--- layout: default --- {% include base_path %}
Explore how IOS-XR’s service layer APIs and application hosting capabilities can be leveraged to host and integrate Open/R as an IGP on IOS-XR. Akshat Sharma explains.
IOS-XR supports Linux applications like iperf, etcd, pipeline etc. running on-box: both natively and inside containers (Docker, LXC).Akshat Sharma explores the application-hosting capabilties in IOS-XR in a tutorial series called #xr-toolbox, all running on Vagrant IOS-XR on the user's laptop
Are you thinking about how to start exploring telemetry? You know how to configure Model Driven Telemetry on IOS XR routers and your next question is how to consume it? Viktor Osipchuk will help you with that. Find some time to go through this Pipeline deep dive. You will find all the answers right here!
The Internet was created to provide transparent data services across interconnected packet switched networks. The interconnection and exchange of Internet routing data between two networks is known as Peering and it represents an important administrative, operational, and security boundary between networks. In this blog, Phil Bedard focuses on the technical aspects of peering covering peering history and current peering architecture in the areas of network, security, and telemetry.
Zero Touch provisioning (ZTP) has become an integral part of the workflow for most Users of IOS-XR. ZTP allows an IOS-XR box to be provisioned without any manual intervention by either running a user-defined script (bash/python) or simply applying a configuration file - all done automatically on boot. Patrick Warichet explains.
The Segment Routing-based fast-reroute solution, TI-LFA, can provide per-destination sub-50msec protection upon any single link, node or SRLG failure regardless the topology. The traffic is rerouted straight to the post-convergence path, hence avoiding any intermediate flap via an intermediate path. The primary and backup path computation is completely automatic by the IGP. Clarence Filsfils and Kris Michielsen dive deeper.


| Total net profit | -72.97 | Gross profit | 229.49 | Gross loss | -302.46 |
| Profit factor | 0.76 | Expected payoff | -0.14 | ||
| Absolute drawdown | -81.18 | Maximal drawdown (%) | 93.81 (742.76%) | ||
| Total trades | 515 | Profit trades (% of total) | 243 (47.18%) | Loss trades (% of total) | 272 (52.82%) |
| Long positions (won %) | 238 (52.52%) | Short positions (won %) | 277 (42.6%) | ||
| Largest | profit trade | 5.01 | loss trade | -7.11 | |
| Average | profit trade | 0.94 | loss trade | -1.11 | |
| Minimum | profit trade | 0.0 | loss trade | -0.01 | |
| Maximum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 3225 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 254.6 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 12.0 (19.01) | consecutive losses (loss in money) | 12.0 (-19.34) | |
| Average | consecutive wins | 2.0 | consecutive losses | 2.0 |
| Total net profit | 19.49 | Gross profit | 113.04 | Gross loss | -93.55 |
| Profit factor | 1.21 | Expected payoff | 0.08 | ||
| Absolute drawdown | -3.13 | Maximal drawdown (%) | 19.11 (62.39%) | ||
| Total trades | 238 | Profit trades (% of total) | 125 (52.52%) | Loss trades (% of total) | 113 (48.74%) |
| Largest | profit trade | 4.04 | loss trade | -4.38 | |
| Average | profit trade | 0.9 | loss trade | -0.83 | |
| Minimum | profit trade | 0.0 | loss trade | -0.01 | |
| Maximum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 3225.0 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 258.1 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 12.0 (19.01) | consecutive losses (loss in money) | 8.0 (-11.27) | |
| Average | consecutive wins | 2.0 | consecutive losses | 2.0 |
| Total net profit | -92.46 | Gross profit | 116.45 | Gross loss | -208.91 |
| Profit factor | 0.56 | Expected payoff | -0.33 | ||
| Absolute drawdown | -104.81 | Maximal drawdown (%) | 110.39 (1978.32%) | ||
| Total trades | 277 | Profit trades (% of total) | 118 (42.6%) | Loss trades (% of total) | 159 (57.4%) |
| Largest | profit trade | 5.01 | loss trade | -7.11 | |
| Average | profit trade | 0.99 | loss trade | -1.31 | |
| Minimum | profit trade | 0.0 | loss trade | -0.02 | |
| Maximum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Average | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Minimum | take profit in pips | 0.0 | stop loss in pips | 0.0 | |
| Maximum | Reward-Risk | 0.0 | Duration in minutes | 3194.0 | |
| Average | Reward-Risk | 0.0 | Duration in minutes | 251.5 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 7.0 (8.28) | consecutive losses (loss in money) | 14.0 (-16.49) | |
| Average | consecutive wins | 2.0 | consecutive losses | 3.0 |


Caching is a cheap way of speeding up slow applications by keeping the result of calculations, renderings, and database calls around for subsequent requests. Action Controller affords you three approaches in varying levels of granularity: Page, Action, Fragment.
You can read more about each approach and the sweeping assistance by clicking the modules below.
Note: To turn off all caching and sweeping, set Base.perform_caching = false.
Caching stores
All the caching stores from ActiveSupport::Cache is available to be used as backends for Action Controller caching. This setting only affects action and fragment caching as page caching is always written to disk.
Configuration examples (MemoryStore is the default):
ActionController::Base.cache_store = :memory_store ActionController::Base.cache_store = :file_store, "/path/to/cache/directory" ActionController::Base.cache_store = :drb_store, "druby://localhost:9192" ActionController::Base.cache_store = :mem_cache_store, "localhost" ActionController::Base.cache_store = MyOwnStore.new("parameter")
Module ActionController::Caching::Fragments
Module ActionController::Caching::Pages
Module ActionController::Caching::Sweeping
[ show source ]
# File vendor/rails/actionpack/lib/action_controller/caching.rb, line 49 49: def self.cache_configured? 50: perform_caching && cache_store 51: end
Defines the storage option for cached fragments
[ show source ]
# File vendor/rails/actionpack/lib/action_controller/caching.rb, line 39 39: def self.cache_store=(store_option) 40: @@cache_store = ActiveSupport::Cache.lookup_store(store_option) 41: end
Convenience accessor
[ show source ]
# File vendor/rails/actionpack/lib/action_controller/caching.rb, line 57 57: def cache(key, options = {}, &block) 58: if cache_configured? 59: cache_store.fetch(ActiveSupport::Cache.expand_cache_key(key, :controller), options, &block) 60: else 61: yield 62: end 63: end


Installation
At the moment there are no executable installers being distributed for Vaultron, but if you are curious and slightly adventurous you can install a development version from our public GitHub repository.
Git Installation Steps
First Node.js will need to be installed on your computer. Any current version should work, for development we are using v8.6.0. Node.js Homepage
Next you will want to clone Vaultron repository. Use the following commands to clone and then install all dependencies to run Vaultron.
git clone https://github.com/vaultron/Vaultron.git
cd Vaultron
npm run-script install
Once installed to start Vaultron run the following command.
npm run-script start
Setting Up Entries
To setup an account for a site first fill out the New Entry tab. After submitted, entries will appear in the main table.
- Website (NOT encrypted): a title of the entry
- Username (NOT encrypted): Arbitrary username for the site
- Password (encrypted): Password for the site
- Notes (NOT encrypted): Comments for the user are stored in the database file
Generating Cryptographically Secure Passwords
It is also possible to generate cryptographically secure passwords using our Password Generator tool. We utilize the node Crypto Library to follow a standard way for pseudo random number generation.
| Option | Included Characters | Ascii Ranges |
|---|---|---|
| Numbers | 0 - 9 | { 48 to 57 } |
| Lowercase | a - z | { 65 to 90 } |
| Uppercase | A - Z | { 97 to 122 } |
| Symbols | ! to -, : to @, { to ~ | { 33 to 47 }, { 58 to 64 }, { 123 to 126 } |
To find exactly which characters these sets include check out The Ascii Code
Output is generated in the text field and can be copied to the clipboard using the COPY button.


In this introduction we are going to have a look at a few things.
First, what is it. Project.clj is a configuration file for leiningen a CLI tool that compiles CLJS -> JS. Similar to Gruntfile.js to Grunt or package.json (more or less).
You will need to install leiningen. They have a nice website and installation steps available leiningen.org
beginning was omitted see full file here
:cljsbuild {
:builds {
:dev {
:source-paths ["src-cljs"]
:compiler {:output-to "dest/index.js"
:optimizations :simple
:target :nodejs}}
The configuration file allows lein to watch the source file and compile automatically. You can start the watch task by using
$ lein cljsbuild auto dev
This will ensure that every time you hit save lein will automatically re compile the code. Be sure to be in the correct folder and to have the project.clj file in the same location.
auto means it will watch and compile automatically. The alternative would be once.
dev refers to the build type we specified in the project.clj, we only have dev, you could also have a production step available.
To require a express in Node and create an app you would do:
var express = require('express')
, app = express();
The syntax is similar in CLJS
(ns node.main) ;; you have to declare a namespace first
(def express (js/require "express"))
(def app (express))
I have skipped the basic http hello world example because there are other examples out there.
First you need to include the fs module and I've also declared a rs variable (from read stream).
(def fs (js/require "fs"))
(def *rs* nil)
And now for reading and redirecting a stream
(set! *rs* (.createReadStream fs "path_to_file"))
(.pipe *rs* process.stdout)
In this example I am opening a read stream for a file and set it to the rs variable. I then pipe the data to process.stdout which would be our terminal. And you can even access the __dirname variable to ensure the correct path. This is accessible via
(str js/__dirname "/static/file_name")
In this example I concatenated the current path with the string "/static/file_name". You might prefer to include the path node module instead.
(ns node.main)
(def express (js/require "express")) ;; require express
(def app (express)) ;; create the app
(def fs (js/require "fs")) ;; require fs
(def *rs* nil)
;; localhost:3000
(.get app "/" (fn [req res]
(.send res "Hello world!")))
;; localhost:3000/user/andrei
(.get app "/user/:name" (fn [req res]
(.send res (aget req "params" "name"))))
;; localhost:3000/read
;; be sure to specify a valid path to read from
(.get app "/read"
(fn [req res]
(set! *rs* (.createReadStream fs "my_file"))
(.pipe *rs* res)))
;; what port to bind the application on
(.listen app 3000)
(defn start [& _]
(println "Server started on port 3000"))
;; must have a main function
(set! *main-cli-fn* start)


|
µEvLoop
A fast and lightweight event loop aimed at embedded platforms in C99.
|
Defines a closure, a tuple <function, context, destructor> More...
Data Fields | |
| uel_closure_function_t | function |
| The function to be run when the closure is invoked. Must take two pointers ar arguments, one for the context and one for closure parameters. Must return a void pointer. | |
| void * | context |
| The context supplied to the closure durint creation. This context will be available during the function execution. | |
Defines a closure, a tuple <function, context, destructor>
A closure is a data structure that binds some function to a predetermined context. When the function is run, this context will be available to the function body.
When invoking the closure, additional parameters may be passed along and will also be available to the function body. The function can also return a value that will be passed back by the uel_closure_invoke
function.


Lock¶
class RWLock¶
-
class
paddle::
RWLock
¶ A simple read-write lock. The RWlock allows a number of readers or at most one writer at any point in time. The RWlock disable copy.
Lock:
Use lock() to lock on write mode, no other thread can get it until unlock.
Use lock_shared() to lock on read mode, other thread can get it by using the same method lock_shared().
Unlock:
Use unlock() to unlock the lock.
Public Functions
-
RWLock
()¶
-
~RWLock
()¶
-
void
lock
()¶ lock on write mode.
- Note
- the method will block the thread, if failed to get the lock.
lock on read mode.
- Note
- if another thread is writing, it can’t get the lock, and will block the thread.
-
void
unlock
()¶
Protected Attributes
-
pthread_rwlock_t
rwlock_
¶
-
class ReadLockGuard¶
-
class
paddle::
ReadLockGuard
¶ The ReadLockGuard is a read mode RWLock using RAII management mechanism.
Public Functions
-
~ReadLockGuard
()¶ Destruct Function.
- Note
- This method just unlock the read mode rwlock, won’t destroy the lock.
-
class Semaphore¶
-
class
paddle::
Semaphore
¶ Public Functions
-
Semaphore
(int initValue = 0)¶ Construct Function.
- Parameters
initValue
: the initial value of the semaphore, default 0.
-
~Semaphore
()¶
-
bool
timeWait
(struct timespec *ts)¶ The same as wait(), except if the decrement can not be performed until ts return false install of blocking.
- Return
- ture if the decrement proceeds before ts, else return false.
- Parameters
ts
: an absolute timeout in seconds and nanoseconds since the Epoch 1970-01-01 00:00:00 +0000(UTC).
-
void
wait
()¶ decrement the semaphore. If the semaphore’s value is 0, then call blocks.
-
class ThreadBarrier¶
-
class
paddle::
ThreadBarrier
¶ Public Functions
-
DISABLE_COPY
(ThreadBarrier)¶
-
ThreadBarrier
(int count)¶ Construct Function. Initialize the barrier should wait for count threads in wait().
-
~ThreadBarrier
()¶
-
void
wait
()¶ If there were count - 1 threads waiting before, then wake up all the count - 1 threads and continue run together. Else block the thread until waked by other thread .
-
class LockedCondition¶
-
class
paddle::
LockedCondition
¶ A wrapper for condition variable with mutex.
Inherits from condition_variable
Public Functions
- template <class Op>
-
void
notify_one
(Op op)¶ execute op and notify one thread which was blocked.
- Parameters
op
: a thread can do something in op before notify.
- template <class Op>
-
void
notify_all
(Op op)¶ execute op and notify all the threads which were blocked.
- Parameters
op
: a thread can do something in op before notify.
- template <class Predicate>
-
void
wait
(Predicate pred)¶ wait until pred return ture.
- Note
- pred shall not apply any non-constant function through the dereferenced iterator.
- Template Parameters
Predicate
: c++ concepts, describes a function object that takes a single iterator argument that is dereferenced and used to return a value testable as a bool.
-
std::mutex *
mutex
()¶ get mutex.
Protected Attributes
-
std::mutex
mutex_
¶


Three passions
simple but overwhelmingly strong
have governed my life
the longing for love
the search for knowledge
and unbearable pity for the suffering of mankind
These passions, like great winds
have blown me hither and thither
in a wayward course, over a great ocean of anguish
reaching to the very verge of despair
What I Have Lived For


Welcome to Professional Consulting Agency
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+91-9915740823
Currently working as a Programmer Analyst Trainee at Cognizant Technology Solutions,Pune
Collected tweets of people for tv series and movies.Analyzed the collected data to generate results pertaining to how many people are positive, negative and neutral about it and represented the data in different charts and graphs.
Collected data of IPL matches played till 2018 and represented the data of which team won the maximum matches,each player individual score,wickets taken by each player and winning ratio.
In this project, we can enter the record of new books and retrieve the details of books available in the library. The records of books issued to the students is also maintained. Late fine is charged for students who returns the issued books after the due date and only one book is issued to students.
Learned basic of Big data, Hadoop ecosystem and various tools like Pig,Hive,Scala. Also,made projects based on sentimental analysis using pig tool.
CGPA: 7.57
Percentage: 75.60
CGPA: 9.0
I enjoy most of my time being outdoors and travelling to differnt tourist destinations. I, also like to play badminton and cricket. During my free time i spend most of the time learning new technologies and keep myself update about latest technology.
My career objective is to work for the upliftment of the organization and myself as a whole.


Validate Page
My Validations - Results List Page
Validation Results Page
Publishing
Published Validations Page
This page contains a quick user tutorial. More comprehensive information can be found in our User Manual.
The validate page is subdivided into areas for the various validation parameters. Each area has menu with two icons on the top right. Hover over the question mark to get help for the respective areas. You can use the +/- icon to fold up or fold down the area.
Step 1 - Choose the data you would like to validate - including the dataset name, the version of the dataset, and one of the soil moisture variables provided in the dataset. All the supported datasets are listed on datasets page here.
Step 2 [optional] - Choose the criteria by which you would like to filter this dataset. The filters available depend on the data contained within the chosen dataset. For example, you can filter the C3S data to include only data with no inconsistencies detected (flag = 0). Details of the filter options provided for each dataset are given on the supported datasets page. You can also hover your mouse pointer over the question mark next to a filter to get a short explanation.
Step 3 [optional] - If you want to intercompare several datasets, you can add more datasets to the validation
using the Add dataset button, up to a maximum of five. Configure the settings for the additional datasets by selecting
the respective tab and repeating steps 1 and 2 above.
Intercomparison: The intercomparison mode of QA4SM validates up to five satellite data sets against a common reference data set. For each reference location (e.g. each ISMN station) it finds the nearest observation series in all selected satellite products. All observations series are then scaled (if selected) and temporally matched to the reference series. For validation only the common time stamps (that are available in all satellite products) are used to calculate validation metrics between the reference and each individual satellite product. This way deviations in the metrics due to different temporal coverage are excluded and validation results represent differences in the performance of the compared satellite products.
Step 4 - Choose the reference dataset you would like to use for the validation including the dataset name, the version of the dataset, and the soil moisture variables provided in the dataset.
Step 5 [optional] - Choose the criteria by which you would like to filter the reference data prior to
running the validation. The filters available depend on the data contained within the chosen dataset. For example,
you can filter the ISMN data to include only data points where the soil_moisture_flag is "G" for "good".
You can also hover your mouse pointer over the question mark next to a filter
to get a short explanation.
If the reference dataset is ISMN you can also choose specific networks and/or measurements in a defined depth range.
To do that you have to open an appropriate window by clicking a 'select...' link next to the filter you want to parameterised.
The window for choosing networks contains a list of continents. When clicking an arrow next to a continent name a list of networks that belong to the continent unfolds. Next to each network there is the name of the country it belongs to. To toggle the particular network it is enough to click on its name. The choice doesn't have to be approved, it is enough to close the dialog window and the choice will be saved.
The window for the depth selection contains two fields for introducing the depth range. Values have to be introduced in meters.
The choice can to be accepted by clicking the 'OK' button or simply by closing the dialog window.
Details of the filter options provided for each dataset are given on the supported datasets page
here. You can also hover your mouse pointer over the question mark next to a filter
to get a short explanation.
Step 6 [optional] - If you want to calculate metrics from anomalies instead of absolute values, select the desired method in the "Method" drop-down menu. The options are:
Step 7 [optional] - Chose the geographic area over which the validation should be performed. You can either
specify a lat/lon bounding
box directly or you can select the area on a map. The trash button will clear all four
bounding
box fields.
If you don't specify an area, a global validation will be done.
Step 8 [optional] - Choose the date range over which the validation should be performed. Accepted formats are: YYYY*MM*DD or DD*MM*YYYY where * can be any of ".", "/" or "-". If you leave the fields empty, the validation period will be implicitly determined through temporal matching of the data and reference selected. For the time range covered by the various datasets, see the datasets page.
Step 9 [optional] - Activate Triple Collocation Analysis (TC) and Bootstrapping Triple Collocation confidence intervals (BTC). By default, QA4SM calculates validation
metrics between dataset pairs (the reference and each candidate data set). If more than 3 datasets
are selected (including the reference), the switch to activate TC becomes available. If
TC is selected, in addition to the basic metrics between dataset pairs, TC metrics between
triples of selected datasets are calculated. The reference is included in all triples and
metrics are found for all candidate datasets. Note that each TC metric is specific to a dataset
(this is evident from the metric name). TC metrics are affected by all 3 used datasets. Only results from TC with
independent datasets should be used.
The bootstrapping option is enabled only if TC is selected.
When it is chosen confidence intervals are calculated via bootstrapping with 1000 repetitions, however the validation time might be up to 5 times longer.
Step 10 - Choose how the data (or reference) will be scaled before metrics calculation. The data can be scaled to the reference (default) or vice versa. Note that in an intercomparision validation (with multiple datasets), only scaling to reference is possible. The scaling method determines how values of one dataset are mapped onto the value range of the other dataset for better comparability.
Step 11 - Optionally name your validation results to make it easier to identify it in the list of all your validations.
Step 12 - Run the validation process. You'll be notified via e-mail once it's finished. You don't need to keep the results window (or even your browser) open for the validation to run. The email will contain a link to your results.
The list shows all your validations, including the currently running ones, sorted by date (latest first).
Note: Your validations will be automatically removed {{ this.getExpiryPeriod() }} days after completion by our auto-cleanup process, unless you extend or archive them. You will be warned via email about validation expiry {{ this.getWarningPeriod() }} days before deletion.
The icons in the validations' title bars indicate the following:
The buttons on the right-hand side of each validation have the following effects:
Once the validation process is finished, you can see a summary of the validation run on the results page.
The buttons at the bottom of the result overview have the following effects:
The following metrics are calculated during the validation process:
| Name | Description |
|---|---|
| Pearson's r | Pearson correlation coefficient |
| Pearson's r p-value | p-value for pearson correlation coefficient |
| Spearman's rho | Spearman rank correlation coefficient |
| Spearman's rho p-value | p-value for Spearman rank correlation coefficient |
| Root-mean-square deviation | Root-mean-square deviation |
| Bias (difference of means) | Average Error |
| # observations | Number of Observations |
| Unbiased root-mean-square deviation | Unbiased root-mean-square deviation |
| Mean square error | Mean square error |
| Mean square error correlation | Mean square error correlation |
| Mean square error bias | Mean square error bias |
| Mean square error variance | Mean square error variance |
| Residual Sum of Squares | Residual Sum of Squares |
| TC: Signal-to-noise ratio | TC: Signal-to-noise ratio |
| TC: Error standard deviation | TC: Error standard deviation' |
| TC: Scaling coefficient | TC: Scaling coefficient |
Visualisations of these metrics are displayed in the Result Files section of the page: boxplots and
geographical
overview maps. You can select the metric shown with the left drop-down button below the graphs.
For an intercomparison validation, all boxplots are combined into one graph. The dataset displayed in the overview
map can be selected with the drop-down button on the right.
You can also download a zipfile of all the plots in png and svg (vector) format by clicking on the Download all
graphs button, and the result NetCDF
file
with all metrics with the Download results in NetCDF button.
This feature allows you to publish the result NetCDF file of your validation to Zenodo under your own name but without creating your own Zenodo account. This gives you a DOI for your results, which you can cite in your publications to give your readers open access to your data.
Once you click the Publish button on the validation result page, you will
be presented with a dialog
containing the metadata the results will be published with. You can change the metadata to your liking (within
some limits) and start the file upload to Zenodo by clicking Publish
results.
Note that we require 'qa4sm' to be one of the keywords, and that Title, Description, Keywords, and
Name are mandatory fields. You don't need to give an affiliation or
ORCID, though. Changes you make to your
author details will not be stored to your user profile - for that, please
got to the Profile page.
The upload can take a few minutes, please be patient. If it should fail, please try
again a few hours later. If it still doesn't work, please email us at {{ this.getAdminMail() }} and include the
error message
you received.
Please be aware that the NetCDF file and the metadata will be stored at Zenodo under the account of the QA4SM
project but with your name as the author. Zenodo is a separate website run at CERN over which the QA4SM team
has no control.
Assigning a DOI to a result also means that it cannot easily be unpublished or
deleted - see also Zenodo's FAQ.
If you prefer to use your own Zenodo account, you can of course do so - the QA4SM publishing feature is just
for convenience. Just download the NetCDF result file and upload it yourself through Zenodo's submission process
with your own account. We'd ask you to use 'qa4sm' as one of the keywords so that we can easily find all QA4SM
results on Zenodo with a keyword search.
The list shows validations on the QA4SM service that have been published to Zenodo by their owners. This means the results can be cited with a DOI.
If you want to email us to send comments, report errors, or ask questions, you can do so at {{ this.getAdminMail() }}.


{% extends "email/email_base.html" %}
{% block content%}
Hi,
Your event titled "{{ event.name }}" is updated and public now. You can share the event using the following url:
{{ site_url }}{{ event.get_absolute_url }}
Here is the link you can use to edit the event: {{ site_url }}{{ event.get_edit_url }}. You can share this link with other whom you want to provide edit access.
If you are having any issues or got any feedback, you can reply to this email and someone from the team will reach out to you.
{% endblock %}


When you are writing a code, sometimes you'll want to create things like headers, footers, navigation bars, anything that kinda resembles a rectangle with a function (even if the function is to stay put and look a specific color, it's doing a job!)
When you create these things, more often than not you'll be using div tags. Divs allow you to create a space for just about whatever you put your mind to, really. Pretty neat, right?
When working with these, you'll most likely think about specific properties such as location of divs on your page, location of content within div, etc. Thats where the margins, borders, and padding come into play!
There are several different ways to move around content within your page. However, when you create something like a div you can change their own unique properties, as just mentioned, to help with the whole process.
Simply put, these things have to do with spacing. The margin is the space surrounding your div. You can think of border as a wall containing your content, which also takes up space in your page (and can be played with to be thick, thin, or even invisible!). Then we have our padding, which is the space between your border and your content. Pretty simple, right? Here's a quick little image to help you better visualize what i'm talking about!
Thanks for reading!


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About online conferences
29 Apr 2020COVID 19 is forcing a lot of conferences to go online. A few notes about this.
Report from the online conference WAGON
There is a maths conference named WAGS that took place virtually this year, and was renamed WAGON for the occasion. One of the organizers wrote report about it here. It explains what worked and what didn’t.
They used zoom, with a license purchased by a university, which worked well even though they had ~1000 participants (but only a few had video). During the talks, there was a chat channel for all participants. There was a few moderators (session chairs and specialists), that would answer some of the questions, and interrupt the speaker only if needed. The speaker did not need to look at the chat. Seemingly the chat was very active and very useful. They also had some other ways to ask questions, such as the “raise your hand” button of zoom, but it seems that the chat was the main channel for this.
There was also some virtual discussion tables for coffee breaks. This was successful up to some extent but it seems that it needs some more iterations to find a good mix. There are questions like: do you register to get on a table? Do you know the names of the people on the table? etc.
Virtual conference schedule
Conferences going online was a topic before the COVID 19, because of climate change and the impact of flying. We are kind of forced to use it now, and that’s a test that might show that at least some conferences can be virtual. For the moment it seems that the choice for these virtual conferences is to make the same event as before but online, in the sense that the schedules are similar, that there are coffee breaks between talks etc. A question is whether this is the best format. Of course for this year, it’s probably the best because it’s what was planned. But if some conferences stay online, maybe another format would be better.
In particular, I have seen comments saying that attending 7 hours of online talks is even more exhausting than 7 hours of real talks, and I can imagine that. Also I’m not sure whether having lunch on your desk with zoom open is great. But still you want people to “attend” the conference and do not just come back and forth between their usual activity and the conference, both because it is more efficient this way, and also because if one can go out of the conference easily then people will be asked to do it (by their colleagues, by the department etc.) although it’s not what they would prefer.
Maybe a good thing is to gather a few people interested in the conference (from your department or city) and to live the conference together. This is probably a much nicer experience (for post-pandemic times).
Impact of video on climate change
Of course virtual conferences have less impact on climate than flying across an ocean, but what is the impact of it anyway? There is a study by a (serious) French think tank about the impact of digital technologies on the climate crisis, and in particular the impact of video. You can find summary here and the full report there.
A few numbers:
- The energy consumption related to digital technologies is distributed the following way: 45% for production of the devices, 19% for data centers, 16% for network, and 20% for terminal use.
- Digital technologies represent 4% of the greenhouse gases emissions (to be compared to the 2% due to aviation) and will most probably reach 8% soon (which is the percentage of car emissions).
- Out of these 4%, 1% is due to video.
- 80% of the global data flow is video, and this 80% can is then divided into two categories: 60% of streaming-like usage (youtube, netflix, social media etc.) and 20% of visioconferences-like usage.
If you want to measure the impact of our Internet activity, the report mentions the firefox add-on carbonalyser .
By the way, there is a manifesto for taking climate change into account in TCS academic life. It is called TCS4F (TCS for future). It has been signed by ~100 people and some organizations such as the conference STACS.
Another relation between conferences and CO2
Another, more unexpected relation between CO2 and conferences: during talks, the CO2 level and the temperature in the room increase in a non-negligible way, which can explain why it’s difficult to stay awake sometimes.
A full (quite old) twitter thread on this here. Also, a New York Times article about this.


An Artist object
id (str): Echo Nest Artist ID
name (str): Artist Name
audio (list): Artist audio
biographies (list): Artist biographies
blogs (list): Artist blogs
familiarity (float): Artist familiarity
hotttnesss (float): Artist hotttnesss
images (list): Artist images
news (list): Artist news
reviews (list): Artist reviews
similar (list): Similar Artists
songs (list): A list of song objects
terms (list): Terms for an artist
urls (list): Artist urls
video (list): Artist video
years_active (list): A list of dictionaries containing start and stop years
You create an artist object like this:
>>> a = artist.Artist('ARH6W4X1187B99274F')
>>> a = artist.Artist('the national')
>>> a = artist.Artist('musicbrainz:artist:a74b1b7f-71a5-4011-9441-d0b5e4122711')
Artist class
Example:
>>> a = artist.Artist('ARH6W4X1187B99274F', buckets=['hotttnesss'])
>>> a.hotttnesss
0.80098515900997658
>>>
Get a list of audio documents found on the web related to an artist
Args:
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An integer starting value for the result set
Example:
>>> a = artist.Artist('alphabeat')
>>> a.get_audio()[0]
{u'artist': u'Alphabeat',
u'date': u'2010-04-28T01:40:45',
u'id': u'70be4373fa57ac2eee8c7f30b0580899',
u'length': 210.0,
u'link': u'http://iamthecrime.com',
u'release': u'The Beat Is...',
u'title': u'DJ',
u'url': u'http://iamthecrime.com/wp-content/uploads/2010/04/03_DJ_iatc.mp3'}
>>>
Get a list of artist biographies
Args:
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An integer starting value for the result set
license (str): A string specifying the desired license type
Example:
>>> a = artist.Artist('britney spears')
>>> bio = a.get_biographies(results=1)[0]
>>> bio['url']
u'http://www.mtvmusic.com/spears_britney'
>>>
Get a list of blog articles related to an artist
Args:
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An ingteger starting value for the result set
Example:
>>> a = artist.Artist('bob marley')
>>> blogs = a.get_blogs(results=1,start=4)
>>> blogs.total
4068
>>> blogs[0]['summary']
But the Kenyans I know relate to music about the same way Americans do. They like their Congolese afropop,
and I've known some to be big fans of international acts like Bob Marley and Dolly Parton.
They rarely talk about music that's indigenous in the way a South African or Malian or Zimbabwean would, and it's
even rarer to actually hear such indigenous music. I do sometimes hear ceremonial chanting from the Maasai, but only
when they're dancing for tourists. If East Africa isn't the most musical part ... "
>>>
Get the number of related documents of various types for the artist. The types include audio, biographies, blogs, images, news, reviews, songs, videos.
Note that these documents can be retrieved by calling artist.<document type>, for example, artist.biographies.
Args:
Example:
>>> a = artist.Artist("The Kinks")
>>> a.get_doc_counts()
{u'audio': 194,
u'biographies': 9,
u'blogs': 379,
u'images': 177,
u'news': 84,
u'reviews': 110,
u'songs': 499,
u'videos': 340}
>>>
Get our numerical estimation of how familiar an artist currently is to the world
Args:
Example:
>>> a = artist.Artist('frank sinatra')
>>> a.get_familiarity()
0.65142555825947457
>>> a.familiarity
0.65142555825947457
>>>
Get the foreign id for this artist for a specific id space
Args:
Example:
>>> a = artist.Artist('fabulous')
>>> a.get_foreign_id('7digital')
u'7digital:artist:186042'
>>>
Get our numerical description of how hottt an artist currently is
Args:
Example:
>>> a = artist.Artist('hannah montana')
>>> a.get_hotttnesss()
0.59906022155998995
>>> a.hotttnesss
0.59906022155998995
>>>
Get a list of artist images
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An integer starting value for the result set
license (str): A string specifying the desired license type
Example:
>>> a = artist.Artist('Captain Beefheart')
>>> images = a.get_images(results=1)
>>> images.total
49
>>> images[0]['url']
u'http://c4.ac-images.myspacecdn.com/images01/5/l_e1a329cdfdb16a848288edc6d578730f.jpg'
>>>
Get a list of news articles found on the web related to an artist
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An integer starting value for the result set
Example:
>>> a = artist.Artist('Henry Threadgill')
>>> news = a.news
>>> news.total
41
>>> news[0]['name']
u'Jazz Journalists Association Announces 2010 Jazz Award Winners'
>>>
Get reviews related to an artist’s work
Args:
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An integer starting value for the result set
Example:
>>> a = artist.Artist('Ennio Morricone')
>>> reviews = a.reviews
>>> reviews.total
17
>>> reviews[0]['release']
u'For A Few Dollars More'
>>>
Return similar artists to this one
Args:
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An integer starting value for the result set
max_familiarity (float): A float specifying the max familiarity of artists to search for
min_familiarity (float): A float specifying the min familiarity of artists to search for
max_hotttnesss (float): A float specifying the max hotttnesss of artists to search for
min_hotttnesss (float): A float specifying the max hotttnesss of artists to search for
reverse (bool): A boolean indicating whether or not to return dissimilar artists (wrecommender). Defaults to False.
Example:
>>> a = artist.Artist('Sleater Kinney')
>>> similars = a.similar[:5]
>>> similars
[<artist - Bikini Kill>, <artist - Pretty Girls Make Graves>, <artist - Huggy Bear>, <artist - Bratmobile>, <artist - Team Dresch>]
>>>
Get the songs associated with an artist
Args:
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An integer starting value for the result set
Example:
>>> a = artist.Artist('Strokes')
>>> a.get_songs(results=5)
[<song - Fear Of Sleep>, <song - Red Light>, <song - Ize Of The World>, <song - Evening Sun>, <song - Juicebox>]
>>>
Get the terms associated with an artist
Args:
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
sort (str): A string specifying the desired sorting type (weight or frequency)
Example:
>>> a = artist.Artist('tom petty')
>>> a.terms
[{u'frequency': 1.0, u'name': u'heartland rock', u'weight': 1.0},
{u'frequency': 0.88569401860168606,
u'name': u'jam band',
u'weight': 0.9116501862732439},
{u'frequency': 0.9656145118557401,
u'name': u'pop rock',
u'weight': 0.89777934440040685},
{u'frequency': 0.8414744288140491,
u'name': u'southern rock',
u'weight': 0.8698567153186606},
{u'frequency': 0.9656145118557401,
u'name': u'hard rock',
u'weight': 0.85738022655218893},
{u'frequency': 0.88569401860168606,
u'name': u'singer-songwriter',
u'weight': 0.77427243392312772},
{u'frequency': 0.88569401860168606,
u'name': u'rock',
u'weight': 0.71158718989399083},
{u'frequency': 0.60874110500110956,
u'name': u'album rock',
u'weight': 0.69758668733499629},
{u'frequency': 0.74350792060935744,
u'name': u'psychedelic',
u'weight': 0.68457367494207944},
{u'frequency': 0.77213698386292873,
u'name': u'pop',
u'weight': 0.65039556639337293},
{u'frequency': 0.41747136183050298,
u'name': u'bar band',
u'weight': 0.54974975024767025}]
>>>
Get the urls for an artist
Args:
Example:
>>> a = artist.Artist('the unicorns')
>>> a.get_urls()
{u'amazon_url': u'http://www.amazon.com/gp/search?ie=UTF8&keywords=The Unicorns&tag=httpechonecom-20&index=music',
u'aolmusic_url': u'http://music.aol.com/artist/the-unicorns',
u'itunes_url': u'http://itunes.com/TheUnicorns',
u'lastfm_url': u'http://www.last.fm/music/The+Unicorns',
u'mb_url': u'http://musicbrainz.org/artist/603c5f9f-492a-4f21-9d6f-1642a5dbea2d.html',
u'myspace_url': u'http://www.myspace.com/iwasbornunicorn'}
>>>
Get a list of video documents found on the web related to an artist
Args:
cache (bool): A boolean indicating whether or not the cached value should be used (if available). Defaults to True.
results (int): An integer number of results to return
start (int): An integer starting value for the result set
Example:
>>> a = artist.Artist('the vapors')
>>> a.get_video(results=1, start=2)
[{u'date_found': u'2009-12-28T08:27:48',
u'id': u'd02f9e6dc7904f70402d4676516286b9',
u'image_url': u'http://i1.ytimg.com/vi/p6c0wOFL3Us/default.jpg',
u'site': u'youtube',
u'title': u'The Vapors-Turning Japanese (rectangular white vinyl promo)',
u'url': u'http://youtube.com/watch?v=p6c0wOFL3Us'}]
>>>
Get a list of years active dictionaries for an artist
Args:
Example:
>>> a = artist.Artist('radiohead')
>>> a.get_years_active()
[{ start: 1985 }]
>>>
Search for artists by name, description, or constraint.
Args:
name (str): the name of an artist
description (str): A string describing the artist
style (str): A string describing the style/genre of the artist
mood (str): A string describing the mood of the artist
start (int): An integer starting value for the result set
results (int): An integer number of results to return
buckets (list): A list of strings specifying which buckets to retrieve
limit (bool): A boolean indicating whether or not to limit the results to one of the id spaces specified in buckets
fuzzy_match (bool): A boolean indicating whether or not to search for similar sounding matches (only works with name)
max_familiarity (float): A float specifying the max familiarity of artists to search for
min_familiarity (float): A float specifying the min familiarity of artists to search for
max_hotttnesss (float): A float specifying the max hotttnesss of artists to search for
min_hotttnesss (float): A float specifying the max hotttnesss of artists to search for
artist_start_year_before (int): Returned artists will have started recording music before this year.
artist_start_year_after (int): Returned artists will have started recording music after this year.
artist_end_year_before (int): Returned artists will have stopped recording music before this year.
artist_end_year_after (int): Returned artists will have stopped recording music after this year.
rank_type (str): A string denoting the desired ranking for description searches, either ‘relevance’ or ‘familiarity’
Example:
>>> results = artist.search(name='t-pain')
>>> results
[<artist - T-Pain>, <artist - T-Pain & Lil Wayne>, <artist - T Pain & 2 Pistols>, <artist - Roscoe Dash & T-Pain>, <artist - Tony Moxberg & T-Pain>, <artist - Flo-Rida (feat. T-Pain)>, <artist - Shortyo/Too Short/T-Pain>]
>>>
Get the top hotttest artists, according to The Echo Nest
Args:
results (int): An integer number of results to return
start (int): An integer starting value for the result set
buckets (list): A list of strings specifying which buckets to retrieve
limit (bool): A boolean indicating whether or not to limit the results to one of the id spaces specified in buckets
Example:
>>> hot_stuff = artist.top_hottt()
>>> hot_stuff
[<artist - Deerhunter>, <artist - Sufjan Stevens>, <artist - Belle and Sebastian>, <artist - Glee Cast>, <artist - Linkin Park>, <artist - Neil Young>, <artist - Jimmy Eat World>, <artist - Kanye West>, <artist - Katy Perry>, <artist - Bruno Mars>, <artist - Lady Gaga>, <artist - Rihanna>, <artist - Lil Wayne>, <artist - Jason Mraz>, <artist - Green Day>]
>>>
Get a list of the top overall terms
Args:
Example:
>>> terms = artist.top_terms(results=5)
>>> terms
[{u'frequency': 1.0, u'name': u'rock'},
{u'frequency': 0.99054710039307992, u'name': u'electronic'},
{u'frequency': 0.96131624654034398, u'name': u'hip hop'},
{u'frequency': 0.94358477322411127, u'name': u'jazz'},
{u'frequency': 0.94023302416455468, u'name': u'pop rock'}]
>>>
Return similar artists to this one
Args:
ids (str/list): An artist id or list of ids
names (str/list): An artist name or list of names
results (int): An integer number of results to return
buckets (list): A list of strings specifying which buckets to retrieve
limit (bool): A boolean indicating whether or not to limit the results to one of the id spaces specified in buckets
start (int): An integer starting value for the result set
max_familiarity (float): A float specifying the max familiarity of artists to search for
min_familiarity (float): A float specifying the min familiarity of artists to search for
max_hotttnesss (float): A float specifying the max hotttnesss of artists to search for
min_hotttnesss (float): A float specifying the max hotttnesss of artists to search for
seed_catalog (str): A string specifying the catalog similar artists are restricted to
Example:
>>> some_dudes = [artist.Artist('weezer'), artist.Artist('radiohead')]
>>> some_dudes
[<artist - Weezer>, <artist - Radiohead>]
>>> sims = artist.similar(ids=[art.id for art in some_dudes], results=5)
>>> sims
[<artist - The Smashing Pumpkins>, <artist - Biffy Clyro>, <artist - Death Cab for Cutie>, <artist - Jimmy Eat World>, <artist - Nerf Herder>]
>>>


这篇文章告诉你如何使用Acard主题！
Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.
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{{ SuspectsFactory.innocents[0].name }} is extremely upset by your accusation and demands that you leave him alone. While arguing, you notice your blood sugar levels have dropped dramatically. You fall to the floor, and see {{ SuspectsFactory.perpetrator.name }} eating a {{ detective.favorite }} donut.


School of Data provides a nationwide educational program covering topics on data handling and related technologies. We address non-profit organisations that are interested in learning how to use data effectively within their work or want to enhance their abilities in processing data. Over the course of three months we develop data-driven projects with our partners. Through our network of trainers we offer tailored workshops and tool trainings which address the needs of our partners.
Our mission is to:
Do you want to know more? Get in touch<EMAIL_ADDRESS>Get to know us and improve your data literacy! Whether you are a novice or an expert in wrangling data, we offer different ways of collaborating with us. See our calendar for our upcoming events.
We organise short, hands-on workshops which provide practical insights on various topics related to data. What is open data, how do you find and clean data for further analysis, and how does one create meaningful visualisations without having a degree in computer science?
Over a period of three months, we support projects and organisations in finding data-driven solutions to their challenges and everyday questions. By combining individual workshops, consultations and technical trainings, we try to improve your organisation’s understanding and use of data.
First of all we want to get to know you, your work and your knowledge about data: Where do you use data in your everyday work? What is your data workflow? Where do you need support? Besides that we're interested in your question: Do you want to know how clean the drinkable water in your area is? Or figure out which neighborhoods are affected the most by aircraft noise? Tell us about your challenges that you want to overcome with data!
As the next step we start researching together. Do we have to collect data first or are there data sets around your question? If so, are openly accessible or do they need to be freed (e.g. from public registers)? Depending on the baseline we'll design a sketch afterwards that will further concretize the project idea and plan how to proceed. For that we'll use the Data Pipeline as orientation - our method of implementing digital projects.
To better prepare and evaluate the data, we'll train you data exterpise through workshops and technical trainings that we'll tailor to your needs, and give you a few tools, tipps and tricks that will support your in your daily work.
Then it's time for the implementation: Together we'll clean, analyze and visualize the data and think about how to best present the results. Depending on the idea or challenge, that could be a new website, a (web-) app or digital map, where your data can be viewed and searched. Maybe you want to start a campaign or put the results on your own website. Do you have more ideas? Do tell us!
After the project has been implemented, we'll evaluate our collaboration together, document the individual steps and learnings and prepare them transparently for interested people. That way we're sharing the gained knowledge with everyone.
Still too abstract? Get inspired by our previous projects and learn more about how we proceeded e.g. with the topic of educational data in our lerning section.
We support organisations and project teams that:
Contact us at<EMAIL_ADDRESS>and give us a short introduction on your organisation and an idea about how we could support you.
Our newsletter provides information on our recent activities, workshops and current topics.


Here are a list of projects that i had embark on. Some were personal pet projects while others were some of the more complicated school projects. (Some of the website link may cease to work due to no maintainence)
A school project from CS3226 Web programming and its applications module. It is a chatroom style web platform for nus students to interact with each other. It was built from laravel framework, using materialize as frontend. This project has gotten second placing in NUS's 10th Steps competition.
This is a personal pet project, an extension from the web platform that was created previously. It is still in production phase with basic feature of creating chatroom and messaging functionality implemented. It is built using Swift 3.0, iOS 10 and Firebase 4.0
Flashpoint is a java desktop application that helps you to keep track of your busy schedules and commitments all in one interactive program. It ensures the easiest way of getting tasks done. This was a school project from CS2103T Software Engineering.


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Reference #18.cdcf180.1518007007.1b10a1f0


Suppose that there are two 4-bit registers, RAX and RBX. As we all know, add and cmp instructions set the eflags register.
Toggle the slider below to see what register values produce what flags.
add rax rbx
0 (0000)
- Overflow flag: 0
- Carry flag: 0
- Zero flag: 1
- Sign flag: 0
cmp rax rbx
- Overflow flag: 0
- Carry flag: 0
- Zero flag: 1
- Sign flag: 0
Signed jumps
- Jump if less (jl/jnge): false
- Jump if less or equal (jle/jng): true
- Jump if greater (jg/jnle): false
- Jump if greater or equal (jge/jnl): true
Unsigned jumps
- Jump if below / jump carry (jb/jnae/jc): false
- Jump if below or equal (jbe/jna): true
Misc Jumps
- Jump if overflow (jo): false
- Jump if sign (js): false
- Jump if equal/zero (je/jz): true
- Jump if parity even (jp/jpe): true
Note: not all the flags are shown, only the ones affected by add/cmp instructions
More on this topic:
- Notes on carry vs overflow flags
- SO post on jumps after
cmp


Before Change
for backend, TransposeLayer in backend_pairs:
for pattern, axes_lengths, result_shape in patterns:
x = numpy.arange(10 * 20 * 30 * 40, dtype="float32").reshape([10, 20, 30, 40])
layer = TransposeLayer(pattern, **axes_lengths)
assert layer(backend.from_numpy(x)).shape == result_shape
for shape in [(), (10,), (10, 10, 10), (15, 20, 31, 40), (10, 1, 1, 1, 1)]:
try:
After Change
// hybridization doesn"t work
// layer3 = layer2.hybridize()
layer3 = deepcopy(layer2)
result3 = backend.to_numpy(layer3(backend.from_numpy(x)))
assert numpy.allclose(result1, result3)
v = backend.from_numpy(x)
if isinstance(v, torch.Tensor):


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price:rs 130
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price:rs 90
product name
price:rs 30
product name
price:rs 130
product name
price:rs 10
product name
price:rs 90
product name
price:rs 30
product name
price:rs 130
product name
price:rs 10
product name
price:rs 90
product name
price:rs 30


your
results tell us:
Because you have both FNA (Fine Needle Aspiration) and Molecular Testing (
) an adjustment to your cancer risk is warranted.
Your adjusted cancer risk


Reconciliation
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(this is the result of the last reconciliation)
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(this is what the statement says)
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Description
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Source: layout/_animation.scss, line 1
3.2 Slide animation
Animate content to slide in from left, right, top or bottom. Note: the deafult is EMPTY because when no animation class is applied the opacity of the elecemnt is 0. The effect is achieved my listening to scroll events using the image-scroll.js file.


The Curious Case of Protractor and Page Synchronization
Protractor is an amazing tool but use it incorrectly and it will make your life miserable. This blog post is about how a simple setTimeout()
made my life miserable.
Protractor is an end-to-end test framework for Angular and AngularJS applications. It is a Node.js program built on top of WebDriverJS. It runs tests against your application running in a real browser, interacting with it as a user would.
Protractor wraps WebDriverJS which is Javascript Selenium bindings — in other words, Protractor interacts with a browser through the Selenium WebDriver. It provides a really convenient API and has some unique Angular-specific features, like Angular specific element locating strategies (by.model()
, by.binding()
, by.repeater()
), automatic synchronization between Protractor and Angular that helps to minimize the use of explicit waits here and there.
The Background —
Before we get to the problem, we need to understand how protractor deals with asynchronous nature of Javascript and provides a synchronous API.
Despite being asynchronous, protractor allows us to write synchronous tests. This is possible because of the WebDriverJS library which uses a promise manager to ease the pain of working with a purely asynchronous API. WebDriverJS maintains a queue of pending promises, called the control flow, to keep execution organized. For example, consider this test:
it('should find an element by text input model', function() {
browser.get('app/index.html#/form');
var username = element(by.model('username'));
username.clear();
username.sendKeys('Jane Doe');
var name = element(by.binding('username'));
expect(name.getText()).toEqual('Jane Doe');
// Point A
});
At Point A, none of the tasks have executed yet. The browser.get
call is at the front of the control flow queue, and the name.getText()
call is at the back. The value of name.getText()
at point A is an unresolved promise object.
Protractor provides two ways to handle asynchronous actions
- Promise Manager/Control Flow
- Async/Await
The Promise Manager/Control Flow Monster
Before performing any action, Protractor waits until there are no pending asynchronous tasks in your Angular application. This means that all timeouts and HTTP requests are finished. For Angular apps, Protractor will wait until the Angular Zone stabilizes. This means long-running asynchronous operations will block your test from continuing.
So, if you have an infinite timeout (usually used to refresh tokens), your tests will wait indefinitely.
In fabric8-ui, we have a piece of code that refreshes the JWT token
setupRefreshTimer(refreshInSeconds: number) {
....
this.clearTimeoutId = setTimeout(() =>
this.refreshToken(), refreshInMs
);
....
}
The above code worked as expected by triggering a new timer just when the existing token was about to expire. This meant the control flow queue would always have a setTimeout() call within it. As soon as the current timeout was about to expire, a new one was added to the queue. This meant that the Angular Zone would never stabilize and our tests would keep waiting indefinitely. Our tests would wait indefinitely and always fail with the following error
Failed: Timed out waiting for Protractor to synchronize with the page after 11 seconds. Please see https://github.com/angular/protractor/blob/master/docs/faq.md.
Error: Timeout - Async callback was not invoked within timeout specified by jasmine.DEFAULT_TIMEOUT_INTERVAL
The easiest way to fix the above error was to run the setTimeout function outside the angular zone with the following code
this.ngZone.runOutsideAngular(() => {
setTimeout(() => {
// Changes here will not propagate into your view.
this.ngZone.run(() => {
// Run inside the ngZone to trigger change detection.
});
}, REALLY_LONG_DELAY);
});
But we decided to fix the underlying issue for once and for all by using Async/Await instead of Control Flow.
Slaying the Monster with Async/Await
The Async/Await way allows us to choose to when to wait for an action. Instead of waiting for angular to stabilize on every action, we can selectively wait for angular to stabilize on selected actions. Let’s look at an example
describe('angularjs homepage', function() {
it('should greet the named user', async function() {
await browser.get('http://www.angularjs.org');
await element(by.model('yourName')).sendKeys('Julie');
var greeting = element(by.binding('yourName'));
expect(await greeting.getText()).toEqual('Hello Julie!');
});
In the above example, protractor will wait only at lines which are awaiting for the promise to resolve. We can add “await” keyword to each operation that we want our program to wait for. Before you start using the Async/Await pattern with Protractor, you’ll have to disable the Control Flow (at least for now). Control Flow will soon be deprecated and Async/Await will be the default.
Protractor won’t wait for HTTP requests/async operations unless it is explicitly specified via await.
Control flow uses a queue for actions to wait upon while async/await waits only at the specified lines. We ended up fixing the Waiting for Protractor to synchronize issue by migrating to async/await from control flow. You can find our typescript based tests here.
Takeaways —
- Use Async/Await instead of Control Flow, unless you have a concrete reason to not use Async/Await.
- Wrap your timeouts and long duration async operations in ngZone.runOutsideAngular() if you plan to use Control Flow.
- The Control Flow will soon be deprecated and async/await will be the default (See this Github issue).


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7. Virtual Machine Example
The example for this tutorial has been altered to run on the tutorial
VM, but it is adapted from an HPC example. The next thing we will do is
run the workflow, which will:
- Create a work directory
- Copy files into the work directory
- Run the simulation, creating a Cinema database in-situ,
according to several files in the workflow.
Execute the workflow, and watch the shell for output during the run:
cd ~/pantheon/tutorial
./execute
After the simulation is done, you can look at the results:
pushd ~/pantheon/work/pantheon/E4S/Ascent_Miniapp_Workflow/results
firefox cinema_databases/cinema_view.html
popd


Imports a module.
__import__ (name[, globals[, locals[, fromlist[, level]]]])
#TODO
#TODO
This is an advanced function that is not needed in everyday Python programming, unlike importlib.import_module().
This function is invoked by the import statement. It can be replaced (by importing the __builtin__ module and assigning to __builtin__.__import__) in order to change semantics of the import statement, but nowadays it is usually simpler to use import hooks (see PEP 302). Direct use of __import__() is rare, except in cases where you want to import a module whose name is only known at runtime.
When the name variable is of the form package.module, normally, the top-level package (the name up till the first dot) is returned, not the module named by name. However, when a non-empty fromlist argument is given, the module named by name is returned. For example, the statement import spam results in bytecode resembling the following code:
>>> spam = __import__('spam', globals(), locals(), [], -1)
The statement import spam.ham results in this call:
>>> spam = __import__('spam.ham', globals(), locals(), [], -1)
Note how __import__() returns the toplevel module here because this is the object that is bound to a name by the import statement.
On the other hand, the statement from spam.ham import eggs, sausage as saus results in
>>> _temp = __import__('spam.ham', globals(), locals(), ['eggs', 'sausage'], -1)
>>> eggs = _temp.eggs
>>> saus = _temp.sausage
Here, the spam.ham module is returned from __import__(). From this object, the names to import are retrieved and assigned to their respective names.
If you simply want to import a module (potentially within a package) by name, use importlib.import_module().
#TODO
#TODO


Trigger (open/close) with selector:
Trigger Emoji Menu
Trigger (open/close) with onClick event:
Trigger Emoji Menu
Emoji Menu location


Indian Legal Dataset Analysis
Indian Institute of Technology, Delhi
National - States
Mean Disposal Time
Coefficient of Variation
Zero Day Disposal Percentage
Tail Bounds - 3 months
Tail Bounds - 6 months
Tail Bounds - 1 year
Tail Bounds - 3 years
Avg No of Hearings
Avg Time Between Hearings
National - Districts
KL Divergence
Mean Disposal Time
Coefficient of Variation
Zero Day Disposal Percentage
Tail Bounds - 3 months
Tail Bounds - 6 months
Tail Bounds - 1 year
Tail Bounds - 3 years
Avg No of Hearings
Avg Time Between Hearings
States
Andhra Pradesh
Assam
Bihar
Chhattisgarh
Delhi
Gujarat
Haryana
Himachal Pradesh
Jammu and Kashmir
Jharkhand
Karnataka
Kerala
Lakshadweep
Madhya Pradesh
Maharashtra
Odisha
Punjab
Rajasthan
Sikkim
Tamil Nadu
Telangana
Tripura
Uttarakhand
Uttar Pradesh
West Bengal
Madhya Pradesh
KL Divergence
Mean Disposal Time
Coefficient of Variation
Zero Day Disposal Percentage
Tail Bounds - 3 months
Tail Bounds - 6 months
Tail Bounds - 1 year
Tail Bounds - 3 years
Avg No of Hearings
Avg Time Between Hearings
FAQ
About Us
Percentage Cases Taking More than 1 Year for Districts of Madhya Pradesh


Screen¶
Screens the genome for ERV like regions by comparing the genome to a set of known retroviral ORFs using Exonerate.
Confirms the Exonerate regions using UBLAST
Finds and confirms ORFs within these regions
Finds the most similar known retroviral ORF in the database to each of the newly identified ORFs
Functions¶
initiate¶
Input Files
pipeline.ini
Output Files
init.txt
Parameters
[genome] file
[paths] path_to_ERVsearch
[paths] path_to_usearch
[paths] path_to_exonerate
Initialises the pipeline and checks that the required parameters in the pipeline.ini are set and valid and that the required software is in your $PATH.
Checks that:
The input genome file exists.
The correct path to ERVsearch is provided.
samtools, bedtools, FastTree and mafft are in the $PATH
The correct paths to usearch and exonerate are provided.
init.txt
is a placeholder to show that this step has been completed.
genomeToChroms¶
Input Files
[genome] file
keep_chroms.txt
Output Files
host_chromosomes.dir/*fasta
Parameters
[genome] file
[genomesplits] split
[genomesplits] split_n
[genomesplits] force
Splits the host genome provided by the user into FASTA files of a suitable size to run Exonerate efficiently.
If genomesplits_split
in the pipeline.ini is False, the genome is split into one fasta file for each sequence - each chromosome, scaffold or contig.
If genomesplits_split
in the pipeline.ini is True, the genome is split into the number of batches specified by the genomesplits_splitn
parameter, unless the total number of sequences in the input file is less than this number.
The pipeline will fail if the number of sequences which would result from the genomesplits settings would result in >500 Exonerate runs, however it is possible to force the pipeline to run despite this by setting genomesplits_force
to True.
If the file keep_chroms.txt exists in the working directory only chromosomes listed in this file will be kept.
An unzipped copy of zipped and gzipped fasta files will be created or a link to the file if it is already unzipped. this will be named genome.fa
and be in the working directory.
This function generates a series of fasta files which are stored in the host_chromosomes.dir directory.
prepDBs¶
Input Files
None
Output Files
gene_databases.dir/GENE.fasta
Parameters
[database] use_custom_db
[database] gag
[database] pol
[database] env
Retrieves the gag, pol and env amino acid sequence database fasta files and puts a copy of each gene_databases.dir directory.
If custom databases are used they are retrieved and named as gag.fasta pol.fasta, env.fasta so the path doesn’t need to be changed every time.
runExonerate¶
Input Files
gene_databases.dir/GENE.fasta
host_chromosomes.dir/*fasta
Output Files
raw_exonerate_output.dir/GENE_*.tsv
Parameters
[paths] path_to_exonerate
Runs the protein2dna
algorithm in the Exonerate software package with the host chromosomes (or other regions) in host_chromosomes.dir
as target sequences and the FASTA files from prepDBs as the query sequences.
The raw output of Exonerate is stored in the raw_exonerate_output directory, one file is created for each combination of query and target sequences.
This step is carried out with low stringency as results are later filtered using UBLAST and Exonerate.
cleanExonerate¶
Input Files
raw_exonerate_output.dir/GENE_*.tsv
Output_Files
clean_exonerate_output.dir/GENE_*_unfiltered.tsv
clean_exonerate_output.dir/GENE_*_filtered.tsv
clean_exonerate_output.dir/GENE_*.bed
Parameters
[exonerate] min_hit_length
Filters and cleans up the Exonerate output.
Converts the raw Exonerate output files into dataframes - GENE_unfiltered.tsv
Filters out any regions containing introns (as defined by Exonerate)
Filters out regions less than
exonerate_min_hit_length
on the host sequence (in nucleotides).Outputs the filtered regions to GENE_filtered.tsv
Converts this to bed format and outputs this to GENE.bed
mergeOverlaps¶
Input Files
clean_exonerate_output.dir/GENE_*.bed
Output_Files
gene_bed_files.dir/GENE_all.bed
,
gene_bed_files.dir/GENE_merged.bed
Parameters
[exonerate] overlap
Merges the output bed files for individual sections of the input genome into a single bed file.
Overlapping regions or very close together regions of the genome detected by Exonerate with similarity to the same retroviral gene are then merged into single regions. This is performed using bedtools merge on the bed files output by cleanExonerate.
If there is a gap of less than exonerate_overlap
between the regions they will be merged.
makeFastas¶
Input Files
gene_bed_files.dir/GENE_merged.bed
genome.fa
Output Files
gene_fasta_files.dir/GENE_merged.fasta
Parameters
None
Fasta files are generated containing the sequences of the merged regions of the genome identified using mergeOverlaps. These are extracted from the host chromosomes using bedtools getfasta.
renameFastas¶
Input Files
gene_fasta_files.dir/GENE_merged.fasta
Output Files
gene_fasta_files.dir/GENE_merged_renamed.fasta
Parameters
None
Renames the sequences in the fasta files of ERV-like regions identified with Exonerate so each record has a numbered unique ID (gag1, gag2 etc). Also removes “:” from sequence names as this causes problems later.
makeUBLASTDb¶
Input Files
gene_databases.dir/GENE.fasta
Output Files
UBLAST_db.dir/GENE_db.udb
Parameters
[paths] path_to_ublast
USEARCH requires an indexed database of query sequences to run. This function generates this database for the three gene amino acid fasta files used to screen the genome.
runUBLASTCheck¶
Input Files
UBLAST_db.dir/GENE_db.udb
gene_fasta_files.dir/GENE_merged.fasta
Output Files
ublast.dir/GENE_UBLAST_alignments.txt
ublast.dir/GENE_UBLAST.tsv
ublast.dir/GENE_filtered_UBLAST.fasta
Parameters
[paths] path_to_usearch
[usearch] min_id
[usearch] min_hit_length
[usearch] min_coverage
ERV regions in the fasta files generated by makeFasta are compared to the ERV amino acid database files for a second time, this time using USEARCH (https://www.drive5.com/usearch/). Using both of these tools reduces the number of false positives.
This allows sequences with low similarity to known ERVs to be filtered out. Similarity thresholds can be set in the pipeline.ini file (usearch_min_id,
- minimum identity between query and target - usearch_min_hit_length
- minimum length of hit on target sequence - and usearch_min_coverage
- minimum proportion of the query sequence the hit should cover).
The raw output of running UBLAST against the target sequences is saved in GENE_UBLAST_alignments.txt (equivalent to the BLAST default output) and GENE_UBLAST.tsv (equivalent to the BLAST -outfmt 6 tabular output) this is already filtered by passing the appropriate parameters to UBLAST. The regions which passed the filtering and are therefore in these output files are then output to a FASTA file GENE_filtered_UBLAST.fasta.
classifyWithExonerate¶
Input Files
ublast.dir/GENE_filtered_UBLAST.fasta
ERVsearch/ERV_db/all_ERVs_nt.fasta
Output Files
exonerate_classification.dir/GENE_all_matches_exonerate.tsv
exonerate_classification.dir/GENE_best_matches_exonerate.tsv
exonerate_classification.dir/GENE_refiltered_matches_exonerate.fasta
Parameters
[paths] path_to_exonerate
[exonerate] min_score
Runs the Exonerate ungapped algorithm with each ERV region in the fasta files generated by makeFasta as queries and the all_ERVs_nt.fasta fasta file as a target, to detect which known retrovirus is most similar to each newly identified ERV region. Regions which don’t meet a minimum score threshold (exonerate_min_score
) are filtered out.
all_ERVs_nt.fasta contains nucleic acid sequences for many known endogenous and exogenous retroviruses with known classifications.
First all seqeunces are compared to the database and the raw output is saved as exonerate_classification.dirGENE_all_matches_exonerate.tsv. Results need a score greater than exonerate_min_score
against one of the genes of the same type (gag, pol or env) in the database. The highest scoring result which meets these critera for each sequence is then identified and output to exonerate_classification.dir/GENE_best_matches_exonerate.tsv. The sequences which meet these critera are also output to a FASTA file exonerate_classification.dir/GENE_refiltered_exonerate.fasta.
getORFs¶
Input Files
exonerate_classification.dir/GENE_refiltered_matches_exonerate.fasta
Output Files
ORFs.dir/GENE_orfs_raw.fasta
xsORFs.dir/GENE_orfs_nt.fasta
ORFs.dir/GENE_orfs_aa.fasta
Parameters
[orfs] translation_table
[orfs] min_orf_len
Finds the longest open reading frame in each of the ERV regions in the filtered output table.
This analysis is performed using EMBOSS revseq and EMBOSS transeq.
The sequence is translated in all six frames using the user specified translation table. The longest ORF is then identified. ORFs shorter than orfs_min_orf_length are filtered out.
The positions of the ORFs are also convered so that they can be extracted directly from the input sequence file, rather than using the co-ordinates relative to the original Exonerate regions.
The raw transeq output, the nucleotide sequences of the ORFs and the amino acid sequences of the ORFs are written to the output FASTA files.
checkORFsUBLAST¶
Input Files
ORFs.dir/GENE_orfs_nt.fasta
UBLAST_dbs.dir/GENE_db.udb
Output Files
ublast_orfs.dir/GENE_UBLAST_alignments.txt
ublast_orfs.dir/GENE_UBLAST.tsv
ublast_orfs.dir/GENE_filtered_UBLAST.fasta
Parameters
[paths] path_to_usearch
[usearch] min_id
[usearch] min_hit_length
[usearch] min_coverage
ERV ORFs in the fasta files generated by the ORFs function are compared to the original ERV amino acid files using UBLAST. This allows any remaining sequences with poor similarity to known ERVs to be filtered out.
This allows ORFs with low similarity to known ERVs to be filtered out. Similarity thresholds can be set in the pipeline.ini file (usearch_min_id,
- minimum identity between query and target - usearch_min_hit_length
- minimum length of hit on target sequence - and usearch_min_coverage
- minimum proportion of the query sequence the hit should cover).
The raw output of running UBLAST against the target sequences is saved in GENE_UBLAST_alignments.txt (equivalent to the BLAST default output) and GENE_UBLAST.tsv (equivalent to the BLAST -outfmt 6 tabular output) this is already filtered by passing the appropriate parameters to UBLAST. The regions which passed the filtering and are therefore in these output files are then output to a FASTA file GENE_filtered_UBLAST.fasta.
assignGroups¶
Input Files
ublast_orfs.dir/GENE_UBLAST.tsv
ERVsearch/ERV_db/convert.tsv
Output Files
grouped.dir/GENE_groups.tsv
Parameters
[paths] path_to_ERVsearch
Many of the retroviruses in the input database all_ERVs_nt.fasta have been classified into groups based on sequence similarity, prior knowledge and phylogenetic clustering. Some sequences don’t fall into any well defined group, in these cases they are just assigned to a genus, usually based on prior knowledge. The information about these groups is stored in the provided file ERVsearch/ERV_db/convert.tsv.
Each sequence in the filtered fasta file of newly identified ORFs is assigned to one of these groups based on the sequence identified as the most similar in the classifyWithExonerate step.
The output table is also tidied up to include the UBLAST output, chromosome, ORF start and end positions, genus and group.
Screen¶
Input Files
None
Output Files
None
Parameters
None
Helper function to run all screening functions (all functions prior to this point).


This demo contains two different AngularJS controllers. That's a common situation - just think of modal dialogs.
The beans don't do much. They simply return random numbers. What makes them interesting is that the random numbers belong to two different AngularJS controllers. Note that as of AngularFaces 2.1.7, both AngularJS controllers contain all the values of both JSF beans. Each AngularJS controller also contains the JSF bean variable of the other AngularJS controller. That's a bug that needs to be fixed, but in most cases it shouldn't be a problem to you.
The first dice shows {{dice1.value}}.
The second dice shows {{dice2.value}}.


--- layout: post title: mint chocolate chip brownies date: 2010-03-12 17:07:05.000000000 -05:00 type: post published: true status: publish categories: - brownies - cookies tags: - egg-free - mint chocolate chip brownies - recipe meta: {} author: login: sophie email<EMAIL_ADDRESS>display_name: Pika first_name: '' last_name: '' ---
Forgive me father, for I have sinned.
I put non-vegan mint chips into a vegan brownie.
But you can TJ’s Mint UFO’s instead, those are 100% vegan.
Vegan-ish Mint Chocolate Chip Brownies (makes 12 squares)
adapted from these
3/4 cup sugar
1/4 cup canola oil
1/2 cup soy milk
1 teaspoon vanilla extract
3/4 cup whole wheat pastry flour
1/2 cup cocoa powder
1/2 teaspoon baking powder
1/4 teaspoon salt
1/2 cup chocolate chips (I used Nestle’s dark chocolate and mint mix)
Notes:
I have been trying to find a good vegan brownie recipe for a long time and these were definitely a bit of an “Eureka!” like the recipe promised. They were almost exactly like non-vegan fudge brownies, even the ones choc-full of butter and eggs. After cooling, they have a great texture and hold together. Even though they are 100% whole wheat, they definitely don’t taste like it and are super-super decadent. I got rave reviews from my classmates and omnivorous friends.
The only caveat for me is that these are very sweet—not any more so than box mix brownies, but I am going to try to use ½ cup sugar next time to see what happens.
UPDATE: two days later I went to a friend's house and ate brownies from a box mix, so I can assure you that yeah, these are the real thing.


|
Character |
Legend |
Example |
Sample Match |
|---|---|---|---|
|
\d |
Most
engines: one digit |
file_\d\d |
file_25 |
|
\d |
.NET, Python 3: one Unicode digit in any script |
file_\d\d |
file_9੩ |
|
\w |
Most engines: "word character": ASCII letter, digit or underscore |
\w-\w\w\w |
A-b_1 |
|
\s |
Most engines: "whitespace character": space, tab, newline, carriage return, vertical tab |
a\sb\sc |
a
b |
|
\s |
.NET, Python 3, JavaScript: "whitespace character": any Unicode separator |
a\sb\sc |
a
b |
|
\D |
One character that is not a digit as defined by your engine's \d |
\D\D\D |
ABC |
|
\W |
One character that is not a word character as defined by your engine's \w |
\W\W\W\W\W |
*-+=) |
|
\S |
One character that is not a whitespace character as defined by your engine's \s |
\S\S\S\S |
Yoyo |
|
. |
Any character except line break |
a.c |
abc |
|
\t |
Tab |
T\t\w{2} |
T ab |
|
\r |
Carriage return character |
see below |
|
|
\n |
Line feed character |
see below |
|
|
\r\n |
Line separator on Windows |
AB\r\nCD |
AB |
|
Character |
Legend |
Example |
Sample Match |
|---|---|---|---|
|
[ … ] |
One of the characters in the brackets |
[AEIOU] |
One uppercase vowel |
|
[ … ] |
One of the characters in the brackets |
T[ao]p |
Tap or Top |
|
- |
Range indicator |
[a-z] |
One lowercase letter |
|
[x-y] |
One of the characters in the range from x to y |
[A-Z]+ |
GREAT |
|
[ … ] |
One of the characters in the brackets |
[AB1-5w-z] |
One of either: A,B,1,2,3,4,5,w,x,y,z |
|
[x-y] |
One of the characters in the range from x to y |
[ -~]+ |
Characters in the printable section of the ASCII table. |
|
[^x] |
One character that is not x |
[^a-z]{3} |
A1! |
|
[^x-y] |
One of the characters not in the range from x to y |
[^ -~]+ |
Characters that are not in the printable section of the ASCII table. |
|
[\d\D] |
One character that is a digit or a non-digit |
[\d\D]+ |
Any
characters, inc- |
|
[\x41] |
Matches the character at hexadecimal position 41 in the ASCII table, i.e. A |
[\x41-\x45]{3} |
ABE |
|
Anchor |
Legend |
Example |
Sample Match |
|---|---|---|---|
|
^ |
Start of string or start of line depending on multiline mode. (But when [^inside brackets], it means "not") |
^abc .* |
abc (line start) |
|
$ |
End of string or end of line depending on multiline mode. Many engine-dependent subtleties. |
.*? the end$ |
this is the end |
|
\A |
Beginning
of string |
\Aabc[\d\D]* |
abc
(string... |
|
\z |
Very
end of the string |
the end\z |
this is...\n...the end |
|
\Z |
End
of string or (except Python) before final line break |
the end\Z |
this is...\n...the end\n |
|
\G |
Beginning
of String or End of Previous Match |
|
|
|
\b |
Word
boundary |
Bob.*\bcat\b |
Bob ate the cat |
|
\b |
Word
boundary |
Bob.*\b\кошка\b |
Bob ate the кошка |
|
\B |
c.*\Bcat\B.* |
copycats |
|
Modifier |
Legend |
Example |
Sample Match |
|---|---|---|---|
|
(?i) |
Case-insensitive
mode |
(?i)Monday |
monDAY |
|
(?s) |
DOTALL mode (except JS and Ruby). The dot (.) matches new line characters (\r\n). Also known as "single-line mode" because the dot treats the entire input as a single line |
(?s)From A.*to Z |
From
A |
|
(?m) |
Multiline
mode |
(?m)1\r\n^2$\r\n^3$ |
1 |
|
(?m) |
In Ruby: the same as (?s) in other engines, i.e. DOTALL mode, i.e. dot matches line breaks |
(?m)From A.*to Z |
From
A |
|
(?x) |
Free-Spacing
Mode mode |
(?x)
# this is a |
abc d |
|
(?n) |
Turns all (parentheses) into non-capture groups. To capture, use named groups. |
|
|
|
(?d) |
The dot and the ^ and $ anchors are only affected by \n |
|
|
Logic |
Legend |
Example |
Sample Match |
|---|---|---|---|
|
| |
Alternation / OR operand |
22|33 |
33 |
|
( … ) |
Capturing group |
A(nt|pple) |
Apple (captures "pple") |
|
\1 |
Contents of Group 1 |
r(\w)g\1x |
regex |
|
\2 |
Contents of Group 2 |
(\d\d)\+(\d\d)=\2\+\1 |
12+65=65+12 |
|
(?: … ) |
Non-capturing group |
A(?:nt|pple) |
Apple |
|
Lookaround |
Legend |
Example |
Sample Match |
|---|---|---|---|
|
(?=…) |
(?=\d{10})\d{5} |
01234 in<PHONE_NUMBER> |
|
|
(?<=…) |
(?<=\d)cat |
cat in 1cat |
|
|
(?!…) |
(?!theatre)the\w+ |
theme |
|
|
(?<!…) |
\w{3}(?<!mon)ster |
Munster |


List of all classes, functions and methods in python-igraph
class Histogram:
Generic histogram class for real numbers
Example:
>>> h = Histogram(5) # Initializing, bin width = 5 >>> h << [2,3,2,7,8,5,5,0,7,9] # Adding more items >>> print(h) N = 10, mean +- sd: 4.8000 +- 2.9740 [ 0, 5): **** (4) [ 5, 10): ****** (6)
| Method | __init__ |
Initializes the histogram with the given data set. |
| Property | n |
Returns the number of elements in the histogram |
| Property | mean |
Returns the mean of the elements in the histogram |
| Property | sd |
Returns the standard deviation of the elements in the histogram |
| Property | var |
Returns the variance of the elements in the histogram |
| Method | add |
Adds a single number to the histogram. |
| Method | add_many |
Adds a single number or the elements of an iterable to the histogram. |
| Method | clear |
Clears the collected data |
| Method | bins |
Generator returning the bins of the histogram in increasing order |
| Method | __plot__ |
Plotting support |
| Method | to_string |
Returns the string representation of the histogram. |
| Method | __str__ |
Undocumented |
| Instance Variable | _bin_width |
Undocumented |
| Instance Variable | _bins |
Undocumented |
| Instance Variable | _min |
Undocumented |
| Instance Variable | _max |
Undocumented |
| Instance Variable | _running_mean |
Undocumented |
| Method | _get_bin |
Returns the bin index corresponding to the given number. |
Initializes the histogram with the given data set.
| Parameters | bin_width | the bin width of the histogram. |
| data | the data set to be used. Must contain real numbers. |
Returns the bin index corresponding to the given number.
| Parameters | num | the number for which the bin is being sought |
| create | whether to create a new bin if no bin exists yet. | |
| Returns | the index of the bin or None if no bin exists yet and {create} is False . |
Adds a single number to the histogram.
| Parameters | num | the number to be added |
| repeat | number of repeated additions |
Adds a single number or the elements of an iterable to the histogram.
| Parameters | data | the data to be added |
Generator returning the bins of the histogram in increasing order
| Returns | a tuple with the following elements: left bound, right bound, number of elements in the bin |
Returns the string representation of the histogram.
| Parameters | max_width | the maximal width of each line of the string This value may not be obeyed if it is too small. |
| show_bars | specify whether the histogram bars should be shown | |
| show_counts | specify whether the histogram counts should be shown. If both show_bars and show_counts are False , only a general descriptive statistics (number of elements, mean and standard deviation) is shown. |


Hi! The website you are previewing is a demonstration for Mundana Jekyll theme, hosted on Github, made by Sal.


QUIT
Check README
Hitting pink bricks wins 10 scores; blue bricks for 50 scores.
Choose Speed:
Slow
Middle
Fast
Choose Ball Size:
Small
Middle
Big
©
Mingyu MA / Derek


{% extends 'emails/base.html' %} {% block content %}
Hi Campus Partners,
We hope this email finds you well!
We are writing to invite you to add keywords to your resource on the Hub@Penn platform. Add keywords to your resource to make it easier to find. This will allow students to more effectively identify your resource in their general searches on Hub@Penn. Please identify any keywords that relate to your resource and add them into the system using the process outlined below.
Through Hub@Penn’s filter algorithm, users are able to easily search and sort the resources that are most likely be useful for them. Your keyword additions will make this experience even easier for students to navigate and find what they are looking for. As illustrated below, these keywords reflect terms, acronyms, abbreviations, or phrases that relate to your resource and ultimately promote its discoverability on the platform.
Please let us know if you have any questions.
Thank you!
Best, Hub@Penn Admin https://hub.provost.upenn.edu/
What is Hub@Penn?Hub@Penn is a platform to help Penn students find and connect with university support resources. Whether you need help with financial wellness, academic support, healthcare, career and fellowship applications, and more, you can use Hub@Penn to find the resource that meets your needs.
Office of New Student Orientation & Academic Initiatives (NSOAI) Online Learning Initiative (OLI) Penn Labs University Life


a web developer
Tribute Page
Random Quote Machine
JavaScript Calculator
Map Data Asross The Globe
Wikipedia Viewer
Tic Tac Toe Game
How do you take your coffee?


By using buffered input and buffered output.
The stream types BufferedInputStream
and BufferedOutputStream
each put a buffer between the program and the disk.
The program logic deals with single bytes;
actual IO is done efficiently.
DataInputStream instr; DataOutputStream outstr; . . . . instr = new DataInputStream( new BufferedInputStream( new FileInputStream( args[0] ))); outstr = new DataOutputStream( new BufferedOutputStream( new FileOutputStream( args[2] ))); try { int data; while ( true ) { data = instr.readUnsignedByte() ; outstr.writeByte( data ) ; } } catch ( EOFException eof ) { outstr.close(); instr.close(); return; }
The names of the files come from the command line. The name of the file to be copied is argument number 0; the copy is argument number 2 (the word "to" is argument number 1).


The simplest solutions of CGLE are plane wave solutions, which take the form of a one-parameter family:
where
We examine the stability of these solutions by setting
which, after substitution into the CGLE equation and linearising, yields
We now set
after which, a little manipulation gives
We seek solutions to these equations in the form which results in a polynomial for . We then expand the solutions for for small (the long-wavelength limit) to find
Thus, travelling waves solutions are long-wave stable as long as the condition
holds. Hence, when the Benjamin-Feir-Newell criterion
holds, there is a spectrum of wavenumbers
including the band centre () state, where plane waves are stable.


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Connection Error
When connecting to a PostgreSQL server, you may get an error message. If you
encounter an error message, please review the message carefully; each error
message attempts to incorporate the information you’ll need to resolve the
problem. For more details about specific errors, please locate the error
message in the list below:
Connection to the server has been lost
This error message indicates that the connection attempt has taken longer than
the specified threshold; there may be a problem with the connection properties
provided on the Server dialog, network connectivity issues, or the server may
not be running.
could not connect to Server: Connection refused
- If pgAdmin displays this message, there are two possible reasons for this:
- the database server isn’t running - simply start it.
- the server isn’t configured to accept TCP/IP requests on the address shown.
For security reasons, a PostgreSQL server “out of the box” doesn’t listen on
TCP/IP ports. Instead, it must be enabled to listen for TCP/IP requests. This
can be done by adding listen_addresses=’*’; this will make the server accept
connections on any IP interface.
For further information, please refer to the PostgreSQL documentation about
runtime configuration.
FATAL: no pg_hba.conf entry
If pgAdmin displays this message when connecting, your server can be contacted
correctly over the network, but is not configured to accept your connection.
Your client has not been detected as a legal user for the database.
To connect to a server, the pg_hba.conf file on the database server must be
configured to accept connections from the host of the pgAdmin client. Modify
the pg_hba.conf file on the database server host, and add an entry in the form:
- host template1 postgres <IP_ADDRESS>/24 md5 for an IPV4 network
- host template1 postgres ::ffff:<IP_ADDRESS>/120 md5 for an IPV6 network
For more information, please refer to the PostgreSQL documentation about
client authentication.
FATAL: password authentication failed
The password authentication failed for user error message indicates there
may be a problem with the password you entered. Retry the password to confirm
you entered it correctly. If the error message returns, make sure that you have
the correct password, that you are authorized to access the server, and that
the access has been correctly configured in the server’s postgresql.conf
configuration file.


The AudioContext of the listener given in the constructor.
Default is null.
A GainNode created using AudioContext.createGain().
Time delta value for audio entities. Use in context of AudioParam.linearRampToValueAtTimeDefault(). Default is 0.
String denoting the type, set to 'AudioListener'.
Returns the value of the filter property.
Return the gainNode.
Return the volume.
Set the filter property to null.
Set the filter property to value.
Set the volume.
Generated using TypeDoc
The AudioListener represents a virtual listener of the all positional and non-positional audio effects in the scene. A three.js application usually creates a single instance of AudioListener. It is a mandatory construtor parameter for audios entities like Audio and PositionalAudio. In most cases, the listener object is a child of the camera. So the 3D transformation of the camera represents the 3D transformation of the listener.


Dallas Buyers Club
My mind's a burnin' hell / I got razors a rippin' and tearin' and strippin' / My heart apart as well / Tonight you told me / That you ache for something new 149 items Come To My Window By Melissa Etheridge. For piano, voice, and guitar. (chords only) Pop; Rock. 6 pages Published by Hal Leonard. - Digital Sheet


First, you'll need to create a post-receive hook on GitHub. Just follow the
step-by-step guide
for adding a webhook and set the URL to
http://plugins.jquery.com/postreceive-hook
.
Once the post-receive hook is setup, publishing your plugin is as simple as tagging the version in git and pushing the tag to GitHub. The post-receive hook will notify the plugins site that a new tag is available and the plugins site will take care of the rest!
The name of the tag must be a valid
semver value. The tag name may contain an
optional v
prefix. The tag name must also match the version listed
in the manifest file. If the manifest file is valid, then the version will be
automatically added to the plugins site.
Unfortunately we do not currently have a system for notifying you if there is a problem. If you're interested in helping improve this aspect of the plugins site, we'd love your help.


Typical SEM images of GdBO 3:Eu 3+ phosphors and the elemental mapping profiles of unannealed precursors synthesized by (a) RPB and (b) conversional STR and annealed products synthesized by (c) RPB route and (d) STR route. The reaction times in the RPB and STR were 2 h and 20 min, respectively.


1 override of Read
System.Management.Automation (1)
engine\CommandProcessor.cs (1)
456
internal sealed override bool
Read
()
4 references to Read
System.Management.Automation (4)
engine\NativeCommandProcessor.cs (1)
436
while (
Read
())
engine\ScriptCommandProcessor.cs (3)
386
while (
Read
())
418
while (
Read
())
445
while (
Read
())


Compodoc is a documentation tool for Angular applications. Nest and Angular are very closed, so Compodoc supports also Nest applications.
Setting up Compodoc inside an existing Nest project is very simple. With npm installed, just add the dev-dependency with the following commands in your OS terminal:
$ npm i -D @compodoc/compodoc
Followin the official documentation, you can generate very easily your documentation with this following command in your OS terminal (npm 6 required):
$ npx compodoc -p tsconfig.json -s
Open http://localhost:8080 in your browser and you should have for an empty Nest CLi project this screenshot :
You can participate and contribute to Compodoc project here.