Teaching
I love teaching! I trace my interest in education back to my time spent at Camp Westmoreland serving National Youth Leadership Training for the Greater Alabama Council, when I was just a teenager. NYLT utilizes youth instructors for teaching all courses, giving a younger me the chance to get up and talk about the things I was passionate about, and was a great inspiration for my professional development as an adult.
While at Auburn University, I had the opportunity to work with hundreds of students as the instructor of several Calculus courses. I think that I’ve done well overall, and this is reflected by the evaluations they’ve left me. I’ve included excerpts from my last four semesters at Auburn below.
Fall 2013
Steven was the best calculus teacher I have had at Auburn. He explained the material very well, breaking it down piece by piece to help everyone understand. He was always willing to help and made class interesting.
Greatest learning experience I’ve ever had.
Fantastic teacher. Easily best math teacher I’ve had in college. I like how it’s a straightforward class with no tricks. The way the material is given and tested on is great. Only thing is maybe show how what we learn is applicable for us especially for engineers, but it’s not that big of a deal.
Best math teacher I’ve had at Auburn. Seems like he’s having fun teaching and really enjoys his job here at Auburn. He isn’t as mundane as the rest of the math department’s professors and teachers. Overall, a funny nice guy with a great attitude and a love for what he’s doing. Could definitely use some people like him in the physics department!
His policy on quizzes was a great way to encourage people to do the homework.
You did a great job this semester Steven. Your presentation of the material was clear and you made your expectations in terms of grading both clear and simple. The end of the course maaaybe felt a little rushed. […] Teach linear algebra next year. I’d appreciate having a teacher like you next semester.
Great teacher. Very approachable and definitely cares about his students. He explains the subject very clearly and effectively.
Great teacher. One of the best I’ve had at auburn! A++++
Liked the Quiz system and how it combined homework.
This class is the first class ive taken in auburn’s math department where the teacher cared about teaching the class. He was so helpful and he honestly wanted us to do well, he gave us material to study and help us and put extra problems on canvas to work that would help with the tests, the resources he provided us with were so helpful. He explained everything well conceptually and didn’t make the math harder than necessary. overall, Mr. Clontz is the bomb dot com, and he wore a tie to class EVERY DAY, which is very impressive. he was very accommodating for excused absences and class was fun. I also learned cal 3
Steven was not only the best Calculus teacher I have had at Auburn, but also one of my best teachers period. His grading techniques were fair, and he did everything possible to make sure that we understood the material being taught. I would absolutely take him again for any future math classes and it was a privilege to have him as a teacher. Steven, don’t change any aspect of how you teach, it was done perfectly. Excellent job.
Summer 2013
By far the best teacher I have ever had!
Overall the class was fairly straight forward. Clontz did not like us using calculators, so he made his own problems that didn’t require calculators. Normally these ended up being much easier than what was in the book though.
He is by far my favorite teacher ever!!
I really like the way you engaged us in the material, despite how quiet the class usually was. I thought you did a great job this summer.
Spring 2013
Great teacher. One of those teachers that you just can’t help but like. Very good with explanations, examples, meeting outside of class. Truly there for the students.
Keep up the great work! War Eagle!
Best math teacher I have ever had.
Mr. Clontz has easily been one of the best instructors I have had in Auburn in every aspect. He makes Calculus about as enjoyable as one could make it. His teaching techniques really helped me to learn the material that much easier. He provides plenty of examples and he leaves no room for doubt for how he makes his test. He creates a study guide that even lists how he gives partial credit so that way there is no room for error. Outside of class he was very easy to approach which was nice for me since I have never been one to visit teachers outside of class but he encourages you to and was very flexible. Mr. Clontz is definitely someone I would recommend to future students.
Really good communication with class. Excellent resources provided online.
Fall 2012
Mr. Clontz did an excellent job teaching this course. His techniques were consistent as far as what we were to expect from him. He was frank with us about grading and made the class schedule very clear to the class. His relaxed methods concerning attendance made it an environment open to learning without the distraction of formalities. He left it up to the student to learn the material. I will say this, while I loved having a study guide for the upcoming test, I must admit I don’t think I did one outside problem. That being said, I did pretty well on the tests and feel as though his teaching style enabled me to understand the material without having to do any outside problems. He wasn’t teaching work ethic, he was teaching Calculus. And while i believe that work ethic is important, his relaxed style and class enabled me to get a core credit without killing myself and i really appreciated that. Things to improve on: Try integrating 3-d visualization software into particular problems. I could get a pretty good idea of the 3-d objects he was drawing on the board (he did do a pretty good job drawing that stuff) but if he used something like Maple, i feel like it could really benefit those not as visually inclined. Dr. Peterson knows her stuff about that so i would ask her where to start. maybe more structure? i didn’t need the structure, but some of these kids who have been spoon fed their entire lives need a boot-in-the-ass to do well. i personally don’t think he HAS to put in more structure, but if there is a gross failing rate or anything, that might help. structure meaning bonus attendance (like x number of points if you show up to 70% or more classes) or some real hw or something. or just let the kids fail out and give me an easier future. That’s pretty much it. Steven Clontz did an overall pretty good job, was open, fair, and wanted the students to succeed. its their fault if they didn’t. Personal note to Mr. Clontz: Thank you so much for instructing this class and may God bless your future endeavors. (if you believe in God, its kinda a hit or miss with these math and science folk.) WDE
It’s hard to fit 15 weeks of math into 10 weeks, but this was not a problem for this class. I would have liked to see more problems worked, though.
Mr. clontz is a great teacher. he explains the material very well. his only issue to me is his grading strategy. he is very strict on minor mistakes and counts off dealy. in all other aspects he is a great teacher though
Great teacher. Better than my professor in Cal II. A little slow on grading tests but understandable. Grades are very fair. Can fight for a grade and if he agrees he will award you a few points. Very quirky, but very funny teacher. Overall: Enjoyed thoroughly
Steven was an awesome teacher! The class notes were really helpful with learning in class, because they provided a bit of an outline of what was going to be covered that day. No complaints.
The study guides were a little too gentle. Sometimes they were carbon copies of the tests. I think making the course a little more challenging would be better, but overall I liked the teaching.


33-year-old riding home falls into pit, dies
Police said the deceased did not notice the pit and fell into it and suffered severe injuries.
by Chaitraly DeshmukhPune: A 33-year-old died after he fell into a pit on the road at Kalyaninagar, while riding home on his bike on Sunday night. According to the Vimannagar police, the deceased was identified as Sunil Malesh Gunjal, 33, a resident of Chandannagar.
Police said Gunjal, who owns a saree store on Laxmi Road, was heading home around 1am. "He did not notice the pit and fell into it and suffered severe injuries. Gunjal was rushed to the hospital but declared dead on arrival," a police officer said, on condition of anonymity.
He said the pit was dug by a contractor to fix a few wires and it did not have a caution or precautionary sign to alert riders. "We have registered a case of negligence against the unidentified contractor and workers. Further investigations are on," the police officer added.
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welcome to this page you will see a lot of online videos like youtube but you will only can watch this and nothing to do with this


SpaCy Wrapper¶
you can read all about Spacy here.
-
class
wrappers.spacy_wrapper.
SpacyWrapper
(spacy_module: str)[source]¶ Wrapper object that load SpaCy module and helps use it.
-
sentence_tokenizer
(text: str) → List[str][source]¶ Tokenize (split) text in sentences.
- for example:
sentence_tokenizer(“Hello, world. Here are two sentences.”)
- will output:
[‘Hello, world.’, ‘Here are two sentences.’]
- Parameters
text – raw text to split into sentences
- Returns
list of strings, each string is a sentence.
-


Memorial football tournament in Telford teenager's honour raises thousands for cystic fibrosis charity
by Rob SmithA football memorial tournament for a Telford teenager has raised more than £4,000 for a charity.
Mechanic Lee Hill, from Sutton Hill, was 19 when he died after being hit by a bus last year.
Since then Lee's parents and their friends have held two fun days including football tournaments to raise money for the Cystic Fibrosis Trust.
The first was held just seven weeks after Mr Hill's death and raised £2,450 with a football tournament and associated entertainment and food, but the organisers wanted to do it again when they had more time to prepare.
A bouncy castle, stalls, raffle, bar and hot food helped raise money at the second, expanded event at Hadley Learning Community on July 28.
Six football teams took part including a veterans’ team which Mr Hill's father Chris played for.
Britannia FC took home the Lee Hill Memorial Shield, sponsored by No Gym Required.
The fun days have been organised by Dave Simmonds, Jamie Farrelly and Brett Shaw, in cooperation with Lee's parents Chris Hill and Kelly Duffy, and with the help of Mike Harvey of No Gym Required.
Mr Simmonds, who has known Chris Hill for about 30 years, said the group had aimed just to beat last year's total of £2,450.
He said they would have been happy to make £2,500 for the charity, and were "gobsmacked" when they in fact counted out £4,350.
An auctioned Fernando Torres shirt that sold for £1,700 made up much of that total.
The organisers thanked everyone who supported the endeavour, and said they plan to hold the tournament again.


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---
layout: post
title: Valentine's Day
categories: []
tags: []
status: publish
type: post
published: true
meta:
_original_post_id: ''
_original_import_author: John Duprey
author:
---
I was determined not to mess up Valentine's day. Especially since
I forgot about our anniversary (January 21, 2003; January 21, 2003; January 21, 2003;....). Monday night was busy for us. Abby was baby sitting for a friend, we had puppy class at 6:30, and then we had Valentine's day which is extra special because I asked Abby to go "steady" with me that night (February 14, 1993; February 14, 1993; February 14, 1993;...).
I came home early to layout the Godiva chocolates I had bought the day before, along with some flowers and a card I picked up that day. We met up at puppy class, then went home where I made a very boring dinner - stir fry. For desert, we ate a few chocolates and then somehow ended up in bed! Don't worry this story isn't going to get nasty. We both went to sleep! This was around 9. Every hour or so Abby would poke me and tell me to let the dog out. I would grunt and go back to sleep. How romantic! About quarter to twelve we both got up. I took the dog out, Abby put the food away. I surfed the web for an hour and we both went back to sleep. I guess for a
pregnant woman, this is considered a good night because Abby was quite content.
For my Valentine's day gift, I received monkey boxers.
I like monkeys.


MKJOBTEXMF(1) User Contributed Perl Documentation MKJOBTEXMF(1) NAME mkjobtexmf -- Generate a texmf tree for a particular job VERSION 2011-11-10 v0.8 SYNOPSIS The progam mkjobtexmf runs a program and tries to find the used file names. Two methods are available, option "-recorder" of TeX (Web2C) or the program strace. Then it generates a directory with a texmf tree. It checks the found files and tries sort them in this texmf tree. It can be used for archiving purposes or to speed up following TeX runs. mkjobtexmf [options] This runs TeX that can be configured by options. Both methods for get‐ ting the used file names are available. mkjobtexmf [options] -- <cmd> [args] The latter form runs program cmd with arguments args instead of TeX. As method only program strace is available. Options: --jobname <name> Name of the job (mandatory). Usually this is the TeX file without extension --texname <file> Input file for TeX. Default is the job name with extension '.tex' --texopt <option> Option for TeX run --destdir <directory> Destination directory, default is `<jobname>.mjt' --output Add also output files --strace Use strace instead of TeX's option -recorder --copy Copy files instead of creating symbol links --flat Junk paths, do not make directories inside the destination directory --(no)texhash Run texhash, use --notexhash for MiKTeX --exclude-ext <ext> Exclude files with extension <ext>. --cmd-tex <cmd> Command for the TeX compiler --cmd-kpsewhich <cmd> Command for kpsewhich --cmd-texhash <cmd> Command for texhash --cmd-strace <cmd> Command for strace --verbose Verbose output --help Brief help message --man Full documentation --version Print version identification DESCRIPTION Running the program First mkjobtexmf runs a program, usually TeX. The TeX compiler is config‐ ured by option "--cmd-tex". Option "--texname" can be used, if the file name extension differs from .tex: mkjobtexmf --jobname foo --texname foo.ltx Even more complicate cases are possible: mkjobtexmf --jobname foo --texname '\def\abc{...}\input{foo}' If another program than TeX should be used (dvips, ...), then this pro‐ gram can be given after "--": mkjobtexmf --jobname foo -- dvips foo File recording Two methods are available to get the used file names: Recorder of TeX Some TeX distributions (e.g. Web2C) support the option -recorder for its TeX compilers. Then the TeX compiler generates a file with exten‐ sion .fls that records the used input and output files. Program strace This program traces system calls and signals. It is used here to log the used files. Analyze and link/copy found files The result directory jobname.mjt is generated. Inside the result TEXMF tree is created. Each found file is compared against a list of paths of TEXMF trees. If a match is found, the file is linked/copied into the TEXMF tree. The list of paths is generated by program kpsewhich. If the file cannot be mapped to a TEXMF tree and the file is a relative file name, then it is directly linked/copied into the result directory jobname.mjt. Absolute file names are not supported and neither paths with links to parent directories. Symbolic links are created by default. The files are copied if option "--copy" is given or symbolic linking is not available. OPTIONS --jobname=<jobname> It is the name of the job. `<jobname>.tex' serves as default for the TeX file and <jobname> is used for naming various directories and files. See section "FILES". --texname=<name> The name of the TeX input file, if it differs from <jobname>.tex. --texopt=<opt> Additional option for the TeX compiler, examples are "--ini" or "--shell-escape". This option can be given more than once. --destdir=<directory> Specifies the name of the destination directory where the result is collected. As default a directory is generated in the current direc‐ tory with the job name and extension `.mjt'. --output Also add output files. --strace Use method with program strace, see "DESCRIPTION". --copy Files are copied instead of creating symbolic links. --flat Files are linked or copied without path elements. The destination directory will contain a flat list of files or links without direc‐ tory. The files `ls-R' and `aliases' are ignored. --exclude-ext=<ext> Files with extension <ext> are excluded. The option can be given sev‐ eral times or a comma separated list of extensions can be used. Exam‐ ples: --exclude-ext aux --exclude-ext log --exclude-ext toc is the same as --exclude-ext aux,log,toc --(no)texhash As default the file `ls-R' is generated in the `texmf' tree, because this is the file name database that might be used in TeX Live. Because MiKTeX uses a different mechanism, its `texhash' does not generate the `ls-R' files and "--notexhash" suppresses the call of `texhash'. --cmd-tex=<cmd> Command for the TeX compiler. Default is pdflatex. --cmd-kpsewhich=<cmd> Command for kpsewhich. --cmd-texhash=<cmd> Command for updating the file name database of the generated texmf tree. Default is texmf. --cmd-strace=<cmd> Command for strace. --verbose Verbose messages. --help Display help screen. -B(-man> Print manual page. --version Print version identification and exit. EXAMPLES TeX file test.tex using TeX's recorder method: mkjobtexmf --jobname test TeX file test.tex using LaTeX: mkjobtexmf --jobname test --cmd-tex latex Format generation: mkjobtexmf --jobname test --texopt -ini --texname pdflatex.ini Example, how the new texmf tree (Linux/bash) can be used: TEXMF=!!test.mjt/texmf pdflatex test Example for generating a zip archive (Linux/bash): (cd test.mjt && zip -9r ../test .) Example for generating a tar archive: tar cjhvf test.tar.bz2 -C test.mjt . UNSOLVED ISSUES, CAVEATS, TODOS Experimental software Options, defaults, how the program works might change in future ver‐ sions. texmf.cnf Currently the method with strace records this files. TeX's recorder does not. Useful are texmf.cnf files for variable settings. Because we have just one TEXMF tree, the path sections should probably rewritten. Settings in environment variables They are not stored at all. Collisions The program uses one destination directory and at most one TEXMF tree for the result. However, the source files can come from different directories and TEXMF trees. Therefore name collisions are possible. The program follows the strategy not to delete files in the destina‐ tion directory. That allows to collect files from differnt runs. Thus collisions are resolved in the manner that the first entry that is made in the destination directory wins. Configuration file It would save the user from retyping the same options again and again. Uncomplete recording Bugs in TeX's file recording might result in incomplete file record‐ ing (e.g. pdfTeX 1.40.3 does not record .pfb and .pk files). ... FILES <jobname>.mjt/ Directory where the resulting texmf tree and symbol links are stored. It can be changed by option "--destdir". <jobname>.fls Name of TeX's recorder file. <jobname>.strace Log file where the result of strace is stored. AUTHOR Heiko Oberdiek, email: heiko.oberdiek at googlemail.com COPYRIGHT AND LICENSE Copyright 2007, 2008, 2011 by Heiko Oberdiek. This library is free software; you may redistribute it and/or modify it under the same terms as Perl itself (Perl Artistic License/GNU General Public License, version 2). HISTORY 2007/04/16 v0.1 * First experimental version. 2007/05/09 v0.2 * Typo in option name fixed. 2007/09/03 v0.3 * New options: "--copy", "--flat", "--destdir" 2007/09/04 v0.4 * Bug fix in map_files_texmf. 2007/09/06 v0.5 * Support for `configure' added. (Thanks to Norbert Preining for writ‐ ing a first version of the configure stuff.) 2008/04/05 v0.6 * Tiny fix in target `uninstall' in file `Makefile.in'. (Thanks to Karl Berry) 2008/06/28 v0.7 * Fix for unknown option `"--cmd-strace"'. (Thanks to Juho Niemelä) 2011/11/10 v0.8 * Remove colon from drive specification when making directories. * Option "--(no)texhash" added. * Some support for MiKTeX (thanks Ulrike Fischer). * Various fixes in the generation of the documentation. * Options "--exclude-ext" and "--version" added. perl v5.8.1 2011-11-10 v0.8 MKJOBTEXMF(1)


The goal of mapboxer is to make it easy to create interactive maps using Mapbox GL JS within R. Visualizations can be used at the R console, embedded in R Markdown documents or Shiny apps.
This guide covers the basic usage.
Map: The map is the main component of your visualization to which you then add other components like layers, controls or sources. Maps are created with mapboxer()
.
Sources: Sources state which type of data should be displayed on the map. R objects can be converted to Mapbox sources with as_mapbox_source()
. With add_source()
or as first parameter of mapboxer()
sources can be added to the map so that they can be used accross layers.
Layers: A layer’s style define how a source is displayed on the map. Furthermore, you can apply filters to the data of a source. With add_layer()
you can add any type of layer to the map but in most cases it is easier to use one of the shortcuts like add_circle_layer()
.
Controls: Controls are used to interact with the map. Besides the standard controls like NavigationControl
included in Mapbox GL JS, mapboxer provides additional controls. For example, add_filter_control()
can be used to filter your data on the fly without having to set up a Shiny app.
Expressions: Expressions are pretty powerful. Among other things, they can be used for data-driven-styling or to filter your data.
Shiny Bindings: With renderMapboxer()
and mapboxerOutput()
you can integrate your visualizations in Shiny apps. Furthermore, you can use mapboxer_proxy()
and update_mapboxer()
to update an already rendered widget. Observe the input$<widget_id>_onclick
event to get the properties for a clicked feature.
# Load the library
library(mapboxer)
# Create a source
motor_vehicle_collisions_nyc %>%
dplyr::mutate(color = ifelse(injured > 0, "red", "yellow")) %>%
as_mapbox_source(lng = "lng", lat = "lat") %>%
# Setup a map with the default source above
mapboxer(
center = c(-73.9165, 40.7114),
zoom = 10
) %>%
# Add a navigation control
add_navigation_control() %>%
# Add a layer styling the data of the default source
add_circle_layer(
circle_color = c("get", "color"),
circle_radius = 3,
# Use a mustache template to add popups to the layer
popup = "Number of persons injured: {{injured}}"
)
With mapboxer()
you create a map object. This is the main component of your vizualization. To add components like layers or controls or to modify your map you use the add_*
and set_*
functions. The options to configure your map are set via the ...
parameter that allows you to pass on any option described in the Map API Reference:
mapboxer(
style = basemaps$Carto$dark_matter,
center = c(-73.9165, 40.7114),
zoom = 9,
minZoom = 8
)
With the optional source
parameter you can add a default source to the map that will be used by the layers if no source is provided. Therefore, it is easy to integrate mapboxer()
into your workflow:
motor_vehicle_collisions_nyc %>%
dplyr::filter(killed > 0) %>%
as_mapbox_source() %>%
mapboxer(
center = c(-73.9165, 40.7114),
zoom = 9
) %>%
add_circle_layer(circle_color = "red")
As you can see above mapboxer is designed to use the widely used piping style provided by magrittr.
The style
parameter passed to mapboxer()
sets the style of the basemap. By default mapboxer uses a Carto vector style. It is also possible to use raster tiles or a background color as basemap. Therefore, you can use the helpers basemap_raster_style()
or basemap_background_style()
:
mapboxer(style = basemap_raster_style())
To use styles from Mapbox it is recommened that your store your API token in an environment variable called MAPBOX_API_TOKEN
. If not set globally you can store it as follows:
Sys.setenv(MAPBOX_API_TOKEN = "<yourSuperSecretToken>")
mapboxer(style = basemaps$Mapbox$satellite_v9)
Sources state which data the map should display. To show the data on the map you need to bind a source to a layer which contains the styling details like color or width. This makes it possible to style the same source in different ways. The easiest way to create a source from an R data object is to use as_mapbox_source()
. Supported structures are sf-objects and data frames that contain longitudes and latitudes:
mvc_sf <- motor_vehicle_collisions_nyc %>%
sf::st_as_sf(coords = c("lng", "lat"), crs = 4326)
mvc_source_from_sf <- mvc_sf %>%
as_mapbox_source()
mvc_source_from_df <- motor_vehicle_collisions_nyc %>%
as_mapbox_source(lng = "lng", lat = "lat")
With the ...
parameter you can pass additional options to the source:
mvc_cluster <- motor_vehicle_collisions_nyc %>%
as_mapbox_source(
lat = "lat",
lng = "lng",
cluster = TRUE,
clusterMaxZoom = 14,
clusterRadius = 50
)
See the Sources API Reference for available options for the used source type. Sources are either passed to the add_*_layer
functions or as first parameter to mapboxer()
setting it as default source. With add_source()
you can add a source to the map that you refer to in the layer definition by its ID.
Layers style the data of the source to which they refer. Optionally you can filter features. Each layer must have a unique ID. If you use the generic function add_layer()
, the type of the layer is specified by the type
property. See the Layers API Reference for available types. In most cases it is convenient to use one of the add_*_layer()
functions:
mapboxer(
center = c(-73.9165, 40.7114),
zoom = 9
) %>%
add_circle_layer(
source = as_mapbox_source(motor_vehicle_collisions_nyc),
circle_color = "red",
circle_radius = 5
)
Usually popups are added to a layer with the popup
parameter of the add_*_layer()
functions. Optionally you can also use add_popups()
. The popup text (HTML) is specified by a mustache template in which the tags refer to the properties of the layer’s data object. If your data contains the properties name
and population
, it could look like this:
popup_template <- "Name: {{name}}</br>Population: {{population}}"
With add_tooltips()
you can add tooltips to a layer in the same way.
Controls are used to interact with the user. They are displayed as overlays on top of the map. Options of the standard controls described in the Markers and Controls API Reference are provided with the ...
parameter. The position is set with the pos
parameter, one of top-left
, top-right
, bottom-right
, bottom-left
:
mapboxer() %>%
add_navigation_control(
pos = "top-left",
# Option passed to the 'NavigationControl'
showCompass = FALSE
) %>%
add_scale_control(
pos = "bottom-left",
# Option passed to the 'ScaleControl'
unit = "nautical"
) %>%
add_text_control(
pos = "top-right",
text = "mapboxer"
)
The value of any layout property, paint property (data-driven-styling) or filter may be specified as an expression. Expressions in Mapbox GL JS use a Lisp-like syntax represented as JSON arrays:
[expression_name, argument_0, argument_1, ...]
Therefore, in R you must use the list
structure:
list(expression_name, argument_0, argument_1, ...)
If all elements are of the same type you can also use a vector:
expr_get_property <- c("get", "<data-property>")`
A simple expression is to use a data property to style your data:
map <- motor_vehicle_collisions_nyc %>%
dplyr::mutate(
color = ifelse(injured > 0, "red", "yellow")
) %>%
as_mapbox_source() %>%
mapboxer(
center = c(-73.9165, 40.7114),
zoom = 9
)
map %>%
add_circle_layer(
# Expression to get the color from the data's color property
circle_color = c("get", "color")
)
You can get the same result for the circle_color
without modifying the data but using expressions only:
map %>%
add_circle_layer(
circle_color = list(
"case",
# 'red' if 'injured > 0'
list(">", c("get", "injured"), 0), "red",
# Defaults to 'yellow'
"yellow"
)
)
A filter could look like this:
map %>%
add_circle_layer(
circle_color = c("get", "color"),
# Expression to display only data where 'injured > 1'
filter = list(">", "injured", 1)
)
See also add_filter_control()
to modify filter expressions on the fly to update your map without the need of a Shiny app, Get started with expressions for a tutorial and the Expressions Style Specification for details.
Use mapboxerOutput()
and renderMapboxer()
to integrate mapboxer in a Shiny app:
library(shiny)
library(mapboxer)
view <- fluidPage(
h1("mapboxer"),
mapboxerOutput("map")
)
backend <- function(input, output) {
output$map <- renderMapboxer({
mapboxer(center = c(9.5, 51.3), zoom = 10) %>%
add_navigation_control() %>%
add_marker(lng = 9.5, lat = 51.3, popup = "mapboxer")
})
}
if (interactive()) shinyApp(view, backend)
With mapboxer_proxy()
and update_mapboxer()
you can update your already rendered map:
LAYER_ID <- "crashes"
START_VALUE <- 4
view <- basicPage(
sliderInput("slider", "Number of persons injured:",
min = 0, max = 7, step = 1, value = START_VALUE),
mapboxerOutput("map")
)
backend <- function(input, output) {
output$map <- renderMapboxer({
mapboxer(
center = c(-73.9165, 40.7114),
zoom = 9
) %>%
add_circle_layer(
source = as_mapbox_source(motor_vehicle_collisions_nyc),
circle_color = "red",
popup = "{{injured}}",
filter = list("==", "injured", START_VALUE),
id = LAYER_ID
)
})
observeEvent(input$slider, {
mapboxer_proxy("map") %>%
set_filter(LAYER_ID, list("==", "injured", input$slider)) %>%
update_mapboxer()
})
}
if (interactive()) shinyApp(view, backend)
Observe the input$<widget_id>_onclick
event to get the properties for a clicked feature.


--- layout: default redirect_from: - /minecraft ---
Online portal of information regarding pixelpox planet minecraft server


| Attribute |
Public noTrailer
|
Details:
| Alias: |
|
| Initial: |
|
| Stereotype: |
<<enum>> |
| Ordered: |
|
| Range: |
|
| Transient: |
False |
| Derived: |
False |
| IsID: |
False |
|
Notes:
|
The vehicle has verified (e.g., with sensors) that no trailer is connected.

|
|
Public trailerPresentWithCertifiedLength
|
Details:
| Alias: |
|
| Initial: |
|
| Stereotype: |
<<enum>> |
| Ordered: |
|
| Range: |
|
| Transient: |
False |
| Derived: |
False |
| IsID: |
False |
|
Notes:
|
The lead vehicle has communicated with a certified trailer and is reporting the supplied information

|
|
Public trailerPresentWithDerivedLength
|
Details:
| Alias: |
|
| Initial: |
|
| Stereotype: |
<<enum>> |
| Ordered: |
|
| Range: |
|
| Transient: |
False |
| Derived: |
False |
| IsID: |
False |
|
Notes:
|
The vehicle has determined that there is a trailer and is reporting a derived length based on information received from other connected devices

|
|
Public trailerPresentWithUnverifiedLength
|
Details:
| Alias: |
|
| Initial: |
|
| Stereotype: |
<<enum>> |
| Ordered: |
|
| Range: |
|
| Transient: |
False |
| Derived: |
False |
| IsID: |
False |
|
Notes:
|
THe vehicle has determined that there is a trailer but is unable to communicate with the trailer and has not yet received enough information from connected devices to verify the length being reported

|
|
Public trailerPresenceUnknown
|
Details:
| Alias: |
|
| Initial: |
|
| Stereotype: |
<<enum>> |
| Ordered: |
|
| Range: |
|
| Transient: |
False |
| Derived: |
False |
| IsID: |
False |
|
Notes:
|
The lead vehicle has not detected a trailer

|
|
Public unavailable
|
Details:
| Alias: |
|
| Initial: |
|
| Stereotype: |
<<enum>> |
| Ordered: |
|
| Range: |
|
| Transient: |
False |
| Derived: |
False |
| IsID: |
False |
|
Notes:
|
Trailer information is not currently available.

|
|


Obviously I haven't figured out how to embed video yet! (it works at home!) 2014 was a sabbatical year for me. One of the adventures I set for myself was to make a planetarium film.
Always start with customer validation so I interviewed planetarium directors. It turns out they do a lot of school programs. Sometimes these programs are pretty dry. Sometimes the kids are intimidated or bored. At the same time there's often a few minutes between presentations.
Thus the idea for Monsters of the Cosmos was born. It would be a science rock video using actual scientists to tell the story of black holes.
Where to start?
I had long been a fan and minor supporter of John Boswell, an incredibly talented young composer in Seattle. I contacted him and gave him a rundown on the planetarium industry. He liked the idea but neither of us knew how to create a video for a screen 5 times larger than IMAX.
I found my way to Matt Mascheri of Dome3D in Chicago. He is a master of the art. We met at my house in California where we produced the first 30 seconds of MOC. We previewed it at the Morrison Planetarium at the California Academy of Sciences. Over the next 3 months we finished it.
So far, MOC has been featured at three film festivals and requested by 150 planetariums.
Mike Farr
Everything that's interesting to do was once impossible.
March 28, 2015


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PARTICIPATING SPONSORS
EPISODE 3
EPISODE 2
EPISODE 1
The Story of Glenn O'Brien's TV Party
As told by Glenn O'Brien to Vice Magazine
In 1978 I started a public-access television show in New York along with a few friends. It was called TV Party, and by the time it ended in 1982 our list of guests included David Bowie, David Byrne, Robert Fripp, the B-52s, Chris Burden, George Clinton, Iggy Pop, Steven Meisel, Mick Jones, James Chance, John Lurie, Klaus Nomi, Kraftwerk, the Screamers, Robert Mapplethorpe, Jean-Michel Basquiat, Nile Rodgers, Kid Creole, the Offs, Alex Chilton, the Brides of Funkenstein, Arthur Russell, David McDermott, and Charles Rocket, just to name a few.
I first became interested in public access when I was invited to appear on the show If I Can't Dance You Can Keep Your Revolution, hosted by a woman named Coca Crystal. It went well enough, but I didn't think much of it. Then, the next day on the subway, several strangers said that they had seen me on the show the night before. This happened more and more. I thought, Wow, people are actually watching this. In those days, the lack of decent cable options meant random dial-spinners had a very good chance of landing on your channel.
My friend Chris Stein, the guitarist of Blondie, lived in a Midtown penthouse around this time and had cable. I often watched it with him while smoking Rasta-sized joints. One night it just hit me and I called him up and said, "Chris, we have to start a public-access show.
And so we did.
Public access offered something strange and exciting. In New York City that meant programming like Telepsychic, the Robin Byrd Show (which featured adult film stars dancing nude), John Wallowitch's show, and the Vole Show. Then there was Hugh Hefner's cocktail party on the airwaves, Playboy After Dark, which featured guests like Ella Fitzgerald and Lenny Bruce. While it wasn't a public-access show, After Dark was still an early influence on TV Party.
The show ran on Channel D and Channel J, and was quite popular with the kids. We lucked into following the Robin Byrd Show for a while, and so inherited an audience of horny guys.  The show never officially ended—Chris got sick and almost died, I got married and decided I needed to make some money, some people went to rehab, some left town, and some died of AIDS, which had just appeared. It seemed like suddenly everything was changing, and it just got to be longer and longer since the last show.  Cursing, getting high, advocating subversion, being party desperados...
It all looks glamorous now, but that's because even with HBO, Showtime, Cinemax, CNN, ESPN, AMC, the Comedy Channel, and hundreds more, television is still monitored and censored by higher powers. And even though we sort of invented reality TV, if anything TV is less real than ever. So is reality. We still have our work cut out for us.
After a nearly 30 year hiatus,
we are proud to relaunch TV Party for a new digital era. We shot 3 episodes—"The Miraculous Resurrection" at LeBaron, "The Pajama Party" at Lafayette House, and "TV Party Goes to Camp" at MoMA PS1—and are shooting new episodes as we go. As reimagined by Glenn for a new digital age, the show is no longer constrained by the television studio and is now done on location as a real party. No longer done for public access cable television, it is now being being made for the Internet. The show is still "the cocktail party that is also a political party" and the aim is still to overthrow the government, only the medium has changed. The Internet is the new public access and TV Party is still the show where "if you can find it, you can come."


12. Clustering Analysis¶
In this chapter we will concern ourselves with the study of cluster analysis. In general terms cluster analysis, or clustering, is the task of grouping a data-set into different distinct categories based on some measure of equality of the data. This measure is often referred to as a metric or similarity measure in the literature (note: sometimes we deal with a dissimilarity measure instead). Usually, these metrics are formulated as some kind of distance function between points in a high-dimensional space.
There exists a lot of such distance measures. The simplest, and also the most common is the Euclidean distance (i.e. Pythagoras). A good source for those of you wanting a thorough overview is the article (DOI:10.5120/ijca2016907841 Irani, Pise, Phatak). A few other metrics mentioned there are: cosine similarity, Manhattan distance, Chebychev distance and the Minkowski distance. The Minkowski distance is a general formulation which encapsulates a range of metrics. All of these, and many more, can be used in clustering. There exists different categories of clustering algorithms. A few of the most common are: centroid-, distribution-, density- and hierarchical- clustering. We will concern ourselves primarily with the first one.
12.1. Basic Idea of the K-means Clustering Algorithm¶
The simplest of all clustering algorithms is the aptly named k-means algorithm , sometimes also referred to as Lloyds algorithm. It is the simplest and also the most common. From its simplicity it obtains both strengths and weaknesses. These will be discussed in more detail later. The k-means algorithm is a centroid based clustering algorithm.
Assume, we are given \(n\) data points and we wish to split the data into \(K < n\) different categories, or clusters. We label each cluster by an integer \(k\in\{ 1, \cdots, K \}\). In the basic k-means algorithm each point is assigned to only one cluster \(k\), and these assignments are non-injective i.e. many-to-one. We can think of these mappings as an encoder \(k = C(i)\), which assigns the \(i\)-th data-point \(\bf x_i\) to the \(k\)-th cluster. Before we jump into the mathematics let us describe the k-means algorithm in words:
We start with guesses / random initializations of our \(k\) cluster centers / centroids
For each centroid the points that are most similar are identified
Then we move / replace each centroid with a coordinate average of all the points that were assigned to that centroid.
Iterate this points 2, 3) until the centroids no longer move (to some tolerance)
Now we consider the method formally. Again, we assume we have \(n\) data-points (vectors)
which we wish to group into \(K < n\) clusters. For our dissimilarity measure we will use the squared Euclidean distance
Next we define the so called within-cluster point scatter which gives us a measure of how close each data point assigned to the same cluster tends to be to the all the others.
where \(\boldsymbol{\overline{x_k}}\) is the mean vector associated with the \(k\)-th cluster, and \(N_k = \sum_{i=1}^nI(C(i) = k)\), where the \(I()\) notation is similar to the Kronecker delta (Commonly used in statistics, it just means that when \(i = k\) we have the encoder \(C(i)\)). In other words, the within-cluster scatter measures the compactness of each cluster with respect to the data points assigned to each cluster. This is the quantity that the \(k\)-means algorithm aims to minimize. We refer to this quantity \(W(C)\) as the within cluster scatter because of its relation to the total scatter.
Which is a quantity that is conserved throughout the \(k\)-means algorithm. It can be thought of as the total amount of information in the data, and it is composed of the aforementioned within-cluster scatter and the between-cluster scatter \(B(C)\). In methods such as principle component analysis the total scatter is not conserved.
Given a cluster mean \(\boldsymbol{m_k}\) we define the total cluster variance
Now we have all the pieces necessary to formally revisit the k-means algorithm. If you at this point feel like some of the above definitions came a bit out of no-where, don’t fret, the method does get a whole lot simpler once we start programming.
12.2. The K-means Clustering Algorithm¶
The k-means clustering algorithm goes as follows (note in my opinion this description is a bit complicated and is lifted directly out of ESL HASTIE for deeper understanding purposes)
For a given cluster assignment \(C\), and \(k\) cluster means \(\{m_1, \cdots, m_k\}\). We minimize the total cluster variance with respect to the cluster means \(\{m_k\}\) yielding the means of the currently assigned clusters.
Given a current set of \(k\) means \(\{m_k\}\) the total cluster variance is minimized by assigning each observation to the closest (current) cluster mean. That is $\(C(i) = \underset{1\leq k\leq K}{\mathrm{argmin}} ||\boldsymbol{x_i} - \boldsymbol{m_k}||^2\)$
Steps 1 and 2 are repeated until the assignments do not change.
As previously stated the above formulation can be a bit difficult to understand, at least the first time, due to the dense notation used. But all in all the concept is fairly simple when explained in words. The math needs to be understood but to help you along the way we summarize the algorithm as follows (try to look at the terms above to match with the summary).
Before we start we specify a number \(k\) which is the number of clusters we want to try to separate our data into.
We initially choose \(k\) random data points in our data as our initial centroids, or means (this is where the name comes from).
Assign each data point to their closest centroid, based on the squared Euclidean distance.
For each of the \(k\) cluster we update the centroid by calculating new mean values for all the data points in the cluster.
Iteratively minimize the within cluster scatter by performing steps (3, 4) until the new assignments stop changing (can be to some tolerance) or until a maximum number of iterations have passed.
That’s it, nothing magical happening.
12.3. Writing Our Own Code¶
In the following section we will work to develop a deeper understanding of the previously discussed mathematics through developing codes to do k-means cluster analysis.
12.3.1. Basic Python¶
Let us now program the most basic version of the algorithm using nothing but Python with numpy arrays. This code is kept intentionally simple to gradually progress our understanding. There is no vectorization of any kind, and even most helper functions are not utilized. Throughout our implementation process it will be helpful to keep in mind both the mathematical description of the algorithm and our summary from above. In addition, try to think of ways to optimize this while reading the next section. We will get to it, take it as a challenge to see if your optimizations are better.
First of all we need a dataset to do our cluster analysis on, for clarity (and lack of googling beforehand) we generate it ourselves using Gaussians. First we import
%matplotlib inline
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import time
from IPython.display import display
np.random.seed(2021)
Next we define functions, for ease of use later, to generate Gaussians and to set up our toy data set.
def gaussian_points(dim=2, n_points=1000, mean_vector=np.array([0, 0]),
sample_variance=1):
"""
Very simple custom function to generate gaussian distributed point clusters
with variable dimension, number of points, means in each direction
(must match dim) and sample variance.
Inputs:
dim (int)
n_points (int)
mean_vector (np.array) (where index 0 is x, index 1 is y etc.)
sample_variance (float)
Returns:
data (np.array): with dimensions (dim x n_points)
"""
mean_matrix = np.zeros(dim) + mean_vector
covariance_matrix = np.eye(dim) * sample_variance
data = np.random.multivariate_normal(mean_matrix, covariance_matrix,
n_points)
return data
def generate_simple_clustering_dataset(dim=2, n_points=1000, plotting=True,
return_data=True):
"""
Toy model to illustrate k-means clustering
"""
data1 = gaussian_points(mean_vector=np.array([5, 5]))
data2 = gaussian_points()
data3 = gaussian_points(mean_vector=np.array([1, 4.5]))
data4 = gaussian_points(mean_vector=np.array([5, 1]))
data = np.concatenate((data1, data2, data3, data4), axis=0)
if plotting:
fig, ax = plt.subplots()
ax.scatter(data[:, 0], data[:, 1], alpha=0.2)
ax.set_title('Toy Model Dataset')
plt.show()
if return_data:
return data
data = generate_simple_clustering_dataset()
Now that we are our, albeit very simple, dataset we are ready to start implementing the k-means algorithm.
n_samples, dimensions = data.shape
n_clusters = 4
# we randomly initialize our centroids
np.random.seed(2021)
centroids = data[np.random.choice(n_samples, n_clusters, replace=False), :]
distances = np.zeros((n_samples, n_clusters))
# first we need to calculate the distance to each centroid from our data
for k in range(n_clusters):
for n in range(n_samples):
dist = 0
for d in range(dimensions):
dist += np.abs(data[n, d] - centroids[k, d])**2
distances[n, k] = dist
# we initialize an array to keep track of to which cluster each point belongs
# the way we set it up here the index tracks which point and the value which
# cluster the point belongs to
cluster_labels = np.zeros(n_samples, dtype='int')
# next we loop through our samples and for every point assign it to the cluster
# to which it has the smallest distance to
for n in range(n_samples):
# tracking variables (all of this is basically just an argmin)
smallest = 1e10
smallest_row_index = 1e10
for k in range(n_clusters):
if distances[n, k] < smallest:
smallest = distances[n, k]
smallest_row_index = k
cluster_labels[n] = smallest_row_index
Let’s plot and see
fig = plt.figure()
ax = fig.add_subplot()
unique_cluster_labels = np.unique(cluster_labels)
for i in unique_cluster_labels:
ax.scatter(data[cluster_labels == i, 0],
data[cluster_labels == i, 1],
label = i,
alpha = 0.2)
ax.scatter(centroids[:, 0], centroids[:, 1], c='black')
ax.set_title("First Grouping of Points to Centroids")
plt.show()
So what do we have so far? We have ‘picked’ \(k\) centroids at random from our data points. There are other ways of more intelligently choosing their initializations, however for our purposes randomly is fine. Then we have initialized an array ‘distances’ which holds the information of the distance, or dissimilarity, of every point to of our centroids. Finally, we have initialized an array ‘cluster_labels’ which according to our distances array holds the information of to which centroid every point is assigned. This was the first pass of our algorithm. Essentially, all we need to do now is repeat the distance and assignment steps above until we have reached a desired convergence or a maximum amount of iterations.
max_iterations = 100
tolerance = 1e-8
start_time = time.time()
for iteration in range(max_iterations):
prev_centroids = centroids.copy()
for k in range(n_clusters):
# this array will be used to update our centroid positions
vector_mean = np.zeros(dimensions)
mean_divisor = 0
for n in range(n_samples):
if cluster_labels[n] == k:
vector_mean += data[n, :]
mean_divisor += 1
# update according to the k means
centroids[k, :] = vector_mean / mean_divisor
# we find the dissimilarity
for k in range(n_clusters):
for n in range(n_samples):
dist = 0
for d in range(dimensions):
dist += np.abs(data[n, d] - centroids[k, d])**2
distances[n, k] = dist
# assign each point
for n in range(n_samples):
smallest = 1e10
smallest_row_index = 1e10
for k in range(n_clusters):
if distances[n, k] < smallest:
smallest = distances[n, k]
smallest_row_index = k
cluster_labels[n] = smallest_row_index
# convergence criteria
centroid_difference = np.sum(np.abs(centroids - prev_centroids))
if centroid_difference < tolerance:
print(f'Converged at iteration {iteration}')
print(f'Runtime: {time.time() - start_time} seconds')
break
elif iteration == max_iterations:
print(f'Did not converge in {max_iterations} iterations')
print(f'Runtime: {time.time() - start_time} seconds')
Converged at iteration 5
Runtime: 0.4887218475341797 seconds
And thats it! We now have an extremely barebones, un-optimized k-means clustering implementation. Lets plot the final result
fig = plt.figure()
ax = fig.add_subplot()
unique_cluster_labels = np.unique(cluster_labels)
for i in unique_cluster_labels:
ax.scatter(data[cluster_labels == i, 0],
data[cluster_labels == i, 1],
label = i,
alpha = 0.2)
ax.scatter(centroids[:, 0], centroids[:, 1], c='black')
ax.set_title("Final Result of K-means Clustering")
plt.show()
Now there are a few glaring improvements to be done here. First of all is organizing things into functions for better readability. Second is getting rid of the small inefficiencies like manually calculating distances and argmin. And finally, we need to optimize for better run-time. It’s like we always say: the best way of looping in Python is to not loop in Python. Let us tackle the first two improvements.
12.4. Towards a More Numpythonic Code¶
def get_distances_to_clusters(data, centroids):
"""
Function that for each cluster finds the squared Euclidean distance
from every data point to the cluster centroid and returns a numpy array
containing the distances such that distance[i, j] means the distance between
the i-th point and the j-th centroid.
Inputs:
data (np.array): with dimensions (n_samples x dim)
centroids (np.array): with dimensions (n_clusters x dim)
Returns:
distances (np.array): with dimensions (n_samples x n_clusters)
"""
n_samples, dimensions = data.shape
n_clusters = centroids.shape[0]
distances = np.zeros((n_samples, n_clusters))
for k in range(n_clusters):
for i in range(n_samples):
dist = 0
for j in range(dimensions):
dist += np.abs(data[i, j] - centroids[k, j])**2
distances[i, k] = dist
return distances
def assign_points_to_clusters(distances):
"""
Function to assign each data point to the cluster to which it is the closest
based on the squared Euclidean distance from the get_distances_to_clusters
method.
Inputs:
distances (np.array): with dimensions (n_samples x n_clusters)
Returns:
cluster_labels (np.array): with dimensions (n_samples)
"""
cluster_labels = np.argmin(distances, axis=1)
return cluster_labels
def k_means(data, n_clusters=4, max_iterations=100, tolerance=1e-8):
"""
Naive implementation of the k-means clustering algorithm. A short summary of
the algorithm is as follows: we randomly initialize k centroids / means.
Then we assign, using the squared Euclidean distance, every data-point to a
cluster. We then update the position of the k centroids / means, and repeat
until convergence or we reach our desired maximum iterations. The method
returns the cluster assignments of our data-points and a sequence of
centroids.
Inputs:
data (np.array): with dimesions (n_samples x dim)
n_clusters (int): hyperparameter which depends on dataset
max_iterations (int): hyperparameter which depends on dataset
tolerance (float): convergence measure
Returns:
cluster_labels (np.array): with dimension (n_samples)
centroid_list (list): list of centroids (np.array)
with dimensions (n_clusters x dim)
"""
samples, dimensions = data.shape
np.random.seed(2021)
centroids = data[np.random.choice(len(data), n_clusters, replace=False), :]
distances = get_distances_to_clusters(data, centroids)
cluster_labels = assign_points_to_clusters(distances)
start_time = time.time()
for iteration in range(max_iterations):
prev_centroids = centroids.copy()
for k in range(n_clusters):
vector_mean = np.zeros(dimensions)
mean_divisor = 0
for n in range(n_samples):
if cluster_labels[n] == k:
vector_mean += data[n, :]
mean_divisor += 1
# And update according to the new means
centroids[k, :] = vector_mean / mean_divisor
distances = get_distances_to_clusters(data, centroids)
cluster_labels = assign_points_to_clusters(distances)
centroid_difference = np.sum(np.abs(centroids - prev_centroids))
if centroid_difference < tolerance:
print(f'Converged at iteration: {iteration}')
print(f'Runtime: {time.time() - start_time} seconds')
return cluster_labels, centroids
print(f'Did not converge in {max_iterations} iterations')
print(f'Runtime: {time.time() - start_time} seconds')
return cluster_labels, centroids
# quirk of numpy / Jupyter need to set seed again
cluster_labels, centroids = k_means(data)
Converged at iteration: 5
Runtime: 0.432811975479126 seconds
Note: the start of the timing is after the random initialization, and first ‘cycle’ of our algorithm. This is technically not the correct way to time it but due to this being in a Jupyter notebook and the way it is structured this way of comparing our algorithms will produce a more equal result. When timing code we should always encapsulate our whole computation block.
So we see an improvement from just switching to numpy’s argmin function. There is a very nice tool (or category of tools) called profilers. These can be utilized to make clearer which improvements to our code we should care most about here is an excellent source on the topic. Even before optimizing we can understand which parts of our code will be taking the most of the run-time. It will be the longest Python loop, i.e. the loop over all the samples. Nonetheless, let us do some profiling!
test_data = generate_simple_clustering_dataset(n_points=10000, plotting=False)
%prun -l 10 cluster_labels, centroids = k_means(test_data)
Converged at iteration: 11
Runtime: 0.8653810024261475 seconds
Here we can see the reason for profiling. We now know for certain a lot can be gained just by vectorizing our distance function. Ideally we wish to perform most of our loops in numpy, i.e. C. To do this we need our array shapes to match and clever reshaping will let us do so.
def np_get_distances_to_clusters(data, centroids):
"""
Squared Euclidean distance between all data-points and every centroid. For
the function to work properly it needs data and centroids to be numpy
broadcastable. We sum along the dimension axis.
Inputs:
data (np.array): with dimensions (samples x 1 x dim)
centroids (np.array): with dimensions (1 x n_clusters x dim)
Returns:
distances (np.array): with dimensions (samples x n_clusters)
"""
distances = np.sum(np.abs((data - centroids))**2, axis=2)
return distances
def np_assign_points_to_clusters(distances):
"""
Assigning each data-point to a cluster given an array distances containing
the squared Euclidean distance from every point to each centroid. We do
np.argmin along the cluster axis to find the closest cluster. Returns a
numpy array with corresponding labels.
Inputs:
distances (np.array): with dimensions (samples x n_clusters)
Returns:
cluster_labels (np.array): with dimensions (samples x None)
"""
cluster_labels = np.argmin(distances, axis=1)
return cluster_labels
def np_k_means(data, n_clusters=4, max_iterations=100, tolerance=1e-8):
"""
Numpythonic implementation of the k-means clusting algorithm.
Inputs:
data (np.array): with dimesions (samples x dim)
n_clusters (int): hyperparameter which depends on dataset
max_iterations (int): hyperparameter which depends on dataset
tolerance (float): convergence measure
progression_plot (bool): activation flag for plotting
Returns:
cluster_labels (np.array): with dimension (samples)
centroid_list (list): list of centroids (np.array)
with dimensions (n_clusters x dim)
"""
n_samples, dimensions = data.shape
np.random.seed(2021)
centroids = data[np.random.choice(len(data), n_clusters, replace=False), :]
distances = np_get_distances_to_clusters(np.reshape(data,
(n_samples, 1, dimensions)),
np.reshape(centroids,
(1, n_clusters, dimensions)))
cluster_labels = np_assign_points_to_clusters(distances)
start_time = time.time()
for iteration in range(max_iterations):
prev_centroids = centroids.copy()
for k in range(n_clusters):
points_in_cluster = data[cluster_labels == k]
mean_vector = np.mean(points_in_cluster, axis=0)
centroids[k] = mean_vector
distances = np_get_distances_to_clusters(np.reshape(data,
(n_samples, 1, dimensions)),
np.reshape(centroids,
(1, n_clusters, dimensions)))
cluster_labels = np_assign_points_to_clusters(distances)
centroid_difference = np.sum(np.abs(centroids - prev_centroids))
if centroid_difference < tolerance:
print(f'Converged at iteration: {iteration}')
print(f'Runtime: {time.time() - start_time} seconds')
return cluster_labels, centroids
print(f'Did not converge in {max_iterations} iterations')
print(f'Runtime: {time.time() - start_time} seconds')
return cluster_labels, centroids
When working towards becoming a data scientist using Python this last step is arguably one of the most important. Thinking of ways to avoid explicitly looping by adding dimensions to our arrays in such a way that they become broadcastable using numpy (also tensorflow and many others). Let us take a look at our the fruits of our labor.
cluster_labels, centroids = np_k_means(data)
Converged at iteration: 5
Runtime: 0.0036406517028808594 seconds


HTML: to create another flippable element, just duplicate the div with a class of 'wrapper' and change the 'box' ID to a unique number on the 'input' and on the 'label' (i.e. box4).
CSS: Create a new 'ID:checked+label' declaration section for each new box you add in the HTML.


Your browser doesn't support the features required by impress.js, so you are presented with a simplified version of this presentation.
For the best experience please use the latest Chrome, Safari or Firefox browser.
ember.js
A very gentle introduction
What is Ember.js?
A bit of history...
Ok... so what is Ember.js?
- MVC Javascript Framework
- Rapid Development
- Enforce Good Practices
- Maintainable Code
Basics
- Application
- Model
- View
- Controller
- Handlebars
Advanced
- Router
- Computed Properties
- Mixins
Conclusions
- The Bad
- The Good
- Resources
Application
- Defines the namespace
- Contains the ember components
- Controls the lifecycle
Definition
var App = Ember.Application.create();
Initialization
App.ready = function() {
/** INIT CODE GOES HERE **/
}
App.initialize();
Model
- Represent objects
- Define how data is stored
- Contain no logic
- Expanded by Ember Data
Definition
App.Movie = Ember.Object.extend({
title : null,
year: null,
rating: 0,
available: null,
imdbLink: null
});
Usage
var movie = App.Movie.create();
movie.set('title','The Shawshank Redemption');
movie.set('year', 1994);
movie.set('rating', 9.2);
movie.set('available', true);
movie.set('imdbLink', 'http://www.imdb.com/title/tt0111161/');
View
- Encapsulates templates
- Defines the UI
- Handles user actions
Definition
App.MoviesView = Ember.View.extend({
templateName: "movies",
baseClass: "table",
additionalClass: "table-hover"
});
Usage
//js
App.moviesView = App.MoviesView.create().append();
//html
{{#view App.moviesView}}
...
{{/view}}
Controller
- Contain the logic of the application
- Manage object lists (Array Controller)
Definition
//Basic Controller
App.ApplicationController = Ember.Controller.extend();
//Array Controller
App.MoviesController = Ember.ArrayController.extend({
content : null,
init: function(){
this.content = []
}
});
Usage
App.moviesController = App.MoviesController.create();
Handlebars
- Templating engine
- Associated context
- Reusable
- Helpers
Usage
//syntax
{{App.myObject.property}}
//iterate collections
{{#each movie in App.moviesController}}
{{movie.title}}
{{/each}}
//conditional
{{#if App.myObject.property}}
//...
{{/if}}
//client side actions
< a href="#" {{action actionName parameter}} >Click me!< /a>
Router
- Manages the state of the application
- Initialize controllers and views
- Serialize/Deserialize urls
- Injects views in templates
Definition
App.Router = Ember.Router.extend({
root: Ember.Route.extend({
index: Ember.Route.extend({
route: '/'
}),
user: Ember.Route.extend({
route: '/users/:idUser',
deserialize: function(router, context){
var user = //get user based on the idUser contained in the context
//return user to put it in the context
return user;
},
serialize: function(router, context){
return {
idUser: //obtain id user from the context
}
},
})
})
});
Computed Properties
- Composition of properties
- Cached
- Setters
- Observers
Definition
//Model computed property
App.Movie = Ember.Object.extend({
...
onSale: function(){
return ((this.rating < 4 ) && this.available) ? true : false;
}.property("rating", "available")
...
});
//Controller aggregated computed property
App.MoviesController = Ember.ArrayController.extend({
...
availableTitles: function(){
return this.content.filterProperty('available',true).get('length');
}.property('content.@each.available'),
...
}
Mixins
- Provides functionality to multiple classes
- Applicable to any object
- Reusable
Definition
//Normal Object
App.Client = Ember.Object.extend(){
name: null
...
}
//Our Mixins
App.GeekMixin = Ember.Mixin.create(){
favouriteMovie: null,
init: function() {
this._super();
this.set("favouriteMovie", "Star Wars");
}
}
App.HipsterMixin = Ember.Mixin.create(){
favouriteDirector: null,
init: function() {
this._super();
this.set("favouriteDirector", "Aronofsky");
}
}
Usage
//Default initialization
var clientA = App.Client.create(GeekMixin,HipsterMixin);
clientA.set('name','Joe');
clientA.get('favouriteMovie'); // Star Wars
//Change default values
var clientB = App.Client.create(GeekMixin,HipsterMixin, {
init:function(){
this._super();
this.set('favouriteMovie', 'Star Trek');
}
});
clientB.get('favouriteMovie', 'Star Trek');
The Bad
- Still Not Final
- Documentation
- Learning Curve
- Handlebars
- Not modular by design
- Performance
The Good
- Productivity
- Forces Best Practices (MVC, OO, Observer)
- Bindings
- Computed Properties
- Handlebars
- Community
- Performance
Resources
Addons
- Ember-data
- Flame.js
- Ember-bootstrap
Documentation
Questions ¿?


PaginatedData
public protocol PaginatedData
The paginated data protocol defines a common interface for result types of paginated requests. The properties of the protocol can be leveraged to enable handling paginated requests automatically by observing the provided properties.
-
The actual type of the requested data (usually provided as a field of the top-level JSON).
Declaration
Swift
associatedtype DataType
-
The actual data that is received by the request.
Declaration
Swift
var data: DataType { get }
-
The index of the current page. By convention, an index of 1 indicates the first page. You will need to overwrite the
zeroBasedPageIndex
property to returntrue
when you want to use an API where the first page has index 0.Declaration
Swift
var page: Int { get }
-
The index of the first element of the data (inclusive).
Declaration
Swift
var from: Int { get }
-
The index of the last element of the data (exclusive).
Declaration
Swift
var to: Int { get }
-
The requested number of items on the page. Might be larger than the actual number of elements.
Declaration
Swift
var chunk: Int { get }
-
The total number of elements that are available.
Declaration
Swift
var totalCount: Int { get }
-
The total number of pages that are available given the chunk.
Declaration
Swift
var totalPageCount: Int { get }
-
zeroBasedPageIndex
Default implementationWhether the first page of the paginated request is indexed with 0. By default, this property is
false
and indicates that the first page is indexed with 1.Default Implementation
Declaration
Swift
var zeroBasedPageIndex: Bool { get }
-
count
Extension methodThe number of elements currently returned.
Declaration
Swift
public var count: Int { get }
-
isLastPage
Extension methodReturns whether the returned page of the data is the last page available.
Declaration
Swift
public var isLastPage: Bool { get }


JAVASCRIPT FUNCTIONS
Enter String to get the Position of the left-most Vowel
Enter Number with its Digits to get the Reverse Order
Enter String/Number
EXECUTE
Result


Government Looking at Credit Rating Process For Corporates: Nirmala Sitharaman
The Finance Minister, without mentioning any names, said corporates have gone bust soon after getting AAA rated, thereby making people wonder.
Finance Minister Nirmala Sitharaman on Saturday said the government is looking at the methodologies used by credit rating agencies in rating corporates.
She also said the Narendra Modi government decided against joining the Regional Comprehensive Economic Partnership as the offer did not match India’s expectations and aspirations.
Delivering the sixth G. Ramachandran Memorial Lecture organises by the Southern India Chamber of Commerce and Industry, Sitharaman said she had met some rating agencies to understand how they rate a company, its assets and how far it is in line with the happenings in the economy.
The Finance Minister, without mentioning any names, said corporates have gone bust soon after getting AAA-rated, thereby making people wonder.
She said a churn is happening in the way banks are assessing their asset quality and also the manner that credit ratings are done.
According to Sitharaman, the government plans to further strengthen the powers of financial sector regulators like Reserve Bank of India, and the Securities and Exchange Board of India.
Citing the reduction in corporate taxes, she said the government is fast responding to the needs of various sectors.
For breaking news and live news updates, like us on Facebook or follow us on Twitter and Instagram. Read more on Business Latest News on India.com.
Story Timeline
- 20 Nov 2019 Union Cabinet Approves Strategic Disinvestment of BPCL Among 4 Other PSUs: FM Nirmala Sitharaman
- 17 Nov 2019 We Will Move Forward on Insurance PSUs Merger, Says Nirmala Sitharaman
- 15 Nov 2019 ‘Govt Planning to Increase Insurance Limit on Banks Deposits,’ Says Finance Minister Nirmala Sitharaman
Comments - Join the Discussion


ABC Skilled Nursing Facility

resourceType: Organization

status: generated

div:
Generated Narrative with Details

id: cms-organization-01

name: ABC Skilled Nursing Facility


TALES FROM THE HOOD A film review by Maoist Internationalist Movement
- MIM Notes 102, July 1995 -
TALES FROM THE HOOD SHOWS THE REAL HORROR: AMERIKKKA
Directed by Rusty Cundieff, 1995
TALES FROM THE HOOD is much better than your average Hollywood flick. It skillfully co-opts the trademarks of the goofier horror movies--action, violence, gore and humor--to make some harsh political points against patriarchy and national oppression. More so than in 1995 Amerikkka, the oppressors tend to get what's coming to them, just as they will in real life in the long run. The audiences' cheers for vengeance are one sign that life will imitate art in this regard. By using the horror medium, Cundieff is able to keep his audience's attention and avoid being accused of being "preachy" as are more sober political directors.
TALES FROM THE HOOD is composed of four stories, each with a strong component of reality. What could make for better horror than the real-life monsters created by imperialism, capitalism and patriarchy?
The monster in the second story is an abusive patriarch. Cundieff deals well with gender, particularly by the inclusion of several strong women characters.
The third story centers on a fascist cracker politician named Duke Metzger. Once again, the bad guy gets his, as does his Uncle Tom advisor. The protagonists here are symbols of slavery who call for reparations--or else.
The last story brings it all home to the young audience TALES FROM THE HOOD is made for. Cundieff makes it clear that these stories add up to genocide, and that fratricidal gang violence is part of this big problem. This bit suffers from a line of dialogue that seems out of place in this otherwise excellent movie. The awkward line says that the government funded a genuine anti-fratricide program, suggesting that the government behind genocide has its sweet side.
Overall though, TALES FROM THE HOOD is a great film in that it advances a lot of heavy--and correct--political points, while staying faithful to the traditions of a playful genre.
--MC49
MIM Notes is not copyrighted. Please credit MIM when redistributing or referring to this material.
MIM Distributors, PO Box 3576, Ann Arbor MI 48106-3576. E-mail<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Chinatown Citizenship
English
US History
N400
Other Resources
Contact Us
English Test
This test consists of 20 questions pulled out of a test bank!


Cancel read
Vertices Viewer is an all in one model viewer for video game and 3D models. It is meant to be a model viewer and light weight editor for lighting values and texture types.
<canvas>


FAX2PS
Section: User Commands (1)
Updated: March 16, 1995
Index
Return to Main Contents
NAME
fax2ps - convert a
TIFF
facsimile to compressed POSTSCRIPTtm
SYNOPSIS
fax2ps
[
options
] [
file...
]
DESCRIPTION
fax2ps
reads one or more
TIFF
facsimile image files and prints a compressed form of
POSTSCRIPT on the standard output that is suitable for printing.
By default, each page is scaled to reflect the
image dimensions and resolutions stored in the file.
The
-x
and
-y
options can be used to specify the horizontal and vertical
image resolutions (lines/inch), respectively.
If the
-S
option is specified, each page is scaled to fill an output page.
The default output page is 8.5 by 11 inches.
Alternate page dimensions can be specified in inches with the
-W
and
-H
options.
By default
fax2ps
generates POSTSCRIPT for all pages in the file.
The
-p
option can be used to select one or more pages from
a multi-page document.
fax2ps
generates a compressed form of POSTSCRIPT that is
optimized for sending pages of text to a POSTSCRIPT
printer attached to a host through a low-speed link (such
as a serial line).

The POSTSCRIPT specification of the black drawing operations
is optimized by using a special font that encodes the
move-draw operations required to fill
the black regions on the page.
This compression scheme typically results in a substantially
reduced POSTSCRIPT description, relative to the straightforward
imaging of the page with a POSTSCRIPT
image
operator.
This algorithm can, however, be ineffective
for continuous-tone and white-on-black images.
For these images, it sometimes is more efficient to send
the raster bitmap image directly; see
tiff2ps(1).
OPTIONS
- -p number
-
Print only the indicated page.
Multiple pages may be printed by specifying
this option more than once.
- -x resolution
-
Use
resolution
as the horizontal resolution, in dots/inch, of the image data.
By default this value is taken from the file.
- -y resolution
-
Use
resolution
as the vertical resolution, in lines/inch, of the image data.
By default this value is taken from the file.
- -S
-
Scale each page of image data to fill the output page dimensions.
By default images are presented according to the dimension
information recorded in the
TIFF
file.
- -W width
-
Use
width
as the width, in inches, of the output page.
The default page width is 8.5 inches.
- -H height
-
Use
height
as the height, in inches, of the output page.
The default page height is 11 inches.
DIAGNOSTICS
Some messages about malformed
TIFF
images come from the
TIFF
library.
Various messages about badly formatted facsimile images
may be generated due to transmission errors in received
facsimile.
fax2ps
attempts to recover from such data errors by resynchronizing
decoding at the end of the current scanline.
This can result in long horizontal black lines in the resultant
POSTSCRIPT image.
NOTES
If the destination printer supports POSTSCRIPT Level II then
it is always faster to just send the encoded bitmap generated
by the
tiff2ps(1)
program.
BUGS
fax2ps
should probably figure out when it is doing a poor
job of compressing the output and just generate
POSTSCRIPT to image the bitmap raster instead.
SEE ALSO
tiff2ps(1),
libtiff(3)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- OPTIONS
-
- DIAGNOSTICS
-
- NOTES
-
- BUGS
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 01:31:47 GMT, November 23, 1999


{% extends 'base.html' %} {% block title_extension %} - {{ organization }}{% endblock %} {% block content %}
Click on a subject area to see the {{ organization.alias_ca }}s and {{ organization.alias_eu }}s for that subject area.
{% if subject_areas %} {# --- subject areas --- #}new {{ organization.alias_sa }}
{% endif %} {% elif user.is_authenticated and user in editors %}This {{ organization.org_type }} does not have any subject areas yet:
This {{ organization.org_type }} does not have any public subject areas yet.
{% endif %} {% endblock %}


Implementation for iSensor-SPI-Buffer IMU interfacing module.
More...
#include "reg.h"
#include "imu.h"
#include "main.h"
#include "timer.h"
#include "stm32f3xx_hal.h"
Implementation for iSensor-SPI-Buffer IMU interfacing module.
Copyright (c) Analog Devices Inc, 2020 All Rights Reserved.
- Date
- 3/18/2020
- Author
- A. Nolan (alex..nosp@m.nola.nosp@m.n@ana.nosp<EMAIL_ADDRESS>◆ ApplySclkDivider()
| static void ApplySclkDivider |
( |
uint32_t |
preScalerSetting | ) |
|
|
static |
Applies baud rate divider setting to master SPI port (to IMU)
- Parameters
-
| preScalerSetting | The SCLK pre-scaler setting to apply. |
- Returns
- void
◆ ConfigureImuCsTimer()
| static void ConfigureImuCsTimer |
( |
uint32_t |
period | ) |
|
|
static |
Sets the TIM3 period for use in PWM mode to drive CS.
- Returns
- void
- Parameters
-
| period | Timer ticks period for TIM3. This corresponds to the CS low pulse width |
TIM3 will be disabled (CS high) by this function. It should not be called while a data capture is in progress. This function must be called whenever the IMU SPI interface settings are updated, either at startup or on demand by the user.
◆ ConfigureImuSpiTimer()
| static void ConfigureImuSpiTimer |
( |
uint32_t |
period | ) |
|
|
static |
Configures the period on TIM4.
- Parameters
-
| period | The timer period (in 72MHz ticks) |
- Returns
- void
This function must be called whenever the IMU SPI interface settings are updated, either at startup or on demand by the user.
◆ IMU_Disable_SPI_DMA()
| void IMU_Disable_SPI_DMA |
( |
| ) |
|
Disable IMU burst data stream.
- Returns
- void
◆ IMU_Read_Register()
| uint16_t IMU_Read_Register |
( |
uint8_t |
RegAddr | ) |
|
Reads 16 bit value from the IMU.
- Parameters
-
| RegAddr | The IMU register address to read |
- Returns
- The 16 bit register data read from the IMU
This function produces two SPI transfers to the IMU. One to send the initial read request, and a second to get back the read result data.
A stall time is inserted between the two words, as well as after the last word. This is needed when accessing the IMU over USB - don't want to read requests to be immediately back to back.
◆ IMU_Reset()
Hardware reset connected IMU.
- Returns
- void
Reset pin is pulled low for 1ms, then brought high
◆ IMU_SPI_Init()
SPI1 Initialization Function (master SPI port to IMU)
- Returns
- void
◆ IMU_SPI_Transfer()
| uint16_t IMU_SPI_Transfer |
( |
uint32_t |
MOSI | ) |
|
Basic IMU SPI data transfer function (protocol agnostic).
- Parameters
-
| MOSI | The 16 bit MOSI data to transmit to the IMU |
- Returns
- The 16 bit MISO data received during the transmission
This function wraps the SPI master HAL layer into something easily usable. All SPI pass through functionality is built on this call.
◆ IMU_Start_Burst()
| void IMU_Start_Burst |
( |
uint8_t * |
bufEntry | ) |
|
Start an IMU burst data capture (using DMA)
- Parameters
-
| bufEntry | Pointer to the buffer entry to recieve data into |
- Returns
- void
This function configures the IMU SPI port for a bi-directional DMA transfer. CS is manually controlled by leaving TIM3 (CS timer) disabled, and manually setting the count register to 0.
◆ IMU_Update_SPI_Config()
| void IMU_Update_SPI_Config |
( |
| ) |
|
Processes any changes to IMU_SPI_CONFIG reg and applies.
- Returns
- void
Sets the stall time (in microseconds) based on lower byte of the IMU_SPI_CONFIG register. Sets the SPI clock frequency divider based on the upper 8 bits. See register documentation for details of what target SCLK frequencies are achievable.
◆ IMU_Write_Register()
| uint16_t IMU_Write_Register |
( |
uint8_t |
RegAddr, |
|
|
uint8_t |
RegValue |
|
) |
| |
Writes an 8 bit value to the IMU.
- Parameters
-
| RegAddr | The IMU register address to write |
| RegValue | The 8 bit value to write |
- Returns
- The MISO data clocked out from the IMU during the transaction
This function produces only a single SPI transaction to the IMU
◆ InitImuCsTimer()
| static void InitImuCsTimer |
( |
| ) |
|
|
static |
Inits TIM3 for use in PWM mode to drive CS.
- Returns
- void
TIM4 is used to drive SPI buffered data acquisition from the IMU. One timer interrupt is generated per SPI word clocked out from the DUT. TIM4 runs at a full 72MHz. With a 16-bit resolution and a time base of 72MHz, TIM4 will roll over every 910us The worst case spi period allowed is 255us stall + (17 bits / 140KHz) -> 376us
◆ InitImuSpiTimer()
| static void InitImuSpiTimer |
( |
| ) |
|
|
static |
Inits TIM4 for use as a IMU spi period timer.
- Returns
- void
TIM4 is used to drive SPI buffered data acquisition from the IMU. One timer interrupt is generated per SPI word clocked out from the DUT. TIM4 runs at a full 72MHz. With a 16-bit resolution and a time base of 72MHz, TIM4 will roll over every 910us The worst case spi period allowed is 255us stall + (17 bits / 140KHz) -> 376us
◆ htim3
◆ htim4
◆ imuStallTimeUs
| uint32_t imuStallTimeUs = 25 |
|
static |
track stall time (microseconds)


ROS Test Results
Pick one and look at some results.
Failed Tests
Unit
Integration
Acceptance
All Tests
Unit
Integration
Acceptance


XXHeadXX
Here are some hints for using the Armory Description Search Form.
The purpose of the form is to find all registered armory that matches a particular description, such as "FIELD DIV.-BENDY:complex line". It is used, for instance, to view a single category from the on-line SCA Ordinary.
Your search will probably fail if you don't type the description exactly right, so until a friendlier interface is available, you should probably access it only through the on-line SCA Ordinary.
The form has a text entry field and a "submit" button. Type the description you are looking for in the box, then click on the button. The armory description search form is case-sensitive.
A description consists of
Here are some typical headings:
Here are some typical features:
Here are some examples of reasonable armory descriptions:
You can also find examples of valid descriptions by enabling the "Armory descriptions" option on the Blazon Pattern Search Form and doing a search.


Use the Config tool to change a target's configuration database.
Also see Manage Config Tree
Functions supported include: inspect a tree, read/write values, and import/export enitre tree sections.
config [OPTIONS]
config get <tree path> [--format=json]
Read a value.
config set <tree path> <new value> [<type>]
Write a value.
config move <node path> <new name>
Move a node.
config copy <node path> <new name>
Copy a node.
config delete <tree path>
Delete a node.
config clear <tree path>
Clear a node. Or create a new empty node if it didn't previously exist.
config import <tree path> <file path> [--format=json]
Import config data.
config export <tree path> <file path> [--format=json]
Export config data.
config list
List all config trees.
config rmtree <tree name>
Delete a tree.
config help
Display help.
<tree path>
Path to the tree and node to configure.
<tree name>
Is the name of a tree in the system, but without a path.
<file path>
Path to the file for import/export.
<new value>
String value to write to the config tree.
<type>
Optional, must be bool, int, float, or string. If tool, must be true or false. If unspecified, default type is string.
--format=json
For imports, then properly formatted JSON will be expected. For exports, then the data will be generated as well. It is also possible to specify JSON for the get sub-command.
A tree path is specified similar to a *nix
path. With the beginning slash being optional.
For example: /a/path/to/somewhere or a/path/to/somewhere
The configTree supports multiple trees: a default tree is assigned per user. If the config tool is run as root, then alternative trees can be specified in the path by entering a tree name, then a colon and the value path.
Here's an example using the tree named 'foo' instead of the default tree:
foo:/a/path/to/somewhere
The trees themselves are stored in the file system at:
/legato/systems/current/configTree
The configTree cycles through the extensions, .rock, .paper, and .scissors to differentiate between versions of the tree file. The base file name is the same as the tree.
A listing for /legato/systems/current/configTree where the system tree and the user trees are foo and bar looks like this:
$ ls /legato/systems/current/configTree/ -l total 32 -rw------- 1 user user 3456 May 12 11:02 bar.rock -rw------- 1 user user 3456 May 9 11:04 foo.scissors -rw------- 1 user user 21037 May 9 11:04 system.paper
The system, or root user, has its own tree; each application has a separate tree.
To dump a tree, run this to get the default tree for the current user:
config get /
Or to get a specific tree:
$ config get foo:/ / helloWorld/ greeted<bool> == true ignored<bool> == false
The config tool can also read and write individual values. You can read the value of greeted like this:
$ config get /helloWorld/greeted true
If you want to see everything under helloWorld:
$ config get /helloWorld helloWorld/ greeted<bool> == true ignored<bool> == false
If you want to change the value of ignored:
$ config set /helloWorld/ignored true bool
You can check it by running:
$ config get /helloWorld/ignored true
If the config tool is listing a tree, it will display the node name and a / if the current node has children (except for the root node, as the root node does not have a name.)
For leaf nodes, the config tool will display the value type in angle brackets, <>, as well as its name and actual value:
/ testValues/ aBoolValue<bool> == true aStringValue<string> == This is some text I saved. anIntValue<int> == 1024 afloatValue<float> == 10.24
Copyright (C) Sierra Wireless Inc. Use of this work is subject to license.


THE OTHERS Written and Directed by Alejandro Amenabar With Nicole Kidman, Fionnula Flanagan De Vargas PG-13 100 min.
`The Others' is an old-fashioned ghost story that aims not to slash the jugular but to constrict the throat, designed not so much for screams as for raising the little hairs on the back of the neck. Writer/director Alejandro Amenabar (Abres los Ojos (Open Your Eyes) simmers his atmospheric broth with a slow, sure relish over a low heat, with help immeasurable from the rich, darkly textured visual flavors supplied by cinematographer Javier Aguirresarobe. The terrors Amenabar treats us to are terrors of the mind, terrors of the soul, terrors of the unknown and unseen, terrors of darkness and repression, terrors of isolation, terrors of a religion designed to put the fear of God into little children.
There is very little that can be said about this movie without giving too much away. Not much happens for long stretches, but it builds its web strand by strand, and its pleasures are in the buildup of little discoveries. It is probably safe to reveal that this is a haunted house yarn set in the formerly Nazi-occupied Isle of Jersey in the English Channel, just after WWII. Nicole Kidman plays Grace, whose husband has gone off to war and never returned. She lives in an isolated mansion with her two young children, who are afflicted with a strange and terrible malady that makes it essential that they never be exposed to daylight. There is precious little daylight to be had in this dark-paneled mansion at best; it is perpetually shrouded in a dense fog, and even to go out for a walk beyond the great iron gate is to risk getting hopelessly and terrifyingly lost in the swirling, changing mists. Nevertheless, all the windows are swathed in thick drawn curtains, and as Grace moves from room to room she carries a heavy ring of keys with which she unlocks each door and locks it again behind her to insure against one being carelessly opened or left ajar to let in lethal daylight.
A trio of servants – a middle-aged housekeeper (Fionnula Flanagan), an old gardener (Eric Sykes) and a young maid (Elaine Cassidy) arrive on the doorstep on the cold, misty morning on which the story begins. They are quickly employed, and set to work to replace the previous staff, which has left without notice, and who's to blame them? Days pass, there are strange noises, strange happenings.
It is surely no coincidence that Amenabar has named his heroine Grace. With her post-war hairdo, her cool control and her exquisite patrician beauty, Nicole Kidman bears a startling resemblance to Grace Kelly. It's a nicely calculated touch, lending an instant Hitchcockian classicism (and an eerie reminder of her bizarre and shocking death) to the atmosphere of the film. Kidman delivers a skilled performance as a woman walking a shaky tightrope between self-control and neurosis. Her religious fanaticism as she home-schools her children with warnings of `The Children's Limbo' and eternal torment delivers some of the movie's most disturbing moments. There is something very fragile rattling beneath Grace's competent exterior, and something very wrong.
Fionnula Flanagan makes Mrs. Mills, the new housekeeper, a nurturing yet vaguely malevolent presence, a sort of dark-side Mrs. Piggle Wiggle. The children are excellent, both the frightened, trembling little Nicholas (James Bentley) and his almost preternaturally self-possessed older sister, Anne (Alakina Mann). Christopher Eccleston adds a haunting cameo.
There are holes in the story and gaps in its logic, and these can be mildly troublesome in the last third of the picture. But Amenabar recovers for an ending that falls squarely within the traditions of the ghost story genre, like a tale well told around the campfire.
And now, children, crawl into your tents and try to go to sleep.
========== X-RAMR-ID: 29125 X-Language: en X-RT-ReviewID: 242805 X-RT-TitleID: 1109257 X-RT-AuthorID: 2779
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Benchmarking outcomes for applying task rules¶
This article describes the benchmarking of outcomes for applying various task rules to a test set of data in a similar environment. The results are useful in providing guidance in how task rules can improve GEE performance of loading assets and build times.
Tip
This set of tests was designed to help assess how GEE performs in a specific environment. Although certain conclusions can be drawn from the test results, your results may differ. The figures presented here can be used as a reference for the GEE performance only. This testing was conducted with GEE 4.4 using publicly available geospatial data to build a 3D database.
GEE System Specifications
The following table lists the software and hardware specifications that were used for testing.
| OS | Ubuntu 12.04.1 LTS |
| Processor | 8x Intel(R) Xeon ® CPU @2.60GHz |
| CPU | 2599.998 MHz |
| Cache size | 20488 KB |
| Memory | 30 GB |
This set of tests used raster data, terrain data, and vector data that are freely available online. Some of them provide global coverage and others provide local high resolution coverage.
Fusion Server Configurations - Task Rules
A number of task rules can be configured to increase processing speed. You configure task rule settings to assign a number of CPUs to processes that can be run concurrently.
Fusion Server has the following two binaries which are multithreaded to increase processing speed when building a 3D database:
gepackgen
gecombineterrain
You can identify the activity of these binaries during the processing of your databases by checking the log files listed in the Version Properties dialog. (Right-click an asset in the Asset Manager dialog.)
gepackgen
generates imagery or terrain project packets.
Multiple gepackgen
processes can run concurrently to speed up
the building of the imagery projects. gecombineterrain
processes 3D databases and merges terrain packets from the
specified indices.
Task rules can be configured based on the number of concurrent
processes required to be run on the Fusion server. Since
gecombineterrain
is a serial process and must be completed
before the other database-level building activities can start, the
CombinedTerrain task rule is used to put all available
CPUs/cores onto the build, assuming that one core is reserved for
the system.
The ImageryPacketLevel and TerrainPacketLevel task rules
configure gepackgen
for concurrent processing. Both assign
more or fewer CPUs to the pack files that are created during
project builds.
The task rules apply to all subsequent tasks that the Fusion server performs, and cannot be applied to individual assets or projects.
To compare the effect of different task rule settings on the performance of the Fusion server when loading assets and building resources, projects, and databases, three different task rule configurations were used: default taskrule, taskrule 2 and taskrule 3. Default task rule is the GEE default setting after installation.
Detailed settings are in the following table.
| Default taskrule | Default taskrule | Taskrule 2 | Taskrule 2 | Taskrule 3 | Taskrule 3 | |
|---|---|---|---|---|---|---|
| Min CPU | Max CPU | Min CPU | Max CPU | Min CPU | Max CPU | |
| CombinedTerrain | 1 | 1 | 7 | 7 | 7 | 7 |
| ImageryPacketLevel | 1 | 1 | 3 | 3 | 2 | 2 |
| TerrainPacketLevel | 1 | 1 | 3 | 4 | 3 | 4 |
In this set of tests, the server was permitted to use 7 CPU cores for Fusion processing. In Taskrule 2, for example, there can be up to 2 gepackgen tasks running concurrently with three CPUs assigned for each task.
Project build time under different taskrules
Total raster, terrain, and vector project build times are
illustrated in the following charts. The vector project build time
does not change much at all because the task rule settings have no
impact on the gevectorfuse
command. For the imagery project,
taskrule 2 provides the best build performance. For the terrain
project, taskrule 3 provides the best build performance.
Total database build time
The database build time with task rule 3 was the fastest - reducing the time to 25.31 hours. Under the default task rule setting, the total database build time took the longest time to finish, 33.52 hours.
Recommendations
Always set the task rule other than default to have better image
project build performance.
With this test dataset, task rule 3 provides the shortest total
database building time on this server - with two imagery
gepackgen
tasks running concurrently with two CPUs assigned for
each task; with all available CPU assigned to CombinedTerrain
and two terrain gepackgen
tasks running concurrently with three or four
CPUs assigned for each task.
Set CombinedTerrain to put all available CPUs/cores onto the
build.
Select a machine with more memory if processing vector data as
fusing vector data (gevectorfuse
) can consume a lot of memory.
.. rubric:: Learn more
:name: learn-more