Environment.usejoblib module¶
Import Parallel and delayed from joblib, safely.
-
class
Environment.usejoblib.
Parallel
(n_jobs=None, backend=None, verbose=0, timeout=None, pre_dispatch='2 * n_jobs', batch_size='auto', temp_folder=None, max_nbytes='1M', mmap_mode='r', prefer=None, require=None)[source]¶ Bases:
joblib.logger.Logger
Helper class for readable parallel mapping.
Read more in the User Guide.
- n_jobs: int, default: None
The maximum number of concurrently running jobs, such as the number of Python worker processes when backend=”multiprocessing” or the size of the thread-pool when backend=”threading”. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. None is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a parallel_backend context manager that sets another value for n_jobs.
- backend: str, ParallelBackendBase instance or None, default: ‘loky’
Specify the parallelization backend implementation. Supported backends are:
“loky” used by default, can induce some communication and memory overhead when exchanging input and output data with the worker Python processes.
“multiprocessing” previous process-based backend based on multiprocessing.Pool. Less robust than loky.
“threading” is a very low-overhead backend but it suffers from the Python Global Interpreter Lock if the called function relies a lot on Python objects. “threading” is mostly useful when the execution bottleneck is a compiled extension that explicitly releases the GIL (for instance a Cython loop wrapped in a “with nogil” block or an expensive call to a library such as NumPy).
finally, you can register backends by calling register_parallel_backend. This will allow you to implement a backend of your liking.
It is not recommended to hard-code the backend name in a call to Parallel in a library. Instead it is recommended to set soft hints (prefer) or hard constraints (require) so as to make it possible for library users to change the backend from the outside using the parallel_backend context manager.
- prefer: str in {‘processes’, ‘threads’} or None, default: None
Soft hint to choose the default backend if no specific backend was selected with the parallel_backend context manager. The default process-based backend is ‘loky’ and the default thread-based backend is ‘threading’.
- require: ‘sharedmem’ or None, default None
Hard constraint to select the backend. If set to ‘sharedmem’, the selected backend will be single-host and thread-based even if the user asked for a non-thread based backend with parallel_backend.
- verbose: int, optional
The verbosity level: if non zero, progress messages are printed. Above 50, the output is sent to stdout. The frequency of the messages increases with the verbosity level. If it more than 10, all iterations are reported.
- timeout: float, optional
Timeout limit for each task to complete. If any task takes longer a TimeOutError will be raised. Only applied when n_jobs != 1
- pre_dispatch: {‘all’, integer, or expression, as in ‘3*n_jobs’}
The number of batches (of tasks) to be pre-dispatched. Default is ‘2*n_jobs’. When batch_size=”auto” this is reasonable default and the workers should never starve.
- batch_size: int or ‘auto’, default: ‘auto’
The number of atomic tasks to dispatch at once to each worker. When individual evaluations are very fast, dispatching calls to workers can be slower than sequential computation because of the overhead. Batching fast computations together can mitigate this. The
'auto'
strategy keeps track of the time it takes for a batch to complete, and dynamically adjusts the batch size to keep the time on the order of half a second, using a heuristic. The initial batch size is 1.batch_size="auto"
withbackend="threading"
will dispatch batches of a single task at a time as the threading backend has very little overhead and using larger batch size has not proved to bring any gain in that case.- temp_folder: str, optional
Folder to be used by the pool for memmapping large arrays for sharing memory with worker processes. If None, this will try in order:
a folder pointed by the JOBLIB_TEMP_FOLDER environment variable,
/dev/shm if the folder exists and is writable: this is a RAM disk filesystem available by default on modern Linux distributions,
the default system temporary folder that can be overridden with TMP, TMPDIR or TEMP environment variables, typically /tmp under Unix operating systems.
Only active when backend=”loky” or “multiprocessing”.
- max_nbytes int, str, or None, optional, 1M by default
Threshold on the size of arrays passed to the workers that triggers automated memory mapping in temp_folder. Can be an int in Bytes, or a human-readable string, e.g., ‘1M’ for 1 megabyte. Use None to disable memmapping of large arrays. Only active when backend=”loky” or “multiprocessing”.
- mmap_mode: {None, ‘r+’, ‘r’, ‘w+’, ‘c’}
Memmapping mode for numpy arrays passed to workers. See ‘max_nbytes’ parameter documentation for more details.
This object uses workers to compute in parallel the application of a function to many different arguments. The main functionality it brings in addition to using the raw multiprocessing or concurrent.futures API are (see examples for details):
More readable code, in particular since it avoids constructing list of arguments.
- Easier debugging:
informative tracebacks even when the error happens on the client side
using ‘n_jobs=1’ enables to turn off parallel computing for debugging without changing the codepath
early capture of pickling errors
An optional progress meter.
Interruption of multiprocesses jobs with ‘Ctrl-C’
Flexible pickling control for the communication to and from the worker processes.
Ability to use shared memory efficiently with worker processes for large numpy-based datastructures.
A simple example:
>>> from math import sqrt >>> from joblib import Parallel, delayed >>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10)) [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
Reshaping the output when the function has several return values:
>>> from math import modf >>> from joblib import Parallel, delayed >>> r = Parallel(n_jobs=1)(delayed(modf)(i/2.) for i in range(10)) >>> res, i = zip(*r) >>> res (0.0, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5, 0.0, 0.5) >>> i (0.0, 0.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0)
The progress meter: the higher the value of verbose, the more messages:
>>> from time import sleep >>> from joblib import Parallel, delayed >>> r = Parallel(n_jobs=2, verbose=10)(delayed(sleep)(.2) for _ in range(10)) [Parallel(n_jobs=2)]: Done 1 tasks | elapsed: 0.6s [Parallel(n_jobs=2)]: Done 4 tasks | elapsed: 0.8s [Parallel(n_jobs=2)]: Done 10 out of 10 | elapsed: 1.4s finished
Traceback example, note how the line of the error is indicated as well as the values of the parameter passed to the function that triggered the exception, even though the traceback happens in the child process:
>>> from heapq import nlargest >>> from joblib import Parallel, delayed >>> Parallel(n_jobs=2)(delayed(nlargest)(2, n) for n in (range(4), 'abcde', 3)) #... --------------------------------------------------------------------------- Sub-process traceback: --------------------------------------------------------------------------- TypeError Mon Nov 12 11:37:46 2012 PID: 12934 Python 2.7.3: /usr/bin/python ........................................................................... /usr/lib/python2.7/heapq.pyc in nlargest(n=2, iterable=3, key=None) 419 if n >= size: 420 return sorted(iterable, key=key, reverse=True)[:n] 421 422 # When key is none, use simpler decoration 423 if key is None: --> 424 it = izip(iterable, count(0,-1)) # decorate 425 result = _nlargest(n, it) 426 return map(itemgetter(0), result) # undecorate 427 428 # General case, slowest method TypeError: izip argument #1 must support iteration ___________________________________________________________________________
Using pre_dispatch in a producer/consumer situation, where the data is generated on the fly. Note how the producer is first called 3 times before the parallel loop is initiated, and then called to generate new data on the fly:
>>> from math import sqrt >>> from joblib import Parallel, delayed >>> def producer(): ... for i in range(6): ... print('Produced %s' % i) ... yield i >>> out = Parallel(n_jobs=2, verbose=100, pre_dispatch='1.5*n_jobs')( ... delayed(sqrt)(i) for i in producer()) Produced 0 Produced 1 Produced 2 [Parallel(n_jobs=2)]: Done 1 jobs | elapsed: 0.0s Produced 3 [Parallel(n_jobs=2)]: Done 2 jobs | elapsed: 0.0s Produced 4 [Parallel(n_jobs=2)]: Done 3 jobs | elapsed: 0.0s Produced 5 [Parallel(n_jobs=2)]: Done 4 jobs | elapsed: 0.0s [Parallel(n_jobs=2)]: Done 6 out of 6 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=2)]: Done 6 out of 6 | elapsed: 0.0s finished
-
__init__
(n_jobs=None, backend=None, verbose=0, timeout=None, pre_dispatch='2 * n_jobs', batch_size='auto', temp_folder=None, max_nbytes='1M', mmap_mode='r', prefer=None, require=None)[source]¶ - depth: int, optional
The depth of objects printed.
-
_dispatch
(batch)[source]¶ Queue the batch for computing, with or without multiprocessing
WARNING: this method is not thread-safe: it should be only called indirectly via dispatch_one_batch.
-
dispatch_next
()[source]¶ Dispatch more data for parallel processing
This method is meant to be called concurrently by the multiprocessing callback. We rely on the thread-safety of dispatch_one_batch to protect against concurrent consumption of the unprotected iterator.
-
dispatch_one_batch
(iterator)[source]¶ Prefetch the tasks for the next batch and dispatch them.
The effective size of the batch is computed here. If there are no more jobs to dispatch, return False, else return True.
The iterator consumption and dispatching is protected by the same lock so calling this function should be thread safe.
-
print_progress
()[source]¶ Display the process of the parallel execution only a fraction of time, controlled by self.verbose.
-
__module__
= 'joblib.parallel'¶


Where To Watch Ulster Vs Clermont? TV Details For Champions Cup Clash
by Conor ByrneThe second round of fixtures for the Heineken Champions Cup kicks off this Friday when Ulster will play host to Clermont Auvergne. Here are all the details for those wondering where to watch Ulster Vs Clermont Auvergne.
Both sides go into the game with a degree of confidence after they were both victorious in their first games of the tournament. Ulster secured a one point win over Bath, while Clermont came away with a convincing 52 - 21 victory over Harlequins.
This match will see the two sides meet in the Champions cup for the fifth time, with the previous four meetings between the two resulting in both sides coming away with two wins each.
The winner of this tie will go top of the group and will become early favourites to come out group winners.
Where To Watch Ulster Vs Clermont Auvergne?
BT Sport 2 will have live coverage of the match, which will begin at 7 pm.
Where Is It On?
Ulster will be at home for the tie with it taking place in the Kingspan Stadium in Belfast.
What Time Is Kick-Off?
The match will kick off at 7.45 pm on Friday, 22 November.
Are The Teams In Good Form?
Both teams are in good form with four wins in their last six games. This will no doubt provide an exciting game for fans and even any neutrals watching.
Ulster Team News
Ulster will be without Jack McGrath, Sam Carter and Rob Lyttle, who were all taken off injured in the previous game against Bath. McGrath was forced to leave the pitch after only 8 minutes of play, Carter was also taken off before the end of the first half. Louis Ludik came on to replace Lyttle in the 63rd minute.
Ulster Team: Will Addison; Louis Ludik, Luke Marshall, Stuart McCloskey, Jacob Stockdale; Billy Burns, John Cooney; Eric O’Sullivan, Rob Herring, Marty Moore; Alan O’Connor, Iain Henderson (Capt.); Sean Reidy, Jordi Murphy, Marcell Coetzee.
Replacements: Adam McBurney, Kyle McCall, Tom O’Toole, Kieran Treadwell, Nick Timoney, David Shanahan, Bill Johnston, Craig Gilroy.


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About Medical Cannabis
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Benefits
The health benefits of medical marijuana include relief from pain and muscle spasm, nausea associated with chemotherapy, and anorexia. Benefits are seen in immune function, neuroplasticity, emotional and mood regulation, vascular health and digestive function.
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Watch this video to learn more!
Dispensaries in Florida
Dadeland
9002 Dadeland Blvd Miami, FL, 33156 USA
Daytona
148 North Federal Highway Deerfield Beach Florida 33441 USA
Deerfield
148 North Federal Highway Deerfield Beach Florida 33441 USA
East Orlando
775 N Semoran Blvd Orlando FL 32807 USA
Ft. Myers
4480 Fowler Street Ft. Myers, FL, 33901 USA
Ft. Pierce
3218 S. US Highway 1 Ft. Pierce Florida 34982 USA
Jacksonville
6361 103rd Street Jacksonville, FL, 32210 USA
Lakeland
3145 US HWY 98 N Lakeland Florida 33805 USA


| Toppings | 12" | 15" | 18" |
|---|---|---|---|
| Cheese | $9.75 | $11.99 | $14.49 |
| Each Extra Topping | $1.00 | $1.25 | $1.50 |
| pepperoni, sausage, ham, hamburger, mushroom, onion, green pepper, black olive, jalapenos, tomato, pineapple, spinach, extra cheese | |||
| Deluxe | $13.99 | $16.99 | $19.99 |
| mushrooms, onions, bell peppers, black olives, sausage, pepperoni, and ham | |||
| Deluxe | $13.99 | $16.99 | $19.99 |
| Hawaiian | $13.99 | $16.99 | $19.99 |
| BBQ Pizza | $13.99 | $16.99 | $19.99 |
| Bacon Cheeseburger | $13.99 | $16.99 | $19.99 |
| Meatball Pizza | $13.99 | $16.99 | $19.99 |
| Stuffed Pizza | $19.99 | $26.99 |
| Slice | Price |
|---|---|
| Jumbo Cheese Slice | $2.25 |
| 1 Topping Slice | $2.35 |
| 2 Topping Slice | $2.60 |
| Jumbo Deluxe Slice | $2.99 |
| Stuffed Pizza Slice | $4.49 |
| Roll | Price |
|---|---|
| Pizza Pocket (Pepperoni & Mozzarella) |
$4.99 |
| Cheese Calzone (Ricotta, Mozzarella, & Parmesan Cheeses) |
$5.75 |
| Hamm & Cheese Calzone | $5.75 |
| Spinach, Pepperoni, or Sausage Calzone | $5.75 |
| Stromboli (Ham, Pepperoni & Mozzarella) |
$5.75 |
| Veggie Roll (Tomato, Onion, Green Peppers, Black Olives, Mushrooms, & Mozzarella) |
$5.75 |
| Sausage Roll (Sausage, Green Peppers, Onions, & Mozzarella) |
$5.75 |
| Chicken Stromboli (Chicken & Mozzarella) |
$5.75 |
| Dish | Price |
|---|---|
| Spaghetti with Sauce | $6.49 |
| Spaghetti with Meatballs or Sausage | $7.49 |
| Fettuccini Alfredo | $7.49 |
| Lasagna | $7.49 |
| Baked Ziti | $7.49 |
| Cheese Ravioli | $7.49 |
| Beef Ravioli | $7.49 |
| Manicotti with Cheese | $7.49 |
| Sandwich | Price |
|---|---|
| Italian Sandwich (Ham, Salami, & Mozzarella) |
$5.49 |
| Ham & Cheese | $5.49 |
| Salami & Cheese | $5.49 |
| Chicken Sandwich (Breaded Chicken & Mozzarella) |
$5.49 |
| Steak Sandwich | $5.49 |
| Meatball Sandwich | $5.49 |
| Chicken Parmesan (Breaded Chicken, Marinara, & Mozzarella) |
$5.49 |
| Pizza Steak Sandwich (Steak, Marinara, & Mozzarella) |
$5.49 |
| Special Steak Sandwich (Grilled Mushrooms, Onions, and Bell Peppers with Marinara on the side) |
$5.99 |
| Salad | Price |
|---|---|
| Side Salad (Lettuce, Tomato, & Cucumber) |
$2.99 |
| Milanos Salad (Lettuce, Tomato, Cucumber, Onion, Black Olives, Pepperochinis, & Mozzarella Cheese) |
$4.99 |
| Chef Salad (Lettuce, Tomato, Cucumber, Onion, Black Olives, Ham, Salami, Pepperochinis, and Mozzarella Cheese) |
$5.99 |
| Pasta Salad (Tri-colored Rotini pasta, House Italian Dressing, Black Olives, Tomatoes, & Bananna Peppers) |
$3.99 |
| Chicken Salad (Fried or Grilled) |
$5.99 |
| Greek Salad | $5.99 |
| Side | Price |
|---|---|
| Breat Sticks (4) | $1.99 |
| Pepperoni Breadsticks (4) | $2.99 |
| Garlick Bread | $2.50 |
| Garlic Knots (6) | $2.50 |
| Mozzarella Cheesticks (5) | $3.99 |
| Toasted Ravioli | $4.99 |
| Hot wings | $4.99 |
| Cheesy Bread | $2.99 |
| Side of Meatballs | $2.99 |
| All Extras | $0.50 |
| Dessert | Price |
|---|---|
| Tiramisu | $3.75 |
| Cheesecake | $2.75 |
| Cannoli | $2.99 |
| Drink | Price |
|---|---|
| 20 oz cup | $1.49 |
| 32 oz cup | $1.75 |
| 20 oz bottled drinks | $1.59 |
| Domestic Beer | $2.50 |
| Imported Beer | $3.00 |


This class provides a useful interface for the user, to easily read some arguments/options from the command-line.
This class provides a useful interface for the user, to easily read some arguments/options from the command-line.Note that here, we make a difference between:
- options which are not required.
- arguments which are required.
Options:
"write log" bool Should I output the cmd.log file ? [true]
Add an option (Int, Bool, Real, String).
The option will be setted to value in the command-line by printing "name value"
Alphabetic index HTML hierarchy of classes or Java


Symbol Index for "Subject Identifier State Type"
Symbol Index for diagram
"Subject Identifier State Type"
Activity State Type (Class)
classifier (Association)
Focus State Type (Class)
Intention State Type (Class)
ST/Direction (Generalization Node)
State Type (Class)
Subject Identifier State (Class)
Subject Identifier State Type (Class)


[1]
gerry the head ranger zak took us on a walking safari - it comes to.
[2]
gerry the head ranger zak took us on a walking safari - it has to.


Link to Documentation: tensorflow.github.io/magenta-js/sketch
This JavaScript implementation of Magenta's sketch-rnn model uses TensorFlow.js for GPU-accelerated inference. sketch-rnn
is a recurrent neural network model described in Teaching Machines to Draw and A Neural Representation of Sketch Drawings.
Examples of vector images produced by this generative model.
This document is an introduction on how to use the Sketch RNN model in Javascript to generate images. The Sketch RNN model is trained on stroke-based vector drawings. The model implementation here is able to handle unconditional (decoder-only) generation of vector images.
For more information, please read original model description and for the Python TensorFlow implementation.
In the .html files, we need to include magentasketch.js
. Our example sketch are built with p5.js and stored in a file such as sketch.js
, so we have also included p5 libraries here too. Please see this minimal example:

The implementation was written in TypeScript and built with the yarn tool:
yarn install
to install dependencies.
yarn build
to compile ts into js
yarn bundle
to produce a bundled version in dist/
.
We have provided around 100 pre-trained sketch-rnn models. We have trained the models with a .gen.json extension.
The models are located in:
https://storage.googleapis.com/quickdraw-models/sketchRNN/large_models/category.gen.json
where category is a quickdraw category such as cat, dog, the_mona_lisa etc., Some models are trained on more than one category, such as catpig or crabrabbitfacepig.
i.e.
https://storage.googleapis.com/quickdraw-models/sketchRNN/large_models/spider.gen.json
or
https://storage.googleapis.com/quickdraw-models/sketchRNN/large_models/the_mona_lisa.gen.json
A set of smaller models (with LSTM node size = 512 only) are located in:
https://storage.googleapis.com/quickdraw-models/sketchRNN/models/category.gen.json
Here is a list of all the models provided:
| Models | ||||
|---|---|---|---|---|
| alarm_clock | ambulance | angel | ant | antyoga |
| backpack | barn | basket | bear | bee |
| beeflower | bicycle | bird | book | brain |
| bridge | bulldozer | bus | butterfly | cactus |
| calendar | castle | cat | catbus | catpig |
| chair | couch | crab | crabchair | crabrabbitfacepig |
| cruise_ship | diving_board | dog | dogbunny | dolphin |
| duck | elephant | elephantpig | eye | face |
| fan | fire_hydrant | firetruck | flamingo | flower |
| floweryoga | frog | frogsofa | garden | hand |
| hedgeberry | hedgehog | helicopter | kangaroo | key |
| lantern | lighthouse | lion | lionsheep | lobster |
| map | mermaid | monapassport | monkey | mosquito |
| octopus | owl | paintbrush | palm_tree | parrot |
| passport | peas | penguin | pig | pigsheep |
| pineapple | pool | postcard | power_outlet | rabbit |
| rabbitturtle | radio | radioface | rain | rhinoceros |
| rifle | roller_coaster | sandwich | scorpion | sea_turtle |
| sheep | skull | snail | snowflake | speedboat |
| spider | squirrel | steak | stove | strawberry |
| swan | swing_set | the_mona_lisa | tiger | toothbrush |
| toothpaste | tractor | trombone | truck | whale |
| windmill | yoga | yogabicycle | everything |
Below is the essence of how a sketch is generated. In addition to the original paper, a simple tutorial for understanding how RNNs can generate a set of strokes is here.
var model;
var dx, dy; // offsets of the pen strokes, in pixels
var pen_down, pen_up, pen_end; // keep track of whether pen is touching paper
var x, y; // absolute coordinates on the screen of where the pen is
var prev_pen = [1, 0, 0]; // group all p0, p1, p2 together
var rnn_state; // store the hidden states of rnn's neurons
var pdf; // store all the parameters of a mixture-density distribution
var temperature = 0.45; // controls the amount of uncertainty of the model
var line_color;
var model_loaded = false;
// loads the TensorFlow.js version of sketch-rnn model, with the "cat" model's weights.
model = new ms.SketchRNN("https://storage.googleapis.com/quickdraw-models/sketchRNN/models/cat.gen.json");
// code that ensures the above line is run before the below lines are run.
function setup() {
x = windowWidth/2.0;
y = windowHeight/3.0;
createCanvas(windowWidth, windowHeight);
frameRate(60);
// initialize the scale factor for the model. Bigger -> large outputs
model.setPixelFactor(3.0);
// initialize pen's states to zero.
[dx, dy, pen_down, pen_up, pen_end] = model.zeroInput(); // the pen's states
// zero out the rnn's initial states
rnn_state = model.zeroState();
// define color of line
line_color = color(random(64, 224), random(64, 224), random(64, 224));
};
function draw() {
// see if we finished drawing
if (prev_pen[2] == 1) {
noLoop(); // stop drawing
return;
}
// using the previous pen states, and hidden state, get next hidden state
// the below line takes the most CPU power, especially for large models.
rnn_state = model.update([dx, dy, pen_down, pen_up, pen_end], rnn_state);
// get the parameters of the probability distribution (pdf) from hidden state
pdf = model.getPDF(rnn_state, temperature);
// sample the next pen's states from our probability distribution
[dx, dy, pen_down, pen_up, pen_end] = model.sample(pdf);
// only draw on the paper if the pen is touching the paper
if (prev_pen[0] == 1) {
stroke(line_color);
strokeWeight(3.0);
line(x, y, x+dx, y+dy); // draw line connecting prev point to current point.
}
// update the absolute coordinates from the offsets
x += dx;
y += dy;
// update the previous pen's state to the current one we just sampled
prev_pen = [pen_down, pen_up, pen_end];
};
There is a small IPython notebook to show how to quickly train a sketch-rnn model with Python-based TensorFlow model, and convert that model over to the JSON format that can be used by by this model.
A few demos are available in demos
directory built to use the Sketch RNN model. You can look at the corresponding code to study in detail how the model works. To run these examples, it is recommended to use a simple local webserver, such as the http-server that can be obtained using npm, or the yarn run-demos
command, and load the local html file from the local server. Some examples require this, since they need to dynamically load .json model files, and local static session doesn't allow for this in many browsers.
If you use yarn run-demos
, running something would be like putting http://localhost:8080/demos/simple.html
in the address tab in Chrome. For debugging, it is recommended you open a console tab on the side of the screen to look at the log messages.
This demo simply generates a bird using the model, using the example code in the earlier section.
Run the simple demo.
This demo attempts to finish the drawing given starting set of strokes. If the user doesn't draw anything, the computer will keep on drawing stuff from scratch.
In this demo, you can also select other classes, like "cat", "ant", "bus", etc. The demo will dynamically load the json files in the models directory but cache previously loaded json models. Hitting restart will clear the current drawing and start from scratch.
Run the predict demo.
Same as the previous demo, but made to be interactive so the user can draw the beginning of a sketch on the canvas. Similar to the first AI experiment. Hitting restart will clear the current human-entered drawing and start from scratch.
Run the interactive predict demo.
When training the models, all the offset data has been normalized to have a standard deviation of 1.0 on the training set, after simplifying the strokes. Neural nets work best when training on normalized data. However, the original data recorded with the QuickDraw web app stored everything as pixels, which was scaled down so that on average the stroke offsets are ~ 1.0 length. Thus each dataclass has its own scale_factors
to scale down, and these numbers are usually between 60 to 120 depending on the dataset. These scale factors are stored into model.info.scale_factor
. The model will assume all inputs and outputs to be in pixel space, not normalized space, and will do all the scaling for you. You can modify these in the model directly, but it is not recommended. Rather than overwriting the scale_factor
value, modify the pixel_factor instead, as described in the next paragraph.
If using PaperJS, it is recommended that you leave everything as it is. When using P5.JS, all the recorded data looks much bigger compared to the original app by a factor of exactly 2, and this is likely due to anti-aliasing functionality of web browsers. Hence the extra scaling factor for the model called pixel_factor
. If you want to make interactive apps and receive realtime drawing data from the user, and you are using PaperJS, it is best to set do a model.set_pixel_factor(1.0)
. For p5.js, do a model.set_pixel_factor(2.0)
. For non-interactive applications, using a larger set_pixel_factor
will reduce the size of the generated image.
Data collected by the original quickdraw app are stored in the below format, which is a list of list of ["x", "y"] pixel points.
[[["x": 123, "y": 456], ["x": 127, "y": 454], ["x": 137, "y": 450], ["x": 147, "y": 440], ...], ...]
The first thing to do is to convert this format into line format, and get rid of the "x" and "y" orderings. In the Line Data format, x always come before y:
Line Data: [[[123, 456], [127, 454], [137, 450], [147, 440], ...], ...]
The model contains helper functions to convert between this formats. This Line Data format must be first simplified using simplify_lines
or simplify_line
(depending if it is a list of polylines or just a single polyline) first. Afterwards, the simplified line will be fed into lines_to_strokes to convert into the Stroke Data format used by the model.
In the Stroke Data format, we assume the drawing starts at the origin, and store only the offset points from the previous location. The format is 2 dimensional, rather than 3 dimensional as in the Line Data format:
Each row of the stroke will be 5 elements:
[dx, dy, p0, p1, p2]
dx, dy
are the offsets in pixels from the previous point.
p0, p1, p2
are binary values, and only one of them will be 1, the other 2 must be 0.
p0 = 1 means the pen stays on the paper at the next stroke.
p1 = 1 means the pen will is now above the paper after this stroke. The next stroke will be the start of a new line.
p2 = 1 means the drawing has stopped. Stop drawing anything!
The drawing will be decomposed into a list of [dx, dy, p0, p1, p2]
strokes.
The mapping from Line Data to Stroke Data will lose the information about the starting position of the drawing, so you may want to record LineData[0][0]
to keep this info.
Generated using TypeDoc


Postbox
Introduction
digi.me is a data portability facilitator. As such, we support the flow of data in both directions - from the user to you, and from you back to the user. This process of ‘giving data back’ is known as Postbox and will henceforth be referred to as such.
Instances may arise where you wish to utilise data not currently supported by digi.me natively, Postbox allows you to do this. It does what it says on the tin, acts as a postbox for data into a user’s digi.me.
Types of Push
When pushing data to Postbox you have two main options:
Data pre-mapped into digi.me’s ontology:
digi.me publishes it’s data ontology here for the various data types. When making a submission, if you push data normalised to this format, it will be displayed in the digi.me more appropriately, with UI specifically engineered to maximise the value of that data. It also means that when you or another third party requests this data via pull, it can be included within a collection of data points of the same type.
Unmapped data (Raw Data):
digi.me can also act as a vault for data that does not fit within our current ontology, whether to collate user data together in one place or to act as a conduit between a data provider and data consumer. When data that doesn’t correspond to one of digi.me’s object types is pushed, this will be rendered within digi.me as a raw ‘data drop’. If we can deserialise this to JSON, we will show the raw JSON tree, otherwise there will be no facility to preview the data - this is for security reasons.
Pushing Data - 5 Simple Steps
The digi.me Private Sharing SDK makes it easy to create a postbox to push data to. Similarly to requesting data, you can achieve this by utilising a client object as follows:
1. Obtaining your Contract ID & Application ID:
Postbox uses the same means of authentication as pulling user data.
To access the digi.me platform, you need to obtain an AppID
for your application. You can get yours by filling out the registration form here.
In a production environment, you will also be required to obtain your own Contract ID
from digi.me support. However, for sandbox purposes, we provide the following example value:
Example Contract ID: Cb1JC2tIatLfF7LH1ksmdNx4AfYPszIn
2. Configuring Callback Forwarding:
Because the digi.me Private Sharing SDK opens the digi.me app for authorization, you are required to forward the openURL
event through to the SDK so that it may process responses. In your application’s delegate (typically AppDelegate
) override application:openURL:options:
method as below:
Objective-C
-(BOOL)application:(UIApplication *)app openURL:(NSURL *)url options:(NSDictionary<UIApplicationOpenURLOptionsKey,id> *)options
{
return [[DMEAppCommunicator shared] openURL:url options:options];
}
Swift
func application(_ app: UIApplication, open url: URL, options: [UIApplication.OpenURLOptionsKey : Any] = [:]) -> Bool {
return DMEAppCommunicator.shared().open(url, options: options)
}
Additionally, you need to whitelist Digi.me app scheme in your Info.plist
:
<key>LSApplicationQueriesSchemes</key>
<array>
<string>digime-ca-master</string>
</array>
And register custom URL scheme so that your app can receive the callback from Digi.me app. Still in Info.plist
add:
<key>CFBundleURLTypes</key>
<array>
<dict>
<key>CFBundleTypeRole</key>
<string>Editor</string>
<key>CFBundleURLName</key>
<string>Consent Access</string>
<key>CFBundleURLSchemes</key>
<array>
<string>digime-ca-YOUR_APP_ID</string>
</array>
</dict>
</array>
where YOUR_APP_ID
should be replaced with your AppID
.
3. Configuring the DMEPushClient
object:
DMEPushClient
is the object you will primarily interface with to use the SDK. It is instantiated with a DMEPushConfiguration
object.
The DMEPushConfiguration
object is instantiated with your AppID
and Private Key
in hex format. The below code snippet shows you how to combine all this to get a configured DMEPushClient
:
Objective-C
DMEPushConfiguration *configuration = [[DMEPushConfiguration alloc] initWithAppId:@"YOUR_APP_ID" contractId:@"YOUR_CONTRACT_ID"];
DMEPushClient *pushClient = [[DMEPushClient alloc] initWithConfiguration:configuration];
Swift
let configuration = DMEPushConfiguration(appId: "YOUR_APP_ID", contractId: "YOUR_CONTRACT_ID")
let pushClient = DMEPushClient(configuration: configuration)
4. Requesting Consent:
Before you can push data into a user’s digi.me, you must obtain their consent. This is achieved by calling createPostbox
on your client object:
Objective-C
[pushClient createPostboxWithCompletion:^(DMEPostbox * _Nullable postbox, NSError * _Nullable error) {
}];
Swift
pushClient.createPostbox { postbox, error in
}
If a user grants consent, a Postbox will be created and returned; this is used by subsequent calls to push data. If the user denies consent, an error stating this is returned. See Handling Errors.
5. Pushing Data:
To push data, you need to build a JSON metadata
object that describes what data your pushing along with the NSData representation of your data itself. An example showing Postbox creation and push can be seen below.:
Objective-C
NSData *data = ... // Obtain the data you wish to post.
NSData *metadata = ... // All Postbox submissions must be pushed with appropriate metadata. See the example apps for more details.
[pushClient pushDataToPostbox:postbox metadata:metadata data:data completion:^(NSError * _Nullable error) {
// Handle error, if any.
}];
Swift
let data: Data = ... // Obtain the data you wish to post.
let metadata: Data = ... // All Postbox submissions must be pushed with appropriate metadata. See the example apps for more details.
pushClient.pushData(to: postbox, metadata: metadata, data: data) { error in
// Handle error, if any.
}
NB: Please refer to our Postbox example in the Swift Example App for more details on data and metadata.


ENGR 697 Final Presentation
-Going to a bar during peak times =! fast service
-Hosting a party =! fun times
-Private bar tenders are expensive to hire.
-Drinks are not always consistently made.

Four diaphragm pumps that can make a drink in < 30 seconds
It is fairly easy to transport
We chose four bottles for the system
Entire project could be made for < $100
/


Yellow bomb is the good one:
Collide with bomb to pick it
Use Space to drop bomb:
Use arrow keys to move dude:


Mondays
I have calculus II from 12pm to 3pm
i have to go to the Jay Pretzler pavillon at 4pm
I have class mondays, wenesday and friday
I have calculus II from 12pm to 3pm
i have to go to the Jay Pretzler pavillon at 4pm
| Mondays | Wenesday | Friday |
|---|---|---|
| Calculus II | Computer systems | Chemistry |
| Lunch | Biology |


Release Engineering encompasses a part of the process between when a Developer makes a software check-in and when that change is ready to ship out to the user.
Release Engineers != Developers
Developers build a product
Release Engineers build a pipeline
So what does this Pipeline look like?
Building/Testing
Building/Testing
Building/Testing
Building/Testing
Riding the Trains
Riding the Trains
Riding the Trains
Riding the Trains
Riding the Trains
Riding the Trains
Riding the Trains
How is the pipeline run?
How is the pipeline run?
How is the pipeline run?
How is the pipeline run?
What's changed in the past 5 years?
Expansion
May 2007
Today
Number of machines running builds/tests
86 Machines
5,400 Machines
Time to ship a chemspill (emergency) release
4-6 weeks
11 hours
Time to ship a 'new feature' release
12-18 months
6 weeks
What's changed in the past 5 years?
Demand
May 2007
Today
Number of branches
1 1/2
42
Number of check-ins per day
15
400
Number of locales (languages) that we ship for
34
94
Compute Time
per Check-in
~9 days
per Month
~191 years
per Year
~1.3k years
For more information
Search on the Google Talks YouTube channel for the video titled
Release Engineering as a Force Multiplier
Projects I contributed to
BuildAPI
Autolander
Nagios Alerts
Reporting
BuildAPI
BuildAPI
BuildAPI
BuildAPI
Gaia-Central Reporting Bug
5/21 to 5/28
BuildAPI
Gaia-Central Reporting Bug
5/29 to 6/5
BuildAPI
Push Report Adjustments
Autolander
Autolander
Autolander
Autolander
Autolander
Autolander
Autolander
Autolander
Autolander
Autolander
Autolander
Autolander
Autolander
Note: Autolander is still in development and is not available for use yet.
Nagios Alerts
Nagios Alerts
Nagios Alerts
Nagios Alerts
Nagios Alerts
Nagios Alerts
Reporting
Reporting
In Conclusion...
Release Engineering is a Force Multiplier
A Force Multiplier is:
A capability that, when added to and employed by a team, significantly increases the potential of that team and thus enhances the probability of success
Defense Technical Information Center (dtic.mil)
Special Thanks
Hal Wine (hwine)
John O'Duinn (joduinn)
Jill Alvarez
Andrea Zingerman
All of RelEng
Aki Sasaki (aki)
Armen Zambrano Gasparnian (armenzg)
Ben Hearsum (bhearsum)
Justin Wood (Callek)
Chris Atlee (catlee)
Chris Cooper (coop)
Joey Armstrong (joey)
John Hopkins (jhopkins)
Jordan Lund (jlund)
Kim Moir (kmoir)
Massimo Gervasini (mgerva)
Michael Shal (mshal)
Nick Thomas (nthomas)
Pete Moore (pmoore)
Rail Aliiev (rail)
Simone Bruno (simone)
For more information
Search on the Google Talks YouTube channel for the video titled


CAMPING WEBSITE
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Campsite name:
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Your rating (out of 5)
5 - Amazing!
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1 - DO NOT GO
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Matlab Server Hello World Example.


SECON
Section: NSA (1)Updated: April 2006
Index Return to Main Contents
NAME
secon - See an SELinux context, from a file, program or user input.SYNOPSIS
secon [-hVurtscmPRfLp] [CONTEXT][--file] FILE
[--link] FILE
[--pid] PID
DESCRIPTION
See a part of a context. The context is taken from a file, pid, user input or the context in which secon is originally executed.
- -V, --version
- shows the current version of secon
- -h, --help
- shows the usage information for secon
- -P, --prompt
- outputs data in a format suitable for a prompt
- -u, --user
- show the user of the security context
- -r, --role
- show the role of the security context
- -t, --type
- show the type of the security context
- -s, --sensitivity
- show the sensitivity level of the security context
- -c, --clearance
- show the clearance level of the security context
- -m, --mls-range
- show the sensitivity level and clearance, as a range, of the security context
- -R, --raw
- outputs the sensitivity level and clearance in an untranslated format.
- -f, --file
- gets the context from the specified file FILE
- -L, --link
- gets the context from the specified file FILE (doesn't follow symlinks)
- -p, --pid
- gets the context from the specified process PID
- --pid-exec
- gets the exec context from the specified process PID
- --pid-fs
- gets the fscreate context from the specified process PID
- --current, --self
- gets the context from the current process
- --current-exec, --self-exec
- gets the exec context from the current process
- --current-fs, --self-fs
- gets the fscreate context from the current process
- --parent
- gets the context from the parent of the current process
- --parent-exec
- gets the exec context from the parent of the current process
- --parent-fs
- gets the fscreate context from the parent of the current process
Additional argument
CONTEXT
may be provided and will be used if no options have been specified to make
secon
get it's context from another source.
If that argument is
-
then the context will be read from stdin.
If there is no arugment,
secon
will try reading a context from stdin, if that is not a tty, otherwise
secon
will act as though --self had been passed.
If none of --user, --role, --type, --level or --mls-range is passed. Then all of them will be output.
SEE ALSO
chcon (1)AUTHORS
James Antill<EMAIL_ADDRESS>Index
This document was created by man2html, using the manual pages.
Time: 05:29:10 GMT, December 24, 2015


Update Security Questions
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tanzania
Question:
Select a question
What is your favourite colour?
What is your mother's maiden name?
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{% extends "base.html" %} {% block content %}
Hey there, {{ name }}.
Now all we need is your CalNetID to verify you are enrolled in the class.
The CalNetID is the part before @iastate.edu incase you forgot.


Gets the best current guess from a vector. :param f: function to evaluate. :param X: locations.
Returns a vector whose components i are the minimum (default) or maximum of Y[:i]
Gradients with respect to x of the moments (mean and sdev.) of the GP :param model: GPy model. :param x: location where the gradients are evaluated.
Moments (mean and sdev.) of a GP model at x
Quantiles of the Gaussian distribution useful to determine the acquisition function values :param acquisition_par: parameter of the acquisition function :param fmin: current minimum. :param m: vector of means. :param s: vector of standard deviations.
Generates a multidimensional lattice :param bounds: box constrains :param Ngrid: number of points per dimension.
Reshapes x into a matrix with input_dim columns
Generates a multidimensional grid uniformly distributed. :param bounds: tuple defining the box constrains. :num_data: number of data to generate.
Function for parallel evaluation of the acquisition function
| Parameters: |
|
|---|


Nicehash owns about 70% of DASH mining network according to a Reddit user (Jan 10), and the news is already causing a wave of concerns within the community Investigation It looks like someone currently controls over 51% of DASHs hashrate over Nicehash mining pools; a recent R...


age
- simple, modern, and secure file encryption
age
[--encrypt
] (-r
RECIPIENT | -R
PATH)... [--armor
] [-o
OUTPUT] [INPUT]
age
[--encrypt
] --passphrase
[--armor
] [-o
OUTPUT] [INPUT]
age
--decrypt
[-i
PATH | -j
PLUGIN]... [-o
OUTPUT] [INPUT]
age
encrypts or decrypts INPUT to OUTPUT. The INPUT argument is
optional and defaults to standard input. Only a single INPUT file may be
specified. If -o
is not specified, OUTPUT defaults to standard output.
If -p
/--passphrase
is specified, the file is encrypted with a passphrase
requested interactively. Otherwise, it's encrypted to one or more
RECIPIENTS specified with -r
/--recipient
or
-R
/--recipients-file
. Every recipient can decrypt the file.
In -d
/--decrypt
mode, passphrase-encrypted files are detected automatically
and the passphrase is requested interactively. Otherwise, one or more
IDENTITIES specified with -i
/--identity
are
used to decrypt the file.
age
encrypted files are binary and not malleable, with around 200 bytes of
overhead per recipient, plus 16 bytes every 64KiB of plaintext.
-o
, --output
=OUTPUT
If encrypting without --armor
, age
will refuse to output binary to a
TTY. This can be forced by specifying -
as OUTPUT.
--version
-e
, --encrypt
-r
, --recipient
=RECIPIENT
This option can be repeated and combined with other recipient flags, and the file can be decrypted by all provided recipients independently.
-R
, --recipients-file
=PATH
#
are ignored as comments.
If PATH is -
, the recipients are read from standard input. In
this case, the INPUT argument must be specified.
This option can be repeated and combined with other recipient flags, and the file can be decrypted by all provided recipients independently.
-p
, --passphrase
age
will offer to auto-generate a secure passphrase.
This option can't be used with other recipient flags.
-a
, --armor
age
armor is a strict version of PEM with type AGE ENCRYPTED FILE
,
canonical "strict" Base64, no headers, and no support for leading and
trailing extra data.
Decryption transparently detects and decodes ASCII armoring.
-i
, --identity
=PATH
age-keygen -y
and then passing that to -R
/--recipients-file
.
For the format of PATH, see the definition of -i
/--identity
in the
Decryption options section.
-e
/--encrypt
must be explicitly specified when using -i
/--identity
in encryption mode to avoid confusion.
-j
PLUGIN
This is equivalent to using -i
/--identity
with a file that contains a
single plugin IDENTITY
that encodes no plugin-specific data.
-e
/--encrypt
must be explicitly specified when using -j
in encryption
mode to avoid confusion.
-d
, --decrypt
If INPUT is passphrase encrypted, it will be automatically detected
and the passphrase will be requested interactively. Otherwise, the
IDENTITIES specified with -i
/--identity
are used.
ASCII armoring is transparently detected and decoded.
-i
, --identity
=PATH
PATH may be one of the following:
a. A file listing IDENTITIES one per line.
Empty lines and lines starting with "#
" are ignored as comments.
b. A passphrase encrypted age file, containing IDENTITIES one per line like above. The passphrase is requested interactively. Note that passphrase-protected identity files are not necessary for most use cases, where access to the encrypted identity file implies access to the whole system.
c. An SSH private key file, in PKCS#1, PKCS#8, or OpenSSH format. If the private key is password-protected, the password is requested interactively only if the SSH identity matches the file. See the SSH keys section for more information, including supported key types.
d. "-
", causing one of the options above to be read from standard input.
In this case, the INPUT argument must be specified.
This option can be repeated. Identities are tried in the order in which are
provided, and the first one matching one of the file's recipients is used.
Unused identities are ignored, but it is an error if the INPUT file is
passphrase-encrypted and -i
/--identity
is specified.
-j
PLUGIN
This is equivalent to using -i
/--identity
with a file that contains a
single plugin IDENTITY
that encodes no plugin-specific data.
RECIPIENTS
are public values, like a public key, that a file can be encrypted
to. IDENTITIES
are private values, like a private key, that allow decrypting
a file encrypted to the corresponding RECIPIENT
.
Native age
key pairs are generated with age-keygen(1), and provide small
encodings and strong encryption based on X25519. They are the recommended
recipient type for most applications.
A RECIPIENT
encoding begins with age1
and looks like the following:
age1gde3ncmahlqd9gg50tanl99r960llztrhfapnmx853s4tjum03uqfssgdh
An IDENTITY
encoding begins with AGE-SECRET-KEY-1
and looks like the
following:
AGE-SECRET-KEY-1KTYK6RVLN5TAPE7VF6FQQSKZ9HWWCDSKUGXXNUQDWZ7XXT5YK5LSF3UTKQ
An encrypted file can't be linked to the native recipient it's encrypted to without access to the corresponding identity.
As a convenience feature, age
also supports encrypting to RSA or Ed25519
ssh(1) keys. RSA keys must be at least 2048 bits. This feature employs more
complex cryptography, and should only be used when a native key is not available
for the recipient. Note that SSH keys might not be protected long-term by the
recipient, since they are revokable when used only for authentication.
A RECIPIENT
encoding is an SSH public key in authorized_keys
format
(see the AUTHORIZED_KEYS FILE FORMAT
section of sshd(8)), starting with
ssh-rsa
or ssh-ed25519
, like the following:
ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABgQDULTit0KUehbi[...]GU4BtElAbzh8=
ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIH9pO5pz22JZEas[...]l1uZc31FGYMXa
The comment at the end of the line, if present, is ignored.
In recipient files passed to -R
/--recipients-file
, unsupported but valid
SSH public keys are ignored with a warning, to facilitate using
authorized_keys
or GitHub .keys
files. (See EXAMPLES.)
An IDENTITY
is an SSH private key file passed individually to
-i
/--identity
. Note that keys held on hardware tokens such as YubiKeys
or accessed via ssh-agent(1) are not supported.
An encrypted file can be linked to the SSH public key it was encrypted to.
This is so that age
can identify the correct SSH private key before
requesting its password, if any.
age
can be extended through plugins. A plugin is only loaded if a corresponding
RECIPIENT
or IDENTITY
is specified. (Simply decrypting a file encrypted with
a plugin will not cause it to load, for security reasons among others.)
A RECIPIENT
for a plugin named example
starts with age1example1
, while an
IDENTITY
starts with AGE-PLUGIN-EXAMPLE-1
. They both encode arbitrary
plugin-specific data, and are generated by the plugin.
When either is specified, age
searches for age-plugin-example
in the PATH
and executes it to perform the file header encryption or decryption. The plugin
may request input from the user through age
to complete the operation.
Plugins can be freely mixed with other plugins or natively supported keys.
A plugin is not bound to only encrypt or decrypt files meant for or generated by
the plugin. For example, a plugin can be used to decrypt files encrypted to a
native X25519 RECIPIENT
or even with a passphrase. Similarly, a plugin can
encrypt a file such that it can be decrypted without the use of any plugin.
Plugins for which the IDENTITY
/RECIPIENT
distinction doesn't make sense
(such as a symmetric encryption plugin) may generate only an IDENTITY
and
instruct the user to perform encryption with the -e
/--encrypt
and
-i
/--identity
flags. Plugins for which the concept of separate identities
doesn't make sense (such as a password-encryption plugin) may instruct the user
to use the -j
flag.
age
will exit 0 if and only if encryption or decryption are successful for the
full length of the input.
If an error occurs during decryption, partial output might still be generated, but only if it was possible to securely authenticate it. No unauthenticated output is ever released.
Files encrypted with a stable version (not alpha, beta, or release candidate) of
age
, or with any v1.0.0 beta or release candidate, will decrypt with any later
version of the tool.
If decrypting older files poses a security risk, doing so might cause an error by default. In this case, a flag will be provided to force the operation.
Generate a new identity, encrypt data, and decrypt:
$ age-keygen -o key.txt
Public key: age1ql3z7hjy54pw3hyww5ayyfg7zqgvc7w3j2elw8zmrj2kg5sfn9aqmcac8p
$ tar cvz ~/data | age -r age1ql3z7hjy54pw3hyww5ayyfg7zqgvc7w3j2elw8zmrj2kg5sfn9aqmcac8p > data.tar.gz.age
$ age -d -o data.tar.gz -i key.txt data.tar.gz.age
Encrypt example.jpg
to multiple recipients and output to example.jpg.age
:
$ age -o example.jpg.age -r age1ql3z7hjy54pw3hyww5ayyfg7zqgvc7w3j2elw8zmrj2kg5sfn9aqmcac8p \
-r age1lggyhqrw2nlhcxprm67z43rta597azn8gknawjehu9d9dl0jq3yqqvfafg example.jpg
Encrypt to a list of recipients:
$ cat > recipients.txt
# Alice
age1ql3z7hjy54pw3hyww5ayyfg7zqgvc7w3j2elw8zmrj2kg5sfn9aqmcac8p
# Bob
age1lggyhqrw2nlhcxprm67z43rta597azn8gknawjehu9d9dl0jq3yqqvfafg
$ age -R recipients.txt example.jpg > example.jpg.age
Encrypt and decrypt a file using a passphrase:
$ age -p secrets.txt > secrets.txt.age
Enter passphrase (leave empty to autogenerate a secure one):
Using the autogenerated passphrase "release-response-step-brand-wrap-ankle-pair-unusual-sword-train".
$ age -d secrets.txt.age > secrets.txt
Enter passphrase:
Encrypt and decrypt with a passphrase-protected identity file:
$ age-keygen | age -p > key.age
Public key: age1yhm4gctwfmrpz87tdslm550wrx6m79y9f2hdzt0lndjnehwj0ukqrjpyx5
Enter passphrase (leave empty to autogenerate a secure one):
Using the autogenerated passphrase "hip-roast-boring-snake-mention-east-wasp-honey-input-actress".
$ age -r age1yhm4gctwfmrpz87tdslm550wrx6m79y9f2hdzt0lndjnehwj0ukqrjpyx5 secrets.txt > secrets.txt.age
$ age -d -i key.age secrets.txt.age > secrets.txt
Enter passphrase for identity file "key.age":
Encrypt and decrypt with an SSH public key:
$ age -R ~/.ssh/id_ed25519.pub example.jpg > example.jpg.age
$ age -d -i ~/.ssh/id_ed25519 example.jpg.age > example.jpg
Encrypt and decrypt with age-plugin-yubikey:
$ age-plugin-yubikey # run interactive setup, generate identity file and obtain recipient
$ age -r age1yubikey1qwt50d05nh5vutpdzmlg5wn80xq5negm4uj9ghv0snvdd3yysf5yw3rhl3t secrets.txt > secrets.txt.age
$ age -d -i age-yubikey-identity-388178f3.txt secrets.txt.age
Encrypt to the SSH keys of a GitHub user:
$ curl https://github.com/benjojo.keys | age -R - example.jpg > example.jpg.age
Filippo Valsorda age@filippo.io


val resumingJob: (ResultHandlerInput) -> ResultHandlerOutput
the non capturing closure which runs on the caller thread and accepts the job
output
as the only argument.
resumingJob
- the non capturing closure which runs on the caller thread and accepts the job
output
as the only argument.


| Home | Trees | Indices | Help |
|---|
|
|
object --+ | YipitObject
An abstract class that implements methods several classes the Yipit API will require. Also outlines some abstract methods to require classes which implement YipitObject to implement.
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Inherited from |
|
An abstract method. Creates a new instance based on a JSON dict. Args: data: A JSON dict, as converted from the JSON in the Yipit API. Returns: A yipit.YipitObject instance specific to the type |
A JSON string representation of the yipit.YipitObject instance. Returns: A JSON string representation of the yipit.YipitObject instance |
An abstract method. A dict representation of a yipit.YipitObject instance. The return value uses the same key names as the JSON representation. Return: A dict represention a yipit.YipitObject instance |
Returns a dictionary of all parameters with specified keys Args: **kwargs: Default python packaging of un-specified params with keys Returns: A dictionary of all params with specified keys |
A string representation of this yipit.Business instance. The return value is the same as the JSON representation. Returns: A string representation of this yipit.Business instance.
|
| Home | Trees | Indices | Help |
|---|
| Generated by Epydoc 3.0.1 on Sun Oct 9 02:19:13 2011 | http://epydoc.sourceforge.net |


| Tuesday, April 10 | |||||||
|---|---|---|---|---|---|---|---|
| Sunrise Room | Mountain View Room | Arbor Hall | Mitchell Theater | Gold Room | Blue Room | Green Room | |
| 8:00 - 10:00 (Marriage Encounter) | 8:00 - 9:00 (Breakfast Club of Ohio) | 8:00 - 12:00 (Computer Expo set up) | 3:00 - 5:00 (Youth Theatre) | 9:00 - 10:30 (The Tompkins Group) | 9:00 - 10:30 (Retirement Planning) | 9:00 - 11:00 (Drivers' Ed) | |
| 12:00 - 1:00 (Marriage Encounter Luncheon) | 11:00 - 1:00 (Ohio Conference of Librarians) | 1:00 - 5:00 (Computer Expo opening session) | 10:30 - 12:00 (Pearson Planning) | 10:30 - 12:00 (The Senior Group) | 11:00 - 1:30 (Movie Club) | ||
| 3:00 - 5:00 (Marriage Encounter Talks) | 2:00 - 5:00 (Ohio Book Resale Setup) | 2:00 - 3:30 (Mothers of Multiples) | 12:00 - 1:00 (Senior Luncheon) | 3:30 - 5:00 (Tutoring) | 3:30 - 5:00 (Teen Connection) | 1:00 - 3:30 (Senior Classes) | |
| 3:30 - 5:00 (Tutoring) |
To reserve a room, please contact Yancy Inwe at extension 388 or by e-mail at<EMAIL_ADDRESS>Reservations should be made at least 48 hours in advance. Cancellations require 24 hour notice. Room deposits will not be returned for cancellations within 24 hours of the scheduled event.
Here are the room descriptions:
Sunrise Room
Maximum Capacity: 80
Moutain View Room
Maximum Capacity: 150
Arbor Hall
Maximum Capacity: 190
Mitchell Theatre
Maximum Capacity: 120
Gold Room
Maximum Capacity: 40
Blue Room
Maximum Capacity: 30
Green Room
Maximum Capacity: 55


Calorimeter
Your weight Kg
Your goal weight Kg
You need loss
Kg
Choice one option the exercise
You need to exercise to
hours with this exercise


To take the curl of a vector, it should be in the covariant representation since we can make use of the fact that . Thus the curl of is written as


Your needs, our goals
Thank to his international experience, his values are rooted in purpose of service, and a mission to inspire positive change every day. We push beyond the boundaries of geographic science in order to improve your business through possibilities of powerful geospatial technologies.
Who Are We,
we ate gidus
what we do
GIS Solution
Business application development for spatial data analysis and visualization.  You will learn how our application improves workflows and efficiencies, and discover how big data and real-time data analytics can unlock the full potential of your business.
Development
Development of Management operational software and web application integrated with GIS platforms and Databases, in order to enhance business processes and enrich customer interactions. All web application are mobile compliant with iOS, Android and Windows phone.


111Francis Ford Coppola's legendary continuation and sequel to his landmark 1972 film, The_Godfather parallels the young Vito Corleone's rise with his son Michael's spiritual fall, deepening The_Godfather's depiction of the dark side of the American dream. In the early 1900s, the child Vito flees his Sicilian village for America after the local Mafia kills his family. Vito struggles to make a living, legally or illegally, for his wife and growing brood in Little Italy, killing the local Black Hand Fanucci after he demands his customary cut of the tyro's business. With Fanucci gone, Vito's communal stature grows.
2222Francis Ford Coppola's legendary continuation and sequel to his landmark 1972 film, The_Godfather parallels the young Vito Corleone's rise with his son Michael's spiritual fall, deepening The_Godfather's depiction of the dark side of the American dream. In the early 1900s, the child Vito flees his Sicilian village for America after the local Mafia kills his family. Vito struggles to make a living, legally or illegally, for his wife and growing brood in Little Italy, killing the local Black Hand Fanucci after he demands his customary cut of the tyro's business. With Fanucci gone, Vito's communal stature grows.
333Francis Ford Coppola's legendary continuation and sequel to his landmark 1972 film, The_Godfather parallels the young Vito Corleone's rise with his son Michael's spiritual fall, deepening The_Godfather's depiction of the dark side of the American dream. In the early 1900s, the child Vito flees his Sicilian village for America after the local Mafia kills his family. Vito struggles to make a living, legally or illegally, for his wife and growing brood in Little Italy, killing the local Black Hand Fanucci after he demands his customary cut of the tyro's business. With Fanucci gone, Vito's communal stature grows.
4444Francis Ford Coppola's legendary continuation and sequel to his landmark 1972 film, The_Godfather parallels the young Vito Corleone's rise with his son Michael's spiritual fall, deepening The_Godfather's depiction of the dark side of the American dream. In the early 1900s, the child Vito flees his Sicilian village for America after the local Mafia kills his family. Vito struggles to make a living, legally or illegally, for his wife and growing brood in Little Italy, killing the local Black Hand Fanucci after he demands his customary cut of the tyro's business. With Fanucci gone, Vito's communal stature grows.
5555Francis Ford Coppola's legendary continuation and sequel to his landmark 1972 film, The_Godfather parallels the young Vito Corleone's rise with his son Michael's spiritual fall, deepening The_Godfather's depiction of the dark side of the American dream. In the early 1900s, the child Vito flees his Sicilian village for America after the local Mafia kills his family. Vito struggles to make a living, legally or illegally, for his wife and growing brood in Little Italy, killing the local Black Hand Fanucci after he demands his customary cut of the tyro's business. With Fanucci gone, Vito's communal stature grows.
6666Francis Ford Coppola's legendary continuation and sequel to his landmark 1972 film, The_Godfather parallels the young Vito Corleone's rise with his son Michael's spiritual fall, deepening The_Godfather's depiction of the dark side of the American dream. In the early 1900s, the child Vito flees his Sicilian village for America after the local Mafia kills his family. Vito struggles to make a living, legally or illegally, for his wife and growing brood in Little Italy, killing the local Black Hand Fanucci after he demands his customary cut of the tyro's business. With Fanucci gone, Vito's communal stature grows.


Omachi Alps Marathon
Omachi JPN
Race Winners
(34 runnings, 25 runnings as a marathon)
14 Oct
2007
15
Oct
2006
16
Oct
2005
03
Oct
2004
19 Oct
2003
06
Oct
2002
07
Oct
2001
2000
03 Oct 1999
04 Oct 1998
05
Oct 1997
06
Oct 1996
08 Oct 1995
1994
10 Oct 1993
11 Oct 1992
13 Oct 1991
1990
1989
Notes: 1984 was the inaugural year. The distance for 1992 and earlier
years was 30 km.
15
Oct 2017
2:30:10
Naohiko Yomura (JPN)
3:15:44
Mariko Tsuzuki (JPN)
16
Oct 2016
2:29:42
Koji Tabata-4
3:00:45
Mika Kobayashi-2
18 Oct
2015
2:32:05
Masakazu Takahashi-2
3:07:21
Mika Kobatashi (JPN)
19 Oct
2014
2:33:26
Koji Tabata-3
3:07:56
Kanako Matsuzawa (JPN)
20
Oct 2013
2:37:41
Koji Tabata-2
3:15:32
Yuko
Shimada-3
21
Oct 2012
2:31:38
Koji Tabata (JPN)
3:10:19
Yumi Otani (JPN)
16
Oct 2011
2:42:08
Tomoyuki Karasawa (JPN)
3:14:52
Yuko
Shimada-2
17 Oct
2010
2:40:41
Ichiro Hirao (JPN)
3:11:17
Yuko Shimada (JPN)
18 Oct
2009
2:37:31
Satoshi Nishizawa-3
3:09:43
Kazuha Matsui-4
19 Oct
2008
2:25:52
Masakazu Takahashi (JPN)
3:12:05
Kazuha Matsui-3
2:29:13
Satoshi Nishizawa-2
3:07:46
Kazuha Matsui-2
2:24:16
Satoshi Nishizawa (JPN)
3:04:46
Midori Misumi-5
2:35:09
Hiroyuki Jingu (JPN)
3:01:50
Kazuha Matsui (JPN)
2:42:40
Kohei Yumoto (JPN)
2:58:48
Midori Misumi-4
2:48:51
Hirokazu Ito (JPN)
2:56:47
Midori Misumi-3
2:35:26
Yuichi Dobashi-4
3:21:45
Kyoko Nitani (JPN)
---
n/a
---
n/a
---
n/a
---
n/a
2:26:25
Yuichi Dobashi-3
2:57:13
Midori Misumi-2
2:33:52
Yuichi Dobashi-2
3:05:35
Midori Misumi (JPN)
2:25:07
Yuichi Dobashi (JPN)
3:01:05
Akiko Kono (JPN)
2:33:29
Tsutomu Matsuura (JPN)
3:08:11
Akemi Shimada (JPN)
2:32:36
Akihiro Shinagawa (JPN)
3:18:59
Sayuri Yuyama (JPN)
---
n/a
---
n/a
2:34:02
Hideki Kita (JPN)
---
n/a
1:35:00
Tadashi Fukushima (JPN)
1:57:12
Noriko Suzuki (JPN)
1:36:18
Tomio Sueyoshi (JPN)
2:00:21
Keiko Kaneko (JPN)
---
n/a
---
n/a
---
n/a
---
n/a
1988
---
n/a
---
n/a
1987
---
n/a
---
n/a
1986
---
n/a
---
n/a
1985
---
n/a
---
n/a
1984
---
n/a
---
n/a
Most Wins
(1984/date)
Men
4
Yuichi Dobashi
(JPN)
1997-99,2002
Koji Tabata (JPN)
2012-14,2016
Women
5
Midori Misumi
(JPN)
1998-99,2003-04,2006
Prize Money
(ARRS qualified runners)
No information on prize money has been received for this race.
Race Time Bias
(sec/km)
Insufficient data to calculate the race time bias.
Competitive Level
(1990-date)
Insufficient data to calculate the competitive level.
Quality Performances
Men No men under 2:20 Women No women under 2:50
Last Updated on 03 Dec 2017
Contributors: Shigenobu Ota (50), Klaas Loonstra (12), Kanji Kono


Contributing to Open Source Part I: The Easy Way
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wikibrowser
A simple browser app provides easy access to wikis for your favorite games
Download for WindowsSettings
Controls to maximize wikibrowser's accessibility while floating atop other applications
Styles
Pick your favorite color
Bonus dark mode!
Notes
Take notes on the fly
Click Through
Click through ignores mouse inputs on the app, hover the red icon to turn it back off
Works great for coding as well as games


zerone
Around the day
12/30 2013
1/2
10,765
1/1
10,765
12/31
10,765
12/30
10,765
12/29
10,765
12/28
10,765
12/27
10,765


Agent Class
Primary client-side abstraction used to establish and manage communication with a central dispatcher
Constructor
Agent
-
cfg
Parameters:
-
cfg
Object
Item Index
Methods
Methods
broadcast
-
event
-
payload
-
options
Send a notification to whomever is listening to. This is not directed to a specific agent, but to anyone on the network, including the dispatcher.
Parameters:
-
event
StringThe key identifying the event
-
payload
ObjectThe event data
-
options
ObjectOptions modifying the behavior of the broadcast.
Returns:
A promise resolved as soon as the event has been broadcasted. Nothing is returned (fire and forget)
connect
-
options
Parameters:
-
options
ObjectSupported options are :
- waitForDispatcher: Indicate if the agent will exit of retry connection with the dispatcher indefinitely. Default to false.
Returns:
execute
-
key
-
payload
-
options
Execute a remote command. Depending on the dispatcher context, the command might be executed by the dispatcher itself or by another agent, having registered a remote command handler with the dispacher.
Parameters:
-
key
ObjectThe key of the command to execute. It must be registered by the dispatcher or an agent somewhere to receive a response.
-
payload
ObjectThe data to send with the command.
-
options
ObjectOptions that may change the way the command is executed. Supported options are:
- timeout : The number of seconds to wait for a response. Default to 30 seconds.
Returns:
A promise for the command result.
executeOn
-
key
-
payload
-
options
Execute a remote command on a specific agent. The command will be forward directly to this agent.
Parameters:
-
key
ObjectThe key of the command to execute. It must be registered by the dispatcher or an agent somewhere to receive a response.
-
payload
ObjectThe data to send with the command.
-
options
ObjectOptions that may change the way the command is executed. Supported options are:
- timeout : The number of seconds to wait for a response. Default to 30 seconds.
Returns:
A promise for the command result.
registerCommandHandler
-
key
-
fn
Add or replace a command handler. This handler will be execute each time a command of type key will be received.
Parameters:
-
key
ObjectThe command key to attach this handler
-
fn
ObjectThe handler that will be executed when a command of the specified key is received
retrieveCommands
()
Force the retrieval of any pending commands from the dispatcher. *
Returns:
retrieveEvents
-
options!~YUIDOC_LINE~!!~YUIDOC_LINE~!-
Retrieve all pending events for this agent.
Parameters:
-
options!~YUIDOC_LINE~!!~YUIDOC_LINE~!-
Objectdelay: The number of seconds we have to wait before retrieving the events. Default to 0.
Returns:
subscribeTo
-
key
-
fn
-
options
Will connect this agent with remote events emitted by the dispatcher. These events may be triggered by the dispatcher or other agents connected on the network. For now, you can only subscribe to events that are sent to you, but in the future, we will add support for channels to receive 'room-like' messages.
Parameters:
-
key
StringThe event key we wish to subscribe to remotely
-
fn
FunctionThe handler that will be called when the event is triggered remotely
-
options
ObjectOptions modifying the behavior to the function.
- force : Avoid reusing a subscription if already in place and send the request to the dispatcher anyway. This is used when the dispatcher just failed and we are instructed to reconnect our event handlers.
unsubscribeFrom
-
key
Parameters:
-
key
Object
Events
network-error
Emitted when there is a network problem and we were unable to communicate with the dispatcher.
Event Payload:
-
action
StringThe name of the action that triggered the error
-
error
Error | StringThe actual error that was thrown
network-error
Emitted when there is a network problem and we were unable to communicate with the dispatcher.
Event Payload:
-
action
StringThe name of the action that triggered the error
-
error
Error | StringThe actual error that was thrown
transport-error
Emitted when there is a network problem and we were unable to communicate with the dispatcher.
Event Payload:
-
action
StringThe name of the action that triggered the error
-
error
Error | StringThe actual error that was thrown


Author: Gustavo A. Brey, Gaston Coco Level: Intermediate Technologies: CDI, JAX-RS Summary: Demonstrates the use of CDI 1.0 and JAX-RS Target Product: EAP
This example demonstrates the use of CDI 1.0 and JAX-RS in JBoss AS 7 or JBoss Enterprise Application Platform 6.
All you need to build this project is Java 6.0 (Java SDK 1.6) or better, Maven 3.0 or better.
The application this project produces is designed to be run on JBoss Enterprise Application Platform 6 or JBoss AS 7.
If you have not yet done so, you must Configure Maven before testing the quickstarts.
The following shows the command line to start the server with the web profile:
For Linux: JBOSS_HOME/bin/standalone.sh
For Windows: JBOSS_HOME\bin\standalone.bat
NOTE: The following build command assumes you have configured your Maven user settings. If you have not, you must include Maven setting arguments on the command line. See Build and Deploy the Quickstarts for complete instructions and additional options.
Type this command to build and deploy the archive:
mvn clean package jboss-as:deploy
This will deploy target/jboss-as-helloworld-rs.war
to the running instance of the server.
The application is deployed to http://localhost:8080/jboss-as-helloworld-rs.
The XML content can be viewed by accessing the following URL: http://localhost:8080/jboss-as-helloworld-rs/rest/xml
The JSON content can be viewed by accessing this URL: http://localhost:8080/jboss-as-helloworld-rs/rest/json
When you are finished testing, type this command to undeploy the archive:
mvn jboss-as:undeploy
You can also start the server and deploy the quickstarts from Eclipse using JBoss tools. For more information, see Use JBoss Developer Studio or Eclipse to Run the Quickstarts
If you want to debug the source code or look at the Javadocs of any library in the project, run either of the following commands to pull them into your local repository. The IDE should then detect them.
mvn dependency:sources
mvn dependency:resolve -Dclassifier=javadoc
If you do not yet have an OpenShift account and domain, Sign in to OpenShift to create the account and domain. Get Started with OpenShift will show you how to install the OpenShift Express command line interface.
Open a shell command prompt and change to a directory of your choice. Enter the following command, replacing APPLICATION_TYPE with jbosseap-6.0
for quickstarts running on JBoss Enterprise Application Platform 6, or jbossas-7
for quickstarts running on JBoss AS 7:
rhc app create -a helloworldrs -t APPLICATION_TYPE
NOTE: The domain name for this application will be helloworldrs-YOUR_DOMAIN_NAME.rhcloud.com
. Here we use the quickstart domain. You will need to replace it with your own OpenShift domain name.
This command creates an OpenShift application named helloworldrs
and will run the application inside the jbosseap-6.0
or jbossas-7
container. You should see some output similar to the following:
Creating application: helloworldrs
Now your new domain name is being propagated worldwide (this might take a minute)...
Warning: Permanently added 'helloworldrs-quickstart.rhcloud.com,<IP_ADDRESS>' (RSA) to the list of known hosts.
Confirming application 'helloworldrs' is available: Success!
helloworldrs published: http://helloworldrs-quickstart.rhcloud.com/
git url<EMAIL_ADDRESS>Successfully created application: helloworldrs
The create command creates a git repository in the current directory with the same name as the application. Notice that the output also reports the URL at which the application can be accessed. Make sure it is available by typing the published url http://helloworldrs-quickstart.rhcloud.com/ into a browser or use command line tools such as curl or wget. Be sure to replace the quickstart
in the URL with your domain name.
Now that you have confirmed it is working you can migrate the quickstart source. You do not need the generated default application, so navigate to the new git repository directory and tell git to remove the source and pom files:
cd helloworldrs
git rm -r src pom.xml
Copy the source for the helloworld-rs
quickstart into this new git repository:
cp -r <quickstarts>/helloworld-rs/src .
cp <quickstarts>/helloworld-rs/pom.xml .
You can now deploy the changes to your OpenShift application using git as follows:
git add src pom.xml
git commit -m "helloworld-rs quickstart on OpenShift"
git push
The final push command triggers the OpenShift infrastructure to build and deploy the changes.
Note that the openshift
profile in the pom.xml
file is activated by OpenShift. This causes the WAR built by OpenShift to be copied to the deployments
directory and deployed without a context path.
When the push command returns you can test the application by getting the following URLs either via a browser or using tools such as curl or wget. Be sure to replace the quickstart
in the URL with your domain name.
You can use the OpenShift command line tools or the OpenShift web console to discover and control the application.
If you plan to test the jax-rs-client
quickstart on OpenShift, you may want to wait to destroy this application because it is also used by that quickstart for testing. When you are finished with the application you can destroy it as follows:
rhc app destroy -a helloworldrs
Note: There is a limit to the number of applications you can deploy concurrently to OpenShift. If the rhc app create
command returns an error indicating you have reached that limit, you must destroy an existing application before you continue.
rhc domain show
rhc app destroy -a APPLICATION_NAME_TO_DESTROY