Trump aide said Ukraine behind Democratic party hack: Documents
WASHINGTON: Documents released by the US Department of Justice on Saturday (Nov 2) indicated that a top advisor to then-candidate Donald Trump said as early as the summer of 2016 that Ukraine, not Russia, was behind a hack of Democratic party emails.
Trump and his surrogates have suggested that Kiev hacked Democratic National Committee servers and planted evidence to frame Russia, as a way of undermining the legitimacy of the US leader's election.
Trump has pressured Ukraine's president to investigate that debunked conspiracy theory as well as his election rival Joe Biden - a move at the crux of an impeachment investigation against him in the House of Representatives.
The hundreds of pages of documents out Saturday were part of an initial tranche released to US media outlets Buzzfeed and CNN, which sued the government for access to notes from the investigation conducted by special counsel Robert Mueller into Russian meddling in the 2016 election.
Installments of the internal documents from the Mueller probe, which concluded in March, will be released every month for the next eight years, Buzzfeed said.
Campaign chairman Paul Manafort spread the theory among his aides that Ukraine - not Russia - had hacked the Democratic National Committee, deputy campaign manager Rick Gates told the FBI, according to a heavily redacted summary of an interview conducted by the agency.
In July 2016, WikiLeaks began publishing thousands of Democratic National Committee emails, embarrassing Trump's election rival Hillary Clinton. US intelligence later said that the emails were hacked by Russian spies and handed over to WikiLeaks.
The documents released Saturday also showed how Trump, his top aides and family members repeatedly discussed how to get the hacked Democratic emails that WikiLeaks had obtained.


## Mako can find this base file because it's in the settings.py -> MAKO_TEMPLATES_DIRS list
<%inherit file="base_template.htm" />
<%block name="content">
Shipped To:
Sam Snow
1090 S 2490 W
West Valley, UT 84119
| Order Num |
Product Name |
Quantity |
Price |
Total |
| #13545 |
Canon DSLR 7D |
1 |
$1350.00 |
$1350.00 |
#13545 |
Canon Capture Camear Bag |
1 |
$105.00 |
$105.00 |
|   |
  |
  |
Subtotal:
Tax:
|
$1455.00
$98.22
|
|   |
  |
  |
Total:
|
$1553.22
|
%block>
<%block name="join_us">
%block>


What comes to mind first when asked about "autism"?
Right: The film Rainman, people with very poor social skills, but with
island talents, people who require permanent support.
We, a group of autistic and non-autistic people, want to correct this wrong picture in the minds of many people.
In fact, it is only this wrong impression of us that makes our lives unnecessarily complicated. We're no different from you.
Did you know that (estimated) 1.1% of humanity is autistic? So roughly: one in 100 people is an autistic. How many people have you met without noticing?
On the following pages you will find a lot of information about autism, about the differences between you and "us", and above all how we can benefit from each other.
And very important: Thank you! Thank you for your interest!


## Daniel A. Spielman
about information transmission and risk-tolerane therein
the author is just excited about a new way to do error correction but never mentions beauty besides the title. maybe to revisit when i’ve researched more on formal math beauty


e
do
rei
Home
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soon in your service
Let's merge your shopping experience
Queries here.
Our Services
Let us help you to unite your shopping experience
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If you're using TLC5940 chips with your controller setup, either with the official expansion boards or with your own custom circuitry, you'll need to attach a couple of the TLC5940 pins to special pins on the KL25Z that connect internally to an on-board subsystem called the SPI Controller.
The pin selector on the configuration page automatically shows you the valid choices for each pin requiring an SPI function, so you just need to choose from the available pins.
The reason that such a small number of pins is shown for each SPI connection is that these are the only pins the KL25Z hardware allows for these functions. The KL25Z's internal wiring makes the SPI Controller reachable only through a very limited set of pins.
(You don't need to know the following details to set up your controller, but in case you're curious: SPI stands for Serial Peripheral Interface, which is one of those electronics-industry standards, in this case for connecting different digital devices together for communications and control functions. The TLC5940's control interface is compatible with SPI, and the KL25Z has a dedicated SPI hardware module, so it's a perfect match to connect the two devices using this module. The big benefit of doing so is that the SPI module works autonomously, freeing up the KL25Z CPU to do other work while TLC5940 data transfers are taking place. Without the hardware helper, TLC5940 communications would impose a very high workload on the CPU, which would cut into our ability to perform other functions, such as monitoring the plunger sensor and sending joystick updates to the host PC.)


4 references to PerformCloseOperation
System.Management.Automation (4)
engine\remoting\fanin\WSManPlugin.cs (2)
463
PerformCloseOperation
(context);
1587
PerformCloseOperation
(cmdCtxt);
engine\remoting\fanin\WSManPluginFacade.cs (1)
721
WSManPluginInstance.
PerformCloseOperation
(context);
engine\remoting\fanin\WSManPluginTransportManager.cs (1)
359
WSManPluginInstance.
PerformCloseOperation
(ctxtToReport);


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Next: boolean, Previous: Types and Classes Dictionary, Up: Types and Classes Dictionary
all types
The type nil contains no objects and so is also called the empty type. The type nil is a subtype of every type. No object is of type nil.
The type containing the object nil is the type null, not the type nil.


This demo that treats the pixels of an image as a learning problem: it takes the (x,y) position on a grid and learns to predict the color at that point using regression to (r,g,b). It's a bit like compression, since the image information is encoded in the weights of the network, but almost certainly not of practical kind :)
Note that the entire ConvNetJS definition is shown in textbox below and it gets eval()'d to create the network, so feel free to fiddle with the parameters and hit "reload". I found that, empirically and interestingly, deeper networks tend to work much better on this task given a fixed parameter budget.
Report questions/bugs/suggestions to @karpathy.


If you prreciate this work you can make a small donation to offer me a coffee, a beer, a pizza or else!
| Choose Manually | Donate |
- wow, a coffee!
- beer for me!
- thank for a McMenu!
- a pizza with friends!
- a beer with me: I'm paying!
- your generosity make me cry!
This plugin purpose is simplify the copy of Latex code in particular domain (wikipedia.org and mathworld.wolfra.org).
If owner's of that domain don't appreciate this work, please contact me ( my email ) and I'll provide for removal from this extension.
Software License Agreement (BSD License 3-Clause)
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
- Neither the name of the author nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.


EESTEC Competition for Android is open to Android enthusiasts from all over the world. This academical event aims at opening doors for creative innovation by putting into practice knowledge about programming and design.
In the past years, the usage of applications has reached immense popularity and their usefulness slowly transformed from what was merely a luxurious advantage into a basic tool for daily needs in terms of information, communication, mobility, entertainment etc.
Because Android has become a part of our everyday life, we ought to participate in enhancing the quality of the services that the platform provides.
As young students and free-thinkers, we can bring ourselves to understand the needs of our generation and help fulfil them.
The 5th EESTEC Competition for Android is a great experience not only because it teaches students about useful technical tools - it also pushes them to research and analyse in order to meet a demand.
This event will be an amazing opportunity for the developers to gain experience about programming and design, show their creativity, and build an application that could be used by millions of people from all around the globe.
EESTEC is a non-political, non-profit organization of and for Electrical Engineering and Computer Science (EECS) students at universities, institutes and technical schools in Europe awarding an engineering degree. The aim is to promote and develop international contacts and the exchange of ideas among the students of EECS. The association achieves its aim through improving technical knowledge of EECS students, introducing them to the industry and the educational system of other countries. The association was founded in The Netherlands in 1986. Since then, EESTEC has spreaded all around Europe and is now connecting over 4000 students from 32 countries and 55 universities. EESTEC addresses students with skills, openness, enthusiasm and ambition. It connects them to a wider range of opportunities in the educational as well as the professional world.
Start thinking of your ideas! Be a creative mind and the best will come!
Teamwork is the key to succeed! Find your teammates and rock the world of apps!
The hard task of debugging is waiting for you! Patience is the key!
Win prizes! And even if you don't the experience in the way till here was valuable!
Rewards the most skilled team with the best implemented idea from the design perspective. The award will go to the app with the best (friendliest) user interface.
Rewards the most innovative idea. This award’s purpose is to encourage participants who didn’t have enough skills to complete the implementing part, but they came up with a great idea and work towards developing it to an application.
Rewards the team with the highest total score in all judging criteria. So, the team which gathers the above characteristics, skillfully designed , innovative, popular and stands out will have this award.
A new prize category that has been added this year, awards the most popular app. The app can be promoted by the team in social media. The app will be uploaded to Google Play by the organization once it is submitted by the team, before the second deadline. The app with the best rating on our Google Play account will have a non monetary prize (special certificate, etc).


--- date: 2010-02-25T00:00:00Z published: true title: About this Blog | Free PeepCode Blog - Damn this is cool. url: /2010/02/25/about-this-blog-free-peepcode-blog-damn-this-is-cool-/ ---
Many developments in computer science have nothing to do with the capabilities of computers, but with the way we use them.
A few years ago, an article on Airbag Industries1 inspired me to think about writing a blog with a unique design per post.
There are many benefits:
- It encourages creativity both at the computer and away from it
- It’s like a code kata for design
- You can easily experiment with cutting-edge CSS3 features or just learn CSS2
- You’ll learn how to build a style foundation for other designs in other applications
On the downside, it takes more time than simply writing prose.
This is great - totally understand about the homogenization of website content. I struggle with this all of the time with our sites and our client sites - it simply takes time (and costs more money) to make custom pages each time.
This is an inspiration on how to do it properly.


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A tortoise, abiding in damp and deep places, asked an eagle to carry him on high. For he wanted to see mountains and valleys and woods. The eagle acquiesced and carried the tortoise up high, and said to him: "You are seeing what you never before saw." The tortoise said: "I see well; but I would prefer to be in my own hole." And so the eagle let him drop, and he was shattered in the fall.
The interpretation. It sometimes happens that someone, living in a poor house, wishes to rise unto the wings of the winds and fly. He asks the eagle, that is, the devil, to elevate him in some way. He gives and receives bribes to be promoted, and so through simony rises to a high position; and thus the devil carries him; but whenever, having looked back, he realizes his own peril he would rather be in his poor house than in the peril of office. But the devil, anticipating the end of his life at some time, destroys him with a sudden death, and so hurls him into the well of Hell, where all is shattered.


Important People
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Description


Gizmo(bpy_struct)¶
base class — bpy_struct
-
class
bpy.types.
Gizmo
(bpy_struct)¶ Collection of gizmos
-
alpha
¶ Type: float in [0, 1], default 0.0
-
alpha_highlight
¶ Type: float in [0, 1], default 0.0
-
bl_idname
¶ Type: string, default “”, (never None)
-
color
¶ Type: float array of 3 items in [0, inf], default (0.0, 0.0, 0.0)
-
color_highlight
¶ Type: float array of 3 items in [0, inf], default (0.0, 0.0, 0.0)
-
group
¶ Gizmo group this gizmo is a member of
Type: GizmoGroup
, (readonly)
-
hide
¶ Type: boolean, default False
-
hide_select
¶ Type: boolean, default False
-
is_highlight
¶ Type: boolean, default False, (readonly)
-
is_modal
¶ Type: boolean, default False, (readonly)
-
line_width
¶ Type: float in [0, inf], default 0.0
-
matrix_basis
¶ Type: float multi-dimensional array of 4 * 4 items in [-inf, inf], default ((0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0))
-
matrix_offset
¶ Type: float multi-dimensional array of 4 * 4 items in [-inf, inf], default ((0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0))
-
matrix_space
¶ Type: float multi-dimensional array of 4 * 4 items in [-inf, inf], default ((0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0))
-
matrix_world
¶ Type: float multi-dimensional array of 4 * 4 items in [-inf, inf], default ((0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 0.0, 0.0)), (readonly)
-
properties
¶ Type: GizmoProperties
, (readonly, never None)
-
scale_basis
¶ Type: float in [0, inf], default 0.0
-
select
¶ Type: boolean, default False
-
select_bias
¶ Depth bias used for selection
Type: float in [-inf, inf], default 0.0
-
use_draw_hover
¶ Type: boolean, default False
-
use_draw_modal
¶ Draw while dragging
Type: boolean, default False
-
use_draw_offset_scale
¶ Scale the offset matrix (use to apply screen-space offset)
Type: boolean, default False
-
use_draw_scale
¶ Use scale when calculating the matrix
Type: boolean, default False
-
use_draw_value
¶ Show an indicator for the current value while dragging
Type: boolean, default False
-
use_event_handle_all
¶ When highlighted, do not pass events through to be handled by other keymaps
Type: boolean, default False
-
use_grab_cursor
¶ Type: boolean, default False
-
use_operator_tool_properties
¶ Merge active tool properties on activation (does not overwrite existing)
Type: boolean, default False
-
use_select_background
¶ Don’t write into the depth buffer
Type: boolean, default False
-
draw
(context)¶
-
draw_select
(context, select_id=0)¶
-
test_select
(context, location)¶ Parameters: location (int array of 2 items in [-inf, inf], (never None)) – Location, Region coordinates Returns: Use -1 to skip this gizmo Return type: int in [-1, inf]
-
modal
(context, event, tweak)¶ Parameters: tweak (enum set in {'PRECISE', 'SNAP'}) – Tweak Returns: result RUNNING_MODAL
Running Modal, Keep the operator running with blender.CANCELLED
Cancelled, The operator exited without doing anything, so no undo entry should be pushed.FINISHED
Finished, The operator exited after completing its action.PASS_THROUGH
Pass Through, Do nothing and pass the event on.INTERFACE
Interface, Handled but not executed (popup menus).
Return type: enum set in {‘RUNNING_MODAL’, ‘CANCELLED’, ‘FINISHED’, ‘PASS_THROUGH’, ‘INTERFACE’}
-
setup
()¶
-
invoke
(context, event)¶ Returns: result RUNNING_MODAL
Running Modal, Keep the operator running with blender.CANCELLED
Cancelled, The operator exited without doing anything, so no undo entry should be pushed.FINISHED
Finished, The operator exited after completing its action.PASS_THROUGH
Pass Through, Do nothing and pass the event on.INTERFACE
Interface, Handled but not executed (popup menus).
Return type: enum set in {‘RUNNING_MODAL’, ‘CANCELLED’, ‘FINISHED’, ‘PASS_THROUGH’, ‘INTERFACE’}
-
exit
(context, cancel)¶ Parameters: cancel (boolean) – Cancel, otherwise confirm
-
select_refresh
()¶
-
draw_preset_box
(matrix, select_id=-1)¶ Draw a box
Parameters: - matrix (float multi-dimensional array of 4 * 4 items in [-inf, inf]) – The matrix to transform
- select_id (int in [-1, inf], (optional)) – Zero when not selecting
-
draw_preset_arrow
(matrix, axis='POS_Z', select_id=-1)¶ Draw a box
Parameters: - matrix (float multi-dimensional array of 4 * 4 items in [-inf, inf]) – The matrix to transform
- axis (enum in ['POS_X', 'POS_Y', 'POS_Z', 'NEG_X', 'NEG_Y', 'NEG_Z'], (optional)) – Arrow Orientation
- select_id (int in [-1, inf], (optional)) – Zero when not selecting
-
draw_preset_circle
(matrix, axis='POS_Z', select_id=-1)¶ Draw a box
Parameters: - matrix (float multi-dimensional array of 4 * 4 items in [-inf, inf]) – The matrix to transform
- axis (enum in ['POS_X', 'POS_Y', 'POS_Z', 'NEG_X', 'NEG_Y', 'NEG_Z'], (optional)) – Arrow Orientation
- select_id (int in [-1, inf], (optional)) – Zero when not selecting
-
draw_preset_facemap
(object, face_map, select_id=-1)¶ Draw the face-map of a mesh object
Parameters: - object (
Object
, (never None)) – Object - face_map (int in [0, inf]) – Face map index
- select_id (int in [-1, inf], (optional)) – Zero when not selecting
- object (
-
target_set_prop
(target, data, property, index=-1)¶ Parameters: - target (string, (never None)) – Target property
- data (
AnyType
, (never None)) – Data from which to take property - property (string, (never None)) – Identifier of property in data
-
target_set_operator
(operator, index=0)¶ Operator to run when activating the gizmo (overrides property targets)
Parameters: - operator (string, (never None)) – Target operator
- index (int in [0, 255], (optional)) – Part index
Returns: Operator properties to fill in
Return type:
-
target_is_valid
(property)¶ Parameters: property (string, (never None)) – Property identifier Return type: boolean
-
draw_custom_shape
(shape, *, matrix=None, select_id=None)¶ Draw a shape created form
bpy.types.Gizmo.draw_custom_shape
.Parameters: - shape (Undefined.) – The cached shape to draw.
- matrix (
mathutils.Matrix
) – 4x4 matrix, when not givenbpy.types.Gizmo.matrix_world
is used. - select_id – The selection id.
Only use when drawing within
bpy.types.Gizmo.draw_select
.
-
static
new_custom_shape
(type, verts)¶ Create a new shape that can be passed to
bpy.types.Gizmo.draw_custom_shape
.Parameters: - type (string) – The type of shape to create in (POINTS, LINES, TRIS, LINE_STRIP).
- verts (sequence of of 2D or 3D coordinates.) – Coordinates.
- display_name (Callable that takes a string and returns a string.) – Optional callback that takes the full path, returns the name to display.
Returns: The newly created shape.
Return type: Undefined (it may change)
-
classmethod
bl_rna_get_subclass
(id, default=None)¶ Parameters: id (string) – The RNA type identifier. Returns: The RNA type or default when not found. Return type: bpy.types.Struct
subclass
-
classmethod
bl_rna_get_subclass_py
(id, default=None)¶ Parameters: id (string) – The RNA type identifier. Returns: The class or default when not found. Return type: type
-
target_get_range(target):
Get the range for this target property.
Parameters: target – Target property name. Returns: The range of this property (min, max). Return type: tuple pair.
-
target_get_value(target):
Get the value of this target property.
Parameters: target (string) – Target property name. Returns: The value of the target property. Return type: Single value or array based on the target type
-
target_set_handler(target, get, set, range=None):
Assigns callbacks to a gizmos property.
Parameters: - get (callable) – Function that returns the value for this property (single value or sequence).
- set (callable) – Function that takes a single value argument and applies it.
- range (callable) – Function that returns a (min, max) tuple for gizmos that use a range.
-
target_set_value(target):
Set the value of this target property.
Parameters: target (string) – Target property name.
-
Inherited Properties
Inherited Functions
References


To whom it may concern,
I am writing in regards to the renewal of our lease at [insert your apartment's address].
On , we moved into a unit in the aforementioned property. Since then, the average rents in have for Class private domestic units.
Data source: Rating and Valuation Department
To whom it may concern,
I am writing in regards to the renewal of our lease at [insert your apartment's address].
On , we moved into a unit in the aforementioned property. Since then, the average rents in have for Class private domestic units.
Based on the historical government data illustrated in the graph above, a rent hike of 10% seems inconsistent with the recent market conditions and unfair to tenants like us with positive rental history.
We are paying [current rate] per month in rent currently. We propose that [your proposed rent increase] would be a more reasonable rent increase to pay for this apartment.
We look forward to continuing the conversation and hearing from you shortly. Thank you.
Sincerely,


A data pro with serious analytical and statistical chops. You know how to take massive amounts of data and find the insights our clients need to help their companies do more.
The work:
At least 3 years working in data science using statistics
At least 2 years developing machine learning methods
Familiarity with techniques like clustering, regression, optimization, and neural networks
At least 2 years working with data science tools like Python, R, Scala, Julia, or SAS
Strong quantitative and analytical skills
A Bachelor’s degree in data science and related disciplines like mathematics, statistics, computer science, physics, or related fields
You’ve got a Master’s degree in statistics, econometrics, mathematics, or statistics
Data science is your passion and you’re creative and forward thinking
You know your way around services like AWS, Google and Azure and big data tools like Hadoop, Hive and Spark
You’ve got know-how in AI, machine learning, data science, and other related areas
Important information
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States and with Accenture.
Equal Employment Opportunity
All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.


My Browser is: FF
My Browser Version is: 9.0.1
My BBLog Addon Version is: 26.01.2012
Changes in the popup options (filters/location) doesn't update the server list.
I understand it's due to the page has to refresh for the update, however, there is a way around this is to automatically refresh when "ok" button pressed.
BrainFooLong
Administrator
Yes, i will bind it also to the popups ok button.
Keep watchin fou updates.


1.1 Change Log - Reference Documentation
Authors:
Version: 0.3.2
1.1 Change Log
Version 0.3.2 "Tick-Tock"- Fixed #3: add gameName config parameter as default title to pages.
- Changed: Run game ticks in a background thread (no more need for a cron job to run ticks).
- Fixed #14: Version link in footer was broken.
- Fixed: admin can edit ItemTypes again.
- New: admin can assign categories to item types.
- Fixed: inputValidationService no longer croaks on 'null'-String.
- Cleanup code.
- Added more documentation (items, guilds)
- Project is now language level Java 7
- Grails 2.2.0
- Updated to Grails 2.1.1.
- Fixed session handling.
- Fixed HTML error in /goblinOrder/_order_list
- Refactoring pages to use sitemesh layout instead of using homebrew solution.
- Fixed item filter: remove unnecessary conversion of list to array. (ItemService).
- Removed old prototyps JS-library files since we have been using jQuery for some time.
- Fixed message id in highscore.gsp.
- Added more documentation.
- Added custom equals & hashCode to many domain classes.
- Fixed: do not double-encode chatterbox messages.
- Fixed: textarea in chatterbox.
- Removed redundant "def inputValidationService", which is inherited from BaseController.
- Fixed cronGoblin and CronController for recurring events (would previously go into an infinite loop).
- Added configurable registration with confirmation mail.
- Added testMode parameter to config file.
- Updated spring-security-core plugin
- Added FeatureAdminController.
- Upgraded to Grails 2.1.1


This DIV has it's own scroll-blocks-on setting which can augment that set by the document.
Scroll-blocks-on:
start-touch
wheel-event
scroll-event
Scroll-blocks-on:
start-touch
wheel-event
scroll-event
This page demonstrates the effect of the proposed scroll-blocks-on CSS property.
When enabled, this page will do a bunch of work in JavaScript to simulate a complex/janky application. Try scrolling on a touchscreen or with a touchpad using various settings for scroll-blocks-on.


tl;dr: See github.com/aschmelyun/cleaver-directory for more info and a quick-start guide.
A few months ago, I published an article about a static site generator I made called Cleaver. Before this weekend, I mainly was just letting it sit idle. Fixing a few issues that sprung up, figuring out what features I should be adding to it, et cetera.
All last week I've been thinking and trying to come up with something that I could build to potentially help, in any way, those around my during the COVID-19 pandemic. I've noticed the large amount of dashboards showing statistics, infection rates, and the like, and I honestly wanted to shy away from that purely for my own sanity. I wanted to do something that would make an impact, and potentially relieve stress, on local businesses in my small Florida city. So, I came up with an idea.
The idea
I decided to build a local directory, a website map of all non-chain restaurants in my county that were still open and serving take-out and curbside food. The goal being to spread awareness to others about dining options that are still available, and hopefully funneling a source of business into local establishments.
The basic gist would be a landing page consisting of a map, along with a list of restaurants that could be filterable by tags, city, or a text box for user input. Clicking on a restaurant would give you details like their open hours, what type of food they're serving, if they have a limited menu, etc.
Also, I wanted this to be 100% open source and easy for other developers to use. That way, someone else could clone this project, and spin it up on a server, and add data for their local city or area. Originally I was planning on using a traditional Laravel backend for this, combined with a MySQL database and Vue frontend. But, I thought, that might hinder some people using it.
Not everyone can, or wants to, purchase hosting that can accommodate those requirements for a non-profit community project. So I went deeper. What if I used flat-file storage, or better yet, what if the whole site could be built into static files and served on a $0 Netlify instance? Then my Cleaver static site generator came to mind, and I started hacking away at it.
The dev process
As it previously stood, Cleaver was incredibly simple. It took Markdown or JSON files as content, looped through them in an array, and with the help of Laravel's Blade templates, built out HTML files that were saved depending on the path you specified. This was great for something like a blog, or a documentation site, but in order for this to work the way I wanted it was missing a crucial piece.
On the home page, I needed to have a running list of all of the restaurants available throughout the site. The problem was that I didn't want to have to manually add each one as an individual content file, AND add it to an index page as well. My goal was, add a restaurant Markdown file and the landing page will automatically pull it in.
It honestly was easy enough to accomplish using Tighten's Collection package. If you're not familiar with Laravel, Collections are basically arrays on steroids. There's literally dozens of methods that you can use to filter, map, sort, and loop through items you add. In my case, I added a block of code that adds every single piece of content across the site as a collection, and makes it available to each template as a $content
variable.
That way, to get my list of restaurants on my index page, I was able to do something like this:
$listings = $content->filter(function($item, $key) {
return $item->view === 'layout.restaurant';
});
Since only the restaurant pages used the layout.restaurant
view, I could filter through the array of content and pull in all the restaurants with their subsequent data. Then, just like an array, I could call a foreach loop on the collection and spit out the details for each of them.
The rest of the development process was pretty easy and straightforward. Using TailwindCSS and a few Blade templates, I hacked together a landing page, a content page (for things like FAQs and contact info), and individual listing pages. The landing page is powered by Vue and contains an embedded Google Map, and live filtering of restaurants. All you have to do is add Markdown files following a specific format and fill in some brief details.
An example restaurant Markdown file might look like this:
---
view: layout.listing
path: /taco-dive
tags: delivery,take-out,mexican,curbside
title: Taco Dive
address: 10501 SW Village Center Dr
city: Port St. Lucie
state: FL
lat: 27.267700
long: -80.432040
---
Not your average hole in the wall. Popular spot for dive tacos, sandwiches, burritos, and salads.
Currently serving a limited menu, for their full menu see [their website](https://tacodive.com).
**Hours:**
- Mon-Sat: 12pm-8pm
- Sun: Closed
So, let's say you want to get started and create a directory of your own, here's how it works!
Building your own directory
Before you get started, there are a few requirements if you want to compile the site and assets locally on your machine. They are:
- PHP 7.1+ installed
- A fairly recent version of Node + NPM
First step, is clone the repo or use Composer to create a new project at a directory of your choosing:
composer create-project aschmelyun/cleaver-directory my-directory
Once that's ready, you'll need to install the Node dependencies:
npm install
Next, it's time to fill your directory with listings! These could be restaurants, shops, or any other businesses that you want to appear on the map. In the resources/content/listings
directory, copy one of the example Markdown files and make your necessary edits.
It should be noted that the filename you choose to save your content as doesn't matter. The path
variable that you set in the file will determine where your page ends up living on your directory.
After you're satisfied with your content choices, the last thing to do is build your site! From your command line in the root directory, you have two choices:
npm run production // this will build your assets quickly, but is recommended for local development
npm run production // this will build, minify, and transpile your assets
Your compiled HTML files will be put in a dist/
directory in your project's folder. It will contain all of the compiled assets, directory structure, and rendered markup for your entire site.
Let's get it up and online!
Deploying your site
If you're using your own server, it's as easy as either:
- Uploading just the
/dist
directory to your webroot, or - Uploading the entire project and modifying the webroot of your server to use the
/dist
folder
From there, you should be able to see your finished site!
But what about that $0 deployment from Netlify that we talked about earlier? It's pretty simple. Just follow these brief steps:
First, sign up for an account at Netlify if you don't already have one.
Then, upload your entire finished project to a GitHub repository, with or without the /dist folder attached to it.

In the build settings, you're going to type in npm run production
as the build command, and dist
as the publish directory. Additionally, in the Environment Variables section, you'll have to specify PHP_VERSION
as 7.2
, since 5.6 is by default the one used.
That's it! After hitting Trigger deploy, Netlify will clone your site, install the Composer and NPM dependencies, run the asset compilation, and output the entire site to the dist
directory. That directory is then served out to the public and your site is ready to be viewed!
All you have to do is either share the randomly-generated domain given, or add in a custom domain name to attach the project to.
That's about it
Well, that's all there is! This project is still very much a work in progress. As it was hacked out over a literal weekend, there's a lot of room for optimization, organization, and general additions.
Please feel free to add any issues, bugs, or feature requests that you might come across to the GitHub repo. You can also reach out to me on Twitter if you have any questions about this project, or web development in general.


| Total net profit | -20.9 | Gross profit | 19.5 | Gross loss | -40.4 |
| Profit factor | 0.48 | Expected payoff | -1.1 | ||
| Absolute drawdown | -29.4 | Maximal drawdown (%) | 25.3 (-617.07%) | ||
| Total trades | 19 | Profit trades (% of total) | 9 (47.37%) | Loss trades (% of total) | 10 (52.63%) |
| Long positions (won %) | 13 (38.46%) | Short positions (won %) | 6 (66.67%) | ||
| Largest | profit trade | 2.4 | loss trade | -5.4 | |
| Average | profit trade | 2.17 | loss trade | -4.04 | |
| Minimum | profit trade | 1.9 | loss trade | -3.65 | |
| Maximum | take profit in pips | 7.0 | stop loss in pips | -10.8 | |
| Average | take profit in pips | 4.5 | stop loss in pips | -7.8 | |
| Minimum | take profit in pips | 3.2 | stop loss in pips | -7.3 | |
| Maximum | Reward-Risk | 0.9 | Duration in minutes | 12031 | |
| Average | Reward-Risk | 0.6 | Duration in minutes | 3477.4 | |
| Minimum | Reward-Risk | 0.4 | Duration in minutes | 205 | |
| Maximum | consecutive wins (profit in money) | 4.0 (8.5) | consecutive losses (loss in money) | 3.0 (-13.35) | |
| Average | consecutive wins | 1.0 | consecutive losses | 1.0 |
| Total net profit | -22.35 | Gross profit | 10.75 | Gross loss | -33.1 |
| Profit factor | 0.32 | Expected payoff | -1.72 | ||
| Absolute drawdown | -26.65 | Maximal drawdown (%) | 22.55 (-550.0%) | ||
| Total trades | 13 | Profit trades (% of total) | 5 (38.46%) | Loss trades (% of total) | 8 (61.54%) |
| Largest | profit trade | 2.4 | loss trade | -5.4 | |
| Average | profit trade | 2.15 | loss trade | -4.14 | |
| Minimum | profit trade | 1.9 | loss trade | -3.75 | |
| Maximum | take profit in pips | 7.0 | stop loss in pips | -10.8 | |
| Average | take profit in pips | 4.5 | stop loss in pips | -8.0 | |
| Minimum | take profit in pips | 3.2 | stop loss in pips | -7.3 | |
| Maximum | Reward-Risk | 0.9 | Duration in minutes | 12031.0 | |
| Average | Reward-Risk | 0.6 | Duration in minutes | 3770.8 | |
| Minimum | Reward-Risk | 0.4 | Duration in minutes | 289.0 | |
| Maximum | consecutive wins (profit in money) | 2.0 (4.3) | consecutive losses (loss in money) | 3.0 (-13.35) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |
| Total net profit | 1.45 | Gross profit | 8.75 | Gross loss | -7.3 |
| Profit factor | 1.2 | Expected payoff | 0.24 | ||
| Absolute drawdown | -5.15 | Maximal drawdown (%) | 3.65 (-243.33%) | ||
| Total trades | 6 | Profit trades (% of total) | 4 (66.67%) | Loss trades (% of total) | 2 (33.33%) |
| Largest | profit trade | 2.2 | loss trade | -3.65 | |
| Average | profit trade | 2.19 | loss trade | -3.65 | |
| Minimum | profit trade | 2.15 | loss trade | -3.65 | |
| Maximum | take profit in pips | 4.5 | stop loss in pips | -7.6 | |
| Average | take profit in pips | 4.4 | stop loss in pips | -7.3 | |
| Minimum | take profit in pips | 4.2 | stop loss in pips | -7.3 | |
| Maximum | Reward-Risk | 0.6 | Duration in minutes | 5912.0 | |
| Average | Reward-Risk | 0.6 | Duration in minutes | 2841.7 | |
| Minimum | Reward-Risk | Duration in minutes | 205.0 | ||
| Maximum | consecutive wins (profit in money) | 3.0 (6.6) | consecutive losses (loss in money) | 1.0 (-3.65) | |
| Average | consecutive wins | 1.0 | consecutive losses | 1.0 |


Screen Class
Screen class can be used to access display information.
Properties
_container
HTMLElement
private
The canvas's parent node in dom.
_frame
HTMLElement
private
The container's parent node in dom.
_frameSize
Vec2
private
Size of parent node that contains container and _canvas
devicePixelRatio
Number
The current device's pixel ratio (for retina displays)
Default: 1
height
Number
The current height of the screen window in pixels
size
Vec2
The current size of the screen window in pixels
width
Number
The current width of the screen window in pixels


New Patient Health Risk Assessment / Update Your Information
Please fill out the following information. Fields marked with an asterisk (*) are required.
Name*
Date of
birth*
Gender
Select...
Female
Male
Phone number*
Address*
Plan name*
Alternate phone
number
Language
English
Spanish
Chinese
French
Other
Subscriber ID*
Best time
to contact
Early morning (7 AM - 9 AM)
Late morning (10 AM - 12 PM)
Early afternoon (12 PM - 3 PM)
Late afternoon (3 PM - 5 PM)
Early evening (5 PM - 7 PM)
Late evening (7 PM - 9 PM)
Disease
management plan
Practitioner
name*


DateInputComponent
Description
Renders a date input box to collect user input from.
This component fires a event change where we select older months using the mouse. That
can be prevented using arrow keys. The component is fully localized according to the selected user locale.
Options
- name
- The name of the component
- type
dateInputComponent
- listeners
- Array - Parameters who this component will react to
- parameter
- Variable where the input is stored
- htmlObject
- Id of the component (usually a div or a span tag) to be replaced by the result
- refreshPeriod
- Time in seconds for this component to refresh. Default is 0 (disabled)
- dateFormat
- String - Format used for date output and parsing (including startDate and endDate fields). Must be a jquery datePicker format string:
- d - day of month (no leading zero)
- dd - day of month (two digit)
- D - day name short
- DD - day name long
- m - month of year (no leading zero)
- mm - month of year (two digit)
- M - month name short
- MM - month name long
- y - year (two digit)
- yy - year (four digit)
- @ - Unix timestamp (also known as epoch seconds or POSIX time) in milliseconds since 01/01/1970
- '...' - literal text
- '' - single quote
- anything else - literal text
Default is 'yy-mm-dd'.
- startDate
- String - Component will only allow selection of dates after this value. Value is a string containing a date compliant to this component's dateFormat. Special value 'TODAY' will translate to current date whenever the component is updated. Empty by default (no restriction)
- endDate
- String - Component will only allow selection of dates before this value. Value is a string containing a date compliant to this component's dateFormat. Special value 'TODAY' will translate to current date whenever the component is updated. Empty by default (no restriction)
- executeAtStart
- True to execute the component at start, false otherwise
- tooltip
- Tooltip to be displayed when mouse hovers
- preExecution
- Function - Function to be called before the component is executed
- postExecution
- Function - Function to be called after the component is executed
- preChange
- Function - Function to be called before the component is changed
- postChange
- Function - Function to be called after the component is changed
Sample


US woos Asia with plan to rival China’s ‘Belt and Road’
by The Associated PressBANGKOK (AP) — Commerce Secretary Wilbur Ross says the U.S. will invest and trade more in Asia as it rolls out an American plan to support “sustainable” projects in Asia.
The plan announced at a U.S. business forum in Bangkok is meant to rival China’s own multibillion dollar “Belt and Road” infrastructure program.
Ross is leading a high-powered trade mission in Asia and attended a regional summit that wrapped up Monday.
As he was talking up U.S. trade and investment, China opened a lavish import expo Tuesday in Shanghai showcasing its own eagerness to do business.
President Xi Jinping vowed to open Chinese markets further and clinked wine glasses with French President Emmanuel Macron, who urged that Beijing and Washington end the trade war that is clouding an already murky global economic outlook.
The Associated Press


The Nav Mesh Obstacle component allows you to describe moving obstacles that Nav Mesh Agents should avoid while navigating the world (for example, barrels or crates controlled by the physics system). While the obstacle is moving, the Nav Mesh Agents do their best to avoid it. When the obstacle is stationary, it carves a hole in the NavMesh. Nav Mesh Agents then change their paths to steer around it, or find a different route if the obstacle causes the pathway to be completely blocked.
| Property | Function |
|---|---|
| Shape | The shape of the obstacle geometry. Choose whichever one best fits the shape of the object. |
| Box | |
| Center | Center of the box relative to the transform position. |
| Size | Size of the box. |
| Capsule | |
| Center | Center of the capsule relative to the transform position. |
| Radius | Radius of the capsule. |
| Height | Height of the capsule. |
| Carve | When the Carve checkbox is ticked, the Nav Mesh Obstacle creates a hole in the NavMesh. |
| Move Threshold | Unity treats the Nav Mesh Obstacle as moving when it has moved more than the distance set by the Move Threshold. Use this property to set the threshold distance for updating a moving carved hole. |
| Time To Stationary | The time (in seconds) to wait until the obstacle is treated as stationary. |
| Carve Only Stationary | When enabled, the obstacle is carved only when it is stationary. See Logic for moving Nav Mesh Obstacles, below, to learn more. |
Nav Mesh Obstacles can affect the Nav Mesh Agent’s navigation during the game in two ways:
When Carve is not enabled, the default behavior of the Nav Mesh Obstacle is similar to that of a Collider. Nav Mesh Agents try to avoid collisions with the Nav Mesh Obstacle, and when close, they collide with the Nav Mesh Obstacle. Obstacle avoidance behaviour is very basic, and has a short radius. As such, the Nav Mesh Agent might not be able to find its way around in an environment cluttered with Nav Mesh Obstacles. This mode is best used in cases where the obstacle is constantly moving (for example, a vehicle or player character).
When Carve is enabled, the obstacle carves a hole in the NavMesh when stationary. When moving, the obstacle is an obstruction. When a hole is carved into the NavMesh, the pathfinder is able to navigate the Nav Mesh Agent around locations cluttered with obstacles, or find another route if the current path gets blocked by an obstacle. It’s good practice to turn on carving for Nav Mesh Obstacles that generally block navigation but can be moved by the player or other game events like explosions (for example, crates or barrels).
Unity treats the Nav Mesh Obstacle as moving when it has moved more than the distance set by the Carve > Move Threshold. When the Nav Mesh Obstacle moves, the carved hole also moves. However, to reduce CPU overhead, the hole is only recalculated when necessary. The result of this calculation is available in the next frame update. The recalculation logic has two options:
Only carve when the Nav Mesh Obstacle is stationary
Carve when the Nav Mesh Obstacle has moved
This is the default behavior. To enable it, tick the Nav Mesh Obstacle component’s Carve Only Stationary checkbox. In this mode, when the Nav Mesh Obstacle moves, the carved hole is removed. When the Nav Mesh Obstacle has stopped moving and has been stationary for more than the time set by Carving Time To Stationary, it is treated as stationary and the carved hole is updated again. While the Nav Mesh Obstacle is moving, the Nav Mesh Agents avoid it using collision avoidance, but don’t plan paths around it.
Carve Only Stationery is generally the best choice in terms of performance, and is a good match when the GameObject associated with the Nav Mesh Obstacle is controlled by physics.
To enable this mode, untick the Nav Mesh Obstacle component’s Carve Only Stationary checkbox. When this is unticked, the carved hole is updated when the obstacle has moved more than the distance set by Carving Move Threshold. This mode is useful for large, slowly moving obstacles (for example, a tank that is being avoided by infantry).
Note: When using NavMesh query methods, you should take into account that there is a one-frame delay between changing a Nav Mesh Obstacle and the effect that change has on the NavMesh.
Creating a Nav Mesh Obstacle - Guidance on creating Nav Mesh Obstacles.
Inner Workings of the Navigation System - Learn more about how Nav Mesh Obstacles are used as part of navigation.
Nav Mesh Obstacle scripting reference - Full description of the Nav Mesh Obstacle scripting API.


[ aws . elastictranscoder ]
The create-pipeline operation creates a pipeline with settings that you specify.
create-pipeline
[--name <value>]
[--input-bucket <value>]
[--output-bucket <value>]
[--role <value>]
[--notifications <value>]
[--content-config <value>]
[--thumbnail-config <value>]
The name of the pipeline. We recommend that the name be unique within the AWS account, but uniqueness is not enforced.
Constraints: Maximum 40 characters.
The Amazon S3 bucket in which you saved the media files that you want to transcode.
The Amazon S3 bucket in which you want Elastic Transcoder to save the transcoded files. (Use this, or use ContentConfig:Bucket plus ThumbnailConfig:Bucket.)
Specify this value when all of the following are true:
- You want to save transcoded files, thumbnails (if any), and playlists (if any) together in one bucket.
- You do not want to specify the users or groups who have access to the transcoded files, thumbnails, and playlists.
- You do not want to specify the permissions that Elastic Transcoder grants to the files. .. warning::When Elastic Transcoder saves files in --output-bucket , it grants full control over the files only to the AWS account that owns the role that is specified by --role .
- You want to associate the transcoded files and thumbnails with the Amazon S3 Standard storage class.
If you want to save transcoded files and playlists in one bucket and thumbnails in another bucket, specify which users can access the transcoded files or the permissions the users have, or change the Amazon S3 storage class, omit --output-bucket and specify values for --content-config and --thumbnail-config instead.
The IAM Amazon Resource --name (ARN) for the role that you want Elastic Transcoder to use to create the pipeline.
The Amazon Simple Notification Service (Amazon SNS) topic that you want to notify to report job status.
Warning
To receive notifications, you must also subscribe to the new topic in the Amazon SNS console.
- Progressing : The topic ARN for the Amazon Simple Notification Service (Amazon SNS) topic that you want to notify when Elastic Transcoder has started to process a job in this pipeline. This is the ARN that Amazon SNS returned when you created the topic. For more information, see Create a Topic in the Amazon Simple Notification Service Developer Guide.
- Completed : The topic ARN for the Amazon SNS topic that you want to notify when Elastic Transcoder has finished processing a job in this pipeline. This is the ARN that Amazon SNS returned when you created the topic.
- Warning : The topic ARN for the Amazon SNS topic that you want to notify when Elastic Transcoder encounters a warning condition while processing a job in this pipeline. This is the ARN that Amazon SNS returned when you created the topic.
- Error : The topic ARN for the Amazon SNS topic that you want to notify when Elastic Transcoder encounters an error condition while processing a job in this pipeline. This is the ARN that Amazon SNS returned when you created the topic.
Shorthand Syntax:
--notifications Progressing=value,Completed=value,Warning=value,Error=value
JSON Syntax:
{
"Progressing": "string",
"Completed": "string",
"Warning": "string",
"Error": "string"
}
The optional --content-config object specifies information about the Amazon S3 bucket in which you want Elastic Transcoder to save transcoded files and playlists: which bucket to use, which users you want to have access to the files, the type of access you want users to have, and the storage class that you want to assign to the files.
If you specify values for --content-config , you must also specify values for --thumbnail-config .
If you specify values for --content-config and --thumbnail-config , omit the --output-bucket object.
- Bucket : The Amazon S3 bucket in which you want Elastic Transcoder to save transcoded files and playlists.
- Permissions (Optional): The Permissions object specifies which users you want to have access to transcoded files and the type of access you want them to have. You can grant permissions to a maximum of 30 users and/or predefined Amazon S3 groups.
- Grantee Type : Specify the type of value that appears in the Grantee object: * Canonical : The value in the Grantee object is either the canonical user ID for an AWS account or an origin access identity for an Amazon CloudFront distribution. For more information about canonical user IDs, see Access Control List (ACL) Overview in the Amazon Simple Storage Service Developer Guide. For more information about using CloudFront origin access identities to require that users use CloudFront URLs instead of Amazon S3 URLs, see Using an Origin Access Identity to Restrict Access to Your Amazon S3 Content. .. warning::A canonical user ID is not the same as an AWS account number.
- Email : The value in the Grantee object is the registered email address of an AWS account.
- Group : The value in the Grantee object is one of the following predefined Amazon S3 groups: AllUsers , AuthenticatedUsers , or LogDelivery .
- Grantee : The AWS user or group that you want to have access to transcoded files and playlists. To identify the user or group, you can specify the canonical user ID for an AWS account, an origin access identity for a CloudFront distribution, the registered email address of an AWS account, or a predefined Amazon S3 group
- Access : The permission that you want to give to the AWS user that you specified in Grantee . Permissions are granted on the files that Elastic Transcoder adds to the bucket, including playlists and video files. Valid values include: * READ : The grantee can read the objects and metadata for objects that Elastic Transcoder adds to the Amazon S3 bucket.
- READ_ACP : The grantee can read the object ACL for objects that Elastic Transcoder adds to the Amazon S3 bucket.
- WRITE_ACP : The grantee can write the ACL for the objects that Elastic Transcoder adds to the Amazon S3 bucket.
- FULL_CONTROL : The grantee has READ , READ_ACP , and WRITE_ACP permissions for the objects that Elastic Transcoder adds to the Amazon S3 bucket.
- StorageClass : The Amazon S3 storage class, Standard or ReducedRedundancy , that you want Elastic Transcoder to assign to the video files and playlists that it stores in your Amazon S3 bucket.
JSON Syntax:
{
"Bucket": "string",
"StorageClass": "string",
"Permissions": [
{
"GranteeType": "string",
"Grantee": "string",
"Access": ["string", ...]
}
...
]
}
--thumbnail-config (structure)
The --thumbnail-config object specifies several values, including the Amazon S3 bucket in which you want Elastic Transcoder to save thumbnail files, which users you want to have access to the files, the type of access you want users to have, and the storage class that you want to assign to the files.
If you specify values for --content-config , you must also specify values for --thumbnail-config even if you don't want to create thumbnails.
If you specify values for --content-config and --thumbnail-config , omit the --output-bucket object.
- Bucket : The Amazon S3 bucket in which you want Elastic Transcoder to save thumbnail files.
- Permissions (Optional): The Permissions object specifies which users and/or predefined Amazon S3 groups you want to have access to thumbnail files, and the type of access you want them to have. You can grant permissions to a maximum of 30 users and/or predefined Amazon S3 groups.
- GranteeType : Specify the type of value that appears in the Grantee object: * Canonical : The value in the Grantee object is either the canonical user ID for an AWS account or an origin access identity for an Amazon CloudFront distribution. .. warning::A canonical user ID is not the same as an AWS account number.
- Email : The value in the Grantee object is the registered email address of an AWS account.
- Group : The value in the Grantee object is one of the following predefined Amazon S3 groups: AllUsers , AuthenticatedUsers , or LogDelivery .
- Grantee : The AWS user or group that you want to have access to thumbnail files. To identify the user or group, you can specify the canonical user ID for an AWS account, an origin access identity for a CloudFront distribution, the registered email address of an AWS account, or a predefined Amazon S3 group.
- Access : The permission that you want to give to the AWS user that you specified in Grantee . Permissions are granted on the thumbnail files that Elastic Transcoder adds to the bucket. Valid values include: * READ : The grantee can read the thumbnails and metadata for objects that Elastic Transcoder adds to the Amazon S3 bucket.
- READ_ACP : The grantee can read the object ACL for thumbnails that Elastic Transcoder adds to the Amazon S3 bucket.
- WRITE_ACP : The grantee can write the ACL for the thumbnails that Elastic Transcoder adds to the Amazon S3 bucket.
- FULL_CONTROL : The grantee has READ , READ_ACP , and WRITE_ACP permissions for the thumbnails that Elastic Transcoder adds to the Amazon S3 bucket.
- StorageClass : The Amazon S3 storage class, Standard or ReducedRedundancy , that you want Elastic Transcoder to assign to the thumbnails that it stores in your Amazon S3 bucket.
JSON Syntax:
{
"Bucket": "string",
"StorageClass": "string",
"Permissions": [
{
"GranteeType": "string",
"Grantee": "string",
"Access": ["string", ...]
}
...
]
}


Guide¶
Introduction
regrnd is a python library for fitting L2 regularised regression models with repeated observations from the same sample. regrnd should be run with Anaconda built with Python 2.7.
A regrnd model (regrnd.model
) consists of a linear model for the mean along
with a vector of sample labels that allows for repeated observations from the same sample.
The documentation for the regrnd module (Documentation for ‘regrnd’ module) contains information on how to define a regrnd.model
,
how to optimise a regrnd.model
given a L2 regularisation parameter, how to predict from
a regrnd.model
, and how to simulate a regrnd.model
.
Running tests
To check that the code is working properly and computing likelihoods and gradients accurately, you can run tests. In the regrnd/ subdirectory, type
python tests.py
The output should say
Ran 4 tests in...
OK
Running the code
In the regrnd/ subdirectory, there is a script ‘met_analysis.py’. The script takes one command line argument that is the location of the training_data.csv file. Unless you wish to install regrnd as a package, the ‘met_analysis.py’ script should be run from the regrnd/ subdirectory so that it can import the code in regrnd/regrnd.py.
The cross-validation code is contained in this script, but it is commmented out as it takes some time to run for all the investigated regularisation parameters.
Without modifying the script, it trains the model on the training data using the regularisation parameters found by cross-validation. It then predicts the ‘mets’ of the test set, and prints the mean squared error on the test set.


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We have previously shown that mutational footprints associated with extrinsic and intrinsic risk factors of cancer can be used to inform aetiology and treatment options in oesophageal adenocarcinoma (Secrier, Li et al, Nat Genet 2016). Specifically, we uncovered three distinct subtypes of this cancer with distinct therapeutic opportunities, including a subgroup with high mutational burden, which could benefit from immunotherapy, and one with prevalent DNA damage defects in the homologous recombination pathway, for which PARP inhibitors may be effective (Fig 1).
We are currently investigating further mechanisms of mutational signature development and their involvement in cancer progression.
Genomic instability is a major determinant of cancer development. We are interested in gaining insights into the timing and mechanisms of processes such as catastrophic events, whole-genome doubling and copy number variation leading to gene dosage-mediated regulation of pathway activity. Ongoing efforts in the group are focused on complex rearrangement events we have previously described in oesophageal adenocarcinoma (Fig 2).
We are interested in developing new methodology for evaluating cancer-immune cell interactions and immune activity in relation to cancer-driven signals (Fig 3). Some of the topics under current investigation include immune checkpoint activity and the identification of rare cell populations in cancer.
This is a new direction of research in the group that we are starting to expand through collaborations with the UCL Institute of Neurology and the German Center for Neurodegenerative Diseases (DFZN, Bonn). The focus is on epigenetic regulation of disease and immune system involvement.
We apply unsupervised and supervised learning approaches for the purpose of patient stratification and prediction of disease outcome or response to therapy. At the cellular level, we apply similar methods to understand dependencies within protein interaction networks and the effect of signal propagation within key pathways regulating oncogenic activation, senescence, dormancy, metastasis etc.
We are developing new tools for multi-omics data integration. Methods employed include: causal reasoning, predictive models, network analysis, hotspot/module identification, deep learning.


Sheffield Wednesday charged by the EFL with breach of financial rules
The EFL began its investigation into the Owls earlier this year.
Sheffield Wednesday have been charged with breaching EFL rules around financial fair play over the sale of Hillsborough.
The EFL opened an investigation into the Championship club’s profitability and sustainability (P&S) submission for 2017-18 earlier this year and has now issued “a number of charges” in relation to that investigation.
“The EFL has reviewed a large number of documents obtained from the club as part of this process and concluded there is sufficient evidence to justify issuing charges of misconduct,” a statement from the league read.
“The charges are in respect of a number of allegations regarding the process of how and when the stadium was sold and the inclusion of the profits in the 2017-18 accounts.”
The EFL said the charges would now be considered by an independent disciplinary commission, with sanctions possibly including a points penalty.
The club’s 2018-19 P&S submission remains under review, the league said.
Wednesday said in a statement: “Sheffield Wednesday note the statement issued by the EFL and the charges contained within.
“These charges will be vigorously defended and the club will be making no further comment at this time.”


{% if message %}
| Title | Description | Client | Priority | Date | Product Area | Actions | {{feature.title}} | {{feature.description}} | {{feature.client}} | {{feature.client_priority}} | {{ feature.target_date.strftime("%m/%d/%Y") }} | {{feature.product_area}} | Edit | Delete | {% endfor %}
|---|


Some quick example text to build on the card title and make up the bulk of the card's content.
Go somewhereI am a software engineer at Microsoft!
I am a software engineer at Microsoft!
I have 2 dogs - Daisy and Tux!
I enjoy music festivals and traveling
Odesza and San Holo are my favorite artists


ui5-segmentedbutton
Segmentedbutton example
ToggleButton
Button
Example with 4 buttons
Button
Button
Click
Pressed ToggleButton
Example with 5 buttons
Word
Pressed ToggleButton With Bigger Text
Button
Pressed ToggleButton
A
Example with Icons and custom width
SegmentedButton with 100% width
Pressed ToggleButton
ToggleButton
ToggleButton
SegmentedButton wrapped in a container with set width
Initial focus test
the focus hould go to the first button
Press
Press


Notebooks
Story: Description of animals and their habits
Description of animals and their habits
Title
Description of animals and their habits
Collection
Summary
A description of the appearance, habits and homes of various animals. Included are more lengthy narratives about the korhaan malkop (who sits on her eggs), the lion (who springs out of a bush and catches a man), and the leopard (or 'tiger') who sits in a tree and catches a man's head.
Comments
1) Notes on the verso pages of this contribution appear to be given by |a!kunta, 2) 'tiger' must refer to another beast of prey, 3) This story is found in Book II-1
Contributors
Date
11 March – 29 May 1871 (given on or about)
Categories
Keywords
animals (names of) , animals (and 'Boers') , animals (Dutch names of) , animals (habits of) , animals (sleeping habits) , animals (description of) , animals (hunting of) , animals (eating of) , animals (which kill people) , animals (poisonous) , animals (game) , plants (eaten as food) , names (of animals) , names (of edible plants) , food (plants and animals eaten as)
Story Pages
200v, 202-209, 211-213


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Livestream: Watch The 2019 American Music Awards Red Carpet
by Katie ColleyThe 2019 American Music Awards are officially here and ET Canada has your ticket to the red carpet.
In the video below, catch all your favourite stars at the 2019 American Music Awards, including Taylor Swift, Shawn Mendes, Halsey, the Jonas Brothers, Shania Twain and more.
The official two-hour pre-show is hosted by Sofia Carson, AJ Gibson, Jaymes Vaughan, Lauren Jauregui, Raja Kumari and Nick Viall and will stream live from the Microsoft Theatre in Los Angeles starting at 6:00 p.m ET.
Streaming exclusively on Twitter, fans can watch and engage in the live red carpet experience during exclusive interviews with nominees, performers and presenters.
The 2019 AMAs will broadcast live from the Microsoft Theatre at 8:00 p.m. ET.
Check out the full list of attendees and performers below:


About Me guessing game
My name is Aya azzam , I'am 24 years
- education : graduate public administration (sub- specialization finance and banking )from yarmouk university .
- experience : i was trained in social security at human resources department for three months aslo
worked in ayla financial as a loan officer for two months
My goals honestly i don't know 100% what the life store for me ,
but i know two things the first is that i will find the right job and develop myself the second is that since i found work ,
i can complete higher studies or take courses to try to develop in my career .
top ten of my favourite movies ?
- Ip man
- old guard
- THe mig
- run all night
- taken
- creed
- holidate
- fighting with my family
- southpaw
- me before you


‘The Big Bang Theory’ and ‘Glee’ actress Jane Galloway Heitz has died aged 78
Also a casting agent, the actress broke the likes of Stephen Colbert and Steve Carrell.
The Big Bang Theory actress Jane Galloway Heitz has died aged 78.
Heitz played Mildred in The Big Bang Theory and was also known for her role as Lilian Adler in Glee, where she played the founder of the William McKinley Glee Club.
According to The Hollywood Reporter, the actress died last week (November 13) of congestive heart failure at a hospital in Illinois and was revealed by her daughter, Amie Richardson.
Other roles in her long career included roles in ER, Grey’s Anatomy and Prison Break. Heitz also had a career as a casting agent, breaking the careers of Stephen Colbert and Steve Carrell, amongst many others.
Tributes have been pouring in for the late actress, with Modern Family star Eric Stonestreet calling her “a great friend and mentor.”
“Jane was the first casting director to ever call me in for an audition in Chicago,” he wrote. “She became a great friend and mentor.
“I tried to always make sure she knew she had a starring role in my career. She put me on that path I’m on. I wish i could have said thank you one more time.”
Heitz sold her casting agency in 1997 to focus on her acting career, with her first big role coming in David Lynch’s 1999 film The Straight Story.


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Sparklyr 0.7 (UNRELEASED)
Added support for
context
parameter inspark_apply()
to allow callers to pass additional contextual information to thef()
closure.Implemented workaround to support in
spark_write_table()
formode = 'append'
.Various ML improvements, including support for pipelines, additional algorithms, hyper-parameter tuning, and better model persistence.
Added
spark_read_libsvm()
for reading libsvm files.Added support for separating struct columns in
sdf_separate_column()
.Fixed collection of
short
,float
andbyte
to properly return NAs.Added
sparklyr.collect.datechars
option to enable collectingDateType
andTimestampTime
ascharacters
to support compatibility with previos versions.Fixed collection of
DateType
andTimestampTime
fromcharacter
to properDate
andPOSIXct
types.
Sparklyr 0.6.4
Added support for HTTPS for
yarn-cluster
which is activated by settingyarn.http.policy
toHTTPS_ONLY
inyarn-site.xml
.Added support for
sparklyr.yarn.cluster.accepted.timeout
underyarn-cluster
to allow users to wait for resources under cluster with high waiting times.Fix to
spark_apply()
when package distribution deadlock triggers in environments where multiple executors run under the same node.Added support in
spark_apply()
for specifying a list ofpackages
to distribute to each worker node.Added support in
yarn-cluster
forsparklyr.yarn.cluster.lookup.prefix
,sparklyr.yarn.cluster.lookup.username
andsparklyr.yarn.cluster.lookup.byname
to control the new application lookup behavior.
Sparklyr 0.6.3
Enabled support for Java 9 for clusters configured with Hadoop 2.8. Java 9 blocked on ‘master=local’ unless ‘options(sparklyr.java9 = TRUE)’ is set.
Fixed issue in
spark_connect()
where usingset.seed()
before connection would cause session ids to be duplicates and connections to be reused.Fixed issue in
spark_connect()
blocking gateway port when connection was never started to the backend, for isntasnce, while interrupting the r session while connecting.Performance improvement for quering field names from tables impacting tables and
dplyr
queries, most noticeable inna.omit
with several columns.Fix to
spark_apply()
when closure returns adata.frame
that contains no rows and has one or more columns.Fix to
spark_apply()
while usingtryCatch()
within closure and increased callstack printed to logs when error triggers within closure.Added support for the
SPARKLYR_LOG_FILE
environment variable to specify the file used for log output.Fixed regression for
union_all()
affecting Spark 1.6.X.Added support for
na.omit.cache
option that when set toFALSE
will preventna.omit
from caching results when rows are dropped.Added support in
spark_connect()
foryarn-cluster
with hight-availability enabled.Added support for
spark_connect()
withmaster="yarn-cluster"
to query YARN resource manager API and retrieve the correct container host name.Fixed issue in
invoke()
calls while using integer arrays that containNA
which can be commonly experienced while usingspark_apply()
.Added
topics.description
underml_lda()
result.Added support for
ft_stop_words_remover()
to strip out stop words from tokens.Feature transformers (
ft_*
functions) now explicitly requireinput.col
andoutput.col
to be specified.Added support for
spark_apply_log()
to enable logging in worker nodes while usingspark_apply()
.Fix to
spark_apply()
forSparkUncaughtExceptionHandler
exception while running over large jobs that may overlap during an, now unnecesary, unregister operation.Fix race-condition first time
spark_apply()
is run when more than one partition runs in a worker and both processes try to unpack the packages bundle at the same time.spark_apply()
now adds generic column names when needed and validatesf
is afunction
.Improved documentation and error cases for
metric
argument inml_classification_eval()
andml_binary_classification_eval()
.Fix to
spark_install()
to use the/logs
subfolder to store locallog4j
logs.Fix to
spark_apply()
when R is used from a worker node since worker node already contains packages but still might be triggering different R session.Fix connection from closing when
invoke()
attempts to use a class with a method that contains a reference to an undefined class.Implemented all tuning options from Spark ML for
ml_random_forest()
,ml_gradient_boosted_trees()
, andml_decision_tree()
.Avoid tasks failing under
spark_apply()
and multiple concurrent partitions running while selecting backend port.Added support for numeric arguments for
n
inlead()
for dplyr.Added unsupported error message to
sample_n()
andsample_frac()
when Spark is not 2.0 or higher.Fixed
SIGPIPE
error underspark_connect()
immediately after aspark_disconnect()
operation.Added support for
sparklyr.apply.env.
underspark_config()
to allowspark_apply()
to initializae environment varaibles.Added support for
spark_read_text()
andspark_write_text()
to read from and to plain text files.Addesd support for RStudio project templates to create an “R Package using sparklyr”.
Fix
compute()
to trigger refresh of the connections view.Added a
k
argument toml_pca()
to enable specification of number of principal components to extract. Also implementedsdf_project()
to project datasets using the results ofml_pca()
models.Added support for additional livy session creation parameters using the
livy_config()
function.
Sparklyr 0.6.2
- Fix connection_spark_shinyapp() under RStudio 1.1 to avoid error while listing Spark installation options for the first time.
Sparklyr 0.6.1
Fixed error in
spark_apply()
that may triggered when multiple CPUs are used in a single node due to race conditions while accesing the gateway service and another in theJVMObjectTracker
.spark_apply()
now supports explicit column types using thecolumns
argument to avoid sampling types.spark_apply()
withgroup_by
no longer requires persisting to disk nor memory.Added support for Spark 1.6.3 under
spark_install()
.Added support for Spark 1.6.3 under
spark_install()
spark_apply()
now logs the current callstack when it fails.Fixed error triggered while processing empty partitions in
spark_apply()
.Fixed slow printing issue caused by
print
calculating the total row count, which is expensive for some tables.Fixed
sparklyr 0.6
issue blocking concurrentsparklyr
connections, which required to setconfig$sparklyr.gateway.remote = FALSE
as workaround.
Sparklyr 0.6.0
Distributed R
Added
packages
parameter tospark_apply()
to distribute packages across worker nodes automatically.Added
sparklyr.closures.rlang
as aspark_config()
value to support generic closures provided by therlang
package.Added config options
sparklyr.worker.gateway.address
andsparklyr.worker.gateway.port
to configure gateway used under worker nodes.Added
group_by
parameter tospark_apply()
, to support operations over groups of dataframes.Added
spark_apply()
, allowing users to use R code to directly manipulate and transform Spark DataFrames.
External Data
Added
spark_write_source()
. This function writes data into a Spark data source which can be loaded through an Spark package.Added
spark_write_jdbc()
. This function writes from a Spark DataFrame into a JDBC connection.Added
columns
parameter tospark_read_*()
functions to load data with named columns or explicit column types.Added
partition_by
parameter tospark_write_csv()
,spark_write_json()
,spark_write_table()
andspark_write_parquet()
.Added
spark_read_source()
. This function reads data from a Spark data source which can be loaded through an Spark package.Added support for
mode = "overwrite"
andmode = "append"
tospark_write_csv()
.spark_write_table()
now supports saving to default Hive path.Improved performance of
spark_read_csv()
reading remote data wheninfer_schema = FALSE
.Added
spark_read_jdbc()
. This function reads from a JDBC connection into a Spark DataFrame.Renamed
spark_load_table()
andspark_save_table()
intospark_read_table()
andspark_write_table()
for consistency with existingspark_read_*()
andspark_write_*()
functions.Added support to specify a vector of column names in
spark_read_csv()
to specify column names without having to set the type of each column.Improved
copy_to()
,sdf_copy_to()
anddbWriteTable()
performance underyarn-client
mode.
dplyr
Support for
cumprod()
to calculate cumulative products.Support for
cor()
,cov()
,sd()
andvar()
as window functions.Support for Hive built-in operators
%like%
,%rlike%
, and%regexp%
for matching regular expressions infilter()
andmutate()
.Support for dplyr (>= 0.6) which among many improvements, increases performance in some queries by making use of a new query optimizer.
sample_frac()
takes a fraction instead of a percent to match dplyr.Improved performance of
sample_n()
andsample_frac()
through the use ofTABLESAMPLE
in the generated query.
Databases
Added
src_databases()
. This function list all the available databases.Added
tbl_change_db()
. This function changes current database.
DataFrames
Added
sdf_len()
,sdf_seq()
andsdf_along()
to help generate numeric sequences as Spark DataFrames.Added
spark_set_checkpoint_dir()
,spark_get_checkpoint_dir()
, andsdf_checkpoint()
to enable checkpointing.Added
sdf_broadcast()
which can be used to hint the query optimizer to perform a broadcast join in cases where a shuffle hash join is planned but not optimal.Added
sdf_repartition()
,sdf_coalesce()
, andsdf_num_partitions()
to support repartitioning and getting the number of partitions of Spark DataFrames.Added
sdf_bind_rows()
andsdf_bind_cols()
– these functions are thesparklyr
equivalent ofdplyr::bind_rows()
anddplyr::bind_cols()
.Added
sdf_separate_column()
– this function allows one to separate components of an array / vector column into separate scalar-valued columns.sdf_with_sequential_id()
now supportsfrom
parameter to choose the starting value of the id column.Added
sdf_pivot()
. This function provides a mechanism for constructing pivot tables, using Spark’s ‘groupBy’ + ‘pivot’ functionality, with a formula interface similar to that ofreshape2::dcast()
.
MLlib
Added
vocabulary.only
toft_count_vectorizer()
to retrieve the vocabulary with ease.GLM type models now support
weights.column
to specify weights in model fitting. (#217)ml_logistic_regression()
now supports multinomial regression, in addition to binomial regression [requires Spark 2.1.0 or greater]. (#748)Implemented
residuals()
andsdf_residuals()
for Spark linear regression and GLM models. The former returns a R vector while the latter returns atbl_spark
of training data with aresiduals
column added.Added
ml_model_data()
, used for extracting data associated with Spark ML models.The
ml_save()
andml_load()
functions gain ameta
argument, allowing users to specify where R-level model metadata should be saved independently of the Spark model itself. This should help facilitate the saving and loading of Spark models used in non-local connection scenarios.ml_als_factorization()
now supports the implicit matrix factorization and nonnegative least square options.Added
ft_count_vectorizer()
. This function can be used to transform columns of a Spark DataFrame so that they might be used as input toml_lda()
. This should make it easier to invokeml_lda()
on Spark data sets.
Broom
- Implemented
tidy()
,augment()
, andglance()
from tidyverse/broom forml_model_generalized_linear_regression
andml_model_linear_regression
models.
R Compatibility
- Implemented
cbind.tbl_spark()
. This method works by first generating index columns usingsdf_with_sequential_id()
then performinginner_join()
. Note that dplyr_join()
functions should still be used for DataFrames with common keys since they are less expensive.
Connections
Increased default number of concurrent connections by setting default for
spark.port.maxRetries
from 16 to 128.Support for gateway connections
sparklyr://hostname:port/session
and usingspark-submit --class sparklyr.Shell sparklyr-2.1-2.11.jar <port> <id> --remote
.Added support for
sparklyr.gateway.service
andsparklyr.gateway.remote
to enable/disable the gateway in service and to accept remote connections required for Yarn Cluster mode.Added support for Yarn Cluster mode using
master = "yarn-cluster"
. Either, explicitly setconfig = list(sparklyr.gateway.address = "<driver-name>")
or implicitlysparklyr
will read thesite-config.xml
for theYARN_CONF_DIR
environment variable.Added
spark_context_config()
andhive_context_config()
to retrieve runtime configurations for the Spark and Hive contexts.Added
sparklyr.log.console
to redirect logs to console, useful to troubleshootingspark_connect
.Added
sparklyr.backend.args
as config option to enable passing parameters to thesparklyr
backend.Improved logging while establishing connections to
sparklyr
.Improved
spark_connect()
performance.Implemented new configuration checks to proactively report connection errors in Windows.
While connecting to spark from Windows, setting the
sparklyr.verbose
option toTRUE
prints detailed configuration steps.Added
custom_headers
tolivy_config()
to add custom headers to the REST call to the Livy server
Compilation
Added support for
jar_dep
in the compilation specification to support additionaljars
throughspark_compile()
.spark_compile()
now prints deprecation warnings.Added
download_scalac()
to assist downloading all the Scala compilers required to build usingcompile_package_jars
and provided support for using anyscalac
minor versions while looking for the right compiler.
Backend
- Improved backend logging by adding type and session id prefix.
Miscellaneous
copy_to()
andsdf_copy_to()
auto generate aname
when an expression can’t be transformed into a table name.Implemented
type_sum.jobj()
(from tibble) to enable better printing of jobj objects embedded in data frames.Added the
spark_home_set()
function, to help facilitate the setting of theSPARK_HOME
environment variable. This should prove useful in teaching environments, when teaching the basics of Spark and sparklyr.Added support for the
sparklyr.ui.connections
option, which adds additional connection options into the new connections dialog. Therstudio.spark.connections
option is now deprecated.Implemented the “New Connection Dialog” as a Shiny application to be able to support newer versions of RStudio that deprecate current connections UI.
Bug Fixes
When using
spark_connect()
in local clusters, it validates thatjava
exists underJAVA_HOME
to help troubleshoot systems that have an incorrectJAVA_HOME
.Improved
argument is of length zero
error triggered while retrieving data with no columns to display.Fixed
Path does not exist
referencinghdfs
exception duringcopy_to
under systems configured withHADOOP_HOME
.Fixed session crash after “No status is returned” error by terminating invalid connection and added support to print log trace during this error.
compute()
now caches data in memory by default. To revert this beavior usesparklyr.dplyr.compute.nocache
set toTRUE
.spark_connect()
withmaster = "local"
and a givenversion
overridesSPARK_HOME
to avoid existing installation mismatches.Fixed
spark_connect()
under Windows issue whennewInstance0
is present in the logs.Fixed collecting
long
type columns when NAs are present (#463).Fixed backend issue that affects systems where
localhost
does not resolve properly to the loopback address.Fixed issue collecting data frames containing newlines
\n
.Spark Null objects (objects of class NullType) discovered within numeric vectors are now collected as NAs, rather than lists of NAs.
Fixed warning while connecting with livy and improved 401 message.
Fixed issue in
spark_read_parquet()
and other read methods in whichspark_normalize_path()
would not work in some platforms while loading data using custom protocols likes3n://
for Amazon S3.Resolved issue in
spark_save()
/load_table()
to support saving / loading data and added path parameter inspark_load_table()
for consistency with other functions.
Sparklyr 0.5.5
- Implemented support for
connectionViewer
interface required in RStudio 1.1 andspark_connect
withmode="databricks"
.
Sparklyr 0.5.4
- Implemented support for
dplyr 0.6
and Spark 2.1.x.
Sparklyr 0.5.3
- Implemented support for
DBI 0.6
.
Sparklyr 0.5.2
Fix to
spark_connect
affecting Windows users and Spark 1.6.x.Fix to Livy connections which would cause connections to fail while connection is on ‘waiting’ state.
Sparklyr 0.5.0
Implemented basic authorization for Livy connections using
livy_config_auth()
.Added support to specify additional
spark-submit
parameters using thesparklyr.shell.args
environment variable.Renamed
sdf_load()
andsdf_save()
tospark_read()
andspark_write()
for consistency.The functions
tbl_cache()
andtbl_uncache()
can now be using without requiring thedplyr
namespace to be loaded.spark_read_csv(..., columns = <...>, header = FALSE)
should now work as expected – previously,sparklyr
would still attempt to normalize the column names provided.Support to configure Livy using the
livy.
prefix in theconfig.yml
file.Implemented experimental support for Livy through:
livy_install()
,livy_service_start()
,livy_service_stop()
andspark_connect(method = "livy")
.The
ml
routines now acceptdata
as an optional argument, to support calls of the form e.g.ml_linear_regression(y ~ x, data = data)
. This should be especially helpful in conjunction withdplyr::do()
.Spark
DenseVector
andSparseVector
objects are now deserialized as R numeric vectors, rather than Spark objects. This should make it easier to work with the output produced bysdf_predict()
with Random Forest models, for example.Implemented
dim.tbl_spark()
. This should ensure thatdim()
,nrow()
andncol()
all produce the expected result withtbl_spark
s.Improved Spark 2.0 installation in Windows by creating
spark-defaults.conf
and configuringspark.sql.warehouse.dir
.Embedded Apache Spark package dependencies to avoid requiring internet connectivity while connecting for the first through
spark_connect
. Thesparklyr.csv.embedded
config setting was added to configure a regular expression to match Spark versions where the embedded package is deployed.Increased exception callstack and message length to include full error details when an exception is thrown in Spark.
Improved validation of supported Java versions.
The
spark_read_csv()
function now accepts theinfer_schema
parameter, controlling whether the columns schema should be inferred from the underlying file itself. Disabling this should improve performance when the schema is known beforehand.Added a
do_.tbl_spark
implementation, allowing for the execution ofdplyr::do
statements on Spark DataFrames. Currently, the computation is performed in serial across the different groups specified on the Spark DataFrame; in the future we hope to explore a parallel implementation. Note thatdo_
always returns atbl_df
rather than atbl_spark
, as the objects produced within ado_
query may not necessarily be Spark objects.Improved errors, warnings and fallbacks for unsupported Spark versions.
sparklyr
now defaults totar = "internal"
in its calls tountar()
. This should help resolve issues some Windows users have seen related to an inability to connect to Spark, which ultimately were caused by a lack of permissions on the Spark installation.Resolved an issue where
copy_to()
and other R => Spark data transfer functions could fail when the last column contained missing / empty values. (#265)Added
sdf_persist()
as a wrapper to the Spark DataFramepersist()
API.Resolved an issue where
predict()
could produce results in the wrong order for large Spark DataFrames.Implemented support for
na.action
with the various Spark ML routines. The value ofgetOption("na.action")
is used by default. Users can customize thena.action
argument through theml.options
object accepted by all ML routines.On Windows, long paths, and paths containing spaces, are now supported within calls to
spark_connect()
.The
lag()
window function now accepts numeric values forn
. Previously, only integer values were accepted. (#249)Added support to configure Ppark environment variables using
spark.env.*
config.Added support for the
Tokenizer
andRegexTokenizer
feature transformers. These are exported as theft_tokenizer()
andft_regex_tokenizer()
functions.Resolved an issue where attempting to call
copy_to()
with an Rdata.frame
containing many columns could fail with a Java StackOverflow. (#244)Resolved an issue where attempting to call
collect()
on a Spark DataFrame containing many columns could produce the wrong result. (#242)Added support to parameterize network timeouts using the
sparklyr.backend.timeout
,sparklyr.gateway.start.timeout
andsparklyr.gateway.connect.timeout
config settings.Improved logging while establishing connections to
sparklyr
.Added
sparklyr.gateway.port
andsparklyr.gateway.address
as config settings.The
spark_log()
function now accepts thefilter
parameter. This can be used to filter entries within the Spark log.Increased network timeout for
sparklyr.backend.timeout
.Moved
spark.jars.default
setting from options to Spark config.sparklyr
now properly respects the Hive metastore directory with thesdf_save_table()
andsdf_load_table()
APIs for Spark < 2.0.0.Added
sdf_quantile()
as a means of computing (approximate) quantiles for a column of a Spark DataFrame.Added support for
n_distinct(...)
within thedplyr
interface, based on call to Hive functioncount(DISTINCT ...)
. (#220)
Sparklyr 0.4.0
- First release to CRAN.


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{% extends 'base.html' %} {% load static %} {% load i18n %} {% load i18n %} {% load widget_tweaks %} {% block title %}{% trans "Coworker - cowork spaces for coworking people" %}{% endblock %} {% block content %}
{% trans "Let everyone contact you quickly" %}
{% trans "When someone wants to contact you through the page, which mailboxaddress do you want to receive information, booking request, travelinformation? (multiple mailbox addresses can be set) " %}


By Paul Hutson / @PaulHutson
It's a GIS (Graphical Information System).
Open standard format for geographical features (or data structures) using JSON.
It differs from out GIS standards as it was written by an internet working group of developers.
Discussion about the format was started in March 2007 and was finalised in June 2008
{ "type": "Point",
"coordinates": [100.0, 0.0]
}
{ "type": "LineString",
"coordinates": [
[100.0, 0.0], [101.0, 1.0]
]
}
{ "type": "Polygon",
"coordinates": [
[ [100.0, 0.0], [101.0, 0.0], [101.0, 1.0], [100.0, 1.0], [100.0, 0.0] ]
]
}
{ "type": "GeometryCollection",
"geometries": [
{ "type": "Point",
"coordinates": [100.0, 0.0]
},
{ "type": "LineString",
"coordinates": [ [101.0, 0.0], [102.0, 1.0] ]
}
]
}
There are a number of different mapping and GIS packages that support GeoJSON.
OpenLayers, Leaflet, Geoforge, GeoServer, GeoDjango, GDAL, Safe Software FME and CartoDB.
Bing Maps, Yahoo! and Google also support GeoJSON in their API services.
Google Maps integration is possible using libraries (or by iterating through the data and creating equivilant objects).
GitHub also supports GeoJSON rendering.
// set up the map
var map = new L.Map('map'); // needs a corresponding on the page somewhere
// create the tile layer
var osmUrl = 'http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png';
var osmAttrib = 'Map data © OpenStreetMap contributors';
var osm = new L.TileLayer(osmUrl, { minZoom: 8, maxZoom: 15, attribution: osmAttrib });
// start the map in London (note, it's y,x for latlng)
map.setView(new L.LatLng(51.505, -0.09), 13);
map.addLayer(osm);
// Create a GeoJSON Feature
var geojsonFeature = {
"type": "Feature",
"properties": {
"name": "Example",
"amenity": "Example",
"popupContent": "Example!"
},
"geometry": {
"type": "Point",
"coordinates": [-0.09, 51.5]
}
};
// Create the layer and add the feature to the map
L.geoJson(geojsonFeature).addTo(map);
// Alternatively, you could do it like the below...
//var myLayer = L.geoJson().addTo(map);
//myLayer.addData(geojsonFeature);
// Point Marker
var marker = L.marker([51.5, -0.09]).addTo(map);
// Circle (not a GeoJSON Format)
var circle = L.circle([51.508, -0.11], 500, {
color: 'red',
fillColor: '#f03',
fillOpacity: 0.5
}).addTo(map);
// Polygon / Coordinate view
var polygon = L.polygon([
[51.509, -0.08],
[51.503, -0.06],
[51.51, -0.047]
]).addTo(map);


|
SdFat
|
Classes | |
| struct | fpos_t |
| internal type for istream do not use in user apps More... | |
| class | SdFile |
| Access FAT16 and FAT32 files on SD and SDHC cards. More... | |
Functions | |
| static uint16_t | FAT_DATE (uint16_t year, uint8_t month, uint8_t day) |
| static uint8_t | FAT_DAY (uint16_t fatDate) |
| static uint8_t | FAT_HOUR (uint16_t fatTime) |
| static uint8_t | FAT_MINUTE (uint16_t fatTime) |
| static uint8_t | FAT_MONTH (uint16_t fatDate) |
| static uint8_t | FAT_SECOND (uint16_t fatTime) |
| static uint16_t | FAT_TIME (uint8_t hour, uint8_t minute, uint8_t second) |
| static uint16_t | FAT_YEAR (uint16_t fatDate) |
Variables | |
| uint16_t const | FAT_DEFAULT_DATE = ((2000 - 1980) << 9) | (1 << 5) | 1 |
| uint16_t const | FAT_DEFAULT_TIME = (1 << 11) |
| uint8_t const | FAT_FILE_TYPE_CLOSED = 0 |
| uint8_t const | FAT_FILE_TYPE_MIN_DIR = FAT_FILE_TYPE_ROOT_FIXED |
| uint8_t const | FAT_FILE_TYPE_NORMAL = 1 |
| uint8_t const | FAT_FILE_TYPE_ROOT32 = 3 |
| uint8_t const | FAT_FILE_TYPE_ROOT_FIXED = 2 |
| uint8_t const | FAT_FILE_TYPE_SUBDIR = 4 |
| uint8_t const | LS_DATE = 1 |
| uint8_t const | LS_R = 4 |
| uint8_t const | LS_SIZE = 2 |
| uint8_t const | O_ACCMODE = (O_READ | O_WRITE) |
| uint8_t const | O_APPEND = 0X04 |
| uint8_t const | O_AT_END = 0X20 |
| uint8_t const | O_CREAT = 0X40 |
| uint8_t const | O_EXCL = 0X80 |
| uint8_t const | O_RDONLY = O_READ |
| uint8_t const | O_RDWR = (O_READ | O_WRITE) |
| uint8_t const | O_READ = 0X01 |
| uint8_t const | O_SYNC = 0X08 |
| uint8_t const | O_TRUNC = 0X10 |
| uint8_t const | O_WRITE = 0X02 |
| uint8_t const | O_WRONLY = O_WRITE |
| uint8_t const | T_ACCESS = 1 |
| uint8_t const | T_CREATE = 2 |
| uint8_t const | T_WRITE = 4 |
SdFile class.
| static uint16_t FAT_DATE | ( | uint16_t | year, |
| uint8_t | month, | ||
| uint8_t | day | ||
| ) | [inline, static] |
date field for FAT directory entry
| [in] | year | [1980,2107] |
| [in] | month | [1,12] |
| [in] | day | [1,31] |
| static uint8_t FAT_DAY | ( | uint16_t | fatDate | ) | [inline, static] |
day part of FAT directory date field
| [in] | fatDate | Date in packed dir format. |
| static uint8_t FAT_HOUR | ( | uint16_t | fatTime | ) | [inline, static] |
hour part of FAT directory time field
| [in] | fatTime | Time in packed dir format. |
| static uint8_t FAT_MINUTE | ( | uint16_t | fatTime | ) | [inline, static] |
minute part of FAT directory time field
| [in] | fatTime | Time in packed dir format. |
| static uint8_t FAT_MONTH | ( | uint16_t | fatDate | ) | [inline, static] |
month part of FAT directory date field
| [in] | fatDate | Date in packed dir format. |
| static uint8_t FAT_SECOND | ( | uint16_t | fatTime | ) | [inline, static] |
second part of FAT directory time field Note second/2 is stored in packed time.
| [in] | fatTime | Time in packed dir format. |
| static uint16_t FAT_TIME | ( | uint8_t | hour, |
| uint8_t | minute, | ||
| uint8_t | second | ||
| ) | [inline, static] |
time field for FAT directory entry
| [in] | hour | [0,23] |
| [in] | minute | [0,59] |
| [in] | second | [0,59] |
| static uint16_t FAT_YEAR | ( | uint16_t | fatDate | ) | [inline, static] |
year part of FAT directory date field
| [in] | fatDate | Date in packed dir format. |
| uint16_t const FAT_DEFAULT_DATE = ((2000 - 1980) << 9) | (1 << 5) | 1 |
Default date for file timestamps is 1 Jan 2000
| uint16_t const FAT_DEFAULT_TIME = (1 << 11) |
Default time for file timestamp is 1 am
| uint8_t const FAT_FILE_TYPE_CLOSED = 0 |
This SdFile has not been opened.
| uint8_t const FAT_FILE_TYPE_MIN_DIR = FAT_FILE_TYPE_ROOT_FIXED |
Test value for directory type
| uint8_t const FAT_FILE_TYPE_NORMAL = 1 |
SdFile for a file
| uint8_t const FAT_FILE_TYPE_ROOT32 = 3 |
SdFile for a FAT32 root directory
| uint8_t const FAT_FILE_TYPE_ROOT_FIXED = 2 |
SdFile for a FAT12 or FAT16 root directory
| uint8_t const FAT_FILE_TYPE_SUBDIR = 4 |
SdFile for a subdirectory
| uint8_t const LS_DATE = 1 |
ls() flag to print modify date
| uint8_t const LS_R = 4 |
ls() flag for recursive list of subdirectories
| uint8_t const LS_SIZE = 2 |
ls() flag to print file size
| uint8_t const O_APPEND = 0X04 |
The file offset shall be set to the end of the file prior to each write.
| uint8_t const O_AT_END = 0X20 |
set the initial position at the end of the file
| uint8_t const O_CREAT = 0X40 |
create the file if nonexistent
| uint8_t const O_EXCL = 0X80 |
If O_CREAT and O_EXCL are set, open() shall fail if the file exists
| uint8_t const O_READ = 0X01 |
open() oflag for reading
| uint8_t const O_SYNC = 0X08 |
synchronous writes - call sync() after each write
| uint8_t const O_TRUNC = 0X10 |
truncate the file to zero length
| uint8_t const O_WRITE = 0X02 |
open() oflag for write
| uint8_t const T_ACCESS = 1 |
set the file's last access date
| uint8_t const T_CREATE = 2 |
set the file's creation date and time
| uint8_t const T_WRITE = 4 |
Set the file's write date and time


Set max, min and initial track zoom. Multiple Canvases will be used if necessary to draw the track, avoiding errors with a canvas exceding max dimensions in the browser.


(PECL maxdb >= 1.0)
maxdb_options -- maxdb::options — Set options
过程化风格
面向对象风格
maxdb_options() can be used to set extra connect options and affect behavior for a connection.
This function may be called multiple times to set several options.
maxdb_options() should be called after maxdb_init() and before maxdb_real_connect().
The parameter option
is the option that you want to set,
the value
is the value for the option. For detailed
description of the options see » http://maxdb.sap.com/documentation/
The parameter option
can be one of the following values:
| Name | Description |
|---|---|
MAXDB_COMPNAME |
The component name used to initialise the SQLDBC runtime environment. |
MAXDB_APPLICATION |
The application to be connected to the database. |
MAXDB_APPVERSION |
The version of the application. |
MAXDB_SQLMODE |
The SQL mode. |
MAXDB_UNICODE |
TRUE, if the connection is an unicode (UCS2) client or FALSE, if not. |
MAXDB_TIMEOUT |
The maximum allowed time of inactivity after which the connection to the database is closed by the system. |
MAXDB_ISOLATIONLEVEL |
Specifies whether and how shared locks and exclusive locks are implicitly requested or released. |
MAXDB_PACKETCOUNT |
The number of different request packets used for the connection. |
MAXDB_STATEMENTCACHESIZE |
The number of prepared statements to be cached for the connection for re-use. |
MAXDB_CURSORPREFIX |
The prefix to use for result tables that are automatically named. |
成功时返回 TRUE
， 或者在失败时返回 FALSE
。
See maxdb_real_connect().


Foundation for all types of skin, shade and coats! The Makeup Respository has it all.
Date night or a girls night? Why don't you try out our midnight purple look!
One palette wonder! Make the most of your makeup with this tutorial created only using Stacey Marie's Carnival Palette.
One palette, a million moods. More magic from Stacey Marie's Carnival Palette.
Create a hot winter look with cool, icy tones.
A run down of our favourite eyeshadows of 2018. We want to know yours too!


Title
Note that this is the title in wikitext, not page title.
Description
You can define your mapping below.
Permission
When you upload a file, you must indicate its license status. See Commons:Licensing for a quick tutorial about licenses.
Source
This is usually the web address where image was downloaded.
Date
Date that the image was created.
Medium
Medium (technique and materials) used to create artwork.
Institution
Accession number
Notes
Notes on images.
Inscriptions
Categories
Template spesific mapping
Here you can match your fields to the fields of wikitext template.
Here you can match your fields to the fields of wikitext template.