Product Features
With rapidly rising global temperatures, natural disasters displacing millions, and deforestation destroying the habitats of animals, there's no doubt that climate change is a significant issue today and will continue to be prevalent. At EcoCraft, we want to inform the next generation about climate science and motivate sustainable actions with some fun!
Gamification of Education
Our story-driven campains are exciting. Students accomplish missions collaboratively. We focus on enjoyment and learning.
Guiding Lesson Plans
Teachers and students can get started seamlessly with simple instructions.
Impactful Technology
Our Minecraft Education experiences allow students to explore and gain knowledge about sustainability. The digital environment is a space for creativity and applied learning.
Education Standards
We worked with elementary school teachers to ensure value. Each plan complies with Washington State's education standards.
Lesson 1: Pollution in Marine Ecosystems
This first lesson introduces to students some of the negative impacts pollutions can have on an ecosystem and the health of the wildlife within it. Paired with our detailed lesson plan, students will be able to collaborate as a team to clean a river and then properly dispose of the various pollutants that they find. Their teacher will be able to join in with them and combined with their own instructions and our digital guidance, students will learn about marine ecosystems and how they can be negatively affected by pollution.
Students will be provided with a series of interactive tasks that they can then complete in order to save the river alongside TreeTown. Upon completion of their tasks, they’ll see in real-time as the wildlife returns to the river, further cementing the lesson plan’s goal of instructing students on connections between our actions and the health of ecosystems.
We aim to teach students about:
- Proper waste disposal.
- The current state of marine ecosystems in the world.
- The effects of pollution on marine life.
Lesson 2: Solar Panel Renewable Energy
The second lesson introduces students to key concepts of clean energy. Students will conduct guided research to learn about different kinds of renewable energy. During their research they will be exposed to materials that teach about the effect of non-renewable energy and how greenhouse gasses have been impacting the climate.
Students will learn where energy in the US comes from and the types of different renewable energy sources that are available. They will have to analyze the needs of a factory owner in TreeTown and choose a type of renewable energy that will meet their needs and help them decrease their carbon footprint.
We aim to teach students about:
- Effect of non-renewable energy.
- Benefits of renewable energy.
- Types of non-renewable and renewable energy sources and how they contribute to the energy in the US.
Lesson 3: Flattening The Curve
The third lesson introduces some integral aspects of the coronavirus that have recently been discussed in the media. Upon interviewing our stakeholders we found that they are most confused about the terminology being used in the news such as “shelter in place,” “flattening the curve,” and the various metrics that are being used to track the spread of the illness. For the activity, we put the student in the place of a medical organizer attempting to supply enough equipment to the medical tents as the virus is transmitted throughout the village. The student can either dedicate their time to constructing the correct equipment or can herd the infected villagers into the medical tents to slow the spread of the disease.
We want students to understand the compromises that are being made by today’s health professionals.
We aim to teach students about:
- How viruses spread.
- Useful strategies for stopping the spread of viruses.
- The necessary balance between health infrastructure and new case rate.


Web Developer & Resources by Madison Woo
Social Media Optimization (SMO)
SMO is a valuable tool in marketing a business and creating more visibility for a website. When thinking about web design, it’s important to consider ways to make a website optimized for good SMO. One method of creating a social media optimized website, is adding links for sharing content for social bookmarking. Another thing to keep in mind when designing a website is installing site plug-ins.
According to Lifewire, “Social bookmarking is an online service which allows you to create and publicly share website bookmarks with other members of a community by simply tagging a web page with a web-based tool so you can easily access it later” (Nations). Big social bookmarking sites include Twitter, Reddit and Pinterest, and to optimize a website for social bookmarking would allow for large social media marketing opportunities. “Increase traffic by adding social bookmarking buttons to your website. Adding social bookmark links to your blog or website makes it easy for readers to save and share your content, and therefore for others to find it” (Network Solutions). With this being said, it is important to design a website that makes it easy for users to share website content to social media to increase social media marketing.
When designing a website, a useful way to build SMO is using plug-ins. A plug-in is a piece of software that is downloaded to add more functionality to a website. For example, a plug-in like the Facebook Like Button for the Web, can be installed to a website and allow users to easily ‘like’ content on your website and have it automatically show on their profile. “A single click on the Like button will 'like' pieces of content on the web and share them on Facebook. You can also display a Share button next to the Like button to let people add a personal message and customize who they share with” (Facebook). A plug-in like the Facebook Like button would keep users engaged with your content, and provide free marketing for your site.


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The HELM tools are hosted here.
Feel free to try them out, but please don't rely on them for production use since we make no guarentees about availability. For more information have a look at our wiki , go to www.openHELM.org or email us at<EMAIL_ADDRESS>|
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How does life look through the 'pleasure, passion and purpose' lens?
For me personally, I would prefer a type of happiness which is easily sustainable.  I'm always trying to find a purpose to what I do and would like to be part of something bigger than myself, something meaningful.
What are your core values?
My core values would be honesty (in it's widest sense), kindness. I am a man of my word. I would say thay I'm quit traditional and old fashioned.
What do you stand for?
I stand for basic human rights, fairness, freedom of speech.


Interactive Newman Projection
Newman projections are used to visualize various conformations of an alkane (a chain of single bonded carbons). This interactive plot can be used to calculate the strain of an alkane as the dihedral angle (angle between two arbitrary substituents) is varied. The left plot will track your mouse as you move it around, and the conformational strain will be plotted to the right. For best results, use a desktop computer.


This is a quiz to probe your knowledge about the Periodic Table Elements.
These are the questions in the quiz.


Featured List of Titles
Search Reference Databases
Inter-library Loan
Other Libraries via Z39.50
Send Suggestions for the Library
Books the Library Should Acquire
COURSE RESERVES:
INFORMATION:
By Course Name | By Instructor Name
About the Library | View Your Record


NAME
csvfind
SYNOPSIS
csvfind OPTIONS TEXT_TO_MATCH
DESCRIPTION
csvfind processes a CSV file as input returning rows that contain the column with matched text. Columns are counted from one instead of zero. Supports exact match as well as some Levenshtein matching.
OPTIONS
- -help
- display help
- -license
- display license
- -version
- display version
- -allow-duplicates
- allow duplicates when searching for matches
- -append-edit-distance
- append column with edit distance found (useful for tuning levenshtein)
- -case-sensitive
- perform a case sensitive match (default is false)
- -col, -cols
- column to search for match in the CSV file
- -contains
- use contains phrase for matching
- -d, -delimiter
- set delimiter character
- -delete-cost
- set the delete cost to use for levenshtein matching
- -i, -input
- input filename
- -insert-cost
- set the insert cost to use for levenshtein matching
- -levenshtein
- use levenshtein matching
- -max-edit-distance
- set the edit distance thresh hold for match, default 0
- -nl, -newline
- include trailing newline from output
- -o, -output
- output filename
- -quiet
- suppress error messages
- -skip-header-row
- skip the header row
- -stop-words
- use the colon delimited list of stop words
- -substitute-cost
- set the substitution cost to use for levenshtein matching
- -trim-leading-space
- trim leadings space in field(s) for CSV input
- -trimspace, -trimspaces
- trim spaces around cell values before comparing
- -use-lazy-quotes
- use lazy quotes on CSV input
EXAMPLES
Find the rows where the third column matches “The Red Book of Westmarch” exactly
csvfind -i books.csv -col=2 "The Red Book of Westmarch"
Find the rows where the third column (colums numbered 1,2,3) matches approximately “The Red Book of Westmarch”
csvfind -i books.csv -col=2 -levenshtein \
-insert-cost=1 -delete-cost=1 -substitute-cost=3 \
-max-edit-distance=50 -append-edit-distance \
"The Red Book of Westmarch"
In this example we’ve appended the edit distance to see how close the matches are.
You can also search for phrases in columns.
csvfind -i books.csv -col=2 -contains "Red Book"
csvfind 1.2.2


{% if form.create_fastq_for_index_reads %}yes{% else %}no{% endif %}
Sheet Sanity Check and Quality Control
Sheet Completeness
{% get_lanes_with_missing_sheets object as lanes_with_missing_sheets %}
{% if lanes_with_missing_sheets %}
Sample sheet missing for {{ lanes_with_missing_sheets|pluralize:"lane,lanes" }} {{ lanes_with_missing_sheets|pretty_range }}.
{% elif flowcell.lanes_suppress_no_sample_sheet_warning %}
{% get_lanes_with_missing_sheets object True as lanes_with_missing_sheets %}
Suppressed warning {{ lanes_with_missing_sheets|pluralize:"s" }} on lanes with missing sample sheets.
{% else %}
The sheet is complete
{% endif %}
All BCL indexes in sheet?
{% get_index_error_lanes object True as all_lanes_with_index_errors %}
{% if flowcell.get_index_errors %}
At least one index adapter was not found in the sample sheet.
{% elif flowcell.lanes_suppress_no_sample_found_for_observed_index_warning %}
Suppressed warnings about BCL index adapters that cannot be found in the sample sheet.
{% else %}
All (non-contamination) index adapters found in sheet.
{% endif %}
All sheet libraries in BCL?
{% get_libraries_with_suppressed_reverse_index_errors object as libraries_with_suppressed_index_errors %}
{% if flowcell.get_reverse_index_errors %}
There is an issue with a library on the sample shet.
{% elif libraries_with_suppressed_index_errors %}
Suppressed warnings about sample sheet index adapters that cannot be found in the BCL indices.
{% else %}
All (non-contamination) index adapters found in sheet.
{% endif %}


| Name |
Time |
Artist |
Album |
Genre |
| Yellow Submarine |
1:00 |
The Beatles |
Yellow Submarine |
Rock |
| Supermassive Black Hole |
5:46 |
Muse |
Blackholes and Revelations |
Metal |
| Yellow Submarine |
1:00 |
The Beatles |
Yellow Submarine |
Rock |
| Supermassive Black Hole |
5:46 |
Muse |
Blackholes and Revelations |
Metal |
| Yellow Submarine |
1:00 |
The Beatles |
Yellow Submarine |
Rock |
| Supermassive Black Hole |
5:46 |
Muse |
Blackholes and Revelations |
Metal |
| Yellow Submarine |
1:00 |
The Beatles |
Yellow Submarine |
Rock |
| Supermassive Black Hole |
5:46 |
Muse |
Blackholes and Revelations |
Metal |
3333 items, 105 hrs total time, 21GB


Receipt search |
| Status. | Building | room no.. | name | Tools |
Building1 |
543 |
ก.ไก่ |
||
Building1 |
544 |
ข.ไข่ |
||
Building1 |
545 |
ค.ไค่ |
||
Building1 |
546 |
ง.ไง่ |
Receipt search |
| Status. | Building | room no.. | name | Tools |
Building1 |
543 |
ก.ไก่ |
||
Building1 |
544 |
ข.ไข่ |
||
Building1 |
545 |
ค.ไค่ |
||
Building1 |
546 |
ง.ไง่ |


WITNESS LIST County Affairs Committee March 16, 2005-8:00A HB 631 For: Mintz, David (Texas Apartment Association) Montgomery, Mike (Harris County Fire & Emergency Services) Registering, but not testifying: For: deLafuente, Veronica (Office of the Cameron County Judge Gilberto Hinojosa, County Judge) Allison, Jim (County Judges and Commissioners Assn. of TX) Cain, Randy (Texas Fire Chiefs Assoc.) Jones, Amanda (Harris County) Lee, Donald (Texas Conference of Urban Counties) Lenz, Carole (Harris County Commissioner Steve Radack) Lewis, Jim (McLennan County) Mendez, Mark (Tarrant County Commissioners Court) Sisk, Cathy (Harris County Attorney) HB 663 For: Copeland, Kelli F. (Montgomerty County Animal Control) Moffett Jr., Jerry G. (Self) HB 950 For: Lee, Donald (Texas Conference of Urban Counties) Lee, Randy M. (Stewart Information Services Corp. LANDALA Technical Services) Mitchell, Cynthia (County & District Clerks Assoc.) On: Lehman, Mark (Texas Association of Realtors) Registering, but not testifying: For: Allison, Jim (County Judges & Commissioners Assn. of TX) Jones, Amanda (Harris County) Lewis, Jim (McLennan County) Mendez, Mark (Tarrant County Commissioners Court) Pardue, Craig (Dallas County) HB 1287 For: Lee, Donald (Texas Conference of Urban Counties) Powell, Leilah (Bexar County Commissioners Court) Sisk, Cathy (Harris County Attorney, Harris County Public Health) Registering, but not testifying: For: Allison, Jim (County Judges and Commissioners Assn. of TX) Lewis, Jim (McLennon County) Mendez, Mark (Tarrant County Commissioners Court) HB 1428 For: Smith, Mike (Texas Department of Public Safety Officers Assoc.) On: Anderson, Captain Trent (Texas Parks & Wildlife Law Enforcement Division) Flores, Colonel Pete (Texas Parks & Wildlife Law Enforcement Division)


Angelique Kerber announces new coach for 2020
by FSA EditorHaving split from Rainer Schuttler in July, Angelique Kerber will be bringing in Dieter Kindlmann as her new coach.
Angelique Kerber will work with Dieter Kindlmann in the next WTA season after announcing her fellow German as her new coach.
The three-time grand slam champion parted ways with coach Rainer Schuttler in July after just eight months.
Kerber, now ranked 20th in the world, confirmed the arrival of Kindlmann in a tweet on Thursday.
“As much as I’ve enjoyed my time off, I’m excited to get back into training,” wrote the former world number one.
“Happy to announce that Didi Kindlmann will be joining my team as the new coach! Work starts here, and I’m thankful for the people in my team that share the same vision moving forward!”
The move comes after she failed to win a title in 2019.
Kindlmann has previously worked with Madison Keys, Elise Mertens and, most recently, Ajla Tomljanovic.
Kerber’s most recent grand slam triumph came at Wimbledon in 2018, but she did not make it past the fourth round at any of the four majors this year.


{% extends 'base.html' %} {% load i18n %} {% load bootstrap %} {% block title %}{% trans 'Add likes to a resource/object' %} | {{ block.super }}{% endblock %} {% block content %} {% trans "Add new likes to a resource" %} Select Resource {% for object in resources %} {{ object }} {% endfor %} × Thank you for your response. You've successfully like this resource. {% endblock %}
You've successfully like this resource.


HEWNIQUORN TEKKNO MUZAK!
- DEVELOPMENT ZONE FOR SQUAWK 2.0: ATMlib -
Ticks: 0
Active: 0
MUTE FOR THE LOVE OF ALL THINGS SACRED!
RETRIG NOISE TEST (WHAT IF U HAD ROBOT EARS?)
PAUSE MAKES THE GEARS STOP TURNING...
EXPRIETTE (EXPERIMENT ONE)
Length:
Start:
...BUT REMEMBER, JOE WILL DANCE INTO ETERNITY!


Overview
Package
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Tree
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G
get(int, int)
- Method in class Maths.
AbstractMatrix
returns a value in the matrix. if the value has not been set yet it returns 0.
get(int, int)
- Method in class Maths.
BandMatrix
Get the entry of a element.
get(int, int)
- Method in class Maths.
Matrix
returns a value in the matrix. if the value has not been set yet it returns 0.
get(int)
- Method in class Maths.
Vector
gets a vector entry.
get(int, int)
- Method in class Model.
Image
returns a pixel value at a given point
getBufferedImage()
- Method in class Model.
Image
Gets this image as Buffered image
getColumnCount()
- Method in class View.ImageTable.
ImageTableModel
getColumnName(int)
- Method in class View.ImageTable.
ImageTableModel
getDescription()
- Method in class View.ImageChooser.
ImageFilter
getDisplay()
- Method in class Ctrl.
Controller
Returns the corresponding UI to be able to display informations.
getDoubleValue()
- Method in class View.NumericTextField.
NumericTextField
getE()
- Method in class Model.
HDRResult
returns radiance map.
getExposureTime()
- Method in class Model.
Image
gets the exposure time of a picture.
getExtension(File)
- Static method in class View.ImageChooser.
Utils
getFormat()
- Method in class View.NumericTextField.
NumericTextField
getG()
- Method in class Model.
HDRResult
returns the camera response curve
getGaussianKernel1D(int, double)
- Static method in class Maths.
Convolution
Returns a approximation of a gaussian kernel as stencil for the 1D.
getGaussianKernel2D(int, double)
- Static method in class Maths.
Convolution
Returns a approximation of a gaussian kernel as stencil for the 2D case.
getHeight()
- Method in class Model.
HDRResult
Height of the HDRI
getHeight()
- Method in class Model.
Image
getHistogram()
- Method in class Model.
Image
Gets the histogram of this image.
getImage(int, int, double[])
- Method in class View.ToneMappers.
ToneMapping
Generates an image out of the value
getImageSize()
- Method in class Model.
Image
getInstance()
- Static method in class Ctrl.
Controller
Singelton pattern for the controller.
getLongValue()
- Method in class View.NumericTextField.
NumericTextField
getMedian()
- Method in class Model.
Image
gets the median of the image.
getNumberValue()
- Method in class View.NumericTextField.
NumericTextField
getPrecision()
- Method in class Maths.
Vector
returns the precision of this vector set by @see setPrecision.
getRowCount()
- Method in class View.ImageTable.
ImageTableModel
getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int)
- Method in class View.ImageTable.
HighlightEmptyRenderer
getTableCellRendererComponent(JTable, Object, boolean, boolean, int, int)
- Method in class View.ImageTable.
LeftDotTableRenderer
getTypeDescription(File)
- Method in class View.ImageChooser.
ImageFileView
getValue(int)
- Method in class Model.
Image
returns grey value of this picture in a given index.
getValueAt(int, int)
- Method in class View.ImageTable.
ImageTableModel
getValues(double[][])
- Method in class View.ToneMappers.
ToneMapping
Public entry point for the Tone-Mapping Plot wheren we return a 2d array of greyvalues to print it.
getValuesIntern(double[][])
- Method in class View.ToneMappers.
LocalReinhardMapping
actual tone mapping function, called by super
getValuesIntern(double[][])
- Method in class View.ToneMappers.
ReinhardMapping
calculation
getVar(String)
- Method in class View.ToneMappers.
LocalReinhardMapping
Gets the vlaue of a parameter
getVar(String)
- Method in class View.ToneMappers.
ReinhardMapping
gets a value of a parameter
getVar(String)
- Method in class View.ToneMappers.
ToneMapping
returns the value of an parameter for this tone mapper
getVars()
- Method in class View.ToneMappers.
LocalReinhardMapping
getVars()
- Method in class View.ToneMappers.
ReinhardMapping
returns the varaiables
getVars()
- Method in class View.ToneMappers.
ToneMapping
Gets the available variables of this tone mapper
getWidth()
- Method in class Model.
HDRResult
Width of the HDRI
getWidth()
- Method in class Model.
Image
getXDescription()
- Method in class View.Plots.
ScatterPlot
Gets the description for the x direction
getYDescription()
- Method in class View.Plots.
ScatterPlot
Gets the description for the y direction
gif
- Static variable in class View.ImageChooser.
Utils
GUIFrame
- Class in
View
Gui of the Calculation program.
GUIFrame(String)
- Constructor for class View.
GUIFrame
Constructor to build up a new GUI for the HDR Solver.
Overview
Package
Class
Use
Tree
Index
Help
PREV LETTER
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All Classes
A
B
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D
E
F
G
H
I
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S
T
U
V
W


Pacific Road Runners First Half Half Marathon
Vancouver BC/CAN
Website: www.pacificroadrunners.ca/firsthalf/
Race Winners
(29 runnings)
1:16:05.9 10 Feb
2008
1:06:02.9
1:16:43.6
11 Feb
2007
1:04:44.7
1:16:42.9
19 Feb
2006
1:06:32.5
1:15:28.5
13 Feb
2005
1:07:08.6
1:15:25.4
08 Feb
2004
1:06:56
1:12:47
09 Feb
2003
1:09:55
1:16:10
10 Feb
2002
1:08:52
1:16:35
11 Feb
2001
1:07:47
1:18:08
13 Feb
2000
1:07:49
1:15:42
14 Feb
1999
1:07:01
1:19:08
15 Feb
1998
1:08:50
1:14:44
16 Feb
1997
1:04:59
1:19:29
11 Feb
1996
1:05:13
1:17:49
12 Feb
1995
1:06:49
1:20:44
13 Feb
1994
1:06:20
1:13:22
14 Feb
1993
1:08:15
1:15:35
09 Feb
1992
1:04:45
1:17:26
10 Feb
1991
1:05:53.6
1:15:08.1
11 Feb
1990
1:09:50
n/a
12 Feb
1989
1:04:23
1:10:45
10 Feb
2019
1:06:00
Dylan Wykes-4
1:17:43
Robyn Mildren (CAN)
11 Feb
2018
1:06:57
Robyn Watson-3
1:12:21
Rachel Cliff (CAN)
14 Feb
2016
1:05:46
Eric Gillis (ON/CAN)
1:15:11
Dayna Pidhoresky-2
15 Feb
2015
1:07:20
Dylan Wykes-3
1:16:10
Dayna Pidhoresky (BC/CAN)
16 Feb
2014
1:06:38
Robyn Watson-2
1:17:04
Natasha Wodak-2
10 Feb
2013
1:05:38.8
Robyn Watson (BC/CAN)
1:17:34.1
Catrin Jones (BC/CAN)
12 Feb
2012
1:04:21.4
Dylan
Wykes-2
1:16:18.6
Natasha Wodak (BC/CAN)
13 Feb
2011
1:04:39.3
Dylan Wykes (BC/CAN)
1:18:49.2
Ellie Greenwood (SCO)
2010
---
cancelled due to winter olympics
---
cancelled due to winter olympics
15 Feb
2009
1:05:42.2
Richard Mosley (BC/CAN)
Cheryl Murphy (BC/CAN)
Ryan Hayden-2
Kirsty Smith (CAN)
Ryan Hayden (PA/CAN)
Lisa Harvey-4
Steve Osaduik (CAN)
Tina Connelly-3
Jeremy Deere (CAN)
Leah Pells (CAN)
Bruce Deacon-3
Tina Connelly-2
Neil Holm (CAN)
Janine Moffett (CAN)
Colin Dignum-2
Lisa Harvey-3
Bruce Deacon-2
Lisa Harvey-2
Norman Tinkham-2
Lisa Harvey (CAN)
Colin Dignum (CAN)
Erin Heffring (CAN)
Norman Tinkham (CAN)
Lucille Smith-2
Phillip Ellis-2
Meghan O'Brian (CAN)
Carey Nelson (CAN)
Tina Connelly (CAN)
Phillip Ellis (CAN)
Lucille Smith (CAN)
Art Boileau-3
Lisa Rainsberger (USA)
Art Boileau-2
Isabelle Dittberner-2
Bruce Deacon (CAN)
Jacqueline Zawertailo (CAN)
Art Boileau (CAN)
Sylviane Puntous (CAN)
Ashley Dustow (CAN)
Carolyn Hubbard (CAN)
Peter Butler (CAN)
Isabelle Dittberner (BEL)
Notes: The 1989 course was about 1000m short. There was no race in 2017.
Most Wins
(1989-date)
Men
4
Dylan Wykes (CAN)
2011-12,2015,2019
Women
4
Lisa Harvey (CAN)
2000-02,2007
Prize Money
(ADR qualified runners)
Year Men Women Total 2016 $1,085 $1,370 $2,455 2015 760 960 1,720 2014 1,045 1,090 2,135 2013 1,250 1,250 2,500 2012 1,250 1,450 2,700 2011 2,630 1,110 3,740 2009 920 960 1,880 2008 1,190 1,140 2,330 2007 1,480 545 2,025 2006 390<PHONE_NUMBER> 765 805 1,570 2004 530 535 1,065 .... 2002 410<PHONE_NUMBER> 435<PHONE_NUMBER> 345 450 795 Sums $14,485 $12,990 $27,475
Race Time Bias (sec/km)
Year Men Women 2016 n/c -0.3 2015 n/c -1.4 .... 2009 -1.0 -1.5 .... 2007 -0.6 n/c .... 2005 -2.5 +0.1 Avgs -1.3 -0.8
Competitive Level (1990-date)
Year Men Women 2016<PHONE_NUMBER> n/c 1 .... 2009 17 30 2008 38 n/c 2007 123 n/c .... 2005 47 141
Quality Performances
Men <1:04:30 1 <1:05:00 6 <1:05:30 9 <1:06:00 16 Women <1:13:00 2 <1:14:00 3 <1:15:00 4 <1:16:00 13 Year Fin 2019 1842 2018 1535 2016 2012 2015 2082 2014 1928 2013 1918 2012 1978 2011 2018 2009 2030 2008 2016 2007 2011 2006 1905 2005 1988 2004 1890 2003 1918 2002 1843 2001 1993 2000 1753 1999 1615 1998 1455 1997 1200 1996 1201 1995 1046 1994 991 1993 454 1992 506 1991 456 1990 1989
Last Updated on 24 Apr 2019
Contributors: Maurice Wilson (50), Juraj Gasparovic (2)


(.) molecules CCSD/daDZ BLYP/daDZ iso values QMb aniso basis \documentclass[pt]{minimal} \setlength{\oddsidemargin}{-pt} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amsmath} \usepackage{amssymb} \usepackage{mathrsfs} \usepackage{upgreek} \usepackage{wasysym} showcase AlphaML molecular set SCAN/daDZ Chem. database properties Phys. {border-style e., Bohr Fig. energy error px;} #ffffff;} calculations .E- Table computed data larger dipole DFT (see BLYP/daTZ polarizability provided ./. analysis polarizabilities using chemical correlation errors quantum theory computational databases used CCSD \begin{document}$${\alpha anisotropic database, database. density excitations incompleteness isotropic large CCSD/daDZ, accurate convergence electron functional largest level molecule moment performed provide px; respect response {font-family Data Quantum RMSE Science comparing computing corresponding employed font-size includes mechanical obtained ref sees small statistical water xyz (and BLYP MSE National Research SCAN/daDZ, aniso) contained depicted double; following quadrupole second significantly structure use vs. work, xx, xy, xz, yy, zz, }_{i}-{\alpha (for (–) Chemical Commons Cornell Creative E_convergence Eq. Jr. Level MAE, No. SCAN Statistical Yang aniso, atomic available border-color border-width cost coupled-cluster criteria d-aug-cc-pVDZ different distributions error, file files functionals g., geometries hybrid iso, mean metadata method molecules, moments, order percent popular prediction sets signed size solid; solution standard static summary tensor total work. {font-style {font-weight {}^{-}$$\end{document} (). (in (which .%, BLYP/daDZ, Computational DNA/RNA Database Department DiStasio FPS- HOMO LR-CCSD/daDZ LUMO Lao MAE Materials Psi Q-Chem Reference Rev. SPE Theory Type(s) University \begin{document}$${\epsilon amino associated benchmark bold calculations, chemistry compared contains courier; cyclic daDZ described electric electronic em; energies finite-field force generation included including initio isomers learning left level, max measure min molecule: number open-chain opposite-spin pharmaceutical predicting probability pt;} quantity reference reliable same-spin smallest spectra sufficiently tot treatment uncharged underlying work yz. {\rm{ }_{{\rm{aniso}}}^{{\rm{CCSD/daDZ}}}=.\times }}$$\end{document}


OpenNaaS Datacenter GUI
This is an example interface for operating the OpenNaaS REST interface.Copyright © 2014
SURFsara B.V.
Project homepage: www.opennaas.org
Developed by:
Erik Ruiter
License:
This software is distributed under the GNU General Public License (GPL).
Disclaimer of warranty:
OpenNaas-DC-GUI is provided as is with NO WARRANTY OF ANY KIND, including the warranty
of design, merchantability, and fitness for a particular purpose.
Powered by:
{% endblock %}


@( appConfig: lib.AppConfig, membership: io.apibuilder.api.v0.models.Membership )
@membership.user.name<EMAIL_ADDRESS>has joined @membership.organization.name as @membership.role.
View all members


STATES
BEATLES
MEATS
Alaska
Ringo
Ham
Ohio
George
Chicken
Florida
Paul
Turkey
Nevada
John
Beef
California
Larry
Zebra
New Jersey
Stig
Pork
Michigan
Kevin
Antelope


{% extends 'layout.html' %} {% set pageHeading = "Before you use NHS login" %} {% block pageTitle %} {{pageHeading}} | {{serviceName}} {% endblock %} {% block beforeContent %} {% endblock %} {% block content %}
If you access this service using your NHS login details, please note that the identity verification services are managed by NHS Digital.
NHS Digital is the controller for any personal information you provided to NHS Digital to get an NHS login account and verify your identity. It uses your personal information solely for that purpose.
For this personal information, our role is as a “processor” only. We must act under the instructions provided by NHS Digital (as the “controller”) when verifying your identity.
Read NHS Digital’s Privacy Notice and Terms and Conditions (opens in a new tab) for more information. This restriction does not apply to the personal information you provide to us separately.


---
layout: default
title: About
permalink: /about/
include_in_nav: true
body-color: my-black lighten-2
---
History
{% include_relative about/history.md %}
Mission
{% include_relative about/mission.md %}
Unify
{% include_relative about/unify.md %}
Reclaim
{% include_relative about/reclaim.md %}
Empower
{% include_relative about/empower.md %}
Structure
{% include_relative about/structure.md %}
Our Team
{% assign team = site.people | where_exp: "person", "person.team == true" %}
{% assign team_ordered = team | sort: 'weight' %}
{% for person in team_ordered %}
{% if person.cloudinary_image %}
{% endif %}
{{person.name}}
{{ person.position }}
{{ person.content | strip_html }}
More Info
{% endfor %}
Member Orgs
{% assign member_orgs = site.orgs | where : "member_org","true" %}
{% for org in member_orgs %}
{% if org.cloudinary_image %}
{% endif %}
{{org.name}}
{{ org.content | strip_html }}
More Info
{% endfor %}
Extended Family
{% assign other_orgs = site.orgs | where : "member_org","false" %}
{% for org in other_orgs %}
{{org.name}}
{{ org.content | strip_html }}
More Info
{% endfor %}


Finished re-reading /A Game of Thrones/ the same day George R. R. Martin announces /A Dance with Dragons/ is finally complete. Sweet!
4/27/2011, 8:50:14 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Finished re-reading /A Game of Thrones/ the same day George R. R. Martin announces /A Dance with Dragons/ is finally complete. Sweet!
4/27/2011, 8:50:14 PM
Favs: 0
Retweets: 0
link


Base Classes¶
Base classes
-
class
base.
Logger
(lvl='INFO')¶ Logger class used by Pipe. There are five levels of logs: INFO, DEBUG, WARNING, ERROR and CRITICAL. By default logger is set to INFO.
- Parameters
lvl – log level, one of: info, debug, warning, error or critical
- Returns
None
-
log
(msg, name, lvl='info')¶ Logs messages.
- Parameters
msg – message to log
name – name of logger
lvl – log level, one of: info, debug, warning, error or critical
- Returns
None
-
class
base.
Pipe
(functor_obj, name, upstreams=None, downstreams=None, ignore_exceptions=None, init_kwargs=None)¶ Base class for all pipe segments. Pipes use two sets of Streams: upstreams and downstreams. Generally Pipes except data from upstreams and pass downstream after a transformation. All pipe segments run on their own thread or process, which allows them to run in parallel with other segments.
Number of upstreams should be equal to number of functor args. Likewise, number of downstreams should be equal to number of functor outputs.
When Pipe produces a None it will not be passed downstream. In this case nothing will be placed on the downstreams. This allows the user to create ‘switches’ based on internal logic in the functor.
- Parameters
functor – Python function, class, generator or corountine
name – String associated with pipe segment
upstreams – List of Streams that are inputs to functor
downstreams – List of Streams that are outputs of functor
ignore_exceptions – List of exceptions to ignore while pipeline is running
init_kwargs – Kwargs to initiate class object on process (no used when func_type = ‘function’)
stateful – Set to True when using a class functor. Class functors must implement a ‘run’ method
-
class
base.
Sentinel
¶ This class is used to indicate the end of a stream. When a instance of Sentinel is passed to a Pipe it will shut itself down.
-
class
base.
Stream
(name='stream', buffer_size=3, timeout=None, monitor=False, queue_type='multiprocessing.Queue')¶ Based off of a multiprocessing queue, Stream handles moving data between Pipe segments.
- Parameters
name – String, name of Stream
buffer_size – Int, max size of queue. Will be ignored if queue_type = “multiprocessing.SimpleQueue”
timeout – Int, timeout(s) value for Queue.put and Queue.get methods
monitor – Bool, log stream I/O times
- Queue_type
String, multiprocesses queue type to be used. Valid types: ‘multiprocessing.Queue’, ‘multiprocessing.SimpleQueue’
- Returns
None


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The point of this example is to have an ESP8266 with Espruino have an interactive connection to a web page. This page can be hosted anywhere. ESP8266 and web page only have to agree on the WebSocket server to connect to.
Set Color: Red Green Blue White Off


v0.4.0-SNAPSHOT
|
Juan Antonio Breña Moral (40) Chapter Lead, Deejay & Mindstorms/STEAM teacher. Twitter | Github | Linkedin |
|
| Purpose: "Riding dragons with innovation & fun" | |
|
Quote: "Pressure makes diamonds." - George S. Patton Jr. |
“Production is the best place on the Internet.”
I wish the audience could:
functional programming is a programming paradigm style of building the structure and elements of computer programs—that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data.
You can model solutions using this paradigm with Scala, Clojure, Kotlin & Java in the JVM.
Agenda:
Skills:
Performance Characteristics of Vavr Collections
Performance Characteristics of Vavr Collections
Destructuring is a special syntax that allows us to “unpack” arrays or objects into a bunch of variables.
Function composition is a mechanism to combine simple functions to build more complicated ones.
The ability to easily compose functions encourages factoring (breaking apart) functions for maintainability and code reuse.
Lambda expressions provide a clear and concise way to represent one method interface using an expression.
java.util.function.*
Agenda:
Skills:
Agenda:
Skills:
Thanks


For further assistance click HELP or contact your Spira system administrator.
The Spira Import/Export Tool helps you: get information from Spira into this spreadsheet (export your data); or prepare data to send to Spira (import new data)
What do you want to do?
1. Choose where to send data to in Spira1. Choose where to get data from in Spira
Fetching the data needed...
The template is set to: , in project: , but you have chosen different options
2. Start the data transfer with Spira
Quick Help Guide
Connecting Your Spira App
You can use this add-on with SpiraTest®, SpiraTeam®, or SpiraPlan®. It works with any cloud Spira app. Any on premise Spira needs to be accessible over the internet.
You can use this add-on with SpiraTest®, SpiraTeam®, or SpiraPlan®. If you are using Excel in the browser, your Spira app needs to be accessible over the internet.
Enter your SpiraTeam URL
Use the web address you use to access Spira in your browser. This is usually of the form 'http://(hostname)/spiraplan'. Make sure you remove any suffixes from the address (e.g. Default.aspx or "/")
Enter your user name
Enter the user name you use to log in to Spira. (Not Case Sensitive)
Enter your RSS token
You can find or generate this from your user profile page inside Spira. Make sure to include the curly braces i.e {ExampleRSS}
Actions
Help
This button will open an in app help menu with basic functionality information.
Logout
This button will close your connection with SpiraTeam and clear your data. WARNING: your spreadsheet data will be erased as a security measure on disconnect.
Projects
Select a project from the menu.
Artifacts
Select an artifact from the menu.
Prepare Template
Click this button to generate the template for the project and artifact you’ve selected.
Get From Spira
Click this button to generate the required template and then fetch the data for the project and artifact selected.
Required Fields
Required fields are marked by their name in the title row shown as bold black text (standard fields are regular light text)
For test steps, required fields are shown in black, but not bold text.
Help For Specific Fields
ID Fields
Stores the id of the item. This field MUST be left blank to add new items to SpiraTeam
Test Step Fields
have their header row shown in a lighter background color. If the system cannot tell whether an entry is a test case or step it is skipped over when sending to Spira.
Name
For REQUIREMENTS and RELEASES this field supports indentation, add a “ > “ symbol to indicate how the items in the artifact hierarchy are organized.
Example:
Item 1
>Item 2 child of item 1
>Item 3 child of item 1
>>Item 4 child of item 3
CommentsNot Currently Supported
Custom Field Entries
Text
Enter any text. Include HTML tags for rich text.
Decimal
Enter any positive number.
Integer
Enter a non-decimal number.
Date
Enter the date in any standard format.
List
Select a value from the dropdown list.
MultiList
Select an item from the provided list. This is not currently supported


Public | ||
| virtual void | Seed (const unsigned int seedVal, const unsigned int seedVal2) | : Seeds the randomizer with a beginning value |
| virtual T | GetRandBetween (const T minVal, const T maxVal) | : Gets a rand between minVal and maxVal inclusive |
| virtual const T | HighestRand () const | : Highest random number that will be generated |
| virtual const T | LowestRand () const | : Lowest random number that will be generated |
| virtual const T | RandGenerationRange () const | : The range (HighestRand - LowestRand) that can be generated |
Protected | ||
| virtual T | GetRand (const long double maxVal) |
In some children of RandGenerator, this function
may be meaningless. Also, seedVal2 need not be
used by all children.
PRECONDITIONS:
virtual T GetRandBetween(const T minVal, const T maxVal)
(minVal <= maxVal),
(minVal >= LowestRand),
(maxVal <= HighestRand),
(RandGenerationRange >= maxVal-minVal)


| PhotoLine Tip: How to morph one image to another |
| 1. Start
with two images, which have to be layer one and two of a PhotoLine 32
document, for example
If the images are in two windows, select the layer tool from the toolbar, press the Alt-key and drag the second image onto the first one. 2. Select
Filter/Shape Tools/Morph.
3. Setup the amount of steps you want to get at Steps. 4. Now press OK and PhotoLine 32 will create an animation. 5. Select
Tool/Animate layers to see the animation. You can save the animation
in PLD- or GIF-format.
|
| ««Previous Tip <Contents> Next Tip»» |
| Please email questions and suggestions on these tips to Gerhard Huber. |


| from |
To |
Date |
time |
price |
requests |
|
| Embakasi |
Ngong |
June 3, 2018 |
08:00 am |
60 kshs |
5 |
|
| Rongai |
Ngong |
June 1, 2018 |
08:00 am |
100 kshs |
6 |
|
| Nakuru |
Thika |
June 10, 2018 |
10:00 am |
70 kshs |
3 |
|
| Lavington |
JKIA |
June 10, 2018 |
03:00 pm |
50 kshs |
2 |
|
| Nairobi |
Kitale |
March 12, 2018 |
08:00 am |
600 kshs |
9 |
|
| Kitale |
Nairobi |
March 10, 2018 |
08:00 am |
600 kshs |
1 |
|
| Machakos |
Westlands |
Feb 14, 2018 |
12:00 pm |
20 kshs |
0 |
|


Dense
keras.layers.core.Dense(output_dim, init='glorot_uniform', activation='linear', weights=None, W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None, bias=True, input_dim=None)
Just your regular fully connected NN layer.
Example
# as first layer in a sequential model:
model = Sequential()
model.add(Dense(32, input_dim=16))
# now the model will take as input arrays of shape (*, 16)
# and output arrays of shape (*, 32)
# this is equivalent to the above:
model = Sequential()
model.add(Dense(32, input_shape=(16,)))
# after the first layer, you don't need to specify
# the size of the input anymore:
model.add(Dense(32))
Arguments
- output_dim: int > 0.
- init: name of initialization function for the weights of the layer
(see initializations),
or alternatively, Theano function to use for weights
initialization. This parameter is only relevant
if you don't pass a
weights
argument. - activation: name of activation function to use (see activations), or alternatively, elementwise Theano function. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
- weights: list of Numpy arrays to set as initial weights.
The list should have 2 elements, of shape
(input_dim, output_dim)
and (output_dim,) for weights and biases respectively. - W_regularizer: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.
- b_regularizer: instance of WeightRegularizer, applied to the bias.
- activity_regularizer: instance of ActivityRegularizer, applied to the network output.
- W_constraint: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.
- b_constraint: instance of the constraints module, applied to the bias.
- bias: whether to include a bias (i.e. make the layer affine rather than linear).
- input_dim: dimensionality of the input (integer).
This argument (or alternatively, the keyword argument
input_shape
) is required when using this layer as the first layer in a model.
Input shape
2D tensor with shape: (nb_samples, input_dim)
.
Output shape
2D tensor with shape: (nb_samples, output_dim)
.
Activation
keras.layers.core.Activation(activation)
Applies an activation function to an output.
Arguments
- activation: name of activation function to use
- (see: activations), or alternatively, a Theano or TensorFlow operation.
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as input.
Dropout
keras.layers.core.Dropout(p)
Applies Dropout to the input. Dropout consists in randomly setting
a fraction p
of input units to 0 at each update during training time,
which helps prevent overfitting.
Arguments
- p: float between 0 and 1. Fraction of the input units to drop.
References
SpatialDropout2D
keras.layers.core.SpatialDropout2D(p, dim_ordering='default')
This version performs the same function as Dropout, however it drops entire 2D feature maps instead of individual elements. If adjacent pixels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout2D will help promote independence between feature maps and should be used instead.
Arguments
- p: float between 0 and 1. Fraction of the input units to drop.
- dim_ordering: 'th' or 'tf'. In 'th' mode, the channels dimension
(the depth) is at index 1, in 'tf' mode is it at index 3.
It defaults to the
image_dim_ordering
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "tf".
Input shape
4D tensor with shape:
(samples, channels, rows, cols)
if dim_ordering='th'
or 4D tensor with shape:
(samples, rows, cols, channels)
if dim_ordering='tf'.
Output shape
Same as input
References
SpatialDropout3D
keras.layers.core.SpatialDropout3D(p, dim_ordering='default')
This version performs the same function as Dropout, however it drops entire 3D feature maps instead of individual elements. If adjacent voxels within feature maps are strongly correlated (as is normally the case in early convolution layers) then regular dropout will not regularize the activations and will otherwise just result in an effective learning rate decrease. In this case, SpatialDropout3D will help promote independence between feature maps and should be used instead.
Arguments
- p: float between 0 and 1. Fraction of the input units to drop.
- dim_ordering: 'th' or 'tf'.
In 'th' mode, the channels dimension (the depth)
is at index 1, in 'tf' mode is it at index 4.
It defaults to the
image_dim_ordering
value found in your Keras config file at~/.keras/keras.json
. If you never set it, then it will be "tf".
Input shape
5D tensor with shape:
(samples, channels, dim1, dim2, dim3)
if dim_ordering='th'
or 5D tensor with shape:
(samples, dim1, dim2, dim3, channels)
if dim_ordering='tf'.
Output shape
Same as input
References
Flatten
keras.layers.core.Flatten()
Flattens the input. Does not affect the batch size.
Example
model = Sequential()
model.add(Convolution2D(64, 3, 3, border_mode='same', input_shape=(3, 32, 32)))
# now: model.output_shape == (None, 64, 32, 32)
model.add(Flatten())
# now: model.output_shape == (None, 65536)
Reshape
keras.layers.core.Reshape(target_shape)
Reshapes an output to a certain shape.
Arguments
- target_shape: target shape. Tuple of integers, does not include the samples dimension (batch size).
Input shape
Arbitrary, although all dimensions in the input shaped must be fixed.
Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
(batch_size,) + target_shape
Example
# as first layer in a Sequential model
model = Sequential()
model.add(Reshape((3, 4), input_shape=(12,)))
# now: model.output_shape == (None, 3, 4)
# note: `None` is the batch dimension
# as intermediate layer in a Sequential model
model.add(Reshape((6, 2)))
# now: model.output_shape == (None, 6, 2)
Permute
keras.layers.core.Permute(dims)
Permutes the dimensions of the input according to a given pattern.
Useful for e.g. connecting RNNs and convnets together.
Example
model = Sequential()
model.add(Permute((2, 1), input_shape=(10, 64)))
# now: model.output_shape == (None, 64, 10)
# note: `None` is the batch dimension
Arguments
- dims: Tuple of integers. Permutation pattern, does not include the
samples dimension. Indexing starts at 1.
For instance,
(2, 1)
permutes the first and second dimension of the input.
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same as the input shape, but with the dimensions re-ordered according to the specified pattern.
RepeatVector
keras.layers.core.RepeatVector(n)
Repeats the input n times.
Example
model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension
model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
Arguments
- n: integer, repetition factor.
Input shape
2D tensor of shape (nb_samples, features)
.
Output shape
3D tensor of shape (nb_samples, n, features)
.
Merge
keras.engine.topology.Merge(layers=None, mode='sum', concat_axis=-1, dot_axes=-1, output_shape=None, output_mask=None, node_indices=None, tensor_indices=None, name=None)
A Merge
layer can be used to merge a list of tensors
into a single tensor, following some merge mode
.
Example usage
model1 = Sequential()
model1.add(Dense(32))
model2 = Sequential()
model2.add(Dense(32))
merged_model = Sequential()
merged_model.add(Merge([model1, model2], mode='concat', concat_axis=1)
- ____TODO__: would this actually work? it needs to.__
# achieve this with get_source_inputs in Sequential.
Arguments
- layers: can be a list of Keras tensors or a list of layer instances. Must be more than one layer/tensor.
- mode: string or lambda/function. If string, must be one
- of: 'sum', 'mul', 'concat', 'ave', 'cos', 'dot', 'max'. If lambda/function, it should take as input a list of tensors and return a single tensor.
- concat_axis: integer, axis to use in mode
concat
. - dot_axes: integer or tuple of integers, axes to use in mode
dot
orcos
. - output_shape: either a shape tuple (tuple of integers), or a lambda/function
to compute
output_shape
(only if merge mode is a lambda/function). If the argument is a tuple, it should be expected output shape, not including the batch size (same convention as theinput_shape
argument in layers). If the argument is callable, it should take as input a list of shape tuples- (1:1 mapping to input tensors) and return a single shape tuple, including the
batch size (same convention as the
get_output_shape_for
method of layers).
- (1:1 mapping to input tensors) and return a single shape tuple, including the
batch size (same convention as the
- node_indices: optional list of integers containing the output node index for each input layer (in case some input layers have multiple output nodes). will default to an array of 0s if not provided.
- tensor_indices: optional list of indices of output tensors to consider for merging (in case some input layer node returns multiple tensors).
- output_mask: mask or lambda/function to compute the output mask (only if merge mode is a lambda/function). If the latter case, it should take as input a list of masks and return a single mask.
Lambda
keras.layers.core.Lambda(function, output_shape=None, arguments={})
Used for evaluating an arbitrary Theano / TensorFlow expression on the output of the previous layer.
Examples
# add a x -> x^2 layer
model.add(Lambda(lambda x: x ** 2))
# add a layer that returns the concatenation
# of the positive part of the input and
# the opposite of the negative part
def antirectifier(x):
x -= K.mean(x, axis=1, keepdims=True)
x = K.l2_normalize(x, axis=1)
pos = K.relu(x)
neg = K.relu(-x)
return K.concatenate([pos, neg], axis=1)
def antirectifier_output_shape(input_shape):
shape = list(input_shape)
assert len(shape) == 2 # only valid for 2D tensors
shape[-1] *= 2
return tuple(shape)
model.add(Lambda(antirectifier, output_shape=antirectifier_output_shape))
Arguments
- function: The function to be evaluated. Takes input tensor as first argument.
- output_shape: Expected output shape from function.
Can be a tuple or function.
If a tuple, it only specifies the first dimension onward;
sample dimension is assumed either the same as the input:
output_shape = (input_shape[0], ) + output_shape
or, the input isNone
and the sample dimension is alsoNone
:output_shape = (None, ) + output_shape
If a function, it specifies the entire shape as a function of the input shape:output_shape = f(input_shape)
- arguments: optional dictionary of keyword arguments to be passed to the function.
Input shape
Arbitrary. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.
Output shape
Specified by output_shape
argument.
ActivityRegularization
keras.layers.core.ActivityRegularization(l1=0.0, l2=0.0)
Layer that passes through its input unchanged, but applies an update to the cost function based on the activity.
Arguments
- l1: L1 regularization factor (positive float).
- l2: L2 regularization factor (positive float).
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as input.
Masking
keras.layers.core.Masking(mask_value=0.0)
Masks an input sequence by using a mask value to identify timesteps to be skipped.
For each timestep in the input tensor (dimension #1 in the tensor),
if all values in the input tensor at that timestep
are equal to mask_value
, then the timestep will masked (skipped)
in all downstream layers (as long as they support masking).
If any downstream layer does not support masking yet receives such an input mask, an exception will be raised.
Example
Consider a Numpy data array x
of shape (samples, timesteps, features)
,
to be fed to a LSTM layer.
You want to mask timestep #3 and #5 because you lack data for
these timesteps. You can:
- set
x[:, 3, :] = 0.
andx[:, 5, :] = 0.
- insert a
Masking
layer withmask_value=0.
before the LSTM layer:
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(timesteps, features)))
model.add(LSTM(32))
Highway
keras.layers.core.Highway(init='glorot_uniform', transform_bias=-2, activation='linear', weights=None, W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None, bias=True, input_dim=None)
Densely connected highway network, a natural extension of LSTMs to feedforward networks.
Arguments
- init: name of initialization function for the weights of the layer
(see initializations),
or alternatively, Theano function to use for weights
initialization. This parameter is only relevant
if you don't pass a
weights
argument. - transform_bias: value for the bias to take on initially (default -2)
- activation: name of activation function to use (see activations), or alternatively, elementwise Theano function. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
- weights: list of Numpy arrays to set as initial weights.
The list should have 2 elements, of shape
(input_dim, output_dim)
and (output_dim,) for weights and biases respectively. - W_regularizer: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.
- b_regularizer: instance of WeightRegularizer, applied to the bias.
- activity_regularizer: instance of ActivityRegularizer, applied to the network output.
- W_constraint: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.
- b_constraint: instance of the constraints module, applied to the bias.
- bias: whether to include a bias (i.e. make the layer affine rather than linear).
- input_dim: dimensionality of the input (integer).
This argument (or alternatively, the keyword argument
input_shape
) is required when using this layer as the first layer in a model.
Input shape
2D tensor with shape: (nb_samples, input_dim)
.
Output shape
2D tensor with shape: (nb_samples, input_dim)
.
References
MaxoutDense
keras.layers.core.MaxoutDense(output_dim, nb_feature=4, init='glorot_uniform', weights=None, W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None, bias=True, input_dim=None)
A dense maxout layer.
A MaxoutDense
layer takes the element-wise maximum of
nb_feature
Dense(input_dim, output_dim)
linear layers.
This allows the layer to learn a convex,
piecewise linear activation function over the inputs.
Note that this is a linear layer;
if you wish to apply activation function
(you shouldn't need to --they are universal function approximators),
an Activation
layer must be added after.
Arguments
- output_dim: int > 0.
- nb_feature: number of Dense layers to use internally.
- init: name of initialization function for the weights of the layer
(see initializations),
or alternatively, Theano function to use for weights
initialization. This parameter is only relevant
if you don't pass a
weights
argument. - weights: list of Numpy arrays to set as initial weights.
The list should have 2 elements, of shape
(input_dim, output_dim)
and (output_dim,) for weights and biases respectively. - W_regularizer: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.
- b_regularizer: instance of WeightRegularizer, applied to the bias.
- activity_regularizer: instance of ActivityRegularizer, applied to the network output.
- W_constraint: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.
- b_constraint: instance of the constraints module, applied to the bias.
- bias: whether to include a bias (i.e. make the layer affine rather than linear).
- input_dim: dimensionality of the input (integer).
This argument (or alternatively, the keyword argument
input_shape
) is required when using this layer as the first layer in a model.
Input shape
2D tensor with shape: (nb_samples, input_dim)
.
Output shape
2D tensor with shape: (nb_samples, output_dim)
.
References
TimeDistributedDense
keras.layers.core.TimeDistributedDense(output_dim, init='glorot_uniform', activation='linear', weights=None, W_regularizer=None, b_regularizer=None, activity_regularizer=None, W_constraint=None, b_constraint=None, bias=True, input_dim=None, input_length=None)
Apply a same Dense layer for each dimension[1] (time_dimension) input. Especially useful after a recurrent network with 'return_sequence=True'.
- Note: this layer is deprecated, prefer using the
TimeDistributed
wrapper:
model.add(TimeDistributed(Dense(32)))
Input shape
3D tensor with shape (nb_sample, time_dimension, input_dim)
.
Output shape
3D tensor with shape (nb_sample, time_dimension, output_dim)
.
Arguments
- output_dim: int > 0.
- init: name of initialization function for the weights of the layer
(see initializations),
or alternatively, Theano function to use for weights
initialization. This parameter is only relevant
if you don't pass a
weights
argument. - activation: name of activation function to use (see activations), or alternatively, elementwise Theano function. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).
- weights: list of Numpy arrays to set as initial weights.
The list should have 2 elements, of shape
(input_dim, output_dim)
and (output_dim,) for weights and biases respectively. - W_regularizer: instance of WeightRegularizer (eg. L1 or L2 regularization), applied to the main weights matrix.
- b_regularizer: instance of WeightRegularizer, applied to the bias.
- activity_regularizer: instance of ActivityRegularizer, applied to the network output.
- W_constraint: instance of the constraints module (eg. maxnorm, nonneg), applied to the main weights matrix.
- b_constraint: instance of the constraints module, applied to the bias.
- bias: whether to include a bias (i.e. make the layer affine rather than linear).
- input_dim: dimensionality of the input (integer).
This argument (or alternatively, the keyword argument
input_shape
) is required when using this layer as the first layer in a model. - input_length: length of inputs sequences (integer, or None for variable-length sequences).


--- layout: page title: UniExtract2 dev snapshots ---
1. Added RTL mirroring code, based upon which language file is
selected (Arabic, Farsi or Hebrew currently) (PR #84 Bioruebe/UniExtract2)
2. Added a preliminary hebrew translation
3. RTL code has issues with the GetPos function inside the program...
need to figure out how autoit works (image shows the issue)


Crystal structures of a) cubic 3D perovskite, b) (001) 2D perovskite ( N‐MPDA)[PbBr 4], and c) (110) 2D perovskite ( N‐MEDA)[PbBr 4]. N‐MPDA = N′‐methylpropyl‐1,3‐diammonium; N‐MEDA = N′‐methylethane‐1,2‐diammonium. a) Reproduced with permission. 9 Copyright 2018, Annual Reviews. b,c) Adapted with permission. 11 Copyright 2014, American Chemical Society.


registers a service to the Services by instantiating a GameObject asset loaded from Resources
More...
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| ResourceServiceRegistration (Type registrationInterface, Type concreteType, int registrationGroup=Services.REGISTRATION_GROUP_DEFAULT, ServiceResourceType resourceType=ServiceResourceType.REQUIRED, string overrideResourcePath=null) |
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ServiceRegistration | SetRegistrationGroup (int registrationGroup) |
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void | SetServiceRegistration (ServiceLoader loader) |
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void | RegisterService (Services toLocator) |
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virtual void | InitService (Services serviceLocator, System.Action onCompleteCallback) |
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bool | UnregisterService (Services toLocator) |
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ServiceType | GetService< ServiceType > (Services serviceLocator) |
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object | GetService (Services serviceLocator) |
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override string | ToString () |
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object | NewInstanceOfService () |
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virtual object | CreateService () |
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virtual object | ConfigureService (object service) |
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bool | isProxy [get] |
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Type | registrationType [get] |
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Type | concreteType [get] |
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ServiceResourceType | resourceType [get] |
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string | overrideResourcePath [get] |
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int | registrationGroup [get] |
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int | registrationGroup [get] |
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bool | isProxy [get] |
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registers a service to the Services by instantiating a GameObject asset loaded from Resources
The documentation for this class was generated from the following file:
- Runtime/services/ServiceRegistration/ResourceServiceRegistration.cs


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lwIP
2.1.3
Lightweight IP stack
|
Macros | |
| #define | LWIP_IPV6_DHCP6 0 |
| #define | LWIP_IPV6_DHCP6_STATEFUL 0 |
| #define | LWIP_IPV6_DHCP6_STATELESS LWIP_IPV6_DHCP6 |
| #define | LWIP_DHCP6_GET_NTP_SRV 0 |
| #define | LWIP_DHCP6_MAX_NTP_SERVERS 1 |
| #define | LWIP_DHCP6_MAX_DNS_SERVERS DNS_MAX_SERVERS |
| #define LWIP_DHCP6_GET_NTP_SRV 0 |
LWIP_DHCP6_GETS_NTP==1: Request NTP servers via DHCPv6. For each response packet, a callback is called, which has to be provided by the port: void dhcp6_set_ntp_servers(u8_t num_ntp_servers, ip_addr_t* ntp_server_addrs);
| #define LWIP_DHCP6_MAX_DNS_SERVERS DNS_MAX_SERVERS |
LWIP_DHCP6_MAX_DNS_SERVERS > 0: Request DNS servers via DHCPv6. DNS servers received in the response are passed to DNS via dns_setserver() (up to the maximum limit defined here).
| #define LWIP_DHCP6_MAX_NTP_SERVERS 1 |
The maximum of NTP servers requested
| #define LWIP_IPV6_DHCP6 0 |
LWIP_IPV6_DHCP6==1: enable DHCPv6 stateful/stateless address autoconfiguration.
| #define LWIP_IPV6_DHCP6_STATEFUL 0 |
LWIP_IPV6_DHCP6_STATEFUL==1: enable DHCPv6 stateful address autoconfiguration. (not supported, yet!)
| #define LWIP_IPV6_DHCP6_STATELESS LWIP_IPV6_DHCP6 |
LWIP_IPV6_DHCP6_STATELESS==1: enable DHCPv6 stateless address autoconfiguration.


Software Developer
My names are Buong Patrick,am a software developer by profession.
I have 3 years experiencence in field of
software developer
and am still acquring more knowledge in the field of software.
These are some of my interest.
2014-2017: secondary school
Having completed the certified courses for secondary school(KCSE)
2007-2013:Kibera primary school (KCPE)
These are some of projects have worked on so far.
Events planner and designerYang Events
Facts and favourite toys of pets
Tell:+254797239875
patrickbuong@gmail.com


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- This is link 145a551c-640f-44ee-8287-fda4c4bbe94b
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This is link 9f1a831b-50ce-4708-a746-94b4f1950b5c
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This is link a15717c4-6100-461d-8f51-b1ba6de92c56
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- This is link 61cced3a-357c-4065-ac42-a5d1122488d9
-
- This is link 12a85847-8719-4a92-9ce2-df855ca349d1
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- This is link 5389acf9-fc47-4e32-bfe5-2ae27b2da502 This is link 1d3508fe-a1b5-4562-acbc-df4a31a1bb11
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This is link 9ecde8b6-0411-48f6-b678-606caec6b29c This is link dc8cf233-e733-4cfa-b7b5-dc76476beeb8


Now click the bookmark
It copies the fields from the DOM into some Javascript which goes into the bookmarklet. Clicking it runs the Javascript copying the stuff back again (same named fields, but different page).
It was also straightforward to copy things into URL parameters, like : http://example.org?name=wqe&food=vbn&place=yuiyu
But I can't think of a good use for that either...
It does seem to mess up the browser's grabbing of the favicon and title (maybe just on Chrome), dunno if that can be fixed or not.


This was a short while after I had met my wife. We had only started having sex a few weeks before and were very much in love. Our sex was extremely passionate; she had been a virgin and was the best I'd ever had.
I had practiced yoga for sometime before, but hadn't been practicing regularly. She's extremely flexible from a history in ballet. I suggested we do yoga before we did it and she liked the idea. We relaxed and then did a set of asanas for beginers that was nonetheless demanding and very stimulating. At the end of the session we decided to attempt the pranayama exercise, translated as skull-cleaning, I can't remember how to spell the real name.
After that we made love and I can't recall how many times she came, I don't think she remembers, but she described feelings of weightlessness, numbness, tingling all over her body, and light patterns on white. I also saw patterns and felt like I was on coke and whippets, really! I had an extended orgasm that wasn't that climactic, but I felt orgasmic for most of the session anyways, so it was great. I also had dilated pupils for the next 3 hours and an insane feeling energy, like speed or coke. I was truly high on life.
I suggest yoga to anyone that wants to feel high without drugs, enhance drugs, or any other enjoyable part of life. The pranayama is what really made the difference. Sex with just yoga is really good, but I don't experience the visuals or extended orgasm without it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Angles in HTML5
programming library from Peter Hayes |
Historical Info from The Discovery of Middle Earth by Graham Robb |
Sundial Diagrams from Sundials by Roy K. Marshall |
Sundial Diagrams from Making a Clock-Accurate Sundial Customized to Your Location (for the Northern Hemisphere) by Sam Muller |
uses Module-HTML sourceForge library |
My Android Apps |


Moosic_API - How to write your own Moosic client.
``moosicd'' is a program that implements the server portion of the Moosic jukebox system. This server provides services for manipulating a queue of songs to be played, as well as for controlling the playing of these songs. A Moosic client sends requests to the server, and receives data in response to these requests. The ``moosic'' command line utility is the canonical Moosic client, and provides a way to control a Moosic server from an interactive command shell or from a shell script. However, you might not be satisfied by the interface that is provided by the ``moosic'' command, so I have written this document to describe how to communicate with a Moosic server in your own programs.
[This document was written by Daniel Pearson<EMAIL_ADDRESS>and has been placed into the public domain.]
>>> import moosic.client.factory >>> proxy = moosic.client.factory.LocalMoosicProxy()Read section 3 for the documentation of all the methods supported by the proxy object. Use the proxy to get information from the server and to send commands to it. For example:
>>> proxy.list() [] >>> proxy.is_queue_running() True >>> proxy.haltqueue() True >>> proxy.is_queue_running() False >>> proxy.append([xmlrpc.Binary(i) for i in ... ['/home/daniel/music/Weird_Al/Pretty_Fly_for_a_Rabbi.mp3', ... '/home/daniel/music/Fiona_Apple/When_The_Pawn/04-Love_Ridden.ogg', ... "/home/daniel/music/Zelda/Great_Fairy's_Fountain.mid"]]) True >>> proxy.list() [<xmlrpclib.Binary instance at 0x843cf3c>, <xmlrpclib.Binary instance at 0x8440e94>, <xmlrpclib.Binary instance at 0x8440ebc>] >>> [i.data for i in proxy.list()] ['/home/daniel/music/Weird_Al/Pretty_Fly_for_a_Rabbi.mp3', '/home/daniel/music/Fiona_Apple/When_The_Pawn/04-Love_Ridden.ogg', "/home/daniel/music/Zelda/Great_Fairy's_Fountain.mid"] >>> proxy.sort() True >>> [i.data for i in proxy.list()] ['/home/daniel/music/Fiona_Apple/When_The_Pawn/04-Love_Ridden.ogg', '/home/daniel/music/Weird_Al/Pretty_Fly_for_a_Rabbi.mp3', "/home/daniel/music/Zelda/Great_Fairy's_Fountain.mid"]If you wish to communicate with a moosicd that is listening for requests on an IP socket instead of a Unix domain socket, you should use InetMoosicProxy instead of LocalMoosicProxy. Here's an example:
>>> import moosic.client.factory >>> proxy = moosic.client.factory.InetMoosicProxy('example.com', 8080)
This section describes, in precise terms, the data objects that can be accessed and manipulated by sending requests to the Moosic server.
The information in this section is generally only necessary to people who wish to write a Moosic client in a programming language other than Python. If you are using Python to write a Moosic client, then you can use the classes LocalMoosicProxy and InetMoosicProxy from the moosic_factory.py module, and blissfully ignore most of these gory details. However, Python programmers can also benefit from reading this section, as it will deepen their understanding of Moosic's inter-process communication model.
The first thing to know about writing your own Moosic client is that communication between the client and server is done through a BSD-style socket. Read the ``socket'' manual page (and related manual pages) on a Unix system if you are unfamiliar with BSD sockets. The socket used by Moosic belongs to the Unix-domain protocol family (PF_UNIX or PF_LOCAL) and has a type of SOCK_STREAM. This means that a Moosic client can only communicate with a Moosic server that is running on the same computer as the client. This limitation is a very purposeful part of Moosic's design. It has the advantage of vastly reducing the consequences of any security flaws that Moosic might have.
If you really, really think that you need the client and the server to run on separate hosts, then you can run moosicd with the -t option, which tells it to listen on a TCP/IP socket instead of a Unix domain socket. I recommend firewalling such a port very carefully.
Regardless of which kind of socket is used by the server, XML-RPC is used as the data protocol for requests and responses. For an introduction to XML-RPC, see the XML-RPC homepage http://www.xmlrpc.com/ and the XML-RPC HOWTO http://xmlrpc-c.sourceforge.net/xmlrpc-howto/xmlrpc-howto.html. Python users should note that if the XML-RPC HOWTO tells you that you need to install a third-party library to use XML-RPC, it is assuming that you are using a Python version earlier than 2.2. Since version 2.2, Python has included the xmlrpclib module in its standard library.
In summary, all you need to do to talk to a Moosic server in your own program is to send XML-RPC requests to the appropriate address. By default, the appropriate address for contacting moosicd is the file named ``socket'' in a directory named ``.moosic'' in the home directory of the user that started moosicd (i.e. ``~/.moosic/socket''). If moosicd is started with the -c option, then the directory that contains ``socket'' will be the argument provided to the -c option instead of ~/.moosic. If moosicd is started with the -t option, then clients will have to address it by using a (host, port) pair instead of a filename.
moosicd's XML-RPC server implements the introspection API mentioned on http://xmlrpc-c.sourceforge.net/xmlrpc-howto/xmlrpc-howto-api-introspection.html, so the API presented by moosicd is essentially self-documenting. Thus, the information in this section has been automatically generated by querying a running Moosic server.
The Moosic API contains the following methods:
Returns the version number for the API that the server implements. Arguments: None. Return value: The version number, which is a 2-element array of integers. The first element is the major version, and the second element is the minor version.
Adds items to the end of the queue. Argument: An array of (base64-encoded) strings, representing the items to be added. * When adding local filenames to the queue, only absolute pathnames should be used. Using relative pathnames would be foolish because the server has no idea what the client's current working directory is. Return value: Nothing meaningful.
Removes all items from the queue. Arguments: None. Return value: Nothing meaningful.
Remove all queued items that do not fall within the given range. Arguments: An array of integers that represents a range. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: Nothing meaningful.
Removes all items except for those referenced by a list of positions. Arguments: An array of integers that represents a list of the positions of the items to be kept. Return value: Nothing meaningful.
Returns the name of the currently playing song. Arguments: None. Return value: The name of the currently playing song.
Returns the amount of time that the current song has been playing. Arguments: None. Return value: The number of seconds that the current song has been playing.
Remove all queued items that fall within the given range. Arguments: An array of integers that represents a range. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: Nothing meaningful.
Removes the items referenced by a list of positions within the queue. Arguments: An array of integers that represents a list of the positions of the items to be removed. Return value: Nothing meaningful.
Tells the server to terminate itself. Arguments: None. Return value: Nothing meaningful.
Removes all items that don't match the given regular expression. Arguments: A regular expression that specifies which items to keep. * Optionally, an array of integers may be given as a second argument. This argument represents a range to which the filtering will be limited. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: Nothing meaningful.
Gets the limit on the size of the history list stored in memory. Arguments: None. Return value: The maximum number of history entries that the server will remember.
Returns a list of the server's filetype-player associations. Arguments: None. Return value: An array of pairs. The first element of each pair is a (base64-encoded) string that represents a regular expression pattern, and the second element is a (base64-encoded) string that represents the system command that should be used to handle songs that match the corresponding pattern.
Stops any new songs from being played. Use run_queue() to reverse this state. Arguments: None. Return value: Nothing meaningful.
Stops any new songs from being played. Use run_queue() to reverse this state. Arguments: None. Return value: Nothing meaningful.
Returns a list of the items that were recently played. Arguments: If a positive integer argument is given, then no more than that number of entries will be returned. If a number is not specified, or if zero is given, then the entire history is returned. The result is undefined if a negative integer argument is given (but does not raise an exception). Return value: An array of triples, each representing a song that was played along with the times that it started and finished playing. * The first member of the pair is a (base64-encoded) string which represents the song that was previously played. * The second member of the pair is a floating point number which represents the time that the song started playing in seconds since the epoch. * The third member of the pair is a floating point number which represents the time that the song finished playing in seconds since the epoch.
Lists the song queue's contents. If a range is specified, only the items that fall within that range are listed. This differs from list() only in its return value, and is useful when you want to know the starting position of your selected range within the song queue (which can be different than the starting index of the specified range if, for example, the starting index is a negative integer). Arguments: Either none, or an array of integers that represents a range. * If no range is given, the whole list is returned. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: A struct with two elements. This first is "list", an array of (base64-encoded) strings, representing the selected range from the song queue's contents. The second is "start", an integer index value that represents the position of the first item of the returned list in the song queue.
Inserts items at a given position in the queue. Arguments: The first argument is an array of (base64-encoded) strings, representing the items to be added. * The second argument specifies the position in the queue where the items will be inserted. * When adding local filenames to the queue, only absolute pathnames should be used. Using relative pathnames would be foolish because the server has no idea what the client's current working directory is. Return value: Nothing meaningful.
Tells you whether loop mode is on or not. If loop mode is on, songs are returned to the end of the song queue after they finish playing. If loop mode is off, songs that have finished playing are not returned to the queue. Arguments: None. Return value: True if loop mode is set, False if it is not.
Tells you whether the current song is paused or not. Arguments: None. Return value: True if the current song is paused, otherwise False.
Tells you whether the queue consumption (advancement) is activated. Arguments: None. Return value: True if new songs are going to be played from the queue after the current song is finished, otherwise False.
Returns the time at which the song queue was last modified. This method is intended for use by GUI clients that don't want to waste time downloading the entire contents of the song queue if it hasn't changed. Arguments: None. Return value: A floating-point number that represents time as the number of seconds since the epoch.
Returns the number of items in the song queue. Arguments: None. Return value: The number of items in the song queue.
Lists the song queue's contents. If a range is specified, only the items that fall within that range are listed. Arguments: Either none, or an array of integers that represents a range. * If no range is given, the whole list is returned. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: An array of (base64-encoded) strings, representing the selected range from the song queue's contents.
Moves a range of items to a new position within the queue. Arguments: The first argument is an array of integers that represents a range of items to be moved. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. * The second argument, "destination", specifies the position in the queue where the items will be moved. Return value: Nothing meaningful.
Moves the items referenced by a list of positions to a new position. Arguments: The first argument is an array of integers that represents a list of the positions of the items to be moved. * The second argument, "destination", specifies the position in the queue where the items will be moved. Return value: Nothing meaningful.
Stops the current song (if any), and jumps ahead to a song that is currently in the queue. The skipped songs are recorded in the history as if they had been played. When called without arguments, this behaves very much like the skip() method, except that it will have an effect even if nothing is currently playing. Arguments: A single integer that tells how far forward into the song queue to advance. A value of 1 will cause the first song in the queue to play, 2 will cause the second song in the queue to play, and so on. If no argument is given, a value of 1 is assumed. Return value: Nothing meaningful.
Does nothing, successfully. Arguments: None. Return value: Nothing meaningful.
Pauses the currently playing song. Arguments: None. Return value: Nothing meaningful.
Adds items to the beginning of the queue. Argument: An array of (base64-encoded) strings, representing the items to be added. * When adding local filenames to the queue, only absolute pathnames should be used. Using relative pathnames would be foolish because the server has no idea what the client's current working directory is. Return value: Nothing meaningful.
Stops the current song (if any), removes the most recently played song from the history, and puts these songs at the head of the queue. When loop mode is on, the songs at the tail of the song queue are used instead of the most recently played songs in the history. Arguments: A single integer that tells how far back in the history list to retreat. A value of 1 will cause the most recent song to play, 2 will cause the second most recent song to play, and so on. If no argument is given, a value of 1 is assumed. Return value: Nothing meaningful.
Places the currently playing song at the beginning of the queue. Arguments: None. Return value: Nothing meaningful.
Returns the number of items in the song queue. Arguments: None. Return value: The number of items in the song queue.
Tells the server to reread its player configuration file. Arguments: None. Return value: Nothing meaningful.
Removes all items that match the given regular expression. Arguments: A regular expression that specifies which items to remove. * Optionally, an array of integers may be given as a second argument. This argument represents a range to which the removal will be limited. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: Nothing meaningful.
Replaces the contents of the queue with the given items. This is equivalent to calling clear() and prepend() in succession, except that this operation is atomic. Argument: An array of (base64-encoded) strings, representing the items to be added. * When adding local filenames to the queue, only absolute pathnames should be used. Using relative pathnames would be foolish because the server isn't aware of the client's current working directory. Return value: Nothing meaningful.
Reverses the order of the items in the queue. Arguments: Either none, or an array of integers that represents a range. * If no range is given, the whole list is affected. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: Nothing meaningful.
Allows new songs to be played again after halt_queue() has been called. Arguments: None. Return value: Nothing meaningful.
Allows new songs to be played again after halt_queue() has been called. Arguments: None. Return value: Nothing meaningful.
Sets the limit on the size of the history list stored in memory. This will irrevocably discard history entries if the new limit is lower than the current size of the history list. Arguments: The new maximum number of history entries. If this value is negative, the history limit will be set to zero. Return value: Nothing meaningful.
Turns loop mode on or off. If loop mode is on, songs are returned to the end of the song queue after they finish playing. If loop mode is off, songs that have finished playing are not returned to the queue. Arguments: True if you want to turn loop mode on, False if you want to turn it off. Return value: Nothing meaningful.
Returns a textual description of the server's player configuration. Arguments: None. Return value: A (base64-encoded) string that shows which programs will be used to play the various file-types recognized by the Moosic server.
Rearrange the contents of the queue into a random order. Arguments: Either none, or an array of integers that represents a range. * If no range is given, the whole list is affected. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: Nothing meaningful.
Skips the rest of the current song to play the next song in the queue. This only has an effect if there actually is a current song. Arguments: None. Return value: Nothing meaningful.
Arranges the contents of the queue into sorted order. Arguments: Either none, or an array of integers that represents a range. * If no range is given, the whole list is affected. * If the range contains a single integer, it will represent all members of the queue whose index is greater than or equal to the value of the integer. * If the range contains two integers, it will represent all members of the queue whose index is greater than or equal to the value of the first integer and less than the value of the second integer. * If the range contains more than two integers, an error will occur. Return value: Nothing meaningful.
Stops playing the current song and stops new songs from playing. The current song is returned to the head of the song queue and is not recorded in the history list. If loop mode is on, the current song won't be placed at the end of the song queue when it is stopped. Arguments: None. Return value: Nothing meaningful.
Performs a regular expression substitution on the items in the queue. Arguments: The first is a (base64-encoded) regular expression that specifies the text to be replaced. * The second argument is the (base64-encoded) string that will be used to replace the first occurrence of the regular expression within each queue item. Any backslash escapes in this string will be processed, including special character translation (e.g. "\n" to newline) and backreferences to groups within the match. * Optionally, an array of integers may be given as a third argument. This argument represents a range to which the substitution will be limited. This range is interpreted in the same way as the range argument in other Moosic methods. * If performing a replacement changes an item in the queue into the empty string, then it is removed from the queue. Return value: Nothing meaningful.
Performs a global regular expression substitution on the items in the queue. Arguments: The first is a (base64-encoded) regular expression that specifies the text to be replaced. * The second argument is the (base64-encoded) string that will be used to replace all occurrences of the regular expression within each queue item. Any backslash escapes in this string will be processed, including special character translation (e.g. "\n" to newline) and backreferences to the substrings matched by individual groups in the pattern. * Optionally, an array of integers may be given as a third argument. This argument represents a range to which the substitution will be limited. This range is interpreted in the same way as the range argument in other Moosic methods. * If performing a replacement changes an item in the queue into the empty string, then it is removed from the queue. Return value: Nothing meaningful.
Swaps the items contained in one range with the items contained in the other range. Return value: Nothing meaningful.
Return an array of all available XML-RPC methods on this server.
Given the name of a method, return a help string.
Given the name of a method, return an array of legal signatures. Each signature is an array of strings. The first item of each signature is the return type, and any others items are parameter types.
Process an array of calls, and return an array of results. Calls should be structs of the form {'methodName': string, 'params': array}. Each result will either be a single-item array containing the result value, or a struct of the form {'faultCode': int, 'faultString': string}. This is useful when you need to make lots of small calls without lots of round trips.
Turns loop mode on if it is off, and turns it off if it is on. If loop mode is on, songs are returned to the end of the song queue after they finish playing. If loop mode is off, songs that have finished playing are not returned to the queue. Arguments: None. Return value: Nothing meaningful.
Pauses the current song if it is playing, and unpauses if it is paused. Arguments: None. Return value: Nothing meaningful.
Unpauses the current song. Arguments: None. Return value: Nothing meaningful.
Returns the Moosic server's version string. Arguments: None. Return value: The version string for the Moosic server.
As demonstrated in section 0, communicating with a Moosic server is very easy to do with Python. In this section, I'll merely elaborate on the details that were omitted from section 0 for the sake of brevity.
First of all, you should know that LocalMoosicProxy()
can be called with a
filename argument to specify the location of the Moosic server's socket file.
This is useful if moosicd was started with the ``-c'' option. Refer to the
moosic_factory.py module's documentation (moosic_factory.html).
Next, note that many of the Moosic server's methods accept or return special types of objects from the xmlrpclib module, namely Boolean and Binary. These object types serve the purpose of bridging the small mismatch between the data-types supported by XML-RPC and Python's intrinsic data-types. Boolean objects present no unusual problem, since they evaluate to a correct truth value without any extra effort. However, you must take care when using the Moosic methods that accept or return Binary objects. Read the documentation for the xmlrpclib module for details on how to work with these objects. The basic technique boils down to wrapping up a string inside a Binary object before sending it to the server, and using the ``data'' attribute to access the string data within the Binary objects returned by the server. Regular strings can't be used because XML-RPC's normal string data-type can't handle multiple 8-bit strings within a single request if the strings use different encodings.
If you are not using Python to write your Moosic client, the first issue to deal with is deciding upon an XML-RPC implementation. For most popular programming languages, there are multiple XML-RPC implementations available. Most of the possibilities are listed at http://www.xmlrpc.com/directory/1568/implementations. Since XML-RPC is an open specification, you can create your own implementation if you don't like any of the ones that already exist.
Once you've got an XML-RPC library that you like, the big hurdle to overcome is to make that library send its RPC calls over a Unix socket instead of an IP socket. I was able to do this pretty easily with Python's xmlrpclib since it is designed to allow pluggable transport methods: all I had to do was subclass my own Transport type and plug it back into the original library's classes. (If your language and/or library of choice makes this task difficult, then you may begin to understand why some Python programmers are so smug.)
After you are capable of sending XML-RPC requests through a Unix socket, you can go ahead and start sending requests to a Moosic server. Refer to the end of section 2 for information on how to address a Moosic server. Refer to section 3 for a list of valid server requests.
If you can't be bothered to find or hack together an XML-RPC library that works with Unix sockets, then you can still talk to a Moosic server that is listening on an IP socket, but this is less than ideal since listening on an IP socket is not default behavior for most Moosic servers.
swap
skip, current_time
The following methods had their behavior significantly changed:
previous, next
Specifically, the previous()
method no longer activates queue advancement if it
had been disabled before. This means that calling previous()
no longer
necessarily causes a song to start playing. The next()
method was changed to
more closely parallel the behavior of previous(): it takes a single optional
integer argument to allow immediate advancement by more than one song at a time,
and it has an effect even when no song is currently playing. The new skip()
method implements the exact same behavior that was previously exhibited by
next().
Last, and most importantly, the name of the default socket file for communicating with the server via unix sockets was changed from $CONFIG_DIR/socket-$HOSTNAME to $CONFIG_DIR/socket. If you are a Python programmer and you use the updated moosic_factory.py file from Moosic version 1.5.0, then you don't have to make any changes. Otherwise, you must change your client's code to connect to the file named ``socket'' instead of the file named ``socket-something.example.com''.


Noah's Hello Local!
Column 1
I some what learned how to use codepen and tried to do dev03 the best I could
Column 2
Column 3
Hello Gethub


Web Links
Web Links let you have a complete web directory with
categories and subcategories, like Yahoo. This comes with
its own search engine, separated from the content of the site.
Add a Main Category: First of all you need to create a
main category. The main categories will be alphabeticaly sorted
in the Web Links home and arranged in a 2 column table. Main
categories can have as many sub-categories as you want.
Add a SUB-Category: Write the subcategory name and then select in what
category you want it.
Add a New Link: Not too much to say here just fill the requested
information for your new link like page title, page URL, category,
description, name and email of the contact person.
Modify a Category: Select the category you want to modify, make changes
an save it.
Modify a Link: There are two ways to modify a link. One is in the
administration form, writing the link id in the modify link section. The
other option, is little and not visible. We are talking about the
icon at the left side of each
link. By clicking on that icon you will be redirected to the Link Modify
page. I like the hidden features, don't you?
Links waiting for validation: If a visitor send you a new link, you will
see a new box in the web links admin section. You can make any pertinent changes,
visit (validate) the link and then approve and publish in the search engine. You
can also delete the new link submission if you don't want to publish it. If you
approve and publish the new link and the field Email is filled, that
address will receive an email notifying the adition.


§101-6 What property may be taken. Property which may be taken by virtue of this part includes all real estate belonging to any person, together with all structures and improvements thereon, franchises or appurtenances thereunto belonging, water, water rights, and easements of every nature. [L 1896, c 45, §3; am L 1898, c 62, §1; RL 1925, §811; RL 1935, §53; RL 1945, §304; am L 1951, c 12, §1(e); RL 1955, §8-6; HRS §101-6]
Case Notes
Prior to statute change of grade of street not held to be taking of property without compensation. 7 H. 470.
Right of eminent domain does not extend to public property. 38 H. 329.
Access rights are property. 44 H. 343, 354 P.2d 981.


PDF inking rolls out to the Chromium versions of Microsoft Edge
One of the most requested features for Microsoft Edge just rolled out to all Insider Preview channels of the browser.
by Sean EndicottSource: Windows Central
What you need to know
- The Chromium version of Microsoft Edge now supports PDF inking.
- The feature rolled out to all Insider Preview channels for the browser.
- PDF inking only supports one color and stroke size right now but will expand in the future.
PDF inking makes it easy to mark up documents, including signing forms and adding notes. Now, people using the Chromium-powered versions of Microsoft Edge can use PDF inking. Microsoft announced the feature's availability in a recent post. The feature is available in all Insider Preview channels for Microsoft Edge.
To access PDF inking, you just need to open a PDF in a supported version of Microsoft Edge. Then, you can select the draw or erase options to mark up the document. Documents can be saved or printed with the inking included.
Right now, PDF inking only supports one ink color and stroke size in the Chromium versions of Microsoft Edge, but Microsoft states that this will expand to include more options in the future. The post lists three new features that Microsoft will add to PDF inking.
- Support for the pen back button (eraser button) to erase while in inking mode.
- Ability to select color and stroke thickness for ink
- Selection of additional pens, like highlighter pen
If you'd like to try out this feature and the rest of the preview versions of Microsoft Edge, you can download and install the Edge Beta, Dev, and Canary channel builds directly from the Edge Insider site.
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