Get all available regions for the Amazon DynamoDB service.
| Return type: | list |
|---|---|
| Returns: | A list of boto.regioninfo.RegionInfo |
This is the lowest-level interface to DynamoDB. Methods at this layer map directly to API requests and parameters to the methods are either simple, scalar values or they are the Python equivalent of the JSON input as defined in the DynamoDB Developer’s Guide. All responses are direct decoding of the JSON response bodies to Python data structures via the json or simplejson modules.
| Variables: | throughput_exceeded_events – An integer variable that keeps a running total of the number of ThroughputExceeded responses this connection has received from Amazon DynamoDB. |
|---|
The error response returned when a conditional check fails
The default region name for DynamoDB API.
The number of times an error is retried.
alias of DynamoDBResponseError
The name of the Service
The error response returned when session token has expired
The error response returned when provisioned throughput is exceeded
The error response returned when an item is invalid in some way
DynamoDB API version.
Return a set of attributes for a multiple items in multiple tables using their primary keys.
| Parameters: | request_items (dict) – A Python version of the RequestItems data structure defined by DynamoDB. |
|---|
This operation enables you to put or delete several items across multiple tables in a single API call.
| Parameters: | request_items (dict) – A Python version of the RequestItems data structure defined by DynamoDB. |
|---|
Add a new table to your account. The table name must be unique among those associated with the account issuing the request. This request triggers an asynchronous workflow to begin creating the table. When the workflow is complete, the state of the table will be ACTIVE.
| Parameters: |
|---|
Delete an item and all of it’s attributes by primary key. You can perform a conditional delete by specifying an expected rule.
| Parameters: |
|
|---|
Deletes the table and all of it’s data. After this request the table will be in the DELETING state until DynamoDB completes the delete operation.
| Parameters: | table_name (str) – The name of the table to delete. |
|---|
Returns information about the table including current state of the table, primary key schema and when the table was created.
| Parameters: | table_name (str) – The name of the table to describe. |
|---|
Return a set of attributes for an item that matches the supplied key.
| Parameters: |
|
|---|
Returns a dictionary of results. The dictionary contains a TableNames key whose value is a list of the table names. The dictionary could also contain a LastEvaluatedTableName key whose value would be the last table name returned if the complete list of table names was not returned. This value would then be passed as the start_table parameter on a subsequent call to this method.
| Parameters: |
|---|
| Raises: | DynamoDBExpiredTokenError if the security token expires. |
|---|
Create a new item or replace an old item with a new item (including all attributes). If an item already exists in the specified table with the same primary key, the new item will completely replace the old item. You can perform a conditional put by specifying an expected rule.
| Parameters: |
|
|---|
Perform a query of DynamoDB. This version is currently punting and expecting you to provide a full and correct JSON body which is passed as is to DynamoDB.
| Parameters: |
|
|---|
Perform a scan of DynamoDB. This version is currently punting and expecting you to provide a full and correct JSON body which is passed as is to DynamoDB.
| Parameters: |
|
|---|
Edits an existing item’s attributes. You can perform a conditional update (insert a new attribute name-value pair if it doesn’t exist, or replace an existing name-value pair if it has certain expected attribute values).
| Parameters: |
|
|---|
Return a set of attributes for a multiple items in multiple tables using their primary keys.
| Parameters: | batch_list (boto.dynamodb.batch.BatchList) – A BatchList object which consists of a list of boto.dynamoddb.batch.Batch objects. Each Batch object contains the information about one batch of objects that you wish to retrieve in this request. |
|---|
Performs multiple Puts and Deletes in one batch.
| Parameters: | batch_list (boto.dynamodb.batch.BatchWriteList) – A BatchWriteList object which consists of a list of boto.dynamoddb.batch.BatchWrite objects. Each Batch object contains the information about one batch of objects that you wish to put or delete. |
|---|
Build a Key structure to be used for accessing items in Amazon DynamoDB. This method takes the supplied hash_key and optional range_key and validates them against the schema. If there is a mismatch, a TypeError is raised. Otherwise, a Python dict version of a Amazon DynamoDB Key data structure is returned.
| Parameters: |
|
|---|
Create a Schema object used when creating a Table.
| Parameters: |
|
|---|
Create a new Amazon DynamoDB table.
| Parameters: |
|
|---|---|
| Return type: | |
| Returns: | A Table object representing the new Amazon DynamoDB table. |
Delete the item from Amazon DynamoDB.
| Parameters: |
|
|---|
Delete this table and all items in it. After calling this the Table objects status attribute will be set to ‘DELETING’.
| Parameters: | table (boto.dynamodb.table.Table) – The Table object that is being deleted. |
|---|
Retrieve information about an existing table.
| Parameters: | name (str) – The name of the desired table. |
|---|
Convert a set of pending item updates into the structure required by Layer1.
Convert an expected_value parameter into the data structure required for Layer1.
Convert a last_evaluated_key parameter into the data structure required for Layer1.
Convert a layer2 range_key_condition parameter into the structure required by Layer1.
Convert a layer2 scan_filter parameter into the structure required by Layer1.
Retrieve an existing item from the table.
| Parameters: |
|
|---|
Retrieve the Table object for an existing table.
| Parameters: | name (str) – The name of the desired table. |
|---|---|
| Return type: | boto.dynamodb.table.Table |
| Returns: | A Table object representing the table. |
Return a list of the names of all tables associated with the current account and region.
| Parameters: | limit (int) – The maximum number of tables to return. |
|---|
Retrieve the Table object for an existing table.
| Parameters: | name (str) – The name of the desired table. |
|---|---|
| Return type: | boto.dynamodb.table.Table |
| Returns: | A Table object representing the table. |
Return a new, empty boto.dynamodb.batch.BatchList object.
Return a new, empty boto.dynamodb.batch.BatchWriteList object.
Store a new item or completely replace an existing item in Amazon DynamoDB.
| Parameters: |
|
|---|
Perform a query on the table.
| Parameters: |
|
|---|---|
| Return type: |
Perform a scan of DynamoDB.
| Parameters: |
|
|---|---|
| Return type: |
Create a Table object from a schema.
This method will create a Table object without making any API calls. If you know the name and schema of the table, you can use this method instead of get_table.
Example usage:
table = layer2.table_from_schema(
'tablename',
Schema.create(hash_key=('foo', 'N')))
| Parameters: |
|
|---|---|
| Return type: | |
| Returns: | A Table object representing the table. |
Commit pending item updates to Amazon DynamoDB.
| Parameters: |
|
|---|
Update the ProvisionedThroughput for the Amazon DynamoDB Table.
| Parameters: |
|
|---|
Use the decimal.Decimal type for encoding/decoding numeric types.
By default, ints/floats are used to represent numeric types (‘N’, ‘NS’) received from DynamoDB. Using the Decimal type is recommended to prevent loss of precision.
This is an object that wraps up the table_generator function. The only real reason to have this is that we want to be able to accumulate and return the ConsumedCapacityUnits element that is part of each response.
| Variables: |
|
|---|
Returns a float representing the ConsumedCapacityUnits accumulated.
The total number of items retrieved thus far. This value changes with iteration and even when issuing a call with count=True, it is necessary to complete the iteration to assert an accurate count value.
Issue a call and return the result. You can invoke this method while iterating over the TableGenerator in order to skip to the next “page” of results.
The current response to the call from DynamoDB.
As above, but representing the total number of items scanned by DynamoDB, without regard to any filters.
An Amazon DynamoDB table.
| Variables: |
|
|---|---|
| Parameters: |
|
Return a set of attributes for a multiple items from a single table using their primary keys. This abstraction removes the 100 Items per batch limitations as well as the “UnprocessedKeys” logic.
| Parameters: |
|
|---|---|
| Returns: | A TableBatchGenerator (generator) object which will iterate over all results |
| Return type: |
Create a Table object.
If you know the name and schema of your table, you can create a Table object without having to make any API calls (normally an API call is made to retrieve the schema of a table).
Example usage:
table = Table.create_from_schema(
boto.connect_dynamodb(),
'tablename',
Schema.create(hash_key=('keyname', 'N')))
| Parameters: |
|
|---|---|
| Return type: | |
| Returns: | A Table object representing the table. |
Delete this table and all items in it. After calling this the Table objects status attribute will be set to ‘DELETING’.
Retrieve an existing item from the table.
| Parameters: |
|
|---|
Checks the table to see if the Item with the specified hash_key exists. This may save a tiny bit of time/bandwidth over a straight get_item() if you have no intention to touch the data that is returned, since this method specifically tells Amazon not to return anything but the Item’s key.
| Parameters: |
|
|---|---|
| Return type: | bool |
| Returns: | True if the Item exists, False if not. |
Retrieve an existing item from the table.
| Parameters: |
|
|---|
Return an new, unsaved Item which can later be PUT to Amazon DynamoDB.
This method has explicit (but optional) parameters for the hash_key and range_key values of the item. You can use these explicit parameters when calling the method, such as:
>>> my_item = my_table.new_item(hash_key='a', range_key=1,
attrs={'key1': 'val1', 'key2': 'val2'})
>>> my_item
{u'bar': 1, u'foo': 'a', 'key1': 'val1', 'key2': 'val2'}
Or, if you prefer, you can simply put the hash_key and range_key in the attrs dictionary itself, like this:
>>> attrs = {'foo': 'a', 'bar': 1, 'key1': 'val1', 'key2': 'val2'}
>>> my_item = my_table.new_item(attrs=attrs)
>>> my_item
{u'bar': 1, u'foo': 'a', 'key1': 'val1', 'key2': 'val2'}
The effect is the same.
| Parameters: |
|
|---|
Perform a query on the table.
| Parameters: |
|
|---|
Refresh all of the fields of the Table object by calling the underlying DescribeTable request.
| Parameters: |
|
|---|
Scan through this table, this is a very long and expensive operation, and should be avoided if at all possible.
| Parameters: |
|
|---|---|
| Returns: | A TableGenerator (generator) object which will iterate over all results |
| Return type: |
Update the state of the Table object based on the response data received from Amazon DynamoDB.
Update the ProvisionedThroughput for the Amazon DynamoDB Table.
| Parameters: |
|---|
A low-level generator used to page through results from batch_get_item operations.
| Variables: | consumed_units – An integer that holds the number of ConsumedCapacityUnits accumulated thus far for this generator. |
|---|
Represents a DynamoDB schema.
| Variables: |
|
|---|
Convenience method to create a schema object.
Example usage:
schema = Schema.create(hash_key=('foo', 'N'))
schema2 = Schema.create(hash_key=('foo', 'N'),
range_key=('bar', 'S'))
| Parameters: |
|
|---|
An item in Amazon DynamoDB.
| Variables: |
|
|---|
Queue the addition of an attribute to an item in DynamoDB. This will eventually result in an UpdateItem request being issued with an update action of ADD when the save method is called.
| Parameters: |
|
|---|
Delete the item from DynamoDB.
| Parameters: |
|
|---|
Queue the deletion of an attribute from an item in DynamoDB. This call will result in a UpdateItem request being issued with update action of DELETE when the save method is called.
| Parameters: |
|---|
Store a new item or completely replace an existing item in Amazon DynamoDB.
| Parameters: |
|
|---|
Queue the putting of an attribute to an item in DynamoDB. This call will result in an UpdateItem request being issued with the update action of PUT when the save method is called.
| Parameters: |
|
|---|
Commits pending updates to Amazon DynamoDB.
| Parameters: |
|
|---|
Used to construct a BatchGet request.
| Variables: |
|
|---|
Convert the Batch object into the format required for Layer1.
A subclass of a list object that contains a collection of boto.dynamodb.batch.Batch objects.
Add a Batch to this BatchList.
| Parameters: |
|
|---|
Resubmit the batch to get the next result set. The request object is rebuild from scratch meaning that all batch added between submit and resubmit will be lost.
Note: This method is experimental and subject to changes in future releases
Convert a BatchList object into format required for Layer1.
Used to construct a BatchWrite request. Each BatchWrite object represents a collection of PutItem and DeleteItem requests for a single Table.
| Variables: |
|
|---|
Convert the Batch object into the format required for Layer1.
A subclass of a list object that contains a collection of boto.dynamodb.batch.BatchWrite objects.
Add a BatchWrite to this BatchWriteList.
| Parameters: |
|
|---|
Convert a BatchWriteList object into format required for Layer1.
Some utility functions to deal with mapping Amazon DynamoDB types to Python types and vice-versa.
Control serialization/deserialization of types.
This class controls the encoding of python types to the format that is expected by the DynamoDB API, as well as taking DynamoDB types and constructing the appropriate python types.
If you want to customize this process, you can subclass this class and override the encoding/decoding of specific types. For example:
'foo' (Python type)
|
v
encode('foo')
|
v
_encode_s('foo')
|
v
{'S': 'foo'} (Encoding sent to/received from DynamoDB)
|
V
decode({'S': 'foo'})
|
v
_decode_s({'S': 'foo'})
|
v
'foo' (Python type)
Takes the format returned by DynamoDB and constructs the appropriate python type.
Encodes a python type to the format expected by DynamoDB.
Use float/int instead of Decimal for numeric types.
This class is provided for backwards compatibility. Instead of using Decimals for the ‘N’, ‘NS’ types it uses ints/floats.
This class is deprecated and its usage is not encouraged, as doing so may result in loss of precision. Use the Dynamizer class instead.
Take a scalar Python value and return a dict consisting of the Amazon DynamoDB type specification and the value that needs to be sent to Amazon DynamoDB. If the type of the value is not supported, raise a TypeError
Take a scalar Python value and return a string representing the corresponding Amazon DynamoDB type. If the value passed in is not a supported type, raise a TypeError.
A custom object hook for use when decoding JSON item bodys. This hook will transform Amazon DynamoDB JSON responses to something that maps directly to native Python types.
Cast a number to a string and perform validation to ensure no loss of precision.


Turkey Burgers
Burgers
Ingredients
Patties
- 1 tablespoon olive oil
- 1 lb. ground turkey
- 3 teaspoons ground ginger
- 3 garlic cloves, minced
- 2 teaspoons soy sauce
- Salt and Pepper to taste
Spicy Mayo
- 1/2 cup of mayonnaise
- 1 lime, juiced
- 1 1/2 teaspoons sriracha
Instructions
In a small bowl, mix the mayonnaise, lime and sriracha. Set aside.
In a large mixing bowl, mix together turkey, ginger, garlic, soy sauce, salt and pepper.
Form into patties - should make about six.
In a large pan, heat the olive oil at medium-high heat. Place patties in pan once heated.
Cook 5-7 minutes each side or until cooked through.
Serve immediately. We like to serve on wheat buns with a dab of the mayo mix, spinach, tomatos and avacado! Enjoy!
Original Source: Best Simple Suppers for Two by Laura Arnold, pg. 58
Sweet Potato Fries
Ingregients
- 2 sweet potatoes
- 1 1/2 tablespoons olive oil
- Salt and pepper to taste
- 1/2 teaspoon paprika
- 1/2 teaspoon garlic powder
- Pinch of dried rosemary
Instructions
Preheat oven to 450˚F.
Slice sweet potatoes into fries. We like ours nice and thin!
In large mixing bowl, combine the sweet potato slices with the olive oil, salt, pepper, paprika, garlic powder and rosemary. Mix until the fries are coated.
On a baking tray lined with parchment paper, spread the fries in a single layer.
Bake for 20–30 minutes (depending on the thickness of the potato slices.) Turn occasionally and keep an eye out so they don’t burn! Serve immediately and enjoy.
Original Source: Mom’s Need To Know


Urban Meyer Sends Video Message To Ohio State Fans
by Tzvi MachlinThe Ohio State Buckeyes take on the Penn State Nittany Lions this weekend in what is essentially a Big Ten Championship Game play-in. Ahead of the game, former Buckeyes head coach Urban Meyer had a message for the fans.
In a message directed at Block O – the student section – he called on the students to show up nice and early to the stadium for FOX Sports’ Big Noon Kickoff Show.
“See you Saturday 10 AM sharp at the RPAC. (Big Noon Kickoff) needs your support,” he tweeted.
Considering that ESPN is going to be hosting College GameDay at the same time, FOX is really bringing out the big guns to make sure they have the bigger crowd.
But there’s no doubting Meyer’s sincerity in wanting to see his former team succeed – especially in this game.
In his seven years at the helm, Meyer went 6-1 against Penn State. But that lone loss in 2016 ultimately cost them a trip to the Big Ten Championship Game.
But Ohio State boasts one of its most complete teams ever, dominating on both sides of the football. They are currently the No. 1 offense in college football (51.5 points per game scored) AND the No. 1 defense in the nation (9.8 points per game allowed).
They’ll be put to the test on both of those fronts against a Penn State team that ranks 15th in scoring and 7th on defense.
Get ready for a fun one.


◊
Apps
Tutorials
Downloads
iOS Apps
They work as intended
Tutorials
Take control of your devices
Downloads
Free software for your computer


In this step, you will implement the phone details view, which is displayed when a user clicks on a phone in the phone list.
To implement the phone details view we used $http to fetch our data, and we
fleshed out the phone-detail.html
view template.
In addition to phones.json
, the app/phones/
directory also contains one json file for each
phone:
app/phones/nexus-s.json
: (sample snippet)
{
"additionalFeatures": "Contour Display, Near Field Communications (NFC),...",
"android": {
"os": "Android 2.3",
"ui": "Android"
},
...
"images": [
"img/phones/nexus-s.0.jpg",
"img/phones/nexus-s.1.jpg",
"img/phones/nexus-s.2.jpg",
"img/phones/nexus-s.3.jpg"
],
"storage": {
"flash": "16384MB",
"ram": "512MB"
}
}
Each of these files describes various properties of the phone using the same data structure. We'll show this data in the phone detail view.
We'll expand the PhoneDetailCtrl
by using the $http
service to fetch the json files. This works
the same way as the phone list controller.
app/js/controllers.js
:
var phonecatControllers = angular.module('phonecatControllers',[]);
phonecatControllers.controller('PhoneDetailCtrl', ['$scope', '$routeParams', '$http',
function($scope, $routeParams, $http) {
$http.get('phones/' + $routeParams.phoneId + '.json').success(function(data) {
$scope.phone = data;
});
}]);
To construct the URL for the HTTP request, we use $routeParams.phoneId
extracted from the current
route by the $route
service.
The TBD placeholder line has been replaced with lists and bindings that comprise the phone details.
Note where we use the Angular {{expression}}
markup and ngRepeat
to project phone data from
our model into the view.
app/partials/phone-detail.html
:

{{phone.name}}

{{phone.description}}

Availability and Networks

Availability

{{availability}}

...

Additional Features

{{phone.additionalFeatures}}

We wrote a new unit test that is similar to the one we wrote for the PhoneListCtrl
controller in
step 5.
test/unit/controllersSpec.js
:
beforeEach(module('phonecatApp'));
...
describe('PhoneDetailCtrl', function(){
var scope, $httpBackend, ctrl;
beforeEach(inject(function(_$httpBackend_, $rootScope, $routeParams, $controller) {
$httpBackend = _$httpBackend_;
$httpBackend.expectGET('phones/xyz.json').respond({name:'phone xyz'});
$routeParams.phoneId = 'xyz';
scope = $rootScope.$new();
ctrl = $controller('PhoneDetailCtrl', {$scope: scope});
}));
it('should fetch phone detail', function() {
expect(scope.phone).toBeUndefined();
$httpBackend.flush();
expect(scope.phone).toEqual({name:'phone xyz'});
});
});
...
You should now see the following output in the Karma tab:
Chrome 22.0: Executed 3 of 3 SUCCESS (0.039 secs / 0.012 secs)
We also added a new end-to-end test that navigates to the Nexus S detail page and verifies that the heading on the page is "Nexus S".
test/e2e/scenarios.js
:
...
describe('Phone detail view', function() {
beforeEach(function() {
browser.get('app/index.html#/phones/nexus-s');
});
it('should display nexus-s page', function() {
expect(element(by.binding('phone.name')).getText()).toBe('Nexus S');
});
});
...
You can now rerun npm run protractor
to see the tests run.
Now that the phone details view is in place, proceed to step 9 to learn how to write your own custom display filter.


VPKit SDK Reference
Basic usage
VPKit provides the public interface for the framework. This is where you will find methods to initialise the SDK.
You will also need to initialise a VPKPreview
(UIImageView sublcass) with a VPKImage
(UIImage sublcass). VPKPreview
has two delegates that may be set to adjust behaviour.
VPKVeepViewer
and VPKVeepEditor
are the view controllers for viewing and creating veeps. There is no need to invoke these objects explicitly - the VPKPreview can handle simple use cases internally.
VPKit
VPKPreview
VPKPreviewDelegate
VPKPreviewPassThroughDelegate
VPKImage
VPKVeepViewer
VPKVeepEditor
For simple usage examples refer to the included demo apps.
Controlling appearance
The VPKStyle classes provide some scope for cusomising the appearance of the Veep Viewer
VPKStyles
VPKFontStyles
VPKColorStyles
Event notifications
A number of event messages are available to intercept as NSNotification objects.
See VPKEvent
constants for a list of event names.
The notification objects include a userInfo dictionary with a single VPKIdentifierKey entry. If a mediaIdentifier string is set on the VPKPreview
‘s VPKImage
, this will be returned as the value. The identifier may aid in ensuring that the correct media events are handled.


Flask is a Python web framework built with a small core and easy-to-extend philosophy.
Flask is considered more Pythonic than Django because Flask web application code is in most cases more explicit. Flask is easy to get started with as a beginner because there is little boilerplate code for getting a simple app up and running.
For example, here's a valid "hello world" web application with Flask (the equivalent in Django would be significantly more code):
from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello World!' if __name__ == '__main__': app.run()
Flask was also written several years after Django and therefore learned from the Python community's reactions as the framework evolved. Jökull Sólberg wrote a great piece articulating to this effect in his experience switching between Flask and Django.
The 18-part Flask mega tutorial is an absolutely amazing starting resource for using the Flask framework. Yes, there are a lot of posts in the series. However, each post is focused on a single topic to contain the complexity while the reader is learning the framework. The whole series is well worth an in-depth read-through. The author also wrote the new O'Reilly Flask Web Development book which is an excellent learning resource.
If you're looking for a fun introduction to Flask and WebSockets, check out my blog post on creating Choose Your Own Adventure Presentations with Reveal.js, Python and WebSockets. That post is my favorite technical walkthrough I've written to date. There is also a companion open source GitHub repository for the app with tags for each step in the blog post.
Branded MMS Coupon Generation with Python and Twilio is a Flask tutorial I wrote for building a web application that can send branded barcode coupons via MMS. The post goes through every step from a blank directory until you have a working app that you can deploy to Heroku.
Building a blog using Flask and AngularJS Part 1 is the first of a multipart series on working with Flask and an AngularJS front end. Part 2 is also available along with the source code.
The Flask Extensions Registry is a curated list of the best packages that extend Flask. It's the first location to look through when you're wondering how to do something that's not in the core framework.
Explore Flask is a public domain book that was previously backed on Kickstarter and cost money for about a year before being open sourced. The book explains best practices and patterns for building Flask apps.
Randall Degges wrote a detailed walkthrough for building a Flask app in 30 minutes.
Building an Analytics App with Flask is a detailed walkthrough for collecting and analyzing webpage analytics with your own Flask app.
Nice post by Jeff Knupp on Productionizing a Flask App.
Building Websites in Python with Flask is another walkthrough tutorial from first steps through getting bigger with Flask.
The Plank & Whittle blog has two posts, one on Packaging a Flask web app and another on Packaging a Flask app in a Debian package once you've built an app and want to deploy it.
The Tuts+ Flask tutorial is another great walkthrough for getting started with the framework.
Create Your Own Obnoxiously Simple Messaging App Just Like Yo is a silly walkthrough of very basic Flask web application that uses Nitrous.io to get started and Twilio for SMS.
The blog post series "Things which aren't magic" covers how Flask's ubiquitous @app.route decorator works under the covers. There are two parts in the series, part 1 and part 2.
Flask by Example: Part 1 shows the basic first steps for setting up a Flask project. Part 2 explains how to use PostgreSQL, SQLAlchemy and Alembic. Part 3 describes text processing with BeautifulSoup and NLTK. Part 4 shows how to build a task queue with Flask and Redis.
Along with the above FLask by Example series, there's also a Discover Flask series of videos. The GitHub repo contains the code and the 25+ videos are hosted on YouTube.
How to Structure Large Flask Applications covers a subject that comes up quickly once you begin adding significant functionality to your Flask application.
Video streaming with Flask is another fantastic tutorial by Miguel Grinberg that covers video streaming.
"One line of code cut our Flask page load times by 60% is an important note about optimizing Flask template cache size to dramatically increase performance in some cases.
Unit Testing Your Twilio App Using Python’s Flask and Nose covers integrating the Twilio API into a Flask application and how to test that functionality with nose.
The Flask documentation has some quick examples for how to deploy Flask with standalone WSGI containers.
Handling Email Confirmation in Flask is a great walkthrough for a common use case of ensuring an email address matches with the user's login information.
Microblog is the companion open source project that goes along with Miguel Grinberg's O'Reilly Flask book.
Flask Foundation is a starting point for new Flask projects. There's also a companion website for the project that explains what extensions the base project includes.
Cookiecutter Flask is a project template for use with Cookiecutter.
Flaskr TDD takes the official Flask tutorial and adds test driven development and JQuery to the project.
Use the Flask App Engine Template for getting set up on Google App Engine with Flask.
Here is a note-taking app along with the source code in Gists.
Bean Counter is an open source Flask app for tracking coffee.
FlaskBB is a Flask app for a discussion forum.
psdash is an app built with Flask and psutils to display information about the computer it is running on.
Install Flask on your local development machine.
Work through the 18-part Flask tutorial listed first under "Flask resources" below.
Read through Flask Extensions Registry to find out what extensions you'll need to build your project.
Start coding your Flask app based on what you learned from the 18 part Flask tutorial plus open source example applications found below.
Move on to the deployment section to get your initial Flask project on the web.


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Lorem ipsum, or lipsum as it is sometimes known, is dummy text used in laying out print, graphic or web designs. The passage is attributed to an unknown typesetter in the 15th century who is thought to have scrambled parts of Cicero's De Finibus Bonorum et Malorum for use in a type specimen book.
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Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Makes 6-8 melts
Dietary
A bit like pizza, these cheese and tomato scones are great to bake with kids. Only a little grownup help required.
Before cooking, wash your hands and put on an apron. Weigh or measure all the ingredients. Ask a grown-up to preheat the oven at 200C/400F/Gas 6. Line a baking tray with some baking paper.
Place the flour into the mixing bowl and add the paprika and butter. Rub the butter into the flour.
Cut the spring onion up into small pieces with the scissors (ask a grown-up to help you) - take off the roots and the dark green leaves first; this is called topping and tailing. Start by cutting it into chunks, then add to the bowl and chop more in the bowl.
Add the grated cheese and stir the mixture together with a fork. Add two tablespoons of tomato juice and mix well with a spoon. If the dough isn’t sticking together then add a little more tomato juice until the mixture becomes a smooth dough.
Roll the dough out on a floured surface to a thickness of about 1cm/½in . Then cut circles with a 5cm/2in round biscuit cutter. Place the biscuits onto the lined baking tray.
Ask a grown-up to put the cheese and tomato melts into the oven for 10-15 minutes or until firm. Leave to cool before eating. Serve them with apple slices.


(defmacro unless (cond T F) ["if" cond F T]) (def sad false) (unless false (console.log "Awesome!")) (defmacro square (x) (mul x x)) (square 12) (defmacro cube (x) (mul x x x)) (square 8) (defmacro half (x) (div x 2)) (half 24) (defmacro third (x) (div x 3)) (third 24) (defmacro fourth (x) (div x 4)) (fourth 20)
Run


|
MRSL JPS3D Library
1.0
An implementaion of Jump Point Search on 3D voxel map
|
#include <map_util.h>
Public Member Functions | |
| MapUtil () | |
| Simple constructor. | |
| Tmap | getMap () |
| Get map data. | |
| decimal_t | getRes () |
| Get resolution. | |
| Veci< Dim > | getDim () |
| Get dimensions. | |
| Vecf< Dim > | getOrigin () |
| Get origin. | |
| int | getIndex (const Veci< Dim > &pn) |
| Get index of a cell. | |
| bool | isOutsideXYZ (const Veci< Dim > &n, int i) |
| Check if the given cell is outside of the map in i-the dimension. | |
| bool | isFree (int idx) |
| Check if the cell is free by index. | |
| bool | isUnknown (int idx) |
| Check if the cell is unknown by index. | |
| bool | isOccupied (int idx) |
| Check if the cell is occupied by index. | |
| bool | isOutside (const Veci< Dim > &pn) |
| Check if the cell is outside by coordinate. | |
| bool | isFree (const Veci< Dim > &pn) |
| Check if the given cell is free by coordinate. | |
| bool | isOccupied (const Veci< Dim > &pn) |
| Check if the given cell is occupied by coordinate. | |
| bool | isUnknown (const Veci< Dim > &pn) |
| Check if the given cell is unknown by coordinate. | |
| void | setMap (const Vecf< Dim > &ori, const Veci< Dim > &dim, const Tmap &map, decimal_t res) |
| Set map. More... | |
| void | info () |
| Print basic information about the util. | |
| Veci< Dim > | floatToInt (const Vecf< Dim > &pt) |
| Float position to discrete cell coordinate. | |
| Vecf< Dim > | intToFloat (const Veci< Dim > &pn) |
| Discrete cell coordinate to float position. | |
| vec_Veci< Dim > | rayTrace (const Vecf< Dim > &pt1, const Vecf< Dim > &pt2) |
| Raytrace from float point pt1 to pt2. | |
| bool | isBlocked (const Vecf< Dim > &p1, const Vecf< Dim > &p2, int8_t val=100) |
| Check if the ray from p1 to p2 is occluded. | |
| vec_Vecf< Dim > | getCloud () |
| Get occupied voxels. | |
| vec_Vecf< Dim > | getFreeCloud () |
| Get free voxels. | |
| vec_Vecf< Dim > | getUnknownCloud () |
| Get unknown voxels. | |
| void | dilate (const vec_Veci< Dim > &dilate_neighbor) |
| Dilate occupied cells. | |
| void | freeUnknown () |
| Free unknown voxels. | |
Public Attributes | |
| Tmap | map_ |
| Map entity. | |
Protected Attributes | |
| decimal_t | res_ |
| Resolution. | |
| Vecf< Dim > | origin_d_ |
| Origin, float type. | |
| Veci< Dim > | dim_ |
| Dimension, int type. | |
| int8_t | val_occ = 100 |
| Assume occupied cell has value 100. | |
| int8_t | val_free = 0 |
| Assume free cell has value 0. | |
| int8_t | val_unknown = -1 |
| Assume unknown cell has value -1. | |
The map util class for collision checking
| Dim | is the dimension of the workspace |
|
inline |
Set map.
| ori | origin position |
| dim | number of cells in each dimension |
| map | array of cell values |
| res | map resolution |


Week 4: Further into Python
Expert Systems
Extend our 20 questions game
Programming styles
Testing code
Week 4: Expert Systems
Simple decision tree
May have probabilities
Is not "trained" like machine learning
Week 4: Expert Systems
Making 20 questions into an expert system
Check for wrong answer
Add correct answer
Add distinguishing question
Save data
Week 4: Coding Styles
The imperative coding style
my_list = [1, 2, 3, 4, 5]
sum = 0
for x in my_list:
sum += x
print(sum)
Week 4: Coding Styles
The procedural coding style
my_list = [1, 2, 3, 4, 5]
# Add function to sum list
def do_add(any_list):
sum = 0
for x in any_list:
sum += x
return sum
print(do_add(my_list))
Week 4: Coding Styles
The object-oriented coding style
my_list = [1, 2, 3, 4, 5]
# Add function to sum list
def do_add(any_list):
sum = 0
for x in any_list:
sum += x
return sum
print(do_add(my_list))


Defining the Goal and Criteria of the Analysis
The goal of the MCDA can be stated as a problem or as question to be answered. For example: Show me the most and the least suitable locations in Brooklyn for adding new Citibike stations.
What makes a location suitable for something? Usually, it’s a combination of factors. Defining the criteria for what makes a place suitable is the core of MDCA. In this example, we will use the following criteria/layers:
Existing citibike stations: we will prioritize areas that are near the the existing network.
Density of the residential units constructed in the past 5 years: we will prioritize areas with higher density over areas of lower density.
Subway stations: we will prioritize areas closer to the subway stations.
Bike lanes: we will prioritize areas closer to the bike routes.
The data for this exercise can be downloaded from NYC Open Data and MapPLUTO. The packaged file of the above data can be downloaded below
Datasets
To create this map we will be using the following datasets:
Borough of Brooklyn - from New York City boroughs. Download from NYC Open Data – Department of City Planning. Choose "Export", under "Borough Boundaries & Community Districts".
Citibike stations. Data available from Citibike in JSON format. A point feature shapefile for the stations can be found in the package below.
MapPLUTO - Brooklyn MapPLUTO file (version 17v1), containing all the lots in Brooklyn and their attributes. The original PLUTO files can be downloaded from NYC Planning - Open Data. Make sure you download the file for the borough of Brooklyn. For more information about the PLUTO files and what each of the fields means take a look at the useful PLUTO data dictionary.
MTA Subway Station Locations. Download from NYC Open Data - Metropolitan Transportation Authority (MTA). Choose "Export", under "Subway Stations", and download in Shapefile format.
Locations of bike lanes and routes throughout the City - from New York City boroughs. Download from NYC Open Data – Department of Transportation. Choose "Export", under "Bike Routes", and download in Shapefile format.
A packaged file of the above data can be found in a downloadable package - brwn.co/mcda. Original data assets (listed above) are located in the directory 00_Original. In addition, we've provided output files for each of the steps below, under 01_Outputs. Throughout the tutorial, we will reference which files were created and at which steps.
Preparing Input Layers
Launch ArcMap and save project as “MCDA_citibike_BK.” Load the following layers:
Border of the Borough of Brooklyn (for geographic reference)
Citibike Stations
Brooklyn PLUTO dataset
MTA subway stations
Locations of bike lanes and routes throughout the City
Preprocessing: Clipping Layers to Brooklyn and Defining the Geoprocessing Extent
Navigate to Selection
> Select by Location
and set the Target Layer to Citibike_Stations.
Set the Source Layer to BK_Borough
and select the features from target layer that are within the source layer feature
. Export selected features by right-clicking on target layer in TOC snd clicking through Data
> Export Data...
Make sure you export only the selected features.
Repeat the same step for subway stations and bike lanes as target layers. Now you should have Citibike Stations, MTA Subway Stations and Bike Lanes/Routes clipped to the borough boundary of Brooklyn. When complete, remove the original feature classes.
Before we create our input layers, we need to make sure they will be perfectly aligned. We can do that by defining a geoprocessing extent for all operations.
To define the geoprocessing extent, navigate to the Geoprocessing
on the top of your toolbar, and select Environments
. In the Environments Workspace, expand the Processing Extent
toolbar and set your Extent to Same as layer BK_borough
Criterion I: Citibike Stations
Average Nearest Neighbor | Buffer | Euclidean Distance
As Citibike is rolled out across neighborhoods and boroughs, a primary requirement for new station placement is nearness to pre-existing stations and network density. When expanding the network, Citibike rolls out collections of stations across an area at once. For this exercise, we will be avoiding densifying pre-existing networks of stations, while at the same time prioritizing distance to the existing network. The way we will accomplish this is by calculating the average nearest neighbor for all stations in Brooklyn, and use that distance to create a buffer area around each station that will be excluded from consideration.
Navigate to Geoprocessing
in your toolbar and select ArcToolbox
. This will open a new tab within your workspace. In this tab, expand the Spatial Statistics Tools
item, and expand its child Analyzing Patterns
. We will use the Average Nearest Neighbor
tool.
To calculate a nearest neighbor index, use the settings below
Set
Input Feature class
to citibike_stations_BKSet
Distance method
to EUCLIDEAN_DISTANCEDo not generate report
Navigate to
Geoprocessing
in your toolbar and selectResults
. Take a moment to observe the results, looking specifically at the Nearest Neighbor Expected/Observed values. Both indexes cluster around 800 feet, so we will use this value as our buffer.
Now that the nearest neighbor index has been calculated, we will create a buffer based upon the results (Nearest Neighbor Expected and Nearest Neighbor Observed values). Navigate to Geoprocessing
in your toolbar and select Buffer
. This tool will create buffer polygons around input features to a specified distance (800 feet). To generate the buffer, use the settings below:
Set
Input Features
to citibike_stations_BKSet
Output Feature Class
to .....shpSet
Distance
to 800 Feet andDissolve Type
to ALL
To finalize preparing the Citibike Station input layer, we need to generate a distance raster, utilizing the Euclidean Distance. Navigate to Geoprocessing
in your toolbar and select ArcToolbox
. This will open a new tab within your workspace. In this tab, expand the Spatial Analyst Tools
item, and expand its child Distance
. We will use the Euclidean Distance
tool.
Set
Input Feature Source Data
to citibike_buffer800ft (the buffer file you just created).Set
Output Distance Raster
to citibike_distance.tif. Be sure to write out the .tif extension to define the file type properly.Set
Output Cell Size
to 20 ft and selectOK
.
We are now ready to work with the Citibike criterion to generate a decision layer for analysis.
Criterion II: Recent Residential Analysis
Select by Attribute | Kernal Density
In addition to considering the existing Citibike network, we will also incorporate recent residential development in our decision analysis. To do so, we will rely on PLUTO data to select residential construction completed within the last 5 years (build date > 2013). To get started, we need to select buildings that fit these characteristics.
Navigate to Selection
in your toolbar and select Select by Attributes
. This will open tabular view of your data.
Set input
Layer
to BKMapPLUTO.In the equation field of the workspace, we need to highlight buildings that have 1 or more residential units, as well as build dates that equal 2013 or greater. To do so, enter
”YearBuilt" >= 2013 AND "UnitsRes" >= 1
and selectOK
.Once you see a highlighted selection of data on your map, right-click on the BKMapPLUTO layer and select
Data
>Export Data
.With Selected Features set as your
Export
option, navigate to an output folder that you would like to house the exported shapefile and selectOK
.Once this updated PLUTO shapefile has been added to your layers panel, you can remove the original Brooklyn MapPLUTO layer.
With an updated PLUTO layer highlighting only buildings that have 1 or more residential units that also were built on or after 2013, we will perform kernel density calculations. This process will produce the ratio of the number of observations to an area using a density function. In this case we will be dividing the number of residential units built in the last 5 years in Brooklyn by an area defined as that encompassed by a radius from the center of each raster cell in the output raster dataset. To begin, navigate to Geoprocessing
in your toolbar and select ArcToolbox
. This will open a new tab within your workspace. In this tab, expand the Spatial Analyst Tools
item, and expand its child Density
. We will use the Kernel Density
tool.
In the
Input Point
field, select BK_PLUTO_res_from2013_points (the shapefile generated in the previous step).Set the
Population
input field to UnitsRes.Set the
Search Radius
to 2,640 feet (half-mile).Set the
Area Units
to Acres.Save the
Output Raster
to Save as “kernel_dens_residential_halfmile.tif” and selectOK
.
We are now ready to work with the Residential criterion to generate a decision layer for analysis.
Criterion III: Subway Stations
Euclidean Distance
To preparing the Subway Station locations for analysis, we need to generate a distance raster, utilizing the Euclidean Distance. Navigate to Geoprocessing
in your toolbar and select ArcToolbox
. This will open a new tab within your workspace. In this tab, expand the Spatial Analyst Tools
item, and expand its child Distance
. We will use the Euclidean Distance
tool.
Set
Input Feature Source Data
to MTA_SubwayStations_BK (the layer you generated of Subway Stations found within the Brooklyn Borough boundary).Set
Output Distance Raster
to subway_distance.tif. Be sure to write out the .tif extension to define the file type properly.Set
Output Cell Size
to 20 ft and selectOK
.
We are now ready to work with the Subway Station criterion to generate a decision layer for analysis.
Criterion IV: Bike Lanes/Routes
Euclidean Distance
To preparing the Bike Lanes/Routes locations for analysis, we need to generate a distance raster, utilizing the Euclidean Distance. Navigate to Geoprocessing
in your toolbar and select ArcToolbox
. This will open a new tab within your workspace. In this tab, expand the Spatial Analyst Tools
item, and expand its child Distance
. We will use the Euclidean Distance
tool.
Set
Input Feature Source Data
to DOT_BikeLanes_BK (the layer you generated of bike lanes/routes found within the Brooklyn Borough boundary).Set
Output Distance Raster
to bikelanes_distance.tif. Be sure to write out the .tif extension to define the file type properly.Set
Output Cell Size
to 20 ft and selectOK
.
We are now ready to work with the Bike Lanes/Routes criterion to generate a decision layer for analysis.
Create Decision Layers
Now that distance rasters have been generated, the output no longer fits neatly into the Brooklyn Borough boudnary. Therefore, we will run Extract by Mask
to extracts cells from a raster within a mask defined by an input polygon shapefile feature class (Brooklyn boundary layer).
Navigate to Geoprocessing
in your toolbar and select ArcToolbox
. This will open a new tab within your workspace. In this tab, expand the Spatial Analyst Tools
item, and expand its child Extraction
. We will use the tool Extract by Mask
.
Set
Input Raster
to citibike_distance.tif.Set
Feature Mask
to BK_Borough.Set
Output Distance Raster
to citibike_stations_BK_distance_clip.tif. Be sure to write out the .tif extension to define the file type properly. Once everything is setup, selectOK
.
Repeat this step for each of the decision layers (subway stations, new residential units, and bike lanes/routes), and remove the non-clipped layers from your Table of Contents.
Reclassification
The reclassification function reclassifies or changes cell values in a raster to alternative values, based on user input. We will use Reclassify tool on all four input rasters in order to create a common scale of values between them. More specifically, we will reclassify input rasters by value ranges into five new classes, assigning “5” to the most suitable areas, and “1” to the least suitable area for citibike expansion in each input raster.
Navigate to Geoprocessing
in your toolbar and select ArcToolbox
. This will open a new tab within your workspace. In this tab, expand the Spatial Analyst Tools
item, and expand its child Reclass
. We will use the tool Reclassify
.
Set
Input Raster
to subway_stations_BK_distance_clip.tifClick on
Classify
, and set theClassification Method
to ManualFor the classes, we will split the values of citibike stations, subway stations, and bike lanes/routes by quarter-mile increments. For recent residential units, we will use the
Classification Method
Quintiles. Quarter-mile increment entries are shown below.1320
2640
3960
5280
Leave as is (automatically generated from the MAX value)
Set the
Output
as DL_subwayStations.tifSelect OK
Repeat this reclassification for each of your remaining inputs (Citibike Stations, Bike Lanes/Routes, and Recent Residential Activity). As stated above, we will use the Manual Classification Method
for Citibike Stations, Bike Lanes/Routes and Subway Stations (done in first example) based upon quarter-mile increments. For recent residential units, use the Classification Method
Quintiles which will automatically generate. The reason we use the Quintiles
is because we don't have any external factors that could influence our classification, so we will classify internally. For example, with subway stations, we would want to classify based upon how quickly one could walk to the subway (quarter-mile increments).
Bike Lanes/Routes
Citibike Stations
Subway Stations
Residential Development
Above are examples of each of the input tools. Now we are ready to add our values to produce a decision map.
Decision Mapping for Analysis
In the final step of this exercise, we will combine our decision layers to represent a final decision map that will highlight where opportune areas for Citibike expansion. This is the simplest step of the entire process, as we are simply using our raster calculator to add our decision layers. There are a variety of ways we can do this addition, weighting certain inputs above others. For this example, we are simply going to weight each input equally. To begin, navigate to Geoprocessing
in your toolbar and select ArcToolbox
. This will open a new tab within your workspace. In this tab, expand the Spatial Analyst Tools
item, and expand its child Map Algebra
. We will use the tool Raster Calculator
.
In the raster calculator, enter
"DL_bikeLanes.tif" + "DL_citibikeStations.tif" + "DL_ResDensity.tif" + "DL_subwayStations.tif"
.For
Ouput
DecisionMap.tif and selectOK
.
The initial output of our Raster Calculator
will be unreadable but with some simple styling, we can create our first decision map. Right click on the newly generated layer DecisionMap.tif and click on Symbology
. In this setting, we will select a new color ramp highlighting the spots for development in dark purple.
With some simple neighborhood outlines, we now have our first legible decision map that tells us which neighborhoods are best suited for the expansion of the Citibike network. From this analysis, we can identify that Prospect Park - Lefferts Garden, Crown Heights (North), and Bushwick (South) are the leading candidates. Remember that there are various inputs that could be taken into consideration were we to perform this analysis for Citibike. In addition, we've calculated this map based upon equal weighting of the selected inputs. One could imagine that prioritizing distance to Subway Stations ("DL_bikeLanes.tif" + "DL_citibikeStations.tif" + "DL_ResDensity.tif" + ("DL_subwayStations.tif"*2)
), for example, might lead to a better designation for expansion.


{% extends "upload_base.html" %} {% load upload_tags %} {% block content %}
Stitch GigaPixel Panoramas in the Cloud. Browsers currently supported: Google Chrome, Mozilla Firefox.


Aardvark Brings Flocks of Apps To SteamVR
A new open source immersive browser prototype, Aardvark, provides spatial "gadgets" that run over SteamVR applications. Gadgets are hosted on web servers and can be bookmarked as well as shared via URLs. This form of simultaneously used XR applications has been raised before in Immersive Web issues #15 and #43 and perhaps Aardvark is a platform where those ideas can grow.
Facebook Wants to Connect With You
Fresh out of a re-brand and some organizational shuffle, Facebook Reality Labs (née Oculus) is hosting an online event in which they are rumored to be unveiling a new version of their Quest headset as well as providing more information about upcoming online social spaces. The community group's very own Jacob Rossi is scheduled to talk about "The Future of the Web in VR for Work and Play". Good luck, Jacob!
Relatvty (sic) Frees a $200 Headset
On the other end of the XR hardware spectrum from the Quest 2 comes this tweet from these can-do kids:
"I’m Max Coutte and when my best friend Gabriel Combe and I were 15 years old we built our own VR headset because we couldn't afford to buy one. Relativty is not a consumer product. We made Relativty in my bedroom with a soldering iron and a 3D printer and we expect you to do the same."
The original North Star AR headset had a similarly hands-on origin but thanks to a large maker/hacker community a more polished version with injection molded parts and a field tested belt pack is close to release.
Ada Introduces The Immersive Web
I suspect that most readers of the Immersive Web Weekly are regularly called on to explain the context and history of the WebXR Device API and the immersive web in general. To help ease people onto the immersive web our very own chair, Ada Rose Canon, has put together a tight 30 minute video that covers the basics of WebXR without assuming that the viewer is already versed in virtual or augmented reality technology.


Leaflet.js web map application which displays bike and pedestrian count information alongside trail network and point of interest data.
Leaflet.js web map application which displays minimum travel times between Amtrak Stations, derived from a GTFS file processed in R using tidytransit.
A visualization of an on-demand rural transportation network, constructed from schedule information using QGIS and incorporated into a Mapbox tileset.
ArcGIS Story Map exploration of parks, trails, and other green recreation areas in McLean County, Illinois.
Collection of historical photos, site information, and location data, presented using ArcGIS Story Maps.
Combined zoning map of Normal and Bloomington using McLean County GIS zoning data, drafted in ArcMap and finished using Adobe Illustrator.
Hi there, I'm Tim! I'm a GIS Planner specializing in spatial data analysis in the R ecosystem and web mapping using Leaflet. I have been working at MCRPC since June 2019 but I am exploring opportunities in the DC-Maryland-Virginia area.
When I'm not working or learning, I love to cook, hike, game, and doomscroll on social media.


With this tool you can cleanup the version history, keeping a given number of versions for each content node.
Please note that Umbraco requires each node to have at least 2 versions (the currently published and the newest). These versions will never be deleted.
Cleanup Summary
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{{ item.Result }} rows deleted in table: {{ item.Type }}
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Cleanup in progress. Please wait until you see results


This syllabus is subject to change based on specific class needs, especially the schedule. Significant deviations will be discussed in class.
Introduces basic operating system concepts including virtualization, memory management, process and thread management, concurrency, file systems, and process scheduling. Students will also gain hands-on experience with the C programming language and Unix-based operating systems via programming projects.
Possible topics include:
| Processes | Concurrent Data Structures |
| CPU Scheduling | Concurrency Bugs |
| Virtual Memory | I/O Devices |
| Free Space Management | Redundant Disk Arrays |
| Locks | File Systems |
The required course textbook is:
It is available online at http://pages.cs.wisc.edu/~remzi/OSTEP/.
Late assignments: You have each been allotted a total of 5 late days. You may apply these without any penalty to any project you see fit, with the exception that you may use at most 2 on any individual project. The whole point here is to give you some flexibility that allows for things like illnesses, long trips, and the like. I am unlikely to grant further extensions. Any projects turned in late will result in a 20% penalty per day.
Academic dishonesty: Monmouth College’s official policy on academic dishonesty can be found here. You are responsible for reading and complying with that policy.
In this course, any violation of the academic honesty policy will have varying consequences depending on the severity of the infraction as judged by the instructor. Minimally, a violation will result in an “F” or 0 points on the assignment in question. Additionally, the student’s course grade may be lowered by one letter grade. In severe cases, the student will be assigned a course grade of “F” and dismissed from the class. All cases of academic dishonesty must be reported to the Associate Dean who may decide to recommend further action to the Admissions and Academic Status Committee, including suspension or dismissal. It is assumed that students will educate themselves regarding what is considered to be academic dishonesty, so excuses or claims of ignorance will not mitigate the consequences of any violations
Collaboration: We encourage you to make use of the resources available to you – it is fine to seek help from a friend, tutor, instructor, internet, etc. However, copying of answers and any act worth of the label “cheating” is never permissible! In addition to listing your sources and collaborators, you should be producing your own writeup in your own words. By “your own words,” we mean you should be producing the text yourself, without some external aid. Verbatim copying of text is specifically disallowed, but so is taking a source and rearranging some phrases and changing some variable names to create a derivative version! Such behavior is definitely NOT “using your own words.” It does not matter if you helped contribute to this source text with others, since then you are still not the sole author of the text. The point of collaborating on an assignment is not to produce a jointly authored set of solutions, since that violates the course policies. Instead, it is to help you solve the problems, which sometimes involve a bit of creativity. After you have jointly come up with the ideas you need to solve the problems, though, you should part ways with your group and sit down to do the writing by yourself. I also advise against sharing the writeup you submit with others, since if someone else uses your text as a source for their own solution (with or without your permission), you will also be implicated in the violation of the academic integrity policy. In any case, if two nearly identical solutions are received, we have no way of tell which is the original, and the policy is to not award credit for either submission.
Electronic devices: Do not use your phone or other devices in class except where necessary. Any computer or tablet usage should be related to the course. If a device is not being used for Zoom or Socrative it should be put away and turned on silent. Other usage is rude and distracting to others.
General expectations: In short, I expect you to be respectful of others and take responsibility for your own learning. You are here to learn, so work hard and be professional.
Just attending class is not sufficient to truly learn the material. Read the text, use the resources available at Monmouth College, and go beyond the material.
If you miss class, you are responsible for everything covered on that day. College is, in some sense, your job. Take pride in creating quality work. Staple your assignments, label problems, and present your answers neatly and orderly.
Your job is to convince me that you have learned the material – show your work! Even if you do not know a particular answer, guide me through your thought process.
The weekly workload for this course will vary by student and over the semester, but on average should be about 12 hours per week. The follow table provides a rough estimate of the distribution of this time over different course components for a 16 week semester.
| Category | Amount | Final Grade Weight | Total Time | Time/Week (Hours) |
|---|---|---|---|---|
| Lectures | 55 | 10% (Participation) | - | 3.5 |
| Projects | 3–5 | 45% | 64 | 4.5 |
| Exam Study | - | - | 27 | 1.5 |
| Exams | 3 | 45% | - | - |
| Reading+Unstructured Study | - | - | 2.5 | |
| 12 |
Your participation grade is based on a variety of activities. During class I will often make use of the Socrative (https://socrative.com) app, so you’ll need to install this on your phones. Participating in Socrative questions and with in-class group activities is required for a decent participation grade; an A includes asking questions either in class or in office hours.
Your final grade is based on a weighted average of particular assignment categories, with weights shown above. You can estimate your current grade based on your scores and these weights. You may always visit the instructor outside of class to discuss your current standing.
This courses uses a standard grading scale. Assignments and final grades will not be curved except in rare cases when its deemed necessary by the instructor. Percentage grades translate to letter grades as follows:
| Score | Grade |
|---|---|
| 94–100 | A |
| 90–93 | A- |
| 88–89 | B+ |
| 82–87 | B |
| 80–81 | B- |
| 78–79 | C+ |
| 72–77 | C |
| 70–71 | C- |
| 68–69 | D+ |
| 62–67 | D |
| 60–61 | D- |
| 0–59 | F |
You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge*.
You are always welcome to challenge a grade that you feel is unfair or calculated incorrectly. Mistakes made in your favor will never be corrected to lower your grade. Mistakes made not in your favor will be corrected. Basically, after the initial grading your score can only go up as the result of a challenge.
The following tentative calendar should give you a feel for how work is distributed throughout the semester. Assignments and events are listed in the week they are due or when they occur. This calendar is subject to change based on the circumstances of the course.
| Date | Topic | Assignment |
|---|---|---|
| Mon 01/14 | Operating Systems Overview | Read OSTEP 1,2 |
| Tue 01/15 | Intro to C | Work through this tutorial |
| Wed 01/16 | More C | |
| Fri 01/18 | Exercise: Linked List Stack in C | |
| Mon 01/21 | CPU Virtualization: Privilege Levels | OSTEP 3-6, Project 1 out |
| Tue 01/22 | Context Switching | |
| Wed 01/23 | Project 1 Walkthrough | |
| Fri 01/25 | CPU Scheduling | OSTEP 7 |
| Mon 01/28 | MLFQ and Lottery Scheduling | OSTEP 8-9 |
| Tue 01/29 | Memory Allocation Algorithms | OSTEP 12,17 |
| Wed 01/30 | (Class Cancelled) | Read OSTEP 13-14 |
| Fri 02/01 | Coalescing; Base and Bounds | Read OSTEP OSTEP 15 |
| Mon 02/04 | Segmentation | Project 1 due, Project 2 out, Read OSTEP 13-16 |
| Tue 02/05 | (Class Cancelled) | |
| Wed 02/06 | Paging | OSTEP 18 |
| Fri 02/08 | Review Project 1; Project 2A | OSTEP 19 |
| Mon 02/11 | Project 2B | |
| Tue 02/12 | TLBs | OSTEP 19 |
| Wed 02/13 | Making Page Tables Smaller | OSTEP 20 |
| Fri 02/15 | Swapping | OSTEP 21-22 |
| Mon 02/18 | (Class Cancelled - Conference) | |
| Tue 02/19 | (Class Cancelled - Conference) | |
| Wed 02/20 | (Class Cancelled - Conference) | |
| Fri 02/22 | Detailed Paging Example | OSTEP 23-24 |
| Mon 02/25 | Exam Review | Project 2 due |
| Tue 02/26 | Exam Review - VAX/VMS | |
| Wed 02/27 | Exam 1 | |
| (Fri 03/01) | (Half-semester course exams – no class) | |
| (03/04–03/08) | (Spring break) | |
| Mon 03/11 | Project 2 Review | |
| Tue 03/12 | Project 2 Review | |
| Wed 03/13 | Project 2 Review | |
| Fri 03/15 | Project 2 Review, Thread API | OSTEP 25, Exam 1 Redux Due |
| Mon 03/18 | Race Conditions | OSTEP 26-27, Project 3 out |
| Tue 03/19 | Project 3 Overview | |
| Wed 03/20 | Locks | OSTEP 28 |
| Fri 03/22 | Concurrent Data Structures | OSTEP 29 |
| Mon 03/25 | Evaluating Locks | |
| Tue 03/26 | Futexes | |
| Wed 03/27 | Condition Variables | OSTEP 30 |
| Fri 03/29 | Producer/Consumer | |
| Mon 04/01 | Producer/Consumer | |
| Tue 04/02 | Producer/Consumer | |
| Wed 04/03 | Project 3 Questions | |
| Fri 04/05 | Semaphores | OSTEP 31 |
| Mon 04/08 | Semaphores | |
| Tue 04/09 | ||
| Wed 04/10 | Project 3 due | |
| Fri 04/12 | Concurrency Bugs | OSTEP 32 |
| Mon 04/15 | Concurrency Bugs and Project 3 Review | Exam 2 out |
| Tue 04/16 | (Scholar’s Day) | |
| Wed 04/17 | I/O Devices, Disks | OSTEP 36 |
| (Fri 04/19) | (Easter Break) | |
| (Mon 04/22) | (Easter Break) | |
| Tue 04/23 | Disk Scheduling | OSTEP 37 |
| Wed 04/24 | Disk Scheduling | |
| Fri 04/26 | RAID | OSTEP 38 |
| Mon 04/29 | Files and Directories | OSTEP 39 |
| Tue 04/30 | File System Implementation | OSTEP 40 |
| Wed 05/01 | Review | |
| Fri 05/03 3:00 PM | Final Exam |
The Teaching and Learning Center offers FREE resources to assist Monmouth College students with their academic success. Programs include supplemental instruction for difficult classes, drop-in and appointment tutoring, and individual academic coaching. The TLC is here to help students excel academically. TLC services are not just for struggling students, but can assist all students to get better grades, practice stronger study skills, and manage time.
Make an appointment with Kam Williams, Director of Academic Support Programs and Student Disability Services, at the TLC on the 2nd floor of Poling Hall. The department phone number is 457-2257, or contact the department online at http://ou.monmouthcollege.edu/academics/teaching-learning-center/. They can also be reached via email at<EMAIL_ADDRESS>Disability Support Services: If you have a disability or had academic accommodations in high school or another college, you may be eligible for academic accommodations at Monmouth College under the Americans with Disabilities Act (ADA). Monmouth College is committed to equal educational access.
Students can meet with Kam Williams about accommodations at the Teaching and Learning Center (TLC). The TLC is located on the 2nd floor of Poling Hall. For more information, call<PHONE_NUMBER> or connect online at http://ou.monmouthcollege.edu/life/disability-services/default.aspx.


Elizabeth
Director: Shekhar Kapur.
Starring: Cate Blanchett, Geoffrey Rush, Christopher Eccleston, Joseph Fiennes, Richard Attenborough.
Well, everything good you've heard about Elizabeth is true. It's a visually stunning movie with the most incredible performances from everyone involved. This is definitely a masterpiece with only one or two minor quibbles. Most certainly something that must be viewed on the big screen.
Cate Blanchett, as everyone has noted, is magnificent in the role of Elizabeth. She's fun, she's haughty, she's naive, she's strong at all the right times, making an Elizabeth who's believable and sympathetic while at the some time being something of a hero.
And Geoffrey Rush is the best I've ever seen him, with perhaps the exception of A Little Bit of Soul. His Walsingham is a wonderful mentor for Elizabeth, with the ruthlessness necessary to get the job done. Similarly, Christopher Eccleston and Joseph Fiennes play their roles magnificently. I had a minor problem with Richard Attenborough, mainly because I feel he plays the exact same role, no matter what movie he's in. As I watched him in Elizabeth, I kept having visions of the guy who owned Jurassic Park!
The biggest problem I have with this film is a problem inherent with almost all historical movies: what is truth? To tell a good story, often facts need to be manipulated, or extra people added. Douglas Adams once said that he never got his books from real life because although real life provides interesting little incidents, it rarely produces a beginning, a middle and an end. So, for Elizabeth, choices need to be made as to what to show and what to leave out; which bits of the life of Elizabeth are going to make up the movie; what is the beginning, the middle and the end? Similarly, if Elizabeth is our hero, who is our villain?
Elizabeth makes Norfolk (Christopher Eccleston) and the Catholic Church the villain. While not arguing with the atrocities committed by the Catholic Church in that and many other eras, this movie suggests very strongly that any evil our heros did, they were driven to by villainy of others. My history of this period is not good enough to know for certain, but I doubt that Elizabeth was quite as blameless as shown. I imagine it would be possible to make a movie of the life of Norfolk, making him a hero and Elizabeth the villain if we make different choices about what to show.
While on the topic of historical accuracy, one wonders whether there were quite so many tall, willowy females in the court of Queen Elizabeth.
Also, at times, the music threatened to swallow us all. There were moments in the film when a less strident approach would have been more suited to the moment. Any time where I notice the music in a way that distracts from whatever is happening on the screen is a bad music moment for me - and there were half a dozen such moments in Elizabeth.
Be warned, however, this is not a Jane Austen period drama; this is very gory stuff. The opening scene, for me, was the worst, more in tone than gore. It begins with three people being burnt alive at the stake. Very horrific. Later scenes of heads on sticks, slashed throats, beatings, all contribute to a very dark mood for Elizabeth, matching probably the mood of the times. If you can stomach the gruesomeness of this move, I wholeheartedly recommend it.
Rating: D -- Nicole Lesley email<EMAIL_ADDRESS>"The world won't end in darkness, it'll end in family fun" The Beautiful South, One God.
Ratings System
HD: High Distinction D: Distinction CR: Credit P: Pass CP: Conceded Pass F: Fail
review page: http://www.cs.usyd.edu.au/~nikki/m_r/Intro.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


You can add text elements by:
map.addText({ lat: 30.257806291133193, lng: -97.72566276602447, text: '♥♥♥ Most Excellent Tacos ♥♥♥' });
You must define latitude, longitude and the text of the text.
Note: Also, you can define a mouse event
callback methods, which will be triggered when the overlay's DOM element is interacted with.


Yongqing Fang Landscape Design / Lab D+H
- Curated by 韩爽 - HAN Shuang
- about 10 hours
- version="1.0" encoding="UTF-8"? Architects: Lab D+H
- AreaArea of this architecture projectArea: 3000.0 m2
- version="1.0" encoding="UTF-8"?YearCompletion year of this architecture project Year: 2017
- version="1.0" encoding="UTF-8"?PhotographsPhotographs: Arch-Exist
- Lead Architects Zhongwei Li
- Design Team Huicheng Zhong, Nan Lin, Zongjie Liang, Hao Lan
- Clients Guangzhou Vanke
- Landscape Lab D+H
- Consultants Vanke
- Collaborators Vanke
More Specs Less Specs
Yongqing Fang
Yongqing Fang, a renovation project as a reaction to the rapid urban transformation of modern-day life, sits right in the heart of Guangzhou old town. Our design strategy emphasizes on renewing the buildings and activating the existing resources in the area to avoid relocating or expelling.
This inner-city renewal project improves the quality of life for all residents but also keeps their place-attachment by preserving the historic neighborhood fabric. The success of Yongqing Fang also provides a new benchmark for a micro-renovation approach – a series of case-specific measures -- towards inner-city rejuvenation.
Micro-transformation
Residential houses, commercial stores, and a combination of historic and modern buildings crowded in Yongqing Fang. To unify the style of the site and to retain the historical context, micro-transformation came into being. At the start of the renovation, most of the dwellings in Yonqing Fang were found abandoned. An overall inventory was established and intensively discussed with all stakeholders involved, leading to a final classification of all buildings into five different renovation categories applied on site: restoration, façade redesign, structural reinforcement, partial rebuilding, and new construction.
Instead of adopting a unified measure, an organic approach toward renewal was taken, which from the start has embraced the coexistence of a wide range of different building types and styles sitting next to each other, at the same time combining restoration with the injection of a new urban program.
Sustainability and Symbiosis
Respecting the local culture by using an environmentally sustainable and low impact approach to rehabilitating the site, we effectively use the original materials of the site such as tiles, brick, natural stone, and wood. We adaptively transform them into landscape elements.
Several moments are utilized in the landscape design: large wall falling into the water, wood ladder, roof garden, and Bruce Lee’s ancestral entrance garden. Those expansions and reconnections respect the local culture. The public space of Yongqing Fang is not only the renovation project of the neighborhood but also the creative prophecy of the outdoor activities.


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Before you start awkwardly mumbling the pop-goes-the-weasel ripoff, I’d like to inform you that there is no need to learn the quadratic formula song. We’ve been told how imperative it is to memorize this complex formula in order to find the roots of a quadratic, but if your memory fails to conjure it up, you can always do complete the square on the quadratic function: ax2 + bx + c = 0
This video goes over finding the quadratic formula.
The distance formula is actually just Pythagorean theorem wearing shades and a tapped mustache. The distance between two points on a coordinate plane can be utilized as the hypotenuse of a right triangle. We can draw the parameters of the triangle in aid of conceptualizing this. Down below is a video that further explains how to derive the values of our two legs of this invisible right triangle that floats on the coordinate plane.
Believe it or not, the distance formula can be derived from the Pythagorean theorem, here’s how!
Basics exponent rules are pretty straight forward and relatively easy to remember. Down below shows how these rules were finalized through the expansion of the terms.
This video goes over the basics of Exponents with proofs.
It’s imperative to understand compound interests in order to save money and not hire an accountant. But all jokes aside, this formula produces the result of the amount of money you end up having depending on the interest rate. It will help you manage personal finance and discover the best loan options.
This video goes over finding the formula for Annual Compound Interest.


Choose your favorite category
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Science-Fiction
Non-Fiction
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Science-Fiction
Non-Fiction
NEXT->


Building RingQt Applications for Mobile¶
In this chapter we will learn about Building RingQt Applications for Mobile.
Download Requirements¶
Check the next link : http://doc.qt.io/qt-5/androidgs.html
Download
The Android SDK Tools
The Android NDK (Tested using android-ndk-r10c)
Apache Ant v1.8 or later
Java SE Development Kit (JDK) v6 or later
Update the Android SDK¶
Update the Android SDK to get the API and tools packages required for development
Tested using Android 4.4.2 (API 19)
- In Windows - Define the next Environment Variables based on your system.
- JAVA_HOME
For Example : C:\Program Files (x86)\Java\jdk1.8.0_05
- ANDROID_HOME
For Example : B:\mahmoud\Tools\Java-Android\adt-bundle-windows-x86-20140702\sdk
Install Qt for Android¶
You can install Qt for Android from the next link
- Run Qt Creator, Select Tools > Options > Android to add the
Android NDK and SDK paths.
Using Ring2EXE¶
Starting from Ring 1.6 we can use Ring2EXE to quickly prepare Qt project for our application
Example:
ring2exe myapp.ring -dist -mobileqt
Note
We can use the Distribute Menu in Ring Notepad
Tip
The option ( Prepare Qt project for Mobile devices ) in the Distribute Menu
The Qt project for your Ring application¶
After using Ring2EXE or the Distribute Menu in Ring Notepad
Using the Qt Creator Open the generated Qt project
Folder : target/mobile/qtproject
Project file : project.pro
Using Qt Creator, You will find the compiled Ring application in the resources (YourAppName.ringo)
This file (Ring Object File) is generated by the Ring compiler using
ring YourAppName.ring -go -norun
- You can build your application using Qt Creator
You can add your application images to the resources
Or You can use any text editor (Notepad) and modify : project.qrc
To find images from your Ring application, You need to use the file name in resources
Example
if isandroid()
mypic = new QPixmap(":/cards.jpg")
else
mypic = new QPixmap("cards.jpg")
ok
Comments about developing for Android using RingQt¶
The main project file is main.cpp
This file load Ring Compiler/Virtual Machine and RingQt
Then get the Ring Object File during the runtime from the resources
Then run the Ring Object File (ringapp.ringo) using the Ring VM
Through main.cpp you can extract more files from the resources to temp. folder once you add them (create projects with many files).
The next functions are missing from this Ring edition
- Database (ODBC, SQLite & MySQL)
- Security and Internet functions (LibCurl & OpenSSL)
- RingAllegro (Allegro Library)
- RingLibSDL (LibSDL Library)
Just use Qt Classes through RingQt.
For database access use the QSqlDatabase Class
Note
All of the missing libraries ((LibCurl, OpenSSL & Allegro) can be compiled for Android, but they are not included in this Qt project.
- use if isandroid() when you want to modify the code just for android
Example:
if isandroid()
// Android code
else
// other platforms
ok
(4) Sometimes you will find that the button text/image is repeated in drawing ! it’s Qt problem that you can avoid using the next code.
if isandroid()
setStyleSheet("
border-style: outset;
border-width: 2px;
border-radius: 4px;
border-color: black;
padding: 6px;")
ok
- Always use Layouts instead of manual setting of controls position and size.
This is the best way to get the expected user interface to avoid problems like (controls with small/extra size)
- When you deal with Qt Classes you can determine the images from resources (you don’t need to copy them using main.cpp)
Example:
if isandroid()
mypic = new QPixmap(":/cards.jpg")
else
mypic = new QPixmap("cards.jpg")
ok
Now RingQt comes with the AppFile() function to determine the file name
Example:
mypic = new QPixmap(AppFile("cards.jpg")) # Desktop or Android
- When you update your project code, You don’t have to use Ring2EXE to generate the Qt project again
Just use the Distribute Menu in Ring Notepad and select (Generate Ring Object File)
Then copy the YourAppName.ringo file to target/mobile/qtproject folder and accept replacing files.
- If your application folder contains a Qt resource file (project.qrc)
Then when you use Ring2EXE or Ring Notepad (Distribute - Prepare Qt project for Mobile devices) the resource file will be used
See ring/applications/cards game as an example.


This map shows countries grouped into clusters of countries with similar constitutions.
There are 4 distinct clusters of countries corresponding to the Commonwealth, former Soviet Union, Iberian
and Francophone countries. These network clusters can be seen in a
network view


$10* for 30 minute introduction to Nordic Walking.
Includes:
*$10 for both individual and groups.
Presentations are suitable for Service Clubs and Social Groups and it is free of charge. I offer 30 minute presentations on the general background of Nordic Walking and the benefits Nordic Walking can have on your physical and mental health.
Contact me today to find out more about presentations and what I offer.
Enter your details in the form provided and I will contact you back via phone or email as soon as possible.


HTML-Hypertext Markup Language, a standardized system for tagging text files to achieve font, color, graphic, and hyperlink effects on World Wide Web pages.
HTTP -Hypertext Transfer Protocol. HTTP is the foundation of data communication for the World Wide Web.
To work with HTML you need to use some particular tags to express what you want in your text. You shouldn't be worried about to remember them all because you can always look up for them.
 Think about what you is writing correctly because your computer will understand you very exactly. And small typo can cause big problems.
Inline or Block
HTML elements can be inline or block. Block
elements form an "invisible box" around the informtion which you puted in them.


The changes
- Addressing performance. The first drop of 5.0
is 20% faster than release 4.3.
- Requires JDK 6.0. 6u10 is the only VM that is being
used for tests.
- SubstanceLookAndFeel class is now abstract and does not
have any public constructors. Use a core skin-based LAF, extend to
set a custom skin or use static setSkin method.
- Removed support for pluggable text rasterizers
(substance-bramble plugin based on SWT). 6u10 addresses poor
Segoe UI rendering on Vista.
- Reworked notion of themes. Split the functionality of SubstanceTheme,
SubstanceComplexTheme and SubstanceSkin into SubstanceColorScheme,
SubstanceColorSchemeBundle and SubstanceSkin. Tweaked most of the
core skins to take advantage of the new more flexible and straighforward
APIs.
- Removed most of the VM flags. The only two flags are for tracing memory
allocation and heap status. Use skin / image watermark / configuration
APIs.
- Reconciled APIs that were added to address specific
user requests and did not result in a consistent API. This
should make making a custom skin / tweaking default
Substance behavior easier and more straightforward.
- New client property to provide per-window skinning.
Use different skins for different top-level windows.
- Removed the client properties and APIs for setting themes and
painters on per-component level. Use colorization client property and
per-window skins for custom tweaking.
- The new default mode does not add any extra UI elements
(menu search, lock icons etc).
- Removed performance-adverse functionality of custom overlay
and composites. Was unintentionally broken since release 4.2 and
intentionally unfixed in both 4.2 and 4.3. A single complaint shows
that this functionality is of miniscule use.
- Removed combobox auto-completion. Use SwingX, GlazedLists or JIDE.
- Moved UI debug mode (additional context menu on title panes and
scroll bars) to a substance-tools plugin
- Consolidated existing theme pack, button shaper pack
and watermark pack into substance-extras plugin
- New SubstanceApi annotation marks public APIs (classes, constructors,
methods)
- Internal restructuring of the packages to make the codebase
more organized.
Java SE 6 specific functionality
- Multi-stop linear gradients
- Support for sort arrow icons on table headers
- Linux-specific bug fix on dragging window by title pane


--- layout: tips permalink: /tips/example title: Example description: This is an example published-url: https://github.com/ tags: ['tip'] require: ['modal', 'menu', 'nav-bar', 'arrow-up'] --- {% include work-head.html %} {% include work-about.html %}
Example text
{% include work-screencaps.html %}{% include work-modal.html %} {% include work-code.html %} {% include work-bugs.html %}


s6
Software
skarnet.org
The s6-fdholder-storec program
s6-fdholder-storec talks to a
fd-holding daemon on its open
descriptors 6 and 7, and passes it a copy of its standard
input.
Interface
s6-fdholder-storec [ -T fdtimeout ] [ -t timeout ] id
- s6-fdholder-storec expects to talk to a listening
s6-fdholderd program on its descriptors 6 and 7.
- It attempts to pass a copy of its fd 0 (standard input) to s6-fdholderd,
with identifier id.
- It then exits 0, or 1 if the server returned an error.
Options
- -T fdtimeout : the descriptor is stored with
an expiration time of fdtimeout milliseconds, which means the
s6-fdholderd daemon will close and get rid of
the descriptor after that time. By default, fdtimeout is 0, which
means infinite - no expiration time.
- -t timeout : if the operation cannot be
processed in timeout milliseconds, then fail with an error message.
Communications with the server should be near-instant, so this option is
only here to protect users against programming errors.
Notes
- s6-fdholder-storec is the "internal" version of
s6-fdholder-store. It simply
expects to be run as a UCSPI client, i.e. talking to the server
over descriptors 6 and 7, instead of connecting to the server
itself.
- The error messages for the s6-fdholder suite are explained
here.


For this HIT to work correctly, you must:
not be on a mobile device
be using a recent version of Chrome
have javascript enabled


{% extends "base.html" %} {% load staticfiles %} {% block title %}{% load strip_up %}{{ X |strip_up:request.path }}{% endblock %} {% block content %}
The sobering tale of the World's Most Ridiculously Hairy Man. A musical adaptation.
What happens when a notorious recluse has a chance encounter and falls in love? Not only was that rhetorical, but you will see why Sasquatch have been become the #1 musical of the pre-teen generation.
Not since that disney movie that spawned the ear worm about letting it go has a musical managed to to make you want to buy so much merchandise while entertaining murderous thoughts for the producers. Sasquatch The Musical will not leave you wanted more, just leave you wanted to get away to buy all the awesome collector's edition stuff.
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo
consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
NAU Ardrey Auditorium
Two hours with One intermission
Ages 16+


The following items area configurable
| Attribute | Usage | Effect | Notes |
|---|---|---|---|
| width | width="200" |
Sets the width of the gauge | The width should be at least twice the height |
| height | height="200 |
Sets the height of the gauge | The height should be at least half of the width |
| min-value | min-value="0" |
Sets the minimum value for the scale | |
| max-value | max-value="200" |
Sets the maximum value for the scale | |
| start-color | start-color="green" |
Sets the initial color for your gauge | You can keep the gauge one color by setting both the start and end to the same value |
| end-color | end-color=""red |
Sets the end color for your gauge | |
| sections | Optional sections="[[0,10],[10,15],[15,30]]" or
sections="angularProperty" |
Optionally sets the sections in your gauge | Use a javascript style array of start and end pairs or bind to an angular property that represents an array of start end pairs. If left blank, you will be given 10 evenly sized sections based on your min/max values |
| border | border="true" |
Use an svg rectangle as a border. Defaults to false if no value is given | |
| value | value="10" or value="angularProperty" |
Sets the value of your gauge | If you bind to an angular property the needle will update as the value changes |
It's very simple to create a static gauge, just don't bind any of the attributes to an angular model

Use the number selector to change the value of the gauge


ActiveJS Tests
Test Group:
All
ActiveController
ActiveEvent
ActiveRecord
ActiveRoutes
ActiveSupport
ActiveView
ActiveRecord Adapter:
Titanium
Logging?
Check console for test results.


|
XLabs
Cross-platform reusable C# libraries
|
Public Member Functions | |
| override void | Flush () |
| Cleans memory. More... | |
| override void | Serialize< T > (T obj, Stream stream) |
| Serializes object to a stream More... | |
| override T | Deserialize< T > (Stream stream) |
| Deserializes stream into an object More... | |
| override object | Deserialize (Stream stream, Type type) |
| Deserializes stream into an object. More... | |
| override T | Deserialize< T > (string data) |
| Deserializes string into an object. More... | |
| override string | Serialize< T > (T obj) |
| Serializes object to a string. More... | |
| Public Member Functions inherited from XLabs.Serialization.StreamSerializer | |
| virtual byte[] | SerializeToBytes< T > (T obj) |
| Serializes object to a string. More... | |
| virtual T | Deserialize< T > (byte[] data) |
| Deserializes string into an object. More... | |
| object | Deserialize (byte[] data, System.Type type) |
| Deserializes string into an object. More... | |
| abstract void | Serialize< T > (T obj, System.IO.Stream stream) |
| Serializes object to a stream. More... | |
| abstract T | Deserialize< T > (System.IO.Stream stream) |
| Deserializes stream into an object. More... | |
| abstract object | Deserialize (System.IO.Stream stream, Type type) |
| Deserializes stream into an object. More... | |
| object | Deserialize (string data, Type type) |
| Deserializes string into an object. More... | |
| Public Member Functions inherited from XLabs.Serialization.IByteSerializer | |
| object | Deserialize (byte[] data, Type type) |
| Deserializes byte array into an object. More... | |
Properties | |
| override SerializationFormat | Format [get] |
| Properties inherited from XLabs.Serialization.StreamSerializer | |
| abstract SerializationFormat | Format [get] |
| Gets the serialization format. More... | |
| Properties inherited from XLabs.Serialization.ISerializer | |
| SerializationFormat | Format [get] |
| Gets the serialization format. More... | |
| override object XLabs.Serialization.SystemXmlSerializer.Deserialize | ( | Stream | stream, |
| Type | type | ||
| ) |
Deserializes stream into an object.
| stream | Stream to deserialize from. |
| type | Type of object to deserialize. |
Implements XLabs.Serialization.IStreamSerializer.
| override T XLabs.Serialization.SystemXmlSerializer.Deserialize< T > | ( | Stream | stream | ) |
Deserializes stream into an object
| T | Type of object to serialize to |
| stream | Stream to deserialize from |
Implements XLabs.Serialization.IStreamSerializer.
|
virtual |
Deserializes string into an object.
| T | Type of object to serialize to. |
| data | Serialized object. |
Reimplemented from XLabs.Serialization.StreamSerializer.
|
virtual |
Cleans memory.
Implements XLabs.Serialization.StreamSerializer.
| override void XLabs.Serialization.SystemXmlSerializer.Serialize< T > | ( | T | obj, |
| Stream | stream | ||
| ) |
Serializes object to a stream
| obj | Object to serialize |
| stream | Stream to serialize to |
Implements XLabs.Serialization.IStreamSerializer.
|
virtual |
Serializes object to a string.
| T | Type of object to serialize. |
| obj | Object to serialize. |
Reimplemented from XLabs.Serialization.StreamSerializer.
|
get |


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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Lead Menager
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum. Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
Web Developer
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Software Developer
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.


Coyote Comparison Results
Output From Tab 1
Output From Tab 2
Comparison Output Page 1
Comparison Output Page 1
Comparison Output Page 2


Experienced in retail and e-commerce operation. Team player who collaborates across multiple teams and with stakeholders to drive digital transformation.
... and counting.
Group project created using Ruby / Rails / Javascript / HTML / SCSS / Bootstrap / PostgreSQL.
Project created using MVC Framework / RESTful API / Node.js / Express.js / React.js / PostgreSQL / AJAX / Heroku / Cloudinary.


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At Turtle Resort, there is so much to do and see within walking distance. Just down the street, at Ohana Surf Shop, you can rent stand-up paddleboards, surfboards, boogie boards, or just about anything else for a water activity you can think of. There are also remarkable snorkeling and diving destinations within a 15-minute drive.  Well, you're in luck! 100% Kona coffee, only found on the Big Island, is produced on numerous farms that are located a stones-throw away from the resort.