| 1 |
Russell_2006 |
Transmission Dynamics and Prospective Environmental Sampling of Adenovirus in a Military Recruit Setting |
Russell, Kevin L.; Broderick, Michael P.; Franklin, Suzanne E.; Blyn, Lawrence B.; Freed, Nikki E.; Moradi, Emily; Ecker, David J.; Kammerer, Peter E.; Osuna, Miguel A.; Kajon, Adriana E.; Morn, Cassandra B.; Ryan, Margaret A. K. |
2006 |
2006-10-01 |
PMC |
N |
PMC7109706 |
16960774.0 |
10.1086/507426 |
olxluuqv |
0.621611 |
|
|
| 2 |
Levy_2015 |
Elevated transmission of upper respiratory illness among new recruits in military barracks in Thailand |
Levy, Jens W; Bhoomiboonchoo, Piraya; Simasathien, Sriluck; Salje, Henrik; Huang, Angkana; Rangsin, Ram; Jarman, Richard G; Fernandez, Stefan; Klungthong, Chonticha; Hussem, Kittinun; Gibbons, Robert V; Yoon, In-Kyu |
2015 |
2015-10-13 |
COMM-USE |
N |
PMC4605412 |
26271648.0 |
10.1111/irv.12345 |
vyh77o4f |
0.530792 |
|
|
| 3 |
Lee_T_2020 |
Predictive value of C-reactive protein for the diagnosis of meningitis in febrile infants under 3 months of age in the emergency department |
Lee, Tae Gyoung; Yu, Seung Taek; So, Cheol Hwan |
2020 |
2020-01-08 |
NONCOMM |
N |
PMC7142029 |
31914719.0 |
10.12701/yujm.2019.00402 |
84etaikv |
0.483433 |
|
|
| 4 |
Coles_2019 |
The Acute Respiratory Infection Consortium: A Multi-Site, Multi-Disciplinary Clinical Research Network in the Department of Defense |
Coles, Christian; Millar, Eugene V; Burgess, Timothy; Ottolini, Martin G |
2019 |
2019-11-28 |
PMC |
N |
PMC6886571 |
31778194.0 |
10.1093/milmed/usz174 |
k8ixv4aa |
0.460162 |
|
|
| 5 |
Noh_J_2015 |
Clinical performance of the Sofia™ Influenza A+B FIA in adult patients with influenza-like illness |
Noh, Ji Yun; Choi, Won Suk; Lee, Jacob; Kim, Hye Lim; Song, Joon Young; Cheong, Hee Jin; Kim, Woo Joo |
2015 |
2015-10-31 |
PMC |
N |
PMC7127480 |
26184128.0 |
10.1016/j.diagmicrobio.2015.05.016 |
t549rf3o |
0.459266 |
|
Falsey_2014 |
| 6 |
Nachtnebel_2012 |
Lessons from a one-year hospital-based surveillance of acute respiratory infections in Berlin- comparing case definitions to monitor influenza |
Nachtnebel, Matthias; Greutelaers, Benedikt; Falkenhorst, Gerhard; Jorgensen, Pernille; Dehnert, Manuel; Schweiger, Brunhilde; Träder, Christian; Buda, Silke; Eckmanns, Tim; Wichmann, Ole; Hellenbrand, Wiebke |
2012 |
2012-03-27 |
COMM-USE |
N |
PMC3362781 |
22452874.0 |
10.1186/1471-2458-12-245 |
esrguepi |
0.428804 |
Murray_2015, Azziz-Baumgartner_2012 |
|
| 7 |
Fitzner_2018 |
Revision of clinical case definitions: influenza-like illness and severe acute respiratory infection |
Fitzner, Julia; Qasmieh, Saba; Mounts, Anthony Wayne; Alexander, Burmaa; Besselaar, Terry; Briand, Sylvie; Brown, Caroline; Clark, Seth; Dueger, Erica; Gross, Diane; Hauge, Siri; Hirve, Siddhivinayak; Jorgensen, Pernille; Katz, Mark A; Mafi, Ali; Malik, Mamunur; McCarron, Margaret; Meerhoff, Tamara; Mori, Yuichiro; Mott, Joshua; Olivera, Maria Teresa da Costa; Ortiz, Justin R; Palekar, Rakhee; Rebelo-de-Andrade, Helena; Soetens, Loes; Yahaya, Ali Ahmed; Zhang, Wenqing; Vandemaele, Katelijn |
2018 |
2018-02-01 |
COMM-USE |
N |
PMC5791775 |
29403115.0 |
10.2471/blt.17.194514 |
86lh3vc3 |
0.406909 |
|
Huang_2014, Sáez-López_2019 |
| 8 |
Khandaker_2012 |
Comparing the use of, and considering the need for, lumbar puncture in children with influenza or other respiratory virus infections |
Khandaker, Gulam; Heron, Leon; Rashid, Harunor; Li‐Kim‐Moy, Jean; Lester‐Smith, David; Kesson, Alison; McCaskill, Mary; Jones, Cheryl; Zurynski, Yvonne; Elliott, Elizabeth J.; Dwyer, Dominic E.; Booy, Robert |
2012 |
2012-11-05 |
PMC |
N |
PMC4634251 |
23122417.0 |
10.1111/irv.12039 |
tgagwgpj |
0.393529 |
|
|
| 9 |
Salmón‐Mulanovich_2010 |
Frequency of human bocavirus (HBoV) infection among children with febrile respiratory symptoms in Argentina, Nicaragua and Peru |
Salmón‐Mulanovich, Gabriela; Sovero, Merly; Laguna‐Torres, V. Alberto; Kochel, Tadeusz J.; Lescano, Andres G.; Chauca, Gloria; Sanchez, J. Felix; Rodriguez, Francisco; Parrales, Eduardo; Ocaña, Victor; Barrantes, Melvin; Blazes, David L.; Montgomery, Joel M. |
2010 |
2010-09-16 |
PMC |
N |
PMC4066840 |
21138534.0 |
10.1111/j.1750-2659.2010.00160.x |
k54vjyiz |
0.345966 |
|
|
| 10 |
O’Shea_2007 |
Respiratory syncytial virus: an important cause of acute respiratory illness among young adults undergoing military training |
O’Shea, Matthew K.; Pipkin, Christopher; Cane, Patricia A.; Gray, Gregory C. |
2007 |
2007-12-10 |
PMC |
N |
PMC2564797 |
18846262.0 |
10.1111/j.1750-2659.2007.00029.x |
pzbh8dd3 |
0.343033 |
|
|
| 11 |
Lau_Y_2018 |
Epidemiologic analysis of respiratory viral infections among Singapore military servicemen in 2016 |
Lau, Yuk-Fai; Koh, Wee-Hong Victor; Kan, Clement; Dua, Poh-Choo Alethea; Lim, Ai-Sim Elizabeth; Liaw, Chin-Wen Jasper; Gao, Qiu-Han; Chng, Jeremiah; Lee, Vernon J.; Tan, Boon-Huan; Loh, Jin-Phang |
2018 |
2018-03-12 |
COMM-USE |
N |
PMC5848554 |
29529993.0 |
10.1186/s12879-018-3040-x |
hj0t4wjj |
0.317106 |
|
Tan_X_2014 |
| 12 |
Seah_2010 |
Viral agents responsible for febrile respiratory illnesses among military recruits training in tropical Singapore |
Seah, Shirley Gek-Kheng; Lim, Elizabeth Ai-Sim; Kok-Yong, Seng; Liaw, Jasper Chin-Wen; Lee, Vernon; Kammerer, Peter; Metzgar, David; Russell, Kevin L.; Tan, Boon-Huan |
2010 |
2010-03-31 |
PMC |
N |
PMC7108280 |
20079688.0 |
10.1016/j.jcv.2009.12.011 |
73591t3m |
0.309436 |
|
Lau_Y_2018 |
| 13 |
Sáez-López_2019 |
Performance of surveillance case definitions for respiratory syncytial virus infections through the sentinel influenza surveillance system, Portugal, 2010 to 2018 |
Sáez-López, Emma; Pechirra, Pedro; Costa, Inês; Cristóvão, Paula; Conde, Patrícia; Machado, Ausenda; Rodrigues, Ana Paula; Guiomar, Raquel |
2019 |
2019-11-07 |
COMM-USE |
N |
PMC6852315 |
31718741.0 |
10.2807/1560-7917.es.2<IP_ADDRESS>0140 |
h2d2368x |
0.306984 |
|
|
| 14 |
Yun_H_2015 |
Changes in Clinical Presentation and Epidemiology of Respiratory Pathogens Associated With Acute Respiratory Illness in Military Trainees After Reintroduction of Adenovirus Vaccine |
Yun, Heather C.; Young, Adam N.; Caballero, Manuel Y.; Lott, Lisa; Cropper, Thomas L.; Murray, Clinton K. |
2015 |
2015-09-01 |
NONCOMM |
N |
PMC4569648 |
26380351.0 |
10.1093/ofid/ofv120 |
qdehf6rb |
0.306940 |
|
|
| 15 |
Domínguez_2020 |
Usefulness of Clinical Definitions of Influenza for Public Health Surveillance Purposes |
Domínguez, Àngela; Soldevila, Núria; Torner, Núria; Martínez, Ana; Godoy, Pere; Rius, Cristina; Jané, Mireia |
2020 |
2020-01-14 |
COMM-USE |
N |
PMC7019582 |
31947696.0 |
10.3390/v12010095 |
0njm2cgh |
0.305278 |
|
Sáez-López_2019, Huang_2014, Ridgway_2015 |


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GGC Alma Mater
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About
GGC Alma Mater
This application was created to help users GGC's alma mater.
This was created in ITEC 4550 Section 01, Mobile Application Development.
Created by Robert Bryan, Fall 2017


Alright was around, but might have gone now... be like 3am where he is.
I am not 100% sure of the relevent people are all in the staker role to do a ping... guess I just do it lol
@LabsAssistant Please update notary nodes.
6
Deleted User29-Nov-18 07:33 AM
no new assetchain.json ?
blackjok3r29-Nov-18 07:36 AM
no its just an update to min sigs
now needs ot be 6
Deleted User29-Nov-18 07:36 AM
oh so just iguana restart
blackjok3r29-Nov-18 07:36 AM
instead of 5... there is also an update to the mining code that fixes random freexes
Deleted User29-Nov-18 07:36 AM
kk
blackjok3r29-Nov-18 07:37 AM
updating komodod and restarting should handle everything required
minsigs is set in komodod for us
Deleted User29-Nov-18 07:37 AM
relaunching all fresh
1
blackjok3r29-Nov-18 07:38 AM
for those using the standard control scripts just ./start.sh
gcharang29-Nov-18 07:39 AM
@blackjok3r have you seen my message in #dev-general , ./komodo-cli minerids 123456 gives wrong notary pubkeys because some of them are uninitialized for komodod through stakednotary. Don't know if it will cause problems elsewhere
blackjok3r29-Nov-18 07:40 AM
LABS notaariesa dont mine ... I could disable that RPC call for staked chains ... but is that really required ?
seems like a lot of extra code for no reason to me...
if you wanted a list of notaries, just use getiguanajson RPC call.
gcharang29-Nov-18 07:42 AM
I wasn't asking to disable it, I was thinking it might cause problems with other useful rpc calls we haven't used.
blackjok3r29-Nov-18 07:42 AM
appreciated testing these things though.
I dont think so ..
if it returns garbled output its because its looking for soething that does not exist
1
I just checked it ... dont think its anything to worry about. if you do it on komodo its correct and thats all that matters.
gcharang29-Nov-18 07:46 AM
Great!
CrisF29-Nov-18 08:28 AM
@blackjok3r Just had a look and getinfo and getbalance are working fine on CFEKLAMEDUCKS now.
No hanging like yesterday.
blackjok3r29-Nov-18 08:32 AM
yeah I think I fixed that. Good we found it.
That fix needs to be pushed to KMD ... but I am not 100% sure what branch.
jorian29-Nov-18 08:37 AM
it's so good to restart your node with just ./stop.sh and ./start.sh
blackjok3r29-Nov-18 08:39 AM
you can just use ./start.sh
but it will move the iguana output to the terminal you run it in. if there is an update to iguana.
in this case we have an update to iguana
jl777c29-Nov-18 08:40 AM
getinfo doesnt hang with latest FSM branch
blackjok3r29-Nov-18 08:40 AM
I still cant bring that in ...
jl777c29-Nov-18 08:40 AM
i did fix some deadlock issues a few weeks back, so depending on when you last rebased to FSM, it could be something that was already fixed
blackjok3r29-Nov-18 08:40 AM
it was the commit before verus
I cant merge anythign after that, without a full rebase of our code
its a few days work
There was another fix that fixed our deadlocks ...(edited)
there is a call to IsInitalBlockDownlaod in the mempool fix, that needs to be removed, and change to just true
and a LOCK(cs.mempool) needs to be added to the second part of the mempool fix.(edited)
Which branch is this meant to be PRd too, as the mempool fix was propagated to all of them no?
jl777c29-Nov-18 08:44 AM
not sure about the mempool fix. verus code made some changes to it and it is running stable in our tests
blackjok3r29-Nov-18 08:45 AM
ok I will look at the code in FSM
maybe it was a totally diffrent issue that we have ... I dont know ... there is too many merge conflits for me to merge that in... I just cant do it.
its 1000x eaier to start at your branch and manually port LABS code into that
as everything is in 2 files with just calls to those.
Komodo. Contribute to jl777/komodo development by creating an account on GitHub.
That one I dont know for 100% sure, but I think it needs to be there.
jl777c29-Nov-18 08:52 AM
yes, it seems a good idea. added
any other surgical fixes?
blackjok3r29-Nov-18 08:53 AM
I think that was all that applies on your end ... my other fix was in the miner... we have an exeption that makes notarisations have the same priority as Z-TX so that under spam conditions notariations are confirmed first. That code was sloppy...
CMaurice29-Nov-18 08:53 AM
updated
1
blackjok3r29-Nov-18 08:53 AM
I improved it yesterday.
And it still needs further fixes to speed it up, even more.(edited)
Once we have enough nodes updated to mine sigs 6 we need to test migrates heavily again... I have a problem, becasue my migrate testing VM is now being used to stake all t the CFEK chains.
So if anyone has a node they can test on, please test them and report any failed imports.
1
Heads up, both reported fails were on CFEKLF ... importing to this chain from CFEK.
Although it should really make 0 diffrence as all chains are the same branch.(edited)
And rember LAMEDUCKS cannot do migrates ... its a seperate cluster ID.
repo for using staked chains, for testing and production use - StakedChain/staked
It has changes to make it much easier .. should be all in readme
ALthough you do need KMD synced for migrates.
You can/have to use the master version of komodod to load KMD. Stating any chain ten manually starting ./komodod is all you need.
I have been thinkign about that, and we can definatly have a centralised service for the KMD signing part.
as the export tx is funded and signed early in the process... so after that it doenst matter if you make it avalible to some other person.
The coins can only be sent to the address you sent them too, during the export.
That seems like a great use for the AWS partnetrship ... Might speak to marketing about it. Ties in with the scaletest well.
CMaurice29-Nov-18 09:44 AM
./update_komodo.sh isn't doing anything for me
blackjok3r29-Nov-18 09:49 AM
sorry
lol
@CMaurice I need more context ?
CMaurice29-Nov-18 09:52 AM
yeah, must be an issue with me, it literally seems to exit straight to cmd line without any output, quite tsrange
blackjok3r29-Nov-18 09:52 AM
What did you do ? You need to run buildkomodo.sh first
otherwise it doesnt have the git repo to build out of
CMaurice29-Nov-18 09:53 AM
oh yeah, just downgraded this old laptop to Stretch, does seem to build fine now
blackjok3r29-Nov-18 09:54 AM
I have used another komodo directory say ~/komodo and symlinked it like so
ln -s /home/<user>/komodo /home/<user>/staked/StakedModo
but I dont quite reccomend doing such thigns unless you kow what your doing.
Also that repo, if someone can make it so that it doesnt require any hard coded paths at all .... I woukd be very happy about it.
currently it seperates everything, so that you can have normal komodo on the same system... but its till hard codes to ~/staked
CMaurice29-Nov-18 10:47 AM
hrm won't build for me gcc 5 & 6, did build jl777 FSM gcc5
Bar_F1sh_Rel29-Nov-18 11:05 AM
Notary updated.
gt29-Nov-18 11:12 AM
updated a few hours ago
smk762 - dracocanis ominator29-Nov-18 11:16 AM
updated. Pool hit a snag and needed komodo rebuilt, but notary was fine. For some reason pool lost kmodod symling
Nabob29-Nov-18 11:20 AM
How to know Nodes are updated ? seems stop and start script pulls nothing annd says it is on latest build already @blackjok3r(edited)
blackjok3r29-Nov-18 11:21 AM
when you run start you can also run htop in another terminal and see if it builds... generally it will say updated to latest if it built
then start
Nabob29-Nov-18 11:24 AM
Already up-to-date.
[master] Checking for updates and building if required...
[master] Updated to latest
[KMD] Stopping ...
okie
blackjok3r29-Nov-18 11:34 AM
yes trhat means it worked
1
blackjok3r29-Nov-18 12:34 PM
Does anyone want to test the streamer in a new build? I am making a PR for FSM. If anyone wants to give it a go, this is a good oppertunity to try it out doing something helpful.
You would need to use a clean VPS most likely. I use 2 VMs on my PC to test it early on, each with 8GB of ram, but you could use far less.
Should be as simple as following the instructionsm to stream and then extract a file once it has sent.


About
I’m an energy researcher and an aspiring physicist. Currently working on projects related to the device physics of organic solar cells. I was born and raised in ‘the land of spices’-Kerala, India. I believe even a small step to reach out the fruits of technology to the society is paramount. As a science student, I realize synergistic efforts in integrating ideas and innovation are vital to the progress of mankind. For me, humanity stands above all differences and regards as key to a peaceful heart.
Researcher
- Birthday: 1 JAN 1992
- Website: www.abdlazeezh.in
- Phone: +123 456 7890
- City: Bengalore
- Degree: PhD Scholar
- Email<EMAIL_ADDRESS>Currently
Molecular Electronic Lab
Chemistry and Physics of Materials Unit
Advanced Materials Research Laboratory (AMRL) Building
Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR)
Jakkur, Bangalore-560 064
Karnataka, India
Education
My Studies
Current
Jawaharlal Nehru Center for Advanced Scientific Research
Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR) is a multidisciplinary research institute situated in Jakkur, a locality north of Bangalore, India. It is relatively young yet well-known around the Globe.
- PhD Scholar
- Molecular Electronics Lab
- Chemistry and Physics of Materials Unit
- Research Supervisor: Prof. K.S. Narayan
Education
Master of Science Physics
2014 - 2015
NIT Calicut, Kerala, India.
Bachelor of Science Physics
2010 - 2013
Govt. Victoria College Palakkad University of Calicut, Kerala, India.
Academic Achievements
CSIR-UGC JRF/Eligibility for Lectureship
2016
JEST (Joint Entrace Screening Test-2016)
2016
GATE (Graduate Aptitude Test in Engineering)
2016
Student Award
2010
Best Outgoing Student Award, Science Batch - Class 12 GHSSK, Palakkad, Kerala(2010)
Lower Secondary Scholarship (LSS)
2002
Board of Public Examination, Government of Kerala
Interests and Activities
- Graphic Designing
- Programming, Automation and Instrumentation.
- Electronics and Opto-electronics.
- Agriculture technology.
- Waste management.
- Solutions to reduce the impact of climate change.
- Interested in teaching Physics.
- I'm a foodie and I love to cook.
- An automotive enthusiast.
- Likes event mangement.
- I enjoy Travelling, Swimming and Playing Football


Web Developer
One of the best wallpapers in the planet. https://github.com/udacity/Appify/
A magical APP that will solve all your problems https://github.com/udacity/Appify/
An APP to help you write better readme files https://github.com/udacity/Appify/


Are you ready to export?
Manitoba Trade is your export partner. Whether you are a first-time exporter, or you want to diversify your existing international business, we have programs and services designed to help you succeed in the global marketplace.
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Upcoming and past events.
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Discover the potential trade opportunities in key sectors in Manitoba.


Configure the project
- Ensure Cocoapods are installed.
- Run pod install in terminal.
- Open VISDK.xcworkspace (don’t use .xcodeproj)
Folder structure
- InternalClient - internal test app which use VISDK public api.
- SDK - VISDK sources
- Scripts ..* build.sh - builds VISDK scheme for device and simulator, then merges them into universal framework ..* submit - stolen from Fabric. Sends specified .ipa to the Fabric. Usage 0a7344d53213d77ea17f37a14f1b6b0d0db0e9e7 1858c198c20cdb55735ed960f83067107428af6d96cdb75acc4ed993abc27d5d -ipaPath Build/InternalClient.ipa -emails<EMAIL_ADDRESS>..* makeIpa.sh - creates .ipa from InternalClient and signs it for AdHoc distribution ..* exportOptions.plist - configuration required by makeIpa.sh ..* unitTest.sh - runs unit tests of libVSDK scheme ..* setVersion.sh - set version of the Build/VISDK.framework by modifying it’s Info.plist. Version is defined by teamcity
Project structure
Pods are only used in unit tests. Try to avoid usage of side pods in VISDK source. Both ‘libVSDK’ and ‘VISDK’ targets are maintained. Currently ‘VISDK’ is used to build a framework. Unit test are assigned to ‘libVSDK’ historically.


silex
package silex
This package is used to write lexers.
Lexer
To define a lexer, mix-in the silex.Lexers trait.
object MyLexer extends Lexers { // Type of characters consumed. type Character = Char // Type of positions. type Position = MyPosition // Type of tokens produced. type Token = MyToken // Then define your lexing rules using regular expressions. val lex = Lexer(...) // Then, you can query the lexer. def apply(source: Source[Character, Position]): Iterator[Token] = lex(source) }
Additional traits can be mixed-in.
See for instance silex.CharLexers
for lexers specialized on Char
.
Data sources
The input of lexers is in a form of a data source.
// Building a source from a string: val stringSource = Source.fromString("[1, 2, 3]") // Or from a file: val fileSource = Source.fromFile("data/clients.json") // With a custom positioner: val customSource = Source.fromFile("data/clients.json", IndexPositioner)
- Source
- package.scala
- Grouped
- Alphabetic
- By Inheritance
- silex
- AnyRef
- Any
- Hide All
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- All
Type Members
-
class
ArraySource[Character, Position] extends Source[Character, Position]
Source over an array of character.
-
trait
CharLexers extends AnyRef
Specialized lexers operating on
Char
characters.Specialized lexers operating on
Char
characters. Expected to be mixed-in with silex.Lexers. -
class
IteratorSource[Character, Position] extends Source[Character, Position]
Source over an interator.
-
trait
Lexers extends RegExps with Zippers
Contains definitions for lexers.
-
trait
Positioner[-Character, Position] extends AnyRef
Indicates the start position and how to increment positions.
-
class
ReaderSource[Position] extends Source[Char, Position]
Source over a
Reader
. -
trait
RegExps extends AnyRef
Contains definitions relating to regular expressions.
Contains definitions relating to regular expressions.
- See also
See silex.CharLexers for useful regular expressions on
Char
.
-
trait
Source[Character, Position] extends Iterator[Character]
Represents iterators with
Position
s and ability tomark
points andreset
to them. -
case class
StringPosition(index: Int, line: Int, column: Int) extends Product with Serializable
Position within a string.
Position within a string.
- index
The index in the string. Zero-based.
- line
The line number. Zero-based.
- column
The column number. Zero-based.
- See also
See silex.StringPositioner for the corresponding positioner.
-
class
StringSource[Position] extends Source[Char, Position]
Source over an array of character.
Value Members
-
object
IndexPositioner extends Positioner[Any, Int]
General index-based positioner.
-
object
NoPositioner extends Positioner[Any, Unit]
Positioner for
Unit
positions. -
object
Source
Builds sources.
-
object
StringPositioner extends Positioner[Char, StringPosition]
Positioner for silex.StringPosition.
Overview
Silex is a library for easily writing lexers in Scala. See the package silex for more information.


This component represents a navigation list.
A navigation list is a special type of list fine-tuned for being used in navigating a page.
The following may be specified as an attribute on a template
element:
list-items
: The content to display in a section within the list representing all items of the list.The following attributes are available:
aligned
: One of center
, left
, right
, or any valid CSS alignment value.vertical
: One of true
or false
.wvre-nav-list
component has the weaver-nav-li
as a sub component.


When you are stuck in some on an the exercise for hours and you are noticing that you breath it is not the same, the feeling of frustration is all over your mind and the anger feeling taking over your body.
Is hard to realize that you are stuck in something really simple, it could be a semi-column that are you missing in your code or something really little as an upper-case or even a simple dot. In that moment before your going to blow up is when we need to use for the Siberian North Railroad, which is a practice for dealing with triggers or negatives emotions. Siberian North Railroad is the short cut for: Stop, breath, notice, reflect and respond. I found this exercise really helpful to maintain and control my emotions in bad times.


CITY OF ANGELS Grade: B (Worth a look)
Directed by Brad Silberling Written for the screen by Dana Stevens Starring Nicolas Cage, Meg Ryan, Dennis Franz Year of Release: 1998 Rating: PG-13
Capsule Review: A Memo to Hollywood; if you must persist in cranking out big-budget retreads of classic foreign films, then this is the way to do it - as a subtle and intelligent reimagining of the source material (in this case Wim Wenders' ethereally lovely 1987 Wings of Desire) rather than a slavish frame-by-frame duplication. It's hard to believe that Nicolas Cage hasn't used up all of his different facets as an actor by now, but he delivers another inspired performance here, comparable in its fumbling otherness to Jeff Bridges' sublime work in Starman, as an Angel who falls in love with Meg Ryan. The plot does come awfully close at times to shifting into Touched by an Angel style hokum (the spirits interact with humanity much more here than in the original film), but director Brad Silberling, displaying a talent that was difficult to spot amid all the ILM effects of his debut Casper, does a superb job of keeping the narrative grounded. (He also makes his L.A. locations look almost as gorgeously fresh and new as Wenders' Berlin.) On its own terms, City of Angels is an unusually resonant romantic drama; as a remake that doesn't insult the original, it's borderline miraculous.
Copyright 1998 by The Critic formerly known as Andrew Wright For more insanely biased reviews, check out http://www.seanet.com/~louk/ e-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
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Related links: index of all rec.arts.movies.reviews reviews


Css, auto-clear floating elements
Features:
- Automatically adds 'div.clear:both' element after floating elements
- Works in any browser, there's no CSS hacks, it uses old "clear: both" (and a little JS), exactly the same way as if you did it by hand
- Browser should have JavaScript enabled
- Current implemetation depends on MooTools (it's trivial to port it to another libraries, jQuer for example, but I'm too lazy to do it, maybe someone else someday will do that)


Contents
Example 2: Apply TDM on an example dataset (quick version)
Download dataset (if necessary) and add TDM to the MATLAB path
setup;
Load in the data
load('exampledataset_inputsonly.mat','data','intensity','tr');
Run extracthrfmanifold.m
results = extracthrfmanifold(data,intensity,tr,1);
calculating particles...
Wait while particle positions are being optimized ...
Iteration # Net Energy Time (sec)
0 36641.355445 0.0
50 33918.540866 0.7
100 33917.775987 1.4
150 33917.749149 2.1
193 33917.745363 2.7
Optimization completed after 193 iterations. Elapsed time: 2.7 sec
done.
initial seed is [56 56 22.2 22.2 0.080723 0.072152 0]
Norm of First-order
Iteration Func-count f(x) step optimality
0 8 53.3073 153
1 16 14.2105 10 20.3
2 24 10.9173 16.2739 54.5
3 32 3.19719 13.0282 9.54
4 40 2.68099 7.0568 1.83
5 48 2.6669 0.725015 0.0444
6 56 2.66683 0.0737062 0.00378
7 64 2.66682 0.00662206 0.000625
Local minimum possible.
lsqcurvefit stopped because the final change in the sum of squares relative to
its initial value is less than the selected value of the function tolerance.


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WITNESS LIST Economic Development Committee April 19, 2005-12:00A HB 1206 Registering, but not testifying: For: Ellis, Randall (Lesbian/Gay Rights Lobby of Texas) Raab, Ted Melina (Texas Federation of Teachers) Riddering, Hannah (Texas National Organization for Women) Against: Hammond, Bill (Texas Association of Business) HB 2781 For: Banner, Bruce (Education Austin) Raab, Ted Melina (Texas Federation of Teachers) Weiser, David (Self) Against: Hahn, Christopher (Texas Employment Law Council) Registering, but not testifying: For: Kelly, Jack (Texas State Teacher's Association) Levy, Rick (TX AFL-CIO) Riddering, Hannah (Texas National Organization for Women) Against: Hammond, Bill (Texas Association of Business) James, Dwain (American Collection Association of Texas) HB 3433 For: Hinojosa, Walter (TX AFL-CIO) Riddering, Hannah (Texas National Organization for Women) Against: Hahn, Christopher (Texas Employment Law Council) Registering, but not testifying: For: Lavine, Dick (Center for Public Policy Priorities) Raab, Ted Melina (Texas Federation of Teachers) Against: Hammond, Bill (Texas Association of Business)


A B C D E F G H I K L M N O P Q R S T U V W X Y
All Classes All Packages
All Classes All Packages
All Classes All Packages
M
- main(String[]) - Static method in class slogo.Main
-
Start of the program.
- Main - Class in slogo
-
Launch point of program.
- Main() - Constructor for class slogo.Main
- MainPane - Interface in view.mainpane
-
Interface representing a main pane object that contains the basic views required for any slogo simulation.
- MainPaneConstants - Class in view.mainpane
-
Class containing constants for classes in the mainpane package.
- MainVisualiser - Class in view.mainpane
-
Main Visualiser class storing the turtle pane and the selector pane.
- MainVisualiser(Node) - Constructor for class view.mainpane.MainVisualiser
-
Instantiates a MainVisualiser, places turtle arena node within the graphical view.
- MAKE_VARIABLE - Static variable in class controller.OperationFinder
- MAKE_VARIABLE - Static variable in class controller.Operator
- MakeVariable - Class in controller.specialOperations
-
instantiates a variable
- MakeVariable(ArgumentType, ControlParser) - Constructor for class controller.specialOperations.MakeVariable
- MAX_COLOR_VAL - Static variable in class view.selector.features.ColorPicker
- MAX_COLOUR_VAL - Static variable in class view.statepane.StatePaneConstants
- MAX_COLUMN_INDEX - Static variable in class view.mainpane.MainPaneConstants
- MAX_COLUMNS - Static variable in class view.mainpane.MainPaneConstants
- Minus - Class in controller.operations
-
returns the negated value of a given parameter
- Minus(Model) - Constructor for class controller.operations.Minus
- model - package model
- Model - Class in model
- Model() - Constructor for class model.Model
- move(double, double) - Method in class view.turtlearena.BasicTurtle
-
Moves turtle to new location absolutely
- move(double, String) - Method in class model.Model
- move(double, String) - Method in interface model.MovementModel
- move(Coordinate, String) - Method in class model.Model
- move(Coordinate, String) - Method in interface model.MovementModel
- MovementModel - Interface in model
- MovementView - Interface in view
-
This view is concerned with methods that change what the user sees on the screen.
- moveOffset(double, double) - Method in class view.turtlearena.BasicTurtle
-
Moves turtle to new location as specified by offset from current location
- moveTurtle(int, double, double) - Method in class view.turtlearena.BasicTurtleArena
- moveTurtle(int, double, double) - Method in interface view.turtlearena.TurtleArena
-
Moves turtle of specified index from old position to new position
- moveTurtle(int, Coordinate, Coordinate) - Method in interface view.MovementView
-
Tells the view to move the turtle with the given index from the start coordinate to the end coordinate
- moveTurtle(int, Coordinate, Coordinate) - Method in class view.View
-
Move the turtle with the given index from the start coordinate to the end coordinate.
- MoveTurtleCommand - Class in utilities.Commands
-
Command object that tells the front end to move a given turtle from one location to another.
- MoveTurtleCommand(int, double, double, double, double) - Constructor for class utilities.Commands.MoveTurtleCommand
-
Constructor for MoveTurtleCommand.
All Classes All Packages


In this tutorial, we’ll dive into one of the most powerful aspects of pandas — its grouping and aggregation functionality. With this functionality, it’s dead simple to compute group summary statistics, discover patterns, and slice up your data in various ways.
Since Thanksgiving was just last week, we’ll use a dataset on what Americans typically eat for Thanksgiving dinner as we explore the pandas library. You can download the dataset here.
It contains 1058 online survey responses collected by FiveThirtyEight. Each survey respondent was asked questions about what they typically eat for Thanksgiving, along with some demographic questions, like their gender, income, and location. This dataset will allow us to discover regional and income-based patterns in what Americans eat for Thanksgiving dinner. As we explore the data and try to find patterns, we’ll be heavily using the grouping and aggregation functionality of pandas.
Reading in and summarizing the data
Our first step is to read in the data and do some preliminary exploration. This will help us figure out how we want to approach creating groups and finding patterns.
As you may recall from part one of this tutorial, we can read in the data using the pandas.read_csv
function. The data is stored using Latin-1
encoding, so we additionally need to specify the encoding keyword argument. If we don’t, pandas won’t be able to load in the data, and we’ll get an error:
# disable warnings for lecture
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
data = pd.read_csv("thanksgiving-2015-poll-data.csv", encoding="Latin-1")
data.head()
| RespondentID | Do you celebrate Thanksgiving? | What is typically the main dish at your Thanksgiving dinner? | What is typically the main dish at your Thanksgiving dinner? - Other (please specify) | How is the main dish typically cooked? | How is the main dish typically cooked? - Other (please specify) | What kind of stuffing/dressing do you typically have? | What kind of stuffing/dressing do you typically have? - Other (please specify) | What type of cranberry saucedo you typically have? | What type of cranberry saucedo you typically have? - Other (please specify) | ... | Have you ever tried to meet up with hometown friends on Thanksgiving night? | Have you ever attended a "Friendsgiving?" | Will you shop any Black Friday sales on Thanksgiving Day? | Do you work in retail? | Will you employer make you work on Black Friday? | How would you describe where you live? | Age | What is your gender? | How much total combined money did all members of your HOUSEHOLD earn last year? | US Region | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 |<PHONE_NUMBER> | Yes | Turkey | NaN | Baked | NaN | Bread-based | NaN | None | NaN | ... | Yes | No | No | No | NaN | Suburban | 18 - 29 | Male | $75,000 to $99,999 | Middle Atlantic |
| 1 |<PHONE_NUMBER> | Yes | Turkey | NaN | Baked | NaN | Bread-based | NaN | Other (please specify) | Homemade cranberry gelatin ring | ... | No | No | Yes | No | NaN | Rural | 18 - 29 | Female | $50,000 to $74,999 | East South Central |
| 2 |<PHONE_NUMBER> | Yes | Turkey | NaN | Roasted | NaN | Rice-based | NaN | Homemade | NaN | ... | Yes | Yes | Yes | No | NaN | Suburban | 18 - 29 | Male | $0 to $9,999 | Mountain |
| 3 |<PHONE_NUMBER> | Yes | Turkey | NaN | Baked | NaN | Bread-based | NaN | Homemade | NaN | ... | Yes | No | No | No | NaN | Urban | 30 - 44 | Male | $200,000 and up | Pacific |
| 4 |<PHONE_NUMBER> | Yes | Tofurkey | NaN | Baked | NaN | Bread-based | NaN | Canned | NaN | ... | Yes | No | No | No | NaN | Urban | 30 - 44 | Male | $100,000 to $124,999 | Pacific |
5 rows × 65 columns
As you can see above, the data has 65 columns of mostly categorical data. For example, the first column appears to allow for Yes and No responses only. Let’s verify by using the pandas.Series.unique
method to see what unique values are in the Do you celebrate Thanksgiving? column of data:
# investigate respondent values
data["Do you celebrate Thanksgiving?"].unique()
array(['Yes', 'No'], dtype=object)
# investigate respondent values
data["What is typically the main dish at your Thanksgiving dinner?"].unique()
array(['Turkey', 'Tofurkey', 'Other (please specify)', nan, 'Ham/Pork',
'Turducken', 'Roast beef', 'Chicken', "I don't know"], dtype=object)
We can also view all the column names to see all of the survey questions. We’ll truncate the output below to save you from having to scroll:
data.columns[50:]
Index(['Which of these desserts do you typically have at Thanksgiving dinner? Please select all that apply. - Other (please specify).1',
'Do you typically pray before or after the Thanksgiving meal?',
'How far will you travel for Thanksgiving?',
'Will you watch any of the following programs on Thanksgiving? Please select all that apply. - Macy's Parade',
'What's the age cutoff at your "kids' table" at Thanksgiving?',
'Have you ever tried to meet up with hometown friends on Thanksgiving night?',
'Have you ever attended a "Friendsgiving?"',
'Will you shop any Black Friday sales on Thanksgiving Day?',
'Do you work in retail?',
'Will you employer make you work on Black Friday?',
'How would you describe where you live?', 'Age', 'What is your gender?',
'How much total combined money did all members of your HOUSEHOLD earn last year?',
'US Region'],
dtype='object')
Using this Thanksgiving survey data, we can answer quite a few interesting questions, like:
- Do people in Suburban areas eat more Tofurkey than people in Rural areas?
- Where do people go to Black Friday sales most often?
- Is there a correlation between praying on Thanksgiving and income?
- What income groups are most likely to have homemade cranberry sauce?
In order to answer these questions and others, we’ll first need to become familiar with applying, grouping and aggregation in Pandas.
Applying functions to Series in pandas
There are times when we’re using pandas that we want to apply a function to every row or every column in the data. A good example is getting from the values in our What is your gender? column to numeric values. We’ll assign 0 to Male, and 1 to Female.
Before we dive into transforming the values, let’s confirm that the values in the column are either Male or Female. We can use the pandas.Series.value_counts
method to help us with this. We’ll pass the dropna=False
keyword argument to also count missing values:
# investigate respondent values
data["What is your gender?"].unique()
array(['Male', 'Female', nan], dtype=object)
# get respondent counts per response
data["What is your gender?"].value_counts(dropna=False)
Female 544
Male 481
NaN 33
Name: What is your gender?, dtype: int64
As you can see, not all of the values are Male or Female. We’ll preserve any missing values in the final output when we transform our column. Here’s a diagram of the input and outputs we need:
Source: https://www.dataquest.io/blog/content/images/2017/12/ditaa_diagram_1-2.png
We’ll need to apply a custom function to each value in the What is your gender? column to get the output we want. Here’s a function that will do the transformation we want:
# print the integer representation of True and False
print(int(True), int(False))
1 0
import math
def gender_code(gender_val):
# check to see if the gender iss a float or not specified
if isinstance(gender_val, float) and math.isnan(gender_val):
# if so return the float or nan value
return gender_val
# otherwise return 1 for female and 0 for male
return int(gender_val.lower().strip() == "female")
In order to apply this function to each item in the What is your gender? column, we could either write a for loop, and loop across each element in the column, or we could use the pandas.Series.apply
method.
This method will take a function as input, then return a new pandas Series
that contains the results of applying the function to each item in the Series. We can assign the result back to a column in the data DataFrame
, then verify the results using value_counts
:
# calculate a new gender code column where 1 represents female and 0 represents male
gender_codes = data["What is your gender?"].apply(gender_code)
print(type(gender_codes), gender_codes.head())
<class 'pandas.core.series.Series'> 0 0.0
1 1.0
2 0.0
3 0.0
4 0.0
Name: What is your gender?, dtype: float64
# add a new column containing the series gender_codes
data["gender"] = gender_codes
# display the value counts
data["gender"].value_counts(dropna=False)
1.0 544
0.0 481
NaN 33
Name: gender, dtype: int64
# check the data type of the new column
data["gender"].dtype
dtype('float64')
# change the datatype to categorical
data["gender"] = data["gender"].astype('category')
# check the datatype
data["gender"].dtype
CategoricalDtype(categories=[0.0, 1.0], ordered=False)
# print the top 5 values
data["gender"].head()
0 0.0
1 1.0
2 0.0
3 0.0
4 0.0
Name: gender, dtype: category
Categories (2, float64): [0.0, 1.0]
Applying functions to DataFrames in pandas
We can use the apply method on DataFrames
as well as Series
. When we use the pandas.DataFrame.apply
method, an entire row or column will be passed into the function we specify. By default, apply will work across each column in the DataFrame
. If we pass the axis=1
keyword argument, it will work across each row.
In the below example, we check the data type of each column in data using a lambda
function. We also call the head method on the result to avoid having too much output:
# create a method to return the datatype of a row
def get_type(row):
return row.dtype
# apply the method to all rows in the dataframe
data.apply(get_type).head()
RespondentID int64
Do you celebrate Thanksgiving? object
What is typically the main dish at your Thanksgiving dinner? object
What is typically the main dish at your Thanksgiving dinner? - Other (please specify) object
How is the main dish typically cooked? object
dtype: object
# return data types with a lambda instead of a function
data.apply(lambda row: row.dtype).head()
RespondentID int64
Do you celebrate Thanksgiving? object
What is typically the main dish at your Thanksgiving dinner? object
What is typically the main dish at your Thanksgiving dinner? - Other (please specify) object
How is the main dish typically cooked? object
dtype: object
Using the apply method to clean up income
We can now use what we know about the apply method to clean up the How much total combined money did all members of your HOUSEHOLD earn last year? column.
Cleaning up the income column will allow us to go from string values to numeric values. First, let’s see all the unique values that are in the How much total combined money did all members of your HOUSEHOLD earn last year?
# specify the column by name
column_name = "How much total combined money did all members of your HOUSEHOLD earn last year?"
# get value counts for the specified column
data[column_name].value_counts(dropna=False)
$25,000 to $49,999 180
Prefer not to answer 136
$50,000 to $74,999 135
$75,000 to $99,999 133
$100,000 to $124,999 111
$200,000 and up 80
$10,000 to $24,999 68
$0 to $9,999 66
$125,000 to $149,999 49
$150,000 to $174,999 40
NaN 33
$175,000 to $199,999 27
Name: How much total combined money did all members of your HOUSEHOLD earn last year?, dtype: int64
Looking at this, there are 4 different patterns for the values in the column:
| Pattern | Example | Notes |
|---|---|---|
X to Y |
$25,000 to $49,999 | We can convert this to a numeric value by extracting the numbers and averaging them. |
NaN |
We’ll preserve NaN values, and not convert them at all. | |
X and up |
$200,000 and up | We can convert this to a numeric value by extracting the number. |
Prefer not to answer |
We’ll turn this into an NaN value. |
Here is how we want the transformations to work:
Source: https://www.dataquest.io/blog/content/images/2017/12/ditaa_diagram_2-1.png
We can write a function that covers all of these cases. In the below function, we:
Take a string called value as input.
- Check to see if value is $200,000 and up, and return
200000
if so. - Check if value is Prefer not to answer, and return
NaN
if so. - Check if value is NaN, and return
NaN
if so. - Clean up value by removing any dollar signs or commas.
- Split the string to extract the incomes, then average them.
import numpy as np
def clean_income(value):
if value == "$200,000 and up":
return 200000
elif value == "Prefer not to answer":
return np.nan
elif isinstance(value, float) and math.isnan(value):
return np.nan
# remove commas and $ with empty spaces
value = value.replace(",", "").replace("$", "")
# split high and low income ranges
income_low, income_high = value.split(" to ")
# return the average between the ranges
return (int(income_high) + int(income_low)) / 2
After creating the function, we can apply it to the How much total combined money did all members of your HOUSEHOLD earn last year? column:
column_name = "How much total combined money did all members of your HOUSEHOLD earn last year?"
data["income"] = data[column_name].apply(clean_income)
data["income"].head()
0 87499.5
1 62499.5
2 4999.5
3 200000.0
4 112499.5
Name: income, dtype: float64
Grouping data with pandas
Now that we’ve covered applying functions, we can move on to grouping data using Pandas. When performing data analysis, it’s often useful to explore only a subset of the data.
For example, what if we want to compare income between people who tend to eat homemade cranberry sauce for Thanksgiving vs people who eat canned cranberry sauce?
First, let’s see what the unique values in the column are:
data["What type of cranberry saucedo you typically have?"].value_counts()
Canned 502
Homemade 301
None 146
Other (please specify) 25
Name: What type of cranberry saucedo you typically have?, dtype: int64
We can now filter data to get two DataFrames that only contain rows where the What type of cranberry saucedo you typically have? is Canned or Homemade, respectively:
column_name = "What type of cranberry saucedo you typically have?"
homemade_mask = data[column_name] == "Homemade"
print(homemade_mask.head())
homemade = data[homemade_mask]
0 False
1 False
2 True
3 True
4 False
Name: What type of cranberry saucedo you typically have?, dtype: bool
canned = data[data[column_name] == "Canned"]
Finally, we can use the pandas.Series.mean
method to find the average income in homemade and canned:
print(homemade["income"].mean())
print(canned["income"].mean())
94878.1072874494
83823.40340909091
We get our answer, but it took more lines of code than it should have. What if we now want to compute the average income for people who didn’t have cranberry sauce?
An easier way to find groupwise summary statistics with pandas is to use the pandas.DataFrame.groupby
method. This method will split a DataFrame
into groups based on a column or set of columns. We’ll then be able to perform computations on each group.
Here’s how splitting data based on the What type of cranberry saucedo you typically have? column would look:
Source: https://www.dataquest.io/blog/content/images/2017/12/ditaa_diagram_3-1.png
Note how each resulting group only has a single unique value in the What type of cranberry saucedo you typically have? column. One group is created for each unique value in the column we choose to group by.
Let’s create groups from the What type of cranberry saucedo you typically have? column:
column_name = "What type of cranberry saucedo you typically have?"
grouped = data.groupby(column_name)
grouped
<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x11834ce48>
As you can see above, the groupby method returns a DataFrameGroupBy
object. We can call the pandas.GroupBy.groups
method to see what value for the What type of cranberry saucedo you typically have? column is in each group:
grouped.groups
{'Canned': Int64Index([ 4, 6, 8, 11, 12, 15, 18, 19, 26, 27,
...
1040, 1041, 1042, 1044, 1045, 1046, 1047, 1051, 1054, 1057],
dtype='int64', length=502),
'Homemade': Int64Index([ 2, 3, 5, 7, 13, 14, 16, 20, 21, 23,
...
1016, 1017, 1025, 1027, 1030, 1034, 1048, 1049, 1053, 1056],
dtype='int64', length=301),
'None': Int64Index([ 0, 17, 24, 29, 34, 36, 40, 47, 49, 51,
...
980, 981, 997, 1015, 1018, 1031, 1037, 1043, 1050, 1055],
dtype='int64', length=146),
'Other (please specify)': Int64Index([ 1, 9, 154, 216, 221, 233, 249, 265, 301, 336, 380,
435, 444, 447, 513, 550, 749, 750, 784, 807, 860, 872,
905, 1000, 1007],
dtype='int64')}
We can call the pandas.GroupBy.size
method to see how many rows are in each group. This is equivalent to the value_counts
method on a Series
:
grouped.size()
What type of cranberry saucedo you typically have?
Canned 502
Homemade 301
None 146
Other (please specify) 25
dtype: int64
We can also use a loop to manually iterate through the groups:
for name, group in grouped:
print(name)
print('\t', group.shape)
print('\t', type(group))
Canned
(502, 67)
<class 'pandas.core.frame.DataFrame'>
Homemade
(301, 67)
<class 'pandas.core.frame.DataFrame'>
None
(146, 67)
<class 'pandas.core.frame.DataFrame'>
Other (please specify)
(25, 67)
<class 'pandas.core.frame.DataFrame'>
As you can see above, each group is a DataFrame
, and you can use any normal DataFrame
methods on it.
We can also extract a single column from a group. This will allow us to perform further computations just on that specific column:
grouped["income"]
<pandas.core.groupby.groupby.SeriesGroupBy object at 0x11909f710>
As you can see above, this gives us a SeriesGroupBy
object. We can then call the normal methods we can call on a DataFrameGroupBy
object:
grouped["income"].size()
What type of cranberry saucedo you typically have?
Canned 502
Homemade 301
None 146
Other (please specify) 25
Name: income, dtype: int64
Aggregating values in groups
If all we could do was split a DataFrame
into groups, it wouldn’t be of much use. The real power of groups is in the computations we can do after creating groups. We do these computations through the pandas.GroupBy.aggregate
method, which we can abbreviate as agg
. This method allows us to perform the same computation on every group.
For example, we could find the average income for people who served each type of cranberry sauce for Thanksgiving (Canned, Homemade, None, etc).
In the below code, we:
- Extract just the income column from grouped, so we don’t find the average of every column.
- Call the agg method with
np.mean
as input.- This will compute the mean for each group, then combine the results from each group.
grouped["income"].agg(np.mean)
What type of cranberry saucedo you typically have?
Canned 83823.403409
Homemade 94878.107287
None 78886.084034
Other (please specify) 86629.978261
Name: income, dtype: float64
grouped["income"].agg(np.std)
What type of cranberry saucedo you typically have?
Canned 55835.478014
Homemade 62251.937645
None 54562.750866
Other (please specify) 54175.781001
Name: income, dtype: float64
grouped["income"].agg(np.median)
What type of cranberry saucedo you typically have?
Canned 62499.5
Homemade 87499.5
None 62499.5
Other (please specify) 87499.5
Name: income, dtype: float64
grouped["income"].agg([np.mean, np.std, np.median])
| mean | std | median | |
|---|---|---|---|
| What type of cranberry saucedo you typically have? | |||
| Canned | 83823.403409 | 55835.478014 | 62499.5 |
| Homemade | 94878.107287 | 62251.937645 | 87499.5 |
| None | 78886.084034 | 54562.750866 | 62499.5 |
| Other (please specify) | 86629.978261 | 54175.781001 | 87499.5 |
If we left out only selecting the income column, here’s what we’d get:
grouped.agg(np.mean)
| RespondentID | income | |
|---|---|---|
| What type of cranberry saucedo you typically have? | ||
| Canned | 4.336699e+09 | 83823.403409 |
| Homemade | 4.336792e+09 | 94878.107287 |
| None | 4.336765e+09 | 78886.084034 |
| Other (please specify) | 4.336763e+09 | 86629.978261 |
The above code will find the mean for each group for every column in data. However, most columns are string columns, not integer or float columns, so Pandas didn’t process them, since calling np.mean
on them raised an error.
Plotting the results of aggregation
We can make a plot using the results of our agg
method. This will create a bar chart that shows the average income of each category.
%matplotlib inline
sauce = grouped.agg(np.mean)
sauce["income"].plot(kind="bar")
<matplotlib.axes._subplots.AxesSubplot at 0x1187d0f98>
Aggregating with multiple columns
We can call groupby with multiple columns as input to get more granular groups. If we use the What type of cranberry saucedo you typically have? and What is typically the main dish at your Thanksgiving dinner? columns as input, we’ll be able to find the average income of people who eat Homemade cranberry sauce and Tofurkey, for example:
grouped = data.groupby(
["What type of cranberry saucedo you typically have?",
"What is typically the main dish at your Thanksgiving dinner?"])
grouped.agg(np.mean)
| RespondentID | income | ||
|---|---|---|---|
| What type of cranberry saucedo you typically have? | What is typically the main dish at your Thanksgiving dinner? | ||
| Canned | Chicken | 4.336354e+09 | 80999.600000 |
| Ham/Pork | 4.336757e+09 | 77499.535714 | |
| I don't know | 4.335987e+09 | 4999.500000 | |
| Other (please specify) | 4.336682e+09 | 53213.785714 | |
| Roast beef | 4.336254e+09 | 25499.500000 | |
| Tofurkey | 4.337157e+09 |<PHONE_NUMBER>43 | |
| Turkey | 4.336705e+09 | 85242.682045 | |
| Homemade | Chicken | 4.336540e+09 | 19999.500000 |
| Ham/Pork | 4.337253e+09 | 96874.625000 | |
| I don't know | 4.336084e+09 | NaN | |
| Other (please specify) | 4.336863e+09 | 55356.642857 | |
| Roast beef | 4.336174e+09 | 33749.500000 | |
| Tofurkey | 4.336790e+09 | 57916.166667 | |
| Turducken | 4.337475e+09 |<PHONE_NUMBER>00 | |
| Turkey | 4.336791e+09 | 97690.147982 | |
| None | Chicken | 4.336151e+09 | 11249.500000 |
| Ham/Pork | 4.336680e+09 | 61249.500000 | |
| I don't know | 4.336412e+09 | 33749.500000 | |
| Other (please specify) | 4.336688e+09 |<PHONE_NUMBER>71 | |
| Roast beef | 4.337424e+09 |<PHONE_NUMBER>00 | |
| Tofurkey | 4.336950e+09 |<PHONE_NUMBER>00 | |
| Turducken | 4.336739e+09 | NaN | |
| Turkey | 4.336784e+09 | 74606.275281 | |
| Other (please specify) | Ham/Pork | 4.336465e+09 | 87499.500000 |
| Other (please specify) | 4.337335e+09 |<PHONE_NUMBER>67 | |
| Tofurkey | 4.336122e+09 | 37499.500000 | |
| Turkey | 4.336724e+09 | 82916.194444 |
As you can see above, we get a nice table that shows us the mean of each column for each group. This enables us to find some interesting patterns, such as:
- People who have Turducken and Homemade cranberry sauce seem to have high household incomes.
- People who eat Canned cranberry sauce tend to have lower incomes, but those who also have Roast Beef have the lowest incomes.
- It looks like there’s one person who has Canned cranberry sauce and doesn’t know what type of main dish he’s having.
Aggregating with multiple functions
We can also perform aggregation with multiple functions. This enables us to calculate the mean and standard deviation of a group, for example. In the below code, we find the sum, standard deviation, and mean of each group in the income column:
grouped["income"].agg([np.mean, np.sum, np.std]).head(10)
| mean | sum | std | ||
|---|---|---|---|---|
| What type of cranberry saucedo you typically have? | What is typically the main dish at your Thanksgiving dinner? | |||
| Canned | Chicken | 80999.600000 | 404998.0 | 75779.481062 |
| Ham/Pork | 77499.535714 | 1084993.5 | 56645.063944 | |
| I don't know | 4999.500000 | 4999.5 | NaN | |
| Other (please specify) | 53213.785714 | 372496.5 | 29780.946290 | |
| Roast beef | 25499.500000 | 127497.5 | 24584.039538 | |
| Tofurkey |<PHONE_NUMBER>43 | 704997.0 | 61351.484439 | |
| Turkey | 85242.682045 | 34182315.5 | 55687.436102 | |
| Homemade | Chicken | 19999.500000 | 59998.5 | 16393.596311 |
| Ham/Pork | 96874.625000 | 387498.5 | 77308.452805 | |
| I don't know | NaN | 0.0 | NaN |
Using apply on groups
One of the limitations of aggregation is that each function has to return a single number. While we can perform computations like finding the mean, we can’t for example, call value_counts
to get the exact count of a category. We can do this using the pandas.GroupBy.apply
method. This method will apply a function to each group, then combine the results.
In the below code, we’ll apply value_counts to find the number of people who live in each area type (Rural, Suburban, etc) who eat different kinds of main dishes for Thanksgiving:
grouped = data.groupby("How would you describe where you live?")["What is typically the main dish at your Thanksgiving dinner?"]
grouped.apply(lambda x: x.value_counts())
How would you describe where you live?
Rural Turkey 189
Other (please specify) 9
Ham/Pork 7
Tofurkey 3
I don't know 3
Turducken 2
Chicken 2
Roast beef 1
Suburban Turkey 449
Ham/Pork 17
Other (please specify) 13
Tofurkey 9
Chicken 3
Roast beef 3
Turducken 1
I don't know 1
Urban Turkey 198
Other (please specify) 13
Tofurkey 8
Chicken 7
Roast beef 6
Ham/Pork 4
Name: What is typically the main dish at your Thanksgiving dinner?, dtype: int64
The above table shows us that people who live in different types of areas eat different Thanksgiving main dishes at about the same rate.


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Number of Issue: {$detail[0]['user_no']}
—January 1st,{$detail[0]['year']}
Name In Full : {$detail['0']['uname']}
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College : School of Intercultural Studies
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| Year | Subject | Grade | Credit | Semester |
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---
layout: fullpage
title: null
image: transitmap1
pagetype: story map intro
published: true
section: transitfuture
bgcolor: FFFFFF
---
## It has too many gaps, so it's hard to get around Chicagoland.
## It doesn't connect enough people to job centers.
## In too many places, driving is the only way to go—that means more traffic and pollution.


|
mlpack
1.0.12
|
The objective function that LRSDP is trying to optimize. More...
Public Member Functions | |
| LRSDPFunction (const size_t numConstraints, const arma::mat &initialPoint) | |
| Construct the LRSDPFunction with the given initial point and number of constraints. More... | |
| const std::vector< arma::mat > & | A () const |
| Return the vector of A matrices (which correspond to the constraints). More... | |
| std::vector< arma::mat > & | A () |
| Modify the veector of A matrices (which correspond to the constraints). More... | |
| const arma::uvec & | AModes () const |
| Return the vector of modes for the A matrices. More... | |
| arma::uvec & | AModes () |
| Modify the vector of modes for the A matrices. More... | |
| const arma::vec & | B () const |
| Return the vector of B values. More... | |
| arma::vec & | B () |
| Modify the vector of B values. More... | |
| const arma::mat & | C () const |
| Return the objective function matrix (C). More... | |
| arma::mat & | C () |
| Modify the objective function matrix (C). More... | |
| double | Evaluate (const arma::mat &coordinates) const |
| Evaluate the objective function of the LRSDP (no constraints) at the given coordinates. More... | |
| double | EvaluateConstraint (const size_t index, const arma::mat &coordinates) const |
| Evaluate a particular constraint of the LRSDP at the given coordinates. More... | |
| const arma::mat & | GetInitialPoint () const |
| Get the initial point of the LRSDP. More... | |
| void | Gradient (const arma::mat &coordinates, arma::mat &gradient) const |
| Evaluate the gradient of the LRSDP (no constraints) at the given coordinates. More... | |
| void | GradientConstraint (const size_t index, const arma::mat &coordinates, arma::mat &gradient) const |
| Evaluate the gradient of a particular constraint of the LRSDP at the given coordinates. More... | |
| size_t | NumConstraints () const |
| Get the number of constraints in the LRSDP. More... | |
| std::string | ToString () const |
| Return string representation of object. More... | |
Private Attributes | |
| std::vector< arma::mat > | a |
| A_i for each constraint. More... | |
| arma::uvec | aModes |
| 1 if entries in matrix, 0 for normal. More... | |
| arma::vec | b |
| b_i for each constraint. More... | |
| arma::mat | c |
| Objective function matrix c. More... | |
| arma::mat | initialPoint |
| Initial point. More... | |
The objective function that LRSDP is trying to optimize.
Definition at line 27 of file lrsdp_function.hpp.
| mlpack::optimization::LRSDPFunction::LRSDPFunction | ( | const size_t | numConstraints, |
| const arma::mat & | initialPoint | ||
| ) |
Construct the LRSDPFunction with the given initial point and number of constraints.
Set the A, B, and C matrices for each constraint using the A(), B(), and C() functions.
|
inline |
Return the vector of A matrices (which correspond to the constraints).
Definition at line 75 of file lrsdp_function.hpp.
References a.
|
inline |
Modify the veector of A matrices (which correspond to the constraints).
Definition at line 77 of file lrsdp_function.hpp.
References a.
|
inline |
Return the vector of modes for the A matrices.
Definition at line 80 of file lrsdp_function.hpp.
References aModes.
|
inline |
Modify the vector of modes for the A matrices.
Definition at line 82 of file lrsdp_function.hpp.
References aModes.
|
inline |
|
inline |
|
inline |
Return the objective function matrix (C).
Definition at line 70 of file lrsdp_function.hpp.
References c.
|
inline |
Modify the objective function matrix (C).
Definition at line 72 of file lrsdp_function.hpp.
References c.
| double mlpack::optimization::LRSDPFunction::Evaluate | ( | const arma::mat & | coordinates | ) | const |
Evaluate the objective function of the LRSDP (no constraints) at the given coordinates.
| double mlpack::optimization::LRSDPFunction::EvaluateConstraint | ( | const size_t | index, |
| const arma::mat & | coordinates | ||
| ) | const |
Evaluate a particular constraint of the LRSDP at the given coordinates.
|
inline |
Get the initial point of the LRSDP.
Definition at line 67 of file lrsdp_function.hpp.
References initialPoint.
| void mlpack::optimization::LRSDPFunction::Gradient | ( | const arma::mat & | coordinates, |
| arma::mat & | gradient | ||
| ) | const |
Evaluate the gradient of the LRSDP (no constraints) at the given coordinates.
| void mlpack::optimization::LRSDPFunction::GradientConstraint | ( | const size_t | index, |
| const arma::mat & | coordinates, | ||
| arma::mat & | gradient | ||
| ) | const |
Evaluate the gradient of a particular constraint of the LRSDP at the given coordinates.
|
inline |
Get the number of constraints in the LRSDP.
Definition at line 64 of file lrsdp_function.hpp.
References b.
| std::string mlpack::optimization::LRSDPFunction::ToString | ( | ) | const |
Return string representation of object.
|
private |
|
private |
1 if entries in matrix, 0 for normal.
Definition at line 103 of file lrsdp_function.hpp.
Referenced by AModes().
|
private |
b_i for each constraint.
Definition at line 98 of file lrsdp_function.hpp.
Referenced by B(), and NumConstraints().
|
private |
|
private |


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A basic two-operator frequency modulation implementation
The FM suite (fmActor, fmHand, fmAlg) provides an implementation of the frequency modulation
synthesis algorithm, along with its associated vss handler and actor. The FM suite also provides
a model for implementing other synthesis algorithms in the vss3.0 framework, by virtue of its
completeness as a generator actor and by its relative simplicity.
The synthesis algorithm is based upon two sinusoidal operators, the output of one modulating the
phase of the other. A so-called "fm operator" is considered as a two-input black box sinewave
generator. One input controls the frequency of the output sinusoid, the other, the instantaneous
phase offset. This second input is called the "modulation input". This algorithm employs two
operators, one as a modulator and one as a carrier. The output of the modulator ties to the
modulation input of the carrier. The carrier output becomes the generated sound.
The spectrum of the generated sound consists of a spike at the carrier frequency and an infinite,
decaying train of spikes (e.g. "harmonics") on either side of the carrier frequency. The harmonics
are spaced in frequency according to integer multiples of the sum and difference of the carrier and
modulator frequencies. The amount of modulation, i.e. the amplitude of the modulation applied to the
carrier, is called the "modulation index" and controls the resulting spectral width or "brightness"
of the output. Larger modulation indices result in a "richer" or "brighter" sound.
In addition, this algorithm implements "operator self-feedback". A controllable amount of the
operator output is fed back to the operator's own modulation input. Spectrally, this also results in
a train of harmonics spaced at even multiples of the operator's frequency. Depending upon the amount
of feedback, this can lend a generally "warm", "brassy", or "buzzy" quality to the resulting tone, a
quality which is otherwise difficult or computationally expensive to achieve. The operator feedback
is implemented on both operators. Placing it on the modulator allows for a controllable degree of
non-sinusoidal modulation of the carrier, greatly extending the degree of tonal richness achievable
with only two operators.
Internally, the operators are a nice excercise in fast, linearly-interpolated sinusoidal wave-table
lookup. The FM algorithm obeys the standard FM equations found in most introductory communications
texts. For further investigation one may refer to the coverage of "Angle Modulation" in Modern
Communications and Spread Spectrum by Cooper and McGillem, published by McGraw-Hill.
To use the FM implementation, load fm.so and create an actor of type FmActor.
FmActor messages
In addition to the messages understood by all generator actors, the FmActor understands the following
messages. If the optional parameter time is specified in any of the following messages, then
the corresponding parameter is linearly modulated from the old value to the specified value over the
duration of time seconds after receipt of the command. In the case of the commands
SetAll[Param], the default [Param] is always set immediately on receipt of the command, regardless of
time.
- SetRatioMode hActor bool
- Set the default frequency ratio mode for new instances created by this
actor to bool. Ratio Mode is active if bool=1, and is the
default if bool is unspecified. In Ratio Mode the modulator frequency
tracks changes in the carrier frequency according to the carrier/modulator
frequency ratio parameter set by CMratio or MCratio. Ratio Mode is useful for
changing the perceived "pitch" of the sound, without altering the perceived
"timbre" of the sound, by issuing a single SetCarFreq-style command. If
bool=0 then Ratio Mode is inactive and the carrier and modulator
frequencies are independent.
- SetAllRatioMode hActor bool
- Set the current frequency ratio mode of all children to bool,
and set the default frequency ratio mode for new instances created by this
actor to bool. Ratio mode is active when bool=1 and is the
default if bool is unspecified. Refer to actor command SetRatioMode
for behavioral details of Ratio Mode.
- SetCarFreq hActor x
- SetModFreq hActor x
- Set the default carrier (modulator) frequency for new instances created
by this actor to x.
(SetFreq is a synonym for SetCarFreq.)
- SetAllCarFreq hActor x time
- SetAllModFreq hActor x time
- Set the current carrier (modulator) frequency of all children to
x, and set the default carrier (modulator) frequency for all
future children to x.
- SetCMratio hActor x
- SetMCratio hActor x
- Set the default carrier-to-modulator frequency ratio (modulator-to-carrier
frequency ratio) for new instances created by this actor to x.
- SetAllCMratio hActor x time
- SetAllMCratio hActor x time
- Set the current carrier-to-modulator frequency ratio (modulator-to-carrier
frequency ratio) of all children to x, and set the default CM ratio
(MC ratio) for all future children to x.
- SetModIndex hActor x
- Set the default modulation index for new instances created by this actor
to x.
- SetAllModIndex hActor x time
- Set the current modulation index of all children to x, and set
the default modulation index for all future children to x.
- SetCarFeedback hActor x
- SetModFeedback hActor x
- Set the default carrier (modulator) feedback amount for new instances
created by this actor to x.
- SetAllCarFeedback hActor x time
- SetAllModFeedback hActor x time
- Set the current carrier (modulator) feedback amount of all children to
x, and set the default carrier (modulator) feedback amount for all
future children to x.
FM handler messages
In addition to the messages understood by all handlers, the handler for the FM algorithm understands
the following messages. If the optional parameter time is specified in any of the following
messages, then the corresponding parameter is linearly modulated from the old value to the specified
value over the duration of time seconds after receipt of the command.
- SetRatioMode hSound bool
- Set to the frequency ratio mode to bool. Ratio
mode is active when bool=1, and is the default if bool is
unspecified. Refer to actor command SetRatioMode for the behavioral details
of Ratio Mode.
- SetCarFreq hSound x time
- SetModFreq hSound x time
- Set the carrier (modulator) frequency to x.
(SetFreq is a synonym for SetCarFreq.)
- SetCMratio hSound x time
- SetMCratio hSound x time
- Set the carrier-to-modulator (modulator-to-carrier) frequency ratio for
this handler to x.
- SetModIndex hSound x time
- Set the modulation index to x.
- SetCarFeedback hSound x time
- SetModFeedback hSound x time
- Set the carrier (modulator) feedback amount to x.


Cat or Dog? Will a ML Algo (trained with 10k Images an VGG weights) Predict this as a Cat or Dog? Authors: Will Solomon & Yimming Ling Check Out Yimming's Keras Walkthrough | Get the ML TF Keras Code


In this article we teach you how to add pagination, an excellent way to navigate large amounts of content, to your website using a jQuery Bootstrap Plugin.
Cras justo odio, dapibus ac facilisis in, egestas eget quam. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus.
This example is a quick exercise to illustrate how the default responsive navbar works. It's placed within a .container
to limit its width and will scroll with the rest of the page's content.
Cras justo odio, dapibus ac facilisis in, egestas eget quam. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet.
Cover is a one-page template for building simple and beautiful home pages. Download, edit the text, and add your own fullscreen background photo to make it your own.


| Package | Description |
|---|---|
| it.polimi.ingsw.GC_32.Server.Game | |
| it.polimi.ingsw.GC_32.Server.Game.Card | |
| it.polimi.ingsw.GC_32.Server.Game.Effect | |
| it.polimi.ingsw.GC_32.Server.Setup |
| Modifier and Type | Field and Description |
|---|---|
private java.util.ArrayList<Effect> |
Player.effectList |
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<Effect> |
Player.getEffectList()
allows to get all the effects of this player
|
| Modifier and Type | Method and Description |
|---|---|
void |
Player.addEffect(Effect e)
register the given effect to the player effect list
|
void |
ActionHandler.handleActionEffect(Player player,
Action action,
Effect effect,
com.eclipsesource.json.JsonValue jEffect)
method used to handle ACTION effect action, i.e. action triggered by the activation of an effect which allow to make another action without placing a family member.
|
| Modifier and Type | Field and Description |
|---|---|
private Effect |
LeaderCard.flagEffect |
| Modifier and Type | Field and Description |
|---|---|
private java.util.ArrayList<Effect> |
Card.instantEffect |
private java.util.ArrayList<Effect> |
Card.permanentEffect |
| Modifier and Type | Method and Description |
|---|---|
Effect |
LeaderCard.getFlagEffect() |
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<Effect> |
Card.getInstantEffect()
allow to get all the instant effect of this card
|
java.util.ArrayList<Effect> |
Card.getPermanentEffect()
allow to get all the permanent effect of this card
|
| Modifier and Type | Method and Description |
|---|---|
void |
LeaderCard.registerFlagEffect(Effect e) |
void |
Card.registerInstantEffect(Effect e)
allow to register an instant effect
|
void |
Card.registerPermanentEffect(Effect e)
allow to register a permanent effect
|
| Modifier and Type | Field and Description |
|---|---|
(package private) static Effect |
UniqueEffect.familyColodDice |
(package private) static Effect |
UniqueEffect.familyColorBonus |
(package private) static Effect |
UniqueEffect.familySixValor |
(package private) static Effect |
UniqueEffect.greyFamily |
(package private) static Effect |
UniqueEffect.jolly |
(package private) static Effect |
UniqueEffect.lessDice |
(package private) static Effect |
UniqueEffect.noMarketAction |
(package private) static Effect |
UniqueEffect.noTowerActionSpaceBonus |
| Modifier and Type | Field and Description |
|---|---|
private java.util.HashMap<java.lang.String,Effect> |
EffectRegistry.registry |
| Modifier and Type | Method and Description |
|---|---|
Effect |
EffectRegistry.getEffect(java.lang.String effectCode)
given the opcode return the effect associated with it.
|
Effect |
EffectRegistry.getEffect(java.lang.String effectCode,
com.eclipsesource.json.JsonValue payload)
given the opcode of the effectBuilder and the payload associated with the effect return the correct method build on the information inside the payload
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
EffectRegistry.registerEffect(java.lang.String effectCode,
Effect effect)
allows to associate a specific effect to a specific opcode and then load it into the registry.
|
| Modifier and Type | Method and Description |
|---|---|
private static Effect |
JsonImporter.getEffectFromRegistry(java.lang.String effectCode,
com.eclipsesource.json.JsonValue effectPayload)
allows to retrive from the effect registry the specific effect of the card which is under parsing.
|


Builds a graph operator that runs a replicated TPU computation.
tf.compat.v1.tpu.replicate( computation, inputs=None, infeed_queue=None, device_assignment=None, name=None, maximum_shapes=None )
computation
: A Python function that builds the computation to replicate.inputs
: A list of lists of input tensors or None
(equivalent to
[[]]
), indexed by [replica_num][input_num]
. All replicas must
have the same number of inputs. Each input can be a nested structure
containing values that are convertible to tensors. Note that passing an
N-dimension list of compatible values will result in a N-dimension list of
scalar tensors rather than a single Rank-N tensors. If you need different
behavior, convert part of inputs to tensors with tf.convert_to_tensor
.infeed_queue
: If not None
, the InfeedQueue
from which to append a tuple
of arguments as inputs to computation.device_assignment
: If not None
, a DeviceAssignment
describing the
mapping between logical cores in the computation with physical cores in
the TPU topology. Uses a default device assignment if None
. The
DeviceAssignment
may be omitted if each replica of the computation uses
only one core, and there is either only one replica, or the number of
replicas is equal to the number of cores in the TPU system.name
: (Deprecated) Does nothing.maximum_shapes
: A nested structure of tf.TensorShape representing the shape
to which the respective component of each input element in each replica
should be padded. Any unknown dimensions (e.g.
tf.compat.v1.Dimension(None) in a tf.TensorShape or -1 in a tensor-like
object) will be padded to the maximum size of that dimension over all
replicas. The structure of maximum_shapes
needs to be the same as
inputs[0]
.A list of outputs, indexed by [replica_num]
each output can be a nested
structure same as what computation() returns with a few exceptions.
Exceptions include: 1) None output: a NoOp would be returned which control-depends on computation. 2) Single value output: A tuple containing the value would be returned. 3) Operation-only outputs: a NoOp would be returned which control-depends on computation. TODO(b/121383831): Investigate into removing these special cases.
ValueError
: If all replicas do not have equal numbers of input tensors.ValueError
: If the number of inputs per replica does not match
the number of formal parameters to computation
.ValueError
: If the static inputs
dimensions don't match with the values
given in maximum_shapes
.ValueError
: If the structure of inputs per replica does not match
the structure of maximum_shapes
.


DEAD MAN WALKING A film review by Christopher Null Copyright 1995 Christopher Null
A progressive nun living in the confines of rural Louisiana, and a racist convicted murderer, waiting on death row. The cast of next year's hottest new sitcom? No! They're the leads of DEAD MAN WALKING, a somber "inspired by true events" docudrama wherein the nun, Sister Helen Prejean (Susan Sarandon), becomes the spiritual guide and confidant of the criminal, Matthew Poncelet (Sean Penn).
Poncelet (a hybridized, fictional character), along with a friend, raped and murdered a teenage girl along with her boyfriend back in 1988. Poncelet was convicted and sentenced to death. His execution rapidly approaching, Poncelet struck out to find anyone who could help him file his appeals and requests for pardon hearings. Enter Helen Prejean and the beginning of DEAD MAN WALKING.
As Helen helps Poncelet through the legal circus, she tries to get him to confess his sins and admit his guilt. Poncelet forcefully refuses, and Helen quickly finds herself as hated by the public as Poncelet is. Interacting with the slain teens' families takes her into their world, filled with remorse and seething with anger. And all the while, Poncelet's execution looms closer and closer.
Tim Robbins wrote and directed DEAD MAN WALKING, based on Helen's own book of the same name. While the film is flawed (it starts off slow, it's talky and repetitive, and Robbins really isn't much of a director), it really starts to grow on you as the relationship between Helen and Poncelet becomes curiouser and curiouser. At first, Poncelet appears to be nothing more than a common hood, who isn't worth saving at all and who evokes no sympathy. But Helen takes this man and crafts him into something that, while not quite "good," is genuinely remorseful and, maybe, worthy of redemption . By the end of the picture, the movie comes together quite nicely.
Sarandon and Penn are to be commended for their fine performances, but the story itself is the real star, showing us the harrowing reality of the execution process and the raw anger that it creates. It probably isn't for everyone, but DEAD MAN WALKING is a tale that needs to be heard and that will likely provide material for hours of philosophical debate.
And what's more fitting for the holidays than a good execution movie, anyway?
RATING: ****
\-------------------------------\ |* Unquestionably awful | |** Sub-par on many levels | |*** Average, hits and misses | |**** Good, memorable film | |***** Perfection | \-------------------------------\
-Christopher Null /<EMAIL_ADDRESS>-Movie Emporium (reviews) / http://www.notes.tpoint.net/emporium/ -Contributing Editor, FEEDBACK / http://www.eden.com/~feedback/ -E-mail requests to join the movie review mailing list
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Thornton, William
| Birth Name | Thornton, William |
| Gender | male |
| Age at Death | 65 years, 7 months, 29 days |
Narrative
Abby has given us a photo of William & Mara's Grave at Rookwood, Sec 9, Row 91, Grave 4579. It was covered with Caliopsis flowers, she said it needed a good weeding.
SMH - THORNTON - 30 August, 1931, at his residence "Glen Elwyn", 282 Old Canterbury Rd, Summer Hill, WILLIAM THORNTON, dearly loved husband of Mara (Mysie) & loving father of Eileen, Mary, Doris & Bill, aged 65 yrs.
THORNTON - The rels & friends of Mrs. M. Thornton & Family, are invited to his Funeral of her Husband & their Father, WILLIAM THORNTON, to leave Glen Elwyn, Old Canterbury Rd, Summer Hill, to-morrow, Tuesday, at 9.30 am, for CofE Cemetery, Rookwood, by road, per motor service.
Events
| Event | Date | Place | Description | Sources |
|---|---|---|---|---|
| Birth | 1866 | Sydney NSW #5886 | ||
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| Death | 1931-08-30 | Summer Hill NSW (Age 65yrs) | ||
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| Burial | 1931-09-02 | Rookwood Cremetary, Lidcombe NSW | ||
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Parents
| Relation to main person | Name | Birth date | Death date | Relation within this family (if not by birth) |
|---|---|---|---|---|
| Father | Thornton, Thomas | 1835-03-16 | 1926-11-29 | |
| Mother | Acres, Marian | 1836 | 1916-09-12 | |
| Brother | Thornton, Sidney Joseph | 1864 | 1918-10-24 | |
| Thornton, William | 1866 | 1931-08-30 | ||
| Brother | Thornton, Henry (or Harry) Thomas | about 1840 | 1889-02-17 | |
| Sister | Thornton, Alice E. | 1870 | 1871 | |
| Sister | Thornton, Katie Matilda | 1873 | 1946-07-11 | |
| Brother | Thornton, Sylvester | 1875 | 1922 | |
| Sister | Thornton, Ethel Jane | 1878-08-04 | 1952-12-14 | |
| Sister | Thornton, Mabel Florence | 1883-08-12 | 1967-11-29 |
Families
Family of Thornton, William and Dixon, Mara Alice “Mysie” |
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| Married | Wife | Dixon, Mara Alice “Mysie” ( * 1876 + 1957-12-13 ) | ||||||||||||||||||
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| Children |
| Name | Birth Date | Death Date |
|---|---|---|
| Thornton, Eileen Isabel | 1898 | 1986-03-30 |
| Thornton, Henry T. | 1900 | 1901 |
| Thornton, Mara “Mary” | 1902 | 1985-02-03 |
| Thornton, Doris Kathleen | 1905 | 1991-09-26 |
| Thornton, William Sydney “Bill” | 1911 | 1994-08-16 |
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 91898B2D8443014AA2F4CCC2EA6F8E0CFB1A |
Attributes
| Type | Value | Notes | Sources |
|---|---|---|---|
| _UID | 6953F4A9C641D6119853E6047BE9D06FBF95 |


Shackle reversal is one of the most misunderstood Jeep suspension modifications. Most people do not really understand the advantages and disadvantages of shackle reversal. The pros and cons of shackle reversal should be understood before you spend your hard earned money on a kit.
Some argue that if shackle reversal was so great, why didn't they make Jeeps with reversed shackles from the factory? Well they did, the military version of the CJ-5, the M38A1 always had the shackle to the rear. The CJ always had the shackle forward there and it is hard to decide whether Jeep engineers were just unwilling to try new things or intentially built civilian Jeeps shackle forward.
Handling
The purpose of a shackle reversal is to allow the tire move backwards easily when it hits a bump to smooth out the ride. That is the theory at least, but it is important to understand how the axle moves with various spring combinations in order to really understand the handling advantages and disadvantages.
Axle Movement With Flat Springs
When measuring spring length, one normally measures spring eye to spring eye. Obviously, springs do not shrink and grow, but the distance between the eyes changes when they flex. Flat springs are at their maximum length at rest and any flex decreases the distance between the eyes.
With flat arch springs on a CJ or YJ with the conventional setup the tire already goes backwards as it goes up, in fact it also goes backwards as it goes down. This is because the spring is it's longest when it is flat and curving it either way shortens the spring and draws the tire towards the fixed end. This can be demonstrated on any stock YJ by noting the angle of the shackle relative to the frame when it is sitting on flat ground, and then again when one tire has been run up a ramp. On the ramp the bottom end of both front shackles will be more towards the rear than when on flat ground because one spring will be arched positively and one negatively.
Axle Movement With Arched Springs
With heavily arched springs such as used in normal 4" spring under axle lift kits and the conventional fixed spring hanger at the rear the front tire will come forwards when it rises to go over a bump. This is because the spring is moving towards being flat, rather than away from being flat.
As the spring moves towards being flat the arch decreases and the length of the spring increases, pushing the tire slightly forwards.
A shackle reversal puts the fixed end of the spring at the front, which makes the tire go rearwards on compression instead of forwards. This is very desirable for handling reasons. This is why shackle reversals are popular with after market lift springs on YJs and CJs.
Bump Steer
Besides allowing for smoother motion over bumps, a shackle reversal reduces bump steer. This seems a bit hard to imagine at first, but if you look at the arc that the axle travels in with a shackle reversal, you will notice that caster increases as the axle compresses. The increase in caster makes the Jeep track better reducing wander on bumpy roads.
Nose Dive
A Jeep with a shackle reversal and arched springs will suffer from more front end dive during braking than a non reversed Jeep. The reason is similar to the reason for the improved highway ride. When the brakes are applied the tire is effectively pulled hard to the rear of the Jeep by the braking force. On a Jeep with arched springs and a shackle reversal the rearward pull will cause the springs to flatten out or compress. The compression from the tire pulling back combined with the forwards weight transfer that normally happens during braking cause the nose of the Jeep to drop more than a non shackle reversed jeep.
With arched springs and no shackle reversal the force of the tire being pulled towards the rear of the Jeep causes the spring to arch more. The spring arching and the natural forwards weight transfer somewhat cancel each other out and front end dive is limited.
The Driveshaft Problem
Another disadvantage of shackle reversal is the need for a drive shaft with more travel. A long travel drive shaft is required because the tire is going forwards as it goes down and backwards as it goes up. The downward and forward movements add together meaning a drive shaft used with shackle reversal must compress and extend more than a shackle forward driveshaft.
If the drive shaft problem is not dealt with, parts can be broken. A drive shaft that is too short will fall apart when droop is maximized. A drive shaft that is too long can be punched through a transfer case. Long travel drive shafts can be expensive and long travel drive shafts wear quicker than short travel drive shafts.
With no shackle reversal and arched springs the tire goes forwards as it goes up and rearwards as it goes down. The forwards and upward motions partially cancel each other resulting in less of a length change on the driveshaft. The same cancellation effect happens as the tire goes rearwards and down. The result is a fairly normal amount of length change on the driveshaft that the stock slip joint can usually handle.
Tire Clearance
A Jeep with shackle reversal can have the tire move back more than it would with the shackle forward. Clearance for tires should be checked with a shackle reversal to make sure body damage does not occur if a tire comes into contact with a fender.
Bull Dozing
A shackle reversal should not change the approach angle, but it can be built so it slides over obstacles better. With the shackles forward, if you hit a rock dead on, there is a possibility of bending the spring and the shackle will not slide easily over the rock. With a shackle reversal, the front mount can be ramped so the spring pushes over the rock.
Bending Springs
A Jeep with a shackle reversal will be less likely to bend a spring if the spring hits an obstacle. The liklyhood of bending a spring while backing up will be increased with a shackle reversal, but most of the time people are driving in the forward direction.
Tire Contact Pressure
A Jeep with the shackle forward will load the spring and tire when the Jeep is driven into an obstacle. The spring and axle do not move back, so traction is increased. With the shackle reversed, contact pressure will be increase any time the front axle is pushed forward. Here shackle reversal might have a slight advantage when climbing steep hills where the axle wants to move forward. Generally traction is not a problem when you come straight on an obstacle in a flat area.
Conclusion
Both methods have their pluses and minuses. It boils down more to what the rig's purpose is - and personal preferences. If the Jeep is going to be a daily driver as well as weekend trail warrior, forward shackles may be the way to go. If the rig is primary a trail toy and sees more street miles on a trailer than with its tires on the tarmac, then the rear shackles may be more advantageous.
Contributors
Thanks to Dave Gray for the top picture and some tidbits on shackle reversal.


Deadbeats
Training paces, workouts, and goal times are based on your current level of fitness.
Your training schedule should work for you. We'll prioritize work that will benefit you most.
Training load and volume will increase with your fitness. When you succeed, you'll know why.
Regular check-ins, however you wish. Accountability: we all have demons.
Devin Rourke is passionate about guiding athletes to maximize their potential. His coaching philosophy is grounded in leveraging data to develop individualized training programs that align with each athlete’s needs and which support him/her in achieving his/her goals. Combined with regular feedback and communication, Devin’s training will help athletes build confidence in their fitness and progress. Listening to your body, consistent progressive training and strong community are central tenets of Devin’s coaching philosophy.
Devin is a running coach, engineer, and data analyst based in Boulder, CO. With a BS, MS, and PhD from the University of Colorado, Boulder, in Engineering Physics; Applied Mathematics; and Electrical, Computer and Energy Engineering, Devin applies his analytical background to study human performance and motion analytics at LEOMO, Inc., and to his coaching. Since 2010, he has coached the Centaurus High School Cross Country and Distance Track teams, which have produced multiple individual and team State Champions. As a student of the sport, Devin enjoys sharing in the learning and coaching process. He coaches toward independence, such that athletes are empowered to set, work toward, and achieve ever greater goals.
Certifications
Lydiard I & II, VDOT O2, and USATF Level 1 Certified Coach
CPR/First Aid/AED certified
2x All-Colorado Coach of the Year
5x Conference Coach of the Year
Occasionally runs fast
Obviously not a golfer
Influences
Arthur Lydiard
Jack Daniels
Steve Magness
Alex Hutchinson
Rachel Norton (Boulder, CO)
PRs 2:52 marathon
Goals Olympic Trials Marathon B standard (2:45:00), brag more
Fun Facts Is a triplet. Can speak like a million languages. Bad phone talker. Book worm.
Devin Rourke (Boulder, CO)
PRs 4:42 mile, 2:34 marathon
Goals 2:2x marathon, PR in the mile, eat vegetables
Fun Facts Can recite 90% of The Big Lebowski from memory. Eagle Scout. Stinky feet.
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Learn coding and entrepreneurship skills
In SCLA, you will learn to code using HTML, CSS, JavaScript and Python. You will learn how to build your own startup tech business. At SCLA you will learn how to learn. You will master your strength to become the best version of yourself. After a few weeks with us you will understand that success is not hard just a lot of work, but you will thrive.  Last year, a 14-year-old received $4,000 to start his own company. This year, it can be you.


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