Original and creative pickers, selectors and cards very useful to create Javascript single page applications.
Awesome smooth dynamic charts to display your data in beatiful ways. Most of the charts are the perfect way to display realtime data.
Other ways to display your products: 360° image viewers, paranoma, Google Map routes and more...
To complete the offer we’re introducing several media components like YouTube and Vimeo players, mp3 and audio players, Spotify support and many others
A set to of utilities to enable new interesting features to your HTML elements like dragging, rotations and many others.
A fresh approach to the user's choices: do you need a color picker? Draw a card! Do you need to set a date? Draw a card!
Angular Cards comes with a whole set of selectors, pickers and utilities to make building your HTML5 desktop, mobile and hybrid applications a breeze!
Augment your web and mobile HTML5 Single Page Applications with powerful yet easy to use controls. Angular Cards do the hard job giving you time to focus on your contents! Try it, it's easy as draw a card!
All directives supports natively Angular Cards! Use given Angular Cards Adapters or modify them to satisfy your needs.
Create complex UI and CRUD web applications on mobile has never been easier... just draw a Card!


Create a test page, using any hosting option you prefer - free sites can be created using https://pages.github.com/ with a free Github account, or sites such as zeit or surge.
Implement Google Tag Manager on the site. Create logic that adds the key and value from query string parameters to the GTM data layer.


float, read-only
read-only
float
float
float
float
float
gravity in domain scale units (0.0-1.0) per second squared
QUUid, read-only
read-only
float get the lifetime in seconds for the entity
read-only
set position in meter units, will be clamped to domain bounds
read-only
velocity in domain scale units (0.0-1.0) per second
Generated using TypeDoc
A collection of properties of an entity item used in the scripting API. Translates between the actual properties of an entity and a JavaScript style hash/QScriptValue storing a set of properties. Used in scripting to set/get the complete set of entity item properties via JavaScript hashes/QScriptValues all units for position, dimensions, etc are in meter units
checked in /libraries/entities/src/EntityItemProperties.{cpp,h} See EntityItemProperties::copyToScriptValue September 18th, 2014


Functions | |
| ENET_API int | enet_initialize (void) |
| Initializes ENet globally. | |
| ENET_API int | enet_initialize_with_callbacks (ENetVersion version, const ENetCallbacks *inits) |
| Initializes ENet globally and supplies user-overridden callbacks. | |
| ENET_API void | enet_deinitialize (void) |
| Shuts down ENet globally. |
| ENET_API void enet_deinitialize | ( | void | ) |
Should be called when a program that has initialized ENet exits.
| ENET_API int enet_initialize | ( | void | ) |
Must be called prior to using any functions in ENet.
Referenced by enet_initialize_with_callbacks().
| ENET_API int enet_initialize_with_callbacks | ( | ENetVersion | version, | |
| const ENetCallbacks * | inits | |||
| ) |
Must be called prior to using any functions in ENet. Do not use enet_initialize() if you use this variant. Make sure the ENetCallbacks structure is zeroed out so that any additional callbacks added in future versions will be properly ignored.
| version | the constant ENET_VERSION should be supplied so ENet knows which version of ENetCallbacks struct to use | |
| inits | user-overriden callbacks where any NULL callbacks will use ENet's defaults |
References enet_initialize().


My second JavaScript, API project a local weather reporter, again this is from FreeCodeCamp and awesome resource to delve into web development.
The projects first challenge was to source the location of the user, to do this I utilised HTML5 Geolocation, this allowed me to get the geographical position of a user although due to privacy issues, the position is not available unless the user allows access. Once the Geolocation was received, in longitude and latitude the next challenge was to utilise this data to access the FreeCodeCamp weather API and return the weather data for that location, and to display this data, for this again I used a JSON request, and displayed the data using JQuery.
Finally the °C to °F uses a small bit of JavaScript code to change the values and JQuery to change the display.
Here is it..


It appears that most of the schematron rules referenced here are now out of date. The rule regarding invalid characters is worth following up. Also, nothing more concrete here regarding the maximum field length allowed, so it's still trial and error.
One takeaway from this file: ASpace silently drops note
elements; should be converted to odd
.
unassigned
by James R. Griffin III at 2020-08-05 13:21:04.This is no longer a relevant issue now that Atmire has successfully deployed a functioning, upgraded DSpace installation for oar-dev.princeton.edu
closed
by James R. Griffin III at 2020-08-05 16:10:45.This should also build off of the comments on https://github.com/pulibrary/dspace-development/issues/99
closed
by James R. Griffin III at 2020-08-05 13:20:29.This should build off of the comments on https://github.com/pulibrary/dspace-development/issues/99
labeled
by James R. Griffin III at 2020-08-05 16:24:32.closed
by James R. Griffin III at 2020-08-05 16:25:11.labeled
by James R. Griffin III at 2020-08-05 16:52:23.This user account was created with the following:
ID: oartrain_sharedaccount
Email<EMAIL_ADDRESS>Username: oartrain
First name: oartrain
Last name: Student Account
However, attempting to enable CAS
for authentication results in the following error message:
I have just confirmed that CAS
authentication does function as expected for my personal account for oaworkflow-dev.princeton.edu
The error message logged is the following:
Message: Violation of UNIQUE KEY constraint 'un_USER_tblUser_UsernameUnique'. Cannot insert duplicate key in object 'dbo.USER_tblUser'. The duplicate key value is (CAS™oartrain).
Perhaps a database synchronization created a record to the oartrain
user with CAS enabled but the user account itself was deleted at some point in the past?
I'm going to proceed by attempting to delete and recreate the account.
This did not resolve the error, I'm going to leave the user account with a temporary password for testing until I can determine a means by which to directly update the database.
labeled
by James R. Griffin III at 2020-08-05 16:28:18.milestoned
by James R. Griffin III at 2020-08-05 16:28:18.assigned
by James R. Griffin III at 2020-08-05 16:28:18.labeled
by James R. Griffin III at 2020-08-05 19:01:07.assigned
by James R. Griffin III at 2020-08-05 19:01:07.closed
by James R. Griffin III at 2020-08-05 19:08:47.This is not related to the Bitstreams for the underlying Item, as these are intact and I can retrieve the data using dspace-jruby
. I instead suspect that this may be related to the URL generation in the theming layer.
https://github.com/jrgriffiniii/oar-jspui-dev/blob/master/src/main/java/org/dspace/app/webui/servlet/BitstreamServlet.java#L87 is where the bug is arising:
@Override
protected void doDSGet(Context context, HttpServletRequest request,
HttpServletResponse response) throws ServletException, IOException,
SQLException, AuthorizeException
{
ReferredObjects ref = new ReferredObjects(context, request);
if (log.isDebugEnabled())
log.debug("doDSGet " + ref);
if (ref.item.isWithdrawn())
{
log.info(LogManager.getHeader(context, "view_bitstream",
"handle=" + ref.item.getHandle() + ",withdrawn=true"));
JSPManager.showJSP(request, response, "/tombstone.jsp");
return;
}
if (ref.bitstream == null || ref.filename == null
|| !ref.filename.equals(ref.bitstream.getName()))
{
// No bitstream found or filename was wrong -- ID invalid
log.info(LogManager.getHeader(context, "invalid_id", "path="
+ ref.idString));
JSPManager.showInvalidIDError(request, response, ref.idString,
Constants.BITSTREAM);
return;
}
Within ReferredObjects
idString = request.getPathInfo();
is used to retrieve the file name from the name of the PDF being downloaded:
class ReferredObjects {
private static Logger log = Logger.getLogger(BitstreamServlet.class);
Item item = null;
Bitstream bitstream = null;
String idString = "";
String filename = null;
ReferredObjects(Context context, HttpServletRequest request) throws SQLException {
// Get the ID from the URL
idString = request.getPathInfo();
String handle = "";
String sequenceText = "";
int sequenceID;
if (idString == null) {
idString = "";
}
// Parse 'handle' and 'sequence' (bitstream seq. number) out
// of remaining URL path, which is typically of the format:
// {handle}/{sequence}/{bitstream-name}
// But since the bitstream name MAY have any number of "/"s in
// it, and the handle is guaranteed to have one slash, we
// scan from the start to pick out handle and sequence:
// Remove leading slash if any:
if (idString.startsWith("/")) {
idString = idString.substring(1);
}
// skip first slash within handle
int slashIndex = idString.indexOf('/');
if (slashIndex != -1) {
slashIndex = idString.indexOf('/', slashIndex + 1);
if (slashIndex != -1) {
handle = idString.substring(0, slashIndex);
int slash2 = idString.indexOf('/', slashIndex + 1);
if (slash2 != -1) {
sequenceText = idString.substring(slashIndex + 1, slash2);
filename = idString.substring(slash2 + 1);
}
}
}
Unfortunately, this is compared with the URI-unescaped name of the Bitstream, raising an error with:
|| !ref.filename.equals(ref.bitstream.getName()))
Attempting to resolve this is going to be blocked until I can attempt to rebuild the JSPUI theme for the OAR.
As this is going to require that the theme be built on the server, I'm going to rebuild and redeploy this within a maintenance period from 05:15PM - 07:00PM this evening.
Attempting to build the DSpace installation on the OAR server is blocked given that there are network connectivity issues:
[ERROR] Failed to execute goal on project dspace-solr: Could not resolve dependencies for project org.dspace:dspace-solr:war:5.5-SNAPSHOT: Failed to collect dependencies at org.restlet.jee:org.restlet:jar:2.1.1: Failed to read artifact descriptor for org.restlet.jee:org.restlet:jar:2.1.1: Could not transfer artifact org.restlet.jee:org.restlet:pom:2.1.1 from/to restlet (http://maven.restlet.org): Transfer failed for http://maven.restlet.org/org/restlet/jee/org.restlet/2.1.1/org.restlet-2.1.1.pom: Connection reset -> [Help 1]
[ERROR]
[ERROR] To see the full stack trace of the errors, re-run Maven with the -e switch.
[ERROR] Re-run Maven using the -X switch to enable full debug logging.
[ERROR]
[ERROR] For more information about the errors and possible solutions, please read the following articles:
[ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/DependencyResolutionException
[dspace@cisdr204l dspace]$ curl -I http://maven.restlet.org/org/restlet/jee/org.restlet/2.1.1/org.restlet-2.1.1.pom
curl: (56) Recv failure: Connection reset by peer
[dspace@cisdr204l dspace]$ curl http://google.com
<HTML><HEAD><meta http-equiv="content-type" content="text/html;charset=utf-8">
<TITLE>301 Moved</TITLE></HEAD><BODY>
Please note that this is not the case from within my local environment:
% curl -I http://maven.restlet.org/org/restlet/jee/org.restlet/2.1.1/org.restlet-2.1.1.pom
HTTP/1.1 301 Moved Permanently
Content-Length: 0
Connection: keep-alive
assigned
by James R. Griffin III at 2020-08-05 13:22:28.referenced
by Trey Pendragon at 2020-08-04 22:18:48.merged
by Trey Pendragon at 2020-08-04 22:18:48.closed
by Trey Pendragon at 2020-08-04 22:18:48.head_ref_deleted
by Trey Pendragon at 2020-08-04 22:18:53.labeled
by None at 2020-08-05 15:27:51.referenced
by Trey Pendragon at 2020-08-05 15:51:33.merged
by Trey Pendragon at 2020-08-05 15:51:33.closed
by Trey Pendragon at 2020-08-05 15:51:33.head_ref_deleted
by Trey Pendragon at 2020-08-05 15:51:38.labeled
by Trey Pendragon at 2020-08-05 17:40:37.review_requested
by Cliff Wulfman at 2020-08-05 14:56:59.review_requested
by Cliff Wulfman at 2020-08-05 16:24:23.closed
by Trey Pendragon at 2020-08-05 19:23:53.referenced
by Trey Pendragon at 2020-08-05 19:23:53.merged
by Trey Pendragon at 2020-08-05 19:23:53.closed
by Trey Pendragon at 2020-08-05 19:23:53.head_ref_deleted
by Trey Pendragon at 2020-08-05 19:23:57.Reviewing:
in spec/resources/cdl/automaticingesterspec.rb:12, shouldn't the code be doing this? It doesn't seem like it should be part of test setup. Line 18 is testing that this happened.
One general question. A file goes into the cdl_in_path
and is moved to cdl_in_path/ingesting
and then ingested via move from there. If the file is corrupted during these moves it seems like we lose the file entirely and it has to be re-scanned to be reingested -- is that correct? Is there some other guard against this?
Line 12 is an extra file to make sure it doesn't reingest files that are currently ingesting by a job which is either running or in the retry queue. I'll add a comment.
We've never really had a problem with file system copies that I can think of. I can adjust it so that it doesn't remove the file until after the ingest is done if that makes you more comfortable?
In that case on line 12, can you use a different filename.
We have definitely had problems that show up during derivative generation and aren't really caught until much later. I'm not sure but I think sometimes the studio has found those files and they looked fine so they just ingested them again. That's the scenario I'm thinking of.
I just realized I forgot to get this to add it to a collection anyways. After I do that maybe we can chat about this on zoom?
Do you think this should get a release note?
Regardless, :thumbsup: approved
I think the release note should be for when the whole feature's done, personally.
referenced
by Trey Pendragon at 2020-08-05 20:49:02.merged
by Trey Pendragon at 2020-08-05 20:49:02.closed
by Trey Pendragon at 2020-08-05 20:49:03.head_ref_deleted
by Trey Pendragon at 2020-08-05 20:49:07.referenced
by Francis Kayiwa at 2020-08-05 12:50:00.closed
by Trey Pendragon at 2020-08-05 21:08:44.@tpendragon deleted the commented code; thanks for catching that.
renamed
by Trey Pendragon at 2020-08-05 20:08:30.ready_for_review
by Trey Pendragon at 2020-08-05 20:08:45.mentioned
by Trey Pendragon at 2020-08-05 20:39:57.subscribed
by Trey Pendragon at 2020-08-05 20:39:57.referenced
by Trey Pendragon at 2020-08-05 21:08:44.merged
by Trey Pendragon at 2020-08-05 21:08:44.closed
by Trey Pendragon at 2020-08-05 21:08:44.head_ref_deleted
by Trey Pendragon at 2020-08-05 21:08:45.closed
by Trey Pendragon at 2020-08-05 21:08:45.Thank you @pmgreen ! I copied the content in a txt file on google drive https://drive.google.com/drive/folders/0ACgqrvF40vkeUk9PVA
mentioned
by Peter Green at 2020-08-05 01:10:33.subscribed
by Peter Green at 2020-08-05 01:10:33.Here's one initial sketch. Thoughts / suggestions? I think doing it this way could be good because we don't have to mix the CDL link into the "in the library" info -- this way no matter how many copies are on CDL there's only one link to the checkout. @escowles
@hackmastera That looks good — and I agree that having the CDL access highlighted in the Online section makes more sense than under the Copies in the Library section. I'm not sure about using the word "Sequestered" though — maybe "Reserved for Digital Lending" or "Available via Digital Lending"?
One other piece of feedback from the DATF meeting this morning: it would be good to have the language for CDL and public/non-CDL items be the same, to help users consistently identify how they can access digital content. Right now, public items have a "Digital content" link. So please make the link text "View digital content" for both CDL and non-CDL items, with "Princeton users:" added for CDL items like you have here.
@escowles :thumbsup: thanks for this feedback
Here are updated wireframes under scenarios 1 and 3. I think scenarios 2 and 4 could easily be extrapolated from these but if not I'm happy to create more or simply clarify.
@hackmastera 👍 Yes, those look great!
Thanks! Also reviewed by @tpendragon and @christinach.
Just realized the capitalization should probably be "View digital content" not "View Digital content" -- will update those images.
mentioned
by Esmé Cowles at 2020-08-05 01:39:40.subscribed
by Esmé Cowles at 2020-08-05 01:39:40.mentioned
by None at 2020-08-05 12:13:33.subscribed
by None at 2020-08-05 12:13:33.mentioned
by Esmé Cowles at 2020-08-05 15:11:33.subscribed
by Esmé Cowles at 2020-08-05 15:11:33.mentioned
by None at 2020-08-05 17:24:33.subscribed
by None at 2020-08-05 17:24:33.closed
by None at 2020-08-05 18:01:02.mentioned
by Trey Pendragon at 2020-08-05 18:01:02.subscribed
by Trey Pendragon at 2020-08-05 18:01:02.mentioned
by Christina Chortaria at 2020-08-05 18:01:02.subscribed
by Christina Chortaria at 2020-08-05 18:01:02.Is it possible to add the "Reserved for Digital Lending" status to the availability call? That can be used to make sure the request button doesn't show up.
labeled
by Kate Lynch at 2020-08-05 20:41:37.referenced
by Francis Kayiwa at 2020-08-05 12:49:59.merged
by Francis Kayiwa at 2020-08-05 12:49:59.closed
by Francis Kayiwa at 2020-08-05 12:49:59.head_ref_deleted
by Francis Kayiwa at 2020-08-05 12:50:03.referenced
by Francis Kayiwa at 2020-08-05 12:51:25.merged
by Francis Kayiwa at 2020-08-05 12:51:25.closed
by Francis Kayiwa at 2020-08-05 12:51:25.head_ref_deleted
by Francis Kayiwa at 2020-08-05 12:51:30.this PR has deployed oar-staging and dev multiple times. (sorry it was this huge 👎 )
head_ref_force_pushed
by Francis Kayiwa at 2020-08-05 22:04:31.ready_for_review
by Francis Kayiwa at 2020-08-05 22:05:32.review_requested
by Francis Kayiwa at 2020-08-05 22:05:41.This project began as METS/MODS, with the items generated by hand from a pre-EAD finding aid to the microfilm. The METS/MODS, plus a version of pudl0066.ead and a final report (anonymous and undated) can be found in the Subversion repository:http://diglibsvn.princeton.edu/svn/puldi/pudl/branches/development/mdata/pudl0066
Introducing the changes on https://github.com/pulibrary/pulfalight/compare/issues-327-jrgriffiniii-component-view yielded the following design updates:
Unfortunately, there are still some indexing issues which seem to keep fields from being populated with the appropriate data from the fixtures. Additionally, the LUX does not maintain some of the icons needed to fully implement the design.
closed
by Kevin Reiss at 2020-08-05 13:46:35.All done except for production
unassigned
by Shaun Ellis at 2020-08-05 13:52:19.referenced
by Axa Mei Liauw at 2020-08-05 14:01:36.merged
by Axa Mei Liauw at 2020-08-05 14:01:36.closed
by Axa Mei Liauw at 2020-08-05 14:01:36.head_ref_deleted
by Axa Mei Liauw at 2020-08-05 14:01:40.head_ref_force_pushed
by None at 2020-08-05 14:51:08.head_ref_force_pushed
by None at 2020-08-05 16:04:57.head_ref_force_pushed
by None at 2020-08-05 18:47:25.ready_for_review
by None at 2020-08-05 18:49:27.head_ref_force_pushed
by None at 2020-08-05 19:03:53.


Hint of the day
“You may ask your employer to provide you with a qualified job reference even during employment in certain circumstances.”
Case law update
Relocation of an employee permissible without rehabilitation measures.
BAG (Az.: 10 AZR 47/17) 18.10.2017
Six month old video footage taken at the worksite may be used as proof of employee-theft; The GDPR has no bearing.
BAG (Az.: 2 AZR 133/18) 23.08.0218


Modeltime is designed for modeling Panel Data. In this short tutorial, we will explain what Panel Data Modeling is and demonstrate how to use the Modeltime Forecasting Workflow for multi-time series datasets (Panel Data).
Time series are increasing at an exponential rate. Organization-wide forecasting demands have changed from top-level to bottom-level forecasting, which has increased the number of forecasts that need to be made from the range of 1-100 to the range of 1,000-10,000.
Think of forecasting by customer for an organization that has 10,000 customers. It becomes a challenge to make these forecasts one at a time in an iterative approach. As that organization grows, moving from 10,000 to 100,000 customers, forecasting with an iterative approach is not scalable.
Modeltime has been designed to take a different approach using Panel Data and Global Models (more on these concepts shortly). Using these approaches, we can dramatically increase the scale at which forecasts can be made. Prior limitations in the range of 1,000 to 10,000 forecasts become managable. Beyond is also possible with clustering techniques and making several panel models. We are only limited by RAM, not modeling time.
In it’s simplest form, Panel Data is a time series dataset that has more than one series. Each time series is stacked row-wise (on-top) of each other.
Traditional modeling techniques like ARIMA can only be used on one time series at a time. The widely accepted forecasting approach is to iterate through each time series producing a unique model and forecast for each time series identifier. The downside with this approach is that it’s expensive when you have many time series. Think of the number of products in a database. As the number of time series approaches the range of 1000-10,000, the iterative approach becomes unscalable.
Global Models are alternatives to the iterative approach. A Global Model is a single model that forecasts all time series at once. Global Models are highly scalable, which solves the problem of 1-10,000 time series. An example is an XGBoost Model, which can determine relationships for all 1000 time series panels with a single model.
The downside is that an iterative approach can be more accurate. To improve accuracy, feature engineering and localized model selection by time series identifier become critical to large-scale forecasting success. If interested, I teach proven feature engineering techniques in my Time Series Forecasting Course.
While Modeltime can perform iterative modeling, Modeltime has been designed for Panel Data using:
Global Modeling: Global model Machine Learning and Deep Learning strategies using the Modeltime Ecosystem (e.g. modeltime
, modeltime.h2o
, and modeltime.gluonts
).
Feature Engineering: Developing calendar features, lagged features, and other time-based, window-based, and sequence-based features using timetk
.
Local Forecast Visualization: Visualizing multiple local time series forecasts at once.
Global and Localized Accuracy Reporting: Generating out-of-sample accuracy both globally and at a local level by time series identifier (available in modeltime
>= 0.7.0)
Global and Localized Confidence Intervals Reporting: Generating out-of-sample confidence intervals both globally and at a local level by time series identifier (available in modeltime
>= 0.7.0)
We’ll cover a short tutorial on modeling panel data with the walmart_sales_weekly
dataset.
Next, collect the walmart_sales_weekly
dataset. The dataset consists of 1001 observations of revenue generated by a store-department combination on any given week. It contains:
data <- walmart_sales_weekly %>%
select(id, Date, Weekly_Sales) %>%
set_names(c("ID", "date", "value"))
data
#> # A tibble: 1,001 x 3
#> ID date value
#> <fct> <date> <dbl>
#> 1 1_1 2010-02-05 24924.
#> 2 1_1 2010-02-12 46039.
#> 3 1_1 2010-02-19 41596.
#> 4 1_1 2010-02-26 19404.
#> 5 1_1 2010-03-05 21828.
#> 6 1_1 2010-03-12 21043.
#> 7 1_1 2010-03-19 22137.
#> 8 1_1 2010-03-26 26229.
#> 9 1_1 2010-04-02 57258.
#> 10 1_1 2010-04-09 42961.
#> # … with 991 more rows
From visualizing, the weekly department revenue patterns emerge. Most of the series have yearly seasonality and long-term trends.
data %>%
group_by(ID) %>%
plot_time_series(
.date_var = date,
.value = value,
.facet_ncol = 3,
.interactive = FALSE
)
We can split the data into training and testing sets using time_series_split()
. We’ll investigate the last 3-months of the year to test a global model on a 3-month forecast. The message on overlapping dates is to let us know that multiple time series are being processed using the last 3-month window for testing.
splits <- data %>% time_series_split(assess = "3 months", cumulative = TRUE)
#> Using date_var: date
#> Data is not ordered by the 'date_var'. Resamples will be arranged by `date`.
#> Overlapping Timestamps Detected. Processing overlapping time series together using sliding windows.
splits
#> <Analysis/Assess/Total>
#> <917/84/1001>
We can move to preprocessing the data. We will use the recipes
workflow for generating time series features. This results in 37 derived features for modeling. We can certainly include more features such as lags and rolling features, which are covered in the High-Performance Time Series Course.
rec_obj <- recipe(value ~ ., training(splits)) %>%
step_mutate(ID = droplevels(ID)) %>%
step_timeseries_signature(date) %>%
step_rm(date) %>%
step_zv(all_predictors()) %>%
step_dummy(all_nominal_predictors(), one_hot = TRUE)
summary(prep(rec_obj))
#> # A tibble: 38 x 4
#> variable type role source
#> <chr> <chr> <chr> <chr>
#> 1 value numeric outcome original
#> 2 date_index.num numeric predictor derived
#> 3 date_year numeric predictor derived
#> 4 date_year.iso numeric predictor derived
#> 5 date_half numeric predictor derived
#> 6 date_quarter numeric predictor derived
#> 7 date_month numeric predictor derived
#> 8 date_month.xts numeric predictor derived
#> 9 date_day numeric predictor derived
#> 10 date_mday numeric predictor derived
#> # … with 28 more rows
We’ll create an xgboost
workflow by fitting the default xgboost model to our derived features from our in-sample training data set.
Training the global xgboost model takes approximately 50 milliseconds.
Conversely, an ARIMA model might take several minutes to iterate through possible parameter combinations for each of the 7 time series.
# Workflow
wflw_xgb <- workflow() %>%
add_model(
boost_tree() %>% set_engine("xgboost")
) %>%
add_recipe(rec_obj) %>%
fit(training(splits))
wflw_xgb
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: boost_tree()
#>
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 5 Recipe Steps
#>
#> • step_mutate()
#> • step_timeseries_signature()
#> • step_rm()
#> • step_zv()
#> • step_dummy()
#>
#> ── Model ───────────────────────────────────────────────────────────────────────
#> ##### xgb.Booster
#> raw: 58.3 Kb
#> call:
#> xgboost::xgb.train(params = list(eta = 0.3, max_depth = 6, gamma = 0,
#> colsample_bytree = 1, colsample_bynode = 1, min_child_weight = 1,
#> subsample = 1, objective = "reg:squarederror"), data = x$data,
#> nrounds = 15, watchlist = x$watchlist, verbose = 0, nthread = 1)
#> params (as set within xgb.train):
#> eta = "0.3", max_depth = "6", gamma = "0", colsample_bytree = "1", colsample_bynode = "1", min_child_weight = "1", subsample = "1", objective = "reg:squarederror", nthread = "1", validate_parameters = "TRUE"
#> xgb.attributes:
#> niter
#> callbacks:
#> cb.evaluation.log()
#> # of features: 37
#> niter: 15
#> nfeatures : 37
#> evaluation_log:
#> iter training_rmse
#> 1 46315.141
#> 2 33001.734
#> ---
#> 14 3676.542
#> 15 3373.945
We’ll step through the modeltime workflow, which is used to test many different models on the time series and organize the entire process.
model_tbl <- modeltime_table(
wflw_xgb
)
model_tbl
#> # Modeltime Table
#> # A tibble: 1 x 3
#> .model_id .model .model_desc
#> <int> <list> <chr>
#> 1 1 <workflow> XGBOOST
A new feature in modeltime
0.7.0 is the ability to calibrate by each time series. Calibration calculates the out of sample residual error.
calib_tbl <- model_tbl %>%
modeltime_calibrate(
new_data = testing(splits),
id = "ID"
)
calib_tbl
#> # Modeltime Table
#> # A tibble: 1 x 5
#> .model_id .model .model_desc .type .calibration_data
#> <int> <list> <chr> <chr> <list>
#> 1 1 <workflow> XGBOOST Test <tibble [84 × 5]>
Next, we measure the global and local accuracy on the global model.
The default is modeltime_accuracy(acc_by_id = FALSE)
, which returns a global model accuracy.
calib_tbl %>%
modeltime_accuracy(acc_by_id = FALSE) %>%
table_modeltime_accuracy(.interactive = FALSE)
| Accuracy Table | ||||||||
|---|---|---|---|---|---|---|---|---|
| .model_id | .model_desc | .type | mae | mape | mase | smape | rmse | rsq |
| 1 | XGBOOST | Test | 3254.56 | 7.19 | 0.1 | 7 | 4574.52 | 0.98 |
By toggling modeltime_accuracy(acc_by_id = TRUE)
, we can obtain the local model accuracy. This can be useful for identifying specifically which time series the model does well on (and which it does poorly on). We can then apply model selection logic to select specific global models for specific IDs.
calib_tbl %>%
modeltime_accuracy(acc_by_id = TRUE) %>%
table_modeltime_accuracy(.interactive = FALSE)
| Accuracy Table | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| .model_id | .model_desc | .type | ID | mae | mape | mase | smape | rmse | rsq |
| 1 | XGBOOST | Test | 1_1 | 1138.25 | 6.19 | 0.85 | 5.93 | 1454.25 | 0.95 |
| 1 | XGBOOST | Test | 1_3 | 3403.81 | 18.47 | 0.57 | 16.96 | 4209.29 | 0.91 |
| 1 | XGBOOST | Test | 1_8 | 1891.35 | 4.93 | 0.86 | 5.07 | 2157.43 | 0.55 |
| 1 | XGBOOST | Test | 1_13 | 1201.11 | 2.92 | 0.53 | 2.97 | 1461.49 | 0.60 |
| 1 | XGBOOST | Test | 1_38 | 8036.27 | 10.52 | 0.99 | 10.64 | 8955.32 | 0.02 |
| 1 | XGBOOST | Test | 1_93 | 3493.69 | 4.50 | 0.34 | 4.64 | 4706.68 | 0.78 |
| 1 | XGBOOST | Test | 1_95 | 3617.45 | 2.83 | 0.46 | 2.83 | 4184.46 | 0.72 |
The last step we’ll cover is forecasting the test dataset. This is useful to evaluate the model using a sampling of the time series within the panel dataset. In modeltime
0.7.0, we now have modeltime_forecast(conf_by_id = TRUE)
to allow the confidence intervals (prediction intervals) to be calculated by time series identifier. Note, that the modeltime_calibrate()
must have been performed with an id
specified.
calib_tbl %>%
modeltime_forecast(
new_data = testing(splits),
actual_data = bind_rows(training(splits), testing(splits)),
conf_by_id = TRUE
) %>%
group_by(ID) %>%
plot_modeltime_forecast(
.facet_ncol = 3,
.interactive = FALSE
)
We just showcased the Modeltime Workflow for Panel Data using a Global XGBOOST Model. But, this is a simple problem. And, there’s a lot more to learning time series:
Your probably thinking how am I ever going to learn time series forecasting.
Become the forecasting expert for your organization
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Time series is changing. Businesses now need 10,000+ time series forecasts every day. This is what I call a High-Performance Time Series Forecasting System (HPTSF) - Accurate, Robust, and Scalable Forecasting.
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I'm a Software Engineer (fancy name to say that I develop web applications and sometimes automate them).
My first experience with code was at the time everybody had a Code Chef Profile. I remember struggling with finding the way to change the standard template library in C++ and take input from user. I used to spend hours figuring out the looping conditions. The excitement of having the power to write code uniquely and simplistically was amazing.  You start move by move, you need to understand what outcomes your current step will generate and bammm... Checkmate.
Here's to building castles with code!
Java Development, Automation Framework Handling and API Automation, C++ and SQL Query Handling
June 2019 - June 2020
June 2018 – August 2018
April 2017 – May 2017
June 2017 – July 2017
Command over programming Language like JAVA,Backend Implementaion with SQL and Service Deployment to Server.
Knowledge of RESTFUL API Services Response Codes and Knowledge of Automation Framework/Scripting Language integrated with Selenium like Webdriver.
Development of responsive websites, cross-browser and cross-platform testing.
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Class std.container
Container prototype.
A container is a std.object with no methods. It's functionality is instead defined by its metamethods.
Where an Object uses the __index
metatable entry to hold object
methods, a Container stores its contents using __index
, preventing
it from having methods in there too.
Although there are no actual methods, Containers are free to use
metamethods (__index
, __sub
, etc) and, like Objects, can supply
module functions by listing them in _functions
. Also, since a
std.container is a std.object, it can be passed to the
std.object module functions, or anywhere else a std.object is
expected.
When making your own prototypes, derive from std.container if you want
to access the contents of your objects with the []
operator, or from
std.object if you want to access the functionality of your objects with
named object methods.
Prototype Chain
table `-> Object `-> Container
Objects
| std.container.Container | Container prototype. |
Objects
- std.container.Container
-
Container prototype.
Container also inherits all the fields and methods from std.object.Object.
Fields:
- _type string object name (default "Container")
See also:
Usage:
local std = require "std" local Container = std.container {} local Graph = Container { _type = "Graph", _functions = { nodes = function (graph) local n = 0 for _ in std.pairs (graph) do n = n + 1 end return n end, }, } local g = Graph { "node1", "node2" } --> 2 print (Graph.nodes (g))


VeriFast Help > Error messages
Error message
Recursive lemma call does not decrease the heap (no full field chunks left) or the derivation depth of the first chunk and there is no inductive parameter.
Context
When a lemma recursively calls itself.
Cause
VeriFast cannot find a suitable measure which proves that the lemma terminates.
Notes
- To ensure soundness, VeriFast checks that lemma functions terminate. To enforce termination, VeriFast checks that one of the following conditions hold when a lemma recursively calls itself:
- After consuming the precondition of the recursive call, the symbolic heap contains a full field chunk.
- The body of the lemma is a switch statement over one of its inductive arguments, and the size of the inductive argument decreases in the recursive call.
- The first conjunct of the precondition is a predicate assertion, the lemma's body begins with an open statement opening this predicate assertion, and the first chunk consumed by the precondition of the recursive call is one of the conjuncts produced by opening the first conjunct.
Suggested solutions
Suggested approaches:
- Rewrite the body of the lemma such that one of the conditions described above holds.


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Introduction
In summer 2017, I worked as the first and the only UX designer & UX researcher on the SHRINE team in Harvard Catalyst, Harvard Medical School. I was tasked with doing user research and redesigning the Data Steward Application, a web application for Data Stewards to check if there is malicious query when medical researchers access patient data.
Tool
- Sketch
- Balsamiq
- Tableau
- Invision
- Principle


|
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a good cause. I'm sending money now to allow Beth to buy more octopii (for
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|


Hey Guys, My name is Pavit Kalra I am interested in IT and everything in its orbit. I recently began to be fascinated by web programming, e.g. developing apps and building websites. I am keen to gain more experience in the field. For this reason, I am looking for a company willing to offer me a placement among their developers. In return, I would offer my full commitment, and be a pleasant and friendly addition to your team. I am therefore currently looking for a job or an internship as a front-end or a back-end developer.


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|
| Local Properties | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |
Properties
| Name | Type | Description |
|---|---|---|
| accel3dSupported | xsd:boolean |
Indicates if 3D hardware acceleration for virtual machines is supported.
Since vSphere API 5.1 |
| backgroundSnapshotsSupported | xsd:boolean |
Flag indicating whether background snapshots are supported on this host.
Since VI API 2.5 |
| cloneFromSnapshotSupported | xsd:boolean |
Indicates whether or not cloning a virtual machine from a snapshot
point is allowed.
This property must be true on the host where the virtual machine
is currently residing. This property need not be true on the
destination host for the clone. |
| cpuHwMmuSupported* | xsd:boolean |
Indicates whether this host supports hardware-based MMU virtualization.
Since vSphere API 6.5 |
| cpuMemoryResourceConfigurationSupported | xsd:boolean |
Flag indicating whether cpu and memory resource configuration is
supported. If this is set to false,
UpdateConfig,
UpdateChildResourceConfiguration
cannot be used for changing the cpu/memory resource configurations.
Since VI API 2.5 |
| cryptoSupported* | xsd:boolean |
Flag indicating whether Cryptographer feature is supported.
Since vSphere API 6.5 |
| datastorePrincipalSupported | xsd:boolean |
Flag indicating whether datastore principal user
is supported on the host.
|
| deltaDiskBackingsSupported | xsd:boolean |
Flag indicating whether explicitly creating arbirary configurations of
delta disk backings is supported.
A delta disk backing is a way to preserve a virtual disk backing at some point in time. A delta disk backing is a file backing which in turn points to the original virtual disk backing (the parent). After a delta disk backing is added, all writes go to the delta disk backing. All reads first try the delta disk backing and then try the parent backing if needed. If this property is false, then delta disk backings can only be implicitly created through using snapshot operations and two virtual machines cannot safely share a parent disk backing. If this property is true, then delta disk backings can be explicitly created and managed, and two virtual machines may safely share a parent disk backing. In the context above, "safely" means that performing operations on one of the virtual machines will not affect the operation of the other virtual machine.
|
| eightPlusHostVmfsSharedAccessSupported | xsd:boolean |
Indicates whether the host is capable of accessing a VMFS disk
when there are eight or more hosts accessing the disk already.
Since vSphere API 5.1 |
| encryptedVMotionSupported* | xsd:boolean |
Indicates whether this host supports encrypted vMotion.
Since vSphere API 6.5 |
| encryptionCBRCSupported* | xsd:boolean |
Indicates whether this host supports encrypting virtual disks with
Content Based Read Cache (digest disks) enabled.
Since vSphere API 6.5 |
| encryptionChangeOnAddRemoveSupported* | xsd:boolean |
Indicates whether this host supports changing the encryption state
of a virtual disk when the disk is being added or removed from a
virtual machine configuration.
Since vSphere API 6.5 |
| encryptionFaultToleranceSupported* | xsd:boolean |
Indicates whether this host supports enabling Fault Tolerance on
encrypted virtual machines.
Since vSphere API 6.5 |
| encryptionHBRSupported* | xsd:boolean |
Indicates whether this host supports encrypting virtual disks with
Host Based Replication enabled.
Since vSphere API 6.5 |
| encryptionHotOperationSupported* | xsd:boolean |
Indicates whether this host supports changing the encryption state
of a virtual machine, or virtual disk, while the virtual machine
is powered on.
Since vSphere API 6.5 |
| encryptionMemorySaveSupported* | xsd:boolean |
Indicates whether this host supports suspending, or creating
with-memory snapshots, encrypted virtual machines.
Since vSphere API 6.5 |
| encryptionRDMSupported* | xsd:boolean |
Indicates whether this host supports encrypting RDM backed virtual
disks.
Since vSphere API 6.5 |
| encryptionVFlashSupported* | xsd:boolean |
Indicates whether this host supports encrypting virtual disks with
vFlash cache enabled.
Since vSphere API 6.5 |
| encryptionWithSnapshotsSupported* | xsd:boolean |
Indicates whether this host supports changing the encryption state
state of a virtual machine, or virtual disk, while the virtual
machine has snapshots present.
Since vSphere API 6.5 |
| featureCapabilitiesSupported | xsd:boolean |
Indicated whether the host supports feature capabilities
for EVC mode.
Since vSphere API 5.1 |
| firewallIpRulesSupported* | xsd:boolean |
Indicates whether this host supports ip address based restrictions in
the firewall configuration.
Since vSphere API 5.0 |
| ftCompatibilityIssues* | xsd:string[] | Deprecated.
as of vSphere API 6.0
For a host which doesn't support Fault Tolerance, indicates all the reasons
for the incompatibility. HostCapabilityFtUnsupportedReason
lists the set of possible values.
Since vSphere API 4.1 |
| ftEfiSupported* | xsd:boolean |
Indicates whether this host supports Fault Tolerance VMs that have
specified UEFI firmware.
Since vSphere API 6.7 |
| ftSupported | xsd:boolean | Deprecated.
as of vSphere API 6.0
Indicates whether this host supports Fault Tolerance
There can be many reasons why a host does not support Fault
Tolerance, which includes CPU compatibility, product
compatibility as well as other host configuration settings.
For specific reasons, look into
replayCompatibilityIssues and
ftCompatibilityIssues
In releases after vSphere API 5.0, vSphere Servers might not
generate property collector update notifications for this property.
To obtain the latest value of the property, you can use
PropertyCollector methods RetrievePropertiesEx or WaitForUpdatesEx.
If you use the PropertyCollector.WaitForUpdatesEx method, specify
an empty string for the version parameter.
Since this property is on a DataObject, an update returned by WaitForUpdatesEx may
contain values for this property when some other property on the DataObject changes.
If this update is a result of a call to WaitForUpdatesEx with a non-empty
version parameter, the value for this property may not be current.
Since vSphere API 4.0 |
| ftVmcpSupported* | xsd:boolean |
Indicates whether this host supports VMCP for Fault Tolerance VMs.
Since vSphere API 6.7 |
| gatewayOnNicSupported* | xsd:boolean |
Flag indicating whether default gateway can be configured on a
vmkernel nic.
Since vSphere API 6.5 |
| hbrNicSelectionSupported | xsd:boolean |
Indicates whether a dedicated nic can be selected for vSphere Replication
LWD traffic, i.e., from the primary host to the VR server.
Since vSphere API 5.1 |
| highGuestMemSupported | xsd:boolean |
Is high guest memory supported.
|
| hostAccessManagerSupported* | xsd:boolean |
Whether this host supports HostAccessManager for controlling direct
access to the host and for better lockdown mode management.
Since vSphere API 6.0 |
| interVMCommunicationThroughVMCISupported | xsd:boolean |
Indicates whether the host supports VMCI for communication
between virtual machines.
Since vSphere API 5.1 |
| ipmiSupported* | xsd:boolean |
Flag indicating whether the host supports
IPMI (Intelligent Platform Management Interface).
XXX - Make ipmiSupported optional until there is a compatible hostagent.
Since vSphere API 4.0 |
| iscsiSupported | xsd:boolean |
Is access to iSCSI devices supported.
|
| latencySensitivitySupported | xsd:boolean |
Indicates whether the host supports latency sensitivity for the
virtual machines.
Since vSphere API 5.1 |
| localSwapDatastoreSupported | xsd:boolean |
Flag indicating whether the host supports selecting a datastore that
that may be used to store virtual machine swapfiles.
Since VI API 2.5 |
| loginBySSLThumbprintSupported* | xsd:boolean |
Flag indicating whether this host supports SSL thumbprint authentication
Since vSphere API 4.0 |
| maintenanceModeSupported | xsd:boolean |
Is maintenance mode supported
|
| markAsLocalSupported* | xsd:boolean |
Indicates whether mark disk as local or remote is supported
on the host. See MarkAsLocal_Task See MarkAsNonLocal_Task Since vSphere API 6.0 |
| markAsSsdSupported* | xsd:boolean |
Indicates whether mark disk as SSD or Non-SSD is supported
on the host. See MarkAsSsd_Task See MarkAsNonSsd_Task Since vSphere API 6.0 |
| maxHostRunningVms* | xsd:int |
The maximum number of virtual machines that can be run on the host.
An unset value indicates that the value could not be obtained. In contrast
to maxRunningVMs, this value is the minimum of (i) the maximum
number supported by the hardware and (ii) the maximum number permitted by
the host license.
Since vSphere API 5.0 |
| maxHostSupportedVcpus* | xsd:int |
The maximum number of virtual CPUs that can be run on the host. An unset
value indicates that the value could not be obtained. In contrast to
maxSupportedVcpus, this value is the minimum of (i) the maximum
number supported by the hardware and (ii) the maximum number permitted by
the host license.
Since vSphere API 5.0 |
| maxMemMBPerFtVm* | xsd:int |
Indicates maximum memory allowed for Fault Tolerance virtual machine.
Since vSphere API 6.7 |
| maxNumDisksSVMotion* | xsd:int |
Maximum number of migrating disks allowed of a migrating VM during SVMotion.
If this capability is not set, then the maximum is considered to be 64.
Since vSphere API 6.0 |
| maxRegisteredVMs* | xsd:int |
The maximum number of registered virtual machines supported by
the host. If this limit is exceeded, the management agent will be
at risk of running out of system resources. configIssue will be posted on
HostSystem in this case.
If this capability is not set, the number is unknown. Since vSphere API 5.1 |
| maxRunningVMs* | xsd:int |
The maximum number of virtual machines that can be running
simultaneously on this host.
If this capability is not set, the number of virtual machines
running simultaneously is unlimited.
|
| maxSupportedVcpus* | xsd:int |
The maximum number of virtual CPUs supported per virtual machine.
If this capability is not set, the number is unlimited.
|
| maxSupportedVMs* | xsd:int |
The maximum number of virtual machines that can exist on this host.
If this capability is not set, the number of virtual machines is
unlimited.
|
| maxVcpusPerFtVm* | xsd:int |
The maximum number of vCPUs allowed for a fault-tolerant virtual machine.
Since vSphere API 6.0 |
| messageBusProxySupported* | xsd:boolean |
Indicates whether the message bus proxy is supported
Since vSphere API 6.0 |
| multipleNetworkStackInstanceSupported* | xsd:boolean |
Indicates whether the host supports Multiple Instance TCP/IP stack
Since vSphere API 5.5 |
| nestedHVSupported | xsd:boolean |
Indicates whether the host supports nested hardware-assisted virtualization.
Since vSphere API 5.1 |
| nfs41Krb5iSupported* | xsd:boolean |
Whether this host support NFS41 Kerberos 5* security type.
Since vSphere API 6.5 |
| nfs41Supported* | xsd:boolean |
Whether this host supports NFS41 file systems.
Since vSphere API 6.0 |
| nfsSupported | xsd:boolean |
Is access to NFS devices supported.
|
| nicTeamingSupported | xsd:boolean |
Is NIC teaming supported.
|
| oneKVolumeAPIsSupported* | xsd:boolean |
Indicates whether this host supports granular datastore cache update.
If value is not specified, it should be considered as not supported.
Since vSphere API 6.5 |
| perVMNetworkTrafficShapingSupported | xsd:boolean |
Indicates whether network traffic shaping on a
per virtual machine basis is supported.
Since vSphere API 4.0 |
| perVmSwapFiles | xsd:boolean |
Flag indicating whether virtual machine execution on this host involves
a swapfile for each virtual machine. If true, the swapfile placement
for a powered-on virtual machine is advertised in its FileLayout by
the swapFile property.
Since VI API 2.5 |
| pMemSnapshotSupported* | xsd:boolean |
Indicates whether this host supports snapshots for VMs with virtual
devices backed by persistent memory.
If value is not specified, it should be considered as not supported.
Since vSphere API 6.7 |
| pMemSupported* | xsd:boolean |
Indicates whether this host supports persistent memory.
If value is not specified, it should be considered as not supported.
Since vSphere API 6.7 |
| preAssignedPCIUnitNumbersSupported | xsd:boolean |
Flag to indicate whether the server returns unit numbers in a
pre-assigned range for devices on the PCI bus.
When the server supports this flag, the device unit number namespace is
partitioned by device type. Different types of devices will sit in
a specific range of unit numbers that may not correspond to physical
slots in the pci bus but present a relative ordering of the devices
with respect to other devices of the same type.
Note that this does not mean that the user can set the relative ordering
between device types, but only allows stable orderings between devices
of the same type. The unit number will now clearly represent an ordering
between devices of the same type.
unitNumber
This property is only available for devices on the pci controller.
Since VI API 2.5 |
| provisioningNicSelectionSupported | xsd:boolean |
Indicates whether a dedicated nic can be selected for vSphere Provisioning
NFC traffic.
Since vSphere API 6.0 |
| quickBootSupported* | xsd:boolean |
Indicates whether this host supports the LoadESX feature
which allows faster reboots. See QueryLoadEsxSupported Since vSphere API 6.7.1 |
| rebootSupported | xsd:boolean |
Flag indicating whether rebooting the host is supported.
|
| recordReplaySupported | xsd:boolean | Deprecated.
as of vSphere API 6.0
Indicates whether this host supports record and replay
Since vSphere API 4.0 |
| recursiveResourcePoolsSupported | xsd:boolean |
|
| reliableMemoryAware* | xsd:boolean |
Indicates that this host uses a reliable memory aware allocation policy.
Since vSphere API 5.5 |
| replayCompatibilityIssues* | xsd:string[] | Deprecated.
as of vSphere API 6.0
For a host which doesn't support replay, indicates all the reasons
for the incompatibility. HostReplayUnsupportedReason
lists the set of possible values.
Since vSphere API 4.1 |
| replayUnsupportedReason* | xsd:string | Deprecated.
As of vSphere API 4.1, use
replayCompatibilityIssues.
For a host whose CPU doesn't support replay, indicates the reason
for the incompatibility. HostReplayUnsupportedReason
represents the set of possible values.
Since vSphere API 4.0 |
| restrictedSnapshotRelocateSupported | xsd:boolean |
Indicates whether this host supports relocation of
virtual machines with snapshots. Must be true on the
source and destination hosts for the relocation to work.
Even if this is true, the following conditions must hold:
1) All the the vm's files are in one directory prior
to the relocate.
2) All of the vm's files will be in one directory
after the relocate.
3) The source and destination hosts are the same product
version.
Since VI API 2.5 |
| sanSupported | xsd:boolean |
Flag indicating whether access to SAN devices is supported.
|
| scaledScreenshotSupported | xsd:boolean |
Indicates whether scaling is supported for screenshots retrieved over https.
If true, screenshot retrieval supports the additional optional
parameters 'width' and 'height'. After cropping, the returned image will be scaled
to these dimensions. If only one of these parameters is specified, default behavior
is to return an image roughly proportional to the source image.
Since VI API 2.5 |
| scheduledHardwareUpgradeSupported* | xsd:boolean |
Indicates whether the host supports scheduled hardware upgrades. See scheduledHardwareUpgradeInfo Since vSphere API 5.1 |
| screenshotSupported | xsd:boolean |
Indicates whether the screenshot retrival over https is supported for this host's
virtual machines.
If true, a screenshot can be retrieved at the HTTPS relative path
/screen?id=<managed object ID of virtual machine or snapshot>.
If any of the optional parameters 'top', 'left', 'bottom', and 'right' is
specified, the returned image will be cropped from the rectangle with upper left
corner (left, top) and bottom right corner (right - 1, bottom - 1). These values
default to the top, left, bottom and right edges of the image.
The client must use an authenticated session with privilege
VirtualMachine.Interact.ConsoleInteract on the requested virtual machine or,
in the case of a snapshot, the virtual machine associated with that snapshot.
Since VI API 2.5 |
| servicePackageInfoSupported* | xsd:boolean |
Indicates whether this host supports package information in service
configuration.
Since vSphere API 5.0 |
| shutdownSupported | xsd:boolean |
Flag indicating whether the host can be powered off
|
| smartCardAuthenticationSupported* | xsd:boolean |
Indicates whether this host supports local two-factor user
authentication using smart cards. See EnableSmartCardAuthentication Since vSphere API 6.0 |
| smpFtCompatibilityIssues* | xsd:string[] |
For a host which doesn't support smp fault tolerance, indicates all the
reasons for the incompatibility.
HostCapabilityFtUnsupportedReason lists the set of possible
values.
Since vSphere API 6.0 |
| smpFtSupported | xsd:boolean |
Indicates whether this host supports smp fault tolerance
Since vSphere API 6.0 |
| snapshotRelayoutSupported | xsd:boolean |
Indicates whether this host supports unrestricted relocation of virtual
machines with snapshots. Only needs to be true on the destination host for
the unrestricted relocation to work. The full snapshot relocation does not
restrict the layout of snapshot files or disks of the virtual machine, nor
its power state. If the virtual machine is powered on, a storage vmotion
will be performed to relocate its snapshots and disks.
Since vSphere API 5.0 |
| standbySupported | xsd:boolean |
Flag indicating whether you can put the host in a power down
state, from which it can be powered up automatically.
Since VI API 2.5 |
| storageIORMSupported | xsd:boolean |
Indicates whether the host supports storage I/O resource
management.
Since vSphere API 4.1 |
| storagePolicySupported* | xsd:boolean |
Indicates that host supports Object-based Storage System and
Storage-Profile based disk provisioning.
Since vSphere API 5.5 |
| storageVMotionSupported | xsd:boolean |
Indicates whether the storage of a powered-on virtual machine may be
relocated.
Since vSphere API 4.0 |
| supportedCpuFeature* | HostCpuIdInfo[] | Deprecated.
As of vSphere API 6.5 use
featureCapability
CPU feature set that is supported by the virtualization platform. This
feature set may reflect characteristics of the product capabilities and
licensing. For any feature marked '-', reference the
cpuFeature array of the host's
HardwareInfo to determine the correct value.
Since vSphere API 4.0 |
| supportedVmfsMajorVersion* | xsd:int[] |
List of VMFS major versions supported by the host.
Since vSphere API 5.0 |
| suspendedRelocateSupported | xsd:boolean |
Indicates whether this host supports relocation of
suspended virtual machines. Must be true on the source
and destination hosts for the relocation to work.
|
| tpmSupported | xsd:boolean |
Flag indicating whether this host supports the integrity measurement using
a TPM device.
Since vSphere API 4.0 |
| tpmVersion* | xsd:string |
TPM version if supported by this host.
Since vSphere API 6.7 |
| turnDiskLocatorLedSupported* | xsd:boolean |
Indicates whether turning on/off local disk LED is supported
on the host. See TurnDiskLocatorLedOn_Task See TurnDiskLocatorLedOff_Task Since vSphere API 6.0 |
| txtEnabled* | xsd:boolean |
Flag indicating whether Intel TXT is enabled on this host.
Since vSphere API 6.7 |
| unmapMethodSupported* | xsd:string |
Indicates which kind of VMFS unmap method is supported. See
HostCapabilityUnmapMethodSupported
Since vSphere API 6.7 |
| unsharedSwapVMotionSupported | xsd:boolean |
Flag indicating whether the host supports participating in a VMotion
where the virtual machine swapfile is not visible to the destination.
Since VI API 2.5 |
| upitSupported* | xsd:boolean |
Indicate whether this host supports UPIT
Since vSphere API 6.5 |
| vFlashSupported* | xsd:boolean |
Indicates whether the host supports vFlash.
Since vSphere API 5.5 |
| virtualExecUsageIgnored* | xsd:boolean |
Indicates that virtualExecUsage is
ignored by the host, always operating as if "hvOn" was selected.
Since vSphere API 6.7 |
| virtualExecUsageSupported | xsd:boolean |
Indicates whether the host supports configuring hardware
virtualization (HV) support for virtual machines.
Since vSphere API 4.0 |
| virtualMmuUsageIgnored* | xsd:boolean |
Indicates that virtualMmuUsage is
ignored by the host, always operating as if "on" was selected.
Since vSphere API 6.7 |
| virtualVolumeDatastoreSupported* | xsd:boolean |
Indicates whether this host supports VirtualVolume based Datastore.
Since vSphere API 6.0 |
| vlanTaggingSupported | xsd:boolean |
Is VLAN Tagging supported.
|
| vmCreateDateSupported* | xsd:boolean |
Indicates that createDate feature is supported by the host.
Since vSphere API 6.7 |
| vmDirectPathGen2Supported | xsd:boolean |
Indicates whether the host supports network passthrough using
VMDirectPath Gen 2. Note that this is a general capability for the host
and is independent of support by a given physical NIC. If false, the
reason(s) for lack of support will be provided in
vmDirectPathGen2UnsupportedReason and/or in
vmDirectPathGen2UnsupportedReasonExtended. See vmDirectPathGen2Supported Since vSphere API 4.1 |
| vmDirectPathGen2UnsupportedReason* | xsd:string[] |
If vmDirectPathGen2Supported is false, this array will be
populated with reasons for the lack of support (chosen from
VmDirectPathGen2UnsupportedReason). If there is a reason for
the lack of support that cannot be described by the available constants,
vmDirectPathGen2UnsupportedReasonExtended will be populated
with an additional explanation provided by the platform.
Note that this list of reasons is not guaranteed to be exhaustive. If the reason "hostNptIncompatibleProduct" is provided, then that will be the only provided reason, as the host software is incapable of providing additional information. Since vSphere API 4.1 |
| vmDirectPathGen2UnsupportedReasonExtended* | xsd:string |
If vmDirectPathGen2Supported is false, this property may
contain an explanation provided by the platform, beyond the reasons (if
any) enumerated in vmDirectPathGen2UnsupportedReason.
Since vSphere API 4.1 |
| vmfs3EOLSupported* | xsd:boolean |
Indicates whether this host supports VMFS-3 EOL.
If value is not specified, it should be considered as not supported.
Since vSphere API 6.7 |
| vmfsDatastoreMountCapable | xsd:boolean |
Indicates whether the host is capable of mounting/unmounting
VMFS datastores.
Since vSphere API 5.0 |
| vmotionAcrossNetworkSupported* | xsd:boolean |
Indicates whether the network of a powered-on virtual machine can be
changed while simultaneously changing the execution host of the
virtual machine.
Since vSphere API 5.5 |
| vmotionSupported | xsd:boolean |
Flag indicating whether you can perform VMotion.
|
| vmotionWithStorageVMotionSupported | xsd:boolean |
Indicates whether the storage of a powered-on virtual machine may be
relocated while simultaneously changing the execution host of the
virtual machine.
Since vSphere API 4.0 |
| vPMCSupported | xsd:boolean |
Indicates whether the host supports virtual CPU performance counters.
Since vSphere API 5.1 |
| vrNfcNicSelectionSupported | xsd:boolean |
Indicates whether a dedicated nic can be selected for vSphere Replication
NFC traffic, i.e., from the VR server to the secondary host.
Since vSphere API 6.0 |
| vsanSupported* | xsd:boolean |
Indicates whether the host supports VSAN functionality. See HostVsanSystem Since vSphere API 5.5 |
| vStorageCapable | xsd:boolean |
Indicates whether the host supports vStorage Hardware
acceleration.
Since vSphere API 4.1 |
| Properties inherited from DynamicData | ||
| None |
| Top of page | Local Properties | ||
| Managed Object Types | Data Object Types | All Properties | All Methods |


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| ml-params |
Spark ML - ML Params |
| ml-persistence |
Spark ML - Model Persistence |
| ml-transform-methods |
Spark ML - Transform, fit, and predict methods (ml_ interface) |
| ml-tuning |
Spark ML - Tuning |
| ml_aft_survival_regression |
Spark ML - Survival Regression |
| ml_als |
Spark ML - ALS |
| ml_als_tidiers |
Tidying methods for Spark ML ALS |
| ml_approx_nearest_neighbors |
Utility functions for LSH models |
| ml_approx_similarity_join |
Utility functions for LSH models |
| ml_association_rules |
Frequent Pattern Mining - FPGrowth |
| ml_binary_classification_eval |
Spark ML - Evaluators |
| ml_binary_classification_evaluator |
Spark ML - Evaluators |
| ml_bisecting_kmeans |
Spark ML - Bisecting K-Means Clustering |
| ml_chisquare_test |
Chi-square hypothesis testing for categorical data. |
| ml_classification_eval |
Spark ML - Evaluators |
| ml_clustering_evaluator |
Spark ML - Clustering Evaluator |
| ml_compute_cost |
Spark ML - K-Means Clustering |
| ml_compute_silhouette_measure |
Spark ML - K-Means Clustering |
| ml_corr |
Compute correlation matrix |
| ml_cross_validator |
Spark ML - Tuning |
| ml_decision_tree |
Spark ML - Decision Trees |
| ml_decision_tree_classifier |
Spark ML - Decision Trees |
| ml_decision_tree_regressor |
Spark ML - Decision Trees |
| ml_default_stop_words |
Default stop words |
| ml_describe_topics |
Spark ML - Latent Dirichlet Allocation |
| ml_evaluate |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_evaluator |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_generalized_linear_regression_model |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_linear_regression_model |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_logistic_regression_model |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_model_classification |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_model_clustering |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_model_generalized_linear_regression |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_model_linear_regression |
Evaluate the Model on a Validation Set |
| ml_evaluate.ml_model_logistic_regression |
Evaluate the Model on a Validation Set |
| ml_evaluator |
Spark ML - Evaluators |
| ml_feature_importances |
Spark ML - Feature Importance for Tree Models |
| ml_find_synonyms |
Feature Transformation - Word2Vec (Estimator) |
| ml_fit |
Spark ML - Transform, fit, and predict methods (ml_ interface) |
| ml_fit_and_transform |
Spark ML - Transform, fit, and predict methods (ml_ interface) |
| ml_fpgrowth |
Frequent Pattern Mining - FPGrowth |
| ml_freq_itemsets |
Frequent Pattern Mining - FPGrowth |
| ml_freq_seq_patterns |
Frequent Pattern Mining - PrefixSpan |
| ml_gaussian_mixture |
Spark ML - Gaussian Mixture clustering. |
| ml_gbt_classifier |
Spark ML - Gradient Boosted Trees |
| ml_gbt_regressor |
Spark ML - Gradient Boosted Trees |
| ml_generalized_linear_regression |
Spark ML - Generalized Linear Regression |
| ml_glm_tidiers |
Tidying methods for Spark ML linear models |
| ml_gradient_boosted_trees |
Spark ML - Gradient Boosted Trees |
| ml_isotonic_regression |
Spark ML - Isotonic Regression |
| ml_isotonic_regression_tidiers |
Tidying methods for Spark ML Isotonic Regression |
| ml_is_set |
Spark ML - ML Params |
| ml_kmeans |
Spark ML - K-Means Clustering |
| ml_kmeans_cluster_eval |
Evaluate a K-mean clustering |
| ml_labels |
Feature Transformation - StringIndexer (Estimator) |
| ml_lda |
Spark ML - Latent Dirichlet Allocation |
| ml_lda_tidiers |
Tidying methods for Spark ML LDA models |
| ml_linear_regression |
Spark ML - Linear Regression |
| ml_linear_svc |
Spark ML - LinearSVC |
| ml_linear_svc_tidiers |
Tidying methods for Spark ML linear svc |
| ml_load |
Spark ML - Model Persistence |
| ml_logistic_regression |
Spark ML - Logistic Regression |
| ml_logistic_regression_tidiers |
Tidying methods for Spark ML Logistic Regression |
| ml_log_likelihood |
Spark ML - Latent Dirichlet Allocation |
| ml_log_perplexity |
Spark ML - Latent Dirichlet Allocation |
| ml_model_data |
Extracts data associated with a Spark ML model |
| ml_multiclass_classification_evaluator |
Spark ML - Evaluators |
| ml_multilayer_perceptron |
Spark ML - Multilayer Perceptron |
| ml_multilayer_perceptron_classifier |
Spark ML - Multilayer Perceptron |
| ml_multilayer_perceptron_tidiers |
Tidying methods for Spark ML MLP |
| ml_naive_bayes |
Spark ML - Naive-Bayes |
| ml_naive_bayes_tidiers |
Tidying methods for Spark ML Naive Bayes |
| ml_one_vs_rest |
Spark ML - OneVsRest |
| ml_param |
Spark ML - ML Params |
| ml_params |
Spark ML - ML Params |
| ml_param_map |
Spark ML - ML Params |
| ml_pca |
Feature Transformation - PCA (Estimator) |
| ml_pca_tidiers |
Tidying methods for Spark ML Principal Component Analysis |
| ml_pipeline |
Spark ML - Pipelines |
| ml_power_iteration |
Spark ML - Power Iteration Clustering |
| ml_predict |
Spark ML - Transform, fit, and predict methods (ml_ interface) |
| ml_predict.ml_model_classification |
Spark ML - Transform, fit, and predict methods (ml_ interface) |
| ml_prefixspan |
Frequent Pattern Mining - PrefixSpan |
| ml_random_forest |
Spark ML - Random Forest |
| ml_random_forest_classifier |
Spark ML - Random Forest |
| ml_random_forest_regressor |
Spark ML - Random Forest |
| ml_recommend |
Spark ML - ALS |
| ml_regression_evaluator |
Spark ML - Evaluators |
| ml_save |
Spark ML - Model Persistence |
| ml_save.ml_model |
Spark ML - Model Persistence |
| ml_stage |
Spark ML - Pipeline stage extraction |
| ml_stages |
Spark ML - Pipeline stage extraction |
| ml_sub_models |
Spark ML - Tuning |
| ml_summary |
Spark ML - Extraction of summary metrics |
| ml_survival_regression |
Spark ML - Survival Regression |
| ml_survival_regression_tidiers |
Tidying methods for Spark ML Survival Regression |
| ml_topics_matrix |
Spark ML - Latent Dirichlet Allocation |
| ml_train_validation_split |
Spark ML - Tuning |
| ml_transform |
Spark ML - Transform, fit, and predict methods (ml_ interface) |
| ml_tree_feature_importance |
Spark ML - Feature Importance for Tree Models |
| ml_tree_tidiers |
Tidying methods for Spark ML tree models |
| ml_uid |
Spark ML - UID |
| ml_unsupervised_tidiers |
Tidying methods for Spark ML unsupervised models |
| ml_validation_metrics |
Spark ML - Tuning |
| ml_vocabulary |
Feature Transformation - CountVectorizer (Estimator) |
| mutate |
Mutate |
| sdf-saveload |
Save / Load a Spark DataFrame |
| sdf-transform-methods |
Spark ML - Transform, fit, and predict methods (sdf_ interface) |
| sdf_along |
Create DataFrame for along Object |
| sdf_bind |
Bind multiple Spark DataFrames by row and column |
| sdf_bind_cols |
Bind multiple Spark DataFrames by row and column |
| sdf_bind_rows |
Bind multiple Spark DataFrames by row and column |
| sdf_broadcast |
Broadcast hint |
| sdf_checkpoint |
Checkpoint a Spark DataFrame |
| sdf_coalesce |
Coalesces a Spark DataFrame |
| sdf_collect |
Collect a Spark DataFrame into R. |
| sdf_copy_to |
Copy an Object into Spark |
| sdf_crosstab |
Cross Tabulation |
| sdf_debug_string |
Debug Info for Spark DataFrame |
| sdf_describe |
Compute summary statistics for columns of a data frame |
| sdf_dim |
Support for Dimension Operations |
| sdf_distinct |
Invoke distinct on a Spark DataFrame |
| sdf_drop_duplicates |
Remove duplicates from a Spark DataFrame |
| sdf_expand_grid |
Create a Spark dataframe containing all combinations of inputs |
| sdf_fit |
Spark ML - Transform, fit, and predict methods (sdf_ interface) |
| sdf_fit_and_transform |
Spark ML - Transform, fit, and predict methods (sdf_ interface) |
| sdf_from_avro |
Convert column(s) from avro format |
| sdf_import |
Copy an Object into Spark |
| sdf_is_streaming |
Spark DataFrame is Streaming |
| sdf_last_index |
Returns the last index of a Spark DataFrame |
| sdf_len |
Create DataFrame for Length |
| sdf_load_parquet |
Save / Load a Spark DataFrame |
| sdf_load_table |
Save / Load a Spark DataFrame |
| sdf_ncol |
Support for Dimension Operations |
| sdf_nrow |
Support for Dimension Operations |
| sdf_num_partitions |
Gets number of partitions of a Spark DataFrame |
| sdf_partition |
Partition a Spark Dataframe |
| sdf_partition_sizes |
Compute the number of records within each partition of a Spark DataFrame |
| sdf_persist |
Persist a Spark DataFrame |
| sdf_pivot |
Pivot a Spark DataFrame |
| sdf_predict |
Spark ML - Transform, fit, and predict methods (sdf_ interface) |
| sdf_project |
Project features onto principal components |
| sdf_quantile |
Compute (Approximate) Quantiles with a Spark DataFrame |
| sdf_random_split |
Partition a Spark Dataframe |
| sdf_rbeta |
Generate random samples from a Beta distribution |
| sdf_rbinom |
Generate random samples from a binomial distribution |
| sdf_rcauchy |
Generate random samples from a Cauchy distribution |
| sdf_rchisq |
Generate random samples from a chi-squared distribution |
| sdf_read_column |
Read a Column from a Spark DataFrame |
| sdf_register |
Register a Spark DataFrame |
| sdf_repartition |
Repartition a Spark DataFrame |
| sdf_residuals |
Model Residuals |
| sdf_residuals.ml_model_generalized_linear_regression |
Model Residuals |
| sdf_residuals.ml_model_linear_regression |
Model Residuals |
| sdf_rexp |
Generate random samples from an exponential distribution |
| sdf_rgamma |
Generate random samples from a Gamma distribution |
| sdf_rgeom |
Generate random samples from a geometric distribution |
| sdf_rhyper |
Generate random samples from a hypergeometric distribution |
| sdf_rlnorm |
Generate random samples from a log normal distribution |
| sdf_rnorm |
Generate random samples from the standard normal distribution |
| sdf_rpois |
Generate random samples from a Poisson distribution |
| sdf_rt |
Generate random samples from a t-distribution |
| sdf_runif |
Generate random samples from the uniform distribution U(0, 1). |
| sdf_rweibull |
Generate random samples from a Weibull distribution. |
| sdf_sample |
Randomly Sample Rows from a Spark DataFrame |
| sdf_save_parquet |
Save / Load a Spark DataFrame |
| sdf_save_table |
Save / Load a Spark DataFrame |
| sdf_schema |
Read the Schema of a Spark DataFrame |
| sdf_separate_column |
Separate a Vector Column into Scalar Columns |
| sdf_seq |
Create DataFrame for Range |
| sdf_sort |
Sort a Spark DataFrame |
| sdf_sql |
Spark DataFrame from SQL |
| sdf_to_avro |
Convert column(s) to avro format |
| sdf_transform |
Spark ML - Transform, fit, and predict methods (sdf_ interface) |
| sdf_unnest_longer |
Unnest longer |
| sdf_unnest_wider |
Unnest wider |
| sdf_weighted_sample |
Perform Weighted Random Sampling on a Spark DataFrame |
| sdf_with_sequential_id |
Add a Sequential ID Column to a Spark DataFrame |
| sdf_with_unique_id |
Add a Unique ID Column to a Spark DataFrame |
| select |
Select |
| separate |
Separate |
| spark-api |
Access the Spark API |
| spark-connections |
Manage Spark Connections |
| spark_adaptive_query_execution |
Retrieves or sets status of Spark AQE |
| spark_advisory_shuffle_partition_size |
Retrieves or sets advisory size of the shuffle partition |
| spark_apply |
Apply an R Function in Spark |
| spark_apply_bundle |
Create Bundle for Spark Apply |
| spark_apply_log |
Log Writer for Spark Apply |
| spark_auto_broadcast_join_threshold |
Retrieves or sets the auto broadcast join threshold |
| spark_available_versions |
Download and install various versions of Spark |
| spark_coalesce_initial_num_partitions |
Retrieves or sets initial number of shuffle partitions before coalescing |
| spark_coalesce_min_num_partitions |
Retrieves or sets the minimum number of shuffle partitions after coalescing |
| spark_coalesce_shuffle_partitions |
Retrieves or sets whether coalescing contiguous shuffle partitions is enabled |
| spark_compilation_spec |
Define a Spark Compilation Specification |
| spark_config |
Read Spark Configuration |
| spark_config_kubernetes |
Kubernetes Configuration |
| spark_config_packages |
Creates Spark Configuration |
| spark_config_settings |
Retrieve Available Settings |
| spark_connect |
Manage Spark Connections |
| spark_connection |
Retrieve the Spark Connection Associated with an R Object |
| spark_connection-class |
spark_connection class |
| spark_connection_find |
Find Spark Connection |
| spark_connection_is_open |
Manage Spark Connections |
| spark_context |
Access the Spark API |
| spark_context_config |
Runtime configuration interface for the Spark Context. |
| spark_dataframe |
Retrieve a Spark DataFrame |
| spark_default_compilation_spec |
Default Compilation Specification for Spark Extensions |
| spark_dependency |
Define a Spark dependency |
| spark_dependency_fallback |
Fallback to Spark Dependency |
| spark_disconnect |
Manage Spark Connections |
| spark_disconnect_all |
Manage Spark Connections |
| spark_extension |
Create Spark Extension |
| spark_get_checkpoint_dir |
Set/Get Spark checkpoint directory |
| spark_home_set |
Set the SPARK_HOME environment variable |
| spark_install |
Download and install various versions of Spark |
| spark_installed_versions |
Download and install various versions of Spark |
| spark_install_dir |
Download and install various versions of Spark |
| spark_install_tar |
Download and install various versions of Spark |
| spark_jobj |
Retrieve a Spark JVM Object Reference |
| spark_jobj-class |
spark_jobj class |
| spark_load_table |
Reads from a Spark Table into a Spark DataFrame. |
| spark_log |
View Entries in the Spark Log |
| spark_read |
Read file(s) into a Spark DataFrame using a custom reader |
| spark_read_avro |
Read Apache Avro data into a Spark DataFrame. |
| spark_read_binary |
Read binary data into a Spark DataFrame. |
| spark_read_csv |
Read a CSV file into a Spark DataFrame |
| spark_read_delta |
Read from Delta Lake into a Spark DataFrame. |
| spark_read_image |
Read image data into a Spark DataFrame. |
| spark_read_jdbc |
Read from JDBC connection into a Spark DataFrame. |
| spark_read_json |
Read a JSON file into a Spark DataFrame |
| spark_read_libsvm |
Read libsvm file into a Spark DataFrame. |
| spark_read_orc |
Read a ORC file into a Spark DataFrame |
| spark_read_parquet |
Read a Parquet file into a Spark DataFrame |
| spark_read_source |
Read from a generic source into a Spark DataFrame. |
| spark_read_table |
Reads from a Spark Table into a Spark DataFrame. |
| spark_read_text |
Read a Text file into a Spark DataFrame |
| spark_save_table |
Saves a Spark DataFrame as a Spark table |
| spark_session |
Access the Spark API |
| spark_session_config |
Runtime configuration interface for the Spark Session |
| spark_set_checkpoint_dir |
Set/Get Spark checkpoint directory |
| spark_statistical_routines |
Generate random samples from some distribution |
| spark_submit |
Manage Spark Connections |
| spark_table_name |
Generate a Table Name from Expression |
| spark_uninstall |
Download and install various versions of Spark |
| spark_version |
Get the Spark Version Associated with a Spark Connection |
| spark_version_from_home |
Get the Spark Version Associated with a Spark Installation |
| spark_web |
Open the Spark web interface |
| spark_write |
Write Spark DataFrame to file using a custom writer |
| spark_write_avro |
Serialize a Spark DataFrame into Apache Avro format |
| spark_write_csv |
Write a Spark DataFrame to a CSV |
| spark_write_delta |
Writes a Spark DataFrame into Delta Lake |
| spark_write_jdbc |
Writes a Spark DataFrame into a JDBC table |
| spark_write_json |
Write a Spark DataFrame to a JSON file |
| spark_write_orc |
Write a Spark DataFrame to a ORC file |
| spark_write_parquet |
Write a Spark DataFrame to a Parquet file |
| spark_write_rds |
Write Spark DataFrame to RDS files |
| spark_write_source |
Writes a Spark DataFrame into a generic source |
| spark_write_table |
Writes a Spark DataFrame into a Spark table |
| spark_write_text |
Write a Spark DataFrame to a Text file |
| src_databases |
Show database list |
| stream_find |
Find Stream |
| stream_generate_test |
Generate Test Stream |
| stream_id |
Spark Stream's Identifier |
| stream_lag |
Apply lag function to columns of a Spark Streaming DataFrame |
| stream_name |
Spark Stream's Name |
| stream_read_csv |
Read CSV Stream |
| stream_read_delta |
Read Delta Stream |
| stream_read_json |
Read JSON Stream |
| stream_read_kafka |
Read Kafka Stream |
| stream_read_orc |
Read ORC Stream |
| stream_read_parquet |
Read Parquet Stream |
| stream_read_socket |
Read Socket Stream |
| stream_read_text |
Read Text Stream |
| stream_render |
Render Stream |
| stream_stats |
Stream Statistics |
| stream_stop |
Stops a Spark Stream |
| stream_trigger_continuous |
Spark Stream Continuous Trigger |
| stream_trigger_interval |
Spark Stream Interval Trigger |
| stream_view |
View Stream |
| stream_watermark |
Watermark Stream |
| stream_write_console |
Write Console Stream |
| stream_write_csv |
Write CSV Stream |
| stream_write_delta |
Write Delta Stream |
| stream_write_json |
Write JSON Stream |
| stream_write_kafka |
Write Kafka Stream |
| stream_write_memory |
Write Memory Stream |
| stream_write_orc |
Write a ORC Stream |
| stream_write_parquet |
Write Parquet Stream |
| stream_write_text |
Write Text Stream |


The template starts out with the magic string:
() { :; };
Note that spaces are required. When not sanitized, the string essentially creates a function and passes it to bash. Execution is obtained by placing your commands at the end of the string.
() { :; }; /bin/sleep 10
It is recommended to use full paths when testing due to edge cases. Also make sure to test for blind execution.
Typically, anything being passed to the server should be tested for this string. Web application-specific testing can usually be started by injecting EVERY header. At minimum, make sure to test for injection in the following:
Hhost:
User-Agent:
Accept-Language:
Cookie:
Referer:
And feel free to add more. Nmap's shellshock nse will also generate a header like:
() { :; }; /bin/sleep 10: () { :; }; /bin/sleep 10
but this can error out some webservers and return false positives (ie 403 errors or such)


Package com.google.cloud.audit
Interface AuthorizationInfoOrBuilder
- All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder
,com.google.protobuf.MessageOrBuilder
- All Known Implementing Classes:
AuthorizationInfo
,AuthorizationInfo.Builder
public interface AuthorizationInfoOrBuilder
extends com.google.protobuf.MessageOrBuilder
-
Method Summary
Modifier and TypeMethodDescriptionboolean
Whether or not authorization for `resource` and `permission` was granted.The required IAM permission.com.google.protobuf.ByteString
The required IAM permission.The resource being accessed, as a REST-style or cloud resource string.Resource attributes used in IAM condition evaluation.Resource attributes used in IAM condition evaluation.com.google.protobuf.ByteString
The resource being accessed, as a REST-style or cloud resource string.boolean
Resource attributes used in IAM condition evaluation.Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder
isInitialized
Methods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
-
Method Details
-
getResource
String getResource()The resource being accessed, as a REST-style or cloud resource string. For example: bigquery.googleapis.com/projects/PROJECTID/datasets/DATASETID or projects/PROJECTID/datasets/DATASETID
string resource = 1;
- Returns:
- The resource.
-
getResourceBytes
com.google.protobuf.ByteString getResourceBytes()The resource being accessed, as a REST-style or cloud resource string. For example: bigquery.googleapis.com/projects/PROJECTID/datasets/DATASETID or projects/PROJECTID/datasets/DATASETID
string resource = 1;
- Returns:
- The bytes for resource.
-
getPermission
String getPermission()The required IAM permission.
string permission = 2;
- Returns:
- The permission.
-
getPermissionBytes
com.google.protobuf.ByteString getPermissionBytes()The required IAM permission.
string permission = 2;
- Returns:
- The bytes for permission.
-
getGranted
boolean getGranted()Whether or not authorization for `resource` and `permission` was granted.
bool granted = 3;
- Returns:
- The granted.
-
hasResourceAttributes
boolean hasResourceAttributes()Resource attributes used in IAM condition evaluation. This field contains resource attributes like resource type and resource name. To get the whole view of the attributes used in IAM condition evaluation, the user must also look into `AuditLog.request_metadata.request_attributes`.
.google.rpc.context.AttributeContext.Resource resource_attributes = 5;
- Returns:
- Whether the resourceAttributes field is set.
-
getResourceAttributes
AttributeContext.Resource getResourceAttributes()Resource attributes used in IAM condition evaluation. This field contains resource attributes like resource type and resource name. To get the whole view of the attributes used in IAM condition evaluation, the user must also look into `AuditLog.request_metadata.request_attributes`.
.google.rpc.context.AttributeContext.Resource resource_attributes = 5;
- Returns:
- The resourceAttributes.
-
getResourceAttributesOrBuilder
AttributeContext.ResourceOrBuilder getResourceAttributesOrBuilder()Resource attributes used in IAM condition evaluation. This field contains resource attributes like resource type and resource name. To get the whole view of the attributes used in IAM condition evaluation, the user must also look into `AuditLog.request_metadata.request_attributes`.
.google.rpc.context.AttributeContext.Resource resource_attributes = 5;
-


This appliction is based on the official block explorer for the EarthCoin currency. Therefore its functionality depends on the correct function of the block explorer.
Block counter In the top left conner of the application window you shloud see the current EarthCoin blockchain height and time from last sync. You can anytime click on the refresh icon to read new blocks. Please do not use this application if you do not see a block number or the time from last block is too high (more that several minutes). Such a case indicate a problem with the block explorer and you can lose you coins if you will use it despite this warning.
Wallet - In this window you can enter an EarthCoin address, your private key or your password for your brain-wallet. It is your responsibility to store the private key or password safely, this application will not remember it for you nor send them to any remote site. An autodetection should recognize EarthCoin addresses and private keys, other inputs are considered as passwords. Please check everytime if the correct indicator is lighting. If you use an EarthCoin address, the application switch itself to the read-only mode and you will not be able to send the EarthCoins.
Balance - In this window you can see the balance on your account. In the advanced mode you can see individual unspent transactions and you can select which one you want to spent. Only transactions with 6 or more confirmations in the blockchain are spendable.
Receive - Here you should see the QR code for your EarthCoin address. Optionally you can enter the requested amount of EarthCoins. The QR-code then show to your customer, who can scan it into his wallet for the payment.
Send - This window is avaiable only if you use your private key or brain-wallet password. There are two fields for destination addresses here by default. The first one is for a "go back" transaction. It is a feature of blockchain transactions that you have always to spent all coins from the source transaction, so if you wish only partial spent, you have to send the rest of the source amount to back your address. In advanced mode you can modify this address for the "go-back" transaction, but it is your responsibility to use the correct address for which you own the private key. Optionally you can add arbitrary number of addresses to which you want send to coins in a single transaction. You can also pay a transaction fee for speeding up your transaction and to support the network. If your device has a camera, you can scan the addresses from QR-codes. For QR-code scan please click on the camera icon in the appropriate row. Inputs are verified automatically and if everything is correct, the button "Send" will be enabled. After click on this button a summary of you transaction will be shown and then you can confirm it or cancel.
List of changes - version 1.1: - a faster library for QR-code scaning - address/private key can be scaned from a QR-code - added an icon for camera turn off after QR-code scan
List of changes - version 1.2: - switched to the new API v.2 - enabled transaction messages
Close


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nips nips2008 nips2008-220 nips2008-220-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Zhi Yang, Qi Zhao, Wentai Liu
Abstract: This paper presents a spike feature extraction algorithm that targets real-time spike sorting and facilitates miniaturized microchip implementation. The proposed algorithm has been evaluated on synthesized waveforms and experimentally recorded sequences. When compared with many spike sorting approaches our algorithm demonstrates improved speed, accuracy and allows unsupervised execution. A preliminary hardware implementation has been realized using an integrated microchip interfaced with a personal computer. 1
[1] Zumsteg ZS, Kemere C, O’Driscoll S, Santhanam G, Ahmed RE, Shenoy KV, et al. Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems. IEEE Trans Neural Syst Rehabil Eng. 2005 Sep;13(3):272–279.
[2] Lewicki MS. Bayesian modeling and classiﬁcation of neural signals. Advances in NIPS. 1994;p. 590–597.
[3] Vargas-Irwin C, Donoghue JP. Automated spike sorting using density grid contour clustering and subtractive waveform decomposition. J Neurosci Methods. 2007;164(1).
[4] Quian Quiroga R, Nadasdy Z, Ben-Shaul Y. Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 2004 Aug;16(8):1661–1687.
[5] Takahashi S, Sakurai Y. Coding of spatial information by soma and dendrite of pyramidal cells in the hippocampal CA1 of behaving rats. Eur J Neurosci Methods. 2007 Oct;26(7):2033–2045.
[6] Jolliffe IT. Principal Component Analysys. New York: Springer-Verlag. 2002;.
[7] Yang Z, Chen T, Liu W. A neuron signature based spike feature extraction algorithm for on-chip implementation. to Appear in Proc 30th Ann Int Conf IEEE EMBS. 2008 Aug;p. 4237–4240.
[8] Chen T, Yang Z, Liu W, Chen L. NEUSORT2.0: a multiple-channel neural signal processor with systolic array buffer and channel-interleaving processing schedule. to appear Proc 30th Ann Int Conf IEEE EMBS. 2008 Aug;p. 6652–6656.
[9] Chae M, Liu W, Yang Z, Chen T, Kim J, Sivaprakasam M, et al. A 128 channel 6mW wireless neural recording IC with on-the-ﬂy spike sorting and UWB transmitter. IEEE ISSCC 2008 Dig Tech Papers. 2008 Feb;7(6):241–261.
[10] Buzsaki G, Penttonen M, Nadasdy Z, Bragin A. Pattern and inhibition-dependent invasion of pyramidal cell dendrites by fast spikes in the hippocampus in vivo. Proc Natl Acad Sci USA. 1996 Sep;93(18):9921– 9925.
[11] Kaiser JF. On a simple algorithm to calculate the energy of a signal. In Proc IEEE Int Conf Acoustic Speech and Signal Processing. 1990;p. 381–384.
[12] Yang Z, Zhao Q, Liu W. Neural signal classiﬁcation using a simpliﬁed feature set with nonparametric clustering. to appear in Neurocomputing;.


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Code highlight is a feature with predefined styles for HTML, CSS, JavaScript and JSON. These styles are a customization for the languages supported by the library highlight.js. See the basic example:
@@include('includes/code-examples/reference/code-highlight/basic-usage.html')
See below the code highlight for each supported language:
@@include('includes/code-examples/reference/code-highlight/code-highlight.html')
@@include('includes/code-examples/reference/code-highlight/code-highlight.css')
@@include('includes/code-examples/reference/code-highlight/code-highlight.js')
@@include('includes/code-examples/reference/code-highlight/code-highlight.json')
@@include('includes/code-examples/reference/code-highlight/each-language.html')


You can reset the password for {email} by visiting this ppage
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--- layout: post title: Schwarzkopf Moisture kick shampoo tags: - Schwarzkopf status: publish type: post published: true meta: _edit_last: '2' _aioseop_title: Schwarzkopf Moisture kick shampoo review _aioseop_description: Schwarzkopf Moisture kick shampoo review _aioseop_keywords: Schwarzkopf Moisture kick shampoo review, moisture shampoo _graphene_slider_img: global _graphene_slider_imgurl: '' _graphene_show_addthis: global _yoast_wpseo_linkdex: '0' _thumbnail_id: '366' --- Was advised this by a stylist at Lakme studio, Bangalore. I had been using T.G.I. CATWALK Elevating shampoo for some time and it had made my hair ends dry. I have a oily scalp and dry hair, I have tough time selecting hair products and haven't found the best combination for me yet. Price: Rs. 540 for 250ml Products boasts of: It cleanses normal to dry or curly hair and infuses it with essential moisture. BC(Boncure) hairtherapy with Hyaluronic Acid Derivative re balances the moisture level of the hair. Aino Cell Rebuild technology recovers the hair's natural elasticity by strengthening the architecture in the hair cell. For soft, supple and nourished hair full of healthy shine and bounce. Application: Work into wet hair and rinse out Product contains: What I love about the product: