|
Nonlinear Model Measures
|
|
| Among
different classes of nonlinear models, the local state space models
are implemented here. This class is popular in nonlinear analysis of
time series because it is effective for fitting and prediction tasks and
it is intuitively simple. Different variations of the local model are
implemented, and different statistical measures of fitting and
prediction can be derived for a number of varying model specific
parameters. These are the delay (t),
embedding dimension (m), number of neighbors (k)
and prediction times (h).
The local region supporting the model for fitting or prediction is determined here by the number of neighbours to the target point formed from the available set of reconstructed points. The k-d-tree data structure is utilized to speed up computation time in the search of neighboring points, as implemented in Matlab (introduced in R2010a). The point distances are computed using the Euclidean (L2) norm. The difference between the fitting and prediction measures is in the set of samples that are used to compute the statistical measure: all the samples are used for fitting and the samples in the so-called test set are used for prediction. Here, the test set is the last part of the time series given by the parameter of fraction for test set (f). For multi-step fit or prediction, both the direct and iterative schemes are implemented. For the direct fit or prediction at a lead time h, the h-step ahead mappings are used to form the estimate of the target points at a lead time h, whereas for the iterative fit or prediction, the one step predictions from previous steps are used to make predictions for the current step until the h-step is reached. The local models implemented here are listed below, where the task is to predict h-step ahead from a current state xi, where all states (points) are reconstructed with a delay t and an embedding dimension m, and the local model is estimated on the basis of the k nearest neighbors of xi, denoted as xi(j), for j=1,...,k. The selection of the specific model is determined by the value of the so-called truncation parameter (q). - The local average model (LAM), called also model of zero order, predicts xi+h from the average of the mappings of the neighbors at lead time h, i.e. xi(j)+h, for j=1,...,k. This model is selected by q = 0. - The local linear model (LLM) is the linear autoregressive model xi+h = a0 + aT xi, based only on the neighboring points xi(j), for j=1,...,k. The parameters a0 and a are estimated by ordinary least squares (OLS) and this requires that k > m+1. The prediction xi+h is computed from the equation above for the target point xi. Note that the solution for the model parameters may be numerically unstable if k is close to m. This model is selected by using any q ≥ m. - The local linear model as above but regularizing the OLS solution for the model parameters with principal component regression (PCR). PCR rotates the parameter space to match the basis formed by the principal components (found by Singular Value Decomposition (SVD) of the matrix formed by the neighboring points). Then the space is projected to the subspace formed by the q first principal axes, the solution for the parameters is found in this subspace and it is transformed back to the original state space to yield the PCR regularized solution for the parameters. In this way, the estimated parameters have smaller variance (they are more stable) at the cost of an introduced bias. Another advantage is that PCR may at cases reduce the effect of noise. The condition for the PCR solution is that k > q+1, so that stable solutions can be reached even when m > k provided that the truncation parameter is sufficiently small. This solution is selected by using 0 < q < m. Four standard statistical measures are encountered, the mean square error (MSE), the normalized mean square error (NMSE), the normalized root mean square error (NMSE) and the correlation coefficient (CC). The three last measures account for the variance of the time series and this allows comparison of the measure across different time series. The local state space models are commonly used under the hypothesis of an underlying deterministic system to the time series, e.g. see Abarbanel H.D.I. (1996), Analysis of Observed Chaotic Data, Springer, New York. Kantz H. and Schreiber T. (1997), Nonlinear Time Series Analysis, Cambridge University Press, Cambridge. The local linear model was first introduced in Farmer J.D. and Sidorowich J.J. (1987), Predicting Chaotic Time Series, Physical Review Letters, Vol 59, pp 845-848. The PCR regularization of the OLS solution for the local linear model and the difference in direct and iterative prediction are presented in Kugiumtzis D., Lingjærde O.C. and Christophersen N. (1998), Regularized Local Linear Prediction of Chaotic Time Series, Physica D, Vol 112, pp 344-360. Kugiumtzis D. (2002), State Space Local Linear Prediction, in Modelling and Forecasting Financial Data, Techniques of Nonlinear Dynamics, eds Soofi A. and Cao L., Kluwer Academic Publishers, Chp 4, pp 95-113. |
|
|
Local Average or Linear Direct Fit (Loc_DirFit) |
|
|
Local Average or Linear Direct Fit gives four statistical measures of
goodness of fit with a local model of order zero (the local average
model, LAM) or one (the local linear model, LLM). A number of different
delays, embedding dimensions and number of neighbors for the local model
can be given and the fit can be computed for a number of lead times
using the direct prediction scheme. The following parameters can be
specified: - delay (t): any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1'. - embedding dimension (m) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that no state space reconstruction will be done and the points are simply the samples of the time series. - number of neighbors (k) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that the computations of the goodness of fit measures will be done for the simplest local model where the prediction is based on the mapping of the nearest neighbor of the target point. This prediction is called also for nearest neighbor prediction. - prediction time (h) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that the computations of the goodness of fit measures will be done only for one-step ahead. - truncation parameter (q) : any positive integer. This parameter actually determines the type of local model to be used. The default is '0' meaning that the local average model will be used. If q ≥ m the standard local linear model with OLS solution for the model parameters will be used. So, if the user wants to run this model for a number of different embedding dimensions, just use q larger than the maximum of the selected embedding dimensions. If 0 < q < m the local linear model with the PCR regularized solution for the model parameters will be used. Care should be taken that the condition q < k (or m < k if q > m) always holds, otherwise the equation system is under-determined and there is no unique solution for the model parameters leading to unstable parameter estimates. The solution for the model parameters can be unstable (in the sense that the variance of the estimates is very large) also when q (or m if q > m) is at the level of k. The user can activate (check) any of the four statistical measures to save results for. If none is checked then results for all four measures will be saved and the measures will be included in the measure list. The statistical measures are the following: - MSE : if checked the mean square error (MSE) measure of goodness of fit for the specified parameters will be included in the list of measures. MSE is the sum of squares of prediction errors defined as
where xi+h is the actual sample and xi(h) is the h-step ahead prediction at current time i, for i=m,...,N-h, and N is the length of the time series. - NMSE : if checked the normalized mean square error (MSE) measure of goodness of fit for the specified parameters will be included in the list of measures. NMSE is the MSE divided by the variance of the samples included in the sum of MSE, defined as
where is the mean of the samples in the sum. - NRMSE : if checked the normalized root mean square error (MSE) measure of goodness of fit for the specified parameters will be included in the list of measures. NRMSE is the square root of NMSE. - CC : if checked the correlation coefficient (CC) measure of goodness of fit for the specified parameters will be included in the list of measures. CC is the standard Pearson correlation coefficient between the actual samples and the respective predictions, i.e. xi+h and xi(h) for h-step ahead prediction at current time i. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for the delay (t) '1', for embedding dimension (m) '5 10', for the number of nearest neighbors (k) '10 20', for prediction time (h) '1:3' and for truncation parameter (q) '20' (no truncation with PCR since q ≥ m), and checks only NRMSE then the NRMSE measure of Local Average or Linear Direct Fit is computed for the combinations of the 2 values of m, 2 values of k and the 3 values of h and in the measure list the following measure names will appear
Loc_DirFitNRMSEt1m5k10q20h1 |
|
| Local Average or Linear Iterative Fit (Loc_IteFit) |
|
|
Local Average or Linear Iterative Fit gives four statistical measures of goodness of fit
with a local model of order zero (the local average model, LAM) or one
(the local linear model, LLM). A number of different delays, embedding
dimensions and number of neighbors for the
local model can be given and the fit can be computed for a number of lead
times using the Iterative prediction scheme. The following parameters can be specified: - delay (t): any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1'. - embedding dimension (m) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that no state space reconstruction will be done and the points are simply the samples of the time series. - number of neighbors (k) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that the computations of the goodness of fit measures will be done for the simplest local model where the prediction is based on the mapping of the nearest neighbor of the target point. This prediction is called also for nearest neighbor prediction. - prediction time (h) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that the computations of the goodness of fit measures will be done only for one-step ahead. - truncation parameter (q) : any positive integer. This parameter actually determines the type of local model to be used. The default is '0' meaning that the local average model will be used. If q ≥ m the standard local linear model with OLS solution for the model parameters will be used. So, if the user wants to run this model for a number of embedding dimensions, just use q larger than the maximum of the selected embedding dimensions. If 0 < q < m the local linear model with the PCR regularized solution for the model parameters will be used. Care should be taken that the condition q < k (or m < k if q > m) always holds, otherwise the equation system is under-determined and there is no unique solution for the model parameters leading to unstable parameter estimates. The solution for the model parameters can be unstable (in the sense that the variance of the estimates is very large) also when q (or m if q > m) is at the level of k. The user can activate (check) any of the four statistical measures to save results for. If none is checked then results for all four measures will be saved and the measures will be included in the measure list. The statistical measures are the following: - MSE : if checked the mean square error (MSE) measure of goodness of fit for the specified parameters will be included in the list of measures. MSE is the sum of squares of prediction errors defined as
where xi+h is the actual sample and xi(h) is the h-step ahead prediction at current time i, for i=m,...,N-h, and N is the length of the time series. - NMSE : if checked the normalized mean square error (MSE) measure of goodness of fit for the specified parameters will be included in the list of measures. NMSE is the MSE divided by the variance of the samples included in the sum of MSE, defined as
where is the mean of the samples in the sum. - NRMSE : if checked the normalized root mean square error (MSE) measure of goodness of fit for the specified parameters will be included in the list of measures. NRMSE is the square root of NMSE. - CC : if checked the correlation coefficient (CC) measure of goodness of fit for the specified parameters will be included in the list of measures. CC is the standard Pearson correlation coefficient between the actual samples and the respective predictions, i.e. xi+h and xi(h) for h-step ahead prediction at current time i. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for the delay (t) '1', for embedding dimension (m) '5 10', for the number of nearest neighbors (k) '10 20', for prediction time (h) '1:3' and for truncation parameter (q) '20' (no truncation with PCR since q ≥ m), and checks only NRMSE then the NRMSE measure of Local Average or Linear Iterative Fit is computed for the combinations of the 2 values of m, 2 values of k and the 3 values of h and in the measure list the following measure names will appear Loc_IteFitNRMSEt1m5k10q20h1 |
|
| Local Average or Linear Direct Prediction (Loc_DirPre) | |
|
Local Average or Linear Direct Prediction gives four statistical measures of goodness of
prediction
with a local model of order zero (the local average model, LAM) or one
(the local linear model, LLM). The local average or linear model is
estimated in the first part of the time series, the so-called training
set, and the predictions are made in the second part of the time series,
the so-called test set. A number of different delays, embedding
dimensions and number of neighbors for the
local model can be given and the prediction can be computed for a number of lead
times using the direct prediction scheme. The following parameters can be specified: - fraction for test set (f) : any number between 0.1 and 0.9. The default is '0.5' meaning that the test set is the second half of the time series. Typically f should be smaller or equal to 0.5, so that the test set does not exceed in size the training set. - delay (t): any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1'. - embedding dimension (m) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that no state space reconstruction will be done and the points are simply the samples of the time series. - number of neighbors (k) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that the computations of the goodness of prediction measures will be done for the simplest local model where the prediction is based on the mapping of the nearest neighbor of the target point in the training set. This prediction is called also for nearest neighbor prediction. - prediction time (h) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that the computations of the goodness of prediction measures will be done only for one-step ahead. - truncation parameter (q) : any positive integer. This parameter actually determines the type of local model to be used. The default is '0' meaning that the local average model will be used. If q ≥ m the standard local linear model with OLS solution for the model parameters will be used. So, if the user wants to run this model for a number of embedding dimensions, just use q larger than the maximum of the selected embedding dimensions. If 0 < q < m the local linear model with the PCR regularized solution for the model parameters will be used. Care should be taken that the condition q < k (or m < k if q > m) always holds, otherwise the equation system is under-determined and there is no unique solution for the model parameters leading to unstable parameter estimates. The solution for the model parameters can be unstable (in the sense that the variance of the estimates is very large) also when q (or m if q > m) is at the level of k. The user can activate (check) any of the four statistical measures to save results for. If none is checked then results for all four measures will be saved and the measures will be included in the measure list. The statistical measures are the following: - MSE : if checked the mean square error (MSE) measure of prediction in the test set for the specified parameters will be included in the list of measures. MSE is the sum of squares of prediction errors defined as
where xi+h is the actual sample and xi(h) is the h-step ahead prediction at current time i, for i=N1,...,N-h, N is the length of the time series and N1 is the length of the training set ( N1 = (1-f)N ). - NMSE : if checked the normalized mean square error (MSE) measure of prediction in the test set for the specified parameters will be included in the list of measures. NMSE is the MSE divided by the variance of the samples included in the sum of MSE, defined as
where is the mean of the samples in the sum - NRMSE : if checked the normalized root mean square error (MSE) measure of prediction in the test set for the specified parameters will be included in the list of measures. NRMSE is the square root of NMSE. - CC : if checked the correlation coefficient (CC) measure of prediction in the test set for the specified parameters will be included in the list of measures. CC is the standard Pearson correlation coefficient between the actual samples and the respective predictions. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for fraction for test set (f) '0.25', for the delay (t) '1', for embedding dimension (m) '5 10', for the number of nearest neighbors (k) '10 20', for prediction time (h) '1:3' and for truncation parameter (q) '20' (no truncation with PCR since q ≥ m), and checks only NRMSE then the NRMSE measure of Local Average or Linear Direct Prediction is computed on a test set of length one forth of time series length for the combinations of the 2 values of m, 2 values of k and the 3 values of h and in the measure list the following measure names will appear Loc_DirPreNRMSEt1m5k10q20h1 |
|
| Local Average or Linear Iterative Prediction (Loc_ItePre) |
|
|
Local Average or Linear Iterative Prediction gives four statistical measures of goodness of
prediction
with a local model of order zero (the local average model, LAM) or one
(the local linear model, LLM). The local average or linear model is
estimated in the first part of the time series, the so-called training
set, and the predictions are made in the second part of the time series,
the so-called test set. A number of different delays, embedding
dimensions and number of neighbors for the
local model can be given and the prediction can be computed for a number of lead
times using the iterative prediction scheme. The following parameters can be specified: - fraction for test set (f) : any number between 0.1 and 0.9. The default is '0.5' meaning that the test set is the second half of the time series. Typically f should be smaller or equal to 0.5, so that the test set does not exceed in size the training set. - delay (t): any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1'. - embedding dimension (m) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that no state space reconstruction will be done and the points are simply the samples of the time series. - number of neighbors (k) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that the computations of the goodness of prediction measures will be done for the simplest local model where the prediction is based on the mapping of the nearest neighbor of the target point in the training set. This prediction is called also for nearest neighbor prediction. - prediction time (h) : any valid matlab format denoting an array of positive integers or a single positive integer. The default is '1' meaning that the computations of the goodness of prediction measures will be done only for one-step ahead. - truncation parameter (q) : any positive integer. This parameter actually determines the type of local model to be used. The default is '0' meaning that the local average model will be used. If q ≥ m the standard local linear model with OLS solution for the model parameters will be used. So, if the user wants to run this model for a number of embedding dimensions, just use q larger than the maximum of the selected embedding dimensions. If 0 < q < m the local linear model with the PCR regularized solution for the model parameters will be used. Care should be taken that the condition q < k (or m < k if q > m) always holds, otherwise the equation system is under-determined and there is no unique solution for the model parameters leading to unstable parameter estimates. The solution for the model parameters can be unstable (in the sense that the variance of the estimates is very large) also when q (or m if q > m) is at the level of k. The user can activate (check) any of the four statistical measures to save results for. If none is checked then results for all four measures will be saved and the measures will be included in the measure list. The statistical measures are the following: - MSE : if checked the mean square error (MSE) measure of prediction in the test set for the specified parameters will be included in the list of measures. MSE is the sum of squares of prediction errors defined as
where xi+h is the actual sample and xi(h) is the h-step ahead prediction at current time i, for i=N1,...,N-h, N is the length of the time series and N1 is the length of the training set ( N1 = (1-f)N ). - NMSE : if checked the normalized mean square error (MSE) measure of prediction in the test set for the specified parameters will be included in the list of measures. NMSE is the MSE divided by the variance of the samples included in the sum of MSE, defined as
where is the mean of the samples in the sum - NRMSE : if checked the normalized root mean square error (MSE) measure of prediction in the test set for the specified parameters will be included in the list of measures. NRMSE is the square root of NMSE. - CC : if checked the correlation coefficient (CC) measure of prediction in the test set for the specified parameters will be included in the list of measures. CC is the standard Pearson correlation coefficient between the actual samples and the respective predictions. Example: If the user selects this measure by activating the check box in the beginning of the measure line and sets for fraction for test set (f) '0.25', for the delay (t) '1', for embedding dimension (m) '5 10', for the number of nearest neighbors (k) '10 20', for prediction time (h) '1:3' and for truncation parameter (q) '20' (no truncation with PCR since q ≥ m), and checks only NRMSE then the NRMSE measure of Local Average or Linear Iterative Prediction is computed on a test set of length one forth of time series length for the combinations of the 2 values of m, 2 values of k and the 3 values of h and in the measure list the following measure names will appear Loc_ItePreNRMSEt1m5k10q20h1 |
|
| OK |
|
| By pressing this button the window of "Nonlinear Model Measures" will disappear and the user will be moved to the "Select / run measures" window. Any changes in the measures and parameter values will be stored. | |
| Cancel |
|
| Quit without doing anything and return to the "Select / run measures" window. Any changes in the measures and parameter values will be ignored. | |
| Help |
|
| This file will be shown. |


Flights
Over the past few years, there has been a marked increase in the number of domestic
travellers preferring flights over other modes of travel. And why not? With so many new airlines
introducing domestic flights to even smaller cities within India at competitive airfare,
there couldn't be a better time to plan your domestic travels.
Book your flights through ‘No suffer’ to avail exclusive benefits and discounts on every flight.
Railways
Train ticket booking through NSF along with exclusive benefits. Free ticket Upgradation,
Choice of berths and tatkaal booking.
Buses
Bus transport has become popular over the years and we at NSF partner
with leading shuttle services to provide maximum transportation facilities
to our customers to help them reach their destination and to make their
dream vacation come true without any hassle.
Car Rentals
At NSF we realise that transportation is necessary not only to reach your destination,
but also to explore your chosen destination. Therefore, we provide car rentals and other
taxi services in various cities across the country, offering exclusive discounts and
at affordable prices.


Merges sequential
if
statements if they have the same conditions.
Note that this intention may change the order of code execution.


Hazaron Kwahishen Aisi
Ki
harkhwahish pe dam nikle..
Bahut nikle .. mere armaan
lekin
phirbhi kam nikle..
- Galib
(This loosely means a lot of heart felt wishes ..and that
wishes never exhaust themselves)
Well there are a few that are in my heart
# Add articles in this website that can help people. ( highly inspired by PG)
# Get SCJP 1.4 Certified
# Get OCP Certified
# Got first certification from AWS - Jan 2021.
# Get a wiki or redshift installed on my website to help my clients. - May 2021
# Visit Paris Stay there for about 6 months
# Get Married (Done: Got married to Preeti She is just a wonderful life partner)
# Do Engineering - Done
# Scuba Diving.
# Reduce weight to 80 Kgs
# .. and stay at that
# Develop a habit to wake up early at 5 AM at least 5 days a weekj
# Study at world class Business School.
# Create value and wealth for my employers and myself and earn their
faith
# Make friends with a person who only speaks Chinese Mandarin as a
language (Yes I am learning Chinese)
# Become an Old man.
# ....and earn my age.
# Have a Pony tail haircut. (or no haircut for 4 months I should say)
# Donate 1 lakh Rs for a social cause.
# Own a flat in lokhandwala Mumbai.
# Have an enviable Library .. of best books -(
I spend at least 4000 Rs per year on it since
past 8 years, before that my father used to buy for me )
# .. and read most of those at least twice (In all my journeys ..
I
# keep at least 2 books along )
# Make a compilation of what all books are there .. ( I want to know
how many I have in every topic)
# Give a presentation to a crowd of 40,000
# Act in a play - Done
# Sing a French Song - Done
(Chanson le'Auvergnea by George Branssens
(Also did best performance and represented my state at national level in Alliance Francaise )
...


Code signing, app id, provisioning profile
Let’s explain the Apple code signing and provisioning profile approach. It all starts with a developer certificate.
Developer or Distribution certificate
As an iOS developer, chances are you have a certificate, a public key, and a private key on your development machine. A certificate is — very broadly speaking — a public key combined with a lot of additional information that was itself signed by Apple Worldwide Developer Relations CA. This is the certificate you need to renew every year.
Code signing
Code signing is a security technology that allows you to certify that an app was created by you. All apps running on iOS must be code signed.
Entitlements
Entitlements specify which resources of the system an app is allowed to use. These entitlements should all be enabled in the developer center’s App ID, and embedded in the provisioning profile. Setting entitlements to enable Push notification is done in Apple App ID and SSL certificate for APNs.
Provisioning profile
A provisioning profile is a collection of all the components needed to determine if a particular app can run on a particular device. It’s a central point where app-id, list all UUID devices where app can run, certificate and entitlements are gathered together.
Code signing and Provisioning Profile in Xcode: automatic mode
Code signing and provisioning profile selection should be in automatic mode by default. Specifically if your project is used by multiple iOS developpers, let Xcode find the appropriate developer provisioning profile.
NOTES: The Provisioning profile setting is set to 'None' in Xcode 5. And in Xcode 6, the Provisioning profile setting should be set to 'Automatic' by default. Check apple documentation for more details.
In Xcode 8, a .entitlements file is needed to make push notifications work. To create it, go to the Capabilities section of the project.
You can see that the Push Notifications switch is in "OFF" state. Click the switch and Xcode 8 will connect with your developer account to add the needed provisioning profiles and will also create the YourProjectName.entitlements file.
Notice that the status of the Push Notifications switch is now "ON" and there is a new HelloWorld.entitlements file in the left side.
Running into "No matching provisioning profiles found" issue?
Check out iOS troubleshooting guide.
UnifiedPush Server
Now, after managing all the Apple certificates, let’s setup the UnifiedPush Server.


Before Change
all_psnr = []
max_shifts = [0, 1, 3, 5]
for n, s in enumerate(max_shifts):
print("ms = {}".format(s))
im_bayescs = cycle_spin(noisy, func=denoise_wavelet, max_shifts=s,
func_kw=denoise_kwargs)
ax[n+1].imshow(im_bayescs)
After Change
ax[5].set_xlabel("max cycle shift along each axis")
ax[5].grid("on")
plt.subplots_adjust(wspace=0.35, hspace=0.35)
// Annotate with a cyan arrow on the 6x6 case vs. no cycle shift case to
// illustrate a region with reduced block-like artifact with cycle shifting
arrowprops = dict(arrowstyle="simple,tail_width=0.1,head_width=0.5",
connectionstyle="arc3",


vignettes/building-custom-docker-images.Rmd
building-custom-docker-images.Rmd
This guide covers how to build and use custom Docker images for training and deploying models with Azure Machine Learning.
For remote training jobs and model deployments, Azure ML has a default environment that gets used. However, this default environment may not be sufficient for the requirements of your particular scenario. And if you specify additional dependencies on top of the default environment, you will have to wait through the image build process when you run the job. For these situations, we recommend that you create and use a custom Docker image. Typically, you create a custom image when you want to use Docker to manage your dependencies, maintain tighter control over component versions or save time during remote training runs or deployment. For example, you might want to standardize on a specific version of R or other component. You might also want to install software required by your training code or model, where the installation process takes a long time. Installing the software when creating the Docker image means that you don’t have to install it for each remote run or deployment.
The first step in building a Docker image is to write the Dockerfile, a text file that contains all the commands needed to build your image. Create a text file named Dockerfile
.
Most Dockerfiles start from a parent image, rather than from scratch. Azure ML has a set of maintained images that serve as base images for training and inference. You can find information on these images, including the Dockerfiles used to build them, at this GitHub repo Azure/AzureML-Containers. Depending on your scenario, you should pick an appropriate CPU or GPU image to use as the parent image. The base images include Miniconda, and the GPU base images include the necessary GPU drivers needed to run GPU jobs on Azure ML. Here is the list of images and associated tags for the Azure ML base images.
As an example, we will use one of the CPU images as the parent image for our Dockerfile:
FROM mcr.microsoft.com/azureml/base:openmpi3.1.2-ubuntu18.04
Since using Azure ML has some Python dependencies, we will add an instruction to install these dependencies via Conda, an open-source package management system:
ggplot2
).azureml-core
and applicationinsights
packages required for tasks such as logging metrics, uploading artifacts, accessing datastores from within runs, and inference. azureml-defaults
should be sufficient for most remote training and deployment scenarios; if for some reason you need the full SDK, you can specify pip installing the full azureml-sdk
package instead.We will also install the R interpreter via conda:
Optionally, you can also install the R Essentials bundle r-essentials, which includes approximately 200 of the most popular R packages for data science, including dplyr, shiny, ggplot2, tidyr, caret, and nnet. For tighter control over package versions and the size of your image, however, it is better to explicitly specify each R package that you need (covered in the following section).
FROM mcr.microsoft.com/azureml/base:openmpi3.1.2-ubuntu18.04
RUN conda install -c r -y pip=20.1.1 openssl=1.1.1c r-base rpy2 && \
conda install -c conda-forge -y mscorefonts && \
conda clean -ay && \
pip install --no-cache-dir azureml-defaults
If you have any other Python dependencies, such as for TensorFlow or Keras, which also leverage reticulate to wrap the corresponding Python SDK like Azure ML does, you should also specify those. The following vignettes have Dockerfiles you can reference:
NOTE:
Although standard installation procedure for the Azure ML SDK (and TensorFlow and Keras) assumes installing the Python SDK via install_azureml()
, you will not need to add an instruction for this in your Dockerfile (or inside your job script) since the above instruction already directly installs the Python SDK and reticulate will find the Python installation. In fact, for remote jobs on Azure ML, you should not use install_azureml()
, as you may run into a “Permissions denied” issue with Docker running as non-root when creating the conda environment (which is what the install_azureml()
method does).
Finally, specify any R packages that are required for your training script or scoring script. The following example installs several R packages from CRAN.
 The following sections show examples for publish your image on either Docker Hub or Azure Container Registry.
You can familiarize yourself first with the official documentation on building Docker images if you are new to it.
If you are using Docker Hub to publish your images, first create a Docker Hub account and a repository where you will publish your image. Then, run the following set of CLI commands.
docker login --username <your username>
Dockerfile
. Build your Docker image and tag it:docker build --tag <your username>/<your repository>:<tag> .
By convention, you can have your repository correspond to the image name. By default, the tag will be given the tag latest
.
docker push <your username>/<your repository>:<tag>
The first time you train or deploy a model using an Azure Machine Learning workspace, an Azure Container Registry is created for your workspace.You can build and publish your image using this registry. (You can also use a standalone ACR registry if you prefer.)
az login
az ml workspace show -w <your workspace> -g <resourcegroup> --query containerRegistry
The information returned is similar to the following text: /subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.ContainerRegistry/registries/<registry_name>
The
az acr login --name <registry_name>
Dockerfile
. Use the following command to upload the Dockerfile and build it:az acr build --image <image_name>:<tag> --registry <registry_name> --file Dockerfile .
Now that your Docker image is published, you can create an Azure ML Environment and specify your custom image.
For Docker Hub:
For ACR:
env <- r_environment("your-env-name",
custom_docker_image = "<repository_name>.azurecr.io/<image_name>:<tag>")
If you want to use an image from a private container registry that is not in your workspace, you must specify the registry details using container_registry
and specify it to the image_registry_details
parameter of r_environment()
:
If you want to use your image for a remote training run, pass the environment you created into the estimator configuration for your job:
ws <- load_workspace_from_config()
compute_target <- get_compute(ws, cluster_name = "your-cluster-name")
exp <- experiment(workspace = ws, name = "your-experiment-name")
est <- estimator(source_directory = ".",
entry_script = "train.R",
compute_target = compute_target,
environment = env)
run <- submit_experiment(exp, est)
If you want to use the image for deploying a model as a web service, pass the environment you created into the inference configuration for your deployment:
model <- get_model(ws, name = "your-model-name")
inference_config = inference_config(entry_script = 'score.R',
source_directory ='.',
environment = env)
aci_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 0.5)
aci_service <- deploy_model(ws,
'your-service-name',
list(model),
inference_config,
aci_config)
For more information on deploying models in Azure ML, see the Deploying models guide.
For additional resources on building and using custom Docker images, you can refer to the following:


17. Help! My boss wants me to load test our application!¶
This is a fairly open-ended proposition. There are a number of questions to be asked first, and additionally a number of resources that will be needed. You will need some hardware to run the benchmarks/load-tests from. A number of tools will prove useful. There are a number of products to consider. And finally, why is Java a good choice to implement a load-testing/Benchmarking product.
17.1 Questions to ask¶
What is our anticipated average number of users (normal load)?
What is our anticipated peak number of users?
When is a good time to load-test our application (i.e. off-hours or week-ends), bearing in mind that this may very well crash one or more of our servers?
Does our application have state? If so, how does our application manage it (cookies, session-rewriting, or some other method)?
What is the testing intended to achieve?
17.2 Resources¶
The following resources will prove very helpful. Bear in mind that if you cannot locate these resources, you will become these resources. As you already have your work cut out for you, it is worth knowing who the following people are, so that you can ask them for help if you need it.
17.2.1 Network¶
Who knows our network topology? If you run into any firewall or proxy issues, this will become very important. As well, a private testing network (which will therefore have very low network latency) would be a very nice thing. Knowing who can set one up for you (if you feel that this is necessary) will be very useful. If the application doesn't scale as expected, who can add additional hardware?
17.2.2 Application¶
Who knows how our application functions? The normal sequence is
- test (low-volume - can we benchmark our application?)
- benchmark (the average number of users)
- load-test (the maximum number of users)
- test destructively (what is our hard limit?)
17.3 What platform should I use to run the benchmarks/load-tests?¶
This should be a widely-used piece of hardware, with a standard (i.e. vanilla) software installation. Remember, if you publish your results, the first thing your clients will do is hire a graduate student to verify them. You might as well make it as easy for this person as you possibly can.
For Windows, Windows XP Professional should be a minimum (the others do not multi-thread past 50-60 connections, and you probably anticipate more users than that).
Good free platforms include the linuxes, the BSDs, and Solaris Intel. If you have a little more money, there are commercial linuxes. This may be worth it if you need the support.
For non-Windows platforms, investigate "ulimit -n unlimited" with a view to including it in your user account startup scripts (.bashrc or .cshrc scripts for the testing account).
Also note that some Linux/Unix editions are intended for server use. These generally have minimal or no GUI support. Such OSes should be OK for running JMeter in non-GUI mode, but JMeter GUI mode probably won't work unless you install a minimal GUI environment.
As you progress to larger-scale benchmarks/load-tests, this platform will become the limiting factor. So it's worth using the best hardware and software that you have available. Remember to include the hardware/software configuration in your published benchmarks.
When you need a lot of machines or want to test the network latency, Cloud can help you. JMeter can easily be installed on Cloud instances as it runs on nearly any architecture available in the Cloud. JMeter is also supported within Commercial Cloud PAAS if you don't want to manage it yourself.
Don't forget JMeter batch (NON-GUI) mode. This mode should be used during load testing for many reasons:
- If you have a powerful server that supports Java but perhaps does not have a fast graphics implementation, or where you need to login remotely.
- Batch (non-GUI) mode can reduce the network traffic compared with using a remote display or client-server mode.
- Java AWT Thread used for GUI mode can alter injection behaviour by blocking sometimes
17.4 Tools¶
The following tools will all prove useful. It is definitely worthwhile to become familiar with them. This should include trying them out, and reading the appropriate documentation (man-pages, info-files, application --help messages, and any supplied documentation).
17.4.1 ping¶
This can be used to establish whether or not you can reach your target site. Options can be specified so that 'ping' provides the same type of route reporting as 'traceroute'.
17.4.2 nslookup/dig¶
While the user will normally use a human-readable internet address, you may wish to avoid the overhead of DNS lookups when performing benchmarking/load-testing. These can be used to determine the unique address (dotted quad) of your target site.
17.4.3 traceroute¶
If you cannot "ping" your target site, this may be used to determine the problem (possibly a firewall or a proxy). It can also be used to estimate the overall network latency (running locally should give the lowest possible network latency - remember that your users will be running over a possibly busy internet). Generally, the fewer hops the better.
17.5 How can I enhance JMeter?¶
There a lot of open-source and commercial providers who provide JMeter plugins or other resources for use with JMeter. Some of these are listed on the JMeter Wiki. They are listed under several categories:
- JMeterPlugins - plugins for extending JMeter
- JMeterAddons - addons for use with JMeter, e.g. plugins for browsers, Maven and Jenkins.
- JMeterServices - 3rd party services, e.g. cloud-based JMeter
17.6 Why Java?¶
Why not Perl or C?
Well, Perl might be a very good choice except that the Benchmark package seems to give fairly fuzzy results. Also, simulating multiple users with Perl is a tricky proposition (multiple connections can be simulated by forking many processes from a shell script, but these will not be threads, they will be processes). However, the Perl community is very large. If you find that someone has already written something that seems useful, this could be a very good solution.
C, of course, is a very good choice (check out the Apache ab tool). But be prepared to write all of the custom networking, threading, and state management code that you will need to benchmark your application.
Java gives you (for free) the custom networking, threading, and state management code that you will need to benchmark your application. Java is aware of HTTP, FTP, and HTTPS - as well as RMI, IIOP, and JDBC (not to mention cookies, URL-encoding, and URL-rewriting). In addition Java gives you automatic garbage-collection, and byte-code level security.


Image
-
packnet_sfm.utils.image.
flip_lr
(image)[source] Flip image horizontally
- Parameters
image (torch.Tensor [B,3,H,W]) – Image to be flipped
- Returns
image_flipped – Flipped image
- Return type
torch.Tensor [B,3,H,W]
-
packnet_sfm.utils.image.
flip_model
(model, image, flip)[source] Flip input image and flip output inverse depth map
- Parameters
model (nn.Module) – Module to be used
image (torch.Tensor [B,3,H,W]) – Input image
flip (bool) – True if the flip is happening
- Returns
inv_depths – List of predicted inverse depth maps
- Return type
list of torch.Tensor [B,1,H,W]
-
packnet_sfm.utils.image.
gradient_x
(image)[source] Calculates the gradient of an image in the x dimension :param image: Input image :type image: torch.Tensor [B,3,H,W]
- Returns
gradient_x – Gradient of image with respect to x
- Return type
torch.Tensor [B,3,H,W-1]
-
packnet_sfm.utils.image.
gradient_y
(image)[source] Calculates the gradient of an image in the y dimension :param image: Input image :type image: torch.Tensor [B,3,H,W]
- Returns
gradient_y – Gradient of image with respect to y
- Return type
torch.Tensor [B,3,H-1,W]
-
packnet_sfm.utils.image.
image_grid
(B, H, W, dtype, device, normalized=False)[source] Create an image grid with a specific resolution
- Parameters
B (int) – Batch size
H (int) – Height size
W (int) – Width size
dtype (torch.dtype) – Meshgrid type
device (torch.device) – Meshgrid device
normalized (bool) – True if grid is normalized between -1 and 1
- Returns
grid – Image grid containing a meshgrid in x, y and 1
- Return type
torch.Tensor [B,3,H,W]
-
packnet_sfm.utils.image.
interpolate_image
(image, shape, mode='bilinear', align_corners=True)[source] Interpolate an image to a different resolution
- Parameters
image (torch.Tensor [B,?,h,w]) – Image to be interpolated
shape (tuple (H, W)) – Output shape
mode (str) – Interpolation mode
align_corners (bool) – True if corners will be aligned after interpolation
- Returns
image – Interpolated image
- Return type
torch.Tensor [B,?,H,W]
-
packnet_sfm.utils.image.
interpolate_scales
(images, shape=None, mode='bilinear', align_corners=False)[source] Interpolate list of images to the same shape
- Parameters
images (list of torch.Tensor [B,?,?,?]) – Images to be interpolated, with different resolutions
shape (tuple (H, W)) – Output shape
mode (str) – Interpolation mode
align_corners (bool) – True if corners will be aligned after interpolation
- Returns
images – Interpolated images, with the same resolution
- Return type
list of torch.Tensor [B,?,H,W]
-
packnet_sfm.utils.image.
load_image
(path)[source] Read an image using PIL
- Parameters
path (str) – Path to the image
- Returns
image – Loaded image
- Return type
PIL.Image
-
packnet_sfm.utils.image.
match_scales
(image, targets, num_scales, mode='bilinear', align_corners=True)[source] Interpolate one image to produce a list of images with the same shape as targets
- Parameters
image (torch.Tensor [B,?,h,w]) – Input image
targets (list of torch.Tensor [B,?,?,?]) – Tensors with the target resolutions
num_scales (int) – Number of considered scales
mode (str) – Interpolation mode
align_corners (bool) – True if corners will be aligned after interpolation
- Returns
images – List of images with the same resolutions as targets
- Return type
list of torch.Tensor [B,?,?,?]
-
packnet_sfm.utils.image.
meshgrid
(B, H, W, dtype, device, normalized=False)[source] Create meshgrid with a specific resolution
- Parameters
B (int) – Batch size
H (int) – Height size
W (int) – Width size
dtype (torch.dtype) – Meshgrid type
device (torch.device) – Meshgrid device
normalized (bool) – True if grid is normalized between -1 and 1
- Returns
xs (torch.Tensor [B,1,W]) – Meshgrid in dimension x
ys (torch.Tensor [B,H,1]) – Meshgrid in dimension y


{include file="../view/common/header.htm"}> <{include file="../view/common/navigation-responsive.htm" theme="bluemode"}>
程序猿同学正在加班加点开发中……


{% extends 'records/base.html' %} {% load widget_tweaks %} {% load record_filters %} {% block content %}
Here you can modify the medicine's information, along with adding and removing schedules from the medicine's plan. Want to add new schedules? There are 2 blank forms at the bottom of the right pane! As a reminder:
"1" for frequency
"weeks" for interval
"adult" for demographics
"2" for doses
means we will remind you to put 2 doses, with 1 week between doses. for any adult horse which has this medicine on it's plan.


Introduction
This document describes how to build ADCIRC and ADCIRC+SWAN, as well as the guidelines that ADCIRC developers should use when developing new code for ADCIRC and its related utilities. It also presents a short tutorial for Subversion (svn), the version control software that we use to coordinate development and ease the task of merging in new features.
Build It
This section contains details for building ADCIRC executables and describes the directories and files related to the build process. For a more complete description of the files and directories that are included with the ADCIRC source code, see the official ADCIRC documentation on adcirc.org:
http://adcirc.org/home/documentation/generic-adcirc-compile-time-operations/
The ADCIRC source tree contains a directory called work where the ADCIRC and ADCIRC+SWAN executables are built. This directory contains the following files:
- config.guess
-
A shell script that is called automatically by make during the build process to guess the hardware and operating system that are being used in the build process. You can execute this shell script yourself on the command line to see what it guesses and confirm that it is guessing correctly. The resulting platform guess is then used automatically in the cmplrflags.mk file.
- cmplrflags.mk
-
Contains sets of compiler flags that are appropriate for various platforms. The build process uses the output from config.guess to select the appropriate set of compiler flags. In the past, in the days of many varieties of proprietary unix, the hardware type and operating system type were sufficient to determine which compiler flags to select. Now that most installations of ADCIRC are on linux, it is often necessary to provide additional hints about which set of compiler flags to use, and these additional hints are provided on the command line when make is executed, as described below.
- makefile
-
The makefile contains instructions for building adcirc, padcirc, padcswan (if the SWAN source code is also present), adcprep, adcpost, hstime, and aswip on linux or unix.
When building ADCIRC for the first time on a new (to me) platform, I normally begin by going to the ADCIRC work directory
cd work
and running config.guess manually to see what it guesses about the platform architecture.
./config.guess
Then I open up the cmplrflags.mk file, to see if the relevant platform and/or compiler is defined in the file, and if it is, how it is triggered on the make command line with the compiler= notation.
I also test the availability of the compilers that I will be using by executing the which command. For example, if I’m using the Intel compilers, I’ll type which ifort. If I’ll be running ADCIRC in parallel, I’d also try which icc and which mpif90 to make sure they’re all available. If they’re not, I take it up with my local sysadmin.
Looking in the cmplrflags.mk, I would find (for example) that I can use a set of Intel-appropriate compiler flags by setting compiler=intel on the make command line. As a result, my first tentative step in building all the ADCIRC executables would be
make adcirc compiler=intel
Assuming that this completes successfully, I would try
make padcirc compiler=intel
Now that padcirc has been built successfully, the parallelization modules used by parallel SWAN are available (they share the same libraries). In other words, the padcswan build depends on the padcirc build, so padcirc must be built before padcswan. If you’d like to build ADCIRC+SWAN, it is time to switch to the SWAN subdirectory.
cd ../swan
SWAN has a completely separate build system, since it is normally a completely separate code. But the SWAN build system must solve the same problems as the ADCIRC build system, that is, detecting the nature of the underlying platform and suggesting an appropriate set of compiler flags.
The SWAN build system has a perl script called platform.pl that performs much the same function as config.guess does for ADCIRC. The difference is that plaform.pl picks the compiler flags and writes them to a file called macros.inc for use during the build process.
Several sets of macros.inc files are already available in the ADCIRC source code for use with certain platforms; type ls macros* to see the currently available choices. To write a set of compiler flags for your platform, type
perl ./platform.pl
You may want to have a look at the resulting macros.inc file to be sure that it represents the compiler you’re using. If it looks ok, the next step is to create a test build of punswan. You won’t actually use punswan; in fact, you’ll completely delete it, even if it builds successfully. You’re just trying to build it to test the validity of your compiler flags.
make punswan
Once punswan builds successfully, go ahead and get rid of it as well as any object files that were built along the way with
make clobber
Now you’re ready to switch back to the work subdirectory and build padcswan, if you’re interested in that:
cd ../work make padcswan compiler=intel
Once padcswan has been built successfully, you can build adcprep, the program that prepares your input files for a parallel run. If you also plan to run ADCIRC+SWAN in parallel, then add the SWAN=enable string to the make command line, as follows:
make adcprep compiler=intel SWAN=enable
You’ll see the invokation of your C compiler during the build process for adcprep, since adcprep uses the metis library (which is written in C) to perform domain decomposition.
Once you’ve successfully built adcprep, the main set of executables is complete. However, there are a couple utility programs you may like to build as well. The first of these is a very small program called hstime whose only purpose is to read the time in seconds from an ADCIRC hotstart file and write it out to the console window.
The final executable that you may like to build is aswip, the Asymmetric Wind Input Preprocessor. This utility program is required for the use of an asymmetric vortex model for tropical cyclones in ADCIRC. One wrinkle for this program is that it calls many subroutines inside ADCIRC, therefore there is a dependency of aswip on adcirc, meaning that adcirc executable must be built first in order for aswip to be built successfully. It is built analogously with the other ADCIRC executables, with a command like make aswip compiler=intel.
Another issue with the building of aswip is that it is built inside the same directory as adcirc, and both programs have a "main" program section inside them, creating a conflict. Therefore, in order to rebuild adcirc after aswip has been built, aswip its object file must be deleted first, to avoid a build error when adcirc is rebuilt.
All of the above instructions will build the ADCIRC executables without support for NetCDF input or output. This was done intentionally, to simplify the build process and avoid complications resulting from the vagaries of building with NetCDF.
On the other hand, the use of NetCDF is recommended as a result of the many advantages of NetCDF files. ADCIRC actually supports the use of three different NetCDF capabilities: NetCDF3, NetCDF4 (classic model), and NetCDF4 with internal compression.
If you’ve already built all the executables without NetCDF, its necessary to get rid of them so that you can build with NetCDF.
make clobber
To enable compilation with NetCDF3, the command line listed at the beginning of these instructions for building ADCIRC would be modified as follows:
make adcirc compiler=intel NETCDF=enable NETCDFHOME=/usr
with the NETCDFHOME variable indicating the directory where NetCDF was installed. The ADCIRC build system expects this directory to contain include/netcdf.inc.
If NetCDF4 without internal compression is installed on your platform, the above would change to the following:
make adcirc compiler=intel NETCDF=enable NETCDFHOME=/usr NETCDF4=enable
in which case the directory specified in the NETCDFHOME variable would also be expected to contain the include/netcdf.mod Fortran module file.
Finally, if your NetCDF4 installation supports internal compression (only introduced in NetCDF4 version 4.1), and you want ADCIRC to automatically make use of internal compression, change the above command line as follows:
make adcirc compiler=intel NETCDF=enable NETCDFHOME=/usr NETCDF4=enable NETCDF4_COMPRESSION=enable
These changes in the make command line must be made for the build of each of the ADCIRC executables so that they are all consistent.
In general, building ADCIRC with NetCDF support is tedious and usually requires several rounds of emails to local sysadmins to determine the right combination of compiler flags and options, because they vary siginificantly from platform to platform. Have a look at the NetCDF-related compiler flags in cmplrflags.mk for ideas to get you started.
Development Strategy
The ADCIRC development strategy consists of the maintenance of two separate versions at any given time: the stable version and the development version. The stable version only receives bug fixes, while the development version receives bug fixes as well as new features.
At some point, the development version becomes stable. At that time, a stable branch is created from it, and then new features are added to the previosly stable code, creating a new development version. At that time, the major version number will be incremented, e.g., stable v48.xx will become the next development version as v49.00.
The minor version number is changed each time there is a change in the code and the modified code is committed back to the repository. Details of the implementation of this strategy with Subversion are provided in the section entitled "Subversion".
Coding Standards
ADCIRC has had many individual contributors and has received code accretions over many years. A set of uniform coding standards has not been defined, and as a result, ADCIRC contains many different styles of Fortran. This section provides a set of style guidelines for contributing code to ADCIRC.
The Basics
-
IMPLICIT NONE at the beginning of each subroutine.
-
Fixed form, never exceeding the 72 column limit, even for comment lines.
-
When adding code to an existing subroutine, make the new code match the style of the surrounding code, even if you’d prefer another style.
Maintainability
When adding code that will be used in slightly different ways in different contexts, make it a subroutine, rather than cutting and pasting several times and making small changes to each cut-and-pasted section. Although it is faster to write code with cut-and-paste, the resulting code is harder to maintain, since each cut-and-pasted section will have to be modified individually later. Also, it is easier to make mistakes when working with cut-and-pasted code.
Rick has expressed a desire for greater modularity … particularly with the number of variables in the global module. When adding a major new feature, please consider the modularity of the data it requires. In other words, if new variables can be made private to a particular module rather than global, please do so.
Release Process
The release process informal, but generally includes the following steps:
-
Set and stick to a list of new features as the goal for the new release.
-
Have a consensus among ADCIRC developers that the goal has been achieved.
-
Run tests that cover the new features as well as making sure old features still work.
-
Fix issues revealed by failed tests.
-
Distribute the release candidate code to collaborative users to make sure their runs perform as expected on the new code.
-
Fix issues revealed by user dismay.
-
Update the docs to reflect the changes. If input files or file formats have changed, produce release notes to highlight the changes.
-
Publicly announce, release, and brag about a shiny new version of ADCIRC.
These tests are often referred to as "regression tests," i.e., a means to detect a regression in correctness or functionality due to changes in the code. A regression test suite can be built up pretty easily. There are a number of small test cases on adcirc.org, so the initial set of tests should consist of those. We may also want to differentiate between tests for correctness and tests for functionality. The former is making sure you’re getting the expected model results and the latter is making sure that nothing is broken (i.e., adcprep, i/o, message passing, etc).
The test suite gets built up based on different purposes. One may want to exercise more options and more cases, but it is also important to test for known bugs that might have been reintroduced. In otherwords, once a bug is discovered and fixed, a test should be created specifically for that bug to make sure it doesn’t appear again.
And lastly, a small test suite should be included with each distribution and a more comprehensive should be created that runs during development and before a release. A step in this direction has been made using ant in the autotest directory.
Subversion
Subversion is a version control system. That is, it provides a means for many software developers to collaborate on a single software project.
A subversion repository is a storehouse of code for a particular project. Subversion repositories consist of a trunk, which is the mainline code that everyone shares, and any number of branches that are created by and for individual users.
A branch may be created by a developer that has a lot of changes to make over a longer period of time. This lone developer can work on a branch without affecting the mainline code. Once the changes in the branch are satisfactory, the branch can be merged back to the trunk, making the changes available to all.
A further advantage of Subversion is that it automates the process of merging new features into ADCIRC. For example, in the past, without Subversion, developers modified their local copies of ADCIRC and wanted to merge the modifications into the mainline code. This required hours or days of painstakingly examining each change they had made and cutting and pasting the changes one by one into the up-to-date version of ADCIRC. With Subversion, the software examines the changes that were made locally and makes corresponding changes to the mainline version in an automated way.
Policies
Working with subversion requires greater coordination among ADCIRC developers. For example, if two developers change the same line of code in two different branches in two different ways, this will create a conflict that will have to be resolved manually.
Furthermore, if one developer makes changes to trunk that prevent the code from compiling, it will be difficult for other developers to continue working if they update to the latest code. As a result, ADCIRC development with Subversion will adhere to the following policies:
-
Communicate. Jason Fleming is responsible for making sure ADCIRC development is smooth, pain-free and productive---when in doubt, email him (jason.fleming@seahorsecoastal.com).
-
Also use the adcirc-dev mailing list to keep everyone informed of what you are doing.
-
If you are not on the adcirc-dev mailing list and would like to be, email Jason Fleming.
-
Make a branch to develop new features, rather than making changes to trunk.
-
Don’t commit changes to trunk that prevent the code from compiling, at least on your platform. You should also confirm that your code compiles with and without NetCDF.
Instructions
Comprehensive documentation for Subversion is available: http://svnbook.red-bean.com/.
The ADCIRC code is hosted at the following Subversion repository location:
https://adcirc.renci.org/svn/adcirc
To check out code from the trunk, please type
svn checkout https://adcirc.renci.org/svn/adcirc/trunk
Here are a few examples of things that we commonly do with svn.
Compare Revisions
In order to see the changes between revisions of the repository, type (for example)
svn diff -r 15:16 owiwind.F
Package Up Code From Repository
In order to create a tar file that contains just the ADCIRC code and excludes the .svn directories, the first step is to export the code to a new subdirectory. For example:
svn export ./trunk ./adc99_99
Would copy the local files under version control from the trunk subdir to the adc99_99 subdir, excluding the .svn files. Particular revisions of the svn repository can also be extracted, see the svn documentation for details on how to do this.
Pull Old Version from Repository
One advantage of using svn is that it is easy to go back to an older version of the code, if necessary. The first thing to do in this case is usually to look at the svn log to see what changes were made, so that you can select the right version of the repository to extract. Go to the subdirectory of the code tree that you want an older version of and issue the command
svn log
Look at the log entries, and note the revision that you’d like to extract. Let’s say you want to retrieve adcirc48/trunk at revision 65 of the repository (for example). Issue the following command:
svn checkout -r 65 https://adcirc.renci.org/svn/adcirc/trunk
Make a Branch
To make a branch off trunk to add a new feature and call it the myfeature branch:
svn copy https://adcirc.renci.org/svn/adcirc/trunk https://adcirc.renci.org/svn/adcirc/branches/myfeature --message "Creating branch to add myfeature"
Then to start using the newly created branch:
svn checkout https://adcirc.renci.org/svn/adcirc/branches/myfeature
Appendix A: This Document
This document was prepared from the text file ADCIRCDevGuide.txt using software called asciidoc (http://www.methods.co.nz/asciidoc/). The document can be formatted as an html page with the command
asciidoc -a toc2 ADCIRCDevGuide.txt
or formatted as a pdf with the command
a2x --format=pdf -a toc2 ADCIRCDevGuide.txt


The future is bright for hydrogen, say experts at #POWERGEN19
by Babalwa BunganeRepresentatives from Clarion Energy, Ansaldo, NEL Hydrogen, and MAN Energy Solutions discussed the future role of hydrogen in the power industry during a session sponsored by the Italian Trade Agency at POWERGEN International.
Jeff Benoit with Ansaldo Energia showed attendees how this resource can be used in gas turbines to produce energy, which is something that Ansaldo client Dow Chemical is doing with the left-over hydrogen it produces from its manufacturing processes.
Benoit further explained the difference between green (produced with renewable energy), blue (produced from natural gas through steam reformation) and black hydrogen (produced with synthetic gas derived from coal).
Following Benoit’s focus on turbines, Jeff Syzmanski with Nel Hydrogen stepped up to the podium to show the audience how his company’s proton exchange membrane (PEM) electrolyser works and talk about some of the projects that the company is involved in.
PEM electrolysis is a technology that splits water into oxygen and hydrogen.
He said his company is scaling quickly. “We are trying to keep pace with the demand for power-to-gas use cases that we see in the pipeline,” he said.
Syzmanski said electrolyser costs are falling along the same trajectory that solar PV followed. “The cost of electrolysers are not going to be a barrier,” he said.
Great interest in hydrogen
According to Syzmanski, there is interest in this energy source from the heavy-duty industry and pointed out that Nikola motor, maker of heavy-duty trucks, plans to roll out a large fleet of hydrogen-powered trucks in 2022. He said they have an order for 800 heavy-duty trucks for Anheuser Busch.
“We are looking at a demand of more than 1GW with this one customer alone,” he said.
Dr Eng Florian Gruschwitz from MAN Energy Solutions said his company has “hydrogen in our blood". He explained how this resource will be an important part of decarbonising the industrial sector.
Paddy Young, event director for POWERGEN Europe owned by Clarion Energy, moderated the panel and referred to himself as an enthusiast in the technology.
Europe is heavily investing in this energy resource as a means to decarbonise the entire industry – not just the electricity sector – and Young said he was pleased to see that the interest is strong here [in the US], too.
Courtesy of Jennifer Runyon for power-eng.com


No form of art goes beyond ordinary consciousness as film does, straight to our emotions, deep into the twilight room of the soul.(Ingmar Bergman)
{{ error }}


To Die For is, I suppose, just that.
As reviewed by James Brundage
I don't know why I didn't see it before. I saw Natural Born Killers, which I will go out on a limb and compare it to (minus excessive senseless violence and profanity and Oliver Stone's foray into the surreal). I simply am left, in the first five minutes after watching it, like I was caught inside a whirlwind of activity, of insanity. Like I just watched a twisted version of the OJ Trial in an hour and a half.
And indeed, that is the best analogy, for To Die For, Gus Van Sant's screwball black comedy about a woman obsessed with being on television... to the point to have her husband killed to do so (they even have the Star Spangled Banner playing as she walks towards the cameras on the night of her husband's death). It's just a perfect illustration, a perfect satire of the black comedy which is violence in America. For an hour and a half it entraps you in the world you're already in, but in such a way as to make you realize just how twisted a place it is. Gus Van Sant preaches to the converted and to the disbelieves and has em all coming out of the theater blinking twice, doing a double take and wondering ... did this really happen? The answer... it did.
No: Tom Cruise isnt' dead by the hands of Nicole Kidman (who plays the very disturbed main character), but dying left and right are the NBKC, The Natural Born Killers copycats, thrill-killers who seek to be immortalized for their crimes. Psychopaths inhabiting our world, obsessed with the media. The violence in film issue, the death of Brandon Lee on the set of The Crow, Kurt Cobain copycat suicides. All of it shows this film for what it is... Not a fiction but a mirror. This is not the world according to Gus Van Sant (which I will stop and watch anyday), but, like Drugstore Cowboy, one of the director's first and most realistic works, a showing of the world as HOW IT IS. Watching this movie, I recognize this, and the line pops into my head from a book in an equal vein of reality, American Psycho : ABANDON ALL HOPE YE WHO ENTER HERE is scrawled in blood.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


SoftBank-backed Banco Inter launches app linking clients to stores
Brazilian mid-sized lender Banco Inter SA on Thursday launched a smartphone app offering clients direct access to more than 60 stores selling products from appliances to sneakers to beauty products.
SAO PAULO: Brazilian mid-sized lender Banco Inter SA on Thursday launched a smartphone app offering clients direct access to more than 60 stores selling products from appliances to sneakers to beauty products.
The launch marks Banco Inter's latest move beyond its existing free checking accounts to lure more clients and increase profitability by becoming a one-stop shop for everything.
Among its retail partners are Alibaba's AliExpress, Lojas Americanas SA , Natura Cosmeticos SA , Nike Inc , Carrefour Brasil and HP Inc .
Chief Executive Joao Vitor Menin said in an interview with Reuters that he expects the so-called super app to help the bank attract new clients, either at a faster pace or at a lower cost.
Menin said the bank could reach roughly 8 million clients by December 2020, more than double its current 3.3 million consumers.
The lender, in which SoftBank Group Corp bought a 14.9per cent stake earlier this year, will share a portion of commissions from the third-party vendors on the app with its clients in the form of cash-back rebates. The commissions will also provide the bank a new revenue stream.
Some analysts still doubt whether such multi-function apps, widely used in China and elsewhere in Asia, will catch on in Latin America's largest economy.
"A bank's app is one that people use more recurrently," he said. "So we believe clients will buy products and services through Inter's app. It will prove to be easier."
Banco Inter posted on Wednesday a 38.1per cent drop in third-quarter net income from a year earlier, to 11.8 million reais, as expenses rose at a faster pace than revenues
(Reporting by Carolina Mandl; Editing by Cynthia Osterman)


Classes
The following classes are available globally.
-
A web token that has a string representation, and can expire.
See moreDeclaration
Swift
public class WebToken
-
Used to execute functions on keypr api as a user, using Federated Authentication Flow.
See moreDeclaration
Swift
open class KeyprApi
-
A json response gotten from a successful OAuth request.
See moreDeclaration
Swift
public class OAuthResponse : Codable
-
A paged json response from keypr server.
See moreDeclaration
Swift
public class PagedResponse<T> : Codable where T : Decodable, T : Encodable
-
A reservation for a guest.
See moreDeclaration
Swift
public class Reservation : Codable
-
A async task that was start or performed on a reservation
See moreDeclaration
Swift
public class ReservationTask : Codable


Standard rate for most legal services, paid in 10 hour increments
If both parties are in agreement, you will only pay a flat rate
Standard with every case
“I care about my clients and will do all I can for them within the bounds of the law”
"With a paralegal degree, and over 5 years of experience in social services work, I'm always ready to help. I have standing as a member of the Northeast Indiana Paralegal Association (NIPA) and an affiliate member of the Allen County Bar Association (ACBA)"
A strong voice with cool common sense; an ear to listen and guide
Yes. Yvonne is licensed to practice all areas of the law in Indiana, however her experience is focused in family law. "Family Law" is an umbrella term describing areas such as filing for divorce, enforcing a divorce decree, and modifying an existing child support or child custody order. Domestic violence can include civil and criminal cases, enforcement of protective orders, and Invasion of Privacy Defense. Child protective services and related issues can include areas of child advocacy, Guardian Ad Litem work, parent’s rights, and grandparent’s rights. Yvonne’s legal services are not limited to family law but this is where her passion lies.
Yvonne M. Spillers has been an attorney since November of 2011 and has her own family law practice. Yvonne has been involved in victim’s advocacy and the legal system since 1995. Yvonne is passionate about helping those in need, protecting victims’ rights, and making sure we all have a voice within the confines of the law. Yvonne graduated from Thomas M. Cooley Law School in 2009. While attending Cooley Law School, Yvonne earned a Family Law, Certificate of Merit, due to receiving the highest score in family law classes. In addition, Yvonne has completed both Family Violence and Child Abuse and Neglect Law course studies. Yvonne attended Indiana-Purdue University of Fort Wayne graduating with a Criminal Justice Bachelor’s degree with Honors and Distinction.
Yvonne is passionate about helping those who fall victim to domestic violence. The honors project Yvonne created while attending Indiana-Purdue University focused on providing education to domestic violence victims. Yvonne’s project was brought to life while working with the Fort Wayne’s Domestic Violence Task Force. DV Information sessions continue to be offered on a weekly basis for victims of domestic violence. The success of the project resulted in a full-time Victims Advocate at the Fort Wayne Police Department. “I am a strong Victim’s Advocate,” says Yvonne “so it is important to make sure I stay on top of the current laws and legal information to help protect my client's rights”. Yvonne currently serves on the Mayors Commissioned Domestic Violence Task Force. She worked as a Victim’s Advocate for the Fort Wayne Police Department before becoming an attorney.
We have two other very professional attorneys within the office. Jody Dietsch, who is a licensed therapist and also practices law, and Jim Hanson, who practices primarily in criminal law.


There’s a philosophical position that goes something like this:
A statement that has no predictive power, and therefore cannot be shown to be true or false, is meaningless.
This is an attractive stance for the scientist who spends his or her life investigating and refining predictive models; it suggests a true objectivity to the world. It narrows the scope of knowledge down to that in which they are an expert. And if there’s one undebatable truth in philosophy, it’s that there are no undebatable truths in philosophy. And even that’s debated.
Philosophical knowledge, by its very nature, is that set of knowledge that cannot be verified by experiment. It can’t have predictive power: if it did it would no longer be philosophical. To the hard-line scientist, this is the ultimate shoot-down to philosophy, it makes philosophy meaningless.
But there is a second role of knowledge missing from this view. Even without predictive power, knowledge and beliefs effect our behaviour and decisions. Enter moral philosophy: the world of knowledge that, despite not being verifiable, influences every action we make. Although, to the scientist, subjectivity may be seen as inherently negative, even the most stringent scientist will hold personal ethical beliefs, out of touch from their science. It’s the point of the quote that ended my last post; talking about the inability of science to guide our decisions, Feynman concludes: “… at the end, you must have some ultimate judgment.”
Moral framework
A moral framework is a set of beliefs – held by an individual – that can be used to ultimately answer all behavioural dilemmas. It fills the gap left by science, and exists as the ultimate end to why we do something the way we do. It adds a connection between our ideas of good and bad and the real world. To use a moral framework is to take a choice of possible actions, judge their worth according to the criteria within the framework, and choose the action of the most moral worth. As a computer scientist, it is analogous to a state-space search or the mini-max algorithm – both exist to make decisions in a simulated world based purely on some external measure of “success”.
Although moral frameworks are subjective, most of us share some intuitive, ambiguous ideas of right and wrong. It’s wrong to hurt someone. It’s wrong to take away someone’s freedom. Our actions may be judged by their consequences to people’s happiness. Our actions may also be judged on our intent. Some actions can be wrong in principle, regardless of circumstance. These views can all be true to different extents for different people. And people struggle internally with how to bind them together into a single unambiguous moral framework. It’s possible to try and reason and justify these views, but being within the realm of philosophical knowledge, it often results in unresolvable ideological food-fights.
Where does science come in?
It’s established that science alone cannot derive an objective moral framework or ideology. It’s the ultimate reason that political debate often seems so futile and endless. It creates a frustration amongst scientists, so used to seeking objectivity in their field that they call for scientific principles to be introduced into policy making – with the hope of ending this ongoing political shouting in favour of evidence based policy. But if our ideologies are personally chosen, what is the role of science in policy making?
A policy is not the same as an ideology. A policy is a set of real-world rules, which have real-world consequences hopefully pushing society closer to the ideals of the policy-creator. Say the politician in charge is a libertarian: he believes the worth of a policy should be judged by its effects in enabling individual freedom. The policy gives people freedom? GOOD. The policy takes away people’s freedom? BAD. The politician justifies his no-taxes, no-regulation economic policies with his ideology:
Taxes and regulation take away the freedom of individuals to trade with each other as they both see fit. Taking away money and enforcing our own rules on other people’s personal trades is wrong because they should have the freedom to trade with each other as they want, so long as the trade doesn’t take away the rights of others.
On its surface, it may look like this policy is only debatable on ideological grounds. But look deeper and we can see how this policy actually has two parts: The ideology is to promote freedom to individuals, the claim is that reducing taxes and regulation will result in satisfying these ends.
Say the policy is implemented. People are free to trade as they see fit within the lenient restraints of the new ‘libertarian’ society. Say, over time the economy grows and large companies are allowed to form and merge. It turns out, in this society, that it is in the large companies’ profit interests to fix prices and create repressive monopolies – harvesting all wealth from the less well-off and restricting their employment options so, given a free choice under the law, citizens have to choose to work for the monopolistic companies. It’s the only option to survive. Would the libertarian politician – who values social mobility and individual freedoms – consider this society ‘free’? If he’d have known that the long term consequences of his policies would be that the 99% do not have a practical choice over their lifestyle or employment?
It’s a hypothetical story, the point is not about economics, and it is especially not a criticism aimed at the libertarian ideology. Instead, the story illustrates that an acceptable justification of a policy rests on more than just an ideology. It should rest on evidence that the policy’s consequences work towards the ideology. The failure of the above policy in achieving its ideological aims was not a fault of the ideology itself, it was the fault of the politician’s naive understanding of the policy’s consequences.
Evidence based policy
When scientists like me call for science based policy, we should call for the consequences of policies to be investigated scientifically, not their ideological foundations. A country being run without this step of investigation cannot make educated choices between policies. The naivety of politicians regarding the effects of their own policies is solvable with science, and it is here that science based policy should flourish. Ignorance about the limitations of science, which politicians may be more aware of than scientists, unnecessarily weakens the otherwise valid and important efforts of scientists in improving the quality of our policies. Science should have its place in politics, but misunderstandings and confusions about the roles of science and ideology need to be tackled before the scientific method can be fully taken advantage of within politics.


Real-time Covid-19 tracker statistics from around the world.
Spring Boot Project Code available on GitHub : www.github.com/dammike/Covid-Tracker/ Author: Dammike Saman


An experimental interactive space scene rendered on the browser using WebGL and Three.js with Javascript.


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Sport Systems Canada Inc.
Mailing Address:
28 Industrial Dr.ALMONTE, Ontario
K0A 1A0
Location Address:
28 Industrial Dr.ALMONTE, Ontario
K0A 1A0
Tel.:<PHONE_NUMBER>
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Logo
Company Profile
SSC Inc. manufactures, distributes & installs athletic facility equipment and possesses over 20 years of experience in the sports infrastructure market. SSC Inc. offers a full range of design assistance, product supply, installation and service to institutional clients including school boards, colleges, universities & municipalities. Private clients include: architects, contractors, private facility owners & corporations.
Full profile
Full profile
Contact information
Company description
238990 - All Other Specialty Trade Contractors
332999 - All Other Miscellaneous Fabricated Metal Product Manufacturing
339990 - All Other Miscellaneous Manufacturing
414470 - Amusement and Sporting Goods Wholesaler-Distributors
Product / Service / Licensing
grade sporting goods and athletic facility components. With an
array of durable merchandise and exceptional product knowledge
we have been pleased to serve a broad base of clientele which
include school boards, municipalities, recration centres,
athletic facilities, transit authorities, construction firms and
private companies.
Our quality products include basketball equipment, athletic
mats, volleyball equipment, bike locker systems, bike racks,
custom made sports & protective netting, soccer goals, wall
padding, athletic floor covers, hockey arena accessories, tennis
court supplies and baseball equipment including pitching
machines & field equipment.
As a premier supplier of athletic equipent, we look forward to
growing our business by maintaining exceptional levels of
customer service & product knowledge.
5670-Building Components, Prefabricated
7810-Athletic and Sporting Equipment
7830-Recreational and Gymnastic Equipment
N000-Installation of Equipment
Y000-Construction
Y514-Assembly and erection of prefabricated constructions
Y515-Special trade construction work
Y516-Installation work
Technology profile
Market profile
Alliances:
- Sales/Marketing
- Technology
Strategic alliances:
SSC Inc. is seeking to partner with firms abroad who specialize in supplying & installing athletic infrastructure products. SSC Inc. possesses industry leading product expertise and installation capabilities that can be exported to any country.Industry sector market interests:
- Construction
- Consumer Products
- Manufacturing
- Wholesale/Retail
Geographic markets:
Export experience:
- Bahrain
- Cyprus
- Ireland
- Kuwait
- Morocco
- Qatar
- Saudi Arabia
- United Arab Emirates
Sector information
Classification codes
Total plant size:
Distribution of employees:
14Key / Major clients:
General ElectricCognos
Nortel
PCL Construction
City of Ottawa
Ottawa Carleton District School Board
Ottawa Carleton Catholic School Board
Putnam County District School Board (USA)
Star Academy
Mohawk School Board of Akwesasne
City of Hamilton
St. Lawrence Valley Parks Commission
Little Tykes Commercial Play Systems (Canada)
PIMA Community College (USA)
Timbertec Inc.
Ford of Canada
Molson Breweries Inc.
Renfrew County Catholic Dist. School Board
City of Vaughan
Town of Mississippi Mills
Play it Again Sports
Source for Sports
Ron Engineering (Eastern) Ltd.
Suncor Energy Inc.
Tofcon Construction
Algoma District School Board
Family Dollar
City of Oshawa
Scott Builders Inc.
Carleton University
University of Ottawa
Algoma University
District School Board Ontario Northeast
Government of NWT
Public Works & Government Services Canada
US Department of Defence
Canadian Forces
YMCA
BC Place
Vancouver Whitecaps Football Club
Town of Conception Bay South
City of St. John's
City of Peterborough
Kawartha Speedway
Medicine Hat Drag Racing Association
City of Spruce Grove
Algonquine & Lakeshore Catholic DSB
Success stories:
Sport Systems Canada Inc. is proud to offer clients a turn-key approach to both indoor & outdoor athletic facility outfitting.As a Canadian-based manufacturer & installer of premium equipment, SSC's clients rely on it to assist in all project aspects: design, budgeting, equipment installation & servicing.
SSC's unique geographical position enable it seamless access to all Canadian and American markets. SSC Inc. has installed projects across Canada & the U.S. and currently exports its products beyond North America.
Product offerings include: basketball backstop installation, telescopic bleacher systems, permanent outdoor grandstands, fixed arena and auditorium seating, court equipment, field equipment, protective netting, custom-made athletic facility padding & protective gym floor covers.
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
238990 - All Other Specialty Trade Contractors
332999 - All Other Miscellaneous Fabricated Metal Product Manufacturing
339990 - All Other Miscellaneous Manufacturing
414470 - Amusement and Sporting Goods Wholesaler-Distributors
Products, services and licensing
Product / Service / Licensing
grade sporting goods and athletic facility components. With an
array of durable merchandise and exceptional product knowledge
we have been pleased to serve a broad base of clientele which
include school boards, municipalities, recration centres,
athletic facilities, transit authorities, construction firms and
private companies.
Our quality products include basketball equipment, athletic
mats, volleyball equipment, bike locker systems, bike racks,
custom made sports & protective netting, soccer goals, wall
padding, athletic floor covers, hockey arena accessories, tennis
court supplies and baseball equipment including pitching
machines & field equipment.
As a premier supplier of athletic equipent, we look forward to
growing our business by maintaining exceptional levels of
customer service & product knowledge.
5670-Building Components, Prefabricated
7810-Athletic and Sporting Equipment
7830-Recreational and Gymnastic Equipment
N000-Installation of Equipment
Y000-Construction
Y514-Assembly and erection of prefabricated constructions
Y515-Special trade construction work
Y516-Installation work
Technology
Technology profile
Market
Market profile
Alliances:
- Sales/Marketing
- Technology
Strategic alliances:
SSC Inc. is seeking to partner with firms abroad who specialize in supplying & installing athletic infrastructure products. SSC Inc. possesses industry leading product expertise and installation capabilities that can be exported to any country.Industry sector market interests:
- Construction
- Consumer Products
- Manufacturing
- Wholesale/Retail
Geographic markets:
Export experience:
- Bahrain
- Cyprus
- Ireland
- Kuwait
- Morocco
- Qatar
- Saudi Arabia
- United Arab Emirates
Sector
Sector information
Classification codes
Total plant size:
Distribution of employees:
14Key / Major clients:
General ElectricCognos
Nortel
PCL Construction
City of Ottawa
Ottawa Carleton District School Board
Ottawa Carleton Catholic School Board
Putnam County District School Board (USA)
Star Academy
Mohawk School Board of Akwesasne
City of Hamilton
St. Lawrence Valley Parks Commission
Little Tykes Commercial Play Systems (Canada)
PIMA Community College (USA)
Timbertec Inc.
Ford of Canada
Molson Breweries Inc.
Renfrew County Catholic Dist. School Board
City of Vaughan
Town of Mississippi Mills
Play it Again Sports
Source for Sports
Ron Engineering (Eastern) Ltd.
Suncor Energy Inc.
Tofcon Construction
Algoma District School Board
Family Dollar
City of Oshawa
Scott Builders Inc.
Carleton University
University of Ottawa
Algoma University
District School Board Ontario Northeast
Government of NWT
Public Works & Government Services Canada
US Department of Defence
Canadian Forces
YMCA
BC Place
Vancouver Whitecaps Football Club
Town of Conception Bay South
City of St. John's
City of Peterborough
Kawartha Speedway
Medicine Hat Drag Racing Association
City of Spruce Grove
Algonquine & Lakeshore Catholic DSB
Success stories:
Sport Systems Canada Inc. is proud to offer clients a turn-key approach to both indoor & outdoor athletic facility outfitting.As a Canadian-based manufacturer & installer of premium equipment, SSC's clients rely on it to assist in all project aspects: design, budgeting, equipment installation & servicing.
SSC's unique geographical position enable it seamless access to all Canadian and American markets. SSC Inc. has installed projects across Canada & the U.S. and currently exports its products beyond North America.
Product offerings include: basketball backstop installation, telescopic bleacher systems, permanent outdoor grandstands, fixed arena and auditorium seating, court equipment, field equipment, protective netting, custom-made athletic facility padding & protective gym floor covers.


| PWG Tampa Meeting Details |
UPDF: March 5
IPP Fax: March 6
Plenary/IPP: March 7
IPP: March 8
voice: +1-813-287-2555
fax: +1-813-289-5464
http://Marriotthotels.com/dpp/PropertyPage.asp?MarshaCode=TPAWE
Hotel Cost: $179/night, single or double occupancy.
***Please contact Cindy Tiritilli at<EMAIL_ADDRESS>if for any reason you are unable to obtain the negotiated room rate.****When making your reservation, the meeting will be listed under "PWG" and "IEEE-ISTO".
Deadline: Please make your reservations by February 16, 2000.
There will be a meeting charge of $60; participants are responsible for their own
lunch.
YOU MUST REGISTER AT: https://www.ieee-isto.org/pwg/registration.html
Please contact Cindy Tiritilli, IEEE-ISTO PWG Program Administrator at
+1-732-981-3434 or<EMAIL_ADDRESS>if you have any questions.


| Author: | Shibukawa Yoshiki |
|---|---|
| Contact: | yoshiki at shibu.jp |
| Copyright: | This document has been placed in the public domain. |
In PySpec, the specification is represented in python function or method. These specification methods have special decorator.
example:
# This is function style spec @spec def New_list_should_be_empty(): About(list()).should_be_empty() # This is method style spec class List_Behavior(object): @spec def New_list_should_be_empty(): About(list()).should_be_empty()
If you want to share any objects between specifications and contexts (context is a setup method), you should write specs in class. PySpec creates one object per one spec. It doesn't pollute other test environment.
PySpec searches specs from top level functions and classes. If you write specs in nested class, that will not be called.
You have two ways to run console runner. First way is cuipyspec.py script. Another is writing startup code into your spec source files. It's easy.
example:
from pyspec import * # Define some specs here if __name__ == "__main__": run_test()
You don't have to register your specs. PySpec searches all modules that are loaded in your interpreter automatically. So, this code can kick specs in it.
If you want to run all specs that are divided into some files, creating startup script is the easiest.
example:
from pyspec import* import behavior_entities import behavior_database_access import behavior_views run_test()
If you have created a spec startup script file and added startup code into all your specs, you would be able to all tests and each test.
You can use GUI runner too. Run wxpyspec.py script. You can load spec module file from [Project] - [Add module...] menu.
| notice: | Now I rebuilding GUI runner. There are some bugs and not fixed. |
|---|
If you want to run GUI startup code from your spec script, please add following code:
if __name__ == "__main__": import pyspec.wxui.controller controller = pyspec.wxui.controller.WxPySpecController(0) controller.MainLoop()
The spec which have good name is good spec. In pyspec, you have 2 ways to set the name. These spec names will appear in the result report.
Using method name is more simple way.
| Method Name | Spec Name |
|---|---|
| A_stack_which_have_one_value() | A stack which have one value |
| version_info__in_sys_module() | version_info in sys module |
| one_of__test_method__should_fail() | one of test_method should fail |
This feature is implemented in pyspec.util.create_spec_name().
Docstring is used for the spec names. Python 2.X can accept ASCII names only as a method name. This function is for programmers in Asia.
If you write docstring in context method and spec method, the first line of docstring is used as a spec name.
PySpec gets docstring encoding from the first line of source code. If you want to use Japanese or Chinese or any languages, you should write as following:
# -*- coding: utf8 -*-
Pyspec can accept any codec which is installed in python.
example:
class StackBehavior(object): @context def A_stack_with_one_item(self): """Youso wo hitotsu motsu stack.""" self.stack = Stack() self.stack.push("one item") @spec def should_not_be_empty(self): """kara dewa nai.""" About(self.stack).should_not_be_empty()
result:
Youso wo hitotsu motsu stack kara dewa nai ... OK
This sample docstrings are Japanese pronunciation in English. Of course, you can write real Japanese.