This is the same as the basic example, but ValidateJS.enableActiveChecking is set to true, meaning fields will be validated on blur.
ValidateJS.enableActiveChecking
true
validation.js example by Andrew Berls. Project on GitHub.


Hello world!
I DID NOT PLAGARIZE THIS WEBSITE
Stop questioning me and just hit the link already...
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This project started out from a personal need: a quick and easy tool to split grocery bills.
My best friend and I live in the same apartment, so we would go grocery shopping together every weekend. The receipts would consist of food that we both share such as ground beef and tomatoes, and food that only belongs to one of us like minted Ben & Jerry ice-cream (Totally recommended if you haven't tried). At the end of each trip, we would always have a receipt marked with three different colors: pink for shared food, orange for her food only, and yellow for my food only. Our receipts looked colorful, but they waste a lot of our time. We need a better way to quickly calculate how much each of us has spent after each grocery trip. That's how this project came to life. I hope this tool will not only help us but many others splitting money faster and more accurately. Thank you for reading!
You can reach out to me at<EMAIL_ADDRESS>Enjoy your day!


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Quick Tour¶
A console script entry point is installed with the package: sparsify
.
This enables easy interaction through your console/terminal.
Note, for some environments the console scripts cannot install properly.
If this happens for your system and the sparsify
command is not available,
scripts/main.py
may be used in its place. Documentation is provided in the
script file.
To launch Sparsify locally, open up a console or terminal window and enter the following:
sparsify
The Sparsify server will begin running locally on the machine and can be accessed through a web browser.
The default host:port Sparsify starts on is <IP_ADDRESS>:5543
.
Therefore, after starting Sparsify with the default commands, you may enter the following into a web browser to begin using Sparsify: http://<IP_ADDRESS>:5543
.
If you are running Sparsify on a separate server from where the web browser is located,
then you will need to substitute in the proper IP address for that server in place of <IP_ADDRESS>
.
Additionally, confirm that the networking rules on your server allow for access to port 5543.
After visiting http://<IP_ADDRESS>:5543
in a web browser, the home page for Sparsify will load if configured correctly:
A quick start flow is given below. For a more in-depth read, check out the Sparsify User Guide.
New Project¶
To begin optimizing a model, a new project must be created. The New Project button is located in the lower right of Sparsify’s home screen. After clicking, the create project popup will be displayed:
Sparsify only accepts ONNX model formats currently. To easily convert to ONNX from common ML frameworks, see the SparseML repository.
To begin creating a project use one of the following flows:
Upload your model file through the browser by clicking on
Click to browse
.Download your model file through a public URL by filling in the field
Remote Path or URL
.Move your model file from an accessible file location on the server by filling in the field
Remote Path or URL
.
Continue through the popup and fill in information as specified to finish creating the project.
Analyzing a Model¶
After model creation, optimization sensitivity analysis for the model are shown under the Performance Profiles
and Loss Profiles
in the left navigation.
The profiles will show the effects that different types of optimizations and degrees of those optimizations have on both the models inference speed and the baseline loss.
Performance Profiles:
Loss Profiles:
Optimizing a Model¶
Click on the Optimization
in the left navigation or the Start Optimizing
button on the analyzing pages to begin optimizing your model. After clicking, the optimization creation popup will be displayed:
Fill in the information as required in the modal. Once completed, Sparsify’s autoML algorithms will choose the best settings it can find for optimizing your model. The resulting recipe will be displayed along with estimated metrics for the optimized model. The recipe can then be further edited if desired:
Exporting a Recipe¶
Currently Sparsify is focused on training-aware optimizations; these allow much better loss recovery for a given target performance. A future release will enable the option of one-shot optimizations with limited to no retraining.
Given that the optimization recipe is created with training-aware optimizations, it must be exported for inclusion in your original training pipeline using SparseML. SparseML enables this inclusion with only a few lines of code for most training workflows.
On the optimization page, click the Export
button in the bottom right.
This will open up the export popup:
Select the framework the model was originally trained in on the upper right of the popup. Once selected, either copy or download the recipe for use with SparseML. In addition, some sample code using SparseML is given to integrate the exported optimization recipe.


Text Classification API
Analytics Zoo provides pre-defined models having different encoders that can be used for classifying texts.
Highlights
- Easy-to-use models, could be fed into NNFrames or BigDL Optimizer for training.
- The encoders we support include CNN, LSTM and GRU.
Build a TextClassifier model
Scala
val textClassifier = TextClassifier(classNum, tokenLength, sequenceLength = 500, encoder = "cnn", encoderOutputDim = 256)
classNum
: The number of text categories to be classified. Positive integer.tokenLength
: The size of each word vector. Positive integer.sequenceLength
: The length of a sequence. Positive integer. Default is 500.encoder
: The encoder for input sequences. String. "cnn" or "lstm" or "gru" are supported. Default is "cnn".encoderOutputDim
: The output dimension for the encoder. Positive integer. Default is 256.
Python
text_classifier = TextClassifier(class_num, token_length, sequence_length=500, encoder="cnn", encoder_output_dim=256)
class_num
: The number of text categories to be classified. Positive int.token_length
: The size of each word vector. Positive int.sequence_length
: The length of a sequence. Positive int. Default is 500.encoder
: The encoder for input sequences. String. 'cnn' or 'lstm' or 'gru' are supported. Default is 'cnn'.encoder_output_dim
: The output dimension for the encoder. Positive int. Default is 256.
Train a TextClassifier model
After building the model, we can use BigDL Optimizer to train it (with validation) using RDD of Sample.
Note that raw text data may need to go through tokenization and vectorization before being fed into the Optimizer. You can refer to the examples we provide for data pre-processing.
Scala
val optimizer = Optimizer(
model = textClassifier,
sampleRDD = trainRDD,
criterion = ClassNLLCriterion[Float](logProbAsInput = false),
batchSize = 128)
optimizer
.setOptimMethod(new Adagrad(learningRate = 0.01, learningRateDecay = 0.001))
.setValidation(Trigger.everyEpoch, valRDD, Array(new Top1Accuracy), 128)
.setEndWhen(Trigger.maxEpoch(20))
.optimize()
Python
optimizer = Optimizer(
model=text_classifier,
training_rdd=train_rdd,
criterion=ClassNLLCriterion(logProbAsInput=False),
end_trigger=MaxEpoch(20),
batch_size=128,
optim_method=Adagrad(learningrate=0.01, learningrate_decay=0.001))
optimizer.set_validation(
batch_size=128,
val_rdd=val_rdd,
trigger=EveryEpoch(),
val_method=[Top1Accuracy()])
Do prediction
After training the model, it can be used to predict probabilities or class labels.
Scala
// Predict for probability distributions.
val results = textClassifier.predict(rdd)
// Predict for class labels. By default, label starts from 0.
val resultClasses = textClassifier.predictClasses(rdd)
Python
# Predict for probability distributions.
results = text_classifier.predict(rdd)
# Predict for class labels. By default, label starts from 0.
result_classes = text_classifier.predict_classes(rdd)
Examples
We provide an example to train the TextClassifier model on 20 Newsgroup dataset and uses the model to do prediction.
See here for the Scala example.
See here for the Python example.


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Lets pretend this is a really good article all about how great Apple is and how much we love the iPhone...
Lorem ipsum dolor sit amet, consectetuer adipiscing elit. Maecenas sit amet metus. Nunc quam elit, posuere nec,
auctor in, rhoncus quis, dui. Aliquam erat volutpat. Ut dignissim, massa sit amet dignissim cursus, quam lacus feugiat.
Lets pretend this is a really good article all about how great coffee is and how much programmers need it to live...
Lorem ipsum dolor sit coffee, consectetuer coffee elit. Maecenas sit amet metus. coffee quam elit, posuere nec,
auctor in, rhoncus coffee, dui. Aliquam erat volutpat. Ut coffee, massa sit amet coffee cursus, quam coffee feugiat.
Mockingbird is a JavaScript Twitter widget that displays related tweets based on the content of the page it lives on.
Most of the content on this sample page is just dummy data. However, there are a few key terms Mockingbird should pickup on. If everything works as expected you should see tweets relating to any of the following topics; the iPhone, Twitter, Yahoo!, coffee, Mockingbird, etc. If you sit on this page for a while you'll notices the tweets refresh every 60 seconds or so.
Loading Related Tweets...
Download Mockingbird from Github here.


What is new in Ring 1.10?¶
In this chapter we will learn about the changes and new features in Ring 1.10 release.
List of changes and new features¶
Ring 1.10 comes with the next features!
- Chess Game
- Minesweeper Game
- Knight Tour Game
- Game of Life Game
- Pong Game
- Snakes and Ladder Game
- More Games
- Ring Extension for Visual Studio Code
- The Ring Package Manager (RingPM)
- Better Tests
- More Improvements
Minesweeper Game¶
The objective of the game is to clear a rectangular board containing hidden “mines” or bombs without detonating any of them.
Knight Tour Game¶
Move to every square on the chess board, using only the moves of a knight.
Can you visit every square in just 63 moves?
Game of Life Game¶
The game is a zero-player game, meaning that its evolution is determined by its initial state, requiring no further input.
One interacts with the Game of Life by creating an initial configuration and observing how it evolves, or, for advanced players, by creating patterns with particular properties.
Pong Game¶
Simple “tennis like” game features two paddles and a ball.
The goal is to defeat your opponent once the opponent misses a ball.
More Games¶
The next games are added to the application folder
- Lights Out Game
- Dots and Boxes Game
- Magic Four Game
- Sum Puzzle Game
The next screen shot for the Sum Puzzle Game
Ring Extension for Visual Studio Code¶
This extension support the Ring programming language in Visual Studio Code
The Ring Package Manager (RingPM)¶
Ring comes with a package manager (RingPM) that we can use to install, update and remove packages.
The Package Manager uses Semantic Versioning to check compatibility between packages
The Package Manager comes with the next options
===========================================================================
Usage : ringpm [command]
Command : search [keywords...]
Command : refresh : Update the Registry (Packages List)
Command : install [ <packagename> [from <UserName>] [branch <branchname>] ]
Command : list [-u : Check updates]
Command : run [packagename]
Command : update <packagename>
Command : remove <packagename>
Command : format : Delete All Packages
Command : new <packagename>
Command : package : Create package in the current folder
===========================================================================
More Improvements¶
- Ring Compiler : Better support for (Operator Operator) to avoid checking the first operator when it’s a literal.
- Ring Compiler : When we load a file that doesn’t exist, display the caller file name in the error message.
- Ring Compiler : Support source code files with one line of comment without end of line.
- Ring Compiler : change nNoAssignment attribute in Parser Structure to nNewObject.
- Ring VM : Better support for the (Return) command inside braces that access new objects.
- Ring VM : Dir() Function - Don’t add ”.” and ”..” to the output
- Ring VM : Dir() Function - Correct output for the item type (file|directory) on Linux and macOS.
- Ring VM : ICO_LISTSTART - Clean pVM->aSetProperty when setting an object attribute.
- Ring VM : ICO_NEWOBJECT - Clean pVM->aSetProperty when setting an object attribute.
- Ring VM : Better code for Setter and Getter methods support.
- Ring2EXE: The libraries information are stored in separated files in ring/ring2exe/libs folder.
- WebLib : Separate the library to many source code files.
- StdLib : IsVowel() function - Better Code.
- RingQt : Count(), Left(), Mid() and Right() methods are added to QString class.
- Better Read Me File


tracklib.analysis.bild¶
tracklib.analysis.bild.amis¶
Implementation of AMIS for posterior sampling at fixed k
This module provides the implementation of the AMIS (Cornuet et al. 2012) sampling scheme for BILD,
at a fixed number of switches k
in the profile. The FixedkSampler
class
provides the full interface, while the other functions in the module define the
sampling scheme.
- We parametrize the binary profiles as
(s, theta)
where s is a vector of interval lengths, as fraction of the whole trajectory. It thus has dimension
k+1
and satisfiessum(s) = 1
, as well ass_i > 0 forall i
.theta is the looping state of the first interval. Since the profiles are binary, this determines the state of all subsequent intervals. Clearly
θ in {0, 1}
.
The conversion from the parametrization (s, θ)
to discretely sampled
profiles is according to the following scheme:
profile: θ p p p p p
trajectory: -----x-----x-----x-----x-----x-----x
position: 0.0 0.2 0.4 0.6 0.8 1.0
example (s): ==0.25==|=====0.5=====|==0.25==
switch positions: |(0.25) |(0.75)
conversion: |<<<<<|<<<<<|<<<<<|<<<<<|<<<<<
result (θ=0): 0 0 ' 1 1 ' 0 0
Note that the conversion step amounts to a simple floor()
operation on the
switch positions. We chose this conversion over the maybe more “natural” one of
rounding to the nearest integer because it guarantees
that a uniformly distributed continuous switch position will still be uniformly distributed over its possible discrete positions.
conservation of switches: a switch at .99 would disappear from the profile with the rounding scheme. Importantly, note that in either case an interval shorter than the framerate will likely not be represented in the profile, and thus switches are not always conserved. However, if such profiles are the main contribution to the evidence for this number of switches, we should (and do!) just choose the appropriate lower number of switches instead.
-
tracklib.analysis.bild.amis.
st2profile
(s, theta, traj)¶ Convert AMIS parameters (s, θ) to Loopingprofile for given trajectory
- Parameters
theta (s,) –
traj (Trajectory) – needed to determine discretization. In fact, only
len(traj)
is evaluated.
- Returns
Loopingprofile
-
tracklib.analysis.bild.amis.
dirichlet_methodofmoments
(ss, normalized_weights)¶ Method of moments estimator for the Dirichlet distribution
- Parameters
ss ((n, k+1) np.ndarray, dtype=float) – the switch positions
s
for a sample of sizen
normalized_weights ((n,) np.ndarray, dtype=float) – the associated weights for all samples. Should satisfy
np.sum(normalized_weights) = 1
.
- Returns
(k+1,) np.ndarray – the concentration parameters for the fitted Dirichlet distribution
Notes
We use the method of moments, because it is straight-forward to apply to a weighted sample. Given an ensemble of switch positions
s
, we havethe mean positions
m = <s>
the variances
v = <(s-m)²>
from this we can find the total concentration as
A = mean(m*(1-m)/v) - 1
thus yielding the final estimate of
α = A*m
.
See also
-
tracklib.analysis.bild.amis.
logL
(params)¶ Evaluate the model likelihood for a given profile
The argument to this function is an aggregate of all the necessary information, such that we can evaluate it in parallel (see
calculate_logLs
). Its structure isparams = ((s, theta), (traj, model))
.- Parameters
theta (s,) –
traj (Trajectory) –
model (MultiStateModel) –
- Returns
float
See also
-
tracklib.analysis.bild.amis.
calculate_logLs
(ss, thetas, traj, model)¶ Evaluate the likelihood on an ensemple of profiles
- Parameters
ss ((n, k+1) np.ndarray, dtype=float) –
thetas ((n,) np.ndarray, dtype=int) –
traj (Trajectory) –
model (MultiStateModel) –
- Returns
(n,) np.ndarray, dtype=float – the likelihoods associated with each profile
Notes
This function is parallel-aware (ordered). See
tracklib.util.parallel
-
tracklib.analysis.bild.amis.
proposal
(a, m, ss, thetas)¶ Evaluate the proposal distribution at a given point
- Parameters
m (a,) – the parameters for the proposal distribution (Dirichlet(α) x Bernoulli(m))
ss ((n, k+1) np.ndarray, dtype=float) –
thetas ((n,) np.ndarray, dtype=int) –
- Returns
float
See also
-
tracklib.analysis.bild.amis.
sample_proposal
(a, m, N)¶ Sample from the proposal distribution
- Parameters
m (a,) – the parameters for the proposal distribution (Dirichlet(α) x Bernoulli(m))
N (int) – size of the sample
- Returns
ss ((N, k+1) np.ndarray, dtype=float) – the switch positions for each sample
thetas ((N,) np.ndarray, dtype=int) – the initial state for each sample
See also
-
tracklib.analysis.bild.amis.
fit_proposal
(ss, thetas, weights)¶ Fit the proposal distribution to a weighted sample
- Parameters
ss ((n, k+1) np.ndarray, dtype=float) –
thetas ((n,) np.ndarray, dtype=int) –
weights ((n,) np.ndarray, dtype=float) – the weights associated with the sample
(ss, thetas)
- Returns
a, m ((k+1,) np.ndarray, dtype=float; float in [0, 1]) – the parameters for the proposal distribution (Dirichlet(α) x Bernoulli(m))
See also
-
exception
tracklib.analysis.bild.amis.
ExhaustionImpractical
¶ Bases:
ValueError
-
class
tracklib.analysis.bild.amis.
FixedkSampler
(traj, model, k, N=100, concentration_brake=0.01, polarization_brake=0.001, max_fev=20000)¶ Bases:
object
Running the AMIS scheme for a fixed number of switches k
The most important method is
step
, which performs one AMIS iteration, i.e. it samplesN
profiles from the current proposal and updates the weights and proposal parameters accordingly.- Parameters
traj (Trajectory) – the trajectory we are sampling for
model (MultiStateModel) – the model to use (defines the likelihood)
k (int) – the number of switches for this run
N (int, optional) – the sample size for each AMIS step
concentration_brake (float, optional) – limits changes in the total concentration of the Dirichlet proposal by constraining
|log(new_concentration/old_concentration)| <= N*concentration_brake
, whereconcentration = sum(α)
.polarization_brake (float, optional) – limits changes in the polarization (Bernoulli part of the proposal) by constraining
|m_new - m_old| <= N*polarization_brake
.max_fev (int, optional) – limit on likelihood evaluations. If this limit is reached, the sampler will go to an “exhausted” state, where it does not allow further evaluations. Will be rounded down to integer multiple of
N
.
-
traj, model
setup for the sampling. See Parameters
- Type
-
k, N
see Parameters
- Type
int
-
brakes
¶ see Parameters
- Type
(concentration, polarization)
-
max_fev
¶ see Parameters
- Type
int
-
samples
¶ list of samples. Each samples is a list
[ss, thetas, logLs, deltas, weights]
where each entry is an(N, ...) np.ndarray
.ss
andthetas
define theN
sample points,logLs
are the associated likelihoods,deltas
are auxiliary variables for AMIS, andweights
are the associated weights for each point (updated at eachstep
).- Type
list
-
parameters
¶ proposal parameters for the samples in
samples
, as tuple(a, m)
.- Type
[((k+1,) np.ndarray, float)]
-
evidences
¶ estimated evidence at each step. Similar to
samples
andparameters
, this is a list with an entry for eachstep
. The entries are tuples of log-evidence, standard error on the log-evidence, and Kullback-Leibler divergenceD_KL( proposal || posterior )
of proposal on posterior. The latter can come in handy in interpreting convergence.- Type
[(logE, dlogE, KL)]
-
max_logL
¶ maximum value of the likelihood. Used internally to prevent overflows.
- Type
float
-
logprior
¶ value of the uniform prior over profiles. Used internally.
- Type
float
-
fix_exhaustive
()¶ Evaluate by exhaustive sampling of the parameter space
- Raises
ValueError – if parameter space is too big to warrant exhaustive sampling. Currently this means
self.k >= 2
.
Notes
Since in this case the evidence is exact, its standard error should be zero. To avoid numerical issues, we set
dlogev = 1e-10
.
-
step
()¶ Run a single step of the AMIS sampling scheme
- Returns
bool – whether the sample was performed.
False
if sampler is exhausted,True
otherwise.
-
t_stat
(other)¶ Calculate separation (by evidence) from another sampler
Inspired by the t-statistic from frequentist inference, we use the separation score
(logev - other_logev) / sqrt( dlogev² + other_dlogev² )
.- Returns
float
-
MAP_profile
()¶ Give the current MAP estimate
- Returns
Loopingprofile
tracklib.analysis.bild.core¶
Core implementation of the bild module
-
tracklib.analysis.bild.core.
sample
(traj, model, dE=0, samples_per_step=100, init_runs=20, significant_separation_sem_fold=5, k_lookahead=2, k_max=20, sampler_kw={}, show_progress=False)¶ Entry point for BILD
This function executes the whole BILD scheme, i.e. it takes a trajectory and returns a best-fit looping profile (plus some of the internally stored variables that might be of interest for downstream analysis.
- Parameters
trajectory (Trajectory) – the trajectory to sample for
model (MultiStateModel) – the model defining the likelihood function
dE (float, optional) – the evidence margin ΔE to apply in finding the actual point estimate
- Other Parameters
samples_per_step (int) – how many profiles are contained in each AMIS step. See ‘FixedkSampler.step`
init_runs (int) – minimum number of AMIS runs for a new value of
k
.significant_separation_sem_fold (float) –
FixedkSampler.t_stat
k_lookahead (int) – how far we look ahead for global maxima. As long as there is a current candidate for global optimum at
k >= k_max - k_lookahead
(wherek_max
is the largestk
sampled so far), sample largerk
instead of refining the ones we have. The default value of 2 comes from the fact that for binary profiles the evidence follows an odd-even pattern, so it is necessary to look ahead by 2 additional switches.k_max (int) – the maximum number of switches to sample to
sampler_kw (dict) – further keyword arguments when setting up the
amis.FixedkSampler
instances for eachk
.show_progress (bool) – whether to show a progress bar
- Returns
dict –
'samplers'
is a list of theamis.FixedkSampler
instances, including the full samples'k_updates'
is a record of which sampler was stepped in order. Can be useful for diagnostics / to look a bit under the hood what happens when'ks'
isnp.array([sampler.k for sampler in samplers])
. For convenience.'evidence'
is an array with the final evidence values for eachk
.'evidence_se'
are the associated standard errors'dE'
the evidence margin applied in finding the point estimate (see next two points)'best_k'
optimal number of switches identified using evidence margindE
'profile'
point estimate with that optimal number of switches
Notes
This function takes a value for the evidence margin ΔE, such that we can immediately give a point estimate. However, one should always make sure to study the behavior of the results under changing ΔE. To that end, you can always find the
best_k
andprofile
from the list of samplers by a construction like this:>>> out = sample(...) # run sampling ... dE = 5 # set dE ... # Find associated profile ... ks_plausible = out['ks'][out['evidence'] >= np.max(out['evidence']) - dE] ... best_k = np.min(ks_plausible) ... profile = samplers[out['best_k']].MLE_profile()
Post-processing functionality is provided in the
postproc
module, but usually the profile found by sampling alone is already pretty good.See also
amis.FixedkSampler()
,postproc()
-
class
tracklib.analysis.bild.core.
SamplingResults
(traj, model, samplers, k_updates=None)¶ Bases:
object
Standard format for
sample
output-
traj
¶ the trajectory these results pertain to
- Type
-
model
¶ the model used in obtaining them
- Type
-
samplers
¶ the samplers run for this inference run
- Type
list of FixedkSampler
-
k_updates
¶ an array containing the order in which the samplers were updated
- Type
np.ndarray, optional
-
k
¶ list of evaluated
k
values- Type
np.ndarray
-
evidence
¶ the corresponding evidences for each
k
- Type
np.ndarray
-
evidence_se
¶ the standard error on the evidence
- Type
np.ndarray
-
property
k
-
property
evidence
-
property
evidence_se
-
best_profile
(dE=0)¶ Find the best profile at given ΔE
- Parameters
dE (float >= 0) – the evidence margin to apply
- Returns
Loopingprofile
-
tracklib.analysis.bild.models¶
Inference models and the interface they should conform to
The MultiStateModel
defines the interface used for inference by the rest of
the BILD module. We provide specific implementations, the MultiStateRouse
model, as well as the FactorizedModel
, which essentially constitutes an HMM.
-
class
tracklib.analysis.bild.models.
MultiStateModel
¶ Bases:
object
Abstract base class for inference models
The most important capability of any model is the likelihood function
logL
for a combination ofLoopingprofile
andTrajectory
. Furthermore, a model should be able to tell how many states it has and what number of spatial dimensions it expects through thenStates
andd
properties respectively.Finally, there are some convenience functions that are recommended, but not required to implement:
provide an initial guess for a good profile by
initial_loopingprofile
sample from the likelihood by
trajectory_from_loopingprofile
-
property
nStates
¶ How many internal states does this model have?
-
property
d
¶ Spatial dimension
-
initial_loopingprofile
(traj)¶ Give a quick guess for a good
Loopingprofile
for aTrajectory
.The default implementation gives a random
Loopingprofile
.- Parameters
traj (Trajectory) –
- Returns
Loopingprofile
-
abstract
logL
(loopingprofile, traj)¶ Calculate log-likelihood for (
Loopingprofile
,Trajectory
) pair.- Parameters
loopingprofile (Loopingprofile) –
traj (Trajectory) –
- Returns
float – log-likelihood associated with the inputs
-
trajectory_from_loopingprofile
(profile, localization_error=None, missing_frames=None, preproc=None)¶ Generate a
Trajectory
for the givenLoopingprofile
- Parameters
profile (Loopingprofile) –
localization_error (float or (d,) np.ndarray, dtype=float) – how much Gaussian noise to add to the trajectory. Specify either as a single float, in which case a Gaussian random variable of that standard deviation will be added to each dimension, or as an array with one value of the standard deviation for each dimension individually.
missing_frames (None, float in [0, 1), int, or np.ndarray) –
- frames to remove from the generated trajectory. Can be
None
or0
: remove no framesfloat in (0, 1)
: remove frames at random, with this probabilityint
: remove this many frames at randomnp.ndarray, dtype=int
: indices of the frames to remove
preproc (string) – specific to this base implementation. Set to
'localization_error'
or'missing_frames'
to resolve the corresponding parameter according to the rules outlined above and return it.
- Returns
Trajectory
Notes
Even though this method provides default preprocessing for
localization_error
andmissing_frames
, implementations are not forced to use them. So the meaning of these arguments might be different from what’s outlined here.
-
class
tracklib.analysis.bild.models.
MultiStateRouse
(N, D, k, d=3, looppositions=(None, (0, -1)), measurement='end2end', localization_error=None)¶ Bases:
tracklib.analysis.bild.models.MultiStateModel
A multi-state Rouse model
This inference model uses a given number of
rouse.Model
instances to choose from for each propagation interval. In the default use case this switches between a looped and unlooped model, but it could be way more general than that, e.g. incorporating different looped states, loop positions, numbers of loops, etc.- Parameters
N (int) – number of monomers
k (D,) – Rouse parameters: 1d diffusion constant of free monomers and backbone spring constant
d (int, optional) – spatial dimension
looppositions (list of tuples) – specification of the extra bonds to add for the different states. Each entry corresponds to one possible state of the model and should be a tuple
(left_mon, right_mon, rel_strength=1)
or a list of such; specification ofrel_strength
is optional. This will introduce an additional bond between the monomer indexedleft_mon
and the one indexedright_mon
, with strengthrel_strength*k
. Note that you can remove the i-th backbone bond by giving(i, i+1, -1)
. Remember that (if relevant) you also have to include the unlooped state, which you can do by givingNone
instead of a tuple; alternatively, give a vacuous extra bond like(0, 0)
.measurement ("end2end" or (N,) np.ndarray) – which distance to measure. The default setting “end2end” is equivalent to specifying a vector
np.array([-1, 0, ..., 0, 1])
, i.e. measuring the distance from the first to the last monomer.localization_error (float, (d,) np.array, or None, optional) – localization error assumed by the model, e.g. in calculating the likelihood or generating trajectories. If
None
, try to usetraj.meta['localization_error']
where possible. If scalar, will be broadcast to all spatial dimensions.
-
models
¶ the Rouse models for the individual states
- Type
list of
rouse.Model
-
measurement
¶ the measurement vector
- Type
(N,) np.ndarray
-
localization_error
¶ if
None
, usetraj.meta['localization_error']
where possible- Type
array or None
Notes
This class also provides a function to convert to a
FactorizedModel
, using the exact steady state distributions of the Rouse models. This is used for example to quickly guess an initial looping profile.See also
MultiStateModel
,rouse.Model
-
property
nStates
¶ How many internal states does this model have?
-
property
d
¶ Spatial dimension
-
initial_loopingprofile
(traj)¶ Give an initial guess for a looping profile
- Parameters
traj (Trajectory) – the trajectory under investigation
- Returns
Loopingprofile
-
logL
(profile, traj)¶ Rouse likelihood, evaluated by Kalman filter
- Parameters
profile (Loopingprofile) –
traj (Trajectory) –
- Returns
float
-
trajectory_from_loopingprofile
(profile, localization_error=None, missing_frames=None)¶ Generative model
- Parameters
profile (Loopingprofile) – the profile from whose associated ensemble to sample
localization_error (float or (d,) np.ndarray, dtype=float) – see
MultiStateModel.trajectory_from_loopingprofile
missing_frames (None, float in [0, 1), int, or np.ndarray) – see
MultiStateModel.trajectory_from_loopingprofile
- Returns
Trajectory
-
toFactorized
()¶ Give the corresponding
FactorizedModel
This is the model that simply calculates likelihoods from the steady state probabilities of each of the individual states.
- Returns
FactorizedModel
-
class
tracklib.analysis.bild.models.
FactorizedModel
(distributions, d=3)¶ Bases:
tracklib.analysis.bild.models.MultiStateModel
A simplified model, assuming time scale separation
This model assumes that each point is sampled from one of a given list of distributions, where there is no correlation between the choice of distribution for each point.
- Parameters
distributions (list of distribution objects) – these will usually be
scipy.stats.rv_continuous
objects (e.g. Maxwell), but can be pretty arbitrary. The only function they have to provide islogpdf()
, which should take a scalar or vector of distance values and return a corresponding number of outputs. If you plan on usingtrajectory_from_loopingprofile
, the distributions should also have anrvs()
method for sampling.
-
distributions
¶ - Type
list of distribution objects
Notes
This being a heuristical model, we assume that the localization error is already incorporated in the
distributions
, as would be the case if they came from experimental data. Therefore, this class ignores themeta['localization_error']
field ofTrajectory
.The
d
attribute mandated by theMultiStateRouse
interface is used only for generation of trajectories.Instances of this class memoize trajectories they have seen before. To reset the memoization, you can either reinstantiate, or clear the cache manually:
>>> model = FactorizedModel(model.distributions) ... model.clear_memo()
If using
scipy.stats.maxwell
, make sure to use it correctly, i.e. you have to specifyscale=...
. Writingscipy.stats.maxwell(5)
instead ofscipy.stats.maxwell(scale=5)
shifts the distribution instead of scaling it and leads to-inf
values in the likelihood.Examples
Experimentally measured distributions can be used straightforwardly using
scipy.stats.gaussian_kde
: assuming we have measured ensembles of distancesdists_i
for reference statesi
, we can use>>> model = FactorizedModel([scipy.stats.gaussian_kde(dists_0), ... scipy.stats.gaussian_kde(dists_1), ... scipy.stats.gaussian_kde(dists_2)])
-
property
nStates
¶ How many internal states does this model have?
-
property
d
¶ Spatial dimension
-
clear_memo
()¶ Clear the memoization cache
-
initial_loopingprofile
(traj)¶ Gives the MLE profile for the given trajectory
- Parameters
traj (Trajectory) –
- Returns
Loopingprofile
-
logL
(profile, traj)¶ Calculate log-likelihood for (
Loopingprofile
,Trajectory
) pair.- Parameters
loopingprofile (Loopingprofile) –
traj (Trajectory) –
- Returns
float – log-likelihood associated with the inputs
-
trajectory_from_loopingprofile
(profile, localization_error=0.0, missing_frames=None)¶ Generative model
- Parameters
profile (Loopingprofile) – the profile from whose associated ensemble to sample
localization_error (float or (d,) np.ndarray, dtype=float) – see
MultiStateModel.trajectory_from_loopingprofile
; note that since the localization error should already be accounted for in thedistributions
of the model, it is not added to the trajectory here. Instead, it is just written totraj.meta['localization_error']
.missing_frames (None, float in [0, 1), int, or np.ndarray) – see
MultiStateModel.trajectory_from_loopingprofile
- Returns
Trajectory
Notes
The
FactorizedModel
only contains distributions for the scalar distance between the points, amounting to the assumption that the full distribution of distance vectors is isotropic. Thus, in generating the trajectory we sample a magnitude from the given distributions and a direction from the unit sphere.
tracklib.analysis.bild.postproc¶
Some post-processing of inferred profiles
This module provides an iterative optimization scheme for inferred looping profiles. Briefly, at each step we evaluate how much the likelihood increases if we were to move each boundary of the profile and then do the move that gives the largest increase.
-
tracklib.analysis.bild.postproc.
logLR_boundaries
(profile, traj, model)¶ Give log-likelihood ratios for moving each boundary either direction
- Parameters
profile (Loopingprofile) – the profile whose boundaries we want to optimize
traj (Trajectory) – the trajectory to which the profile applies
model (MultiStateModel) – the model providing the likelihood function
- Returns
(k, 2) np.ndarray, dtype=float – the log(likelihood ratio) for moving any of the
k
boundaries to the left ([i, 0]
) / right ([i, 1]
).
See also
-
exception
tracklib.analysis.bild.postproc.
BoundaryEliminationError
¶ Bases:
Exception
-
tracklib.analysis.bild.postproc.
optimize_boundary
(profile, traj, model, max_iteration=10000)¶ Locally optimize the boundaries of a profile
- Parameters
profile (Loopingprofile) – the profile whose boundaries we want to optimize
traj (Trajectory) – the trajectory to which the profile applies
model (MultiStateModel) – the model providing the likelihood function
max_iteration (int, optional) – limit on the number of iterations through the (find boundaries) –> (make best move) cycle
- Raises
BoundaryEliminationError – if the local optimization tries to shrink an interval to zero, such that the total number of boundaries would change. This usually suggests that the original sampling might not have been extensive enough.
- Returns
Loopingprofile – the optimized profile
tracklib.analysis.bild.util¶
Utilities for BILD
-
class
tracklib.analysis.bild.util.
Loopingprofile
(states=None)¶ Bases:
object
This class represents a single looping profile
This is a thin wrapper around a
state
array, whose entries indicate which state of the model should be used for propagation from frame to frame. Specifically, the following scheme applies to the relation between aLoopingprofile
profile
and the associatedTrajectory
traj
:profile[0] profile[1] profile[-1] -------------> traj[0] ------------> traj[1] --- ... ---> traj[-2] -------------> traj[-1]
Note that
profile[i]
can be interpreted as “the state of the model used to propagate totraj[i]
”. Furthermore,profile[0]
indicates the model state used to calculate the steady state ensemble each trajectory starts from.- Operators defined for a
Loopingprofile
profile
: len(profile)
gives the length in framesprofile[i]
for element-access (get/set)profile0 == profile1
if the associated states are equal
-
states
¶ the internal state array. If this is not given upon initialization, initialized to an empty array.
- Type
np.ndarray, dtype=int
-
copy
()¶ Copy to a new object
Because we can copy just
self.state
, this is faster than doingdeepcopy(profile)
.- Returns
Loopingprofile
-
count_switches
()¶ Give number of switches in the profile
- Returns
int
-
intervals
()¶ Find intervals of constant state in the profile
- Returns
list of tuples – each entry signifies an interval, in the format
(start, end, state)
, wherestart
orend
areNone
for the first/last interval in the profile, respectively.
-
plottable
()¶ For plotting profiles
- Returns
t, y (np.ndarray) – coordinates for plotting the profile as proper step function. See below for usage
Example
Given a profile
profile
, you might visualize it as>>> from matplotlib import pyplot as plt ... plt.plot(*profile.plottable(), color='tab:green') ... plt.show()
Note that another way to achieve the same thing is to just use
matplotlib
’sstairs
function:>>> plt.stairs(profile, edges=np.arange(-1, len(profile)))
- Operators defined for a
-
tracklib.analysis.bild.util.
state_probabilities
(profiles, nStates=None)¶ Calculate marginal probabilities for an ensemble of profiles
- Parameters
profiles (list of Loopingprofile) – the profiles to use
nStates (int, optional) – the number of states in the model associated with the profiles. Since a profile might be (e.g.) flat zero, it is not in general possible to determine this from the profiles. Does not affect the calculated probabilities, but (“just”) the shape of the output. If
None
, will be identified from the profiles (i.e.nStates = (largest state index found) + 1
)
- Returns
(nStates, T) np.ndarray – the marginal probabilities for each state at each time point


As well as working with local in-memory data stored in data frames, dplyr also works with remote on-disk data stored in databases. This is particularly useful in two scenarios:
Your data is already in a database.
You have so much data that it does not all fit into memory simultaneously and you need to use some external storage engine.
(If your data fits in memory there is no advantage to putting it in a database: it will only be slower and more frustrating).
This vignette focusses on the first scenario because it’s the most common. If you’re using R to do data analysis inside a company, most of the data you need probably already lives in a database (it’s just a matter of figuring out which one!). However, you will learn how to load data in to a local database in order to demonstrate dplyr’s database tools. At the end, I’ll also give you a few pointers if you do need to set up your own database.
To use databases with dplyr you need to first install dbplyr:
install.packages("dbplyr")
You’ll also need to install a DBI backend package. The DBI package provides a common interface that allows dplyr to work with many different databases using the same code. DBI is automatically installed with dbplyr, but you need to install a specific backend for the database that you want to connect to.
Five commonly used backends are:
RMySQL connects to MySQL and MariaDB
RPostgreSQL connects to Postgres and Redshift.
RSQLite embeds a SQLite database.
odbc connects to many commercial databases via the open database connectivity protocol.
bigrquery connects to Google’s BigQuery.
If the database you need to connect to is not listed here, you’ll need to do some investigation (i.e. googling) yourself.
In this vignette, we’re going to use the RSQLite backend which is automatically installed when you install dbplyr. SQLite is a great way to get started with databases because it’s completely embedded inside an R package. Unlike most other systems, you don’t need to setup a separate database server. SQLite is great for demos, but is surprisingly powerful, and with a little practice you can use it to easily work with many gigabytes of data.
To work with a database in dplyr, you must first connect to it, using DBI::dbConnect()
. We’re not going to go into the details of the DBI package here, but it’s the foundation upon which dbplyr is built. You’ll need to learn more about if you need to do things to the database that are beyond the scope of dplyr.
library(dplyr)
con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
The arguments to DBI::dbConnect()
vary from database to database, but the first argument is always the database backend. It’s RSQLite::SQLite()
for RSQLite, RMySQL::MySQL()
for RMySQL, RPostgreSQL::PostgreSQL()
for RPostgreSQL, odbc::odbc()
for odbc, and bigrquery::bigquery()
for BigQuery. SQLite only needs one other argument: the path to the database. Here we use the special string “:memory:” which causes SQLite to make a temporary in-memory database.
Most existing databases don’t live in a file, but instead live on another server. That means in real-life that your code will look more like this:
con <- DBI::dbConnect(RMySQL::MySQL(),
host = "database.rstudio.com",
user = "hadley",
password = rstudioapi::askForPassword("Database password")
)
(If you’re not using RStudio, you’ll need some other way to securely retrieve your password. You should never record it in your analysis scripts or type it into the console.)
Our temporary database has no data in it, so we’ll start by copying over nycflights13::flights
using the convenient copy_to()
function. This is a quick and dirty way of getting data into a database and is useful primarily for demos and other small jobs.
copy_to(con, nycflights13::flights, "flights",
temporary = FALSE,
indexes = list(
c("year", "month", "day"),
"carrier",
"tailnum",
"dest"
)
)
As you can see, the copy_to()
operation has an additional argument that allows you to supply indexes for the table. Here we set up indexes that will allow us to quickly process the data by day, by carrier, by plane, and destination. Creating the write indices is key to good database performance, but is unfortunately beyond the scope of this article.
Now that we’ve copied the data, we can use tbl()
to take a reference to it:
flights_db <- tbl(con, "flights")
When you print it out, you’ll notice that it mostly looks like a regular tibble:
flights_db
#> Source: table<flights> [?? x 19]
#> Database: sqlite 3.11.1 []
#>
#> # S3: tbl_dbi
#> year month day dep_time sched_dep_time dep_delay arr_time
#> <int> <int> <int> <int> <int> <dbl> <int>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> 5 2013 1 1 554 600 -6 812
#> 6 2013 1 1 554 558 -4 740
#> # ... with more rows, and 12 more variables: sched_arr_time <int>,
#> # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> # minute <dbl>, time_hour <dbl>
The main difference is that you can see that it’s a remote source in a SQLite database.
To interact with a database you usually use SQL, the Structured Query Language. SQL is over 40 years old, and is used by pretty much every database in existence. The goal of dbplyr is to automatically generate SQL for you so that you’re not forced to use it. However, SQL is a very large language and dbplyr doesn’t do everything. It focusses on SELECT
statements, the SQL you write most often as an analyst.
Most of the time you don’t need to know anything about SQL, and you can continue to use the dplyr verbs that you’re already familiar with:
flights_db %>% select(year:day, dep_delay, arr_delay)
#> Source: lazy query [?? x 5]
#> Database: sqlite 3.11.1 []
#>
#> # S3: tbl_dbi
#> year month day dep_delay arr_delay
#> <int> <int> <int> <dbl> <dbl>
#> 1 2013 1 1 2 11
#> 2 2013 1 1 4 20
#> 3 2013 1 1 2 33
#> 4 2013 1 1 -1 -18
#> 5 2013 1 1 -6 -25
#> 6 2013 1 1 -4 12
#> # ... with more rows
flights_db %>% filter(dep_delay > 240)
#> Source: lazy query [?? x 19]
#> Database: sqlite 3.11.1 []
#>
#> # S3: tbl_dbi
#> year month day dep_time sched_dep_time dep_delay arr_time
#> <int> <int> <int> <int> <int> <dbl> <int>
#> 1 2013 1 1 848 1835 853 1001
#> 2 2013 1 1 1815 1325 290 2120
#> 3 2013 1 1 1842 1422 260 1958
#> 4 2013 1 1 2115 1700 255 2330
#> 5 2013 1 1 2205 1720 285 46
#> 6 2013 1 1 2343 1724 379 314
#> # ... with more rows, and 12 more variables: sched_arr_time <int>,
#> # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> # minute <dbl>, time_hour <dbl>
flights_db %>%
group_by(dest) %>%
summarise(delay = mean(dep_time))
#> Source: lazy query [?? x 2]
#> Database: sqlite 3.11.1 []
#>
#> # S3: tbl_dbi
#> dest delay
#> <chr> <dbl>
#> 1 ABQ 2006
#> 2 ACK 1033
#> 3 ALB 1627
#> 4 ANC 1635
#> 5 ATL 1293
#> 6 AUS 1521
#> # ... with more rows
However, in the long-run, I highly recommend you at least learn the basics of SQL. It’s a valuable skill for any data scientist, and it will help you debug problems if you run into problems with dplyr’s automatic translation. If you’re completely new to SQL you might start with this codeacademy tutorial. If you have some familiarity with SQL and you’d like to learn more, I found how indexes work in SQLite and 10 easy steps to a complete understanding of SQL to be particularly helpful.
The most important difference between ordinary data frames and remote database queries is that your R code is translated into SQL and executed in the database, not in R. When working with databases, dplyr tries to be as lazy as possible:
It never pulls data into R unless you explicitly ask for it.
It delays doing any work until the last possible moment: it collects together everything you want to do and then sends it to the database in one step.
For example, take the following code:
tailnum_delay_db <- flights_db %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay),
n = n()
) %>%
arrange(desc(delay)) %>%
filter(n > 100)
Suprisingly, this sequence of operations never touches the database. It’s not until you ask for the data (e.g. by printing tailnum_delay
) that dplyr generates the SQL and requests the results from the database. Even then it tries to do as little work as possible and only pulls down a few rows.
tailnum_delay_db
#> Source: lazy query [?? x 3]
#> Database: sqlite 3.11.1 []
#> Ordered by: desc(delay)
#>
#> # S3: tbl_dbi
#> tailnum delay n
#> <chr> <dbl> <int>
#> 1 N11119 30.3 148
#> 2 N16919 29.9 251
#> 3 N14998 27.9 230
#> 4 N15910 27.6 280
#> 5 N13123 26.0 121
#> 6 N11192 25.9 154
#> # ... with more rows
Behind the scenes, dplyr is translating your R code into SQL. You can see the SQL it’s generating with show_query()
:
tailnum_delay_db %>% show_query()
#> <SQL>
#> SELECT *
#> FROM (SELECT *
#> FROM (SELECT `tailnum`, AVG(`arr_delay`) AS `delay`, COUNT() AS `n`
#> FROM `flights`
#> GROUP BY `tailnum`)
#> ORDER BY `delay` DESC)
#> WHERE (`n` > 100.0)
If you’re familiar with SQL, this probably isn’t exactly what you’d write by hand, but it does the job. You can learn more about the SQL translation in vignette("sql-translation")
.
Typically, you’ll iterate a few times before you figure out what data you need from the database. Once you’ve figured it out, use collect()
to pull all the data down into a local tibble:
tailnum_delay <- tailnum_delay_db %>% collect()
tailnum_delay
#> # A tibble: 1,201 × 3
#> tailnum delay n
#> <chr> <dbl> <int>
#> 1 N11119 30.3 148
#> 2 N16919 29.9 251
#> 3 N14998 27.9 230
#> 4 N15910 27.6 280
#> 5 N13123 26.0 121
#> 6 N11192 25.9 154
#> # ... with 1,195 more rows
collect()
requires that database does some work, so it may take a long time to complete. Otherwise, dplyr tries to prevent you from accidentally performing expensive query operations:
Because there’s generally no way to determine how many rows a query will return unless you actually run it, nrow()
is always NA
.
Because you can’t find the last few rows without executing the whole query, you can’t use tail()
.
nrow(tailnum_delay_db)
#> [1] NA
tail(tailnum_delay_db)
#> Error: tail() is not supported by sql sources
You can also ask the database how it plans to execute the query with explain()
. The output is database dependent, and can be esoteric, but learning a bit about it can be very useful because it helps you understand if the database can execute query efficiently, or if you need to create new indices.
If you don’t already have a database, here’s some advice from my experiences setting up and running all of them. SQLite is by far the easiest to get started with, but the lack of window functions makes it limited for data analysis. PostgreSQL is not too much harder to use and has a wide range of built-in functions. In my opinion, you shouldn’t bother with MySQL/MariaDB: it’s a pain to set up, the documentation is subpar, and its less featureful than Postgres. Google BigQuery might be a good fit if you have very large data, or if you’re willing to pay (a small amount of) money to someone who’ll look after your database.
All of these databases follow a client-server model - a computer that connects to the database and the computer that is running the database (the two may be one and the same but usually isn’t). Getting one of these databases up and running is beyond the scope of this article, but there are plenty of tutorials available on the web.
In terms of functionality, MySQL lies somewhere between SQLite and PostgreSQL. It provides a wider range of built-in functions, but it does not support window functions (so you can’t do grouped mutates and filters).
PostgreSQL is a considerably more powerful database than SQLite. It has:
a much wider range of built-in functions
support for window functions, which allow grouped subset and mutates to work.
BigQuery is a hosted database server provided by Google. To connect, you need to provide your project
, dataset
and optionally a project for billing
(if billing for project
isn’t enabled).
It provides a similar set of functions to Postgres and is designed specifically for analytic workflows. Because it’s a hosted solution, there’s no setup involved, but if you have a lot of data, getting it to google can be an ordeal (especially because upload support from R is not great currently). (If you have lots of data, you can ship hard drives!)


We are two artists who work well independently, and create amazing works of art together. Typically we work with acrylic on canvas in many different styles. However, we also use oils, spraypaint, pencils, and house paint. We also enjoy painting on odd surfaces from rocks, to wood, murals, slate, glass, and anything else we (or you) can think of.
We are local to Cleveland Ohio and love to share our creativity with others! If you need help with any artistic conundrums you may have do not be shy and contact us! Things we can help with include murals and indoor painting, filling your home or office with paintings made to match your aesthetic, creative consultation, and anything else you can think of!


Depending on power supply voltage and motor coil inductance, current through the motor can change relatively slowly or extremely rapidly. A physically larger DC Motor will typically have a lower inductance, requiring a higher current regulator gain. A higher power supply voltage will result in motor current changing more rapidly, requiring a higher current regulator gain. If the current regulator gain is too small, spikes in current will occur, causing large variations in torque, and possibly damaging the motor controller. If the current regulator gain is too high, the current will jitter, causing the motor to sound 'rough', especially when changing directions.
|
Device In Device # Identification |
|
error in (no error) error in can accept error information wired from VIs previously called. |
|
status status is TRUE (X) if an error occurred or FALSE (checkmark) to indicate a warning or that no error occurred. Right-click the error in control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
code code is the error or warning code. Right-click the error in control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
source source describes the origin of the error or warning. Right-click the error in control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
Current Regulator Gain The current regulator gain value 1060_0 (ver: 100, 110) DC Motor Controller: Unsupported
1064_0 (ver: 100, 101, 102) DC Motor Controller: Unsupported
1064_1 (ver: 102) DC Motor Controller: Unsupported
1065_0 (ver: 100, 101) DC Motor Controller: Unsupported
1065_1 (ver: 100, 101) DC Motor Controller: Unsupported
DCC1000_0 (ver: 115) DC Motor Controller: Min: 1 Max: 100Val: 10
DCC1000_0 (ver: 204, 205, 206) DC Motor Controller: Min: 1 Max: 100Val: 10
DCC1001_0 (ver: 114, 116, 117) DC Motor Controller: Unsupported
DCC1002_0 (ver: 100) DC Motor Controller: Unsupported
DCC1003_0 (ver: 100) DC Motor Controller: Unsupported |
|
Device Out Same as Device In |
|
error out error out passes error or warning information out of a VI to be used by other VIs. |
|
status status is TRUE (X) if an error occurred or FALSE (checkmark) to indicate a warning or that no error occurred. Right-click the error out control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
code code is the error or warning code. Right-click the error out control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |
|
source source describes the origin of the error or warning. Right-click the error out control on the front panel and select Explain Error or Explain Warning from the shortcut menu for more information about the error. |


Hi, Trent!
Dont forget to get your 7,000 step average this week! Currently,
you are averaging 5,700 steps per day. You can do this!
- Your health coaching team
Nutrition plan requested information
Hi, Trent!
Here is that information you requested on your last
health-coaching call.
- Your health coaching team
Upcoming program deadline
Hi, Trent!
The third interval of your Tobacco Cessation program is coming up
soon. If you have any questions or concerns, don't hesitate to
request an appointment with a health coach. Keep up the great
work!


An embedded system is a computer system—a combination of a computer processor, computer memory, and input/output peripheral devices—that has a dedicated function within a larger mechanical or electrical system. It is embedded as part of a complete device often including electrical or electronic hardware and mechanical parts. Because an embedded system typically controls physical operations of the machine that it is embedded within, it often has real-time computing constraints. Embedded systems control many devices in common use today.Ninety-eight percent of all microprocessors manufactured are used in embedded systems.


EBD - Easton Bible DIctionary
Ague
AGUE
"AGUE "the translation in
Leviticus 26:16
(R.V., “fever”) of the Hebrew word kaddah’ath, meaning “kindling”, i.e., an inflammatory or burning fever. In
Deuteronomy 28:22
the word is rendered “fever.”
"AGUE "the translation in Leviticus 26:16 (R.V., “fever”) of the Hebrew word kaddah’ath, meaning “kindling”, i.e., an inflammatory or burning fever. In Deuteronomy 28:22 the word is rendered “fever.”


Semrush
¶ ↑
Semrush
is a ruby wrapper for the SEMRush API.
Requirements¶ ↑
-
Ruby 1.9, 2.x, 3.1<
-
ActiveSupport
Installation¶ ↑
Add the gem to your Gemfile:
gem install semrush
Create an initializer, for instance semrush.rb with the following:
Semrush.config do |config| config.api_key = "7899esf6874" end
Replace ‘7899esf6874’ with your SEMRush api key.
You may also use the following parameters in config:
config.before = lambda{|params| puts params} # (Proc or lambda) will be called before the call to SemRush (even if this return false it still runs the request) config.after = lambda{|params, results| puts results} # (Proc or lambda) will be called after the call to SemRush (if the call returns an exception, this will not be executed)
Getting started¶ ↑
First, create a report for a domain, a URL or a phrase with:
report = Semrush::Report.domain("seobook.com") # for the "seobook.com" domain report = Semrush::Report.url("http://tools.seobook.com/") # for the "http://tools.seobook.com/" url report = Semrush::Report.phrase("search+engine+optimization") # for the "search+engine+optimization" phrase
Then run the report type you need:
basic_data = report.basics
or
keywords_data = report.keywords_organic
If you want to know your remaining api units:
units = Semrush::Report.remaining_quota
Parameters¶ ↑
You may use the following parameters:
:db # (String) select the google engine ('us' for google.com 'fr' for google.fr) :api_key # (String) change the api_key :limit # (Integer) select only the first 'limit' entries (This parameter is required in order to avoid uncontrolled heavy usage) :offset # (Integer) skip the first 'offset' entries :export_columns # (String) select the columns you want to fetch, for instance: :export_columns => "Dn,Rk" :display_sort # (String) select the column and the order you want to sort with, for instance: :display_sort => 'tr_asc' :display_filter # (String) list of filters for the report, separated by '|' (maximum number - 25). A filter consists of <sign>|<field>|<operation>|<value> (read http://www.semrush.com/api), for instance: :display_filter => '+|Po|Lt|5' :display_date # (String) select the date for the report data, in the format: YYYYMM15
Some examples:
report = Semrush::Report.domain("seobook.com", :db => 'us', :limit => 100) data = report.basics
or
report = Semrush::Report.domain("seobook.com") data = report.basics(:db => 'us', :limit => 100)
or
analytics = Semrush::Analytics.backlinks_refdomains("seobook.com")
You will find more information about these parameters at www.semrush.com/api.html
Reports¶ ↑
Source of reports¶ ↑
They are 3 sources for the reports: domain, url and phrase.
report = Semrush::Report.domain("seobook.com") # for the "seobook.com" domain report = Semrush::Report.url("http://tools.seobook.com/") # for the "http://tools.seobook.com/" url report = Semrush::Report.phrase("search+engine+optimization") # for the "search+engine+optimization" phrase
Report types¶ ↑
You may call for one of the following report types:
data = report.basics # main report for either a domain or a phrase data = report.organic # organic report for either a domain, a URL or a phrase data = report.adwords # adwords report for either a domain or a URL data = report.competitors_organic # for a domain data = report.competitors_adwords # for a domain data = report.competitors_organic_by_adwords # for a domain data = report.competitors_adwords_by_organic # for a domain data = report.related # keywords related report for a phrase data = report.fullsearch # phrase fullsearch report data = report.history_adwords # ads history for either a domain or a phrase
For more information about the report types, please read www.semrush.com/api.html
ChangeLog¶ ↑
3.0.24, 2023-05-15¶ ↑
-
Added Analytics class for the new Analytics API, not covered all the reports yet
3.0.23, 2020-03-19¶ ↑
-
Add New Zealand ‘nz’ to allowed databases (countries)
3.0.22, 2019-06-14¶ ↑
-
Add India to the list of allowed countries
3.0.21, 2018-04-14¶ ↑
-
Move to SSL (https)
3.0.20, 2017-03-16¶ ↑
-
Add Phrase KDI support
3.0.19, 2016-01-16¶ ↑
-
Fix map bug with filtered organic report that has no results
3.0.18, 2014-10-03¶ ↑
-
Add new databases (countries)
3.0.17, 2014-08-01¶ ↑
-
Remove Pony dependency
3.0.16, 2014-07-21¶ ↑
-
Add support for fullsearch reports
-
Add support for Adwords history reports
3.0.15, 2013-06-26¶ ↑
-
activesupport dependency to >= 3.2.0 to support rails 4
3.0.14, 2013-06-10¶ ↑
-
parameter ‘display_filter’ has to be url-encoded in a separate way
3.0.13, 2013-06-10¶ ↑
-
Add report type ‘phrase_oganic’
-
Add parameter ‘display_sort’
-
Add parameter ‘display_filter’
3.0.12, 2013-05-31¶ ↑
-
Force parameter ‘limit’ > 0 to prevent limit=0
3.0.11, 2013-05-22¶ ↑
-
Fix parameters accessible through Semrush.before & Semrush.after
3.0.10, 2013-05-21¶ ↑
-
Fix remaining_quota (problem with redirections)
3.0.9, 2013-05-21¶ ↑
-
Add url in parameters accessible through Semrush.before & Semrush.after
3.0.8, 2013-05-21¶ ↑
-
Move after & before callbacks to
Semrush
module: Semrush.before & Semrush.after
3.0.7, 2013-05-21¶ ↑
-
Add after & before callbacks
3.0.6, 2013-05-7¶ ↑
-
Add method remaining_quota
About authors¶ ↑
This gem is inspired by the work of Cramer Development for the semrush-client plugin (github.com/cramerdev/semrush-client).
It has been rewritten and gemified for the internal use in Weboglobin (fr.weboglobin.com).
This project rocks and uses MIT-LICENSE.


--- title: Services layout: default bodyClass: page-services-list ---
"Everything starts with one step, or one brick, or one word or one day" --Jeremy Gilley
We restore habitats so that native plants and creatures can find somewhere safe to live. To achieve this, we need to link as many areas together as possible. Some of these creatures are very small, so walking a long way is difficult! Imagine yourself struggling from oasis to oasis in the desert - wouldn't you want more oases?
You can help nature by being a part of the movement to create stepping stones for biodiversity in your garden. Each new stepping stone is a vital oasis in the grand scheme of things. We can provide a full service to build stepping stones for biodiversity not just for you, but for nature and for the future.
Remember - There is no planet "B"


Submitting Survey Response
How to submit a response to a survey on the Survey module. Note: Access to view results may be restricted.
- If a message reading "You must sign in to vote in this survey" is displayed below the questions you must Login to the site to submit a response to the survey.
- Click in the selection boxes to select your answer(s) for each question.
- Click the Submit Survey link.
- If you are authorized to view survey results, the accumulative results are displayed. If you are unable to view results the message "Thank You for Participating" is displayed.


v1.8.1 [MOD-603] Fixed useLED to properly report if the LED will be used. Also added a button to the example overlay for toggling it on and off.v1.8 [MOD-434][MOD-439] Fixed a crash when scanning a VEVENT code, and improved the durability of scanning to not crash the application upon parse errors. - BREAKING CHANGE: iOS error event's property "reason" has been renamed to "message" for parity with Android and the documentation.
v1.7 [MOD-377] Fixed regression introduced by Titanium Mobile 1.8.1 [MOD-403] Fixed crash after parsing an invalid barcode.
v1.6 [MOD-304] Added "acceptedFormats" and relevant constants. Check out the documentation and example to find out more. [MOD-202] Added "parse" method for finding barcodes in blobs. See documentation and example for more information. [MOD-250] Added "useLED" property to the module for lighting the LED during scanning, if available.
v1.5 [MOD-241][MOD-256] Added a "useFrontCamera" property to the module to control which camera is used. [MOD-254][MOD-253] "capture" now accepts the "keepOpen" boolean key. Check out the example and documentation to find out more. [MOD-90] "capture" now accepts several new keys: overlay, which takes a view; and showRectangle, which takes a boolean. [MOD-223] Established parity between iOS and Android Barcode modules. - BREAKING CHANGE: iOS now uses event listeners instead of callbacks! Use Ti.Barcode.addEventListener('success', ...) instead of capture({ success: ...})! - BREAKING CHANGE: Android's contentType property is now an integer, instead of a string! Check out the example and documentation to find out more. - BREAKING CHANGE: All automatically parsed "data" keys will now be lower case. This ensures consistency across the API and ease of access. - BREAKING CHANGE: Android now properly fires the "cancel" event, as documented. It was firing the "canceled" event. - Updated examples to be identical; only difference is iOS's example utilizes the allowRotation property. - Added support for processing "WIFI" QR Codes. - Android now supports the "data" property, offering up a easy to use dictionary of the properties from the scanned barcode. - Android now has a "allowMenu" property on the module. See the documentation to find out more. - Updated example to use 'var' for module object returned from require. [MOD-293] Fixed issue with 'Cancel' button not displaying on capture screen
v1.4 MOD-232 Added ability to disable animation when dismissing camera to eliminate redraw artifacts
v1.3 MOD-114 Resolved problems with scanning Code-128 barcodes by updating to the latest version of ZXing
v1.2 MOD-141 Updated ZXingWidgetController.m to remove references to AVFoundation APIs when compiled to run in the simulator. Updated barcode system requirements to state that barcodes are supported in 4.0 and newer Exposed "displayedMessage" property to let developers customize the text that is shown to the user
v1.1 Updated to the latest version of ZXing Exposed "allowRotation" property to let the module scan in portrait and landscape orientations
v1.0 Initial Release


So, now there's all these stories about Microsoft entering the business of online music. Will it be better or worse than Apple? I can't say I'm happy with Apple. The songs are overpriced, and I personally can't burn my songs anymore -- somewhere in one of the upgrades it lost my key. I contacted support, but they ignored me. I admit I haven't tried very hard, but I can't help but feel that they've stolen something from me.  It's fundamentally shitty, and it's still shitty when Apple does it. Will it be more or less shitty with Microsoft? I don't know, but I am 100% certain it will still be shitty.
Should we even care? So, there's this company, AllOfMP3.com, that sells unencumbered audio. It's a Russian company, with a license to sell the music from a Russian copyright agency. Is it legal for me, a US citizen, to buy this music? If it is legal, is there any reason why iTMS should even exist, when compared to this vastly superior competitor?
I've tried them out. The rate is $0.01/Mb, or about $0.70 for an album (with my settings), encoded in whatever format and bitrate I choose. It all works pretty smoothly. The selection is a bit odd (they have a ton of Frank Zappa; I guess it's an Eastern European thing), but it's reasonably extensive, and they add 10-15 albums a day.
In this interview of the AllOfMP3 CIO, he emphasizes that the service is intended for Russian users, though maybe that's just the story he needs to tell. This article claims that AllOfMP3.com is illegal (in Australia); specifically that ROMS (a Russian equivalent of ASCAP or BMI) doesn't have the right to authorize the music. ROMS claims otherwise of course. Do I, as a consumer, need to worry about any of this? If I buy the music in good faith, isn't that good enough?
Of course, it's not quite that clear -- it feels like a loophole, at best. There's a certain pleasure in this; if corporations can shop around in various countries for low wages, poor environmental controls, etc, then can't I shop around for permissive copyright laws? At the prices AllOfMP3 charges, there can't be a lot of money going to licensing, but then they have to compete against widespread CD piracy in Russia, and I'm guessing pirated CDs are in the $1-$3 range(?) So the prices make sense in Russia, even if they seem suspiciously low for the US.
Lastly, there's the ethical question.  Plus it's not stealing, since I'm clearly not taking anything from anyone. But there is the issue of the artists. I somehow doubt that the estate of Frank Zappa is getting regular royalty checks from ROMS (though maybe I'm wrong). The RIAA doesn't have the interest of the artists in mind either, so I'm not enthusiastic about either alternative. I'm also okay with the idea that I may not support every artist I like, and I may not support them in every transaction, but if ultimately I'm supporting musicians in a sustainable way, that it's ethical. If no one is really offering a complete, ethical manner to support artists while enjoying their works, is there a balance I can create as an individual?
I'm interested in hearing other people's thoughts.
AOM seems to be legal in the US (the RIAA said they won't pursue any of its users). The only reason they don't have the entire online music downloading marketshare is because they have absolutely no marketing in the western countries and people believe it is to good to be true.
One possible alternative, although everyone says this and no-one does it, is to listen to music not made by famous artists. There's places like http://mperia.com/ and so on serving up this sort of thing. Obviously, there's the issue of finding good music from that without the radio to help out...
Lo Ian.
I read a couple of posts here in France about the AOM. In france this is clearly illegal. In fact AOM buy the right for songs at the Russian Price. So they can resell it in Russia, that's all. So if they sell songs out of Russia, they can be sue by record's company. And worst, in france, the record company can sue the people's too because they own music that doesn't pay the rights for that. This has never happen, but ..
Check this http://linuxfr.org/~yaubi/13539.html (use a translator) for additionnal information.# Jkx
What about the hymn project? It removes Apple's DRM to enable fair use rights.
http://hymn-project.org/# BCC
The going rate for a pirate CD in Moscow was about three or four dollars when I was there a couple of years go. There's a huge market my girlfriend (Russian) goes to to stock up whenever she's in town.
Is it illegal if I go to russia and buy (legit) CD's there and take them home with me? What about if I go to russia and download them onto my laptop there? I guess I am probably liable for import duty, but isn't this equivalent to buying cheap CDs from Amazon.com and having them posted to the UK.
Thanks for the pointer Ian. I had a look at it and got some great stuff. Further thoughts at http://www.alanlittle.org/weblog/AllofMP3.html
BTW, for people who have problems downloading from AOM, you can just copy the links into a text file, and run:
for LINE in `cat linkfile.txt` ; do wget $LINE ; done
I believe wget is standard on Mac OS X, so this should work well. You don't have to be logged in to download the files; I assume the links are personalized in some fashion.# Ian Bicking
Lo IanCan you explain that a little further? Having problems downloading from AOM with Mac OSX.
Please? thanks!
# Gina Quiroga
Well, I'm not sure what else to say... I think you can use SimpleText to make a text file with all the URLs. Save it to your desktop, then open the Terminal, which you can find in Applications:Utilities, and type at the command line:
cd Desktop for LINE in `cat linkfile.txt` ; do wget $LINE ; doneAnd it'll just download them all.
You need to get a program like Three Ten or iGetter to download with a Mac.
# Billy
I guess you just discover the new promotional from emusic.com. I haven't join but, but while browsing the website, i discover that they sell my favorite bands for great price. For example, some rare Sonic Youth album are sell at ~30$ here in France, because it's a 'import'. So emusic sells them without any extra cost .# Jkx


Online Registration form for Iskra2k19
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A basic screenshot application to capture screenshot of website with the use of puppeteer and cloud functions.


| Name | Role | iLevel |
|---|
| Main hand |
| Off hand |
| Head |
| Neck |
| Shoulder |
| Back |
| Chest |
| Wrist |
| Hands |
| Waist |
| Legs |
| Feet |
| Finger 1 |
| Finger 2 |
| Trinket 1 |
| Trinket 2 |
| Main hand |
| Off hand |
| Head |
| Neck |
| Shoulder |
| Back |
| Chest |
| Wrist |
| Hands |
| Waist |
| Legs |
| Feet |
| Finger 1 |
| Finger 2 |
| Trinket 1 |
| Trinket 2 |
| Main hand |
| Off hand |
| Head |
| Neck |
| Shoulder |
| Back |
| Chest |
| Wrist |
| Hands |
| Waist |
| Legs |
| Feet |
| Finger 1 |
| Finger 2 |
| Trinket 1 |
| Trinket 2 |
| Token |


public class RtpMediaDecoder extends java.lang.Object implements Decoder
| Modifier and Type | Field and Description |
|---|---|
java.lang.String |
bufferType |
static java.lang.String |
CONFIG_BUFFER_TYPE |
static java.lang.String |
CONFIG_RECEIVE_BUFFER_SIZE |
static java.lang.String |
CONFIG_USE_NIO |
static boolean |
DEBUGGING |
static java.lang.String |
DEBUGGING_PROPERTY |
int |
receiveBufferSize |
boolean |
useNio |
| Constructor and Description |
|---|
RtpMediaDecoder(SurfaceView surfaceView)
Creates an RTP decoder indicating where to play the video
|
RtpMediaDecoder(SurfaceView surfaceView,
java.util.Properties properties)
Creates an RTP decoder
|
| Modifier and Type | Method and Description |
|---|---|
void |
decodeFrame(BufferedSample decodeBuffer)
Decodes a frame
|
BufferedSample |
getSampleBuffer()
Retrieves a buffer from the decoder to be filled with data getting it from the Android
decoder input buffers
|
void |
release()
Stops the underlying RTP session and properly releases the Android API decoder
|
void |
restart()
Restarts the underlying RTP session
|
void |
setTraceOutputStream(java.io.OutputStream outputStream)
Defines the output stream where to trace packet's data while they arrive to the decoder
|
void |
start()
Starts decoder, including the underlying RTP session
|
void |
surfaceChanged(SurfaceHolder holder,
int format,
int width,
int height)
Starts playing video when surface view is ready
|
void |
surfaceCreated(SurfaceHolder holder)
Resizes surface view to 640x480
|
void |
surfaceDestroyed(SurfaceHolder holder) |
public static final java.lang.String DEBUGGING_PROPERTY
public static final java.lang.String CONFIG_USE_NIO
public static final java.lang.String CONFIG_BUFFER_TYPE
public static final java.lang.String CONFIG_RECEIVE_BUFFER_SIZE
public static boolean DEBUGGING
public java.lang.String bufferType
public boolean useNio
public int receiveBufferSize
public RtpMediaDecoder(SurfaceView surfaceView)
surfaceView
- view where video will be displayedpublic RtpMediaDecoder(SurfaceView surfaceView, java.util.Properties properties)
surfaceView
- view where to play video streamingproperties
- used to configure the debugging variablepublic void setTraceOutputStream(java.io.OutputStream outputStream)
outputStream
- stream where to dump datapublic void start()
public void restart()
public void release()
public BufferedSample getSampleBuffer() throws RtpPlayerException
getSampleBuffer
in interface Decoder
RtpPlayerException
- if no such buffer is currently availablepublic void decodeFrame(BufferedSample decodeBuffer) throws java.lang.Exception
decodeFrame
in interface Decoder
decodeBuffer
- java.lang.Exception
public void surfaceCreated(SurfaceHolder holder)
holder
- public void surfaceChanged(SurfaceHolder holder, int format, int width, int height)
holder
- format
- width
- height
- public void surfaceDestroyed(SurfaceHolder holder)


% set title="Mimir's Well: Guitar Pedal Platform powered by the FV-1 DSP" %>
<% extends "partials/_template.html" %>
<% block body %>
Mimir's Well
FV-1 DSP Pedal Platform
Walkthrough
What are the controls? What does it look like?
<% include "partials/labels.html" %>
<% include "partials/gallery.html" %>
Overview
23 Pedals in One Enclosure
<% include "partials/mimirs-well-intro.html" %>
Patches
What sounds can you make?
<% include "partials/patches.html" %>
3 Versions
<% include "partials/versions.html" %>
Perfect for...
<% include "partials/use-cases.html" %>
Free and Open Source
<% include "partials/oss.html" %>
FAQ
<% include "partials/faq.html" %>
<#
How It Works
(the quick and simple explanation)
#>
Swapping Banks
<% include "partials/swap-banks.html" %>


Wake Atoll is under the control of the US Air Force and is an active military base. General visitors are not permitted.
This wildlife atoll lies about 1500nm south west of Midway.
Last updated June 2015.


Indicators are composable - meaning they can be chained together to create unique combinations much like lego blocks. We support several indicator extensions as outlined below:
| Extension | Example Usage |
|---|---|
| .Plus() |
var emaSum = ema5.Plus(ema20);
Add a fixed value or indicator value to another indicator
|
| .Minus() |
var emaDelta = ema20.Minus(ema5);
Find the difference between two indicators
|
| .Times() |
var rsiSafe = rsi.Times(0.95);
Multiply one indicator or constant value by another.
|
| .Over() |
var emaAverage = ema5.Plus(ema10).Over(2);
Divide indicator chain by constant or indicator.
|
| .Of() |
var sma = new SimpleMovingAverage("SPY", 14);
Feed an indicator output into input of another.
var rsiAverage= sma.Of( rsi ); |
| .SMA(int period) |
var rsiAvg = rsi.SMA(10);
Of extension helper for SMA method.
|
| .EMA(int period) |
var rsiAvg = rsi.EMA(10);
Of extension helper for EMA method.
|
| .MAX(int period) |
var rsiMax = rsi.MAX(10);
Of extension helper for MAX method, get max in i-samples.
|
| .MIN(int period) |
var rsiMin = rsi.MIN(10);
Of extension helper for MIN method, get min in i-samples.
|


Burger
HOME
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CONTACT
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Whats @ Tha Spot?!
Fast Tasty Food Now....
Let me see the menu@ The Spot..


--- layout: default title: 'Designs' ---
Explore the designs and concepts that reflect the precision prism principles.
Concepts for views of the patient summary portion of the health record based on serveral different use cases: geriatric, pediatric, emergency, and self care. (View PDF)


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This Bible contradiction is from the Skeptic's Annotated Bible.
Verses
No.
Exodus 1:18-20 View context
And the king of Egypt called for the midwives, and said unto them, Why have ye done this thing, and have saved the men children alive?
And the midwives said unto Pharaoh, Because the Hebrew women [are] not as the Egyptian women; for they [are] lively, and are delivered ere the midwives come in unto them.
Therefore God dealt well with the midwives: and the people multiplied, and waxed very mighty.
Joshua 2:4-6 View context
And the woman took the two men, and hid them, and said thus, There came men unto me, but I wist not whence they [were]:
And it came to pass [about the time] of shutting of the gate, when it was dark, that the men went out: whither the men went I wot not: pursue after them quickly; for ye shall overtake them.
But she had brought them up to the roof of the house, and hid them with the stalks of flax, which she had laid in order upon the roof.
1 Samuel 21:2 View context
And David said unto Ahimelech the priest, The king hath commanded me a business, and hath said unto me, Let no man know any thing of the business whereabout I send thee, and what I have commanded thee: and I have appointed [my] servants to such and such a place.
1 Kings 15:5 View context
Because David did [that which was] right in the eyes of the LORD, and turned not aside from any [thing] that he commanded him all the days of his life, save only in the matter of Uriah the Hittite.
1 Kings 22:21-22 View context
And there came forth a spirit, and stood before the LORD, and said, I will persuade him.
And the LORD said unto him, Wherewith? And he said, I will go forth, and I will be a lying spirit in the mouth of all his prophets.

When he had said these words unto them, he abode [still] in Galilee.
But when his brethren were gone up, then went he also up unto the feast, not openly, but as it were in secret.
James 2:25 View context
Likewise also was not Rahab the harlot justified by works, when she had received the messengers, and had sent [them] out another way?
Yes.
Exodus 20:16 View context
Thou shalt not bear false witness against thy neighbour.
Exodus 23:1-7 View context
Thou shalt not raise a false report: put not thine hand with the wicked to be an unrighteous witness.

Thou shalt not wrest the judgment of thy poor in his cause.
Keep thee far from a false matter; and the innocent and righteous slay thou not: for I will not justify the wicked.
Leviticus 6:2-4 View context
If a soul sin, and commit a trespass against the LORD, and lie unto his neighbour in that which was delivered him to keep, or in fellowship, or in a thing taken away by violence, or hath deceived his neighbour;
Or have found that which was lost, and lieth concerning it, and sweareth falsely; in any of all these that a man doeth, sinning therein:
Then it shall be, because he hath sinned, and is guilty, that he shall restore that which he took violently away, or the thing which he hath deceitfully gotten, or that which was delivered him to keep, or the lost thing which he found,
Leviticus 19:11 View context
Ye shall not steal, neither deal falsely, neither lie one to another.
Deuteronomy 5:20 View context
Neither shalt thou bear false witness against thy neighbour.
Proverbs 12:22 View context
Lying lips [are] abomination to the LORD: but they that deal truly [are] his delight.
Proverbs 13:5 View context
A righteous [man] hateth lying: but a wicked [man] is loathsome, and cometh to shame.
Proverbs 24:28 View context
Be not a witness against thy neighbour without cause; and deceive [not] with thy lips.
Luke 3:14 View context
And the soldiers likewise demanded of him, saying, And what shall we do? And he said unto them, Do violence to no man, neither accuse [any] falsely; and be content with your wages.
Ephesians 4:25 View context
Wherefore putting away lying, speak every man truth with his neighbour: for we are members one of another.
Colossians 3:9 View context
Lie not one to another, seeing that ye have put off the old man with his deeds;
James 3:14 View context
But if ye have bitter envying and strife in your hearts, glory not, and lie not against the truth.
Revelation 21:8-27 View context
But the fearful, and unbelieving, and the abominable, and murderers, and whoremongers, and sorcerers, and idolaters, and all liars, shall have their part in the lake which burneth with fire and brimstone: which is the second death.
And there came unto me one of the seven angels which had the seven vials full of the seven last plagues, and talked with me, saying, Come hither, I will shew thee the bride, the Lamb's wife.
And he carried me away in the spirit to a great and high mountain, and shewed me that great city, the holy Jerusalem, descending out of heaven from God,
Having the glory of God: and her light [was] like unto a stone most precious, even like a jasper stone, clear as crystal;
And had a wall great and high, [and] had twelve gates, and at the gates twelve angels, and names written thereon, which are [the names] of the twelve tribes of the children of Israel:
On the east three gates; on the north three gates; on the south three gates; and on the west three gates.
And the wall of the city had twelve foundations, and in them the names of the twelve apostles of the Lamb.
And he that talked with me had a golden reed to measure the city, and the gates thereof, and the wall thereof.
And the city lieth foursquare, and the length is as large as the breadth: and he measured the city with the reed, twelve thousand furlongs. The length and the breadth and the height of it are equal.
And he measured the wall thereof, an hundred [and] forty [and] four cubits, [according to] the measure of a man, that is, of the angel.
And the building of the wall of it was [of] jasper: and the city [was] pure gold, like unto clear glass.
And the foundations of the wall of the city [were] garnished with all manner of precious stones. The first foundation [was] jasper; the second, sapphire; the third, a chalcedony; the fourth, an emerald;
The fifth, sardonyx; the sixth, sardius; the seventh, chrysolite; the eighth, beryl; the ninth, a topaz; the tenth, a chrysoprasus; the eleventh, a jacinth; the twelfth, an amethyst.
And the twelve gates [were] twelve pearls; every several gate was of one pearl: and the street of the city [was] pure gold, as it were transparent glass.
And I saw no temple therein: for the Lord God Almighty and the Lamb are the temple of it.
And the city had no need of the sun, neither of the moon, to shine in it: for the glory of God did lighten it, and the Lamb [is] the light thereof.
And the nations of them which are saved shall walk in the light of it: and the kings of the earth do bring their glory and honour into it.
And the gates of it shall not be shut at all by day: for there shall be no night there.
And they shall bring the glory and honour of the nations into it.
And there shall in no wise enter into it any thing that defileth, neither [whatsoever] worketh abomination, or [maketh] a lie: but they which are written in the Lamb's book of life.


Watir 6.8.0 is now available on RubyGems! Several new features including new methods to increase performance.
To install:
or in your Gemfile:
Bang Methods
Drivers are slow when it comes to typing text. Typically the intent of a test that enters text into a field is to verify:
- the user can type into the field
- the actions associated with events get fired
- the application properly handles the data
None of these three things require that a driver type each character.
By using a Element#set!
instead of Element#set
, Watir will:
- send clear via the applicable driver command to ensure the user can interact with the field
- use JavaScript to quickly fill the field with the specified text
- type the last character via the applicable driver command to ensure that appropriate events get fired
Depending on how much text is being entered, this could provide a huge performance improvement without negatively impacting the effectiveness of the test.
Along with Element#set!
, Watir now supports bang (!
) methods with
JavaScript implementations for:
Element#click!
Element#double_click!
Select#select!
Select#select_all!
The clicks are unlikely to provide a performance improvement and should be used sparingly. The Select methods have the potential to decrease the time it takes to interact with especially large select lists.
Let us know what kind of performance improvements you see with these methods.
Radio Sets
Input elements of type="radio"
are not independent (clicking one radio button in a
set will de-select another radio button in the set), but until now Watir has required
users to treat them independently. Element#radio_set
now allows users to
identify the collection of related radio buttons and interact with it
similarly to how one currently interacts with a Select List.
A RadioSet
is initialized with a locator for any of the constituent radio buttons.
When the name
value is the same between radio buttons, the RadioSet
initialized
by their locators will be the same:
A RadioSet
:
- Can be iterated over
- Returns currently selected radio button with
#selected
- Returns text and value of the selected radio button with
#text
and#value
- Returns whether the radio button with the provided label/text is
currently selected with
#selected?(#{label})
- Selects the radio button with the specified label/text with:
#select(#{label})
Selecting drop down options
Select#select_value
has been deprecated. Select#select
will now allow users
to locate elements by text, label or value. Having one primary way to select
options both matches the original Watir implementation as well as making it
easier to support automatic form filling methods in Page Object libraries.
Input Label
Sometimes a user might want to click on the label of an input element instead of the
element itself. With the new Input#label
, a user can now do:
See the Changelog for the complete history of updates.


Filton & Bradley Stoke constituency: Candidates, boundaries and all you need to know
Everything you need to know for voting in the General Election 2019 in Filton & Bradley Stoke
by Geraint TuckerThe Filton and Bradley Stoke constituency is a key Labour target in the 2019 General Election.
Tory MP and Brexiter Jack Lopresti has held the seat since the constituency was first established in 2010, but in 2017 his majority dropped to 4,182 votes.
We're taking a closer look at all of the constituencies in Bristol, South Gloucestershire and North Somerset where our readers will be taking part.
We'll include all the candidates standing for election plus information on the area covered by the constituency.
This is the guide for Filton and Bradley Stoke.
Filton and Bradley Stoke make-up
According to the latest census figures 72,569 are eligible to vote in Filton and Bradley Stoke.
Filton and Bradley Stoke boundaries
The Filton and Bradley Stoke constituency includes Almondsbury, Bradley Stoke, Downend, Filton, Frenchay & Stoke Park, Patchway, Pilning and Severn Beach, Staple Hill, Stoke Gifford and Winterbourne.
Previous voting habits
2010 – Conservative, Jack Lopresti
2015 - Conservative, Jack Lopresti
2017 - Conservative, Jack Lopresti
In 2017 voter turnout was 70 per cent in the ward.
Candidates for Filton and Bradley Stoke in 2019
Labour - Mhairi Threlfall
Conservatives - Jack Lopresti
Greens - Jenny Vernon
Liberal Democrats - Louise Harris
The Citizens Movement Party UK - Elaine Hardwick
Analysis of the ward
Filton and Bradley Stoke in South Gloucestershire is home to some of the biggest employers in the area including Rolls Royce, Airbus, Cribbs Causeway, the Ministry of Defence and the University of the West of England.
The seat is currently held by Brexiteer Jack Lopresti, but as a constituency it voted to remain in the 2016 referendum.
Unemployment in the area is low and house ownership high.
Housing and transport could be big issues for constituents - there are thousands of new housing developments planned across the constituency and serious concerns that the infrastructure is not in place to support this level of housebuilding.
For the latest news in and around Bristol, visit and bookmark Bristol Live's homepage.


:navtop
+callout-info
You can add markers that represent other entries in your RAMADDA.
-callout
:heading Entry Markers
The Entry Marker and the Multi Entry allow you to add markers
for RAMADDA entries. Clicking on the marker will show some content
from that entry.
For example, RAMADDA supports 3D Models as an entry type.
Choosing New->Entry Marker and adding it to the map will add a marker. Clicking on that marker
will show a 3D view of the model:
When adding a marker for an entry that has geolocation you will be prompted if you want to
use the entry's latitide/longitude as the position for the marker or if you want to position
the marker manually. If you opt to use the entry's position then, if the position of the entry
is changed, that change is reflected in the map. You can manually move the marker and that new
position will then be used. Likewise, the name and label for the marker is taken from the entry
name. If the name of the entry changes then that will be reflected in the map. This can be changed
by bringing up the properties for the entry marker:
:heading Data Displays
RAMADDA provides very rich capabilities to show data in maps, e.g.
on
ramadda.org.
These displays are accomplished through RAMADDA's
wiki
facility. You can import one or more map data displays into an IMDV.
:p
First, upload your data to RAMADDA and create a map display for that data
using RAMADDA's
wiki facility.
Next, from the IMDV New menu select Data and select the data you uploaded.
The IMDV will extract from you data's wiki the arguments for a map display.
These will be shown and can be edited if needed. Here we've selected some aircraft
track data:
With the result map:
The map display properties can be edited through the Edit form.
Other data displays can be added and overlaid on the IMDV map.
:p
You can play around with this by importing the file
https://ramadda.org/repository/a/userguide_showing_data_in_maps
to your RAMADDA. This is an export of the example showing Colorado aircraft crashes and
Colorado mines on
ramadda.org.


Jan Verbesselt, 2016-02-01
Loic Dutrieux, Ben DeVries, Johannes Reiche, Lammert Kooistra, Martin Herold, Brice Mora
Laboratory of Geo-Information Science and Remote Sensing
Wageningen University, The Netherlands
"Changes in forest cover affect the delivery of important ecosystem services, including biodiversity richness, climate regulation, carbon storage, and water supplies"(Foley et al. 2005; Hansen et al. 2013)
Between 2000 and 2012, 2.3 million km² of forest were lost and 0.8 million km² were established (Hansen et al. 2013)
Verbesselt, J., Zeileis, A., & Herold, M. (2012). Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment
Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote sensing of Environment
Change detection and monitoring research research task
DeVries, B., Verbesselt, J., Kooistra, L., & Herold, M. (2015). Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sensing of Environment
Dutrieux, L. P., Verbesselt, J., Kooistra, L., & Herold, M. (2015). Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia. ISPRS Journal of Photogrammetry and Remote Sensing.
Reiche, J., Verbesselt, J., Hoekman, D., & Herold, M. (2015). Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sensing of Environment
A change map in 3 lines of code
Update link http://loicdutrieux.com/talks/SCERIN-2015-change-detection


| Interface | Description |
|---|---|
| Operator |
Interface for all genetic operators.
|
| Class | Description |
|---|---|
| AveragingCrossoverOperator |
An arithmetic crossover operator.
|
| BitFlipMutation |
Simple mutation for the simple genetic algorithm.
|
| GaussianMutationOperator |
Performs the Gaussian mutation.
|
| IntegerMutation |
A mutation for integer encoded individuals.
|
| OnePtXOver |
Performs a one point crossover.
|
| PolynomialMutationOperator |
Performs the polynomial mutation as described in:
Deb, Kalyanmoy and Mayank Goyal (1996).
|
| SBXoverOperator |
Simulated binary crossover operator.
|
| SearchItemIndividualMutation |
A mutation operator for individuals represented as a vector of
SearchItem s. |
| SwappingMutationOperator |
A mutation which swaps the values on different positions in a single individual.
|


Our radar locates questions based on keywords generated by an AI in real time. The larger and more central a keyword appears on the radar, the more numerous or significant the associated questions.


Folders
<parent>
images
public
test
app
map
queries
tests
async-await
migrations
seeders
then-catch
config
models
src
views
css
problems
templates
yoush
Folders: 20


Check out Sanfrancisco tourist information. It's a great resource for learning about San Francisco!
Check out Boston tourist information. It's a great resource for learning about Boston!
Check out Chicago tourist information. It's a great resource for learning about Chicago!
Here are the three places that I like to visit:
Here are the reasons why I would to visit these cities: