Speak to God
You have the opportunity to say everything you would like to say to God .
To do this, you do not necessarily have to be a believer . Just speak out .


name: inverse layout: true class: center, middle, inverse --- #GitHub .footnote[Go directly to [project site](https://github.com/zmon/legal-git-hub)] --- ## What is it, what can I do with it, and why should I be using it? --- layout: false .left-column[ ## Why ] .right-column[ GitHub allows you to: - Share/Publish - This presentation is a GitHub document - Collaborate - Communicate Examples: - Class Glossary [https://github.com/UMKC-Law/Glossary/blob/master/Version1.md](https://github.com/UMKC-Law/Glossary/blob/master/Version1.md) - [http://gsa.github.io/Open-And-Structured-Content-Models/index.html](http://gsa.github.io/Open-And-Structured-Content-Models/index.html) - [http://labs.data.gov/dashboard/offices](http://labs.data.gov/dashboard/offices) - [https://github.com/GSA/open-data-program-template](https://github.com/GSA/open-data-program-template) ] ---
---
---
--- .left-column[ ## Why ## What ] .right-column[ Git + Hub, two words, CamelCased - Git is a Version Control System - Allows you to track changes and versions - Hub is the social aspect - README.md - Issues - with labels - Wiki - for persistent backgound - Collaborators/team - easy access ] --- .left-column[ ## Why ## What ### Git = Versions ] .right-column[ - Versions - Branch - Final/master - Draft/develop - Distributed - Tags - iOS 5.0, 6.0, 7.0 - Presented-2-Sep-2015 ] --- .left-column[ ## Why ## What ### Git = Versions ] .right-column[
* GitHub generally _only adds_ data * Integrity: Every file is check-summed before it is stored and checked every time it is retrieved. ] --- .left-column[ ## Why ## What ### Git = Branches ] .right-column[
] --- .left-column[ ## Why ## What ### Git = Commits ] .right-column[
*
Add
and
Commit
to a repository ] --- .left-column[ ## Why ## What ### Git = Distributed ] .right-column[
* Three repositories *
Pull request
if you do not have write access *
Push
if you DO HAVE write access *
Clone
to copy a repository ] --- .left-column[ ## Why ## What ## Social (Hub) ] .right-column[ There are three major social aspects that the Hub has: - To easily see what the project is about [README.md](https://gist.github.com/susanBuck/d5173196babd1cedfd06) a [better](https://github.com/codeforamerica/CutePets/blob/master/README.md) example - The ability to suggest and converse about improvements, [issues](https://github.com/UMKC-Law/DataSharingAgreement/issues/7) - A place to talk about and document a project [Wiki](https://github.com/h5bp/html5-boilerplate/wiki) Collaborative: - See each other's work - Know when changes are made, **Follow** - See changes made to the work - Politely suggest changes - **Fork** a copy - **Edit** your copy - **Commit** your changes to your copy - **Pull Request**, ask for your changes to be included ] --- ### Organization/Person Home
--- ### Repository Home
--- ### https://github.com/codeforamerica/howto/blob/master/Good-READMEs.md
---
---
--- ### Wiki
--- ### Edit
--- ### Commit
--- ### Changes
* Lines added are in green. * Lines removed are in red. * Checksum for the file is in the red circle. * Commit message "Added Checksum" is at the start, work that was done on the file. --- ## Create your account * Goto http://github.com * Click Sign Up, in the upper right hand corner * Choose the free plan --- ## Lets follow Glossary Project .left-column[ ## Create Account ## Follow ] .right-column[ We are all going follow this semesters class - Browse over to [https://github.com/UMKC-Law](https://github.com/UMKC-Law) - Search for Glossary - Click the name - Click the **Watch** pulldown and change to Watching ] --- ## Lets follow another GitHub Project .left-column[ ## Create Account ## Follow ] .right-column[ We are all going follow this semesters class - Browse over to [https://github.com/UMKC-Law](https://github.com/UMKC-Law) - Search for spring-2016-class - Click the name - Click the **Watch** pulldown and change to Watching ] --- ### Lets make a suggestion .left-column[ ## Create Account ## Follow ## Issue ] .right-column[ We will create an issue suggesting that we edit the readme using: ## MarkDown Instead of HTML ```html
List of items
This is item one
This is item two
``` MarkDown ```html List of Items ------------- * This is item one * This is item two ``` ] --- .left-column[ ## Edit ## Create a Repository ] .right-column[ Create a repository in GitHub: 1. Go Back to your organization/list of repositories 1. Click on your name in the upper right hand corner 2. Select "Your Profile" 3. Click on Repositories in the middle of the tabs 2. Click
New
green button 3. Fill in: * Repository name * Description * Public * Initialize this repository with a README * Add a MIT License ] --- .left-column[ ## Edit ## Create a Repository ## README.md ] .right-column[ What a README should do and be: * Use it to orient people to your project. * It should be simple and short. * Could contain: * A clear description of the project * how and why you would use it * list of authors * instructions on how to contribute, fork/pull, issues * instructions on use and/or install ] --- ### Better looking issues, iron them with waffle.io
--- ## Lets politely make a change .left-column[ ## Create Account ## Follow ## Issue ## Fork ] .right-column[ We are all going to suggest a change: - We are going to add our names to [http://codeforkc.org/meeting-notes/coders.html](http://codeforkc.org/meeting-notes/coders.html) - Follow the instructions at [http://goo.gl/tVYEBZ](http://goo.gl/tVYEBZ) - Fork the KC Brigade Website - View your fork in a browser - Add your name to the list of coders - Commit your changes - Issue a Pull Request ] --- .left-column[ ## Fin ] .right-column[ Some resources: - This slide deck http://zmon.github.io/legal-git-hub - Markdown Basics [https://help.github.com/articles/markdown-basics/](https://help.github.com/articles/markdown-basics/) - Mastering Markdown [https://guides.github.com/features/mastering-markdown/](https://guides.github.com/features/mastering-markdown) - Deep dive Mastering Issues [https://guides.github.com/features/issues/](https://guides.github.com/features/issues/) - Pro Git [https://git-scm.com/doc](https://git-scm.com/doc) - Waffle.io http://waffle.io ] --- name: last-page template: inverse ## That's all folks (for now)! Slideshow created using [remark](http://github.com/gnab/remark).


Using data from soundcloud, I've performed a statistical analysis on all the episodes generated aprés timesonline.
See interactive chart »
See ipython notebook »


Howard Goodall's Big Bangs - Rotten Tomatoes
New on DVD. The Bourne . Do you want to see 'Howard Goodall's Big Bangs'? Howard . There are no critic reviews yet for Howard Goodall's Big Bangs.
http://www.rottentomatoes.com/m/howard_goodalls_big_bangs-2008/
Treatment for Herniated or Bulging Discs
Howard Goodall's Big Bangs - Shop Online for Movies, DVDs in ...
Fishpond Australia, Howard Goodall's Big Bangs [Region 2]. Buy Movies, DVDs online: Howard Goodall's Big Bangs [Region 2], 2009, Fishpond.com.au.
http://www.fishpond.com.au/Movies/Howard-Goodalls-Big-Bangs/5051442882025
Howard Goodall - Wikipedia, the free encyclopedia
Howard Goodall at St. John the Baptist Church in Devon, United Kingdom, May . of composing one of these works is noted in the book of his Big Bangs series), .
http://en.wikipedia.org/wiki/Howard_Goodall
howard goodall's big bangs dvd Herniated or Bulging Discs
Howard Goodall DVD Movies and Videos
Howard Goodall DVD movies and videos to buy at CD Universe, Great service secure ordering and fast shipping at . Howard Goodall's Big Bangs DVDs (2008) .
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"Howard Goodall's Big Bangs" DVD Set (A2279)
With intriguing anecdotes and witty humour, composer Howard Goodall presents five innovations in European musical history, which have overwhelmingly .
http://www.friendshiphouse.com/product/item-2d49a1d9-117a-402d-a166-51cf3366e437.aspx
Howard Goodall's Big Bangs [DVD] [2009] [NTSC] - Amazon.co.uk
Amazon.co.uk - Buy Howard Goodall's Big Bangs at a low price; free delivery on qualified orders. See reviews & details on a wide selection of Blu-ray & DVDs, .
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Official Impairment Listing for Spinal Disorders
Howard Goodall's Big Bangs: Turning Points in Music History Video ...
Howard Goodall's Big Bangs: Turning Points in Music History Video(VHS/DVD) Howard Goodall's Big Bangs: Turning Points in Music History. Armed with wit .
http://www.madisonartshop.com/howard-history.html
- why cant we be friendslyrics (narrowing of the spine)
Buy Howard Goodall's Big Bangs by on CD at Mighty Ape NZ
Buy Howard Goodall's Big Bangs by online and save! . DVDs & Blu-ray > . 14.29% of people buy Howard Goodall's Big Bangs and Big Bangs: Five Musical .
http://www.mightyape.co.nz/product/Howard-Goodalls-Big-Bangs/8742501/Review of Big Bangs by Howard Goodall
Big Bangs: The Story of Five Discoveries that Changed Musical History by Howard Goodall. If you love classical or popular music, you'll love this book!
http://www.musicwithease.com/big-bangs-goodall.html
Howard Goodall's Big Bangs: Turning Points in Music History
Howard Goodall's Big Bangs: Turning Points in Music History . date: ©2000. DVD ISBN 978-0-7365-6292-8 . DVD + 3-Year Streaming, $1,274.63. 3-Year .
http://ffh.films.com/id/744/Howard_Goodalls_Big_Bangs_Turning_Points_in_Music_History.htm
Big Bangs: Five Musical Revolutions: Amazon.co.uk: Howard ...
Big Bangs: Five Musical Revolutions: Amazon.co.uk: Howard Goodall: Books. . Howard Goodall's Big Bangs [DVD] [2009] [NTSC]. Howard Goodall. 4.0 out of 5 .
http://www.amazon.co.uk/Big-Bangs-Five-Musical-Revolutions/dp/0099283549
Medical-Vocational Allowance for Herniated Disc
Howard Goodall's Big Bangs - YouTube
Nov 24, 2009 . Written and Presented by Howard Goodall Please visit http://www.kultur.com/ Howard-Goodalls-Big-Bangs-p/d4334.htm for more information on this program . I have the DVD, and? I love it! It's a must have for any serious .
http://www.youtube.com/watch?v=XgQptKXzcTY
Amazon.com: Howard Goodall's Big Bangs: Howard Goodall, David ...
This item: Howard Goodall's Big Bangs ~ Howard Goodall DVD $26.99. Howard Goodall: Choir Works / Choirs Perform ~ Howard Goodall DVD $24.99. Howard .
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Appealing a Denial of Benefits
Big Bangs (TV mini-series 2000 ) - IMDb
With Howard Goodall. . Howard Goodall's Big Bangs See more » . I'm so happy that Kultur recently released the DVD of the series and I'm looking forward to .
http://www.imdb.com/title/tt0282381/
Watch Howard Goodall's 20th Century Greats Free Online
Watch Howard Goodall's 20th Century Greats TV Show Free Online. Full Howard . Cast: Howard Goodall. Categories: . 2:12min. Howard Goodall's Big Bangs.
http://www.ovguide.com/tv/howard_goodall_s_20th_century_greats.htm


{{> head}}
{{date}} {{title}}
{{#previous}} ← Newer posts {{/previous}} page {{current}} of {{total}} {{#next}} Older posts → {{/next}}


My 10 year journey: A trip through my ups and downs and how this community kept me going.
You said:
Awesome inspirational post!! We're all so proud of the incredible progress you've made and are so happy to have you as a member of the community :). Your posts and support in the past have really helped me along in my journey to sobriety.
See the full post!


{% if include.email %}{% assign formEmail = include.email %}{% else %}{% assign formEmail = site.email %}{% endif %} Apply to speak at future events.


Contents
Add actor/director
Add movie
Add review
Add movie/actor relation
Add movie/director relation
Show actor info
Show movie info
Search for actors/movies


Accessing External IDs
GNSDK can match identified media with External IDs (XIDs). These are third-party identifiers provided by preferred Gracenote partners that allow applications to match identified media to merchandise IDs in online stores and other services – facilitating transactions by helping connect queries directly to commerce. Gracenote has several preferred partnerships and pre-matches partner XIDs to Gracenote media IDs. Entitled applications can retrieve these IDs and present them to users.
XIDs are only available using GDO keys. So for albums for example, you set ENABLE_LINK, call find_albums(), then the resultant album GDO will have the XIDs in it.
- Initialize manager and music modules.
- Pass in a source GDO.
- Call gnsdk_*_query_option_Set() with the option OPTION_ENABLE_EXTERNAL_ID_DATA.
- Call gnsdk_*_query_find_albums() and navigate through the returned Album GDO for possible XIDs.
- Get the External IDs using gnsdk_manager_gdo_value_get() and GNSDK_GDO_VALUE_EXTERNALID_VALUE.
- Get the External IDs data using gnsdk_manager_gdo_value_get() and GNSDK_GDO_VALUE_EXTERNALID_DATA.
The following table lists options for gnsdk_*_query_option_Set() to access XIDs (External IDs).
| To Retrieve | Function | Use |
|---|---|---|
| XIDs |
gnsdk_*_query_option_set() |
GNSDK_*_OPTION_ENABLE_EXTERNAL_ID_DATA query key. |
where * is MUSICID or MUSICIDFILE.
Example
... if (GNSDK_SUCCESS == error) { gnsdk_uint32_t count = 0; gnsdk_uint32_t i = 0; gnsdk_gdo_handle_t xid_gdo = GNSDK_NULL; error = gnsdk_manager_gdo_child_count(album_gdo, GNSDK_GDO_CHILD_EXTERNAL_ID, &count); if (GNSDK_SUCCESS == error) { printf( " We have %d xid child\n", count ); for (i = 1; i <= count; i++) { error = gnsdk_manager_gdo_child_get( album_gdo, GNSDK_GDO_CHILD_EXTERNAL_ID, i, &xid_gdo ); if (GNSDK_SUCCESS == error) { error = gnsdk_manager_gdo_value_get( xid_gdo, GNSDK_GDO_VALUE_EXTERNALID_VALUE, 1, &value ); if (GNSDK_SUCCESS == error) { printf( " xid_value: %s\n", value ); } if (GNSDKERR_NotFound == GNSDKERR_ERROR_CODE(error)) { /*These fields are optional so it is totally fine if they are not found at all*/ error = GNSDK_SUCCESS; } error = gnsdk_manager_gdo_value_get( xid_gdo, GNSDK_GDO_VALUE_EXTERNALID_SOURCE, 1, &value ); if (GNSDK_SUCCESS == error) { printf( " xid_source: %s\n", value ); } if (GNSDKERR_NotFound == GNSDKERR_ERROR_CODE(error)) { /*These fields are optional so it is totally fine if they are not found at all*/ error = GNSDK_SUCCESS; } error = gnsdk_manager_gdo_value_get( xid_gdo, GNSDK_GDO_VALUE_EXTERNALID_TYPE, 1, &value ); if (GNSDK_SUCCESS == error) { printf( " xid_type: %s\n", value ); } if (GNSDKERR_NotFound == GNSDKERR_ERROR_CODE(error)) { /*These fields are optional so it is totally fine if they are not found at all*/ error = GNSDK_SUCCESS; } error = gnsdk_manager_gdo_value_get( xid_gdo, GNSDK_GDO_VALUE_EXTERNALID_DATA, 1, &value ); if (GNSDK_SUCCESS == error) { printf( " xid_data: %s\n", value ); } if (GNSDKERR_NotFound == GNSDKERR_ERROR_CODE(error)) { /*These fields are optional so it is totally fine if they are not found at all*/ error = GNSDK_SUCCESS; } } gnsdk_manager_gdo_release(xid_gdo); } } }


Best Coast return with first album in five years and share new track, ‘For The First Time’
They're back!
Best Coast have announced their return with the release of a new single, ‘For The First Time’. You can listen to the new track below.
The new single is taken from their first new album in five years, ‘Always Tomorrow’, which the band are still “completing work on.”
The album is set for release some time in 2020. You can listen to the new track here:
Back in 2017, Best Coast’s Bethany Cosentino wrote an opinion piece regarding the sexual assault allegations against Harvey Weinstein.
Her article came weeks after the Hollywood film producer was accused of rape, sexual harassment and other forms of misconduct by numerous women – allegations he “unequivocally denies”.
In the piece for Billboard, Cosentino said that if “we look back on history we might be able to see where this horrific reality came from,” adding that “the attack on women and the idea that men with power abuse it is nothing new. She went on to allege Weinstein was “the dictionary image of a systematic problem facing our nation — The Man With Power Who Abuses It.”
In a four star review of Best Coast’s last album, ‘Californian Nights’, NME said: “Five years on from ‘Crazy For You’, their weed- and reverb-drenched surf-punk opus, Best Coast have come of age. The dreamy, sun-streaked yearning remains, but the humour that laced Bethany Cosentino’s bratty ennui has gone.
“‘California Nights’ is, instead, full of more sobering concerns – medication, heartbreak, insomnia…Cosentino’s honeyed vocal is the only true constant. It’s a radiating sunbeam, particularly so on the giddy, shimmering ‘Heaven Sent’.”


Please paste the URL of your Table of Content Document
The Content Viewer uses two widgets to operate. The first widget becomes the Table of Content where users can click the ToC items, and the linked-to content is then loaded into the viewer panel. The second widget code drives the Viewer Panel.


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{% if compensate == True %}To enquire about compensation, please contact the researcher at {{contact_address}} and quote the following information.
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Exp. 1
Exp. 2
Exp. 3
Exp. 4
Exp. 5
Exp. 6
Exp. 7
Exp. 8
Exp. 9
Exp. 10
Round
Next
Confirm Load Session
×
Loading a new session will reset all data.


Computer account information
PIRCS systems
URL: http://www.iitap.iastate.edu/ees/intranet
These are the current user accounts on the PIRCS systems. If you know
of other who should be on please let me know (fils@iastate.edu). I will
be setting up the proceedures and documenting these so that those with
root access can create accounts on their own if they so desire or you
can still request me to do it.
Users
- gstakle
all systems
- rwturner
all systems
- gutowski
all systems
- panz
all systems
- zekai
all systems
- fils
all systems


Your IP address works much like your physical home address,
in that it acts as a way for people to easily find your exact location–except that with an IP address,
it’s the exact location of your computer, right down to the latitude and longitude in some cases,
rather than your house. Your IP address allows your computer to connect to other computers over the Internet;
in essence, it’s vital for you to get Internet access, so that one set of numbers is exactly why you can
binge watch Orange is the New Black on Netflix over a weekend.
It’s also how anyone else on the Internet can track you, especially if you’re using a Static IP address as
most people are. This number never changes, and it can show information like what Internet Service Provider
you’re using, what kind of router, and even what operating system you’re on. Hackers, surveillance systems,
and even companies trying to directly target ads towards you can use this information for nefarious purposes,
and if there’s anyone actively looking for your, having a public IP address makes their job a whole lot easier.
Simply put, having an unsecured IP address can jeopardize your Internet security.
Source: www.surfeasy.com


{% extends 'base.html' %} {% block content %}
Forgot your password? Enter your email below, and we will send you an email with instructions for changing your password.


|
AceTime
2.3.0
Date and time classes for Arduino that support timezones from the TZ Database.
|
Class that allows looking up the ZoneInfo (ZI) from its TZDB identifier (e.g. More...
#include <ZoneRegistrar.h>
Public Member Functions | |
| ZoneRegistrarTemplate (uint16_t zoneRegistrySize, const ZI *const *zoneRegistry) | |
| Constructor. | |
| uint16_t | zoneRegistrySize () const |
| Return the number of zones and (fat) links. | |
| const ZI * | getZoneInfoForIndex (uint16_t i) const |
| Return the ZoneInfo at index i. More... | |
| const ZI * | getZoneInfoForName (const char *name) const |
| Return the ZoneInfo corresponding to the given zone name. More... | |
| const ZI * | getZoneInfoForId (uint32_t zoneId) const |
| Return the ZoneInfo using the zoneId. More... | |
| uint16_t | findIndexForName (const char *name) const |
| Find the index for zone name. More... | |
| uint16_t | findIndexForId (uint32_t zoneId) const |
| Find the index for zone id. More... | |
Static Public Attributes | |
| static const uint16_t | kInvalidIndex = 0xffff |
| Invalid index to indicate error or not found. | |
Protected Member Functions | |
| friend | void ::runBasicRegistrarFindIndexForName () |
| friend | void ::runBasicRegistrarFindIndexForIdBinary () |
| friend | void ::runBasicRegistrarFindIndexForIdLinear () |
| friend | void ::runExtendedRegistrarFindIndexForName () |
| friend | void ::runExtendedRegistrarFindIndexForIdBinary () |
| friend | void ::runExtendedRegistrarFindIndexForIdLinear () |
| friend | void ::runCompleteRegistrarFindIndexForName () |
| friend | void ::runCompleteRegistrarFindIndexForIdBinary () |
| friend | void ::runCompleteRegistrarFindIndexForIdLinear () |
| uint16_t | findIndexForIdLinear (uint32_t zoneId) const |
| Exposed only for benchmarking purposes. | |
| uint16_t | findIndexForIdBinary (uint32_t zoneId) const |
| Exposed only for benchmarking purposes. | |
Static Protected Member Functions | |
| static bool | isSorted (const ZI *const *registry, uint16_t registrySize) |
| Determine if the given zone registry is sorted by id. | |
| static uint16_t | linearSearchById (const ZI *const *registry, uint16_t registrySize, uint32_t zoneId) |
| Find the registry index corresponding to zoneId using linear search. More... | |
| static uint16_t | binarySearchById (const ZI *const *registry, uint16_t registrySize, uint32_t zoneId) |
| Find the registry index corresponding to zoneId using a binary search. More... | |
Static Protected Attributes | |
| static const uint8_t | kBinarySearchThreshold = 8 |
| Use binarySearchById() if zoneRegistrySize >= threshold. | |
Class that allows looking up the ZoneInfo (ZI) from its TZDB identifier (e.g.
"America/Los_Angeles"), zoneId (hash from its name), or the index in the zone registry.
| ZI | ZoneInfo type (e.g. basic::ZoneInfo) |
| ZIB | ZoneInfoBroker type (e.g. basic::ZoneInfoBroker) |
| ZRGB | ZoneRegistryBroker type (e.g. basic::ZoneRegistryBroker) |
Definition at line 49 of file ZoneRegistrar.h.
|
inlinestaticprotected |
Find the registry index corresponding to zoneId using a binary search.
Returns kInvalidIndex if not found.
The largest registrySize is UINT16_MAX so the largest valid index is UINT16_MAX - 1. This allows us to set kInvalidIndex to UINT16_MAX to indicate "Not Found".
Definition at line 188 of file ZoneRegistrar.h.
|
inline |
Find the index for zone id.
Return kInvalidIndex if not found.
Definition at line 108 of file ZoneRegistrar.h.
|
inline |
Find the index for zone name.
Return kInvalidIndex if not found.
Definition at line 92 of file ZoneRegistrar.h.
|
inline |
Return the ZoneInfo using the zoneId.
Return nullptr if not found.
Definition at line 85 of file ZoneRegistrar.h.
|
inline |
Return the ZoneInfo at index i.
Return nullptr if i is out of range.
Definition at line 68 of file ZoneRegistrar.h.
|
inline |
Return the ZoneInfo corresponding to the given zone name.
Return nullptr if not found.
Definition at line 78 of file ZoneRegistrar.h.
|
inlinestaticprotected |
Find the registry index corresponding to zoneId using linear search.
Returns kInvalidIndex if not found.
Definition at line 155 of file ZoneRegistrar.h.


Bayesian Workflow Example (Police Officer’s Dilemma)¶
[1]:
import arviz as az
import bambi as bmb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
[2]:
az.style.use('arviz-darkgrid')
Here we will analyze a dataset from experimental psychology in which a sample of 36 human participants engaged in what is called the shooter task, yielding 3600 responses and reaction times (100 from each subject). The link above gives some more information about the shooter task, but basically it is a sort of crude first-person-shooter video game in which the subject plays the role of a police officer. The subject views a variety of urban scenes, and in each round or “trial” a person or “target” appears on the screen after some random interval. This person is either Black or White (with 50% probability), and they are holding some object that is either a gun or some other object like a phone or wallet (with 50% probability). When a target appears, the subject has a very brief response window – 0.85 seconds in this particular experiment – within which to press one of two keyboard buttons indicating a “shoot” or “don’t shoot” response. Subjects receive points for correct and timely responses in each trial; subjects’ scores are penalized for incorrect reponses (i.e., shooting an unarmed person or failing to shoot an armed person) or if they don’t respond within the 0.85 response window. The goal of the task, from the subject’s perspective, is to maximize their score.

Load and examine data¶
[3]:
shooter = pd.read_csv('data/shooter.csv', na_values='.')
shooter.head(10)
[3]:
| subject | target | trial | race | object | time | response | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | w05 | 19 | white | nogun | 658.0 | correct |
| 1 | 2 | b07 | 19 | black | gun | 573.0 | correct |
| 2 | 3 | w05 | 19 | white | gun | 369.0 | correct |
| 3 | 4 | w07 | 19 | white | gun | 495.0 | correct |
| 4 | 5 | w15 | 19 | white | nogun | 483.0 | correct |
| 5 | 6 | w96 | 19 | white | nogun | 786.0 | correct |
| 6 | 7 | w13 | 19 | white | nogun | 519.0 | correct |
| 7 | 8 | w06 | 19 | white | nogun | 567.0 | correct |
| 8 | 9 | b14 | 19 | black | gun | 672.0 | incorrect |
| 9 | 10 | w90 | 19 | white | gun | 457.0 | correct |
The design of the experiment is such that the subject, target, and object (i.e., gun vs. no gun) factors are fully crossed: each subject views each target twice, once with a gun and once without a gun.
[4]:
pd.crosstab(shooter['subject'], [shooter['target'], shooter['object']])
[4]:
| target | b01 | b02 | b03 | b04 | b05 | ... | w95 | w96 | w97 | w98 | w99 | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| object | gun | nogun | gun | nogun | gun | nogun | gun | nogun | gun | nogun | ... | gun | nogun | gun | nogun | gun | nogun | gun | nogun | gun | nogun |
| subject | |||||||||||||||||||||
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 9 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 10 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 11 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 12 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 13 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 14 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 15 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 16 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 17 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 18 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 19 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 20 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 21 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 22 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 23 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 24 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 25 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 26 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 27 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 28 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 29 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 30 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 31 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 32 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 33 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 34 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 35 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 36 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
36 rows × 100 columns
The response speeds on each trial are recorded given as reaction times (milliseconds per response), but here we invert them to and multiply by 1000 so that we are analyzing response rates response rates (responses per second). There is no theoretical reason to prefer one of these metrics over the other, but it turns out that response rates tend to have nicer distributional properties than reaction times (i.e., deviate less strongly from the standard Gaussian assumptions), so response rates will be a little more convenient for us by allowing us to use some fairly standard distributional models.
[5]:
shooter['rate'] = 1000.0/shooter['time']
INFO:numexpr.utils:NumExpr defaulting to 4 threads.
[6]:
plt.hist(shooter['rate'].dropna());
Fit response rate models¶
Random subject effects only¶
Our first model is analogous to how the data from the shooter task are usually analyzed: incorporating all subject-level sources of variability, but ignoring the sampling variability due to the sample of 50 targets. This is a Bayesian generalized linear mixed model (GLMM) with a Normal response and with intercepts and slopes that vary randomly across subjects. Of note here is the C(x, Sum)
syntax, which is from the Patsy library
that we use to parse formulae. This instructs Bambi to use contrast codes of -1 and +1 for the two levels of each of the fixed factors of race
(black vs. white) and object
(gun vs. no gun), so that the race
and object
coefficients can be interpreted as simple effects on average across the levels of the other factor (directly analogous, but not quite equivalent, to the main effects). This is the standard coding used in ANOVA.
[7]:
subj_model = bmb.Model(shooter, dropna=True)
subj_fitted = subj_model.fit('rate ~ C(race, Sum)*C(object, Sum)',
random=['C(race, Sum)*C(object, Sum)|subject'],
samples=1000, init=None)
/home/osvaldo/proyectos/00_BM/Bambi/bambi/bambi/models.py:160: UserWarning: Automatically removing 98/3600 rows from the dataset.
warnings.warn(msg)
Multiprocess sampling (2 chains in 2 jobs)
INFO:pymc3:Multiprocess sampling (2 chains in 2 jobs)
NUTS: [rate_sd, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_offset, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_sd, C(object, Sum)[S.gun]|subject_offset, C(object, Sum)[S.gun]|subject_sd, C(race, Sum)[S.black]|subject_offset, C(race, Sum)[S.black]|subject_sd, 1|subject_offset, 1|subject_sd, C(race, Sum):C(object, Sum), C(object, Sum), C(race, Sum), Intercept]
INFO:pymc3:NUTS: [rate_sd, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_offset, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_sd, C(object, Sum)[S.gun]|subject_offset, C(object, Sum)[S.gun]|subject_sd, C(race, Sum)[S.black]|subject_offset, C(race, Sum)[S.black]|subject_sd, 1|subject_offset, 1|subject_sd, C(race, Sum):C(object, Sum), C(object, Sum), C(race, Sum), Intercept]
The number of effective samples is smaller than 25% for some parameters.
INFO:pymc3:The number of effective samples is smaller than 25% for some parameters.
First let’s visualize the default priors that Bambi automatically decided on for each of the parameters. We do this by calling the .plot()
method of the Model
object.
[8]:
subj_model.plot();
The priors on the fixed effects seem quite reasonable. Recall that because of the -1 vs +1 contrast coding, the coefficients correspond to half the difference betwen the two levels of each factor. So the priors on the fixed effects essentially say that the black vs. white and gun vs. no gun (and their interaction) response rate differences are very unlikely to be as large as a full response per second. The priors on the standard deviations of the random effects are yoked to the corresponding fixed effect priors (see our technical paper for more info).
Now let’s visualize the model estimates. We do this by calling the .plot()
method of the MCMCResults
object that resulted from our call to Model.fit()
.
[9]:
subj_fitted.posterior
m_vars = [str(v) for v in subj_model.backend.model.vars]
plot_vars = []
# is not str(v).endswith(offset)]
[10]:
az.plot_trace(subj_fitted, compact=True);
Each distribution in the plots above has 4 densities because we used 4 MCMC chains, so we are viewing the results of all 4 chains prior to their aggregation. The main message from the plot above is that the chains all seem to have converged well and the resulting posterior distributions all look quite reasonable. It’s a bit easier to digest all this information in a concise, tabular form, which we can get by calling the .summary()
method of the MCMCResults
object.
[11]:
# by default the variables plotted by subj_model.plot() are different than those from:
az.summary(subj_fitted)
[11]:
| mean | sd | hpd_3% | hpd_97% | mcse_mean | mcse_sd | ess_mean | ess_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept[0] | 1.708 | 0.014 | 1.680 | 1.733 | 0.001 | 0.000 | 431.0 | 431.0 | 432.0 | 736.0 | 1.01 |
| C(race, Sum)[0] | -0.001 | 0.004 | -0.009 | 0.007 | 0.000 | 0.000 | 2629.0 | 949.0 | 2623.0 | 1536.0 | 1.00 |
| C(object, Sum)[0] | 0.093 | 0.006 | 0.082 | 0.105 | 0.000 | 0.000 | 1310.0 | 1306.0 | 1304.0 | 1255.0 | 1.00 |
| C(race, Sum):C(object, Sum)[0] | 0.023 | 0.004 | 0.015 | 0.031 | 0.000 | 0.000 | 3674.0 | 3629.0 | 3712.0 | 1418.0 | 1.00 |
| 1|subject_offset[0] | -0.033 | 0.318 | -0.617 | 0.565 | 0.009 | 0.007 | 1271.0 | 1184.0 | 1269.0 | 1553.0 | 1.00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject[32] | 0.000 | 0.006 | -0.012 | 0.012 | 0.000 | 0.000 | 2749.0 | 1052.0 | 2899.0 | 1477.0 | 1.00 |
| C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject[33] | -0.000 | 0.006 | -0.012 | 0.013 | 0.000 | 0.000 | 2596.0 | 1409.0 | 2748.0 | 1702.0 | 1.00 |
| C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject[34] | 0.000 | 0.006 | -0.011 | 0.014 | 0.000 | 0.000 | 2413.0 | 1170.0 | 2865.0 | 1709.0 | 1.01 |
| C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject[35] | -0.001 | 0.007 | -0.016 | 0.011 | 0.000 | 0.000 | 2759.0 | 1287.0 | 2959.0 | 1852.0 | 1.00 |
| rate_sd | 0.240 | 0.003 | 0.234 | 0.245 | 0.000 | 0.000 | 3892.0 | 3875.0 | 3916.0 | 1447.0 | 1.01 |
297 rows × 11 columns
The take-home message from the analysis seems to be that we do find evidence for the usual finding that subjects are especially quick to respond (presumably with a shoot response) to armed black targets and especially slow to respond to unarmed black targets (while unarmed white targets receive “don’t shoot” responses with less hesitation). We see this in the fact that the marginal posterior for the C(race, Sum)[S.black]:C(object, Sum)[S.gun]
interaction coefficient is concentrated strongly
away from 0.
Random stimulus effects¶
A major flaw in the analysis above is that random stimulus effects are ignored. The model does include random effects for subjects, reflecting the fact that the subjects we observed are but a sample from the broader population of subjects we are interested in and that potentially could have appeared in our study. But the targets we observed – the 50 photographs of white and black men that subjets responded to – are also but a sample from the broader theoretical population of targets we are interested in talking about, and that we could have just as easily and justifiably used as the experimental stimuli in the study. Since the stimuli comprise a random sample, they are subject to sampling variability, and this sampling variability should be accounted in the analysis by including random stimulus effects. For some more information on this, see here, particularly pages 62-63.
To account for this, we let the intercept and object
slope vary randomly across targets. Random object slopes across targets are possible because, if you recall, the design of the study was such that each target gets viewed twice by each subject, once with a gun and once without a gun. However, because each target is always either white or black, it’s not possible to add random slopes for the race
factor or the interaction.
[12]:
stim_model = bmb.Model(shooter, dropna=True)
stim_fitted = stim_model.fit('rate ~ C(race, Sum)*C(object, Sum)',
random=['C(race, Sum)*C(object, Sum)|subject',
'C(object, Sum)|target'],
samples=1000)
/home/osvaldo/proyectos/00_BM/Bambi/bambi/bambi/models.py:160: UserWarning: Automatically removing 98/3600 rows from the dataset.
warnings.warn(msg)
Auto-assigning NUTS sampler...
INFO:pymc3:Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
INFO:pymc3:Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (2 chains in 2 jobs)
INFO:pymc3:Multiprocess sampling (2 chains in 2 jobs)
NUTS: [rate_sd, C(object, Sum)[S.gun]|target_offset, C(object, Sum)[S.gun]|target_sd, 1|target_offset, 1|target_sd, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_offset, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_sd, C(object, Sum)[S.gun]|subject_offset, C(object, Sum)[S.gun]|subject_sd, C(race, Sum)[S.black]|subject_offset, C(race, Sum)[S.black]|subject_sd, 1|subject_offset, 1|subject_sd, C(race, Sum):C(object, Sum), C(object, Sum), C(race, Sum), Intercept]
INFO:pymc3:NUTS: [rate_sd, C(object, Sum)[S.gun]|target_offset, C(object, Sum)[S.gun]|target_sd, 1|target_offset, 1|target_sd, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_offset, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_sd, C(object, Sum)[S.gun]|subject_offset, C(object, Sum)[S.gun]|subject_sd, C(race, Sum)[S.black]|subject_offset, C(race, Sum)[S.black]|subject_sd, 1|subject_offset, 1|subject_sd, C(race, Sum):C(object, Sum), C(object, Sum), C(race, Sum), Intercept]
The estimated number of effective samples is smaller than 200 for some parameters.
ERROR:pymc3:The estimated number of effective samples is smaller than 200 for some parameters.
Now let’s look at the results…
[13]:
az.plot_trace(stim_fitted, var_names=stim_model.term_names, compact=True);
[14]:
az.summary(stim_fitted, var_names=stim_model.term_names)
[14]:
| mean | sd | hpd_3% | hpd_97% | mcse_mean | mcse_sd | ess_mean | ess_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept[0] | 1.705 | 0.019 | 1.665 | 1.737 | 0.002 | 0.001 | 124.0 | 124.0 | 125.0 | 366.0 | 1.01 |
| C(race, Sum)[0] | -0.001 | 0.014 | -0.025 | 0.025 | 0.001 | 0.001 | 163.0 | 163.0 | 167.0 | 200.0 | 1.02 |
| C(object, Sum)[0] | 0.094 | 0.015 | 0.067 | 0.121 | 0.001 | 0.001 | 224.0 | 224.0 | 232.0 | 560.0 | 1.02 |
| C(race, Sum):C(object, Sum)[0] | 0.024 | 0.015 | -0.003 | 0.051 | 0.001 | 0.001 | 189.0 | 189.0 | 189.0 | 390.0 | 1.01 |
| 1|subject[0] | -0.005 | 0.023 | -0.046 | 0.042 | 0.001 | 0.001 | 392.0 | 392.0 | 391.0 | 894.0 | 1.00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| C(object, Sum)[S.gun]|target[45] | -0.176 | 0.030 | -0.232 | -0.123 | 0.001 | 0.001 | 498.0 | 474.0 | 493.0 | 991.0 | 1.01 |
| C(object, Sum)[S.gun]|target[46] | 0.078 | 0.032 | 0.019 | 0.139 | 0.001 | 0.001 | 607.0 | 607.0 | 601.0 | 1180.0 | 1.01 |
| C(object, Sum)[S.gun]|target[47] | 0.006 | 0.030 | -0.052 | 0.063 | 0.001 | 0.001 | 525.0 | 525.0 | 516.0 | 949.0 | 1.01 |
| C(object, Sum)[S.gun]|target[48] | 0.086 | 0.030 | 0.027 | 0.140 | 0.001 | 0.001 | 464.0 | 464.0 | 466.0 | 802.0 | 1.01 |
| C(object, Sum)[S.gun]|target[49] | -0.020 | 0.031 | -0.076 | 0.038 | 0.001 | 0.001 | 583.0 | 583.0 | 585.0 | 1142.0 | 1.01 |
248 rows × 11 columns
There are two interesting things to note here. The first is that the key interaction effect, C(race, Sum)[S.black]:C(object, Sum)[S.gun]
is much less clear now. The marginal posterior is still mostly concentrated away from 0, but there’s certainly a nontrivial part that overlaps with 0; 3.2% of the distribution, to be exact.
[15]:
(stim_fitted.posterior['C(race, Sum):C(object, Sum)'] < 0).mean()
[15]:
<xarray.DataArray 'C(race, Sum):C(object, Sum)' ()>
array(0.048)
The second interesting thing is that the two new variance components in the model, those associated with the random stimulus effects, are actually rather large. This actually largely explains the first fact above, since if these where estimated to be close to 0 anyway, the model estimates wouldn’t be much different than they were in the subj_model
. It makes sense that there is a strong tendency for different targets to elicit difference reaction times on average, which leads to a large
estimate of 1|target_sd
. Less obviously, the large estimate of C(object, Sum)[S.gun]|target_sd
(targets tend to vary a lot in their response rate differences when they have a gun vs. some other object) also makes sense, because in this experiment, different targets were pictured with different non-gun objects. Some of these objects, such as a bright red can of Coca-Cola, are not easily confused with a gun, so subjects are able to quickly decide on the correct response. Other objects,
such as a black cell phone, are possibly easier to confuse with a gun, so subjects take longer to decide on the correct response when confronted with this object. Since each target is yoked to a particular non-gun object, there is good reason to expect large target-to-target variability in the object
effect, which is indeed what we see in the model estimates.
Fit response models¶
Here we seek evidence of the second traditional finding, that subjects are more likely to response ‘shoot’ toward black targets than toward white targets, regardless of whether they are armed or not. Currently the dataset just records whether the given response was correct or not, so first we transformed this into whether the response was ‘shoot’ or ‘dontshoot’.
[16]:
shooter['shoot_or_not'] = shooter['response'].astype(str)
# armed targets
new_vals = {'correct': 'shoot', 'incorrect':'dontshoot', 'timeout': np.nan}
shooter['shoot_or_not'][shooter['object']=='gun'] = \
shooter['response'][shooter['object']=='gun'].astype(str).replace(new_vals)
# unarmed targets
new_vals = {'correct': 'dontshoot', 'incorrect':'shoot', 'timeout': np.nan}
shooter['shoot_or_not'][shooter['object']=='nogun'] = \
shooter['response'][shooter['object']=='nogun'].astype(str).replace(new_vals)
# view result
shooter.head(20)
/home/osvaldo/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/home/osvaldo/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
# Remove the CWD from sys.path while we load stuff.
[16]:
| subject | target | trial | race | object | time | response | rate | shoot_or_not | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | w05 | 19 | white | nogun | 658.0 | correct | 1.519757 | dontshoot |
| 1 | 2 | b07 | 19 | black | gun | 573.0 | correct | 1.745201 | shoot |
| 2 | 3 | w05 | 19 | white | gun | 369.0 | correct | 2.710027 | shoot |
| 3 | 4 | w07 | 19 | white | gun | 495.0 | correct | 2.020202 | shoot |
| 4 | 5 | w15 | 19 | white | nogun | 483.0 | correct | 2.070393 | dontshoot |
| 5 | 6 | w96 | 19 | white | nogun | 786.0 | correct | 1.272265 | dontshoot |
| 6 | 7 | w13 | 19 | white | nogun | 519.0 | correct | 1.926782 | dontshoot |
| 7 | 8 | w06 | 19 | white | nogun | 567.0 | correct | 1.763668 | dontshoot |
| 8 | 9 | b14 | 19 | black | gun | 672.0 | incorrect | 1.488095 | dontshoot |
| 9 | 10 | w90 | 19 | white | gun | 457.0 | correct | 2.188184 | shoot |
| 10 | 11 | w91 | 19 | white | nogun | 599.0 | correct | 1.669449 | dontshoot |
| 11 | 12 | b17 | 19 | black | nogun | 769.0 | correct | 1.300390 | dontshoot |
| 12 | 13 | b04 | 19 | black | nogun | 600.0 | correct | 1.666667 | dontshoot |
| 13 | 14 | w17 | 19 | white | nogun | 653.0 | correct | 1.531394 | dontshoot |
| 14 | 15 | b93 | 19 | black | gun | 468.0 | correct | 2.136752 | shoot |
| 15 | 16 | w96 | 19 | white | gun | 546.0 | correct | 1.831502 | shoot |
| 16 | 17 | w91 | 19 | white | gun | 591.0 | incorrect | 1.692047 | dontshoot |
| 17 | 18 | b95 | 19 | black | gun | NaN | timeout | NaN | NaN |
| 18 | 19 | b09 | 19 | black | gun | 656.0 | correct | 1.524390 | shoot |
| 19 | 20 | b02 | 19 | black | gun | 617.0 | correct | 1.620746 | shoot |
Let’s skip straight to the correct model that includes random stimulus effects. This looks quite similiar to the stim_model
from above except that we change the response to the new shoot_or_not variable
– notice the [shoot]
syntax indicating that we wish to model the prbability that shoot_or_not=='shoot'
, not shoot_or_not=='dontshoot'
– and then change to family='bernoulli'
to indicate a mixed effects logistic regression.
[17]:
stim_response_model = bmb.Model(shooter.dropna())
stim_response_fitted = stim_response_model.fit(
'shoot_or_not[shoot] ~ C(race, Sum)*C(object, Sum)',
random=['C(race, Sum)*C(object, Sum)|subject',
'C(object, Sum)|target'],
samples=1500, family='bernoulli')
/home/osvaldo/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:2963: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self[k1] = value[k2]
/home/osvaldo/proyectos/00_BM/Bambi/bambi/bambi/models.py:269: UserWarning: Modeling the probability that shoot_or_not=='shoot'
self.y.name, str(self.clean_data[self.y.name].iloc[event])
Auto-assigning NUTS sampler...
INFO:pymc3:Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
INFO:pymc3:Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (2 chains in 2 jobs)
INFO:pymc3:Multiprocess sampling (2 chains in 2 jobs)
NUTS: [C(object, Sum)[S.gun]|target_offset, C(object, Sum)[S.gun]|target_sd, 1|target_offset, 1|target_sd, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_offset, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_sd, C(object, Sum)[S.gun]|subject_offset, C(object, Sum)[S.gun]|subject_sd, C(race, Sum)[S.black]|subject_offset, C(race, Sum)[S.black]|subject_sd, 1|subject_offset, 1|subject_sd, C(race, Sum):C(object, Sum), C(object, Sum), C(race, Sum), Intercept]
INFO:pymc3:NUTS: [C(object, Sum)[S.gun]|target_offset, C(object, Sum)[S.gun]|target_sd, 1|target_offset, 1|target_sd, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_offset, C(race, Sum)[S.black]:C(object, Sum)[S.gun]|subject_sd, C(object, Sum)[S.gun]|subject_offset, C(object, Sum)[S.gun]|subject_sd, C(race, Sum)[S.black]|subject_offset, C(race, Sum)[S.black]|subject_sd, 1|subject_offset, 1|subject_sd, C(race, Sum):C(object, Sum), C(object, Sum), C(race, Sum), Intercept]
There were 2 divergences after tuning. Increase `target_accept` or reparameterize.
ERROR:pymc3:There were 2 divergences after tuning. Increase `target_accept` or reparameterize.
There were 3 divergences after tuning. Increase `target_accept` or reparameterize.
ERROR:pymc3:There were 3 divergences after tuning. Increase `target_accept` or reparameterize.
The number of effective samples is smaller than 25% for some parameters.
INFO:pymc3:The number of effective samples is smaller than 25% for some parameters.
Show the trace plot
[18]:
az.plot_trace(stim_response_fitted, compact=True);
Looks pretty good! Now for the more concise summary.
[19]:
az.summary(stim_response_fitted)
[19]:
| mean | sd | hpd_3% | hpd_97% | mcse_mean | mcse_sd | ess_mean | ess_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept[0] | -0.022 | 0.149 | -0.300 | 0.246 | 0.004 | 0.003 | 1265.0 | 1265.0 | 1262.0 | 1462.0 | 1.0 |
| C(race, Sum)[0] | 0.219 | 0.141 | -0.044 | 0.490 | 0.004 | 0.003 | 1507.0 | 1339.0 | 1516.0 | 1516.0 | 1.0 |
| C(object, Sum)[0] | 4.168 | 0.251 | 3.711 | 4.627 | 0.009 | 0.007 | 709.0 | 704.0 | 724.0 | 889.0 | 1.0 |
| C(race, Sum):C(object, Sum)[0] | 0.203 | 0.173 | -0.107 | 0.526 | 0.005 | 0.004 | 1048.0 | 773.0 | 1104.0 | 1151.0 | 1.0 |
| 1|subject_offset[0] | -0.005 | 0.953 | -1.874 | 1.710 | 0.019 | 0.019 | 2501.0 | 1318.0 | 2498.0 | 1802.0 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| C(object, Sum)[S.gun]|target[45] | 0.362 | 0.607 | -0.791 | 1.471 | 0.013 | 0.013 | 2110.0 | 1076.0 | 2197.0 | 1705.0 | 1.0 |
| C(object, Sum)[S.gun]|target[46] | 0.043 | 0.569 | -1.073 | 1.046 | 0.013 | 0.012 | 1936.0 | 1209.0 | 2003.0 | 1562.0 | 1.0 |
| C(object, Sum)[S.gun]|target[47] | 0.298 | 0.581 | -0.814 | 1.363 | 0.012 | 0.011 | 2181.0 | 1427.0 | 2245.0 | 1661.0 | 1.0 |
| C(object, Sum)[S.gun]|target[48] | 0.355 | 0.600 | -0.695 | 1.544 | 0.013 | 0.011 | 2300.0 | 1383.0 | 2360.0 | 1727.0 | 1.0 |
| C(object, Sum)[S.gun]|target[49] | 0.036 | 0.563 | -0.993 | 1.106 | 0.012 | 0.011 | 2115.0 | 1266.0 | 2153.0 | 1708.0 | 1.0 |
498 rows × 11 columns
There is some slight evidence here for the hypothesis that subjects are more likely to shoot the black targets, regardless of whether they are armed or not, but the evidence is a little weak. The marginal posterior for the C(race, Sum)
coefficient is mostly concentrated away from 0, but it overlaps even more in this case with 0 than did the key interaction effect in the previous model.
[20]:
(stim_response_fitted.posterior['C(race, Sum)'] < 0).mean()
[20]:
<xarray.DataArray 'C(race, Sum)' ()>
array(0.05933333)


national-oilwell-varco
Businessmodel of National Oilwell Varco
Customer Segments
NOV provides a broad range of services to customers across the oil and gas industry. The Company’s customers can be organised into groups according to the Company’s operating segments and services provided. This includes:
- Rig Systems and Rig Aftermarket Customers, which principally comprise multinational, national, and independent oil and gas companies that are engaged in land and offshore drilling operations;
- Wellbore Technologies Customers, which principally comprises full-service oil and gas companies, drilling contractors, oilfield service companies, and oilfield equipment rental companies; and
- Completion and Production Solutions Customers, which comprises oilfield service companies, and full-service oil and gas companies. NOV serves an international customer base. It largest market remains the US, which account for the largest portion of its sales, followed by South Korea, China, Singapore, the UK, Brazil, Norway, and Canada. The Company serves customers in other markets across the Americas, Asia Pacific, the Middle East, and Africa.
Value Propositions
NOV provides value to its customers in the following ways:
- Its industry standing and reputation, with the Company recognised as a reliable and effective provider of products to the oil and gas sector, having established a positive track record over many years in operations;
- Its international operational reach, with the Company providing abroad range of products and services to customers across the Americas, Europe, Asia Pacific, the Middle East, and Africa;
- Its commitment to innovation, with the Company dedicating significant fund to research and development activity, and having a tradition of pioneering innovations which improve the cost-effectiveness, efficiency, safety and environmental impact of oil and gas operations;
- Its extensive manufacturing infrastructure, with the Company owning and operating a network of manufacturing, machining, welding and fabrication, heat treating, and assembly facilities across the world, enabling it to serve customers quickly and efficiently; and
- Its industry expertise and experience, with the Company employing specialist personnel across its four operating segments, as well as a team of experienced industry executives. ### Channels
NOV operates a website at www.nov.com, through which it provides information on its various products, services, and locations. The Company also operates an online customer portal under the name MyNOV, which allows customers to access technical information, tools, and production specific applications.
NOV’s Rig Systems capital equipment and Rig Aftermarket spare parts sales, and a large portion of its smaller pumps and parts sales, are made through the Company’s own in-house direct sales force, which operates out of the Company’s offices across its various operating regions. The Company’s Wellbore Technologies and Completion and Production Solutions segments rent and sell products and services worldwide through their own dedicated sales force and through commissioned representatives. The Company’s sales in and to foreign jurisdictions are often made through joint ventures, license arrangements, and other business agreements with local nationals.
NOV also operates its own manufacturing and distribution infrastructure. This includes a network of machining, welding and fabrication, heat treating, assembly, testing, and distribution and service centre facilities, based principally in the US, South Korea, the UK, Singapore, Dubai, Mexico, Brazil, and Malaysia.
Customer Relationships
NOV provides a range of tools and resources to its customers on a self-service basis through its MyNOV customer portal. This channel enables customers to use various applications, and access technical information and tools without interacting with members of NOV’s sales and service teams.
NOV’s sales teams, which are organised by segment and geographic region, work closely with customers to ascertain their specific individual needs and circumstances. The Company seeks to establish long-term relationship with its customers, providing support through dedicated customer relationship and support personnel.
NOV provides ongoing customer support to its customers, who are able to access personalised assistance over the phone and online. The Company also provides various online resources, such as news articles, as well as information on its upcoming events, where it can interact with customers in-person. Additionally, NOV operates social media accounts with Facebook, LinkedIn, Twitter, YouTube, and Instagram, through which it is able to interact with clients directly.
Key Activities
NOV is engaged in the design, manufacture and sale of equipment and components used in oil and gas drilling, completion and production operations.
The Company aligns its activities into four business segments: Rig Systems, which manufactures and maintains equipment and integrated systems used in drilling oil and gas wells on land and offshore; Rig Aftermarket, which provides a range of aftermarket products and services to support land and offshore rigs; Wellbore Technologies, which sells and rents solids control equipment, and provides solids control, waste management and drilling fluids services; and Completion and Production Solutions, which provides technologies for well completions and oil and gas production.
NOV additionally provides certain oilfield services to customers in the upstream oil and gas industry. The Company operates globally, serving customers primarily across the Americas, Asia Pacific, and Europe.
Key Partners
NOV works closely with a network of partner companies and organisations across its manufacturing and distribution operations. These partnerships can be categorised broadly as:
- Supplier and Procurement Partners, comprising suppliers of raw materials, tools, and equipment that support the Company’s manufacturing operations, as well as suppliers of services and technologies that are utilised more broadly across the Company’s four operating segments;
- Channel and Distribution Partners, comprising the Company’s network of license partners, distributors, and independent sales representatives that extend the Company’s sales and marketing reach, and facilitate business in foreign jurisdictions;
- Joint Venture Partners, comprising various manufacturing and distribution companies within the oil and gas sector, with which the Company jointly produces and distributes products through mutually owned entities; and
- Strategic and Alliance Partners, comprising companies from multiple business sectors, with which the Company shares resources and collaborates on joint projects. NOV has a number of partnerships in place. This includes a tie-up with GE Oil and Gas to provide integrated solutions for floating production storage and offloading projects, and a deal with Global Industrial Water to market innovative technology products.
Key Resources
NOV’s key resources are its technologies and intellectual properties, its materials and supply chain, its manufacturing and other industrial facilities, its IT and communications infrastructure, its sales and distribution networks, its partnerships, and its personnel.
NOV owns a number owns a substantial number of US and foreign patents that are utilised in the manufacture of its products. Searches of records published by the US Patent and Trademark Office identified numerous patent applications filed in NOV’s name, including applications entitled ‘Radial valves and pumps including radial valves’, ‘System and method for controlling a downhole tool’ and ‘Rotary steerable push-the-bit drilling apparatus with self-cleaning fluid filter’.
NOV owns and or leases approximately 835 facilities worldwide, including its key manufacturing, service, distribution and administrative facilities across the US, South Korea, Singapore, the UK, Mexico, the United Arab Emirates, Denmark, Brazil, and Malaysia.
Cost Structure
NOV incurs costs in relation to the development of its products and technologies, the procurement of supplies and materials, the operation of its manufacturing facilities, the maintenance of its IT and communications infrastructure, the operation of its sales and distribution channels, the implementation of advertising and marketing campaigns, the management of its partnerships, and the retention of its employees.
In 2015 NOV recorded total cost of revenue in the amount of $11.69 billion, comprising $9.36 billion in cost of sales and $2.33 in cost of services. The Company accrued selling, general, and administrative costs – including the payment of salaries and benefits to its international workforce of 50,197 employees – totalling $1.76 billion for the year.
Revenue Streams
NOV generates revenue through the manufacture, marketing, and sale of various products and services related to oil and gas drilling projects around the world. The Company’s products include pressure pumping trucks and pumps, blenders, sanders, hydration units, injection units, flowline, and manifolds and wellheads.
In 2015 NOV generated annual total revenue of $14.76 billion, down considerably on the $21.44 billion recorded by the Company in 2014. This 31% drop in revenue was attributed to declining oil and gas prices resulting in reduced drilling activity and demand for oilfield equipment and services. The Company’s largest revenue generator was its Rig Systems segment, which alone recorded revenue for the year of $6.96 billion. This Company’s Wellbore Technologies, Completion and Production Solutions, and Rig Aftermarket segments recorded revenue of $3.72 billion, $3.37 billion, and $2.52 billion respectively.


Squarepusher announces first album in five years, Be Up A Hello
by Henry Bruce-JonesPhoto by: Donald Milne
Marking a return to the vintage analogue equipment with which the producer developed his sound in the early ’90s.
Squarepusher is back after a five-year hiatus with a new album on Warp.
Be Up A Hello sees producer Tom Jenkinson abandoning the software he developed over 15 years to make albums like Ufabulum and Damogen Furies, instead returning to the equipment he used to define his sound in the early ’90s, including a collection of analogue synths, vintage effects units and a Commodore Vic20.
Since his 2015 album Damogen Furies, Jenkinson has scored a one hour episode of a show on CBeebies and collaborated with James McVinnie on an album of organ music, All Night Chroma. He also formed the speed jazz band Shobaleader One to reinterpret classic Squarepusher tracks.
Be Up A Hello arrives on January 31 and is available to pre-order now. The first single, ‘Vortrack’ and a ‘Fracture Remix’ is out on December 6. Check out the album artwork and tracklist below.
Tracklist:
A1. ‘Oberlove’
A2. ‘Hitsonu’
A3. ‘Nervelevers’
A4. ‘Speedcrank’
A5. ‘Detroit People Mover’
B1. ‘Vortrack’
B2. ‘Terminal Slam’
B3. ‘Mekrev Bass’
B4. ’80 Ondula’
World tour dates:
Jan 31 – CTM Festival, Berlin – Germany
March 14 – Bangface Weekender, Southport – UK
April 09 – Royale, Boston – USA
April 11 – Brooklyn Steel, NYC – USA
April 14 – SAT, Montreal – USA
April 15 – Danforth, Toronto- USA
April 16 – St. Andrews, Detroit – USA
April 17 – Metro, Chicago – USA
April 19 – Bluebird Theater, Denver – USA
April 22 – Neumos, Seattle – USA
April 23 – Wonder Ballroom, Portland – USA
April 24 – The Midway, SF – USA
April 25 – 1720, LA – USA
May 01 – LEV Festival, Gijon – Spain
May 08 – Les Nuits Botanique, Brussels – Belgium
May 09 – Melkweg, Amsterdam – Netherlands
May 13 – Brudenell, Leeds – UK
May 14 – Concorde2, Brighton – UK
May 15 – Roundhouse, London – UK
Read next: Deep Inside – November 2019’s must-hear house and techno
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§304A-3153 REPEALED. L 2014, c 13, §2.
Cross References
Complaints by students of post-secondary educational institutions, see §305J-17.


fields in project.mdb / Registration
// VALUES -> values in webform "biom9450_webpage_register.html"
// how to add marketing?
// how to check banned?
// checks if input was added to .mdb, probably, but this code was made for MySQLi so idk XD
if ($conn->query($sqlInsert) === TRUE) {
echo "New record created successfully";
}
else {
echo "Error: " . $sql . "
" . $conn->error;
}
// closes db connection, but this is for MySQLi so...
$conn->close();
?>
Registration Form


Rationale
Beefs
Whom?
All about us. The procratinator (moi) and the database champ (Hector). Insert text here.


Index
All Classes|All Packages
G
- GCD - Class in tools.gcs
- GCD(String) - Constructor for class tools.gcs.GCD
-
Initiate a GCD object for Google cloud datastore operations.
- GCV - Class in tools.gcs
- GCV() - Constructor for class tools.gcs.GCV
- getComments() - Method in class tools.gcs.Photo
-
Get the comments of the photo.
- getEntity(Entity) - Method in class tools.gcs.GCD
-
Get an Entity from the cloud datastore.
- getEntity(String, String) - Method in class tools.gcs.GCD
-
Get an Entity from the cloud datastore.
- getImageLabels(byte[]) - Static method in class tools.gcs.GCV
-
Get the array list containing the label objects of an image stored in the cloud storage.
- getImageLabels(Image) - Static method in class tools.gcs.GCV
-
Get the array list containing the label objects of an image stored in the cloud storage.
- getImageLabels(String) - Static method in class tools.gcs.GCV
-
Get the array list containing the label objects of an image stored in the cloud storage.
- getImageLabels(String, String, String) - Static method in class tools.gcs.GCV
-
Get the array list containing the label objects of an image stored in the cloud storage.
- getLabel(String) - Method in class tools.gcs.Word2VecGCD
-
Get the label object with the specified label name
- getLabelCounts() - Method in class tools.gcs.PhotoSet
-
Get the label counting results.
- getLabels() - Method in class tools.gcs.Photo
-
Get the label objects of the photo.
- getLabels(String[]) - Method in class tools.gcs.Word2VecGCD
-
Get the label objects with a string array of word.
- getLabels(ArrayList<String>) - Method in class tools.gcs.Word2VecGCD
-
Get the label objects with an array list of words.
- GetLabels - Class in servlet.gcs
-
Servlet implementation class GetLabels2
- GetLabels() - Constructor for class servlet.gcs.GetLabels
- getLikes() - Method in class tools.gcs.Photo
-
Get the like count of the photo.
- getName() - Method in class tools.gcs.Label
-
Get the name of the label
- getName() - Method in class tools.gcs.Photo
-
Get the name of this photo.
- GetPageId - Class in servlet.gcs
-
Servlet implementation class GetPageId
- GetPageId() - Constructor for class servlet.gcs.GetPageId
- getPagePhotos(String) - Method in class tools.gcs.GCD
-
Get the photo entities of a specified FB page from the cloud datastore.
- getPhotoNames() - Method in class tools.gcs.PhotoSet
-
Get the name of the photo objects belonging to this set.
- getPhotos() - Method in class tools.gcs.PhotoSet
-
Get the photo objects belonging to this set.
- getPhotos(String) - Static method in class servlet.gcs.Sample
- getPhotoSamples() - Static method in class servlet.gcs.Sample
- getScore() - Method in class tools.gcs.Label
-
Get the score of the label
- getScore() - Method in class tools.gcs.Photo
-
Get the score of the photo.
- getTotalCommentsCount() - Method in class tools.gcs.PhotoSet
-
Get the sum of the comment counts of all the belonging photos.
- getTotalLikesCount() - Method in class tools.gcs.PhotoSet
-
Get the sum of the like counts of all the belonging photos.
- getTotalScore() - Method in class tools.gcs.PhotoSet
-
Get the sum of the scores of all the belonging photos.
- getUrl() - Method in class tools.gcs.Photo
-
Get the FB URL of the photo.
- getVector() - Method in class tools.gcs.Label
-
Get the vector of the label.
- getVectorEntity(String) - Method in class tools.gcs.Word2VecGCD
-
Get the vector entity object from the cloud datastore.
All Classes|All Packages


Barcelona 'cannot afford' to lose Vidal in January, says Blaugrana president Bartomeu
by OmnisportBarcelona "cannot afford" to let unsettled midfielder Arturo Vidal leave the club in January, according to president Josep Maria Bartomeu.
Vidal has started just four of Barca's 20 matches this season and last month vowed to quit the Spanish champions next year if he is not made to "feel important".
Barca boss Ernesto Valverde has opened the door for the Chile international to find a new team, with Inter CEO Giuseppe Marotta recently hinting his side are interested.
However, Bartomeu is expecting Vidal - a reported €18million signing from Bayern Munich in August 2018 - to remain at Camp Nou until at least the end of the season.
"No movement is expected in January," he told La Repubblica. "We have a balanced squad - we don't lack anything, but we also cannot afford to lose someone.
"Several times before a player has showed a willingness to leave, but never in January. In some cases we have let them go, on other occasions we have convinced them to stay."
Barcelona travel to Inter in the Champions League on Tuesday with top spot already assured in Group F, while the Italians need to match Borussia Dortmund's result against Slavia Prague.
Lautaro Martinez has been in fine form for the Serie A leaders this season, leading to reports he is on Barca's radar, but Bartomeu was unwilling to comment on the speculation.
"This is a trap question," he said. "What I can say is that Inter are playing very well and he is not the only player who has impressed me. It is no coincidence that Inter are top of Serie A."


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Security Policy
The Open Policy Agent (OPA) community has adopted this security disclosures and response policy to ensure we responsibly handle critical issues.
Reporting a Security Bug
- If you think you have found a security issue in an OPA project, please send an
email to open-policy-agent-security.
This list is delivered to a small security team. We will then acknowledge receipt
of your report and prioritize initial analysis of severity.
- After the initial reply to your report, the security team will endeavor to keep you informed
of the progress being made towards a fix and full announcement, and may ask for additional
information or guidance surrounding the reported issue.
- If you have not received a reply to your report within two days, please reach out on our
Slack
by posting a message in the
#contributors
channel.
- Note that the
#contributors
channel is public, so please don't discuss details of your
issue there. Instead, simply say that you're trying to get a hold of someone from the security team.
Disclosure Policy
- The security report is received and is assigned a primary handler.
This person will coordinate the fix and release process.
The problem is confirmed and a list of all affected versions is determined.
Code is audited to find any potential similar problems.
Fixes are prepared for all releases which are still under maintenance.
These fixes are not committed to the public repository but rather held locally or in a private fork pending the announcement.
- A suggested embargo date for this vulnerability is chosen and a CVE
(Common Vulnerabilities and Exposures (CVE®)) is requested for the vulnerability,
through the GitHub Advisory database.
- A prenotification may be published on the security announcements channels listed below,
providing information about affected projects, severity, and the embargo date.
- On the embargo date, the announcement is published.
The changes are pushed to the public repository and new builds are deployed.
- Typically the embargo date will be set 72 hours from the time the CVE is issued.
However, this may vary depending on the severity of the bug or difficulty in applying a fix.
- This process can take some time, especially when coordination is required with maintainers of
other projects. Every effort will be made to handle the bug in as timely a manner as possible;
however, it’s important that we follow the release process above to ensure that the disclosure
is handled in a consistent manner.
Receiving Security Updates


The FileIcon
is an abstract class creates a javax.swing.Icon
for a File
.
A file icon is a small (usually about 16x16) icon you can place next to a file name. Usually all files with the same file extension share the same file icon.
An "icon" is different from a "thumbnail". (A thumbnail is an image that is a rendering of a specific file.) And sometimes the word "preview" also carries special implications of interactivity (for ex: a video). So "icon", "thumbnail" and "preview" are all separate buzzwords.
The simplest one-line invocation resembles:
Icon icon = FileIcon.get().getIcon(file);
Each subclass is responsible for deciding how to identify the appropriate icon. And the FileIcon
class has a static getter/setter method for maintaining the active default FileIcon
on a given system.
There are currently 2 subclasses of FileIcon
. (There used to a 3rd that involved reflection, but in accordance with Java guidelines I'm trying to phase out reflection.)
This implementation uses FileSystemView
, and it resembles:
public class FileSystemViewFileIcon extends FileIcon { @Override public Icon getIcon(File file) { FileSystemView fsv = FileSystemView.getFileSystemView(); Icon icon = fsv.getSystemIcon(file); if (icon != null) return icon; return super.getDefaultIcon(file); } }
This looks like it should be the ideal way to go, because it's so clean/direct. But on Mac it only ever returns a generic file or folder icon. I tried rummaging through the source code for FileSystemView
(and related classes) to see if I could modify (hack) it without reflection, but there aren't a lot of entry points.
This implementation uses a JFileChooser's
FileView
, and it resembles:
public class FileViewFileIcon extends FileIcon { FileView fileView; public FileViewFileIcon() { JFileChooser chooser = new JFileChooser(); fileView = chooser.getUI().getFileView(chooser); } @Override public Icon getIcon(File file) { return fileView.getIcon(file); } }
I don't like that this is so indirect and clunky. Sometimes creating a JFileChooser
is not trivially cheap, either. But on Mac: this works great. It returns scalable icons for most every file type.
On Windows: both this approach and the FileSystemView
approach produce unscaleable (kind of crappy/pixelated) 16x16 icons. But today as I wrote this I saw emails trickle in about a pull request to update this in future Java versions, so that's exciting.
This demo includes an option to scale the icon.
On Mac the icons we get back are high-resolution, so they can scale to most any size with great clarity.
On Windows the icons are not (yet) high-resolution. (Although in a future Java release that should change.)  (And even that, unfortunately, will look pixelated on a high-resolution monitor on Windows.)


AssetsFacade |
Encapsulates logic, associated with echo blockchain assets use cases interface AssetsFacade |
AuthenticationFacade |
Encapsulates logic, associated with user authentication and account configuration processes interface AuthenticationFacade |
BitcoinSidechainFacade |
Encapsulates logic, associated with bitcoin sidechain functionality interface BitcoinSidechainFacade |
CommonSidechainFacade |
Encapsulates logic, associated with common sidechain functionality interface CommonSidechainFacade |
ContractsFacade |
Encapsulates logic, associated with various blockchain smart contract processes interface ContractsFacade |
ERC20SidechainFacade |
Encapsulates logic, associated with erc20 sidechain functionality interface ERC20SidechainFacade |
EthereumSidechainFacade |
Encapsulates logic, associated with ethereum sidechain functionality interface EthereumSidechainFacade |
FeeFacade |
Encapsulates logic, associated with fee configuration processes interface FeeFacade |
InformationFacade |
Encapsulates logic, associated with receiving blockchain information processes interface InformationFacade |
InitializerFacade |
Encapsulates logic, associated with socket and blockchain apis connection interface InitializerFacade |
SubscriptionFacade |
Encapsulates logic, associated with subscription on blockchain objects interface SubscriptionFacade |
TransactionsFacade |
Encapsulates logic, associated with various blockchain transactions processes interface TransactionsFacade |


Designs¶
Synbio designs.
Design encode plasmids, libraries (of plasmids) or combinatorial builds for DNA assembly. They are the design specification used for the assembly step.
-
class
synbio.designs.
Combinatorial
(records: Iterable[Bio.SeqRecord.SeqRecord] = None, linear: bool = True)¶ A list of SeqRecords. Find and use all valid combinations.
Structure-wise it’s the same as a Plasmid but multiple assemblies are possible given a single set of SeqRecords. List of SeqRecords in, list of SeqRecords out. Versus List of SeqRecords in one SeqRecord out for a Plasmid design
-
records
¶ list of SeqRecord
-
linear
¶ Whether the SeqRecords of the design are linear
-
-
class
synbio.designs.
CombinatorialBins
(bins: Iterable[List[Bio.SeqRecord.SeqRecord]] = None, linear: bool = True)¶ A list of bins of SeqRecords. All combinations between bins are attempted.
-
bins
¶ list of SeqRecord bins
-
linear
¶ Whether the SeqRecords of the design are linear
-
append
(records: Union[Bio.SeqRecord.SeqRecord, List[Bio.SeqRecord.SeqRecord]])¶ Add the SeqRecords as a bin for combinatorial design
- Parameters
record – the records to add as a bin
-
-
class
synbio.designs.
Plasmid
(records: Iterable[Bio.SeqRecord.SeqRecord] = None, linear: bool = True)¶ Create a plasmid from a list of SeqRecords
-
records
¶ SeqRecords to concatenate into a plasmid
-
linear
¶ Whether the SeqRecords of the design are linear
-
append
(records: Union[Bio.SeqRecord.SeqRecord, List[Bio.SeqRecord.SeqRecord]])¶ Add the SeqRecord to this plasmid’s list of SeqRecords/fragments
- Parameters
record – the SeqRecord to add to the plasmid specification
-
-
class
synbio.designs.
PlasmidLibrary
(sets: Iterable[List[Bio.SeqRecord.SeqRecord]] = None, linear: bool = True)¶ A list of SeqRecord lists. Each nested list is combined into a Plasmid.
-
sets
¶ Iterable of SeqRecord sets
-
linear
¶ Whether the SeqRecords of the design are linear
-
append
(records: Union[Bio.SeqRecord.SeqRecord, List[Bio.SeqRecord.SeqRecord]])¶ Add a new set or SeqRecord to the library
- Parameters
records – the records to add as a set
-


--- layout: post title: 'Picasa 3 for Linux: Proxy setting' date: 2010-12-28 20:36:48.000000000 +05:30 type: post published: true status: publish categories: - Tech Bytes - Ubuntu tags: - GNU/Linux - google - picasa - proxy - Ubuntu meta: _wp_old_slug: '' _syntaxhighlighter_encoded: '1' _edit_last: '1' dsq_thread_id: '477794949' _thumbnail_id: '841' dsq_needs_sync: '1' author: login: satyadeep email<EMAIL_ADDRESS>display_name: Satyadeep first_name: Satyadeep last_name: K ---
Its been a long time since Google upgraded Picasa for Linux from 2.7 to 3.0 adding new features like 'Sync to Web', retouching images etc. Even though it is tagged beta it is quite stable with some minor issues though. It is actually ported through Wine directly.
Uploading to Web albums is one major reason why I love Picasa. But the current version has issue of connecting to web albums through a proxy giving an error like "Check your internet connection". Going through the FAQ http://picasa.google.com/linux/faq.html#31gives a solution to this.
Run
[shell light="true"] /opt/google/picasa/3.0/bin/wrapper regedit[/shell]
in terminal to start Picasa's regedit.
Then the proxy settings can be changed by following the instructions given for tweaking windows in general at http://www.pctools.com/guides/registry/detail/292/ .


Gemma Collins trolled over make-up disaster as GMB viewers ask why she's 'painted her nose brown'
They're calling for her make-up artist to be sacked
by Nancy BrownDiva Forever star Gemma Collins has been targeted by trolls following her appearance on Good Morning Britain.
The star – who is set to make a return to Dancing On Ice for the show's Christmas special – chatted about the latest showbiz stories as she appeared on the show.
However, while she did let slip that she "can't wait much longer" for on-again boyfriend James Argent to propose, viewers only had one thing on their minds.
Appearing on the show before 7am, it appears Gemma's make-up artist could've been feeling a little sleepy as they got to work on the star.
Read more: Simon Cowell planning Christmas charity single featuring X Factor: Celebrity stars
They missed a crucial bit of blending when it came to Gemma's contouring.
As a result, the TOWIE icon was left with a two-tone nose – and the make-up mishap didn't go unnoticed by viewers.
Read more: Married Justin Timberlake spotted holding hands with new co-star
Others called for the make-up artist in question to be "sacked".
Some also wondered if Gemma had done her make-up "in the dark".
Gemma has yet to respond to the comments, but we expect the star is too busy to even notice she's been trolled.
It was recently revealed Gemma had ditched plans to release a Christmas single after not having any time to finish it.
Head to our Facebook page @EntertainmentDailyFix and let us know what you think of our story.


AQUARIUS (JANUARY 20 – FEBRUARY 18)
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You’re the rebel of the zodiac and you march to the beat of your own drum on the field. This is the only
apricot-flavored sparkling water on the market, making it just as rare as you and your plays are.
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PISCES (FEBRUARY 19 – MARCH 20)
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You’re super empathetic, and you love giving spirit gifts and helping others on the field. But today, it’s
time to be a little selfish. Save this super-sweet flavor duo all for yourself.
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ARIES (MARCH 21 – APRIL 19)
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You’re a pioneer, with daring—OK, sometimes impulsive—tendencies. You’re always ready to try different
plays. So stand out from the crowd and try an out-of-the-box variety, like this cola-flavored sparkling water.
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TAURUS (APRIL 20 – MAY 20)
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You love luxury, and let’s face it: You definitely wish you were getting a massage right now. This
cucumber-and-blackberry-flavored seltzer is the closest thing to spa water you’ll find at Sunbreak—is there
anything fancier than that?
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GEMINI (MAY 21 – JUNE 20)
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Sometimes, you’re fun-loving and talkative at Sunbreak. Other times, you just want to hang out alone and
read. This flavor is just like you: It mixes the sweet (cantaloupe) with the tart (grapefruit).
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CANCER (JUNE 21 – JULY 22)
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Your world revolves around home, family, and team. This crisp, unflavored option is no-frills and
down-to-earth, just the way you like your throws and your hydration #TakeYourHydrationSeriously.
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LEO (JULY 23 – AUGUST 22)
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You have a big presence on the field and consider the world your stage. You’re also very generous and
loving—everything you do comes from the heart. So naturally, a bright, citrus flavor is right up your alley.
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VIRGO (AUGUST 23 – SEPTEMBER 22)
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You’re efficient, organized and detail-oriented, so you want a no-nonsense drink that cuts straight to the
point just like your cuts on the field. Get the game started on the right foot with a burst of lemony
goodness.
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LIBRA (SEPTEMBER 23 – OCTOBER 21)
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You’re the peacemaker and diplomat of your group, but you’re also very social and can totally dominate the
dance floor at the Sunbreak party. This fun, fruity blend of kiwi and watermelon is right up your alley.
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SCORPIO (OCTOBER 22 – NOVEMBER 21)
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You always say exactly what you’re thinking with zero filter. Which is why you need an equally bold flavor
for your bold plays, like this strong cherry-and-lime combo.
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SAGITTARIUS (NOVEMBER 22 - DECEMBER 21)
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You’re happy-go-lucky and optimistic, and you love spending time at tournaments. This Sunbreak, grab a spot
on the field and sip on this berry-infused fizz to match your mood.
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CAPRICORN (DECEMBER 22 – JANUARY 19)
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It’s official: You’re the leader of the zodiac, the spirit game, ands the LaCroix flavor family. You’re
practical and ambitious, and your goal is to rise to the occasion. So guzzle grapefruit, LaCroix’s best
seller.
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