In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
input_learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return inputs_real, inputs_z, input_learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha=0.2):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
with tf.variable_scope('discriminator', reuse=reuse):
out = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
out = tf.maximum(alpha * out, out)
# 14 * 14 now
out = tf.layers.conv2d(out, 128, 5, strides=2, padding='same')
out = tf.layers.batch_normalization(out, training=True)
out = tf.maximum(alpha * out, out)
# 7 * 7 now
out = tf.layers.conv2d(out, 256, 3, strides=2, padding='valid')
out = tf.layers.batch_normalization(out, training=True)
out = tf.maximum(alpha * out, out)
# 3 * 3 now
out = tf.reshape(out, (-1, 3 * 3 * 256))
logits = tf.layers.dense(out, 1, activation=None)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
# If is_train is True, set reuse to False, if is_train is False, set reuse to True because
# when we want to generate images, we would like to reuse the weights.
reuse = not is_train
with tf.variable_scope('generator', reuse=reuse):
out = tf.layers.dense(z, 3 * 3 * 512)
out = tf.reshape(out, (-1, 3, 3, 512))
out = tf.layers.batch_normalization(out, training=is_train)
out = tf.maximum(alpha * out, out)
out = tf.layers.conv2d_transpose(out, 512, 3, strides=2, padding='valid')
out = tf.layers.batch_normalization(out, training=is_train)
out = tf.maximum(alpha * out, out)
out = tf.layers.conv2d_transpose(out, 256, 5, strides=2, padding='same')
out = tf.layers.batch_normalization(out, training=is_train)
out = tf.maximum(alpha * out, out)
out = tf.layers.conv2d_transpose(out, out_channel_dim, 5, strides=2, padding='same')
out = tf.tanh(out)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
g_model = generator(input_z, out_channel_dim, alpha=alpha)
d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_real, labels=tf.ones_like(d_logits_real)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_opt, g_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# TODO: Build Model
# For the mnist, the data_image_mode is 'L', shape is (60000, 28, 28, 1)
# For the celebA, the date_image_mode is 'RGB', shape is (202599, 28, 28, 3)
_, image_width, image_height, image_channels = data_shape
# Get model input and output
input_real, input_z, input_learning_rate = model_inputs(image_width, image_height, image_channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, image_channels)
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
step = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# save graph
graph_writer = tf.summary.FileWriter('log' + '/train',
sess.graph)
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
step += 1
# Sample z
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Scale the images'
batch_images = batch_images * 2
# Run optimizers
_ = sess.run(d_opt, feed_dict=
{input_real: batch_images, input_z: batch_z, input_learning_rate: learning_rate})
_ = sess.run(g_opt, feed_dict=
{input_z: batch_z, input_real: batch_images, input_learning_rate: learning_rate})
if step % 100 == 0:
# Print loss
train_loss_d = sess.run(d_loss, feed_dict={input_real: batch_images, input_z: batch_z})
train_loss_g = sess.run(g_loss, feed_dict={input_z: batch_z})
print ('train loss for discriminator is {}, for generator is {}'.
format(train_loss_d, train_loss_g))
show_generator_output(sess, 16, input_z, image_channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
tf.reset_default_graph()
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


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C++ pointers are easy and fun to learn. Some C++ tasks are performed more easily with pointers, and other C++ tasks, such as dynamic memory allocation, cannot be performed without them.
As you know every variable is a memory location and every memory location has its address defined which can be accessed using ampersand (&) operator which denotes an address in memory. Consider the following which will print the address of the variables defined:
#include <iostream> using namespace std; int main () { int var1; char var2[10]; cout << "Address of var1 variable: "; cout << &var1 << endl; cout << "Address of var2 variable: "; cout << &var2 << endl; return 0; }
When the above code is compiled and executed, it produces result something as follows:
Address of var1 variable: 0xbfebd5c0 Address of var2 variable: 0xbfebd5b6
A pointer is a variable whose value is the address of another variable. Like any variable or constant, you must declare a pointer before you can work with it. The general form of a pointer variable declaration is:
type *var-name;
Here, type is the pointer's base type; it must be a valid C++ type and var-name is the name of the pointer variable. The asterisk you used to declare a pointer is the same asterisk that you use for multiplication. However, in this statement the asterisk is being used to designate a variable as a pointer. Following are the valid pointer declaration:
int *ip; // pointer to an integer double *dp; // pointer to a double float *fp; // pointer to a float char *ch // pointer to character
The actual data type of the value of all pointers, whether integer, float, character, or otherwise, is the same, a long hexadecimal number that represents a memory address. The only difference between pointers of different data types is the data type of the variable or constant that the pointer points to.
There are few important operations, which we will do with the pointers very frequently. (a) we define a pointer variables (b) assign the address of a variable to a pointer and (c) finally access the value at the address available in the pointer variable. This is done by using unary operator * that returns the value of the variable located at the address specified by its operand. Following example makes use of these operations:
#include <iostream> using namespace std; int main () { int var = 20; // actual variable declaration. int *ip; // pointer variable ip = &var; // store address of var in pointer variable cout << "Value of var variable: "; cout << var << endl; // print the address stored in ip pointer variable cout << "Address stored in ip variable: "; cout << ip << endl; // access the value at the address available in pointer cout << "Value of *ip variable: "; cout << *ip << endl; return 0; }
When the above code is compiled and executed, it produces result something as follows:
Value of var variable: 20 Address stored in ip variable: 0xbfc601ac Value of *ip variable: 20
Pointers have many but easy concepts and they are very important to C++ programming. There are following few important pointer concepts which should be clear to a C++ programmer:
| Concept | Description |
|---|---|
| C++ supports null pointer, which is a constant with a value of zero defined in several standard libraries. | |
| There are four arithmetic operators that can be used on pointers: ++, --, +, - | |
| There is a close relationship between pointers and arrays. Let us check how? | |
| You can define arrays to hold a number of pointers. | |
| C++ allows you to have pointer on a pointer and so on. | |
| Passing an argument by reference or by address both enable the passed argument to be changed in the calling function by the called function. | |
| C++ allows a function to return a pointer to local variable, static variable and dynamically allocated memory as well. |


{$ doc.animations | marked $}
{$ 'module:ngAnimate.$animate' | link('Click here', doc) $} to learn more about the steps involved in the animation.
{%- endif -%}
{% include "lib/params.template.html" %}
{% include "lib/events.template.html" %}
{% endblock %}


Richard Kaine performance - JSA
Show/hide filters
| On-flow | Off-flow | % achieved | Into work % | Claimants to standard | |
|---|---|---|---|---|---|
| April | 6 | 3 | 50 | 50 | 5 (13 weeks) |
| May | 5 | 2 | 40 | 40 | 4 (52 weeks) |
| June | 7 | 3 | 42.85 | 42.85 | 3 (26 weeks) |
| Year to date | 70 | 35 | 45 | 45 | 36 |
| On-flow | Off-flow | % achieved | Into work % | Claimants to standard | |
|---|---|---|---|---|---|
| April | 6 | 3 | 50 | 50 | 5 (13 weeks) |
| May | 5 | 2 | 40 | 40 | 4 (52 weeks) |
| June | 7 | 3 | 42.85 | 42.85 | 3 (26 weeks) |
| Year to date | 70 | 35 | 45 | 45 | 36 |


Showing code examples¶
Examples of Python source code or interactive sessions are represented using
standard reST literal blocks. They are started by a ::
at the end of the
preceding paragraph and delimited by indentation.
Representing an interactive session requires including the prompts and output along with the Python code. No special markup is required for interactive sessions. After the last line of input or output presented, there should not be an “unused” primary prompt; this is an example of what not to do:
>>> 1 + 1
2
>>>
Syntax highlighting is done with Pygments and handled in a smart way:
There is a “highlighting language” for each source file. Per default, this is
'python'
as the majority of files will have to highlight Python snippets, but the doc-wide default can be set with the :confval:`highlight_language` config value.Within Python highlighting mode, interactive sessions are recognized automatically and highlighted appropriately. Normal Python code is only highlighted if it is parseable (so you can use Python as the default, but interspersed snippets of shell commands or other code blocks will not be highlighted as Python).
The highlighting language can be changed using the
highlight
directive, used as follows:-
.. highlight::
language
¶ Example:
.. highlight:: c
This language is used until the next
highlight
directive is encountered.
-
For documents that have to show snippets in different languages, there’s also a
code-block
directive that is given the highlighting language directly:-
.. code-block::
language
¶ Use it like this:
.. code-block:: ruby Some Ruby code.
The directive’s alias name
sourcecode
works as well.
-
The valid values for the highlighting language are:
none
(no highlighting)python
(the default when :confval:`highlight_language` isn’t set)guess
(let Pygments guess the lexer based on contents, only works with certain well-recognizable languages)rest
c
- … and any other lexer alias that Pygments supports.
If highlighting with the selected language fails (i.e. Pygments emits an “Error” token), the block is not highlighted in any way.
Line numbers¶
Pygments can generate line numbers for code blocks. For
automatically-highlighted blocks (those started by ::
), line numbers must be
switched on in a highlight
directive, with the linenothreshold
option:
.. highlight:: python
:linenothreshold: 5
This will produce line numbers for all code blocks longer than five lines.
For code-block
blocks, a linenos
flag option can be given to
switch on line numbers for the individual block:
.. code-block:: ruby
:linenos:
Some more Ruby code.
The first line number can be selected with the lineno-start
option. If
present, linenos
is automatically activated as well.
10 Some more Ruby code, with line numbering starting at 10.
Additionally, an emphasize-lines
option can be given to have Pygments
emphasize particular lines:
.. code-block:: python
:emphasize-lines: 3,5
def some_function():
interesting = False
print 'This line is highlighted.'
print 'This one is not...'
print '...but this one is.'
Changed in version 1.1: emphasize-lines
has been added.
Changed in version 1.3: lineno-start
has been added.
Includes¶
-
.. literalinclude::
filename
¶ Longer displays of verbatim text may be included by storing the example text in an external file containing only plain text. The file may be included using the
literalinclude
directive. [1] For example, to include the Python source fileexample.py
, use:.. literalinclude:: example.py
The file name is usually relative to the current file’s path. However, if it is absolute (starting with
/
), it is relative to the top source directory.Tabs in the input are expanded if you give a
tab-width
option with the desired tab width.Like
code-block
, the directive supports thelinenos
flag option to switch on line numbers, thelineno-start
option to select the first line number, theemphasize-lines
option to emphasize particular lines, and alanguage
option to select a language different from the current file’s standard language. Example with options:.. literalinclude:: example.rb :language: ruby :emphasize-lines: 12,15-18 :linenos:
Include files are assumed to be encoded in the :confval:`source_encoding`. If the file has a different encoding, you can specify it with the
encoding
option:.. literalinclude:: example.py :encoding: latin-1
The directive also supports including only parts of the file. If it is a Python module, you can select a class, function or method to include using the
pyobject
option:.. literalinclude:: example.py :pyobject: Timer.start
This would only include the code lines belonging to the
start()
method in theTimer
class within the file.Alternately, you can specify exactly which lines to include by giving a
lines
option:.. literalinclude:: example.py :lines: 1,3,5-10,20-
This includes the lines 1, 3, 5 to 10 and lines 20 to the last line.
Another way to control which part of the file is included is to use the
start-after
andend-before
options (or only one of them). Ifstart-after
is given as a string option, only lines that follow the first line containing that string are included. Ifend-before
is given as a string option, only lines that precede the first lines containing that string are included.With lines selected using
start-after
it is still possible to uselines
, the first allowed line having by convention the line number1
.When lines have been selected in any of the ways described above, the line numbers in
emphasize-lines
also refer to the selection, with the first selected line having number1
.When specifying particular parts of a file to display, it can be useful to display the original line numbers. This can be done using the
lineno-match
option, which is however allowed only when the selection consists of contiguous lines.You can prepend and/or append a line to the included code, using the
prepend
andappend
option, respectively. This is useful e.g. for highlighting PHP code that doesn’t include the<?php
/?>
markers.If you want to show the diff of the code, you can specify the old file by giving a
diff
option:.. literalinclude:: example.py :diff: example.py.orig
This shows the diff between example.py and example.py.orig with unified diff format.
New in version 0.4.3: The
encoding
option.New in version 0.6: The
pyobject
,lines
,start-after
andend-before
options, as well as support for absolute filenames.New in version 1.0: The
prepend
andappend
options, as well astab-width
.New in version 1.3: The
diff
option. Thelineno-match
option.Changed in version 1.6: With both
start-after
andlines
in use, the first line as perstart-after
is considered to be with line number1
forlines
.
Dedent¶
New in version 1.3.
A dedent
option can be given to strip indentation characters from the code
block. For example:
.. literalinclude:: example.rb
:language: ruby
:dedent: 4
:lines: 10-15
code-block
also supports the dedent
option.
Footnotes
| [1] | There is a standard .. include directive, but it raises errors if the
file is not found. This one only emits a warning. |


Contributing to eq.js
We love contributors! Yes we do! If you would like to contribute to eq.js, please follow the following guidelines:
- DO NOT ISSUE A PULL REQUEST WITHOUT RELATED ISSUE!! All pull requests must reference an issue in the issue queue and will only be looked at after discussion about that issue has taken place. Any pull request created that does not reference an issue will be closed.
- Each individual feature you would like add, or bug you would like to squash, should be an individual pull request. Each pull request should be from an individual feature branch to either the latest stable or development branch. The current stable branch is 1.x.x. The current development branch is 1.x.x. Contributions that are not in the form of a pull request will not be considered. If your pull request does not apply cleanly we will ask you to fix that before we will look into pulling it in. We may ask you to update or make changes to the code you've submitted, please don't take this the wrong way. If a pull request smells (such as if a large amount of code is all within a single commit, or the coding standards aren't in line with core
eq.js
) we may ask you to rewrite your commit. - Follow all coding standards as defined in the current code base. Any code that does not follow the existing coding standards will be asked to be re-written to follow said standards.
eq.js
is very specifically not a jQuery/Zepto/Other Framework plugin! Any issues opened related to turning this entirely into a jQuery/Zepto/Other Framework plugin or making the core require jQuery/Zepto/Other Framework will not be considered. It's JavaScript people! Just JavaScript!- We are actively trying to keep
eq.js
as small and as fast as possible. Any PR that significantly increases size or decreases speed will be scrutinized heavily against the benchmark speed of the providedindex.html
.
 Either grunt server
to work on the project, or grunt build
to build the dist versions.


My friend owed me a favor and I was surprised to get a call from him so soon. He wouldn't realy tell me what he had for me but he said it was waiting in his freezer in an aluminum altoids box.
I showed up at his appt. in the afternoon. I arrived and smoked a bowl of marijuana. Upon the arrival of my friend we smoked some more and he told me about the research chemical he had for me. I had been waiting for an opportunity to try a new psychedelic compound; it had been months since I last tripped. So around 3:00 I ingested two gelcaps both containing 30 mg.'s AMT. My friend laughed and asked if I ate both of them. I said yes and then he told me that he had never heard of someone taking so much at once. I thought oh well, here goes nothing.
At T:30 I was definately beginning to feel the onslaught. I can only describe the initial onset as a sensation of vibratory energy slowly gaining momentum. At this point all systems go.
At T:1HOUR I began to hallucinate. It started as a glistening or reflective glow kind of a shimmering that would occour randomly. Then I noticed that my body felt cramped or awkward so I did some stretching on the floor. When I stood up it was like the ceiling of the appartment had evaporated, all of a sudden there was no feeling of being held in or contained. I steped out on the porch and watched the bustle of the city night. Now I was triping.
At T:1:30 Now I began to experience the physical rushing sensation which is similar to ecstacy. I experienced random physical quirks such as the twitching of my eye and the clenching of my jaw, which started as almost a shivering although I was not at all cold. It was like a frequency was coming out of my cells and my mind.
I narrowly avoided a bad trip at this time because I vomited quite ferosciously. Looking up from the toilet all of a sudden there was this green slime/ooze that was all over everything and it was moving like there was worms in it. I went and appologized to my friend and his g.f. was like dude what are you talking about there's no mess in here at all! Then I returned to the act of just chillin. I read some of the teachings of Jesus.
At T:2HOURS I was now completely gone, lost in my artwork. I began to paint without any preconcieved idea about what I wanted to do. It was some of the best art I had ever done. It was like rediscovering paint as a child. After doing a couple pieces I tried to sit and meditate in the lotus position. But again the energy kept building up the back of my spine untill all I could do was stand up and spread my arms to embrace all that is. At this point the two couples I was with began to wind down, and it was apparent they were becoming ready to retire. I then thanked my friend for the AMT and his hospitality and said goodnight.
At T:3HOURS I went over to my brother's appartment and watched KOYAANISQATSI: LIFE OUT OF BALANCE directed by Godfrey Regio with score by Phillip Glass. The film would have blown open my mind sober but MY GOD!! It was like watching the Earth from the begining of time to present day in 2 hours. The best movie ever.
After viewing it my bro and I smoked some weed then he passed out. Time seemed to stand still and/or evaporate at this point. My mind was still racing a million miles a second from the film. It had completely changed the way I thought of myself as an individual. The film challanged me to the core. So I was left sitting in the dark on a large dose of AMT all by myself. PERFECT!
At who knows when: Gradually I began to stop trying to sleep. I realized there was just no way I could pass out with so much mental activity present. I started to see outlines of what appeared to be a shadowy figure at the foot of the couch I was on. It gradually morphed into a very tall hooded shadow person who was clearly standing just a few feet ahead of me. Everytime my bro's VCR would flash 12:00 I would see his outline against the ceiling.
I was not scared though. It was definately a male entity and it seemed he was challanging me psychicly. He seemed to be non-hostile. I kept waiting for him to go away with no avail. I began to theorize that this shadow person was realy just my shadow self and that I was having an all night stare-down with that part of the self which is denied during normal waking states of consciousness. I came to terms with a lot that night. Looking back on it, it was like experiencing thousands of dollars worth of psychotherapy in just one night. A truly mystical experience!!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


2017-2019
Graduated and received the Game Programming diploma
2010-2014
Graduated and Received the Ontario Secondary School Diploma
Benson Auto Parts
2021-Present
Managed SQL database and website development
Ensured internet connectivity at 200 locations
Helped employees remotely with Teamviewer
Expected to use osTicket to manage tickets and finish them effectively
Xplornet
2017-2021
Provided technical support for inbound and outbound callers
Retained over 90% of my customers for over 12 months when dealing with billing
Excellent interpersonal skills build rapport and help educate the customer
Worked within a CRM environment which was powered by Salesforce and helped manage tickets
SLC Research
2019
Managed code and developed architecture for a Virtual Reality Experience
Gathered information from real life scenarios to help simulate in-game experiences
Released for Oculus Rift S, Go and HTC VIVE and was functional and was trusted to fix final bugs
Managed project through Trello and helped manage teams and select individuals for tickets
Falsetto was a game that was created as a final project for our game sounds course. Our goal was to create a game that relied heavily on sounds. We realized quickly that the pitch of someones voice has been a myth that it could potentially break glass, going further we learned that bats have some of the most sensitive hearing relying on it for their eyesight. These details tied perfectly into our game which was working perfectly at the time of the presentation. This game included a main menu, options screen with adjustable sound settings, leaderboards, and a replayable procedurally generated level.
Frogger Re-imagined was a final project for first semester students at the college. This was one of my very first projects, the background images and sprites were sourced from free to use and share, modify commercial use websites and the rest was coded by me using the SDL game engine. I really enjoyed this experience because the amount of C++ that was required to make this game have zero bugs was really fun to program!


This is not a report with the results of the pipeline, but the log of the events occurred to generate it.
This reports contain technical details on the execution of Dadaist2.
| Parameter | Value |
|---|---|
| Input | /Users/telatin/git/dadaist2/data/16S |
| Output | /Users/telatin/git/dadaist2/test/auto |
| Timestamp | 20210527 12:00:28 |
| Execution time | 1m 41s |
Executed at 2021-05-27 12:00:57 for 0.77s and returned 0
'Command
fastp
-w 2
-i "/Users/telatin/git/dadaist2/data/16S/A01_S0_L001_R1_001.fastq.gz"
-I "/Users/telatin/git/dadaist2/data/16S/A01_S0_L001_R2_001.fastq.gz"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV"/for/A01_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV"/rev/A01_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/A01.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110
Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 6137 total bases: 1847237 Q20 bases: 1648961(89.2663%) Q30 bases: 1490142(80.6687%) Read2 before filtering: total reads: 6137 total bases: 1847237 Q20 bases: 1577946(85.422%) Q30 bases: 1382606(74.8472%) Read1 after filtering: total reads: 6105 total bases: 1689825 Q20 bases: 1504522(89.0342%) Q30 bases: 1353780(80.1136%) Read2 aftering filtering: total reads: 6105 total bases: 1689825 Q20 bases: 1439363(85.1782%) Q30 bases: 1251309(74.0496%) Filtering result: reads passed filter: 12210 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 64 reads with adapter trimmed: 1090 bases trimmed due to adapters: 64672 Duplication rate: 7.27273% Insert size peak (evaluated by paired-end reads): 465 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/A01.json HTML report: /dev/null fastp -w 2 -i /Users/telatin/git/dadaist2/data/16S/A01_S0_L001_R1_001.fastq.gz -I /Users/telatin/git/dadaist2/data/16S/A01_S0_L001_R2_001.fastq.gz -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/for/A01_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/rev/A01_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/A01.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 0 secondsShell command fastp
Executed at 2021-05-27 12:00:58 for 0.71s and returned 0
'Command
fastp
-w 2
-i "/Users/telatin/git/dadaist2/data/16S/A02_S0_L001_R1_001.fastq.gz"
-I "/Users/telatin/git/dadaist2/data/16S/A02_S0_L001_R2_001.fastq.gz"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV"/for/A02_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV"/rev/A02_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/A02.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 5414 total bases: 1629614 Q20 bases: 1454370(89.2463%) Q30 bases: 1311878(80.5024%) Read2 before filtering: total reads: 5414 total bases: 1629614 Q20 bases: 1412945(86.7043%) Q30 bases: 1240421(76.1175%) Read1 after filtering: total reads: 5399 total bases: 1508588 Q20 bases: 1338035(88.6945%) Q30 bases: 1200838(79.6001%) Read2 aftering filtering: total reads: 5399 total bases: 1508588 Q20 bases: 1299416(86.1346%) Q30 bases: 1130799(74.9574%) Filtering result: reads passed filter: 10798 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 30 reads with adapter trimmed: 648 bases trimmed due to adapters: 23266 Duplication rate: 14.6341% Insert size peak (evaluated by paired-end reads): 465 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/A02.json HTML report: /dev/null fastp -w 2 -i /Users/telatin/git/dadaist2/data/16S/A02_S0_L001_R1_001.fastq.gz -I /Users/telatin/git/dadaist2/data/16S/A02_S0_L001_R2_001.fastq.gz -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/for/A02_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/rev/A02_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/A02.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 0 secondsShell command fastp
Executed at 2021-05-27 12:00:59 for 0.64s and returned 0
'Command
fastp
-w 2
-i "/Users/telatin/git/dadaist2/data/16S/F99_S0_L001_R1_001.fastq.gz"
-I "/Users/telatin/git/dadaist2/data/16S/F99_S0_L001_R2_001.fastq.gz"
-o "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV"/for/F99_R1.fastq.gz
-O "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV"/rev/F99_R2.fastq.gz
--trim_front1 20
--trim_front2 20
--detect_adapter_for_pe
--disable_quality_filtering
--n_base_limit 1
--json "/var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/F99.json"
--html /dev/null
--trim_tail1 0
--trim_tail2 0
--length_required 110Standard output
Standard error
Detecting adapter sequence for read1... No adapter detected for read1 Detecting adapter sequence for read2... No adapter detected for read2 Read1 before filtering: total reads: 4553 total bases: 1370453 Q20 bases: 1233254(89.9888%) Q30 bases: 1118036(81.5815%) Read2 before filtering: total reads: 4553 total bases: 1370453 Q20 bases: 1175086(85.7443%) Q30 bases: 1025042(74.7959%) Read1 after filtering: total reads: 4536 total bases: 1267283 Q20 bases: 1134520(89.5238%) Q30 bases: 1023409(80.7562%) Read2 aftering filtering: total reads: 4536 total bases: 1267283 Q20 bases: 1078894(85.1344%) Q30 bases: 932192(73.5583%) Filtering result: reads passed filter: 9072 reads failed due to low quality: 0 reads failed due to too many N: 0 reads failed due to too short: 34 reads with adapter trimmed: 432 bases trimmed due to adapters: 21554 Duplication rate: 10.8911% Insert size peak (evaluated by paired-end reads): 465 JSON report: /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/F99.json HTML report: /dev/null fastp -w 2 -i /Users/telatin/git/dadaist2/data/16S/F99_S0_L001_R1_001.fastq.gz -I /Users/telatin/git/dadaist2/data/16S/F99_S0_L001_R2_001.fastq.gz -o /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/for/F99_R1.fastq.gz -O /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/rev/F99_R2.fastq.gz --trim_front1 20 --trim_front2 20 --detect_adapter_for_pe --disable_quality_filtering --n_base_limit 1 --json /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/F99.json --html /dev/null --trim_tail1 0 --trim_tail2 0 --length_required 110 fastp v0.20.1, time used: 0 seconds
Executed at 2021-05-27 12:00:59 for 35.45s and returned 0
'Command
Rscript
--vanilla /Users/telatin/miniconda3/envs/dadaist/bin/D2-dada.R /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/for /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/rev /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/dada2/dada2.tsv /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/dada2/stats.tsv /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/for/filtered /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/rev/filtered 265 207 0 0 1 1.5 10 consensus 1 2 10000 /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV do_plots refs/rdp_train_set_16.fa.gz no 0 0
Standard output
R version 4.0.3 (2020-10-10) #DADA2: 1.18.0 / Rcpp: 1.0.6 / RcppParallel: 5.1.2 2021-05-27 12:01:08 [1] Filtering reads ... 2021-05-27 12:01:23 [2] Learning Error Rates 1579400 total bases in 5960 reads from 3 samples will be used for learning the error rates. 1233720 total bases in 5960 reads from 3 samples will be used for learning the error rates. 2021-05-27 12:01:26 [3] Denoise remaining samples * Sample by sample 2021-05-27 12:01:28 [4] Remove chimeras (method = consensus) 2021-05-27 12:01:28 [5.1] Taxonomy 2021-05-27 12:01:35 [6] Write output * /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/dada2/dada2.tsv * /var/folders/v_/xwpxqp0s5jj2n4xq_hrh2r_c0000gn/T/dadaist2_1nociV/taxonomy.tsvStandard error
Loading required package: Rcpp
Not available.
Executed at 2021-05-27 12:00:30 for 0.16s and returned 0
'Command
Rscript
--version 2>&1
Standard output
R scripting front-end version 4.0.3 (2020-10-10)Standard error
Shell command /Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-assigntax
Executed at 2021-05-27 12:00:31 for 0.38s and returned 0
'Command
/Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-assigntax --versionStandard output
dadaist2-assigntax 1.1.0Standard error
[35;1m DADAIST2 Assign Taxonomy[0mShell command /Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-assigntax
Executed at 2021-05-27 12:00:31 for 0.41s and returned 0
'Command
/Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-assigntax --versionStandard output
dadaist2-assigntax 1.1.0Standard error
[35;1m DADAIST2 Assign Taxonomy[0mShell command clustalo
Executed at 2021-05-27 12:00:31 for 0.29s and returned 0
'Command
clustalo
--version 2>&1Standard output
1.2.4Standard error
Shell command echo
Executed at 2021-05-27 12:00:32 for 23.62s and returned 0
'Command
echo "library(dada2)" | R
--no-save > /dev/nullStandard output
Standard error
Loading required package: RcppShell command /Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-metadata
Executed at 2021-05-27 12:00:55 for 0.24s and returned 0
'Command
/Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-metadata --versionStandard output
dadaist2-metadata 1.0.0Standard error
Shell command /Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-exporter
Executed at 2021-05-27 12:00:56 for 0.27s and returned 0
'Command
/Users/telatin/miniconda3/envs/dadaist/bin/dadaist2-exporter --versionStandard output
dadaist2-exporter 1.4.0Standard error
Shell command fastp
Executed at 2021-05-27 12:00:56 for 0.15s and returned 0
'Command
fastp
--version 2>&1Standard output
fastp 0.20.1Standard error
Shell command fasttree
Executed at 2021-05-27 12:00:56 for 0.17s and returned 0
'Command
fasttree 2>&1 | head
-n 1 | cut
-c 11-Standard output
FastTree version 2.1.10 Double precision (No SSE3):Standard error
Shell command fu-primers
Executed at 2021-05-27 12:00:56 for 0.32s and returned 0
'Command
fu-primers
--version 2>&1Standard output
fu-primers 0.9.6Standard error


String
struct String
Codable String Conversion Extension
-
Converts the given String to an Int?
Declaration
Swift
public var int: Int?
-
Converts the given String to a Int8?
Declaration
Swift
public var int8: Int8?
-
Converts the given String to a Int16?
Declaration
Swift
public var int16: Int16?
-
Converts the given String to a Int32?
Declaration
Swift
public var int32: Int32?
-
Converts the given String to a Int64?
Declaration
Swift
public var int64: Int64?
-
Converts the given String to a UInt?
Declaration
Swift
public var uInt: UInt?
-
Converts the given String to a UInt8?
Declaration
Swift
public var uInt8: UInt8?
-
Converts the given String to a UInt16?
Declaration
Swift
public var uInt16: UInt16?
-
Converts the given String to a UInt32?
Declaration
Swift
public var uInt32: UInt32?
-
Converts the given String to a UInt64?
Declaration
Swift
public var uInt64: UInt64?
-
Converts the given String to a Float?
Declaration
Swift
public var float: Float?
-
Converts the given String to a Double?
Declaration
Swift
public var double: Double?
-
Converts the given String to a Bool?
Declaration
Swift
public var boolean: Bool?
-
Converts the given String to a String
Declaration
Swift
public var string: String
-
Converts the given String to an [Int]?
Declaration
Swift
public var intArray: [Int]?
-
Converts the given String to an [Int8]?
Declaration
Swift
public var int8Array: [Int8]?
-
Converts the given String to an [Int16]?
Declaration
Swift
public var int16Array: [Int16]?
-
Converts the given String to an [Int32]?
Declaration
Swift
public var int32Array: [Int32]?
-
Converts the given String to an [Int64]?
Declaration
Swift
public var int64Array: [Int64]?
-
Converts the given String to an [UInt]?
Declaration
Swift
public var uIntArray: [UInt]?
-
Converts the given String to an [UInt8]?
Declaration
Swift
public var uInt8Array: [UInt8]?
-
Converts the given String to an [UInt16]?
Declaration
Swift
public var uInt16Array: [UInt16]?
-
Converts the given String to an [UInt32]?
Declaration
Swift
public var uInt32Array: [UInt32]?
-
Converts the given String to an [UInt64]?
Declaration
Swift
public var uInt64Array: [UInt64]?
-
Converts the given String to a [Float]?
Declaration
Swift
public var floatArray: [Float]?
-
Converts the given String to a [Double]?
Declaration
Swift
public var doubleArray: [Double]?
-
Converts the given String to a [Bool]?
Declaration
Swift
public var booleanArray: [Bool]?
-
Converts the given String to a [String]
Declaration
Swift
public var stringArray: [String]
-
Method used to decode a string into the given type T
Declaration
Swift
public func decodable<T: Decodable>(_ type: T.Type) -> T?
Return Value
The Date? object. Some on success / nil on failure
-
Converts the given String to a Date?
Declaration
Swift
public func date(_ formatter: DateFormatter) -> Date?
Return Value
The Date? object. Some on success / nil on failure
-
Converts the given String to a [Date]?
Declaration
Swift
public func dateArray(_ formatter: DateFormatter) -> [Date]?
Return Value
The [Date]? object. Some on success / nil on failure
-
Creates a string identifier from a given string value.
Declaration
Swift
public init(value: String)
-
The string representation of the identifier.
Declaration
Swift
public var value: String


Forget Click Frenzy: Here's How To Prep For Black Friday
by Anthony CaruanaBlack Friday is coming on soon and that means your credit card could take a hammering if you don’t have a plan. In order to make sure you get the best from this shopping event and don’t blow your budget here are some tips to make the most out of Black Friday.
#1 Make A Shopping List
Make a list of the items you most want and do some research so you know what a reasonable price to pay for each item is. Have that list next to you when you’re shopping.
#2 Set A Budget
Create a plan for how much you’re going to spend and don’t get caught up in the excitement and spend outside your limit.
#3 Check Where Items Are Coming From
Make sure items are coming from retailers that you’re confident will honour warranties in case something goes wrong.
#4 Shipping
Watch out for retailers who drop the price of items but increase shipping costs to offset discounts. That may be the case, especially on sites like Ebay where small retailers sometimes inflate shipping costs.
#5 Delivery Dates
If you’re planning on using Black Friday to buy up Christmas gifts make sure that shipping dates line up in case you need to onsend gifts to family and friends.
Start now!
Despite not officially starting until November 29, a bunch of retailers have already released their Black Friday deals. Click here for six bargains you can get right now!


The first part of this tutorial covered how to create a HTML form so that users can input information
The second part used jQuery to test the answers submitted.
In this final part we’ll use more jQuery to tell the user whether they are correct or wrong, and calculate a score.
There are many ways of doing this, but we’re going to just use the following:
html()
method to put one message into that element if the answer is correct; and another if it’s wrong.The extra HTML element looks like this:

This goes wherever you want the user to see their result - most likely immediately after the form like so:
<form>

Which of the following is spelt correctly?

<input type="radio" name="question1" value="their">They cocked their hats

<input type="radio" name="question1" value="theyre">They cocked they're hats

<input type="radio" name="question1" value="there">They cocked there hats

<input type="submit" value="Submit" id="button">
</form>

When the page loads this

is empty, so the user sees nothing. But its id
attribute means we can select and change that with some jQuery.
Here’s the jQuery that does that:
$('div#result1').html('
CORRECT!
');
You should be able to recognise familiar processes here: 'div#result'
selects

and the dollar sign turns that into a jQuery object. The html()
method then adds some HTML into that.
The HTML added is '
CORRECT!
'
- a paragraph tag and the text CORRECT!
This needs to go within our if
statement like so (just for good measure, I’ve added a similar line to the else
statement too):

Now when the Submit button is clicked, and the click
method tests the value of the selected radio button, if it is correct that

gets some extra HTML showing a paragraph saying ‘CORRECT!’.
You can even insert variables into the string by using the +
operator like so:
$('div#result1').html('
CORRECT! Your score is '+score+'
');
This is a little more complicated, so it bears taking apart:
'
CORRECT! Your score is '
+
operator which concatenates this with whatever comes next:score
.+
operator to concatenate with the final part:'
'
- a closing paragraph tag.The result is
CORRECT! Your score is 1

before that mix of HTML tags and text is put into the

Our code so far has a bug: once the user has clicked the right answer once for a score of 1, he or she can click it again to get a score of 2, 3, 4, and so on.
We also want to be able to display a new question - what’s the point of a score if there’s only one question?
We can deal with both by using the hide()
and show()
methods I mentioned briefly when introducing jQuery.
To do this we again need to have an element in our HTML that can be hidden or shown: we’ll pick the existing <form>
tag as it contains the whole question.
To make sure we only hide this form, we’ll add an id
attribute to the form like so:
<form id="hidemefirst">
The jQuery to hide that form would look like this:
$('form#hidemefirst').hide();
And below is the same code inserted into the code in the middle of our if
statement:

If you test this you’ll see that a correct answer makes the form vanish so quickly it doesn’t seem quite right. Fortunately, the hide()
method does allow you to specify how quickly it vanishes.
In the parentheses you can enter a number to specify that duration. The default is 400, so anything higher will take longer.
Try the duration 1000, like so:
$('form#hidemefirst').hide(1000);
Now the form hides at a speed which is visible, and less disconcerting for the user.
The reverse approach can be used to reveal a second question at the same time, using the .show()
method.
To show an element, it must first be hidden. So we need to do the following:
The second HTML form might look like this:
<form id="hidemesecond">

Which of the following is spelt correctly?

<input type="radio" name="question2" value="theyre">Their out of they're minds

<input type="radio" name="question2" value="their">They're out of their minds

<input type="radio" name="question2" value="there">There out of there minds

<input type="submit" value="Submit" id="button2">
</form>

The HTML is copied and pasted from the first form, and then changed as follows:
id
for the <form>
tag at the start, and the

tag at the end are changedname
attribute for the radio buttons are changed to "question2"
value
attribute for each answer is changed to reflect the answerid
for the Submit button is changedThe first element we need to manipulate is the form as a whole. This is done with the following code:
$('form#hidemesecond').hide();
This line needs to run as soon as the document is ready, so we put it in the existing script as the first line within the .ready()
method’s function, between the first curly bracket and the line setting the score
variable:

Now when you test your page and click the right answer, the first question should disappear and the second be revealed.
Now that you’ve got as far as the second question, it’s largely a matter of repeating what you did for the first question. You can copy and adapt the code for that so that it works for the second question too.
But where do you put your next lot of code?
The key is to identify the different chunks of code nested in your JavaScript so far. These are:
$(document).ready()
and includes:$('input#button').click()
Your next chunk of code should still be within $(document).ready()
but not within $('input#button').click()
.
So you need to find the closing parenthesis for the latter. This is here:
console.log(answer);
})
You could identify this by working backwards from the end of the code: the last )
closes the ready()
method. The last }
before that closes the function first opened within that method.
So, after those lines:
console.log(answer);
})
…you can add a new raft of code repeating what that .click()
method did for the first Submit button. Here’s what it might look like:

If you’re copying and pasting the previous code and then adapting it, you’ll need to make sure you change all the selectors accordingly, and the correct answer value (although in this case it’s the same).
There’s also nothing to use .show()
on for this second question because you haven’t added a third form.
Once you’ve got this you can repeat it for third, fourth, and further questions.
Of course, there is a lot more you can do here. For starters, it’s not considered good practice in programming to repeat the same code over and over again. You could create a separate function which performs the ‘checking an answer’ process and use that for each question. This would require learning more about creating functions and ‘calling’ them.
You could also make the quiz look nicer, using more jQuery methods to create nicer buttons and effects (the hide and show are a bit clunky for example).
You could add better ways of handling situations where the user doesn’t select anything, for example.
And you could use different types of questions, and ways of testing and responding to them.
Those are all good ways to improve your coding. Just try to think of how those processes might be described, and search for methods and tutorials that give you the skills to tackle them.
Written with StackEdit.


This Codegasm is for the people who aspire to score high marks in exams at free of cost. This site will be your practice ground. You can write various model online tests available here and evaluate yourself based on your score. Questions are collected from various competitive exams and presented here for your self training. you need to register for write the exam Click herefor registration.
Lead Developer
Designer


Top Bus Routes & Bus Schedules
From: 04.00 AM
To: 9.30 PM
( Once 15 minutes )
From: 04.00 AM
To: 9.30 PM
( Once 15 minutes )
From: 06.00 AM
To: 7.30 PM
( Once 30 minutes )
From: 06.00 AM
To: 7.30 PM
( Once 30 minutes )
From: 06.30 AM
To: 8.00 PM
( Once 30 minutes )
From: 06.30 AM
To: 8.00 PM
( Once 30 minutes )
From: 06.30 AM
To: 7.00 PM
( Once 1 hours )
From: 07.00 AM
To: 4.30 PM
( Once 4 hours )


Mup is a music publication program by Arkkra Enterprises. Music is described by a text file that you can create using your favorite editor or the included Mupmate program. Mup then determines how to lay out the music, and generates PostScript output for displaying or printing it, or a MIDI file for playing it.
See the included documents overview.html, mupfeat.html, and the Arkkra website for details.
The prebuilt version Mup available from Arkkra Enterprises has been built for x86_64. It can be compiled for other architectures, and should work back to at least OS-X 10.5
To install, double-click the MupMate-70.dmg disk image to get it mounted. You could then drag and drop the MupMate folder to a convenient place on your hard disk. The global folder /Applications or your personal folder ~/Applications would be a good place. If you are upgrading, be careful not to overwrite any files you might have stored in the folders MupIncludes or MupMusic.
(This is an optional step. It is not required to use the MupMate application.) If you want to invoke Mup directly from a Unix command line (e.g. the Terminal application), you can copy the commands from the folder optional_CmdLineTools/bin to /usr/local/bin or some other folder on your Unix command search path. The associated manual pages are available in optional_CmdLineTools/man/man1.
MupMate is a cross-platform application based on the FLTK library. It looks and behaves like FLTK applications on Linux and Windows platforms, not like a true Mac OS X application. For example, menus, keyboard shortcuts, and file dialogs are different.
If there are international characters in the path to the MupMate application, there may be cosmetic glitches with displaying and editing file paths in dialogs. Despite their garbled appearance, the paths should still work correctly.
If the Preview application fails to convert and display a generated Postscript file of typeset music, only a generic error message is shown.
For many years, the port to Mac was provided by Michael Thies at http://www.mthies.de/mup Additional hints were provided by Christoph Dalitz.


Welcome to My Homepage
I live in Duckburg.
My name is Donald
I live in Duckburg.
I live in Duckburg. div div span
I live in Duckburg.
I live in Duckburg.
I live in Duckburg.
My best friend is Mickey.
I will not be styled.


Hi, you don't run any arbitrary scripts either!
Unfortunately, this website depends on JavaScript and CSS to work. It doesn’t use any Google services but it does use OpenStreetMap, W3W and Leaflet. It requests your location but that is for the functionality of the map API.
Please use a browser that supports JavaScript for full functionality of this site!


{%- include head.html -%}
{%- include navbar.html -%}
{%- include post-info.html -%} {% if page.title == "National Parks Gallery" %} {%- include national-parks-gallery.html -%} {% endif %} {{ content }}
{%- include contactbar.html -%}


Hi, So I want to install emoncms on the Raspberry Pi but I couldn't when I run
sudo apt-get install emoncms
it says
E: Unable to locate package emoncms
so any help? thanks.
Archived Forum |
|
Install emoncms on DebianSubmitted by Guest on Fri, 02/01/2015 - 15:03Hi, So I want to install emoncms on the Raspberry Pi but I couldn't when I run it says E: Unable to locate package emoncms so any help? thanks. » |
Re: Install emoncms on Debian
Did you add the repository location to the "sources list" and use sudo apt-get update first ?
The debian repo hasn't been updated in a while and the last included update had a couple of bugs, these have since been ironed out in the main git repo but not the debian repo.
Paul
Re: Install emoncms on Debian
Ah I see thanks a lot


Your Contacts
First Name
Last Name
Email Address
{% for contact in contacts %}
{{ contact.GivenName }}
{{ contact.Surname }}
{{ contact.EmailAddresses.0.Address }}
{% endfor %}


nips nips2005 nips2005-192 nips2005-192-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Brad Schumitsch, Sebastian Thrun, Gary Bradski, Kunle Olukotun
Abstract: This paper presents a new ﬁlter for online data association problems in high-dimensional spaces. The key innovation is a representation of the data association posterior in information form, in which the “proximity” of objects and tracks are expressed by numerical links. Updating these links requires linear time, compared to exponential time required for computing the exact posterior probabilities. The paper derives the algorithm formally and provides comparative results using data obtained by a real-world camera array and by a large-scale sensor network simulation.
[1] Y. Bar-Shalom and X.-R. Li. Estimation and Tracking: Principles, Techniques, and Software. YBS, Danvers, MA, 1998.
[2] F. Dellaert, S.M. Seitz, C. Thorpe, and S. Thrun. EM, MCMC, and chain ﬂipping for structure from motion with unknown correspondence. Machine Learning, 50(1-2):45–71, 2003.
[3] A. Doucet, J.F.G. de Freitas, and N.J. Gordon, editors. Sequential Monte Carlo Methods in Practice. Springer, 2001.
[4] M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model ﬁtting with applications to image analysis and automated cartography. Communications of the ACM, 24:381–395, 1981.
[5] P. Maybeck. Stochastic Models, Estimation, and Control, Volume 1. Academic Press, 1979.
[6] H. Pasula, S. Russell, M. Ostland, and Y. Ritov. Tracking many objects with many sensors. IJCAI-99.
[7] J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.
[8] J. Shin, N. Lee, S. Thrun, and L. Guibas. Lazy inference on object identities in wireless sensor networks. IPSN-05.
[9] D.B. Reid. An algorithm for tracking multiple targets. IEEE Transactions on Aerospace and Electronic Systems, AC-24:843–854, 1979.
[10] S. Thrun, Y. Liu, D. Koller, A.Y. Ng, Z. Ghahramani, and H. Durrant-Whyte. Simultaneous localization and mapping with sparse extended information ﬁlters. IJRR, 23(7/8), 2004.
[11] D. Fox, J. Hightower, L. Lioa, D. Schulz, and G. Borriello. Bayesian Filtering for Location Estimation. IEEE Pervasive Computing, 2003.


Before Change
files_to_download = OriginalFile.objects.filter(
samples__in=samples.values("pk"), is_downloaded=False)
downloaded_urls = []
for original_file in files_to_download:
// We don"t need to create multiple downloaders for the same file.
if original_file.source_url in downloaded_urls:
continue
// There is already a downloader job associated with this file.
old_assocs = DownloaderJobOriginalFileAssociation.objects.filter(
original_file__source_url=original_file.source_url)
if len(old_assocs) > 0:
logger.debug("We found an existing DownloaderJob for this file/url.",
After Change
// We don"t need to create multiple downloaders for the same file.
// However, we do want to associate original_files with the
// DownloaderJobs that will download them.
if original_file.source_url in download_urls_with_jobs.keys():
asoc = DownloaderJobOriginalFileAssociation()
asoc.downloader_job = download_urls_with_jobs[original_file.source_url]
asoc.original_file = original_file
asoc.save()
continue
// There is already a downloader job associated with this file.
old_assocs = DownloaderJobOriginalFileAssociation.objects.filter(
original_file__source_url=original_file.source_url)
if len(old_assocs) > 0:
logger.debug("We found an existing DownloaderJob for this file/url.",


Class: Utopia::Tags::Environment
- Inherits:
-
Object
- Object
- Utopia::Tags::Environment
- Defined in:
- lib/utopia/tags/environment.rb
Overview
A conditional tag which only exposes it’s content in certain environments.
Instance Method Summary collapse
-
#call(document, state) ⇒ Object
-
#initialize(environment) ⇒ Environment
constructor
A new instance of Environment.
Constructor Details
#initialize(environment) ⇒ Environment
Returns a new instance of Environment
26 27 28 |
# File 'lib/utopia/tags/environment.rb', line 26 def initialize(environment) @environment = environment end |
Instance Method Details
#call(document, state) ⇒ Object
TODO:
improve implementation
33 34 35 36 37 38 39 |
# File 'lib/utopia/tags/environment.rb', line 33 def call(document, state) only = state[:only].split(",").collect(&:to_sym) rescue [] if only.include?(@environment) document.parse_markup(state.content) end end |


The Data Scientista Society has been providing data scientistas an essential way to support one another in the Triangle since May 23, 2016. If you've watched us make a difference, and you are ready to level-up, join Data Scientista Society Funders now, for the purpose of submitting membership dues: $50. Else, keep reading!
The mission of the Data Scientista Society is to educate women pursuing careers related to data science in techniques and competencies needed for career advancement and achieving leadership positions in the field.
The modus operandi of the Data Scientista Society is to build relationships among co-located women enthusiastic about data, a.k.a data scientistas, while also practicing mutual supportiveness towards competency, success, and leadership.
Founding members are committed to this modus operandi.
Even though we are launching official membership, all our programs are available to all participants of the regular Data Scientista Society site.
If you are ready to be involved in a cooperative effort, and we’ve proven to you that our programs and methods will help us cooperate to achieve the mission, and you are already receiving the benefits of fellowship, or you want to give for any reason, then you are are ready to level up!
Join the Meetup group Data Scientista Society Funders, supply your LinkedIn profile, designate yourself as a founding member, and submit $50 in dues for 2020.
A funding plan is published online. The bulk of the funds will go towards meeting space rentals and app & platform fees.
As a proper 501(c)(3) organization, we will have a treasurer’s report at every meeting, and perform many other customary legal and financial duties.
While this formality is extra work, we believe it best positions us to achieve or our mission and grow.
All organizational work is on a volunteer basis. Our officers and their understudies are intensely cooperating to divide the duties.
Any questions? Reach out to datascientistasociety@gmail.com.


---
layout: default
title: Fantastic Activist
cover: 'assets/images/fantastic-activist-interviews.jpg'
class: 'home-template'
navigation: True
logo: 'assets/images/fantastic-activist-logo-white.png'
current: home
---
{{ site.name }}
{{ site.description }}
Scroll Down
{% include loop.html %}


The exercise below will test your newly acquired knowledge on the Boyer-Moore algorithm. Assume the two characters highlighted in red were just compared and it is discovered they don't match. Please click the index of the master string that will be compared
after the current mismatch and then the index of the substring that will be compared to it. When you are finished hit
Check Answer to see if you are correct. The values in the strings that have already been compared are highlighted in green so there is no confusion, and the characters that were just mismatched are in red. In effect, what you are doing is tracing the string
sub_temp.join("") through the string
master_temp.join("") using the KMP Algorithm for just one step.


Research
Overview
My research covers mathematical modeling and numerical simulation of acoustic systems. My approach to
modeling and simulation is guided by several core concepts in differential geometry, including conformal
invariance, differential forms, and geometric variational principles. These geometric tools can be used to overcome
many difficulties and limitations that exist in standard Newtonian models and coordinate-based Euclidean spaces.
Currently, I am investigating applications of this geometric approach in three areas of computational acoustics:
boundary modeling for acoustic wave simulation; sound source modeling for computer animation;
and physical simulation of musical instruments, speech, and hearing. My research integrates knowledge and
practices from multiple academic disciplines, including physics, mathematics, computer science, and biology, and
it provides tools for industrial and scientific applications such as experimental validation, prototyping,
data generation, and structural analysis.
Boundary Modeling for Acoustic Wave Simulation
I have developed differential geometric methods for handling boundaries in acoustic wave propagation simulations, including
a wave-equation solver for infinite spacetime domains and an absorbing boundary layer treatment. A promising
future direction of my research in this area involves developing boundary flattening methods for curved obstacles.
A common theme in this work is the discovery of conformally invariant transformations for wave propagation, an approach
that derives from Felix Klein's Erlangen program. Application areas include spatial audio, architectural acoustics,
artificial reverberation, and hearing devices.
Sound Source Modeling for Computer Animation
Below is an image of four of the harmonic modes of the Stanford
bunny mesh. From left to right, I've plotted the \(0^{th}\), \(1^{st}\), \(2^{nd}\), and \(6^{th}\) modes. In my methodology, I compute the
displacement of each mode by numerically solving the Helmholtz equation at every vertex on
the triangulated mesh. The purple portions represent regions where the displacement is maximally negative, and the red portions
represent where it is maximally positive. Notably, the governing model is expressed using coordinate-free differential forms, which allow one to
more easily simulate physics on arbitrary geometries. I am interested
in using my methodology to synthesize sounds emitted by complex
geometries and synchronize them with interactive physics-based computer animations.
Physical Simulation of Musical Instruments, Speech, and Hearing
Physical simulation is the computational
emulation of natural phenomena using physics-based models. Physical simulation is useful for producing
very accurate emulations and offering intuitive user control of modeling parameters. In my research, I have
developed physics-based modal synthesis and finite-difference time-domain simulations of musical instruments
and human speech. Currently, I am interested in using geometric variational principles to simulate fluid-structure
interactions in wind instruments, the vocal folds, and the mammalian cochlea. Application areas include musical
instrument design, speech therapy, and hearing devices.


Software Engineer/Founding Team Member (at) OmniLabs, Inc. with 4 years of experience in Android SDK Engineering, DSP and Interaction Design. Avid dreamer, doodler, caffeine-dependent life-form and FOSS lover.
Lead organizer (at) Google Developer Group, New Delhi
Recommended by 4 out of 5 people that recommend things.
Omnilabs, Inc. | June 2014 - Current.
Silverpush | Nov 2013 - June 2014.
Mindfire Solutions | July 2013 - Nov 2013.
B.Tech[Computer Science] | Gautam Budh Technical University
Android, Java, UI/UX, Web Development, NodeJS, C


[gecode-users] Trigonometric functions in Gecode
Jonathan Skovhus Andersen
jonathanskovhus at gmail.com
Thu Mar 17 10:34:35 CET 2011
Hello,
I'm a beginner in Gecode, so there is something that I don't quite
understand. First of all I understand that Gecode only uses integers. But in
my implementation I have to use arcus tangent which gives me some problems.
I have to following line in my code:
*for(int i = 0; i < p.size(); i++)*
* rel(*this, angle[i] ==
180/3.1415*atan((sqrt(sqr(coordinatesArray[i][0]-x)+sqr(coordinatesArray[i][1]-y))/constantsArray[2][0])));
*
Which gives me the following error:
*Error 12 error C2665: 'atan' : none of the 3 overloads could convert all
the argument types ...source.cpp 107 TSP*
Is there any way to work around this? I don't really have any ideas...
*Regards,*
*Jonathan Skovhus Andersen*
*Aalborg University*
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{% component '@apostrophecms/template:inject' with { where: 'body', end: 'append' } %}
{% block extraBody %}{% endblock %}
{% if data.scene == 'apos' %}
{% endif %}
{# Scripts must load after apos-modal in the DOM #}
{# This call is still here for backwards compatibility, but does nothing #}
{{ apos.asset.scripts(data.scene) }}
{# For project-level webpack injection in dev environments #}
{% block afterAposScripts %}{% endblock %}
{# Automatically does nothing in production #}
{{ apos.asset.refreshOnRestart() }}


ROS Domain Verification
This is an easy way to prove that you own a domain by adding a TXT record to your website's DNS records.
- Verification status
- Domain name
- Verification TXT record value
- Last checked
For developers
Getting verified
In order to be verified, you need to set a TXT record for
with the value


{{/*
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
*/}}
{{ define "animation-header" }}
Apache Airflow™
Airflow™ is a platform created by the community to programmatically author, schedule and monitor
workflows.
{{ partial "buttons/button-filled" (dict "text" "Install Airflow™") }}


You will be asked to mark the dead buttons because the software is not sure of the conditions alive of each button. Press the button and that block will be marked dead. You can unmark them using Control while pressing the mouse. While you end the mark, the system will ask for your partner's agreement. If your partner has some other ideas, he/she will remark(mark or unmark) them, and then system will let you know and asks for your agreement. The marking will loop again and again until one agrees the other person. While the mark ends, the points alive will be reported to you and your partner.


Hello! I'm an experienced copywriter with roots (and a B.A.) in creative writing. I spend my days writing web content, crafting marketing emails, and triple-checking my grammar.
I'm happiest in a collaborative, forward-thinking environment (with good coffee).
Check out my video résumé: kaytee.me/video
How would I describe myself?
Any opportunity to bring a project to life through fresh and experimental writing is definitely my jam. Whether it's blogs, creative content, newsletters, or the occasional southern gothic short story, there's always room for a memorable flourish.
Because my writing is founded by a love for language, I approach every project with thoughtful creativity and precision.
I've worked with agencies, tech startups, universities, small businesses and non-profits creating and curating awesome content that readers can really get into.
In my current portfolio, you can find examples of web content, landing pages, email marketing, newsletters, videos, and blog posts. I'm happy to provide project-specific samples upon request.
View my portfolio here: www.clippings.me/kayteechamplin.
I'd love to hear about your project needs and chat about how my skills can contribute.
Let's chat!
| Video Resume


Here you can integrate a whole project previously saved with Linda. You only have to download the zip file produced when you saved your project from Linda and that's it.


Touch anywhere to begin...
Pick up your item from drawer 12
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Ford's Mustang Mach E is set to compete with Tesla Model Y
Ford Mustang Mach-E marks the foray of the automobile giant into the electric vehicle world, where it will compete with the likes of Tesla and Porsche. Watch the video for more on the new car.


| Package | Description |
|---|---|
| com.compomics.util.experiment.io.identification |
Experiment identification classes.
|
| com.compomics.util.experiment.io.identification.idfilereaders |
Experiment classes related to reading search engine files.
|
| Modifier and Type | Method and Description |
|---|---|
IdfileReader |
IdfileReaderFactory.getFileReader(File aFile)
This method returns the proper identification file reader depending on
the format of the provided file.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AndromedaIdfileReader
This IdfileReader reads identifications from an Andromeda result file.
|
class |
DirecTagIdfileReader
An identification file reader for Direct tag results.
|
class |
MascotIdfileReader |
class |
MsAmandaIdfileReader
This IdfileReader reads identifications from an MS Amanda csv result file.
|
class |
MzIdentMLIdfileReader
This IdfileReader reads identifications from an mzIdentML result file.
|
class |
NovorIdfileReader
This IdfileReader reads identifications from a Novor csv result file.
|
class |
OnyaseIdfileReader
Id file reader for the scripts in Onyase.
|
class |
PepNovoIdfileReader
This class can be used to parse PepNovo identification files.
|
class |
PepxmlIdfileReader
Simple IdfileReader for Pepxml files.
|
class |
PNovoIdfileReader
This class can be used to parse pNovo identification files.
|
class |
TideIdfileReader
This IdfileReader reads identifications from an Tide tsv results file.
|
class |
XTandemIdfileReader |
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Set up by Act No. 14 of 2002 vide Extraordinary Gazette notification of Government of Himachal Pradesh dated May 23, 2002. Approved by the University Grants Commission under section 2(f) of the UGC Act, vide letter no. F.9-10/2002(CPP-I) dated 09 Dec. 2002.
The University commenced academic activities from July 2002 with undergraduate B.Tech. degree programs in Electronics & Communication Engineering, Computer Science & Engineering, Information Technology, Bioinformatics, and Civil Engineering (from July 2003).
The University is offering M.Tech (Electronics & Communication Engineering), M.Tech (Computer Science & Engineering), M.Tech (Applied and Computational Mathematics), M.Tech (Biotechnology), M.Tech (Environmental Engineering), M.Tech (Structural Engineering), M.Tech(Construction Management), M.Tech(Computational Biology) and M Pharma.
The University is offering Ph.D. in Bioinformatics, Biotechnology, Civil Engineering, Computer Science & Engineering, Electronics & Communication Engineering, Humanities & Social Sciences, Mathematics, Pharmacy and Physics & Materials Science.
Vision
To become a Centre of Excellence in the field of IT and related emerging areas in education, training and research comparable to the best in the world for producing professionals who shall be leaders in innovation, entrepreneurship, creativity and management.
Mission
1. To develop as a benchmark University in emerging technologies;
2. To provide state of the art teaching-learning process and a stimulating R&D environment; and
3. To harness human capital for sustainable competitive edge and social relevance.
JUIT Youth Club (JYC) is the name of enthusiasm, development, consistency and effort. It is the body which widens student's view from academics to various fields such as sports, literary, cultural and technical and provides opportunity to explore them. It encourages in student's expression, celebration, recognition and integration. It helps students acquire qualities like team work, sportsmanship and leadership. Not only this, it retreats students from academics load by organizing events, fests and other extra-curricular activities. Focusing on technical, literary, sports, and cultural scopes these competitive activities, apart from serving as a relief from intense academic load, present an opportunity to instill confidence, encourage teamwork and give students a strong sense of achievement and belonging.