JavaScript Fundamentals
Material based on the lecture by Carlos Scheidegger
If you’re following the text below, my suggestion is that you either open the Developer Tools’s JavaScript console on a browser window, and type the examples to see what they do, like we go over in class. You should also try variants, and just generally play around with the console, to get a feel for the language.
Before we get started, though, a few words of warning: there is a lot of bad JavaScript advice on the internet. For example, although StackOverflow is typically a high-quality Q&A website, I would stay well away from it when it comes to JavaScript (why that is the case is beyond my understanding). Finally, the introduction below is not meant to give you a comprehensive description of JavaScript, but rather a foothold.
Once you become proficient in the language, then you can start worrying about best practices and special cases, especially as they related to performance and portability across browsers. It’s easier for you simply not to worry about that kind of stuff right now. (This does mean that if you’re a veteran JavaScript programmer, you’ll spot places where what I’m writing is not 100% accurate. If you were to complain, you’d be technically correct (which is the best kind of correct), but what are you doing reading a JavaScript beginner’s guide?
JavaScript Background
Why do we want to use JavaScript? So far we have only written static HTML and SVG code. In your homework, for example, you have to encode a dataset by hand every time, for your line-chart, bar chart, scatterplot, etc.
If we had a dynamic and general way to load the data, we could use the same variables for all charts and we could also plot the charts with various datasets of different size and with different characteristics. I like to think of pure HTML and SVG as analogous to pen and paper - you can create things that look nice once, but you can’t re-use them efficiently and you can’t interact with them. Computer programming brings interactivity and generalizability (re-use) to the table. We can tell the computer how to do something for certain classes of (legal) input, and the computer will do that for all possible cases of legal input.
JavaScript is the most important programming language of the web, and the only programming language that can be used on most web-browsers without any plugins. Alternatives such as Java Applets or Flash were popular in the past but have lost significant ground to JavaScript. JavaScript is mostly used on the client-side of a client-server application, other languages such as Java and Python are popular on the server, but JavaScript is nowadays also used on the server e.g., using Node.js. We will be focusing on the client-side in this class.
JavaScript can be used with imperative/procedural, object-oriented, and functional programming styles. It is a dynamically typed language, which can be strange for developers who mainly work with strongly typed languages such as C/C++ and Java. Also, Javascript uses prototypical inheritance instead of a class-based model for it’s object oriented purposes. That means that there is no “class” that is defined centrally, instead you rely on objects’ prototypes for inheritance that can be extended at runtime. If this doesn’t mean much to you now, don’t worry - we’ll go through it slowly.
JavaScript - The Very Basic
If you know any other mainstream programming language, JavaScript will feel sufficiently familiar.
We can write to the console (helpful for debugging by calling the log method of the console object). Hint: open up your browser’s developer tools to see the output generated by the examples here.
See output in new page.It has variables which hold values:
See output in new page.The first thing to notice is that JavaScript’s variables are dynamically typed: you don’t need to declare their types before using them, and they can refer to values of different types at different times in the program execution. (This is convenient but quite error-prone: it’s usually a bad idea to make too much use of this feature.)
You also do not need to declare a variable ahead of time. If you don’t, then JavaScript either assumes you’re referring to an already existing variable, or it creates a new global variable. Again, this is convenient but very error-prone (this is a theme, as you’ll see). One common source of confusion is that typos in variable assignments are not caught: they just become global variables.
To create a local variable, use the keyword var
. Local here refers to the current execution context. When used within a function these variables are private to that function, however, when they are declared outside a function, as in the above example, they are still global. As a general rule: minimize your use of global variables, and never define global variables in a function.
You can execute operations on these variables. Below are a couple of important examples, refer to the MDN documentation for the full list:
See output in new page.Arrays
Array literals are declared using square brackets and addressed with square brackets and a reference to the index starting with 0:
See output in new page.You can do much more with arrays than shown here, again, check out the MDN documentation. Arrays are very important for data visualization, so take the time to go through this!
Objects
Objects are the second type of compound values in JavaScript.
See output in new page.Control Structures
See output in new page.Loops
See output in new page.Functions
Functions are one of the key features in computer science and are common to almost all programming languages. JavaScript uses function both the way we know them from languages such as C or Java, but also allows anonymous functions.
See output in new page.None of the calls above cause runtime errors. If you call a function with too many parameters, JavaScript will simply ignore the extra ones. If you call a function with too few parameters, JavaScript gives the local parameters the special value undefined.
See output in new page.There is an alternative way of defining functions:
See output in new page.Pay attention to what’s happening here: this is assigning a value to a variable, in the same way that x = “hi” assigns the string value “hi” to the variable x. But that value is a function! This is important. In JavaScript, functions are values that can be stored in variables. This is your first exposure to the idea that JavaScript is a “functional” language. In the same way that you can store function values in variables, you can pass them around as parameters, store them in arrays, object fields, and even use them as return values of other functions! This is a powerful idea that we will use a lot.
Object Oriented JavaScript
If we create an object with slots that hold functions, this starts to look like methods from Java and Python. If we create a function that returns these objects, this starts to look like class contructors:
See output in new page.Inheritance, without Classes
JavaScript does support a notion of inheritance, but it does it without any classes. This means that there’s no subclasses, so how does it work?
Instead of subclasses, JavaScript has the notion of a prototype chain. Every JavaScript object has a special field which points to another object. Then, every time you tell JavaScript to access a field from an object, it tries to find the field. If the field exists, then the lookup is performed. If, however, the field doesn’t exist, then JavaScript checks for the presence of a special prototype field in the object. If that field is not null, then the JavaScript runtime performs a recursive access of the field in the prototype object. This is more obvious with an example. Make sure to run these in your JavaScript console:
See output in new page.The special variable this
JavaScript has a special variable that is available at every scope called this
. When a function is called with a notation that resembles methods in typical object-oriented languages, say obj.method()
, then this
is bound to the object holding the method (in this case obj). this allows you to make changes to the local object:
So far, so good: we’ve used this
to change the value bound to the x field in the object. That’s pretty convenient.
However, the convenience comes with a caveat. The way JavaScript decides to associate this
with a given object is simple to explain, but sometimes leads to strange behavior. The way it works is that although obj.someFunction
is the syntax to access the someFunction
slot from obj and someFunction(parameter)
is the syntax to call a the function value bound to someFunction with parameter parameter, the syntax obj.someFunction(parameter)
is not equivalent to storing obj.someFunction
to some temporary variable and calling that. In other words, the syntax obj.someFunction(parameter)
consisting of those three things next to one another is special to JavaScript.
Here’s what can go wrong:
See output in new page.What happened in the example that goes wrong is that when t() is called, JavaScript doesn’t see the special notation obj.method(params), and so it keeps the binding of this to its present value.
Anytime you use this and something goes wrong, the first you should try to check is whether some call in your chain forgot to set the binding by using the right syntax.


Marquez:
Go and defeat the
Velociraptor
and bring back the
50-page journal
. Travel east to the
Lost Nest
and I have no doubt that you will find the
Velociraptor
.
Good hunting!


In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[['yr', 'cnt']].groupby('yr').count()
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*10].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save the last 21 days
test_data = data[-21*24:]
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.
Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.import random
class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
random.seed(1)
self.weights_input_to_hidden = np.random.normal(0.0,
self.hidden_nodes ** -0.5,
(self.hidden_nodes,
self.input_nodes))
self.weights_hidden_to_output = np.random.normal(0.0,
self.output_nodes ** -0.5,
(self.output_nodes,
self.hidden_nodes))
self.lr = learning_rate
#### Set this to your implemented sigmoid function ####
# Activation function is the sigmoid function
self.activation_function = lambda x: 1 / (1 + np.exp(-x))
def train(self, inputs_list, targets_list):
# Convert inputs list to 2d array
inputs = np.array(inputs_list, ndmin=2).T
targets = np.array(targets_list, ndmin=2).T
#### Implement the forward pass here ####
### Forward pass ###
# TODO: Hidden layer
hidden_inputs = np.dot(self.weights_input_to_hidden,
inputs) # signals into hidden layer
hidden_outputs = self.activation_function(
hidden_inputs) # signals from hidden layer
# TODO: Output layer
final_inputs = np.dot(self.weights_hidden_to_output,
hidden_outputs) # signals into final output layer
final_outputs = final_inputs
#self.activation_function(final_inputs) # signals from final output layer
### Implement the backward pass here ####
## Backward pass ###
# TODO: Output error
# Output layer error is the difference between desired target and actual output.
output_errors = targets - final_outputs
output_grad = output_errors #self.grid_error(output_errors, final_outputs)
# # TODO: Backpropagated error
# errors propagated to the hidden layer
hidden_errors = np.dot(output_grad, self.weights_hidden_to_output)
# hidden layer gradients
hidden_grad = self.grid_error(hidden_errors, hidden_outputs.T)
# # TODO: Update the weights
self.weights_hidden_to_output += self.delta_w(self.lr, output_grad,
hidden_outputs).T
# update hidden-to-output weights with gradient descent step
self.weights_input_to_hidden += self.delta_w(self.lr, hidden_grad,
inputs).T
def run(self, inputs_list):
# Run a forward pass through the network
inputs = np.array(inputs_list, ndmin=2).T
#### Implement the forward pass here ####
# TODO: Hidden layer
hidden_inputs = np.dot(self.weights_input_to_hidden,
inputs) # signals into hidden layer
hidden_outputs = self.activation_function(
hidden_inputs) # signals from hidden layer
# TODO: Output layer
final_inputs = np.dot(self.weights_hidden_to_output,
hidden_outputs) # signals into final output layer
final_outputs = final_inputs
#self.activation_function(final_inputs) # signals from final output layer
return final_outputs
def grid_error(self, error, output):
output_error = error * output * (1 - output)
return output_error
def delta_w(self, lr, grid_error, input_before_weight):
return lr * input_before_weight * grid_error
def MSE(y, Y):
return np.mean((y-Y)**2)
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.
You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.
This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.
This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.
The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.
import sys
### Set the hyperparameters here ###
epochs = 1000
learning_rate = 0.02
hidden_nodes = 5
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for e in range(epochs):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
for record, target in zip(train_features.ix[batch].values,
train_targets.ix[batch]['cnt']):
network.train(record, target)
# Printing out the training progress
train_loss = MSE(network.run(train_features), train_targets['cnt'].values)
val_loss = MSE(network.run(val_features), val_targets['cnt'].values)
sys.stdout.write("\rProgress: " + str(100 * e/float(epochs))[:4] \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
plt.ylim(ymax=2)
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features)*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
rides[-24*21:].plot(x='dteday', y='cnt')
rides[-24*60:-24*21].plot(x='dteday', y='cnt')
rides[:-24*60].plot(x='dteday', y='cnt')
Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project.
import unittest
inputs = [0.5, -0.2, 0.1]
targets = [0.4]
test_w_i_h = np.array([[0.1, 0.4, -0.3],
[-0.2, 0.5, 0.2]])
test_w_h_o = np.array([[0.3, -0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self):
# Test that file path to dataset has been unaltered
self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
def test_data_loaded(self):
# Test that data frame loaded
self.assertTrue(isinstance(rides, pd.DataFrame))
##########
# Unit tests for network functionality
##########
def test_activation(self):
network = NeuralNetwork(3, 2, 1, 0.5)
# Test that the activation function is a sigmoid
self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))
def test_train(self):
# Test that weights are updated correctly on training
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
network.train(inputs, targets)
self.assertTrue(np.allclose(network.weights_hidden_to_output,
np.array([[ 0.37275328, -0.03172939]])))
self.assertTrue(np.allclose(network.weights_input_to_hidden,
np.array([[ 0.10562014, 0.39775194, -0.29887597],
[-0.20185996, 0.50074398, 0.19962801]])))
def test_run(self):
# Test correctness of run method
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)


Relationships
One to Many
- Inside the
settings.json
file, find the table you are looking for
- Inside that table's columns array find the foreign key column you want to use for the relation
- Inside that column's object insert
oneToMany
key
- Change the column's control type to
select
"control": {
"select": true
},
"oneToMany": {
"table": "user",
"pk": "id",
"columns": [
"firstname",
"lastname"
]
}
The oneToMany
key additionally can contain a schema
key, specifying PostgreSQL schema name for the relation table
-
oneToMany - contains information about the table that this foreign key references
-
table - name of the table that's being referenced
-
pk - name of the referenced table primary key column (can be array as well, see compound primary key documentation)
-
columns - array of columns to select from the referenced table
view on GitHub


Entfernung zur Sonne:
2.877.000.000 km
Bahngeschwindigkeit:
6,81 km/s
Temperatur:
-197 °C
Dichte:
1,27 g/cm³
"I have looked further into space than ever human being did before me. I have observed stars of which the light it can be proved, must take two million years to reach the earth. "
— William Herschel, nachdem er Uranus endeckte


You currently do not have any payment histories.
No disposals recorded
Trucks phone no +2340987482748
Lagos Mainland


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.agentsubscriptionfrontend.config.view.AnswerRow @import uk.gov.hmrc.govukfrontend.views.html.components.{SummaryListRow, Text, HtmlContent, Key, Value, Actions, ActionItem} @import uk.gov.hmrc.govukfrontend.views.html.components.implicits._ @import uk.gov.hmrc.hmrcfrontend.views.html.components.implicits._ @this() @(id: String, answerRow: AnswerRow)(implicit msgs: Messages)


FullStack Web Developer
My name is Jorge Luis Ferrer, I am from Cuba. I studied Computer Software Engineer on Camilo Cienfuegos University, Matanzas. In my career I learn programming languages like C# and Java and practice agile metodologies like Extreme Programming and SCRUM.
Also I am a Linux and Open Source entusiast, usually I use Fedora like my principal OS. I am currently studying a Web Developer Fullstack certification in the MITXPRO program in which I am incorporating skills in the use of the MERN stack.


{% load i18n %}
{% trans "Please select the instance that you need to modify." %}
{% trans "When there are some dev_snapshot in instance you select, they will be deleted." %}
{% trans "The disk of flavor can not be less than the original disk of instance." %}


We at CodeGenetics strives to inculcate the approach of Problem Solving in students.The teaching pattern and video contents at CodeGenetics is not only limited to solving questions but to grasp how to think about a problem.
We provide learning of Data Structure and Algorithm to the students aspiring to excel as a Software Engineer.We prepare students for ACM-ICPC,Google Code Jam ,Facebook Hackercup and other competitions and along with it we prepare students for Jobs at tech gaint companies such as Google,Amazon,Walmart.
For tracking the progress Weekly Online Test will be Taken on Hackerrank.
Assignment Will be Topic Wise so You Don't miss anything and will be on same page.
Encourage Writing Blogs While You are Learning something.
Regular problem discussion make your doubts clear and give you more grip on what you are learning.


jQuery can associate arbitrary data with page elements. Use the controls below to exercise the data() and removeData() functions on the example DIV element
This is paragraph 1
This is paragraph 2
This is paragraph 3
This is paragraph 4
This is paragraph 5


Namespace with functions relating to metainfo file and Torrent struct.
More...
|
| TorrentFile | parse_file_dict (const be_node *file_node) |
| | Parse a be_node dictionary corresponding to a file of a torrent. This function is called only when parsing a torrent with multiple files. More...
|
| |
| void | parse_info_dict (const be_node *info_node, Torrent &new_torrent) |
| | Parse a be_node dictionary corresponding to the "info" key of the torrent. More...
|
| |
| void | parse_torrent_node (const be_node *node, Torrent &new_torrent) |
| | Parse a be_node dictionary corresponding to the decoded torrent file. Parsed keys: announce, announce-list, info. The first element of "announce-list" is equal to "announce", but sometimes there is no "announce-list" key, so we must parse "announce" everytime. Save all the information in a Torrent struct passed as a parameter. More...
|
| |
| char * | get_info_node_hash (const string *file, const string *pieces_string) |
| | Extract the bencoded dictionary from the metainfo file and hash (with SHA1) the content. More...
|
| |
| void | print_file (const TorrentFile &torrent_file) |
| | Print path and length of a file in a torrent. More...
|
| |
| void | print_torrent (const Torrent &torrent) |
| | Print all the parsed information of a torrent. More...
|
| |
| int | parse_torrent (Torrent &torrent, string &torrent_str, const char *filename) |
| | Main function for parsing a .torrent file. More...
|
| |
|
int | parse_torrent (Torrent &torrent, std::string &torrent_str, const char *filename) |
| |
|
char * | get_info_node_hash (const std::string *file, const std::string *pieces_string) |
| |
Namespace with functions relating to metainfo file and Torrent struct.
◆ get_info_node_hash()
| char* torr::get_info_node_hash |
( |
const string * |
file, |
|
|
const string * |
pieces_string |
|
) |
| |
Extract the bencoded dictionary from the metainfo file and hash (with SHA1) the content.
- Parameters
-
| *file | : a string cointaining the entire file |
- Returns
- the uchar array of the hash
◆ parse_file_dict()
Parse a be_node dictionary corresponding to a file of a torrent. This function is called only when parsing a torrent with multiple files.
- Parameters
-
| file_node | be_node dictionary corresponding to the file |
- Returns
- TorrentFile struct with path and length
◆ parse_info_dict()
| void torr::parse_info_dict |
( |
const be_node * |
info_node, |
|
|
Torrent & |
new_torrent |
|
) |
| |
Parse a be_node dictionary corresponding to the "info" key of the torrent.
Parsed keys: name, piece length, pieces, length, files. Save all the information in a Torrent struct passed as a parameter. In the case of a single file torrent, this function parse file info without calling "parse_file_dict".
- Parameters
-
| info_node | be_node dictionary corresponding to "info" key |
| new_torrent | Torrent struct where to save the parsed info |
◆ parse_torrent()
| int torr::parse_torrent |
( |
Torrent & |
torrent, |
|
|
string & |
torrent_str, |
|
|
const char * |
filename |
|
) |
| |
Main function for parsing a .torrent file.
- Parameters
-
| torrent | Torrent struct |
| torrent_str | String for storing the torrent file |
| filename | Path of the .torrent file |
- Returns
- int Return negative number in case of error, otherwise 0
◆ parse_torrent_node()
| void torr::parse_torrent_node |
( |
const be_node * |
node, |
|
|
Torrent & |
new_torrent |
|
) |
| |
Parse a be_node dictionary corresponding to the decoded torrent file. Parsed keys: announce, announce-list, info. The first element of "announce-list" is equal to "announce", but sometimes there is no "announce-list" key, so we must parse "announce" everytime. Save all the information in a Torrent struct passed as a parameter.
- Parameters
-
| node | be_node dictionary corresponding to the torrent file |
| new_torrent | Torrent struct where to save the parsed info |
◆ print_file()
| void torr::print_file |
( |
const TorrentFile & |
torrent_file | ) |
|
Print path and length of a file in a torrent.
- Parameters
-
| torrent_file | File parsed with parse_file_dict |
◆ print_torrent()
| void torr::print_torrent |
( |
const Torrent & |
torrent | ) |
|
Print all the parsed information of a torrent.
- Parameters
-
| torrent | Torrent parsed with parse_torrent |


"NEAR"
"BETWEEN"
"BETWEEN+"
Driving
Biking
Walking
View All POIs
CLOSER POI
1
SHORTER TRIP
WITHIN
30
MINUTES
WITHIN
30
MILES
WITHIN
180
DEGREE NORTH


Terrorists 13 times attack Ukrainian troops in Donbas in past day
Ukraine says one soldier was injured in action.
Over the past day, November 15, Russian occupation forces 13 times violated the ceasefire in eastern Ukraine's Donbas.
Joint Forces positions came under fire of 120mm and 82mm mortars proscribed by the Minsk agreements, weapons installed on IFVs, grenade launchers of various types, and small arms, including heavy machine guns.
That's according to a morning war update by the Joint Forces Operation Command.
In the area of responsibility of the Skhid [East] tactical grouping, Russian occupation forces shelled Ukrainian troops six times, engaging Ukrainian troops near Chernenko – with SPG sniper rifles, automatic grenade launchers and heavy machine guns; twice near Zaitseve – with small arms, 120 mm mortars and automatic grenade launchers; near Lebedynske and Berezovye – with automatic grenade launchers and heavy machine guns; and outside Novohnativka – with heavy machine guns.
In the area of responsibility of the Pivnich [North] tactical grouping, seven enemy attacks were recorded: twice near Mayorske where the enemy fired easel anti-tank grenade launchers and heavy machine guns; near Novotoshkovske – 82 mm mortars, IFV-2 guns, grenade launchers of various types, and heavy machine guns, near Luhanske – easel anti-tank grenade launchers; two times near Novoluhanske – 82 mm mortars, hand anti-tank grenade launchers, and small arms, and near Khutir Vilniy – easel anti-tank grenade launchers.
Yesterday, November 15, one Ukrainian soldier sustained a combat injury.
Since day-start on Saturday, Russian occupation troops have not opened fire.


2 instantiations of EventAction
System.Management.Automation (2)
engine\EventManager.cs (2)
976
AddAction(new
EventAction
(subscriber, newEvent), processSynchronously);
983
AddAction(new
EventAction
(subscriber, newEvent), processSynchronously);
8 references to EventAction
System.Management.Automation (8)
engine\EventManager.cs (8)
330
_actionQueue = new Queue<
EventAction
>();
336
private readonly Queue<
EventAction
> _actionQueue;
1027
private void AddAction(
EventAction
action, bool processSynchronously)
1093
EventAction
nextAction;
1168
private
EventAction
_processingAction = null;
1198
EventAction
[] pendingActions = _actionQueue.ToArray();
1201
foreach (
EventAction
pendingAction in pendingActions)
1228
private void InvokeAction(
EventAction
nextAction, out bool addActionBack)


CPU Scheduler Simulator
A simple CPU scheduler written in Python for COSCA365, Operating Systems. This simulation implements a FCFS scheduling algorithm.
To run simply input the following into your cli:
$ python scheduler.py 10
The number 10
here is your inputed time_quantum
for the program. You can input any time_quantum
you like as long as its an int
.
Dictionary of Variables
Note that because Python does not inherently declare variable types, we will list the type of value the variables will be used according to other languages. Additionally, other variables may be necessary, such as temp
variables, which may not be reflected in the dictionary of variables. These will be made clear inside of the code.
Basic housekeeping variables
sysclock
- this variable will take theint
type and represent the system clock, beginning from 0.time_quantum
- time quantum for the program. Givin by a command line argument to the scheduler, ex.python scheduler.py 10
where the number10
is the programtime_quantum
number_of_processes
- total number of processes for the scheduler. Hard-coded into the pogram and is decremented whenever a process is terminated; oncenumber_of_processes == 0
the main loop breaks and the program is halted.total_processes
- set equal to thenumber_of_processes
in the beginning of the pogram. Sincenumber_of_processes
is decremented,total_processes
is used make runtime calculations after main loop is broken.switch_counter
- used to count number of loops during acontext_switch
. Initially it is set to1
and whenswitch_counter > 5
thecontext_switch
breaksnum_context
- this variable will take theint
type and it will hold the total number of context switches performed.cpu_util
- this variable will take thefloat
type and it will display the percentage of total time the CPU is running.parser
- theArgumentParser
object imported from Python’sargparse
module. Allows the use of command line arguments, so the user can inputtime_quantum
args
- arguments parsed by theparser
.
Process-specific variables
In this program, we will be creating and using a process
object.
process[]
- process dictionary that holds aprocess[i]
wherei = pid
; the dictionary containsProcess
objects. Used to iterate through while creating the processes from the text files provided.pid
- this will be the Process ID, anint
, assigned to each process uniquely.burst_times
- deque containing individualburst_time
variables. Used to pop and appendburst_time
for running or waiting process.wait_times
- deque containing individualwait_time
variables. Used to pop and appendwait_time
for running or waiting process.arrival_time
- time at which the process arrives in the program. Checked in the beginning of the program with a loop testingarrival_time
againstsysclock
.inputfile
- file to create the process from.


Timeout in seconds for SMTP transactions
opts_smtpd_timeout
This parameter limits the time in seconds to send an SMTP server response and
to receive an SMTP client request.
Note: if you set SMTP timeouts to large values you must update the
ipc_timeout parameter as well, reachable
from the General Options section.


What programming language suits me best?
Select your answers below to find out!
Which logo do you prefer?
Gem
Snake
Letters
What's your favorite movie genre?
Horror
Comedy
Drama
What's your favorite holiday?
Christmas
Thanksgiving
Halloween
What's your favorite ice cream flavor?
Vanilla
Strawberry
Chocolate
What's your favorite Spielberg movie?
Jaws
Jurassic Park
Schindler's List
What's your drink of choice?
Beer
Wine
Water
Submit
Your recommended first programming language is
dismiss


In order to fetch the sentinel datapoints, only the columns longitude, latitude and
Aridity_zone must be present. This is only feasible for a small number of datapoints.
plot_id may also be specified manually.
Alternatively, a CSV containing the above columns and the necessary features can be uploaded.


The greatest exploration vehicle of all time.
2020 Explorer
Something for everyone
See our full lineup of vehicles and find the best one that suits you.


We are generating the statistics for roads within the OpenRoads Geostore. Please refresh this page in one to two minutes for the updated statistics.


please use any two digits to recieve a response
we are very sorry but Mr. Roboger is currently experiencing tech issues. If you can not decipher his message please try again later..


Google ScholarWebsite
Xudong Wang received the B.S. degree in automation from Sichuan University, Sichuan, China, in 2014 and the M.S. degree in automation from Beihang University, Beijing, China, in 2017. She is currently working toward the Ph.D. degree in the Department of Civil Engineering and Applied Mechanics at McGill University, Montreal, QC, Canada.


virsh connect- Connect as root to the local QEMU/KVM hypervisor:
virsh connect qemu:///system- Launch a new instance of the hypervisor and connect to it as the local user:
virsh connect qemu:///session- Connect as root to a remote hypervisor using ssh:
virsh connect qemu+ssh://user_name@host_name/system


Version 0.3.0 - August 16 2018
CCircles Loader added
MultiColors Loader Added
HeartBeat Loader started
Version 0.0.1 - August 2 2018
Initial Commit


Video Blur
In this tutorial we will explain the logic behind an image blur in relation to videos. We recommend that you start our by reading our tutorial on Blur Logic
Video Capture
Steps:
- Create variable for your video = capture
- Use the createCapture method to capture the video. This opens the digitizer
- Set video size to the same dimensions as canvas
- Use hide method to hide capture feed.
Vocabulary:
| Capture Feed | A display showing whatthe capmera is capturing in real time. |
var capture; // this is the video camera
function setup() {
createCanvas(640, 480);
pixelDensity(1);
background(100);
capture = createCapture(VIDEO); // this opens the digitizer
capture.size(640, 480);
capture.hide();
}
For more information on video capture, click this link!
Copying Capture
Now let's create a varible containing a copy of our video feed. We will need this copy in order to have access to the pixel array. Use the get( ) method to create a pixel array out of our video frames:
var capture; // this is the video camera
var capimg;
function setup() {
createCanvas(640, 480);
pixelDensity(1);
background(100);
capture = createCapture(VIDEO); // this opens the digitizer
capture.size(640, 480);
capture.hide();
}
function draw() {
capimg = capture.get(); // copying the video
}
Blur
The Logic behind our motion blur relies on understanding that every new frame is influenced by the previous frame. The copy, as mentioned, holds the pixel array for our captured video. The blur is an image we create based on our copy. The blur is essentially holding the last frame we created, and the more weight it has on our final image, the more motion blur our video will have. On the other hand, if our copy has a really heigh ratio, our blur will be brief because we will see more of the live footage.
Steps:
- Create a variable to hold our blur
- Our blur will be an image we create with the same dimensions as our video. Use the createImage( ) method.
- Create two variables: copyRatio and blurRatio. They will hold the weight the copy of the video and our blur will, respectively, have in out final result. The more weight the blur has on the final image, the more blur you’ll have.
- Set a ratio for how much of the copy you want and set the blur to the inverse of that (1 - copyRatio)
- Load Pixels for both copy and blur
- Iterate through all the red values in our pixel array. Check out our Basic Setup for help
- Set RGB values of the blur to the chosen ratio distribution (see code below for further explanation).
var capture; // this is the video camera
var capimg;
var theblur; // this is the blur
var a = 0.1;
var b = 1.0-a;
function setup() {
createCanvas(640, 480);
pixelDensity(1);
background(100);
capture = createCapture(VIDEO); // this opens the digitizer
theblur = createImage(640, 480);
capture.size(640, 480);
capture.hide();
}
function draw() {
capimg = capture.get(); // copying the video
if(capimg.width>0) {
capimg.loadPixels();
theblur.loadPixels();
for(var i = 0; i < theblur.pixels.length;i+=4)
{
theblur.pixels[i] = a*capimg.pixels[i] + b*theblur.pixels[i];
theblur.pixels[i+1] = a*capimg.pixels[i+1] + b*theblur.pixels[i+1];
theblur.pixels[i+2] = a*capimg.pixels[i+2] + b*theblur.pixels[i+2];
theblur.pixels[i+3] = capimg.pixels[i+3];
}
}
}
Display
To display our final result, all we have to do is update pixels for our blur and use the image() method to display it.
var capture; // this is the video camera
var capimg;
var theblur; // this is the blur
var a = 0.1;
var b = 1.0-a;
function setup() {
createCanvas(640, 480);
pixelDensity(1);
background(100);
capture = createCapture(VIDEO); // this opens the digitizer
theblur = createImage(640, 480);
capture.size(640, 480);
capture.hide();
}
function draw() {
capimg = capture.get(); // copying the video
if(capimg.width>0) {
capimg.loadPixels();
theblur.loadPixels();
for(var i = 0; i < theblur.pixels.length;i+=4)
{
theblur.pixels[i] = a*capimg.pixels[i] + b*theblur.pixels[i];
theblur.pixels[i+1] = a*capimg.pixels[i+1] + b*theblur.pixels[i+1];
theblur.pixels[i+2] = a*capimg.pixels[i+2] + b*theblur.pixels[i+2];
theblur.pixels[i+3] = capimg.pixels[i+3];
}
theblur.updatePixels();
image(theblur, 0, 0, width, height);
}
}


NDI Upload
{{errorMessage}}
{{additionalMessage}}
Upload instructions:
- Tab delimited ".txt" file
- Following columns are required
| participantId |
First |
Middle |
Last |
Year |
Month |
Day |
- Year = year of DOB in yyyy format
- Month = month of DOB
- Day = day of DOB
-
First = First Name
-
Last = Last Name
-
Middle = just the initial, don't include anything if it doesn't exist.
-
Middle name column is the only one that could be left empty.


Tools Used
JavaScript
GitHub
Tech Stack
Project Details
Goal: Create a game where the player has 8 chances to guess the correct word.
Role: Created the JavaScript for the game to function and additional JavaScript to enhance the player's experience.
Features: The word is initially displayed with dots and then switches to the correct letters as they are guessed, the game counts down the number of guesses the player has left, displays the guessed letters, displays messages to the player depending on the input, and resets the game if they want to play again.
Challenges: The project initially was only coded and designed with HTML and CSS. By adding JavaScript and using functions and methods, I was able to create all the features listed above and ensure the player has a seamless user experience.


Depending on the decade, one could have attended the Roaring ‘20s Valentine’s Day Massacre Party, Alpha Gamma Delta annual dinner dance featuring the University Stringed Orchestra or even be at a bar enjoying 30 cent beer bottles with a sweetheart.
In the Feb. 13, 1969, edition of The Post, the Roaring ‘20s Lounge in Athens posted an ad for a Roaring ‘20s St. Valentine’s Day Massacre Party to “relive” all the excitement on the 40th anniversary of the 1929 St. Valentine’s Day massacre in Chicago.
“Celebrate the 40th Anniversary of the Valentine’s Day massacre the way it used to be,” the advertisement read, “By getting destroyed Friday night. It’ll be a real ‘cop out.’ ”
The ad also states that violin cases were checked at the door.
“It sounds interesting, as long as no one actually, like, got hurt,” Claire Keiser, a freshman studying actuarial science, said.
Going back to the beginning of the 20th century, as is listed in the Feb. 15, 1917, edition of Green and White, the Alpha Gamma Delta Valentine Dinner Dance was listed as one of the “most charming social functions of the college year.”
The event decor featured lighting by red candles in crystal candlesticks and baskets of carnations. The University Stringed Orchestra and two featured soloists performed through the event. After a grand march, the dancing began for about 50 couples, Green and White said.
Many other formal Valentine’s Day dances were held throughout the ‘40s and ‘50s, hosted by Ohio University Greek Life organizations.
Some students today still have an appreciation for events like these dances.
“I feel like our generation nowadays is so secretive… There's a lot of naysayers when it comes to events,” Harrison Grady, a senior studying applied management, said. “Nowadays, people like the idea of having fun, they want the idea of having fun but sometimes they don't want to do the activities that are there. They’re worried about what my friends might think about doing this...it'd be cool to kind of just dress up and have fun and a special day like that.”
Comparatively, The Post on Feb. 14, 1964, listed that readers may want to take a “certain someone” out for a 30 cent bottle of beer. The “Never on weekends” column states all the bars in town were hosting the special.
Georgie Naumoff, a junior studying health services administration, said she would for sure take up the special.
“30 cents is a good deal,” Naumoff said.
The rest of the Valentine’s Day events listed include a swim meet against Cincinnati and a dinner at the “Campus Skillet.”
While many activities were put on for students on campus, the Valentine’s Day celebrations throughout the years were not only limited to those attending the university.
In February 1957, a man listed only as “Grandpappy” wrote his appreciation for a young woman who came to perform on the accordion and sing for “the boys on the ward” in Green Hill News. He thanked her, said that if he were 45 years younger he would send her a Valentine featuring the poem “Once In Awhile.”
Some students spent their Valentine’s Day not with their sweetheart, but visiting those who may not get out often. The students from Tiffin and Gamertsfelder Hall delivered Valentines to the residents of Kimes Nursing Home who were featured in the Feb. 15, 2006, edition of The Post.
Many students today still feel that this is important.
“A lot of people in nursing homes don't really have a lot of people to talk to,” Lucia Aponte, a freshman studying geography and meteorology, said. “So it is a great idea [because] they don’t really see many people.”
Aponte believes he would most definitely be interested in participating in an event like that.
Looking back at the history of Valentine’s Day shopping in Athens, students in previous years made some very different purchases than students often do today.
Members of Sigma Alpha Iota sold singing Valentine’s Day calls, according to the Feb 13. 1970, edition of The Post. Options included “Happy Valentine’s Day to you” sung to the tune “Happy Birthday” for 25 cents, or for those “big spenders”, “My Funny Valentine” could be sent for $1.
In more recent history, according to an ad in the Feb. 12, 2002, edition of The Post, a $14 heart-shaped cheesecake from the now defunct Zachary’s could be purchased as a special.
Today, students still find ways to spread love on and off campus, spending quality time with those they love.


Get to know just a little bit more about the developers that worked on bringing Trip Split to you!
James Starks is a Software Engineer who currently leads over 300 web developers and works hard to inspire thousands of people on a daily basis.
Hugo Oliveira is an aspiring web developer from Kansas City and does his best to balance time between coding and enjoying the outdoors until a big football or basketball game comes on.
Zeleke Dema is a developer who loves coding. He can be found working tirelessly on various other projects that pique his interest, when not busy with the awesome apps he makes.
Kyle is currently a full time student attending Lambda for full stack web development. Kyle is located in Jacksonville, FL and when he is not coding, can be found completing home renovations as well as helping friends buy and sell real estate.
Cody M Russell is a UI Dev from Detroit, MI and is currently attending Lambda for Full Stack Web Development, when not setting up floral arrangements for weddings on the weekends.


This package has implementations of tags that have functionality beyond the capability of a generic tag. For example, the<EMAIL_ADDRESS>} tag has methods to get the {@link org.htmlparser.tags.MetaTag#getMetaContent CONTENT} and {@link org.htmlparser.tags.MetaTag#getMetaTagName NAME} attributes (although this could be done with generic attribute manipulation) and an implementation of {@link org.htmlparser.tags.MetaTag#doSemanticAction doSemanticAction} that alters the lexer's encoding.
The classes in this package have been added in an ad-hoc fashion, with the most useful ones having existed a long time, while some obvious ones are rather new. Please feel free to add your own custom tags, and register them with the {@link org.htmlparser.PrototypicalNodeFactory PrototypicalNodeFactory}, and they will be treated like any other in-built tag. In fact tags do not need to reside in this package.
Creating custom tags is fairly straight forward. Simply copy one of the simpler tags you find in this package and alter it as follows.
If the tag can contain other nodes, i.e<EMAIL_ADDRESS>If it is a simple tag, i.e<EMAIL_ADDRESS>}, then it should derive from
{@link org.htmlparser.nodes.TagNode TagNode}. See for example
{@link org.htmlparser.tags.MetaTag}
or {@link org.htmlparser.tags.ImageTag}.
To be registered with {@link org.htmlparser.PrototypicalNodeFactory#registerTag},
and especially if it is a composite tag, the tag needs to implement
getIds
which returns the UPPERCASE list of names for the tag
(usually only one), for example "HTML". If the tag can be smart enough to know
what other tags can't be contained within it, it should also implement
{@link org.htmlparser.nodes.TagNode#getEnders getEnders()} which returns the
list of other tags that should cause this tag to close itself, and
{@link org.htmlparser.nodes.TagNode#getEndTagEnders getEndTagEnders()} which
returns the list of end tags (i.e<EMAIL_ADDRESS>}), other than it's own name, that
should cause this tag to close itself. When these 'ender' lists cause a tag to
end before seeing it's own end tag, a virtual end tag is created and 'inserted'
at the location where the end tag should have been. These end tags can be
distinguished because their {@link org.htmlparser.Node#getStartPosition starting}
and {@link org.htmlparser.Node#getEndPosition ending} locations are the same
(i.e. they take up no character length in the HTML stream).
For example, the<EMAIL_ADDRESS>}, or {@.html


Special Thanks to: Authors of Firefly and Retinex papers: Nikola Banic and Sven Loncaric.
Copyright: 2020, https://github.com/k-gintaras License Apache 2.


{{spend_point.reward_item.item_name}} {{ spend_point.date }} Category -> Coupen Coupen Worth -> Rs.{{spend_point.reward_item.item_price}} Points Spend -> {{spend_point.reward_item.points_required}} status -> {% if spend_point.status == "rejected" %} Failed {% elif spend_point.status == "completed" %} Success {%endif%}
{{spend_point.special_deals.hotel.name}} {{ spend_point.date|date:"F d, Y" }} Category -> Special Offer Description -> {{spend_point.special_deals.description}} Points Spend -> {{spend_point.special_deals.points_required}} status -> {% if spend_point.status == "rejected" %} Failed {% elif spend_point.status == "completed" %} Success {%endif%}


Start Record
Stop Record
Message for users when recording is not possible.
{{minutes|numberpad:2}}:{{seconds|numberpad:2}}
Recorder playback
Your browser does not support the audio element.
{{item.channelName}}
push to talk
Connecting
Connected
Your browser does not support the audio element.
L
R


Top 5 Best Chinese Phones for Under $500 – November 2019 - Gizchina.com
by PierreTop 5 Best Chinese Phones for Under $500 – November 2019
3.6 (71.11%) 9 votes
Welcome back to our monthly rendezvous with some of the best Chinese phones currently on the market. This list includes all the smartphones for under $500, this means flagship-grade devices at very competitive prices. Interested? Then keep reading!
Best Chinese Phones for Under $500
1. Realme X2 Pro
The first phone on our list comes with a bit of a surprise as we’re used to see Xiaomi phones top the charts, but the latest Realme X2 Pro really is an extraordinary phone. The handset does indeed manage to pack the latest Qualcomm Snapdragon 855 Plus for less than $500; heck you can even get it for under $400 if you’re willing to only have 64GB of storage.
The Realme X2 Pro then features a large 6.5-inch display of the AMOLED type with Full HD+ resolution and 90Hz refresh rate. Meanwhile in the camera department we find a 64MP primary shooter accompanied by an 8 MP ultra wide angle lens, a 13MP telefoto camera and a 2MP depth sensor. At the front we have a 16MP camera for high res selfies.
Finally, Realme’s flagship packs a large 4000mAh capacity battery with support for 50W fast charging, headphone jack, NFC and runs ColorOS 6.1 based on Android 9.
2. Xiaomi Mi 9T Pro
Next we have Xiaomi’s international variant of the Redmi K20 Pro flagship-killer – the Xiaomi Mi 9T Pro. The smartphone comes with a large 6.39-inch AMOLED panel with a real full screen design an under display fingerprint scanner. The handset is then powered by a Snapdragon 855 coupled with 6GB of RAM and 128GB of internal storage.
On the camera side, we find a main 48MP Sony IMX586 shooter paired with an 8MP telefoto and a 13MP ultra wide-angle lens. Meanwhile in the pop-up camera we find a 20MP snapper.
The Xiaomi Mi 9T Pro is fueled by a 4000mAh, supports fast charge at 27W and runs MIUI based on Android 9.
If you’re willing to lack some global frequency bands, you can also check out the Redmi K20 Pro Premium Edition coming with the slightly more powerful Snapdragon 855 Plus CPU.
3. Vivo iQoo Pro
Yet another great option powered by a Qualcomm Snapdragon 855 Plus is the Vivo iQOO Pro which can now be found for less than $500. The handset comes with 8GB of RAM and 128GB of internal storage along with the previously mentioned CPU. We then have a 6.41-inch Super Amoled display with Full HD+ resolution and a 19.5:9 aspect ratio.
In the camera department, the smartphone is equipped with a main 48 MP Sony IMX582 Exmor RS sensor, a secondary 13MP sensor with ultra wide lens and a third 2MP depth sensor; on the front we instead have a 12MP selfie snapper.
Finally, the Vivo iQOO Pro comes with an extra large 4500mAh capacity battery, supports fast charging at 44W and runs Funtouch 9.0 OS based on Android 9 Pie out of the box.
Best Chinese Phones for Under $500
4. Oppo Reno Ace
Next on the list we have the powerhouse recently launched by Oppo’s sub-brand – the Reno Ace. The smartphone does indeed pack the new Qualcomm Snapdragon 855 Plus along with a minimum of 8GB of RAM an 128GB of internal storage.
We then find a 6.5-inch 90Hz e-sports ready screen and four rear cameras on the back. Including a main 48MP camera, along with a 13MP ultra wide angle, an 8MP telephoto and 2MP depth sensor. For selfies we instead have a single 16MP selfie snapper.
The smartphone supports the new 65W SuperVOOC super flash charging. Meaning it can charge the 4000mAh capacity battery completely in just 30 minutes. With 5 minutes of charging that can bring around 2 hours to 6 hours of autonomy.
5. Nubia Red Magic 3S
Finally, for the gamers out there we have the latest gaming phone by Nubia, the Red Magic 3S. The phone packs a massive 6.65-inch display which should be a joy to play on with its FHD+ resolution and 90Hz refresh rate.
The smartphone is then powered by the beastly Qualcomm Snapdragon 855 Plus CPU along with 8GB of RAM and 128GB of internal storage for an AnTuTu score of over 460k points.
Nubia’s Red Magic 3S is also well equipped in the camera department with a single but high-end Sony IMX586 sensor with 48MP resolution and OIS. Meanwhile at the front we find a 16MP selfie snapper.
Other features on the gaming phone include a 3.5mm audio jack, IR emitter, a large 5000mAh capacity battery and Android 9 Pie as the OS out of the box.
Best Chinese Phones for Under $500
With the Nubia Red Magic 3S powerhouse we conclude our monthly Top 5 Best Chinese Phones list. Did you guys find your next daily driver? If that so let us know down below, other wise tell us what you’d have liked to see on the list!


PUL GitHub Issues
2022-05-25
#260 DSS Site Can't Send Email
Creator:
Kevin Reiss
Created:
2022-05-24 12:59:08
Status:
Open
Events:
labeled
by Kevin Reiss at 2022-05-25 02:52:39.
#770 Capybara screenshot will fail when headless is running
Creator:
Christina Chortaria
Created:
2022-05-18 16:40:49
Status:
Open
Events:
labeled
by Kevin Reiss at 2022-05-25 02:52:10.
#49 adds admin resources from depricated DRDS handbook and provides link …
Creator:
Shaun Ellis
Created:
2022-03-24 16:32:15
Status:
Closed
(pull request )
Events:
referenced
by Eliot Jordan at 2022-05-25 15:55:03.
merged
by Eliot Jordan at 2022-05-25 15:55:03.
closed
by Eliot Jordan at 2022-05-25 15:55:03.
head_ref_deleted
by Eliot Jordan at 2022-05-25 15:55:03.
#39 Inclusive language
Creator:
hackartisan
Created:
2022-03-02 16:00:51
Status:
Open
Events:
assigned
by Shaun Ellis at 2022-05-25 17:23:39.
#55 Adds page on inclusive language
Creator:
Shaun Ellis
Created:
2022-05-25 17:48:39
Status:
Open
(pull request )
#5087 Adding nested structure for a microfilm reel in figgy structure manager is not saving
Creator:
Kim Leaman
Created:
2022-04-22 18:11:56
Status:
Open
Events:
assigned
by Eliot Jordan at 2022-05-25 19:06:22.
#5091 update node
Creator:
hackartisan
Created:
2022-04-24 17:51:04
Status:
Open
Comments:
Events:
assigned
by Shaun Ellis at 2022-05-25 19:30:13.
labeled
by Shaun Ellis at 2022-05-25 19:40:16.
#3224 Add documentation link to the front page of figgy
Creator:
hackartisan
Created:
2019-07-26 12:44:52
Status:
Open
Comments:
Events:
mentioned
by None at 2022-05-25 21:12:48.
subscribed
by None at 2022-05-25 21:12:48.
#5115 Upgrade Bootstrap and Rails
Creator:
Trey Pendragon
Created:
2022-05-12 17:44:48
Status:
Open
(pull request )
Events:
head_ref_force_pushed
by None at 2022-05-25 14:12:09.
head_ref_force_pushed
by Eliot Jordan at 2022-05-25 15:49:28.
#8 Removing music reserves
Creator:
carolyncole
Created:
2022-05-25 20:01:38
Status:
Closed
(pull request )
Events:
referenced
by Kevin Reiss at 2022-05-25 20:09:01.
merged
by Kevin Reiss at 2022-05-25 20:09:01.
closed
by Kevin Reiss at 2022-05-25 20:09:01.
#252 Fund Adjustment
Creator:
carolyncole
Created:
2021-10-12 18:26:53
Status:
Open
Events:
labeled
by Kevin Reiss at 2022-05-25 02:45:56.
#324 SCSB renewal due date not being calculated correctly
Creator:
mzelesky
Created:
2022-05-19 20:33:51
Status:
Open
Events:
labeled
by Kevin Reiss at 2022-05-25 02:46:54.
#340 Date in the body of the voucher feed email has an incorrect date
Creator:
mzelesky
Created:
2022-05-24 15:34:17
Status:
Open
Events:
labeled
by Kevin Reiss at 2022-05-25 02:47:57.
#335 Don't send an ncip renewal when the primary identifier is 'None' or the expiration date is nil
Creator:
Christina Chortaria
Created:
2022-05-24 13:49:33
Status:
Open
Events:
labeled
by Kevin Reiss at 2022-05-25 02:50:14.
#327 Error handling for voucher feed
Creator:
mzelesky
Created:
2022-05-20 21:04:07
Status:
Open
Events:
labeled
by Kevin Reiss at 2022-05-25 15:38:38.
#325 Adding bursar credit and fine jobs to the schedule
Creator:
carolyncole
Created:
2022-05-20 19:42:49
Status:
Closed
(pull request )
Events:
referenced
by Christina Chortaria at 2022-05-25 17:20:09.
merged
by Christina Chortaria at 2022-05-25 17:20:09.
closed
by Christina Chortaria at 2022-05-25 17:20:09.
head_ref_deleted
by Christina Chortaria at 2022-05-25 17:20:10.
#343 Adding documents for web-staff and bursar
Creator:
carolyncole
Created:
2022-05-25 19:08:23
Status:
Open
(pull request )
#342 Change the time on bursar for both.
Creator:
carolyncole
Created:
2022-05-25 18:50:36
Status:
Closed
Events:
assigned
by None at 2022-05-25 19:08:51.
unassigned
by None at 2022-05-25 19:10:51.
assigned
by Christina Chortaria at 2022-05-25 19:10:59.
closed
by Christina Chortaria at 2022-05-25 19:22:15.
#344 set the same time for credits and fines
Creator:
Christina Chortaria
Created:
2022-05-25 19:09:39
Status:
Closed
(pull request )
Events:
head_ref_force_pushed
by Christina Chortaria at 2022-05-25 19:13:20.
referenced
by Christina Chortaria at 2022-05-25 19:22:15.
merged
by Christina Chortaria at 2022-05-25 19:22:15.
closed
by Christina Chortaria at 2022-05-25 19:22:15.
head_ref_deleted
by Christina Chortaria at 2022-05-25 19:22:16.
#82 Fix the scp command
Creator:
Jane Sandberg
Created:
2022-05-23 17:14:25
Status:
Open
(pull request )
Events:
labeled
by Kevin Reiss at 2022-05-25 02:51:55.
#85 Add lando
Creator:
Jane Sandberg
Created:
2022-05-23 20:41:43
Status:
Open
(pull request )
Events:
labeled
by Kevin Reiss at 2022-05-25 02:52:26.
#396 Provide better documentation around "component key" in the MenuBar component
Creator:
Shaun Ellis
Created:
2022-05-25 16:33:57
Status:
Open
#3011 Should we send HTML email to work around Safelinks URL mangling?
Creator:
Esmé Cowles
Created:
2022-05-25 11:27:51
Status:
Open
Events:
labeled
by Esmé Cowles at 2022-05-25 11:27:51.
#2799 Links must have discernible text
Creator:
Christina Chortaria
Created:
2021-11-08 22:11:06
Status:
Open
#3010 update accessibility spec
Creator:
Christina Chortaria
Created:
2022-05-25 01:03:21
Status:
Closed
(pull request )
Comments:
Events:
head_ref_force_pushed
by Christina Chortaria at 2022-05-25 01:43:57.
referenced
by Kevin Reiss at 2022-05-25 16:01:22.
merged
by Kevin Reiss at 2022-05-25 16:01:22.
closed
by Kevin Reiss at 2022-05-25 16:01:22.
head_ref_deleted
by Kevin Reiss at 2022-05-25 16:01:23.
#127 Create/update users with ORCID IDs from a spreadsheet
Creator:
Kate Lynch
Created:
2022-05-18 14:41:09
Status:
Open
Events:
assigned
by Kate Lynch at 2022-05-25 17:06:28.
#19 Authentication / Authorization
Creator:
Bess Sadler
Created:
2022-03-22 13:38:59
Status:
Closed
Events:
closed
by Kate Lynch at 2022-05-25 17:11:18.
#22 Collections
Creator:
Bess Sadler
Created:
2022-03-22 18:39:59
Status:
Closed
Events:
closed
by Kate Lynch at 2022-05-25 17:11:36.
#130 Flesh out accessibility test suite for views
Creator:
Kate Lynch
Created:
2022-05-18 23:18:44
Status:
Closed
Events:
closed
by Hector Correa at 2022-05-25 17:27:19.
#142 130 flesh out axe suite
Creator:
Kate Lynch
Created:
2022-05-25 17:04:19
Status:
Closed
(pull request )
Events:
referenced
by Hector Correa at 2022-05-25 17:27:19.
merged
by Hector Correa at 2022-05-25 17:27:19.
closed
by Hector Correa at 2022-05-25 17:27:20.
head_ref_deleted
by Hector Correa at 2022-05-25 17:27:20.
#143 Feature: Turn off/on Measures Display for individual records
Creator:
Hannah H
Created:
2022-05-25 21:15:03
Status:
Open
#299 Track citation copy/download behaviors
Creator:
Kate Lynch
Created:
2022-05-25 19:21:38
Status:
Open
#300 Message for Measure Feature (Views/Downloads)
Creator:
Hannah H
Created:
2022-05-25 19:23:03
Status:
Open
#3030 Adding instructions for running the playbook
Creator:
carolyncole
Created:
2022-05-10 19:27:27
Status:
Closed
(pull request )
Events:
referenced
by None at 2022-05-25 15:45:18.
merged
by None at 2022-05-25 15:45:18.
closed
by None at 2022-05-25 15:45:18.
head_ref_deleted
by None at 2022-05-25 15:45:18.
#3068 add annotations-staging renewed TLS certs
Creator:
Francis Kayiwa
Created:
2022-05-25 15:07:56
Status:
Closed
(pull request )
Events:
referenced
by Francis Kayiwa at 2022-05-25 16:39:38.
merged
by Francis Kayiwa at 2022-05-25 16:39:38.
closed
by Francis Kayiwa at 2022-05-25 16:39:38.
head_ref_deleted
by Francis Kayiwa at 2022-05-25 16:39:39.
#3054 globus playbook
Creator:
Francis Kayiwa
Created:
2022-05-18 18:12:16
Status:
Open
(pull request )
Comments:
Events:
convert_to_draft
by Francis Kayiwa at 2022-05-25 16:40:19.
#3069 annotate deploy user playbook
Creator:
Alicia Cozine
Created:
2022-05-25 17:04:37
Status:
Open
(pull request )
Events:
review_requested
by Alicia Cozine at 2022-05-25 17:04:42.
#3070 Changing the vaulted staging postgres to postgres3
Creator:
carolyncole
Created:
2022-05-25 19:52:59
Status:
Closed
(pull request )
Events:
referenced
by Christina Chortaria at 2022-05-25 20:10:05.
merged
by Christina Chortaria at 2022-05-25 20:10:05.
closed
by Christina Chortaria at 2022-05-25 20:10:05.
head_ref_deleted
by Christina Chortaria at 2022-05-25 20:10:06.
#157 Bump grunt from 1.3.0 to 1.5.3 in /libraries/superfish
Creator:
dependabot[bot]
Created:
2022-05-26 00:06:39
Status:
Open
(pull request )
Events:
labeled
by None at 2022-05-26 00:06:42.
labeled
by None at 2022-05-26 00:06:42.
#154 Bump grunt from 1.3.0 to 1.5.2 in /libraries/superfish
Creator:
dependabot[bot]
Created:
2022-04-27 10:38:13
Status:
Closed
(pull request )
Comments:
Events:
closed
by None at 2022-05-26 00:06:44.
head_ref_deleted
by None at 2022-05-26 00:06:45.
#709 Bump grunt from 1.3.0 to 1.5.3 in /sites/all/themes/omega/ohm
Creator:
dependabot[bot]
Created:
2022-05-25 23:41:25
Status:
Open
(pull request )
Events:
labeled
by None at 2022-05-25 23:41:27.
labeled
by None at 2022-05-25 23:41:27.
#705 Bump grunt from 1.3.0 to 1.5.2 in /sites/all/themes/omega/ohm
Creator:
dependabot[bot]
Created:
2022-04-27 08:00:42
Status:
Closed
(pull request )
Comments:
Events:
closed
by None at 2022-05-25 23:41:29.
head_ref_deleted
by None at 2022-05-25 23:41:30.
#710 Bump grunt from 1.3.0 to 1.5.3 in /sites/all/themes/omega/omega
Creator:
dependabot[bot]
Created:
2022-05-25 23:41:45
Status:
Open
(pull request )
Events:
labeled
by None at 2022-05-25 23:41:47.
labeled
by None at 2022-05-25 23:41:47.
#704 Bump grunt from 1.3.0 to 1.5.2 in /sites/all/themes/omega/omega
Creator:
dependabot[bot]
Created:
2022-04-27 08:00:42
Status:
Closed
(pull request )
Comments:
Events:
closed
by None at 2022-05-25 23:41:49.
head_ref_deleted
by None at 2022-05-25 23:41:50.


MyReleaseFeed
{{timeDiff}}
Filter Configuration
×
Filter Configuration
Highlight results?
Erase All
All:
Movies:
TV Shows:
Games:
Music:
Apps:
eBooks:
Highlight new items since last visit?
Loading...
Nothing to see here :( Please come back later!
All
Movies
{{movieHighlightCount}}
TV Shows
{{showsHighlightCount}}
Music
{{musicHighlightCount}}
Games
{{gameHighlightCount}}
Apps
{{appHighlightCount}}
eBooks
{{ebookHighlightCount}}
Movies
Name
Download Links
{{movie.title}}
.torrent
TV Shows
Name
Download Links
{{tvshow.title}}
.torrent
Music
Name
Download Links
{{music.title}}
.torrent
Games
Name
Download Links
{{game.title}}
.torrent
Apps
Name
Download Links
{{app.title}}
.torrent
eBooks
Name
Download Links
{{ebook.title}}
.torrent


JUnit 4 test classes need to be public
and have a public
no-arg constructor or no constructor at all
(implicit default constructor) and no other public
constructors.
JUnit 3 test classes need to be public
and need either a public
no-arg constructor
or a public
constructor with a single parameter of String
type, which calls the matching super constructor.
Otherwise the test classes cannot be run by standard JUnit test runners.
Example:
public class MyTest {
private MyTest() {} // no-arg constructor is private
@Test
public void testSomething() {
assertEquals(1, 1);
}
}


--- title: "Cleaning and Tidying Survey Data" author: "Corey Pembleton" date: "2018-12-05" output: html_document categories: ["R"] tags: ["tidyverse", "readxl", "data cleaning"] thumbnailImagePosition: "left" thumbnailImage: "/img/messydata.png" ---
Depending on the survey software used, the results exported as excel or .csv can often be fairly messy, and processing that data manually in excel can be time consuming and is prone to copy/paste or other munging errors. Using a real example (questions and stakeholder names changed), I present here one fairly replicable way to process survey data exported from SurveyMonkey. I should note here that I didn’t design this survey, there are several methodological flaws regarding response options and questions themselves, but that conversation is for another day.
survey <- read_excel("./anon_survey.xlsx")
SurveyMonkey results are exported with two or more header rows. The first row is descriptive, for “parent” main questions, the second for the “child” subquestions, the data required is stored in the latter.
In addition to Questions, the first 1-10 columns contain descriptive information on the respondent, and other descriptive elements. These descriptive aspects are stored in the first row (along with the parent questions), which read_excel()
correctly sets as the header.
Because of this structure, name()
can’t be set on import, and some manipulation is required. This method, which I’ve taken from Lisa DeBruine, is helpful in that columns don’t have to be manually identified, and is reusable for any survey.
new_names <- survey %>%
t() %>%
as_tibble() %>%
mutate(name = paste(V1, sep = "_")) %>%
pull(name)
## Warning: The `x` argument of `as_tibble.matrix()` must have column names if `.name_repair` is omitted as of tibble 2.0.0.
## Using compatibility `.name_repair`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
new_names[1:4] <- c("stakeholder_group", "gender", "position", "sector")
Which can now be applied to the survey data, and the first row dropped.
names(survey) <- new_names
survey <- survey[-1,]
There are duplicate columns names which will cause trouble if you want to analyze right away, but they’ll get dealt with in the next step.
Separating the quantitative response values from the qualitative is a necessary step for future gathering and tallying responses. The qualitative responses vary in length, and sometimes are their own question, or are an addition to a numeric question.
In this case, the qualitative questions are all the same, there are “Other” categories sometimes used when none of the options are suitable but a response is required. I generally reccommend avoiding these, as the single-value responses like this take longer to analyze and more often than not are closely aligned to one of the options.
survey_text <- survey %>%
setNames(make.unique(names(.))) %>%
select(stakeholder_group:sector, contains("Other"))
survey <- survey %>%
setNames(make.unique(names(.))) %>%
select(everything(), -contains("Other"))
###3. Removing the non-responsive respondents
Some respondents exported from SurveyMonkey often appear to have dropped the survey, and these respondents have NA
values for all responses. Any one of the respondent characteristics can be filtered out to remove these non-respondents.
survey <- survey %>% filter(!is.na(gender))
Now that the data is renamed and cleaned, the tidying and counting can begin!
The problem with the way the data is presently structured is that each question “value” has its own column (opposed to being combined in the same “question” variable column), the current ratings (e.g. good or very good) are text values, but we need a count, and there are values where respondents could choose not to answer, giving a
All of these can be sorted out with the following code chunk:
survey_counts <- survey %>%
gather(key = Question, value = Response,
"1.1 Refugees have been consulted in the design of AGENCY-funded projects":"1.4 A gender-sensitive needs assessment, or a gender analysis, was undertaken in the design and implementation of interventions") %>%
group_by(stakeholder_group, Question, Response) %>%
tally %>%
spread(Response, n, fill = 0) %>%
select(stakeholder_group, Question,`Strongly disagree`, Disagree, `Do not know/ Not applicable`, Agree,
`Strongly agree`, no_response = `<NA>`) %>%
filter(!is.na(stakeholder_group))
the weakness of this approach is that the gather questions need to be specified (will need to be changed for every survey), and the variables selected will need to be changed according to each question type. The latter will be experimented with further to see how different question response scales can be analyzed differently.
| stakeholder_group | Question | Strongly disagree | Disagree | Do not know/ Not applicable | Agree | Strongly agree | no_response |
|---|---|---|---|---|---|---|---|
| AGENCY | 1.1 Refugees have… | 0 | 5 | 3 | 9 | 4 | 0 |
| EU Member State | 1.1 Refugees have… | 0 | 1 | 5 | 1 | 0 | 4 |
| INGO | 1.1 Refugees have… | 0 | 6 | 5 | 14 | 4 | 1 |
| Local NGO | 1.1 Refugees have… | 1 | 2 | 0 | 6 | 7 | 8 |
| Other EU | 1.1 Refugees have… | 0 | 1 | 2 | 1 | 1 | 0 |
Counts are helpful, but most of the time what we really want is to know the proportion of their response compared to the overall responses. This is also a helpful step to get out of the way when preparing graphics to display results (as found here).
Considering how often I work with surveys, I created a function to determine the proportions. Like above, it requires some minor tweaking on a project-by-project basis, and I hope to improve it in the not-so-distant future.
#create a function to determine proportions
Proportions_Function <- function(tbl){
tbl %>%
summarise(`Number of Responses` = sum(`Strongly disagree` + `Disagree`+`Do not know/ Not applicable`+
`Agree`+`Strongly agree` + no_response, na.rm = TRUE),
`Strongly disagree`= sum(`Strongly disagree`/`Number of Responses`, na.rm = TRUE) * 100,
`Disagree` = sum(`Disagree` / `Number of Responses`, na.rm = TRUE) * 100,
`Do not know/ Not applicable` = sum(`Do not know/ Not applicable` / `Number of Responses`, na.rm = TRUE)* 100,
`Agree` = sum(`Agree` / `Number of Responses`, na.rm = TRUE) * 100,
`Strongly agree` = sum(`Strongly agree` / `Number of Responses`, na.rm = TRUE) * 100,
`no_response` = sum(no_response / `Number of Responses`, na.rm = TRUE) * 100
) %>%
mutate_if(is.numeric, round, digits = 2) %>%
arrange(desc(`Number of Responses`))
}
stakeholder_proportions <- survey_counts %>%
Proportions_Function() %>%
arrange(Question)
## `mutate_if()` ignored the following grouping variables:
## Column `stakeholder_group`
And with that, we now have two tables, one for stakeholder response proportions, and another for stakeholder responses by counts. In my experience, this workflow can bring on massive time savings, is fairly replicable across survey projects, and lends very well to excel table or graphic exports with anonymized findings for clients.
To find out how to export these as tabbed or single-page excel documents, check out my other post on that topic, and to learn how to quickly produce graphics for those proportion tables, be sure to check this purrr::map2()
workflow.
Creating a set of tables which count the results from surveys can be a straight-forward process. The more I work with this type of data, the more I improve the efficiency of the script itself. learning to add tally
without the brackets was a breakthrough, and to change the columns with the same name “Other - please respond” with setNames(make.unique(names(.)))
is an unelegant solution I would like to improve with rename_at()
, but haven’t figured out as of yet. Lastly, the proportions function needs to become more functional. it’s currently prone to error, and needs to be able to better incorporate changes, for example to account for different sums, unique column names and question types, and generally to be more flexible.


Star warsintro CReator download A long time ago in a galaxy far, far away.... Star wars The FIRST ORDER reigns. Having decimated the peaceful Republic, Supreme Leader Snoke now deploys his merciless legions to seize military control of the galaxy. Only General Leia Organa’s band of RESISTANCE fighters stand against the rising tyranny, certain that Jedi Master Luke Skywalker will return and restore a spark of hope to the fight. But the Resistance has been exposed. As the First Order speeds toward the rebel base, the brave heroes mount a desperate escape.... Center text Play and request your video to download


Factsheet
Developer:
Nuldrums
Based in Raleigh, North Carolina
Founding date:
January, 2016
Website:
nativethegame.com
Press / Business contact:
<EMAIL_ADDRESS>Description
Native, is a cartoon 2D survival side-scroller set in an alien rainforest with a couple twists.  Many key gameplay mechanics are integrated with the environment or come in the form of audio queues forcing the player to be observant to become a true native of the forest.
History
Development for Native began in August 2014 after Troy Maynard completed college. Streaming the process under Twitch's game development category started at the same time. Almost everyday of development has been streamed since and has built a small following on twitch. Native's first show was Playthrough GC in February 2016 then ECGC in April 2016. It will be showing as a finalist at Momocon this year in May 2016, the Kickstarter is going to be launched on the first day of the show, May 26th 2016 with the initial goal of $10,000. If the Kickstarter is successful a Steam Early Access release will be planned for January – February of 2017 with roughly double the amount of content that is in the current demo. There will be some major stretch goals if it exceeds expectations.
Awards & Recognition
- "Momocon Indie Award Showcase Finalist" - Georgia, May, 2016
Additional Links
Development Streams
Trickeri (Troy)
Okaymax (Max)
Fam
Troy Maynard (Trickeri)
Game Design, Everything
Max Mayhew (Okaymax)
Artist, Programmer, Web Dev
Adam Lee
Music
presskit() by Rami Ismail (Vlambeer) - also thanks to these fine folks


$timeout
ng
Angular's wrapper for window.setTimeout
. The fn
function is wrapped into a try/catch
block and delegates any exceptions to
$exceptionHandler
service.
The return value of registering a timeout function is a promise, which will be resolved when the timeout is reached and the timeout function is executed.
To cancel a timeout request, call $timeout.cancel(promise)
.
In tests you can use $timeout.flush()
to
synchronously flush the queue of deferred functions.
$timeout(fn[, delay][, invokeApply]);
| Param | Type | Details |
|---|---|---|
| fn | function() | A function, whose execution should be delayed. |
| delay (optional) | number | Delay in milliseconds. (default: 0) |
| invokeApply (optional) | boolean | If set to (default: true) |
| Promise | Promise that will be resolved when the timeout is reached. The value this
promise will be resolved with is the return value of the |