R
package to impute prior spring NWEA MAP RIT scores from current fall scores.
mapspringr
is an R
package for imputing missing prior spring RIT scores from current fall RIT scors on the NWEA MAP assessmemt. Tha pacakge has one function: impute_spring
that takes as paramters fall map data adn the subject and returns the original data plus an estimated prior spring score, the standard deviation of the score, as well as high and low scores (+/- 2 standard deviations).
You can try out the imputation for a single student by filling in the form below or uploading a CDF of fall data for students with missing spring scores.
In addition to the using the R
package, the impute_spring
function can be called from any programming language via JSON RPC (thanks to the excellent folks at OpenCPU for making this process dead simple).
Predictions are from random forest regression models fit to KIPP Chicago student data. The data are from spring 2011 through fall 2014.
Springr is maintained by Chris Haid at KIPP Chicago.
impute_spring
on a Comprehensive Data File (as a CSV)You can impute spring scores for multiple students by uploading uploading the fall comprehesive data file. Your data will be returned with 4 additional columns: imputed RIT, standard deviation of imputed RIT, and a high and low RIT scores (i.e., +/- two standard deviations).
R
packagerequire(devtools) install_github("chrishaid/mapspringr") require(mapspringr) data(fallmap) fallmap_reading <- fallmap[fallmap$MeasurementScale=="Reading"] impute_spring(fallmap_reading, subject="reading")
impute_spring
from the command line with curl
To call the impute_spring
function with curl
using basic JSON RPC:
curl https://chrishaid.ocpu.io/mapspringr/R/impute_spring -d \ 'input=[{"Grade":"8",'\ '"TestRITScore":201,'\ '"Goal1RitScore":200,'\ '"Goal2RitScore":189,'\ '"Goal3RitScore":203,'\ '"Goal4RitScore":211,'\ '"PercentCorrect":54,'\ '"TestDurationInMinutes":26}]'\ '&subject="math"'
The motivation is relatively straightforward: if you use MAP results to drive instruction and differentiate learning and you measure student achievement as growth over a 12 month, spring-to-spring period, then you need last year's spring scores to understand what students are ready to learn. Yet we don't always have scores from last spring for every one of our students at the beginning of our school years. Some students are new to our schools and have never taken the MAP or perhpas they are returning students and missed the opportunity in the prior spring to sit for the assessment.
One remedy the quandry posed by missing prior spring RIT scores is to use current fall scores (i.e. scores earned at the beginning of the school year) in lieu of spring scores. Fall scores however typically lower than the scores students earned in the spring. The lower score might be caused by summer loss or low student investment/effort on the fall assessemt. I tend to think that low invesment/effort drives most the observed learning loss between spring and fall. To be sure, summer loss is component of the difference, yet even if it is, would you rather plan off a measruement that demonstrates your students highest recent ability (i.e., the spring test taken onoy a few months prior) or a measurement that suggests lower abilty and may be the reflect low motivation?
So this package and application is best suited for providing a best estimate of a missing spring score. If you aren't using last year's spring score to plan, then the isn't the most useful tool. But if you are, then this tool provides an estimate of how that student might have done had they taken the test in spring last year. Hopefully, this makes makes grouping students for differentiated learning and goal setting for growth a bit more consistent across students.
Feel free to email me any feedback you might have!


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about Kurt
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$filter
ng
Filters are used for formatting data displayed to the user.
The general syntax in templates is as follows:
{{ expression [| filter_name[:parameter_value] ... ] }}
$filter(name);
Name of the filter function to retrieve
the filter function


Simulate expected equilibrium length composition, yield-per-recruit, and the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR model to length data to estimate selectivity, relative apical fishing mortality, and the spawning potential ratio for data-limited fisheries. See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the LBSPR assessment method.
| Version: | 0.1.2 |
| Depends: | R (≥ 3.2.4) |
| Imports: | dplyr, ggplot2, graphics, gridExtra, methods, plotrix, Rcpp, RColorBrewer, shiny, tidyr |
| LinkingTo: | Rcpp |
| Suggests: | colorspace, knitr, scales, shinyBS, rmarkdown |
| Published: | 2017-08-15 |
| Author: | Adrian Hordyk [aut, cre] |
| Maintainer: | Adrian Hordyk <ar.hordyk at gmail.com> |
| BugReports: | https://github.com/AdrianHordyk/LBSPR/issues |
| License: | GPL-3 |
| URL: | https://github.com/AdrianHordyk/LBSPR |
| NeedsCompilation: | yes |
| CRAN checks: | LBSPR results |
| Reference manual: | LBSPR.pdf |
| Vignettes: |
LBSPR: An R package for simulation and estimation using life-history ratios and length composition data |
| Package source: | LBSPR_0.1.2.tar.gz |
| Windows binaries: | r-devel: LBSPR_0.1.2.zip, r-release: LBSPR_0.1.2.zip, r-oldrel: LBSPR_0.1.2.zip |
| OS X binaries: | r-release: LBSPR_0.1.2.tgz, r-oldrel: LBSPR_0.1.2.tgz |
| Old sources: | LBSPR archive |
Please use the canonical form https://CRAN.R-project.org/package=LBSPR to link to this page.


I've been failing herecollect2: error: ld returned 1 exit status
Makefile:2200: recipe for target 'komodod' failed
make[2]: *** [komodod] Error 1
make[2]: *** Waiting for unfinished jobs....
collect2: error: ld returned 1 exit status
Makefile:2192: recipe for target 'komodo-test' failed
make[2]: *** [komodo-test] Error 1
make[2]: Leaving directory '/home/trist/staked/StakedModo/src'
Makefile:4982: recipe for target 'all-recursive' failed
make[1]: *** [all-recursive] Error 1
make[1]: Leaving directory '/home/trist/staked/StakedModo/src'
Makefile:659: recipe for target 'all-recursive' failed
make: *** [all-recursive] Error 1
mv: cannot stat 'src/komodod': No such file or directory


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--- permalink: /AmoebaDB/about ---
|
General Information AmoebaDB belongs to the EuPathDB family of databases and is an integrated genomic and functional genomic database for Entamoeba and Acanthamoeba parasites. In its first iteration (released in early 2010), AmoebaDB contains the genomes of three Entamoeba species (see below). AmoebaDB integrates whole genome sequence and annotation and will rapidly expand to include experimental data and environmental isolate sequences provided by community researchers . The database includes supplemental bioinformatics analyses and a web interface for data-mining. For a list of the data integrated into AmoebaDB and the related publications please see Data Sets. Please check our annual EuPathDB Workshop at UGA exercises to see the scope of data and functionality in our sites. |
|
Citing Data Providers Much of the data in AmoebaDB is provided by independent researchers. For a list of the data integrated into AmoebaDB and the related publications see Data Sets. Please cite the authors/providers when you use their data. |
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Scientific Advisory Team EuPathDB databases rely heavily on community input. Each project in EuPathDB benefits from the advice and mentoring of members from each specific community. We hold monthly organism calls for each of our databases with members of the scientific advisory teams and others from the community. Anyone interested in joining one of these conference calls is encouraged to contact us. |
|
Funding AmoebaDB is a member of pathogen-databases that are housed under the NIAID-funded EuPathDB Bioinformatics Resource Center (BRC) umbrella. The EuPathDB team is mainly distributed between the University of Pennsylvania in Philadelphia, Pennsylvania and the University of Georgia in Athens, Georgia. |
|
Data Access Policy The data at this site is provided freely for public use, through the contributions of many researchers including those involved in generating the genome sequences. The data is not guaranteed to be accurate as it often reflects the results of ongoing research. When using data obtained from any EuPathDB project, it is important to cite the original publications and contributors of that data. Please see Citing AmoebaDB below. |
|
Website Privacy Policy We do not use or share any of your personal information for any purpose unrelated to the functionality of the websites; however, we do collect some information to help us understand how our sites are being used and to improve community support. See the Website Privacy Policy for details. |
|
Citing AmoebaDB When citing AmoebaDB itself, please use: AmoebaDB and MicrosporidiaDB: functional genomic resources for Amoebozoa and Microsporidia species. When citing EuPathDB itself, please use: EuPathDB: the eukaryotic pathogen genomics database resource. 2016 Nov 29. pii: gkw1105. [Epub ahead of print] Aurrecoechea C, et al. Much of the data in AmoebaDB is provided by independent researchers. Please cite them if you use their data. For a list of the data integrated into AmoebaDB and the related publications please see Data Sets. |


We offer tours that gives you the opportunity to ride trails that span from the roots of Sólheimajökull glacier down to the Atlantic ocean. Join us on a trip to explore the canyons, rivers and valleys that cover the landscape.
During this three hour tour you get to experience the nature on a mixture of flow and technical trails. You get to see views that come second to none and experience a different side of Iceland.
Two available starting times.
During this six hour tour you get to experience the Icelandic nature by riding a mizture of flow and technical trails. You get to see views that come second to none and experience a different side of Iceland.
During this trip there will be a lunchbreak at base camp.
If you are new to mountain biking or feel as you need to work on particular skills you can join us on one day mountain biking coures. We offer sessions for every skill level, simply select skill level during the booking process.
During this training session you get to experience the views and trail network we have to offer at Black Sand Adventures.
If you are new to mountain biking and don't have a bike or if you want the chance to try something new we offer rentalbikes along with our tours.
Get information about our rental bikes in the link below.
Rental Bikes


Recap
Created by
Łukasz Ziobroń
What do you remember from today's session?
Recalling and repetition is the key to effective learning and remembering.
Pre-test answers
1
A B C
2
A
3
C
4
A B C D
5
C
6
A-H
7
A B C E
8
A D
9
A C D
10
A
Post-work
Online post-test
- do it about a week after the training and check out how much do you remember
Other modern C++ stuff
- Check out README.md for more tasks
Feedback
What could be improved in this training?
What was the most valuable for you?
Thank you :)


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const reg = /???/
const str = "My grandma has a cat. My grandpa has a dog."
str.replace(reg, "family")
"Solution" with .
const reg = /grand.a/g
const str = "My grandma has a cat. My grandpa has a dog."
str.replace(reg, "family")
// "My family has a cat. My family has a dog."
reg.test(str)
// true
reg.test("My grandxa has a hamster")
// true
Character Class
const reg = /grand[mp]a/g
const str = "My grandma has a cat. My grandpa has a dog."
str.replace(reg, "family")
// "My family has a cat. My family has a dog."
reg.test(str)
// true
reg.test("My grandxa has a hamster")
// false
const reg = /???/g
const str = "My brother has a ferret. My sister has a cat."
str.replace(reg, "family")
// "My family has a ferret. My family has a cat."
Alternation in Groups
const reg = /(brother|sister)/g
const str = "My brother has a ferret. My sister has a cat."
str.replace(reg, "family")
// "My family has a ferret. My family has a cat."
const extract_comment = /\/\*(.+)\*\//s
const contains_comment = `this.bit.is.code()
/* this is a comment
this too */
more.code()`
contains_comment.match(extract_comment)[1].strip
// "this is a comment\nthis too"
const es3_extract_comment = /\/\*([\w\W]+)\*\//


We welcome you to explore our great platform and work in it for your needs of Event Management
Explore the world of infinite possibilities and stand before the global challenge. You can take advantage over this platform


{% extends 'base.html' %} {% block content %}
Navigate to "Accounts" tab to watch and modify account list


node.js Alternate Configurations
Deprecation Warning
Older versions of this library required different mechanism to instantiate the Client object in nodejs than Web Browsers. In recent versions that is no longer the case. Please refer Usage for suggested way to use Stomp JS.
If you are already using one of the techniques mentioned in this page and will like to upgrade please refer to Upgrade.
Code for these methods are not actively maintained. These have some known issues that can hang the node environment on certain types connectivity failures.
Stomp.overTCP
This connects over STOMP protocol, unlike other methods in this library which connect using STOMP over WebSocket.
Add npm modules
@stomp/stompjs
andnet
to your project.- using
npm
$ npm install @stomp/stompjs net --save
- using
yarn
$ yarn add @stomp/stompjs net
- using
Require the module
var Stomp = require('@stomp/stompjs');
To instantiate the client use
Stomp.overTCP(host, port)
method:var client = Stomp.overTCP('localhost', 61613);
- Please note you need to pass the STOMP port of the broker.
Stomp.overWS
Add npm modules
@stomp/stompjs
andwebsocket
to your project.- using
npm
$ npm install @stomp/stompjs websocket --save
- using
yarn
$ yarn add @stomp/stompjs websocket
- using
Require the module
var Stomp = require('@stomp/stompjs');
To instantiate the client use
Stomp.overWS(url)
method:var client = Stomp.overWS('ws://localhost:15674/ws');
What Next
Apart from this initialization, the STOMP API remains the same. Please refer to Usage.


The modern virtual keyboard for React. Customizable. Responsive. Lightweight.
Simple-keyboard is a fast, dependency-free and customizable virtual keyboard for rich and snappy web applications.


Swivel Testing Proxy
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Docs
About
Socketry allows for an easy way to set up websocket servers and clients. It is built to allow anybody to easily set up websockets without any hastle.
-
Use
Socketry has an easy way to access all of the clients connected to the server.
const Socket = require('./index.js');
const server = new Socket.Server(8080);
console.log(server.clients); // array
You can filter through these clients to target the current client as well, since each client has their own unique id. You can find it by just using client.id
.
-
Basic server file
To start using socketry, you will need a server file.
const Socket = require('./index.js');
// Create a new server on port `8080`
const server = new Socket.Server(8080);
// Create a room that messages will be sent to (can have multiple rooms)
const room = new server.Room('foo');
const room2 = new server.Room('bar');
// Fires when a client connects to the server
server.on('connection', usr => {
console.log('User connected!');
// Fires when the client closes or disconnects from the server
usr.on('end', extUsr => {
console.log('User Disconnected');
});
// fires when a message is sent to the server
usr.on('message', (data, room) => {
// Parse the data so that you can use each part
data = JSON.parse(data);
console.log(data);
// Sends the message back to every client in the room
room.clients.forEach(c => {
c.send(`${data.usr}: ${data.msg}`);
});
});
});
This is just the barebones of a server. It just receives the websockets, and sends them out again to each client.
-
Basic client file
You will also need a way to communicate to the server. You can do this in node by using another barebones file.
const Socket = require('./index.js');
// Create a client that connects to localhost, on port `8080`
const socket = new Socket.Client('ws://localhost:8080');
// Fires when connected to a server
socket.on('open', usr => {
// Join a room
socket.joinRoom('foo');
socket.joinRoom('bar');
console.log('Connected!');
// Fires when the client joins the room
socket.on('join', room => {
console.log('Joined Room');
// Fires when server sends a message to the client in the room
room.on('message', (msg) => {
console.log(msg);
});
// Send a message to the server in object form.
socket.send({msg: 'Message', usr: 'SharkFin'});
// Leave the room
room.leave();
// Close the websocket connection
// socket.end();
});
// Fires when the client leaves a room
socket.on('leave', room => {
console.log('Left Room');
});
});
Rooms
The websockets are split into rooms. This means that you can have something that acts as two or more servers, when it's really just one! This means you can host the socket server on one node process. The server can do whatever it wants with the rooms messages. When a message is recieved, the server can send it to all teh clients in the room, just a few clients, or to every room. This would be good for if you wan't to make announcements to every room about an event or such.
.send()
When sending with the client, you send an object. You can have any property inside the object, as long as the server will accept it.


{% load static %}
Let us handle the overhead of appointments
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Manage your employees and provide service
Manage your resources effectively
What does a hospital need more than good doctors? Good software developers. Meet the team:
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Description
DEWAG make Oil Content Meter (OCM) is an integral part of all oily water
separator (OWS) systems. The OCM continuously monitors how much oil
is in the water that is pumped out the discharge line of the OWS
system. The OCM will not allow the oil concentration of the exiting
water to be above the Marpol standard of 15 ppm. The oil content
meter will sound an alarm if the liquid leaving the system has an
unsatisfactory amount of oil in the mixture. If it is still above
that standard, then the bilge water will be reentered into the
system until it meets the required criteria. The OCM uses light
beams to determine how oily the water in the system is.
Dimensions
- 450(H) X 350(W) X 325(D) mm
- Enclosure - Wall mounting type
- Electrical connections - On base plate
Technical Specifications
- Range: 0 – 30 ppm
- Accuracy - Within IMCO limits
- Alarm Indication 1 - Instant LED: Settable (0 to 99ppm), Factory set to 15 ppm
- Alarm Relay 1 & Indication 2 - Delayed LED & Relay: Delay time settable (0 to 99 secs.), factory set to 15 secs.
- Alarm Relay 2 - Buzzer relay: Energises with Relay 1 and de-energises when Acknowledge push button is pressed
- Alarm Relay 1 contacts - Changeover 1: For Overboard(O/B) and Recirculation(R/C) valve operations, rated 5Amps. @ 230V AC
- Changeover 2: For O/B and R/C valve status indications, potential free
- Alarm Relay 2 contacts - Changeover 1: For external buzzer operation, reated 5Amps. @ 230V AC
- Concentration readout - By 7-segment Red LED display on front panel
- Light source - Low power IR LED
- Supply - 230V AC, 1Ph., 50Hz, 20VA
Ordering Information


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
real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='real_inputs')
z_inputs = tf.placeholder(tf.float32, (None, z_dim))
learning_rate = tf.placeholder(tf.float32)
return real_inputs, z_inputs, 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):
"""
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)
"""
keep_prob = 0.65
alpha = 0.2
with tf.variable_scope("discriminator", reuse=reuse):
layer1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
relu1 = tf.maximum(alpha*layer1, layer1)
layer2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
batch_norm2 = tf.layers.batch_normalization(layer2, training=True)
dropout2 = tf.layers.dropout(batch_norm2, rate=keep_prob, training=True)
relu2 = tf.maximum(alpha*dropout2, dropout2)
layer3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
batch_norm3 = tf.layers.batch_normalization(layer3, training=True)
dropout3 = tf.layers.dropout(batch_norm3, rate=keep_prob, training=True)
relu3 = tf.maximum(alpha*dropout3, dropout3)
flat = tf.reshape(relu3, (-1, 4*4*256))
logits = tf.layers.dense(flat, 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):
"""
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
reuse = not is_train
keep_prob = 0.65
alpha = 0.2
with(tf.variable_scope('generator', reuse=reuse)):
layer1 = tf.layers.dense(z, 4*4*512)
layer1 = tf.reshape(layer1, (-1, 4, 4, 512))
layer1 = tf.layers.batch_normalization(layer1, training=is_train)
layer1 = tf.layers.dropout(layer1, rate=keep_prob, training=is_train)
layer1 = tf.maximum(alpha*layer1, layer1)
layer2 = tf.layers.conv2d_transpose(layer1, 256, 5, strides=2, padding='same')
layer2 = tf.layers.batch_normalization(layer2, training=is_train)
layer2 = tf.layers.dropout(layer2, rate=keep_prob, training=is_train)
layer2 = tf.maximum(alpha*layer2, layer2)
layer3 = tf.layers.conv2d_transpose(layer2, 128, 5, strides=2, padding='same')
layer3 = tf.layers.batch_normalization(layer3, training=is_train)
layer3 = tf.layers.dropout(layer3, rate=keep_prob, training=is_train)
layer3 = tf.maximum(alpha*layer3, layer3)
logits = tf.layers.conv2d_transpose(layer3, out_channel_dim, 5, strides=2, padding='same')
logits = tf.image.resize_images(logits, (28,28))
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)
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)
"""
# TODO: Implement Function
smooth = 0.1
g_model = generator(input_z, out_channel_dim, is_train=True)
d_model_real, d_logits_real = discriminator(input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*1-(smooth)))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
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)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
def get_vars_prefixed(variables, prefix):
new_vars = []
for variable in variables:
if(variable.name[0:len(prefix)] == prefix):
new_vars.append(variable)
return new_vars
g_vars = get_vars_prefixed(t_vars, 'generator')
d_vars = get_vars_prefixed(t_vars, 'discriminator')
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")
"""
# TODO: Build Model
#saver = tf.train.Saver(var_list = g_vars)
sample_z = np.random.uniform(-1, 1, size=(72, z_dim))
samples, losses = [], []
steps = 0
print_every=50
show_every=100
if(data_image_mode == 'RGB'):
input_channels = 3
else:
input_channels = 1
input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], input_channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, input_channels)
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
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):
# TODO: Train Model
# Rescale images to be between -1 to 1
batch_images = batch_images * 2
steps += 1
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images, lr: learning_rate})
if steps % print_every == 0:
# At the end of each epoch, get the losses and print them out
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),
"Step {}...".format(steps),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
if steps % show_every == 0:
show_generator_output(sess, 25, input_z, input_channels, data_image_mode)
return losses, samples
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 = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
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 = 0.0002
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.


Yes we accept Skrill(Moneybooker).
Yes you can cancel your order anytime you want.
You can receive 100% refund anytime for any reason, there are no strings attached.
You can receive invoice either by mail or by e-mail.


This class provides Postmarkapp Functionality that just plain works.
Methods
OwpPostmark constructor.
__construct($key, $from, string $reply = ''
)
Parameters
$key
$from
$reply
string
Add Attachment Method
attachment($name, $content, $content_type) : \OwpPostmark
| method |
Add Attachment Method |
| access |
public |
| fluent |
This method is part of a fluent interface and will return the same instance |
Parameters
$name
$content
$content_type
Returns
\OwpPostmark
Set BCC To Method
bcc($bcc) : \OwpPostmark
| method |
Set BCC To Method |
| access |
public |
| fluent |
This method is part of a fluent interface and will return the same instance |
Parameters
$bcc
Returns
\OwpPostmark
Set CC To Method
cc($cc) : \OwpPostmark
| method |
Set CC To Method |
| access |
public |
| fluent |
This method is part of a fluent interface and will return the same instance |
Parameters
$cc
Returns
\OwpPostmark
Set HTML Message Method
html_message($html) : \OwpPostmark
| method |
Set HTML Message Method |
| access |
public |
| fluent |
This method is part of a fluent interface and will return the same instance |
Parameters
$html
Returns
\OwpPostmark
Set PLAIN Text Message Method
plain_message($msg) : \OwpPostmark
| method |
Set PLAIN Text Message Method |
| access |
public |
| fluent |
This method is part of a fluent interface and will return the same instance |
Parameters
$msg
Returns
\OwpPostmark
Send method
send() : boolean
| method |
Send method |
| access |
public |
Returns
boolean
Set Subject Method
subject($subject) : \OwpPostmark
| method |
Set Subject Method |
| access |
public |
| fluent |
This method is part of a fluent interface and will return the same instance |
Parameters
$subject
Returns
\OwpPostmark
Set Tag Method
tag($tag) : \OwpPostmark
| method |
Set Tag Method |
| access |
public |
| fluent |
This method is part of a fluent interface and will return the same instance |
Parameters
$tag
Returns
\OwpPostmark
Set Send To Method
to($to) : \OwpPostmark
| method |
Set Send To Method |
| access |
public |
| fluent |
This method is part of a fluent interface and will return the same instance |
Parameters
$to
Returns
\OwpPostmark
Properties
Postmarkapp API Key
$api_key : string
Attachment Counter
$attachment_count : integer


THE MESSENGER: THE STORY OF JOAN OF ARC -------------------------------------------------------------- GRADE: C-
I guess that, sooner or later, someone had to reinvent Joan of Arc for the multiplex. Kathryn Bigelow (Strange Days) reportedly wanted the assignment, and planned to cast Claire Danes. It's hard to tell what that movie would have looked like, but it couldn't have been much more of a disappointment than what Luc Besson did with it.
Don't get me wrong -- I tend to appreciate Besson's naive, unashamedly romantic worldview, an artsy European sensibility gone thoroughly Hollywood. His 1994 Leon is exciting and absurdly moving, thanks mainly to the interplay between Jean Reno and a young Natalie Portman, and 1997's The Fifth Element is a goofy paean to love, L-U-V, that's even more of a hoot on repeated viewings. But his Joan of Arc is a mess, despite some stirring battle scenes and the mostly knockout presence of a close-cropped Milla Jovovich as the maiden herself.
Jovovich, Besson's own personal muse through the making of his last two films (though they've since separated), is magnetic and energetic enough to play a credible Joan (as long as you believe Joan was not just a warrior, but also a fabulous, supermodel-level babe). And Besson remains a servicable action film director, though not an especially facile one. Leading her army against the seemingly impenetrable fortresses of the English, Joan's struggle against great odds is undeniably stirring, and Besson helps you understand how the French could believe, fervently, that God is on her side.
Besson's big mistake, I think, is trying to dramatize the interior life of his subject. Yes, Joan had visions -- but I guarantee that whatever image a sympathetic audience conjures in its collective mind will be more compelling than the blue-eyed Christ figure on display in Besson's hallucinatory dream sequences. The whole conceit turns into a disaster right about the time Dustin Hoffman (billed as "The Conscience") shows up, spouting platitudes and sending poor Milla running around her cell, babbling like Ally McBeal. That's kind of a shame, since she does quite well up to that point, charging into battle with a banshee wail that suggests the eruption of a sublimated bloodthirstiness. That darkly attractive aspect of her character -- that she's as likely a charismatic madwoman as the vessel of the lord -- is more or less betrayed by the final reels, in which The Conscience browbeats her over the killing she's overseen and Joan starts to look like little more than a very confused young lady who's about to take a fall.
As the story winds down, with Joan condemned to a horrible death by fire, Besson is aiming for tragedy and psychological significance. Trouble is, he's chosen a lurid, often jokey tone for the balance of the picture -- including some blithe cgi bloodshed in the battle scenes -- and it's impossible to shake the feeling that this particular vision of the middle ages is, to a great extent, nothing more than a put-on. This glib, pre-fab version of such a great story makes me all the more grateful for Dreyer's The Passion of Joan of Arc (1928), a film whose dialogue is drawn from the actual transcripts of Joan's trial, and which stands as one of the most harrowing experiences in all of cinema. Compared to that film, The Messenger plays as a crime against history.
-------------------------------------------------------------- Directed by Luc Besson Written by Besson and Andrew Birkin Cinematography by Thierry Arbogast Music by Eric Serra Starring Milla Jovovich France/USA, 1999
Theatrical aspect ratio: 2.35:1 --------------------------------------------------------------
DEEP FOCUS (Movie Reviews) http://www.panix.com/~bfrazer/flicker/ "Malkovich Malkovich."
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Dependency injection
Spice# has a basic implementation for inversion of control, which allows you to register your own behaviors for components. The class that tracks all of this is DI.
By default, the static DI class automatically searches the assembly for components and behaviors the first time it is invoked. This is done using reflection, and if you want to keep the startup very light-weight you can choose to disable this behavior by clearing the ScanIfNotFound flag, in which case you will need to add the necessary behaviors and components manually. Searching the assembly is done by looking at the BehaviorForAttribute on behavior classes. This allows you to flag a behavior to be used for a specific component.
The class allows you to manually register all the behaviors of an assembly, or add behaviors for entities manually using the RegisterBehaviorFor method. Overriding existing behaviors can be done by registering behaviors with a higher priority such that they are checked first.
Deciding on the right behavior
Once the DI container needs to decide on behaviors for the specified simulation and component, it will go over each found behavior in order of decreasing priority. If the behavior implements all the necessary IBehavior interfaces that are requested by the simulation, then the behavior is created and added to the simulation.
Example: the diode
A transient simulation requests the following behaviors from a Diode:
The container checks the following behaviors:
- Noise has the highest priority so it is checked first. It implements the INoiseBehavior among others which isn't requested by the simulation so it isn't created.
- Frequency has the same priority as the next step, but since it implements IFrequencyBehavior which isn't requested by the simulation, it isn't created.
- Time implements ITimeBehavior, and since it inherits from its biasing behavior it also implements IBiasingBehavior, IConvergenceBehavior and ITemperatureBehavior. All of these are requested by the simulation so this behavior is created!
- Biasing contains interfaces that are already created, so this behavior is skipped.
- Temperature contains interfaces that are already created so this behavior is skipped.
In a lot of models, quantities and computations need to be shared between different behaviors of a component. The ordering by priority gives the freedom necessery to deal with inheritance to avoid code duplication.
WARNING
Note that the DI does not need to concern itself with the order of creation of the behaviors. It only makes sure that behaviors of the same behavior type are not added twice.


module File::Constants
File::Constants provides file-related constants. All possible file constants are listed in the documentation but they may not all be present on your platform.
If the underlying platform doesn't define a constant the corresponding Ruby constant is not defined.
Your platform documentations (e.g. man open(2)) may describe more detailed information.
Constants
- APPEND
append on each write
- BINARY
disable line code conversion
- CREAT
create file if it does not exist
- DIRECT
Try to minimize cache effects of the I/O to and from this file.
- DSYNC
any write operation perform synchronously except some meta data
- EXCL
error if CREAT and the file exists
- FNM_CASEFOLD
Makes File.fnmatch patterns case insensitive (but not Dir.glob patterns).
- FNM_DOTMATCH
The '*' wildcard matches filenames starting with “.” in File.fnmatch and Dir.glob patterns
- FNM_EXTGLOB
Allows file globbing through “{a,b}” in File.fnmatch patterns.
- FNM_NOESCAPE
Disables escapes in File.fnmatch and Dir.glob patterns
- FNM_PATHNAME
Wildcards in File.fnmatch and Dir.glob patterns do not match directory separators
- FNM_SHORTNAME
Makes patterns to match short names if existing. Valid only on Microsoft Windows.
- FNM_SYSCASE
System default case insensitiveness, equals to FNM_CASEFOLD or 0.
- LOCK_EX
exclusive lock. see File#flock
- LOCK_NB
non-blocking lock. used with LOCK_SH or LOCK_EX. see File#flock
- LOCK_SH
shared lock. see File#flock
- LOCK_UN
unlock. see File#flock
- NOATIME
do not change atime
- NOCTTY
not to make opened IO the controlling terminal device
- NOFOLLOW
do not follow symlinks
- NONBLOCK
do not block on open or for data to become available
- NULL
Name of the null device
- RDONLY
open for reading only
- RDWR
open for reading and writing
- RSYNC
any read operation perform synchronously. used with SYNC or DSYNC.
- SHARE_DELETE
can delete opened file
- SYNC
any write operation perform synchronously
- TMPFILE
Create an unnamed temporary file
- TRUNC
truncate size to 0
- WRONLY
open for writing only


WELCOME TO CAIRFREE
Ease your travel experience with the assistance of cAirFree
We provide a flight time estimator and general
information that revolves around your flight
{% extends "login/index.html" %} {% load static %} {% block content %}
Ease your travel experience with the assistance of cAirFree
We provide a flight time estimator and general
information that revolves around your flight


Payments-o-matic
{{payment.counterparty}}
{{payment.valueDate | date: 'dd/mm/yyyy'}}
Create new payment
Counterparty
Value Date
Credit Account
Amount
Currency
Save
Save
Cancel
Delete


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modal
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To the given UNIX date, get its Julian date.
Syntax
JulianDate ( date )
| Parameter | Description |
|---|---|
date |
A number representing the input date between January 1, 1970 and Dec 31, 2037. The number is the number of seconds elapsed since midnight, January 1, 1970. To retrieve this number, use any of the following functions: Today(), TodateEx(), GetFirstDate(), GetLastDate(), DateRoll(). Date-Time type attribute properties of a member can also be used to retrieve this number. For example: Product.currentmember.[Intro Date] returns the Introduction or release date for the current product in context. [Cola].[Intro Date] returns the Introduction or release date for the “Cola” product. |
Notes
This function is useful in converting the UNIX date to Julian Date or the 1900 Date system recognized by Microsoft Excel.
In the 1900 date system, the first day that is supported is January 1, 1900. When you enter a date, the date is converted into a serial number that represents the number of elapsed days since January 1, 1900. For example, if you enter July 5, 1998, Microsoft Excel converts the date to the serial number 35981.
By default, Microsoft Excel for Windows uses the 1900 date system. The 1900 date system allows greater compatibility between Microsoft Excel and other spreadsheet programs, such as Lotus 1-2-3, that are designed to run under MS-DOS or Microsoft Windows.
Return Value
This function returns juliandate, a number representing the Julian date. This number is a continuous count of days and fractions elapsed since noon Universal Time on January 1, 4713 BC in the proleptic Julian calendar.
Note:
For Excel workbooks using 1900 date system, (JulianDate – 2415018.50) gets the sequential serial number as per 1900 date system.
Example
The following query returns the total monthly sales for all Colas along with their release dates as in 1900 Date system in market “California” for “March 2007.”
WITH MEMBER Measures.[Product Intro Date] AS 'JulianDate(Product.CurrentMember.[Intro Date]) – 2415018.50' SELECT {Measures.[Product Intro Date], Measures.Sales} ON COLUMNS, {Colas.Children} ON ROWS FROM Sample.Basic WHERE (California, [March 2007], Actual);
See Also


| Interface Summary | |
|---|---|
| EffectHandle | Encapsulates an effect handle. |
| Class Summary | |
|---|---|
| Device | Encapsulates a device handle representing an open device. |
| ImmVibe | Defines general constants and methods. |
| IVTBuffer | Stores IVT data. |
| IVTElement | Base class for elements of a Timeline effect in an IVT buffer. |
| IVTMagSweepElement | Represents a MagSweep effect within a Timeline effect in an IVT buffer. |
| IVTPeriodicElement | Represents a Periodic effect within a Timeline effect in an IVT buffer. |
| IVTRepeatElement | Represents a Repeat element within a Timeline effect in an IVT buffer. |
| IVTWaveformElement | Represents a Waveform effect within a Timeline effect in an IVT buffer. |
| Launcher | Provides a simple interface to play built-in effects. |
| MagSweepEffectDefinition | Encapsulates the parameters of a MagSweep effect. |
| PeriodicEffectDefinition | Encapsulates the parameters of a Periodic effect. |
| WaveformEffectDefinition | Encapsulates the parameters of a Waveform effect. |


Marjor Third. Consectetur adipisicing elit. Deleniti, illo recusandae a hic iure autem laboriosam mollitia ut ipsum! Laborum velit ab dicta commodi, non!
What looked like a small patch of purple grass, above five feet square, was moving across the sand in their direction. When it came near enough he perceived that it was not grass; there were no blades, but only purple roots. The roots were revolving, for each small plant in the whole patch, like the spokes of a rimless wheel.
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Laborum sit provident iste magni quae reiciendis delectus corrupti! Non fugiat rerum culpa quas, numquam, aperiam porro repudiandae, cum ad reiciendis, magni ut nisi reprehenderit neque? Omnis tempora maiores, numquam veniam sapiente!
Behave yourself
What looked like a small patch of purple grass, above five feet square, was moving across the sand in their direction. When it came near enough he perceived that it was not grass; there were no blades, but only purple roots. The roots were revolving, for each small plant in the whole patch, like the spokes of a rimless wheel.
Learn as much as possible
Eaque provident sapiente inventore reprehenderit autem delectus facere sit, consequatur. Inventore nihil consequatur quod maxime quam vitae ipsum, quibusdam recusandae quidem et commodi adipisci molestiae qui eos omnis magnam quaerat, delectus! Tempore explicabo quibusdam sint facere asperiores, aliquid deleniti libero.


Custom Constraints
Let's continue the custom rules example, where we want to solve a puzzle that also enforces
that the diagonals contain all unique values. In this case, however, we'll solve this by adding a
new constraint: the
DiagonalUniquenessConstraint
.
If you haven't yet read the constraints summary, read that first!
Creating a constraint
A single constraint object can be used to solve multiple puzzles. Where possible, a constraint should be flexible to multiple puzzle sizes.
The TryConstrain
method
The IConstraint.TryConstrain
operation adds new
Objective
s to the given
ExactCoverGraph
. It must also drop any
Possibility
s that are now impossible based on
applying this constraint to the puzzle's preset values.
It should return false if the given puzzle cannot satisfy this constraint, else true.
Define your objectives
The first thing we need to identify is what an objective looks like for this constraint. We could state this constraint as follows:
Each forward diagonal, and each backward diagonal, needs to contain all possible values.
We can implement this by adding an objective for each possible value on each diagonal.
Determine possible coordinates
The possible coordinates here are those on the forward diagonal, and those on the backward diagonal. We can find all the coordinates on the forward diagonal as follows:
public bool TryConstrain(TPuzzle puzzle, ExactCoverGraph graph)
{
Span<Coordinate> forwardDiagonalCoordinates = stackalloc Coordinate[puzzle.Size];
for (int row = 0, col = puzzle.Size - 1; row < puzzle.Size; row++, col--)
{
forwardDiagonalCoordinates[row] = new Coordinate(row, col);
}
// TODO: Get backward diagonal coordinates.
}
Identify possible values
We need to identify which values are actually possible on each diagonal based on the preset
values. Within the constraint-based solver, we map a puzzle's possible values to zero-based indices
based on the order they are returned when we first call puzzle.AllPossibleValues
. This mapping is
handled by the ExactCoverGraph
.
public bool TryConstrain(TPuzzle puzzle, ExactCoverGraph graph)
{
...
Span<bool> isConstraintSatisfiedAtIndex =
stackalloc bool[graph.AllPossibleValues.Length];
// Zero the array since stackalloc does not guarantee this.
isConstraintSatisfiedAtIndex.Clear();
for (int i = 0; i < forwardDiagonalCoordinates.Length; i++)
{
var puzzleValue = puzzle[forwardDiagonalCoordinates[i]];
if (puzzleValue.HasValue)
{
var possibilityIndex = graph.ValuesToIndices[puzzleValue.Value];
if (isConstraintSatisfiedAtIndex[possibilityIndex])
{
// This value is duplicated on the diagonal. This is not allowed.
return false;
}
isConstraintSatisfiedAtIndex[possibilityIndex] = true;
}
}
// TODO: Check this for backward diagonal coordinates.
}
Add objectives and drop impossible possibilities
Now we can iterate through each possible value on each diagonal, drop possibilities that are no longer possible, and add constraint headers for the rest.
public bool TryConstrain(TPuzzle puzzle, ExactCoverGraph graph)
{
...
Possibility?[]?[] squares = new Possibility[forwardDiagonalCoordinates.Length][];
for (int i = 0; i < squares.Length; i++)
{
squares[i] = graph.GetAllPossibilitiesAt(in squareCoordinates[i]);
}
for (
int possibilityIndex = 0;
possibilityIndex < isConstraintSatisfiedAtIndex.Length;
possibilityIndex++)
{
if (isConstraintSatisfiedAtIndex[possibilityIndex])
{
if (!ConstraintUtil.TryDropPossibilitiesAtIndex(squares, possibilityIndex))
{
return false;
}
continue;
}
// Each possible value should only be present once on the diagonal, so set
// requiredCount to 1. The objective will be linked into the graph, so discard the out
// parameter.
if (!ConstraintUtil.TryAddObjectiveForPossibilityIndex(
squares, possibilityIndex, graph, requiredCount: 1, objective: out _))
{
return false;
}
}
// TODO: Add objectives and drop possibilities for the backward diagonal.
...
return true;
}
Note that here we made use of the
ConstraintUtil
to easily
add objectives and drop possibilities. This provides a few useful functions for implementing
constraints. In fact, we could have replaced all of this work for handling the forward diagonal
with the following:
Span<Coordinate> coordinates = stackalloc Coordinate[puzzle.Size];
for (int row = 0, col = puzzle.Size - 1; row < puzzle.Size; row++, col--)
{
coordinates[row] = new Coordinate(row, col);
}
return ConstraintUtil.TryImplementUniquenessConstraintForSquares(puzzle, coordinates, graph);


 Allow wild turkeys to hob-gobble about
Connecticut and you get letters to the editor. Release a prairie
dog in Colorado and you have the locals up in arms. Literally.
But up here in the Great Land, talk of troublesome turkeys and
pesky prairie dogs is met with a great big yawn.
Why? Read the license plates, fella. This is The Last Frontier.
The corner of America where critters outnumber people 5-to-1. (Only
critters larger than Dall Sheep are counted in Alaska.) The land
humans and hairy ones share and share alike.
As a moose controller on the Talkeetna rail gang, Sunday
Bumanglag's job is to make sure moose don't become moose meat when
the Seward-to-Fairbanks train passes through. Mind you, moose
control can be tricky. Especially if a 7-foot, 1,600-pound bull has
taken a liking to a particular section of track.
Bumanglag normally tries the whistle first.
No response? He blink-blinks the headlights.
Moose didn't blink? The foreman CA-RONG!s a warning shot with a
hunting rifle.
If Bullwinkle still doesn't get the picture, Bumanglag goes to
Plan D.
He waits.
Sometimes, for half an hour. If passengers start to grumble,
Bumanglag makes an announcement: ``We have a stubborn moose, ladies
and gentlemen.'' That's usually enough to turn scowls to smiles.
``I mean, this is moose country,'' Bumanglag says. ``We're just
guests in the mooses' living room.''
In Alaska, critters are everywhere and humans do a pretty good
job of living alongside them. Non-Alaskans might say that's because
farming never took root here and because the state's so big that
humans and animals don't trip over each other.
But folks here note that while the Paul Bunyans of the Lower 48
were scalping forests and pushing many animals toward oblivion,
Alaskans came to appreciate their critters.
``The bush makes you a different person,'' says Steve Mahay, an
outdoorsman in Talkeetna, an assemblage of log cabins and house
trailers in Susitna Valley. He earns a living by taking visitors on
trails and on his riverboat to gawk at things non-human in the
bush.
In Saratoga, N.Y., where he was raised on a farm in the '60s,
Mahay loved catching muskrats in steel traps. When suburbia came
creeping, he gathered his guns and his wife and rode the ``Alaska
Highway'' west in 1972.
Mahay built a trapper's cabin and lived off game meat. With a
.44 magnum and a .22 rifle he killed 11 bears during his first few
years in the bush.
He doesn't do it anymore, though. ``I don't get any pleasure
shooting them,'' Mahay says. ``Funny how that changed.'' Now he
counts how many bears he spots from his riverboat. His single-day
record: 16.
Carole Lloyd, director of the Nature Center at Chugach State
Park, doesn't know Steve Mahay but insists she can explain his
change of heart. ``When you live in the wild,'' she says, ``you
learn tolerance for all living things.''
This is a land where bald eagles lunch on poodles and
dachshunds; where flocks of Canada geese keep jetliners in holding
patterns; where an intruder in your home is likely to slobber and
leave a pile on the rug.
In Juneau, the state capital, black bears poke through trash
bins, raid bird feeders, even duck into supermarkets. Eight years
ago, a cub entered the Barlett Regional Hospital emergency ward.
Nobody called 911.
Laura Stats, a nurse, found the bear sitting on a bed. She fed
it some milk and apples, sedated it, called the Fish and Game
Department and returned to her rounds.
In Anchorage, where half of human Alaska resides, nobody is
surprised to drive home from work and find a moose on the roof.
``Happens all the time,'' says Rick Sinnott, a wildlife biologist.
At his desk at the Fish and Game Department, the phone rings
nonstop, people calling to report critter capers:
-- A porcupine falling out of a tree into some woman's new
hairdo.
-- A squadron of ravens chewing up the windshield wipers on cars
parked at a mall.
-- A moose dragging away a kid's swing set.
-- A bear mauling a bear-shaped archery target.
``We still aren't sure if the bear was playing or trying to mate
with it,'' Sinnott says.
If there is one misconception humans have about critter country,
it is that pets will feel at home here.
A couple on vacation from Georgia learned otherwise a few years
ago after stopping at a Valdez gas station. When the woman let her
Chihuahua out to relieve itself, a bald eagle swooped down,
snatched it by the scruff and circled up and off.
``The dog gave one yelp,'' Dennis Fleming, the station
attendant, says, ``and that was it.''
Alaskans know wild animals are not Disney characters. Moose
cause more than 100 traffic accidents a year in Anchorage. They
destroy fences, eat shrubbery, and ``we tend to have a stomping
every couple of years,'' says Bruce Bartley, a spokesman at Fish
and Game.
The last death by moose stomping in Anchorage was in January,
1995. Myong Chin Ra, 71, was walking on the University of Alaska
campus when a cow moose charged. He died hours later of multiple
head fractures and internal injuries.
Last month in Hoonah, a village in southeastern Alaska, a
grizzly attacked 14-year-old Cody Mills as he walked a trail. The
boy was in surgery for three hours.
``This ain't the zoo,'' says Sgt. Darlene Turner, chief of state
troopers in Kodiak. ``You should appreciate animals, respect them.
But you can't romanticize them.''
Particularly not at a crime scene.
Investigators were taking photographs at a murder scene when a
moose appeared -- head down, ears back, hackles up. Then it charged.
``We were diving over balconies, running back and forth, around
parked cars trying to get away from that thing,'' Turner remembers.

After three times around the lot, an idea struck: The siren.
``Once he heard it,'' she says, ``Mr. Moose was off like a
shot.''


|
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| Packages that use PDStream | |
| org.pdfbox.pdfparser | The pdfparser package contains classes to parse PDF documents and objects within the document. |
| org.pdfbox.pdmodel | The PDModel package represents a high level API for creating and manipulating PDF documents. |
| org.pdfbox.pdmodel.common | High level PD classes that are used throughout several packages are placed in the PDModel common package. |
| org.pdfbox.pdmodel.common.filespecification | The file specification package defines classes that are used for the PDF File Specification logic. |
| org.pdfbox.pdmodel.common.function | This package contains functions that are available in the PDF specification. |
| org.pdfbox.pdmodel.font | Classes to deal with font functionality in a PDF Document. |
| org.pdfbox.pdmodel.graphics.color | This package deals with colors that are stored in a PDF document. |
| org.pdfbox.pdmodel.graphics.xobject | This package deals with images that are stored in a PDF document. |
| Uses of PDStream in org.pdfbox.pdfparser |
| Constructors in org.pdfbox.pdfparser with parameters of type PDStream | |
PDFStreamParser(PDStream stream)
Constructor. |
| Uses of PDStream in org.pdfbox.pdmodel |
| Methods in org.pdfbox.pdmodel that return PDStream | |
PDStream |
PDPage.getContents()
This will get the contents of the PDF Page, in the case that the contents of the page is an array then then the entire array of streams will be be wrapped and appear as a single stream. |
| Methods in org.pdfbox.pdmodel with parameters of type PDStream | |
void |
PDPage.setContents(PDStream contents)
This will set the contents of this page. |
| Uses of PDStream in org.pdfbox.pdmodel.common |
| Subclasses of PDStream in org.pdfbox.pdmodel.common | |
class |
PDMemoryStream
A PDStream represents a stream in a PDF document. |
class |
PDMetadata
This class represents metadata for various objects in a PDF document. |
class |
PDObjectStream
A PDStream represents a stream in a PDF document. |
| Methods in org.pdfbox.pdmodel.common that return PDStream | |
static PDStream |
PDStream.createFromCOS(COSBase base)
Create a pd stream from either a regular COSStream on a COSArray of cos streams. |
| Uses of PDStream in org.pdfbox.pdmodel.common.filespecification |
| Subclasses of PDStream in org.pdfbox.pdmodel.common.filespecification | |
class |
PDEmbeddedFile
This represents an embedded file in a file specification. |
| Uses of PDStream in org.pdfbox.pdmodel.common.function |
| Constructors in org.pdfbox.pdmodel.common.function with parameters of type PDStream | |
PDStreamFunction(PDStream functionDictionary)
Constructor. |
|
PDFunctionType4(PDStream functionDictionary)
Constructor. |
|
PDFunctionType0(PDStream functionDictionary)
Constructor. |
| Uses of PDStream in org.pdfbox.pdmodel.font |
| Methods in org.pdfbox.pdmodel.font that return PDStream | |
PDStream |
PDSimpleFont.getToUnicode()
This will get the ToUnicode stream. |
PDStream |
PDFontDescriptorDictionary.getFontFile()
A stream containing a Type 1 font program. |
PDStream |
PDFontDescriptorDictionary.getFontFile2()
A stream containing a true type font program. |
PDStream |
PDFontDescriptorDictionary.getFontFile3()
A stream containing a font program that is not true type or type 1. |
| Methods in org.pdfbox.pdmodel.font with parameters of type PDStream | |
void |
PDSimpleFont.setToUnicode(PDStream unicode)
This will set the ToUnicode stream. |
void |
PDFontDescriptorDictionary.setFontFile(PDStream type1Stream)
Set the type 1 font program. |
void |
PDFontDescriptorDictionary.setFontFile2(PDStream ttfStream)
Set the true type font program. |
void |
PDFontDescriptorDictionary.setFontFile3(PDStream stream)
Set a stream containing a font program that is not true type or type 1. |
| Uses of PDStream in org.pdfbox.pdmodel.graphics.color |
| Methods in org.pdfbox.pdmodel.graphics.color that return PDStream | |
PDStream |
PDICCBased.getPDStream()
Get the pd stream for this icc color space. |
| Uses of PDStream in org.pdfbox.pdmodel.graphics.xobject |
| Methods in org.pdfbox.pdmodel.graphics.xobject that return PDStream | |
PDStream |
PDXObject.getPDStream()
Returns the stream. |
| Constructors in org.pdfbox.pdmodel.graphics.xobject with parameters of type PDStream | |
PDXObjectImage(PDStream imageStream,
String fileSuffix)
Standard constuctor. |
|
PDXObjectForm(PDStream formStream)
Standard constuctor. |
|
PDXObject(PDStream xobj)
Standard constuctor. |
|
PDPixelMap(PDStream pdStream)
Standard constructor. |
|
PDJpeg(PDStream jpeg)
Standard constructor. |
|
PDCcitt(PDStream ccitt)
Standard constructor. |
|
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Get to know us!
Hoosi Design is the work of Danny Skelley. I have over 12 years of professional web design experience. I relocated to Bloomington with my wife Stephanie in 2017 because we love it here.
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Answer TRUE or FALSE.
In an array-based list implementation, to access the predecessor of the current node we must start at the first node in the list.
Is it possible to go backwards? If not, what can we do intead?


Changeset 744 for mystic/scripts
- Timestamp:
- 08/07/14 10:24:49 (22 months ago)
- Location:
- mystic/scripts
- Files:
-
- 5 edited
-
mystic_log_reader.py (modified) (3 diffs)
-
support_convergence.py (modified) (4 diffs)
-
support_hypercube.py (modified) (4 diffs)
-
support_hypercube_measures.py (modified) (4 diffs)
-
support_hypercube_scenario.py (modified) (4 diffs)
Legend:
- Unmodified
- Added
- Removed
-
mystic/scripts/mystic_log_reader.py
r724 r744 11 11 plot parameter convergence from file written with 'LoggingMonitor' 12 12 13 The option "param" takes an indicator string. This indicator string is a list 14 of strings, with each entry in the list corresponding to an array slice. 15 For example, params = "[':']" will plot all parameters. Alternatively, 16 params = "[':2','3:']" will plot all parameters except for the third parameter, 17 while params = "['0']" will only plot the first parameter. 18 13 The option "param" takes an indicator string. The indicator string is built 14 from comma-separated array slices. For example, params = ":" will plot all 15 parameters. Alternatively, params = ":2, 3:" will plot all parameters except 16 for the third parameter, while params = "0" will only plot the first parameter. 19 17 20 18 Required Inputs: … … 37 35 help="id # of the nth simultaneous points to plot") 38 36 parser.add_option("-p","--param",action="store",dest="param",\ 39 metavar="STR",default=" [':']",37 metavar="STR",default=":", 40 38 help="indicator string to select parameters") 41 39 #parser.add_option("-f","--file",action="store",dest="filename",metavar="FILE",\ … … 69 67 70 68 try: # select which parameters to plot 71 select = eval(parsed_opts.param) # format is "[':2','2:4','5','6:']"69 select = parsed_opts.param.split(',') # format is ":2, 2:4, 5, 6:" 72 70 except: 73 71 select = [':'] -
mystic/scripts/support_convergence.py
r742 r744 11 11 generate parameter convergence plots from file written with 'write_support_file' 12 12 13 The option "param" takes an indicator string. This indicator string is a list 14 of strings, with each entry in the list corresponding to an array slice. 15 For example, params = "[':']" will plot all parameters in a single plot. 16 Alternatively, params = "[':2','2:']" will split the parameters into two plots, 17 while params = "['0']" will only plot the first parameter. 18 19 The option "label" takes a list of strings. For example, label = "['x','y','']" 13 The option "param" takes an indicator string. The indicator string is built 14 from comma-separated array slices. For example, params = ":" will plot all 15 parameters in a single plot. Alternatively, params = ":2, 2:" will split the 16 parameters into two plots, and params = "0" will only plot the first parameter. 17 18 The option "label" takes comma-separated strings. For example, label = "x,y," 20 19 will label the y-axis of the first plot with 'x', a second plot with 'y', and 21 20 not add a label to a third or subsequent plots. If more labels are given than 22 21 plots, then the last label will be used for the y-axis of the 'cost' plot. 23 LaTeX is also accepted. For example, label = "[r'$ h$',r'$ {\alpha}$',r'$ v$']"24 willlabel the axes with standard LaTeX math formatting. Note that the leading25 space isrequired, and the text is aligned along the axis.22 LaTeX is also accepted. For example, label = r"$ h$, $ {\alpha}$, $ v$" will 23 label the axes with standard LaTeX math formatting. Note that the leading 24 space and leading 'r' are required, and the text is aligned along the axis. 26 25 27 26 Required Inputs: … … 67 66 default=None,help="the largest iteration to plot") 68 67 parser.add_option("-p","--param",action="store",dest="param",\ 69 metavar="STR",default=" [':']",68 metavar="STR",default=":", 70 69 help="indicator string to select parameters") 71 70 parser.add_option("-l","--label",action="store",dest="label",\ 72 metavar="STR",default=" ['']",71 metavar="STR",default="", 73 72 help="string to assign label to y-axis") 74 73 parser.add_option("-n","--nid",action="store",dest="id",\ … … 102 101 103 102 try: # select which parameters to plot 104 select = eval(parsed_opts.param) # format is "[':2','2:4','5','6:']"103 select = parsed_opts.param.split(',') # format is ":2, 2:4, 5, 6:" 105 104 except: 106 105 select = [':'] … … 112 111 113 112 try: # select labels for the axes 114 label = eval(parsed_opts.label) # format is "['x','y','z']"113 label = parsed_opts.label.split(',') # format is "x, y, z" 115 114 label += [''] * max(0, plots - len(label)) 116 115 except: -
mystic/scripts/support_hypercube.py
r742 r744 19 19 the y direction, and the third tuple for the z direction. Thus, axes = 20 20 "[(2,3),(6,7),(10,11)]" would set the 2nd and 3rd parameters along x. Iters, 21 however, accepts a list of strings instead of a list of tuples. For example,22 iters = "[':']" will plot all iters in a single plot. Alternatively,23 iters = " [':2','2:']" will split the iters into two plots, while24 iters = "['0']" willonly plot the first iteration.25 26 The option "label" takes a list of strings. For example, label = "['x','y','']"21 however, accepts a string built from comma-separated array slices. For 22 example, iters = ":" will plot all iters in a single plot. Alternatively, 23 iters = ":2, 2:" will split the iters into two plots, while iters = "0" will 24 only plot the first iteration. 25 26 The option "label" takes comma-separated strings. For example, label = "x,y," 27 27 will place 'x' on the x-axis, 'y' on the y-axis, and nothing on the z-axis. 28 LaTeX is also accepted. For example, label = "[r'$ h $',r'$ {\alpha}$',r'$ v$']"29 willlabel the axes with standard LaTeX math formatting. Note that the leading30 space is required, while the trailing space aligns the text with the axis31 instead of the plot frame.28 LaTeX is also accepted. For example, label = r"$ h $, $ {\alpha}$, $ v$" will 29 label the axes with standard LaTeX math formatting. Note that the leading 30 space and leading 'r' are required, while a trailing space aligns the text 31 with the axis instead of the plot frame. 32 32 33 33 Required Inputs: … … 50 50 help="indicator string to assign parameter to axis") 51 51 parser.add_option("-i","--iters",action="store",dest="iters",\ 52 metavar="STR",default=" ['-1']",52 metavar="STR",default="-1", 53 53 help="indicator string to select iterations to plot") 54 54 parser.add_option("-l","--label",action="store",dest="label",\ 55 metavar="STR",default=" ['','','']",55 metavar="STR",default=",,", 56 56 help="string to assign label to axis") 57 57 parser.add_option("-n","--nid",action="store",dest="id",\ … … 82 82 83 83 try: # select labels for the axes 84 label = eval(parsed_opts.label) # format is "['x','y','z']"84 label = parsed_opts.label.split(',') # format is "x, y, z" 85 85 except: 86 86 label = ['','',''] … … 88 88 x = params[max(xyz[0])] 89 89 try: # select which iterations to plot 90 select = eval(parsed_opts.iters) # format is "[':2','2:4','5','6:']"90 select = parsed_opts.iters.split(',') # format is ":2, 2:4, 5, 6:" 91 91 except: 92 92 select = ['-1'] -
mystic/scripts/support_hypercube_measures.py
r742 r744 22 22 and 0.0 is white. For example, using weight = "[(0,1),(4,5),(8,9)]" would use 23 23 the 0th and 1st parameters to weight x. Iters is also similar, however only 24 accept a list of ints. Hence, iters = "[-1]" will plot the last iteration,25 while iters = " [0,300,700]" will plot the 0th, 300th, and 700th in three plots.24 accepts comma-separated ints. Hence, iters = "-1" will plot the last iteration, 25 while iters = "0,300,700" will plot the 0th, 300th, and 700th in three plots. 26 26 ***Note that if weights are not normalized (to 1), an error will be thrown.*** 27 27 28 The option "label" takes a list of strings. For example, label = "['x','y','']"28 The option "label" takes comma-separated strings. For example, label = "x,y," 29 29 will place 'x' on the x-axis, 'y' on the y-axis, and nothing on the z-axis. 30 LaTeX is also accepted. For example, label = "[r'$ h $',r'$ {\alpha}$',r'$ v$']"31 willlabel the axes with standard LaTeX math formatting. Note that the leading32 space is required, while the trailing space aligns the text with the axis33 instead of the plot frame.30 LaTeX is also accepted. For example, label = r"$ h $, $ {\alpha}$, $ v$" will 31 label the axes with standard LaTeX math formatting. Note that the leading 32 space and leading 'r' are required, while a trailing space aligns the text 33 with the axis instead of the plot frame. 34 34 35 35 INTENDED FOR VISUALIZING WEIGHTED MEASURES (i.e. weights and positions) … … 57 57 help="indicator string to assign weight parameter to axis") 58 58 parser.add_option("-i","--iters",action="store",dest="iters",\ 59 metavar="STR",default=" ['-1']",59 metavar="STR",default="-1", 60 60 help="indicator string to select iterations to plot") 61 61 parser.add_option("-l","--label",action="store",dest="label",\ 62 metavar="STR",default=" ['','','']",62 metavar="STR",default=",,", 63 63 help="string to assign label to axis") 64 64 parser.add_option("-n","--nid",action="store",dest="id",\ … … 94 94 95 95 try: # select labels for the axes 96 label = eval(parsed_opts.label) # format is "['x','y','z']"96 label = parsed_opts.label.split(',') # format is "x, y, z" 97 97 except: 98 98 label = ['','',''] … … 100 100 x = params[max(xyz[0])] 101 101 try: # select which iterations to plot 102 select = eval(parsed_opts.iters) # format is "[2,4,5,6]"102 select = [int(i) for i in parsed_opts.iters.split(',')]# format is "[2,5,6]" 103 103 except: 104 104 select = ['-1'] -
mystic/scripts/support_hypercube_scenario.py
r742 r744 19 19 The dim (dimensions of the scenario) should be a quoted tuple. For example, 20 20 dim = "(1,1,2)" will convert the params to a two-member 3-D dataset. Iters 21 takes a list of strings instead of a tuple or a list of tuples. For example, 22 iters = "[':']" will plot all iters in a single plot. Alternatively,23 iters = " [':2','2:']" will split the iters into two plots, while24 iters = "['0']" willonly plot the first iteration.25 26 The option "label" takes a list of strings. For example, label = "['x','y','']"21 however, accepts a string built from comma-separated array slices. For 22 example, iters = ":" will plot all iters in a single plot. Alternatively, 23 iters = ":2, 2:" will split the iters into two plots, while iters = "0" will 24 only plot the first iteration. 25 26 The option "label" takes comma-separated strings. For example, label = "x,y," 27 27 will place 'x' on the x-axis, 'y' on the y-axis, and nothing on the z-axis. 28 LaTeX is also accepted. For example, label = "[r'$ h $',r'$ {\alpha}$',r'$ v$']"29 willlabel the axes with standard LaTeX math formatting. Note that the leading30 space is required, while the trailing space aligns the text with the axis31 instead of the plot frame. The option "filter" is used to select datapoints 32 from a given dataset, and takes a quoted list. A "mask" can be given as an 33 integer or a tuple of integers; when the mask is a tuple, the plot will be 2D. 34 The option "vertical" will plot the dataset values on the vertical axis; for 35 2D plots, cones are always plotted on the vertical axis.28 LaTeX is also accepted. For example, label = r"$ h $, $ {\alpha}$, $ v$" will 29 label the axes with standard LaTeX math formatting. Note that the leading 30 space and leading 'r' are required, while a trailing space aligns the text 31 with the axis instead of the plot frame. The option "filter" is used to select 32 datapoints from a given dataset, and takes a quoted list. A "mask" can be 33 given as an integer or a tuple of integers; when the mask is a tuple, the plot 34 will be 2D. The option "vertical" will plot the dataset values on the vertical 35 axis; for 2D plots, cones are always plotted on the vertical axis. 36 36 37 37 Required Inputs: … … 282 282 help="indicator string to set hypercube bounds") 283 283 parser.add_option("-i","--iters",action="store",dest="iters",\ 284 metavar="STR",default=" ['-1']",284 metavar="STR",default="-1", 285 285 help="indicator string to select iterations to plot") 286 286 parser.add_option("-l","--label",action="store",dest="label",\ 287 metavar="STR",default=" ['','','']",287 metavar="STR",default=",,", 288 288 help="string to assign label to axis") 289 289 parser.add_option("-d","--dim",action="store",dest="dim",\ … … 370 370 371 371 try: # select labels for the axes 372 label = eval(parsed_opts.label) # format is "['x','y','z']"372 label = parsed_opts.label.split(',') # format is "x, y, z" 373 373 except: 374 374 label = ['','',''] … … 376 376 x = params[-1] 377 377 try: # select which iterations to plot 378 select = eval(parsed_opts.iters) # format is "[':2','2:4','5','6:']"378 select = parsed_opts.iters.split(',') # format is ":2, 2:4, 5, 6:" 379 379 except: 380 380 select = ['-1']
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TLogger class.
TLogger records log messages in memory and implements the methods to retrieve the messages with filter conditions, including log levels and log categories.
Located in /Util/TLogger.php (line 25)
TComponent | --TLogger
Retrieves log messages.
Messages may be filtered by log levels and/or categories. A level filter is specified by an integer, whose bits indicate the levels interested. For example, (TLogger::INFO | TLogger::WARNING) specifies INFO and WARNING levels. A category filter is specified by concatenating interested category names with commas. A message whose category name starts with any filtering category will be returned. For example, a category filter 'System.Web, System.IO' will return messages under categories such as 'System.Web', 'System.IO', 'System.Web.UI', 'System.Web.UI.WebControls', etc. Level filter and category filter are combinational, i.e., only messages satisfying both filter conditions will they be returned.
Logs a message.
Messages logged by this method may be retrieved via getLogs.
Inherited From TComponent
TComponent::addParsedObject()
TComponent::attachEventHandler()
TComponent::canGetProperty()
TComponent::canSetProperty()
TComponent::createdOnTemplate()
TComponent::detachEventHandler()
TComponent::evaluateExpression()
TComponent::evaluateStatements()
TComponent::getEventHandlers()
TComponent::getSubProperty()
TComponent::hasEvent()
TComponent::hasEventHandler()
TComponent::hasProperty()
TComponent::raiseEvent()
TComponent::setSubProperty()
TComponent::__get()
TComponent::__set()
Documentation generated on Mon, 22 Feb 2010 12:18:08 +0000 by phpDocumentor 1.3.0RC4


A stop is a value that an elevator can arrive at. This value varies depending on the class of elevator, and can be floor altitude, ceiling altitude, ambience, degrees etc. Stops can be used practically, such as different heights a lift stops at, or can be used purely for level control as elevators can also send a message, page a sound, or create an adjoin upon arriving at a stop.
Note: Elevators can have any number of stops. If no stops are given, the elevator will start at value 0 and keep increasing its value throughout the entire level. This may be appropriate for an "elevator scroll_floor", but not for an "elevator move_floor" !!!
Note: Door elevators should NOT be given stops. They will have automatic stops set depending on the altitudes of the floor and ceiling of their sector.
Usage:
| stop: [value1] [value2]
The first value can be given in three ways:
| | [num] | absolute stop |
| | @[num] | relative stop |
| | [sectorname] | equal the value of the sector [sectorname] |
The second value can be given in 4 ways:
| | [time] | time in sec that elevator remains at stop |
| | hold | elevator will remain at stop indefinitely |
| | terminate | elevator will stay at the stop permanently |
| | complete | mission will be complete when elev arrives at stop |


The play area represents the level users play in. To move a game object in the play area, click on it. It will then follow your mouse. Click again to stop moving it. To add a game object, click on one in the palette. Move to the desired location in the play area and click to place the new game object.


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_
A
ACTIVE
- Enum Constant in
TESTENUM
adate
- Property in
Solr1
adate
- Property in
Solr1Annotated
adate
- Property in
Solr1Disabled
adate
- Property in
Solr1Override
adate
- Property in
Solr2
addCategory
(String)
- Method in
Listing
addEventTypeListener
(def, def, def)
- Method in
SolrGrailsPlugin
address1
- Property in
Listing
address2
- Property in
Listing
afloat
- Property in
Solr1
afloat
- Property in
Solr1Annotated
afloat
- Property in
Solr1Disabled
afloat
- Property in
Solr1Override
afloat
- Property in
Solr2
aint
- Property in
Solr1
aint
- Property in
Solr1Annotated
aint
- Property in
Solr1Disabled
aint
- Property in
Solr1Override
aint
- Property in
Solr2
along
- Property in
Solr1
along
- Property in
Solr1Annotated
along
- Property in
Solr1Disabled
along
- Property in
Solr1Override
along
- Property in
Solr2
asText
- Field in
Solr
asTextAlso
- Field in
Solr
astring
- Property in
Solr1
astring
- Property in
Solr1Annotated
astring
- Property in
Solr1Disabled
astring
- Property in
Solr1Override
astring
- Property in
Solr2
author
- Property in
SolrGrailsPlugin
authorEmail
- Property in
SolrGrailsPlugin
averageLastUpdated
- Property in
Listing
averageRating
- Property in
Listing
B
bstring
- Property in
Solr1Annotated
C
categories
- Property in
Listing
categories
- Property in
Solr2
city
- Property in
Listing
composition
- Property in
Solr2
compute
(def, def, def, def, def)
- Method in
Haversine
computeKm
(def, def, def, def)
- Method in
Haversine
computeMi
(def, def, def, def)
- Method in
Haversine
Config
- Class in
grails-app.conf
Config
()
- Constructor in
Config
constraints
- Property in
Listing
constraints
- Property in
Solr2
country
- Property in
Listing
D
DataSource
- Class in
grails-app.conf
DataSource
()
- Constructor in
DataSource
dateCreated
- Property in
Listing
DeleteSolrIndex
- Class in
scripts
DeleteSolrIndex
()
- Constructor in
DeleteSolrIndex
dependsOn
- Property in
SolrGrailsPlugin
description
- Property in
SolrGrailsPlugin
DISABLED
- Enum Constant in
TESTENUM
documentation
- Property in
SolrGrailsPlugin
DomainMethodTests
- Class in
test.integration
DomainMethodTests
()
- Constructor in
DomainMethodTests
doWithApplicationContext
- Property in
SolrGrailsPlugin
doWithDynamicMethods
- Property in
SolrGrailsPlugin
doWithSpring
- Property in
SolrGrailsPlugin
doWithWebDescriptor
- Property in
SolrGrailsPlugin
E
email
- Property in
Listing
enableSolrSearch
- Property in
Listing
enableSolrSearch
- Property in
Solr1
enableSolrSearch
- Property in
Solr1Annotated
enableSolrSearch
- Property in
Solr1Disabled
enableSolrSearch
- Property in
Solr1Override
enableSolrSearch
- Property in
Solr2
F
facet
(def)
- Method in
SearchResults
facet
- Property in
SolrTagLib
field
- Field in
Solr
fromSolrAstring
(def)
- Method in
Solr1Override
G
getCategoriesAsList
- Property in
Listing
getKey
()
- Method in
TESTENUM
getServer
()
- Method in
SolrService
Return a SolrServer {
getSolrId
(def)
- Method in
SolrUtil
Get the Id that will be stored in the SOLR index: ClassName-Id
getSpatialQuery
(def, def, def, def, def, def, def, def, def, def, def)
- Method in
SolrService
Constitute SolrQuery for a haversine based spatial search.
getStreamingUpdateServer
(def, def)
- Method in
SolrService
getTotal
()
- Method in
SearchResults
getValue
()
- Method in
TESTENUM
grailsApplication
- Property in
SolrService
grailsVersion
- Property in
SolrGrailsPlugin
H
hasMany
- Property in
Solr2
Haversine
- Class in
src.groovy.org.grails.solr
Haversine algorithmu Derived from http://www.movable-type.co.uk/scripts/latlong.html)
Haversine
()
- Constructor in
Haversine
I
ifEnabled
(def)
- Method in
SolrIndexListener
ignore
- Field in
Solr
IGNORED_PROPS
- Property in
SolrUtil
IndexDomain
- Class in
scripts
IndexDomain
()
- Constructor in
IndexDomain
indexDomain
(def, def, def, def, def)
- Method in
SolrGrailsPlugin
indexSolrAstring
(def)
- Method in
Solr1Override
indexSolrCategories
(def)
- Method in
Solr2
indexSolrLatitude
(def)
- Method in
Listing
indexSolrLongitude
(def)
- Method in
Listing
indexSolrSolrsWithOverride
(def)
- Method in
Solr2
initialize
(Configuration)
- Method in
SolrIndexListener
J
K
L
lastUpdated
- Property in
Listing
latitude
- Property in
Listing
leadershipContact
- Property in
Listing
Listing
- Class in
grails-app.domain.org.grails.solr
Listing
()
- Constructor in
Listing
log
- Property in
SolrIndexListener
log
- Property in
SolrUtil
longDescription
- Property in
Listing
longitude
- Property in
Listing
M
mappings
- Property in
UrlMappings
N
namespace
- Property in
SolrTagLib
NEW
- Enum Constant in
TESTENUM
O
onChange
- Property in
SolrGrailsPlugin
onConfigChange
- Property in
SolrGrailsPlugin
onPostDelete
(PostDeleteEvent)
- Method in
SolrIndexListener
onPostInsert
(PostInsertEvent)
- Method in
SolrIndexListener
onPostUpdate
(PostUpdateEvent)
- Method in
SolrIndexListener
P
parseSolrId
(def)
- Method in
SolrUtil
Return a map with values class and id parse from the SolrDomainId ie Book-21 would return [class: "Book", id: 21]
phone
- Property in
Listing
pluginExcludes
- Property in
SolrGrailsPlugin
postalCode
- Property in
Listing
Q
queryResponse
- Property in
SearchResults
querySpatial
(SolrQuery, def, def, def, def)
- Method in
SolrService
Expected to be called after getSpatialQuery, assuming you need to further manipulate the SolrQuery object before executing the query.
R
replaceCategories
(String[])
- Method in
Listing
resultAsObject
(def)
- Method in
SolrUtil
Parse a Solrj result document and attempt to return an object
resultLink
- Property in
SolrTagLib
resultList
- Property in
SearchResults
Rk
- Property in
Haversine
Rm
- Property in
Haversine
S
search
(SolrQuery)
- Method in
SolrService
Given SolrQuery object, execute Solr query
SearchResults
- Class in
src.groovy.org.grails.solr
SearchResults
()
- Constructor in
SearchResults
SearchTests
- Class in
test.integration
SearchTests
()
- Constructor in
SearchTests
serviceDetails
- Property in
Listing
setUp
()
- Method in
DomainMethodTests
setUp
()
- Method in
SolrServiceTests
setUp
()
- Method in
SolrUtilTests
sicCode
- Property in
Listing
sicDescription
- Property in
Listing
Solr
- Annotation Type in
src.java.org.grails.solr
Describes the Solr annotated API element.
Solr1
- Class in
grails-app.domain.org.grails.solr
Solr1
()
- Constructor in
Solr1
Solr1Annotated
- Class in
grails-app.domain.org.grails.solr
Solr1Annotated
()
- Constructor in
Solr1Annotated
Solr1Disabled
- Class in
grails-app.domain.org.grails.solr
Solr1Disabled
()
- Constructor in
Solr1Disabled
Solr1Override
- Class in
grails-app.domain.org.grails.solr
Solr1Override
()
- Constructor in
Solr1Override
Solr2
- Class in
grails-app.domain.org.grails.solr
Solr2
()
- Constructor in
Solr2
solrAutoIndex
- Property in
Solr1
solrAutoIndex
- Property in
Solr1Annotated
SolrGrailsPlugin
- Class in
DefaultPackage
SolrGrailsPlugin
()
- Constructor in
SolrGrailsPlugin
SolrIndexListener
- Class in
src.groovy.org.grails.solr
SolrIndexListener
()
- Constructor in
SolrIndexListener
SolrService
- Class in
grails-app.services.org.grails.solr
solrService
- Property in
DomainMethodTests
solrService
- Property in
SearchTests
SolrService
()
- Constructor in
SolrService
SolrServiceTests
- Class in
test.unit
SolrServiceTests
()
- Constructor in
SolrServiceTests
SolrTagLib
- Class in
grails-app.taglib.org.grails.solr
SolrTagLib
()
- Constructor in
SolrTagLib
solrTitle
()
- Method in
Solr1
SolrUtil
- Class in
src.groovy.org.grails.solr
SolrUtil
()
- Constructor in
SolrUtil
SolrUtilTests
- Class in
test.unit
SolrUtilTests
()
- Constructor in
SolrUtilTests
StartSolr
- Class in
scripts
StartSolr
()
- Constructor in
StartSolr
state
- Property in
Listing
StopSolr
- Class in
scripts
StopSolr
()
- Constructor in
StopSolr
stripFieldName
(def)
- Method in
SolrUtil
T
tearDown
()
- Method in
DomainMethodTests
tearDown
()
- Method in
SolrServiceTests
tearDown
()
- Method in
SolrUtilTests
testAnnotation
()
- Method in
DomainMethodTests
testBasicIndex
()
- Method in
DomainMethodTests
testBasicOverride
()
- Method in
DomainMethodTests
testBasicQuery
()
- Method in
SearchTests
testBasicSearch
()
- Method in
DomainMethodTests
testDelete
()
- Method in
DomainMethodTests
testDeleteByEvent
()
- Method in
DomainMethodTests
testDomainProperty
()
- Method in
DomainMethodTests
TESTENUM
- Enum in
grails-app.domain.org.grails.solr
testenum
- Property in
Solr2
TESTENUM
(String)
- Constructor in
TESTENUM
testFacetedQuery
()
- Method in
SearchTests
testHasManyOverride
()
- Method in
DomainMethodTests
This tests two things.
testListingDomain
()
- Method in
DomainMethodTests
testQuerySpatial
()
- Method in
SearchTests
testReinstantiateClass
()
- Method in
SolrUtilTests
testSearchWithHasManyOverride
()
- Method in
DomainMethodTests
testSolrDisabled
()
- Method in
DomainMethodTests
testSolrFieldName
()
- Method in
DomainMethodTests
testSolrId
()
- Method in
SolrUtilTests
testSpatialQueryBuilder
()
- Method in
SolrServiceTests
testStreamingUpdateServer
()
- Method in
SearchTests
testStrippedSolrFieldName
()
- Method in
DomainMethodTests
testTitleIndex
()
- Method in
DomainMethodTests
TITLE_FIELD
- Property in
SolrUtil
title
- Property in
Listing
title
- Property in
SolrGrailsPlugin
toString
()
- Method in
TESTENUM
transactional
- Property in
SolrService
TYPE_FIELD
- Property in
SolrUtil
typeMapping
- Property in
SolrUtil
typeSuffixes
- Property in
SolrUtil
U
url
- Property in
Listing
UrlMappings
- Class in
grails-app.conf
UrlMappings
()
- Constructor in
UrlMappings
V
value
- Property in
TESTENUM
valueOf
(String)
- Method in
TESTENUM
Returns the enum constant of this type with the specified name.
values
()
- Method in
TESTENUM
Returns an array containing the constants of this enum type, in the order they are declared.
version
- Property in
SolrGrailsPlugin
W
X
Y
Z
_
_Install
- Class in
scripts
_Install
()
- Constructor in
_Install
_Uninstall
- Class in
scripts
_Uninstall
()
- Constructor in
_Uninstall
_Upgrade
- Class in
scripts
_Upgrade
()
- Constructor in
_Upgrade
Groovy Documentation


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{%extends display.suMain%} {%block content%}
{%trans "A special handle to give barcode or rfid code"%}
{%trans "This type of input filled only with readers, because of type speed!"%}


Hello, this is The Official AssureBOT and Assure-Music Discord Bot Website.
AssureBOT will assure 😀 good care of your server.
more. He can help a lot, make sure
to check out the commands page [or help command] and the other ones before you invite
AssureBOT to your server, so you know what an amzing bot you're inviting!


TBaseActiveControl class provided additional basic property for every active control. An instance of TBaseActiveControl or its decendent TBaseActiveCallbackControl is created by TActiveControlAdapter::getBaseActiveControl() method.
The EnableUpdate property determines wether the active control is allowed to update the contents of the client-side when the callback response returns.
Located in /Web/UI/ActiveControls/TBaseActiveControl.php (line 30)
TComponent | --TBaseActiveControl
| Class | Description |
|---|---|
| TBaseActiveCallbackControl | TBaseActiveCallbackControl is a common set of options and functionality for active controls that can perform callback requests. |
Constructor. Attach a base active control to an active control instance.
Returns true if callback response is allowed to update the browser contents.
Is is true if the control is initilized, and is a callback request and the EnabledUpdate property is true and the page is not loading post data.
Gets an option named value. Options are used to store and retrive named values for the base active controls.
Sets a named options with a value. Options are used to store and retrive named values for the base active controls.
Inherited From TComponent
TComponent::addParsedObject()
TComponent::attachEventHandler()
TComponent::canGetProperty()
TComponent::canSetProperty()
TComponent::createdOnTemplate()
TComponent::detachEventHandler()
TComponent::evaluateExpression()
TComponent::evaluateStatements()
TComponent::getEventHandlers()
TComponent::getSubProperty()
TComponent::hasEvent()
TComponent::hasEventHandler()
TComponent::hasProperty()
TComponent::raiseEvent()
TComponent::setSubProperty()
TComponent::__get()
TComponent::__set()
Documentation generated on Sun, 11 Jan 2009 20:17:38 -0500 by phpDocumentor 1.3.0RC4


Gareth Bale’s celebration adds more uncertainty to Real Madrid future
Zinedine Zidane faces another crossroads in his handling of Gareth Bale, who appeared to mock Real Madrid after Wales' 2-0 victory over Hungary yesterday morning (Singapore time).
After sealing qualification for Euro 2020 with a wild win in Cardiff, an ecstatic Bale celebrated next to his teammates behind a flag that read "Wales. Golf. Madrid. In that order".
The flag was taken from the crowd after Welsh fans made a slogan out of comments made by former Real star and director of football Predrag Mijatovic during an interview with Spanish radio station Cadena Ser last month.
"Bale thinks first about the Wales national team, then about golf and then about Madrid," said Mijatovic.
"I haven't talked to him, but that is the impression I get."
Zidane must now decide whether to punish Bale and what form that punishment should take.
Bale's Wales boss, however, was in a distinctly happier mood.
Manchester United legend Ryan Giggs hailed Wales' qualification for Euro 2020 as one of the best days of his life after Aaron Ramsey's double saw off Hungary in a winner-takes-all clash in Group E.
"It doesn't get any better," said Giggs, who won 25 major trophies as a United player.
"It's one of the best days of my life."
This is only the second time Wales have qualified for the European Championship. - AFP


Individual:
Tickets are limited. Be sure to get your ticket before they are sold out. We will send out electronic invoices by email after the conference. If you need reimbursement from your company, you might want a corporate ticket to meet our local tax regulation.