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Law Journals and Reviews
Hawaii's Land Reform Act: Is it Constitutional? 6 HBJ 31.


| Package | Description |
|---|---|
| org.rendersnake | |
| org.rendersnake.error | |
| org.rendersnake.ext.jquery | |
| org.rendersnake.ext.spring | |
| org.rendersnake.internal | |
| org.rendersnake.tools |
| Modifier and Type | Class and Description |
|---|---|
class |
DocType
DocType is used to set the doctype of markup language based document such as HTML and XML.
|
class |
RenderableWrapper
RenderableWrapper is an abstract class to create decorator components such as
a site layout.
|
class |
StringResource
StringResource holds a cache for text content such as Html snippets that is
read from resources on the classpath.
|
| Modifier and Type | Field and Description |
|---|---|
protected Renderable |
RenderableWrapper.component
The wrapped component.
|
| Modifier and Type | Method and Description |
|---|---|
HtmlCanvas |
HtmlCanvas.render_if(Renderable component,
boolean condition)
Conditionally render a component based on the condition.
|
HtmlCanvas |
HtmlCanvas.render(Renderable component)
Render the component using the receiver.
|
| Constructor and Description |
|---|
RenderableWrapper(Renderable component) |
| Modifier and Type | Class and Description |
|---|---|
class |
RenderException
RenderException is a RuntimeException that is thrown in the following cases
sending close() when there is no open tag
sending close(someTag) where otherTag was expected
general exception occurred when rendering a component
|
| Modifier and Type | Class and Description |
|---|---|
class |
JQueryLibrary
JQueryLibrary is a convenience component to add HTML instructions to load
libraries from http://code.jquery.com/ (or your own domain in BASE_RESOURCE_URL).
|
| Constructor and Description |
|---|
RenderableView(Renderable render,
String contentType) |
| Modifier and Type | Method and Description |
|---|---|
HtmlCanvas |
SinkCanvas.render(Renderable component) |
| Modifier and Type | Class and Description |
|---|---|
class |
Inspector
Inspector can display debugging information for a component.
|
| Modifier and Type | Field and Description |
|---|---|
Renderable |
Inspector.target |
| Modifier and Type | Method and Description |
|---|---|
HtmlCanvas |
DebugHtmlCanvas.render(Renderable component) |
static String |
PrettyWriter.toString(Renderable component)
Answer a pretty formatted HTML output for a Renderable component.
|
| Constructor and Description |
|---|
Inspector(Renderable toInspect) |
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DtCraft is a general-purpose programming system to make parallel and distributed computing easier to handle. Our programming model allows you to focus on high-level developments without worrying about difficult system details.
Latest News
- DtCraft Webinar by VSD (May 26, 2018)
- DtCraft Presented at GLSVLSI18 (May 24, 2018)
- DtCraft Accepted by IEEE TCAD (May 7, 2018)
- DtCraft Presented at CSLSC18 (April 19, 2018)
Productivity
Boost your productivity in writing parallel programs.
Performance
Leverage your time to produce promising results.
Scalability
Scale out your applications in large clusters.
API
Modernize your parallel code with our API in C++17.
Stream Graph
Parallelize your applications from sequential building blocks.
Resource
Schedule your jobs through in-conext resource controls.
Container
Improve your runtime reliability and security.
Web UI
Monitor your workload through built-in web interface.
Stream Graph Programming
DtCraft introduces a new powerful programming model based on stream graph that eliminates the traditional dataflow limitation and associated performance bottleneck. Stream graph provides a high-level abstraction over parallel operations and resource controls on a cluster, and enables computation to quickly take place whenever data is available. This allows DtCraft to efficiently solve a wide range of optimization and simulation problems.
Performance and Productivity
Building a DtCraft application is natural to parallel thinking. We use advanced C++17 technology to deliver the best performance and programmability. Deploying a DtCraft application to a computer cluster is as simple as running a binary from a local console. Compared with traditional high-performance programming libraries, our API allows you to quickly build up parallel and distributed applications at scale far more productive.
auto L = [=] (auto& vertex, auto& istream) {
if(string data; istream(data) != -1) {
// Your program control flow.
} else { ... }
};


Despite of having ability to have a team, let me enumerate what qualifications I personally have:
Web
Full-cycle development of web applications, from specification to ready and tested product. Based on following technologies:
- — JVM based languages (Java, Groovy, Scala)
- — JavaScript (jQuery, AngularJS, Backbone, ExtJS, NodeJS)
- — PHP
- — HTML5/CSS3/XML
- — WebServices/SOAP/REST
- — SQL (MySQL, PostgreSQL, Oracle)
- — NoSQL (MongoDB, Redis, Firebase)
Mobile
High-quality mobile applications for most popular platforms:
- — Android
- — iOS
- — Phonegap framework for all platforms
- — Integration with BaaS like Parse.com
Consulting
As I have extensive experience in software development, I find myself able to help you and your business in terms of consultation on following topics:
- — Advise on technologies stack to be used in application
- — Figure out scope of the application based on specifications
- — Help in recruiting developers in my local area


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본 내용은 Coursera Build Basic Generative Adversarial Networks (GANs) Lecture 기반으로 구성했습니다.
지난주, Coursera Andrew Ng 교수님의 Deep learning specialization을 마무리했습니다. 이번주부터는 굉장히 섬세한 fake 이미지를 생성할 수 있는 GAN 모델을 다루는 강의를 시청하고 리뷰할 예정입니다.
GAN 이란 Generative Adverarial Network의 약자로, 하나의 모델이 가짜 이미지를 생성하고 다른 모델이 진짜 이미지와 가짜 이미지를 구분? 하면서 Generator 모델이 더욱 완벽한 fake 이미지를 만들 수 있도록 training 합니다.
Generative Models
Generator
- Random noise를 input에 주고 fake image를 생성합니다.
Discriminator
- Real image를 input에 주고 학습시킵니다.
학습이 완료되면 Random Noise를 input으로 Generator에 주게 되면 정교한 fake 이미지를 생성하게 됩니다. 그리고 위 모델들이 경쟁하며 성능을 향상시키는 구조를 갖고 있습니다.
Summary
- Generative models learn to produce realistic examples
- Discriminative models distinguish between classes
Real Life GANs
Ian Goodfellow is known as the creator of the GAN.
not limited to human faces
image translation : horse to zebra, vice versa
draw something -> able to produce realistic image
Monariza -> moving
Companies Using GANs
- Adobe
- Google (text generation)
- IBM (data augmentation) : x enough data -> augmentation
- whatever you like
Summary
- GANs’ performance is rapidly improving
- Huge opportunity to work in this space!
- Major companies are using them
Intuition Behind GANs
Outline
-
the goal of the generator and the discriminator
-
the competition between them
Generator : learn to make fakes that look real
Discriminator : learns to distinguish real from fake
First, train a discriminator
Summary
- The generator’s goal is to fool the discriminator
- The discriminator’s goal is to distinguish between real and fake
- They learn from the competition with each other
- At the end, fakes look real
Discriminator
Outline
- Review of classifiers
- The role of classifiers in terms of probability
- Discriminator
Discriminator : Distinguish between different classes
Summary
- The discriminator is a classifier
- It learns the probability of class Y (real or fake) given features X
- The probabilities are the feedback for the generator
Generator
Outline
- What the generator does
- How it improves its performance
- Generator in terms of probability
Noise vector - different output
Noise -> Generator -> Features(X hat) -> **Discriminator ** -> Output (Y hat) -> Cost -> update Generator’s Parameters
Summary
- The Generator produces fake data
- It learns the probability of features X
- The Generator takes as input noise (random features)
BCE Cost Function
Outline
- Binary Cross Entropy(BCE) Loss equation by parts
- How it looks graphically
Summary
- The BCE cost function has two parts (one relevant for each class)
- Close to zero when label and the prediction are similar
- Approaches infinity when the label and the prediction are different
Putting It All Together
Outline
- How the whole architecture looks
- How to train GANs
Summary
- GANs train in an alternating fashion
- The two models should always be at a similar “skill” level
Intro to PyTorch
Outline
- Comparison with TensorFlow
- Dfining Models
- Training
Summary
- PyTorch makes computations on the run
- Dynamic computational graphs in PyTorch
- Just another framework, and similar to Tensorflow!


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Mass Conservation Approach Walkthrough¶
Using the SBML file constructed as in CellDesigner Walkthrough, we will proceed by completing a more in-depth
explanation of running the mass conservation approach of [OMYS17]. Note that the mass conservation approach can
be ran on any uniterminal network that has conservation laws, even if that network does have a sink/source. One can test whether or
not there are conservation laws by seeing if the output of crnt4sbml.Cgraph.get_dim_equilibrium_manifold()
is
greater than zero. This tutorial will use Fig1Ci.xml
.
The following code will import crnt4sbml and the SBML file. For a little more detail on this process consider Low Deficiency Approach.
import crnt4sbml
network = crnt4sbml.CRNT("/path/to/Fig1Ci.xml")
If we then want to conduct the mass conservation approach of [OMYS17], we must first initialize the mass_conservation_approach, which is done as follows:
approach = network.get_mass_conservation_approach()
This command creates all the necessary information to construct the optimization problem to be solved. Along with this, the initialization will also attempt to obtain a linear form of the Equilibrium Manifold. Note that this process may take several minutes for larger networks. For more detail on this process consider Creating the Equilibrium Manifold. The following is the output provided by the initialization:
Creating Equilibrium Manifold ...
Elapsed time for creating Equilibrium Manifold: 1.992364
One very important value that must be provided to the optimization problem are the bounds for the decision vector of the optimization problem. For this reason, it is useful to see what decision vector was constructed. To do this one can add the following command to the script:
print(approach.get_decision_vector())
This provides the following output:
[re1, re1r, re2, re3, re3r, re4, re5, re5r, re6, s2, s6, s15]
To obtain more available functions that this initialization provides, see crnt4sbml.MassConservationApproach()
.
Using the decision vector provided, one can then construct the bounds which are necessary for the optimization problem
by creating a list of tuples where the first element corresponds to the lower bound value of the parameter and the second
element is the upper bound value of the parameter.
In addition to the bounds for the decision vector, we must also supply the bounds for those species’ concentrations that are not defined in the decision vector. To see the order and those species’ concentration bounds that you need to provide bounds for, we can use the following command:
print(approach.get_concentration_bounds_species())
This provides the following output:
[s1, s3, s7, s16]
This tells us that we need to provide a list of four tuples that correspond to the lower and upper bounds for the species s1, s3, s7, and s16, in that order.
As creating these bounds is not initially apparent to novice users or may become cumbersome, we have created a function call that will automatically generate physiological bounds based on the C-graph. To use this functionality one can add the following code:
bnds, conc_bnds = approach.get_optimization_bounds()
This provides the following values:
bnds = [(1e-08, 0.0001), (1e-05, 0.001), (0.001, 1.0), (1e-08, 0.0001), (1e-05, 0.001), (0.001, 1.0),
(1e-08, 0.0001), (1e-05, 0.001), (0.001, 1.0), (0.5, 500000.0), (0.5, 500000.0), (0.5, 500000.0)]
conc_bnds = [(0.5, 500000.0), (0.5, 500000.0), (0.5, 500000.0), (0.5, 500000.0)]
For more information and the correctness on these bounds please refer to Creating Physiological Bounds.
The next most important parameter for optimization is the number of initial points in the feasible point method (please see Numerical Optimization Routine for a detailed description of the optimization routine). It is usually good practice to run the optimization with 100 initial points and observe the minimum objective function value achieved. If an objective function value smaller than machine epsilon is not achieved, it is best to rerun the optimization with more initial points. If 10000 or more points are used and an objective function value smaller than machine epsilon is not achieved, then it is possible that the network does not produce bistability (although this test does not exclude the possibility for bistability to exist, as stated in the theory). We state the number of feasible points below.
num_itr = 100
The last values that can be defined before the optimization portion are the sys_min_val which states what value of the
objective function should be considered as zero (below we set this to machine epsilon), the seed for the random number
generation in the optimization method (below we set this to 0 so we can reproduce the results, None should be used if we
want the method to be random), the print_flag which tells the program if the objective function value and decision
vector for the feasible point and multi-start method should be printed out (here we set it to False, which means no
output will be provided), and numpy_dtype which tells the program the numpy data type that should be used in the
optimization method (here we set it to a float with 64 bits). Note that higher precision data types will increase the
runtime of the optimization, but may produce better results. See crnt4sbml.MassConservationApproach.run_optimization()
for the default values of the routine.
import numpy
sys_min = numpy.finfo(float).eps
sd = 0
prnt_flg = False
num_dtype = numpy.float64
Using these values, we run the optimization problem using the following command, which returns a list of the parameters (which correspond to the decision vectors) and corresponding objective function values that produce an objective function value smaller than machine epsilon.
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(bounds=bnds, concentration_bounds=conc_bnds,
iterations=num_itr, seed=sd, print_flag=prnt_flg,
numpy_dtype=num_dtype, sys_min_val=sys_min)
The following is the output obtained by the constructed model:
Running feasible point method for 100 iterations ...
Elapsed time for feasible point method: 14.034250974655151
Running the multistart optimization method ...
Elapsed time for multistart method: 67.97679090499878
At this point it may also be helpful to generate a report on the optimization routine that provides more information. To do this execute the following command:
approach.generate_report()
This will provide the following output:
Smallest value achieved by objective function: 0.0
28 point(s) passed the optimization criteria.
The first line tells one how the smallest value of the objective function that was found after all points have been ran. The second line describes the number of feasible points that produce an objective function value smaller than sys_min_val that also pass all of the constraints of the optimization problem. Given the optimization may take a long time to complete, it may be important to save the parameters produced by the optimization. This can be done as follows:
numpy.save('params.npy', params_for_global_min)
this saves the list of numpy arrays representing the parameters into the npy file params. The user can then load these values at a later time by using the following command:
params_for_global_min = numpy.load('params.npy')
Now that we have obtained some parameters that have achieved an objective function value smaller than sys_min_val, we can conduct numerical continuation to see if the parameters produce bistability for the ODE system provided by the network. The most important parameters that must be provided by the user are the principal continuation parameter (PCP) and the species you would like to compare it against. For more information on numerical continuation and these values see Numerical Continuation Routine. To select the PCP one needs to know which conservation law to choose. The following command will provide the conservation laws derived by the deficiency manager:
print(approach.get_conservation_laws())
This provides the following output:
C1 = 1.0*s16 + 1.0*s7
C2 = 1.0*s2 + 1.0*s3
C3 = 1.0*s1 + 2.0*s15 + 1.0*s16 + 1.0*s3 + 1.0*s6
here the left hand side of the equation corresponds to the constant that reflects the total amount of the leading species. It is one of these constants that should be provided to the numerical continuation routine. For this example we choose a PCP of C3 (total amount of species ) and the species s15 (species ) for the y-axis of the bifurcation diagram.
spcs = "s15"
PCP_x = "C3"
Now we can call the numerical continuation routine. First we set the species and pass in the parameters we obtained from
the optimization routine. The next input we provide is a dictionary representation of the AUTO 2000 parameters, to obtain
a description of these parameters and more options refer to AUTO parameters
. Please note
that one should not set ‘SBML’ or ‘ScanDirection’ in these parameters as these are automatically assigned. It is
absolutely necessary to set PrincipalContinuationParameter in this dictionary.
Here we set the maximum stepsize for numerical continuation, DSMAX to 1e3. However, for certain runs of the numerical continuation this may produce jagged plots. Smaller values should be used if one wants to obtain a smoother plot, although it should be noted that this will increase the runtime of the numerical continuation. We also state the principal continuation parameter range by defining ‘RL0’ and ‘RL1’, the lower and upper bound for the parameter, respectively. In addition to this range, the lower and upper bounds for the measure of the error is also provided as ‘A0’ and ‘A1’, respectively.
Once we have set the AUTO parameters, we tell the numerical continuation routine whether or not to print out the labels obtained by the numerical continuation routine. Please refer to Numerical Continuation Routine for a description of this print out. The next value we provide is the string representation of the directory where we would like to store the multistability plots, if any are found (here we choose to create the stability_graphs directory in the current directory).
Using this input we can now run the numerical continuation routine on the parameters that pass the constraints of the optimization problem and produce an objective function value smaller than sys_min_val. This is done below.
multistable_param_ind, plot_specifications = approach.run_continuity_analysis(species=spcs, parameters=params_for_global_min,
auto_parameters={'PrincipalContinuationParameter': PCP_x,
'RL0': 1e2, 'RL1': 1e6, 'A0': 0.0, 'A1': 5e6,
'DSMAX': 1e3},
print_lbls_flag=False, dir_path="./stability_graphs")
In addition to putting the multistability plots found into the path dir_path, this routine will also return the indices of params_for_global_min that correspond to these plots named “multistable_param_ind” above. Along with these indices, the routine will also return the plot specifications for each element in “multistable_param_ind” that specify the range used for the x-axis, y-axis, and the x-y values for each special point in the plot (named “plot_specifications” above). Also note that if multistability plots are produced, the plot names will have the following form: PCP_species id_index of params_for_global.png. The output provided by the numerical continuation run is as follows:
Running continuity analysis ...
Elapsed time for continuity analysis in seconds: 26.88336992263794
Again, we can generate a report that will contain the numerical optimization routine output and the now added information provided by the numerical continuation run.
approach.generate_report()
This provides the following output that describes that of the 28 parameter sets that passed the constraints of the optimization problem, 14 of them produce multistability for the given input. In addition to this, it also tells one the indices in params_for_global_min that produce multistability. In practice, larger ranges for the principal continuation parameter may be needed, but this will increase the runtime of the numerical continuation routine.
Smallest value achieved by objective function: 0.0
28 point(s) passed the optimization criteria.
Number of multistability plots found: 14
Elements in params_for_global_min that produce multistability:
[0, 1, 5, 7, 8, 12, 13, 14, 15, 20, 23, 25, 26, 27]
The following is a bistability plot produced by element 27 of params_for_global_min. Here the solid blue line indicates stability, the dashed blue line is instability, and the red stars are the special points produced by the numerical continuation.
In addition to providing this more hands on approach to the numerical continuation routine, we also provide a greedy version of the numerical continuation routine. With this approach the user just needs to provide the species, parameters, and PCP. This routine does not guarantee that all multistability plots will be found, but it does provide a good place to start finding multistability plots. Once the greedy routine is ran, it is usually best to return to the more hands on approach described above. Note that as stated by the name, this approach is computationally greedy and will take a longer time than the more hands on approach. Below is the code used to run the greedy numerical continuation:
multistable_param_ind, plot_specifications = approach.run_greedy_continuity_analysis(species=spcs, parameters=params_for_global_min, dir_path="./stability_graphs",
auto_parameters={'PrincipalContinuationParameter': PCP_x})
approach.generate_report()
This provides the following output:
Running continuity analysis ...
Elapsed time for continuity analysis in seconds:<PHONE_NUMBER>2476196
Smallest value achieved by objective function: 0.0
28 point(s) passed the optimization criteria.
Number of multistability plots found: 19
Elements in params_for_global_min that produce multistability:
[0, 1, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20, 23, 25, 26, 27]
Note that some of these plots will be jagged or have missing sections in the plot. To produce better plots the hands on approach should be used.
Although numerical continuation can be used by most examples, in some cases, the input vectors found by the optimization method yield an ODE system that has a singular or ill-conditioned Jacobian. For this reason, the numerical continuation method will be unsuccessful. To provide an alternative method to numerical continuation, we have constructed a routine that performs direct simulation in order to construct the bifurcation diagram. See section Direct Simulation for the General Approach for further information on the method.
To run bistability analysis using the direct simulation approach, we run the following routine:
import crnt4sbml
network = crnt4sbml.CRNT("/path/to/Fig1Ci.xml")
approach = network.get_mass_conservation_approach()
bounds, concentration_bounds = approach.get_optimization_bounds()
params_for_global_min, obj_fun_val_for_params = approach.run_optimization(bounds=bounds, concentration_bounds=concentration_bounds)
approach.run_direct_simulation(response="s15", signal="C3", params_for_global_min=params_for_global_min)
approach.generate_report()
This routine will use the input vectors (named params_for_global_min) provided by the optimization and perform the direct simulation approach for bistability analysis, then puts the plots produced in the directory path ./dir_sim_graphs. This provides the following output for the simple_biterminal example:
Creating Equilibrium Manifold ...
Elapsed time for creating Equilibrium Manifold: 2.384094
Running feasible point method for 10 iterations ...
Elapsed time for feasible point method: 1.722398281097412
Running the multistart optimization method ...
Elapsed time for multistart method: 8.421388149261475
Starting direct simulation ...
Elapsed time for direct simulation in seconds:<PHONE_NUMBER>38797
Smallest value achieved by objective function: 0.0
4 point(s) passed the optimization criteria.
Along with this, it also produces the following bifurcation diagram.
Similar to the optimization for the mass conservation approach, we can see that direct simulation can take a long time to complete. For this reason, we have a parallel version of the direct simulation approach and optimization. The parallel version can be ran by setting parallel_flag=True and then running with mpiexec. For further details on running in parallel see section Parallel CRNT4SBML.
For more examples of running the mass conservation approach please see Further Examples.


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| Path: | README |
| Last Update: | Fri Nov 21 23:28:58 +0400 2008 |
ArabicHelper
A plugin to help you localize your Rails app to Arabic, this include few useful helpers and ActiveRecord localization.
This plugin is made before Rails 2.2, in Rails 2.2, I18n has been added as part of ActionPack, which is the answer to the need of this plugin. So use it only for versions before 2.2
Arabic Helper consists of two parts, model extension and views helper methods. Model extension is to help you have Arabic error messages and attributes names in your model. Helper methods are made to support, pluralizing, country names and time distance in Arabic.
I’ve been also following the updates in rails-I18n - github.com/svenfuchs/rails-i18n/tree/master and did some work on the Arabic locale file, you can find it on: github.com/rbarazi/rails-i18n/tree/master. You can simply use it if you are on Rails 2.2 or Rails Edge, or if you are not then use ArabicHelper.
Example
Model extension:
class Post has_arabic_attributes :body => 'النص', :user_id => 'الكاتب' end
View/Controller
@post = Post.first @post.arabic_full_messages
Available helpers:
arabic_country_options_for_select(selected = nil, priority_countries = nil) arabic_country_select(object, method, priority_countries = nil, options = {}, html_options = {}) arabic_time_ago_in_words(from_time, include_seconds = false) arabic_distance_of_time_in_words(from_time, to_time = 0, include_seconds = false) arabic_pluralize(number, singular, double, plural, gender_class='male')
TODO:
Copyright (c) 2008 Rida Al Barazi, released under the MIT license


Master index
Index for ./bFunctions
Matlab files in this directory:
DecimationFilters
DecimatorQuantizationCoefficientSensitivity
NormalizedCoefficients
TuneK
TuneR
cDecimationFilters
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Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
Note that when possible I link to the page containing the link to the actual PDF or PS of the preprint.
I prefer this as it gives some context to the paper and avoids possible copyright problems with direct linking.
Thus you may need to search on the page to find the actual document.
Changelog
Differential Geometry
- Robust statistical estimation of curvature on discretized surfaces
-
Evangelos Kalogerakis,
Patricio Simari,
Derek Nowrouzezahrai,
Karan Singh
- Focal Surfaces of Discrete Geometry
-
Jingyi Yu,
Xiaotian Yin,
Xianfeng Gu,
Leonard McMillan,
Steve Gortler
- Discrete Laplace operators: No free lunch
-
Max Wardetzky,
Saurabh Mathur,
Felix Kalberer,
Eitan Grinspun
Surface Reconstruction I
- Voronoi-based Variational Reconstruction of Unoriented Point Sets
-
Pierre Alliez,
David Cohen-Steiner,
Yiying Tong,
Mathieu Desbrun
- Reconstruction of Deforming Geometry from Time-Varying Point Clouds
-
Michael Wand,
Philipp Jenke,
Qi-Xing Huang,
Martin Bokeloh,
Leonidas Guibas,
Andreas Schilling
- Data-Dependent MLS for Faithful Surface Approximation
-
Yaron Lipman,
Daniel Cohen-Or,
David Levin
Surface Reconstruction II
- Multilevel Streaming for Out-of-Core Surface Reconstruction
-
Matthew Bolitho,
Michael Kazhdan,
Randal Burns,
Hugues Hoppe
- A Streaming Algorithm for Surface Reconstruction
-
Remi Allegre,
Raphaelle Chaine,
Samir Akkouche
- Shape reconstruction from unorganized cross-sections
-
Jean-Daniel BOISSONNAT,
Pooran MEMARI
Short Papers I
- Bayesian Surface Reconstruction via Iterative Scan Alignment to an Optimized Prototype
-
Qi-Xing Huang,
Bart Adams,
Michael Wand
- Laplace-Beltrami Eigenfunctions for non-rigid Shape Matching
-
Raif M. Rustamov
- Symmetry-Enhanced Remeshing of Surfaces
-
Joshua Podolak,
Aleksey Golovinskiy,
Szymon Rusinkiewicz
- Ridge Based Curve and Surface Reconstruction
-
Jochen Susmuth,
Gunther Greiner
Shape Deformation
- Elastic Secondary Deformations by Vector Field Integration
-
Wolfram von Funck,
Holger Theisel,
Hans-Peter Seidel
- As-Rigid-As-Possible Surface Modeling
-
Olga Sorkine,
Marc Alexa
- GPU-assisted Positive Mean Value Coordinates for Mesh Deformation
-
Yaron Lipman,
Johannes Kopf,
Daniel Cohen-Or,
David Levin
Meshing and Parameterization
- Unconstrained Isosurface Extraction on Arbitrary Octrees
-
Michael Kazhdan,
Allison Klein,
Ketan Dalal,
Hugues Hoppe
- Linear Angle Based Parameterization
-
Rhaleb Zayer,
Bruno Levy,
Hans-Peter Seidel
- Triangulations with locally optimal Steiner points
-
Hale Erten,
Alper Ungor
Short Papers II
- Surface Reconstruction using Local Shape Priors
-
Ran Gal,
Ariel Shamir,
Tal Hassner,
Mark Pauly,
Daniel Cohen-Or
- Fast Normal Vector Compression with Bounded Error
-
Eric J. Griffith,
Michal Koutek,
Frits Post
- Delaunay Mesh Construction
-
Ramsay Dyer,
Hao Zhang,
Torsten Möller
Modeling and Registration
- Example-Based Skeleton Extraction
-
Scott Schaefer,
Can Yuksel
- Developable Surfaces from Arbitrary Sketched Boundaries
-
Kenneth Rose,
Alla Sheffer,
Jamie Wither,
Marie-Paule Cani,
Boris Thibert
- Dynamic Geometry Registration
-
Niloy J. Mitra,
Simon Flory,
Maks Ovsjanikov,
Natasha Gelfand,
Leonidas Guibas,
Helmut Pottmann
Surface Fairing and Optimization
- Generalized Surface Flows for Mesh Processing
-
Ilya Eckstein,
Jean-Philippe Pons,
Yiying Tong,
C.-C. Jay Kuo,
Mathieu Desbrun
- Shape Optimization Using Reflection Lines
-
Elif Tosun,
Yotam I. Gingold,
Jason Reisman,
Denis Zorin
- Constraint-based fairing of surface meshes
-
Klaus Hildebrandt,
Konrad Polthier
kesen.huang@gmail.com


Posen Chai
School
National Taiwan Normal University
Date of Birth
08/18/1994
Gender
Male
Email
<EMAIL_ADDRESS>Phone Number
0987620952
FACEBOOK


This is a simple page to display some of the work I have done, enjoy!
Remember PokemonGo? If not, you must have been on a remote island during the summer of 2016. You probably could've caught some pretty nice Pokemon there too.
During the PokemonGo frenzy, a couple co-workers and I wanted to cheat the system a bit. We wanted to crack the spawning algorithm and figure out where the hell we could catch a dragonite. To do so, we did a 24 hour hack-a-thon, creating this PokemonGo sightings page. A simple right click allowed you to add a sighting to the map, and a click on an existing Pokemon allowed you to verify the sighting. We even had a 'live' mode, only showing Pokemon sighted in the last 15 minutes. At its peak, we were getting over 1000 visits per day, with a total of over 2000 Pokemon locations entered.
Unfortunately for our map, we soon learned that Pokemon spawns were fairly random, enough to render our map useless. However, in our short time, we did get to solve some pretty cool problems: Spam protection, so people can't draw male genitalia out of Abras in the Atlantic ocean (yes, that happened), clustering, and creating the sleekest interface of all the PokemonGo maps out there!
For the time being, this site is not being hosted anywhere. I may work on getting it back online, but if you'd like to see it, it'll have to be in person.


Chemical development of photography began around the early 1800's, the first attempts were very imprecise and nothing was done to preserve what little shades the photosensitive substances did produce. The earlier chemicals used included sivler nitrate, silve chloride, and others.
After decades of development by inventors such as Nicéphore Niépce, and Frederick Scott Archer, the technology for capturing images had evolved enough for everyday people to replicate. Archer was the one to invent the earliest practical process, called the collodion process.
In the collodion process, Archer used (you guessed it) collodion to coat a glass plate. What is collodion? Collodion is simply an assembly of organic compounds that is highly flammable and unstable, but also works well for acting as a catalyst (it speeds up the reaction) for imagining incoming light. When the photographer prepared a slide, he took a glass panel and spread collodion on it. The plate would then be dunked, or otherwise covered with silver nitrate, the compound that is actually photosensitive and will be responsible for capturing the image. Silver nitrate is also somewhat corrosive, and it reacts explosively with ethanol.
It is very important at this stage to keep the plate away from light, as it will pick up the light and will ruin the slide. When the time came to take a picture, the plate was carefully loaded into the camera, and the plate is exposed for a short amount of time capturing the image.
The plate was then developed in a solution of pyrogallic acid to produce a positive image. Completing the image, the photography generally covered the whole plate with a layer of varnish to give it a shiny and clean look.
Many cameras use lenses to focus on the subject of their picture. One can explain how the light is refracted by the lenses using Snell's Law, as shown below:
This equation describes the relationship between the angle of light entering the medium that is the lense, and the anlge of the light in that lense. Diffraction of light simply changes the angle of the light, and you can see this whenever you put a straw into a glass of water.
On the left side, θ would represent the angle of the light with respect to the surface of the lense entering the lense, and n would be the refractive index of the air. (The refractive index is the value c/v where c is the speed of light and v is the speed of light in whatever medium we are talking about.
A simple application of this equation would be knowing the refractive index of the glass lense(n1), and the angle entering the lense(θ), but we wouldn't know what angle it would diffract to. In this situation we could divide both sides by n2 (which is approximately equal to one because there isn't much difference between the speed of light in a vacuum and the speed of light in air), and just get (n1 sin(θ1))/n2 = sin(θ2)


This page shows dialog to select files or directories to be uploaded from local repository to user's Virtual Folder.
1.Select files or directories that will be uploaded:
| filename | date | action | |
|---|---|---|---|
| ${item.name} | ${item.date} |
1.Selected files: ${selectedfiles}
2.Select Virtual Folder:
3. Press the button to start
... upload in progress ...


Deval Patrick to speak in Las Vegas on Sunday
by The Associated PressFormer Massachusetts Gov. Deval Patrick will make one of his first appearances as a presidential candidate in Las Vegas on Sunday night.
Patrick, who jumped into the packed primary race on Thursday, is among more than a dozen 2020 candidates who will speak at a fundraiser for the Nevada Democratic Party this weekend.
Unlike former New York City Mayor Michael Bloomberg, who is expected to skip early states like Nevada if he jumps in the race, Patrick plans to compete in the traditional early voting states of Iowa, New Hampshire, Nevada and South Carolina.
Bloomberg, who is also weighing a last-minute bid for the party’s nomination, is not among the slate of scheduled speakers in Las Vegas.


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CIFXMarker Class ReferenceInheritance diagram for CIFXMarker:
List of all members.
|
Public Member Functions |
| virtual void IFXAPI | GetCountX (U32 &rCount) const |
| virtual IFXRESULT IFXAPI | GetIndex (const IFXString &rKey, U32 &uIndex) |
| virtual void IFXAPI | GetKeyX (U32 index, IFXString &rOutKey) |
| virtual void IFXAPI | GetAttributeX (U32 uIndex, IFXMetaDataAttribute &rValueType) |
| virtual void IFXAPI | SetAttributeX (U32 uIndex, const IFXMetaDataAttribute &rValueType) |
| virtual void IFXAPI | GetBinaryX (U32 uIndex, U8 *pDataBuffer) |
| virtual void IFXAPI | GetBinarySizeX (U32 uIndex, U32 &rSize) |
| virtual void IFXAPI | GetStringX (U32 uIndex, IFXString &rValue) |
| virtual void IFXAPI | GetPersistenceX (U32 uIndex, BOOL &rPersistence) |
| virtual void IFXAPI | SetBinaryValueX (const IFXString &rKey, U32 length, const U8 *data) |
| virtual void IFXAPI | SetStringValueX (const IFXString &rKey, const IFXString &rValue) |
| virtual void IFXAPI | SetPersistenceX (U32 uIndex, BOOL value) |
| virtual void IFXAPI | DeleteX (U32 uIndex) |
| virtual void IFXAPI | DeleteAll () |
| virtual void IFXAPI | AppendX (IFXMetaDataX *pSource) |
| virtual void IFXAPI | GetEncodedKeyX (U32 uIndex, IFXString &rOutKey) |
| virtual void IFXAPI | GetSubattributesCountX (U32 uIndex, U32 &rCountSubattributes) |
| virtual IFXRESULT IFXAPI | GetSubattributeIndex (U32 uIndex, const IFXString &rSubattributeName, U32 &rSubattributeIndex) |
| virtual void IFXAPI | GetSubattributeNameX (U32 uIndex, U32 uSubattributeIndex, IFXString &rSubattributeName) |
| virtual void IFXAPI | GetSubattributeValueX (U32 uIndex, U32 uSubattributeIndex, IFXString *&pSubattributeValue) |
| virtual void IFXAPI | SetSubattributeValueX (U32 uIndex, const IFXString &rSubattributeName, const IFXString *pSubattributeValue) |
| virtual void IFXAPI | DeleteSubattributeX (U32 uIndex, U32 uSubattributeIndex) |
| virtual void IFXAPI | DeleteAllSubattributes (U32 uIndex) |
Protected Member Functions |
| | CIFXMarker () |
| virtual | ~CIFXMarker () |
| IFXRESULT IFXAPI | SetSceneGraph (IFXSceneGraph *pInSceneGraph) |
| IFXRESULT IFXAPI | GetSceneGraph (IFXSceneGraph **ppOutSceneGraph) |
| IFXRESULT IFXAPI | Mark () |
| IFXRESULT IFXAPI | Marked (BOOL *pbOutMarked) |
| U32 IFXAPI | GetPriority () |
| void IFXAPI | SetPriority (U32 uInPriority, BOOL bRecursive, BOOL bPromotionOnly) |
| void IFXAPI | SetExternalFlag (BOOL bExternalFlag) |
| void IFXAPI | GetExternalFlag (BOOL *bExternalFlag) |
| virtual void IFXAPI | SetQualityFactorX (U32 uQualityFactor, U32 uQualityFactorMask=(U32) IFXMarkerX::ALL) |
| virtual void IFXAPI | GetQualityFactorX (U32 &ruQualityFactor, U32 uQualityFactorMask=(U32) IFXMarkerX::ALL) |
| void IFXAPI | GetEncoderX (const IFXCID &, IFXEncoderX *&rpEncoderX) |
| virtual IFXRESULT IFXAPI | InitializeObject () |
Protected Attributes |
| IFXSceneGraph * | m_pSceneGraph |
| U32 | m_uMark |
| U32 | m_uPriority |
| U32 | m_uDefaultQualityFactor |
| U32 | m_uPositionQualityFactor |
| U32 | m_uTexCoordQualityFactor |
| U32 | m_uNormalQualityFactor |
| U32 | m_uDiffuseQualityFactor |
| U32 | m_uSpecularQualityFactor |
Private Attributes |
| IFXMetaDataX * | m_pMetaData |
| BOOL | m_bExternalFlag |
Constructor & Destructor Documentation
| CIFXMarker::CIFXMarker |
( |
|
) |
[protected] |
|
| CIFXMarker::~CIFXMarker |
( |
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) |
[protected, virtual] |
|
Member Function Documentation
| void CIFXMarker::AppendX |
( |
IFXMetaDataX * |
pSource |
) |
[virtual] |
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Method that Appends pSource Meta Data to the existing one.
Implements IFXMetaDataX. |
| void CIFXMarker::DeleteAll |
( |
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) |
[virtual] |
|
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|
Method that delete all of the key/value pairs that exist.
It can be successfully called even when no key/value pairs exist.
Implements IFXMetaDataX. |
| void CIFXMarker::DeleteAllSubattributes |
( |
U32 |
uIndex |
) |
[virtual] |
|
|
|
Method that delete all of the additinal attributes that exist.
It can be successfully called even when no additinal attributes exist.
Implements IFXMetaDataX. |
| void CIFXMarker::DeleteSubattributeX |
( |
U32 |
uIndex, |
|
|
U32 |
uSubattributeIndex |
|
) |
[virtual] |
|
|
|
Method that deletes additional attribute that is specified by index.
Implements IFXMetaDataX. |
| void CIFXMarker::DeleteX |
( |
U32 |
uIndex |
) |
[virtual] |
|
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|
Method that deletes the key/value pair that is specified by index.
Implements IFXMetaDataX. |
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Method that returns the value's enumerated type (either binary or string) associated with the key/value pair specified by index.
Implements IFXMetaDataX. |
| void CIFXMarker::GetBinarySizeX |
( |
U32 |
uIndex, |
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|
U32 & |
rSize |
|
) |
[virtual] |
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|
Method that returns the U32 size in bytes of the value binary data associated with the key/value pair specified by index.
The key/value pair must be of type binary.
Implements IFXMetaDataX. |
| void CIFXMarker::GetBinaryX |
( |
U32 |
uIndex, |
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|
U8 * |
pDataBuffer |
|
) |
[virtual] |
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|
Method that copies the value binary data (buffer of U8s) associated with the key/value pair specified by index into the caller's buffer.
The key/value pair must be of type binary.
Implements IFXMetaDataX. |
| void CIFXMarker::GetCountX |
( |
U32 & |
rCount |
) |
const [virtual] |
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|
Method that returns the number of key/value pairs.
Implements IFXMetaDataX. |
| void CIFXMarker::GetEncodedKeyX |
( |
U32 |
uIndex, |
|
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IFXString & |
rOutKey |
|
) |
[virtual] |
|
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|
Method that returns the "packed" string including key and all additional attributes .
Implements IFXMetaDataX. |
| void CIFXMarker::GetEncoderX |
( |
const IFXCID & |
, |
|
|
IFXEncoderX *& |
rpEncoderX |
|
) |
[protected] |
|
| void IFXAPI CIFXMarker::GetExternalFlag |
( |
BOOL * |
bExternalFlag |
) |
[inline, protected, virtual] |
|
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|
Returns flag indicating if object was loaded from external refernced file.
Implements IFXMarker. |
| IFXRESULT CIFXMarker::GetIndex |
( |
const IFXString & |
rKey, |
|
|
U32 & |
uIndex |
|
) |
[virtual] |
|
|
|
Method that hands back the index of the specified key if it exists.
If the specified key does not exists, an IFXRESULT is returned instead of an exception being thrown. It is expected that this might be a typical usage scenario. Method can return IFX_OK or IFX_E_NO_METADATA result.
Implements IFXMetaDataX. |
| void CIFXMarker::GetKeyX |
( |
U32 |
index, |
|
|
IFXString & |
rOutKey |
|
) |
[virtual] |
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|
Method that copies the key string associated with the key/value pair specified by index into the caller's string.
Implements IFXMetaDataX. |
| void CIFXMarker::GetPersistenceX |
( |
U32 |
uIndex, |
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|
BOOL & |
rPersistence |
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) |
[virtual] |
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|
Method that returns a BOOL that if TRUE indicates that the specified key/value pair is serialized when the CIFXWriteManager is instructed to write meta-data for the associated component's style of component.
Otherwise if FALSE, it is not serialized when serialization occurs. The key/value pair is specified by index.
Implements IFXMetaDataX. |
| U32 IFXAPI CIFXMarker::GetPriority |
( |
|
) |
[inline, protected, virtual] |
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|
Gets the streaming sequence relative to other components.
Implements IFXMarker. |
| void CIFXMarker::GetQualityFactorX |
( |
U32 & |
ruQualityFactor, |
|
|
U32 |
uQualityFactorMask = (U32) IFXMarkerX::ALL |
|
) |
[protected, virtual] |
|
|
|
Function used to get a single quality factor setting.
- Parameters:
-
| ruQualityFactor | Reference to a value that upon success is initalized with a quality factor setting in the range [0, IFX_MAXIMUM_QUALITY_FACTOR]. |
| uQualityFactorMask | A single EIFXQualityFactorFlags flag that identifies the quality factor setting to be obtained. Use IFXMarkerX::ALL to get the default quality setting. The behavior of other flag combinations are undefined. |
Implements IFXMarkerX. |
| IFXRESULT CIFXMarker::GetSceneGraph |
( |
IFXSceneGraph ** |
ppOutSceneGraph |
) |
[protected, virtual] |
|
|
|
Returns the scenegraph that was last passed into Initialize().
Implements IFXMarker. |
| void CIFXMarker::GetStringX |
( |
U32 |
uIndex, |
|
|
IFXString & |
rValue |
|
) |
[virtual] |
|
|
|
Method that copies the value string associated with the key/value pair specified by index into the caller's string.
The specified key/value pair must be of type string.
Implements IFXMetaDataX. |
| IFXRESULT CIFXMarker::GetSubattributeIndex |
( |
U32 |
uIndex, |
|
|
const IFXString & |
rSubattributeName, |
|
|
U32 & |
rSubattributeIndex |
|
) |
[virtual] |
|
|
|
Method that hands back the index of the specified additional attribute if it exists.
If the specified additional attribute does not exist, an IFXRESULT is returned instead of an exception being thrown. It is expected that this might be a typical usage scenario. Method can return IFX_OK or IFX_E_NO_METADATA result.
Implements IFXMetaDataX. |
| void CIFXMarker::GetSubattributeNameX |
( |
U32 |
uIndex, |
|
|
U32 |
uSubattributeIndex, |
|
|
IFXString & |
rSubattributeName |
|
) |
[virtual] |
|
|
|
Method that copies the name of the additional attribute specified by indexes into the caller's string.
Implements IFXMetaDataX. |
| void CIFXMarker::GetSubattributesCountX |
( |
U32 |
uIndex, |
|
|
U32 & |
rCountSubattributes |
|
) |
[virtual] |
|
|
|
Method that returns the number of additional attributes .
Implements IFXMetaDataX. |
| void CIFXMarker::GetSubattributeValueX |
( |
U32 |
uIndex, |
|
|
U32 |
uSubattributeIndex, |
|
|
IFXString *& |
pSubattributeValue |
|
) |
[virtual] |
|
|
|
Method that copies the value string associated with the additional attribute specified by indexes into the caller's string.
NULL means that no value exist.
Implements IFXMetaDataX. |
| IFXRESULT CIFXMarker::InitializeObject |
( |
|
) |
[protected, virtual] |
|
| IFXRESULT CIFXMarker::Mark |
( |
|
) |
[protected, virtual] |
|
| IFXRESULT CIFXMarker::Marked |
( |
BOOL * |
pbOutMarked |
) |
[protected, virtual] |
|
|
|
Determines whether a component is marked.
Implements IFXMarker. |
|
|
Method that sets the value's enumerated type (either binary or string) associated with the key/value pair specified by index.
Implements IFXMetaDataX. |
| void CIFXMarker::SetBinaryValueX |
( |
const IFXString & |
rKey, |
|
|
U32 |
length, |
|
|
const U8 * |
data |
|
) |
[virtual] |
|
|
|
Method that adds a new key/value pair or replaces an existing key/value pair using the the specified key and U8 binary data buffer.
The U32 size in bytes of the data buffer is also specified, as well as the persistence BOOL. The binary data is copied out of the caller's buffer and into a buffer managed by the component that exposes this interface.
Implements IFXMetaDataX. |
| void IFXAPI CIFXMarker::SetExternalFlag |
( |
BOOL |
bExternalFlag |
) |
[inline, protected, virtual] |
|
|
|
Sets flag to indicate if object was loaded from external referenced file or not.
Implements IFXMarker. |
| void CIFXMarker::SetPersistenceX |
( |
U32 |
uIndex, |
|
|
BOOL |
value |
|
) |
[virtual] |
|
|
|
Method that sets if the specified key/value pair is serialized when the CIFXWriteManager is instructed to write meta-data for the associated component's style of component.
If TRUE, it's serialized. Otherwise, it is not serialized. The key/value pair is specified by index.
Implements IFXMetaDataX. |
| void CIFXMarker::SetPriority |
( |
U32 |
uInPriority, |
|
|
BOOL |
bRecursive, |
|
|
BOOL |
bPromotionOnly |
|
) |
[protected, virtual] |
|
|
|
Set the streaming priority ( zero = initial load segment ) The two default, optional arguments are used in the following way:
- by default, bRecursive == TRUE, indicating that components should propagate the SetPriority() call to any subordinate (child) components.
If bRecursive == FALSE, the SetPriority() only applies to the component upon which the method was called (i.e. local only).
- by default, bPromotionOnly == TRUE, indicating that the specified priority (uInPriority) is only applied if it is a greater priority (lower U32) than the current priority of the component. In other words, by default, you cannot lower a components priority. If bPromotionOnly == FALSE, then the component accepts the new priority irregardless of its value relative to its old priority.
Implements IFXMarker.
Reimplemented in CIFXModel, CIFXNode, CIFXShaderLitTexture, and CIFXTextureObject. |
| void CIFXMarker::SetQualityFactorX |
( |
U32 |
uQualityFactor, |
|
|
U32 |
uQualityFactorMask = (U32) IFXMarkerX::ALL |
|
) |
[protected, virtual] |
|
|
|
Function used to set the quality factors of one or more indentified quality settings.
- Parameters:
-
| uQualityFactor | New quality factor that must be in the range [0, IFX_MAXIMUM_QUALITY_FACTOR]. As quality factors increase, the actual quality also increases. The default quality factor is IFX_DEFAULT_QUALITY_FACTOR. |
| uQualityFactorMask | Mask of EIFXQualityFactorFlags flags that defines the set of quality settings that the new quality factor is to be assigned to upon success. IFXMarkerX::ALL is used to identify the default quality setting. |
Implements IFXMarkerX. |
| IFXRESULT CIFXMarker::SetSceneGraph |
( |
IFXSceneGraph * |
pInSceneGraph |
) |
[protected, virtual] |
|
| void CIFXMarker::SetStringValueX |
( |
const IFXString & |
rKey, |
|
|
const IFXString & |
rValue |
|
) |
[virtual] |
|
|
|
Method that adds a new key/value pair or replaces an existing key/value pair using the the specified key, string value and persistence BOOL.
The string is copied from the caller's string to one managed by the component that exposes this interface.
Implements IFXMetaDataX. |
| void CIFXMarker::SetSubattributeValueX |
( |
U32 |
uIndex, |
|
|
const IFXString & |
rSubattributeName, |
|
|
const IFXString * |
pSubattributeValue |
|
) |
[virtual] |
|
|
|
Method that adds a new additional attribute or replaces an existing additional attribute using the the specified index, name and string value.
If pSubattributeValue equals to NULL, this means that additional attribute has no value.
Implements IFXMetaDataX. |
Member Data Documentation
© 1999 - 2006 Intel Corporation. All rights reserved.
* Other names and brands may be claimed as the property of others.
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Lambert Wilderness got its start over 60 (?) years ago with the help of Bill Lambert.(he started it- not helped) Soon after his return home from WWII, Bill found a teaching job in (wasilla before Fairbanks). Fairbanks and moved to Alaska with his wife, Carol "Sweetpea" Lambert. (Like the nickname /human , its humanizing) Bill and Carol quickly fell in love with the mountains, and in 19?? they purchased a small plot of land in the Alaska Range. (Not sure if they actually purchased it or homesteaded iit. Know they got horses and bought the camper and horse trailer before buying camp. Lots of family trips that way).
Hello there here's a quote
At Lambert Wilderness, we want to give others an opportunity to experience the land we've come to love. We strive to share our knowledge and our passion for nature, through personalized wilderness tours from experienced guides. Come join us in the wilderness, and witness the stunning beauty of Alaska.


snakemake --rerun-incomplete {{ rulegraph['target'] }}
snakemake --rerun-incomplete {{ rulegraph['target'] }} --drmaa " -t TIME -A account -p core -n {threads}"
No rulegraph has been defined


Your telephone number and email address will ONLY be used to contact you about your current registration.
It is strongly encouraged to provide your telephone number and/ or email address as a point of contact, so your local Emergency Support Services (ESS) team can easily support you.
Do you authorize Emergency Management British Columbia and your Local Emergency Program to communicate with you by email or phone regarding your emergency situation. Note: if you do not authorize communications, you can still complete your registration and receive emergency supports, however you will not receive your ESS file number confirmation and will not be able to receive e-transfer based supports.
If the evacuee is not able to provide an email address or contact telephone number, should they be eligible for emergency supports due to an evacuation, they will not be able to receive an e-Transfer. They will instead receive referrals.


module Slot: sig .. end
type t = Printexc.backtrace_slot;
let is_raise: t => bool;
is_raise slot
is true
when slot
refers to a raising
point in the code, and false
when it comes from a simple
function call.let location: t => option(Printexc.location);
location slot
returns the location information of the slot,
if available, and None
otherwise.
Some possible reasons for failing to return a location are as follow:
-g
)let format: (int, t) => option(string);
format pos slot
returns the string representation of slot
as
raw_backtrace_to_string
would format it, assuming it is the
pos
-th element of the backtrace: the 0
-th element is
pretty-printed differently than the others.
Whole-backtrace printing functions also skip some uninformative
slots; in that case, format pos slot
returns None
.
Since 4.02


-
- All Implemented Interfaces:
-
java.io.Serializable
,kotlin.sequences.Sequence
public class BigIntegerProgression implements Serializable, Sequence
Arithmetic progression of BigIntegers with non-null step.
-
-
Field Summary
Fields Modifier and Type Field Description private final BigInteger
last
private final BigInteger
first
private final BigInteger
step
-
Method Summary
Modifier and Type Method Description final Boolean
isEmpty()
Indicates if the BigIntegerProgression is empty. final Boolean
isNotEmpty()
Indicates if the BigIntegerProgression is not empty. final Boolean
isInfinite()
Indicates if the BigIntegerProgression is infinite. final Boolean
isFinite()
Indicates if the BigIntegerProgression is finite. final BigInteger
count()
Finds count of the sequence (yields O(1) time capacity assuming BigInteger arithmetic operations to take O(1) time). final Boolean
isSingle()
Indicates if the BigIntegerProgression contains exactly 1 element. Boolean
equals(Object other)
Two BigIntegerProgressions are equal when they are equal as sequences. Iterator<BigInteger>
iterator()
Returns an Iterator over values from the sequence. final Number
average()
Finds average element of the sequence (yields O(1) time capacity assuming BigDecimal arithmetic operations to take O(1) time). final Number
sum()
Finds sum of the sequence (yields O(1) time capacity assuming BigInteger arithmetic operations to take O(1) time). Integer
hashCode()
String
toString()
String representation for the BigIntegerProgression. final Boolean
contains(BigInteger number)
Indicates if the sequence contains the BigInteger yielding O(1) time capacity. final Boolean
contains(Number number)
Indicates if the sequence contains the number (converted to BigInteger) yielding O(1) time capacity. final Boolean
contains(IntProgression subProgression)
Indicates if the sequence contains all Ints (converted to BigInteger) of subProgression yielding O(1) time capacity. final Boolean
contains(LongProgression subProgression)
Indicates if the sequence contains all Longs (converted to BigInteger) of subProgression yielding O(1) time capacity. final Boolean
contains(BigIntegerProgression subProgression)
Indicates if the sequence contains all BigIntegers of subProgression yielding O(1) time capacity. final BigInteger
last()
Gets actual last element or throws NoSuchElementException if it does not exist. final List<BigInteger>
distinct()
Gets List of distinct elements. final BigIntegerProgression
drop(BigInteger n)
Skips n first elements. BigIntegerProgression
drop(Number n)
Skips n (converted to BigInteger) first elements. final BigInteger
elementAtOrNull(Number index)
Gets element by the specified index (since 0) (converted to BigInteger) or null if the index is out of range. final BigInteger
elementAtOrNull(BigInteger index)
Gets element by the specified index (since 0) or null if the index is out of range. final BigInteger
elementAtOrElse(Number index, Function1<Number, BigInteger> defaultValue)
Gets element by the specified index (since 0) (converted to BigInteger) or calls defaultValue with index if the index is out of range. final BigInteger
elementAt(Number index)
Gets element by the specified index (since 0) (converted to BigInteger) or throws IndexOutOfBoundsException if the index is out of range. final BigInteger
indexOf(BigInteger element)
Returns index (since 0) of the element in the progression or -1 if the progression does not contain the element. final BigInteger
indexOf(Number element)
Returns index (since 0) of the element (converted to BigInteger) in the progression or -1 if the progression does not contain the element. final BigInteger
lastIndexOf(Number element)
Returns last index of the element that is always equal to BigIntegerProgression.indexOf because all elements are unique in the sequence. final Number
lastOrNull()
ReturnsYields O(1) time capacity. final Number
max()
Gets max element yielding O(1) time capacity. final Number
min()
Gets min element yielding O(1) time capacity. final Sequence<BigInteger>
plus(Number other)
Concatenates the BigIntegerProgression with the other if the BigIntegerProgression is finite. final Sequence<BigInteger>
plus(Array<Out Number> other)
Concatenates the BigIntegerProgression with the other if the BigIntegerProgression is finite. final Sequence<BigInteger>
plus(Iterable<Number> other)
Concatenates the BigIntegerProgression with the other if the BigIntegerProgression is finite. final Sequence<BigInteger>
plus(Sequence<Number> other)
Concatenates the BigIntegerProgression with the other if the BigIntegerProgression is finite. final Sequence<BigInteger>
plusElement(Number other)
Adds the other to the end of the sequence if it is finite. final BigIntegerProgression
reversed()
Reverses order of the sequence if possible. final BigIntegerProgression
flip()
Changes direction of the infinite BigIntegerProgression to the opposite one. BigIntegerProgression
extend(BigInteger n)
Adds n elements to the end of the progression according to its step if it has at least 2 elements. BigIntegerProgression
extend(Number n)
Adds n (converted to BigInteger) elements to the end of the progression according to its step if it has at least 2 elements. BigIntegerProgression
shr(Number number)
Makes number steps forward for each item. BigIntegerProgression
shl(Number number)
Makes number steps backward for each item. BigIntegerProgression
step(Number step)
Changes step not changing direction of progression. BigIntegerProgression
take(Number n)
Limits to n (converted to BigInteger) first elements. final BigIntegerProgression
take(BigInteger n)
Limits to n first elements. final <C extends MutableCollection<BigInteger>> C
toCollection(C destination)
Appends all elements to the given destination collection. final List<BigInteger>
toList()
Converts the progression into the List. final List<BigInteger>
toMutableList()
Converts the progression into the MutableList. final HashSet<BigInteger>
toHashSet()
Converts the progression into the HashSet. final Set<BigInteger>
toMutableSet()
Converts the progression into the MutableSet. final Set<BigInteger>
toSet()
Converts the progression into the Set. final SortedSet<BigInteger>
toSortedSet()
Converts the progression into the SortedSet. final SortedSet<BigInteger>
toSortedSet(Comparator<In BigInteger> comparator)
Converts the progression into the SortedSet with specified comparator. BigInteger
component1()
final BigInteger
component2()
final BigInteger
component3()
final BigInteger
component4()
final BigInteger
component5()
final BigInteger
getLast()
The last element when possible. final BigInteger
getFirst()
final BigInteger
getStep()
-
-
Method Detail
-
isEmpty
final Boolean isEmpty()
Indicates if the BigIntegerProgression is empty.
-
isNotEmpty
final Boolean isNotEmpty()
Indicates if the BigIntegerProgression is not empty.
-
isInfinite
final Boolean isInfinite()
Indicates if the BigIntegerProgression is infinite.
-
isFinite
final Boolean isFinite()
Indicates if the BigIntegerProgression is finite.
-
count
final BigInteger count()
Finds count of the sequence (yields O(1) time capacity assuming BigInteger arithmetic operations to take O(1) time).
-
isSingle
final Boolean isSingle()
Indicates if the BigIntegerProgression contains exactly 1 element.
-
equals
Boolean equals(Object other)
Two BigIntegerProgressions are equal when they are equal as sequences.
-
iterator
Iterator<BigInteger> iterator()
Returns an Iterator over values from the sequence.
-
average
final Number average()
Finds average element of the sequence (yields O(1) time capacity assuming BigDecimal arithmetic operations to take O(1) time).
-
sum
final Number sum()
Finds sum of the sequence (yields O(1) time capacity assuming BigInteger arithmetic operations to take O(1) time).
-
toString
String toString()
String representation for the BigIntegerProgression.
-
contains
final Boolean contains(BigInteger number)
Indicates if the sequence contains the BigInteger yielding O(1) time capacity.
-
contains
final Boolean contains(Number number)
Indicates if the sequence contains the number (converted to BigInteger) yielding O(1) time capacity.
-
contains
final Boolean contains(IntProgression subProgression)
Indicates if the sequence contains all Ints (converted to BigInteger) of subProgression yielding O(1) time capacity.
-
contains
final Boolean contains(LongProgression subProgression)
Indicates if the sequence contains all Longs (converted to BigInteger) of subProgression yielding O(1) time capacity.
-
contains
final Boolean contains(BigIntegerProgression subProgression)
-
last
final BigInteger last()
Gets actual last element or throws NoSuchElementException if it does not exist.
-
distinct
final List<BigInteger> distinct()
Gets List of distinct elements.
-
drop
final BigIntegerProgression drop(BigInteger n)
Skips n first elements.
-
drop
BigIntegerProgression drop(Number n)
Skips n (converted to BigInteger) first elements.
-
elementAtOrNull
final BigInteger elementAtOrNull(Number index)
Gets element by the specified index (since 0) (converted to BigInteger) or null if the index is out of range.
-
elementAtOrNull
final BigInteger elementAtOrNull(BigInteger index)
Gets element by the specified index (since 0) or null if the index is out of range.
-
elementAtOrElse
final BigInteger elementAtOrElse(Number index, Function1<Number, BigInteger> defaultValue)
Gets element by the specified index (since 0) (converted to BigInteger) or calls defaultValue with index if the index is out of range.
-
elementAt
final BigInteger elementAt(Number index)
Gets element by the specified index (since 0) (converted to BigInteger) or throws IndexOutOfBoundsException if the index is out of range.
-
indexOf
final BigInteger indexOf(BigInteger element)
Returns index (since 0) of the element in the progression or -1 if the progression does not contain the element.
-
indexOf
final BigInteger indexOf(Number element)
Returns index (since 0) of the element (converted to BigInteger) in the progression or -1 if the progression does not contain the element.
-
lastIndexOf
final BigInteger lastIndexOf(Number element)
Returns last index of the element that is always equal to BigIntegerProgression.indexOf because all elements are unique in the sequence.
-
lastOrNull
final Number lastOrNull()
ReturnsYields O(1) time capacity.
-
plus
final Sequence<BigInteger> plus(Number other)
-
plus
final Sequence<BigInteger> plus(Array<Out Number> other)
-
plus
final Sequence<BigInteger> plus(Iterable<Number> other)
-
plus
final Sequence<BigInteger> plus(Sequence<Number> other)
-
plusElement
final Sequence<BigInteger> plusElement(Number other)
Adds the other to the end of the sequence if it is finite.
-
reversed
final BigIntegerProgression reversed()
Reverses order of the sequence if possible.
-
flip
final BigIntegerProgression flip()
Changes direction of the infinite BigIntegerProgression to the opposite one.
-
extend
BigIntegerProgression extend(BigInteger n)
-
extend
BigIntegerProgression extend(Number n)
Adds n (converted to BigInteger) elements to the end of the progression according to its step if it has at least 2 elements.
-
shr
BigIntegerProgression shr(Number number)
Makes number steps forward for each item.
-
shl
BigIntegerProgression shl(Number number)
Makes number steps backward for each item.
-
step
BigIntegerProgression step(Number step)
Changes step not changing direction of progression.- Parameters:
step
- positive number to be new property step
-
take
BigIntegerProgression take(Number n)
Limits to n (converted to BigInteger) first elements.
-
take
final BigIntegerProgression take(BigInteger n)
Limits to n first elements.
-
toCollection
final <C extends MutableCollection<BigInteger>> C toCollection(C destination)
Appends all elements to the given destination collection.
-
toList
final List<BigInteger> toList()
Converts the progression into the List.
-
toMutableList
final List<BigInteger> toMutableList()
Converts the progression into the MutableList.
-
toHashSet
final HashSet<BigInteger> toHashSet()
Converts the progression into the HashSet.
-
toMutableSet
final Set<BigInteger> toMutableSet()
Converts the progression into the MutableSet.
-
toSet
final Set<BigInteger> toSet()
Converts the progression into the Set.
-
toSortedSet
final SortedSet<BigInteger> toSortedSet()
Converts the progression into the SortedSet.
-
toSortedSet
final SortedSet<BigInteger> toSortedSet(Comparator<In BigInteger> comparator)
Converts the progression into the SortedSet with specified comparator.
-
component1
BigInteger component1()
-
component2
final BigInteger component2()
-
component3
final BigInteger component3()
-
component4
final BigInteger component4()
-
component5
final BigInteger component5()
-
getLast
final BigInteger getLast()
The last element when possible.
-
getFirst
final BigInteger getFirst()
-
getStep
final BigInteger getStep()
-
-
-
-


coil_utils module¶
Calculate the magnetic field from a arbitrary current loop and a anti-helmholtz coil.
-
coil_utils.
field_anti_helmholtz
(coil_turns, R, D, I, r)¶ Calculate field inside anti-helmholtz coils.
Their normal vector points in the z direction.
First coil is in the position [0, 0, D] and its normal vector n points in the +z direction. The current I direction follows the right hand-rule regarding the normal vector.
- Parameters
coil_turns – number of turns in the coil.
R – coil radius in m.
D – distance between the coordinate system and the coils in m.
I – electric current in A.
r – position vector r in the space to evaluate B field
- Returns
vector representing B field in position r (in Tesla).
-
coil_utils.
field_loop
(r, r0, n, R, I)¶ Return the magnetic field from an arbitrary current loop.
See eqns (1) and (2) in Phys Rev A Vol. 35, N 4, pp. 1535-1546; 1987.
- Parameters
r (list [x y z]) –
- Position vector where the magnetc field is to be evaluated,
where x, y, z in meters.
r0 (list [x y z]) – Position vector of the loop center, where x, y, z in meters.
n (list [x y z]) – Normal vector of loop plane at its center. Current I is oriented by the right-hand-rule.
R (float) – Radius of the loop in meters.
I (float) – Electrical current in A.
- Returns
Magnetic field vector at point r in Tesla.
- Return type
numpy array [x y z]


Provides a parallel estimation method for generalized linear models without compiling with a multithreaded LAPACK or BLAS.
| Version: | 0.1.0 |
| Imports: | Rcpp, Matrix |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat, SuppDists, knitr, rmarkdown, speedglm, microbenchmark, R.rsp |
| Published: | 2018-12-09 |
| Author: | Benjamin Christoffersen [cre, aut], Anthony Williams [cph], Boost developers [cph] |
| Maintainer: | Benjamin Christoffersen <boennecd at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Materials: | README |
| CRAN checks: | parglm results |
| Reference manual: | parglm.pdf |
| Vignettes: |
Introduction to the paglm package |
| Package source: | parglm_0.1.0.tar.gz |
| Windows binaries: | r-devel: parglm_0.1.0.zip, r-release: parglm_0.1.0.zip, r-oldrel: parglm_0.1.0.zip |
| OS X binaries: | r-release: parglm_0.1.0.tgz, r-oldrel: parglm_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=parglm to link to this page.


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 In particular, many CPUs cannot load a 16-bit value from an address which isn't a multiple of two and similarly for larger values. The helper functions and macros defined here will ensure that the optimum method of accessing unaligned variables is selected based on the target platform.
Since unaligned fields are commonly found in protocol-mandated structures, such fields may often need to be accessed using non-native endian as well, so endian-aware versions of the accessors are also included.
uint16_t read_inode_number(const uint8_t *data, unsigned int offset) { return read_unaligned_le16(&data[offset]); } void write_file_size(uint8_t *data, unsigned int offset, uint32_t size) { write_unaligned_le16(&data[offset], size); }
| #define read_unaligned_be16 | ( | ptr | ) | be16_to_cpu(read_unaligned_16(ptr)) |
Read 16-bit Big Endian value from a possibly unaligned address.
Definition at line 72 of file unaligned-direct.h.
| #define read_unaligned_be32 | ( | ptr | ) | be32_to_cpu(read_unaligned_32(ptr)) |
Read 32-bit Big Endian value from a possibly unaligned address.
Definition at line 73 of file unaligned-direct.h.
| #define read_unaligned_le16 | ( | ptr | ) | le16_to_cpu(read_unaligned_16(ptr)) |
Read 16-bit Little Endian value from a possibly unaligned address.
Definition at line 79 of file unaligned-direct.h.
| #define read_unaligned_le32 | ( | ptr | ) | le32_to_cpu(read_unaligned_32(ptr)) |
Read 32-bit Little Endian value from a possibly unaligned address.
Definition at line 80 of file unaligned-direct.h.
| #define write_unaligned_be16 | ( | ptr, | |||
| value | ) | write_unaligned_16(ptr, cpu_to_be16(value)) |
Write 16-bit Big Endian value to a possibly unaligned address.
Definition at line 74 of file unaligned-direct.h.
| #define write_unaligned_be32 | ( | ptr, | |||
| value | ) | write_unaligned_32(ptr, cpu_to_be32(value)) |
Write 32-bit Big Endian value to a possibly unaligned address.
Definition at line 76 of file unaligned-direct.h.
| #define write_unaligned_le16 | ( | ptr, | |||
| value | ) | write_unaligned_16(ptr, cpu_to_le16(value)) |
Write 16-bit Little Endian value to a possibly unaligned address.
Definition at line 81 of file unaligned-direct.h.
| #define write_unaligned_le32 | ( | ptr, | |||
| value | ) | write_unaligned_32(ptr, cpu_to_le32(value)) |
Write 32-bit Little Endian value to a possibly unaligned address.
Definition at line 83 of file unaligned-direct.h.
| uint16_t read_unaligned_16 | ( | const void * | ptr | ) | [static] |
Read 16-bit value from a possibly unaligned address.
Definition at line 52 of file unaligned-direct.h.
| uint32_t read_unaligned_32 | ( | const void * | ptr | ) | [static] |
Read 32-bit value from a possibly unaligned address.
Definition at line 57 of file unaligned-direct.h.
| void write_unaligned_16 | ( | void * | ptr, | |
| uint16_t | value | |||
| ) | [static] |
Write 16-bit value to a possibly unaligned address.
Definition at line 62 of file unaligned-direct.h.
| void write_unaligned_32 | ( | void * | ptr, | |
| uint32_t | value | |||
| ) | [static] |
Write 32-bit value to a possibly unaligned address.
Definition at line 67 of file unaligned-direct.h.


| mono.con {mgcv} | R Documentation |
Finds linear constraints sufficient for monotonicity (and
optionally upper and/or lower boundedness) of a cubic regression
spline. The basis representation assumed is that given by the
gam
, "cr"
basis: that is the spline has a set of knots,
which have fixed x values, but the y values of which constitute the
parameters of the spline.
mono.con(x,up=TRUE,lower=NA,upper=NA)
x |
The array of knot locations. |
up |
If |
lower |
This specifies the lower bound on the spline unless it is
|
upper |
This specifies the upper bound on the spline unless it is
|
Consider the natural cubic spline passing through the points (x_i,p_i), i=1..n. Then it is possible to find a relatively small set of linear constraints on p sufficient to ensure monotonicity (and bounds if required): Ap >= b. Details are given in Wood (1994).
a list containing constraint matrix A
and constraint vector b
.
Simon N. Wood<EMAIL_ADDRESS>Gill, P.E., Murray, W. and Wright, M.H. (1981) Practical Optimization. Academic Press, London.
Wood, S.N. (1994) Monotonic smoothing splines fitted by cross validation. SIAM Journal on Scientific Computing 15(5), 1126–1133.
http://www.maths.bris.ac.uk/~sw15190/
## see ?pcls


Electronic waste pileup sparks warnings
by Brooke CrothersThe pileup of electronic waste will only accelerate with consumers buying more and more devices.
A whopping 52 percent of 16- to 24-year-olds in the U.K. have 10 or more gadgets in their home, according to a survey by the Royal Society of Chemistry.
Thirty-nine percent of 35- to 44-year-olds have 10 or more devices, and 30 percent of people aged between 55 and 75.
APPLE’S SECRET LAB WHERE ROBOTS PULL APART IPHONES TO INVENT ‘FUTURE OF RECYCLING’ REVEALED
That’s a problem when 82 percent of those households have no plans to recycle or sell their devices after they stop using them, the society noted.
Other key results from the survey:
- 51 percent of households have at least one unused electronic device at home
- 45 percent have between two and five
- 25 percent have an unused laptop
- 23 percent have an unused mobile phone
Waste pileup + conflict elements = opportunity
Each year, approximately 50 million tons of electronic and electrical waste (e-waste) is produced, “equivalent in weight to all commercial aircraft ever built,” according to a report from World Economic Forum (WEF) — yet only 20 percent is recycled. At that rate, the amount of waste will more than double by 2050, to 120 million tons annually.
Besides the raw waste, mobile devices contain “conflict elements” like gold, toxic elements such as arsenic and rare elements like indium, the Royal Society of Chemistry said. “Natural sources of six of the elements found in mobile phones are set to run out within the next 100 years,” it added.
But e-waste is an opportunity, according to the WEF, which values the “waste” material at $62.5 billion, more than three times the annual output of the world’s silver mines and more than the gross domestic product of most countries.
AIR FORCE TESTS PLASMA REACTOR TO DESTROY 'FOREVER CHEMICALS' IN CONTAMINATED GROUNDWATER
“There is 100 times more gold in a ton of mobile phones than in a ton of gold ore,” the WEF said.
And harvesting the material from used electronics produces substantially less carbon-dioxide emissions than mining the Earth’s crust, the organization said.
CLICK HERE TO GET THE FOX NEWS APP
That means there is an economic opportunity to build a more circular electronics system. “One in which resources are not extracted, used and wasted," the WEF said. "But valued and reused in ways that create decent, sustainable jobs. In short, we need a new vision for electronics.”


Linus Liu
There's a lot of new and confusing terminology
Code Signing
Provisioning Profile
Certificates, Public & Private keys, Loing or System keychains etc. ...
You're building:
correct branch of code
correct target: UAT, QA, Staging or Live?
correct version No.
debug or release version?
囧。。。
You can NOT copy the .ipa file exported from Xcode to users and tell them to install the app.
Install iOS app
a Web server hosting your ipa files
HTTPS support
SSL certificates needed
Correct Plist file generated for each ipa file
TestFlight is great!! But ...
TestFlight is acquired by Apple
For Beta testers(up to 1000), builds will need to be reviewed and approved by Apple before testers will have access to them, only iOS 8 & above supported
For Internal testers, up to 25 members who have been assigned the Technical or Admin role in iTunes Connect
Internal testers are still limited
We may need a tool of our own to distribute iOS apps for internal testers
With Floating House:
You're not required to have an Apple ID
You don't have to be invited and enabled for iTunes Connect
No limit on the number of internal testers
Faster uploading & downloading speed than TestFlight
Linus Liu
the'QA Tool' is Excellent! Without the QA tool it's very difficult to test complex scenarios
QA tool cannot be used directly from automated test scripts
I created a wrapper of the QA tool in Ruby
Repeated the code in different projects
Some functions I need for automated scripts are not provided by the QA tool
A set of APIs for test, more suitable for testing purpose
Easy to be called from automated scripts
Simplify the communication between front-end UI automation and backend databases


#include <basicComponents.h>
◆ operator<()
| bool Edge::operator< |
( |
Edge |
other | ) |
const |
|
inline |
Less than operator defined for faster comparisons between Edges.
◆ operator==()
| bool Edge::operator== |
( |
Edge |
b | ) |
|
|
inline |
Equality operator defined for faster comparisons between Edges.
◆ projected()
| Edge Edge::projected |
( |
int |
plane | ) |
|
|
inline |
Generates the 2D projection of a of the edge on a plane
- Parameters
-
| plane | The plane on which projection is to be taken is passed through this |
plane = 0 -> XY plane plane = 1 -> YZ plane plane = 2 -> XZ plane plane = 3 -> Isometric View
2D projection of edge
◆ vertices
The documentation for this struct was generated from the following file:


MY LEARNING PLAN
My long term goal and career pathway, is that i wish to work with IT and fashion at the same time, possibly a role in editing or creating websites in the fashion industry or it may turn into photography even I just plan to combine my two loves together, i also plan to do this overseas.
I would like to develop my working skills though doing EDA, i would like to teach myself how to not get so distracted and how to get the most out of my work and the time i put into it.
My strengths are that I’m extremely creative minded and i have a really good idea about design on lots of different levels. I have good social skills and I’m friendly and chatty. I have drive to finish my work the best i can. My limitations are for one that i have ADD so i find it extremely difficult to pay attention for long periods of time, although there are ways around that. And another is that I’m not very good with numbers, so i make sure to pay special attention to what I’m doing when i am working with numbers.
I will make sure i go into Dev academy to do any planned work, as opposed to working from home as i know i get really distracted at home.
Im currently pretty happy with the facilitation team, I’m finding that I’m receiving just the amount of help i need and if i needed more they would definitely be there to help me.


In this vignette, we demonstrate how complex structural types in Presto can be translated into R types (e.g., vectors, list, and tibbles).
library(RPresto)
You can check your RPresto
version by running the
packageVersion()
function. You need version 1.3.9
or later to have a more comprehensive and robust complex types
support.
packageVersion("RPresto")
#> [1] '1.4.5'
Complex types refer to structural
types including ARRAY
, MAP
and
ROW
. Those data types are basically containers to hold
other data types (hence complex).
We summarize the similarities and differences between the complex types below.
| Type | Is atomic? | Has names/keys? |
|---|---|---|
| ARRAY | Yes | No |
| MAP | Yes | Yes |
| ROW | No | Yes |
Atomic here means all elements in the container share the same type
(usually primitive types, but can be complex types too). For example, an
ARRAY
of integer can only hold integer type elements
whereas a ROW
can have elements of different types (e.g.,
one element is integer and the other boolean).
We assume that the user already have a Presto server with a memory connector set up. If you don’t have such a server set up, refer to the Presto documentation for instructions if you want to follow along.
We first create a PrestoConnection
which will serve as
the bridge between the Presto server and R.
<- DBI::dbConnect(
con drv = RPresto::Presto(),
host = "http://localhost",
port = 8080,
user = Sys.getenv("USER"),
catalog = "memory",
schema = "default"
)
We first issue a simple query to see if the Presto connection is working properly.
::dbGetQuery(con, "SELECT 1+1 AS res")
DBI#> # A tibble: 1 × 1
#> res
#> <int>
#> 1 2
| Presto type | R type |
|---|---|
| ARRAY | unnamed typed vector |
| MAP | named typed vector |
ARRAY
sGiven the atomic and unnamed nature of ARRAY
s, we map
them to unnamed typed vectors in R.
ARRAY
s of primitive typesWe first create a table with ARRAY
s of all supported
primitive Presto data types using the
create_primitive_arrays_table()
function included in the
RPresto
package.
:::create_primitive_arrays_table(
RPrestotable_name = "presto_primitive_arrays", verbose = FALSE
con, )
We can check if the table now exists in Presto.
::dbExistsTable(con, "presto_primitive_arrays")
DBI#> [1] TRUE
We can list the fields in the table. They are named after the Presto types they represent.
::dbListFields(con, "presto_primitive_arrays")
DBI#> [1] "array_boolean" "array_tinyint"
#> [3] "array_smallint" "array_integer"
#> [5] "array_bigint" "array_real"
#> [7] "array_double" "array_decimal"
#> [9] "array_varchar" "array_char"
#> [11] "array_varbinary" "array_date"
#> [13] "array_time" "array_time_with_tz"
#> [15] "array_timestamp" "array_timestamp_with_tz"
#> [17] "array_interval_year_to_month" "array_interval_day_to_second"
ARRAY
s to R vectors
(<- dbGetQuery(
df.array_of_primitive_types
con,"SELECT * FROM presto_primitive_arrays",
bigint = "integer64"
)
)#> # A tibble: 1 × 18
#> array_boolean array_tinyint array_smallint array_integer array_bigint
#> <list> <list> <list> <list> <list>
#> 1 <lgl [3]> <int [3]> <int [3]> <int [3]> <int64 [3]>
#> # ℹ 13 more variables: array_real <list>, array_double <list>,
#> # array_decimal <list>, array_varchar <list>, array_char <list>,
#> # array_varbinary <list>, array_date <list>, array_time <list>,
#> # array_time_with_tz <list>, array_timestamp <list>,
#> # array_timestamp_with_tz <list>, array_interval_year_to_month <list>,
#> # array_interval_day_to_second <list>
We can verify the R types of each column.
::enframe(purrr::map_chr(df.array_of_primitive_types, ~class(.[[1]])[1]))
tibble#> # A tibble: 18 × 2
#> name value
#> <chr> <chr>
#> 1 array_boolean logical
#> 2 array_tinyint integer
#> 3 array_smallint integer
#> 4 array_integer integer
#> 5 array_bigint integer64
#> 6 array_real numeric
#> 7 array_double numeric
#> 8 array_decimal character
#> 9 array_varchar character
#> 10 array_char character
#> 11 array_varbinary list
#> 12 array_date Date
#> 13 array_time hms
#> 14 array_time_with_tz hms
#> 15 array_timestamp POSIXct
#> 16 array_timestamp_with_tz POSIXct
#> 17 array_interval_year_to_month Duration
#> 18 array_interval_day_to_second Duration
All vectors are unnamed.
::every(df.array_of_primitive_types, ~is.null(names(.[[1]])))
purrr#> [1] TRUE
We can also call functions such as length()
on each of
the columns to get the ARRAY
cardinality. It shows that all
ARRAY
s have 3 elements in them.
::enframe(purrr::map_int(df.array_of_primitive_types, ~length(.[[1]])))
tibble#> # A tibble: 18 × 2
#> name value
#> <chr> <int>
#> 1 array_boolean 3
#> 2 array_tinyint 3
#> 3 array_smallint 3
#> 4 array_integer 3
#> 5 array_bigint 3
#> 6 array_real 3
#> 7 array_double 3
#> 8 array_decimal 3
#> 9 array_varchar 3
#> 10 array_char 3
#> 11 array_varbinary 3
#> 12 array_date 3
#> 13 array_time 3
#> 14 array_time_with_tz 3
#> 15 array_timestamp 3
#> 16 array_timestamp_with_tz 3
#> 17 array_interval_year_to_month 3
#> 18 array_interval_day_to_second 3
MAP
sA MAP
in Presto can be thought as a combination of two
same-length ARRAY
s. The first ARRAY
contains
the keys of the MAP
and the second contains the values of
the MAP
. In fact, that’s exactly how MAP
literals are created in Presto (e.g.,
MAP(ARRAY[1, 2], ARRAY['a', 'b'])
creates a 2-element
MAP
).
Following the logic, we translate MAP
s to named
typed vectors in R.
MAP
S of primitive typesWe first create a table with MAPS
s of all supported
primitive Presto data types using the
create_primitive_maps_table()
function included in the
RPresto
package.
:::create_primitive_maps_table(
RPrestotable_name = "presto_primitive_maps", verbose = FALSE
con, )
We can check if the table now exists in Presto.
::dbExistsTable(con, "presto_primitive_maps")
DBI#> [1] TRUE
We can list the fields in the table. They are named after the Presto types they represent.
::dbListFields(con, "presto_primitive_maps")
DBI#> [1] "map_boolean" "map_tinyint"
#> [3] "map_smallint" "map_integer"
#> [5] "map_bigint" "map_real"
#> [7] "map_double" "map_decimal"
#> [9] "map_varchar" "map_char"
#> [11] "map_varbinary" "map_date"
#> [13] "map_time" "map_time_with_tz"
#> [15] "map_timestamp" "map_timestamp_with_tz"
#> [17] "map_interval_year_to_month" "map_interval_day_to_second"
MAP
s to R vectors
(<- dbGetQuery(
df.map_of_primitive_types
con,"SELECT * FROM presto_primitive_maps",
bigint = "integer64"
)
)#> # A tibble: 1 × 18
#> map_boolean map_tinyint map_smallint map_integer map_bigint map_real
#> <list> <list> <list> <list> <list> <list>
#> 1 <lgl [3]> <int [3]> <int [3]> <int [3]> <int64 [3]> <dbl [3]>
#> # ℹ 12 more variables: map_double <list>, map_decimal <list>,
#> # map_varchar <list>, map_char <list>, map_varbinary <list>, map_date <list>,
#> # map_time <list>, map_time_with_tz <list>, map_timestamp <list>,
#> # map_timestamp_with_tz <list>, map_interval_year_to_month <list>,
#> # map_interval_day_to_second <list>
We can verify the R types of each column.
::enframe(purrr::map_chr(df.map_of_primitive_types, ~class(.[[1]])[1]))
tibble#> # A tibble: 18 × 2
#> name value
#> <chr> <chr>
#> 1 map_boolean logical
#> 2 map_tinyint integer
#> 3 map_smallint integer
#> 4 map_integer integer
#> 5 map_bigint integer64
#> 6 map_real numeric
#> 7 map_double numeric
#> 8 map_decimal character
#> 9 map_varchar character
#> 10 map_char character
#> 11 map_varbinary list
#> 12 map_date Date
#> 13 map_time hms
#> 14 map_time_with_tz hms
#> 15 map_timestamp POSIXct
#> 16 map_timestamp_with_tz POSIXct
#> 17 map_interval_year_to_month Duration
#> 18 map_interval_day_to_second Duration
All vectors are named.
::none(df.map_of_primitive_types, ~is.null(names(.[[1]])))
purrr#> [1] TRUE
ARRAY
s and MAP
sIt’s possible to have repeated ARRAY
s and
MAP
s in Presto in the form of ARRAY
s of
ARRAY
s and ARRAY
s of MAP
s.
| Repeated Presto type | R type |
|---|---|
| ARRAY of ARRAY | not supported |
| ARRAY of MAP | unnamed list of named typed vectors |
We are not supporting nested ARRAY
s at the moment
although it’s technically possible in Presto.
For ARRAY
s of MAP
s, we translate the
ARRAY
container into an unnamed list and each of the
MAP
element into a named typed vector.
We first create an array-of-maps table by using the
create_array_of_maps_table()
function.
:::create_array_of_maps_table(
RPrestotable_name = "presto_array_of_maps", verbose = FALSE
con, )
We can check if the table now exists in Presto.
::dbExistsTable(con, "presto_array_of_maps")
DBI#> [1] TRUE
We can list the fields in the table.
::dbListFields(con, "presto_array_of_maps")
DBI#> [1] "array_map_boolean" "array_map_tinyint"
#> [3] "array_map_smallint" "array_map_integer"
#> [5] "array_map_bigint" "array_map_real"
#> [7] "array_map_double" "array_map_decimal"
#> [9] "array_map_varchar" "array_map_char"
#> [11] "array_map_varbinary" "array_map_date"
#> [13] "array_map_time" "array_map_time_with_tz"
#> [15] "array_map_timestamp" "array_map_timestamp_with_tz"
#> [17] "array_map_interval_year_to_month" "array_map_interval_day_to_second"
Let’s import all the data into R.
(<- dbGetQuery(
df.array_of_maps
con,"SELECT * FROM presto_array_of_maps",
bigint = "integer64"
)
)#> # A tibble: 1 × 18
#> array_map_boolean array_map_tinyint array_map_smallint array_map_integer
#> <list> <list> <list> <list>
#> 1 <list [2]> <list [2]> <list [2]> <list [2]>
#> # ℹ 14 more variables: array_map_bigint <list>, array_map_real <list>,
#> # array_map_double <list>, array_map_decimal <list>,
#> # array_map_varchar <list>, array_map_char <list>,
#> # array_map_varbinary <list>, array_map_date <list>, array_map_time <list>,
#> # array_map_time_with_tz <list>, array_map_timestamp <list>,
#> # array_map_timestamp_with_tz <list>,
#> # array_map_interval_year_to_month <list>, …
We need to pry open the wrapping unnamed list to reveal the types of the vectors underneath.
::enframe(purrr::map_chr(df.array_of_maps, ~class(.[[1]][[1]])[1]))
tibble#> # A tibble: 18 × 2
#> name value
#> <chr> <chr>
#> 1 array_map_boolean logical
#> 2 array_map_tinyint integer
#> 3 array_map_smallint integer
#> 4 array_map_integer integer
#> 5 array_map_bigint integer64
#> 6 array_map_real numeric
#> 7 array_map_double numeric
#> 8 array_map_decimal character
#> 9 array_map_varchar character
#> 10 array_map_char character
#> 11 array_map_varbinary list
#> 12 array_map_date Date
#> 13 array_map_time hms
#> 14 array_map_time_with_tz hms
#> 15 array_map_timestamp POSIXct
#> 16 array_map_timestamp_with_tz POSIXct
#> 17 array_map_interval_year_to_month Duration
#> 18 array_map_interval_day_to_second Duration
ROW
typeThe easiest way to think about the ROW
type in Presto is
to think of it literally as a row of a table. Just as a table
can have multiple columns of different data types, a ROW
can have multiple elements of different types. And just like a table
having a name for each column, every element of a ROW
has a
name associated with the value.
Depending on whether the ROW
type is repeated (i.e.,
wrapped in an ARRAY
), the translation into R is
different.
We translate single ROW
value to a named list in
R.
Rather than interpret repeated ROW
s (i.e.,
ARRAY
of ROW
s) as a list of named lists, we
translate the collection of ROW
s into a tibble.
| Presto type | R type |
|---|---|
| Single ROW | named list |
| Repeated ROWs | tibble |
ROW
translationTo demonstrate how ROW
types are translated into R
types, we first create a table using an auxiliary
create_primitive_rows_table()
function included in the
package. The resulting table has only 1 column named
row_primitive_types
which is a ROW
that
includes 18 sub-columns representing all supported primitive types.
:::create_primitive_rows_table(
RPrestotable_name = "presto_primitive_rows", verbose = FALSE
con, )
We can check if the table now exists in Presto.
::dbExistsTable(con, "presto_primitive_rows")
DBI#> [1] TRUE
We can list the fields in the table.
::dbListFields(con, "presto_primitive_rows")
DBI#> [1] "row_primitive_types"
We can then retrieve all the data from the table.
(<- dbGetQuery(
df.row_of_primitive
con,"SELECT row_primitive_types FROM presto_primitive_rows",
bigint = "integer64"
)
)#> # A tibble: 3 × 1
#> row_primitive_types
#> <list>
#> 1 <named list [18]>
#> 2 <named list [18]>
#> 3 <named list [18]>
We can check the R types of each element in the named list.
::enframe(
tibble::map_chr(df.row_of_primitive$row_primitive_types[[1]], ~class(.)[1])
purrr
)#> # A tibble: 18 × 2
#> name value
#> <chr> <chr>
#> 1 boolean logical
#> 2 tinyint integer
#> 3 smallint integer
#> 4 integer integer
#> 5 bigint integer64
#> 6 real numeric
#> 7 double numeric
#> 8 decimal character
#> 9 varchar character
#> 10 char character
#> 11 varbinary list
#> 12 date Date
#> 13 time hms
#> 14 time_with_tz hms
#> 15 timestamp POSIXct
#> 16 timestamp_with_tz POSIXct
#> 17 interval_year_to_month Duration
#> 18 interval_day_to_second Duration
ROW
s translationTo construct a repeated ROW
column, we use the auxiliary
create_array_of_rows_table()
function.
:::create_array_of_rows_table(
RPrestotable_name = "presto_array_of_rows", verbose = FALSE
con, )
We can check if the table now exists in Presto and the field name.
::dbExistsTable(con, "presto_array_of_rows")
DBI#> [1] TRUE
::dbListFields(con, "presto_array_of_rows")
DBI#> [1] "array_of_rows"
We can import the whole data into R.
(<- dbGetQuery(
df.array_of_rows
con,"SELECT array_of_rows FROM presto_array_of_rows",
bigint = "integer64"
)
)#> # A tibble: 3 × 1
#> array_of_rows
#> <list>
#> 1 <tibble [2 × 18]>
#> 2 <tibble [2 × 18]>
#> 3 <tibble [2 × 18]>
We can verify the tibble’s column types.
::enframe(
tibble::map_chr(df.array_of_rows$array_of_rows[[1]], ~class(.)[1])
purrr
)#> # A tibble: 18 × 2
#> name value
#> <chr> <chr>
#> 1 boolean logical
#> 2 tinyint integer
#> 3 smallint integer
#> 4 integer integer
#> 5 bigint integer64
#> 6 real numeric
#> 7 double numeric
#> 8 decimal character
#> 9 varchar character
#> 10 char character
#> 11 varbinary list
#> 12 date Date
#> 13 time hms
#> 14 time_with_tz hms
#> 15 timestamp POSIXct
#> 16 timestamp_with_tz POSIXct
#> 17 interval_year_to_month Duration
#> 18 interval_day_to_second Duration
::dbDisconnect(con)
DBI#> [1] TRUE


{% extends "sentry/projects/manage.html" %} {% load i18n %} {% block title %}{% trans "Client Configuration" %} | {{ block.super }}{% endblock %} {% block inner %}
{% trans "To start sending events, configure your client with the following DSN value:" %}
{{ dsn }}
{% blocktrans with 'https://github.com/lincolnloop/raven-js' as link %}If this is a client that only needs public access (like raven-js), you can use the following public DSN:{% endblocktrans %}
{{ dsn_public }}
{% trans "Choose your platform for quick setup instructions:" %}
{% include "sentry/partial/_client_config.html" %} {% else %}{% trans "There is no key configured for your account with access to this project." %}
{% endif %} {% endblock %}