Want to put some text here? Cool! This can be useful for the people visiting your website.
Be sure to check out the SASS files for what classes we have and edit the template (as the yellow might be too much for you!)... or make your own!
We offer a basic amount of colors, so the rest it up to your :) don't let your dreams be dreams!
Just do it!
This is going to be filled with lorem ipsum from now on, We'll try to find a funny lorem ipsum generator.
There was a circumstance which at first sight seemed to entangle his delirious but still methodical scheme. But not so in the reality, perhaps. Though the gregarious sperm whales have their regular seasons for particular grounds, yet in general you cannot conclude that the herds which haunted such and such a latitude or longitude this year, say, will turn out to be identically the same with those that were found there the preceding season; though there are peculiar and unquestionable instances where the contrary of this has proved true. In general, the same remark, only within a less wide limit, applies to the solitaries and hermits among the matured, aged sperm whales.
So that though Moby Dick had in a former year been seen, for example, on what is called the Seychelle ground in the Indian ocean, or Volcano Bay on the Japanese Coast; yet it did not follow, that were the Pequod to visit either of those spots at any subsequent corresponding season, she would infallibly encounter him there.
So, too, with some other feeding grounds, where he had at times revealed himself. But all these seemed only his casual stopping-places and ocean-inns, so to speak, not his places of prolonged abode. And where Ahab's chances of accomplishing his object have hitherto been spoken of, allusion has only been made to whatever way-side, antecedent, extra prospects were his, ere a particular set time or place were attained, when all possibilities would become probabilities, and, as Ahab fondly thought, every possibility the next thing to a certainty. That particular set time and place were conjoined in the one technical phrase—the Season-on-the-Line.
You get the jist of it. Going to leave this empty :)
Wow, I had no idea hwo to make my hackathon website. Cool I found this website.
Even better, it has a timeline!
Your date here.It will switch between sides so you don't have a monotone, one-sided timeline.
Well, except on mobile, considering it's responsive and all.
Some more date.You can also link to other sites or blogposts! We don't have a blogpost feature as this is a "one pager" (bar code-of conduct).
If you want blogposts and more, check our Apps For X hackathon/event template with CMS.
Go! 1 June


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Ayn Rand was a bestselling author and social critic who wrote novels, plays, and philosophy books.
Her philosophy, which she described as Objectivism, holds that any proper moral system must use objective reality as its starting point.
Rand believed that humans are beings with certain natural needs and wants, and that any action to fulfill those needs and wants is
perfectly just and moral so long as each individual never initiates the use of force against a fellow human.
Rand's writings have been an inspiration for countless people around the globe, from business tycoons to libertarian activists.
For more information on Ayn Rand's life and writings, please check out the Atlas Society.


The simplest syntax highlighting for the Web
old version 0.2
Created for everyone, everywhere. Is a free and open source forever
Very easy to include in your application , just download or clone from github
You will need to include the style coderStyle.min.css
and the lib jquery.min.js
and coderStyle.min.js
files you downloaded or by cdn in your layout or pages.
Next just call coderstyle function with your id
or class
like :
You can specify your programming language code with attribut cs-lang
or in script function by default language :
You can also specify your template with attribut cs-theme
or in script function by default theme :
And you can specify your in-put
or out-put
code data :
14/12/2017 : add bash command language
13/12/2017 : add github template
24/07/2017 : use gulp system
1/04/2017 : start the project


Any element that has a behavior declared on it gets an event collection object added to it. This event collection keeps track of anything that is subscribed to it, and invokes any delegates when an event is fired.
In TactJS behaviors, there is no need to explicitly create events. Simply firing an event is enough. Subscribing to events works the same way; you simply call eventSubscribe(eventName, delegate) against an element. Here's how easy it is to subscribe, unsibscribe, and fire events on TactJS behaviors:
$(_context).eventSubscribe('onToggle', toggleElement); $(_context).eventUnsubscribe('onToggle', toggleElement); $(_context).events('onToggle').fire();
You don't have to have a behavior declared on an element to bind or subscribe to an event on it. Any element will do, although we recommend sticking to element with behaviors on them just because it should make your scripts easier to understand.


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Learn to code by watching others
See how experienced developers solve problems in real-time. Watching scripted tutorials is great, but understanding how developers think is invaluable.
Try it free 7 days, then $20/mo. thereafter.
See how experienced developers solve problems in real-time. Watching scripted tutorials is great, but understanding how developers think is invaluable.
Try it free 7 days, then $20/mo. thereafter.


--- layout: default title: Animate In Demo description: When elements enter the viewport, they transition in from zero opacity and from the bottom. image: /demos/animated.jpg extraJS: [ "viewport.js", "demos/animate-in.js" ] ---
{{ site.longDescription }}
This demo was inspired by codrops’ demo Loading effects for grid items with css animations.


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import include._ @import forms2._ @import config.{ ApplicationConfig} @import models.confirmation.Currency @this(main: include.main) @(isUK: Boolean, fee: Currency, paymentReference: String, sectionHeading: String)(implicit request: Request[_],m:Messages, lang: Lang, appConfig: ApplicationConfig) @header = { @components.back_link() @heading("payments.bankdetails.header", sectionHeading.toString()) } @main( title = Messages("payments.bankdetails.title") + " - " + Messages(sectionHeading), heading = header ) {
@Messages("payments.bankdetails.hint")


Srei Infra to focus on co-lending, leasing to scale up revenue
Awaits clarity on converting into a bank
Shobha Roy
Srei Infrastructure Finance, which is exploring the possibility of converting into a bank to give the much-needed push to its business, is awaiting for regulatory clarity on the same. Meanwhile, it will focus on leasing and co-lending businesses to scale up its revenue.
According to Sunil Kanoria, Vice-Chairman, Srei, it is exploring the possibility of entering into a partnership for its asset financing and leasing business.
“We had attempted that (converting into a bank) in the past….we will be open to look at it if the regulatory system allows that. But at the moment, not assuming anything of that sort happens, we have to put our house in order and that is what we are working on for the last one year,” Kanoria told BusinessLine.
With a view to consolidating its lending operations into one entity, Srei Infrastructure Finance had recently hived off its lending business into subsidiary company, Srei Equipment Finance. The aim was to grow the equipment finance business and reduce the infrastructure loan portfolio.
The move would also help Srei Equipment attract strategic investors and prepare the company for conversion into a bank, as and when the Reserve Bank of India decides to allow systematically important NBFCs to convert into banks, the company had said earlier.
Merging NBFC into a bank would help mitigate the systemic risk and also aid in scaling up growth.
Citing instances of some NBFCs such as Capital First, which merged with IDFC Bank, and Gruh Finance, which is set to be merged into Bandhan Bank, he said the regulator should proactively push it just like the government pushed the merger of public sector banking system to strengthen them and rationalise the cost structure.
“An NBFC is like a caterpillar and a bank is a butterfly, so if you have to fly you have to transform into a butterfly and the ecosystem should allow that transformation,” he said.
Co-lendin business
Srei, which has been reducing its focus on infrastructure financing, would continue to sell assets, thereby releasing capital and focus more on asset financing and leasing business.
“We believe there is a strong value proposition for that (asset finance and leasing). We will look at partners, whether domestically or globally,” he said.
Srei Equipment had BNP Paribas as its JV partner till around 2015 after which it exited the JV in lieu of a 5 per cent stake in Srei Infrastructure.
Asset leasing, which was earlier not considered to be not a very lucrative business due to tax-related issues, is now a viable model. The company started growing its leasing portfolio after GST kicked in and has a book of around ₹6,000 crore, when compared with Srei Equipment’s AUM of around ₹46,500 crore. “We are building that (leasing business) up. At the moment we are facing challenges in regulatory environment, but if that ecosystem builds up I would love to write more leases. Our core focus would be to become a 90 per cent leasing company,” he said.
The co-lending model has also been picking up, and the company has tied up with as many as eight banks. From ₹40 croer to 50 crore a month in early February and March, the company is able to co-lend close to ₹200 crore every month. The aim would be to grow it to ₹1,000 crore a month in the next one year.
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OAI_PMH
Base class for all OAI-PMH serialisable objects to extend from
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GetRecord
Serialisable object representing a GetRecord response
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Identify
Serialisable object representing an Identify response
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ListIdentifiers
Serialisable object representing a ListIdentifiers response
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ListMetadataFormats
Serialisable object representing a ListMetadataFormats response
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ListRecords
Serialisable object representing a ListRecords response
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ListSets
Serialisable object representing a ListSets response
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OAIPMHError
Base class to represent a serialisable error document
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BadArgument
Serialisable object representing a BadArgument response
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BadResumptionToken
Serialisable object representing a BadResumptionToken response
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BadVerb
Serialisable object representing a BadVerb response
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CannotDisseminateFormat
Serialisable object representing a CannotDisseminateFormat response
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IdDoesNotExist
Serialisable object representing an IdDoesNotExist response
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NoRecordsMatch
Serialisable object representing a NoRecordsMatch response
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NoMetadataFormats
Serialisable object representing a NoMetadataFormats response
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NoSetHierarchy
Serialisable object representing a NoSetHierarchy response
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oaipmh(repository_id=None)
main web route for oai-pmh. If the request is to /all this will be called
with repository_id=None, otherwise, the repository_id will be taken from the url
path |
source code
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list_records(dao,
base_url,
metadata_prefix=None,
from_date=None,
until_date=None,
oai_set=None,
resumption_token=None)
Actions a request to ListRecords |
source code
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_parameterised_list_records(dao,
base_url,
metadata_prefix=None,
from_date=None,
until_date=None,
oai_set=None,
start_number=0)
Carry out a ListRecords request based on the set of actual paramters (i.e. not via a resumption token) |
source code
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list_identifiers(dao,
base_url,
metadata_prefix=None,
from_date=None,
until_date=None,
oai_set=None,
resumption_token=None)
Actions a request to ListIdentifiers |
source code
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_parameterised_list_identifiers(dao,
base_url,
metadata_prefix=None,
from_date=None,
until_date=None,
oai_set=None,
start_number=0)
Carry out a ListIdentifiers request based on the set of actual paramters (i.e. not via a resumption token) |
source code
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get_record(dao,
base_url,
identifier=None,
metadata_prefix=None)
Action a reuest to GetRecord |
source code
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{% extends "base.html" %} {% load static %} {% block title %}Books and Courses - {{ block.super }}{% endblock %} {% block analytics %} analytics.page("Books and Courses"); {% endblock %} {% block content %}
I love to teach beginner web skills! See everything I've released so far:
Design shortcuts and fundamentals aimed for programmers, developers, marketers, and non-designers. Learn just enough design.
{% comment %}Pricing starts at $24.95{% endcomment %}Want to build your first web app? Hello Web App walks you through building and launching a simple web app using Python and Django.
{% comment %}Pricing starts at $34.95{% endcomment %}Free intro to the command line. Free zine, printable, and online intro to the command line. Written as a companion to Hello Web App.
Free foreverFree intro to Git. Free zine, printable, and online intro to Git version control system. Written as a companion to Hello Web App.
Free forever


Helpful resources so you don't have to reinvent the wheel each year!
What parts of the IEW DVD should I view? What should I say at my Orientation Meeting?
Orientation ResourcesSome quick example text to build on the card title and make up the bulk of the card's content.
Go somewhere


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compiler(self,
**compileParameters)
This method will parse the openspeak file and will write to a python
module with the equivalent translations. |
source code
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translate_repeat(self,
**repeatStatement)
this will transalte the repeat statement into a python equivalen
while loop |
source code
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interpret(self,
**interpetParameters)
This method will accept the OpenSpeak syntax into a string and will
return a python equivalent translations statement |
source code
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translate_logs(self,
**logStatement)
This will translate the OpenSpeak log message statements into python
equivalent to resultString and returns resultString |
source code
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translate_log_message(self,
**messageStatement)
This API will translate log messages if it is a string or Variable or
combination of string and variable. |
source code
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translate_assertion(self,
**assertStatement)
This will translate the ASSERT <value1> <COMPARISON
OPERATOR> <value2> into python equivalent to resultString
and returns resultString |
source code
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translate_store(self,
**storeStatement)
This will translate the STORE <variable> IN <value> or
<variable> = <value> into python equivalent to
resultString and returns resultString |
source code
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translate_disconnect(self,
**disconnectStatement)
This will translate the DISCONNECT <component_name> into python
equivalent to resultString and returns resultString |
source code
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translate_onDOAs(self,
**onDoStatement)
This will translate the ON <component> DO <action> USING
<arg1> AS <value1>,<arg2> AS <value2> into
python equivalent to resultString and returns resultString |
source code
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handle_conjuction(self,
**conjuctStatement)
This will handle the conjuctions |
source code
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translate_connect(self,
**connectStatement)
This will translate the CONNECT <component_name> USING1
<arg1> AS <value1>, <arg2> AS <value2> into
python equivalent to resultString and returns resultString |
source code
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translate_parameters(self,
**parameterStatement)
This will translate the OpenSpeak Case and Params parameters into
python equivalent to resultString and returns resultString |
source code
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_argsCheck(self,
**args)
This API will check if given argument is varibale reference or String
and will translate accordingly. |
source code
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translate_step(self,
**stepStatement)
This will translate the STEP "DO SOMETHING HERE" into
python equivalent to resultString and returns resultString |
source code
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translate_comment(self,
**commentStatement)
This will translate the COMMENT "DO SOMETHING HERE" into
python equivalent to resultString and returns resultString |
source code
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translate_testcase_name(self,
**nameStatement)
This method will convert NAME "<Testcase_name>" into
python equivalent statement to resultString and returns resultString |
source code
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translate_conjuction(self,
conjuctionStatement)
This will translate the AND conjuction statements into python
equivalent to resultString and returns resultString |
source code
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parse_args(self,
args,
**kwargs)
It will accept the (key,value) pair and will return the (key,value)
pairs with keys in uppercase. |
source code
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SGL Carbon delivers composites for Airbus Helicopter rotor blades 25 Nov 2019, 16:22 by Mike Richardson Share This Article Tweet ShareShareShare Subscribe to our FREE Newsletter Tags


Floats are pretty useful, but the only snag is that we can only float left or right and not in the center (or anywhere else for that matter). And if we have two adjecent divs or columns, we can't have the float affect the text-flow in both columns. Thankfully, this will change with CSS Exclusions.
Demo time!
But...how?
First thing to mention is that the image isn't a true float anymore, because it is actually just positioned absolute. The image is now called an exclusion, and its shape should be excluded from the text around it.
To get this thing working we have to do two things: make the exclusion have position:
absolute;
, and define the way the content wraps around the exclusion with the wrap-flow
CSS
property. You can see the possible warp-flow values in the examples above.
You can influence wether or not the surrounding content should wrap around the the exclusion
or not. Setting wrap-through: none
makes the element ignore any exclusion it encounters.
Useful?
Very useful, and it's just the tip of the iceberg of what CSS Exclusions has to offer in the future. These kind of features allow us to create layouts that were previously either impossible, or only possible with ginormous amounts of hacking (which makes us feel dirty inside afterwards).
Is it real world proof then?
Cool as they might be, this is where they come up short. Nope. Sorry. You could, theoretically, but the positioned floats only work in IE10. Thanks to Adobe there is experimental support in Chrome Canary for some other (truly awesome) exclusion features, but work is still in progress. And even in IE10 it's not the most stable feature out there — we managed to crash the browser once or twice while playing with multiple exclusions.
Show me the source!
Feel free to look around the differente source files we used for this example.


Horse rescued from water-filled ditch near Whitchurch
by Aimee JonesA horse had to be rescued after it got stuck in a water-filled ditch near Whitchurch
Fire crews from Whitchurch and Wellington were called to rescue the animal at Old Woodhouses, Broughall, at 12.20pm on Saturday.
A vet and farmer were also on hand to free the horse, which who was stuck in a ditch.
Firefighters used a light portable pump to stem the flow of water in the ditch, along with specialist animal equipment, with assistance from a local farmer with a tractor to lift the horse free.
It took about two hours to rescue the animal.


Class: Aws::ApplicationAutoScaling::Types::TargetTrackingScalingPolicyConfiguration
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
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|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-applicationautoscaling/lib/aws-sdk-applicationautoscaling/types.rb |
Overview
Represents a target tracking scaling policy configuration to use with Application Auto Scaling.
For more information, see Target tracking scaling policies in the Application Auto Scaling User Guide.
Constant Summary
Instance Attribute Summary
-
#customized_metric_specification ⇒ Types::CustomizedMetricSpecification
rw
A customized metric.
-
#disable_scale_in ⇒ Boolean
rw
Indicates whether scale in by the target tracking scaling policy is disabled.
-
#predefined_metric_specification ⇒ Types::PredefinedMetricSpecification
rw
A predefined metric.
-
#scale_in_cooldown ⇒ Integer
rw
The amount of time, in seconds, after a scale-in activity completes before another scale-in activity can start.
-
#scale_out_cooldown ⇒ Integer
rw
The amount of time, in seconds, to wait for a previous scale-out activity to take effect.
-
#target_value ⇒ Float
rw
The target value for the metric.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#customized_metric_specification ⇒ Types::CustomizedMetricSpecification (rw)
A customized metric. You can specify either a predefined metric or a customized metric.
# File 'gems/aws-sdk-applicationautoscaling/lib/aws-sdk-applicationautoscaling/types.rb', line 3907
class TargetTrackingScalingPolicyConfiguration < Struct.new( :target_value, :predefined_metric_specification, :customized_metric_specification, :scale_out_cooldown, :scale_in_cooldown, :disable_scale_in) SENSITIVE = [] include Aws::Structure end
#disable_scale_in ⇒ Boolean
(rw)
Indicates whether scale in by the target tracking scaling policy is
disabled. If the value is true
, scale in is disabled and the
target tracking scaling policy won't remove capacity from the
scalable target. Otherwise, scale in is enabled and the target
tracking scaling policy can remove capacity from the scalable
target. The default value is false
.
# File 'gems/aws-sdk-applicationautoscaling/lib/aws-sdk-applicationautoscaling/types.rb', line 3907
class TargetTrackingScalingPolicyConfiguration < Struct.new( :target_value, :predefined_metric_specification, :customized_metric_specification, :scale_out_cooldown, :scale_in_cooldown, :disable_scale_in) SENSITIVE = [] include Aws::Structure end
#predefined_metric_specification ⇒ Types::PredefinedMetricSpecification (rw)
A predefined metric. You can specify either a predefined metric or a customized metric.
# File 'gems/aws-sdk-applicationautoscaling/lib/aws-sdk-applicationautoscaling/types.rb', line 3907
class TargetTrackingScalingPolicyConfiguration < Struct.new( :target_value, :predefined_metric_specification, :customized_metric_specification, :scale_out_cooldown, :scale_in_cooldown, :disable_scale_in) SENSITIVE = [] include Aws::Structure end
#scale_in_cooldown ⇒ Integer
(rw)
The amount of time, in seconds, after a scale-in activity completes before another scale-in activity can start. For more information and for default values, see Define cooldown periods in the Application Auto Scaling User Guide.
# File 'gems/aws-sdk-applicationautoscaling/lib/aws-sdk-applicationautoscaling/types.rb', line 3907
class TargetTrackingScalingPolicyConfiguration < Struct.new( :target_value, :predefined_metric_specification, :customized_metric_specification, :scale_out_cooldown, :scale_in_cooldown, :disable_scale_in) SENSITIVE = [] include Aws::Structure end
#scale_out_cooldown ⇒ Integer
(rw)
The amount of time, in seconds, to wait for a previous scale-out activity to take effect. For more information and for default values, see Define cooldown periods in the Application Auto Scaling User Guide.
# File 'gems/aws-sdk-applicationautoscaling/lib/aws-sdk-applicationautoscaling/types.rb', line 3907
class TargetTrackingScalingPolicyConfiguration < Struct.new( :target_value, :predefined_metric_specification, :customized_metric_specification, :scale_out_cooldown, :scale_in_cooldown, :disable_scale_in) SENSITIVE = [] include Aws::Structure end
#target_value ⇒ Float
(rw)
The target value for the metric. Although this property accepts numbers of type Double, it won't accept values that are either too small or too large. Values must be in the range of -2^360 to 2^360. The value must be a valid number based on the choice of metric. For example, if the metric is CPU utilization, then the target value is a percent value that represents how much of the CPU can be used before scaling out.
ALBRequestCountPerTarget
predefined metric, specify the target utilization as the optimal
average request count per target during any one-minute interval.
# File 'gems/aws-sdk-applicationautoscaling/lib/aws-sdk-applicationautoscaling/types.rb', line 3907
class TargetTrackingScalingPolicyConfiguration < Struct.new( :target_value, :predefined_metric_specification, :customized_metric_specification, :scale_out_cooldown, :scale_in_cooldown, :disable_scale_in) SENSITIVE = [] include Aws::Structure end


Change key assignments
The combo goes item (Eul's) + skill + skill + skill + invoke + cast
Finished with the task! Download and email the .json file to ___ to be entered into Steam raffle!


FAQ#
How to deal with a dataframe as input?
Each column of the dataframe is considered as an named input. The first step is to make sure that every column type is correct. pandas tends to select the least generic type to hold the content of one column. ONNX does not automatically cast the data it receives. The data must have the same type with the model is converted and when the converted model receives the data to predict.
<<<
from io import StringIO
from textwrap import dedent
import numpy
import pandas
from pyquickhelper.pycode import ExtTestCase
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from mlprodict.onnx_conv import to_onnx
from mlprodict.onnxrt import OnnxInference
text = dedent('''
__SCHEMA__
7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4,5,red
7.8,0.88,0.0,2.6,0.098,25.0,67.0,0.9968,3.2,0.68,9.8,5,red
7.8,0.76,0.04,2.3,0.092,15.0,54.0,0.997,3.26,0.65,9.8,5,red
11.2,0.28,0.56,1.9,0.075,17.0,60.0,0.998,3.16,0.58,9.8,6,red
''')
text = text.replace(
"__SCHEMA__",
"fixed_acidity,volatile_acidity,citric_acid,residual_sugar,chlorides,"
"free_sulfur_dioxide,total_sulfur_dioxide,density,pH,sulphates,"
"alcohol,quality,color")
X_train = pandas.read_csv(StringIO(text))
for c in X_train.columns:
if c != 'color':
X_train[c] = X_train[c].astype(numpy.float32)
numeric_features = [c for c in X_train if c != 'color']
pipe = Pipeline([
("prep", ColumnTransformer([
("color", Pipeline([
('one', OneHotEncoder()),
('select', ColumnTransformer(
[('sel1', 'passthrough', [0])]))
]), ['color']),
("others", "passthrough", numeric_features)
])),
])
pipe.fit(X_train)
pred = pipe.transform(X_train)
print(pred)
model_onnx = to_onnx(pipe, X_train, target_opset=12)
oinf = OnnxInference(model_onnx)
# The dataframe is converted into a dictionary,
# each key is a column name, each value is a numpy array.
inputs = {c: X_train[c].values for c in X_train.columns}
inputs = {c: v.reshape((v.shape[0], 1)) for c, v in inputs.items()}
onxp = oinf.run(inputs)
print(onxp)
>>>
[[1.000e+00 7.400e+00 7.000e-01 0.000e+00 1.900e+00 7.600e-02 1.100e+01
3.400e+01 9.978e-01 3.510e+00 5.600e-01 9.400e+00 5.000e+00]
[1.000e+00 7.800e+00 8.800e-01 0.000e+00 2.600e+00 9.800e-02 2.500e+01
6.700e+01 9.968e-01 3.200e+00 6.800e-01 9.800e+00 5.000e+00]
[1.000e+00 7.800e+00 7.600e-01 4.000e-02 2.300e+00 9.200e-02 1.500e+01
5.400e+01 9.970e-01 3.260e+00 6.500e-01 9.800e+00 5.000e+00]
[1.000e+00 1.120e+01 2.800e-01 5.600e-01 1.900e+00 7.500e-02 1.700e+01
6.000e+01 9.980e-01 3.160e+00 5.800e-01 9.800e+00 6.000e+00]]
{'transformed_column': array([[1.000e+00, 7.400e+00, 7.000e-01, 0.000e+00, 1.900e+00, 7.600e-02,
1.100e+01, 3.400e+01, 9.978e-01, 3.510e+00, 5.600e-01, 9.400e+00,
5.000e+00],
[1.000e+00, 7.800e+00, 8.800e-01, 0.000e+00, 2.600e+00, 9.800e-02,
2.500e+01, 6.700e+01, 9.968e-01, 3.200e+00, 6.800e-01, 9.800e+00,
5.000e+00],
[1.000e+00, 7.800e+00, 7.600e-01, 4.000e-02, 2.300e+00, 9.200e-02,
1.500e+01, 5.400e+01, 9.970e-01, 3.260e+00, 6.500e-01, 9.800e+00,
5.000e+00],
[1.000e+00, 1.120e+01, 2.800e-01, 5.600e-01, 1.900e+00, 7.500e-02,
1.700e+01, 6.000e+01, 9.980e-01, 3.160e+00, 5.800e-01, 9.800e+00,
6.000e+00]], dtype=float32)}
(original entry : convert.py:docstring of mlprodict.onnx_conv.convert.to_onnx, line 36)
Profile the execution
py-spy can be used to profile the execution of a program. The profile is more informative if the code is compiled with debug information.
py-spy record --native -r 10 -o plot_random_forest_reg.svg -- python plot_random_forest_reg.py
(original entry : plot_opml_random_forest_reg.rst, line 51)


Select board size
Easy (2x3)
Normal (4x3)
Hard (6x3)
Pairs:
Inspired by Gwent - The Witcher Card Game
Select board size
Easy (2x3)
Normal (4x3)
Hard (6x3)
Pairs:


The Java EE 7 Tutorial
17.12 Asynchronous Processing
Web containers in application servers normally use a server thread per client request. Under heavy load conditions, containers need a large amount of threads to serve all the client requests. Scalability limitations include running out of memory or exhausting the pool of container threads. To create scalable web applications, you must ensure that no threads associated with a request are sitting idle, so the container can use them to process new requests.
There are two common scenarios in which a thread associated with a request can be sitting idle.
-
The thread needs to wait for a resource to become available or process data before building the response. For example, an application may need to query a database or access data from a remote web service before generating the response.
-
The thread needs to wait for an event before generating the response. For example, an application may have to wait for a JMS message, new information from another client, or new data available in a queue before generating the response.
These scenarios represent blocking operations that limit the scalability of web applications. Asynchronous processing refers to assigning these blocking operations to a new thread and retuning the thread associated with the request immediately to the container.
17.12.1 Asynchronous Processing in Servlets
Java EE provides asynchronous processing support for servlets and filters. If a servlet or a filter reaches a potentially blocking operation when processing a request, it can assign the operation to an asynchronous execution context and return the thread associated with the request immediately to the container without generating a response. The blocking operation completes in the asynchronous execution context in a different thread, which can generate a response or dispatch the request to another servlet.
To enable asynchronous processing on a servlet, set the parameter asyncSupported
to true
on the @WebServlet
annotation as follows:
@WebServlet(urlPatterns={"/asyncservlet"}, asyncSupported=true) public class AsyncServlet extends HttpServlet { ... }
The javax.servlet.AsyncContext
class provides the functionality that you need to perform asynchronous processing inside service methods. To obtain an instance of AsyncContext
, call the startAsync()
method on the request object of your service method; for example:
public void doGet(HttpServletRequest req, HttpServletResponse resp) { ... AsyncContext acontext = req.startAsync(); ... }
This call puts the request into asynchronous mode and ensures that the response is not committed after exiting the service method. You have to generate the response in the asynchronous context after the blocking operation completes or dispatch the request to another servlet.
Table 17-3 describes the basic functionality provided by the AsyncContext
class.
Table 17-3 Functionality Provided by the AsyncContext Class
| Method Signature | Description |
|---|---|
|
|
The container provides a different thread in which the blocking operation can be processed. You provide code for the blocking operation as a class that implements the |
|
|
Returns the request used to initialize this asynchronous context. In the example above, the request is the same as in the service method. You can use this method inside the asynchronous context to obtain parameters from the request. |
|
|
Returns the response used to initialize this asynchronous context. In the example above, the response is the same as in the service method. You can use this method inside the asynchronous context to write to the response with the results of the blocking operation. |
|
|
Completes the asynchronous operation and closes the response associated with this asynchronous context. You call this method after writing to the response object inside the asynchronous context. |
|
|
Dispatches the request and response objects to the given path. You use this method to have another servlet write to the response after the blocking operation completes. |
17.12.2 Waiting for a Resource
This section demonstrates how to use the functionality provided by the AsyncContext
class for the following use case:
-
A servlet receives a parameter from a GET request.
-
The servlet uses a resource, such as a database or a web service, to retrieve information based on the value of the parameter. The resource can be slow at times, so this may be a blocking operation.
-
The servlet generates a response using the result from the resource.
The following code shows a basic servlet that does not use asynchronous processing:
@WebServlet(urlPatterns={"/syncservlet"}) public class SyncServlet extends HttpServlet { private MyRemoteResource resource; @Override public void init(ServletConfig config) { resource = MyRemoteResource.create("config1=x,config2=y"); } @Override public void doGet(HttpServletRequest request, HttpServletResponse response) { response.setContentType("text/html;charset=UTF-8"); String param = request.getParameter("param"); String result = resource.process(param); /* ... print to the response ... */ } }
The following code shows the same servlet using asynchronous processing:
@WebServlet(urlPatterns={"/asyncservlet"}, asyncSupported=true) public class AsyncServlet extends HttpServlet { /* ... Same variables and init method as in SyncServlet ... */ @Override public void doGet(HttpServletRequest request, HttpServletResponse response) { response.setContentType("text/html;charset=UTF-8"); final AsyncContext acontext = request.startAsync(); acontext.start(new Runnable() { public void run() { String param = acontext.getRequest().getParameter("param"); String result = resource.process(param); HttpServletResponse response = acontext.getResponse(); /* ... print to the response ... */ acontext.complete(); } }
AsyncServlet
adds asyncSupported=true
to the @WebServlet
annotation. The rest of the differences are inside the service method.
-
request.startAsync()
causes the request to be processed asynchronously; the response is not sent to the client at the end of the service method. -
acontext.start(new Runnable() {...})
gets a new thread from the container. -
The code inside the
run()
method of the inner class executes in the new thread. The inner class has access to the asynchronous context to read parameters from the request and write to the response. Calling thecomplete()
method of the asynchronous context commits the response and sends it to the client.
The service method of AsyncServlet
returns immediately, and the request is processed in the asynchronous context.


Statistics Essentials¶
Where are we going?¶
Within data science (and perhaps at its core) is the field of Machine learning, which seeks to accomplish two objectives:
Supervised learning - learn a mapping from inputs \(x\) to outputs \(y\)
Unsupervised learning - given only \(x\), learn interesting patterns in \(x\)
These tasks are a form of artificial intelligence that endow a computer with the capability to represent a general class of patterns.
Then through that representation they have the ability to predict outputs and identify patterns. Note that this is different than explicitly hardcoding some data relationship into a computer as though the specific relationship was already known beforehand.
Show More
In order to identify which specific patterns (out of a general class of patterns) are present in the data, machine learning makes extensive use of linear algebra—the branch of mathematics that works directly with matrices—in conjunction numerical optimization procedures.
This process of identifying a specific instance (out of a general class of patterns) that looks as similar to the data as possible is called “model fitting”. Once a machine has such a model representation of the data, then it has learned the pattern in the data and can use it as a part of other programatic instructions designed to accomplish some objective.
Machine Learning versus Statistics¶
What is the difference between statistics and machine learning?
Statistics and Machine Learning represent distinct quantitative analysis traditions that developed towards distinctive objectives that suited their idiosyncratic access to (primarily computationally) different problem solving methodologies and philosophies; however, both disciplines are rooted in the common enterprise of “data analysis” and so have found common ground on which to reconcile and merge methodologies, leading to the current situation in which the line between the two has become increasingly blurred. Nonetheless, some general statements related to the traditional domains and expertise claimed by each discipline can be made:
Statistics
utilizes confidence intervals, hypothesis tests, and optimal estimators
places paramount importance on characterizing uncertainty in estimation
bases methodological development on distribution and asymptotic theory
Machine Learning
utilizes nonparametric and complex models harnessed via regularization
places paramount importance on “out of sample” generalizability/performance
bases methodological development on empirical and computational techniques
Objectives¶
The purpose of this short course is to (a) equip you with actual quantitative tools that you can apply to more effectively tackle problems you’re interested in using data, and (b) to provide you with a appropriate foundation on which you can effectively build a synergistic data science skill set that leverages
Descriptive statistics - mean, median, skewness…
Inferential statistics - hypothesis testing, interval estimation…
Predictive analytics - supervised learning: regression, classification…
Prescriptive analytics - unsupervised learning, recommender systems…


Links úteis:
What's the difference between a mock & stub?
Testes Unitários 101: Mocks, Stubs, Spies e todas essas palavras difíceis
Smoke Testing
Sanity Testing Vs Smoke Testing: Introduction & Differences
Diferença entre Smoke e Acceptance Test num contexto ágil


Some of figures from different segments of the analysis were colated for the manuscript.
Here we present the code/process used to put them together.
Number of treatments and proportion of the total dose used in fungicide trials.
list.of.packages <-
c(
"tidyverse",
"readxl",
"devtools",
"ggpubr",
"zoo",
"kableExtra",
"conflicted",
"here",
"lubridate",
"magick",
"egg",
"conflicted",
"poppr",
"magick"
)
new.packages <-
list.of.packages[!(list.of.packages %in% installed.packages()[, "Package"])]
#Download packages that are not already present in the library
if (length(new.packages))
install.packages(new.packages)
packages_load <-
lapply(list.of.packages, require, character.only = TRUE)
#Print warning if there is a problem with installing/loading some of packages
if (any(as.numeric(packages_load) == 0)) {
warning(paste("Package/s: ", paste(list.of.packages[packages_load != TRUE], sep = ", "), "not loaded!"))
} else {
print("All packages were successfully loaded.")
}
## [1] "All packages were successfully loaded."
rm(list.of.packages, new.packages, packages_load)
conflict_prefer("filter", "dplyr")
#if instal is not working try
#install.packages("ROCR", repos = c(CRAN="https://cran.r-project.org/"))
df <-
read_csv(
here::here("data", "weather" , "hly375.csv"),
col_types = cols(
clamt = col_skip(),
clht = col_skip(),
vis = col_skip(),
w = col_skip(),
ww = col_skip()
),
skip = 17
)
df$date <-
lubridate::dmy_hm(df$date)
df<- add_column(df, short_date = as.Date(df$date), .after=1)
df<- add_column(df, year = year(df$date), .after=2)
df<- add_column(df, month = month(df$date), .after=3)
df<- add_column(df, day = day(df$date), .after=4)
infil_gap <- 8 #Maximum length of the infill gap
df$temp <-
round(na.spline(df$temp, na.rm = FALSE, maxgap = infil_gap), 1)
df$rhum <-
round(na.spline(df$rhum, na.rm = FALSE, maxgap = infil_gap), 0)
df$rhum <- sapply(df$rhum, function(x)
ifelse(x > 100, x <- 100, x))
df <-
df %>%
filter(year %in% 2016:2019)%>%
filter(month %in% c(4:9))
#Check if the imputation worked
df %>%
summarize(
NA_rain = sum(is.na(rain)),
NA_temp = sum(is.na(temp)),
NA_rhum = sum(is.na(rhum))
)%>%
kable(format = "html") %>%
kableExtra::kable_styling(latex_options = "striped",full_width = FALSE)
| NA_rain | NA_temp | NA_rhum |
|---|---|---|
| 2 | 0 | 0 |
#Only two missing values for rain should not be a problem, so we will just do a linear interpolation
df$rain <-
round(na.approx(df$rain, na.rm = FALSE, maxgap = infil_gap), 1)
dfday <-
df %>%
group_by(year, month, short_date) %>%
summarize(
mintemp = round(min(temp), 1),
minrhum = round(min(rhum), 1),
temp = round(mean(temp), 1),
rain = sum(rain),
rhum = round(mean(rhum), 1),
meantemp = round(mean(temp), 1),
meanrhum = round(mean(rhum), 1)
)
#REad disease data
disdata <-
read_rds( path = here::here("results", "dis", "Duration of the epidemics in years.Rdata"))
colnames(disdata) <-
c("year", "disout", "disend")
dfday <-
left_join(dfday, disdata, by = "year")
# rolling mean
rlmean <- 10
dfday$wrhum <- zoo::rollmean(dfday$meanrhum, rlmean, mean, align = 'center', fill = NA)
dfday$wtemp <- zoo::rollmean(dfday$mintemp, rlmean, mean, align = 'center', fill = NA)
#conducive days
templine <- 10
dfday <-
dfday %>%
mutate(condday = ifelse( wtemp>=templine&rain>=.2&rhum>=80& short_date<disout, 50,NA))
#sum of conditions prior to outbreak
dfday <-
dfday %>%
group_by(year) %>%
summarise(consum= sum(condday/50, na.rm = TRUE )) %>%
left_join(dfday,., by = "year")
#LAbels for cumulative sum of critical days
dfday <-
dfday %>%
mutate(symptoms = paste0("Symptoms (", consum, " cond. days)"))
linesize <- .35 #size of the line for variables
dfday %>%
mutate(rain = ifelse(rain == 0, NA, rain)) %>%
rename(date = short_date) %>%
ggplot() +
geom_point(aes(disout, 46),shape =2, size = 1.5, fill ="black")+
geom_point(aes(disend, 46),shape =2, size = 1.5, fill ="black")+
geom_point(aes(date, condday), shape = 3,size = .55)+
geom_line(
aes(
x = date,
y = rhum,
colour = "Relative humidity (%)"
),
size = linesize
) +
geom_line(aes(
x = date,
y = temp,
colour = "Temperature (˚C)",
),
size = linesize) +
geom_line(aes(
x = date,
y = wtemp,
colour = "Rolling mean temp. (˚C)"
),
linetype = "dashed",
size = linesize) +
geom_col(
aes(date,
rain,
fill = "Total precipitation (mm/day)"),
size = 1.2 ,
inherit.aes = TRUE,
width = 0.8
) +
scale_colour_manual(
"Daily weather:",
values = c(
"Relative humidity (%)" = "#0EBFE9",
"Temperature (˚C)" = "#ED2939",
"Rolling mean temp. (˚C)"= "darkred"
)
) +
scale_fill_manual(name= NA, values = c("Total precipitation (mm/day)" = "blue")
) +
scale_y_continuous( breaks = seq(0,100,10), limits = c(-1,100),expand = c(0, 0))+
scale_x_date(date_labels = "%b", date_breaks = "1 month",expand = c(.03, .04))+
facet_wrap( ~ year, scales = "free", ncol = 1,strip.position="right") +
guides(fill=guide_legend(nrow=1,byrow=TRUE,title.position = "top"),
color=guide_legend(nrow=1,byrow=TRUE,title.position = "top"),
linetype=guide_legend(nrow=1,byrow=TRUE,title.position = "top"))+
theme_bw()+
theme(
text = element_text(size=10.8),
# axis.text.x = element_blank(),
# axis.ticks.x = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
# axis.text.y = element_blank(),
# axis.ticks.y = element_blank(),
legend.position = "top",
strip.background =element_rect(colour = "black",size=0.75),
strip.placement = "outside",
legend.title = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = "lightgray",size=linesize),
plot.margin = margin(0, 0, 0, 0, "cm"),
panel.spacing.y=unit(0, "lines"),
legend.box.spacing = unit(0.1, 'cm'),
legend.margin = margin(0, 0, 0, 0, "cm"),
)+
geom_text(aes(label= symptoms, y = 39, x = disout),size = 2.5)+
geom_text(aes(label= "Last assesment", y = 39, x = disend-1.5),size = 2.5)+
ggsave(filename= here::here("results", "wth", "all_wth_dis.png"),
width = 6.5, height =8, dpi = 720)
GGplots from the frequency analysis wee colated using ‘ggpubr’ and DAPC plots using ‘magick’.
plotls <- readRDS(file = here::here("results", "gen", "freq","freq_plots.RDS"))
plotls <-
lapply(plotls, function(x)
x <- x+
theme(plot.background = element_rect(size=.3,linetype="solid",color="black")))
plotf <-
ggpubr::ggarrange(plotlist = plotls,
heights = c(1,1,1),
# labels = c("r","rr"),
nrow = 3)
ggsave(
plot = plotf,
filename = here::here("results", "gen", "freq", "Freq_all.png"),
width = 2.9,
height = 9,
dpi = 820
)
# Read external images
#shell.exec(here::here("results", "gen","dapc", "treatment.png"))
t = image_read(here::here("results", "gen","dapc", "treatment.png"))
v = image_read(here::here("results", "gen","dapc", "variety.png"))
y = image_read(here::here("results", "gen","dapc", "year.png"))
imgs = c(v,t,y)
# concatenate them left-to-right (use 'stack=T' to do it top-to-bottom)
side_by_side = image_append(imgs, stack=T)
# save
image_write(side_by_side,
path = here::here("results", "gen","dapc","dapcs", "full_dapc.png"),
format = "png")
#add the frequency plots
f = image_read(here::here("results", "gen", "freq", "Freq_all.png"))
dapcs = image_read(here::here("results", "gen","dapc","dapcs", "full_dapc.png"))
#Append and save the image
scale <- paste0("x", 7380)
# concatenate them left-to-right (use 'stack=T' to do it top-to-bottom)
side_by_side <- image_append(c(f, image_scale(dapcs, scale)), stack = FALSE)
image_write(side_by_side,
path = here::here("results", "gen","dapc","dapcs", paste0(scale, "full.png")),
format = "png",
# width = 4500,
# height = 3700
)
#To open the figure in external viewer uncoment the following lines
#shell.exec(here::here("results", "gen","dapc","dapcs", paste0(scale, "full.png")))
Results of the foliar disease controll yield analysis were colated in single figure using ‘magick’ package.
dd <-
readRDS( file = here::here("results", "dis", "dis_fin.RDS"))
yd <-
readRDS( file = here::here("results", "yield", "yld_fin.RDS"))
yld <-
readRDS( file = here::here("results", "yield", "yld_dat_fin.RDS"))
audpc_data <-
readRDS( file = here::here("results", "dis", "dis_audpc_fin.RDS"))
dodging <- .8
pdfin <-
dd %>%
mutate(
line_positions = as.numeric(factor(variety, levels = unique(variety))),
line_positions = line_positions + .5,
line_positions = ifelse(line_positions == max(line_positions), NA, line_positions),
line_positions = ifelse(year == 2016 &
variety == "SM", 5.5, line_positions),
line_positions = ifelse(year == 2016 &
variety == "CL", 4.5, line_positions),
line_positions = ifelse(year == 2016 &
variety == "SE", 3.5, line_positions),
line_positions = ifelse(year == 2016 &
variety == "KE", 1.5, line_positions)
) %>%
ggplot(data = ., aes(x = variety, y = prop)) +
geom_errorbar(
aes(
ymin = lower.CL,
ymax = upper.CL,
group = treatment,
color = treatment
),
position = position_dodge(width = dodging),
width = .2
) +
geom_point(
aes(y = prop, group = treatment, color = treatment),
size = 1,
shape = 2,
position = position_dodge(width = dodging)
)+
geom_point(
data = subset(audpc_data),
aes(y = rAUDPC_adj, color = treatment, group = treatment),
size = .2,
alpha = .5,
position = position_dodge(width = dodging)
) +
facet_wrap(~ year, ncol = 1)+
scale_color_brewer("Programme:", palette = "Dark2") +
theme_article() +
theme(legend.position = "bottom",
text = element_text(size = 13)) +
geom_vline(aes(xintercept = line_positions),
size = .2,
alpha = .6) +
labs(colour = "Programme:",
x = "Variety",
y = "rAUDPC")
ggsave(plot = pdfin,
filename = here::here("results", "dis", "Effects final ver.png"),
width = 4,
height = 7,
dpi = 620
)
py_fin <-
yd %>%
mutate(
line_positions = as.numeric(factor(variety, levels = unique(variety))),
line_positions = line_positions + .5,
line_positions = ifelse(line_positions == max(line_positions), NA, line_positions),
line_positions = ifelse(year == 2016 &
variety == "SM", 5.5, line_positions),
line_positions = ifelse(year == 2016 &
variety == "SE", 4.5, line_positions)
) %>%
ggplot(data = ., aes(x = variety, y = lsmean)) +
geom_errorbar(
aes(
ymin = lower.CL,
ymax = upper.CL,
group = treatment,
color = treatment
),
position = position_dodge(width = dodging),
width = .2
) +
geom_point(
aes(y = lsmean, group = treatment, color = treatment),
size = 1,
shape = 2,
position = position_dodge(width = dodging)
) +
geom_point(
data = yld,
aes(y = marketable, color = treatment, group = treatment),
size = .2,
alpha = .5,
position = position_dodge(width = dodging)
) +
facet_wrap(~ year, ncol = 1) +
scale_color_brewer("Programme:", palette = "Dark2") +
theme_article() +
theme(legend.position = "bottom",
text = element_text(size = 13)) +
geom_vline(aes(xintercept = line_positions),
size = .2,
alpha = .6) +
labs(colour = "Programme:",
x = "Variety",
y = "Yield (t per ha)")
py_fin <-
py_fin+
facet_wrap(~ year, ncol = 1)
ggsave(
py_fin,
filename = here::here("results", "yield", "Effects final ver.png"),
width = 3.5,
height = 7,
dpi = 620
)
plotls <- list(pdfin,py_fin)
plotls <-
lapply(plotls, function(x)
x <- x+
theme(plot.background = element_rect(size=.3,linetype="solid",color="black"),
text = element_text(size= 11))
)
ggpubr::ggarrange(plotlist = plotls,
widths = c(2.5,2.5),
heights = c(.5,.5),
labels = c("a)","b)"),
nrow = 1,
common.legend = TRUE,
legend = "bottom")+
ggsave(filename= here::here("results", "dis", "dis_yld.png"),
width = 7.5, height =10, dpi = 620)


The Syriac Bible has a long and notable history. It's origins (both the Syriac Old and New Testament) are cloaked in obscurity.
This website is dedicated to
just one part of the Syriac Bible: the study of the
Syriac New Testament and English language resources.
The motivation and the genesis of this website grew out of two things:
Frustration with the lack of English resources for the study of Syriac, and
Superficial nature of a vast majority of the preexisting resources.
Included here are English language resources which explain to the layman what the text says. The most basic place to start is the Syriac Bible.
That is why, I have brought together the Syriac New Testament translations of J.W. Etheridge, James Murdock and George Lamsa and combined them into one book, The Syriac Parallel New Testament.
These three English translations from the Syriac text are time tested, straightforward and easy to understand.
Checkout the translators!
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The aim of these articles is both general in their scope and scholarly in their orientation. All the content on this website was written with the layman in mind.
As you may know, the study of the Syriac New Testament in English is only in its infancy. You have plenty of choices on the internet and I appreciate the time you spend with us.
I hope you enjoy the content and if we deserve it, please consider sharing.
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Gecode::VarImp< VIC > Class Template Reference
[Programming variables]
Inherits Gecode::VarImpBase.
Detailed Description
template<class VIC>
class Gecode::VarImp< VIC >
Base-class for variable implementations.
Implements variable implementation for variable implementation configuration of type VIC.
Definition at line 284 of file core.icc.
Reflection | |
| static const Support::Symbol | vti = VIC::vti |
| Variable type identifier. | |
Dependencies | |
| void | subscribe (Space *home, Propagator *p, PropCond pc, bool assigned, ModEvent me, bool schedule) |
| Subscribe propagator p with propagation condition pc. | |
| void | cancel (Space *home, Propagator *p, PropCond pc, bool assigned) |
| Cancel subscription of propagator p with propagation condition pc. | |
| void | subscribe (Space *home, Advisor *a, bool assigned) |
| Subscribe advisor a to variable. | |
| void | cancel (Space *home, Advisor *p, bool assigned) |
| Cancel subscription of advisor a. | |
| void | cancel (Space *home) |
| Cancel all subscriptions when variable implementation is assigned. | |
| unsigned int | degree (void) const |
| Return degree (number of subscribed propagators and advisors). | |
| bool | advise (Space *home, ModEvent me, Delta *d) |
| Run advisors when variable implementation has been modified with modification event me and domain change d. | |
Cloning variables | |
| VarImp (Space *home, bool share, VarImp &x) | |
| Constructor for cloning. | |
| bool | copied (void) const |
| Is variable already copied. | |
| VarImp * | forward (void) const |
| Use forward pointer if variable already copied. | |
| VarImp * | next (void) const |
| Return next copied variable. | |
Variable implementation-dependent propagator support | |
| static void | schedule (Space *home, Propagator *p, ModEvent me) |
| Schedule propagator p with modification event me. | |
| static ModEvent | me (ModEventDelta med) |
| Project modification event for this variable type from med. | |
| static ModEventDelta | med (ModEvent me) |
| Translate modification event me into modification event delta. | |
| static ModEvent | me_combine (ModEvent me1, ModEvent me2) |
| Combine modifications events me1 and me2. | |
Memory management | |
| static void * | operator new (size_t, Space *) |
| Allocate memory from space. | |
| static void | operator delete (void *, Space *) |
| Return memory to space. | |
| static void | operator delete (void *) |
| Needed for exceptions. | |
Public Member Functions | |
| VarImp (Space *home) | |
| Creation. | |
| VarImp (void) | |
| Creation of static instances. | |
| unsigned int | bits (void) const |
| Provide access to free bits. | |
| unsigned int & | bits (void) |
| Provide access to free bits. | |
Protected Member Functions | |
| void | schedule (Space *home, PropCond pc1, PropCond pc2, ModEvent me) |
| Schedule subscribed propagators. |
Constructor & Destructor Documentation
template<class VIC>
| Gecode::VarImp< VIC >::VarImp | ( | Space * | home | ) | [inline] |
template<class VIC>
| Gecode::VarImp< VIC >::VarImp | ( | void | ) | [inline] |
template<class VIC>
| Gecode::VarImp< VIC >::VarImp | ( | Space * | home, | |
| bool | share, | |||
| VarImp< VIC > & | x | |||
| ) | [inline] |
Member Function Documentation
template<class VIC>
| void Gecode::VarImp< VIC >::subscribe | ( | Space * | home, | |
| Propagator * | p, | |||
| PropCond | pc, | |||
| bool | assigned, | |||
| ModEvent | me, | |||
| bool | schedule | |||
| ) | [inline] |
Subscribe propagator p with propagation condition pc.
In case schedule is false, the propagator is just subscribed but not scheduled for execution (this must be used when creating subscriptions during propagation).
In case the variable is assigned (that is, assigned is true), the subscribing propagator is scheduled for execution. Otherwise, the propagator subscribes and is scheduled for execution with modification event me provided that pc is different from PC_GEN_ASSIGNED.
template<class VIC>
| void Gecode::VarImp< VIC >::cancel | ( | Space * | home, | |
| Propagator * | p, | |||
| PropCond | pc, | |||
| bool | assigned | |||
| ) | [inline] |
template<class VIC>
| void Gecode::VarImp< VIC >::subscribe | ( | Space * | home, | |
| Advisor * | a, | |||
| bool | assigned | |||
| ) | [inline] |
template<class VIC>
| void Gecode::VarImp< VIC >::cancel | ( | Space * | home, | |
| Advisor * | p, | |||
| bool | assigned | |||
| ) | [inline] |
template<class VIC>
| void Gecode::VarImp< VIC >::cancel | ( | Space * | home | ) | [inline] |
template<class VIC>
| unsigned int Gecode::VarImp< VIC >::degree | ( | void | ) | const [inline] |
template<class VIC>
| bool Gecode::VarImp< VIC >::advise | ( | Space * | home, | |
| ModEvent | me, | |||
| Delta * | d | |||
| ) | [inline] |
template<class VIC>
| bool Gecode::VarImp< VIC >::copied | ( | void | ) | const [inline] |
template<class VIC>
| VarImp< VIC > * Gecode::VarImp< VIC >::forward | ( | void | ) | const [inline] |
template<class VIC>
| VarImp< VIC > * Gecode::VarImp< VIC >::next | ( | void | ) | const [inline] |
template<class VIC>
| void Gecode::VarImp< VIC >::schedule | ( | Space * | home, | |
| Propagator * | p, | |||
| ModEvent | me | |||
| ) | [inline, static] |
template<class VIC>
| ModEvent Gecode::VarImp< VIC >::me | ( | ModEventDelta | med | ) | [inline, static] |
template<class VIC>
| ModEventDelta Gecode::VarImp< VIC >::med | ( | ModEvent | me | ) | [inline, static] |
template<class VIC>
| ModEvent Gecode::VarImp< VIC >::me_combine | ( | ModEvent | me1, | |
| ModEvent | me2 | |||
| ) | [inline, static] |
template<class VIC>
| unsigned int Gecode::VarImp< VIC >::bits | ( | void | ) | const [inline] |
template<class VIC>
| unsigned int & Gecode::VarImp< VIC >::bits | ( | void | ) | [inline] |
template<class VIC>
| void Gecode::VarImp< VIC >::schedule | ( | Space * | home, | |
| PropCond | pc1, | |||
| PropCond | pc2, | |||
| ModEvent | me | |||
| ) | [inline, protected] |
template<class VIC>
| void * Gecode::VarImp< VIC >::operator new | ( | size_t | s, | |
| Space * | home | |||
| ) | [inline, static] |
template<class VIC>
| void Gecode::VarImp< VIC >::operator delete | ( | void * | , | |
| Space * | ||||
| ) | [inline, static] |
template<class VIC>
| void Gecode::VarImp< VIC >::operator delete | ( | void * | ) | [inline, static] |
Member Data Documentation
template<class VIC>
const Support::Symbol Gecode::VarImp< VIC >::vti = VIC::vti [inline, static] |
The documentation for this class was generated from the following file:
- gecode/kernel/core.icc (Revision: 7332)


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We love roasting great coffee. But, we equally love doing good things in the world. That's why when you drink our coffee, we plant trees. Tiny Footprint Coffee is the carbon negative, earth positive coffee that takes sustainability to the next level, one tasty sip at a time.
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OPELIKA, Ala. (AP) — A preliminary hearing is scheduled for a man charged in the kidnapping of a missing college student who's the stepdaughter of UFC heavyweight Walt Harris.
Ibraheem Yazeed is scheduled to appear before a judge Wednesday.
Yazeed is charged in the disappearance of 19-year-old Aniah Blanchard. She was last seen Oct. 23 in Auburn, Alabama. She hasn't been found.
Prosecutors on Tuesday filed a motion seeking permission to take a DNA sample from Yazeed. A judge said he will hear arguments on the request Wednesday.
Blanchard's car was found abandoned more than 50 miles (90 kilometers) away. Police said the car contained evidence that Blanchard had suffered a life-threatening injury.
The charging document says a witness identified 29-year-old Yazeed as the man he saw force Blanchard into a car.


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This record contains the user name, which is the name entered when installing Excel.
Record Data — BIFF8
|
Offset |
Name |
Size |
Contents |
|
4 |
stName |
112 |
User name as an unformatted unicode string . The name is always padded with spaces so the size of the stName field is exactly 112 bytes. |
Record Data — BIFF7 and earlier
|
Offset |
Name |
Size |
Contents |
|
4 |
cch |
1 |
Length of the user name |
|
5 |
stName |
31 |
User name, padded with spaces (20h) so the size of the stName field is exactly 31 bytes |


Nivritti is a student run group of highly motivated undergraduate students from diverse majors of Birla institute of technology, Mesra. Its objectives are to explore the untrodden avenues of robotics and machine learning, to initiate the culture of Research amongst young minds and to keep them updated regarding the recent trends in Robotics and Automation.
With the goal of advancing and promoting A.I and giving our community an unparalleled opportunity to showcase their talent,this spring,we have partnered with Intel Softwares,to bring you the first ever kind of event that BIT Mesra has ever seen.We'll be up with a mega Symposium on Artificial Intelligence and Automation.Hold on to your seats and take a deep breath as we dwell into the realms of A.I and give you a mesmerising memory to hold on .
You will soon know why Spring’18, won’t be, like Spring’18.
For further information of proceeedings in regards with participation, keep an eye on this space.


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The Razer Kraken Gaming Headset is a really great headset to pick up, since it does work with all consoles and even the PC. So no matter what platform you like to game on, this gaming headset is going to work out well for you. That includes PlayStation 4, Xbox One, Nintendo Switch and PC.
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class Foo { public int taskDecorator(int a, int b) { return 0; } public void execute(int a) { } public void decoratedTaskMap(int a, int b, int c) { } int decorate = 0; int command = 0; int put = 0; public void foo() { execute(taskDecorator(decorate, command)); } }
class Foo { public int taskDecorator(int a, int b) { // this is insanely long striiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiingggggggggggggggggggggggggggggggggggggggg ggggggg return 0; } public void execute(int a) { } public void decoratedTaskMap(int a, int b, int c) { } int decorate = 0; int command = 0; int put = 0; public void foo() { int decorated = taskDecorator(decorate, command); if (decorated != command) { decoratedTaskMap(put, decorated, command); } execute(decorated); } }


§712-1270.5 Injunctions against persons. Nothing in this part shall be construed to prohibit injunctions against persons causing, maintaining, aiding, abetting, or permitting a nuisance from entering or residing in any public or private building, premises, or place, in or upon which the nuisance exists. [L 1998, c 286, §1; am L 2005, c 123, §2]


--- layout: example title: Grid template ---
Basic grid layouts to get you familiar with building within the Bootstrap grid system.
No grid classes are necessary for full-width elements.


| MATLAB File Help: prtDataSetClass/getObservationsByClass |
getObservationsByClass Return the observations by class OBS = dataSet.getObservationsByClass(CLASS) returns the observations of the dataSet object correspoding to the class CLASS. CLASS must be an integer index corresponding to one of the values contained in dataSet.uniqueClasses
| Access | public |
| Sealed | false |
| Static | false |


Code:
User:
Balance:
Type:
Currency:
Amount:
Fee:
Subtype:
Account:
Status:
Reference:
Note:
Metadata:
{{formatted.metadata}}
Date:
{{transaction.created | date:'mediumDate'}} {{transaction.created | date:'shortTime'}}


CoachStat aims to help coaches track all the statistics about their teams, from players stats to games stats and also club stats. Try out the drag 'n' drop formation builder to quickly look at how you want to set up your team. Please use the contact us page to provide any feedback you may have.
Create multiple clubs to manage your players and fixtures. You can even create oppsoition clubs if you would like to track your rivals.
Create players and add them in fixture to track their performance. Use the formation builder to place them exactly where you want on the pitch.
Once you have have created your club and added players, add fixtures to quickly help you get monitoring how your team and players are performing.


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picante: Integrating Phylogenies and Ecology
Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) <doi:10.1093/bioinformatics/btq166>.
| Version: |
1.7 |
| Depends: |
ape, vegan, nlme, methods |
| Suggests: |
brglm, circular, corpcor, quantreg |
| Published: |
2018-05-14 |
| Author: |
Steven W. Kembel, David D. Ackerly, Simon P. Blomberg, Will K. Cornwell, Peter D. Cowan, Matthew R. Helmus, Helene Morlon, Campbell O. Webb |
| Maintainer: |
Steven W. Kembel <steve.kembel at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| Citation: |
picante citation info |
| Materials: |
README |
| In views: |
Phylogenetics |
| CRAN checks: |
picante results |
Downloads:
Reverse dependencies:
| Reverse depends: |
CommEcol, idar, RPANDA, spacodiR |
| Reverse imports: |
betapart, DAMOCLES, lefse, metricTester, nodiv, PCPS, pez, recluster |
| Reverse suggests: |
cati, EcoIndR, ecolottery, enveomics.R |
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=picante
to link to this page.


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Appendix C - Internal Number Representation
There are two different techniques whereby most computers today store the values of numbers: integer and floating-point. Integer storage has the nice property that it represents the exact value of the number stored; floating-point storage only guarantees a certain number of digits of precision. There is an upper limit to the values that can be stored in both integer and floating-point form. However, significantly larger numbers can be stored in floating-point storage than in integer storage. By default, MATLAB sets the storage of numbers to double-precision floating-point representation. However, operations like reading images into MATLAB present the large volume of data in the more compact unsigned integer form.
Integers
Integers are represented in computer memory by blocks of data bits of various sizes. Memory is allocated in 8-bit increments, usually referred to as bytes; therefore, it is not surprising that integer storage comes in the same size increments. For a given size, the values of the data bits are represented in two different ways—signed or unsigned. Normally, of course, we expect a number to have both positive and negative values, and when the number of bits is large, this does not seem to have much impact. However, when a small number of bits are used to store a value, one of those bits must be used to show that the number is positive or negative. The range of numbers that can be stored is therefore reduced by 1 bit, a factor of 2. The following figure illustrates the internal storage of 8-bit unsigned and signed values. Clearly, for 8 bits, the maximum value is 127 signed, or 255 unsigned. If this is not sufficient storage, numbers can be stored in 16-, 32-, or 64-bit words, with the corresponding increase in the maximum stored size.
Floating-Point Numbers
Floating-point numbers are stored in single precision (32 bits) or double precision (64 bits) using the IEEE 754 standard. As the name suggests, the storage format includes a mantissa and an exponent, each expressed internally in a manner similar to integer storage. The fixed size of the mantissa leads to the fixed amount of precision of each storage type. The float data type gives 7 significant decimal digits; the double data type gives 15 significant decimal digits.Note that although this seems to be a large amount of precision, you must always design your programs to preserve that precision. If, for example, you were to subtract two numbers almost equal in value, the precision of the result would be significantly worse than that of the original numbers. For details of these storage types, search the Web for “IEEE 754 standard.” At the time of writing, there was a good explanation at: http://www. geocities.com/SiliconValley/Pines/6639/docs/fp_summary.html Parameters of Each Storage Type The following table describes the most commonly used storage types available in MATLAB, their minimum and maximum values, and their equivalent names in C.
| MATLAB Name | Size (Bytes) | Minimum Value | Maximum Value | C Name |
|---|---|---|---|---|
| uint8 | 1 | 0 | 255 | unsigned char |
| int8 | 1 | −128 | 127 | char |
| uint16 | 2 | 0 | 65,536 | unsigned short |
| int16 | 2 | −32,768 | 32,767 | short |
| uint32 | 4 | 0 | 4,294,967,295 | unsigned int |
| int32 | 4 | −2,147,483,648 | 2,147,483,647 | int |
| float | 4 | ~−3.4x1038 | ~3.4x1038 | float |
| double | 8 | ~−1.7x10308 | ~1.7x10308 | double |


{% extends 'base.html' %} {% load i18n %} {% block content %}
{% trans "You can use this form to send an email to all people enrolled in your course." %}
{% trans "Be aware that lots of email spam will most likely put off your students." %}
{% endblock %}


{% include "scaffold/incbootstrap.html" %}
Conference is currently: {{crrt_conf.state()}} {% if crrt_conf.user_rights().has_permission(usr_email, "ChangeConferenceState"): %} - Change state (Open/Close, Start/End reviews, etc.) {% endif %} {% include "conference_lib/image_frag.html" %} {% endif %}
Name: (essential)
Dates: (essential)
Image: {% if not(crrt_conf.has_image()): %}No image suppplied{%endif%}
Shortname: (essential, 8 characters maximum, no punctuation) {% if crrt_conf.shortname != "": %}
Conference welcome page: https://{{home_url}}/welcome?cf={{crrt_conf.shortname}} {% endif %}
Contact address:
Note: this e-mail address is used as the from address in all mail sent.
Website:
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{% if conf_key==None: %} {% else %} {% endif %}


Design good objects and let them do their job!
The ability of a system to achieve its purpose depends on how well the parts work together, not just how well they perform individually.
Layers provide high cohesion within the layer and a separation of concerns between the layers, two fundamental architectural patterns in software design.
High cohesion is a pattern that attempts to keep objects appropriately focused. Cohesion refers to the degree to which elements of an object or module belong together.
For each one concern, other concerns are irrelevant. Separation of concerns is a means of hiding implementation behavior and deferring decision making.
Since you can't build complete flexibility into a system, you should try to group things that are likely to change together and hide them from the rest of the system.
Putting things that vary together ... and hiding them from the rest of the system allows changes to be made that have only local impact and do not disrupt huge parts of the system.
A class should do the smallest possible useful thing; that is, it should have a single responsibility.
A class should have only one reason to change


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Breakers overrun Patterson and Bullets
Lamar Patterson’s hot hand wasn’t enough to help the Brisbane Bullets, whose NBL road woes continued with a 96-85 loss to the New Zealand Breakers in Auckland.
American forward Patterson scored 36 points, a career-high in the NBL, but fatigue caught up with him in the final quarter when his influence waned and the Breakers held on for their fourth win of the season.
The seventh-placed Bullets have lost six of their past seven on the road and slip to 6-8 for the season, with the Breakers (4-9) one spot behind them on the ladder.
It was a crucial result for the Kiwi hosts, who suffered another setback in a tumultuous season when American import Glen Rice Jr. was sacked earlier on Monday, for a breach of his bail conditions.
They were also without rising star RJ Hampton, who is awaiting scan results on a hip injury.
Those blows were countered by the return from injury of big-scoring guards Corey Webster and Scotty Hopson, who were both influential.
Webster top-scored for the hosts with 23, supported by captain Tom Abercrombie’s 19. A third Kiwi, Finn Delany, mixed 14 points with eight rebounds.
The Brisbane stats sheet was more lop-sided. While Patterson’s haul was the most by any Bullets player since their return to the NBL in 2016, their next biggest contributor was Jason Cadee with 11.
Three-point shooting was a major differential, with the hosts landing nine of 21 compared to Brisbane’s lamentable six of 25 from beyond the arc.
Both teams spurned decent early leads to be level 47-47 at half-time but the New Zealand held sway after the main break, helped by some spectacular Hopson dunks in the final quarter.
AAP


Each ST5 spacecraft includes a low power science-grade fluxgate magnetometer. The magnetometer design is based on heritage from earlier missions such as Polar, FAST, and the Australian mission, FedSat. The magnetometers were developed at the University of California, Los Angelese (UCLA).
UCLA's ST5 fluxgate magnetometer is the product of a long series of successful spaceflight magnetometers. The sensors are boom mounted and have no active components. Drive, sense and feedback signals travel along the boom cable between the sensors and the electronics board on the spacecraft. The electronics generates the fluxgate drive signal, detects the second harmonic of this signal, nulls the field surrounding the sensor, and provides a digital reading of the current needed to null each of the sensors. This signal is then sent to the telemetry system. There is no microprocessor in this simple design. The magnetometer has two commandable ranges, 64,000 and 1000 nT.
The fluxgate sensors in the UCLA magnetometer are manufactured using the latest low-noise ring-core technology. UCLA purchased the last of the ring-core material in 1998 and had it fabricated into small (3/8" and 5/8") cores in anticipation of future missions such as ST5. The sensors are similar to the standard UCLA design flown on many previous missions. The feedback windings enclose the cores so that in operation the cores themselves are never exposed to strong fields orthogonal to the sense axis that can cause distortions at the level of about 1 part in 104. Thus the sensors are ultralinear and no harmonics of the spin tone is produced in high fields as have been reported for other missions. The lack of active components on the sensors means that they are very tolerant of temperature extremes. They were qualified at over 100C on Galileo and have operated after being immersed in liquid nitrogen (-196C).
The ST5 magnetometer uses the classic fluxgate circuit. Sensor mass and power are kept low with a dual core series drive circuit. The dynamic range is changed from 64,000 nT to 1000 nT by altering the closed loop response from 64,000 nT range to 5000 nT, and then amplifying the signal to get to a 1000 nT range. This method keeps the noise low in both ranges. One gain change command line switches both elements.
ST5 Magnetometer Design Specifications:
Total Mass: 361g + 250g (chassis)
Electronics Board Mass: 220g
Chassis: 250g
Sensor Mass: 75g
Interface Cable: < 66g
Electronic Unit Volume: 10x20x5 cm
Sensor Volume: 4x4x6 cm
Interface Cable: < 100 cm
Power Consumption, Electronics: 500 mW
Power Consumption, Sensor: 50 mW
Range Selection: 1000, 64,000 nT
Data Rate 16 vectors/s
Data Resolution: 18 bits (1:range, 1:sign, 16:value)
Version:2.0.0
Each ST5 spacecraft includes a low power science-grade fluxgate magnetometer. The magnetometer design is based on heritage from earlier missions such as Polar, FAST, and the Australian mission, FedSat. The magnetometers were developed at the University of California, Los Angelese (UCLA).
UCLA's ST5 fluxgate magnetometer is the product of a long series of successful spaceflight magnetometers. The sensors are boom mounted and have no active components. Drive, sense and feedback signals travel along the boom cable between the sensors and the electronics board on the spacecraft. The electronics generates the fluxgate drive signal, detects the second harmonic of this signal, nulls the field surrounding the sensor, and provides a digital reading of the current needed to null each of the sensors. This signal is then sent to the telemetry system. There is no microprocessor in this simple design. The magnetometer has two commandable ranges, 64,000 and 1000 nT.
The fluxgate sensors in the UCLA magnetometer are manufactured using the latest low-noise ring-core technology. UCLA purchased the last of the ring-core material in 1998 and had it fabricated into small (3/8" and 5/8") cores in anticipation of future missions such as ST5. The sensors are similar to the standard UCLA design flown on many previous missions. The feedback windings enclose the cores so that in operation the cores themselves are never exposed to strong fields orthogonal to the sense axis that can cause distortions at the level of about 1 part in 104. Thus the sensors are ultralinear and no harmonics of the spin tone is produced in high fields as have been reported for other missions. The lack of active components on the sensors means that they are very tolerant of temperature extremes. They were qualified at over 100C on Galileo and have operated after being immersed in liquid nitrogen (-196C).
The ST5 magnetometer uses the classic fluxgate circuit. Sensor mass and power are kept low with a dual core series drive circuit. The dynamic range is changed from 64,000 nT to 1000 nT by altering the closed loop response from 64,000 nT range to 5000 nT, and then amplifying the signal to get to a 1000 nT range. This method keeps the noise low in both ranges. One gain change command line switches both elements.
ST5 Magnetometer Design Specifications:
Total Mass: 361g + 250g (chassis)
Electronics Board Mass: 220g
Chassis: 250g
Sensor Mass: 75g
Interface Cable: < 66g
Electronic Unit Volume: 10x20x5 cm
Sensor Volume: 4x4x6 cm
Interface Cable: < 100 cm
Power Consumption, Electronics: 500 mW
Power Consumption, Sensor: 50 mW
Range Selection: 1000, 64,000 nT
Data Rate 16 vectors/s
Data Resolution: 18 bits (1:range, 1:sign, 16:value)
| Role | Person | |
|---|---|---|
| 1. | PrincipalInvestigator | spase://SMWG/Person/Robert.J.Strangeway |
UCLA web page describing history and design of the ST5 fluxgate magnetometers


Chart.js has had an incredibly last year. In April, we successfully launched v2.0 culminating a year and a half of hard work. We’ve had a number of smaller releases after that, each of which has helped build on the incredibly interactivity that comes standard in v2. We also released our first new chart type in a long time; the horizontal bar chart. This was a long sought request, that fit in well with the work we did in v2.
Coupled with this work, we saw a dramatic increase in use this year, with monthly NPM installs going from ~20k to over 200k per month. It’s amazing and humbling to see so many users choosing Chart.js for their projects.
In 2017, we are committed to working on improving the ability to run Chart.js in an environment other than the browser. In version 2.5, we will have factored out the browser specific events into a removable piece known as a platform
. With this change, it will be possible to create a build that runs in a JavaScript environment that does not have the DOM APIs.
We have also committed to improving our documentation. We are working on creating auto-generated documentation so that we can have the latest dev docs auto update when new commits are added. This will allow us to be more responsive to user feedback and ensure timely corrections when the docs have errors.
Overall, 2017 is shaping up to be a great year for Chart.js. Have a safe and happy new year!


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URSHIFU QUIZ
All teams are top placing teams in Battle Stadium Singles Series 7!
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