class CacheBasedSessionHandler implements SessionHandlerInterface
Methods
| void |
__construct(Repository $cache, int $minutes)
Create a new cache driven handler instance. |
|
|
open($savePath, $sessionName)
{@inheritDoc} |
||
|
close()
{@inheritDoc} |
||
|
read($sessionId)
{@inheritDoc} |
||
|
write($sessionId, $data)
{@inheritDoc} |
||
|
destroy($sessionId)
{@inheritDoc} |
||
|
gc($lifetime)
{@inheritDoc} |
||
| Repository |
getCache()
Get the underlying cache repository. |
Details
at line 28
public void
__construct(Repository $cache, int $minutes)
Create a new cache driven handler instance.
at line 37
public
open($savePath, $sessionName)
{@inheritDoc}
at line 45
public
close()
{@inheritDoc}
at line 53
public
read($sessionId)
{@inheritDoc}
at line 61
public
write($sessionId, $data)
{@inheritDoc}
at line 69
public
destroy($sessionId)
{@inheritDoc}
at line 77
public
gc($lifetime)
{@inheritDoc}
at line 87
public Repository
getCache()
Get the underlying cache repository.


CDS
is an Agile Software Outsourcing Company in Vietnam
Our website is under construction. Please comeback later!


A Paragraph.
This paragraph will change it's color.
5 + 6 =
Finnish alphabet has letters.
Life is awful!
Array list with for loop:

How old are you?
Test error messages.


Type: Minor scuffle or petty theft.
Difficulty Class: DC 10
CHECKS
The Captain: Charisma (Intimidation)
First Mate: Charisma (Intimidation)
Cook: Intelligence (brewer's supplies)
Crew: [[1d20]]
CHECK RESULTS
Total Success: The crew's quality score increases by 1 for [[1d4]] days and the hazard ends.
Success: The hazard ends.
Failure: The crew's quality score decreases by 1.
Total Failure: The crew's quality score decreases by 1, and the crew immediately mutinies.
Type: Brawl involving several people, theft of a valuable.
Difficulty Class: DC 15
CHECKS
The Captain: Charisma (Intimidation)
First Mate: Charisma (Intimidation)
Cook: Intelligence (brewer's supplies)
Crew: [[1d20]]
CHECK RESULTS
Total Success: The crew's quality score increases by 1 for [[1d4]] days and the hazard ends.
Success: The hazard ends.
Failure: The crew's quality score decreases by 1.
Total Failure: The crew's quality score decreases by 1, and the crew immediately mutinies.
Type: Large brawl resulting in several injuries, theft of a prized item.
Difficulty Class: DC 20
CHECKS
The Captain: Charisma (Intimidation)
First Mate: Charisma (Intimidation)
Cook: Intelligence (brewer's supplies)
Crew: [[1d20]]
CHECK RESULTS
Total Success: The crew's quality score increases by 1 for [[1d4]] days and the hazard ends.
Success: The hazard ends.
Failure: The crew's quality score decreases by 1.
Total Failure: The crew's quality score decreases by 1, and the crew immediately mutinies.
Type: Murder, serious brawl involving most of the crew.
Difficulty Class: DC 25
CHECKS
The Captain: Charisma (Intimidation)
First Mate: Charisma (Intimidation)
Cook: Intelligence (brewer's supplies)
Crew: [[1d20]]
CHECK RESULTS
Total Success: The crew's quality score increases by 1 for [[1d4]] days and the hazard ends.
Success: The hazard ends.
Failure: The crew's quality score decreases by 1.
Total Failure: The crew's quality score decreases by 1, and the crew immediately mutinies.


---
title: "Download Scrubs With Heart Logo
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date: "2021-01-10"
categories:
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featuredImage: "https://www.uniformsnw.com/wp-content/uploads/2020/01/SPRING-2020-SP_1_1080x1080.jpeg"
featured_image: "https://cdn.pulseuniform.com/images/emb-cate-1.jpg"
image<EMAIL_ADDRESS>---
Download Scrubs With Heart Logo
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Alibaba.com offers 8,139 scrubs with logo products. Sign up to receive the latest information, news, exclusive invites and other good stuff from heartsoul. 20,443 likes · 1,209 talking about this.
Logo design for online company named open heart scrubs that sells scrubs to different personnel in need of one.
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Routes Controller¶
Controller Hub, help you import other controllers as childs.
Include¶
Load an imported controller, and initializes it with the right data, takes 3 parameters (controller | any
, url? | string
, namespace? | string
)
Example¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | import Routes from "../../abstract/controllers/routes";
import IndexController from "./controllers/index";
/**
* Centralized Controller Routes Loader
*
* @return RoutesController
*/
export class Urls extends Routes {
/*** Configure routes */
protected init(): void {
this.include(IndexController);
}
}
|


Format: EditTextColor red green blue alpha,
Example: EditTextColor 1.0 1.0 1.0,
Purpose: Defines the color of the EditBox's Text. The alpha value can be skiped then it is 1.0. FontColor inside EditTextFrame is stronger.
Used in: EDITBOX, GLUEEDITBOX, SLASHCHATBOX


My Story
I was born in a little town just 20 minutes south of Montgomery, AL called Hayneville, AL.
As a child I played a lot in the wood surrounding my parents property which taught me a lot about the outdoors and how
to appreciate it. After my father died when I was 12 I moved to Auburn, AL to finish out my school years.
After finishing high school I moved to Seattle, WA on June 12, 2012. I came here for no particulary reason other
than to visit my uncle who I had'nt seen in 12 years at the time. Being a young man coming from the south I found Seattle
rather weird and awkward, but this place did have a lot to offer me.
Shortly after moving to Seattle I started college at Seattle Central where I met most of the friends I have today.
It was a very hard transition from a simple life back to the fast paced life in the city trying to balance work and school.
Aside from it being difficult it was also very interesting to see new things and meet new people in my time there. now I am putting myself to the test by becoming apart of code follows and trying to grow as much as I can while I am here as well.


tensorgraph package¶
Subpackages¶
- tensorgraph.dataset package
- tensorgraph.layers package
- Submodules
- tensorgraph.layers.activation module
- tensorgraph.layers.backbones module
- tensorgraph.layers.cast module
- tensorgraph.layers.conv module
- tensorgraph.layers.linear module
- tensorgraph.layers.merge module
- tensorgraph.layers.misc module
- tensorgraph.layers.noise module
- tensorgraph.layers.normalization module
- tensorgraph.layers.recurrent module
- tensorgraph.layers.sampling module
- tensorgraph.layers.template module
- Module contents
- tensorgraph.models_zoo package
Submodules¶
tensorgraph.cost module¶
-
tensorgraph.cost.
binary_f1
(ytrue, ypred)[source]¶ ytrue and ypred is one-hot, and have to be of type int and shape [batchsize, 2] since it is binary, the values must be 0 and 1
-
tensorgraph.cost.
binary_precision
(ytrue, ypred)[source]¶ ytrue and ypred is one-hot, and have to be of type int and shape [batchsize, 2] since it is binary, the values must be 0 and 1
-
tensorgraph.cost.
binary_recall
(ytrue, ypred)[source]¶ ytrue and ypred is one-hot, and have to be of type int and shape [batchsize, 2] since it is binary, the values must be 0 and 1
-
tensorgraph.cost.
generalised_dice
(y_true, y_pred, smooth=1.0)[source]¶ Dice Loss Cost Function, where smaller groundtruth channels is penalised more heavily y_true: tensor of shape (?, D, H, W, c)
tensorgraph.data_iterator module¶
-
class
tensorgraph.data_iterator.
DataBlocks
(data_paths, train_valid_ratio=[5, 1], batchsize=32, load_func=<function np_load_func>, allow_preload=False)[source]¶ Bases:
tensorgraph.data_iterator.SimpleBlocks
This is class for processing blocks of data, whereby dataset is loaded and unloaded into memory and split into train and valid one block at a time.
Parameters: - data_paths (list or list of list) –
contains list of paths for data loading, .. rubric:: example
[f1a.npy, f1b.npy, f1c.npy] or [(f1a.npy, f1b.npy, f1c.npy), (f2a.npy, f2b.npy, f2c.npy)]
- train_valid_ratio (list) – ratio for splitting the data into train and valid
- load_func (function) – function for loading the data_paths, default to numpy file loader
- allow_preload (bool) – by allowing preload, it will preload the next data block while training at the same time on the current datablock, this will reduce time but will also cost more memory.
- data_paths (list or list of list) –
-
class
tensorgraph.data_iterator.
DataIterator
(*data, **params)[source]¶ Bases:
object
Parameters: fullbatch (bool) – decides if the number of examples return after every iteration should be always a full batch.
-
class
tensorgraph.data_iterator.
SequentialIterator
(*data, **params)[source]¶ Bases:
tensorgraph.data_iterator.DataIterator
Example
when
batchsize = 3
then the generated index will be[0, 1, 2], [3, 4, 5], [6, 7, 8]
Parameters: fullbatch (bool) – decides if the number of examples return after every iteration should be always a full batch.
-
class
tensorgraph.data_iterator.
SimpleBlocks
(data_paths, batchsize=32, load_func=<function np_load_func>, allow_preload=False)[source]¶ Bases:
object
This is class for processing blocks of data, whereby dataset is loaded and unloaded into memory one block at a time.
Parameters: - data_paths (list or list of list) –
contains list of paths for data loading, .. rubric:: example
[f1a.npy, f1b.npy, f1c.npy] or [(f1a.npy, f1b.npy, f1c.npy), (f2a.npy, f2b.npy, f2c.npy)]
- load_func (function) – function for loading the data_paths, default to numpy file loader
- allow_preload (bool) – by allowing preload, it will preload the next data block while training at the same time on the current datablock, this will reduce time but will also cost more memory.
-
nblocks
¶
- data_paths (list or list of list) –
-
class
tensorgraph.data_iterator.
StepIterator
(*data, **params)[source]¶ Bases:
tensorgraph.data_iterator.DataIterator
Example
when
batchsize = 3
step = 1
then the generated index wil be[0, 1, 2], [1, 2, 3], [2, 3, 4]
tensorgraph.graph module¶
tensorgraph.node module¶
-
class
tensorgraph.node.
EndNode
(prev, input_merge_mode=<tensorgraph.node.NoChangeState object>)[source]¶ Bases:
object
EndNode is where we want to get the output from the graph. It can be connected to a HiddenNode or a StartNode.
Parameters: - input_merge_mode (tensorgraph.layers.Merge) –
Merge
Layer for merging the multiple inputs coming into this hidden node - prev (list) – list of previous nodes to link to
- input_merge_mode (tensorgraph.layers.Merge) –
-
class
tensorgraph.node.
HiddenNode
(prev, input_merge_mode=<tensorgraph.node.Sum object>, layers=[])[source]¶ Bases:
object
HiddenNode encapsulates a list of layers, it can be connected to a StartNode or another HiddenNode
Parameters: - input_merge_mode (tensorgraph.layers.Merge) –
Merge
Layer for merging the multiple inputs coming into this hidden node - layers (list) – the sequential layers within the node
- prev (list) – list of previous nodes to link to
-
variables
¶
- input_merge_mode (tensorgraph.layers.Merge) –
-
class
tensorgraph.node.
NoChangeState
[source]¶ Bases:
object
tensorgraph.progbar module¶
tensorgraph.sequential module¶
tensorgraph.stopper module¶
-
class
tensorgraph.stopper.
EarlyStopper
(max_epoch=100, epoch_look_back=None, percent_decrease=None)[source]¶ Bases:
object
Use for doing early stopping during training
Parameters: - max_epoch (int) – if the training reach the
max_epoch
, it stops - epoch_look_back (int) – the number of epoch to look to check the percent decrease in validation loss. If the percent decrease is smaller than the desired value within the number of epoch look back then it stops
- percent_decrease (float) – between
0 to 1.0
, if within theepoch_look_back
and the decrease in validation error is smaller than this percentage then it stops.
-
continue_learning
(valid_error, epoch=None)[source]¶ check if should continue learning, by default first epoch starts with 1.
Parameters: - valid_error (float) – validation error to be keep track by early stopper, smaller is better
- epoch (int) – the training epoch, if not specified, the stopper will auto keep track
- max_epoch (int) – if the training reach the
tensorgraph.trainobject module¶
tensorgraph.utils module¶
-
class
tensorgraph.utils.
MakeTFRecords
[source]¶ Bases:
object
general framework for creating and reading tfrecords
-
static
make_tfrecords_from_arrs
(save_path, dtype=<class 'numpy.float32'>)[source]¶ Parameters: data_records – dictionary of arrs of format {‘X’: X_arr, ‘y’: y_arr, …} where first dimension of the array is the example number.
-
static
read_and_decode
(data_shapes, batch_size, dtype=tf.float32, capacity=None, min_after_dequeue=None)[source]¶ Parameters: - tfrecords_filename_list (list) – list of tfrecords paths
- data_shapes (dict) – dictionary of the record name and shape example: {‘X’:[32,32], ‘y’:[10]} where the shape is the shape per image
-
static
-
class
tensorgraph.utils.
MakeTFRecords_tfdata
[source]¶ Bases:
object
general framework for creating and reading tfrecords using tf.data API
-
static
make_tfrecords_from_arrs
(save_path, dtypes=[<class 'numpy.float32'>, <class 'numpy.float32'>])[source]¶ Make tfrecords from numpy array (and list of string (patientnames etc.))
Parameters: - data_records – dictionary. contains data to be made into tfrecords. Can be numpy array.
- save_path – string. Path to save tfrecords.
- dtypes – list of dtype in numpy. Should have same length of data_records.
Returns: None
-
static
read_tensor_from_tfrecords
(tfrecords_filenames, data_shape, dtypes=[tf.float32, tf.float32], batch_size=8, num_epochs=1, phase='training')[source]¶ Read tensors from numpy array (and list of string (patientnames etc.))
Parameters: - n_exp – total number of sample of all the tfrecords_filenames
- tfrecords_filenames – list of tfrecords files
- data_shape – dictionary of data shape
- dtypes – list of dtypes in tf.
- batch_size – integer. batch size.
- num_epochs – integer. number of epochs.
- phase – string. Either “training” or “validation”. To determine whether shuffle.
Returns: trainiterator – an iterator. To be initialize later! element: dictionary of data.
-
static
-
tensorgraph.utils.
cat_to_num
(cat, start_idx=0)[source]¶ Convert categorical values to numeric values
Parameters: cat (list or 1d array) – an array of categorical values Returns: return numeric list and a categorical dictionary map
-
tensorgraph.utils.
desame
(in_height, in_width, stride, kernel_size)[source]¶ Calculate the input height and width from output height and width for deconvolution with
SAME
paddingParameters: - in_height (int) – input height
- in_width (int) – input width
- stride (tuple) – tuple of (h, w)
- kernel_size (tuple) – tuple of (h, w)
Example
>>> out_height = ceil(in_height * float(stride[0])) >>> out_width = ceil(in_width * float(stride[1]))
-
tensorgraph.utils.
desame_nd
(shape, stride, kernel_size)[source]¶ Calculate the input height and width from output height and width for deconvolution with
SAME
padding for arbitrary n dimensional imageParameters: - shape (list of int) – list of dimensions of n dimensional image
- stride (list of int) – list of same dimension as shape
- kernel_size (list of int) – list of same dimension as shape
Example
>>> out_shape[0] = ceil(in_height * float(stride[0])) >>> out_shape[1] = ceil(in_width * float(stride[1])) >>> ..
-
tensorgraph.utils.
devalid
(in_height, in_width, stride, kernel_size)[source]¶ Calculate the input height and width from output height and width for deconvolution with
VALID
paddingParameters: - in_height (int) – input height
- in_width (int) – input width
- stride (tuple) – tuple of (h, w)
- kernel_size (tuple) – tuple of (h, w)
Example
>>> out_height = ceil(in_height * float(stride[0])) - 1 + kernel_size[0] >>> out_width = ceil(in_width * float(stride[1])) - 1 + kernel_size[1]
-
tensorgraph.utils.
devalid_nd
(shape, stride, kernel_size)[source]¶ calculate the input height and width from output height and width for deconvolution with
VALID
padding for arbitrary n dimensional imageParameters: - shape (list of int) – list of dimensions of n dimensional image
- stride (list of int) – list of same dimension as shape
- kernel_size (list of int) – list of same dimension as shape
Example
>>> out_shape[0] = ceil(in_height * float(stride[0])) - 1 + kernel_size[0] >>> out_shape[1] = ceil(in_width * float(stride[1])) - 1 + kernel_size[1]
-
tensorgraph.utils.
make_one_hot
(X, onehot_size)[source]¶ Make a one-hot version of X
Parameters: - X – 1d numpy with each value in X representing the class of X
- onehot_size – length of the one hot vector
Returns: 2d numpy tensor, with each row been the onehot vector
-
tensorgraph.utils.
put_kernels_on_grid
(kernel, num_kernels, pad=1)[source]¶ Visualize conv. features as an image (mostly for the 1st layer). Place kernel into a grid, with some paddings between adjacent filters.
Parameters: - kernel – tensor of shape [NumKernels, Y, X, NumChannels]
- grid_X) ((grid_Y,) – shape of the grid. Require: NumKernels == grid_Y * grid_X User is responsible of how to break into two multiples.
- num_kernels – batchsize or number of kernels
- pad – number of black pixels around each filter (between them)
Returns: Tensor of shape [(Y+2*pad)*grid_Y, (X+2*pad)*grid_X, NumChannels, 1].
-
tensorgraph.utils.
same
(in_height, in_width, stride, kernel_size)[source]¶ Calculate the output dimension of an input image during pooling or convolution using padded with
SAME
modeParameters: - in_height (int) – input height
- in_width (int) – input width
- stride (tuple) – tuple of (h, w)
- kernel_size (tuple) – tuple of (h, w)
Example
>>> pad_along_height = ((out_height - 1) * stride[0] + filter_height - in_height) >>> pad_along_width = ((out_width - 1) * stride[1] + filter_width - in_width)
-
tensorgraph.utils.
same_nd
(shape, stride, kernel_size)[source]¶ Calculate the output dimension of an input image during pooling or convolution when padded with
SAME
mode for arbitrary n dimensional array.Parameters: - shape (list of int) – list of dimensions of n dimensional image
- stride (list of int) – list of same dimension as shape
- kernel_size (list of int) – list of same dimension as shape
Returns: return the new dimension of the image after convolve with
stride
andkernel_size
-
tensorgraph.utils.
valid
(in_height, in_width, stride, kernel_size)[source]¶ Describe the output dimension of an input image during pooling or convolution using padded with
VALID
modeParameters: - in_height (int) – input height
- in_width (int) – input width
- stride (tuple) – tuple of (h, w)
- kernel_size (tuple) – tuple of (h, w)
Example
>>> pad_along_height = 0 >>> pad_along_width = 0
-
tensorgraph.utils.
valid_nd
(shape, stride, kernel_size)[source]¶ Calculate the output dimension of an input image during pooling or convolution when padded with
VALID
mode for arbitrary n dimensional array.Parameters: - shape (list of int) – list of dimensions of n dimensional image
- stride (list of int) – list of same dimension as shape
- kernel_size (list of int) – list of same dimension as shape
Returns: return the new dimension of the image after convolve with
stride
andkernel_size


“If there is magic on this planet, it is contained in water.” — Loren Eiseley
Any alien life visiting us would be extremely confused about the fact that our planet is called the Earth, even though it is 2/3rd water. In this water there are creatures of magical existence, unfathomable in awe and undeniably alien to land-based life.
Whales, dolphins, and porpoises together are called cetaceans. Cetaceans evolved from "hoofed" terrestrial ancestors and they share common features like fins, tails and blowholes, making them appear more like fish than their closest relative, the Hippo.
Cetaceans are not fish. They are mammals just like humans. They breathe air and are warm blooded. It is difficult for us (being land based mammals) to understand cetaceans like we understand elephants or tigers, but their lore and enchantment has driven many to pursue a career in cetacean research and uncover many of their secrets. Thank god for such people!
Cetaceans come in all shapes and sizes and are found all around the planet. Here, we have created interactive maps to help visualise and compare the distribution of different species of whales, dolphins, and porpoises, while also providing a size-wise comparison of them.


Vendor Name:
AFFINITY PRODUCTIONS
Reference Number:
6017627
Contract Date:
2015-01-05
Description of Work:
0232 Image/Video Communications Services (includes Telepresence/Video Conferencing Services)
Contract Period:
2015-01-05 to 2018-31-03
Delivery Date:
2018-31-03
Original Contract Value:
$225,997.74
Current Contract Value:
$225,997.74
Comments:
This contract was competitively sourced.This contract is a multi-year contract.
Additional Comments:
Transcription
Former Public Servant in Receipt of a Pension under the Public Service Superannuation Act:
No


Velvet Goldmine
Velvet Goldmine is a movie starring Ewan McGregor, Jonathan Rhys Meyers, and Christian Bale. In 1984, British journalist Arthur Stuart investigates the career of 1970s glam superstar Brian Slade, who was heavily influenced in his...


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Kyle Newport
Edit Teams


Here you will find the technical aspects covered in my sprint journey
Here, you'll find the reflections and exercises I used to enhance my soft skills.
Here's what you didn't know
Kia ora!
Kia ora and welcome! This little wee blog is dedicated to some of the key learnings and skills I've picked up on
and aiming to master while here at EDA. I can't wait to share what I've got with you.
Thank you so much for joining me!
Xoxo


Application submitted
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We will email you with our decision and further instructions within 10 working days.


|
|
| Person () |
| | Constructor of the class.
|
| |
| | Person (std::string name, int age, std::string gender) |
| | Constructor of the class. More...
|
| |
| std::string | getName () const |
| | Getter of the name of the person. More...
|
| |
| virtual void | storeInfo (std::ofstream &outfile, int &indentation) |
| | Store in the information of the Person to a file. More...
|
| |
| virtual void | loadClass (std::ifstream &inpfile) |
| | Reading the information of a User from a file. More...
|
| |
|
virtual void | show () const |
| | Showing the information of the person on the screen.
|
| |
|
virtual void | cleanVectors () |
| | cleaning all vectors inside the person
|
| |
| bool | operator== (const Person &p1) |
| | Comparing two People. More...
|
| |
| void | editGender (std::string gender) |
| | Changing the gender of said person. More...
|
| |
| void | editAge (int age) |
| | Changing the age of said person. More...
|
| |
| void | editName (std::string name) |
| | Changing the name of said person. More...
|
| |
The class that stores the information of the people in the company


{% extends "base.html" %} {% block content %}
Price: ${{api.latestPrice}}
Previous Close: {{api.previousClose}}
Market Cap: {{api.marketCap}}
YTD Change: {{api.ytdChange}}
52wk High: {{api.week52High}}
52 wk low: {{api.week52Low}}
{# {% for key, item in api.items %} #} {# {{key}}: {{item}}


Query Converter online
Design, ER diagrams and database schemas
Functional Dependencies, Keys and Normalizations
3NF and 4NF analysis
SQL to Relational Algebra
SQL interpreter
XML validation and XPath queries


There are two main interfaces in Objenesis:
interface ObjectInstantiator { Object newInstance(); }
interface InstantiatorStrategy { ObjectInstantiator newInstantiatorOf(Class type); }
Note: All Objenesis classes are in the
org.objenesis
package.
There are many different strategies that Objenesis uses for instantiating objects based on the JVM vendor, JVM version, SecurityManager and type of class being instantiated.
We have defined that two different kinds of instantiation are required:
readResolve()
) are not called and we never check if the object is serializable.The simplest way to use Objenesis is by using ObjenesisStd (Standard) and ObjenesisSerializer (Serializable compliant). By default, automatically determines the best strategy - so you don't have to.
Objenesis objenesis = new ObjenesisStd(); // or ObjenesisSerializer
Once you have the Objenesis implementation, you can then create an ObjectInstantiator
, for a specific type.
ObjectInstantiator thingyInstantiator = objenesis.getInstantiatorOf(MyThingy.class);
Finally, you can use this to instantiate new instances of this type.
MyThingy thingy1 = (MyThingy)thingyInstantiator.newInstance(); MyThingy thingy2 = (MyThingy)thingyInstantiator.newInstance(); MyThingy thingy3 = (MyThingy)thingyInstantiator.newInstance();
To improve performance, it is best to reuse the ObjectInstantiator
objects as much as possible. For example, if you are instantiating multiple instances of a specific class,
do it from the same ObjectInstantiator
.
Both InstantiatorStrategy
and ObjectInstantiator
can be shared between multiple
threads and used concurrently. They are thread safe.
(For the impatient)
Objenesis objenesis = new ObjenesisStd(); // or ObjenesisSerializer MyThingy thingy1 = (MyThingy) objenesis.newInstance(MyThingy.class); // or (a little bit more efficient if you need to create many objects) Objenesis objenesis = new ObjenesisStd(); // or ObjenesisSerializer ObjectInstantiator thingyInstantiator = objenesis.getInstantiatorOf(MyThingy.class); MyThingy thingy2 = (MyThingy)thingyInstantiator.newInstance(); MyThingy thingy3 = (MyThingy)thingyInstantiator.newInstance(); MyThingy thingy4 = (MyThingy)thingyInstantiator.newInstance();


{{.Title}}
Extend CloudFormation with plugins
Write Native CloudFormation
Kombustion builds upon native CloudFormation, so you don't need to learn a new syntax or DSL.
Extend with Plugins
Kombustion uses plugins to preprocess and extend your CloudFormation templates.
Upgrade Incrementally
Kombustion can be adopted incrementally, instead of requiring a full rewrite of your infrastructure as code.
Decentralised Plugins
Kombustion plugins can be installed from a plugins Github repository.
Cross-Platform
Kombustion works on Linux, FreeBSD and MacOS.
License
Kombustion is absolutely free for personal or commercial use, under MIT


{% extends 'base.html' %} {% block content %}
This script will go through your library and add a 4k banner to your posters. Configurable options include:

This script will go through your TV Shows and add a 4k banner to your posters. Configurable options follow the same options as your Movie posters.
Much like the 4K poster script, this will go through your films and add a 3D banner to your films. Currently as Plex has no support for labelling content as 3D this will only work for 3D films keptin a separate library. You can configure the script to have full width banners or the mini corner banners.
If you don't like the banners or want to try a different style, this script will restore your posters. First it will try to restore from a local backup, if this isn't enabled or a poster doesn't have a local backup file for whatever reason it will fallback to themoviedb to find a poster for you.
You must sign up for and account on TheMoviedb to get an api key for this to work.
If you don't like the banners or want to try a different style, this script will restore your posters. First it will try to restore from a local backup, if this isn't enabled or a poster doesn't have a local backup file for whatever reason it will fallback to themoviedb to find a poster for you.
You must sign up for and account on TheMoviedb to get an api key for this to work.
For those of you who don't separate your 4K and non-4K content into separate libraries. This script will go through your films library and add an "Untranscodable" tag to any of your films that only have a single 4K copy of the film available. By adding this tag to your users restrictions, they simply won't see these 4K films giving your hardware a break.
If your hardware is powerful enough, there is the option to create an optimized version so that your users can play this version. This will send your film to be optimized with the Plex optimize feature baked directly into Plex. This means all the hardwork is done by your plex server, (useful if you're hosting these scripts on a lower powered device.) Once the transcode has finished, the untranscodable tag will be removed the next time the script runs and your users will be able to see the film.
This script will create a "Popular", "Top Rated" and a "Recommended" collection. The idea being that you should set these collections to be visible on your and your users home pages for easier content discovery.
As the films in these collections will change over time this is designed to be a good place to find content quickly, without having to dig through your library. Especially useful for those with large libraries.
The "Top Rated" and "Popular" Collections are based on the TheMovieDb user votes. The Reccomended collection takes the film with the highest play count from your server and shows recommended movies based on that film.
This is now the script that will create the Disney, Pixar and Marvel Cinematic Universe collections. These can be individually toggled in the config options page.
This script will go through your TV posters and blur the thumbnail for all the unwatched episodes. A Backup will be made in the database and restored once the episode has been watched.
The blur will be removed either once the script runs again as scheduled. Alternatively you can set up a Tautulli webhook that will remove the blur as soon as the episode has been watched.


Structures
The following structures are available globally.
-
Simple Point structure used to pass Gaze points and Position around
See moreDeclaration
Swift
public struct Point : Hashable
-
Message describing a user profile
See moreDeclaration
Swift
public struct Skyle_Profile
extension Skyle_Profile: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message to request resets
See moreDeclaration
Swift
public struct Skyle_ResetMessage
extension Skyle_ResetMessage: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
General status message
See moreDeclaration
Swift
public struct Skyle_StatusMessage
extension Skyle_StatusMessage: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message to wrap the options message: either empty or filled with options
See moreDeclaration
Swift
public struct Skyle_OptionMessage
extension Skyle_OptionMessage: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message wrapping possible calibration control messages for a client
See moreDeclaration
Swift
public struct Skyle_calibControlMessages
extension Skyle_calibControlMessages: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message describing the calibration status
See moreDeclaration
Swift
public struct Skyle_CalibControl
extension Skyle_CalibControl: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message with screen resolution of the client
See moreDeclaration
Swift
public struct Skyle_ScreenResolution
extension Skyle_ScreenResolution: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message to improve a calibration
See moreDeclaration
Swift
public struct Skyle_CalibImprove
extension Skyle_CalibImprove: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message wrapping calibration host messages
See moreDeclaration
Swift
public struct Skyle_CalibMessages
extension Skyle_CalibMessages: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message for a 2D point
See moreDeclaration
Swift
public struct Skyle_Point
extension Skyle_Point: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message describing a calibration point
See moreDeclaration
Swift
public struct Skyle_CalibPoint
extension Skyle_CalibPoint: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message describing the quality of a calibration
See moreDeclaration
Swift
public struct Skyle_CalibQuality
extension Skyle_CalibQuality: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message with eye positions and quality indicators
See moreDeclaration
Swift
public struct Skyle_PositioningMessage
extension Skyle_PositioningMessage: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Option message for configuration
See moreDeclaration
Swift
public struct Skyle_Options
extension Skyle_Options: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
iPad Option message for configuration
See moreDeclaration
Swift
public struct Skyle_IPadOptions
extension Skyle_IPadOptions: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Message containing all build versions
See moreDeclaration
Swift
public struct Skyle_DeviceVersions
extension Skyle_DeviceVersions: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Representing the three available button actions
See moreDeclaration
Swift
public struct Skyle_ButtonActions
extension Skyle_ButtonActions: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Button message
See moreDeclaration
Swift
public struct Skyle_Button
extension Skyle_Button: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding
-
Filter configuration message
See moreDeclaration
Swift
public struct Skyle_FilterOptions
extension Skyle_FilterOptions: SwiftProtobuf.Message, SwiftProtobuf._MessageImplementationBase, SwiftProtobuf._ProtoNameProviding


Note
Click here to download the full example code
Tutorial 02: Targets and options¶
Many useful local averages (see Tutorial 05: Kernels and averages) are pre-defined and may be directly used in the simulation of new or classical models. This tutorial showcases the basic usage of the target methods to simulate the variants of the Vicsek model.
An example: variants of the Vicsek model¶
In its most abstract form, the Vicsek model reads:
where \(c_0\) is the speed, \(\mathsf{P}(v)\) is the orthogonal projection on the orthogonal plane to \(v\) and \(\sigma\) is the diffusion coefficient. The drift coefficient \(J^i_t\) is called a target. In synchronous and asynchronous Vicsek models, the target is the center of the sampling distribution. A comparison of classical targets is shown below.
First, some standard imports…
import time
import torch
import pprint
from matplotlib import pyplot as plt
from sisyphe.models import Vicsek
from sisyphe.display import display_kinetic_particles
use_cuda = torch.cuda.is_available()
dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
The classical non-normalised Vicsek model¶
The target is:
The parameters of the model…
N = 100000
L = 100
pos = L*torch.rand((N,2)).type(dtype)
vel = torch.randn(N,2).type(dtype)
vel = vel/torch.norm(vel,dim=1).reshape((N,1))
R = 3.
c = 3.
nu = 5.
sigma = 1.
dt = .01
The choice of the target is implemented in the keyword argument variant
. For the classical normalised target, it is given by the following dictionary.
variant = {"name" : "normalised", "parameters" : {}}
simu = Vicsek(
pos = pos.detach().clone(),
vel = vel.detach().clone(),
v = c,
sigma = sigma,
nu = nu,
interaction_radius = R,
box_size = L,
dt = dt,
variant = variant,
block_sparse_reduction = True,
number_of_cells = 40**2)
Finally run the simulation over 300 units of time….
frames = [0., 5., 10., 30., 42., 71., 100, 124, 161, 206, 257, 300]
s = time.time()
it, op = display_kinetic_particles(simu, frames, order=True)
e = time.time()
Out:
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Print the total simulation time and the average time per iteration.
print('Total time: '+str(e-s)+' seconds')
print('Average time per iteration: '+str((e-s)/simu.iteration)+' seconds')
Out:
Total time:<PHONE_NUMBER>0064087 seconds
Average time per iteration: 0.004855963850021362 seconds
Plot the histogram of the angles of the directions of motion.
angle = torch.atan2(simu.vel[:,1],simu.vel[:,0])
angle = angle.cpu().numpy()
h = plt.hist(angle, bins=1000)
plt.xlabel("angle")
plt.show()
After an initial clustering phase, the system self-organizes into a uniform flock.
Non-normalised Vicsek model¶
The target is:
Define the corresponding dictionary…
kappa_0 = 15.
variant = {"name" : "max_kappa", "parameters" : {"kappa_max" : kappa_0}}
simu = Vicsek(
pos = pos.detach().clone(),
vel = vel.detach().clone(),
v = c,
sigma = sigma,
nu = nu,
interaction_radius = R,
box_size = L,
dt = dt,
variant = variant,
block_sparse_reduction = True,
number_of_cells = 40**2)
Finally run the simulation over 300 units of time….
frames = [0., 5., 10., 30., 42., 71., 100, 124, 161, 206, 257, 300]
s = time.time()
it, op = display_kinetic_particles(simu, frames, order=True)
e = time.time()
Out:
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Print the total simulation time and the average time per iteration.
print('Total time: '+str(e-s)+' seconds')
print('Average time per iteration: '+str((e-s)/simu.iteration)+' seconds')
Out:
Total time:<PHONE_NUMBER>180359 seconds
Average time per iteration: 0.005226768350601196 seconds
Plot the histogram of the angles of the directions of motion.
angle = torch.atan2(simu.vel[:,1],simu.vel[:,0])
angle = angle.cpu().numpy()
h = plt.hist(angle, bins=1000)
plt.xlabel("angle")
plt.show()
The system self-organizes into a strongly clustered flock with band-like structures.
Nematic Vicsek model¶
The target is:
where \(\overline{\Omega}^i_t\) is any unit eigenvector associated to the maximal eigenvalue of the average Q-tensor:
Define the corresponding dictionary…
variant = {"name" : "nematic", "parameters" : {}}
simu = Vicsek(
pos = pos.detach().clone(),
vel = vel.detach().clone(),
v = c,
sigma = sigma,
nu = nu,
interaction_radius = R,
box_size = L,
dt = dt,
variant = variant,
block_sparse_reduction = True,
number_of_cells = 40**2)
Finally run the simulation over 100 units of time… The color code indicates the angle of the direction of motion between \(-\pi\) and \(\pi\).
frames = [0., 5., 10., 30., 42., 71., 100]
s = time.time()
it, op = display_kinetic_particles(simu, frames, order=True, color=True)
e = time.time()
Out:
Progress:0%
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Print the total simulation time and the average time per iteration.
print('Total time: '+str(e-s)+' seconds')
print('Average time per iteration: '+str((e-s)/simu.iteration)+' seconds')
Out:
Total time: 56.330119132995605 seconds
Average time per iteration: 0.00563301191329956 seconds
Plot the histogram of the angles of the directions of motion.
# sphinx_gallery_thumbnail_number = -1
angle = torch.atan2(simu.vel[:,1],simu.vel[:,0])
angle = angle.cpu().numpy()
h = plt.hist(angle, bins=1000)
plt.xlabel("angle")
plt.show()
There are two modes separated by an angle \(\pi\) which indicates that two groups of equal size are moving in opposite direction. This a nematic flock.
The target dictionary¶
Several other targets are implemented. The complete list of available targets can be found in the dictionary of targets target_method
.
pprint.pprint(simu.target_method)
Out:
{'max_kappa': <bound method KineticParticles.max_kappa of <sisyphe.models.Vicsek object at 0x7efb4e14e970>>,
'mean_field': <bound method KineticParticles.mean_field of <sisyphe.models.Vicsek object at 0x7efb4e14e970>>,
'morse': <bound method Particles.morse_target of <sisyphe.models.Vicsek object at 0x7efb4e14e970>>,
'motsch_tadmor': <bound method KineticParticles.motsch_tadmor of <sisyphe.models.Vicsek object at 0x7efb4e14e970>>,
'nematic': <bound method KineticParticles.nematic of <sisyphe.models.Vicsek object at 0x7efb4e14e970>>,
'normalised': <bound method KineticParticles.normalised of <sisyphe.models.Vicsek object at 0x7efb4e14e970>>,
'overlapping_repulsion': <bound method Particles.overlapping_repulsion_target of <sisyphe.models.Vicsek object at 0x7efb4e14e970>>,
'quadratic_potential': <bound method Particles.quadratic_potential_target of <sisyphe.models.Vicsek object at 0x7efb4e14e970>>}
Custom targets can be added to the dictionary of targets using the method add_target_method()
. Then a target can be readily used in a simulation using the method compute_target()
.


The KUTE.js Attributes Plugin extends the core engine and enables animation for any numeric presentation attribute, with or without a measurement unit or what we know as suffix. The plugin can be a great asset for creating complex animations in combination with the SVG Plugin as we'll see in the following examples. As a quick refference, the basic synthax goes like this:
// basic synthax for unitless attributes
var myAttrTween = KUTE.to('selector', {attr: {attributeName: 75}});
// OR for attributes that are ALWAYS suffixed / have a measurement unit
var mySufAttrTween = KUTE.to('selector', {attr:{attributeName: '15%'}});
The Attributes Plugin does support color attributes such as fill
or stroke
starting with KUTE.js v1.5.8, but doesn't support attributes with multiple values like stroke-dasharray
, viewBox
or transform
for simplicity reasons. To animate the stroke/fill or transform attribute, the SVG Plugin has some handy solutions for you. Despite the limitations of this plugin, you have access to just
about any SVGElement/Element presentation attribute available.
Starting with KUTE.js version 1.5.5, the Attributes Plugin can handle all possible single value attributes with both dashed string and non-dashed string notation. Let's have a look at an example so you can get the idea:
// dashed attribute notation
var myDashedAttrStringTween = KUTE.to('selector', {attr: {'stroke-width': 75}});
// non-dashed attribute notation
var myNonDashedAttrStringTween = KUTE.to('selector', {attr:{strokeWidth: '15px'}});
The strokeWidth
example is very interesting because this attribute along with many others can work with px
, %
or with no unit/suffix.
Starting with KUTE.js version 1.5.7, the Attributes Plugin can also animate color attributes: fill
, stroke
and stopColor
. If the elements are affected by their CSS counterparts, the effect is not visible,
so always make sure you know what you're doing.
// some fill rgb, rgba, hex
var fillTween = KUTE.to('#element-to-fill', {attr: { fill: 'red' }});
// some stopColor or 'stop-color'
var stopColorTween = KUTE.to('#element-to-do-stop-color', {attr: {stopColor: 'rgb(0,66,99)'}});
If in this example the fill
attribute value would reference a gradient, then rgba(0,0,0,0) is used.
In the first example, let's play with the attributes of a <circle>
element: radius and center coordinates.
// radius attribute
var radiusTween = KUTE.to('#circle', {attr: {r: 75}});
// coordinates of the circle center
var coordinatesTween = KUTE.to('#circle', {attr:{cx:0,cy:0}});
A quick demo with the above:
Similar to the example on gradients with SVG Plugin, we can also animate the gradient positions, and the plugin will make sure to always include the suffix for you, as in this example the %
unit
is found in the current value and used as unit for the DOM update:
// gradient positions to middle
var closingGradient = KUTE.to('#gradient', {attr: {x1:'49%', x2:'49%', y1:'49%', y2:'49%'}});
// gradient positions rotated
var rotatingGradient = KUTE.to('#gradient', {attr: {x1:'49%', x2:'51%', y1:'51%', y2:'51%'}});
This plugin is quite handy and a great addition to the SVG Plugin.


{% extends "base.html" %} {% block content %}
Multimail is a Django plugin that manages the creation of multiple verified e-mail accounts for your users.
{% if user.is_authenticated %}You are currently logged in as {{ user.username }}. Go to your Profile page or Logout
{% endif %}You will also want to be sure to:


A Traditional Greek Taberna, specializing in food from across Greece. We make everything from scratch here to preserve both the spirit and quality of our food. Happy Hours starts at 4:00pm and lasts until 7:00pm. We love friends, so please, like us on Facebook!


Dialog Test
Another Page (normal)
A Dialog (dialog)
The Second
This is the Second
The Third
This is the Third
Page 2
Cancel


Ayush Gupta is skilled and enthusiast about web & mobile development. He has a keen interest in designing. Photography is his passion!


Change the theme of the dashboard. This will be saved to this browser ONLY
{{ headerChoice.description }} This will be saved to this browser ONLY
{{ navChoice.description }} This will be saved to this browser ONLY
Reset and clear local storage to default.
Change the theme of the dashboard. This will be saved to this browser ONLY


Demo: Get Started Debugging JavaScript with Chrome DevTools
Image URL
Additional Text
Launch the API


Running tclib
will download/install all required Python dependencies defined in the Apps requirements.txt file to a local lib_<version>
directory.
Note
Typically calling tclib
with no arguments is the most common use case. If building an App for distribution then using a configuration file to define the lib structure is preferable. The alternative is to run tclib using each Python version the App should support.
Warning
If you are using macOS and have Python installed via Homebrew, there is a known bug that requires you to create a setup.cfg
file in the same directory as the requirements.txt
. The setup.cfg
file should have the following code at minimum:
[install]
prefix=
Usage¶
usage: tclib [-h] [--app_name APP_NAME] [--app_path APP_PATH]
[--config CONFIG] [--no_cache_dir] [--tcex_develop]
optional arguments:
-h, --help show this help message and exit
--app_name APP_NAME Fully qualified path of App.
--app_path APP_PATH Fully qualified path of App.
--config CONFIG Configuration file for gen lib. (Default: tcex.json)
--no_cache_dir Do not use pip cache directory.
--tcex_develop Replace tcex version in config with github develop branch.
Using Configuration File¶
By default the tcex.json configuration file will be loaded if it exists. If the configuration includes the lib_versions
parameter array the tclib
command will use the values defined in the configuration to build the lib directories. For ease of management when building multiple Apps the tcex.json file can contain environment vars defining the Python version (e.g., $env.PY36 for export PY36='3.6.5'
).
Linux
<...snipped>
"lib_versions": [{
"lib_dir": "lib_2.7.13",
"python_executable": "~/.pyenv/versions/2.7.13/bin/python"
},
{
"lib_dir": "lib_3.4.6",
"python_executable": "~/.pyenv/versions/3.4.6/bin/python"
},
{
"lib_dir": "lib_3.5.3",
"python_executable": "~/.pyenv/versions/3.5.3/bin/python"
},
{
"lib_dir": "lib_$env.PY36",
"python_executable": "~/.pyenv/versions/$env.PY36/bin/python"
}
],
<snipped...>
Windows
<...snipped>
"lib_versions": [{
"lib_dir": "lib_2.7.13",
"python_executable": "~\\AppData\\Local\\Programs\\Python\\Python27\\python.exe"
}, {
"lib_dir": "lib_3.6.2",
"python_executable": "~\\AppData\\Local\\Programs\\Python\\Python36\\python.exe"
}
],
<snipped...>


Rotation 2: CAD, Fusion 360 and Design
Project Description:
For my first project, I decided to make a stamp. Since I don't know how to make a stamp or use Fusion 360, I watched several videos which helped me understand how to use the software better. Following the instructions in the video was a straighforward procecss. I decided to do my own stamp design rather than follow the video's design. I started off by inserting a cylinder to create a round base, then I made a taller and skinnier cylinder and placed it a top the round base. For the handle, I chamfered the edges to make it more smoother and comfortable to grip. To personalize my stamp, I decided to sketch a paw print, which I find not difficult to do. I used the line, ellipse, and circle sketch tools to create my design. When my stamp was done 3D printing, it turned out to be larger than I expected, nontheless, I'm satisfied with the end result.


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This tests that calling NPN_GetURLNotify on a URL that never even loads still calls NPP_URLNotify.
FAILURE


{% load static %}
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Plan another trip
Welcome, {{user}} !
History
All the places you've visited before:
{% for i in history%}
{{i}}
{% endfor %}
User Details
Username:
{{user}}
Email:
{{user.email}}
{% csrf_token %}
Enter old password:
Enter New Password:
Re-Enter New Password:
{% for message in messages %}
{{message}}
{% endfor %}