Untangle Puzzle Game in Canvas
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Use the slider to define a lower and upper r-value. The highest the r-value can be is 1, and that means that two vectors are a perfect match. Zero is no match, and -1 is a perfect anti-correlation, i.e. the expression response is exactly opposite to that of your gene of interest. The default lower r-value cutoff is 0.75, and the default upper r-value cutoff is 1.00. A tighter r-value range will result in smaller output files.
Or limit the results to:
Choose the number of best (or worst) correlated genes you'd like to see.


I like to consider myself as a bit of a unicorn. Why? Because I was born and raised here in the Portland, Oregon! For twenty-eight years I've been swimming, hiking, biking, snowboarding, and driving all over the beautiful Pacific Northwest, and although I've moved around the area, I've never been too far from home.
Hobbies
Depending on the time of year I can often be found watching football or basketball in one of the many incredible local breweries here in town. (If you've got time, sign up for a tour with Brewvana. It will be worth it!)
Education
I graduated from Clackamas High School in 2008, and Multnomah University in 2015 with a degree in Bible and Theology.
Work Experience
Nuna Baby International, B.V.
DC Consumer Services / Quality Assurance Technician
Feb. 2016 - Mar. 2018
- Managed and executed the functions of the Quality and Compliance department at the only independently owned and operated distribution center for Nuna International B.V.
- Provided detailed and accurate analysis of returned product.
- Perform product testing upon receipt of new product at the West Coast Distribution Center.
- Work with our Global Brand Manager to organize and execute large scale donations to local hospitals and charities.
- Work closely with our Account/Consumer Service teams to ensure that excellent customer service is maintained among consumers and retail customers.
- Represent Nuna on monthly trips to the Nordstrom Quality Center to inspect product and arrange LTL return shipments to our Distribution Center.
DC Fulfillment Specialist
Oct. 2015 - Feb. 2016
- Responsible for maintaining the standard of quality that Nuna has become known for in the eyes of consumers.
- Ensured accuracy in picking and shipping while fulfill orders in an expedient manner
- Used LISA Warehouse management software to process orders
- Transitioned to SAP's ECC software and used Process Weaver to generate shipping labels.
- Organized and fulfilled LTL shipments for our retail customers
For full details please see my LinkedIn profile.
Why Web Development?
I chose to pursue front-end web development after two years in the warehouse management industry because I found that creating new spreadsheets was my favorite part of the job! I wanted to take that mindset to the next level and web development was a perfect fit. I pursued front-end development because am passionate about creating websites that are not only functional, but are also sleek and user-friendly. I lovethe beauty of the Northwest, and it's that rustic style and sense of adventure that I bring to my development and design.


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Above you will see the contents of primitives from several AS7Leak objects which were constructed in order to render this view. Clicking the buttons, and then navigating back to this view will generate additional AS7Leak objects.
Instructions:
- Count these objects using a monitoring tool, for example:
jvisualvm -> Applications -> pid for jvm -> Sampler -> Memory -> Filter -> AS7Leak
- Generate several of them by clicking the commandButtons to get to view2, and then click the buttons to return here.
- Then click the sign out link to sign out of the portal and end your session.
- Check the console log for the server to make sure there were no Exceptions thrown during the sign out.
- Check the number of AS7Leak objects still in memory after signing out, and after garbage collection.
- If there are any Exceptions thrown during the sign out, or any AS7Leak objects left after garbage collection, let us know.


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Welcome to my todolist app
Task
Deadline
Category
Horror
Mythopoeia
Fantasy
Tall tale
Comic/Graphic Novel
Fanfiction
Horror
Brandy of the Damned , to read: 07/18/21
Consider Phlebas , to read: 05/02/21
To a God Unknown , to read: 07/22/21
Mythopoeia
Butter In a Lordly Dish , to read: 11/09/20
Françoise Sagan , to read: 05/10/21
The Moon by Night , to read: 03/07/21
Fantasy
I Know Why the Caged Bird Sings , to read: 02/23/21
A Handful of Dust , to read: 10/04/20
The Curious Incident of the Dog in the Night-Time , to read: 05/03/21
Buy milk , to read: 08/27/20
Buy sugar , to read: 08/27/20
Buy 1kg apple , to read: 08/27/20
Buy 1kg apple , to read: 08/27/20
Tall tale
Consider the Lilies , to read: 09/11/20
The Daffodil Sky , to read: 08/29/20
The Far-Distant Oxus , to read: 08/31/20
Comic/Graphic Novel
Let Us Now Praise Famous Men , to read: 09/14/20
Consider Phlebas , to read: 09/19/20
A Scanner Darkly , to read: 09/03/20
Fanfiction
Unweaving the Rainbow , to read: 09/04/20
Blithe Spirit , to read: 08/28/20
An Acceptable Time , to read: 09/08/20
abc


--- layout: page status: publish published: true title: DEQ Land-Related Contaminant and Cleanup Sites author: display_name: Jessie Pechmann login: jpechmann email<EMAIL_ADDRESS>url: '' author_login: jpechmann author_email<EMAIL_ADDRESS>wordpress_id: 4482 wordpress_url: http://gis.utah.gov/?page_id=4482 date: '2011-12-08 14:21:24 -0700' date_gmt: '2011-12-08 21:21:24 -0700' categories: [] tags: - sgid - Data - utah - gis - map - mapping - dataset - download - agrc - layer - shapefile - geodatabase - metadata - shp - gdb - kml - lyr - digital - geographic - information - database - state - statewide - deq - environment ---
Data Type: GIS Data Layer
Steward(s): Utah Department of Environmental Quality
Abstract:
US EPA and State designated hazardous materials storage sites and contamination clean up program sites including CERLA, National Priority List, Underground Storage Tanks (UST), Brownfields, Voluntary Cleanup (VCP), Tier 2, Toxic Release Inventory (TRI), User Oil Permitted Facilities, Formerly Used Defense (FUD), RCRA Large & Small Quantity Hazardous Waste Generators, Solid Waste Facilities, and Enforceable Written Assurance sites. To view all DEQ geographic data online, see the DEQ Interactive Map Viewer.
Data Content:
The following data is available for download:
Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS><EMAIL_ADDRESS>Other Links:
Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS>Contact<EMAIL_ADDRESS>Contact: haroldsandbeck@utah.gov


-
DO ONE GOAL AT A TIME
Give your dog time to focus
Infographics are visual representations of data, making complex info easier to share and digest. When making your own, simply organize your images, charts, and text. Finally, cite your sources.
-
COMMUNICATE CLEARLY
Use clear cues and gestures
Infographics are visual representations of data, making complex info easier to share and digest. When making your own, simply organize your images, charts, and text. Finally, cite your sources.
-
REWARD PROPERLY
Motivate your dog with treats
Infographics are visual representations of data, making complex info easier to share and digest. When making your own, simply organize your images, charts, and text. Finally, cite your sources.
-
PRACTICE REGULARLY
Repetition is key to success
Infographics are visual representations of data, making complex info easier to share and digest. When making your own, simply organize your images, charts, and text. Finally, cite your sources.
-
TAKE IT ONE DAY AT A TIME
Give your dog time to rest too
Infographics are visual representations of data, making complex info easier to share and digest. When making your own, simply organize your images, charts, and text. Finally, cite your sources.


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WolfNT90
the path of least resistance is the preference of procrastinators
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The Logos project is a sure tested formula that was created purely for those who are looking to begin their road on maximizing potential. Although some of the tasks may be difficult, you can find solace in your coaches expansive knowledge in the program. In short this is your journey to a fitter, stronger, and better you!


Posted on June 05, 2016
Top seed Novak Djokovic beat second seed Andy Murray 3-6, 6-1, 6-2, 6-4 to complete the career Grand Slam, and became just the eighth man to accomplish the feat.
He joined Rod Laver, Rafael Nadal, Roger Federer, Andre Agassi, Roy Emerson, Don Budge and Fred Perry, as the only men to win all four major tournaments. By winning his first French Open crown, his 12th Grand Slam trophy, Novak cemented his place among the game’s all-time greats!
Nole and Andy squared off for the 34th time on tour, Djokovic now leads their ATP Head2Head rivalry 24-10 and has won 13 of their last 15 meetings.
 The Brit responded strong, making breaks in the second and fourth games. The Serb pushed his rival to deuce three times in the seventh game, but Andy held and closed out the set two games later.
The 29-year-old Belgrade native played fantastic tennis in the second set. He broke Murray twice, in the second and sixth games, to clinch it 6-1 in 36 minutes.
Djokovic moved a double break up in the third set, then saved four break points in the sixth game before sealing it 6-2.
The no.1 seed continued the momentum into the fourth set and broke Andy’s serve in the first and seventh games for 5-2 lead. He got broken serving for the win, but regrouped and served it out two games later, capturing his 65th career title, sixth in 2016.
The Serbian is most impressively just the third to hold all four major crowns at the same time, joining Don Budge (1938) and Rod Laver (1962 & ’69).
“It’s a thrilling moment,” said Djokovic. “One of the most beautiful I have had in my career… It’s incredibly flattering to know that Rod Laver is the last one that managed to do that. There are not many words that can describe it. It’s one of the ultimate challenges that you have as a tennis player. I’m very proud and very thrilled. It’s hard for me to reflect on what has happened before and what’s going to happen after. I’m just so overwhelmed with having this trophy next to me that I’m just trying to enjoy this moment.”
It was Novak’s 20th major final (12-8 record), and his fourth appearance in the Roland Garros final (having lost in 2012, 2014 and 2015).
Nole next heads to Wimbledon, the third Grand Slam event of the season, where he lifted his third Championships trophy last year, beating seven-time former titlist Roger Federer 7-6 (1), 6-7 (10), 6-4, 6-3 in the final.


From Peter Andre to Barbara Windsor - Christmas light switch-ons through the years in Bristol
From Peter Andre in 2014 to Josie Gibson in 2010 - Bristol's had quite the array of Christmas light switch-ons
24 Nov 2019, 10:00 by Briana Millett
Christmas is a mere month away , with Bristol well and truly geared up for the big day.
The lights are switched on , people are preparing to buy their Christmas trees, and the shops are busy with prepared people buying their loved-ones gifts.
But how much has Christmas changed in Bristol over the years?
We've been delving through the archives to find some festive snaps from Christmas light switch-ons of years gone by.
There was Big Brother star Josie Gibson at the Yate switch-on in 2010, while Peter Andre doing it not once but twice - in 2009 and 2014.
Christmas in Bristol 2019
Wallace and Gromit have made an appearance at the Brailsford family home in Brentry, which is famed for its impressive displays.
Here's some of our favourites throughout the years
Kate Ford from Corrie in Yate in 2007(Image: BristolLive)
The Wrington switch-on in 2007(Image: BristolLive)
Peter Andre at Yate Shopping Centre in 2009(Image: BristolLive)
The Cabot switch-on in 2008(Image: BristolLive)
People waiting for Peter Andre in 2009(Image: BristolLive)
Peter Andre at Yate Shopping Centre in 2009(Image: BristolLive)
Barbara Windsor switches on the Christmas Lights at The Mall at Cribbs Causeway in 2010(Image: Bristol News and Media)
Barbara Windsor in 2010(Image: Bristol News and Media)
Commonwealth games champion Imogen Cairns switches on the Christmas lights in Portishead in 2010(Image: Gavin Crilly)
Josie Gibson waves to the crowd after switching on the Christmas lights at the Yate shopping centre in 2010(Image: Dan Regan)
Josie Gibson with Craig Gazey and dancers push the button to turn on the Christmas lights(Image: Dan Regan)
Josie Gibson in Yate in 2010(Image: Dan Regan)
James Argent from The Only Way is Essex and Johnny Robinson from X Factor switch on the Yate Christmas lights in 2011(Image: BristolLive)
The crowd waits at Cribbs Causeway for Olly Murs to switch on the Christmas lights in 2011(Image: Northcliffe Media Ltd)
Olly Murs at Cribbs Causeway before switching on the Christmas lights in 2011(Image: Northcliffe Media Ltd)
Father Christmas joins TV Weather girl Jemma Cooper (left) with Lorraine and Helen Schneider (right) in Portishead High Street in 2011(Image: Chas Breton)
The Cabot switch-on was full of fun in 2011(Image: Michael Lloyd)
The 2012 switch-on in St Nick's Market(Image: BristolLive)
Wallace and Gromit at the Brailsford switch-on in 2012(Image: BristolLive)
The Saturdays turn on the Christmas Lights at Cribbs Causeway in 2013(Image: BristolLive)
The Saturdays at The Mall(Image: BristolLive)
Peter Andre in Yate in 2014(Image: BristolLive)
Union J perform at The Mall Christmas lights switch on in 2014(Image: BristolLive)
Union J in Bristol in 2014(Image: BristolLive)
Entertainment at Cabot Circus in 2014(Image: BristolLive)


Museum API v1 Help
Hastily written. Incredibly not thoroghly tested. Incredibly not helpful if you have errors.
Requests
API requests should be sent to: https://museumofzzt.com/api/<version_number>/
This is currently v1.
Parameters
All parameters are sent using GET. There are no tokens, keys, headers, or authentication required currently.
Endpoints
/get/file
Parameters:
- id: The id of the File object on the Museum.
/search/files
Parameters:
- title: String to search for in a File's title field
- author: String to search for in a File's author field
- filename: String to search for in a File's filename field
- company: String to search for in a File's company field
- genre: String to search for in a File's genre field
- year: Integer year to search for in a File's release_date field (the range is automatically expanded to cover Jan. 1 through Dec. 31 of the given year)
- sort: String to determine how results are sorted. Defaults to "title". See SORT_CODES in source file museum_site/common.py for complete list.
- offset: Integer to determine starting row in search results. Results are restricted to 25 at a time.
Responses
All endpoints return JSON data on a successful call, and fail spectacularly when they fail. (Nah, you'll just get a 500 error probably. I just haven't coded any error handling at all right now.)


Structures
The following structures are available globally.
-
Undocumented
See moreDeclaration
Swift
public struct AbiFunctionModel : Equatable, Hashable
-
Declaration
Swift
public struct Account : ECHOObject, ECHOCodable, Decodable, Hashable
-
Class used to represent the weighted set of keys and accounts that must approve operations.
AccountAuthority model documentation
See moreDeclaration
Swift
public struct AccountAuthority : ECHOCodable, Decodable
-
Declaration
Swift
public struct AccountBalance : ECHOObject, Decodable
-
Declaration
Swift
public struct AccountOptions : ECHOCodable, Decodable
-
Declaration
Swift
public struct Address : ECHOCodable
-
Class used to represent the weighted set of keys and accounts that must approve operations.
AddressAuthority model documentation
See moreDeclaration
Swift
public struct AddressAuthority : ECHOCodable, Decodable
-
Class used to represent a specific asset on the Graphene platform
See moreDeclaration
Swift
public struct Asset : ECHOObject, BytesCodable, Decodable
-
Class used to represent a specific amount of a certain asset
See moreDeclaration
Swift
public struct AssetAmount : ECHOCodable, Decodable
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< allow the asset to be fed by the committee Represents account model in Graphene blockchain
See moreDeclaration
Swift
public struct AssetOptions : ECHOCodable, Decodable
-
Class used to represent the weighted set of keys and accounts that must approve operations.
See moreDeclaration
Swift
public struct Authority : ECHOCodable, Decodable
-
The bitasset_options struct contains configurable options available only to BitAssets
BitassetOptions model documentations
See moreDeclaration
Swift
public struct BitassetOptions : ECHOCodable, Decodable
-
Declaration
Swift
public struct Block : Decodable
-
Represents contract_object from blockchain
See moreDeclaration
Swift
public struct Contract : ECHOObject, Decodable
-
Represents Contract History object from blockchain
See moreDeclaration
Swift
public struct ContractHistory : ECHOObject, Decodable
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Undocumented
See moreDeclaration
Swift
public struct ContractInfo : ECHOObject, Decodable
-
Represents Contract Log object from blockchain
See moreDeclaration
Swift
public struct ContractLog : Decodable
-
Model of contract operation result for VM type Etherium
See moreDeclaration
Swift
public struct ContractResultEVM : Decodable
-
Information about operation executing
See moreDeclaration
Swift
public struct ExecRes : Decodable
-
Information about transaction
See moreDeclaration
Swift
public struct TrReceipt : Decodable
-
Information about log
See moreDeclaration
Swift
public struct Log : Decodable
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Model of contract operation result for VM type x86
See moreDeclaration
Swift
public struct ContractResultx86 : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct ContractStructEVM : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct ContractStructx86 : Decodable
-
Declaration
Swift
public struct DynamicGlobalProperties : ECHOObject, Decodable
-
Represent response from chain
See moreDeclaration
Swift
public struct ECHODirectResponse : Decodable
-
Represent notification from chain
See moreDeclaration
Swift
public struct ECHONotification : Decodable
-
Represent response error from chain
See moreDeclaration
Swift
public struct ECHOResponseError : Decodable
-
The Base58 encoding used is home made, and has some differences. Especially, leading zeroes are kept as single zeroes when conversion happens.
See moreDeclaration
Swift
public struct Base58
-
Undocumented
See moreDeclaration
Swift
public struct DepositEth : ECHOObject, Decodable
-
Undocumented
See moreDeclaration
Swift
public struct EthAddress : ECHOObject, Decodable
-
Undocumented
See moreDeclaration
Swift
public struct WithdrawalEth : ECHOObject, Decodable
-
Represents additional payload of models
See moreDeclaration
Swift
public struct Extensions : ECHOCodable
-
Undocumented
See moreDeclaration
Swift
public struct CallContractFee : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct GlobalProperties : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct GlobalPropertiesParameters : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct CurrentFeesGlobalPropertiesParameters : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct EchorandConfig : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct SidechainConfig : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct EthMethod : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct GasPriceGlobalProperties : Decodable
-
This struct encapsulates single history item
See moreDeclaration
Swift
public struct HistoryItem : Decodable
-
Container template class used whenever we have an optional field.
The idea here is that the binary serialization of this field should be performed in a specific way determined by the field implementing the {@link ByteSerializable} interface, more specifically using the {@link ByteSerializable#toBytes()} method.
However, if the field is missing, the Optional class should be able to know how to serialize it, as this is always done by placing an zero byte.
See moreDeclaration
Swift
public struct OptionalValue<T> : ECHOCodable where T : ECHOCodable
-
Represents Options in Graphene blockchain
See moreDeclaration
Swift
public struct Options : Decodable
-
The price struct stores asset prices in the Graphene system.
A price is defined as a ratio between two assets, and represents a possible exchange rate between those two assets. prices are generally not stored in any simplified form, i.e. a price of (1000 CORE)/(20 USD) is perfectly normal.
The assets within a price are labeled base and quote. Throughout the Graphene code base, the convention used is that the base asset is the asset being sold, and the quote asset is the asset being purchased, where the price is represented as base/quote, so in the example price above the seller is looking to sell CORE asset and get USD in return.
See moreNote
Taken from the Graphene doxygen.Declaration
Swift
public struct Price : ECHOCodable, Decodable
-
Represents Statistics in Graphene blockchain
See moreDeclaration
Swift
public struct Statistics : ECHOObject, Decodable
-
Represents full information about user account
See moreDeclaration
Swift
public struct UserAccount : Decodable
-
Undocumented
See moreDeclaration
Swift
public struct ETHAddressValidator
-
Struct used to encapsulate operations related to the OperationType.generateEthAddressOperation
See moreDeclaration
Swift
public struct GenerateEthAddressOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.accountCreateOperation
See moreDeclaration
Swift
public struct AccountCreateOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.accountUpdateOperation
See moreDeclaration
Swift
public struct AccountUpdateOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.callContractOperation
See moreDeclaration
Swift
public struct CallContractOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.contractTransferOperation Operation will be visible only when contract transfer assets to account
See moreDeclaration
Swift
public struct ContractTransferOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.assetCreateOperation
See moreDeclaration
Swift
public struct CreateAssetOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.createContractOperation
See moreDeclaration
Swift
public struct CreateContractOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.assetIssueOperation
See moreDeclaration
Swift
public struct IssueAssetOperation : BaseOperation
-
Undocumented
See moreDeclaration
Swift
public struct SidechainBurnOperation : BaseOperation
-
Undocumented
See moreDeclaration
Swift
public struct SidechainIssueOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.transferOperation
See moreDeclaration
Swift
public struct TransferOperation : BaseOperation
-
Struct used to encapsulate operations related to the OperationType.withdrawEthOperation
See moreDeclaration
Swift
public struct WithdrawalEthOperation : BaseOperation
-
Undocumented
Declaration
Swift
public struct AssetsServices
-
Services for TransactionFacade
Declaration
Swift
public struct ContractsFacadeServices
-
Undocumented
Declaration
Swift
public struct CustomOperationsFacadeServices
-
Services for TransactionFacade
Declaration
Swift
public struct EthFacadeServices
-
Services for FeeFacade
Declaration
Swift
public struct FeeFacadeServices
-
Services for TransactionFacade
Declaration
Swift
public struct TransactionFacadeServices


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|
SLinkExtra
|
0 %
(0/5)
|
|
src/app/app-engine/apps/d3js/sankey/sankey.component.ts
|
interface
|
SNodeExtra
|
0 %
(0/3)
|
|
src/app/app-engine/apps/d3js/stacked-bar-chart/data04.ts
|
variable
|
SAMPLE_DATA
|
0 %
(0/1)
|
|
src/app/app-engine/apps/d3js/stacked-bar-chart/stacked-bar-chart.component.ts
|
component
|
StackedBarChartComponent
|
0 %
(0/19)
|
|
src/app/app-engine/apps/d3js/stacked-bar-chart/stacked-bar-chart.component.ts
|
interface
|
Margin
|
0 %
(0/5)
|
|
src/app/app-engine/apps/development-views/create-resource-view/create-resource-view/create-resource-view.component.ts
|
component
|
CreateResourceViewComponent
|
20 %
(1/5)
|
|
src/app/app-engine/apps/development-views/edit-resource-view/edit-resource-view/edit-resource-view.component.ts
|
component
|
EditResourceViewComponent
|
10 %
(1/10)
|
|
src/app/app-engine/apps/development-views/image-view/image-view/image-view.component.ts
|
component
|
ImageViewComponent
|
10 %
(1/10)
|
|
src/app/app-engine/apps/development-views/metadata-view/metadata-view/metadata-view.component.ts
|
component
|
MetadataViewComponent
|
14 %
(1/7)
|
|
src/app/app-engine/apps/development-views/text-dev-view/text-dev-view/page-transcription.ts
|
variable
|
PAGETRANSCRIPTION
|
0 %
(0/1)
|
|
src/app/app-engine/apps/development-views/text-dev-view/text-dev-view/text-dev-view.component.ts
|
component
|
TextDevViewComponent
|
0 %
(0/12)
|
|
src/app/app-engine/apps/fact-sheet/fact-sheet/fact-sheet.component.ts
|
component
|
FactSheetComponent
|
0 %
(0/5)
|
|
src/app/app-engine/apps/grapesjs/grapesjs.component.ts
|
component
|
GrapesjsComponent
|
0 %
(0/3)
|
|
src/app/app-engine/apps/grapesjs/grapesjs.component.ts
|
variable
|
grapesjs
|
0 %
(0/1)
|
|
src/app/app-engine/apps/grapesjs/new-gjs-box-dialog/new-gjs-box-dialog.component.ts
|
component
|
NewGjsBoxDialogComponent
|
0 %
(0/6)
|
|
src/app/app-engine/apps/image-frame/image-frame-sizes/image-frame-sizes.component.ts
|
component
|
ImageFrameSizesComponent
|
0 %
(0/8)
|
|
src/app/app-engine/apps/image-frame/image-frame/image-frame.component.ts
|
component
|
ImageFrameComponent
|
0 %
(0/11)
|
|
src/app/app-engine/apps/image-frame/image-frame/image-frame.component.ts
|
variable
|
OpenSeadragon
|
0 %
(0/1)
|
|
src/app/app-engine/apps/image-frame/image-with-overlay/image-with-overlay.component.ts
|
component
|
ImageWithOverlayComponent
|
0 %
(0/15)
|
|
src/app/app-engine/apps/image-frame/image-with-overlay/image-with-overlay.component.ts
|
variable
|
OpenSeadragon
|
0 %
(0/1)
|
|
src/app/app-engine/apps/image-frame/region-to-svg.service.ts
|
injectable
|
RegionToSvgService
|
0 %
(0/3)
|
|
src/app/app-engine/apps/imageViewer/imageViewer.model.ts
|
class
|
ImageViewer
|
0 %
(0/5)
|
|
src/app/app-engine/apps/imageViewer/imageViewer.service.ts
|
injectable
|
ImageViewerService
|
0 %
(0/2)
|
|
src/app/app-engine/apps/models/basic-model.ts
|
class
|
BasicModel
|
0 %
(0/1)
|
|
src/app/app-engine/apps/models/julian-date.ts
|
class
|
JulianDate
|
0 %
(0/1)
|
|
src/app/app-engine/apps/models/person.ts
|
class
|
Person
|
0 %
(0/3)
|
|
src/app/app-engine/apps/models/place.ts
|
class
|
Place
|
0 %
(0/2)
|
|
src/app/app-engine/apps/models/standoff.ts
|
class
|
Standoff
|
0 %
(0/1)
|
|
src/app/app-engine/apps/resource-form/resource-form/resource-form.component.ts
|
component
|
ResourceFormComponent
|
90 %
(20/22)
|
|
src/app/app-engine/apps/resource-form/resource-value-history-value/resource-value-history-value.component.ts
|
component
|
ResourceValueHistoryValueComponent
|
83 %
(5/6)
|
|
src/app/app-engine/apps/resource-form/resource-value-history/resource-value-history.component.ts
|
component
|
ResourceValueHistoryComponent
|
87 %
(7/8)
|
|
src/app/app-engine/apps/shared/IIIFImage.ts
|
class
|
IIIFImage
|
0 %
(0/9)
|
|
src/app/app-engine/apps/shared/base-type-forms/boolean-value-editor/boolean-value-editor.component.ts
|
component
|
BooleanValueEditorComponent
|
80 %
(4/5)
|
|
src/app/app-engine/apps/shared/base-type-forms/date-value-editor/date-value-editor.component.ts
|
component
|
DateValueEditorComponent
|
94 %
(16/17)
|
|
src/app/app-engine/apps/shared/base-type-forms/date-value-viewer/date-value-viewer.component.ts
|
component
|
DateValueViewerComponent
|
85 %
(6/7)
|
|
src/app/app-engine/apps/shared/base-type-forms/decimal-value-editor/decimal-value-editor.component.ts
|
component
|
DecimalValueEditorComponent
|
80 %
(4/5)
|
|
src/app/app-engine/apps/shared/base-type-forms/int-value-editor/int-value-editor.component.ts
|
component
|
IntValueEditorComponent
|
80 %
(4/5)
|
|
src/app/app-engine/apps/shared/base-type-forms/link-value-editor/link-value-editor.component.ts
|
component
|
LinkValueEditorComponent
|
87 %
(7/8)
|
|
src/app/app-engine/apps/shared/base-type-forms/link-value-label-viewer/link-value-label-viewer.component.ts
|
component
|
LinkValueLabelViewerComponent
|
80 %
(4/5)
|
|
src/app/app-engine/apps/shared/base-type-forms/text-value-editor/text-value-editor.component.ts
|
component
|
TextValueEditorComponent
|
85 %
(6/7)
|
|
src/app/app-engine/apps/shared/spa-text/spa-text/spa-text.component.ts
|
component
|
SpaTextComponent
|
75 %
(3/4)
|
|
src/app/app-engine/apps/simple-image-app/simple-image-app.component.ts
|
component
|
SimpleImageAppComponent
|
0 %
(0/4)
|
|
src/app/app-engine/apps/standoff/standoff-reconciler.service.ts
|
injectable
|
StandoffReconcilerService
|
0 %
(0/3)
|
|
src/app/app-engine/apps/synopsis/drag.service.ts
|
injectable
|
DragService
|
0 %
(0/9)
|
|
src/app/app-engine/apps/synopsis/drag.service.ts
|
interface
|
ObjectDimensions
|
0 %
(0/5)
|
|
src/app/app-engine/apps/synopsis/draggable.directive.ts
|
directive
|
DraggableDirective
|
0 %
(0/7)
|
|
src/app/app-engine/apps/synopsis/drop-target-options.ts
|
interface
|
DropTargetOptions
|
0 %
(0/2)
|
|
src/app/app-engine/apps/synopsis/dummy-synopsis-objects.ts
|
variable
|
DUMMYSYNOPSISOBJECTS
|
0 %
(0/1)
|
|
src/app/app-engine/apps/synopsis/float-drop-target.directive.ts
|
directive
|
FloatDropTargetDirective
|
0 %
(0/9)
|
|
src/app/app-engine/apps/synopsis/float-light-table/float-light-table.component.ts
|
component
|
FloatLightTableComponent
|
0 %
(0/25)
|
|
src/app/app-engine/apps/synopsis/float-light-table/float-light-table.component.ts
|
interface
|
ComponentRefTracker
|
0 %
(0/1)
|
|
src/app/app-engine/apps/synopsis/floating-image-object/floating-image-object.component.ts
|
component
|
FloatingImageObjectComponent
|
0 %
(0/4)
|
|
src/app/app-engine/apps/synopsis/floating-text-object/floating-text-object.component.ts
|
component
|
FloatingTextObjectComponent
|
0 %
(0/7)
|
|
src/app/app-engine/apps/synopsis/light-table-layout.service.ts
|
injectable
|
LightTableLayoutService
|
0 %
(0/7)
|
|
src/app/app-engine/apps/synopsis/light-table-menu/light-table-menu.component.ts
|
component
|
LightTableMenuComponent
|
0 %
(0/12)
|
|
src/app/app-engine/apps/synopsis/light-table-stash.service.ts
|
injectable
|
LightTableStashService
|
20 %
(1/5)
|
|
src/app/app-engine/apps/synopsis/light-table/light-table.component.ts
|
component
|
LightTableComponent
|
0 %
(0/3)
|
|
src/app/app-engine/apps/synopsis/load-light-table/load-light-table.component.ts
|
component
|
LoadLightTableComponent
|
0 %
(0/7)
|
|
src/app/app-engine/apps/synopsis/modifiable.directive.ts
|
directive
|
ModifiableDirective
|
0 %
(0/11)
|
|
src/app/app-engine/apps/synopsis/remove-objects-by-id/remove-objects-by-id.component.ts
|
component
|
RemoveObjectsByIdComponent
|
0 %
(0/6)
|
|
src/app/app-engine/apps/synopsis/save-light-table/save-light-table.component.ts
|
component
|
SaveLightTableComponent
|
0 %
(0/8)
|
|
src/app/app-engine/apps/synopsis/save-light-table/save-light-table.component.ts
|
class
|
InstantErrorStateMatcher
|
0 %
(0/2)
|
|
src/app/app-engine/apps/synopsis/selectable.directive.ts
|
directive
|
SelectableDirective
|
0 %
(0/12)
|
|
src/app/app-engine/apps/synopsis/share-light-table/share-light-table.component.ts
|
component
|
ShareLightTableComponent
|
0 %
(0/11)
|
|
src/app/app-engine/apps/synopsis/stubs/directive-stubs.ts
|
directive
|
DraggableStubDirective
|
0 %
(0/2)
|
|
src/app/app-engine/apps/synopsis/stubs/directive-stubs.ts
|
directive
|
ModifiableStubDirective
|
0 %
(0/2)
|
|
src/app/app-engine/apps/synopsis/stubs/directive-stubs.ts
|
directive
|
SelectableStubDirective
|
0 %
(0/2)
|
|
src/app/app-engine/apps/synopsis/stubs/synopsis-object-serializer-service-stub.ts
|
injectable
|
SynopsisObjectSerializerServiceStub
|
0 %
(0/13)
|
|
src/app/app-engine/apps/synopsis/synopsis-item.ts
|
class
|
SynopsisItem
|
0 %
(0/4)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-anchor.directive.ts
|
directive
|
SynopsisObjectAnchorDirective
|
33 %
(1/3)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-data.ts
|
class
|
SynopsisImageData
|
0 %
(0/13)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-data.ts
|
class
|
SynopsisTextData
|
0 %
(0/13)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-data.ts
|
interface
|
SynopsisObjectData
|
0 %
(0/11)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-manager/synopsis-object-manager.component.ts
|
component
|
SynopsisObjectManagerComponent
|
0 %
(0/11)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-modifier.service.ts
|
injectable
|
SynopsisObjectModifierService
|
6 %
(1/16)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-selector.service.ts
|
injectable
|
SynopsisObjectSelectorService
|
20 %
(1/5)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-serializer.service.ts
|
class
|
LightTableStorage
|
14 %
(1/7)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-serializer.service.ts
|
injectable
|
SynopsisObjectSerializerService
|
5 %
(1/20)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-serializer.service.ts
|
interface
|
StorageObject
|
0 %
(0/1)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-storage.service.ts
|
injectable
|
SynopsisObjectStorageService
|
12 %
(1/8)
|
|
src/app/app-engine/apps/synopsis/synopsis-object-toolbox/synopsis-object-toolbox.component.ts
|
component
|
SynopsisObjectToolboxComponent
|
0 %
(0/13)
|
|
src/app/app-engine/apps/synopsis/synopsis-object.ts
|
interface
|
SynopsisObject
|
0 %
(0/2)
|
|
src/app/app-engine/apps/synopsis/synopsis.component.ts
|
component
|
SynopsisComponent
|
0 %
(0/3)
|
|
src/app/app-engine/apps/synopsis/thumbnailbar/thumbnailbar.component.ts
|
component
|
ThumbnailbarComponent
|
0 %
(0/8)
|
|
src/app/app-engine/apps/synopsis/tile-drop-target.directive.ts
|
directive
|
TileDropTargetDirective
|
0 %
(0/12)
|
|
src/app/app-engine/apps/synopsis/tiled-image-object/tiled-image-object.component.ts
|
component
|
TiledImageObjectComponent
|
0 %
(0/3)
|
|
src/app/app-engine/apps/synopsis/tiled-light-table/tiled-light-table.component.ts
|
component
|
TiledLightTableComponent
|
0 %
(0/20)
|
|
src/app/app-engine/apps/synopsis/tiled-text-object/tiled-text-object.component.ts
|
component
|
TiledTextObjectComponent
|
0 %
(0/6)
|
|
src/app/app-engine/apps/tag-chips/tag-chips/tag-chips.component.ts
|
component
|
TagChipsComponent
|
0 %
(0/5)
|
|
src/app/app-engine/apps/tag-chips/tag-chips/tag.ts
|
class
|
SubjectTag
|
20 %
(1/5)
|
|
src/app/app-engine/apps/text-structure/text-line-group/text-line-group.component.ts
|
component
|
TextLineGroupComponent
|
75 %
(3/4)
|
|
src/app/app-engine/apps/text-structure/text-line-margin/text-line-margin.component.ts
|
component
|
TextLineMarginComponent
|
87 %
(7/8)
|
|
src/app/app-engine/apps/text-structure/text-line-number/text-line-number.component.ts
|
component
|
TextLineNumberComponent
|
77 %
(7/9)
|
|
src/app/app-engine/apps/text-structure/text-line/text-line.component.ts
|
component
|
TextLineComponent
|
62 %
(5/8)
|
|
src/app/app-engine/apps/text-view/canvas-options.service.ts
|
injectable
|
CanvasOptionsService
|
0 %
(0/18)
|
|
src/app/app-engine/apps/text-view/models/text.ts
|
class
|
Text
|
0 %
(0/7)
|
|
src/app/app-engine/apps/text-view/result-to-model-mapper.service.ts
|
injectable
|
ResultToModelMapperService
|
0 %
(0/2)
|
|
src/app/app-engine/apps/text-view/result-to-text-mapper.service.ts
|
injectable
|
ResultToTextMapperService
|
0 %
(0/3)
|
|
src/app/app-engine/apps/text-view/templates/text-template.ts
|
class
|
TextTemplate
|
0 %
(0/1)
|
|
src/app/app-engine/apps/text-view/test-data.ts
|
variable
|
author
|
0 %
(0/1)
|
|
src/app/app-engine/apps/text-view/test-data.ts
|
variable
|
editor
|
0 %
(0/1)
|
|
src/app/app-engine/apps/text-view/test-data.ts
|
variable
|
place
|
0 %
(0/1)
|
|
src/app/app-engine/apps/text-view/test-data.ts
|
variable
|
text
|
0 %
(0/1)
|
|
src/app/app-engine/apps/text-view/text-view-canvas/text-view-canvas.component.ts
|
component
|
TextViewCanvasComponent
|
0 %
(0/19)
|
|
src/app/app-engine/apps/text-view/text-view-metadata/text-view-metadata.component.ts
|
component
|
TextViewMetadataComponent
|
0 %
(0/8)
|
|
src/app/app-engine/apps/text-view/text-view-structure/text-view-structure.component.ts
|
component
|
TextViewStructureComponent
|
0 %
(0/4)
|
|
src/app/app-engine/apps/text-view/text-view-tools/text-view-tools.component.ts
|
component
|
TextViewToolsComponent
|
0 %
(0/9)
|
|
src/app/app-engine/apps/text-view/text-view-tools/text-view-tools.component.ts
|
class
|
MyErrorStateMatcher
|
50 %
(1/2)
|
|
src/app/app-engine/apps/text-view/text-view.component.ts
|
component
|
TextViewComponent
|
0 %
(0/4)
|
|
src/app/app-engine/apps/textlist-viewer/textlist-viewer.component.ts
|
component
|
TextlistViewerComponent
|
0 %
(0/5)
|
|
src/app/app-engine/load-variables/load-variables.component.ts
|
component
|
LoadVariablesComponent
|
0 %
(0/21)
|
|
src/app/app-engine/page/frame-settings/frame-settings.component.ts
|
component
|
FrameSettingsComponent
|
0 %
(0/10)
|
|
src/app/app-engine/page/frame/frame.ts
|
component
|
Frame
|
0 %
(0/48)
|
|
src/app/app-engine/page/page/page.component.ts
|
component
|
PageComponent
|
0 %
(0/46)
|
|
src/app/app-engine/page/page/page.component.ts
|
variable
|
grapesjs
|
0 %
(0/1)
|
|
src/app/app.component.ts
|
component
|
AppComponent
|
0 %
(0/3)
|
|
src/app/notes-and-tutorials/how-to-program-with-nie-os/dictionary/dictionary.component.ts
|
component
|
DictionaryComponent
|
0 %
(0/5)
|
|
src/app/notes-and-tutorials/how-to-program-with-nie-os/my-practise-component/my-practise-component.component.ts
|
component
|
MyPractiseComponentComponent
|
0 %
(0/7)
|
|
src/app/notes-and-tutorials/how-to-program-with-nie-os/navigation/navigation.component.ts
|
component
|
NavigationComponent
|
0 %
(0/9)
|
|
src/app/notes-and-tutorials/how-to-program-with-nie-os/text-display/text-display.component.ts
|
component
|
TextDisplayComponent
|
0 %
(0/6)
|
|
src/app/notes-and-tutorials/mockup-tutorial/my-main-component/my-main-component.component.ts
|
component
|
MyMainComponentComponent
|
0 %
(0/7)
|
|
src/app/notes-and-tutorials/mockup-tutorial/navigation/navigation.component.ts
|
component
|
NavigationComponent
|
0 %
(0/11)
|
|
src/app/notes-and-tutorials/mockup-tutorial/text-display/text-display.component.ts
|
component
|
TextDisplayComponent
|
0 %
(0/6)
|
|
src/app/query-app-interface/data-management-tool/data-chooser-settings/data-chooser-settings.component.ts
|
component
|
DataChooserSettingsComponent
|
0 %
(0/41)
|
|
src/app/query-app-interface/data-management/data-assignment/data-assignment.component.ts
|
component
|
DataAssignmentComponent
|
4 %
(1/22)
|
|
src/app/query-app-interface/data-management/data-chooser/data-chooser.component.ts
|
component
|
DataChooserComponent
|
0 %
(0/24)
|
|
src/app/query-app-interface/data-management/data-management/data-management.component.ts
|
component
|
DataManagementComponent
|
0 %
(0/36)
|
|
src/app/query-app-interface/data-management/response-tree/generate-path.service.ts
|
injectable
|
GeneratePathService
|
0 %
(0/8)
|
|
src/app/query-app-interface/data-management/response-tree/response-tree.component.ts
|
component
|
ResponseTreeComponent
|
0 %
(0/21)
|
|
src/app/query-app-interface/data-management/response-tree/response-tree.component.ts
|
class
|
FileFlatNode
|
16 %
(1/6)
|
|
src/app/query-app-interface/data-management/response-tree/response-tree.component.ts
|
class
|
FileNode
|
25 %
(1/4)
|
|
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FileDatabase
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src/app/query-engine/knora/gravsearch/sendGravSearchQuery.service.ts
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src/app/query-engine/knora/knora-v1-request.service.ts
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80 %
(16/20)
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src/app/query-engine/knora/knora-v2-request.service.ts
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src/app/query-engine/sparql/sparql-request.service.ts
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src/app/user-action-engine/dashboard/dashboard/dashboard.component.ts
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The Spark Foundation
Data Science and business analytics | Internship
Feb 2021 - March 2021
Worked on Supervised Machine learning In this regression task, we have predicted the percentage of marks that a student is expected to score based upon the number of hours they studied.


My Wishlist
My Library
Sheldon's Wishlist
Book Name
Author
Date Added
Status
Mocking Jay
Suzanne Collins
10/16/2015
Recently Added
The Lord of the Rings
JRR Tolkien
10/11/2015
Trade Accepted


Side Notes (Note-Taking Web App)
Side Notes is a minimal, light-weight, todo-style note keeping web application. Side Notes are emphemeral, short lasting and residing solely in your browser storage. This web application keeps all data stored in the browser's internal database, meaning that all side-notes reside only on this browser. Absolutely no data is ever sent away from your local browser.


Matlab-problems:
#
java.lang.runtimeexception: std::exception for "matlab.desktop.currentfolder.GroupColumn": Cannot write to the level specified even after multiple times of retry. It'S mostly due to no write permission.
# get the path
at Matlab prompt(Matlab界面): prefdir
# change the write permission
$ sudo chmod 777 /home/rocky/.matlab/R2012b
$ cd /home/rocky/.matalb/R2012b
$ sudo chmod 777 *
#
MATLAB cannot save changes to the path
# search the location of pathdef.m.
# expample: /home/rocky/Matlab/2012b/toolbox/local
$ cd /home/rocky/Matlab/2012b/toolbox/local
$ sudo chmod 777 pathdef.m
#
Could not update the Matlab JAVA classpath.txt file due to the following error:
Error using PsychtoolboxPostInstallRoutine (line 589)
Could not open Matlab's JAVA path definition file 'classpath.txt' for write access.
Probably you do not have sufficient access permissions for the Matlab application folder
or the file itself to change the file /home/rocky/Matlab/2012b/toolbox/local/classpath.txt .
Please ask the system administrator to enable write-access to that file and its
containing folder and then repeat the update procedure.
Alternatively, ask the administrator to add the following line:
/home/rocky/Matlab/2012b/toolbox/Psychtoolbox/PsychJava
$ gedit /Matlab/2012b/toolbox/local/classpath.txt
# add a line at the end:
/home/rocky/Matlab/2012b/toolbox/Psychtoolbox/PsychJava
#
Can not run Eyelink
% necessary for windows:
# bakcup
$ sudo cp /etc/apt/sources.list /etc/apt/sources.list_bak
$ sudo gedit /etc/apt/sources.list
# 加入源地址 ［example for
eyelink-display-software1.9
］
deb
http://archive.canonical.com/ lucid partner
deb http://download.sr-support.com/x64 /
$ sudo apt-get install eyelink-display-software1.9


QuickNote is a free to download app with In App purchase for Pro features.
3rd party service provider
We use Microsoft Appcenter for Analytics and crash reporting
Privacy Policy
Analytics
Analytics are helpful to evaluate user behavior and for diagnostics. While doing so no personal information of the user is being shared with the 3rd party service. Diagnostic data is collected anonymously and cannot be used to identify you.
Crash Reporting
Crash reporting helps Quick Note with stability and performance by sending the crash report if it is terminated unexpectedly. This crash report is collected anonymously and contain no personal information.
Age Rating
Quick Note does not contain any objectionable material and qualifies for 4+ rating on the App Store
Changes to This Privacy Policy
I may update our Privacy Policy from time to time. Thus, you are advised to review this page periodically for any changes. I will notify you of any changes by posting the new Privacy Policy on this page.
This policy is effective as of 2020-11-27
Contact Us
If you have any questions or suggestions about the Privacy Policy, do not hesitate to contact me.


Index of /themes/arjuna/plugins/morris.js-0.5.1/spec/lib
Parent Directory
area/
bar/
commas_spec.coffee
donut/
grid/
hover_spec.coffee
label_series_spec.coffee
line/
pad_spec.coffee
parse_time_spec.coffee


Non-default parameters: things we tried and their results¶
There are a number of uncurl parameters (well, not necessarily parameters, more like... run configurations?) that we experimented with. Here are some results.
Cell normalization¶
This option involves normalizing the cells by their read counts. First, we calculate the total read count of each cell, and divide all counts for cell i by its total read count. Then, we find the median total read count over all cells, and multiply the entire matrix by that value. This method has been used previously for scRNA-seq datasets [see paper for reference].
The clustering performance after cell normalization were substantially better on count-valued datasets, and either had no effect or were marginally worse on RPKM-normalized and other forms of data that have already been normalized in some other way. So we would suggest using this option for unnormalized count-valued datasets. The downside is that it might lose some information (if certain cell types were correlated to larger read counts?), but I’m not sure if that happens in practice.
[TODO: include graphs]
To use this option, run data_normalized = uncurl.preprocessing.cell_normalize(data)
, and run uncurl on data_normalized
.
Constrained W¶
When this option is activated, the W
matrix is normalized so that its columns sum to 1 after each round of alternating minimization. Without this option, W
is only constrained to be nonnegative during the optimization process, and normalized after the end of the optimization.
In clustering experiments, this option had mixed results. It performed marginally better on some datasets and marginally worse on others. On the 10X datasets, constrained W performed slightly better when combined with cell normalization, and worse without cell normalization.
[TODO: include graphs]
To use this option, add the argument constrain_w=True
to run_state_estimation
or poisson_estimate_state
. This does not work for the NMF-based methods.
Uncurl initialization options¶
We provide a variety of initialization options for uncurl. Most initialization methods first perform a clustering, initialize M based on the cluster means, and W based on the cluster assignments. The default initialization is based on truncated SVD followed by K-means. We also provide initializations based on Poisson clustering, and Poisson k-means++ with randomized W.
In clustering experiments, truncated SVD initialization usually performed the best, but there were some datasets under which Poisson clustering initialization performed better. For example, on randomly downsampled data, Poisson clustering initialization seems to perform better.
To use different initializations, use the argument initialization=<method>
, where <method>
can be one of tsvd
(truncated SVD + K-means), cluster
(Poisson clustering), kmpp
(Poisson k-means++), or km
(k-means on the full data).
Alternative to QualNorm: mean-normalized initialization¶
Given prior gene expression data, there are a variety of methods for initializing uncurl. QualNorm
is one way of doing this initialization. Another way, when we have real-valued prior data, we could normalize the prior data so that each cell type sums to 1, and then multiply that by the mean per-cell read count of the actual data.
This performed better than QualNorm on sparse datasets such as the 10X datasets.
Optimization methods¶
The default optimization method for Poisson state estimation is NoLips [see paper for reference].
Before settling on NoLips as a default, we also tried a variety of different optimization methods. The first was L-BFGS, as implemented in scipy. We also tried gradient descent, stochastic gradient descent, and a custom method based on alternating iteratively reweighted least squares on a Poisson regression model. These methods are not included in the uncurl package because they had poor performance characteristics compared to NoLips. We settled on NoLips because it was easy to port to sparse matrices and was easily parallelizable. L-BFGS tends to converge in fewer iterations, but the per-iteration time for NoLips is much less: it has closed-form updates that don’t require gradient or objective value calculations, and the updates take advantage of data sparsity.
To use different optimization methods, use the argument method=<method>
, where <method>
can be either NoLips
(default) or L-BFGS-B
.


This describes newlib
, a freely available libc replacement. Most
applications use calls in the standard C library. When initially linking
in libc.a, several I/O functions are undefined. If you don't plan on
doing any I/O, then you're OK, otherwise they need to be created. These
routines are read, write, open, close. sbrk, and kill. Open & close
don't need to be fully supported unless you have a filesystems, so
typically they are stubbed out. Kill is also a stub, since you can't do
process control on an embedded system.
Sbrk() is only needed by applications that do dynamic memory
allocation. It's uses the symbol _end
that is setup in the linker
script. It also requires a compile time option to set the upper size
limit on the heap space. This leaves us with read and write, which are
required for serial I/O. Usually these two routines are written in C,
and call a lower level function for the actual I/O operation. These two
lowest level I/O primitives are inbyte() and outbyte(), and are also
used by GDB back ends if you've written an exception handler. Some
systems also implement a havebyte() for input as well.
Other commonly included functions are routines for manipulating LED's on the target (if they exist) or low level debug help. Typically a putnum() for printing words and bytes as a hex number is helpful, as well as a low-level print() to output simple strings.
As libg++ uses the I/O routines in libc.a, if read and write work, then libg++ will also work with no additional changes.


It really is a question of body chemistry.
I am a self-proclaimed hardhead. I have tried a few substances in my travels - MJ, coke, salvia, MDMA, ayahuasca, kratom, P. Cubensis, Wild Dagga (flowers and leaves), and Sinicuichi, and now Amanita Muscaria and Amanita Pantherina. Of all these things, only MJ, coke, and P. Cubensis have ever affected me. I have taken substances with groups only to watch serious trips of others go downhill (Ayahuasca) and me going home afterward without any affect.
I decided to try Amanita Muscaria and Amanita Pantherina after much research since they are both organic and legal. I purchased 28g of EACH type from a reputable online ethnobotantist. They were Grade A caps picked in Washington state. I was planning to do these by myself, as I do all of my travels. The caps arrived dried so I did not heat them. since just under the skin is reported to be the most effective part, I decided to just eat them as is. The caps also contained the dried gills which I was a little bit concerned about for the nausea, but decided to eat anyways.
Because I knew I was going to have to assess/regulate my dosage over time I decided that my day off was ideal and would start early in the day. The research suggested eating a small meal beforehand to counter the nausea, so I consumed some minestrone soup and a small beef burrito. I had also read a lot about respecting this ally, so I meditated beforehand and said a simple prayer to the universe.
T + 0:00 Since I weigh 240 lbs. (108kg) I decided to ingest 10g of the Muscaria first and did so at 10:00 AM. After an hour I basically felt nothing so I decided to do laundry which is what I would normally do on my day off.
T + 2:00 By 12:00 PM, I felt absolutely nothing - no nausea, no sweating, no delerium. I decided to consume another 10g of Muscaria caps and waited.
T + 3:30 PM I felt absolutely nothing. Expecting I was going to be in the same boat as with the other ethnobotanicals I had tried, I ate 2 medium-sized Pantherina caps. From the research I had done, it was clear that Pantherina requires caution and is reputed to have higher concentrations of active compounds. One writer had compared Muscaria to 'riding a tiger' and Pantherina to 'riding a raging herd of tigers.' Many had indicated that they simply would never try them again. I put on a DVD and started watching a movie. Still feeling nothing, I dozed off (as I usually take a mid-day nap on my day off) and woke up at about 4:00 PM. At this point I had consumed about 20g of Muscaria and 5g of Pantherina caps with NO effect.
T + 4:00 I decided to eat more Pantherina so I ingested 4 more medium-sized Pantherina caps.
T + 6:30 I started to feel a sense of euphoria and very relaxed. This continued and kind of intensified. I had no sense of any nausea, delerium, or hallucinations. I had no OEVs or CEVs. Just a nice euphoria. Getting to this state required approx. 37g of Amanitas.
T + 7:30 I decided to eat another 2 medium-sized Pantherina caps to see how far the buzz would intensify.
T + 8:00 I had a nice light feeling to my body and the euphoria continued. Still, no no nausea, no sweating, no delerium, no hallucinations, no time shifty feelings, no inner dialogue. Nothing pushing me over the edge of the euphoria. Speech and thought are fully intact. Maybe feel a little giddy.
T+ 8:30 I consumed another 2 medium-sized Pantherina caps to see if I could even get beyond the mild euphoria - this state is quite enjoyable and relaxing, but nothing more. I have lost a little coordination but can handle tasks as if lucid. By this time I have consumed 20g of Amanita Muscaria and 20g of Amanita Pantherina - 40g of Amanitas.
T + 1
12:00 This is where I am right now. I think I am just going to consume the remaining 16g of shrooms. I've peaked and am beginning to come down.
For all the research and warning, I have experienced none of the harsh side-affects and only a mild sedation/euphoria. I have no idea why this is, or why my body seems to process things differently from the majority of other people. Apparently, I must be the biggest hardhead in the world. And then, I can't explain why cannabis affects me so greatly if other ethnobotanicals provide only mild affects, if any.
If anyone knows the answer to this mystery or is interested in conducting research with me, I welcome it. of course, I understand that my experiences are unique, and most people would probably be hospitalized on 20g of each Amanita.
At the end of the day,I am thankful for the experience though.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Hundreds tested over tuberculosis diagnosis at SC med center
WEST COLUMBIA, S.C. (AP) - Hundreds of people have been screened for tuberculosis after possible exposure to a patient recently diagnosed with the contagious disease.
Lexington Medical Center spokeswoman Jennifer Wilson says more than 300 free screenings have been completed on people who possibly came into contact with the Lexington Oncology Infusion Center patient between August and September. Citing a statement by Wilson, news outlets report none of the screenings conducted by the center and the state have come back positive so far.
The infusion center is a physician practice on the medical center system’s West Columbia campus and is not inside the main campus building.
TOP STORIES
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Tuberculosis is a potentially deadly disease that spreads through the air when an infected person talks, coughs or sneezes. It can be treated with medication.
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6.1 Ecoinformatics
6.1.1 Michener et al, 2001
“Defining and unraveling biocomplexity” (Michener et al. 2001)
Key contribution: This study’s primary contribution to the literature is a formal definition of biocomplexity, and an investigation of its emergence within the scientific disciplines. The article primarily discusses:
- Defining biocomplexity
- Examining the characteristics of biocomplexity
- Speculating on the future of biocomplexity
Biocomplexity - “properties emerging from the interplay of behavioral, biological, chemical, physical, and social interactions that affect, sustain, or are modified by living organisms, including humans,” as defined by (Michener et al. 2001)
Key notes:
- Biocomplexity exists in contrast to the Popperian type of reductionalist science (causality & falsification), and recognizes that reduced causal mechanisms often are not robust within real-world situations
- “myriad confounding factors operating at different temporal and spatial scales”
- Two features of “biocomplexity”
- biocomplexity occurs as temporal, spatial and conceptual boundaries are broken-down and worked across
- emergent & unexpected properties may (or likely will) arise in response to biocomplexity
Characteristics of “biocomplexity”:
- Relevant for a wide range of organisms; from microbial to megafauna (including humans)
- Relevant for a wide range of environments
- Reflected in nonlinear, chaotic, or unpredictable behaviors – which likely necessitates sophisticated and innovative quantitative procedures
- Likely to span many hierarchical levels as well as across multiples scales in time and space
Key research trajectories for ecology (as posed by Thompson 2001):
- “Dynamics of coalescence in complex communities” - how do environmental constraints and species availability result in current complex ecological communities
- “Ecological memory” - how do past events & conditions influence current ecological structure and conditions
- “Emergent properties” - examining whether the first principles of “hard” sciences can predict composition, structure & function
- “Ecological topology” - identifying “rules” that occur over different scales of time and space, and examining how they interact to produce ecosystems
Further reading:
- (Thompson et al. 2001) - Frontiers of ecology: As ecological research enters a new era of collaboration, integration, and technological sophistication, four frontiers (listed above) seem paramount for understanding how biological and physical processes interact over multiple spatial and temporal scales to shape the Earth’s biodiversity
6.1.2 Jones et al, 2006
“The new bioinformatics: Integrating ecological data from the gene to the biosphere” (Jones et al. 2006)
Key contribution: This paper makes the case for “locating, accessing and integrating” data within ecology. They provide examples of the successes associated with doing so in alternative fields (genetics), and make a case for the need within ecology. They also outline vertically integrated databases (data warehouses) versus metadata-driven databases (data collections), and the relative pros/cons of the two.
Examples of metadata and data collections in ecology:
Key notes: Some relevant points from the article:
A focus on “experimental” design and testing of multiple hypotheses makes ecological data inherently heterogeneous.
The importance of ecoinformatics is housed in its ability to “allow a broader perspective over time and space, and across many disciplines, than is possible from one or a few studies,” and in allowing data to be reused for questions that may emerge in the future (thus may fit well with “biocomplex” framework).
Current standard for storing of data is in heterogeneous spreadsheets, which are termed “data islands”.
The response of ecoinformatics to resolving issues related to data islands are largely:
- Data warehouses (vertically-integrated) - databases that coalesce data from many investigators, but focus on one theme and are searchable often through an online interface
- Often represents a “least-common denominator” approach in that they are less complex than original dataset
- Examples: GenBank, VegBank or TreeBase
- Data collections (metadata-driven) - collection of datasets that are loosely structured together by well-crafted metadata. A good example if the Global Biodiversity Information Facility (GBIF), which compiles data from 450 museum collections. Also, Data Dryad, which links datasets to papers and is a member of the dataOne network. Some advantages of meta-data driven databases are:
- Data of disparate types can be housed within a single location
- Focuses on project-level data and thus is familiar to researchers
- Metadata often more detailed
- Metadata often more precise and thus can be used in search functions
- Metadata standards - “consistent and rigorous sets of definitions for metadata categories that are broadly adopted by a research community.” Several examples exist within ecology:
- Ecological Metadata Language (EML)
- Biological Data Profile (BDP)
- Geographic Markup Language (GML)
- Knowledge Network for Biocomplexity (KNB) - a metadata-drive repo that provides mappings from EML to BDP (but not from BDP to EML as of time of article publication)
Databasing and development of meta data is often subject to entrenched domain-specific vocabularies and also assumptions; ontologies may be a way of resolving this limitation
“Ontologies” - well-defined concepts or terms that have precise meanings within a specific domain or field, as well as formal mappings of how the terms interrelate
- “Science Environment for Ecological Knowledge (SEEK)” - one example of a system developing ontologies within ecology
- “Darwin Core”- another provider of ontologies, particularly for evolution related concepts; extension of Dublin Core for biodiversity informatics (GBIF uses it)
Further reading:
- Look into metadata languages (e.g., EML)
- Look into some of the earlier work on databases by Michener
6.1.3 Michener, 2006
“Meta-information concepts for ecological data management” (Michener 2006)
Definitions:
- Metadata - information about data, i.e. higher level descriptions of data
- Meta-information - information derived from subsequent data processing and analyses; are these data products?
Key contribution: Michener outlines some of the key criteria, challenges and opportunities for metadata within an ecological context.
Notes on metadata:
Some benefits of metadata include:
- Longevity of the data is greatly increased
- Reuse of the data both by the original researcher and others is facilitated
- Well-documented data may be used to expand scale of ecological inquiry
The primary functionality of metadata is:
- Supports data discovery (most basic level)
- Facilitates the acquisition and use of data by humans (requires additional metadata)
- Enables automatic discovery, ingestion and analysis of data (requires more sophisticated metadata), which needs:
- research context (hypothesis, study site, experimental design, etc.)
- status of dataset
- physical structure of dataset
- info to facilitate use and comprehension
Ecological Metadata Language (EML):
- EML is a standard for metadata that provides structure that supports automated functions of metadata listed above and is implemented in eXtensible Markup Language (XML)
- Organized into modules, such as “dataset”, “access”, “physical”, “party coverage”, “project”, “methods”, “datatable”, “attribute”, and “physical”
- Additional info at ecoinformatics.org
6.1.4 Bekker et al, 2007
“Long term datasets: From descriptive to prescriptive data using ecoinformatics” (Bekker et al. 2007)
Key contribution: This is the lead article to a special issue in Journal of Vegetation Science that focuses on ecoinformatics driven approaches to understanding complexity within vegetation systems.
The article is less informative in its own right as much as a good pointer to other key studies working with vegetation science and ecoinformatics. It appears to be one of the earliest explicit applications of ecoinformatics to vegetation science.
Further reading:
- Le Duc, 2007 - A database application for long-term ecological field experiments
- Peacock, 2007 - The RAINFOR database, for monitoring forest biomass and dynamics
6.1.5 Hale & Hollister, 2009
“Beyond data management: How ecoinformatics can benefit environmental monitoring programs” (Hale and Hollister 2009)
Key contribution: This is a less seminal study. They describe the relevance of ecoinformatics for environmental monitoring, but do not go much beyond some of the other pieces on ecoinformatics and its relevance for ecology. Some relevant points that stood out to me:
- Standardization of taxonomic classes may be facilitated by ecoinformatics and application of ontologies in ecoinformatics
- Establishing standards and protocols can help in synthesizing studies and results across time and space
Note: Likely better to focus on other studies within the literature than this one as they don’t bring up many points beyond those expressed in more seminal works.
6.1.6 Feller et al, 2010
“Biocomplexity in mangrove ecosystems” (Feller et al. 2010)
Key contribution: Aim to elucidate the factors playing in at different hierarchical levels in mangrove ecosystems (biocomplexity). They use emergent properties (which they define as “patterns or processes that occur at multiple hierarchical levels within ecosystems”) to examine underlying ecological processes within mangroves.
Biocomplexity appears to be thinking about ecology through a hierarchical lens, in which processes that exist at more baseline levels (physics, chemistry, etc.) result in ecological processes > functions > services.
Key findings: The article doesn’t differ much from other reviews of controls on mangrove processes, apart from the structure in which they examine:
- Controls of emergent properties (namely specializations for variable salinity, flooding and nutrient availability)
- e.g., salinity tolerance, flooding tolerance, reproductive traits/regeneration, nutrient availability
- perhaps analogous to “ecosystem processes”
- Collective properties of forests (e.g., zonation, productivity, nutrient cycling, food webs)
- perhaps analogous to “functions”
- Emergent/collective properties of ecosystems & landscapes (e.g., habitat stability & connectivity of ecosystems)
- perhaps analogous to “services” or “benefits”
Emergent properties - concept that the whole is greater than the sum of its parts when examining ecological phenomena and the underlying processes that drive them. Emergent properties can exist at any scale, and may build upon themselves
- e.g., morphology and physico-chemical conditions leading to ecophysiology (emergent property) which then leads to spatial assemblages (2nd emergent property), etc.).
- it may be helpful to think of different emergent properties at different scales of time and space.
Three primary characteristics of emergent properties exist:
- Emergent properties are not simply the sum of their parts
- Emergent properties exist as a type that is different from their underlying components
- They cannot be easily predicted from individual components
Examples of emergent properties within mangroves are ecophysiological traits (as listed above) that emerge from the interaction between biochemical and morphological traits with environmental factors.
Individual Based Models (IBMs) are discussed as useful though limited tools for examining biocomplexity within mangroves, but note that there is relatively little work in this field to date. See section 3.1.12 for an example of using IBMs within mangroves.
6.1.7 Michener & Jones, 2012
“Ecoinformatics: Supporting ecology as a data intensive science” (Michener and Jones 2012)
Key contribution: This is a seminal study on the relevance of ecoinformatics written by two of the leading thinkers in the field.
Key notes: Several key notes from the study are:
The authors describe the “data life cycle,”" with 8 steps that do not necessarily occur in sequential order and depend upon the analysis and research question at hand:
- Plan - derive a data management plan that is often a living document as needs and uses for data will change over time
- Collect - there are highly variable mechanisms under which this occur given the vast amount of data types and collecting methods that exist; standardization in collection procedures is key for synthesizing data at later times
- Assure - measures taken to ensure data quality both before and after collection of the data; may consist of standardization of codes, units or metadata (e.g., using EML)
- Describe - sufficiently describing the data via metadata (who, what, when, where, why)
- Preserve - depositing of data in a repository such that they may be verified, maintained and accessed through time
- Discover - identifying appropriate data for research question of interest; double-edged sword in that you can get millions of hits or alternatively have tons of distinct data sources that exist only on disparate, local machines. DataONE is one such tool that drills down into data to identify datasets of actual interest; this is where ontologies come into play
- Integrate - understanding methodological differences, transforming to common representations and converting and recoding data to comparable semantics
- Analyze - reproducibility in analysis, with a particular focus on scientific workflows
A schematic of the data life cycle that the authors provide is:
DataONE: DataONE is a research platform that seeks to support the entire data life cycle, as well as integrate many different forms of data. There are three principle components:
- Member Nodes - contribute data, computing resources and services (e.g., data repositories, universities, computing centers)
- Coordinating Nodes - support network-wide services such as indexing, replication, and interactability of Member Nodes
- Investigator Toolkit - tools for researchers to employ in all steps of the data life cycle
6.1.8 Ruegg, 2014
“Completing the data life cycle: Using information management in macrosystems ecology research” (Rüegg et al. 2014)
Key contribution: This article promotes the premise that macrosystem ecology requires integrated information management at its inception. In calling for “completing the data cycle,” they refer to well-documenting datasets such that data may be re-used to answer new and different questions from those that are conceived by those involved in the original projects.
The article is largely an extension of Michener and Jones (2012), and argues largely for extensive documentation of datasets to enhance longevity.
Key notes: The study notes several key areas within the data life cycle:
- Data collection: Incorporating information management early within the data collection process; critical to consider the structure and longevity of the dataset prior to collection such that it may be built upon and reused in the future
- Data integration (1): Integrating data from many sources. Currently integration is not possible through an automated manner, though tools exist to help in data integration
- Data integration (2): Integrating data from local observations to models and back requires unique considerations
- Must understand spatial resolution of different datasets (e.g., field and RS data).
- Spatial and temporal issues exist for interpolation of field (e.g., point) datasets.
- “Completing the data cycle”: Documenting well and sharing facilitate analysis. Tools that show full data provenance and work flow can be effective in showing the full analysis and collection of a particular dataset.
6.1.9 Soranno, 2014
“Cross-scale interactions: Quantifying multi-scaled cause – effect relationships in macrosystems” (Soranno et al. 2014)
Key significance: This study calls for more explicit investigation of cross scale interactions (CSIs) both in time and space. They lay out a framework for investigating CSIs that consists of:
- Conceptual mapping
- Construction of an appropriate database (ecoinformatics)
- Modeling strategies that specifically investigate interactions (e.g., interaction terms in hierarchical modeling).
Notes:
The authors define two primary types of cross scale interactions (CSIs).
- Interactions among driver variables at different spatial scales that influence a focal resonse variable
- Interactions between transport processes that link fine- and broad-scaled processes
The authors explicitly model the interaction using Bayesian hierarchical models via an interaction term between regional and local driver variables.
Steps for quantifying CSIs:
- Conceptual model of cross-scale relationships that explicitly considers interactions and interdependencies. Development of the conceptual model is key to the process and entails four key components:
- Identifying relevant spatial and temporal extents and resolutions of driver variables
- Considering the types of the interactions that are expected
- Determining whether relationships are linear or nonlinear
- Identifying critical assumptions and uncertainties that may generate additional testable hypotheses
- Compilation of multi-scaled, multi-themed databases
- Summarize independent variables at multiple scales
- Resolve differences in data resolution (ecoinformatics helpful here)
- Test for CSI as interaction term using hierarchical models
Example provided by authors
The authors exemplify these steps in examining P nutrient additions in lake ecosystems in the US.
Specifically, they encounter the issue in that wetland cover surrounding lakes leads to both increases and decreases in concentrations of P. They hypothesize that there are interactive effects between agricultural presence and wetland presence on lake P, and model the cross spatial scale interactivity between the two as an interaction term.
The results are significant and lead the authors to additional hypotheses. The particular case study is facilitated by standardization of lake environmental quality data.
References
Michener, William K, Thomas J Baerwald, Penelope Firth, Margaret A Palmer, James L Rosenberger, Elizabeth A Sandlin, and Herman Zimmerman. 2001. “Defining and Unraveling Biocomplexity.” BioScience 51 (12): 1018–23. doi:10.1641/0006-3568(2001)051[1018:DAUB]2.0.CO;2.
Thompson, John N, OJ Reichman, Peter J Morin, Gary A Polis, Mary E Power, Robert W Sterner, Carol A Couch, et al. 2001. “Frontiers of Ecology: As Ecological Research Enters a New Era of Collaboration, Integration, and Technological Sophistication, Four Frontiers Seem Paramount for Understanding How Biological and Physical Processes Interact over Multiple Spatial and Temporal Scales to Shape the Earth’s Biodiversity.” BioScience 51 (1): 15–24. doi:10.1641/0006-3568(2001)051[0015:FOE]2.0.CO;2.
Jones, Matthew B, Mark P Schildhauer, OJ Reichman, and Shawn Bowers. 2006. “The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere.” Annual Review of Ecology, Evolution, and Systematics 37. doi:10.1146/annurev.ecolsys.37.091305.110031.
Michener, William K. 2006. “Meta-Information Concepts for Ecological Data Management.” Ecological Informatics 1 (1): 3–7. doi:10.1016/j.ecoinf.2005.08.004.
Bekker, Renée M, Eddy van der Maarel, Helge Bruelheide, and Kerry Woods. 2007. “Long-Term Datasets: From Descriptive to Predictive Data Using Ecoinformatics.” Journal of Vegetation Science 18 (4): 458–62. doi:10.1111/j.1654-1103.2007.tb02559.x.
Hale, Stephen S, and Jeffrey W Hollister. 2009. “Beyond Data Management: How Ecoinformatics Can Benefit Environmental Monitoring Programs.” Environmental Monitoring and Assessment 150 (1-4): 227. doi:10.1007/s10661-008-0675-x.
Feller, Ilka C, CE Lovelock, U Berger, KL McKee, SB Joye, and MC Ball. 2010. “Biocomplexity in Mangrove Ecosystems.” Annual Review of Marine Science 2: 395–417. doi:10.1146/annurev.marine.010908.163809.
Michener, William K, and Matthew B Jones. 2012. “Ecoinformatics: Supporting Ecology as a Data-Intensive Science.” Trends in Ecology & Evolution 27 (2): 85–93. doi:10.1016/j.tree.2011.11.016.
Rüegg, Janine, Corinna Gries, Ben Bond-Lamberty, Gabriel J Bowen, Benjamin S Felzer, Nancy E McIntyre, Patricia A Soranno, Kristin L Vanderbilt, and Kathleen C Weathers. 2014. “Completing the Data Life Cycle: Using Information Management in Macrosystems Ecology Research.” Frontiers in Ecology and the Environment 12 (1): 24–30. doi:10.1890/120375.
Soranno, Patricia A, Kendra S Cheruvelil, Edward G Bissell, Mary T Bremigan, John A Downing, Carol E Fergus, Christopher T Filstrup, et al. 2014. “Cross-Scale Interactions: Quantifying Multi-Scaled Cause–effect Relationships in Macrosystems.” Frontiers in Ecology and the Environment 12 (1): 65–73. doi:10.1890/120366.


Hi, my name is
Mohammad Sadi
I am a Developer & Teacher
Welcome to my portfolio. I am a developer, photographer, and father of a bear puppy named Oganesson IT based in Chittagong, Bangladesh.
Select Projects
Here are some of the projects I have worked on.
You can find more on GitHub.
A redesigned Amazon clone inspired by this concept design on Dribbble. You can add items to cart, delete items from the cart, and increase or decrease the quantity of an item in the cart.
This is a recurrent neural network Twitter bot written in Python
with Tweepy. It will tweet newly generated text from the trained
model every hour to a Twitter account
My version of this Twitter bot was trained to generate text with
Emily Dickinson
poems. It was originally deployed on a Google
Cloud Platform virtual machine
instance. However, the free
trial period ended, so it is now scheduled to run locally on PC
startup.


Moonlight Chemistry2014
Lovely Lights2014
A quick process sketch2016
Fan Art for Puzzles and Dragon2015


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**Programming with MATLAB for BARC0130: Advanced Mathematical Modelling & Analysis**
**Tutorial 5: Notes and Exercises**
The fundamental data type in MATLAB is a MATLAB array (or matrix). MATLAB arrays can be classified into the following types:
- **Numeric**
- **Cell**
- Boolean
- **Char**
- Struct
- Table
We have come across numeric, char and cell arrays in previous sessions. The only data types in MATLAB that are not arrays are function (or object) handles, such as figure handles, file handles etc. These object handles are scalar types.
In this tutorial, we will first focus on the **boolean** array types in MATLAB. In a boolean array, all of the elements can take one of only two values:
- 1, corresponding to True
- 0, corresponding to False
We will also cover special values in MATLAB (including Inf and NaN). Then, we look into writing outputs from a MATLAB program and saving it into a data file. This should prepare you for the final exercise, which requires you to take inputs from two data files, which are on Moodle: 'nyc_building_energy_usage.csv' (which is missing some key data) and 'nyc_building_energy_usage_missing_data.csv' to produce a third data set that is complete in the sense that the missing data issue is resolved. In the real-world, data is messy and the ability to deal with data quality issues and merge data sets together is a useful skill to have.
# Boolean Variables and Arrays
Boolean variables are variables that can have either of two values: 0 or 1. Boolean variables are incredibly important in programming. They are used to control how programs operate i.e. they are used in loops and conditional statements to execute instructions in the manner that is desired by the programmers, using relational (see Table [relational_operators]) and logical operators(see Table [logical_operators]).
| Relational Operator | Description | Example: (x=10) |
|:----------------:|:---------------------------:|:--------------:|
| $==$ | is equal to | $(x==5) = 0$ |
| $~=$ | is not equal to | $(x~=5) = 1$ |
| $>$ | is greater than | $(x>5) = 1$ |
| $>=$ | is greater than or equal to | $(x>=5) = 1$ |
| $<$ | is less than | $(x< 5) = 0$ |
| $<=$ | is less than or equal to | $(x<=5) = 0$ |
[Table [relational_operators]: Relational Operators in MATLAB.]
| Logical Operator | MATLAB Symbol | Example: A=[1 0 0 1]; B=[0 1 0 1] |
|:----------------:|:-------------:|:--------------------------------:|
| AND | & | A&B=[0 0 0 1] |
| OR | $\mid$ | A $\mid$ B=[1 1 1 1] |
| NOT | ~ | ~A=[0 1 1 0]; ~B=[1 0 1 0] |
| XOR | xor | xor(A,B)=[1 1 0 0] |
[Table [logical_operators]: Logical Operators in MATLAB.]
!!!
A key point to note is that when we use relational or logical operators, we are making a statement (e.g. $x<=5$ is read as "$x$ is less than or equal to 5"), and that statement must always have a true or false answer. Therefore, the output of such a statement will be a boolean variable.
!!!WARNING
In the examples above all of the boolean operators are acting on integer data types. If performing comparisons between two float or double data types, it is important to be aware of round-off and truncation errors that can occur when real numbers are represented by float or double data types, and to use an appropriate tolerance when making comparisons between to floating point data types. For example, suppose the same number is represented as $a=6.400000000000$ and $b=6.3999999999$. In order to make a valid comparison between $a$ and $b$, you must set an appropriate tolerance, e.g. $tol=1\times10^{-10}$. In such cases, $a==b$ becomes $abs(a-b)< tol$.
**Exercise 1.** Let $x=6$ and $y=10$. What is the result of each of the following statements:
- $x==6$
- $x~=10$
- $x==10$
- $x>10$
- $x<=10$
- $x< 10$ | $y< 10$
- $x< 10$ & $y< 10$
- $x~=10$ & $y==6$
- $x==6$ & $y==10$
# Special Values in MATLAB
There are some special values in MATLAB that you should not use as variables names (if you can avoid it).
**Exercise 2.** Type the following variable names into the MATLAB command window:
- pi
- i, j
- realmax
- realmin
- intmax
- intmin
- Inf
- NaN (you will come across NaN in the final exercise)
# Writing Output to a Data file
**Exercise 3.** For $x=1:1:50$, find $\log_{10}(x)$ and output the results to a data file in the format shown below, using tabs to
separate between the columns.
| $x$ | $log_{10}(x)$ |
|-----|----------------|
| 1 | $log_{10}(1)$ |
| 2 | $log_{10}(2)$ |
| ... | ... |
| 49 | $log_{10}(49)$ |
| 50 | $log_{10}(50)$ |
You may find it useful to use the following MATLAB functions: fullfile, fopen and fprintf.
# Combining Data from Two Sources
**Final Exercise.** Load the New York City Government Buildings Energy Usage (2015) dataset (data1: 'nyc_building_energy_usage.csv') into MATLAB using textscan function. Extract the data from the following four columns:
| 2 | 9 | 10 | 11 |
|---------------|--------------------|---------------------|-----------|
| Building Name | Energy Use (MMBTU) | Latitude (Northing) | Longitude (Easting) |
You will notice that some buildings are missing the coordinates of their location. These missing coordinates are found in a second data file (data2: 'nyc_building_energy_usage_missing_data.csv'). The data in this second file has the following format:
| 1 | 2 | 3 |
|---------------|-------------------|-------------------|
| Building Name | Latitude ($\circ N$)| Longitude($\circ W$) |
Use the second data file to populate the missing data points in the original file. Note that the Longitude in data1 and data2 are using a different direction. Using data1 and data2, create a new dataset that has the following format:
| 1 | 2 | 3 | 4 |
|---------------|---------------------|---------------------|------------|
| Building Name | Latitude (Northing) | Longtitude(Easting) | Energy Use |
Try your best to get this exercise done in time for the next MATLAB tutorial. We will use this dataset next time to learn how to create a map of energy usage by goverment buildings in New York City.


{% extends "rebranded-base.html" %} {% block body_content %} {% from "_macros.html" import render_field, render_hidden %} {% include "_logout.html" %}
Learn to be a better Civil Servant.


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Hybrids.ox
DDP
Solution Methods that rely on algorithms in
FiveO
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This module defines
DDP
solution methods that rely directly on the algorithms in
FiveO
.
Author:
© 2011-2015
Christopher Ferrall


Speed
QTable States
X-Target distance (cm)
Y-Target distance (cm)
Velocity (cm/s)
Action
Score
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Penalty


| DOSE: T+ 0:00 |
100 mg |
oral |
Pharms - Propoxyphene |
(pill / tablet) |
| T+ 0:00 |
650 mg |
oral |
Acetaminophen |
(pill / tablet) |
| T+ 1:15 |
50 mg |
insufflated |
Methcathinone |
(powder / crystals) |
| T+ 2:00 |
65 mg |
oral |
Methcathinone |
(capsule) |
| T+ 4:30 |
60 mg |
oral |
Methcathinone |
(capsule) |
| T+ 4:45 |
100 mg |
oral |
Pharms - Propoxyphene |
(pill / tablet) |
| T+ 4:45 |
650 mg |
oral |
Acetaminophen |
(pill / tablet) |
Methcathinone and Darvocet (100 mg propoxyphene + 650 mg acetaminophen).
I have experience with a wide range of stimulants, and this was one I had not tried. The Darvocet I am very familar with, as I take it occasionally for pain.
9:30 pm -- I take one Darvocet (for moderate pelvic pain). I don't feel much effect from this.
10:45 -- I snorted a line about ~50 mg. The drug appears as a fluffy white powder, and smells slightly sweet.  Not as much as meth, but a fair deal. The subsequent drip is pretty awful, too.
11:00 -- I can feel effects, though mild. Slight stimulation (coke-like), jitteriness.
11:30 -- I decide to try taking some orally, because I've found that stimulants tend to be alot smoother when adminstered that way.  Not strong like meth, and it doesn't really give me that 'alert' feeling. Neither does it make me social or talkative.
The effects continue increasing, but overall, I'm feeling neutral about the drug; even amounts of bad and good effects. I'm feeling antisocial, jittery, anxious. Somewhat lightheaded and 'dumb' (can't think clearly, trouble concentrating, bad memory). My heart is beating quickly (very unusual for me, I've never felt this on any stimulant, I have a very low pulse naturally), and my chest feels generally tight, which I think contributes to my mild anxiety.
But, I do feel some pleasant effects, and a mild body high.
12:15 -- I feel some mild euphoria; my state is now positive as opposed to neutral. It's not a STRONG positive but quite tangible. My head feels good. I wonder if the Darvocet (which takes a while to kick in, and always gives me mild-moderate headaches) is responsible for me feeling only 'neutral' earlier. Thus, maybe I'm feeling better because the Darvocet is wearing off a bit...
12:30-1:30 -- I continue feeling pretty nice, mild surface-of-the-skin body high.
1:45 -- I'm feeling fewer effects, anxious/crash-like feelings returning...
I go for a walk in the rain.
2:00 -- I take another 60 mg orally, it is like I am 'fiending' a bit (very rare for me).
The high isn't great but it is pleasant, and I don't feel like being sober right now.
2:15 -- I take another Darvocet, because my pelvic pain is returning (I guess the Darvocet is wearing off substantially). Then I eat an english muffin in order to prevent opioid-nausea.
2:45 -- I'm really feel effects, very nice tingly body high, nice tingly sensation in the head (though not as strong as with amphetamines), mild euphoria returns.
3:00 -- The effects at this point are at their highest for the night. There definitely seems to be some synergy between the methcat and darvocet resulting in the aforementioned body high. I'm feeling quite warm (pleasantly so, as I'm usually cold).
4:00 -- I start feeling shitty/anxious/antisocial again; guess I'm coming down.
5:00 -- I'm still feeling warm, and some body high. I've developed headache from the Darvocet. Though I'm still kinda stimmed/fucked up, I'm also feeling tired. The body warmth is strongest on my skin, which feels almost unpleasantly tingly/stimulated at this point. It's almost like a cross between really mild sunburn and pins&needles. VERY similar to the physical effects I get on amphetamine (dex, adderall) comedowns.
SUMMARY
This is not the worst stimulant I've done. I found it to be closest in effects to coke (which, I might add, has never made me talkative or social), although it resembled bzp in its body high and head-tingling effects.
It was an overall pleasant experience, but nothing amazing, and quite frankly I'd rather do meth any day.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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