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')
lr = tf.placeholder(tf.float32, name='learning_rate')
return inputs_real, inputs_z, lr
"""
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)
"""
drop = 0.1
with tf.variable_scope('discriminator', reuse=reuse):
#print ('Images:',images.shape)
#Input is 28x28x3
x1 = tf.layers.conv2d(images, 64, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
padding='same', use_bias=False, activation=None)
x1 = tf.layers.batch_normalization(x1, training=True)
x1 = tf.layers.dropout(x1, rate=drop, training=True)
relu1 = tf.maximum(alpha * x1, x1)
#relu1 = tf.nn.dropout(relu1, drop)
#16x16x32
#print ('L1:',relu1.shape)
x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
padding='same',use_bias=False, activation=None)
bn2 = tf.layers.batch_normalization(x2, training=True)
bn2 = tf.layers.dropout(bn2, rate=drop, training=True)
relu2 = tf.maximum(alpha * bn2, bn2)
#relu2 = tf.nn.dropout(relu2, drop)
#8x8x128
#print ('L2:',relu2.shape)
x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
padding='same',use_bias=False, activation=None)
bn3 = tf.layers.batch_normalization(x3, training=True)
bn3 = tf.layers.dropout(bn3, rate=drop, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
#relu3 = tf.nn.dropout(relu3, drop)
#print ('L3:',relu3.shape)
#Flatten
flat = tf.reshape(relu3, (-1, 4*4*256))
#print ('Out:', flat.shape)
logits = tf.layers.dense(flat, 1)
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
"""
drop = 0.5
with tf.variable_scope('generator', reuse=(not is_train)):
#print ('z:',z.shape)
x1 = tf.layers.dense(z, 4*4*256)
x1 = tf.reshape(x1, (-1, 4, 4, 256))
bn1 = tf.layers.batch_normalization(x1, training=is_train)
bn1 = tf.layers.dropout(bn1, rate=drop, training=True)
relu1 = tf.maximum(alpha * bn1, bn1)
#print ('GL1:',relu1.shape)
#256
x2 = tf.layers.conv2d_transpose(relu1, 128, 4, strides=1, kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
padding='valid')
#x2 = tf.layers.dropout(x2, rate=drop, training=True)
bn2 = tf.layers.batch_normalization(x2, training=is_train)
relu2 = tf.maximum(alpha * bn2, bn2)
#print ('GL2:',relu2.shape)
#128
x3 = tf.layers.conv2d_transpose(relu2, 64, 5, strides=2, kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(),
padding='same')
bn3 = tf.layers.batch_normalization(x3, training=is_train)
bn3 = tf.layers.dropout(bn3, rate=drop, training=True)
relu3 = tf.maximum(alpha * bn3, bn3)
#print ('GL3:',relu3.shape)
#Output
logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5, strides=2, padding='same')
#print ('Out:',logits.shape)
out = tf.tanh(logits)
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)
import numpy as np
def model_loss(input_real, input_z, out_channel_dim):
"""
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)
"""
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
#Label Smoothing, i.e. if you have two target labels: Real=1 and Fake=0, then for each incoming sample, if it is real, then replace the label with a random number between 0.7 and 1.2, and if it is a fake sample, replace it with 0.0 and 0.3 (for example).
#Salimans et. al. 2016
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*np.random.uniform(0.0, 0.9)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)*np.random.uniform(0.7, 0.9)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_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)
"""
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')]
#optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_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")
"""
input_real, input_z, learn_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
steps = 10
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
steps += 1
#Normalize
batch_images *= 2
#Sample Noise for Generator
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
#Optimizer
_ = sess.run(d_opt, feed_dict={input_real: batch_images,
input_z: batch_z, learn_rate: learning_rate})
_ = sess.run(g_opt, feed_dict={input_real: batch_images,
input_z: batch_z, learn_rate: learning_rate})
if steps % 10 == 0:
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i + 1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if steps % 100 == 0:
show_generator_output(sess, 16, input_z, data_shape[3], 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 = 16
z_dim = 100
learning_rate = 0.0005
beta1 = 0.2
"""
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 = 32
z_dim = 100
learning_rate = 0.0006
beta1 = 0.3
"""
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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What gets you in a haunted house movie are the quiet moments, when you expect something to happen. A good haunted house movie incubates this, stacks up a few false positives, then knocks them down all at once in a crash of cymbals, only to start the whole process over. Think The Shining, Amityville, even Poltergeist. Jan de Bont's The Haunting, a liberal remake of the 1962 Haunting of Hill House, based on the Shirley Jackson novel, doesn't so much concern itself with this second stage--remounting the tension. Instead it jacks it up and keeps it there, until the baseline (the quiet moments) are the moments when only some of the faces on the wall are tracking a character's progress across a room, down a hall, through one of the countless thirty-foot tall doors of Hill House. This isn't to say there isn't escalation, though.  The place is an absolute amusement park of horror, the ideal setting for Dr. Marrow (Liam Neeson) to stage an experiment on fear; he wants to know what use sweaty palms, rapid heartbeats, pupil dilation and all that have in today's world. His idea is that they're vestigial responses, of no use anymore. How observing a handful of insomniacs is supposed to answer his question is a bit murky, though, which is just as well, as the sweaty palms etc are never given a chance to redeem themselves. So it goes. Suffice it to say that the three subjects--homebody Eleanor (Lily Taylor), bisexual party gal Theo (Catherine-Zeta-Jones), and sardonic Owen (the intractable Luke Wilson)--walk the halls all night trying to get to the bottom of the many strange goings-on. Voices, cherubic faces in the curtains, interdimensional gongs, the whole gamut. And of course no two of them ever see the same phenomenon simultaneously, which isn't so much a trick of camerawork as an accepted convention. Where The Haunting departs from tradition, however, is in the noticeable absence of sexual tension. Nevermind that Catherine Zeta-Jones is splashed all over the trailer, still in supermodel shape from Zorro and Entrapment. She's evidently just window dressing. The one or two token innuendoes (invitations?) her Theo directs Eleanor's way hardly even register, perhaps because Eleanor--suddenly the central character--has other things to worry about. Like distinguishing the scary but good ghosts from the scary and bad ghosts, and how she may or may not fit into the history of Hill House. About this scary and bad ghost: aside from being a Scrooge lookalike, he seems more intent upon frightening Eleanor & Co. to death than upon actually killing them, which--judging by his insanity level and the power he wields--would be a short night's work. But then we wouldn't get startled, right? And The Haunting does startle, something of a feat in itself, taking into account a slightly jaded audience. It does this by not allowing us to anticipate the narrative, and it does that by more or less replacing the narrative with special effects. The same ones introduced with The Frighteners and abused in The Mummy, both of which fell similarly short on story but spent a lot of money doing so. Like them, The Haunting is best seen in the theater, where you can be wowed by sound and lights and not have time to consider what might be missing. (c) 1999 Stephen Graham Jones
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Hilaria Baldwin says she suffered her second miscarriage this year
by New York PostHilaria Baldwin revealed the heartbreaking news that she suffered a miscarriage — her second one this year.
“We are very sad to share that today we learned that our baby passed away at 4 months,” Hilaria, who has four children with husband Alec Baldwin, announced on Instagram Monday night.
The couple was expecting a girl.
Hilaria, 35, said she learned of the miscarriage during a scan on Monday — and the results left her “devastated.”
Despite the heartache, Hilaria wrote that, “even though we are not ok right now, we will be.”
A tearful Hilaria posted a video with the announcement alongside the couple’s eldest daughter, 6-year-old Carmen.
“Thank you for saying you’re sorry, Mommy’s sad,” Hilaria tells her daughter in the clip.


Authenticating with AuthSubThe AuthSub mechanism enables you to write web applications that acquire authenticated access Google Data services, without having to write code that handles user credentials. See » http://code.google.com/apis/accounts/AuthForWebApps.html for more information about Google Data AuthSub authentication. The Google documentation says the ClientLogin mechanism is appropriate for "installed applications" whereas the AuthSub mechanism is for "web applications." The difference is that AuthSub requires interaction from the user, and a browser interface that can react to redirection requests. The ClientLogin solution uses PHP code to supply the account credentials; the user is not required to enter her credentials interactively. The account credentials supplied via the AuthSub mechanism are entered by the user of the web application. Therefore they must be account credentials that are known to that user.
Creating an AuthSub authenticated Http ClientYour PHP application should provide a hyperlink to the Google URL that performs authentication. The static function Zend_Gdata_AuthSub::getAuthSubTokenUri() provides the correct URL. The arguments to this function include the URL to your PHP application so that Google can redirect the user's browser back to your application after the user's credentials have been verified. After Google's authentication server redirects the user's browser back to the current application, a GET request parameter is set, called token. The value of this parameter is a single-use token that can be used for authenticated access. This token can be converted into a multi-use token and stored in your session. Then use the token value in a call to Zend_Gdata_AuthSub::getHttpClient(). This function returns an instance of Zend_Http_Client, with appropriate headers set so that subsequent requests your application submits using that HTTP Client are also authenticated. Below is an example of PHP code for a web application to acquire authentication to use the Google Calendar service and create a Zend_Gdata client object using that authenticated HTTP Client.
Revoking AuthSub authenticationTo terminate the authenticated status of a given token, use the Zend_Gdata_AuthSub::AuthSubRevokeToken() static function. Otherwise, the token is still valid for some time.
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Nextcloud Deployment Recommendations¶
What is the best way to install and maintain Nextcloud? The answer to that is “it depends” because every Nextcloud customer has their own particular needs and IT infrastructure. Nextcloud and the LAMP stack are highly-configurable, so we will present three typical scenarios and make best-practice recommendations for both software and hardware.
General Recommendations¶
Note
Whatever the size of your organization, always keep one thing in mind: the amount of data stored in Nextcloud will only grow. Plan ahead.
Consider setting up a scale-out deployment, or using Federated Cloud Sharing to keep individual Nextcloud instances to a manageable size.
- Operating system: Linux.
- Web server: Apache 2.4.
- Database: MySQL/MariaDB.
- PHP 5.6+. PHP 5.6 is the minimum supported version. We recommend to deploy
on PHP 7 if possible. This version is known to offer significan performance
advantages.
mod_php
is the recommended Apache module due to vendor support and ease of configuration.php-fpm
with Apache Event MPM (or nginx) is an alternative with potentially better scalability in high load and limited RAM environments. For the best results we recommend working with the Nextcloud GmbH enterprise suppor team for large deployments.
Small Workgroups or Departments¶
- Number of users
Up to 150 users.
- Storage size
100 GB to 10TB.
- High availability level
Zero-downtime backups via Btrfs snapshots, component failure leads to interruption of service. Alternate backup scheme on other filesystems: nightly backups with service interruption.
Recommended System Requirements¶
One machine running the application server, Web server, database server and local storage.
Authentication via an existing LDAP or Active Directory server.
- Components
One server with at least 2 CPU cores, 16GB RAM, local storage as needed.
- Operating system
Enterprise-grade Linux distribution with full support from OS vendor. We recommend Red Hat Enterprise Linux or SUSE Linux Enterprise Server 12.
- SSL Configuration
The SSL termination is done in Apache. A standard SSL certificate is needed, installed according to the Apache documentation.
- Load Balancer
None.
- Database
MySQL, MariaDB or PostgreSQL. We currently recommend MySQL / MariaDB, as our customers have had good experiences when moving to a Galera cluster to scale the DB.
- Backup
Install Nextcloud, Nextcloud data directory and database on Btrfs filesystem. Make regular snapshots at desired intervals for zero downtime backups. Mount DB partitions with the “nodatacow” option to prevent fragmentation.
Alternatively, make nightly backups with service interruption:
- Shut down Apache.
- Create database dump.
- Push data directory to backup.
- Push database dump to backup.
- Start Apache.
Then optionally rsync to a backup storage or tape backup. (See the Maintenance section of the Administration manual for tips on backups and restores.)
- Authentication
User authentication via one or several LDAP or Active Directory servers. (See User Authentication with LDAP for information on configuring Nextcloud to use LDAP and AD.)
- Session Management
Local session management on the application server. PHP sessions are stored in a tmpfs mounted at the operating system-specific session storage location. You can find out where that is by running
grep -R 'session.save_path' /etc/php5
and then add it to the/etc/fstab
file, for example:echo "tmpfs /var/lib/php5/pool-www tmpfs defaults,noatime,mode=1777 0 0" >> /etc/fstab
.
- Memory Caching
A memcache speeds up server performance, and Nextcloud supports four memcaches; refer to Configuring Memory Caching for information on selecting and configuring a memcache.
- Storage
Local storage.
Mid-sized Enterprises¶
- Number of users
150 to 1,000 users.
- Storage size
Up to 200TB.
- High availability level
Every component is fully redundant and can fail without service interruption. Backups without service interruption
Recommended System Requirements¶
2 to 4 application servers.
A cluster of two database servers.
Storage on an NFS server.
Authentication via an existing LDAP or Active Directory server.
- Components
- 2 to 4 application servers with 4 sockets and 32GB RAM.
- 2 DB servers with 4 sockets and 64GB RAM.
- 1 HAproxy load balancer with 2 sockets and 16GB RAM.
- NFS storage server as needed.
- Operating system
Enterprise grade Linux distribution with full support from OS vendor. Red Hat Enterprise Linux or SUSE Linux Enterprise Server 12 are recommended.
- SSL Configuration
The SSL termination is done in the HAProxy load balancer. A standard SSL certificate is needed, installed according to the HAProxy documentation.
- Load Balancer
HAProxy running on a dedicated server in front of the application servers. Sticky session needs to be used because of local session management on the application servers.
- Database
MySQL/MariaDB Galera cluster with master-master replication.
- Backup
Minimum daily backup without downtime. All MySQL/MariaDB statements should be replicated to a backup MySQL/MariaDB slave instance.
- Create a snapshot on the NFS storage server.
- At the same time stop the MySQL replication.
- Create a MySQL dump of the backup slave.
- Push the NFS snapshot to the backup.
- Push the MySQL dump to the backup.
- Delete the NFS snapshot.
- Restart MySQL replication.
- Authentication
User authentication via one or several LDAP or Active Directory servers. (See User Authentication with LDAP for information on configuring Nextcloud to use LDAP and AD.)
- LDAP
Read-only slaves should be deployed on every application server for optimal scalability
- Session Management
Session management on the application server. PHP sessions are stored in a tmpfs mounted at the operating system-specific session storage location. You can find out where that is by running
grep -R 'session.save_path' /etc/php5
and then add it to the/etc/fstab
file, for example:echo "tmpfs /var/lib/php5/pool-www tmpfs defaults,noatime,mode=1777 0 0" >> /etc/fstab
.
- Memory Caching
A memcache speeds up server performance, and Nextcloud supports four memcaches; refer to Configuring Memory Caching for information on selecting and configuring a memcache.
- Storage
Use an off-the-shelf NFS solution, such as IBM Elastic Storage or RedHat Ceph.
Large Enterprises and Service Providers¶
- Number of users
5,000 to >100,000 users.
- Storage size
Up to 1 petabyte.
- High availabily level
Every component is fully redundant and can fail without service interruption. Backups without service interruption
Recommended System Requirements¶
4 to 20 application/Web servers.
A cluster of two or more database servers.
Storage is an NFS server, or an object store that is S3 compatible.
Cloud federation for a distributed setup over several data centers.
Authentication via an existing LDAP or Active Directory server, or SAML.
- Components
- 4 to 20 application servers with 4 sockets and 64GB RAM.
- 4 DB servers with 4 sockets and 128GB RAM
- 2 Hardware load balancer, for example BIG IP from F5
- NFS storage server as needed.
- Operating system
RHEL 7 with latest service packs.
- SSL Configuration
The SSL termination is done in the load balancer. A standard SSL certificate is needed, installed according to the load balancer documentation.
- Load Balancer
A redundant hardware load-balancer with heartbeat, for example F5 Big-IP.
- Database
MySQL/MariaDB Galera Cluster with 4x master – master replication.
- Backup
Minimum daily backup without downtime. All MySQL/MariaDB statements should be replicated to a backup MySQL/MariaDB slave instance.
- Create a snapshot on the NFS storage server.
- At the same time stop the MySQL replication.
- Create a MySQL dump of the backup slave.
- Push the NFS snapshot to the backup.
- Push the MySQL dump to the backup.
- Delete the NFS snapshot.
- Restart MySQL replication.
- Authentication
User authentication via one or several LDAP or Active Directory servers, or SAML/Shibboleth. (See User Authentication with LDAP.)
- LDAP
Read-only slaves should be deployed on every application server for optimal scalability.
- Session Management
Redis should be used for the session management storage.
- Caching
Redis for distributed in-memory caching (see Configuring Memory Caching).
- Storage
An off-the-shelf NFS solution should be used. Examples are IBM Elastic Storage or RedHAT Ceph. Optionally, an S3 compatible object store can also be used.
Hardware Considerations¶
- Solid-state drives (SSDs) for I/O.
- Separate hard disks for storage and database, SSDs for databases.
- Multiple network interfaces to distribute server synchronisation and backend traffic across multiple subnets.
Single Machine / Scale-Up Deployment¶
The single-machine deployment is widely used in the community.
Pros:
- Easy setup: no session storage daemon, use tmpfs and memory caching to enhance performance, local storage.
- No network latency to consider.
- To scale buy a bigger CPU, more memory, larger hard drive, or additional hard drives.
Cons:
- Fewer high availability options.
- The amount of data in Nextcloud tends to continually grow. Eventually a single machine will not scale; I/O performance decreases and becomes a bottleneck with multiple up- and downloads, even with solid-state drives.
Scale-Out Deployment¶
Provider setup:
- DNS round robin to HAProxy servers (2-n, SSL offloading, cache static resources)
- Least load to Apache servers (2-n)
- Memcached/Redis for shared session storage (2-n)
- Database cluster with single Master, multiple slaves and proxy to split requests accordingly (2-n)
- GPFS or Ceph via phprados (2-n, 3 to be safe, Ceph 10+ nodes to see speed benefits under load)
Pros:
- Components can be scaled as needed.
- High availability.
- Test migrations easier.
Cons:
- More complicated to setup.
- Network becomes the bottleneck (10GB Ethernet recommended).
- Currently DB filecache table will grow rapidly, making migrations painful in case the table is altered.
What About Nginx / PHP-FPM?¶
Could be used instead of HAproxy as the load balancer. But on uploads stores the whole file on disk before handing it over to PHP-FPM.
A Single Master DB is Single Point of Failure, Does Not Scale¶
When master fails another slave can become master. However, the increased complexity carries some risks: Multi-master has the risk of split brain, and deadlocks. Nextcloud tries to solve the problem of deadlocks with high-level file locking.
Software Considerations¶
Operating System¶
We are dependent on distributions that offer an easy way to install the various components in up-to-date versions. Nextcloud has a partnership with RedHat and SUSE for customers who need commercial support. Canonical, the parent company of Ubuntu Linux, also offers enterprise service and support. Debian and Ubuntu are free of cost, and include newer software packages. CentOS is the community-supported free-of-cost Red Hat Enterprise Linux clone. openSUSE is community-supported, and includes many of the same system administration tools as SUSE Linux Enterprise Server.
Web server¶
Taking Apache and Nginx as the contenders, Apache with mod_php is currently the best option, as Nginx does not support all features necessary for enterprise deployments. Mod_php is recommended instead of PHP_FPM, because in scale-out deployments separate PHP pools are simply not necessary.
Relational Database¶
More often than not the customer already has an opinion on what database to use. In general, the recommendation is to use what their database administrator is most familiar with. Taking into account what we are seeing at customer deployments, we recommend MySQL/MariaDB in a master-slave deployment with a MySQL proxy in front of them to send updates to master, and selects to the slave(s).
The second best option is PostgreSQL (alter table does not lock table, which makes migration less painful) although we have yet to find a customer who uses a master-slave setup.
What about the other DBMS?
- Sqlite is adequate for simple testing, and for low-load single-user deployments. It is not adequate for production systems.
- Microsoft SQL Server is not a supported option.
- Oracle DB is the de facto standard at large enterprises and is currently only supported with an Nextcloud Enterprise Support Subscription only.
File Storage¶
While many customers are starting with NFS, sooner or later that requires scale-out storage. Currently the options are GPFS or GlusterFS, or an object store protocol like S3 or Swift. S3 also allows access to Ceph Storage.
Session Storage¶
- Redis: provides persistence, nice graphical inspection tools available, supports Nextcloud high-level file locking.
- If Shibboleth is a requirement you must use Memcached, and it can also be used to scale-out shibd session storage (see Memcache StorageService).


To reserve any of the following books for parents/carers and children dealing with fear and anxiety to your local library, click on the links to reserve them from our online catalogue, visit any of our libraries, or contact us on 01473 351249 or<EMAIL_ADDRESS>You can sign up for a library card online for free. You can sign your child up for a library card at any age.
Why Do I Feel Scared? A first look at being brave, by Pat Thomas & Lesley Harker
“This delightful picture book explores what it means to be brave. Young children can discover that there are many ways of being brave, from standing up for yourself or others, to trying new things. Discussion boxes offer an adult opportunities to discuss the issues directly with their child audience.”
Who’s Afraid of the Dark?: a story about being brave, by Nancy Parent, Britt Allcroft & W. Awdry
“When Gordon gets asked to pull the night train, he’s too embarrassed to admit he’s afraid of the dark! Can Thomas help his friend overcome his fear and get the job done?”
 She’s off to climb to the top of the tallest tree in the jungle and she’s going to do it all on her own! Except that there’s someone following close behind - someone with claws and stripes and rather sharp teeth!”
A Little Bit Brave, by Nicola Kinner
“Logan is a stay-at-home bunny - but he’s about to discover how brave he really is. It’s time for his first adventure, and he doesn’t want to go. But there’s an amazing world outside, if he can just pluck up courage to look.
“This is a funny, reassuring story which shows that we’re all braver than we think.”
Max the Brave, by Ed Vere
“This is Max. Max the Brave, Max the Fearless, Max the Mouse-catcher. But, in order to be a Mouse-catcher, Max needs to know what a mouse is, so off he goes to find out.”
Take a Deep Breath, by Sue Graves & Desideria Guicciardini
“Josh doesn’t like people looking at him and he’s in the school play! Can Miss Button help him to be brave? And can Miss Button be brave when she is faced by something she doesn’t like?”
The Brave Beast, by Chris Judge
“The Beast is back and this time its up to him to help restore peace to an island, which is being threatened by a scary monster. Determined to face his fears, the Beast sets out to find the monster. But, perhaps, the island monster isn’t so scary after all.”
Emily Brown and the Thing, by Cressida Cowell & Neal Layton
“Emily Brown and her old grey rabbit, Stanley, are trying to sleep. But a noise is keeping them awake. When they find the Thing crying on the windowsill, intrepid explorers Emily and Stanley, set out to bring him everything he wants.”
William Worrydactyl, by Brian Moses & Mike Gordon
“William Worrysaurus was a little dinosaur with big worries. He worried every day about everything, from being scared of the dark to not being as good as his friends. Will he ever be able to stop worrying?
“Written by Brian Moses, this humorous picture book explores different things that children might worry about, from sleeping in the dark to going to school. Ways of dealing with these fears are then set out showing that there are ways to cope with these, sometimes overwhelming, emotions.
“The clever and funny illustrations, by internationally renowned illustrator Mike Gordon, bring a light-touch to these stories, helping children to learn about their feelings in a fun and light-hearted way.
“Excellent notes for parents and teachers at the back of this books show how to approach these issues with children.”
Who Feels Scared?, by Sue Graves & Desideria Guicciardini
“Jack is having a sleepover at his house. But the boys hear noises and they get very scared. Jack’s big sister Ellie says she gets scared sometimes as well. Perhaps Dad can make them all feel better?”
The Monster Bed, by Jeanne Willis & Susan Varley
“Little Dennis doesn’t want to go to sleep because there might be something under his bed. Dennis isn’t afraid of monsters - after all, he’s a monster himself. He’s afraid that humans will get him. His mother says that humans are only in made-up stories, but Dennis isn’t convinced.”
Under the Bed, by Paul Bright
“Under the bed are bugs and beasts. A dragon, an alligator, eating midnight feasts. And under the bed is something more, something bigger than a stable door. This is a picture book for children aged 3-7 years which will dispel night-time beastie fears!”
Walter and the No-Need-to-Worry Suit, by Rachel Bright
“We all worry, but nobody worries quite like Walter. As the Seriously Competitive Sports and Funday approaches, Walter’s worries start to roll out of control. What can his best friend Winnie and the other Woollybottomers do to help?”
What If…?, by Anthony Browne
“Joe is nervous about his first big party, and as Mum walks him along the darkening street to his friend’s house, his imagination starts to run wild. They search for the right place, looking through the windows, wondering ‘what if …?’ while making surprising discoveries along the way.”
The Huge Bag of Worries, by Virginia Ironside
“Wherever Jenny goes, her worries follow her - in a big blue bag. They are there when she goes swimming, when she is watching TV, even when she is in the lavatory. Jenny decides they will have to go. But who can help her?”
Scaredy Squirrel, by Melanie Watt
“There are dangers all around - germs, green Martians, sharks. But Scaredy Squirrel is well-prepared - or thinks he is. He’s packed an emergency kit with anti-bacterial soap, sticking plaster and a parachute.”
I Want My Light On!, by Tony Ross
“Everyone insists there are no such things as ghosts. But if that is the case, just what is it that is lurking under the Little Princess’s bed?”
Ruby’s Worry, by Tom Percival
“Ruby loves being Ruby. Until, one day, she finds a worry. at first it’s not such a big worry, and that’s alright, but then it starts to grow. It gets bigger and bigger every day and it makes Ruby sad. How can Ruby get rid of her worry and feel like herself again?”
Where the Bugaboo Lives, by Sean Taylor & Neal Layton
“A wonderfully interactive, which-way adventure story which follows Floyd and his sister into the dark, shadowy valley behind their house. The valley is full of all kinds of creepy, freaky, weaselly creatures and the scariest of them all is the bugaboo!”
Willy and the Cloud, by Anthony Browne
“A stunning and perceptive story about worry and anxiety, featuring his much loved chimp, Willy.
“One day Willy goes to the park. It’s a sunny day, but a cloud hovers over him and he can’t join in the fun. What can Willy do to make this mysterious cloud go away?”
Little Mouse’s Big Book of Fears, by Emily Gravett
“Young children will identify with the little mouse who documents his fears in the pages of this book - from loud noises and the dark to being sucked down the plughole.”
Is a Worry Worrying You?, by Ferida Wolff
“Adults think of childhood as a carefree time, but the truth is that children worry, and worry a lot, especially in our highly pressurized era. This book addresses children’s worries with humor and imagination, as hilarious scenarios teach kids the use of perspective and the art of creative problem-solving.”
What to do When You Worry Too Much: a kid’s guide to overcoming anxiety, by Dawn Huebner & Bonnie Matthews
“What to Do When You Worry Too Much is an interactive self-help book designed to guide 6-12 year olds and their parents through the cognitive-behavioral techniques most often used in the treatment of generalised anxiety.”
My Hidden Chimp: helping children to understand and manage their emotions, thinking and behaviour with ten helpful habits, by Steve Peters & Jeff Battista
“Using the Chimp Model from the best-selling ‘Chimp Paradox’, this is a guide with easy to follow explanations and simple strategies to help children understand how their chimp brain works.
“With 10 habits to put into practice, such as smiling, saying sorry and talking about your feelings, children can work on controlling their anxiety and emotions and understanding why they sometimes behave and feel the way they do.”
Isle of You, by David LaRochelle & Kim Jaine
“Some days are harder to get through than others. Whenever you’re sad, or lonely, or angry, there’s one place you can always go: the Isle of You. Take a moment to shake off your worries, then set sail to a land where your dreams can come true. Soar on the back of an eagle, hunt for treasure, watch dancing polar bears on roller skates - anything you can imagine is yours.”
Me and My Fear, by Francesca Sanna
“When a young refugee girl has to travel to a new country and start at a new school, she is accompanied by her Fear who tells her to be alone and afraid, growing bigger and bigger every day with questions like ‘how can you hope to make new friends if you don’t understand their language?’ But this little girl is stronger than her Fear.”
Outsmarting Worry, by Dawn Huebner & Margaret Suggs
“This easy-to-follow illustrated text presents strategies for 9-13 year olds with anxiety. It teaches them about, and normalizes, their symptoms and provides a set of concrete steps to overcome them. The tried and tested techniques are based on the most up-to-date psychological treatments of anxiety, including CBT and ACT.”
Looking After Your Mental Health, by Alice James, Louie Stowell, Freya Harrison & Nancy Leschikoff
“The mental of health of children and teenagers hits the headlines on a regular basis, with more and more young people reporting mental health difficulties. This book provides a gentle guide to good mental health, with input from experts in both psychology and psychiatry.”
The Book of No Worries: a survival guide for growing up, by Lizzie Cox & Tanya Stevanovic
“A survival guide to help young people cope with peer pressure, puberty and all the worries in between; focusing on the emotional wellbeing and mental health of tweens.”


We craft beautiful, engaging websites for small businesses.
At Fump we are focused on delivering results for our clients.
Whatever marketing goals you have – Brand Exposure, Lead Generation, Website Traffic –
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We can adjust your website content to direct more traffic in your direction
Your business is growing steadily,, there are quiet patches, but we all have those, don’t we? When things are quiet, do you find yourself looking on the Internet? wondering “Why do I need a website? What can it do for me?”...
Read More →How Much Does A Website Cost? From any sensible viewpoint it doesn’t matter how much a website costs. because it’s value is so, so much more than its price tag. Your website is the cheapest employee you’ll ever have. Can you afford not to have him work for you?...
Read More → Your website must communicate your brand values, attract high-quality visitors,
be easy to navigate and provide a hub for your wider marketing strategy. It must also achieve commercial objectives.
your website must perform well on Google so that potential customers can easily find it.
it must look professional and give a good impression of your company and make customers feel confident in doing business with you.
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The Fump Web team are truly talented professionals and jolly nice people so please do get in touch.


Christine Quinn Burtt
Republican
CQB & Associates, Inc. , Strategic Communications, president since 1982
MPA - Univ of CO; master's thesis on Section 2 of the Voting Rights Act
80210
Consultant or manager to more than 69 political candidate or issues campaigns; political trainer in emerging democracies with International Republican Institute; past president and board member of Colorado Republican Business Coalition; current Board member of the Taxpayers Bill of Rights Foundation and TABOR Committee
Taxpayers Bill of Rights Fndn; Taxpayers Bill of Rights Committee; Colorado Republican Precinct committeewoman
I have a 30+ year career in creating strategic plans for political campaigns, non-profit organizations, and small businesses. In that capacity, I have often been the spokesperson for the organization.
I understand the importance of fair reapportionment and redistricting. Every citizen is entitled to be fairly represented in government, and that starts with the legislative and congressional boundaries drawn to reflect the legal requirements of Section 2 of the Voting Rights Act.
The beauty of our democratic republic is that the citizens empower government and the military. It's a good system and a model for the world. My work in developing democracies around the globe, my work in Colorado to protect free and fair elections, and my interest in preserving our state and federal Constitution for my grandchildren motivates me to volunteer for this assignment.
I have made a living for 30+ years helping others manage their businesses, their non-profit organizations, and campaigns, for a successful outcome. I am adept at budgeting, statistical analysis, and working with a team toward an identifiable goal.
Female


PDP’s NWC Members Deny Knowledge Of Elumelu’s Suspension Extension
Some members of the National Working Committee of the party have denied knowledge of extending the suspension on Elumelu and others.
by SaharaReporters, New York
There appears to be no end in sight in the crisis generated by the suspension of Ndudi Elumelu and six others by the Peoples Democratic Party over the minority leadership in the House of Representatives.
Some members of the National Working Committee of the party have denied knowledge of extending the suspension on Elumelu and others.
The Deputy National Chairman (South) of PDP, Elder Yemi Akinwonmi, and the National Youth Leader, Sunday Okoye, denied knowledge of the renewed suspension of Elumelu and others, stating that it was not discussed at the meeting.
Akinwonmi said, “I am not aware of the extension of the suspension. I am not aware. I do not remember any occasion where it was elongated.
“I am only aware that he was suspended only one month, after that I am not aware of any further action.
“I am aware that he was suspended for one month and that was about six months ago.”
Also speaking, Okoye said that he was not aware of the extended suspension of Elumelu and others by the PDP NWC.
He disclosed that NWC didn't elongate his suspension and the statement to that effect was not from the NWC.
Recall that the NWC had in a statement by its National Publicity Secretary, Kola Ologbondiyan, extended the suspension of Elumelu and others, stating that it will not reward disobedience and disloyalty.
The party also appointed Kingsley Chinda and others as leaders of the PDP caucus in the House of Representatives.


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Published on 14 jun 2019
Daughter - Medicine (Sound Remedy Remix)
⬙ FAVOURITES ON SPOTIFY ⬙
⇥ http://mrsuicidesheep.com/favourites
Phenomenal remix!
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quarantine makes me return to all the precious songs that i used to listen to UwU
AUTOPLAY
When The Lights Go Down
DJ Snake
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quarantine makes me return to all the precious songs that i used to listen to UwU
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Grand Hotel finds its inspiration from the title screen of the 1937 film "Cafe Metropole" starring Tyrone Power. This condensed upright connecting script has a classic vibe to it.
It has a wonderful weight to it that feels subtly tied to Holiday and Bakery themed designs, even though it can work outside that genre.
Designed by Brian J. Bonislawsky and Jim Lyles for Astigmatic (AOETI). To contribute to the project contact Brian J. Bonislawsky.


libksba-1.3.5
Introduction to Libksba
The Libksba package contains a
library used to make X.509 certificates as well as making the CMS
(Cryptographic Message Syntax) easily accessible by other
applications. Both specifications are building blocks of S/MIME and
TLS. The library does not rely on another cryptographic library but
provides hooks for easy integration with Libgcrypt.
This package is known to build and work properly using an LFS-9.1
platform.
Package Information
Libksba Dependencies
Required
libgpg-error-1.37
Optional
Valgrind-3.15.0
User Notes: http://wiki.linuxfromscratch.org/blfs/wiki/libksba
Installation of Libksba
Install Libksba by running the
following commands:
./configure --prefix=/usr &&
make
To test the results, issue: make
check.
Now, as the root
user:
make install
Contents
Installed Program:
ksba-config
Installed Library:
libksba.so
Installed Directory:
None
Short Descriptions
|
ksba-config
|
is a utility used to configure and build applications
based on the libksba(3) library. It can be used to query
the C compiler and linker flags which are required to
correctly compile and link the application against the
libksba(3) library.
|
|
libksba.{so,a}
|
contains the cryptographic API functions.
|
Last updated on 2020-02-16 15:15:05 -0800


--- layout: post date: 2012-03-18 time: "10:00:00" authors: ["Greg Wilson"] title: The Dark Matter of Computational Science tags: ["Opinion"] ---
Scott Hanselman's recent post "Dark Matter Developers" got me thinking once again about what Software Carpentry is about. He says:
[We] hypothesize that there is another kind of developer than the ones we meet all the time. We call them Dark Matter Developers. They don't read a lot of blogs, they never write blogs, they don't go to user groups, they don't tweet or facebook, and you don't often see them at large conferences... Where are [they]? Probably getting work done.
The same is true of computing in the sciences. For everyone who shows up at Supercomputing or blogs about porting the eigenvalue package they wrote in Haskell to a GPU cluster, there are a hundred (or more) who are busy actually doing organic chemistry or neuropsychology. My question is, how do we reach them? Some ideas can be found in this recent talk by Jessica McKellar and Asheesh Laroia about how they increased both the size and diversity of the Boston Python user group:
{% assign video_title="Diversity in practice: How the Boston Python User Group grew to 1700 people and over 15% women" %} {% assign video_slug="QrITN6GZDu4" %} {% assign video_time="00:41:09" %} {% include youtube %}


{% capture nowunix %}{{'now' | date: '%s'}}{% endcapture %} {% assign nowunix = nowunix | plus: 0 %} {% assign one_year_ago = nowunix | minus: 31556926 %} {% assign two_years_ago = nowunix | minus: 63113851 %} {% assign three_years_ago = nowunix | minus: 94670777 | minus: 63113851 %} {% capture posttime %}{{page.date | date: '%s'}}{% endcapture %} {% assign postint = posttime | plus: 0 %} {% if postint < three_years_ago %}
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An example of changing markers by using d3 selections and
map.refresh
with
d3.carto.map
.
Click on the "Change Markers" button to update the marker styling.


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THREEx is a way to write three.js extension ala vendor.js.
So as little dependancy as possible.
It is modules to help you write three.js stuff, not a framework.
Existing Extensions
Here is a list of the existing extensions. Feel free to contribute your own. Just do a
pull request
, edit this file, copy an existing line and fit it to your own needs.
Wanna try live in the browser ? There is a threex playground waiting for you!
If you want to create your own, here is
three.sample
to start with.
It provide a simple laser beam display with
generated texture and additive blending.
For cooler impact effects, there is dynamic collision,
point light and impact sprites.
It handles videos in texture.
It is possible to put html5 video output in texture,
even put the webcam in it, or to connect it to WebAudio API
to get localized sounds.
It is a three.js extension to keep the current state of the keyboard.
It may be queried at any time.
This is convenient in loop driven case, like games.
It provides a tunable depth of field to your scene.
It gives it a nice bokeh effect.
It is very easy to include.
It provides realistic physics in your 3d.


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Competitive Scholarship Portfolio 2017
Dust Jacket Design
LGBTQ+ Advocacy Pamphlet / Poster
Book Design
Written and Illustrated Bilingual Children's Book
Logo and Type Design
Branding and Logo Design - Hike for Healing
Book Design
Drawing - Dear Ones
Competitive Scholarships Portfolio 2016
Drawing - From Across The Room
Illustration - LGBTQ+ Women
Mother's and Father's Day Cards
LGBTQ+ Sex and Consent Zine


{% extends "layouts/base_dashboard.html" %} {% block content_dashboard %} {% if not figures1 %}
Click any button below for plotting.
Descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution
The {{name}} aims to show the distribution of numerical data.


You can define a tab title in the comment section before the query. In the Treat text as title after
field, you can reserve a combination of symbols or characters, and any text after this combination will be treated
as a tab title. By default, no combination is used, so any text after
--
or /*
is treated
as a tab title.
To disable this feature, open settings Ctrl+Alt+S, navigate to %database_general%, and clear the Create title for results from comment before query
checkbox.
Open settings by pressing Ctrl+Alt+S, navigate to %database_general%.
In the Treat text as title after field, define a combination for tab titles.


Constant Field Values
Contents
com.blakfx.*
-
com.blakfx.chat.Chat Modifier and Type Constant Field Value protected static final String
EVERYONE
"EVERYONE"
private static final int
MAX_EXECUTOR_THREADS
10
private final String
prompt
"(type &help for assistance)> "
-
com.blakfx.protocols.DeviceProtocol.ChatMsg Modifier and Type Constant Field Value public static final int
CONTENT_FIELD_NUMBER
2
public static final int
FILENAME_FIELD_NUMBER
4
private static final long
serialVersionUID
0L
public static final int
TARGET_FIELD_NUMBER
3
public static final int
USERNAME_FIELD_NUMBER
1
-
com.blakfx.protocols.DeviceProtocol.Device Modifier and Type Constant Field Value public static final int
ACTION_FIELD_NUMBER
1
public static final int
DESCRIPTION_FIELD_NUMBER
6
public static final int
ERROR_FIELD_NUMBER
9
public static final int
ID_FIELD_NUMBER
2
public static final int
MESSAGE_FIELD_NUMBER
8
public static final int
NAME_FIELD_NUMBER
3
public static final int
OWNER_FIELD_NUMBER
7
private static final long
serialVersionUID
0L
public static final int
STATUS_FIELD_NUMBER
4
public static final int
TYPE_FIELD_NUMBER
5