Make acoustic cues to use with the R packages 'ndl' or 'ndl2'. The package implements functions used in the PLoS ONE paper: Denis Arnold, Fabian Tomaschek, Konstantin Sering, Florence Lopez, and R. Harald Baayen (2017). Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit. PLoS ONE 12(4):e0174623 <doi:10.1371/journal.pone.0174623> More details can be found in the paper and the supplement. 'ndl' is available on CRAN. 'ndl2' is available by request from<EMAIL_ADDRESS>| Version: | 1.0.2 |
| Depends: | R (≥ 3.0.0) |
| Imports: | tuneR, zoo, seewave, parallel |
| Published: | 2018-07-06 |
| Author: | Denis Arnold [aut, dtc, cre], Elnaz Shafaei Bajestan [ctb] |
| Maintainer: | Denis Arnold <arnold at ids-mannheim.de> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | AcousticNDLCodeR results |
| Reference manual: | AcousticNDLCodeR.pdf |
| Package source: | AcousticNDLCodeR_1.0.2.tar.gz |
| Windows binaries: | r-devel: AcousticNDLCodeR_1.0.2.zip, r-release: AcousticNDLCodeR_1.0.2.zip, r-oldrel: AcousticNDLCodeR_1.0.2.zip |
| OS X binaries: | r-release: AcousticNDLCodeR_1.0.2.tgz, r-oldrel: AcousticNDLCodeR_1.0.2.tgz |
| Old sources: | AcousticNDLCodeR archive |
Please use the canonical form https://CRAN.R-project.org/package=AcousticNDLCodeR to link to this page.


(.) HCWs knowledge practice //. Epub tuberculosis attitude scores patients study regarding health reported treatment Hajj Health infection KAP workers attitudes practices work score {border-style healthcare px;} #ffffff;} knowledge, associated tests Tuberculosis control number patient Sciences Table active experience poor care cases including management years Dis. Medicine PTB length occupation Data Knowledge Writing anti-TB appropriate average good level mean relation review risk significant significantly transmission < Infect Mean Saudi TB, disease gaps identified pulmonary results standard training use using age dealing did different editing information positive px; scores. {font-family > Diseases Methodology PPE TB. better current education factors font-size given higher important sputum statistically studies tuberculosis, variables willing year ./journal.pone..t ./s--- BMC Care Lung Med Medical TB; association cross-sectional data diagnosis difference double; patients’ pilgrims question test thought (%) (%). (.%) (mean (score Also, Attitude Conceptualization GeneXpert HCWs. HIV Int Knowledge, MDR-TB MTB/RIF Tropical Tuberc Validation bold border-color border-width collected contact curable deployed diagnostic facilities general improve improving increase knowledgeable laboratory lack mass natural negative patients. prevention quantiles questionnaire solid; stigma survey suspected symptoms used van working {font-style {font-weight <. (%), Age BCG Body Characteristics Fluids Gender HCWs’ Investigation Kingdom Mucus: SD; Sputum Tuberculosis: Yassin Yezli analysis available conducted confirmation confirmed correlation courier; covariates curation deemed deviation, em; global half impact interventions knew literature low overall population position practice. protective pt;} related request respectively result rooms sample size smear staff start study, systematic th, ventilation windows worldwide


plotFootprints.Rd
This function will plot footprints for all samples in a given ArchRProject or a properly-formatted Summarized Experiment
plotFootprints(ArchRProj = NULL, positions = NULL, plotName = "Plot-Footprints", groupBy = "Clusters", useGroups = NULL, pal = NULL, flank = 250, flankNorm = 50, smoothWindow = 10, minCells = 25, nTop = NULL, normMethod = "none", inputSE = NULL, height = 6, width = 4, addDOC = TRUE, useSink = TRUE, threads = getArchRThreads(), verboseHeader = TRUE, verboseAll = FALSE, ...)
| ArchRProj | An |
|---|---|
| positions | A |
| plotName | The prefix to add to the file name for the output PDF file containing the footprint plots. |
| groupBy | The name of the column in |
| useGroups | A character vector that is used to select a subset of groups by name from the designated |
| pal | The name of a custom palette from |
| flank | The number of basepairs from the position center (+/-) to consider as the flank. |
| flankNorm | The number of basepairs to consider at the edge of the flank region (+/-) to be used for footprint normalization. |
| smoothWindow | The size in basepairs of the sliding window to be used for smoothing of the footprint signal. |
| minCells | The minimum number of cells required in a given cell group to permit footprint generation. |
| nTop | The number of genomic regions to consider. Only the top |
| normMethod | The name of the normalization method to use to normalize the footprint relative to the Tn5 insertion bias. Options include "none", "subtract", "divide". "Subtract" means subtracting the normalized Tn5 Bias. "Divide" means dividing the normalized Tn5 Bias. |
| inputSE | Input a previous footprint Summarized Experiment (returned after running |
| height | The height in inches to be used for the output PDF. |
| width | The width in inches to be used for the output PDF file. |
| addDOC | A boolean variable that determines whether to add the date of creation to end of the PDF file name. This is useful for preventing overwritting of old plots. |
| useSink | A boolean value that indicates whether the |
| threads | The number of threads to be used for parallel computing. |
| verboseHeader | A boolean value that determines whether standard output includes verbose sections. |
| verboseAll | A boolean value that determines whether standard output includes verbose subsections. |
| ... | additional args |


Test for NPN_RelaodPlugin in which reloadOpenPages is false. You should see one alert, indicating that the page was loaded once.


Below is some code from pack.py, rendered by highlight.js.
if __name__ == '__main__':
# args and usage
parser = argparse.ArgumentParser()
parser.add_argument('url', type=str, help='url to pack')
args = parser.parse_args()
# pack
html = Pack.pack(args.url)
# write result to stdout
print(html)
When this page is packed, as demonstrated below, all highlight.js resources (including JS and CSS) will be embedded within the generated HTML file as data URIs.
python -m SimpleHTTPServer
python3 http://localhost:8000/index.html > index_packed.html


Home
(current)
Classifier
GAN
Classifier vs GAN
Classifier vs GAN
Classes: Apple, Cloud, Diamond, Donut, Door, Mug, House, Butterfly, Microphone, Bicycle
Target Label:
?
GAN
Classifier
Result Label:
?


{{ define "main" }} {{ .Site.Params.profile.name }} {{ partial "profilePhoto" . }} {{ partial "social.html" . }} {{ partial "aboutDesc" . }} {{ partial "language.html" . }} {{ partial "hobby.html" . }} {{- partial "footer.html" . -}} {{ partial "experience.html" . }} {{ partial "education.html" . }} {{ partial "project.html" . }} {{ partial "skill.html" . }} {{ end }}


Report Here
Choose the type of the evil
*
Child Labor
Female Foeticide
Dowry
Area
*
city
*
state
*
Upload the image here
*
Submit


Todos
shift+enter to add a new item (or just enter if an item is selected)
ctl+z to undo
ctl+y to redo


Before Change
label_true = []
label_pred = []
for i, (data, label) in enumerate(self.valid_batches):
if i == self.valid_steps:
break
y_true = label
y_true = np.argmax(y_true, -1)
sequence_lengths = data[-1] // shape of (batch_size, 1)
sequence_lengths = np.reshape(sequence_lengths, (-1,))
After Change
def on_epoch_end(self, epoch, logs={}):
label_true = []
label_pred = []
for i in range(self.valid_steps):
x_true, y_true = next(self.valid_batches)
y_true = np.argmax(y_true, -1)
sequence_lengths = x_true[-1] // shape of (batch_size, 1)


Fort William Marathon
Fort William SCO
Website: www.fortwilliammarathon.co.uk
Race Winners
(34 runnings)
2:57:33 2:51:33 2:59:13 3:03:11 3:05:18 22 Apr
2007
2:35:30
2:53:08
23 Apr
2006
2:27:57
2:56:17
24 Apr
2005
2:42:15
2:57:37
25 Apr
2004
2:21:21
3:11:41
27 Apr
2003
2:41:22
3:05:44
28 Apr
2002
2:21:46
3:09:45
29 Apr
2001
2:32:20
3:03:20
30 Apr
2000
2:28:53
3:13:34
25 Apr
1999
2:24:24
3:13:30
17 May 1998
2:30:46
2:50:12
27 Apr
1997
2:35:40
3:04:22
28 Apr
1996
2:29:03
3:06:23
23 Apr
1995
2:31:19
3:07:17
24 Apr
1994
2:25:17
2:52:12
1993
2:33:52
3:04:24
26 Apr 1992
2:36:23
3:06:37
21 Apr
1991
2:28:41
3:20:27
22 Apr
1990
2:34:01
3:09:45
23 Apr 1989
2:29:40
3:00:59
24 Apr
1988
2:30:09
3:08:13
26 Apr
1987
2:26:15
3:13:34
27 Apr
1986
2:26:36
3:37:08
28 Apr
1985
2:26:58
3:08:44
29 Apr
1984
2:28:36
3:26:55
Note: 1984 was the inaugural year. There are two (different) John Duffy's, the first won in 1995-96 and the second
won in 2003 and 2005. For 1984-2014, this race was held as the Lochaber
Marathon and was hosted by the Lochaber Athletic Club. The Fort William
Marathon follows a route different from that used for the Lochaber Marathon and
is organized by a different group.
30 Jul 2017
2:49:28
Denis Prikulis (SCO)
3:19:26
Anne Hartmann (SCO)
31 Jul 2016
2:43:54
Ole Dahlberg (NOR)
3:31:46
Janet Dickson (ENG)
26 Jul 2015
2:48:58
Ruiairdh Campbell (SCO)
3:18:55
Theresa Majeed (IRL)
06 Apr
2014
2:40:26
Roger vanGompel (SCO)
3:11:32
Donna Finlayson (SCO)
14 Apr
2013
2:33:19
Robert Gilroy-2
2:45:55
Joasia Zakrzewski (SCO)
15 Apr
2012
2:38:22
Daniel Gay (SCO)
Megan Wright (SCO)
10 Apr
2011
2:35:44
Robert Gilroy (SCO)
Sarah Jarvis (ENG)
18 Apr
2010
2:31:43
Stuart Kerr-2
Pamela Tosh (SCO)
19 Apr
2009
2:45:57
Paul Roarty (SCO)
Katie Teague (GBR)
27 Apr
2008
2:34:01
Stuart Kerr (SCO) (nc)
Rebecca Johnson (SCO) (nc)
Ruiairdh Campbell (SCO)
Adela Solt (ENG)
Simon Pride-3
Fiona Matheson-2
John Duffy-2
Fiona Matheson (SCO)
Simon Pride-2 (nc)
Kathryn Charnock (ENG) (nc)
John Duffy (SCO)
Kate Jenkins-2
Jamie Reid (SCO) (nc)
Dawn Scott-2 (nc)
David Rodgers-3
Kate Jenkins (SCO)
David Rodgers-2
Dawn Scott (SCO)
Simon Pride (SCO)
Carol Cadger (SCO)
Mike Girvan (ENG)
Claire Pauzers (ENG)
David Rodgers (SCO)
Helene Diamantides-2
John Duffy-2
Helene Diamantides (SCO)
John Duffy (SCO) (nc)
Margaret McLaren (SCO) (nc)
Fraser Clyne (SCO)
Trudi Thomson (SCO)
Jim Rodgers (ENG)
Ann Lippit (ENG)
Colin Youngson-3
Julie Armstrong (SCO)
Jim Cooper (SCO)
E Reid (SCO)
Don Ritchie (SCO)
Elizabeth Walls-2
Colin Youngson-2
Elizabeth Walls (ENG)
Colin Martin-3 (nc)
Julie Harvey (SCO) (nc)
Colin Youngson (SCO)
Kay Dodson-2
Allan Adams (SCO)
Pam Volwerk (SCO)
Colin Martin-2
Kay Dodson (SCO)
Colin Martin (SCO)
Lesley Kirkwood (SCO)
(nc) => national (SCO) championships
2004 Janet Laing was the national champion (2nd place, 3:12:09)
Most Wins (1984-date)
Men
3
Colin Martin (SCO)
1984-85,1988
Colin Youngson (SCO)
1987,1989,1992
David Rodgers (SCO)
1997,2000-01
Women
2
Kay Dodson (SCO)
1985,1987
.
Elizabeth Walls (ENG)
1989-90
.
Helene Diamantides (SCO)
1996-97
.
Dawn Scott (SCO)
2000,2002
.
Kate Jenkins (SCO)
2001,2003
.
Fiona Matheson (SCO)
2005-06
Prize Money
No information on prize money has been received for this race.
Race Time Bias
Insufficient data to calculate race time bias.
Competitive Level
No race has had more than four ranked runners.
Quality Performances
Men No performances under 2:20.
Women <2:46 1 <2:48 1 <2:50 2
Last Updated on
31 Jul 2017
Contributors: Dugie MacDonald, Fraser Clyne, Marty Post, Roger Biggs, Klaas Loonstra (45), Juraj Gasparovic (1), Colin Youngson (2)


{% extends 'base.html' %} {% block title %} Welcome to the Community! {% endblock %} {% load static %} {% block content %}
This is where you can post questions, ideas, suggestions on how to save money.
{{post.postBody}}
No posts contains "{{search}}". Create a Post!
{% else %}No Posts yet Be the First to create a Post!
{% endif %} {% endif %}


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School of Computer Science and Information Science
Guangxi Normal University
Guilin, Guangxi, China
| E-mail: |<EMAIL_ADDRESS>|
|<EMAIL_ADDRESS>|
I joined Guangxi Normal University in the early of 2019, which is located at Guilin, a famous tourism city of Guangxi province of China. It is well-known for its beautiful scenes of natural rivers and mountains, as well as the native songs of minority.
Publication Teaching Resource
My research interests mainly lie at the latent applications of Adaptive Learning, Computational Intelligence, and Signal Processing. They typically fall under one of the following categories: efficient algorithm design, adaptive machine learning, structured data analysis, signal analysis and their applications.


SAML integration of Salesforce.com with Gluu Server
By default Salesforce suggest deployers to implement IDP-initiated SSO. The initialization of IDP-initiated SSO is little bit complex as it requires a big hostname which includes IDP's SSO link as well as SP's login uri. So we prefer SP-initiated SSO and here in this documentation we are presenting a very simple SP-initiated SSO steps with Salesforce and Gluu Server. Still, you can go for IDP-initiated SSO if you prefer. Further documentation is available at the Salesforce.com site.
Prepare Salesforce.com
- Log into Salesforce.com with your administrative account.
- Click on Setup in the right upper corner of the page.
- You need to add a custom domain name for your Salesforce.com site if you do not have any yet.
- Go to Domain Management –> My Domain
- Add your custom domain
- Wait for some time. Salesforce.com will register this domain name
for you. As an example we use
testgluu-dev-ed.my.salesforce.com
here.
- Register your Gluu Server information in Salesforce.com
- Go to Security Controls –> Single Sign On Settings
- Click New
-
Now you need to add the information of your Gluu Server here
- Name: Anything, whichever is easier for you to recognize this
setup, i.e.
Gluu Server
- API Name: Gluu Server.
- Issuer: EntityID of your Gluu Server, i.e.
https://test.gluu.org/idp/shibboleth
- EntityID: Your Salesforce.com custom domain name as chosen
above, i.e.
https://testgluu-dev-ed.my.salesforce.com
- Identity Provider Certificate: Grab your Gluu Server's SAML certificate. SAML certificate can be grabbed from your Gluu Sever's metadata. Save the certificate and upload it.
- Request Signing Certificate: Default certificate
- Request Signature Method: RSA-SHA1
- Assertion Decryption Certificate: Assertion not encrypted.
- SAML Identity Type: Assertion contains user's Salesforce.com username
- SAML Identity Location: Identity is in an Attribute element
- Attribute Name: Provide 'SAML2 URI' of your attribute. For our test case we are using Gluu Server's Email attribute. How to check the information of your attribute is available here.
- NameID Format: Leave this field empty.
- Identity Provider Login URL:
https://test.gluu.org/idp/profile/SAML2/Redirect/SSO
- Service Provider Initiated Request Binding: HTTP-Redirect
- Here is how our example setup looks like:
- Name: Anything, whichever is easier for you to recognize this
setup, i.e.
Prepare Gluu Server
- How to create SAML trust relationship is available here.
- Grab Salesforce.com metadata from the Salesforce.com website. There is an option named 'Download Metadata':
- Modify Salesforce.com metadata a bit:
- Remove AuthnRequestsSigned=“true” from metadata.
- Save metadata
- Create Trust Relationship:
- Display Name: Anything, whichever is easier for you to recognize this trust relationship.
- Description: Anything, whichever is easier for you to recognize this trust relationship
- Metadata Type: 'File'
- Upload salesforce's metadata (your modified one)
- Releases attributes: TransientID and Email
- 'Add' this trust
- Configure Specific Relying: It can be done from Gluu Server's GUI (named: oxTrust)
- Select 'SAML2SSO'
- includeAttributeStatement: Enabled
- assertionLifetime: keep the default one
- assertionProxyCount: keep the default one
- signResponses: conditional
- signAssertions: never
- signRequests: conditional
- encryptAssertions: never
- encryptNameIds: never
- Save it
- Select 'SAML2SSO'
- 'Update' the trust relationship
- Here is how it looks like in our example setup:
Test SSO
- Go back to Salesforce.com setup
- Security Controls –> Single Sign On Settings
- Enable 'Federated Single Sign-On Using SAML'
- Go to 'Domain Management'
- Configure 'Authentication Configuration'
- Select 'Gluu Server'
- Save it
- Here is how the 'Authentication Configuration' looks like:
-
This is SP-initiate SSO. So hit your Salesforce.com website link to initiate the SSO.
-
Here is a video link of this SSO.


---
layout: post
title: "Making the jump to OSX in Sublime Text: keyboard shortcut conversions"
date: 2015-10-20T22:19:31-04:00
categories:
- articles
tags:
- Sublime Text
- Mac
published: true
---
If you are like me and have spent most of your time programming in Linux or Windows, then switching over to OSX can be tricky. Keyboard shortcuts have changed and you have to deal with the placement of that pesky fn
key. I've listed below my complete Default (OSX).sublime-keymap
file.
The keymap file contains many common keyboard shortcut conversions. Most of these deal with switching the apple key
(command key) for the ctrl
key. I've made a note at the bottom about the annoying f5
keymapping for sorting a document alphabetically. There is probably a way to remove the original mapping or set it to do nothing, I have tried to use the noop
command. But so far it is still persisting. If anyone knows of a fix I would love to see it in the comments.
Additionally, I added some layout mappings for some swanky custom layouts.
Default (OSX).sublime-keymap
{% highlight json %}
[
{ "keys": ["ctrl+x"], "command": "cut" },
{ "keys": ["ctrl+c"], "command": "copy" },
{ "keys": ["ctrl+v"], "command": "paste_and_indent" },
{ "keys": ["ctrl+s"], "command": "save" },
{ "keys": ["ctrl+z"], "command": "undo" },
{ "keys": ["ctrl+shift+z"], "command": "redo" },
{ "keys": ["f3"], "command": "find_next" },
{ "keys": ["ctrl+g"], "command": "find_next" },
{ "keys": ["ctrl+shift+g"], "command": "find_prev" },
{ "keys": ["shift+f3"], "command": "find_prev" },
{
"keys": ["super+shift+1"],
"command": "set_layout",
"args":
{
"cols": [0.0, 1.0],
"rows": [0.0, 1.0],
"cells": [[0, 0, 1, 1]]
}
},
{
"keys": ["super+shift+2"],
"command": "set_layout",
"args":
{
"cols": [0.0, 0.5, 1.0],
"rows": [0.0, 1.0],
"cells": [[0, 0, 1, 1], [1, 0, 2, 1]]
}
},
// side-by-side column with small row underneath
{
"keys": ["super+shift+3"],
"command": "set_layout",
"args":
{
"cols": [0.0, 0.5, 1.0],
"rows": [0.0, 0.8, 1.0],
"cells": [[0, 0, 1, 1], [1, 0, 2, 1], [0, 1, 2, 2]]
}
},
// 2 rows, 3 columns
{
"keys": ["super+shift+5"],
"command": "set_layout",
"args":
{
"cols": [0.0, 0.33, 0.67, 1.0],
"rows": [0.0, 0.5, 1.0],
"cells": [[0, 0, 1, 1], [1, 0, 2, 1], [2, 0, 3, 1],
[0, 1, 1, 2], [1, 1, 2, 2], [2, 1, 3, 2]]
}
},
{ "keys": ["ctrl+p"], "command": "show_overlay", "args": {"overlay": "goto", "show_files": true} },
{ "keys": ["ctrl+shift+p"], "command": "show_overlay", "args": {"overlay": "command_palette"} },
{ "keys": ["super+ctrl+p"], "command": "prompt_select_workspace" },
{ "keys": ["ctrl+r"], "command": "show_overlay", "args": {"overlay": "goto", "text": "@"} },
{ "keys": ["ctrl+k"], "command": "run_macro_file", "args": {"file": "res://Packages/Default/Delete Line.sublime-macro"} },
{ "keys": ["home"], "command": "move_to", "args": {"to": "bol"} }, // fix the home key
{ "keys": ["end"], "command": "move_to", "args": {"to": "eol"} }, // fix the end key
{ "keys": ["super+shift+a"], "command": "alignment" },
{ "keys": ["ctrl+enter"], "command": "run_macro_file", "args": {"file": "res://Packages/Default/Add Line.sublime-macro"} },
{ "keys": ["ctrl+shift+enter"], "command": "run_macro_file", "args": {"file": "res://Packages/Default/Add Line Before.sublime-macro"} },
{ "keys": ["ctrl+d"], "command": "find_under_expand" },
{ "keys": ["ctrl+shift+up"], "command": "swap_line_up" },
{ "keys": ["ctrl+shift+down"], "command": "swap_line_down" },
{ "keys": ["f1"], "command": "dash_doc" },
{ "keys": ["ctrl+shift+super+d"], "command": "clone_file" }, // Duplicate the current file view, useful for looking at different places at the same time in the same file
{
"keys": ["ctrl+forward_slash"], "command": "toggle_comment", "args": { "block": false }
},
// Attempting to reset the sort lines shortcut. On OSX when using workspaces quickly
// switching between a browser and sublime while pressing f5 will sort text editor lines
// alphabetically.
{ "key": ["f5"], "command": "noop" },
{ "key": ["ctrl+f5"], "command": "noop" },
{ "keys": ["shift+ctrl+f5"], "command": "sort_lines", "args": {"case_sensitive": false} },
{ "keys": ["shift+super+ctrl+f5"], "command": "sort_lines", "args": {"case_sensitive": true} },
]
{% endhighlight %}


home
Hair
Tutorials
Tips
Celebrities
Makeup
Tutorials
Tips
Celebrity Looks
Nails
Tutorials
Tips
Colors
Designs
Skin Care
Daily Routine
Tips
Problem Areas
Learn About Your Skin


Blue Assistant User Manual
This manual contains instructions for setting up all the components of blue assistant and integrating them into a final working product. There will be links to pre-compiled and pre-packaged zip files that will greatly simplify the setup process, but is not suitable for development. Developers working on this project should follow the setup instructions in README.md instead.
In this manual:
Agree to the Let’s Encrypt terms of use
Setting the endpoint URL of your skill
Applying the floor plan to Unity
The skill server setup and start-up scripts use Let’s Encrypt to get https certificates for your cloud server. By using those scripts, you acknowledge that you have read and agree to the Let’s Encrypt usage policies, which can be found at https://letsencrypt.org/repository/ .
To run the Blue Assistant you would need a Windows 10 machine with sound and microphone. Face tracking feature also require a front-facing webcam. You would also need a Debian-based Linux virtual machine with a public IP address.
The skill server must have a publicly accessible domain name. In this document we use “skill-server-install-test.mww.moe” as an example.
· Clone the git repository on your Windows machine.
· Download and extract https://mw-public-data.s3.eu-west-2.amazonaws.com/e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855/avs.zip into a new folder “avs”.
· Download and extract https://mw-public-data.s3.eu-west-2.amazonaws.com/e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855/unity-build.zip into a new folder “unity-build”.
· Go to https://www.java.com/en/download/windows_offline.jsp and download and install Java.
Firstly, remove any existing versions of python which you have installed. Later versions of Python works as well but will require some tinkering.
Run the two installers in the “AVSClientFiles” folder of the git repository, which will install Python and MinGW (MSYS2), which will be used by the AVS client. Note that:
·
When installing Python, you must select “Add Python 3.6 to PATH”:
·
When installing MinGW (MSYS2), you must install it to the default
location:
MinGW might also be useful for ssh-ing into your cloud server, if you want to perform the skill server setup on Windows and do not have another ssh client.
You will need access to an Amazon account with administrator rights to the Alexa skill.
Since this document will be referring to files with their extension, make sure this is checked:
First, check if you can ssh into your cloud server. You can use the ssh command in the MinGW shell to do this if you are on Windows. Keep this window open.
Then, copy the “API-code” folder onto your server. In Linux and MinGW, this can be done with the “scp” program. For example:
scp -r API-code your-server-domain:.
will copy the API-code folder to your home directory on the server.
Now, on the previous ssh window, run the following commands to start the config script:
cd API-code
./configure.sh your-server-domain
This might take a while, but your API server will be fully ready after the script is finished, and the skill server will keep running even if you close your ssh connection, and will also start up automatically when your cloud server boots.
 Then, navigate to the “Endpoint” page:
Make sure that “HTTPS” is selected, and under “Default Region” enter “https://your-server-domain/api/v1/blueassistant” and make sure “My development endpoint has a certificate from a trusted certificate authority” is selected.
Open the folder “AVSClientFiles” and locate the “mingw-setup.sh” file. Open a MinGW window and drag-and-drop the file into it, then press enter.
This process can take a very long time. You can close the window after it’s done.
Run “startsample.bat” under your extracted “avs” folder. Wait for a while, then scroll up until you see this:
Copy the code, go to https://amazon.com/us/code and enter it (you might have to login to your Amazon account first). Click “continue” on this page:
If this is successful, you will then see this from your client:
You can then close the client and your browser tab. Then, run “startapp.bat”. After a while you should see two windows, one is the Alexa client and another is the python bridge, which is used to connect the client with Unity. These need to be kept open while running the unity client.
Open Avatar/ConfigGen/index.html in the git repository with your browser, then follow the instruction. Do not check “Use test client”. Once you’re done, click “Generate config.json” and save (copy) the file into the Avatar/avatar_Data folder under your extracted “unity-build”, overriding the existing “config.json”.
At this point all setup procedure has completed. If you have kept the two windows spawned by the Alexa client earlier open, then simply run “Avatar/Avatar.exe” under “unity-build” to run the assistant. In case you have closed the Alexa client, run “startapp.bat” again to reopen.
You can use Alt+F4 to exit the unity client.
The assistant comes with a default set of rooms which you can test by asking, for example, “(ask Blue Assistant) how to get to the conference room”, without the need to do this configuration process. Nevertheless, here are the steps to customize it to your building.
Before running the configurator, you need to have a top view image of each floor of the building (or each floor which contains some places you want the user to be able to navigate). All the black pixels in the image should represent walls, but it can otherwise contain other colours. An example image suitable for this purpose is shown below:
Run “Navigation/run.bat”, and click “upload” in the graphical interface to select a floor image, then enter the floor number. It will hang for a while to process the rooms, and then you shall see something like this:
The program will iterate through all the rooms and highlight one at a time. You should give a name for each room. It will also highlight the hallway and lift area, and you must press “Hall” or “Lift” for those. Each floor must have exactly one Hall and exactly one Lift. At the end of this, click “Upload another” to repeat this process for another floor, or “Quit” to finish off.
After you click “Quit”, please wait for the command window to close before continuing with…
Copy the entire “Navigation” folder to the “unity-build” folder, overriding any existing files. Afterward the unity client should be able to show video of the new floor plan.
You would also need to change the skill server in order for the Alexa skill to recognize any queries for the rooms you created. For this you would need to manually edit the JSON files in the API-code folder of the repository and re-deploy the skill server (just scp the files again and run sudo systemctl restart skill-backend). Follow the instructions in Navigation/README.md for how to modify the JSON files.


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An AI-generated dance experiment and artistic residency at Amherst College.
I was a dancer in two performances by Kinetech Arts inspired by entropy, AI, and technology.
An interview on the Cognicast podcast, hosted by technologist and musician Robert Randolph, about my research across AI, physics, and the performing arts.
A talk at the StrangeLoop conference in St. Louis, MO, on AI, dance, and the creative process.
A 1-hour pop-up exhibit featuring AI-generated bird calls situated in nature.
As an intern with Intel's AI Lab, I developed a Graph Neural Network to learn a latent graph representation of my dancing body.
An AI-generated duet with myself.
Featured at the 2020 NeurIPS AI Art Gallery, the AI Governance Forum, and the Boston Cyberarts Gallery (cancelled due to COVID-19).
A short film of entirely AI-generated movements.
Featured at the 2019 NeurIPS AI Art Gallery for the Workshop on ML for Creativity and Design.
I led a research project using variational autoencoders to generate choreography.
Published in the proceedings of the International Conference on Computational Creativity (ICCC '19).


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Hi, my name is Nic Ollis and I’m a software development consultant
who specializes in business growth through increasing conversion rates, developing new services, and bringing new businesses online, whether startup or an established enterprise.
Unlike other developers or consultants, I’m both an experienced multiindustry tech consulting and an active developer, so I can create a path that achieves your business goals
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to build a successful web presence. Own a business online or off? I can help you with that
If you need more specialized support
or have an idea you want to be built you may be able to hire me. Let's schedule a call and talk.
Hello there :) I’m Nic Ollis and I can help you design and build a mobile and web presence that puts your users first
, helping you grow your business.
Whether you are a funded start-up hoping to build your initial app to pull in more investments, or a brick-and-mortar shop trying to expand sales online and grow your user base.
I can assist in developing a roadmap and solutions
to help you reach your goals.


Events > Christmas in the Adobes > Monterey, CA > The Californian
Friday and Saturday with tickets on sale at the museum store from 10 a.m. to 4 p.m. daily. submit your review. ShareThis Print Email this Facebook .
http://search.thecalifornian.com/localevents/event//36075-Christmas-in-the-Adobes
Treatment for Herniated or Bulging Discs
Programs and Events - California State Parks - State of California
Stop Numbers correspond to various Monterey State Historic Park adobes in the downtown area. Listeners recieve a two-minute narration for each building.
http://www.parks.ca.gov/?page_id=24072
Christmas in the Adobes - Monterey, CA - Kid friendly activity ...
Stroll thought the streets of Historic Downtown Monterey and visit 19th Century adobes for refreshments, games, history, and fun during Monterey's signature .
http://www.trekaroo.com/activities/christmas-in-the-adobes-monterey-california
christmas at the adobes monterey ca Herniated or Bulging Discs
Christmas in the Adobes | MHAA Museum of Monterey
Dec 10, 2011 . Location: Adobes throughout downtown Monterey 5pm-9pm each night. Stroll through 23 historic downtown Monterey adobes including our .
http://museumofmonterey.org/christmas-in-the-adobes/
Christmas in the Adobes Info & Tickets - California State Parks ...
Christmas in the Adobes is supported by: Inns of Monterey, Fish Hopper . League of Monterey, Monterey State Historic Park Association, California State Parks.
http://www.parks.ca.gov/?page_id=26922
Monterey State Historic Park Association: Programs and Events
The association also assists all of the California State Parks by raising funds and increasing public awareness. Learn More » . Christmas in the Adobes 2012 . Stroll through 23 historic adobes throughout downtown Monterey. Learn to .
http://montereystatehistoricparkassociation.org/news-events.html
Official Impairment Listing for Spinal Disorders
Christmas in the Adobes 2012 | MHAA Museum of Monterey
Nov 15, 2012 . Christmas in the Adobes is supported by: Inns of Monterey, Fish . Monterey, Monterey State Historic Park Association, California State Parks.
http://museumofmonterey.org/christmas-in-the-adobes-2/
- low volume connector 3 ml (narrowing of the spine)
Monterey's holiday traditions sparkle and resonate - SFGate
Dec 5, 2010 . Inside, the thick adobe walls help keep the room warm, and scents of fresh . is captured on November 30, 2010, at Fisherman's Wharf in Monterey, Calif. . characters during the annual Christmas in the Adobes celebration.
http://www.sfgate.com/travel/article/Monterey-s-holiday-traditions-sparkle-and-resonate-2454678.phpChristmas in the Adobes | Monterey State Historic Park | 22 Historic ...
Refreshments and entertainment. Monterey History and holiday tradition combine for the 26th annual Christmas in the Adobes celebration. Tour 22 historic .
http://www.montereybay365.com/event/detail/440967198/Christmas_in_the_Adobes
Christmas in the Adobes « Monterey Bay News & Views Monterey ...
6 days ago . You are here: Home > 'Christmas in the Adobes' . The Robert L. Stevenson House on Houston Street is one of the many Monterey adobes .
http://montereybayareanews.com/tag/christmas-in-the-adobes/
Christmas in the Adobes - Monterey State Historic Park - Monterey ...
Christmas in the Adobes. Friday, Dec 7 5:00p. at Monterey State Historic Park, Monterey, CA. Price: $20; $2 children. Age Suitability: None Specified .
http://events.montereyherald.com/monterey_ca/events/show/291764307-christmas-in-the-adobes
Medical-Vocational Allowance for Herniated Disc
Christmas in the Adobes 2011 - California State Parks - State of ...
Christmas in the Adobes 2011 - Thursday December 8 & Saturday December 10 - 5 p.m. to 9 p.m. Stroll through 23 historic downtown Monterey adobes as the .
http://www.parks.ca.gov/Events/EventDetail.aspx?id=3361
Dec - Monterey County California - Things to Do - Outdoor Activities
The choices are as varied as Monterey County's landscape. Quaint . Christmas Dinners in Monterey County. Dec. 25 . California Restaurant Month. Jan. 1-Jan .
http://www.seemonterey.com/things-to-do/shopping/holiday/
Appealing a Denial of Benefits
Celebrate Christmas in Monterey's Historic Adobes
Monterey's annual Christmas in the Adobes celebration is a wonderful holiday tradition enjoyed by everyone. Thursday, December 8 and Saturday, December .
http://www.innsofmonterey.com/monterey-music-arts/christmas-in-the-adobes/
BlogMonterey
16 minutes ago . Covering Monterey, Big Sur, Salinas, Pacific Grove, . dives into the life of the Founder of the California Missions system, including the Carmel Mission. . Old Whaling Station by Candlelight during Christmas in the Adobes .
http://www.blogmonterey.com/


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The exact address of the class will be emailed to you once your request is accepted by Colin.


Hosting
I have to find hosting for this site. I think hosting it would add to the impression. Anywhich ways the source code is freely available on my github page. Make sure to visit it. I have written some good programs.
I am Pranit Bauva. I am a programmer, sports player, IITian and what not. I love to travel to different places and explore stuff. I am a very fast learner. Reach me on Facebook, Twitter, G+, GitHub, and AskUbuntu
You can contact the admin of the webpage at<EMAIL_ADDRESS>have to find hosting for this site. I think hosting it would add to the impression. Anywhich ways the source code is freely available on my github page. Make sure to visit it. I have written some good programs.
As I had to make a website for KRSSG web team selections, I had to stay awake the whole night on sunday (technically monday) and code. I was familiar with HTML but did not know much about CSS. So I went to w3schools and learned it. I also learned PHP to just find out it would not be efficient for my work. So in turn I choose to work with python which I already knew. I have done a lot of backend coding before, but this was my first time doing front end web development. I just hope the selectors like it!
We had an introduction seminar on the KRSSG research group. It was very good. They told about the work they do. They also told that its divided between many teams like electronics, mechanical, CS, management, web, design teams. I really want to join the CS department because Artifical Intelligence and Machine Learning fascinates me. Currently I think I will apply for the web team and hopefull (fingers crossed) will go to the CS team. In fact I have made this website to show it to the selectors. I hope they like it!


pomdp_problems.rocksample.cythonize package¶
Submodules¶
pomdp_problems.rocksample.cythonize.rocksample_problem.cpython-37m-x86_64-linux-gnu module¶
pomdp_problems.rocksample.cythonize.rocksample_problem.cpython-38-x86_64-linux-gnu module¶
pomdp_problems.rocksample.cythonize.rocksample_problem module¶
RockSample(n,k) problem
Origin: Heuristic Search Value Iteration for POMDPs (UAI 2004)
Description:
State space:
Position {(1,1),(1,2),…(n,n)} \(\times\) RockType_1 \(\times\) RockType_2, …, \(\times\) RockType_k where RockType_i = {Good, Bad} \(\times\) TerminalState
- (basically, the positions of rocks are known to the robot,
but not represented explicitly in the state space. Check_i will smartly check the rock i at its location.)
Action space:
North, South, East, West, Sample, Check_1, …, Check_k The first four moves the agent deterministically Sample: samples the rock at agent’s current location Check_i: receives a noisy observation about RockType_i (noise determined by eta (\(\eta\)). eta=1 -> perfect sensor; eta=0 -> uniform)
Observation: observes the property of rock i when taking Check_i.
- Reward: +10 for Sample a good rock. -10 for Sampling a bad rock.
Move to exit area +10. Other actions have no cost or reward.
Initial belief: every rock has equal probability of being Good or Bad.
- class pomdp_problems.rocksample.cythonize.rocksample_problem.CheckAction¶
Bases:
pomdp_problems.rocksample.cythonize.rocksample_problem.RSAction
- rock_id¶
- class pomdp_problems.rocksample.cythonize.rocksample_problem.MoveAction¶
Bases:
pomdp_problems.rocksample.cythonize.rocksample_problem.RSAction
- EAST = (1, 0)¶
- NORTH = (0, 1)¶
- SOUTH = (0, -1)¶
- WEST = (-1, 0)¶
- motion¶
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RSAction¶
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RSObservation¶
Bases:
pomdp_py.framework.basics.Observation
- quality¶
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RSObservationModel¶
Bases:
pomdp_py.framework.basics.ObservationModel
- argmax()¶
Returns the most likely observation
- probability(self, observation, next_state, action)¶
Returns the probability of \(\Pr(o|s',a)\).
- Parameters
observation (Observation) – the observation \(o\)
next_state (State) – the next state \(s'\)
action (Action) – the action \(a\)
- Returns
the probability \(\Pr(o|s',a)\)
- Return type
float
- sample(self, next_state, action)¶
Returns observation randomly sampled according to the distribution of this observation model.
- Parameters
- Returns
the observation \(o\)
- Return type
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RSPolicyModel¶
Bases:
pomdp_py.algorithms.po_uct.RolloutPolicy
Simple policy model according to problem description.
- argmax()¶
Returns the most likely reward
- get_all_actions(self, *args)¶
Returns a set of all possible actions, if feasible.
- probability(self, action, state)¶
Returns the probability of \(\pi(a|s)\).
- rollout(self, State state, tuple history=None)¶
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RSRewardModel¶
Bases:
pomdp_py.framework.basics.RewardModel
- argmax(self, state, action, next_state)¶
Returns the most likely reward. This is optional.
- probability(self, reward, state, action, next_state)¶
Returns the probability of \(\Pr(r|s,a,s')\).
- sample(self, state, action, next_state)¶
Returns reward randomly sampled according to the distribution of this reward model. This is required, i.e. assumed to be implemented for a reward model.
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RSState¶
Bases:
pomdp_py.framework.basics.State
- position¶
- rocktypes¶
- terminal¶
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RSTransitionModel¶
Bases:
pomdp_py.framework.basics.TransitionModel
The model is deterministic
- argmax()¶
Returns the most likely next state
- probability(self, next_state, state, action)¶
Returns the probability of \(\Pr(s'|s,a)\).
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RockSampleProblem¶
Bases:
pomdp_py.framework.basics.POMDP
- static generate_instance()¶
Returns init_state and rock locations for an instance of RockSample(n,k)
- in_exit_area()¶
- print_state()¶
- static random_free_location()¶
returns a random (x,y) location in nxn grid that is free.
- class pomdp_problems.rocksample.cythonize.rocksample_problem.RockType¶
Bases:
object
- BAD = 'bad'¶
- GOOD = 'good'¶
- static invert()¶
- static random()¶
- class pomdp_problems.rocksample.cythonize.rocksample_problem.SampleAction¶
Bases:
pomdp_problems.rocksample.cythonize.rocksample_problem.RSAction
- pomdp_problems.rocksample.cythonize.rocksample_problem.euclidean_dist()¶
- pomdp_problems.rocksample.cythonize.rocksample_problem.init_particles_belief()¶
- pomdp_problems.rocksample.cythonize.rocksample_problem.main()¶
- pomdp_problems.rocksample.cythonize.rocksample_problem.test_planner()¶


Jeremiah 51:32
And that the passages are stopped, and the reeds they have burned with fire, and the men of war are affrighted.


Controls:
Left/Right Arrows: Move
Space: Attack (or Upgrade on Altar upgrade screens)
Up Arrow: Use (on Altars and Fireplaces)
Escape or Q: Exit menus
Backstory:
You are Marik, a Demonslayer. Many moons ago, the demon Lord Yenu slaughtered your village, or most of it anyway. A great wizard, Sir Phenemon, wounded Yenu, but at the cost of his own life.
Since that day, you seek Lord Yenu's death, and you will not rest easy until his cruel soul is in your hands.
You travel to Hell's Crypt, rumored to house Lord Yenu in its deepest levels. Oh, and it's crawling with the undead. Nobody said it would be easy.


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Introduction
The secret of constructing behavioral models is to think of everything as an amount rather than a thing. We deal with variables, not objects or acts. In this series we will model compensatory tracking, which as you know from Part 1 involves moving a handle that affects a cursor; the cursor is also subject to an independent random disturbance. The first step is to see the handle and cursor not as objects but as variables. All that matters about either one is represented by a number. A number represents handle position, a number represents cursor position. The physical environment (the computer) makes the cursor number depend on both the handle number and the disturbance number.
The links between handle and cursor or disturbance and cursor are established by properties of the environment, which we will show in boxes. We can give the boxes any properties we like. Of course we choose properties that will make the model cursor depend on handle position and disturbance as realistically as possible. The next step is concerned with setting up the environment model.
Modeling the environment
In the following series we will build up the model of a control system step by step. The first step is to model the environment, which consists of the control handle, the cursor seen on the screen, and the disturbance (which acts invisibly inside the computer).
The handle position is represented by h, a variable number. The value of h is multiplied by a "feedback constant" Kf. The product h * Kf represents the contribution of handle position to the cursor position.
The magnitude of disturbance is represented by d, another variable number. The value of d is multiplied by a "disturbance constant" Kd. The product d * Kd represents the contribution of the disturbance to the cursor position.
Finally, cursor position c is found by summing the effects of the handle via Kf and the disturbance via Kd, so that c = h * Kf + d * Kd.
You can use the control handle to keep the cursor between two target marks, a standard compensatory tracking experiment. A "strip chart" graph shows handle and cursor recordings. You can alter Kf and Kd. Experiment until you see and feel just how the disturbance and handle effects add up to position the cursor. Generally, do the tracking for 15-20 seconds, then click the Pause button to freeze the action so you can look at the numbers. You can always alter the constants. Click and hold the constant with the mouse and change it by dragging the number left to decrease it, or right to increase it.
The "disturbance constant" Kd determines how much effect a disturbance will have on the cursor. Step Kf down to 0 by dragging the number in the box to see the disturbance's effect by itself (the handle can move as usual but has no effect on the cursor). Change Kd to see how different values of Kd give the same disturbance different amounts of effect on the cursor.
The "feedback constant" Kf determines how much effect a given handle position will have on the cursor. Set the disturbance factor Kd to zero to see how changing Kf alters the handle's effect on the cursor. With a large value of Kf (3.00), a given change in cursor position can be produced with a small handle movement. Reducing Kf to a small number (like 0.5) means that a larger handle movement is needed to give the same effect.
The perceptual or input function
As you watch the cursor, you get a continual sense of its position. This sense is modeled as a "perceptual signal", generated by a combination of a visual image and neural computations that create a signal representing the state of the variable. As the cursor moves up, the signal increases; as it moves down, the signal decreases. Here we allow the perceptual signal to go negative when the cursor is left of the target. In a real nervous system there would have to be a "left" signal and a "right" signal because the magnitude of neural signals (measured in impulses per second) can't ever be less than zero.
Modeling perception is easy because we don't know any of the details of how sensing position works. We simply draw a box called the "input function" and declare that its input is the actual state of the cursor, and its output is a signal ("p") whose magnitude is the input times some input constant, Ki.
We will now begin using more general labels: "qi" for "input quantity" instead of "cursor", and "qo" for "output quantity" instead of "handle". We will keep "d" for disturbance just to prove that one needn't be compulsive about these labels (we could say "qd" -- disturbing quantity). A dashed line now separates the behaving system from its environment. The magnitude of the perceptual signal is now p = qi * Ki.
The "input constant" Ki determines the scaling factor that relates physical units (cursor position) to nervous-system units or NSU, related to frequency of impulses. A factor of 1.00 means that one physical unit of input creates one NSU of perceptual signal. Try different values of Ki by dragging it left or right.
The comparator and reference signal
Control behavior is based on the difference between the perceived state of the input quantity and a "reference" state -- the state intended. Just seeing if the cursor is left or right from the target is not enough. You have to know how much right or left you want it to be. Normal tracking implies that there should be no "rightness" or "leftness", but you can easily decide to keep the cursor any amount left or right of the target. In the model there is a "comparator" that receives the perceptual signal and a new signal, the "reference signal." The reference signal specifies the desired level of the perceptual signal. The comparator creates an error signal that tells how far the reference signal is to the right or to the left of the perceptual signal: the magnitude of the error signal is just the magnitude of the reference signal minus the magnitude of the perceptual signal.
Using the mouse, you can increase or decrease the reference signal in the range -200 to +200. Watch the effects on the error signal as you move the handle. You can also set Kf to zero, so the handle has no effect, and let the disturbance variations affect the model while you experiment.
The error signal (e) is affected equally by the reference signal (r) and by the perceptual signal (p). The error signal becomes zero only when reference and perceptual signals match. Zero error is defined by the reference signal. A POSITIVE error signal says the handle should move RIGHT. You can change the reference signal; watch the effect on the error signal.
The Output Function
We have an error signal that shows, by its magnitude and sign, how much and in what direction the cursor needs to move to make the perceptual signal match the reference signal. Now we add the last function, an "output function" that converts the error signal into action.
This output function makes the handle move to a position that is some constant times the error signal. The constant is the "output constant" or Ko, which you can adjust as before. For reasons that will be explained later, the output function also contains a slowing factor, which you will see as a lag in the output when the factor Ko is large (10 or so).
In this step we will NOT close the loop. The output will just be shown as a number, which you can compare with the handle number (qo). After you have understood this final part of the control system, you can go to the next step to see what happens when the output actually substitutes for your own handle movements, making this into a working model.
The feedback factor is now set to zero so the handle has no effect. Only the disturbance affects the perceptual, error, and output signals. Watch the disturbance and change Ko until you think the output is changing the way it should to cancel the effects of the disturbance. You are not likely to guess right unless you know control theory! You can still change all the constants.
Closing the Feedback Loop
The situation is now just what it was in the last step, except that the 'Close loop' button is enabled. Pressing 'Close loop' button makes the model's output quantity qo determine the handle position, so your handle movements now have no effect. (Pressing it again allows you to control the model's handle position again)
The model is now controlling the cursor. You may have set Ko, in the last step, so the model's output was equal and opposite to the disturbance (with the handle ineffective, Kf = 0). That would require Ko to be 1.00. Now you will find that this gives very poor control. Watch the error signal with Kd = 1.00, Ki = 1.00, and Kf = 1.00.
For the model to keep the error small, you will have to increase Ko to at least 10. It can be set as high as 50. The larger Ko is, the better the control. With Ko set between 5 and 20, the model will control about as well as you do. Now you can experiment with all the constants to see their effects on the way the model controls. Be sure to check the effects of varying the reference signal -- it determines where the cursor will be maintained.
Use the 'Close loop' button to open and close the loop. With the loop open, you control the handle and the model just produces an output number. With the loop closed, the handle position IS the system's output number, qo. To keep the error signal small, you have to make Ko much larger than 1: the open-loop output changes must be much larger than the handle movements.
A higher-level control system
The control-system model, with the loop closed, keeps the perceptual signal nearly matching the reference signal. With the reference signal set to zero, this means it keeps the cursor in line with the target marks, which are set to the zero (screen-center) position. This system, however, is controlling the absolute cursor position on the screen, not its position relative to the target. It isn't perceiving the target position.
You, however, can see that relationship. You can decide, when you are controlling, to keep the cursor some distance above or below the target. To do this, you pick an intended position, and use your own lower-level control systems to bring the target to the intended position. You can make this model act like your own lower level control systems -- you can specify where you want it to hold the cursor. You do this by altering the reference signal for the model. You don't have to watch the reference signal--just the relationship between cursor and target. Now you are acting like a higher level control system, controlling a RELATIONSHIP by varying the reference signal for a lower-order system that controls only POSITION. Notice that the position control system automatically cancels most of the disturbance effect.
Try changing the reference signal. The control system model will keep the perceptual signal matching the reference signal. To change the perceptual signal, the system has to move the cursor. The result is that the cursor is kept in a position, called its reference level, determined by the setting of the reference signal. The control sysstem automatically resists the disturbance.
The Equations of Control
In all traditional models of behavior, only a single equation is needed to capture the relationship proposed to exist between inputs and outputs: however written, it says that output is a function of input, or in our terms, qo = f(qi).
The control concept adds another statement: input is a function of both output and independent disturbances: qi = g(qo) + h(d). Now there are two equations. They must both hold true all of the time. The final relationship between input and output is not correctly described by either equation alone.
The traditional role of "stimulus" is played by the disturbance, not by the input quantity qi. The real input, qi, is jointly determined by the disturbance, d, and by the behavioral output, qo. The true independent variables are the reference signal, r, and the disturbance, d.
Only certain values of qi and qo will satisfy both equations at once. Both qo and qi are dependent variables: both the input and the output come to values determined by the whole feedback relationship.
From the block diagram built up during Part 2 we can read off four equations that are as important for the life sciences as Maxwell's equations of electromagnetism were for physics. In the next frame you will see how these equations interact to give a picture of the behavior of a control process.
From the diagram above we can write four equations:| 1. Environment: | qi = Kf * qo + Kd * d |
| 2. Perception: | p = Ki * qi |
| 3. Comparison: | e = r - p |
| 4. Action: | qo = Ko * e |
Now you can set the values of the two independent variables, r and d, and the values of the constants Kf, Kd, Ki, and Ko. Each of the equations remains true at all times, as you can verify by checking each equation manually. The values of qi and qo agree with the solutions of the four equations solved as a simultaneous system (solutions shown below). The dynamics are still hidden; they are explained in later frames.
Solutions:
qi = [(KfKo)r + (Kd)d]/[1 + KiKoKf]
qo = [(Ko)r - (KoKiKd)d]/[1 + KiKoKf]
Dynamics: The Slowing Factor
Real systems can't change their output actions instantly from one value to another. To this point, that fact about physical variables and physical time has been handled in the background. Whenever a jump in the output quantity was computed, the actual change was allowed to go only a fraction of the way toward the new value on any one computing cycle (once around the four equations). This fraction is 1/S, where S is called the "slowing factor." If the present value of output is 10 and the computation makes the next value 30, the computed jump is 20 units of output. With a slowing factor of 10, we would multiply the jump by 1/10, and so alter the output only 2 units instead of 20. You will see how this works in the next frame.
The nature of digital computations puts a lower limit on the number S. This limit is related to the "loop gain", the product Ki * Ko * Kf. If S is set to 1 + Ki * Ko * Kf, the digital computation reaches the correct final value in one jump. We have been using that optimum slowing factor. If S is 1/2 that amount or less, the computations will start oscillating and the results will no longer mean anything (you might even have to start the program over!). In simulations of real systems, the best slowing factor is considerably larger than this computer-imposed limit, so the problem doesn't arise. In the next frame, however, you can set the slowing factor to any value greater than 0.001, and see what happens. There's no closed loop to cause a problem.
In this example, all factors are adjustable. Change the value of the output constant Ko, the error signal e, or the slowing factor S. Press "Step" to compute the value of the expression below over and over. Eventually you will find that the output quantity qo comes exactly to the value Ko * e. How fast it does this depends on the slowing factor S (always positive). When S = 1 there is no slowing. qo is limited to +/- 10,000.
We are computing this expression (qo starts at zero):
When the slowing factor is used in simulations of a control system, there is a lower limit on the value of S. If S is less than this limit, the digital nature of the computations will cause a spurious runaway condition that would not appear in an analogue computation or in a normal control system. In the next frame, S is NOT forced to stay above this minimum value -- that's up to you. It is, however, made to stay larger than 0.001, to prevent dividing by 0. If the program runs away, press the 'Reset' button.
The Equations of Control
This demonstration works as before. Also, you can now change the slowing factor rather than having it computed automatically. The loop gain and the optimum slowing factor are shown below. The fourth equation now shows how the slowing factor is applied to calculate a new output quantity. S = 1 means no slowing.
Loop Gain = ...
Optimum Slowing Factor = ...
Matching the Model to Real Behavior
We will now set up a standard compensatory tracking experiment. You will go through a one-minute run while 3600 data points are taken (60 to 70 seconds). Then the same model we have been using will be run with the same disturbance. To "run" the model means feeding it values of the same disturbance, one after the other, and doing one cycle of the computations each time, saving the results. The model's behavior and the real behavior are then plotted.
You can adjust the reference signal r, the output constant Ko, and the slowing factor S for the model. The difference between model and real handle positions is plotted in the middle, with the root-mean-square (RMS) of the difference.
Also shown is the ratio Ko/S, which is the "integration factor" valid for large values of S. When S is a large number, the output function acts almost like a pure time integrator, summing the values of error signal. You will find that the best fit of model to real behavior occurs when Ko is greater than 100, and S is about 5 to 7 times as large as Ko. The most important number is Ko/S.
The numbers you end up with are inferences about the properties of the real person, under the assumption that the person is organized as the model is organized. You can judge for yourself whether this model is adequate.
Select the type of variable you want to control: Cursor Sound NumberSelect the degree of difficulty: 1 2 3 4
This is a compensatory tracking experiment. Hold the cursor between the two target bars. The run lasts about one minute.
Adjust the parameters of the model to get a better fit.
Integration factor, Ko/S:
RMS difference:
Handle vs model-handle correlation:
Matching the Model to Real Behavior, Revised Model
This time, after the experimental tracking run (from the previous step), the model will include a sensory lag adjustment, as well as the adjustments you've already seen. This is accomplished by using a slowing factor just as in the output function. You will find that with the right amount of sensory slowing factor (ss), somewhere around 4 to 6, you can get the model to fit the data even better than before -- more of the rapid changes are imitated (although still not all of them).
When you set the sensory slowing factor to 1.0 (as low as it will go), there is no sensory slowing. So you can compare the best parameter adjustments with and without sensory slowing.
A sensory slowing factor of 5 means that after a sudden change in cursor position, the perception of cursor position changes by 20 per cent in each 1/30 second interval -- 20 per cent of the remaining distance to the final value. This corresponds to a sensory time constant of about 0.15 seconds, a reasonable value for visual perception.
Below are show calculation of the correlation between the model's and the person's handle position, and you can see how well this model matches behavior.
Integration factor, Ko/S:
RMS difference:
Handle vs model-handle correlation:
Text and concept by W.T. Powers, circa 1989
Original programs for DOS: Demo2


Press s to start rating images
Press left arrow key for bad coregistration
Press right arrow key for good coregistration
Press space bar to return to the previous image to re-rate
Press f to flag the image for extra review
subject id


VII_combine_raster_point.Rmd
Observational and gridded data can be visualized or analyzed together for a variety of purposes. Users may want to identify spatial patterns in the alignment of dogwood bloom and the Spring Index bloom model. The current year’s lilac leaf out observations may be compared to the 30 year average lilac sub-model of the spring index to see how well the model predicts the observations.
This example shows several data access calls to assemble observational and gridded data.
Option 1: You can add a parameter to an observational data call to additionally get a gridded layer value for each observation location/date. Note that if you don’t specify which sub model of the Spring Index you want, you will get the SI-x Average layers.
npn_download_site_phenometrics( request_source = 'Your Name Here', years = '2013', num_days_quality_filter = '30', species_ids = '35', phenophase_ids = '373', download_path = 'cl_lilac_data_2013_SIxLeaf.csv', six_leaf_layer = TRUE, six_sub_model = 'lilac' )
If you want to append raster data other than Spring Index, Leaf values, there’s alternative boolean flags that can be set, including six_bloom_layer for Spring Index, Bloom data, and agdd_layer. Instead of TRUE or FALSE agdd_layer takes 32 or 50 and will correlate each data point with the corresponding AGDD value for the given date using either 32 or 50 base temperature.
Option 2: You can create a combined plot of observational data with modeled/raster data.
Building on the approach for accessing point data from earlier vignettes describing Individual Phenometrics and getting raster data, we can access and plot these products together. In this example, we will look at how well cloned lilac leaf out observations in 2018 are predicted by the lilac leaf sub model of the Spring Index.
LilacLeaf2018
## [1] "https://geoserver.usanpn.org/geoserver/wcs?service=WCS&version=2.0.1&request=GetCoverage&format=geotiff&coverageId=si-x:lilac_leaf_ncep&SUBSET=time(\"2018-12-31T00:00:00.000Z\")"
LilacLeaf2018Obs
## using a custom handler function.
## opening curl input connection.
##
Found 180 records...
## closing curl input connection.


Used when client wants to get its account information (screen-name formating, email info, etc)
Server should respond with SNAC(07,03), that contain requested
information.
| 00 07 |
|
word |
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SNAC family |
| 00 02 |
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word |
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SNAC subtype |
| 00 00 |
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word |
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SNAC flags |
| xx xx xx xx |
|
dword |
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SNAC request-id |
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| 00 01 |
|
word |
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TLV.Type(0x01) - request nickname |
| 00 00 |
|
word |
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TLV.Length |
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| 00 11 |
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word |
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TLV.Type(0x11) - request e-mail info |
| 00 00 |
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word |
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TLV.Length |
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| 00 13 |
|
word |
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TLV.Type(0x13) - request registration status |
| 00 00 |
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word |
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TLV.Length |
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Richard Linsdale - Dinghy Racing Coach
Home (Richard Linsdale)
Welcome to my Dinghy Coaching Home page.
Page under construction


Upcoming Race Meetings in Ireland and the UK
Upcoming Race Meetings
Race Meeting ID
Race Meeting Name
Countrycode
OpenDate
Upcoming Races
Race Meeting ID
Race Meeting Name
Race ID
Race Name
Race Start Time


A Queue implementation based on a double-linked list.
Allows constant time add, remove-at-ends and peek operations.
Can do removeAll and retainAll in linear time.
Adds value at the end of the queue.
Adds all elements of iterable at the end of the queue. The length of the queue is extended by the length of iterable.
Adds value at the beginning of the queue.
Adds value at the end of the queue.
Removes all elements in the queue. The size of the queue becomes zero.
Remove a single instance of value from the queue.
Returns true
if a value was removed, or false
if the queue contained no element equal to value.
Removes and returns the first element of this queue. Throws an StateError exception if this queue is empty.
Removes and returns the last element of the queue. Throws an StateError exception if this queue is empty.
Returns a string representation of this object.
An entry in a doubly linked list. It contains a pointer to the next entry, the previous entry, and the boxed element.
WARNING: This class is temporary located in dart:core. It'll be removed at some point in the near future.
The HasNextIterator class wraps an Iterator and provides methods to iterate over an object using hasNext
and next
.
An HasNextIterator does not implement the Iterator interface.
A hash-table based implementation of Map.
The keys of a HashMap
must have consistent Object.operator== and Object.hashCode implementations. This means that the ==
operator must define a stable equivalence relation on the keys (reflexive, anti-symmetric, transitive, and consistent over time), and that hashCode
must be the same for objects that are considered equal by ==
.
The map allows null
as a key.
Returns the value for the given key or null if key is not in the map. Because null values are supported, one should either use containsKey to distinguish between an absent key and a null value, or use the putIfAbsent method.
Associates the key with the given value.
Removes all pairs from the map.
Returns whether this map contains the given key.
Returns whether this map contains the given value.
Applies f to each {key, value} pair of the map.
It is an error to add or remove keys from the map during iteration.
If key is not associated to a value, calls ifAbsent and updates the map by mapping key to the value returned by ifAbsent. Returns the value in the map.
It is an error to add or remove keys from map during the call to ifAbsent.
Removes the association for the given key. Returns the value for key in the map or null if key is not in the map. Note that values can be null and a returned null value does not always imply that the key is absent.
Returns a string representation of this object.
A HashSet is a hash-table based Set implementation.
The elements of a HashSet
must have consistent Object.operator== and Object.hashCode implementations. This means that the ==
operator must define a stable equivalence relation on the elements (reflexive, anti-symmetric, transitive, and consistent over time), and that hashCode
must be the same for objects that are considered equal by ==
.
The set allows null
as an element.
Most simple operations on HashSet
are done in constant time: add(), contains(), remove(), and length.
Adds value into the set.
The method has no effect if value is already in the set.
Adds all of elements to this Set.
Equivalent to adding each element in elements using add, but some collections may be able to optimize it.
Removes all elements in the set.
Check whether the collection contains an element equal to element.
Removes value from the set. Returns true if value was in the set. Returns false otherwise. The method has no effect if value value was not in the set.
Removes each element of elements from this set.
Removes all elements of this set that satisfy test.
Removes all elements of this set that fail to satisfy test.
Base class for implementing Iterable.
This class implements all methods of Iterable except Iterable.iterator in terms of iterator
.
Returns true if one element of this collection satisfies the predicate test. Returns false otherwise.
Check whether the collection contains an element equal to element.
Returns the indexth element.
If this
has fewer than index elements throws a RangeError.
Note: if this
does not have a deterministic iteration order then the function may simply return any element without any iteration if there are at least index elements in this
.
Returns true if every elements of this collection satisify the predicate test. Returns false
otherwise.
Expand each element of this Iterable into zero or more elements.
The resulting Iterable will run through the elements returned by f for each element of this, in order.
The returned Iterable is lazy, and will call f for each element of this every time it's iterated.
Returns the first element that satisfies the given predicate test.
If none matches, the result of invoking the orElse function is returned. By default, when orElse is null
, a StateError is thrown.
Reduces a collection to a single value by iteratively combining each element of the collection with an existing value using the provided function.
Use initialValue as the initial value, and the function combine to create a new value from the previous one and an element.
Example of calculating the sum of an iterable:
iterable.fold(0, (prev, element) => prev + element);
Applies the function f to each element of this collection.
Converts each element to a String and concatenates the strings.
Converts each element to a String by calling Object.toString on it. Then concatenates the strings, optionally separated by the separator string.
Returns the last element that satisfies the given predicate test.
If none matches, the result of invoking the orElse function is returned. By default, when orElse is null
, a StateError is thrown.
Returns a lazy Iterable where each element e
of this
is replaced by the result of f(e)
.
This method returns a view of the mapped elements. As long as the returned Iterable is not iterated over, the supplied function f will not be invoked. The transformed elements will not be cached. Iterating multiple times over the the returned Iterable will invoke the supplied function f multiple times on the same element.
Reduces a collection to a single value by iteratively combining elements of the collection using the provided function.
Example of calculating the sum of an iterable:
iterable.reduce((value, element) => value + element);
Returns the single element that satisfies test. If no or more than one element match then a StateError is thrown.
Returns an Iterable that skips the first n elements.
If this
has fewer than n elements, then the resulting Iterable will be empty.
Returns an Iterable that skips elements while test is satisfied.
The filtering happens lazily. Every new Iterator of the returned Iterable iterates over all elements of this
.
As long as the iterator's elements satisfy test they are discarded. Once an element does not satisfy the test the iterator stops testing and uses every later element unconditionally. That is, the elements of the returned Iterable are the elements of this
starting from the first element that does not satisfy test.
Returns an Iterable with at most n elements.
The returned Iterable may contain fewer than n elements, if this
contains fewer than n elements.
Returns an Iterable that stops once test is not satisfied anymore.
The filtering happens lazily. Every new Iterator of the returned Iterable will start iterating over the elements of this
.
When the iterator encounters an element e
that does not satisfy test, it discards e
and moves into the finished state. That is, it will not ask or provide any more elements.
Creates a List containing the elements of this Iterable.
The elements will be in iteration order. The list is fixed-length if growable is false.
Creates a Set containing the elements of this Iterable.

This method returns a view of the mapped elements. As long as the returned Iterable is not iterated over, the supplied function test will not be invoked. Iterating will not cache results, and thus iterating multiple times over the the returned Iterable will invoke the supplied function test multiple times on the same element.
This Iterable mixin implements all Iterable members except iterator
.
All other methods are implemented in terms of iterator
.
Returns true if one element of this collection satisfies the predicate test. Returns false otherwise.
Check whether the collection contains an element equal to element.
Returns the indexth element.
If this
has fewer than index elements throws a RangeError.
Note: if this
does not have a deterministic iteration order then the function may simply return any element without any iteration if there are at least index elements in this
.
Returns true if every elements of this collection satisify the predicate test. Returns false
otherwise.
Expand each element of this Iterable into zero or more elements.
The resulting Iterable will run through the elements returned by f for each element of this, in order.
The returned Iterable is lazy, and will call f for each element of this every time it's iterated.
Returns the first element that satisfies the given predicate test.
If none matches, the result of invoking the orElse function is returned. By default, when orElse is null
, a StateError is thrown.
Reduces a collection to a single value by iteratively combining each element of the collection with an existing value using the provided function.
Use initialValue as the initial value, and the function combine to create a new value from the previous one and an element.
Example of calculating the sum of an iterable:
iterable.fold(0, (prev, element) => prev + element);
Applies the function f to each element of this collection.
Converts each element to a String and concatenates the strings.
Converts each element to a String by calling Object.toString on it. Then concatenates the strings, optionally separated by the separator string.
Returns the last element that satisfies the given predicate test.
If none matches, the result of invoking the orElse function is returned. By default, when orElse is null
, a StateError is thrown.
Returns a lazy Iterable where each element e
of this
is replaced by the result of f(e)
.
This method returns a view of the mapped elements. As long as the returned Iterable is not iterated over, the supplied function f will not be invoked. The transformed elements will not be cached. Iterating multiple times over the the returned Iterable will invoke the supplied function f multiple times on the same element.
Reduces a collection to a single value by iteratively combining elements of the collection using the provided function.
Example of calculating the sum of an iterable:
iterable.reduce((value, element) => value + element);
Returns the single element that satisfies test. If no or more than one element match then a StateError is thrown.
Returns an Iterable that skips the first n elements.
If this
has fewer than n elements, then the resulting Iterable will be empty.
Returns an Iterable that skips elements while test is satisfied.
The filtering happens lazily. Every new Iterator of the returned Iterable iterates over all elements of this
.
As long as the iterator's elements satisfy test they are discarded. Once an element does not satisfy the test the iterator stops testing and uses every later element unconditionally. That is, the elements of the returned Iterable are the elements of this
starting from the first element that does not satisfy test.
Returns an Iterable with at most n elements.
The returned Iterable may contain fewer than n elements, if this
contains fewer than n elements.
Returns an Iterable that stops once test is not satisfied anymore.
The filtering happens lazily. Every new Iterator of the returned Iterable will start iterating over the elements of this
.
When the iterator encounters an element e
that does not satisfy test, it discards e
and moves into the finished state. That is, it will not ask or provide any more elements.
Creates a List containing the elements of this Iterable.
The elements will be in iteration order. The list is fixed-length if growable is false.
Creates a Set containing the elements of this Iterable.

This method returns a view of the mapped elements. As long as the returned Iterable is not iterated over, the supplied function test will not be invoked. Iterating will not cache results, and thus iterating multiple times over the the returned Iterable will invoke the supplied function test multiple times on the same element.
A hash-table based implementation of Map.
Keys insertion order is remembered, and keys are iterated in insertion order. Values are iterated in their corresponding key's order.
The keys of a HashMap
must have consistent Object.operator== and Object.hashCode implementations. This means that the ==
operator must define a stable equivalence relation on the keys (reflexive, anti-symmetric, transitive, and consistent over time), and that hashCode
must be the same for objects that are considered equal by ==
.
The map allows null
as a key.
The keys of the map, in insertion order.
The values of the map, in the order of their corresponding keys.
Returns the value for the given key or null if key is not in the map. Because null values are supported, one should either use containsKey to distinguish between an absent key and a null value, or use the putIfAbsent method.
Associates the key with the given value.
Removes all pairs from the map.
Returns whether this map contains the given key.
Returns whether this map contains the given value.
Applies f to each {key, value} pair of the map.
It is an error to add or remove keys from the map during iteration.
If key is not associated to a value, calls ifAbsent and updates the map by mapping key to the value returned by ifAbsent. Returns the value in the map.
It is an error to add or remove keys from map during the call to ifAbsent.
Removes the association for the given key. Returns the value for key in the map or null if key is not in the map. Note that values can be null and a returned null value does not always imply that the key is absent.
Returns a string representation of this object.
A LinkedHashSet is a hash-table based Set implementation.
The LinkedHashSet
also keep track of the order that elements were inserted in, and iteration happens in first-to-last insertion order.
The elements of a LinkedHashSet
must have consistent Object.operator== and Object.hashCode implementations. This means that the ==
operator must define a stable equivalence relation on the elements (reflexive, anti-symmetric, transitive, and consistent over time), and that hashCode
must be the same for objects that are considered equal by ==
.
The set allows null
as an element.
Most simple operations on HashSet
are done in constant time: add(), contains(), remove(), and length.
Return an iterator that iterates over elements in insertion order.
Adds value into the set.
The method has no effect if value is already in the set.
Adds all of elements to this Set.
Equivalent to adding each element in elements using add, but some collections may be able to optimize it.
Removes all elements in the set.
Check whether the collection contains an element equal to element.
Perform an operation on each element in insertion order.
Removes value from the set. Returns true if value was in the set. Returns false otherwise. The method has no effect if value value was not in the set.
Removes each element of elements from this set.
Removes all elements of this set that satisfy test.
Removes all elements of this set that fail to satisfy test.
Abstract implementation of a list.
All operations are defined in terms of length
, operatorListBase()
, operatorListBase()=
and length=
, which need to be implemented.
Base implementation of a List class.
This class can be used as a mixin.
This implements all read operations using only the length
and operatorListMixin()
members. It implements write operations using those and length=
and operatorListMixin()=
A fixed-length list should mix this class in, and the FixedLengthListMixin as well, in that order, to overwrite the methods that modify the length.
An unmodifiable list should mix UnmodifiableListMixin on top of this mixin to prevent all modifications.
Adds value at the end of the list, extending the length by one.
Throws an UnsupportedError if the list is not extendable.
Appends all elements of the iterable to the end of this list.
Extends the length of the list by the number of elements in iterable. Throws an UnsupportedError if this list is not extensible.
Returns true if one element of this collection satisfies the predicate test. Returns false otherwise.
Returns an unmodifiable Map view of this
.
It has the indices of this list as keys, and the corresponding elements as values. The Map.keys Iterable will iterate the indices of this list in numerical order.
Removes all elements in the list.
The length of the list becomes zero.
Throws an UnsupportedError, and retains all elements, if the length of the list cannot be changed.
Check whether the collection contains an element equal to element.
Returns the indexth element.
If this
has fewer than index elements throws a RangeError.
Note: if this
does not have a deterministic iteration order then the function may simply return any element without any iteration if there are at least index elements in this
.
Returns true if every elements of this collection satisify the predicate test. Returns false
otherwise.
Expand each element of this Iterable into zero or more elements.
The resulting Iterable will run through the elements returned by f for each element of this, in order.
The returned Iterable is lazy, and will call f for each element of this every time it's iterated.
Sets the elements in the range start to end exclusive to the given fillValue.
It is an error if start..end is not a valid range pointing into the this
.
Returns the first element that satisfies the given predicate test.
If none matches, the result of invoking the orElse function is returned. By default, when orElse is null
, a StateError is thrown.
Reduces a collection to a single value by iteratively combining each element of the collection with an existing value using the provided function.
Use initialValue as the initial value, and the function combine to create a new value from the previous one and an element.
Example of calculating the sum of an iterable:
iterable.fold(0, (prev, element) => prev + element);
Applies the function f to each element of this collection.
Returns an Iterable that iterators over the elements in the range start to end exclusive. The result of this function is backed by this
.
It is an error if end is before start.
It is an error if the start and end are not valid ranges at the time of the call to this method. The returned Iterable behaves similar to skip(start).take(end - start)
. That is, it will not throw exceptions if this
changes size.
Example:
var list = [1, 2, 3, 4, 5]; var range = list.getRange(1, 4); print(range.join(', ')); // => 2, 3, 4 list.length = 3; print(range.join(', ')); // => 2, 3
Returns the first index of element in the list.
Searches the list from index start to the length of the list. The first time an element e
is encountered so that e == element
, the index of e
is returned. Returns -1 if element is not found.
Inserts the element at position index in the list.
This increases the length of the list by one and shifts all elements at or after the index towards the end of the list.
It is an error if the index does not point inside the list or at the position after the last element.
Inserts all elements of iterable at position index in the list.
This increases the length of the list by the length of iterable and shifts all later elements towards the end of the list.
It is an error if the index does not point inside the list or at the position after the last element.
Converts each element to a String and concatenates the strings.
Converts each element to a String by calling Object.toString on it. Then concatenates the strings, optionally separated by the separator string.
Returns the last index in the list a of the given element, starting the search at index startIndex to 0. Returns -1 if element is not found.
Returns the last element that satisfies the given predicate test.
If none matches, the result of invoking the orElse function is returned. By default, when orElse is null
, a StateError is thrown.
Returns a lazy Iterable where each element e
of this
is replaced by the result of f(e)
.
This method returns a view of the mapped elements. As long as the returned Iterable is not iterated over, the supplied function f will not be invoked. The transformed elements will not be cached. Iterating multiple times over the the returned Iterable will invoke the supplied function f multiple times on the same element.
Reduces a collection to a single value by iteratively combining elements of the collection using the provided function.
Example of calculating the sum of an iterable:
iterable.reduce((value, element) => value + element);
Removes value from the list. Returns true if value was in the list. Returns false otherwise. The method has no effect if value value was not in the list.
Removes the element at position index from the list.
This reduces the length of this
by one and moves all later elements down by one position.
Returns the removed element.
Throws an ArgumentError if index is not an int.
Throws an RangeError if the index does not point inside the list.
Throws an UnsupportedError, and doesn't remove the element, if the length of this
cannot be changed.
Pops and returns the last element of the list. Throws a UnsupportedError if the length of the list cannot be changed.
Removes the elements in the range start to end exclusive.
It is an error if start..end is not a valid range pointing into the this
.
Removes all elements of this list that satisfy test.
An elements e
satisfies test if test(e)
is true.
Removes the elements in the range start to end exclusive and replaces them with the contents of the iterable.
It is an error if start..end is not a valid range pointing into the this
.
Example:
var list = [1, 2, 3, 4, 5]; list.replaceRange(1, 3, [6, 7, 8, 9]); print(list); // [1, 6, 7, 8, 9, 4, 5]
Removes all elements of this list that fail to satisfy test.
An elements e
satisfies test if test(e)
is true.
Overwrites elements of this
with the elemenst of iterable starting at position index in the list.
This operation does not increase the length of this
.
It is an error if the index does not point inside the list or at the position after the last element.
It is an error if the iterable is longer than length - index.
Copies the elements of iterable, skipping the skipCount first elements, into the range start to end exclusive of this
.
If start equals end and start..end represents a legal range, this method has no effect.
It is an error if start..end is not a valid range pointing into the this
.
It is an error if the iterable does not have enough elements after skipping skipCount elements.
Example:
var list = [1, 2, 3, 4]; var list2 = [5, 6, 7, 8, 9]; list.setRange(1, 3, list2, 3); print(list); // => [1, 8, 9, 4]
Returns the single element that satisfies test. If no or more than one element match then a StateError is thrown.
Returns an Iterable that skips the first n elements.
If this
has fewer than n elements, then the resulting Iterable will be empty.
Returns an Iterable that skips elements while test is satisfied.
The filtering happens lazily. Every new Iterator of the returned Iterable iterates over all elements of this
.
As long as the iterator's elements satisfy test they are discarded. Once an element does not satisfy the test the iterator stops testing and uses every later element unconditionally. That is, the elements of the returned Iterable are the elements of this
starting from the first element that does not satisfy test.
Sorts the list according to the order specified by the compare function.
The compare function must act as a Comparator.
The default List implementations use Comparable.compare if compare is omitted.
Returns a new list containing the elements from start to end.
If end is omitted, the length of this
is used.
It is an error if start or end are not indices into this
, or if end is before start.
Returns an Iterable with at most n elements.
The returned Iterable may contain fewer than n elements, if this
contains fewer than n elements.
Returns an Iterable that stops once test is not satisfied anymore.
The filtering happens lazily. Every new Iterator of the returned Iterable will start iterating over the elements of this
.
When the iterator encounters an element e
that does not satisfy test, it discards e
and moves into the finished state. That is, it will not ask or provide any more elements.
Creates a List containing the elements of this Iterable.
The elements will be in iteration order. The list is fixed-length if growable is false.
Creates a Set containing the elements of this Iterable.
Returns a string representation of this object.

This method returns a view of the mapped elements. As long as the returned Iterable is not iterated over, the supplied function test will not be invoked. Iterating will not cache results, and thus iterating multiple times over the the returned Iterable will invoke the supplied function test multiple times on the same element.
List based Queue.
Keeps a cyclic buffer of elements, and grows to a larger buffer when it fills up. This guarantees constant time peek and remove operations, and amortized constant time add operations.
The structure is efficient for any queue or stack usage.
Operations like removeAll and removeWhere() are very inefficient. If those are needed, use a DoubleLinkedQueue instead.
Create an empty queue.
If initialCapacity is given, prepare the queue for at least that many elements.
Create a queue initially containing the elements of source.
Adds value at the end of the queue.
Adds all elements of iterable at the end of the queue. The length of the queue is extended by the length of iterable.
Adds value at the beginning of the queue.
Adds value at the end of the queue.
Removes all elements in the queue. The size of the queue becomes zero.
Returns the indexth element.
If this
has fewer than index elements throws a RangeError.
Note: if this
does not have a deterministic iteration order then the function may simply return any element without any iteration if there are at least index elements in this
.
Applies the function f to each element of this collection.
Remove a single instance of value from the queue.
Returns true
if a value was removed, or false
if the queue contained no element equal to value.
Removes and returns the first element of this queue. Throws an StateError exception if this queue is empty.
Removes and returns the last element of the queue. Throws an StateError exception if this queue is empty.
Remove all elements matched by test.
This method is inefficient since it works by repeatedly removing single elements, each of which can take linear time.
Remove all elements not matched by test.
This method is inefficient since it works by repeatedly removing single elements, each of which can take linear time.
Creates a List containing the elements of this Iterable.
The elements will be in iteration order. The list is fixed-length if growable is false.
Returns a string representation of this object.
Returns a string representing the specified map. The returned string looks like this: '{key0: value0, key1: value1, ... keyN: valueN}'
. The value returned by its toString method is used to represent each key or value.
If the map collection contains a reference to itself, either directly as a key or value, or indirectly through other collections or maps, the contained reference is rendered as '{...}'
. This prevents the infinite regress that would otherwise occur. So, for example, calling this method on a map whose sole entry maps the string key 'me' to a reference to the map would return '{me: {...}}'
.
A typical implementation of a map's toString method will simply return the results of this method applied to the collection.
A Queue is a collection that can be manipulated at both ends. One can iterate over the elements of a queue through forEach or with an Iterator.
Creates a queue.
Creates a queue with the elements of other. The order in the queue will be the order provided by the iterator of other.
Adds value at the end of the queue.
Adds all elements of iterable at the end of the queue. The length of the queue is extended by the length of iterable.
Adds value at the beginning of the queue.
Adds value at the end of the queue.
Removes all elements in the queue. The size of the queue becomes zero.
Remove a single instance of value from the queue.
Returns true
if a value was removed, or false
if the queue contained no element equal to value.
Removes and returns the first element of this queue. Throws an StateError exception if this queue is empty.
Removes and returns the last element of the queue. Throws an StateError exception if this queue is empty.
Returns the value for the given key or null if key is not in the map. Because null values are supported, one should either use containsKey to distinguish between an absent key and a null value, or use the putIfAbsent method.
Associates the key with the given value.
Removes all pairs from the map.
Returns whether this map contains the given key.
Returns whether this map contains the given value.
Get the first key in the map. Returns null if the map is empty.
Get the first key in the map that is strictly larger than key. Returns null if no key was not found.
Applies f to each {key, value} pair of the map.
It is an error to add or remove keys from the map during iteration.
Get the last key in the map. Returns null if the map is empty.
Get the last key in the map that is strictly smaller than key. Returns null if no key was not found.
If key is not associated to a value, calls ifAbsent and updates the map by mapping key to the value returned by ifAbsent. Returns the value in the map.
It is an error to add or remove keys from map during the call to ifAbsent.
Removes the association for the given key. Returns the value for key in the map or null if key is not in the map. Note that values can be null and a returned null value does not always imply that the key is absent.
Returns a string representation of this object.
An unmodifiable List view of another List.
The source of the elements may be a List or any Iterable with efficient Iterable.length and Iterable.elementAt.
Create an unmodifiable list backed by source.


This package contains various example models that demonstrates
how to utilize the blocks from sublibrary Blocks.Noise.
| Name |
Description |
| UniformNoise |
Demonstrates the most simple usage of the UniformNoise
block |
| AutomaticSeed |
Demonstrates noise with startTime and automatic local seed for
UniformNoise |
| Distributions |
Demonstrates noise with different types of distributions |
|
UniformNoiseProperties |
Demonstrates the computation of properties for uniformally
distributed noise |
|
NormalNoiseProperties |
Demonstrates the computation of properties for normally
distributed noise |
| Densities |
Demonstrates how to compute distribution densities (=
Probability Density Function) |
| ImpureGenerator |
Demonstrates the usage of the impure random number
generator |
| ActuatorWithNoise |
Demonstrates how to model measurement noise in an actuator |
|
DrydenContinuousTurbulence |
Demonstrates how to model wind turbulence for aircraft with the
BandLimitedWhiteNoise block (a simple model of vertical Dryden gust
speed at low altitudes < 1000 ft) |
| Utilities |
Library of utility models used in the examples |