Note: failures are anticipated and checked for with assertions while errors are unanticipated.
Note: package statistics are not computed recursively, they only sum up all of its testsuites numbers.
|
Class |
Tests |
Errors |
Failures |
Skipped |
Success Rate |
Time |
|
MyEdgeStringerTest |
4 |
0 |
0 |
0 |
100% |
0.046 |
|
MyVertexStringerTest |
6 |
0 |
0 |
0 |
100% |
0.024 |
|
MyVertexPaintFunctionTest |
7 |
0 |
0 |
0 |
100% |
0.035 |
|
MyVertexShapeFunctionTest |
6 |
0 |
0 |
0 |
100% |
0.075 |
|
testGetLabelNotDependencyEdge |
0.005 |
|
testGetLabelDependencyEdge |
0.035 |
|
testGetLabelSuppressedScope |
0 |
|
testGetLabelNotSuppressedScope |
0.001 |
|
testOverviewTestEnvironment |
1.221 |
|
testOverviewTestEnvironmentIncludesSet |
0.163 |
|
testOverviewTestEnvironmentMultipleExlusionsSet |
0.157 |
|
testOverviewTestEnvironmentExlusionsSetEmpty |
0.178 |
|
testOverviewTestEnvironmentIncludesExclusionsSet |
0.14 |
|
getLabelNullArtifact |
0.002 |
|
getLabelNotArtifactVertex |
0 |
|
getLabelShort |
0.001 |
|
getLabelLong |
0.001 |
|
getVersionedLabel |
0 |
|
makeSureLongLabelOverridesVersioned |
0 |
|
getDrawPaint |
0.026 |
|
getFillPaint_nullParam |
0 |
|
getFillPaint_paramNotArtifactVertex |
0.001 |
|
getFillPaint_Level0 |
0 |
|
getFillPaint_Level1 |
0 |
|
getFillPaint_Level2 |
0.001 |
|
getFillPaint_Level3 |
0 |
|
getShape_nullParam |
0.025 |
|
getShape_paramNotArtifactVertex |
0.015 |
|
getShape_Level0 |
0.005 |
|
getShape_Level1 |
0 |
|
getShape_Level2 |
0 |
|
getShape_Level3 |
0 |
|
testBothNull |
0.087 |
|
testExcludeEmptyIncludesNull |
0 |
|
testExclusionsMatchIncludesNull |
0.006 |
|
testExcludeNotMatchIncludesNull |
0.001 |
|
testIncludeEmptyExcludesNull |
0 |
|
testIncludeMatchExcludesNull |
0.001 |
|
testIncludeNotMatchExcludesNull |
0 |
|
testScope |
0 |


Function of apply style using 'MPI' provides better 'HPC' environment on R. And this package supports long vector, can deal with slightly big data.
| Version: | 0.18-204 |
| Depends: | R (≥ 3.0.0) |
| Imports: | parallel |
| Published: | 2018-07-24 |
| Author: | Junji NAKANO and Ei-ji Nakama |
| Maintainer: | Ei-ji Nakama <nakama at ki.rim.or.jp> |
| License: | AGPL-3 |
| URL: | http://prs.ism.ac.jp/~nakama/Rhpc/ |
| NeedsCompilation: | yes |
| SystemRequirements: | R built as a shared or static library, 'MPI' library. |
| Materials: | README ChangeLog |
| In views: | HighPerformanceComputing |
| CRAN checks: | Rhpc results |
| Reference manual: | Rhpc.pdf |
| Package source: | Rhpc_0.18-204.tar.gz |
| Windows binaries: | r-devel: Rhpc_0.18-204.zip, r-release: Rhpc_0.18-204.zip, r-oldrel: Rhpc_0.18-204.zip |
| OS X binaries: | r-release: not available, r-oldrel: not available |
| Old sources: | Rhpc archive |
Please use the canonical form https://CRAN.R-project.org/package=Rhpc to link to this page.


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This list will help me organize everything I have to do today.
I have to finish the replica of Twitter and Trello.
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Take my puppy out for a walk.


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Maintaining Bug Fix Releases¶
Astropy releases, as recommended for most Python projects, follows a <major>.<minor>.<micro> version scheme, where the “micro” version is also known as a “bug fix” release. Bug fix releases should not change any user- visible interfaces. They should only fix bugs on the previous major/minor release and may also refactor internal APIs or include omissions from previous releases–that is, features that were documented to exist but were accidentally left out of the previous release.
Bug fix releases are typically managed by maintaining one or more bug fix branches separate from the master branch (the release procedure below discusses creating these branches). Typically, whenever an issue is fixed on the Astropy master branch a decision must be made whether this is a fix that should be included in the Astropy bug fix release. Usually the answer to this question is “yes”, though there are some issues that may not apply to the bug fix branch. For example, it is not necessary to backport a fix to a new feature that did not exist when the bug fix branch was first created. New features are never merged into the bug fix branch–only bug fixes; hence the name.
In rare cases a bug fix may be made directly into the bug fix branch without going into the master branch first. This may occur if a fix is made to a feature that has been removed or rewritten in the development version and no longer has the issue being fixed. However, depending on how critical the bug is it may be worth including in a bug fix release, as some users can be slow to upgrade to new major/micro versions due to API changes.
Issues are assigned to an Astropy release by way of the Milestone feature in the GitHub issue tracker. At any given time there are at least two versions under development: The next major/minor version, and the next bug fix release. For example, at the time of writing there are two release milestones open: v0.2.2 and v0.3.0. In this case, v0.2.2 is the next bug fix release and all issues that should include fixes in that release should be assigned that milestone. Any issues that implement new features would go into the v0.3.0 milestone–this is any work that goes in the master branch that should not be backported. For a more detailed set of guidelines on using milestones, see Using Milestones and Labels.
Backporting fixes from master¶
Most fixes are backported using the git cherry-pick command, which applies the diff from a single commit like a patch. For the sake of example, say the current bug fix branch is ‘v0.2.x’, and that a bug was fixed in master in a commit abcd1234. In order to backport the fix, simply checkout the v0.2.x branch (it’s also good to make sure it’s in sync with the main Astropy repository) and cherry-pick the appropriate commit:
$ git checkout v0.2.x
$ git pull upstream v0.2.x
$ git cherry-pick abcd1234
Sometimes a cherry-pick does not apply cleanly, since the bug fix branch represents a different line of development. This can be resolved like any other merge conflict: Edit the conflicted files by hand, and then run git commit and accept the default commit message. If the fix being cherry-picked has an associated changelog entry in a separate commit make sure to backport that as well.
What if the issue required more than one commit to fix? There are a few possibilities for this. The easiest is if the fix came in the form of a pull request that was merged into the master branch. Whenever GitHub merges a pull request it generates a merge commit in the master branch. This merge commit represents the full difference of all the commits in the pull request combined. What this means is that it is only necessary to cherry-pick the merge commit (this requires adding the -m 1 option to the cherry-pick command). For example, if 5678abcd is a merge commit:
$ git checkout v0.2.x
$ git pull upstream v0.2.x
$ git cherry-pick -m 1 5678abcd
In fact, because Astropy emphasizes a pull request-based workflow, this is the most common scenario for backporting bug fixes, and the one requiring the least thought. However, if you’re not dealing with backporting a fix that was not brought in as a pull request, read on.
See also
Merge commits and cherry picks for further explanation of the cherry-pick command and how it works with merge commits.
If not cherry-picking a merge commit there are still other options for dealing with multiple commits. The simplest, though potentially tedious, is to simply run the cherry-pick command once for each commit in the correct order. However, as of Git 1.7.2 it is possible to merge a range of commits like so:
$ git cherry-pick 1234abcd..56789def
This works fine so long as the commits you want to pick are actually congruous with each other. In most cases this will be the case, though some bug fixes will involve followup commits that need to back backported as well. Most bug fixes will have an issues associated with it in the issue tracker, so make sure to reference all commits related to that issue in the commit message. That way it’s harder for commits that need to be backported from getting lost.
Making fixes directly to the bug fix branch¶
As mentioned earlier in this section, in some cases a fix only applies to a bug fix release, and is not applicable in the mainline development. In this case there are two choices:
- An Astropy developer with commit access to the main Astropy repository may check out the bug fix branch and commit and push your fix directly.
- Preferable: You may also make a pull request through GitHub against the bug fix branch rather than against master. Normally when making a pull request from a branch on your fork to the main Astropy repository GitHub compares your branch to Astropy’s master. If you look on the left-hand side of the pull request page, under “base repo: astropy/astropy” there is a drop-down list labeled “base branch: master”. You can click on this drop-down and instead select the bug fix branch (“v0.2.x” for example). Then GitHub will instead compare your fix against that branch, and merge into that branch when the PR is accepted.
Preparing the bug fix branch for release¶
There are two primary steps that need to be taken before creating a bug fix release (the rest of the procedure is the same as any other release as described in the release procedure below).
- Any existing fixes to the issues assigned to the current bug fix release milestone, or labeled with the relevant “backport-x.y.z” label must be merged into the bug fix branch.
- The Astropy changelog must be updated to list all issues–especially user-visible issues–fixed for the current release. The changelog should be updated in the master branch, and then merged into the bug fix branch. Most issues should already have changelog entries for them. But it’s typical to forget this, so if doesn’t exist yet please add one in the process of backporting. See Updating and Maintaining the Changelog for more details.
To aid in this process there is a script called suggest_backports.py at https://gist.github.com/iguananaut/4497178. The script is not perfect and still needs a little work, but it will get most of the work done. For example, if the current bug fix branch is called ‘v0.2.x’ run it like so:
$ suggest_backports.py astropy astropy v0.2.x -f backport.sh
This will search GitHub for all issues assigned to the next bug fix release milestone that’s associated with the given bug fix branch (‘v0.2.2’ for example), find the commits that fix those issues, and will generate a shell script called backport.sh containing all the git cherry-pick commands to backport all those fixes.
The suggest_backports.py script will typically take a couple minutes to run, but once it’s done simply execute the generated script from within your local clone of the Astropy repository:
$ ./backport.sh
This will checkout the appropriate bug fix branch (‘v0.2.x’ in this example), do a git pull upstream v0.2.x to make sure it’s up to date, and then start doing cherry-picks into the bug fix branch.
Note
As discussed earlier, cherry-pick may result in merge conflicts. If this occurs, the backport.sh script will exit and the conflict should be resolved manually, followed by running git commit. To resume the backport.sh script after the merge conflict, it is currently necessary to edit the script to either remove or comment out the git cherry-pick commands that already ran successfully.
The author of the script hopes to improve it in the future to add git rebase like functionality, such that running backport.sh --continue or backport.sh --skip will be possible in such cases.
Warning
It has also been noted that the suggest_backports.py script is not perfect, and can either miss issues that need to be backported, and in some cases can report false positives.
It’s always a good idea before finalizing a bug fix release to look on GitHub through the list of closed issues in the release milestone and check that each one has a fix in the bug fix branch. Usually a quick way to do this is for each issue to run:
$ git log --oneline <bugfix-branch> | grep #<issue>
Most fixes will mention their related issue in the commit message, so this tends to be pretty reliable. Some issues won’t show up in the commit log, however, as their fix is in a separate pull request. Usually GitHub makes this clear by cross-referencing the issue with its PR. A future version of the suggest_backports.py script will perform this check automatically.
Finally, not all issues assigned to a release milestone need to be fixed before making that release. Usually, in the interest of getting a release with existing fixes out within some schedule, it’s best to triage issues that won’t be fixed soon to a new release milestone. If the upcoming bug fix release is ‘v0.2.2’, then go ahead and create a ‘v0.2.3’ milestone and reassign to it any issues that you don’t expect to be fixed in time for ‘v0.2.2’.
Release Procedure¶
The automated portion of the Astropy release procedure uses zest.releaser to create the tag and update the version. zest.releaser is extendable through hook functions–Astropy already includes a couple hook functions to modify the default behavior, but future releases may be further automated through the implementation of additional hook functions. In order to use the hooks, Astropy itself must be installed alongside zest.releaser. It is recommended to create a virtualenv specifically for this purpose.
This may seem like a lot of steps, but most of them won’t be necessary to repeat for each release. The advantage of using an automated or semi-automated procedure is that ensures a consistent release process each time.
Update the list of contributors in the creditsandlicense.rst file. The easiest way to check this is do:
$ git shortlog -sAnd just add anyone from that list who isn’t already credited.
Install virtualenv if you don’t already have it. See the linked virtualenv documentation for details. Also, make sure that you have cython installed, as you will need it to generate the .c files needed for the release.
Create and activate a virtualenv:
$ virtualenv --system-site-packages --distribute astropy-release $ source astropy-release/bin/activateObtain a clean version of the Astropy repository. That is, one where you don’t have any intermediate build files. Either use a fresh git clone or do git clean -dfx.
Be sure you’re the “master” branch or, for a bug fix release, on the appropriate bug fix branch. For example, if releasing version 0.2.2 make sure to:
$ git checkout v0.2.xNow install Astropy into the virtualenv:
$ python setup.py installThis is necessary for two reasons. First, the entry points for the releaser scripts need to be available, and these are in the Astropy package. Second, the build process will generate .c files from the Cython .pyx files, and the .c files are necessary for the source distribution.
Install zest.releaser into the virtualenv; use --upgrade --force to ensure that the latest version is installed in the virtualenv (if you’re running a csh variant make sure to run rehash afterwards too):
$ pip install zest.releaser --upgrade --forceEnsure that all changes to the code have been committed, then start the release by running:
$ fullreleaseYou will be asked to enter the version to be released. Press enter to accept the default (which will normally be correct) or enter a specific version string. A diff will then be shown of CHANGES.rst and setup.py showing that a release date has been added to the changelog, and that the version has been updated in setup.py. Enter ‘Y’ when asked to commit these changes.
You will then be shown the command that will be run to tag the release. Enter ‘Y’ to confirm and run the command.
When asked “Check out the tag (for tweaks or pypi/distutils server upload)” enter ‘N’: zest.releaser does not offer enough control yet over how the register and upload are performed so we will do this manually until the release scripts have been improved.
You will be asked to enter a new development version. Normally the next logical version will be selected–press enter to accept the default, or enter a specific version string. Do not add ”.dev” to the version, as this will be appended automatically (ignore the message that says ”.dev0 will be appended”–it will actually be ”.dev” without the 0). For example, if the just-released version was “0.1” the default next version will be “0.2”. If we want the next version to be, say “1.0” then that must be entered manually.
You will be shown a diff of CHANGES.rst showing that a new section has been added for the new development version, and showing that the version has been updated in setup.py. Enter ‘Y’ to commit these changes.
When asked to push the changes to a remote repository, enter ‘Y’. This should complete the portion of the process that’s automated at this point.
Check out the tag of the released version. For example:
$ git checkout v0.1Create the source distribution by doing:
$ python setup.py sdistCopy the produced .tar.gz somewhere and verify that you can unpack it, build it, and get all the tests to pass. It would be best to create a new virtualenv in which to do this.
Register the release on PyPI with:
$ python setup.py registerUpload the source distribution to PyPI; this is preceded by re-running the sdist command, which is necessary for the upload command to know which distribution to upload:
$ python setup.py sdist uploadUpdate the website to reflect the fact there is now a stable release.
Update Readthedocs so that it builds docs for the corresponding github tag, and set the default page to the new release.
If this was a major/minor release (not a bug fix release) create a bug fix branch for this line of release. That is, if the version just released was “v<major>.<minor>.0”, create bug fix branch with the name “v<major>.<minor>.x”. Starting from the commit tagged as the release, just checkout a new branch and push it to the remote server. For example, after releasing version 0.3, do:
$ git checkout -b v0.3.xThen edit setup.py so that the VERSION variable is '0.3.1.dev', and commit that change. Then, do:
$ git push upstream v0.3.xNote
You may need to replace upstream here with astropy or whatever remote name you use for the main astropy repository.The purpose of this branch is for creating bug fix releases like “0.3.1” and “0.3.2”, while allowing development of new features to continue in the master branch. Only changesets that fix bugs without making significant API changes should be merged to the bug fix branches.
- Create a bug fix label on GitHub; this should have the same name as the just created bug fix branch prepended with “backport-”. For the previous example this would be “backport-0.3.x” This label should be applied to all issues that should be backported to the bug fix branch. Also create a milestone for the next bug fix release if it hasn’t been made already.
Creating a MacOS X Installer on a DMG¶
The bdist_dmg command can be used to create a .dmg disk image for MacOS X with a .pkg installer. In order to do this, you will need to ensure that you have the following dependencies installed:
To create a .dmg file, run:
python setup.py bdist_dmg
Note that for the actual release version, you should do this with the Python distribution from python.org (not e.g. MacPorts, EPD, etc.). The best way to ensure maximum compatibility is to make sure that Python and Numpy are installed into /Library/Frameworks/Python.framework using the latest stable .dmg installers available for those packages. In addition, the .dmg should be build on a MacOS 10.6 system, to ensure compatibility with 10.6, 10.7, and 10.8.
Before distributing, you should test out an installation of Python, Numpy, and Astropy from scratch using the .dmg installers, preferably on a clean virtual machine.


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제품 1
Product 1 description
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12 Bugs
There is a slight difference in syntax between ML-Lex and ML-Yacc.
In ML-Lex, semantic actions must be followed by a semicolon but
in ML-Yacc semantic actions cannot be followed by a semicolon.
The syntax should be the same. ML-Lex also produces structures with
two different signatures, but it should produce structures with just
one signature. This would simplify some things.


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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | ENUM CONSTANTS | FIELD | METHOD | DETAIL: ENUM CONSTANTS | FIELD | METHOD |
java.lang.Object java.lang.Enum<DetonationResult> edu.nps.moves.disenum.DetonationResult
public enum DetonationResult
Enumeration values for DetonationResult The enumeration values are generated from the SISO DIS XML EBV document (R35), which was obtained from http://discussions.sisostds.org/default.asp?action=10&fd=31
Note that this has two ways to look up an enumerated instance from a value: a fast but brittle array lookup, and a slower and more garbage-intensive, but safer, method. if you want to minimize memory use, get rid of one or the other.
Copyright 2008-2009. This work is licensed under the BSD license, available at http://www.movesinstitute.org/licenses
| Field Summary | |
|---|---|
java.lang.String |
description
Text/english description of the enumerated value |
static DetonationResult[] |
lookup
This is an array, with each slot corresponding to an enumerated value. |
int |
value
The enumerated value |
| Method Summary | |
|---|---|
static boolean |
enumerationForValueExists(int aVal)
Returns true if there is an enumerated instance for this value, false otherwise. |
java.lang.String |
getDescription()
Returns a text descriptioni for this enumerated value. |
static java.lang.String |
getDescriptionForValue(int aVal)
Returns the string description associated with the enumerated instance with this value. |
static DetonationResult |
getEnumerationForValue(int aVal)
Returns the enumerated instance with this value. |
int |
getValue()
Returns the enumerated value for this enumeration |
static DetonationResult |
valueOf(java.lang.String name)
Returns the enum constant of this type with the specified name. |
static DetonationResult[] |
values()
Returns an array containing the constants of this enum type, in the order they're declared. |
| Methods inherited from class java.lang.Enum |
|---|
clone, compareTo, equals, getDeclaringClass, hashCode, name, ordinal, toString, valueOf |
| Methods inherited from class java.lang.Object |
|---|
finalize, getClass, notify, notifyAll, wait, wait, wait |
| Enum Constant Detail |
|---|
public static final DetonationResult OTHER
public static final DetonationResult ENTITY_IMPACT
public static final DetonationResult ENTITY_PROXIMATE_DETONATION
public static final DetonationResult GROUND_IMPACT
public static final DetonationResult GROUND_PROXIMATE_DETONATION
public static final DetonationResult DETONATION
public static final DetonationResult NONE_OR_NO_DETONATION_DUD
public static final DetonationResult HE_HIT_SMALL
public static final DetonationResult HE_HIT_MEDIUM
public static final DetonationResult HE_HIT_LARGE
public static final DetonationResult ARMOR_PIERCING_HIT
public static final DetonationResult DIRT_BLAST_SMALL
public static final DetonationResult DIRT_BLAST_MEDIUM
public static final DetonationResult DIRT_BLAST_LARGE
public static final DetonationResult WATER_BLAST_SMALL
public static final DetonationResult WATER_BLAST_MEDIUM
public static final DetonationResult WATER_BLAST_LARGE
public static final DetonationResult AIR_HIT
public static final DetonationResult BUILDING_HIT_SMALL
public static final DetonationResult BUILDING_HIT_MEDIUM
public static final DetonationResult BUILDING_HIT_LARGE
public static final DetonationResult MINE_CLEARING_LINE_CHARGE
public static final DetonationResult ENVIRONMENT_OBJECT_IMPACT
public static final DetonationResult ENVIRONMENT_OBJECT_PROXIMATE_DETONATION
public static final DetonationResult WATER_IMPACT
public static final DetonationResult AIR_BURST
public static final DetonationResult KILL_WITH_FRAGMENT_TYPE_1
public static final DetonationResult KILL_WITH_FRAGMENT_TYPE_2
public static final DetonationResult KILL_WITH_FRAGMENT_TYPE_3
public static final DetonationResult KILL_WITH_FRAGMENT_TYPE_1_AFTER_FLY_OUT_FAILURE
public static final DetonationResult KILL_WITH_FRAGMENT_TYPE_2_AFTER_FLY_OUT_FAILURE
public static final DetonationResult MISS_DUE_TO_FLY_OUT_FAILURE
public static final DetonationResult MISS_DUE_TO_END_GAME_FAILURE
public static final DetonationResult MISS_DUE_TO_FLY_OUT_AND_END_GAME_FAILURE
| Field Detail |
|---|
public final int value
public final java.lang.String description
public static DetonationResult[] lookup
Use as DetonationResult.lookup[aVal] to get the enumeration that corresponds to a value.
In non-realtime environments, the prefered method is getEnumerationForValue().
| Method Detail |
|---|
public static final DetonationResult[] values()
for(DetonationResult c : DetonationResult.values()) System.out.println(c);
public static DetonationResult valueOf(java.lang.String name)
name
- the name of the enum constant to be returned.
java.lang.IllegalArgumentException
- if this enum type has no constant
with the specified namepublic static java.lang.String getDescriptionForValue(int aVal)
public static DetonationResult getEnumerationForValue(int aVal) throws edu.nps.moves.siso.EnumNotFoundException
edu.nps.moves.siso.EnumNotFoundException
public static boolean enumerationForValueExists(int aVal)
public int getValue()
public java.lang.String getDescription()
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | ENUM CONSTANTS | FIELD | METHOD | DETAIL: ENUM CONSTANTS | FIELD | METHOD |


Finds optimal designs for nonlinear models using a metaheuristic algorithm called imperialist competitive algorithm ICA. See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014>.
| Version: | 0.9.7 |
| Depends: | R (≥ 3.1.3) |
| Imports: | Rcpp, nloptr, stats, graphics, grDevices, cubature, sn, mnormt, methods |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | rgl, lattice, knitr, rmarkdown |
| Published: | 2018-08-05 |
| Author: | Ehsan Masoudi [aut, cre], Heinz Holling [aut], Weng Kee Wong [aut] |
| Maintainer: | Ehsan Masoudi <ehsan.masoudi at wwu.de> |
| BugReports: | https://github.com/ehsan66/ICAOD/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/ehsan66/ICAOD |
| NeedsCompilation: | yes |
| In views: | ExperimentalDesign |
| CRAN checks: | ICAOD results |
| Reference manual: | ICAOD.pdf |
| Package source: | ICAOD_0.9.7.tar.gz |
| Windows binaries: | r-devel: ICAOD_0.9.7.zip, r-release: ICAOD_0.9.7.zip, r-oldrel: ICAOD_0.9.7.zip |
| OS X binaries: | r-release: ICAOD_0.9.7.tgz, r-oldrel: ICAOD_0.9.7.tgz |
| Old sources: | ICAOD archive |
Please use the canonical form https://CRAN.R-project.org/package=ICAOD to link to this page.


Frontend application
Short description
Create a responsive web frontend application for managing the articles and comments.
Registered user can post Artiles (artiles are up to 255 characters) and post comments on their and
someone else's articles (comments are up to 255 characters).
Specification
General requirements
- Responsive UI, based on Bootstrap 3
- App should support Desktop and mobile at least
- App should consume specified API
- Please make frontend user registration form with frontend form validation.
User stories
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D.rabbani
27-Nov-18 02:09 PM
sorry i mean the buffer cache
i have set the cron jobs on the buffer cache
to clear up after every 8 hours
as it was the issue before as well
so we had to restart the server
on first node it says ht.20463 error validating winning blocktime<PHONE_NUMBER> -><PHONE_NUMBER>.00000 eligible.1543328146 test prior
tried the zap wallettxes as well
both nodes are on set generate true 0


public class DateLib
extends java.lang.Object
| Constructor and Description |
|---|
DateLib() |
| Modifier and Type | Method and Description |
|---|---|
static void |
addDays(java.util.Date date,
int days)
Advances the given Date object by a number of days
|
static void |
addHours(java.util.Date date,
int hours)
Advances the given Date object by a number of hours
|
static void |
addMinutes(java.util.Date date,
int minutes)
Advances the given Date object by a number of minutes
|
static void |
addMonths(java.util.Date date,
int months)
Advances the given Date object by a number of months
|
static void |
addSeconds(java.util.Date date,
int seconds)
Advances the given Date object by a number of seconds
|
static void |
addYears(java.util.Date date,
int years)
Advances the given Date object by a number of years
|
static int |
compare(java.util.Date date1,
java.util.Date date2)
Compares two Date Objects, down to the second
|
static java.util.Date |
copyDateObject(java.util.Date date)
Creates and returns a new Date object, which is an identical copy of the parameter Date object
Eliminates the extra milliseconds after the last second
|
static java.lang.String |
dateAsString(java.util.Date date)
Returns a String representation for a given Date object
|
static boolean |
equals(java.util.Date date1,
java.util.Date date2)
Compares two Date Objects for equality, down to the second
|
static java.util.Date |
getDateObject()
Creates and returns a new Date object which refers to the system time at the time of creation
Eliminates the extra milliseconds after the last second
|
static java.util.Date |
getDateObject(int day,
int month,
int year)
Creates and returns a new Date object with the specified values for day, month & year
Eliminates the extra milliseconds after the last second
Hour defaults to 12 (24-hour based day)
Minute defaults to 0
Second defaults to 0
|
static java.util.Date |
getDateObject(int day,
int month,
int year,
int hour,
int minute,
int second)
Creates and returns a new Date object with the specified values for day, month, year, hour, minute & second
Eliminates the extra milliseconds after the last second
|
static int |
getWeekday(java.util.Date date)
Returns an integer representing the day of the week for the specified date
Sunday = 1, Monday = 2, Tuesday = 3, Wednesday = 4, Thursday = 5, Friday = 6, Saturday = 7
|
static boolean |
preceeds(java.util.Date date1,
java.util.Date date2)
Checks if the first Date object represents an earlier point in time than the second
|
static boolean |
succeeds(java.util.Date date1,
java.util.Date date2)
Checks if the first Date object represents a later point in time than the second
|
public static java.util.Date getDateObject()
public static java.util.Date getDateObject(int day, int month, int year)
day
- day of monthmonth
- month of year (1 for January, 2 for February, etc.)year
- yearpublic static java.util.Date getDateObject(int day, int month, int year, int hour, int minute, int second)
day
- day of monthmonth
- month of year (1 for January, 2 for February, etc.)year
- yearhour
- hour of day (24-hour based day)minute
- minute of hoursecond
- second of minutepublic static java.util.Date copyDateObject(java.util.Date date)
date
- Date object to be copiedpublic static void addDays(java.util.Date date, int days)
date
- Date object to modifydays
- days to advance (or recur if negative)public static void addMonths(java.util.Date date, int months)
date
- Date object to modifymonths
- months to advance (or recur if negative)public static void addYears(java.util.Date date, int years)
date
- Date object to modifyyears
- years to advance (or recur if negative)public static void addHours(java.util.Date date, int hours)
date
- Date object to modifyhours
- hours to advance (or recur if negative)public static void addMinutes(java.util.Date date, int minutes)
date
- Date object to modifyminutes
- minutes to advance (or recur if negative)public static void addSeconds(java.util.Date date, int seconds)
date
- Date object to modifyseconds
- seconds to advance (or recur if negative)public static int getWeekday(java.util.Date date)
date
- Date object to extract day of the week frompublic static boolean equals(java.util.Date date1, java.util.Date date2)
date1
- the first Date objectdate2
- the second Date objectpublic static boolean succeeds(java.util.Date date1, java.util.Date date2)
date1
- the first Date objectdate2
- the second Date objectpublic static boolean preceeds(java.util.Date date1, java.util.Date date2)
date1
- the first Date objectdate2
- the second Date objectpublic static int compare(java.util.Date date1, java.util.Date date2)
date1
- the first Date objectdate2
- the second Date objectpublic static java.lang.String dateAsString(java.util.Date date)
date
- the Date object to be represented as String


team (noun):
The ones who redefine fashion.
Meet the Team
Anton Rufino
CEO and Co-founder
Anton is among the chief visionaries who redefined not only Philippine fashion but also international fashion. His choice of career belies the fact that he graduated summa cum laude with a bachelor's degree in computer science from the University of the Philippines Los Banos (UPLB). Besides leading the company, he is also pursuing graduate studies at the Massachusetts Institue of Technology (MIT).
Christie
Cheif Fashion Designer and Co-founder
Christie is the leader the company's world renowned team of fashion designers. She was originally an undergraduate at UPLB studying computer science where she met Anton Rufino and Lemuel Maranan, both of whome would become the co-founders of Wall. She later transferred to the Julliard School in New York City to pursue studies in performing arts. She would reunite with Anton and Lemuel to form Wall.
Lemuel Maranan
Secretary of the Department of Redefinition and Co-founder
As the company's quirky and self-proclaimed Secretary of the Department of Redefinition (more commonly known as the Marketing Division), Lemuel is the chief force in creating the company's brand. It is also thanks to him that the company has a very lively and informal atmosphere which largely contributes to its massive creative output. He graduated summa cum laude from UPLB with a bachelor's degree in computer science. Currently, he is hailed as the second coming of Steve Jobs for his marketing prowess.


We brought Emma home from the hospital today. We got home around 1PM. It was a crazy drive home... probably the slowest I've ever driven my 'rex. I'd love to chat about it, but I'm tired. Pics are here: http://www.iduprey.com/gallery/baby1
--- layout: post title: Home Coming for Emma; Pictures categories: [] tags: [] status: publish type: post published: true meta: _original_post_id: '' _original_import_author: John Duprey author: ---
We brought Emma home from the hospital today. We got home around 1PM. It was a crazy drive home... probably the slowest I've ever driven my 'rex. I'd love to chat about it, but I'm tired. Pics are here: http://www.iduprey.com/gallery/baby1


This section provides information regarding any expenses or losses you are claiming as a result of the crime. Please keep receipts for all expenses you are claiming. The program will require you to submit original receipts.
For a list of benefits available through CVAP, please view the Summary of Benefits.
Original receipts are required for expenses not covered by your extended health or other insurance plan
Please check all that apply (at least one is required):
If you have received or will recieve other benefits as a result of the crime, check all that apply:
If the victim is deceased as a result of the crime, please indicate which additional expenses or benefits you wish to claim (at least one is required):
The following section provides information regarding employment information. This information is necessary if you are requesting benefits for lost employment income.
An Immediate Family Member is eligible for up to 5 days of bereavement leave benefits if the absence from work occurred within 2 weeks of the death of the victim or the date the Immediate Family Member became aware of the death.
If you have received or will recieve other benefits as a result of the crime, check all that apply:
If the victim is deceased as a result of the crime, please indicate which additional expenses or benefits you wish to claim (at least one is required):
For Immediate Family Members who need to travel some distance to obtain counselling services
For Witnesses who need to travel some distance to obtain counselling services
Examples:
Victims who need healthcare because of their injuries from the crime
Examples:
Victims who need dental care because of their injuries from the crime
Examples:
Victims who need prescription drugs to recover from, or manage the effects of, their injuries from the crime
For Immediate Family Members who need prescription drugs to recover from the psychological injury caused by the crime
For Witnesses who need prescription drugs to recover from the psychological injury caused by the crime
Examples:
For all victims of crime
For Immediate Family Members who need counselling to recover from the psychological injury caused by the crime
For Witnesses who need counselling to recover from the psychological injury caused by the crime
Examples:
Victims whose injuries have a short-term or long-term effect on their ability to work and who had a job when the crime occurred (special eligibility for minors)
Examples:
Victims who are at risk of additional harm from the perpetrator or are traumatized to the extent that they cannot perform regular daily activities
Examples:
Victims who are at risk of additional harm from the perpetrator or are traumatized to the extent that they cannot perform regular daily activities
Examples:
Victims who have to travel some distance to obtain medical, dental, counselling, or vocational services provided as crime victim assistance benefits
Examples:
Victims whose eyeglasses/contacts, disability aids, or articles of clothing were damaged or destroyed because of the crime
Repair or replacement of:
Victims who need healthcare because of their injuries from the crime
Examples:
Victims who need disability aids because of their injuries from the crime, to reduce the effects of the injuries, improve their quality of life, and assist in daily living
Examples:
Victims who were injured at their home or in their vehicle and need specialized cleaning of the home or vehicle due to the nature of the crime.
Examples:
Witnesses who need specialized cleaning of their home or vehicle because the crime was committed there
Examples:
Lost wages or income
If you are claiming for lost wages or income, we will need to verify your employment
Spouses of deceased victims who need training or education to prepare for employment or improve their earning capacity (and who are eligible for income support benefits from CVAP)
Examples:
Spouses of deceased victim
Children of deceased victims
Other immeditate family members of deceased victims if they were financially dependent on the victim
Examples:
Spouses of deceased victims who need to pay for childcare because there is no one in the household to share this responsibility
Examples:
For Immediate Family Members of deceased victims who have to travel over 100 km to attend legal proceedings regarding the death
Examples:
Immediate Family Members of deceased victims who have to pay for funeral and related costs
Examples:
Immediate Family Members of deceased victims who lose earnings from taking time off work for the funeral or other matters related to the victim’s death
Examples:
Children of deceased victims who were under 19 when their parent was killed
Examples:
Spouses of deceased victims who need to pay for homemaker services because there is no one in the household to share household tasks
Examples:
Immediate Family Members of deceased victims who need specialized cleaning of the victim’s home or vehicle, or their own home or vehicle, because the crime was committed there
Examples:


Tyler Wians
CSC 317: Computer Networks
Home → 10.10
Multiplexing and Demultiplexing
A process can have one or more sockets, which are doors through which data passes from the
process to the network. Each transport-layer segment has a set of fields to direct an
incoming segment to the appropriate socket. The transport-layer then examines these fields to identify
the receiving socket and directs the segment to the socket.
Demultiplexing
- Delivers data in a transport-layer segment to the correct socket
Multiplexing
- Gathers data chunks at the source host (from different sockets)
- Encapsulates each data chunk with header information (used for demultiplexing)
- Creates segments to pass to the network layer
How does it work?
- Source port number field: the unique identifier field in a segment
- Destination port number field: field that indicates what socket to deliver to
Each port number is a 16-bit number, ranging from 0 to 65535. Well known port numbers, ranging from 0-1023, are
restricted and reserved for well-known application protocols like HTTP (port no. 80), and FTP (port no. 21). An
application must be assigned a port number in order to transfer data.
Connectionless (UDP)
- Gets assigned a non-well-known port number
- Chooses an available port number not being used by another application
- If a port is chosen that is already being used, the return address field sends it back
Connection-Oriented (TCP)
Identified by a four-tuple which uses the four values,
- Source IP Address
- Source Port Number
- Destination IP Address
- Destination Port Number
to direct the segment to the appropriate socket.


Mempool.Space is a realtime Bitcoin blockchain explorer and mempool visualizer.
Created by @softbtc
Hosted by @wiz
Designed by @markjborg
| Fee API |
|
| Mempool blocks |
|
| Upon connection, send object {{ '{' }} action: 'want', data: ['blocks', ...] {{ '}' }} to express what you want pushed. Available: 'blocks', 'mempool-blocks', 'live-2h-chart' and 'stats'. |
|


Previous Next
What Is Grepy?
Grepy is a Windows utility that allows you to quickly and easily search through files for a text string.
Features:
- Allows you to search through files for text using regular expressions.
- The search results window displays file text before and after the search match to let you see the context around the match.
- Grepy has a clean and uncluttered interface that allows you to search for what you want with minimal keystrokes and mouse clicks.
- Uses Windows File Explorer integration so you can quickly search specific folders by right-clicking on them.
- Saves previously used search expressions, filespecs and folders in a drop down list so you can quickly reuse previous entries (saves typing).
- Saves the position and size of all windows, sliders and column sizes so the layout stays the way you want it to be.
- Will automatically skip binary files (.exe, .obj, .lib, .bin, etc.) during searches to save time.
- Lets you assign a custom editor application to use to open files (to override the default Windows File Association).
- Uses a configurable number of background threads for file searching which allows you to immediately open files as the search results are populated.
- Grepy is small (loads quickly) and fast (searches very quickly).
- If you have the Everything search engine installed, Grepy will automatically use it to collect the list of files to be searched.
Here's a sample screenshot of what Grepy looks like:
Previous Next


It is a fast detector
]]>ExecutorService
objects must be explicitly shutdown,
otherwise, their internal threads can prolong the running of the JVM, even when everything
else has stopped.
FindBugs has detected that there are no calls to either the shutdown()
or shutdownNow()
method, and thus, the ExecutorService
is not guaranteed to ever terminate. This is especially
problematic for Executors.newFixedThreadPool()
and most of the other convenience methods in
the Executors
class.
Even though there are some exceptions to this, particularly when a custom ThreadFactory
is
provided, or for ThreadPoolExecutor
s with allowsCoreThreadTimeOut()
set to true,
it is good practice to explictly shutdown the ExecutorService
when its utility is done.


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Physical fitness may help prevent depression, anxiety
Fitness: The best exercise to lose belly fat and tone up...


First content
Vertical slides are using level 2 sections (===)
===
Press down arrow.
Level 3 (====), rendered as <h3> is now supported.
====
<h3>
But they issue warnings during render


--- title: "'System.String' must be Xml serializable" layout: post ---
Several people have begun to encounter a problem compiling projects after installing BizTalk Server 2004 SP2. Fortunately this problem has been encountered before and there is a Windows HotFix which solves it. Despite the name, this fix will work for SP2 just as well as SP1. You can find it here.


Consider the infinite polynomial series AG(x) = xG1 + x2G2 + x3G3 + ..., where Gk is the kth term of the second order recurrence relation Gk = Gk1 + Gk2, G1 = 1 and G2 = 4; that is, 1, 4, 5, 9, 14, 23, ... .
For this problem we shall be concerned with values of x for which AG(x) is a positive integer.
The corresponding values of x for the first five natural numbers are shown below.
| x | AG(x) |
| (51)/4 | 1 |
| 2/5 | 2 |
| (222)/6 | 3 |
| (1375)/14 | 4 |
| 1/2 | 5 |
We shall call AG(x) a golden nugget if x is rational, because they become increasingly rarer; for example, the 20th golden nugget is 211345365.
Find the sum of the first thirty golden nuggets.


AMAZING PEOPLE ARE MAKING AMAZING DESIGNS IN A FUN ENVIRONMENT
When you work with us, you are part of a team of committed collaborators. All aspects of our software
development process are intergrated, interactive,
and agile.
Our approach unifies design, development, and product management to create exceptional products.
We start each project by understanding your business goals, the user's needs and the product requirements.
Then we help translate them into effective and elegant solutions.
Before we sign a contract or write a line of code, our time will spend the necessary time needed to understand your product vision.
The outcome of this scoping session is a proposal or high-level statement of work.
Based on your requirements, we can provide a complete offering or just the pieces you need.
Our design practice offers a full range of services
including brand strategy, interaction and visual
design
and user experience testing.
Throughout your project, our designers create and
implement visual design and workflows, solicit
user
feedback and work with you to make sure what gets built
is what is needed.
All our engineers are fluent in the latest enterprise, mobile, and web development technologies.
They collaborate with your team to write and improve code on a daily basis using proven practices such as test-driven development and pair programming.
Planning and development is iterative. Because we are
constantly coding and testing, the products we
build are
always ready to go live.
This iterative process allows for changes as business
requirements evolve.
DELANI STUDIO
2021


csv¶
CSV importer supports both positional and name based loading. The loading is stream based and thus can be used to load very large files that do not fit into the memory.
CSV importer also supports schema discovery.
Options¶
-c,--charset <arg> sets the file
character set
-d,--delimiter <arg> specifies the
delimiter
-e,--encoding <arg> specifies the external
file character set
-f,--nafilter enables N/A value
filtering
-h,--header indicates the file has
a header
-j,--clob <arg> specifies the external
text file column
-k,--blob <arg> specifies the external
binary file column
-p,--precise Obtain precise decimal
points if possible.
This option is only
meaningful in
generating a table
schema. By default,
floating values are
treated as DOUBLE
PRECISION.
-r,--rowthreshold <arg> sets row threshold in
schema determination.
-t,--type <default | excel | rfc4180 | mysql | tdf> sets the csv type.
-v,--navalues <arg> specifies a comma
delimited list of N/A
values. If it is not
specified and
--nafilter is enabled,
then the default list
is used.
Note
- Binary types are assumed to be in hexadecimal format.
- N/A values are converted to NULL.
NULL and N/A Handling¶
There are many possible ways to store NULL and N/A values in CSV. CSV
format provides a --nafilter
and --navalues
options to specify
the list. If --nafilter
is specified, the default N/A value list
are the following, which
pandas.read_csv
uses.
“-1.#IND”, “1.#QNAN”, “1.#IND”, “-1.#QNAN”, “#N/A N/A”, “#N/A”, “N/A”, “NA”, “#NA”, “NULL”, “NaN”, “-NaN”, “nan”, “-nan”, “”
Schema Detection¶
CSV importer supports schema discovery which allows .importschema and .importtable to generate database compatible table information.
The default algorithm for schema detection is the following
- For each column, 1000 non-N/A values are analyzed. If all the values are either fixed length string, or numeric values, the scan stops.
- If any of the analyzed column contains variable length string, the entire data file needs to be scanned to determine the maximum string length.
CSV importer scans most CSV files pretty fast even with full file scan. 100MB can be scanned and imported in a few seconds. Of course, your mileage may vary depending on the disk and network speed.
For files over 100GB, you do want to be slightly careful since it can take hours to import the data.
- Make sure the server has enough storage.
- The default schema detection algorithm works fine for the data.
- Or just manually specify the import INSERT statement.
Example¶
CREATE TABLE MyTable(a INTEGER, b INTEGER);
-- success. update count = 0
-- 9/11 - 0 ---------------------------------------------------------------
-- test csv with header
.import csv -h lib/import1.csv
-- 9/12 - 0 ---------------------------------------------------------------
INSERT INTO MyTable VALUES (?, ?);
Parameter Count 2
Index 1
Type INTEGER
SQL Type INTEGER
Java Class java.lang.Integer
Precision 10
Scale 0
Nullable 1
Signed Y
Mode IN
Index 2
Type INTEGER
SQL Type INTEGER
Java Class java.lang.Integer
Precision 10
Scale 0
Nullable 1
Signed Y
Mode IN
-- success. update count = 1
-- 10/12 - 0 --------------------------------------------------------------
SELECT * FROM MyTable ORDER BY a;
-- success --
A B
- -
1 1
2 2
3 3
4 4
-- activity count = 4


(Thank you @anatomecha for the sweet hex)
The goal of simeta
is to make it easier to simulate meta-analysis data.
These are a sometimes-useful modular set of tools for simulating meta-anlaysis data I mostly developed during my doctoral project. For technical details, see the associated manuscript (freely-accessible arxiv version):
Charles T. Gray. “code::proof: Prepare for Most Weather Conditions”. en. In: Statistics and Data Science. Communications in Computer and Information Science (2019). Ed. by Hien Nguyen, pp. 22–41. DOi: 10.1007/978-981-15-1960-4_2
In particular, I’m often interested in simulating meta-analysis data for different values of
This readme is not comprehensive, but updated as collaborators need scripts and examples.
Have ideas? Want to contribute? Request a workflow, etc. Create an issue.
You can install simeta
from github with:
# install.packages("devtools")
devtools::install_github("softloud/simeta")
Suppose we are interested in comparing how variation between studies and overall sample size influence the likelihood of significance in meta-analyses with small to large effects.
# set default parameters, useful to store as an object for visualisation
# labelling
# set effect ratios of interest
sim_effect_ratio <- c(1, 1.5)
# set desired variance between studies
sim_tau_sq <- c(0, 0.2)
# set minimum sample size per study
sim_min_n <- 5
# set maximum sample size per study
sim_max_n <- 150
Use sim_df
to set up a dataframe of simulation parameters, wherein every row represents one combination of simulation parameters.
Use sim_samples
to create a dataframe where each row of the simulation parameter level dataframe is repeated trials
times, and a new list-column of meta-analysis samples are generated using the row-level simulation parameters.
# Generate simulated meta-analyses from simulation parameters dataframe
# Number of trials represents number of repeated rows per simulation parameter
# set
samples_df <-
sim_samples(
measure = "mean",
measure_spread = "sd",
sim_dat = sim_parameters,
# small for the purposes of example
trials = 3
)
# take a look at a few rows
samples_df %>% sample_n(5)
#> # A tibble: 5 × 8
#> k tau_sq_true effect_ratio rdist parameters n sim_id sample
#> <dbl> <dbl> <dbl> <chr> <list> <list> <chr> <list>
#> 1 7 0.2 1 pareto <named list> <tibble> sim 13 <tibble>
#> 2 7 0 1.5 pareto <named list> <tibble> sim 22 <tibble>
#> 3 7 0 1 pareto <named list> <tibble> sim 4 <tibble>
#> 4 3 0 1.5 norm <named list> <tibble> sim 21 <tibble>
#> 5 3 0.2 1.5 pareto <named list> <tibble> sim 28 <tibble>
# example simulated meta-analysis dataset
samples_df %>% sample_n(1) %>% pluck("sample") %>% kable()
|
# Generate meta-anlyses and extract a parameter of interest, such as p-value.
sim_metafor <-
samples_df %>%
# making small for purposes of example (simulations scale fast!)
sample_n(20) %>%
mutate(
rma = map(sample,
function(x) {
metafor::rma(data = x,
measure = "SMD",
m1i = effect_c,
sd1i = effect_spread_c,
n1i = n_c,
m2i = effect_i,
sd2i = effect_spread_i,
n2i = n_i
)}
)
) %>%
# extract pvalues
mutate(
p_val = map_dbl(rma, pluck, "pval")
)
# take a look
sim_metafor %>%
mutate(p_val = round(p_val, 2)) %>%
select(p_val, everything()) %>%
# see first three rows
head(3)
#> # A tibble: 3 × 10
#> p_val k tau_sq_true effect_r…¹ rdist parameters n sim_id sample
#> <dbl> <dbl> <dbl> <dbl> <chr> <list> <list> <chr> <list>
#> 1 0.03 3 0 1 lnorm <named list> <tibble> sim 2 <tibble>
#> 2 0.41 3 0.2 1.5 lnorm <named list> <tibble> sim 29 <tibble>
#> 3 0.02 7 0.2 1.5 pare… <named list> <tibble> sim 31 <tibble>
#> # … with 1 more variable: rma <list>, and abbreviated variable name
#> # ¹effect_ratio
I find I run into problems very quickly with memory. The targets
package can help. For example, the following example samples from several distributions, each with different parameter sets. This combinatorially increases the number of simulations.
| dist | par |
|---|---|
| pareto | 2, 1 |
| norm | 50, 17 |
| lnorm | 4.0, 0.3 |
| exp | 10 |
| pareto | 3.576119, 2.745808 |
| norm | 75.209383, 6.739041 |
| lnorm | 2.3900182, 0.3383603 |
| exp | 4.86717 |
See _targets.R for an example script that produces this visualisation adapting the workflow above. Each point in this plot represents one p-value from a meta-analysis on a randomly-generated dataset.
include_graphics("man/figures/example_sim.png")
include_graphics("man/figures/example_pipeline.png")
dim(trial_results)
#> [1] 21596 12
# move to
trial_results %>%
group_by(sim_id) %>%
summarise(
n_trials = n()
) %>%
full_join(trial_results, by = "sim_id", multiple = "all") %>%
select(-p_value_result, -dist_label) %>%
head()
#> # A tibble: 6 × 11
#> sim_id n_trials k tau_sq_true effect_…¹ rdist parameters parti…² study…³
#> <chr> <int> <dbl> <dbl> <dbl> <chr> <list> <int> <fct>
#> 1 sim 1 100 3 0 1 pare… <named list> 90 3 stud…
#> 2 sim 1 100 3 0 1 pare… <named list> 90 3 stud…
#> 3 sim 1 100 3 0 1 pare… <named list> 90 3 stud…
#> 4 sim 1 100 3 0 1 pare… <named list> 90 3 stud…
#> 5 sim 1 100 3 0 1 pare… <named list> 90 3 stud…
#> 6 sim 1 100 3 0 1 pare… <named list> 90 3 stud…
#> # … with 2 more variables: p_value <dbl>, significant <lgl>, and abbreviated
#> # variable names ¹effect_ratio, ²participants, ³study_n_label
Without specification, the function uses the default parameters dataset (?default_parameters
).
| dist | par |
|---|---|
| pareto | 2, 1 |
| norm | 50, 17 |
| lnorm | 4.0, 0.3 |
| exp | 10 |
| pareto | 3.576119, 2.745808 |
| norm | 75.209383, 6.739041 |
| lnorm | 2.3900182, 0.3383603 |
| exp | 4.86717 |
This dataset also provides a template for how to set up a dataframe specifying the distributions and parameters of interest for sim_df
. The default sampling distributions are designed to provide a mix of common symmetric and asymmetric families, with both fixed and randomly-generated parameters.
sim_dat <-
# defaults to using default_parameters if we do not specify dist_df argument
sim_df(
# different effect sizes
# what is small, medium large effect
effect_ratio = sim_effect_ratio,
# what is small medium large variance
tau_sq = sim_tau_sq,
min_n = sim_min_n,
max_n = sim_max_n
)
# take a look at the top of the dataset
sim_dat %>% head(3)
#> # A tibble: 3 × 7
#> k tau_sq_true effect_ratio rdist parameters n sim_id
#> <dbl> <dbl> <dbl> <chr> <list> <list> <chr>
#> 1 3 0 1 pareto <named list [2]> <tibble [6 × 3]> sim 1
#> 2 3 0 1 norm <named list [2]> <tibble [6 × 3]> sim 2
#> 3 3 0 1 lnorm <named list [2]> <tibble [6 × 3]> sim 3
# the end of the dataset
sim_dat %>% tail(3)
#> # A tibble: 3 × 7
#> k tau_sq_true effect_ratio rdist parameters n sim_id
#> <dbl> <dbl> <dbl> <chr> <list> <list> <chr>
#> 1 20 0.2 1.5 norm <named list [2]> <tibble [40 × 3]> sim 94
#> 2 20 0.2 1.5 lnorm <named list [2]> <tibble [40 × 3]> sim 95
#> 3 20 0.2 1.5 exp <named list [1]> <tibble [40 × 3]> sim 96
# take a look at a random handful of rows
sim_dat %>% sample_n(5)
#> # A tibble: 5 × 7
#> k tau_sq_true effect_ratio rdist parameters n sim_id
#> <dbl> <dbl> <dbl> <chr> <list> <list> <chr>
#> 1 3 0 1.5 pareto <named list [2]> <tibble [6 × 3]> sim 49
#> 2 20 0.2 1 norm <named list [2]> <tibble> sim 46
#> 3 7 0 1.5 exp <named list [1]> <tibble> sim 64
#> 4 3 0.2 1 lnorm <named list [2]> <tibble [6 × 3]> sim 27
#> 5 3 0 1 lnorm <named list [2]> <tibble [6 × 3]> sim 7
sim_df
uses sim_n
as explained below to create each dataset of sample sizes.
This is a function I have often wished I’ve had on hand when simulating meta-analysis data. Thing is, running, say, 1000 simulations, I want to do this for the same sample sizes. So, I need to generate the sample sizes for each study and for each group (control or intervention).
Given a specific k, generate a set of sample sizes.
| study | group | n |
|---|---|---|
| Estelmo_1960 | control | 89 |
| Snaga_1953 | control | 20 |
| Théoden_1959 | control | 76 |
| Estelmo_1960 | intervention | 91 |
| Snaga_1953 | intervention | 20 |
| Théoden_1959 | intervention | 77 |
| study | group | n |
|---|---|---|
| Núneth_2002 | control | 68 |
| Folcred_1957 | control | 105 |
| Boromir_1957 | control | 14 |
| Núneth_2002 | intervention | 64 |
| Folcred_1957 | intervention | 93 |
| Boromir_1957 | intervention | 14 |
| study | group | n |
|---|---|---|
| Gwaihir_2002 | control | 42 |
| Amarië_1964 | control | 80 |
| Nori_1964 | control | 42 |
| Salmar_1987 | control | 92 |
| Anárion_1973 | control | 62 |
| Ulwarth_2007 | control | 95 |
| Gwaihir_2002 | intervention | 43 |
| Amarië_1964 | intervention | 67 |
| Nori_1964 | intervention | 33 |
| Salmar_1987 | intervention | 99 |
| Anárion_1973 | intervention | 59 |
| Ulwarth_2007 | intervention | 93 |
| study | group | n |
|---|---|---|
| Telchar_1972 | control | 14 |
| Asgon_1998 | control | 14 |
| Vëantur_1955 | control | 18 |
| Telchar_1972 | intervention | 16 |
| Asgon_1998 | intervention | 11 |
| Vëantur_1955 | intervention | 18 |
We expect cohorts from the same study to have roughly the same size, proportional to that size. We can control this proportion with the prop
argument.
Suppose we wish to mimic data for which the cohorts are almost exactly the same (say becaues of classes of undergrads being split in half and accounting for dropouts).
| study | group | n |
|---|---|---|
| Beren_1979 | control | 28 |
| Azog_2013 | control | 19 |
| Beren_1979 | intervention | 2 |
| Azog_2013 | intervention | 1 |
It can be useful, for more human-interpretable purposes, to display the sample sizes in wide format.
Adding a few values of τ, different numbers of studies k, and so forth can ramp up the number of combinations of simulation parameters very quickly.
I haven’t settled on a way of simulating data, and haven’t found heaps in the way of guidance yet. So, this is all a bit experimental. My guiding star is packaging what I’d use right now.
What I do always end up with is generating a dataset that summarises what I would like to iterate over in simulation.
The sim_df
takes user inputs for distributions, numbers of studies, between-study error τ, within-study error ε, and the proportion ρ of sample size we expect the sample sizes to different within study cohorts.
# defaults
sim_df()
#> # A tibble: 216 × 7
#> k tau_sq_true effect_ratio rdist parameters n sim_id
#> <dbl> <dbl> <dbl> <chr> <list> <list> <chr>
#> 1 3 0 1 pareto <named list [2]> <tibble> sim 1
#> 2 3 0 1 norm <named list [2]> <tibble> sim 2
#> 3 3 0 1 lnorm <named list [2]> <tibble> sim 3
#> 4 3 0 1 exp <named list [1]> <tibble> sim 4
#> 5 3 0 1 pareto <named list [2]> <tibble> sim 5
#> 6 3 0 1 norm <named list [2]> <tibble> sim 6
#> 7 3 0 1 lnorm <named list [2]> <tibble> sim 7
#> 8 3 0 1 exp <named list [1]> <tibble> sim 8
#> 9 7 0 1 pareto <named list [2]> <tibble> sim 9
#> 10 7 0 1 norm <named list [2]> <tibble> sim 10
#> # … with 206 more rows
sim_df() %>% str(1)
#> tibble [216 × 7] (S3: tbl_df/tbl/data.frame)
# only consider small values of k
sim_df(k = c(2, 5, 7)) %>% str(1)
#> tibble [216 × 7] (S3: tbl_df/tbl/data.frame)
For the list-column of tibbles n
, the sim_df
function calls sim_n
and generates a set of sample sizes based on the value in the column k
.
demo_k <- sim_df()
# the variable n is a list-column of tibbles
demo_k %>% pluck("n") %>% head(3)
#> [[1]]
#> # A tibble: 6 × 3
#> study group n
#> <chr> <chr> <dbl>
#> 1 Mandos_2000 control 23
#> 2 Niënor_1955 control 62
#> 3 Yavanna_2015 control 17
#> 4 Mandos_2000 intervention 25
#> 5 Niënor_1955 intervention 70
#> 6 Yavanna_2015 intervention 19
#>
#> [[2]]
#> # A tibble: 6 × 3
#> study group n
#> <chr> <chr> <dbl>
#> 1 Aravir_1953 control 35
#> 2 Anborn_1982 control 74
#> 3 Elphir_1984 control 17
#> 4 Aravir_1953 intervention 34
#> 5 Anborn_1982 intervention 71
#> 6 Elphir_1984 intervention 17
#>
#> [[3]]
#> # A tibble: 6 × 3
#> study group n
#> <chr> <chr> <dbl>
#> 1 Aragost_1981 control 67
#> 2 Orcobal_1994 control 88
#> 3 Khîm_1954 control 15
#> 4 Aragost_1981 intervention 64
#> 5 Orcobal_1994 intervention 82
#> 6 Khîm_1954 intervention 16
# compare the number of rows in the dataframe in the n column with the k value
# divide by two because there are two rows for each study,
# one for each group, control and intervention
demo_k %>% pluck("n") %>% map_int(nrow) %>% head(3) / 2
#> [1] 3 3 3
demo_k %>% pluck("k") %>% head(3)
#> [1] 3 3 3
Once we have established a set of sample sizes for a given distribution, with parameters, and so forth, I usually want to generate a sample for each of those n
. We need to adjust the value of the sampled data based on the median ratio, and whether the n
is from a control or intervention group.
A random effect is added to account for the between study error τ and within study error ε.
For meta-analysis data, we work with summmary statistics, so we drop the sample and return tabulated summary stats.
| study | effect_c | effect_spread_c | n_c | effect_i | effect_spread_i | n_i |
|---|---|---|---|---|---|---|
| Bob_2004 | 58.77079 | 0.2146638 | 48 | 51.03713 | 0.1810027 | 42 |
| Oromë_1981 | 49.68544 | 0.2232972 | 45 | 60.28628 | 0.1896956 | 43 |
| Ufthak_1965 | 68.67402 | 0.2019829 | 42 | 43.72458 | 0.1893396 | 48 |
In a trial, we’d first want to simulate some data, for a given distribution, for this we use the sim_stats
function discussed in the above section.
With the summary statistics, we then calculate an estimate of the effect or the variance of the effect.
The first two steps are taken care of by the sim_stats
function. The third step will by necessity be bespoke.
But the rest could be automated, assuming there are the same kinds of results.
| step | input | output |
|---|---|---|
| calculate estimates | summary statistics as produced by sim_n
|
summary stats |
| calculate effects | summary stats |
effect and effect_se
|
| meta-analyse |
effect and effect_se
|
rma object |
| summary stats |
rma object |
some kind of broom ing script |
metatrial()
So, now we can put together some generic summarisations. Things I always want to do. Like calculate the coverage probability, confidence interval width, and bias. Most results here are mean values across all trials, the exceptions being cp_
variables.
metasim
calls metatrial
many times and summarises the results.
metasim()
sim %>% coverage_plot()
Ideally this would be configurable but let’s hardcode it for now.↩


WhatsApp for Windows 10 Mobile will stop working from 31st December
by StipeIf you have been following the history of Nokia phones for a bit longer than HMD is alive, you should’ve noticed by now that Nokia phones used a plethora of operating systems. Even HMD or Nokia Mobile as they are called are using 4 operating systems on their feature phones, while Nokia smartphones through history used Symbian, MeeGo, five versions of Windows for phones and other systems as well.
With the Lumia devices using Microsoft’s mobile operating system, Nokia showed design and camera excellence, but at the end that wasn’t enough, because to succeed, Lumia devices needed apps and Microsoft’s platform didn’t have them enough.
One of the most important apps today, Whatsapp, is still available and working on Lumia devices running Windows 10 Mobile, but not for long. On 31st December 2019, WhatsApp for Windows 10 Mobile will stop working.
Users of Lumia phones that also use Whatsapp should consider making a switch to another phone before the deadline, because it’s good to finish things before deadlines.
via: Neowin.net , WindowsArea.de


I'm assuming that anyone who has read this far is familiar with the series, so I'll keep the plot description to a minimum. Set mostly in Hollywood, "Scream 3" continues the movie-within-a-movie conceit, as production of "Stab 3" comes screeching to a halt when a killer, dressed in the familiar ghost mask and black cowl, appears on the scene. In short order, all of our old favorite characters, along with a host of new ones, are scrambling to stay alive and unmask Ghost Face once and for all.
The character dynamics have significantly changed along the way. Where central target Sidney Prescott (Neve Campbell) was once the focal point of the action, she now plays second banana to Deputy Dewey Riley and cutthroat TV tabloid reporter Gale Weathers, played by the recently married David Arquette and Courteney Cox Arquette. Although "Scream 2" ended with Dewey and Gale as honey-bunnies, this storyline has the couple estranged again, thereby allowing the Tom and Roseanne of the 21st century to bicker a lot before their relationship heats up again.
While Dewey and Gale play junior detective on the "Stab 3" set, Sidney is holed up far, far away in a high-security mountain cabin, counseling abused women via a series of high-security phone lines. When she learns of Ghost Face's Tinseltown Tour 2000, she leaves her sanctuary and reluctantly heads for Los Angeles (incidentally, reluctantly is the only way anyone should ever head for Los Angeles).
Fine. The veterans are in place, newcomers are running around all over the set and the killer is slashing away. So what are we dealing with here? Kevin Williamson, who became white hot thanks to his dandy script for the original "Scream," was too busy to write the finale (he reportedly was trying to salvage his wretched TV series, the aptly named "Wasteland"), leaving flavor-of-the-month Ehren Kruger ("Arlington Road," "Reindeer Games") to fill in, with mixed results.
One of the high points is his pairing of Gale Weathers with Jennifer Jolie (Parker Posey), the actress playing Gale in "Stab 3." The combination of a hyperactive, manipulative reporter and a ditzy starlet determined to copy her every move is inspired, and Posey is a riot. The only thing that hampers their scenes is the alarming physical appearance of Courteney Cox Arquette. With her increasingly gaunt features (please, somebody feed this woman!) and godawful Cleopatra-with-hacked-bangs hairdo, she looks scarier than Ghost Face.
While going lighter on the pop culture references than Williamson, Kruger does get in some good ones. In addition to the pointed one-liners, check the closing credits for the character names of the "Stab 3" players, which can be taken as a nod, or a swipe, at a gaggle of popular young actors.
Director Wes Craven has some fine moments as well. His composition work is exceptional, particularly in a haunting shot where Sidney emerges from a studio hallway and the camera slowly pulls away as she finds herself on a movie set recreating a street scene straight out of her personal nightmares. Throughout the film, Craven uses lights and darks masterfully, and his positioning of music to heighten tension is far more effective than in most films of the genre.
Had he only paid as much attention to the script. In climatic scenes, Sidney, Dewey and Gale abandon the very survival rules their characters supposedly learned in the first two films. Even more damaging to "Scream 3" is the simple passage of time. Since the series burst on the scene in 1996, other artists have upped the humor-meets-horror ante. On any given week, shows like the outstanding "Buffy the Vampire Slayer" mix chills and giggles as adroitly as anything in "Scream," while creating characters with considerably more substance. Regardless, though "Scream 3" represents a franchise running on fumes, there's still enough gas in this old buggy for a moderately entertaining final ride.
© 2000 Ed Johnson-Ott
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--- layout: artwork title: "The Incendiary Anarchist Gets Upcycled" author: HAU characters: Moxie date: 2018-09-01 ---
The Incendiary Anarchist Gets Upcycled by HAU


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This application was designed to quickly fetch your class data (from banner) and import it to your Google Calendar
This is accomplished in a series of steps:
No userdata is saved within this process
Please note: This may not work for all classes (such as classes without a start and end time).
you have any questions about privacy of data or the process, please email me.
If you want to look into code which makes this possible, take a look at my GitHub
Did I save you any time ⏰? I hope so!
Websites like this take time (100 x ⏰) to build..
If you venmo me I can justify the time spent making this application
Venmo: @GrantGap


Welcome to RestraintMaker¶
RestraintMaker is a tool that allows automatic distance restraint assignment for dual-topology relative free energy calculations. The package can be used either in a scripting mode or with a GUI-based in PyMOL(see [2]).
This repository is heavy under development! It will be nicer soon ;)
Introduction¶
coming soon
Examples¶
coming soon
Content¶
Using¶
For usage examples, checkout the examples folder.
Development¶
Restraint make is split two parts.
RestraintMaker This part is the core of the program. It can be executed as standalone.
algorithm: restraint selecting algorithms
tools_Rdkit: additional functions for filtering or selecting (see [1])
io: writing/importing outputfiles
utils
- Interface PyMOL:
Allows interfacing to PyMOL and generates the GUI-Expierence (see [2])
Installation¶
You can retrieve the repository from GitHub: https://github.com/rinikerlab/restraintmaker
Install via Pymol Plugin Manager
coming soon
Install with Anaconda
#!/usr/bash # 1. Retrieve the repository git clone https://github.com/rinikerlab/restraintmaker.git cd restraintmaker # 2. activate submodules git submodule init git submodule update # 3. generate an Anaconda environment with the environment file and add the repository to its path: conda env create --file devtools/conda-envs/dev_env.yaml -n restraintmaker conda develop -n restraintmaker /absolute_path/to/restraintmaker # 4. Test conda activate restraintmaker python examples/example_gui.py
GUI Tutorial¶
Author¶
Benjamin Ries, Salomé Rieder Clemens Rhiner
Acknowledgments¶
The authors want to thank Carmen Esposito and Dominik Sidler for the great discussions. Project based on the Computational Molecular Science Python Cookiecutter version 1.3.
References¶
[1] RDKit: Cheminformatics and machine learning software (2021) - http://www.rdkit.org
[2] The PyMOL Molecular Graphics System, Version 2.5 Schrödinger, LLC. (Anaconda OpenSource Version)
Copyright¶
Copyright (c) 2021, Benjamin Ries (@SchroederB), Salomé R. Rieder (@SalomeRonja), Clemens Rhiner (@ClemensRhiner)


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Label: K4KWQ Server
Address: k4kwq.com
Nickname: YOURCALL - Yourname
Important: K4KWQ requests your callsign in Capitals followed by space than a - and then your name.
Non-HAMs use "NOCALL"
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Planet Sick-Boy: http://www.sick-boy.com "We Put the SIN in Cinema"
© Copyright 2001 Planet Sick-Boy. All Rights Reserved.
The fourth and final of the original Dogme 95 films, and the first to both be shot outside Denmark and feature non-Danish acting talent, The King is Alive is just as ambitious as its heavily hyped cinematic brethren, but like the last two (Mifune and The Idiots), it just doesn't measure up to the first (The Celebration). It does, however, come the closest.
[Dogme, in case you've been held captive in a multiplex for the last three years, is the experimental Danish film aesthetic (created by that Dancer in the Dark madman, Lars von Trier) that, among other things, demands directors use handheld cameras and shoot their scenes sequentially while forbidding the use of artificial lighting, sound (including music) and props.]
The film begins in the North African desert (it was shot in Namibia), where a bus with ten international tourists hurtles through the barren, sandy terrain. But the onboard compass is on the fritz, and the bus has traveled several hundred miles off course before driver Moses (Vusi Kunene) notices. To make matters worse, when the bus stops to refuel, it is discovered that the giant petrol cans in the storage area are empty. The passengers now find themselves stranded in an abandoned German mining town that offers only shelter and a potentially crazy old man named Kanana (Peter Khubeke), who narrates the film in Swahili (it's subtitled - don't panic).
Aussie passenger Jack (Miles Anderson), the only experienced outdoorsman of the lot, decides to try his luck at hiking to the nearest town, which, according to Kanana, is over 150 miles away. The remaining travelers choose the houses they want to call home and have a big party their first night in the middle of nowhere. But after several days pass with no word from Jack, it becomes clear the vacationers might be in for an extended stay. They survive on canned carrots abandoned by the Germans and dew (as in condensation, not Mountain). Kind of makes the soupy rice from the Kucha tribe sound pretty good, huh?
If you were stranded in an abandoned town, staging a production of King Lear would probably be the furthest thing from your mind, but that's just what Henry (David Bradley) decides the group should do to take their minds off their potentially grave predicament. He knows Lear by heart and begins to handwrite parts for his fellow castaways. The play, however, is merely the catalyst for all of the wanton sex acts and post-coital jealousy you'd expect in such a tiny community. Envy and lust run unchecked among the group of seemingly self-destructive men, leaving the women in a rare leadership role. Lear, at least according to strandee Liz (Janet McTeer, Tumbleweeds), is the perfect play to produce because "nobody has to fall in love, and everybody gets to die in the end."
This isn't the breathtaking desert we saw in The English Patient - it looks like it could eat you alive when the sun goes down (in fact, if this was a Hollywood film, something would probably be stalking them once it got dark). Although The King is full of stark images and vivid colors, it looks its best at night, when the group is gathered around their fire Director Kristian Levring, who co-wrote the film with Mifune's Anders Thomas Jensen, and his digital camera catch every flaw on the face of each actor's weather-worn skin. It's as gritty as the Mexican portion of Traffic, but with more color than yellow. There also seems to be more editing than the previous Dogme films.
You could think of The King as a combination of The Blair Witch Project and Survivor targeted toward a highbrow arthouse audience instead of the teeming, unwashed masses. The film reminded me of so many other things - Cast Away, The Lord of the Flies, Hitchcock's Lifeboat, Apocalypse Now - that it's easy to think The King will follow one of their trajectories, but it really doesn't.
The King was shot in the summer of 1999 and wrapped just a few days before brilliant character actor Brion James, best known for his work in Blade Runner, died of a heart attack. It's ironic that his character is the first of the film's passengers to experience any type of physical ailment. Although the acting here is more than solid, Jennifer Jason Leigh (eXistenZ) and beautiful French actress Romane Bohringer are the standouts.
1:49 - Rated R for sexuality (nudity) and language
========== X-RAMR-ID: 29178 X-Language: en X-RT-ReviewID: 217876 X-RT-TitleID: 1107873 X-RT-SourceID: 595 X-RT-AuthorID: 1146
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Help pay for gaps in your insurance with Med Supp Plans - includes minimal standard coverages. Find the best plans for you!


Gateway
BOT
06-Jun-19 02:56 AM
[dscrd-hush] <Akira X> thanks
[dscrd-hush] <Akira X> !balance
[dscrd-hush] <Akira X> !tip dukeleto 1 hush
(edited)
[dscrd-hush] <dukeleto> @Akira X you need to do !tip @dukeleto 1 hush i think
[dscrd-hush] <dukeleto> but also, just tip a random person
[dscrd-hush] <dukeleto> !tip a random person 1 hush
[dscrd-hush] <dukeleto> I feel the love, no worries. Spread the Hush around
[dscrd-hush] <Akira X> !tip @Akira X 10000 hush
[dscrd-hush] <dukeleto> lol
[dscrd-hush] <dukeleto> @Akira X you can mine many things with your A9: HUSH, KMD, ARRR, LABS, RFOX, ZEN, ZEC, there are more
[dscrd-hush] <dukeleto> and you can mine some of various things and then switch, to fill various bags
[dscrd-hush] <dukeleto> https://whattomine.com/asic will give you a rough estimate to daily profit
Calculate how profitable it is to ASIC mine selected altcoins in comparison to bitcoin
Calculate Hush (HUSH) mining profitability in realtime based on hashrate, power consumption and electricity cost. HUSH exchange rates, mining pools.
[dscrd-hush] <dukeleto> you need to put the price data in for Hush still
[dscrd-hush] <Akira X> yes, but i dont want 50 wallets
[dscrd-hush] <Akira X> not sure which wallet to use now.
[dscrd-hush] <dukeleto> you can mine to the same address on HUSH, KMD, LABS, RFOX and all asset chains
[dscrd-hush] <dukeleto> but i hear what you mean
[dscrd-hush] <Akira X> should i move my hush? i never created a seed with agama
[dscrd-hush] <Akira X> i just used my hush key
[dscrd-hush] <dukeleto> if you never used agama then nothing is needed
[dscrd-hush] <dukeleto> well, you used agama tho?
[dscrd-hush] <dukeleto> with a hush private key?
[dscrd-hush] <Akira X> yes
[dscrd-hush] <Akira X> so i sholuld mvoe to verus address?
[dscrd-hush] <dukeleto> that single private key, you should move funds out of it
[dscrd-hush] <dukeleto> and consider it compromised
[dscrd-hush] <dukeleto> @Akira X verus agama or KomodoOcean will let you manage many coins


| add_months(start_date, num_months) |
Returns the date that is `num_months` after `start_date`. |
| current_date() |
Returns the current date at the start of query evaluation. All calls of current_date within the same query return the same value. |
| current_date |
Returns the current date at the start of query evaluation. |
| current_timestamp() |
Returns the current timestamp at the start of query evaluation. All calls of current_timestamp within the same query return the same value. |
| current_timestamp |
Returns the current timestamp at the start of query evaluation. |
| date_add(start_date, num_days) |
Returns the date that is `num_days` after `start_date`. |
| date_format(timestamp, fmt) |
Converts `timestamp` to a value of string in the format specified by the date format `fmt`. |
| date_part(field, source) |
Extracts a part of the date/timestamp or interval source. |
| date_sub(start_date, num_days) |
Returns the date that is `num_days` before `start_date`. |
| date_trunc(fmt, ts) |
Returns timestamp `ts` truncated to the unit specified by the format model `fmt`. |
| datediff(endDate, startDate) |
Returns the number of days from `startDate` to `endDate`. |
| dayofweek(date) |
Returns the day of the week for date/timestamp (1 = Sunday, 2 = Monday, ..., 7 = Saturday). |
| dayofyear(date) |
Returns the day of year of the date/timestamp. |
| from_unixtime(unix_time[, format]) |
Returns `unix_time` in the specified `format`. |
| from_utc_timestamp(timestamp, timezone) |
Given a timestamp like '2017-07-14 02:40:00.0', interprets it as a time in UTC, and renders that time as a timestamp in the given time zone. For example, 'GMT+1' would yield '2017-07-14 03:40:00.0'. |
| hour(timestamp) |
Returns the hour component of the string/timestamp. |
| last_day(date) |
Returns the last day of the month which the date belongs to. |
| make_date(year, month, day) |
Create date from year, month and day fields. |
| make_timestamp(year, month, day, hour, min, sec[, timezone]) |
Create timestamp from year, month, day, hour, min, sec and timezone fields. |
| minute(timestamp) |
Returns the minute component of the string/timestamp. |
| month(date) |
Returns the month component of the date/timestamp. |
| months_between(timestamp1, timestamp2[, roundOff]) |
If `timestamp1` is later than `timestamp2`, then the result
is positive. If `timestamp1` and `timestamp2` are on the same day of month, or both
are the last day of month, time of day will be ignored. Otherwise, the difference is
calculated based on 31 days per month, and rounded to 8 digits unless roundOff=false. |
| next_day(start_date, day_of_week) |
Returns the first date which is later than `start_date` and named as indicated. |
| now() |
Returns the current timestamp at the start of query evaluation. |
| quarter(date) |
Returns the quarter of the year for date, in the range 1 to 4. |
| second(timestamp) |
Returns the second component of the string/timestamp. |
| to_date(date_str[, fmt]) |
Parses the `date_str` expression with the `fmt` expression to
a date. Returns null with invalid input. By default, it follows casting rules to a date if
the `fmt` is omitted. |
| to_timestamp(timestamp_str[, fmt]) |
Parses the `timestamp_str` expression with the `fmt` expression
to a timestamp. Returns null with invalid input. By default, it follows casting rules to
a timestamp if the `fmt` is omitted. |
| to_unix_timestamp(timeExp[, format]) |
Returns the UNIX timestamp of the given time. |
| to_utc_timestamp(timestamp, timezone) |
Given a timestamp like '2017-07-14 02:40:00.0', interprets it as a time in the given time zone, and renders that time as a timestamp in UTC. For example, 'GMT+1' would yield '2017-07-14 01:40:00.0'. |
| trunc(date, fmt) |
Returns `date` with the time portion of the day truncated to the unit specified by the format model `fmt`. |
| unix_timestamp([timeExp[, format]]) |
Returns the UNIX timestamp of current or specified time. |
| weekday(date) |
Returns the day of the week for date/timestamp (0 = Monday, 1 = Tuesday, ..., 6 = Sunday). |
| weekofyear(date) |
Returns the week of the year of the given date. A week is considered to start on a Monday and week 1 is the first week with >3 days. |
| year(date) |
Returns the year component of the date/timestamp. |


Here is a list of all namespaces with brief descriptions:
[detail level 12]
| Ncamera | This namespace is used whenever referring to the camera module |
| Nconvert_to_speech | This package is used for speech translation module through gTTS |
| Ncv2 | This package is used for all image handling and image processing |
| NCV_algo | This is the CV_algo.py script located within Software that is imported to allow use of its various functions |
| Ndriver | This namespace is used whenever referring to the driver script |
| Ngoogletrans | This package is ued for the translation functionality of the processing |
| Ngtts | This package is the google text-to-speech package allowing us to use the Google Translate API for audio output in the correct dialect |
| Nled | This is the led.py script located within Software that was used for testing |
| Npicamera | This package is used to startup the camera from code |
| Npygame | This package is used to output audio cues for the user |
| Npytesseract | This package is used for the optical character recognition to retrieve text from a picture |
| Nrequests | This package is used so we can transmit data to the Google Translate API |
| ▼NRPi | |
| NGPIO | Import Raspberry Pi GPIO library to be used to identify and use pins within Raspberry Pi |
| Nsubprocess | Subprocess package to be used to call the other main python script |
| Ntime | This packaged is mostly used for button debouncing |


Hi there! I'm Amrut. I'm pursuing my Bachelor's degree in Computer Science (CS) at the National University of Singapore. At the moment, I'm looking for a full time job! I'm interested in Software Enginnering (Backend or Fullstack) and Machine Learning roles. I can start from
So, you're probably reading this to get to know me better. Admittedly, I'm not really that good at talking about myself, but I'll give it a shot and try to tell you things that you can't find in the sections below.
I started learning CS when I was 12, and programming when I was 15. One of the reasons why I love CS is the feeling of joy that comes with every success. That makes all the hours of head-scratching and debugging worth it!
My main interests lie in Software Development and Artificial Intellligence. This has largely determined the nature of projects that I've worked on. However, I have also worked on non-technical projects that focus on product development and ideas rather than the actual implementation (like this). Working with a large number of people from diverse backgrounds (academically as well as culturally) has taught me how to adjust to different personalities so that we can sucessfully work together.
When I'm not creating magic through code, you'll still likely catch me on my computer, surfing YouTube or playing FIFA. However, I do also enjoy playing sports like basketball, badminton and football, or just hanging out and having conversations with friends.
Worked as a Mobile Engineer (React Native) in the Shopee App team.
Worked as a full stack developer in the Master Maintenance team for 12 weeks.
Teaching, creating course material, facilitating discussions and evaluation of undergraduate students in a variety of CS modules:
Concentrations in Software Engineering and Artificial Intellligence
Minor in Management of Technology
Courses taken are listed below.
Activities:
Result: 97.2% in Grade 12 AISSCE Exam
Subjects:
Activities:
An open source project with >150 KLOC. It is a feedback platform that has served over 300,000 users. I am leading the Continuous Profiling team to design and implement a performance testing framework, which is to be integrated into the project's CI pipeline. I also worked on implementing a full-stack feature - Link Recovery. Apart from that, I have fixed multiple issues and reviewed PRs of other contributors.
Developed Using: Java 8, Angular (+TypeScript), JMeter
View ProjectMachine Learning model built on aggregate data from the College Scorecard dataset. It predicts the future earnings and loan repayment rates of a given student. The highest accuracy of 85.73% on the test set was achieved using a Gradient Boosting Regressor with Regressor chain model.
Developed Using: Python 3, Matplotlib, Scikit-learn, Pandas
View ProjectVisualisation of Conference Data from the Microsoft CMT system. Displays visualisations to show users insights derived from the uploaded file(s).
Developed Using: Python 2 (Django), Vue.js
View Project3D visualisation of the trajectory of a table tennis ball. The ball is tracked (using background subtraction and a sliding window) in the videos captured by three synchronized cameras from different angles. The 3D position is then dervied using Perspective Projection equations (using data from the camera calibrations and 2D positions of the ball).
Developed Using: Matlab, Python 3
View ProjectDesktop application that aims to make management of clubs in NUS easier. Includes features like user profiles, data import/export, email, polls, and task management.
Developed Using: Java 8, JUnit, JavaFX, FXML
View ProjectChrome Extension that helps visitors gather important information quickly. It uses data from users' activity to determine the most important text on any webpage. This text is then highlighted for visitors of the page. (Similar to Medium's Highlights feature)
Developed Using: JavaScript, HTML, CSS
View ProjectA small-scale version of the classic GameBoy Pokémon game developed using C++. Includes a couple of characters and Pokémon to choose from.
Developed Using: C++, SFML
View ProjectDeveloped to make time table scheduling for classes easier, with my high school in mind. Assigns subjects to classrooms, which are reflected in the corresponding teacher's schedule.
Developed Using: C++, WinBGIm
View ProjectMy first coding project. Developed using C++. Used WinBGIm for graphics. It served as an introduction to the world of self-learning required in CS and taught me how to go through documentation and tutorials to learn how to use the library. Unfortunately, it doesn't get you an actual ticket.
Developed Using: C++, WinBGIm
View ProjectA blockchain powered platform for consumers. Aims to make the donation process more transparent so that both retailers as well as beneficiaries can benefit more from cause marketing. This is done by showing consumers where their donated money goes and helps increase consumer trust in brands and retailers.
View ProjectAnalysis of the marketing strategy of a P&G subsidiary, Oral B. Includes maket analysis, SWOT analysis and the 4 Ps. Recommendations are made based on these findings.
View Project


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import include._ @import uk.gov.hmrc.thirdpartydeveloperfrontend.controllers.routes @import uk.gov.hmrc.thirdpartydeveloperfrontend.domain.models.applications.Application @import uk.gov.hmrc.thirdpartydeveloperfrontend.domain.models.developers.DeveloperSession @import uk.gov.hmrc.thirdpartydeveloperfrontend.domain.models.views.BackButtonToUrl @this(devMain: DevMain) @(app: Application)(implicit request: play.api.mvc.Request[Any], loggedIn: DeveloperSession, messages: Messages, applicationConfig: ApplicationConfig) @devMain( title = "You are now responsible for the application", userFullName = loggedIn.loggedInName, developerSession = Some(loggedIn), back = BackButtonToUrl(routes.ManageResponsibleIndividualController.showResponsibleIndividualDetails(app.id).url) ) {


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This list view checklist component displays a SKY UX-themed checklist of data. It includes
the ability to defined a custom search function for the data given. Ideally it is used in
conjunction with the list component.
When set to single, the checklist will only allow selection of one item in the checklist.
Specifies the name of the view.
Acceptable values: string
.
Specifies the name of the label field selector.
Acceptable values: string
.
Specifies the name of the description field selector.
Acceptable values: string
.
Specific a search function to apply on the view data. Default searches view data on label,
description, and category if field selectors are defined for the given field.
Acceptable value: (data: any, searchText: string) => boolean
.