Everything I want to say.
“He was not at all sure he was doing the right thing, but he was sure he was doing what he wanted to do.”
15 Feb 2017
I'm afraid to see my parents cry. I wouldn't know what to do. They're supposed to be the strong ones.... read more


Library: Foundation
Package: Dynamic
Header: Poco/Alignment.h
Description
A templated class that contains an enum value representing the alignment of the template argument. For example, AlignOf<int>::Alignment represents the alignment of type "int". The alignment calculated is the minimum alignment, and not necessarily the "desired" alignment returned by GCC's __alignof__ (for example). Note that because the alignment is an enum value, it can be used as a compile-time constant (e.g., for template instantiation).
Enumerations
Anonymous
Alignment = static_cast < unsigned int > (sizeof (AlignmentCalcImpl < T >)- sizeof (T))
Anonymous
Alignment_GreaterEqual_2Bytes = Alignment >= 2 ? 1 :0
Anonymous
Alignment_GreaterEqual_4Bytes = Alignment >= 4 ? 1 :0
Anonymous
Alignment_GreaterEqual_8Bytes = Alignment >= 8 ? 1 :0
Anonymous
Alignment_GreaterEqual_16Bytes = Alignment >= 16 ? 1 :0
Anonymous
Alignment_LessEqual_2Bytes = Alignment <= 2 ? 1 :0
Anonymous
Alignment_LessEqual_4Bytes = Alignment <= 4 ? 1 :0
Anonymous
Alignment_LessEqual_8Bytes = Alignment <= 8 ? 1 :0
Anonymous
Alignment_LessEqual_16Bytes = Alignment <= 16 ? 1 :0


Download Xcode for Free
Xcode 7 betaPre-release
This release include the Xcode IDE, Swift 2 compiler, Instruments, Simulator, and latest SDKs for OS X, iOS, and watchOS.
Released: Jun 8, 2015
Build: 7A120f
SDK: iOS 9 beta, OS X v10.11 beta
Xcode 6.3.1
This release includes the Xcode IDE, LLVM compiler, Instruments, iOS Simulator, the latest OS X and iOS SDKs, WatchKit and the Swift 1.2 programming language.
Updated: Apr 21, 2015
Build: 6D1002
SDK: iOS 8.3, OS X v10.10
Xcode 6.4 beta 3Pre-release
This is a pre-release version of the complete Xcode developer toolset for Mac, iPhone, iPad, and Apple Watch. This release requires OS X Yosemite.
Released: May 11, 2015
Build: 6E23
SDK: iOS 8.4 beta 3, OS X v10.10
Related Downloads and Resources
Sign in with your Apple ID to download previous versions of Xcode or additional tools from the developer downloads site.
Watch and learn from Apple engineers as they explain how to bring the best of Apple technologies to your apps.
Find a comprehensive set of programming guides, reference, and sample code for developing with the latest tools and technologies on iOS and OS X.
Post questions and share thoughts about using Xcode with fellow developers.


Your Music Hub
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/
75.2%
Statements
737/980
65.3%
Branches
207/317
75.79%
Functions
191/252
75.93%
Lines
735/968
| File |
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Statements |
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Branches |
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Functions |
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Lines |
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| src/ |
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100% |
113/113 |
98.68% |
75/76 |
100% |
11/11 |
100% |
111/111 |
| src/knowledge/ |
|
38.46% |
50/130 |
100% |
0/0 |
35.59% |
21/59 |
38.46% |
50/130 |
| src/language/ |
|
59.77% |
208/348 |
26.43% |
37/140 |
69.77% |
30/43 |
61.54% |
208/338 |
| src/search/ |
|
100% |
119/119 |
100% |
87/87 |
100% |
34/34 |
100% |
119/119 |
| src/speech/ |
|
94.81% |
73/77 |
50% |
5/10 |
95.45% |
21/22 |
94.81% |
73/77 |
| src/vision/ |
|
90.16% |
174/193 |
75% |
3/4 |
89.16% |
74/83 |
90.16% |
174/193 |


|
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Confidence |
Impact |
Impact Detail |
Type of entry |
Entry source |
Year |
Type |
Type other |
URL |
Founding year |
Machine-readability |
Size |
HQ city |
HQ province |
Email |
First name |
Last name |
Phone |
Title |
HQ country |
WB income level |
WB region |
Latitude |
Longitude |
Data type used |
Advocacy |
Advocacy detail |
Org optimization |
Org optimization detail |
Other use |
Other use detail |
New product/service |
New product/service detail |
Research |
Research detail |
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Popular Areas in red rose inn and restaurant
Red Rose Inn in Plant City closes unexpectedly | wtsp.com
May 21, 2012 . PLANT CITY, Fla. - The Red Rose Inn and Suites, a place where southern hospitality prevails, unexpectedly closed its doors Sunday night.
http://www.wtsp.com/news/local/story.aspx?storyid=256017
Red Rose Inn & Suites - Plant City, Florida - Yahoo! Travel
Red Rose Inn & Suites, Plant City - find photos, descriptions, maps, reviews, and expert advice on Yahoo! Travel. . See all Plant City Restaurants ». Map .
http://travel.yahoo.com/p-travelguide-14435949R-red_rose_inn_suites-i
Plant City's Red Rose Inn closes | Tampa Bay Times
May 21, 2012 . PLANT CITY The Red Rose Inn & Suites is staying closed after all. The longtime fixture at State Road 39 and Interstate 4 abruptly closed .
http://www.tampabay.com/news/business/plant-citys-red-rose-inn-closes/1231285
Red Rose Inn & Suites, Plant City - Deals and Guest Reviews
The Red Rose Inn & Suites has a restaurant and a bar. Breakfast is provided at the hotel daily. Attractions near the Red Rose Inn & Suites include the Everest .
http://www.hotelscombined.com/Hotel/Red_Rose_Inn_Suites.htm
The Red Rose Inn & Suites - Plant City, Florida - Hotel
The Red Rose Inn and Suites - Plant City, Florida - Hotel. . Red Rose Dining Room is Now Star . Hear what's happening this week at Red Rose Inn & Suites .
http://www.redroseinnandsuites.com/index1.htm
Red Rose Inn & Suites - Tampa, FL Metro - Citysearch
Red Rose Inn & Suites in Plant City, FL. Come to Citysearch® to get information, directions, and reviews on Red Rose Inn & Suites and other Restaurants in .
http://tampabay.citysearch.com/profile/36473097/plant_city_fl/red_rose_inn_suites.html


--- layout: default ---
Simple and engaging - monsters to kill, quests to complete. You don't even need a terminal to play.
The Land of the Rair is a game inspired by the MUDs of olde. You begin as an intrepid adventurer who joins a vast new world. You start your adventure with a tunic and a weapon. Kill monsters, solve puzzles, and defeat those who oppose the ways of the realm. Stake your claim in the world and find your place amongst the ranks of the greatest heroes to travel the land.
You can play in your web browser from anywhere. Works best in Chrome and Firefox. Or, download and play on Windows.
Hone your combat skills killing monsters or craft your way to the top of the marketboard.
Free to play, but never pay to win. Subscribe or purchase individual boosts for your account for additional perks.


{% extends "base_project.html" %} {% load humanize static hc_extras %} {% block title %}Opsgenie Integration for {{ site_name }}{% endblock %} {% block content %}
Opsgenie provides alerting, on-call scheduling, escalation policies, and incident tracking. You can integrate it with your {{ site_name }} account in a few simple steps.
Log into your Opsgenie account, select a team, and go to the team's Integrations › Add integration page.
The integration needs a Create and Update Access permission, make sure it is checked.
The integration does not need the Read Access and Delete Access checkboxes – make sure they are unchecked.
Give the integration a descriptive name, take note of its API Key, then save the integration.
Paste the API Key down below, and make sure the correct service region is selected. Save the integration, and you are done!


To align with government regulations for safe distancing, guests will be seated in two separate zones. Mingling between zones and after service is not permitted.
Please help us keep our special day within safe distancing regulations!
We seek your understanding if you have been seated in a zone that is not of your preference.


#assign spring="active">
DInject has moved to avaje.io/inject - taking you there now ...
Code Generation
- DInject generates source code where Micronaut generates bytecode
- DInject determines the ordering of dependency injection at compile time and not runtime
Both
- Do not use classpath scanning
- Support modularity (injecting dependencies across modules)


Class ru.stqa.rep.addressbook.tests.GroupCreationTests
|
100%
successful |
Tests
| Test | Duration | Result |
|---|---|---|
| testGroupCreation[0](GroupData{name='Р”СЂСѓР·СЊСЏ 0', id='2147483647', footer='Р”РѕРјР°С€РЅСЏСЏ РіСЂСѓРїРїР°, 0 '}) | 2.696s | passed |
| testGroupCreation[1](GroupData{name='Р”СЂСѓР·СЊСЏ 1', id='2147483647', footer='Р”РѕРјР°С€РЅСЏСЏ РіСЂСѓРїРїР°, 1 '}) | 1.854s | passed |
| testGroupCreation[2](GroupData{name='Р”СЂСѓР·СЊСЏ 2', id='2147483647', footer='Р”РѕРјР°С€РЅСЏСЏ РіСЂСѓРїРїР°, 2 '}) | 2.227s | passed |
Standard output
Было: 3 Стало: 4 Было: 4 Стало: 5 Было: 5 Стало: 6
Standard error
1507273356792 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/"<PHONE_NUMBER>839 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/"<PHONE_NUMBER>841 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/"<PHONE_NUMBER>852 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>938 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>266 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>307 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>308 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>320 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>343 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>595 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>015 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>553 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>687 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>912 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>913 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>923 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>955 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>034 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>083 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>084 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>092 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>119 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>456 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>493 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>495 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>505 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>525 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>616 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>665 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>667 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>677 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>709 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>953 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>253 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>567 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>674 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>822 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>823 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>832 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>848 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>911 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>952 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>953 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>966 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>986 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>338 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>370 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>371 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>383 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>401 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>456 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>501 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>502 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>514 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>545 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>822 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>145 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>500 Marionette DEBUG Canceled page load listener because no navigation has been detected<PHONE_NUMBER>635 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>841 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>843 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>853 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>908 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>995 Marionette DEBUG Received DOM event "beforeunload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>041 Marionette DEBUG Received DOM event "pagehide" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>042 Marionette DEBUG Received DOM event "unload" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>053 Marionette DEBUG Received DOM event "DOMContentLoaded" for "http://localhost/addressbook/group.php"<PHONE_NUMBER>161 Marionette DEBUG Received DOM event "pageshow" for "http://localhost/addressbook/group.php"


All cars have Dual Motor All-Wheel Drive, Autopilot, adaptive air suspension, premium interior and sound. You can return your car for a full refund within 7 days or 1,000 miles, whichever comes first. Learn more »


Founded in 2007, Twende helps you to join trips without having to worry about finding company! Choose a trip of your liking and have the benefit of having affordable and fun trips while networking and making new friends. Additionally, our coordinators are well trained to handle you in the most professional manner and thus you do not have to worry about any logistics.
We noticed that individuals find it hard to travel to new destinations without company. Sometimes, the people they would want to tag along with them are committed and thus unavailable. We make it our responsibility to make group-traveling possible for such individuals!


Gyratonic Hotta-Tanaka Solution
Gyratonic metric with off-diagonal terms can be generalized to the case of non-zero cosmological constant. The metric in conformally flat coordinates then reads $$ \mathrm{d}s^2=\frac{2\mathrm{d}\eta~\mathrm{d}\bar{\eta} - 2 \mathrm{d}\mathcal{U}~\mathrm{d}\mathcal{V} + 2H(\eta, \bar{\eta}) \delta(\mathcal{U}) ~\mathrm{d}\mathcal{U}^2}{\left[1+\frac{1}{6}\Lambda(\eta \bar{\eta}-\mathcal{U}\mathcal{V})\right]^2} + \frac{2J\left(\eta, \bar{\eta}, \mathcal{U}\right) \mathrm{d}\eta~\mathrm{d}\mathcal{U} +2\overline{J}\left(\eta, \bar{\eta}, \mathcal{U}\right) \mathrm{d}\bar{\eta}~\mathrm{d}\mathcal{U}}{\left[1+\frac{1}{6}\Lambda(\eta \bar{\eta}-\mathcal{U}\mathcal{V})\right]^2}.$$ We will consider class of solutions analogous to the gyratonic Aichelburg-Sexl solution (in sense of generalizing it to the $\Lambda \neq 0$ case) and Hotta-Tanaka solution (in sense that the function $H$ is exactly the same). Thus, we have $J = \frac{\chi}{2 \mathrm{i} \eta} \Theta(\mathcal{u})$ for $\chi = const.$ and profile of Hotta-Tanaka solution.
Visualisation of geodesic motion
Click on any picture to open the interactive version. It might take several seconds for interactive view to render.
Null geodesics intersecting gyratonic Hotta-Tanaka impulsive surface at $\eta \bar{\eta} = 1$ and with velocity components equal to $\dot{u} = 1, \dot{v} = 0, \dot{\eta} = 0$. Parameters of gyratonic impulse are set to $\chi = \pi, b = 1$.
Timelike geodesics intersecting gyratonic Hotta-Tanaka impulsive surface at $\eta \bar{\eta} = 1$ and with velocity components equal to $\dot{u} = \frac{1}{2}, \dot{v} = 1, \dot{\eta} = 0$. Parameters of gyratonic impulse are set to $\chi = \pi, b = 1$.
Timelike geodesics intersecting gyratonic Hotta-Tanaka impulsive surface at $\eta \bar{\eta} = 1$ and with velocity components equal to $\dot{u} = \frac{1}{2}, \dot{v} = 1, \dot{\eta} = 0$. Parameters of gyratonic impulse are set to $\chi = \pi, b = 1$.


At Crossing point 50-100 degrees, 25 Jul '18 in Khuvsgul province.
CCS Launching with Minister of Health, Dr A.Tsogtsetseg, 27 Sep '17 in Ministry of Foreign Affairs.
Khatgal soum health center, 25 Jul '18 in Khuvsgul province.
A moment of relaxation by Khuvsgul Lake. 25 July 2018.
Launching Ceremony of Emergency Operation Point, Health Department of Khuvsgul province, 24 Jul '18
Meeting with Honorable Minister of Health, Ms D. Sarangerel with MOH officials and WHO staff, 24 Jul '18 in Ministry of Health
Meeting with Ministry of Health officials, 26th September 2017.
RD awarded Medal of Friendship by Mr Z. Enkhbold, Chief Executive, President's Office, 26 Sep '17 in Government House.
Visit to FHC of Songino-Khairkhan District, 26 Sep 2017
With Dr Sergey Diorditsa in Government House, 26 Sep '17


We're having trouble communicating with the ThinkGeo Cloud. Please check the API key being used in
this
sample's JavaScript source code, and ensure it has access to the ThinkGeo Cloud services you are
requesting. You can create and manage your API keys at https://cloud.thinkgeo.com.


This page exists as an easy reference to compare and contrast the available open source Clojure-Datalog databases. Only databases under active development are listed.
| DataScript by Nikita Prokopov | An immutable in-memory Clojure(Script) database - "What if creating a database would be as cheap as creating a Hashmap?" |
| Datalevin by Juji | Datalevin extends DataScript with durable storage and has been designed to be embedded in applications to manage state. Data durability is handled by LMDB, which is optimized for concurrent read-intensive workloads. Datalevin does not store transaction history. |
| Datahike by lamdaforge | Datahike extends DataScript with durable storage via Hitchhiker Tree (a persistent off-heap data structure). Datahike provides similar functionality to Datomic On-Prem and can be used as a drop-in replacement for a subset of use-cases. Datahike is part of the replikativ toolbox for building distributed data management solutions. |
| Asami by Cisco Secure Malware Analytics | Schema-less triple store designed for Clojure(Script) graph analytics. Sibling project of the Naga Datalog rules engine |
| Crux by JUXT | Bitemporal document database for graph and SQL queries, with HTTP+JSON and Java APIs. Based on an "unbundled" architecture with pluggable storage. |
The following matrix may contain mistakes and be missing various important dimensions. If you believe other projects should be added or if any of the data is incorrect, please submit a PR. Thanks!
| Capabilities | DataScript | Datalevin | Datahike | Asami | Crux |
| Durable storage | N/A | Y | Y | Y | Y |
| Transaction-Time history | N/A | N/A | Y | Y | Y |
| Explicit Schema-on-Write | Y | Y | Y | N/A | N/A |
| “Datom” transaction semantics | Y | Y | Y | Y | N/A |
| Query planner | N | Proposed (Issue) | Proposed (Issue) | Y | Y |
| Speculative transactions | Y | N | Y | Planned (Issue) | Y |
| Transaction functions | Y | Partial (Issue) | Y | Proposed (Issue TBD) | Y |
| Valid-Time history | N/A | N | N | N | Y |
| Lazy queries | N | N | N | Y | Y |
| Ad-hoc "document" storage (i.e. maps of arbitrary types) | Y | Y | N | N | Y |
| Pull syntax | Y | Y | Y | Proposed (Issue) | Y |
| Evict / Excise / Purge (for data privacy compliance) | Y | Y | Y | N | Y |
| `byte-array` value type | Y | Y | N | N | Y |
| Full-text search (Lucene) | N | Proposed (Issue) | N | N | Y |
| Wildcard attributes | Y | Y | Y | Y | N |
| Advanced graph features (paths, transitive edges, multigraph) | N | N | N | Y | N |
| Composite tuples | Y | Planned (Issue) | Y | Planned (Issue TBD) | N |
| ClojureScript runtime | Y | N | Y | Y | N |
| Typed Java API | N | N | Y | N | Y |
| Graal native image | Y | Y | Possible | Possible | Possible (Issue) |
| HTTP API | N | N | Y | N | Y |
| Remote client API | N | Proposed (Issue) | Y | N | Y |
| GUI console | N | N | N | N | Y |
| CLI console | N | Y | N | N | N |
| SQL | N | N | N | N | Y |
| Online index upgrades | N | N | N | N | Y |
| Low-latency commits (asynchronous indexing) | N | N | N | N | Y |
| Single-writer atomic transactions | Y | Y | Y | Y | Y |
| Horizontal scaling (clustering) | N/A | Proposed (Issue) | Y | Proposed (Issue TBD) | Y |
| Commercial support | N | Planned | Y | N | Y |
| Legend: "N/A" ("not applicable") means the capability is explicitly a non-goal of the project | |||||
| Suggestions? Questions? |


A proxied Solr REST API to enable easy read-only access to any Solr instance.
| Path | Method | Description |
|---|---|---|
| /admin | GET | Administration Overview |
| /admin/{log} | GET | Inspect Logs |
| /admin/{log}/{field} | GET | Get Log Settings |
| /admin/{log}/{field}/{value} | POST | Set Log Settings |
| /solr/{query} | GET | Solr Search |
| query | Solr search query | path | string |
application/json
| default |
|
| log | The log on which to operate, i.e. cli, rest or proxy. | path | string |
| append | Whether to only append new log entries; default is true, will serve log entries from log files otherwise. | query | boolean |
application/json
| default |
|
| log | The log on which to operate, i.e. cli, rest or proxy. | path | string |
| field | The field of information to retrieve, i.e. level, location. | path | string |
application/json
| default |
|
| log | The log on which to operate, i.e. cli, rest or proxy. | path | string |
| field | The field of information to retrieve, i.e. level, location. | path | string |
application/json
| default |
|
| query | Solr search query | path | string |
| page | Pagination offset, e.g. 5 | query | integer |
| rows | Number of results per page, e.g. 100 | query | integer |
| sort | Sort function, e.g. id desc, price asc | query | string |
| rangeFilter | Restrict results to field values in range, e.g. price,5,30 | query | string |
| matchFilter | Restrict results to matching field values, e.g. author, tolkien | query | string |
| restrict | Restrict results to specified fields, e.g. id,size,weight | query | string |
| groupBy | Group results with the specified field, e.g. publisher | query | string |
| timeout | Limit response time in milliseconds and get preliminary results, e.g. 10 | query | integer |
application/json
| default |
|


beautiful soup is probably standard, but quick XML parsing can be done with xml.etree.ElementTree (note that this is not secure against malicious data)
import xml.etree.ElementTree as ET
tree = ET.parse('my_data.xml')
root = tree.getroot()
for child in root:
print(child.tag, child.attrib)
for neighbor in root.iter('neighbor'):
print(neighbor.attrib)
for country in root.findall('country'):
rank = country.find('rank').text
name = country.get('name')
print(name, rank)


Vertical-Align, all you need to know
Vertically alignment
Inline ELement
Inline-Block Element
Line Box
Aligning element's baselin relative to the line box's baseline
Aligning elements' outer edges relative to the line box's baseline
Aligning the element's outer edges relative to the line box's text box
Aligning the element's outer edges relative to the line box's outer edges
Axyz
zyxA


Hi Craig,
I realize this is a long shot but we are doing really
well and have lots of interesting problems to solve so
I thought I would reach out. David Underwood (one of our
developers) sent me this (and I quote):
"Hey Doug,
Craig contributed a widget to Dashing to display info
from Last.fm about the currently playing track. I checked
out his site and saw some encouraging stuff there.
He seems most comfortable with front-end/js, so maybe he'd
be a good fit for the Admin team.
Let's drop him a line and see if he's interested. Thanks!"
Any interest in a conversation? BTW - we are in Ottawa.
Rgs,
Doug Tetzner
Head of Talent Acquisition
Shopify


GA-01 GA-02 GA-03 GA-04 GA-05 GA-06 GA-07 GA-08 GA-09 GA-10 GA-11 GA-12 GA-13 GA-14
GA – Sen. Johnny Isakson (R) and Sen. David Perdue (R)
HEP Grants
In the past 9 years, this state has received: 2 HEP grants, totalling $500,000
Year Institution Amount ($)
------ ------------------------------- ------------
2019 Georgia Institute of Technology 0
2018 Georgia Institute of Technology 500,000
SC Contracts
In the past 9 years, this state has received: 9 Office of Science contracts, totalling $150,061
Year Vendor Amount ($) Item
------ ----------------------------- ------------ ----------------------------------------
2015 Nextel South Corp 4,500 Telephone And Telegraph Equipment
2015 Syscom Technologies, Inc. 104,635 ADPE System Configuration
2014 Nextel South Corp 5,000 Telephone And Telegraph Equipment
2013 Nextel South Corp 18,000 Telephone And Telegraph Equipment
2012 Global Equipment Company Inc. 4,926 Chain And Wire Rope
2012 Nextel South Corp 13,000 IT And Telecom- Other IT And Telecommuni
NSF MPS Grants
In the past 9 years, this state has received: 908 NSF MPS grants, totalling $222,656,101
Institution Amount ($)
----------------------------------------------------------- ------------
Georgia Tech Research Corporation 108,565,116
Emory University 57,796,081
Georgia State University Research Foundation 19,329,408
University Of Georgia Research Foundation In 12,304,487
University Of Georgia Research Foundation 11,683,117
Clark Atlanta University 1,996,362
Spelman College 1,513,576
Georgia Tech Research Corp 1,263,895
Kennesaw State University Research And Service Foundation 1,010,004
Multiple Recipients 984,000
Georgia Southern University Research And Serv 870,128
Georgia Southern University Research And Service Foundation 736,487
Morehouse College 620,675
University Of West Georgia 611,848
Kennesaw State University Research And Servic 609,000
Savannah State University 547,229
Agnes Scott College 457,119
Pii Redacted 450,000
University Of Georgia Res Fdn 325,423
Ga State U Res Foundation 239,530
University Of North Georgia 215,469
Georgia Gwinnett College 188,374
Ga. Southern Univ. R&S Foundation 149,997
Kennesaw State University Research And Servi 120,000
University Of Georgia 45,400
Augusta University Research Institute 23,376
SULI/CCI Interns
From 2014-2018, this state had 102 SULI/CCI interns
# Interns Program College
----------- --------- -------------------------------------
43 SULI Georgia Institute of Technology
9 SULI University of Georgia
7 SULI Georgia State University
7 SULI University of North Georgia
5 SULI Emory University
4 SULI Southern Polytechnic State University
4 SULI Kennesaw State University
3 SULI Berry College
3 SULI Covenant College
3 SULI Georgia College and State University
2 SULI Georgia Perimeter College
2 SULI Young Harris College
2 SULI Fort Valley State University
1 SULI Augusta University
1 SULI Mercer University
1 SULI Morehouse College
1 SULI Spelman College
1 SULI Emmanuel College
1 SULI Columbus State University
1 SULI Wesleyan College
1 SULI Agnes Scott College
102 — — Back to top
GA-01 – Rep. Earl L. “Buddy” Carter (R) – Wikipedia
Committees
Earl L. “Buddy” Carter is the #22 Republican on the House Committee on Energy and Commerce
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
This district received no NSF MPS grants from 2012-2020
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
GA-02 – Rep. Sanford D. Bishop, Jr. (D) – Wikipedia
Committees
Sanford D. Bishop, Jr. is the #8 Democrat on the House Committee on Appropriations
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
This district received no NSF MPS grants from 2012-2020
SULI/CCI Interns
From 2014-2018, this district had 4 SULI/CCI interns
Term Name College Host Lab Program
----------- ---------------------- ---------------------------- ------------------------------ ---------
Summer 2015 Dominic Frazier Brooks Fort Valley State University Los Alamos National Laboratory SULI
Summer 2015 Arnold Eatmon Fort Valley State University Los Alamos National Laboratory SULI
Summer 2014 Kirsten Alyssa Brown Mercer University Oak Ridge National Laboratory SULI
Summer 2014 Christopher Scanlon Columbus State University Oak Ridge National Laboratory SULI
GA-03 – Rep. A. Drew Ferguson IV (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $18,750 in NSF MPS grants.
Year Institution Amount ($)
------ -------------------------- ------------
2017 University Of West Georgia 18,750
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
GA-04 – Rep. Henry C. “Hank” Johnson, Jr. (D) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $24,901,032 in NSF MPS grants.
Year Institution Amount ($)
------ ------------------- ------------
2015 Emory University 1,368,202
2014 Emory University 5,844,459
2013 Emory University 10,402,280
2012 Agnes Scott College 201,886
2012 Emory University 7,084,205
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
GA-05 – Rep. John Lewis (D) – Wikipedia
HEP Grants
In the past 9 years, this district has received: $500,000 in SC HEP grants.
Institution Amount ($) Start End Principal Investigator Project Title
------------------------------- ------------ ------- ------- ------------------------ ------------------------------------------------------------------------------------------------------------------------------
Georgia Institute of Technology 0 2019 2020 Otte, Adam Nepomuk Research and Development of High-Sensitivity UV Solid-State Photon-Counting Devices for High-Energy Physics and Related Fields
Georgia Institute of Technology 500,000 2018 Ongoing Otte, Nepomuk Research and Development of High-Sensitivity UV Solid-State Photon-Counting Devices for High-Energy Physics and Related Fields
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $168,164,730 in NSF MPS grants.
Year Institution Amount ($)
------ -------------------------------------------- ------------
2020 Georgia Tech Research Corporation 62,625
2020 Emory University 929,304
2019 Morehouse College 499,931
2019 Georgia Tech Research Corporation 16,983,206
2019 Georgia State University Research Foundation 4,250,451
2019 Emory University 6,295,579
2018 Georgia Tech Research Corporation 18,859,145
2018 Emory University 7,410,402
2018 Georgia State University Research Foundation 1,482,921
2018 Pii Redacted 150,000
2018 Spelman College 599,016
2017 Spelman College 16,400
2017 Pii Redacted 150,000
2017 Georgia Tech Research Corporation 16,181,056
2017 Georgia State University Research Foundation 1,919,482
2017 Emory University 7,272,584
2017 Clark Atlanta University 405,008
2016 Georgia State University Research Foundation 5,198,815
2016 Spelman College 319,451
2016 Georgia Tech Research Corporation 11,617,698
2016 Emory University 6,815,919
2016 Clark Atlanta University 693,854
2016 Agnes Scott College 255,233
2015 Georgia Tech Research Corporation 9,352,206
2015 Georgia Tech Research Corp 1,263,895
2015 Georgia State University Research Foundation 2,200,010
2015 Ga State U Res Foundation 239,530
2015 Emory University 4,373,147
2014 Georgia Tech Research Corporation 6,388,629
2014 Clark Atlanta University 87,500
2014 Georgia State University Research Foundation 1,477,583
2014 Morehouse College 120,744
2014 Multiple Recipients 534,000
2013 Spelman College 550,000
2013 Multiple Recipients 450,000
2013 Georgia Tech Research Corporation 16,916,747
2013 Georgia State University Research Foundation 1,667,590
2013 Clark Atlanta University 405,000
2012 Georgia Tech Research Corporation 12,203,804
2012 Georgia State University Research Foundation 1,132,556
2012 Spelman College 28,709
2012 Clark Atlanta University 405,000
SULI/CCI Interns
From 2014-2018, this district had 60 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------------------- ------------------------------- ---------------------------------------------- ---------
Summer 2018 Jesse Thomas Goodwin Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2018 Pranav Gandham Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2018 Christian Alexander Chaney Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2018 Joseph Donald Litrel Georgia Institute of Technology Idaho National Laboratory SULI
Summer 2018 Gordon Hua Emory University Ames National Laboratory SULI
Summer 2018 Cassiopeia Cartwright Georgia Institute of Technology Ames National Laboratory SULI
Summer 2018 Kenneth Jon Higginbotham Georgia Institute of Technology SLAC National Accelerator Laboratory SULI
Summer 2018 D. Keith Coffman Georgia Institute of Technology Argonne National Laboratory SULI
Summer 2018 Matthew David Legate Georgia State University Oak Ridge National Laboratory SULI
Summer 2018 Tyler Quill Georgia Institute of Technology National Renewable Energy Laboratory SULI
Summer 2018 Alana Mitchell Homa Georgia Institute of Technology National Renewable Energy Laboratory SULI
Summer 2017 Andy Chen Georgia Institute of Technology SLAC National Accelerator Laboratory SULI
Summer 2017 Claire Reanne Arthurs Georgia Institute of Technology National Renewable Energy Laboratory SULI
Summer 2017 Joseph Martin Colosimo Georgia Institute of Technology Brookhaven National Laboratory SULI
Summer 2017 John Daniel Faught Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2017 Rohan Patrick Kadambi Georgia Institute of Technology Pacific Northwest National Laboratory SULI
Summer 2017 Samori Roberts Morehouse College SLAC National Accelerator Laboratory SULI
Summer 2017 Ramya Natarajan Georgia Perimeter College Brookhaven National Laboratory SULI
Summer 2017 Todd Elder Georgia State University Los Alamos National Laboratory SULI
Summer 2017 Kyle Harper Georgia Institute of Technology Savannah River National Laboratory SULI
Summer 2016 Andrew Royappa Georgia Institute of Technology National Renewable Energy Laboratory SULI
Summer 2016 Chaker Fares Georgia Institute of Technology National Renewable Energy Laboratory SULI
Summer 2016 Michael Lane Georgia Institute of Technology Los Alamos National Laboratory SULI
Summer 2016 Joshua Barnett Georgia Institute of Technology Brookhaven National Laboratory SULI
Summer 2016 Patrick Gabriel Gaulden Georgia Perimeter College Savannah River National Laboratory SULI
Summer 2016 Akhil Hemant Chavan Georgia Institute of Technology Argonne National Laboratory SULI
Summer 2016 Emily Victoria Ferrando Georgia Institute of Technology Argonne National Laboratory SULI
Summer 2016 Adrian Glass Georgia Institute of Technology Argonne National Laboratory SULI
Summer 2016 Gui Xin Zhang Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2016 Kristina Elisabeth Reed Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2016 Millie Ma Emory University Brookhaven National Laboratory SULI
Summer 2015 Maximilian Minhthong Nguyen Georgia Institute of Technology Argonne National Laboratory SULI
Summer 2015 Brian Allen Jollie Georgia Institute of Technology Thomas Jefferson National Accelerator Facility SULI
Summer 2015 Yohan John Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2015 Kaitlyn Ann Lapen Emory University Argonne National Laboratory SULI
Summer 2015 Zoe Klesmith Georgia Institute of Technology Argonne National Laboratory SULI
Summer 2015 Madeline M Dekarske Agnes Scott College Oak Ridge National Laboratory SULI
Summer 2015 Daniel Engel Georgia Institute of Technology Lawrence Livermore National Laboratory SULI
Summer 2014 Gregory Szalkowski Georgia Institute of Technology Princeton Plasma Physics Laboratory SULI
Summer 2014 Dustin Yang Georgia Institute of Technology Lawrence Livermore National Laboratory SULI
Summer 2014 Varija Agarwal Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2014 Andrea Vetrone Georgia Institute of Technology Argonne National Laboratory SULI
Summer 2014 Dana Max Emory University National Renewable Energy Laboratory SULI
Summer 2014 Jennifer Nguyen Georgia Institute of Technology Oak Ridge National Laboratory SULI
Summer 2014 Allyson Rogers Georgia Institute of Technology National Renewable Energy Laboratory SULI
Summer 2014 Benjamin Charles Musall Georgia Institute of Technology Savannah River National Laboratory SULI
Summer 2014 Kierra Shanelle Brown Spelman College Brookhaven National Laboratory SULI
Summer 2014 Karla Brooke Wagner Georgia Institute of Technology Pacific Northwest National Laboratory SULI
Summer 2014 Christian Tuchez Georgia Institute of Technology Idaho National Laboratory SULI
Spring 2018 Claire Reanne Arthurs Georgia Institute of Technology Lawrence Berkeley National Laboratory SULI
Spring 2018 Lindsey Danielle Ulmer Georgia Institute of Technology Oak Ridge National Laboratory SULI
Spring 2017 Diana Snyder Georgia State University National Renewable Energy Laboratory SULI
Spring 2017 Jesse Alexander Fernandez Georgia Institute of Technology Los Alamos National Laboratory SULI
Spring 2017 Matthew Leo Wyatt Georgia Institute of Technology National Renewable Energy Laboratory SULI
Spring 2016 Salene Marie Rowe Georgia State University Idaho National Laboratory SULI
Spring 2016 Bryan Jeffrey Mann Emory University Los Alamos National Laboratory SULI
Spring 2015 Arielle Hackel Georgia State University Pacific Northwest National Laboratory SULI
Spring 2015 Matthew Rager Georgia State University Oak Ridge National Laboratory SULI
Fall 2015 Katherine Myers Georgia Institute of Technology Los Alamos National Laboratory SULI
Fall 2014 Matthew Rager Georgia State University Oak Ridge National Laboratory SULI
GA-06 – Rep. Lucy McBath (D) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
This district received no NSF MPS grants from 2012-2020
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
GA-07 – Rep. Rob Woodall (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
In the past 9 years, this district has received: 7 Office of Science contracts, totalling $45,426
Year Vendor Amount ($) Item
------ ----------------------------- ------------ ----------------------------------------
2015 Nextel South Corp 4,500 Telephone And Telegraph Equipment
2014 Nextel South Corp 5,000 Telephone And Telegraph Equipment
2013 Nextel South Corp 18,000 Telephone And Telegraph Equipment
2012 Global Equipment Company Inc. 4,926 Chain And Wire Rope
2012 Nextel South Corp 13,000 IT And Telecom- Other IT And Telecommuni
NSF MPS Grants
In the past 9 years, this district has received: $188,374 in NSF MPS grants.
Year Institution Amount ($)
------ ------------------------ ------------
2018 Georgia Gwinnett College 39,931
2016 Georgia Gwinnett College 148,443
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
GA-08 – Rep. Austin Scott (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $593,098 in NSF MPS grants.
Year Institution Amount ($)
------ -------------------------- ------------
2016 University Of West Georgia 23,380
2014 University Of West Georgia 340,019
2013 University Of West Georgia 229,699
SULI/CCI Interns
From 2014-2018, this district had 1 SULI/CCI intern
Term Name College Host Lab Program
----------- --------------- ---------------- ------------------------------------- ---------
Summer 2014 Chelsea Widener Wesleyan College Pacific Northwest National Laboratory SULI
GA-09 – Rep. Doug Collins (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $215,469 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------------- ------------
2018 University Of North Georgia 129,000
2014 University Of North Georgia 86,469
SULI/CCI Interns
From 2014-2018, this district had 10 SULI/CCI interns
Term Name College Host Lab Program
----------- ------------------------ --------------------------- ------------------------------------- ---------
Summer 2018 Lydia Nicole Skolrood University of North Georgia Oak Ridge National Laboratory SULI
Summer 2017 Daniel Dean Mihok University of North Georgia Oak Ridge National Laboratory SULI
Summer 2017 Sonia Alcantar University of North Georgia Pacific Northwest National Laboratory SULI
Summer 2016 Christopher Luke Baglivi Emmanuel College Brookhaven National Laboratory SULI
Summer 2014 Karissa Lynn Cross Young Harris College Oak Ridge National Laboratory SULI
Spring 2017 Sonia Alcantar University of North Georgia Pacific Northwest National Laboratory SULI
Spring 2017 Donna K Mccullough University of North Georgia Los Alamos National Laboratory SULI
Spring 2017 Kimberly Theresa Wright University of North Georgia Los Alamos National Laboratory SULI
Spring 2014 Ashley Renee Cross Young Harris College Oak Ridge National Laboratory SULI
Fall 2018 Lydia Nicole Skolrood University of North Georgia Oak Ridge National Laboratory SULI
GA-10 – Rep. Jody B. Hice (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $24,508,427 in NSF MPS grants.
Year Institution Amount ($)
------ -------------------------------------------- ------------
2020 University Of Georgia Research Foundation 86,764
2019 University Of Georgia Research Foundation 4,922,324
2018 Pii Redacted 150,000
2018 University Of Georgia Research Foundation 3,682,678
2017 University Of Georgia Research Foundation 2,991,351
2017 University Of Georgia Research Foundation In 56,853
2016 University Of Georgia 38,000
2016 University Of Georgia Research Foundation In 1,885,938
2015 University Of Georgia 7,400
2015 University Of Georgia Res Fdn 325,423
2015 University Of Georgia Research Foundation In 1,640,523
2014 University Of Georgia Research Foundation In 4,416,673
2013 University Of Georgia Research Foundation In 1,802,950
2012 University Of Georgia Research Foundation In 2,501,550
SULI/CCI Interns
From 2014-2018, this district had 12 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------------- ------------------------------------ -------------------------------------- ---------
Summer 2018 Daniel Garon University of Georgia Savannah River National Laboratory SULI
Summer 2017 Sarah Robinson University of Georgia Oak Ridge National Laboratory SULI
Summer 2017 William Moxley Georgia College and State University National Renewable Energy Laboratory SULI
Summer 2017 Jacob Beckham University of Georgia Lawrence Livermore National Laboratory SULI
Summer 2016 Forrest Brown Georgia College and State University Oak Ridge National Laboratory SULI
Summer 2014 Ryan Edward Brush University of Georgia National Renewable Energy Laboratory SULI
Spring 2017 Forrest Brown Georgia College and State University Oak Ridge National Laboratory SULI
Spring 2016 Chloe Moore University of Georgia Brookhaven National Laboratory SULI
Spring 2016 Sarah Schyck University of Georgia Savannah River National Laboratory SULI
Spring 2014 Tim Dinh Tran University of Georgia Oak Ridge National Laboratory SULI
Fall 2018 Gregory Paul Coughlin University of Georgia Lawrence Berkeley National Laboratory SULI
Fall 2014 Roger Zhong University of Georgia Oak Ridge National Laboratory SULI
GA-11 – Rep. Barry Loudermilk (R) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
In the past 9 years, this district has received: 2 Office of Science contracts, totalling $104,635
Year Vendor Amount ($) Item
------ ------------------------- ------------ -------------------------
2015 Syscom Technologies, Inc. 104,635 ADPE System Configuration
NSF MPS Grants
In the past 9 years, this district has received: $1,739,004 in NSF MPS grants.
Year Institution Amount ($)
------ --------------------------------------------------------- ------------
2019 Kennesaw State University Research And Service Foundation 480,773
2018 Kennesaw State University Research And Service Foundation 469,210
2017 Kennesaw State University Research And Service Foundation 60,021
2016 Kennesaw State University Research And Servic 240,000
2015 Kennesaw State University Research And Servic 369,000
2012 Kennesaw State University Research And Servi 120,000
SULI/CCI Interns
From 2014-2018, this district had 8 SULI/CCI interns
Term Name College Host Lab Program
----------- --------------------- ------------------------------------- ------------------------------------- ---------
Summer 2017 Brodie Schroeder Kennesaw State University Ames National Laboratory SULI
Summer 2016 Pearl Jean Kennesaw State University Ames National Laboratory SULI
Summer 2015 Adam Amster Kennesaw State University Pacific Northwest National Laboratory SULI
Summer 2015 Edward Wachira Mwangi Kennesaw State University Pacific Northwest National Laboratory SULI
Summer 2014 Chase Davani Southern Polytechnic State University Oak Ridge National Laboratory SULI
Spring 2016 Phuong H Pham Southern Polytechnic State University Oak Ridge National Laboratory SULI
Spring 2014 David Bush Lay II Southern Polytechnic State University Oak Ridge National Laboratory SULI
Fall 2015 Phuong H Pham Southern Polytechnic State University Oak Ridge National Laboratory SULI
GA-12 – Rep. Rick W. Allen (R) – Wikipedia
Committees
Rick W. Allen is the #1 Republican on the House Subcommittee on Early Childhood, Elementary, and Secondary Education
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
In the past 9 years, this district has received: $2,327,217 in NSF MPS grants.
Year Institution Amount ($)
------ ----------------------------------------------------------- ------------
2019 Georgia Southern University Research And Service Foundation 92,362
2018 Augusta University Research Institute 23,376
2018 Georgia Southern University Research And Service Foundation 577,125
2017 Georgia Southern University Research And Serv 34,000
2017 Georgia Southern University Research And Service Foundation 67,000
2015 Ga. Southern Univ. R&S Foundation 149,997
2014 Georgia Southern University Research And Serv 364,440
2013 Georgia Southern University Research And Serv 196,780
2012 Georgia Southern University Research And Serv 274,908
2012 Savannah State University 547,229
SULI/CCI Interns
From 2014-2018, this district had 1 SULI/CCI intern
Term Name College Host Lab Program
----------- ------------ ------------------ ---------------------------------- ---------
Spring 2017 David Meeker Augusta University Savannah River National Laboratory SULI
GA-13 – Rep. David Scott (D) – Wikipedia
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
This district received no NSF MPS grants from 2012-2020
SULI/CCI Interns
This district had no SULI/CCI interns from 2014-2018
GA-14 – Rep. Tom Graves (R) – Wikipedia
Committees
Tom Graves is the #4 Republican on the House Appropriations Subcommittee on Commerce, Justice, and Science
Tom Graves is the #9 Republican on the House Committee on Appropriations
HEP Grants
This district received no SC HEP grants from 2012-2020
SC Contracts
This district received no SC contracts from 2012-2020
NSF MPS Grants
This district received no NSF MPS grants from 2012-2020
SULI/CCI Interns
From 2014-2018, this district had 6 SULI/CCI interns
Term Name College Host Lab Program
----------- -------------------------- ---------------- ------------------------------------- ---------
Summer 2018 Abigail Faith Gianis Covenant College Brookhaven National Laboratory SULI
Summer 2018 Abbygail Louise Ochs Berry College Oak Ridge National Laboratory SULI
Summer 2018 Nicholas Stephen Kiratzis Covenant College Pacific Northwest National Laboratory SULI
Summer 2018 Parker John Roberts Berry College Princeton Plasma Physics Laboratory SULI
Summer 2015 Julia Lynn Adkins Berry College Oak Ridge National Laboratory SULI
Summer 2015 Christopher Ronald Elliott Covenant College Oak Ridge National Laboratory SULI


Event-driven Programming
When you perform an action on a graphical component you
generate an event.
In event-driven programming the program responds to events.
The program responds to events that the user
generates in interacting with GUI components.
The order of events is controlled by the user.


{include file="public/static/index/common/head.html"/}
{include file="public/static/index/common/nav.html"/}
密码找回
*手机号
*验证码
发送
{include file="public/static/index/common/footer.html"/}


Kiosk Example
Order
Help
Order Here
Welcome to the Kiosk Demo! Feel free to place an order!
Menu
Waiting to initialize...
Order
As you add items to your order, they will appear here.
Card Number
Expiration
CVV
Postal
Pay Now


Notebooks
Story: Oh old woman! At what place did you grow up?
Oh old woman! At what place did you grow up?
Title
Oh old woman! At what place did you grow up?
Collection
Summary
An insert containing an address to an old woman in which she is asked where she grew up as a child and what the place's name is. It asks her who her friends (or companions) were, and what her people's names are.
Comments
1) This story is a loose insert found inside the back cover of Book VIII-15, along with some other pages containing untranslated |xam text and a scrap of paper on which the Latin name of the Blue crane is written (Tetrapteryx paradisea), 2) See also Thy name which is a Bushman name, what is it?' (at Breakwater)
Contributors
possibly Dia!kwain (David Hoesar) (V) ?
Date
30 July 1878 (partly written or rewritten on)
Categories
Keywords
language (|xam) , language ('Oh old woman!') , old woman (her place) , old woman (her relations) , old woman ('At what place did you grow up?') , old woman (her personal history) , personal history (of an old woman) , personal history ('Oh old woman! At what place did you grow up?')
Story Pages
inserts in Book VIII-15


FAQ
Ordering a product is easy. Just click on the product you are interested in and you will see a page showing detail of that product. In the same page you will see a button saying Add to cart. Click on that button and you will see a page showing detail of your cart and a Order button. Click on that button and provide your information.
We need to verify the phonenumber to prevent fake orders by making sure that the person ordering the product is owner of that phonenumber.
Typically, you should receive your products within 48 hours but this can vary due to strikes and traffic jam.
No need to worry. You can always contact us and ask us to deliver your product again.
When you order something we need to visit the vendor of that product first and then deliver it to you. This process can be expensive so you need to pay the delivery charge yourself.
In order to sell with us you need to fill this form first. Once we received your application we will review it and may contact you for further information. If your application is accepted we will contact you.


--- layout: post title: Why I Love Extension Methods date: 2009-11-30 09:52:08.000000000 +01:00 categories: [] tags: - .NET - Extension Methods - Html Agility Pack status: publish type: post published: true meta: {} author: login: admin email<EMAIL_ADDRESS>display_name: Philippe first_name: Philippe last_name: Vlérick excerpt: !ruby/object:Hpricot::Doc options: {} ---
The other day, I was using for the first time the Html Agility Pack library.
The method I use the most is the SelectNodes to which you give an XPath and that returns an HtlmNodeCollection containing the resulting HtmlNodes or null if no node where found.
I don’t know if this is a design decision, but returning null when there is no match is not very nice. If you use the expression as is in a foreach statement, it will throw a NullReferenceException if no match.
A simple solution is to use the coalescing operator next to the function’s call, in order to give the foreach an empty Enumerable to avoid the exception.
htmlNode.SelectNodes(xpath) ?? Enumerable.Empty<HtmlNode>()
This is working well, but it’s a bit ugly to repeat that in every foreach statement.
This is where Extension Methods are so enjoyable. Let’s just add a new method to our HtmlNode friend that returns an empty enumerable when SelectNodes return null.
internal static class HtmlAgilityPackExtension { internal static IEnumerable<HtmlNode> SelectNodesOrEmpty(this HtmlNode htmlNode, String xpath) { return htmlNode.SelectNodes(xpath) ?? Enumerable.Empty<HtmlNode>(); } }
There we go. From now on, I can simply foreach over a SelectNodesOrEmpty result of any HtmlNode, with no fear of any exception.


{% extends "base.html" %} {% load widget_tweaks %} {% block main %}
If you already have an account Sign in instead.


{{define "Head"}}
Oh snap! Our team of gophers could not find the web page you are looking for. Try one of these pages:


{% extends "layout.html" %} {% load static %} {% block style %} {% endblock %} {% block body %}
Is my implementation of the game 2048, made for CS50W final project
I Hope you enjoy


My Project
// A code block. // Spanning multiple lines.
// Special handling of some extensions
Inline=code
at the start of a line. Then a code that is inside of cd dir
a text, should be delimited by spaces. But not when it is in (alert('www');
) parentheses.
![mosra@don-perverzo m.css]$
{ // a block // that is indented // but has a lot of trailing whitespace which should be removed }
Look:
void foo() { return; }
A paragraph following a one line snippet should not be merged with it.
void foo() { return; }
This one also not.
#include <cmath> void bar() { return; }
And this should not produce a warning.
Another paragraph.
Code block inside a list (has to be done using HTML):
A paragraph.
#include <os>
Another paragraph.
Yet another
- A single paragraph, rendered without the wrapping tag


[INFO] BackWPup 3.2.5; A project of Inpsyde GmbH
[INFO] BackWPup job: Weekly lightweight backup
[INFO] Logfile is: backwpup_log_0adefe_2016-08-06_03-00-48.html
[INFO] Backup file is: backwpup_daily_0adefe_2016-08-06_03-00-48.tar.gz
[06-Aug-2016 03:00:48] 1. Try to backup database …
[06-Aug-2016 03:00:48] Connected to database openarko_wp01 on localhost
[06-Aug-2016 03:00:52] Added database dump "openarko_wp01.sql" with 44.17 MB to backup file list
[06-Aug-2016 03:00:52] Database backup done!
[06-Aug-2016 03:00:52] 1. Trying to create a WordPress export to XML file …
[06-Aug-2016 03:01:00] Check WP Export file …
[06-Aug-2016 03:01:01] WP Export file is a valid WXR file.
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[06-Aug-2016 03:01:01] 1. Trying to generate a file with installed plugin names …
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[06-Aug-2016 03:01:04] Backup archive created.
[06-Aug-2016 03:01:04] Archive size is 6.39 MB.
[06-Aug-2016 03:01:04] 5 Files with 50.08 MB in Archive.
[06-Aug-2016 03:01:04] 1. Try to send backup with email …
[06-Aug-2016 03:01:04] Sending email to<EMAIL_ADDRESS>[06-Aug-2016 03:01:32] Email sent.
[06-Aug-2016 03:01:32] One old log deleted
[06-Aug-2016 03:01:32] Job done in 44 seconds.


--- layout: base title: Global Day Of Coderetreat audience: participants extraStyles: ["/js/bundled/events.css"] ---


A random number will be generated.
Each of the four crystals will add points to your total that is randomly generated.
The game is won once you match the generated number and lost if you go over.
The value of the crystals is unknown.
The crystal's value will change each game.


Stopped Jobs
No stopped jobs found
Id
Queue
Class
Method
TimeZone
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{{job.TimeZoneId}}


| G.A.R. 34 |
| [ See rules 147, 150and
159 (1) ] |
| GRANT-IN-AID-BILL |
| Bill No........................................... |
| Head of Account........................................... |
Received the sum of Rs...........................................(Rupees
.........................................being
the grant-in-aid for the period .........................................sanctioned
by .........................................in
his letter No...........................................dated
...........................................(copy
enclosed).
|
| |
Signature |
| Dated........................................... |
Designation |
| |
|
| Countersigned for Rs........................................... |
Signature |
| Dated........................................... |
Designation of Drawing Officer |
|
| |
| FOR USE IN PAY AND ACCOUNTS OFFICE |
| Passed for payment of Rs...........................................(Rupees...................................................................... |
| Payment by |
| Cheque No........................ |
| Date........................ |
| Pay and Accounts Officer |


Learn about Lushootseed
Let's start learning Lushootseed! In this section you can view flashcards of Lushootseed vocabulary. After viewing six flashcards you'll be presented with the option to take a fun quiz! Or you can continue learning more words!


Next we check how well the total energy of the system is conserved in a total-f simulation. The total physical particles in the system is given by
|
| (127) |
The spatial volume occupied by these physical particles is given by V = Ns∕n0, where n0 is the equilibrium electron number density. Since the length along the x direction of the system is L, the cross section Syz of volume occupied by these physical particles is given by
|
| (128) |
Then the total electrical energy in the volume is given by
|
| (129) |
Deﬁne W0 = (mv02∕2)∑ wj, and the normalized electric energy WE = WE∕W0, which can be further written as
|
| (130) |
The total particle kinetic energy in the system is given by
|
| (131) |
Deﬁne the normalized kinetic energy Wk = Wk∕W0, which can be further written as
|
| (132) |
Figure 9 plots the time evolution of WE, Wk −Wk(t = 0), and Wk + WE −Wk(t = 0), which indicates the total energy is approximately conserved.


See https://www.publictendering.com/cpv-codes/list-of-the-cpv-codes for a description of the CPV classification codes.


Customer Section
After order has gone through, Customers can view their orders in their email also have an order section on site where they can see their order details
Admin Section
Customer Order Details
Customer Full name:
Customer Email:
Phone No:
Order no:
Product
Frame Style with images:
Sales page- should show list of sales(order) and have total at the bottom of the list
Backend Settings, GEO currency management
Price Management – Section where Admin can edit price of the frame.


I graduated from the University of Colorado Boulder with a BS and MS in Aerospace Engineering with a focus in Fluids and Propulsion. I currently work in the Thermal Fluids Analysis department at SpaceX, where I help design Starship. I like fluids, propulsion, thermodynamics and heat transfer, and software. I've linked my email, CV, Github, and LinkedIn below!


| View source on GitHub |
TPUEstimator
Estimator with TPU support.
Inherits From: Estimator
TPUEstimator also supports training on CPU and GPU. You don't need to define
a separate tf.estimator.Estimator
.
TPUEstimator handles many of the details of running on TPU devices, such as replicating inputs and models for each core, and returning to host periodically to run hooks.
TPUEstimator transforms a global batch size in params to a per-shard batch
size when calling the input_fn
and model_fn
. Users should specify
global batch size in constructor, and then get the batch size for each shard
in input_fn
and model_fn
by params['batch_size']
.
For training, model_fn
gets per-core batch size; input_fn
may get
per-core or per-host batch size depending on per_host_input_for_training
in TPUConfig
(See docstring for TPUConfig for details).
For evaluation and prediction, model_fn
gets per-core batch size and
input_fn
get per-host batch size.
model_fn
should return TPUEstimatorSpec
, which expects the eval_metrics
for TPU evaluation. If eval_on_tpu is False, the evaluation will execute on
CPU or GPU; in this case the following discussion on TPU evaluation does not
apply.
TPUEstimatorSpec.eval_metrics
is a tuple of metric_fn
and tensors
, where
tensors
could be a list of any nested structure of Tensor
s (See
TPUEstimatorSpec
for details). metric_fn
takes the tensors
and returns
a dict from metric string name to the result of calling a metric function,
namely a (metric_tensor, update_op)
tuple.
One can set use_tpu
to False
for testing. All training, evaluation, and
predict will be executed on CPU. input_fn
and model_fn
will receive
train_batch_size
or eval_batch_size
unmodified as params['batch_size']
.
TPU evaluation only works on a single host (one TPU worker) except BROADCAST mode.
input_fn
for evaluation should NOT raise an end-of-input exception
(OutOfRangeError
or StopIteration
). And all evaluation steps and all
batches should have the same size.
# The metric Fn which runs on CPU. def metric_fn(labels, logits): predictions = tf.argmax(logits, 1) return { 'accuracy': tf.compat.v1.metrics.precision( labels=labels, predictions=predictions), } # Your model Fn which runs on TPU (eval_metrics is list in this example) def model_fn(features, labels, mode, config, params): ... logits = ... if mode = tf.estimator.ModeKeys.EVAL: return tpu_estimator.TPUEstimatorSpec( mode=mode, loss=loss, eval_metrics=(metric_fn, [labels, logits])) # or specify the eval_metrics tensors as dict. def model_fn(features, labels, mode, config, params): ... final_layer_output = ... if mode = tf.estimator.ModeKeys.EVAL: return tpu_estimator.TPUEstimatorSpec( mode=mode, loss=loss, eval_metrics=(metric_fn, { 'labels': labels, 'logits': final_layer_output, }))
Prediction on TPU is an experimental feature to support large batch inference.
It is not designed for latency-critical system. In addition, due to some
usability issues, for prediction with small dataset, CPU .predict
, i.e.,
creating a new TPUEstimator
instance with use_tpu=False
, might be more
convenient.
Note: In contrast to TPU training/evaluation, the input_fn
for prediction
should raise an end-of-input exception (OutOfRangeError
or
StopIteration
), which serves as the stopping signal to TPUEstimator
. To be
precise, the ops created by input_fn
produce one batch of the data.
The predict()
API processes one batch at a time. When reaching the end of
the data source, an end-of-input exception should be raised by one of these
operations. The user usually does not need to do this manually. As long as the
dataset is not repeated forever, the tf.data
API will raise an end-of-input
exception automatically after the last batch has been produced.
Note: Estimator.predict returns a Python generator. Please consume all the data from the generator so that TPUEstimator can shutdown the TPU system properly for user.
TPU prediction only works on a single host (one TPU worker).
input_fn
must return a Dataset
instance rather than features
. In
fact, .train() and .evaluate() also support Dataset as return value.
height = 32 width = 32 total_examples = 100 def predict_input_fn(params): batch_size = params['batch_size'] images = tf.random.uniform( [total_examples, height, width, 3], minval=-1, maxval=1) dataset = tf.data.Dataset.from_tensor_slices(images) dataset = dataset.map(lambda images: {'image': images}) dataset = dataset.batch(batch_size) return dataset def model_fn(features, labels, params, mode): # Generate predictions, called 'output', from features['image'] if mode == tf.estimator.ModeKeys.PREDICT: return tf.contrib.tpu.TPUEstimatorSpec( mode=mode, predictions={ 'predictions': output, 'is_padding': features['is_padding'] }) tpu_est = TPUEstimator( model_fn=model_fn, ..., predict_batch_size=16) # Fully consume the generator so that TPUEstimator can shutdown the TPU # system. for item in tpu_est.predict(input_fn=input_fn): # Filter out item if the `is_padding` is 1. # Process the 'predictions'
export_saved_model
exports 2 metagraphs, one with saved_model.SERVING
, and
another with saved_model.SERVING
and saved_model.TPU
tags. At serving
time, these tags are used to select the appropriate metagraph to load.
Before running the graph on TPU, the TPU system needs to be initialized. If
TensorFlow Serving model-server is used, this is done automatically. If not,
please use session.run(tpu.initialize_system())
.
There are two versions of the API: ExportSavedModelApiVersion.V1 and V2.
In V1, the exported CPU graph is model_fn
as it is. The exported TPU graph
wraps tpu.rewrite()
and TPUPartitionedCallOp
around model_fn
so
model_fn
is on TPU by default. To place ops on CPU,
tpu.outside_compilation(host_call, logits)
can be used.
def model_fn(features, labels, mode, config, params): ... logits = ... export_outputs = { 'logits': export_output_lib.PredictOutput( {'logits': logits}) } def host_call(logits): class_ids = math_ops.argmax(logits) classes = string_ops.as_string(class_ids) export_outputs['classes'] = export_output_lib.ClassificationOutput(classes=classes) tpu.outside_compilation(host_call, logits) ...
In V2, export_saved_model()
sets up params['use_tpu']
flag to let the user
know if the code is exporting to TPU (or not). When params['use_tpu']
is
True
, users need to call tpu.rewrite()
, TPUPartitionedCallOp
and/or
batch_function()
. Alternatively use inference_on_tpu()
which is a
convenience wrapper of the three.
def model_fn(features, labels, mode, config, params): ... # This could be some pre-processing on CPU like calls to input layer with # embedding columns. x2 = features['x'] * 2 def computation(input_tensor): return layers.dense( input_tensor, 1, kernel_initializer=init_ops.zeros_initializer()) inputs = [x2] if params['use_tpu']: predictions = array_ops.identity( tpu_estimator.inference_on_tpu(computation, inputs, num_batch_threads=1, max_batch_size=2, batch_timeout_micros=100), name='predictions') else: predictions = array_ops.identity( computation(*inputs), name='predictions') key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY export_outputs = { key: export_lib.PredictOutput({'prediction': predictions}) } ...
TIP: V2 is recommended as it is more flexible (eg: batching, etc).
__init__
__init__( model_fn=None, model_dir=None, config=None, params=None, use_tpu=True, train_batch_size=None, eval_batch_size=None, predict_batch_size=None, batch_axis=None, eval_on_tpu=True, export_to_tpu=True, export_to_cpu=True, warm_start_from=None, embedding_config_spec=None, export_saved_model_api_version=ExportSavedModelApiVersion.V1 )
Constructs an TPUEstimator
instance.
model_fn
: Model function as required by Estimator
which returns
EstimatorSpec or TPUEstimatorSpec. training_hooks
, 'evaluation_hooks',
and prediction_hooks
must not capure any TPU Tensor inside the
model_fn.model_dir
: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator to
continue training a previously saved model. If None
, the model_dir in
config
will be used if set. If both are set, they must be same. If
both are None
, a temporary directory will be used.config
: An tpu_config.RunConfig
configuration object. Cannot be None
.params
: An optional dict
of hyper parameters that will be passed into
input_fn
and model_fn
. Keys are names of parameters, values are
basic python types. There are reserved keys for TPUEstimator
,
including 'batch_size'.use_tpu
: A bool indicating whether TPU support is enabled. Currently, -
TPU training and evaluation respect this bit, but eval_on_tpu can
override execution of eval. See below.train_batch_size
: An int representing the global training batch size.
TPUEstimator transforms this global batch size to a per-shard batch
size, as params['batch_size'], when calling input_fn
and model_fn
.
Cannot be None
if use_tpu
is True
. Must be divisible by total
number of replicas.eval_batch_size
: An int representing evaluation batch size. Must be
divisible by total number of replicas.predict_batch_size
: An int representing the prediction batch size. Must be
divisible by total number of replicas.batch_axis
: A python tuple of int values describing how each tensor
produced by the Estimator input_fn
should be split across the TPU
compute shards. For example, if your input_fn produced (images, labels)
where the images tensor is in HWCN
format, your shard dimensions would
be [3, 0], where 3 corresponds to the N
dimension of your images
Tensor, and 0 corresponds to the dimension along which to split the
labels to match up with the corresponding images. If None is supplied,
and per_host_input_for_training is True, batches will be sharded based
on the major dimension. If tpu_config.per_host_input_for_training is
False or PER_HOST_V2
, batch_axis is ignored.eval_on_tpu
: If False, evaluation runs on CPU or GPU. In this case, the
model_fn must return EstimatorSpec
when called with mode
as EVAL
.export_to_tpu
: If True, export_saved_model()
exports a metagraph for
serving on TPU. Note that unsupported export modes such as EVAL will be
ignored. For those modes, only a CPU model will be exported.
Currently, export_to_tpu only supports PREDICT.export_to_cpu
: If True, export_saved_model()
exports a metagraph for
serving on CPU.warm_start_from
: Optional string filepath to a checkpoint or SavedModel to
warm-start from, or a tf.estimator.WarmStartSettings
object to fully
configure warm-starting. If the string filepath is provided instead of
a WarmStartSettings
, then all variables are warm-started, and it is
assumed that vocabularies and Tensor names are unchanged.embedding_config_spec
: Optional EmbeddingConfigSpec instance
to support using TPU embedding.export_saved_model_api_version
: ExportSavedModelApiVersion, V1 or V2.
With V1, export_saved_model()
adds rewrite() and TPUPartitionedCallOp()
for user; while in v2, user is expected to add rewrite(),
TPUPartitionedCallOp() etc in their model_fn.
A helper function inference_on_tpu
is provided for V2.
brn_tpu_estimator.py includes examples for both versions
i.e. TPUEstimatorExportTest and TPUEstimatorExportV2Test.ValueError
: params
has reserved keys already.config
model_dir
model_fn
Returns the model_fn
which is bound to self.params
.
The model_fn
with following signature:
def model_fn(features, labels, mode, config)
params
eval_dir
eval_dir(name=None)
Shows the directory name where evaluation metrics are dumped.
name
: Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data. Metrics for
different evaluations are saved in separate folders, and appear
separately in tensorboard.A string which is the path of directory contains evaluation metrics.
evaluate
evaluate( input_fn, steps=None, hooks=None, checkpoint_path=None, name=None )
Evaluates the model given evaluation data input_fn
.
For each step, calls input_fn
, which returns one batch of data.
Evaluates until:
- steps
batches are processed, or
- input_fn
raises an end-of-input exception (tf.errors.OutOfRangeError
or
StopIteration
).
input_fn
: A function that constructs the input data for evaluation. See
Premade Estimators
for more information. The
function should construct and return one of the following: * A
tf.data.Dataset
object: Outputs of Dataset
object must be a tuple
(features, labels)
with same constraints as below. * A tuple
(features, labels)
: Where features
is a tf.Tensor
or a dictionary
of string feature name to Tensor
and labels
is a Tensor
or a
dictionary of string label name to Tensor
. Both features
and
labels
are consumed by model_fn
. They should satisfy the expectation
of model_fn
from inputs.steps
: Number of steps for which to evaluate model. If None
, evaluates
until input_fn
raises an end-of-input exception.hooks
: List of tf.train.SessionRunHook
subclass instances. Used for
callbacks inside the evaluation call.checkpoint_path
: Path of a specific checkpoint to evaluate. If None
, the
latest checkpoint in model_dir
is used. If there are no checkpoints
in model_dir
, evaluation is run with newly initialized Variables
instead of ones restored from checkpoint.name
: Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data. Metrics for
different evaluations are saved in separate folders, and appear
separately in tensorboard.A dict containing the evaluation metrics specified in model_fn
keyed by
name, as well as an entry global_step
which contains the value of the
global step for which this evaluation was performed. For canned
estimators, the dict contains the loss
(mean loss per mini-batch) and
the average_loss
(mean loss per sample). Canned classifiers also return
the accuracy
. Canned regressors also return the label/mean
and the
prediction/mean
.
ValueError
: If steps <= 0
.experimental_export_all_saved_models
experimental_export_all_saved_models( export_dir_base, input_receiver_fn_map, assets_extra=None, as_text=False, checkpoint_path=None )
Exports a SavedModel
with tf.MetaGraphDefs
for each requested mode.
For each mode passed in via the input_receiver_fn_map
,
this method builds a new graph by calling the input_receiver_fn
to obtain
feature and label Tensor
s. Next, this method calls the Estimator
's
model_fn
in the passed mode to generate the model graph based on
those features and labels, and restores the given checkpoint
(or, lacking that, the most recent checkpoint) into the graph.
Only one of the modes is used for saving variables to the SavedModel
(order of preference: tf.estimator.ModeKeys.TRAIN
,
tf.estimator.ModeKeys.EVAL
, then
tf.estimator.ModeKeys.PREDICT
), such that up to three
tf.MetaGraphDefs
are saved with a single set of variables in a single
SavedModel
directory.
For the variables and tf.MetaGraphDefs
, a timestamped export directory
below
export_dir_base
, and writes a SavedModel
into it containing
the tf.MetaGraphDef
for the given mode and its associated signatures.
For prediction, the exported MetaGraphDef
will provide one SignatureDef
for each element of the export_outputs
dict returned from the model_fn
,
named using the same keys. One of these keys is always
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
,
indicating which
signature will be served when a serving request does not specify one.
For each signature, the outputs are provided by the corresponding
tf.estimator.export.ExportOutput
s, and the inputs are always the input
receivers provided by
the serving_input_receiver_fn
.
For training and evaluation, the train_op
is stored in an extra
collection,
and loss, metrics, and predictions are included in a SignatureDef
for the
mode in question.
Extra assets may be written into the SavedModel
via the assets_extra
argument. This should be a dict, where each key gives a destination path
(including the filename) relative to the assets.extra directory. The
corresponding value gives the full path of the source file to be copied.
For example, the simple case of copying a single file without renaming it
is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}
.
export_dir_base
: A string containing a directory in which to create
timestamped subdirectories containing exported SavedModel
s.input_receiver_fn_map
: dict of tf.estimator.ModeKeys
to
input_receiver_fn
mappings, where the input_receiver_fn
is a
function that takes no arguments and returns the appropriate subclass of
InputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory
within the exported SavedModel
, or None
if no extra assets are
needed.as_text
: whether to write the SavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. If None
(the default),
the most recent checkpoint found within the model directory is chosen.The string path to the exported directory.
ValueError
: if any input_receiver_fn
is None
, no export_outputs
are provided, or no checkpoint can be found.export_saved_model
export_saved_model( export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False, checkpoint_path=None, experimental_mode=ModeKeys.PREDICT )
Exports inference graph as a SavedModel
into the given dir.
For a detailed guide, see Using SavedModel with Estimators.
This method builds a new graph by first calling the
serving_input_receiver_fn
to obtain feature Tensor
s, and then calling
this Estimator
's model_fn
to generate the model graph based on those
features. It restores the given checkpoint (or, lacking that, the most
recent checkpoint) into this graph in a fresh session. Finally it creates
a timestamped export directory below the given export_dir_base
, and writes
a SavedModel
into it containing a single tf.MetaGraphDef
saved from this
session.
The exported MetaGraphDef
will provide one SignatureDef
for each
element of the export_outputs
dict returned from the model_fn
, named
using
the same keys. One of these keys is always
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
,
indicating which
signature will be served when a serving request does not specify one.
For each signature, the outputs are provided by the corresponding
tf.estimator.export.ExportOutput
s, and the inputs are always the input
receivers provided by
the serving_input_receiver_fn
.
Extra assets may be written into the SavedModel
via the assets_extra
argument. This should be a dict, where each key gives a destination path
(including the filename) relative to the assets.extra directory. The
corresponding value gives the full path of the source file to be copied.
For example, the simple case of copying a single file without renaming it
is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}
.
The experimental_mode parameter can be used to export a single
train/eval/predict graph as a SavedModel
.
See experimental_export_all_saved_models
for full docs.
export_dir_base
: A string containing a directory in which to create
timestamped subdirectories containing exported SavedModel
s.serving_input_receiver_fn
: A function that takes no argument and returns a
tf.estimator.export.ServingInputReceiver
or
tf.estimator.export.TensorServingInputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory
within the exported SavedModel
, or None
if no extra assets are
needed.as_text
: whether to write the SavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. If None
(the default),
the most recent checkpoint found within the model directory is chosen.experimental_mode
: tf.estimator.ModeKeys
value indicating with mode
will be exported. Note that this feature is experimental.The string path to the exported directory.
ValueError
: if no serving_input_receiver_fn
is provided, no
export_outputs
are provided, or no checkpoint can be found.export_savedmodel
export_savedmodel( export_dir_base, serving_input_receiver_fn, assets_extra=None, as_text=False, checkpoint_path=None, strip_default_attrs=False )
Exports inference graph as a SavedModel
into the given dir. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
This function has been renamed, use export_saved_model
instead.
For a detailed guide, see Using SavedModel with Estimators.
This method builds a new graph by first calling the
serving_input_receiver_fn
to obtain feature Tensor
s, and then calling
this Estimator
's model_fn
to generate the model graph based on those
features. It restores the given checkpoint (or, lacking that, the most
recent checkpoint) into this graph in a fresh session. Finally it creates
a timestamped export directory below the given export_dir_base
, and writes
a SavedModel
into it containing a single tf.MetaGraphDef
saved from this
session.
The exported MetaGraphDef
will provide one SignatureDef
for each
element of the export_outputs
dict returned from the model_fn
, named
using
the same keys. One of these keys is always
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
,
indicating which
signature will be served when a serving request does not specify one.
For each signature, the outputs are provided by the corresponding
tf.estimator.export.ExportOutput
s, and the inputs are always the input
receivers provided by
the serving_input_receiver_fn
.
Extra assets may be written into the SavedModel
via the assets_extra
argument. This should be a dict, where each key gives a destination path
(including the filename) relative to the assets.extra directory. The
corresponding value gives the full path of the source file to be copied.
For example, the simple case of copying a single file without renaming it
is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}
.
export_dir_base
: A string containing a directory in which to create
timestamped subdirectories containing exported SavedModel
s.serving_input_receiver_fn
: A function that takes no argument and returns a
tf.estimator.export.ServingInputReceiver
or
tf.estimator.export.TensorServingInputReceiver
.assets_extra
: A dict specifying how to populate the assets.extra directory
within the exported SavedModel
, or None
if no extra assets are
needed.as_text
: whether to write the SavedModel
proto in text format.checkpoint_path
: The checkpoint path to export. If None
(the default),
the most recent checkpoint found within the model directory is chosen.strip_default_attrs
: Boolean. If True
, default-valued attributes will be
removed from the NodeDef
s. For a detailed guide, see Stripping
Default-Valued Attributes.The string path to the exported directory.
ValueError
: if no serving_input_receiver_fn
is provided, no
export_outputs
are provided, or no checkpoint can be found.get_variable_names
get_variable_names()
Returns list of all variable names in this model.
List of names.
ValueError
: If the Estimator
has not produced a checkpoint yet.get_variable_value
get_variable_value(name)
Returns value of the variable given by name.
name
: string or a list of string, name of the tensor.Numpy array - value of the tensor.
ValueError
: If the Estimator
has not produced a checkpoint yet.latest_checkpoint
latest_checkpoint()
Finds the filename of the latest saved checkpoint file in model_dir
.
The full path to the latest checkpoint or None
if no checkpoint was
found.
predict
predict( input_fn, predict_keys=None, hooks=None, checkpoint_path=None, yield_single_examples=True )
Yields predictions for given features.
Please note that interleaving two predict outputs does not work. See: issue/20506
input_fn
: A function that constructs the features. Prediction continues
until input_fn
raises an end-of-input exception
(tf.errors.OutOfRangeError
or StopIteration
).
See Premade Estimators
for more information. The function should construct and return one of
the following:
tf.data.Dataset
object: Outputs of Dataset
object must have
same constraints as below.tf.Tensor
or a dictionary of string feature name to
Tensor
. features are consumed by model_fn
. They should satisfy
the expectation of model_fn
from inputs.predict_keys
: list of str
, name of the keys to predict. It is used if
the tf.estimator.EstimatorSpec.predictions
is a dict
. If
predict_keys
is used then rest of the predictions will be filtered
from the dictionary. If None
, returns all.
hooks
: List of tf.train.SessionRunHook
subclass instances. Used for
callbacks inside the prediction call.checkpoint_path
: Path of a specific checkpoint to predict. If None
, the
latest checkpoint in model_dir
is used. If there are no checkpoints
in model_dir
, prediction is run with newly initialized Variables
instead of ones restored from checkpoint.yield_single_examples
: If False
, yields the whole batch as returned by
the model_fn
instead of decomposing the batch into individual
elements. This is useful if model_fn
returns some tensors whose first
dimension is not equal to the batch size.Evaluated values of predictions
tensors.
ValueError
: If batch length of predictions is not the same and
yield_single_examples
is True
.ValueError
: If there is a conflict between predict_keys
and
predictions
. For example if predict_keys
is not None
but
tf.estimator.EstimatorSpec.predictions
is not a dict
.train
train( input_fn, hooks=None, steps=None, max_steps=None, saving_listeners=None )
Trains a model given training data input_fn
.
input_fn
: A function that provides input data for training as minibatches.
See Premade Estimators
for more information. The function should construct and return one of
the following:tf.data.Dataset
object: Outputs of Dataset
object must be
a tuple (features, labels)
with same constraints as below.(features, labels)
: Where features
is a tf.Tensor
or
a dictionary of string feature name to Tensor
and labels
is a
Tensor
or a dictionary of string label name to Tensor
. Both
features
and labels
are consumed by model_fn
. They should
satisfy the expectation of model_fn
from inputs.hooks
: List of tf.train.SessionRunHook
subclass instances. Used for
callbacks inside the training loop.steps
: Number of steps for which to train the model. If None
, train
forever or train until input_fn
generates the tf.errors.OutOfRange
error or StopIteration
exception. steps
works incrementally. If you
call two times train(steps=10)
then training occurs in total 20 steps.
If OutOfRange
or StopIteration
occurs in the middle, training stops
before 20 steps. If you don't want to have incremental behavior please
set max_steps
instead. If set, max_steps
must be None
.max_steps
: Number of total steps for which to train model. If None
,
train forever or train until input_fn
generates the
tf.errors.OutOfRange
error or StopIteration
exception. If set,
steps
must be None
. If OutOfRange
or StopIteration
occurs in the
middle, training stops before max_steps
steps. Two calls to
train(steps=100)
means 200 training iterations. On the other hand, two
calls to train(max_steps=100)
means that the second call will not do
any iteration since first call did all 100 steps.saving_listeners
: list of CheckpointSaverListener
objects. Used for
callbacks that run immediately before or after checkpoint savings.self
, for chaining.
ValueError
: If both steps
and max_steps
are not None
.ValueError
: If either steps
or max_steps <= 0
.


Tic-Tac-Toe
Choose your game:
Human vs. Computer
Human vs. Human
Computer vs. Computer
Tic-Tac-Toe
Which player will go first?


SIMILE Widgets
Timeline
Examples
Test Dynamic Loading : Fully Dynamic
Test Dynamic Loading : Fully Dynamic
Timeline version
.
Load APIs and Create Timeline


Contains an abstract base class that supports different ML models.
deepchem.models.models.
Model
(model_instance=None, model_dir=None, verbose=True, **kwargs)[source]¶Bases: sklearn.base.BaseEstimator
Abstract base class for different ML models.
evaluate
(dataset, metrics, transformers=[], per_task_metrics=False)[source]¶Evaluates the performance of this model on specified dataset.
| Parameters: |
|
|---|---|
| Returns: | Maps tasks to scores under metric. |
| Return type: |
fit
(dataset, nb_epoch=10, batch_size=50, **kwargs)[source]¶Fits a model on data in a Dataset object.
get_model_filename
(model_dir)[source]¶Given model directory, obtain filename for the model itself.
get_params
(deep=True)¶Get parameters for this estimator.
| Parameters: | deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. |
|---|---|
| Returns: | params – Parameter names mapped to their values. |
| Return type: | mapping of string to any |
get_params_filename
(model_dir)[source]¶Given model directory, obtain filename for the model itself.
predict
(dataset, transformers=[], batch_size=None)[source]¶Uses self to make predictions on provided Dataset object.
| Returns: | numpy ndarray of shape (n_samples,) |
|---|---|
| Return type: | y_pred |
predict_on_batch
(X, **kwargs)[source]¶Makes predictions on given batch of new data.
| Parameters: | X (np.ndarray) – Features |
|---|
predict_proba
(dataset, transformers=[], batch_size=None, n_classes=2)[source]¶TODO: Do transformers even make sense here?
| Returns: | numpy ndarray of shape (n_samples, n_classes*n_tasks) |
|---|---|
| Return type: | y_pred |
predict_proba_on_batch
(X)[source]¶Makes predictions of class probabilities on given batch of new data.
| Parameters: | X (np.ndarray) – Features |
|---|
save
()[source]¶Dispatcher function for saving.
Each subclass is responsible for overriding this method.
set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
| Returns: | |
|---|---|
| Return type: | self |
Convenience class that lets singletask models fit on multitask data.
deepchem.models.multitask.
SingletaskToMultitask
(tasks, model_builder, model_dir=None, verbose=True)[source]¶Bases: deepchem.models.models.Model
Convenience class to let singletask models be fit on multitask data.
Warning: This current implementation is only functional for sklearn models.
evaluate
(dataset, metrics, transformers=[], per_task_metrics=False)¶Evaluates the performance of this model on specified dataset.
| Parameters: |
|
|---|---|
| Returns: | Maps tasks to scores under metric. |
| Return type: |
fit
(dataset, **kwargs)[source]¶Updates all singletask models with new information.
Warning: This current implementation is only functional for sklearn models.
fit_on_batch
(X, y, w)¶Updates existing model with new information.
get_model_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_num_tasks
()¶Get number of tasks.
get_params
(deep=True)¶Get parameters for this estimator.
| Parameters: | deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. |
|---|---|
| Returns: | params – Parameter names mapped to their values. |
| Return type: | mapping of string to any |
get_params_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_task_type
()¶Currently models can only be classifiers or regressors.
predict_proba
(dataset, transformers=[], n_classes=2)[source]¶Concatenates results from all singletask models.
set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
| Returns: | |
|---|---|
| Return type: | self |
Contains Sequential model adapted from keras/keras/models.py.
This class is adapted from Keras directly. Have cut out functionality and changed API to match DeepChem style.
deepchem.models.sequential.
Sequential
(name=None, logdir=None)[source]¶Bases: deepchem.models.models.Model
Linear stack of layers.
| Parameters: | layers (list of layers to add to the model.) – |
|---|
Note
The first layer passed to a Sequential model should have a defined input shape. What that means is that it should have received an input_shape or batch_input_shape argument, or for some type of layers (recurrent, Dense...) an input_dim argument.
Example
>>> import deepchem as dc
>>> model = dc.models.Sequential()
>>> # Add features
>>> model.add_features(dc.nn.Input(shape=(50,)))
>>> # Add labels
>>> model.add_labels(dc.nn.Input(shape=(1,)))
>>> model.add(dc.nn.Dense(32, 50))
>>> model.add(dc.nn.Dense(64, 32))
add
(layer)[source]¶Adds a layer instance on top of the layer stack.
| Parameters: | layer (layer instance.) – |
|---|
evaluate
(x, y, batch_size=32, verbose=1, sample_weight=None, **kwargs)[source]¶Computes the loss on some input data, batch by batch.
| Parameters: |
|
|---|---|
| Returns: |
|
fit
(dataset, nb_epoch=10, max_checkpoints_to_keep=5, log_every_N_batches=50, learning_rate=0.001, batch_size=50, checkpoint_interval=10)[source]¶Trains the model for a fixed number of epochs.
TODO(rbharath0: This is mostly copied from TensorflowGraphModel. Should eventually refactor both together.
| Parameters: |
|
|---|
fit_on_batch
(X, y, w)¶Updates existing model with new information.
get_model_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_num_tasks
()¶Get number of tasks.
get_params
(deep=True)¶Get parameters for this estimator.
| Parameters: | deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. |
|---|---|
| Returns: | params – Parameter names mapped to their values. |
| Return type: | mapping of string to any |
get_params_filename
(model_dir)¶Given model directory, obtain filename for the model itself.
get_task_type
()¶Currently models can only be classifiers or regressors.
predict
(x, batch_size=32, verbose=0)[source]¶Generates output predictions for the input samples, processing the samples in a batched way.
predict_proba
(x, batch_size=32, verbose=1)[source]¶Generates class probability predictions for the input samples batch by batch.
batch_size: integer. verbose: verbosity mode, 0 or 1.
predict_proba_on_batch
(X)¶Makes predictions of class probabilities on given batch of new data.
| Parameters: | X (np.ndarray) – Features |
|---|
reload
()¶Reload trained model from disk.
save
()¶Dispatcher function for saving.
Each subclass is responsible for overriding this method.
set_params
(**params)¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
| Returns: | |
|---|---|
| Return type: | self |
test_on_batch
(x, y, sample_weight=None, **kwargs)[source]¶Evaluates the model over a single batch of samples.
y: labels, as a Numpy array. sample_weight: sample weights, as a Numpy array.
train_on_batch
(x, y, class_weight=None, sample_weight=None, **kwargs)[source]¶Single gradient update over one batch of samples.
y: labels, as a Numpy array. class_weight: dictionary mapping classes to a weight value,
used for scaling the loss function (during training only).
sample_weight: sample weights, as a Numpy array.
uses_learning_phase
¶Gathers all models in one place for convenient imports


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