Introduction
In this tutorial we will learn how to do efficient neural machine translation
using the Marian toolkit by optimizing the speed, accuracy and use of resources
for training and decoding of NMT models.
No background knowledge about Marian is required, but if you are completely new
to neural machine translation and Marian, you may take a look at at the
introductory tutorial to Marian first. We assume that the
reader is familiar with basic Linux commands.
The tutorial requires Marian in version v1.7.12+, which is currently only
available in the marian-dev
repository. However, most parts are also applicable for older versions of
Marian.
More information about Marian:
Installation
Marian can be compiled on machines with NVIDIA GPU devices and CUDA 8.0+ or on
CPU-only machines. For this tutorial we need Marian compiled with support for
CPU and SentencePiece. This requires installing Intel
MKL and Protocol Buffers first.
To download the repository and compile Marian, run the following commands:
git clone https://github.com/marian-nmt/marian-dev
cd marian-dev
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DUSE_SENTENCEPIECE=on -DCOMPILE_CPU=on
make -j
cd ../..
In this tutorial we assume that Marian is compiled in your home directory.
If everything worked correctly you can display the list of options with:
~/marian-dev/build/marian --help |& less
Models and data
First we need to download models and data (611MB) that we will use for the tutorial:
wget http://data.statmt.org/romang/marian-examples/marian-tutorial-mtm19.tgz
tar zxvf marian-tutorial-mtm19.tgz
cd marian-tutorial-mtm19
This will give you the following files:
marian-tutorial-mtm19
├ 1_decoding
│ ├ data
│ │ ├ newstest2013.de
│ │ ├ newstest2013.en
│ │ ├ newstest2014.de
│ │ └ newstest2014.en
│ ├ model.npz
│ ├ run-me.sh
│ └ vocab.spm
├ 2_training
│ ├ config.yml
│ ├ download-data.sh
│ ├ run-me.sh
│ └ train.log
├ 3_student
│ ├ data
│ │ ├ newstest2014.bpe.en
│ │ ├ newstest2014.de
│ ├ lex.s2t
│ ├ model.student.base
│ │ └ model.npz
│ ├ model.student.small
│ │ └ model.npz
│ ├ model.student.small.aan.nogate.noffn
│ │ └ model.npz
│ ├ run-me.sh
│ └ vocab.ende.yml
├ detruecase.perl
└ multi-bleu.perl
1. Decoding
For this part of the tutorial we will use an RNN model trained on the WMT’14
English-German corpus provided by the Stanford NLP
Group pre-processed with a true-caser
and SentencePiece-based subword segmentation. The model is in the
marian-tutorial-mtm19/1_decoding
folder.
Models trained with Marian can be decoded using the marian-decoder
command.
The basic usage requires specifying paths to the model and vocabularies:
~/marian-dev/build/marian-decoder -m model.npz -v vocab.spm vocab.spm < data/newstest2014.en > output.de
where newstest2014.en
is an input file with pre-processed source sentences
and output.de
is an output file with translations.
Now we want to calculate the speed and quality of the translation. Marian
already prints the total time required to translate the input file (exclusive
of loading times of models and vocabularies) at the end of the decoding, for
example:
[2019-08-25 13:17:33] Total time: 119.24629s wall
To estimate the quality, we need to run a BLEU scorer:
cat output.de | ../multi-bleu.perl data/newstest2014.de
This should display:
BLEU = 23.90, 56.0/29.6/17.7/11.1 (BP=1.000, ratio=1.003, hyp_len=59469, ref_len=59297)
Batched decoding
Batched decoding parallelizes translation of multiple sentences. It generates
translation for whole mini-batches and significantly increases translation
speed, roughly by a factor of 10 or more.
In Marian there are a couple of options that are important for batched
translation:
--mini-batch
enables translation with a mini-batch size of N, i.e.
translating N sentences at once.
allows for better packing of the batch.
--mini-batch-words
allows to specify the size of a mini-batch in terms of a
number of words, not a number of sentences.
--maxi-batch
preloads M mini-batches, i.e. M x N sentences.
--maxi-batch-sort
sorts them according to source sentence length, this
An important option is also --workspace
or -w
, which set the working
memory. The default working memory is 512 MB and Marian will increase it to
match to requirements during translation, but pre-allocating memory makes it
usually a bit faster.
Last but not least, you can also parallelize translation by running it on
multiple GPUs using the --devices
option.
Task 1
Translate newstest2014.en using different settings for batched
translation, compare decoding times, and complete the table below.
Which parameter improves the translation speed the most? Are you getting
exactly the same outputs from line-by-line and batched translation?
| System |
Speed (sec.) |
| Line-by-line translation |
|
| Adding mini-batch of 32 |
|
| Switching to mini-batch of 64 |
|
| Increasing workspace to 4GB |
|
| Adding maxi-batch of 1000 |
|
| Adding mini-batch-words of 2000 |
|
Optimizing parameters
There are a few options that impact the translation speed and quality, and beam
size is one of the most important. It determines a number of translation
hypotheses considered for each input word. Exploring too few hypotheses per
word may lead to a globally suboptimal translation, while using too large beam
size may increase resource usage and computation time without improving the
translation quality that much.
Options that can be easily tuned include:
--beam-size
determines a number of translation hypotheses explored at each
step of the beam search algorithm. Common beam sizes are 8 to 12, depending
on the model.
--max-length-factor
sets maximum target length as source length times
factor. The default value is 3.
--normalize
divides translation score by the translation length to the
power of \(\alpha\).
Task 2
Explore different configurations of the beam size and maximum length factor and
try to improve the translation time, keeping BLEU at a good level. Next grid
search the value of the length normalization parameter on newstest2013
and check if the improvement of BLEU transfers into newstest2014.de.
What are the reasonable values for the beam size? What is a danger of using too
small value for the maximum length factor?
This can be done with a simple bash script:
for i in 1 2 4 8 12 24; do
~/marian-dev/build/marian-decoder -b $i [OTHER OPTIONS] < data/newstest2013.en > output.b$i.de
done
A reasonable values to consider for --max-length-factor
are in a range of 1
to 3. The normalization parameter \(\alpha\) can be grid searched for values
around 1.0, usually in a range of 0.2 to 2.
Back-translation
Generation of back-translations
often require translation of a large amount of monolingual data (sometimes even
hundreds of millions of sentences), so optimizing this process can save you
quite a lot of time.
- If your dataset is large, consider splitting it into smaller chunks (see
GNU split)
and translate the chunks sequentially. This will not make your translation
faster, but more stable.
- Use batched translation and adjust your decoding parameters too speed up the
translation.
- Because monolingual data, especially if it come from web crawling, can
contain very long sentences, use smaller
--max-length
and turn on
--max-length-crop
.
- Use sampled back-translations with
--output-sampling
.
To test sampled translations, run several times:
head data/newstest2014.en | ~/marian-dev/build/marian-decoder -m model.npz -v vocab.spm vocab.spm \
--output-sampling
2. Training
An efficient training of an NMT model might mean minimizing the training time
or improving the convergence. This is also choosing a model architecture that
is best suited for yours needs.
Model architecture
Transformer models often offer best quality, but are also more difficult to
train than RNNs, and need more careful setting of training parameters.
A good starting point for finding a good set of training hyper-parameters is
our repository with Marian
examples.
Since version 1.7.12 Marian provides the --task
option, which provides
pre-defined options for transformer-base, and transformer-big:
~/marian-dev/build/marian --task transformer-base --dump-config expand
You should keep in mind that those settings are just a starting point and they
should be adjusted for your scenario. For example, a low-resource scenario will
probably require stronger regularization, etc.
GPU memory
Training of some model architectures like Transformer may benefit from large
mini-batches.
--mini-batch-fit
overrides the specified mini-batch size and automatically
chooses the largest mini-batch for a given sentence length that fits the
specified memory. When --mini-batch-fit
is set, memory requirements are
guaranteed to fit into the specified workspace. Choosing a too small workspace
will result in small mini-batches which can prohibit learning.
In multi-GPU setting, a synchronous training (--sync-sgd
) can also serve as a
method to increase the effective size of mini-batches.
The option --workspace
sets the size of the memory available for the forward
and backward step of the training procedure. This does not include model size
and optimizer parameters that are allocated outsize workspace. Hence you cannot
allocate all GPU memory to workspace.
It is a bit tricky to determine the effective size of a mini-batch with
dynamically sized mini-batches. An alternative is to operate in terms of the
total workspace size that is used to generate mini-batches for one update,
which can be calculated with the following formula:
\[\text{Workspace (MB)} \times \text{Number of GPUs} \times \text{Delayed updates}\]
Cumulative or delayed gradient
updates increase the effective
batch size, or the amount of data used at each step of training. It can be
enabled with --optimizer-delay N
.
Task 3
Setup training of a transformer-base model with the total workspace of
ca. 48 GB per update, and calculate the average size of mini-batches from
training logs.
Download the training data:
cd ../2_training
bash download-data.sh
Start with the following command and set --workspace
, --devices
and
--optimizer-delay
options:
~/marian-dev/build/marian --task transformer-base -c config.yml
The optimal values for these options will differ depending on the number of
GPUs and available memory per single GPU.
3. Teacher-student
Knowledge distillation approaches, like the teacher-student
method, are used for training a
smaller student model to perform better by learning from a larger teacher
model.
In NMT it might mean training a strong (and slow) ensemble of Transformer
models as a teacher, then translating an entire source side of the training
data with the teacher, and finally training a small (and fast) model on
original source and translated target data. The student model has usually worse
BLEU, but is much faster. A useful side effect of the teacher-student learning
is an improved performance of the student with smaller beam sizes.
More details can be found in our paper on cost-effective and high-quality NMT
with Marian.
CPU decoding
The teacher-student method enables efficient translation on CPU. A couple of
things can speed it up more:
- Batched translation as presented in the first part of the tutorial.
- A lexical shortlist restricts the output
vocabulary to a small subset of translation candidates for each words.
- Auto-tuning of matrix product implementation with
--optimize
.
The --cpu-threads
option turns on the decoding on CPU.
Task 4
Experiment with different settings for decoding on a single CPU. Check
different student models, batched translation, lexical shortlists and
auto-tuning.
How does a student model perform with small beam sizes? What is the speed
improvement from a lexical shortlist on CPU and GPU? What are architectures of
student models and how large are they? What is the issue with decoding on
multiple CPU threads with model files in .npz format?
The models can be found in the ./3_student
folder. They have been trained on
true-cased and BPE-segmented data, so require pre- and post-processing:
cd ../3_student
cat data/newstest2014.bpe.en \
| ~/marian-dev/build/marian-decoder -m model.student.small/model.npz -v vocab.ende.yml vocab.ende.yml \
-d 0 --cpu-threads 1 \
--mini-batch 64 --maxi-batch 100 \
-b 1 --max-length-factor 1.5 -n 0.6 \
| perl -pe 's/@@ //g' \
| ../detruecase.perl \
| ../multi-bleu.perl data/newstest2014.de
The shortlist file is 3_student/data/lex.s2t
. Model-specific parameters can
be read from a model.npz
file using the script
marian-dev/scripts/contrib/model_info.py
.
That’s all for now. Hope you have found something useful here!


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| gam.side {mgcv} | R Documentation |
GAM formulae with repeated variables may only correspond to
identifiable models given some side conditions. This routine works
out appropriate side conditions, based on zeroing redundant parameters.
It is called from mgcv:::gam.setup
and is not intended to be called by users.
The method identifies nested and repeated variables by their names, but numerically evaluates which constraints need to be imposed. Constraints are always applied to smooths of more variables in preference to smooths of fewer variables. The numerical approach allows appropriate constraints to be applied to models constructed using any smooths, including user defined smooths.
gam.side(sm,Xp,tol=.Machine$double.eps^.5,with.pen=FALSE)
sm |
A list of smooth objects as returned by
|
Xp |
The model matrix for the strictly parametric model components. |
tol |
The tolerance to use when assessing linear dependence of smooths. |
with.pen |
Should the computation of dependence consider the penalties or not. Doing so will lead to fewer constraints. |
Models such as y~s(x)+s(z)+s(x,z)
can be estimated by
gam
, but require identifiability constraints to be applied, to
make them identifiable. This routine does this, effectively setting redundant parameters
to zero. When the redundancy is between smooths of lower and higher numbers
of variables, the constraint is always applied to the smooth of the higher
number of variables.
Dependent smooths are identified symbolically, but which constraints are
needed to ensure identifiability of these smooths is determined numerically, using
fixDependence
. This makes the routine rather general, and not
dependent on any particular basis.
Xp
is used to check whether there is a constant term in the model (or
columns that can be linearly combined to give a constant). This is because
centred smooths can appear independent, when they would be dependent if there
is a constant in the model, so dependence testing needs to take account of this.
A list of smooths, with model matrices and penalty matrices adjusted
to automatically impose the required constraints. Any smooth that has been
modified will have an attribute "del.index"
, listing the columns of its
model matrix that were deleted. This index is used in the creation of
prediction matrices for the term.
Much better statistical stability will be obtained by using models like
y~s(x)+s(z)+ti(x,z)
or y~ti(x)+ti(z)+ti(x,z)
rather than
y~s(x)+s(z)+s(x,z)
, since the former are designed not to require
further constraint.
Simon N. Wood<EMAIL_ADDRESS>## The first two examples here iluustrate models that cause ## gam.side to impose constraints, but both are a bad way ## of estimating such models. The 3rd example is the right ## way.... set.seed(7) require(mgcv) dat <- gamSim(n=400,scale=2) ## simulate data ## estimate model with redundant smooth interaction (bad idea). b<-gam(y~s(x0)+s(x1)+s(x0,x1)+s(x2),data=dat) plot(b,pages=1) ## Simulate data with real interation... dat <- gamSim(2,n=500,scale=.1) old.par<-par(mfrow=c(2,2)) ## a fully nested tensor product example (bad idea) b <- gam(y~s(x,bs="cr",k=6)+s(z,bs="cr",k=6)+te(x,z,k=6), data=dat$data) plot(b) old.par<-par(mfrow=c(2,2)) ## A fully nested tensor product example, done properly, ## so that gam.side is not needed to ensure identifiability. ## ti terms are designed to produce interaction smooths ## suitable for adding to main effects (we could also have ## used s(x) and s(z) without a problem, but not s(z,x) ## or te(z,x)). b <- gam(y ~ ti(x,k=6) + ti(z,k=6) + ti(x,z,k=6), data=dat$data) plot(b) par(old.par) rm(dat)


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In this demo jPlayer is combined with HTML and CSS, along with javascript code to add playlist functionality to a video and audio player.
You can easily customise the way they look and make it fit your pages colours and style.
This demo will use the HTML solution if it can, otherwise the Flash fallback solution will be used. The supplied media formats are OGV and M4V for video. OGA and MP3 for audio.
Additional $.jPlayer.event.play
event handlers have been used to avoid both jPlayers playing together.
The playlist item object can include an optional {free:true}
property to make the media easily available via a right-click. (Ctrl+click on Mac.)
Note that all media is freely available since it is distrubuted over the internet, it just makes it easier for a novice to obtain the media.
Direct clicks are disabled, otherwise the browser attempts to play the media directly in the browser and leaves the current page.
(A server/backend Guru could make it very hard to get the media directly if they wanted, but that is for them to figure out.)
Press play to start the media. When the media playlist has ended, the playlist will auto-repeat.
Media used in demo:
© 2008, Blender Foundation - bigbuckbunny.org
© 2003-2004, Disney / Pixar - pixar.com
The Audio player below is a different jPlayer instance to the one used above in the video player. Note that the playlist JavaScript code has been written to facilitate multiple playlist instances, as this page demonstrates.
Media used in demo: © 2003 Miaow / Arnaud Laflaquiere - MiaowMusic.com
Developing and supporting jPlayer is almost a full-time job and we are really just beginning. Help us continue to help you.
Need a media based solution realized or just need some help. Hire Happyworm! Contact<EMAIL_ADDRESS>Massively multiplayer real-time quiz game. Cross platform and tuned for the iPad.


You fill up a cup with the water and place it in your pack. After you remove the cup from the water, the remaining water suddenly drains to the bottom, revealing a large amount of gold coins in the bottom of the fountain. What do you do now?


Dependency Graph for atmat/pubtools/LatticeTuningFunctions/correction/tune
GitHub Website
at
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Generated by
m2html
© 2005


See: Description
| Interface | Description |
|---|---|
| Credential |
A credential for an entity.
|
| CredentialContext |
Marker interface for implementations which hold information specific to a a particular context within
which a
CredentialResolver resolves a Credential . |
| CredentialResolver |
A resolver which uses
Criteria to resolve and return instances of Credential . |
| Class | Description |
|---|---|
| AbstractCredential |
Base class for
Credential implementations. |
| AbstractCredentialResolver |
Abstract base class for
CredentialResolver implementations. |
| AbstractCriteriaFilteringCredentialResolver |
An abstract implementation of
CredentialResolver which filters the returned Credentials
based on the instances of EvaluableCredentialCriteria which are present in the set of
criteria, or which are obtained via lookup in the EvaluableCredentialCriteriaRegistry . |
| BasicCredential |
A basic implementation of
Credential . |
| BasicKeyInfoGeneratorFactory |
A factory implementation which produces instances of
KeyInfoGenerator capable of
handling the information contained within a Credential . |
| ChainingCredentialResolver |
An implementation of
CredentialResolver which chains together one or more underlying credential resolver
implementations. |
| CollectionCredentialResolver |
An implementation of
CredentialResolver which uses a Collection as the
underlying credential source. |
| CredentialContextSet |
This class holds instances of
CredentialContext which represent information
about the context in which a CredentialResolver has resolved a Credential . |
| FilesystemCredentialResolver |
NOTE: this class is not yet implemented
A
CredentialResolver that pulls credential information from the file system. |
| KeyStoreCredentialResolver |
A
CredentialResolver that extracts Credential 's from a key store. |
| StaticCredentialResolver |
Simple implementation of
CredentialResolver which just stores and returns a static set of credentials. |
| Enum | Description |
|---|---|
| UsageType |
Credential usage types.
|
Copyright © 1999-2014. All Rights Reserved.


Logged in as collylogic
“Climb the mountains and get their good tidings. Nature's peace will flow into you as sunshine flows into trees. The winds will blow their own freshness into you, and the storms their energy, thile cares will drop away from you like the leaves of autumn. “
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Copyright &cope; 2009 Simon Collison. All rights reserved
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Job¶
The basic unit of execution, say a python method or a callable.
for methods without arguments¶
from pypette import Job
def method():
print("Hello!")
job = Job(method)
for methods with arguments¶
from pypette import Job
def method(msg):
print("Hello " + msg + "!")
# As argument list
job = Job(method, args=("World",))
# As key word arguments
job = Job(method, kwargs={"msg":"World"})
for lambda methods¶
from pypette import Job
job = Job(lambda: print("Hello World!"))
BashJob¶
The basic unit of execution, which runs a bash command.
from pypette import BashJob
job = BashJob(['ls', '-l'])
job = BashJob(['pwd'])
job = BashJob(['cat', 'file.txt', '|', 'grep', 'colours'])
Pipe¶
Structure to specify the flow in which the jobs need to be executed.
Creation Modes¶
FAIL_FAST (Default)¶
Pipe objects created in this mode do not execute any stages to come after an exception is thrown
from pypette import Pipe, Gate
p = Pipe('Test')
p = Pipe('Test', gate=Gate.FAIL_FAST)
EXECUTE_ALL¶
Pipe objects created in this mode continue to execute all stages to come after an exception is thrown i,e these pipelines are resilient to exceptions.
from pypette import Pipe, Gate
p = Pipe('Test', gate=Gate.EXECUTE_ALL)
Adding jobs to pipeline¶
Any object of the type BashJob
,
Job
or Pipe
can be added as a
job to the pipeline
in series¶
from pypette import Pipe
p = Pipe('Test')
# A list of jobs can be added in series as
p.add_stage(job1)
p.add_stage(job2)
p.add_stage(job3)
p.add_stage(job4)
# Or as a job list
p.add_jobs([job1, job2, job3, job4])
in parallel¶
from pypette import Pipe
p = Pipe('Test')
# A list of jobs can be added in series as
p.add_stage(job1, job2, job3, job4)
# Or as a job list
p.add_jobs([job1, job2, job3, job4], run_in_parallel=True)
Adding dependencies to pipelines¶
Irrespective of the mode the Pipe
object is created in,
we several times come across scenarios where we want to create dependencies i,e
we do not want a pipeline to run unless some other pipeline has succeeded. We
can add one Pipe
object as a dependency to another
Pipe
object as follows
from pypette import Pipe
build = Pipe('Build')
test = Pipe('Test')
# Run test pipeline only if build has run and it was successful.
test.add_dependency(build)
cleanup = Pipe('Cleanup')
# Run cleanup pipeline only if build and test have run and completed
# successfully
cleanup.add_dependency(build, test)
Visualizing the pipeline structure¶
We can visualize the entire pipeline within the comfort of the terminal itself using the graph() method
from pypette import Pipe
test = Pipe('Test')
...
test.graph()
Running the pipeline¶
We can start executing the stages of the pipeline by using the run() method
from pypette import Pipe
test = Pipe('Test')
...
test.run()
Generating report for the pipeline run¶
Once the pipleline has been run, we can generate a report of the run using the report() method
from pypette import Pipe
test = Pipe('Test')
...
test.report()
Building complex pipelines¶
Jobs submitted to pipeline should be callables i.e. structures which can be run. This means python methods, lambdas etc qualify.
What about Pipe itself?
 This way you can build small pipelines which achieve a specific task and then combine them to create more complex pipelines.
Let us understand building pipelines using these jobs
from pypette import Job
def _good():
print("I am good.")
def _bad():
raise Exception("I am bad.")
def _ugly(): print("I"
+ "am"
+ "ugly.")
def _dummy():
pass
good = Job(_good)
bad = Job(_bad)
ugly = Job(_ugly)
dummy = Job(_dummy)
Exception scenarios¶
Let us understand how a pipeline behaves in case of exception scenarios
Exception thrown in series¶
from pypette import Pipe
p = Pipe("Test") # Note is similar to Pipe("Test", gate=Gate.FAIL_FAST)
p.add_stage(good)
p.add_stage(bad)
p.add_stage(ugly)
p.run()
# Executes good, bad and as bad throws exception, exits pipeline without
# executing ugly.
Exception thrown in parallel¶
from pypette import Pipe
p = Pipe("Test") # Note is similar to Pipe("Test", gate=Gate.FAIL_FAST)
p.add_stage(good, bad)
p.add_stage(ugly)
p.run()
# Executes both good and bad as they need to be executed in parallel and we
# have no control over what goes before what. And as bad throws an exception
# does not execute the next stage which has ugly.
Resilient mode¶
from pypette import Gate, Pipe
p = Pipe("Test", gate=Gate.EXECUTE_ALL)
# For a pipe created in the following way, ugly would have been executed for
# both scenarios of exception in series and parallel listed above. What this
# means is that bad would have thrown an exception but that wouldn't have
# stopped pipeline from executing stages after it.
Combining everything¶
Combining all the scenarios listed above we can create a complex pipeline with jobs and sub pipes etc as follows.
from pypette import Gate, Pipe
jenkins = Pipe("Jenkins", gate=Gate.EXECUTE_ALL)
build = Pipe("Build", gate=Gate.EXECUTE_ALL)
build.add_stage(good)
# Test pipeline will throw an exception.
test = Pipe("Test", gate=Gate.EXECUTE_ALL)
test.add_stage(bad)
# Cleanup pipeline is dependent on both build and test pipeline success.
cleanup = Pipe("Cleanup", gate=Gate.EXECUTE_ALL)
cleanup.add_stage(ugly)
cleanup.add_dependency(build, test)
jenkins.add_stage(dummy, build)
jenkins.add_stage(test, dummy)
jenkins.add_stage(cleanup, dummy, dummy)
jenkins.graph()
# Would output a graph on the lines of
#
# Pipe(Jenkins)
# |
# |----------------------------
# | | |
# | dummy Pipe(Build)
# |----------------------------
# | | |
# | Pipe(Test) dummy
# |------------------------------------------
# | | |
# Pipe(Cleanup) dummy dummy
#
# Pipe(Build)
# |
# |----------- good
#
# Pipe(Test)
# |
# |------------ bad
#
# Pipe(Cleanup)
# |
# |------------ ugly
jenkins.run()
# Take a minute before reading the answer and make a mental note of what
# all gets executed and why?
#
# Answer:
# dummy job gets executed 4 times as EXECUTE_ALL is exception resilient.
#
# build pipeline gets successfully executed (implying all internal jobs get
# executed successully)
#
# test pipeline fails (implying one or more jobs threw an exception. Note
# that even though other jobs do get executed within this pipeline as it is
# resilient, it still is marked as overall failure as one ore more jobs
# threw an exception.)
#
# cleanup pipeline wont start executing as it is dependent on build and test
# being successful and test pipe has failed.
#
# jenkins overall gets marked as failed as one or more jobs/sub pipes have
# failed.
jenkins.report()
# Would provide a report on the lines of
#
# Pipe(Jenkins)
# |
# |----------------------------
# | | |
# | SUCCESS SUCCESS
# |----------------------------
# | | |
# | FAILED SUCCESS
# |------------------------------------------
# | | |
# FAILED SUCCESS SUCCESS
#
# Pipe(Build)
# |
# |----------- SUCCESS
#
# Pipe(Test)
# |
# |------------ FAILED
#
# Pipe(Cleanup)
# |
# |------------ FAILED


I am a full stack web developer with a passion for coding and developing simple solutions to complex problems. I have experience in Ruby, Nodejs, JavaScript(ES6+) and React.
I appreciate the uniqueness of programming in that it combines my love for maths with elements of architecture. I get to use the fun analytical side of maths to break down problems and assimilate them, whilst using the creative side of architecture to create new solutions and eventually brand new spanking apps!
A dynamic web app using React and Rails for uploading your wardrobe collection and creating customizable styles. User authentication is done using JWT tokens. Heavily inspired by my interest in fashion and wanting to look fashionable whilst being able to save time.
Technology: React, Rails, Bootstrap (CSS), Postgre
A responsive e-commerce web app that resembles an online shopping cart for the purchasing of beverages. Technologies used were React and Rails. Data is called from a third party API and displayed on the client side. Includes the ability to search by letter or price. Inspired by my general use of shopping online.
Technology: React, Nodejs, Third party API
A responsive app using Javascript and Rails for music lovers that supports a digital platform for listening to music. Inspired by my love for music, usage of spotify and the inner workings of music platforms.
Technology: Vanillajs, Rails
A comic-based web app built in Rails using restful routes that lets users add characters to their account and view the stories associated with each character. Designed by forming many to many relationship tables and assigning routes for different end points. Inspired by my childish side and interest in all things cartoonish.
Technology: Rails
A CLI app built using ruby for querying the TFL API for live news on the lines, stations or any disruptions. Developed by pair programming and using git/github for source code management and development.
Technology: Ruby
I am also part of a local amateur football team. We meet up every week and it is a nice opportunity to do some exercise and have fun.
BEng in Mechanical Engineering


## Parsing in Rust with `nom`
#### Michael Layzell
## What is a parser?
note: What is a parser?
## Rust has a parser!
```rust
"10".parse::<i32>() // Ok(10)
"32.5".parse::<f64>() // Ok(32.5)
"cheese".parse::<i32>() // Err(ParseIntError { ... })
```
What does that look like?
note: Well, rust has a parser. There's a parse method on `str` which
you can call to interpret it as one of the primitive data types, like
i32 or f64.
Let's use that as a starting place.
Somewhere deep in the standard library...
```rust
impl str {
fn parse<F>(&self) -> Result<F, F::Err>
where F: FromStr { ... }
}
```
note: This is what that parse method looks like. The basic idea here
is that the function can take a string in as an argument, and produces
either structured data, or a result.
```rust
fn parse(&str) -> Result<Thing, Err> { ... }
```
> "A parser takes a string, and either returns the parsed value, or an
error"
note: If we simplify this down to the minimal definition, this is what
it looks like.
This is a relatively limited parser, however. It cannot handle
interesting cases, such as parsing data off of the beginning of a
string, and returning the remaining data.
## Incremental Parsing?!?
#### Key-value pairs
`key: 10`
#### Delimited lists
"apple, cider", "pear"
note: This is important for most interesting parser problems. For
example, if you have a parser for a word, and a parser for a number,
you would want to be able to combine them to get a key-value parser.
It would really suck if you had to write a brand new parser from
scratch for every combination of numbers, and strings.
In many of these cases we can't do the easy thing and split on a
delimiter character either, such as when you want to parse a
comma-separated list of strings, which might contain commas.
## Ta Da!
```rust
fn parse(&str) -> Result<(&str, Thing), Err> { ... }
```
> "A parser takes a string, and either returns the parsed value, and the rest of the string, or an error"
note: So, we can expand upon the basic parser, and bring it new power!
## Results in `nom`
```rust
enum IResult<I, R> { // * Not actually IResult
Done(I, R),
Error,
Incomplete,
}
```
note: But nom does something a little bit different. It was designed
to be as flexible and useful in as many situations as possible, so it
extracted one of the possible "error" states into a new Result
variant.
nom uses `IResult` which is like result, except that it also has
support for an `Incomplete` case. This is because nom was designed to
support parsing potentially-incomplete data streams, and needs a way
to distinguish between a parsing failure due to incorrectly formatted
data, versus a parsing failure due to simply not having received
enough data from the stream yet.
The actual definition of IResult is a bit longer, because nom likes to
be super fancy, but we can ignore it.
## Example Time!
 If you set up a local project, you can
easily get to them with `cargo doc --open`, or if you've been
following along in the playground, they're also avaliable on docs.rs.


Source code of this website on Github.
TIP: Protect your self before using this downloader.
I suggest using Tor
.


Trail Building - Community Involvement
Sustainable Trails
Team Dirt only supports the creation of sustainable trails. A sustainable trail is one which does not require constant maintenance under normal riding traffic. The best way to achieve this is through proper drainage of water. Building sustainable trails is part art and science. The science part is using the correct dirt (avoiding organic material, like buried rotten logs), bench cutting into hills with the proper slope gradient, planning for turn exits, berm height and angle, placing drain exits on straight away, reinforcing high impact areas, blocking alternative routes, etc. The art part is trying to add signature to an engineered trail, placement of features, flow, leaving specific roots and rocks, importing rocks, placing a trail cue, etc.
A trail starts with a general topographic route, approved by the land manager and championed by the Team Dirt area manager. However, as the trail is created different terra aspects are factored in as they are encountered. By showing up to build days and discussing your thoughts with other builders, you have the ability to add your own requirements to the trail, hence leaving a signature of your own.
In partnership with the BLM, IMBA has created a Mountain Bike Trail Guide Book, available for free here.
Meet the Trail Leaders
Tim Maddux
The Declaration of Independence is the statement adopted by the Second Continental Congress meeting at the Pennsylvania State House (Independence Hall) in Philadelphia on July 4, 1776.
Favorite TeamDirt Trail:
Tool Assets
Hand Tools
Team Dirt owns and provides to its volunteers hand tools for build days. Most of the tools are repurposed from Wild Fire Suppression and include Pulaski Tools, McLeod Tools, Hoe Tools, Fire Rakes, Garden Rakes, Hand Saws, Hedge Shears, Limb Loppers, Shovels, and buckets.
Power Buckets
Team Dirt has two skid steer power wheel barrows. The power wheel barrow includes a hydraulic lift on the bucket. This tool is essential for moving large amounts of dirt (e.g., when building berms). A lighter duty version of this tool is planned for the McDonald Forest.
Shuttle Wagon and Trailer
Team Dirt has a repurposed short school bus and custom trailer for shuttle days. The trailer has custom bike hitches welded to the frame, which allow for quick mounting. Come on out to a shuttle day to hope on the short bus!
Kubota U17 mini Excavator
A mini excavator may be used to rough in tread on approved projects. This greatly speeds up the trail build process. After the rough tread is laid down, trail builders will build in features, fine tune the final tread by hand, then re-vegitate the area to quicken the natural aspects of the trail.


Hi All, I've had a Raspberry Pi for a few years and tinkered with them now and again, nothing overly complex though. I've recently bought a Geo Minim to monitor our electricity usage but thought it would be really useful if I could record this information using the raspberry pi, key benefits would be to show hour-by-hour historical information and trends by hour/day. At the moment to receiver just shows current usage, weekly and monthly totals. I've found one example that uses a webcam to strip the information and record using a RaspPi but I was wondering if I could intercept the radio signals from the monitor directly. From their website it says it uses the 868 MHz Legato Protocol but not entirely sure where to start.
Through my search I've come across this site and wondering if there is anyway the base unit will be able to reeive information from the Geo Minim Sensor currently paired to my Geo Minim Energy monitor.
Thanks for any help.
Gareth
Re: Intercept Geo Minim Energy Monitor Data
I haven't personally come across that device before so cannot say either way, you will need to do a bit of leg work and see if you can find any examples of someone doing this, if you can provide some links etc we may be able to help but it's not known to be compatible as far as I'm aware.
Paul
Re: Intercept Geo Minim Energy Monitor Data
I have never seen a reference here either to Geo Minim or the Legato protocol. Taking a quick look, the Legato protocol appears to apply only to the information layer, you still need to find out the details of how the information is carried on the radio channel (the transport layer). And when you've done that, you still need to identify which parts of the protocol are being used in your particular situation, because Legato appears to be very comprehensive.
When you can read the messages from your sensor, it should be a comparatively simple task to repackage the data in a suitable format (presumably for emoncms).
I think you have a significant task ahead of you.


The arteries, which start out smooth and elastic, get plaque on their inner walls, which can make them more rigid and narrowed. This restricts blood flow to your heart, which can then become starved of oxygen.The plaque could rupture, leading to a heart attack or sudden cardiac death.
The most common symptoms of coronary artery disease are angina (say "ANN-juh-nuh" or "ann-JY-nuh") and shortness of breath when exercising or doing other vigorous activity. Women are somewhat more likely than men to have other symptoms like nausea and back or jaw pain.
Lower your blood pressure. Prevent blood clots, which can lead to heart attack or stroke. Prevent or delay the need for a stent or percutaneous coronary intervention (PCI) or surgery, such as coronary artery bypass grafting (CABG). Reduce your heart's workload and relieve coronary heart disease symptoms.


{% extends "teacheradmin/base_teacheradmin.html" %}
{% load i18n gravatar compress %}
{% block nav-teacheradmin-guestlist %}active{% endblock %}
{% block extrajs %}
{% compress js %}
{% endcompress %}
{% endblock %}
{% block section %}
{% trans "Success" %}
{% trans "The student has been invited." %}
{% trans "Success" %}
{% trans "The student has been removed from the course." %}
{% trans "Error" %}
{% trans "That student doesn't exists. Please, introduce a valid user or an email." %}
{% trans "Error" %}
{% trans "The email introduced has already received an invitation." %}
{% trans "Error" %}
{% trans "Something went wrong, please try again later." %}
{% if students_enrolled %}
{% trans "Invited students who are already enrolled" %}
{% for element in students_enrolled %}
| {{ element.id }} |
{% gravatar_img_for_email element.student.email 20 %} |
{% firstof element.student.get_full_name element.student.username %} |
{{ element.student.email }} |
|
{% endfor %}
{% endif %}
{% if students_invited %}
{% trans "Invited students who are not enrolled yet" %}
{% for element in students_invited %}
| {{ element.id }} |
{% gravatar_img_for_email element.email 20 %} |
{% firstof element.student.get_full_name element.student.username '--' %} |
{% firstof element.student.email element.email %} |
|
{% endfor %}
{% endif %}
{% endblock %}


Steve
Crouch is a software architect at
the Software Sustainability
Institute, and is based at
the University of
Southampton. He assists researchers and their communities by
consulting on software that is integral to their research.
Instructor Trainer


Guide to Publishing Humanities Data Analysis
Now that you’re undertaking humanities data analysis a natural question is how to present and make your analyses accessible and publicly available. This is a BIG question with many answers depending on your tools, audiences, and timelines. However, for the sake of your final projects, I wanted to cover some of the options you might pursue to present your work.
First though let’s refresh on how the web works.
What is the Web?
From Wikipedia: “The World Wide Web (WWW), commonly known as the Web, is an information system where documents and other web resources are identified by Uniform Resource Locators (URLs, such as https://www.example.com/), which may be interlinked by hypertext, and are accessible over the Internet.[1][2] The resources of the WWW are transferred via the Hypertext Transfer Protocol (HTTP) and may be accessed by users by a software application called a web browser and are published by a software application called a web server.”
What does this mean exactly?
When we type a url for a webpage (say google.com), we’re actually sending an HTTP request to a server that hosts the actual HTML files and data. If the server decides that our request is ok (200), then it will give us access to the webpage.
If you’ve ever seen an error message when you go to a webpage saying the page doesn’t exist, that means that you received a 404 error, which is a type of status code you get back from an HTTP request.
So what does this mean for publishing on the web?
To publish humanities data analysis requires having your files hosted on a server that are accessible by url - essentially you need a website.
Most DH projects are hosted from Github, which is a service that allows you to host files and keep versions of those files (this is what I use for the course website.)
If you decide you want to setup your own website, I would recommend following this tutorial on creating Github websites and this book on using git for DH projects.
Publishing Jupyter Notebooks
One of the easiest ways to publish your data analysis is to simply publish a Jupyter notebook. There’s a few ways you can go about publishing these depending on whether you want to embed the notebook in a website or simply have an accessible link.
- The simplest option is to upload your Jupyter notebook to Google Colab https://colab.research.google.com/notebooks/intro.ipynb#recent=true and https://colab.research.google.com/notebooks/welcome.ipynb. You can run your entire notebook in the Google Colab environment and even do your analysis there instead of running it locally.
- You can also use Binder https://mybinder.org/ and https://mybinder.readthedocs.io/en/latest/introduction.html to host notebooks that you have in a Github repository.
- Lastly, you can convert a Jupyter notebook to HTML https://reproducible-science-curriculum.github.io/publication-RR-Jupyter/02-exporting_the_notebook/index.html either in the terminal or through the web interface. You’ll still need to host your html page, but this can be useful if you have a website and want to have the notebook as a page on the site.
One concern with using Altair and Jupyter notebooks is that it tends to make the file very large since it loads the data into the notebook. The size of file can be a problem for Github, so if you are ending with very large files, check out these instructions from Altair’s documentation https://altair-viz.github.io/user_guide/faq.html#why-does-altair-lead-to-such-extremely-large-notebooks
Publishing Altair Charts
While publishing Jupyter notebooks might be an easier solution, often times you’ll want a different reading experience than the cell format of a notebook. In that case, you can save your Altair charts and embed them on your website.
For general guidelines on saving Altair charts, check out the documentation here https://altair-viz.github.io/user_guide/saving_charts.html
- You can save your Altair chart directly to html with the
chart.save('chart.html')
syntax. You’ll still need to host this html page on a website, but now you only have the chart rather than the entire notebook. - The other option is to download the compiled JSON of the chart and embed it using JavaScript.
You have the option to download the compiled json from your chart by clicking the Open in Vega Editor
.
This will open a new window with the full Vega schema. You can select the export option, which will allow you to download the JSON (you can select either compiled or vega-lite).
The next step is to import this compiled json into your webpage.
<!DOCTYPE html>

<title>Embedding Vega-Lite</title>

In this example from the Vega docs, yourV1spec
is our compiled chart.
So for our purposes we would have the following:
<!DOCTYPE html>

<title>Embedding Vega-Lite</title>

Obviously we haven’t covered much about HTML and haven’t discussed at all JavaScript, but you might recognize some of the syntax inherently.
Our script
tags at the beginning of our webpage are importing the required libraries (just like our import
statements).
In our last script
tag, we’re writing our code. First we are assigning a variable called yourVlSpec
and passing it a string that contains the path to our compiled chart. Then we pass that variable to the class of vegaEmbed
.
// More argument info at https://github.com/vega/vega-embed
vegaEmbed('#vis', yourVlSpec);
This class takes two arguments. The first is where we want the chart displayed (in this case a div
with the id
of vis
). Then the second argument is the variable with the compiled chart.
The benefit of this approach let’s you include other materials on your webpage, like additional text and images, as well as allowing different layouts.
However, using this approach also requires quite a bit of additional work so you may want to start with publishing your notebooks before you start building an actual website.


Set sqlplus system settings and defaults.
Syntax:
SET option value SHO[W] option Options: most of the options listed below have an abbreviated and a long form e.g. APPINFO or APPI will do the same thing APPI[NFO]{ON|OFF|text} Application info for performance monitor (see DBMS_APPLICATION_INFO) ARRAY[SIZE] {15|n} Fetch size (1 to 5000) the number of rows that will be retrieved in one go. AUTO[COMMIT] {OFF|ON|IMM[EDIATE]|n} Autocommit commits after each SQL command or PL/SQL block AUTOP[RINT] {OFF|ON} Automatic PRINTing of bind variables.(see PRINT) AUTORECOVERY [ON|OFF] Configure the RECOVER command to automatically apply archived redo log files during recovery - without any user confirmation. AUTOT[RACE] {OFF|ON|TRACE[ONLY]} [EXP[LAIN]] [STAT[ISTICS]] Display a trace report for SELECT, INSERT, UPDATE or DELETE statements EXPLAIN shows the query execution path by performing an EXPLAIN PLAN. STATISTICS displays SQL statement statistics. Using ON or TRACEONLY with no explicit options defaults to EXPLAIN STATISTICS BLO[CKTERMINATOR] {.|c|OFF|ON} Set the non-alphanumeric character used to end PL/SQL blocks to c CMDS[EP] {;|c|OFF|ON} Change or enable command separator - default is a semicolon (;) COLSEP { |text} The text to be printed between SELECTed columns normally a space. COM[PATIBILITY] {V5|V6|V7|V8|NATIVE} Version of oracle - see also init.ora COMPATIBILITY= You can set this back by up to 2 major versions e.g. Ora 9 supports 8 and 7 CON[CAT] {.|c|OFF|ON} termination character for substitution variable reference default is a period. COPYC[OMMIT] {0|n} The COPY command will fetch n batches of data between commits. (n= 0 to 5000) the size of each fetch=ARRAYSIZE. If COPYCOMMIT = 0, COPY will commit just once - at the end. COPYTYPECHECK {OFF|ON} Suppres the comparison of datatypes while inserting or appending to DB2 DEF[INE] {&|c|OFF|ON} c = the char used to prefix substitution variables. ON or OFF controls whether to replace substitution variables with their values. (this overrides SET SCAN) DESCRIBE [DEPTH {1|n|ALL}][LINENUM {ON|OFF}][INDENT {ON|OFF}] Sets the depth of the level to which you can recursively describe an object (1 to 50) see the DESCRIBE command ECHO {OFF|ON} Display commands as they are executed EMB[EDDED] {OFF|ON} OFF = report printing will start at the top of a new page. ON = report printing may begin anywhere on a page. ESC[APE] {\|c|OFF|ON} Defines the escape character. OFF undefines. ON enables. FEED[BACK] {6|n|OFF|ON} Display the number of records returned (when rows >= n ) OFF (or n=0) will turn the display off ON will set n=1 FLAGGER {OFF|ENTRY|INTERMED[IATE]|FULL} Checks to make sure that SQL statements conform to the ANSI/ISO SQL92 standard. non-standard constructs are flagged as errors and displayed See also ALTER SESSION SET FLAGGER. FLU[SH] {OFF|ON} Buffer display output (OS) (no longer used in Oracle 9) HEA[DING] {OFF|ON} print column headings HEADS[EP] {||c|OFF|ON} Define the heading separator character (used to divide a column heading onto > one line.) OFF will actually print the heading separator char see also: COLUMN command INSTANCE [instance_path|LOCAL] Change the default instance for your session, this command may only be issued when not already connected and requires Net8 LIN[ESIZE] {150|n} Width of a line (before wrapping to the next line) Earlier versions default to 80, Oracle 9 is 150 LOBOF[FSET] {n|1} Starting position from which CLOB and NCLOB data is retrieved and displayed LOGSOURCE [pathname] Change the location from which archive logs are retrieved during recovery normally taken from LOG_ARCHIVE_DEST LONG {80|n} Set the maximum width (in chars) for displaying and copying LONG values. LONGC[HUNKSIZE] {80|n} Set the fetch size (in chars) for retrieving LONG values. MARK[UP] HTML [ON|OFF] [HEAD text] [BODY text] [TABLE text] [ENTMAP {ON|OFF}][SPOOL {ON|OFF}] [PRE[FORMAT] {ON|OFF}] Output HTML text, which is the output used by iSQL*Plus. NEWP[AGE] {1|n} The number of blank lines between the top of each page and the top title. 0 = a formfeed between pages. NULL text Replace a null value with 'text' The NULL clause of the COLUMN command will override this for a given column. NUMF[ORMAT] format The default number format. see COLUMN FORMAT. NUM[WIDTH] {10|n} The default width for displaying numbers. PAGES[IZE] {14|n} The height of the page - number of lines. 0 will suppress all headings, page breaks, titles PAU[SE] {OFF|ON|text} press [Return] after each page enclose 'text' in single quotes RECSEP {WR[APPED]|EA[CH]|OFF} Print a single line of the RECSEPCHAR between each record. WRAPPED = print only for wrapped lines EACH=print for every row RECSEPCHAR {_|c} Define the RECSEPCHAR character, default= ' ' SCAN {OFF|ON} OFF = disable substitution variables and parameters SERVEROUT[PUT] {OFF|ON} [SIZE n] [FOR[MAT] {WRA[PPED]|WOR[D_WRAPPED]|TRU[NCATED]}] whether to display the output of stored procedures (or PL/SQL blocks) i.e., DBMS_OUTPUT.PUT_LINE SIZE = buffer size (2000-1,000,000) bytes SHOW[MODE] {OFF|ON} Display old and new settings of a system variable SPA[CE] {1|n} The number of spaces between columns in output (1-10) SQLBL[ANKLINES] {ON|OFF} Allow blank lines within an SQL command. reverts to OFF after the curent command/block. SQLC[ASE] {MIX[ED]|LO[WER]|UP[PER]} Convert the case of SQL commands and PL/SQL blocks (but not the SQL buffer itself) SQLPLUSCOMPAT[IBILITY] {x.y[.z]} Set the behavior or output format of VARIABLE to that of the release or version specified by x.y[.z]. SQLCO[NTINUE] {> |text} Continuation prompt (used when a command is continued on an additional line using a hyphen -) SQLN[UMBER] {OFF|ON} Set the prompt for the second and subsequent lines of a command or PL/SQL block. ON = set the SQL prompt = the line number. OFF = set the SQL prompt = SQLPROMPT. SQLPRE[FIX] {#|c} set a non-alphanumeric prefix char for immediately executing one line of SQL (#) SQLP[ROMPT] {SQL>|text} Set the command prompt. SQLT[ERMINATOR] {;|c|OFF|ON}| Set the char used to end and execute SQL commands to c. OFF disables the command terminator - use an empty line instead. ON resets the terminator to the default semicolon (;). SUF[FIX] {SQL|text} Default file extension for SQL scripts TAB {OFF|ON} Format white space in terminal output. OFF = use spaces to format white space. ON = use the TAB char. Note this does not apply to spooled output files. The default is system-dependent. Enter SHOW TAB to see the default value. TERM[OUT] {OFF|ON} OFF suppresses the display of output from a command file ON displays the output. TERMOUT OFF does not affect the output from commands entered interactively. TI[ME] {OFF|ON} Display the time at the command prompt. TIMI[NG] {OFF|ON} ON = display timing statistics for each SQL command or PL/SQL block run. OFF = suppress timing statistics TRIM[OUT] {OFF|ON} Display trailing blanks at the end of each line. ON = remove blanks, improving performance OFF = display blanks. This does not affect spooled output. SQL*Plus ignores TRIMOUT ON unless you set TAB ON. TRIMS[POOL] {ON|OFF} Allows trailing blanks at the end of each spooled line. This does not affect terminal output. UND[ERLINE] {-|c|ON|OFF} Set the char used to underline column headings to c. VER[IFY] {OFF|ON} ON = list the text of a command before and after replacing substitution variables with values. OFF = dont display the command. WRA[P] {OFF|ON} Controls whether to truncate or wrap the display of long lines. OFF = truncate ON = wrap to the next line The COLUMN command (WRAPPED and TRUNCATED clause) can override this for specific columns.
The items in Gray on this page are deprecated from Oracle 9 onwards - also note that several of the options above have 'gone missing' from the official documentation set - HELP SET is a more accurate reference.
Get a list of these SET options in sql*plus with the command:
SQLPLUS> HELP SET


Graduate Student in USC
Network Protocols: What has happended when you type "http://www.linkedin.com"
Mar 29, 2018 / Written by Chengyu
Thanks to the powerful search engines, like Google, Bing and Baidu, cyber-citizens find it is quiet convenient to search needed information by simply typing some keywords in the search bar. However, do you know what has happened when you type a famous link, such as “https://www.linkedin.com”? This article explores the “magic” behind the web browser and afterward, you are about to have a better understanding on how to get corresponding context when you type a URL. Read More
How to understand HTTP?
Mar 13, 2018 / Written by Chengyu
The internet permeates into our life nowadays. Thanks to the appearances of various web-related applications, everyone could visit website conveniently. However, even though the majority of websites starting with http(s), only a few cybercitizens know the meaning of this. In order to get a better understanding of HTTP, this article explain this protocol's mechanism in an easy way.
Definition
Establish MySQL and Node.js environment in Ubuntu Server
Nov 26, 2017 / Written by Chengyu
This article shared my experience in deploying MySQL and Node.js remotelly with SSH Encryption connection and terminal.
1. Connect to the remote server
You can use embedded Terminal in Mac or PuTTY in Windows to connect to the server.
ssh 'username'@'Ip_address'
After you typing so, it will require you to enter the corresponding password if necessary. If you can see the following textfield. Congratulation! You have logged in successfully.
Swift Core Motion
Oct 26, 2017 / Written by Chengyu
This article shared my experience in using the CMMotionManager() in Swift.
1. What is Core Motion? What can we do with this package?
If the developer wants to access to the motion- and environment-related data from the embedded hardware from the iPhone, including the accelerometers, gyroscopes, pedometers, magnetometers and barometers, the core motion library provides corresponding functions. One prevalent example is a game might use accelerometer and gyroscope data.
美国驾照，银行卡补办攻略
May 03, 2017 / Written by Chengyu
本人学生党，来美国也快一年了。由于自己马虎的性格，小到晚饭，大到钱包，除了护照以外什么都丢过 (所幸没丢护照，不然要到局子里坐一坐)。经过一系列的丢失经历我也总结了下补办各种卡的过程，在这里分享给大家，也未以后丢失东西的朋友一个参考（不过最好还是别丢，哈哈）。
Read More


click here to learn more about me on linkedin. :)
Bongani is a programmer by nature. Anything that has anything to do with programming he learns, and performs exceptionally well at it.


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Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.
| Version: |
1.0 |
| Depends: |
R (≥ 3.0.0) |
| Imports: |
grDevices, graphics, methods, stats, utils, aqp, ape, soilDB, igraph, cluster, lattice, latticeExtra, vegan, sp, reshape2, Hmisc, scales, circular, RColorBrewer, plyr, digest |
| Suggests: |
MASS, rgdal, spdep, circlize, rvest, xml2, rgeos, raster |
| Published: |
2016-08-30 |
| Author: |
USDA-NRCS Soil Survey Staff |
| Maintainer: |
Dylan Beaudette <dylan.beaudette at ca.usda.gov> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/ncss-tech/sharpshootR |
| NeedsCompilation: |
no |
| CRAN checks: |
sharpshootR results |


Arrays And Hashes
October 5th, 2014
Arrays and hashes are both ruby objects that are used to store collections of data. They are similar in many ways but there are some key differences.
In an array, each object has a unique key assigned to it. We can access any object in the array using this unique key. The positions in an array starts from “0”. The first element is located at “0”, the second at “1” etc…
animals = [“dog”, “cat”, “bird”, “frog”, “cow”]
In the example above, animals is the name of the array. The elements of the array are strings. You can call each element using it’s index. For example:
animals[0] = “dog”
animals[1] = “cat”
animals[2] = “bird”
animals[3] = “frog”
animals[4] = “cow”
If you wanted to add a new animal to the array, you would assign the next index to the type of animal you wanted to add. For example, to add a lizard you would type:
animals[5] = “lizard”
animals = [“dog”, “cat”, “bird”, “frog”, “cow”, “lizard”]
In a hash, each object is assigned a unique key and a value. Where arrays use integers as it’s index, hashes allow you to use any object type.
- animals = {
- :dog => “Buddy”,
- :cat => “Oscar”,
- :bird => “Tweety”,
- :frog => “Kermit”,
- :cow => “Betsy”,
- }
In this example the key value is a symbol (that is why it is preceded by the ':') of the type of animal, and the value is the name for that particular animal. To get the name of each animal, you can call the key associated with it. For example:
animals[:dog] = “Buddy”
animals[:frog] = “Kermit”
To add a new element to the hash, you would need to assign a new key value pair. For example, if you wanted to add a lizard named Lizzy to your animals hash you would type the following:
animals[:lizard] => "Lizzy"
- animals = {
- :dog => “Buddy”,
- :cat => “Oscar”,
- :bird => “Tweety”,
- :frog => “Kermit”,
- :cow => “Betsy”,
- :lizard => “Lizzy”,
- }
Hopefully this post helps you understand the difference between hashes and arrays a little bit better! Thanks for reading.


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Chao Pan
Project Portfolio
Chao is a Computer Science major student at UCSD where he is taking the course CSE170 and doing lab projects and learning about HCI Design.
Projects
- Splurge Restaurant Android Application
- Habit Tracker Application "Virtue/Vice"
- Retail sales website with working backend


Adam Carlton's Project 4
Use the Arrow keys to move the Ogre!! Under the Red bars signify the areas in which a video will play
Press me for a treat! ;)


The lights are displayed under the Lights tab
on the left hand side of the viewer. Right clicking a highlighted light
or set of highlighted lights will open a menu to manipulate them.
- New [Ctrl-N] will add a light to
the stage. The initial position and focal point of the new
light will be the same as the selected camera but will not
follow the camera.
- Edit Light(s) [Ctrl-E] will open a
dialog for editing the highlighted light(s). The dialog has a
number of tabbed panels for manipulating the light. After making
the desired changes, Apply will apply the changes.
Cancel will restore the settings back to the
last applied values.
- Remove Light(s) [Ctrl-R] will remove
the highlighted light(s) from the stage.
- Select Light [Ctrl-S] will select the
highlight light. The selected light is identified by
bold red text.
The Light menu on the top menu bar is a shortcut for the
selected light. Light > Reset Light will reset the light
to the values of the selected scene in the script.


JavaScript is not enabled, unable to run animation
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001// *************************************************************************************************************************** 002// * Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file * 003// * distributed with this work for additional information regarding copyright ownership. The ASF licenses this file * 004// * to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance * 005// * with the License. You may obtain a copy of the License at * 006// * * 007// * http://www.apache.org/licenses/LICENSE-2.0 * 008// * * 009// * Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an * 010// * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the * 011// * specific language governing permissions and limitations under the License. * 012// *************************************************************************************************************************** 013package org.apache.juneau.http.header; 014 015import java.util.function.*; 016 017import org.apache.juneau.http.annotation.*; 018 019/** 020 * Represents a parsed <l>Content-Range</l> HTTP response header. 021 * 022 *
 023 * Where in a full body message this partial message belongs. 024 * 025 *
Example
 026 *
 027 * Content-Range: bytes 21010-47021/47022 028 *
 029 * 030 *
RFC2616 Specification
 031 * 032 * The Content-Range entity-header is sent with a partial entity-body to specify where in the full entity-body the 033 * partial body should be applied. 034 * Range units are defined in section 3.12. 035 *
 036 * Content-Range = "Content-Range" ":" content-range-spec 037 * content-range-spec = byte-content-range-spec 038 * byte-content-range-spec = bytes-unit SP 039 * byte-range-resp-spec "/" 040 * ( instance-length | "*" ) 041 * byte-range-resp-spec = (first-byte-pos "-" last-byte-pos) 042 * | "*" 043 * instance-length = 1*DIGIT 044 *
 045 * 046 *
 047 * The header SHOULD indicate the total length of the full entity-body, unless this length is unknown or difficult to 048 * determine. 049 * The asterisk "*" character means that the instance-length is unknown at the time when the response was generated. 050 * 051 *
 052 * Unlike byte-ranges-specifier values (see section 14.35.1), a byte- range-resp-spec MUST only specify one range, and 053 * MUST contain absolute byte positions for both the first and last byte of the range. 054 * 055 *
 056 * A byte-content-range-spec with a byte-range-resp-spec whose last- byte-pos value is less than its first-byte-pos 057 * value, or whose instance-length value is less than or equal to its last-byte-pos value, is invalid. 058 * The recipient of an invalid byte-content-range- spec MUST ignore it and any content transferred along with it. 059 * 060 *
 061 * A server sending a response with status code 416 (Requested range not satisfiable) SHOULD include a Content-Range 062 * field with a byte-range- resp-spec of "*". 063 * The instance-length specifies the current length of the selected resource. 064 * A response with status code 206 (Partial Content) MUST NOT include a Content-Range field with a byte-range-resp-spec 065 * of "*". 066 * 067 *
 068 * Examples of byte-content-range-spec values, assuming that the entity contains a total of 1234 bytes: 069 *
 070 * The first 500 bytes: 071 * bytes 0-499/1234 072 * The second 500 bytes: 073 * bytes 500-999/1234 074 * All except for the first 500 bytes: 075 * bytes 500-1233/1234 076 * The last 500 bytes: 077 * bytes 734-1233/1234 078 *
 079 * 080 *
 081 * When an HTTP message includes the content of a single range (for example, a response to a request for a single range, 082 * or to a request for a set of ranges that overlap without any holes), this content is transmitted with a Content-Range 083 * header, and a Content-Length header showing the number of bytes actually transferred. 084 * For example: 085 *
 086 * HTTP/1.1 206 Partial content 087 * Date: Wed, 15 Nov 1995 06:25:24 GMT 088 * Last-Modified: Wed, 15 Nov 1995 04:58:08 GMT 089 * Content-Range: bytes 21010-47021/47022 090 * Content-Length: 26012 091 * Content-Type: image/gif 092 *
 093 * 094 *
 095 * When an HTTP message includes the content of multiple ranges (for example, a response to a request for multiple 096 * non-overlapping ranges), these are transmitted as a multipart message. 097 * The multipart media type used for this purpose is "multipart/byteranges" as defined in appendix 19.2. 098 * See appendix 19.6.3 for a compatibility issue. 099 * 100 *
 101 * A response to a request for a single range MUST NOT be sent using the multipart/byteranges media type. 102 * A response to a request for multiple ranges, whose result is a single range, MAY be sent as a multipart/byteranges 103 * media type with one part. 104 * A client that cannot decode a multipart/byteranges message MUST NOT ask for multiple byte-ranges in a single request. 105 * 106 *
 107 * When a client requests multiple byte-ranges in one request, the server SHOULD return them in the order that they 108 * appeared in the request. 109 * 110 *
 111 * If the server ignores a byte-range-spec because it is syntactically invalid, the server SHOULD treat the request as 112 * if the invalid Range header field did not exist. 113 * (Normally, this means return a 200 response containing the full entity). 114 * 115 *
 116 * If the server receives a request (other than one including an If- Range request-header field) with an unsatisfiable 117 * Range request- header field 118 * (that is, all of whose byte-range-spec values have a first-byte-pos value greater than the current length of the 119 * selected resource), 120 * it SHOULD return a response code of 416 (Requested range not satisfiable) (section 10.4.17). 121 * 122 *
 123 * Note: clients cannot depend on servers to send a 416 (Requested range not satisfiable) response instead of a 200 (OK) 124 * response for 125 * an unsatisfiable Range request-header, since not all servers implement this request-header. 126 * 127 *
See Also:

 128 *
juneau-rest-common 129 *
Hypertext Transfer Protocol -- HTTP/1.1 130 *
 131 * 132 * @serial exclude 133 */ 134@Header("Content-Range") 135public class ContentRange extends BasicStringHeader { 136 137 //----------------------------------------------------------------------------------------------------------------- 138 // Static 139 //----------------------------------------------------------------------------------------------------------------- 140 141 private static final long serialVersionUID = 1L; 142 private static final String NAME = "Content-Range"; 143 144 /** 145 * Static creator. 146 * 147 * @param value 148 * The header value. 149 *
Can be <jk>null</jk>. 150 * @return A new header bean, or <jk>null</jk> if the value is <jk>null</jk>. 151 */ 152 public static ContentRange of(String value) { 153 return value == null ? null : new ContentRange(value); 154 } 155 156 /** 157 * Static creator with delayed value. 158 * 159 *
 160 * Header value is re-evaluated on each call to {@link #getValue()}. 161 * 162 * @param value 163 * The supplier of the header value. 164 *
Can be <jk>null</jk>. 165 * @return A new header bean, or <jk>null</jk> if the value is <jk>null</jk>. 166 */ 167 public static ContentRange of(Supplier<String> value) { 168 return value == null ? null : new ContentRange(value); 169 } 170 171 //----------------------------------------------------------------------------------------------------------------- 172 // Instance 173 //----------------------------------------------------------------------------------------------------------------- 174 175 /** 176 * Constructor. 177 * 178 * @param value 179 * The header value. 180 *
Can be <jk>null</jk>. 181 */ 182 public ContentRange(String value) { 183 super(NAME, value); 184 } 185 186 /** 187 * Constructor with delayed value. 188 * 189 *
 190 * Header value is re-evaluated on each call to {@link #getValue()}. 191 * 192 * @param value 193 * The supplier of the header value. 194 *
Can be <jk>null</jk>. 195 */ 196 public ContentRange(Supplier<String> value) { 197 super(NAME, value); 198 } 199}


BioGraphVisart is a web-based tool to interactively visualize networks, especially with biological background.
It is written in javascript based on Cytoscape.js.
The input is a graphml-file (graphml-example) or sif-file (sif-example).
In addition to nodes and edges (genes and interaction types, respectively), GraphML can store information such as expression values as node attributes (more on GraphML Format).
SIF cannot store additional information (more on SIF Format).
Fig. 1 shows the network graph of the graphml-example. Here the example is displayed in BioGraphVisart.
A more detailed description is given in the Documentation.
Figure 1. The red and blue numbers indicate static sections and interactive sections modifying the representation of network graph, respectively: (1) upload, (2) selection of node color attribute (number, Boolean), (3) node color legend, (4) edge style legend (According to KEGG), (5) collapse multiple edges between two nodes, (6) mouse over node displays name and value, mouse over edge displays interaction type(s), (7) layout selection, (8) highlight boolean attribute by node shape, (9) node shape legend, (10) search node by its label, (11) withdrawal of node deletion, (12) layout reset for user can zoom in and out, relocate individual nodes or entire graph, and (13) highlight KEGG pathways that appear most for displayed genes.
Comparison
For direct Comparison of network graphs the node overlap can be calculated and be displayed in a heatmap. Also a merged network graph of two selected network graphs can be generated.
The input are multiple graphml-files.
Here an example with four network graphs is displayed in BioGraphVisart.


x = gan.graph.x
s = [int(x) for x in net.get_shape()]
shape = [s[1], s[2]]
if(shape[0]>32 or shape[1]>32):
return None
x = tf.image.resize_images(x, shape, 1)
print("Created bw ", x)
x = gan.graph.xfiltered
x = tf.image.resize_images(x, shape, 1)
else:
x = tf.image.resize_images(x, shape, 1)
gan.graph.xfiltered = x
print("set xfiltered", x, shape)
return x


I learned a lot during the last month. While the content of the One Month course was great, the main benefit for me was actually something else: it got me interested in a bunch of topics which I then pursued on my own.
I completed a few freecodecamp, codeschool, frontend masters courses as well as a few youtube playlists about computer science (e.g. cs50 from Harvard). I learned about VPS, installed a LAMP stack myself and coded + uploaded the website you’re reading this on. I even have a github account.
I’ve been working with startups (in marketing) for years and my next goal is to have my own bootstrapped SaaS business. That’s why my goal is to get my coding skills to a level where I can build MVPs myself.
Here are my objectives for the next few months:


Flexhang1357.github.io
SHURE MV51 USER MANUAL Pdf Download. Shure Mx690 Bedienungsanleitung. View and Download Shure MV51 user manual online. MV51 Microphone pdf manual download. SHURE MV5 USER MANUAL Pdf Download. View and Download Shure MV5 user manual online. USB MICROPHONE. MV5 Microphone pdf manual download.


WITNESS LIST Finance Committee April 25, 2003 - 8:00A HB 1 ON: Furgeson, Kelly Manager (Legislative Budget Board), Austin, TX Keel, John Director (Legislative Budget Board), Austin, TX Newton, John Assistant Director (Legislative Budget Board), Austin, TX Norris, Robert Analyst (Legislative Budget Board), Austin, TX Priest, Paul Senior Analyst (Legislative Budget Board), Austin, TX Scallion, Marva Manager (Legislative Budget Board), Austin, TX


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This demo application can either show random tweets based on a sample of the current English twitter feed or you can enter a username and generate random tweets based on a user's timeline. Use the button below to to switch between the views.
On the left you see a status overview of the current assembled Markov chain as well as a random live tweet. On the right you see a list of randomly generated tweets based on the current Markov chain. Although the status only gets updated with an interval of 1 second the feed into the chain is constant.
In this view you can generate random tweets based on the timeline of the user. Input a valid twitter username and press 'Go' or click one of the users in the 'history' list. Unlike the random sameple feed the timeline of the user is only downloaded once.


A proprieda de overflow especifica quando o conteúdo de um elemento de nível de bloco deve ser cortado, exibido barra de rolagem ou se transborda do elemento
Utiliza-se a propriedade overflow quando um elemento for maior que o elemento que ocontémVisible: É o valor padrão e define que o conteúdo não será cortado se exceder o tamanho doelemento
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
Hidden: O conteúdo excedido não ficará visível
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
Scroll: O conteúdo excedido será cortado mas haverá uma barra de rolagem para permitir a visualização completa domesmo.
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
Auto: A barra de rolagem será adicionada automaticamente se o conteúdo exceder o tamanho doelemento.
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000


You have no geotagged Videos yet!
Some cameras (and most smartphones) save the location where a video was taken. When we detect that location, we'll add the video to your map.
Right now, none of your videos have location data.
Coming soon:
Add to Map


| HOME | DOWNLOAD | DOCUMENTATION | FAQ |
> Home > Documentation > Fortran 95 > Modules
PlanetsConstants
SHTOOLS Module containing planetary constants.
Description
PlanetsConstants
defines several constants that are useful when working with gravity and topography data of the Earth, Mars, Venus, and Moon. All units are SI. Confer with the Python documentation or fortran source file for exact values and references.
Constants
Fundamental Constants
Grav_constant
- Gravitational Constant.
pi_constant
- Pi.
mu0_constant
- Magnetic constant.
The Moon
R_Moon
- Mean radius of the Moon.
GM_Moon
- Gravitational constant times the mass of the Moon.
Mass_Moon
- Mass of the Moon.
rho_bar_Moon
- Average density of the Moon.
a_Moon
- Semi-major axis of the lunar orbit.
g0_Moon
- Mean gravitational acceleration of the Moon at the mean surface radius (R_Moon), not including rotation.
R0_pot_Moon
- Reference radius of the gravitational-potential models of the Moon.
Omega_Moon
- Angular rotation rate of the Moon.
C_MOI_Moon
- Polar moment of inertia of the Moon, using R=1738 km.
I_MOI_Moon
- Average moment of inertia of the Moon, using R=1738 km.
Gamma_MOI_Moon
- Libration parameter of the Moon, (B-A)/C.
Beta_MOI_Moon
- Libration parameter of the Moon, (C-A)/B.
Mars
R_Mars
- Average radius of Mars.
GM_Mars
- Gravitational constant times the mass of the Mars.
Mass_Mars
- Mass of Mars.
rho_bar_Mars
- Average density of Mars.
g0_Mars
- Gravitational acceleration of Mars at R_Mars, not including rotation.
R0_pot_Mars
- Reference radius of the gravitational-potential models of Mars.
Omega_Mars
- Angular rotation rate of Mars.
f_Mars
- Topographic flattening of Mars.
a_Mars
- Semi-major axis radius of Mars.
b_Mars
- Semi-minor axis radius of Mars.
W0_mars
- Reference potential of Mars.
Venus
R_Venus
- Average radius of Venus.
R0_pot_Venus
- Reference radius of the gravitational-potential models of Venus.
GM_Venus
- Gravitational constant times the mass of the Venus.
Mass_Venus
- Mass of Venus.
Omega_Venus
- Angular rotation rate of Venus.
rho_bar_Venus
- Average density of Venus.
g0_Venus
- Gravitational acceleration at R_Venus, not including rotation.
Earth
GM_Earth
- Gravitational constant times the mass of the Earth.
R0_pot_Earth
- Reference radius of the terrestrial gravitational-potential models.
Mass_Earth
- The mass of the Earth.
WGS84_a
- The semi-major axis of the WGS84 ellipsoid.
WGS84_b
- The semi-minor axis of the WGS84 ellipsoid.
WGS84_r3
- The radius of a sphere of Earth’s volume.
WGS84_f
- The flattening of the WGS84 ellipsoid.
WGS84_gm
- The adopted GM of the WGS84 model, which includes the atmosphere.
WGS84_gma
- The GM of the atmosphere adopted by the WGS84 model.
WGS84_omega
- The adopted angular rotation rate of the Earth of the WGS84 model.
WGS84_U0
- The theoretical normal potential associated with the WGS84 model.
Mercury
R_Mercury
- Average radius of Mercury.
GM_Mercury
- Gravitational constant times the mass of the Mercury.
Mass_Mercury
- Mass of Mercury.
R0_pot_Mercury
- Reference radius used for the spherical harmonic gravity solutions.
Omega_Mercury_orbit
- Angular rotation rate of Mercury about the Sun.
Omega_Mercury_spin
- Angular rotation rate of Mercury.
rho_bar_Mercury
- Average density of Mercury.
g0_Mercury
- Gravitational acceleration at R_Mercury, not including rotation.
See also
shtools
> Home > Documentation > Fortran 95 > Modules
| Laboratoire Lagrange | Observatoire de la Côte d'Azur | © 2017 SHTOOLS |


public class SimulationConfigurations
extends java.lang.Object
| Modifier and Type | Field | Description |
|---|---|---|
static int |
ABILITY_MAX_BOUND |
Maximum ability of the pairs Horse/Jockey.
|
static int |
ABILITY_MIN_BOUND |
Minimum ability of the pairs Horse/Jockey.
|
static int |
MONEY_MAX_BOUND |
Maximum amount of money a Spectator could have.
|
static int |
MONEY_MIN_BOUND |
Minimum amount of money a Spectator could have.
|
static int |
NUMBER_OF_PAIRS_HORSE_JOCKEY |
Number of Pairs Horse/Jockey to compete on the simulation.
|
static int |
NUMBER_OF_RACES |
Number of races of a simulated event.
|
static int |
NUMBER_OF_SPECTATORS |
Number of Spectators to attend these events.
|
static int |
NUMBER_OF_TRACKS |
Number of tracks on the racing track.
|
static boolean |
ONLY_LOG_ON_FILE |
Flag to control if user wants to have colored information about the system's status on the
stdout and
simple one on file (behavior activated if false ); otherwise, with true , only on file, without
colors. |
static int |
TRACK_DISTANCE_MAX_BOUND |
Maximum distance of a race track.
|
static int |
TRACK_DISTANCE_MIN_BOUND |
Minimum distance of a race track.
|
| Constructor | Description |
|---|---|
SimulationConfigurations() |
public static final int NUMBER_OF_PAIRS_HORSE_JOCKEY
public static final int NUMBER_OF_SPECTATORS
public static final int NUMBER_OF_RACES
public static final int NUMBER_OF_TRACKS
public static final int MONEY_MAX_BOUND
public static final int MONEY_MIN_BOUND
public static final int ABILITY_MAX_BOUND
public static final int ABILITY_MIN_BOUND
public static final boolean ONLY_LOG_ON_FILE
stdout
and
simple one on file (behavior activated if false
); otherwise, with true
, only on file, without
colors.public static final int TRACK_DISTANCE_MAX_BOUND
public static final int TRACK_DISTANCE_MIN_BOUND


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I recently ran into the altogether common problem of the Html.DropDownList helper rendering a drop down list with no value selected. This is a major problem when editing data as by default, the first value is selected and saving would mean the first value is used.
There have been a few issues resulting in the same error. My issue was that I was setting the Name of the drop down list to be equal to the property on my model. I was using the Entity Framework, and had an Image class with a navigation property called Category. I was using this to render the ddl:
<%= Html.DropDownList("Category", (IEnumerable<SelectListItem>)ViewData["categories"])%>
In my controller, I was setting the ViewData like this:
this.ViewData["categories"] = new SelectList(db.CategorySet.ToList(), "CategoryId", "Title", img.CategoryReference.EntityKey);
Unfortunately, even though I had set the selected value (third parameter to the SelectList constructor), the ddl had no value selected.
The fix was quite simple:
<%= Html.DropDownList("CategoryId", (IEnumerable<SelectListItem>)ViewData["categories"])%>
I just changed the Name of the drop down and handled the assignment in the controller.
The reason behind this problem is that asp.net MVC first looks for a match between the name of the drop down and a property on the model. If there’s a match, the selected value of the SelectList is overridden. Changing the name of the drop down is all it takes to remedy the issue.
Hope that helps.


GUI.SetEchoChar
Syntax
GUI.SetEchoChar (widgetID : int, echoChar : char)
Description
The GUI.SetEchoChar procedure is used with text fields,
especially when using a text field to input a password.
When the GUI.SetEchoChar is called with a text field,
any character entered into the text field will appear as the
character specified by inputChar.
This allows you to use the text field to enter a password.
The characters that the user types will be echoed with the
character specified by inputChar (often an asterisk).
Example
This program displays a text field. As the user enters
characters, each character is represented as a '*'. When
the user enters return, the program exits after displaying
the actual text entered into the text field.
import GUI
procedure EchoString (s : string)
put "You entered \"", s, "\""
GUI.Quit
end EchoString
var tf := GUI.CreateTextField (10, 100, 100, "", EchoString)
GUI.SetEchoChar (tf, '*')
loop
exit when GUI.ProcessEvent
end loop
Details
Note that the echoChar argument to GUI.SetEchoChar
must be a character, not a string. This means the character
should be enclosed in single quote marks ('), not double
quotes (").
Status
Exported qualified.
This means that you can only call the function by calling
GUI.SetEchoChar, not by calling SetEchoChar.


Available cards:
{{card.name | wordfilter}}
Type: {{card.type}}
Mana cost: {{card.manaCost}}
Health: {{card.health}}
Attack: {{card.attack}}
Description:
Add card for deck
Cards for deck:
{{card.name | wordfilter}}
Type: {{card.type}}
Mana cost: {{card.manaCost}}
Health: {{card.health}}
Attack: {{card.attack}}
Description:
Remove card from deck


I am not an artist, and my profession plays an important role in this project: I am a software developer and work in the field of language technologies. One could then say that my main interest is language, as in both programming and natural languages.
The project draws inspiration from a book about creative writing that stands out for its engineering-like approach: Grammatica della Fantasia ("The Grammar of Fantasy") by Gianni Rodari. Rodari encourages learning through play and proposes a series of exercises that encourage both adults and children to play with words. The goal is, as the author explains, to make everyone, regardless their social status, able to use language in all sorts of ways, including creatively. "Not so that they all become artists", he writes, "but so that nobody becomes a slave"¹.
In my opinion, this reasoning can be applied to any art form, and anyone can benefit from practicing one. With this project, then, it is my objective to learn to work with photography systematically, and speaking of systematicity another literary work comes to mind: Raymond Queneau's Exercices de Style² ("Exercices in Style"), where the same story is transformed and told in 99 different ways.
The word transform is important, because my "exercises in style" of choice are achitectural transformations. Photographs, showing non-touristic builings and landmarks in Gothenburg, are taken in pairs: an experimental picture, where the subject is transformed, and a "postcard" or reference picture.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113489-new-members/topic/dual.20spaces.html ---
Hello everyone, I am Fabian Glöckle, masters student at University of Freiburg, Germany. I just got excited for formal theorem proving, know some (graduate level) number theory and sheaf theory and will be happy to learn and join in the movement! Currently, I am working with @Johan Commelin on a small file about dual vector spaces.
Last time Johan mentioned dual vector spaces he seemed pretty depressed. Is it better now? It's a pretty important topic
Yes, we could prove that for finite dimensional vector spaces, a basis induces an isomorphism from the vector space to its dual. The proofs are a bit lengthy though and may need some refactoring before going to the library. I think we should also include the canonical injection into the bidual space into the file, this yet has to be done.
It seemed to me that for finite-dimensional spaces, people often think of a basis as a sequence (so order matters), whereas for infinite-dimensional spaces they often think of a basis as a subset. The behaviour of dual spaces depends on whether the space is finite-dimensional or not -- you have basic observations about the dual space which work in general, but then these extra facts coming from the fact that duality is an equivalence of categories for the category of finite-dimensional spaces but this is no longer true for general spaces. So will you make the general theory for general spaces and then put extra theorems in under a finite-dimensionality hypothesis? I guess this is the way to go. Is there an ordered_basis
for a finite-dimensional space? The dual of a finite-dimensional space with an ordered basis naturally gets an ordered basis -- usually called "the dual basis" but now I see it could really be called the dual ordered basis.
Yes, we are aiming at both the basis-free theorems that work in general and the dual basis approach in the finite-dimensional case, and their relationship.
If mathlib currently does not make use of ordered bases, we will not start that now; however, there is the map taking a basis vector to its corresponding dual basis vector (which could then be used to transport an order on the first onto the latter).
One thing I would like to look into is porting the stuff about bases to families of elements rather than subsets of the vector space. You get ordered bases for free this way if the index type happens to be ordered
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


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Jose Mourinho claims Tottenham can win PL next season but not this term
- Jose Mourinho spoke on a number of issues in an interesting first presser as Spurs manager
- The tactician admitted Spurs were not title contenders this season but expect to mount some competition next year
- The Special One also admitted he made mistakes in his past jobs and would try to “make new mistakes”
New Tottenham manager Jose Mourinho has ruled out his own club from much success this season in his very first media interaction since his appointment.
Mourinho was in attendance at a press conference on Thursday, November 21, ahead of Tottenham’s trip to the London Stadium to take on Westham on Saturday, November 23.
In an interaction that was highly anticipated by fans across the world, the Special One did not disappoint as he openly discussed a wide range of subjects, including what people should expect from the club this season.
Spurs are currently in 14th place, a staggering 11 points from Manchester City in fourth, and Mourinho was quick to admit their title chances this season was pretty much none existent.
“We will not fight for the title this year. But next season we will definitely try,” he explained.
The Portuguese also spoke on his previous stints where he was quick to admit that he might have made some mistakes in the way he handled problems and players.
He, however, vowed to do things a bit differently at the Tottenham, saying he was ready to make “new mistakes”.
"I realized I made mistakes and I'm not going to make the same mistakes.
“I'll make new mistakes - but not the same," he said.
Efforts from journalists to get him to break down the mistakes he was talking about were fruitless.
Mourinho was appointed new Spurs manager barely hours after the sacking of Mauricio Pochettino.
Speaking on Pochettino’s sacking, the new Spurs manager attempted to show some respect by saying the club will always be the Argentine’s home.
"I have to congratulate him (Pochettino) on the incredible job he did. I share with you what we shared inside. This club will always be his home, this training ground his training ground. He is always welcome here," he said.
One interesting fact about Mourinho is that he has never managed a club for more than four years, in what fans have come to refer as the “Third Season Syndrome”.
On whether joining Spurs was the right move, he tried to be politically correct by insisting he had joined a big football club with loads of quality and “structure”.
“Deep inside, I’m very very happy and I know that my option is correct. I’m enjoying a big football club with great structure and organization. I’m really just focused on my job and players and not with everything that surrounds me,” he said.
Journalists would not let Mourinho leave without bringing up some of his old viral sentiments and the most popular one was his brutal words in 2015 where he dismissed the possibility of ever managing Spurs.
The Portuguese in 2015 said he would never manage Tottenham because he had too much love for Chelsea but when asked what had changed in the years, he simply said, “I was sacked”.
Mourinho also gave Spurs fans a reminder of his credentials by responding to a question about Tottenham losing the Champions League final last season with: "I don't know because I never lost a Champions League final."


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