Bash Basics
Now that you have access to a shell, open it up to practice some basics.
Type the following code into your shell and press enter:
echo "Hello World"
Did you type it out? Seriously try it! Programming is just a dialogue between you and your computer, and like any language you need to read, write and speak (or at least input) code to become fluent.
Navigation
Before we decide where to go, we can check where we are with the pwd
(print working directory) command.
(Note: the #
(hash) symbol below denotes a comment in bash. I will use comments to show my output so that you can compare. You don’t need to type the comment into your shell, but you can try it and see what happens!):
pwd
The path shows me I am in my home directory (or folder). Each user on a Linux computer has their own home directory. The symbol ~
(tilde) is shorthand for your home directory. To make sure we’re on the same page, we can use the cd
(change directory) command to navigate to home (~
).
cd ~
pwd
Your computer’s file system can be thought of as a tree. We will learn to navigate up and down directories (branches) and make new files (leaves). Your home directory is the trunk from which all your files and folders branch out. This isn’t the start of the tree, though.
All Unix filesystems branch out from a single root directory (get it? like the root(s) of a tree?). The root directory is simply the /
at the start of every file path.
Let’s go to the root directory.
cd /
pwd
Okay, our path is now /
so we know we’re in root, but what if we want to know what files and folders we can go to from here? Try using the ls
(list) command.
ls
Here are the non-hidden files and folders that branch out from root. Many of them are system files that you don’t need to worry about.
Recall the path to your home directory /home/<your_username>
. Let’s see if we can get there from the root directory. First we want to change directory to /home/
.
cd home # or 'cd home/' works too
pwd
If we want to check out which other users are on this machine, we can use list to see all of the home directories.
ls
Now we’re one step away from our home directory (replace <your_username>
with your username).
cd <your_username>
pwd
We made it back home! Now let’s check out two shortcuts bash has for navigating, (.
) and (..
). Try to change directory to (.
).
cd .
pwd
Were you surprised you didn’t move? The .
is shorthand for the current directory that we are in. Why would we ever want to do this? Well, we probably wouldn’t want to change directory to our current one, but it will be convenient to use .
to refer to our current path later on.
Now let’s try to cd to ..
cd ..
pwd
This appears to have moved up one level to the /home
directory. The ..
is shorthand for the parent directory of the diectory you are currently in. In our tree analogy ..
is a way to move up the branches towards root /
. Now change directory to ..
once more.
cd ..
pwd
Great! What do you think happens if we cd ..
while we are in the root directory?
cd ..
pwd
Since root is the highest level directory, it has no parent directory, so cd ..
keeps us here at /
.
Now let’s take our handy shortcut back to home and start making our own directories.
cd ~
pwd
Making files and directories
If this is your first time on this Linux machine, your home directory might look pretty empty.
ls
There may be some hidden files, though. Let’s check with the -a
option to the list command.
ls -a
My home directory has a .bashrc
and a .bash_profile
in it (it is ok if you do not have these, we will work with them in the next lesson).  We won’t worry about them for now.
Let’s make a directory that we can practice in. We do this with the mkdir (make directory) command. I will call mine test
.
mkdir test
You can check if you were successful with the ls command.
ls
Now navigate to your new directory.
cd test
pwd
Now let’s make a new file. A combination of the echo
(print) command and bash’s >
(redirection) command can do this quite quickly. Let’s make a new file called test_file.txt
containing one line with the words Hello World
.
echo 'Hello world' > test_file.txt
We know how to check if the file was created,
ls
And we can quickly view (but not edit) a file with the less
command.
less test_file.txt
Press q
to exit less. Great! We’ve now learned to navigate directories, make our own directories and files, and read files, all without leaving the shell.
Manipulating files
Say we want to duplicate a file. To do this, we use the cp
command. Let’s give our copy a creative new name like test_file2.txt
.
cp test_file.txt test_file2.txt
ls
You can check using less
that your important file contents got copied over too.
less test_file2.txt
Let’s try to move our new file to our home directory using the mv
(move) command. As we know, there are many ways to specify the home directory. Try one of the following:
mv test_file2.txt ~
mv test_file2.txt ..
mv test_file2.txt /home/<your_username>
To test if we were successful, we could cd ~
to go to home and run ls
to see what is there. But we can also check without changing directories by giving a path to ls
.
ls ~
Great! The mv
command also has another fun trick which is renaming files. To do this, you “move” the file to the same directory, but with a new name. Rename test_file.txt
to awesome_file.txt
.
mv test_file.txt awesome_file.txt
ls
What if we want to delete a file or directory? We do this with the rm
(remove) command.
rm awesome_file.txt
ls
We deleted our file, now let’s delete our directory. First navigate up to the parent directory then remove the test directory with rm
.
cd ..
rm test/
Here, rm is making sure we don’t accidentally delete a directory full of files we may need. To proceed with removing a directory we need to add an option to the rm
command. Options are used to modify the behavious of bash commands. To see what options are available, we can use the info
(information) command.
info rm
Remember to hit q
to leave most scary dialogs in the shell. Did you notice the -d
or --dir
option? It sounded like the one we need. Both -d
and --dir
do the same thing, as do other options that you see listed together. The -d
is just shorter (because programmers are lazy and typing the 3 extra characters is too much work).
Let’s give it a shot.
rm -d test
ls
And it’s gone! Great, you now know how to move, copy, and delete files and directories. You also know how to get help on any command with info
. Now let’s cover one more important basic shell option.
Wrapping up
I hope this foray into the basics of bash has helped with your aversion to using the shell. We will build on these basics and apply them to actual scientific coding through the next 5 lessons.
I think a big source of shell anxiety comes from thinking you’ll destroy the computer that you are using if you start typing into the black box.  You can inadvertantly delete all of your data (never run rm -rf ~
), which is why it is useful to always have backups and snapshots of your super important files.
How you ask?
Tune in next time when we learn about the power of Git and version control. Don’t forget to work through the Learn Git modules before the next class!


| How to draw 2d objects |
This tutorial starts with a classical Iris Initialisation (see tutorial
1).
IrisContext &c = IrisContext::Get();
c.InitContext(1024 * 1024);
c.GetGeometryPipeline().SetProjection(90.0f, 0.00001f,
5000.0f);
c.GetGeometryPipeline().SetViewport(0.0f, 0.0f, 640.0f,
480.0f);
c.GetGeometryPipeline().SetView(vect3d(0.0f, 150.0f, 0.0f),
vect3d(0.0f,
0.0f, 0.0f),
vect3d(0.0f,
0.0f, 1.0f));
c.GetGeometryPipeline().Update();
c.GetLightingPipeline().SetAmbientColor(IrisColor(0.0f, 0.0f,
0.0f, 0.0f));
c.GetLightingPipeline().Update();
|
|
After testing if at least one controller is plugged in, we declare an
IrisObject and load a mesh. Then, we declare an IrisManualObject. The
first step is to allocate memory for the 4 vertices (the 4 points that
define a square). We assign coordinates to each of these vertices, then
we assign a color to the manual object.
!! Notice that the manual object must be stripped manually.
IrisManualObject image;
image.Allocate(4);
image.GetVertex(0) = vect3d(25.0f, 385.0f, 1.0f);
image.GetVertex(1) = vect3d(75.0f, 385.0f, 1.0f);
image.GetVertex(2) = vect3d(25.0f, 435.0f, 1.0f);
image.GetVertex(3) = vect3d(75.0f, 435.0f, 1.0f);
image.SetColor(IrisColor(1.0f, 1.0f, 1.0f, 1.0f));
|
|
We load a texture to apply to our manual object. We declare a variable
of type IrisMaterial*. The texture is loaded by the LoadMaterial
function, found in our MaterialManager. This function takes only one
parameter: a char * containing the path and the filename of the texture.
The texture is then applied to our manual object using the SetMaterial
function (method of the IrisManualObject class).
IrisMaterial *material;
material =
c.GetMaterialManager().LoadMaterial("/rd/gruiiik.png");
image.SetMaterial(material);
|
|
We have to set the UV coordinates to tell how the texture will be
applied to our manual object.
image.SetUV(0, 0.0f, 0.0f);
image.SetUV(1, 1.0f, 0.0f);
image.SetUV(2, 0.0f, 1.0f);
image.SetUV(3, 1.0f, 1.0f);
|
You must call 'Precalculate2D' after initialising the manual object to
convert directly 3D coordinate to 2d.
|
Our manual object is ready. There is nothing really important in our
main loop, it just gives motion to the whole thing. You can notice the
SetDepthWrite command that allows us to draw the 2d object not writting
in the Z-Buffer in order to be sure that our sprite will always be in
the background (usefull to render a skybox for example). Notice that we
call 'Render2D()' rather than 'Render()'.
context.BeginScene();
mesh.Render();
context.SetDepthWrite(false);
image.Render2D();
context.SetDepthWrite(true);
context.EndScene();
|
|


http://action.vday.org/site/R?i=SsYLHM_US4TaL0t251jv9w
http://action.vday.org/site/R?i=hjEn7oDwP2_6oU5GTGpY-g
http://action.vday.org/site/R?i=dpbM-nkyWfJzv3gyQktq4A
http://action.vday.org/site/R?i=ID5TP5ymLCXiUW3Wgmdn0g
NEW City of Joy Website Launches!
V-Day is proud to announce the launch of the new City of Joy website!
Join us as drc.vday.org where you will meet the people of City of Joy;
http://action.vday.org/site/R?i=w55tE3owd9FGDhArNST1rg
hear voices from Bukavu and beyond;
http://action.vday.org/site/R?i=oA8xWUR8gvOCZuxEtmrdqw
visit the V-World Farm through incredible images by Paula Allen;
http://action.vday.org/site/R?i=Fv83emjGG4395_IHcGUKYQ
explore the history of City of Joy and V-Day’s work in Congo;
http://action.vday.org/site/R?i=5gMLLyGoDBS3HPohTAzFeQ
learn the 10 guiding principals, and more.
http://action.vday.org/site/R?i=jE4SJ071gSS0qnsHdm9xPA
“When the girls arrive here they don’t know their value, they
don’t know that they can do the work, but time after time they
always discover that they are the most beautiful flowers.” - Christine
Schuler Deschryver, V-Day Congo Director & Director of City of Joy
LEARN more and VISIT US at drc.vday.org >


HackGTeeny is an annual hackathon oriented towards beginner hackers at Georgia Tech. You can learn the basics of hacking with our workshops. They include git, web development, and Android. Everyone will start with the git tutorial, and then you can choose other workshops to get hacking! Each team will be paired with a mentor that will help you with your hack, and prizes will be awarded once the hackathon ends for both participants and mentors.
| Saturday, January 14th | |
| 11:59 PM | Applications close |
| Saturday, January 21st | |
| 9:00 AM | Breakfast / Team formation |
| 9:30 AM | Opening ceremony / Team formation |
| 10:00 AM | Hacking begins |
| 10:30 AM | Git tutorial and workshop |
| 12:00 PM | Front-end web dev workshop |
| 1:00 PM | Lunch |
| 2:00 PM | Android workshop |
| 3:30 PM | Back-end web dev workshop |
| 7:00 PM | Dinner |
| 9:30 PM | Hacking end / Expo begins |
| 10:30 PM | Expo ends |
As long as you're a current Georgia Tech student, you're eligible. Prior experience isn't required, but if you do have some, you can mentor for us here! Teams can be up to four members — you can register with your team or create a team at the event.
Bring your laptop, headphones, and anything else you need to hack comfortably. We'll supply hardware, snacks, and meals for the whole day. This hackathon won't last too long, but feel free to bring anything that makes you comfortable. If you're still not sure, ask us!
Everything at this hackathon is absolutely free. The only part that makes it worthwhile is your effort and determination. Indicate any needs in your application and we’ll reach out to take care of you.
As said before, the universe discourages it because it’s an illusion of true innovation. Well maybe, but we'll be teaching you a better way to hack and build your own projects, so why take all the fun away?
HackGTeeny will take place in the CULC in lecture halls 144 and 152.


atod.tests package¶
Submodules¶
atod.tests.test_ability module¶
atod.tests.test_cleaninng_abilities module¶
atod.tests.test_dictionary module¶
atod.tests.test_hero module¶
-
class
atod.tests.test_hero.
TestHero
(methodName='runTest')¶ Bases:
unittest.case.TestCase
-
setUp
()¶ Creates 2 Shadow Fiends to test methods.
-
test_camel2python
()¶
-
test_from_name
()¶
-
test_get_description
()¶
-
test_init
()¶ Tests different ways to create hero.
-
test_patches
()¶ Tests that hero reads only data from needed patch.
If there are some patches in table, Hero should contain only one set of abilities assigned with the right patch.
-
test_primary_attribute
()¶
-
atod.tests.test_hero_model module¶
atod.tests.test_heroes module¶
-
class
atod.tests.test_heroes.
TestHeroes
(methodName='runTest')¶ Bases:
unittest.case.TestCase
-
setUp
()¶
-
test_description
()¶
-
test_get_ids
()¶
-
test_init_with_ids
()¶ I’m not sure how to test classes like this.
So, what tests are doing is basically: call method with some arguments, check if something goes wrong.
-
test_init_with_names
()¶ I’m not sure how to test classes like this.
So, what tests are doing is basically: call method with some arguments, check if something goes wrong.
-
test_iter
()¶ One more time:
I’m not sure what to do there, but user should be able to iterate over heroes.
-


CFIC which stands for Christian Formation for Ignacian Leadership Center molds the spiritual aspect of the student as well as the personnel in St. Michael's College. It caters with services like Retreats and Recollection, spiritual growth or formation, liturgical services such as mass and sacraments like confession, baptism, BEC or Basic Eklesia Community which points out the formation for non-Catholics or Catholics.
Learn More About Us!is believing in the substance of things hoped for, and in the sign that the things not seen are true (Hebrews 11).
is the unwavering trust in the complete fidelity of God's love; Clinging to God in good and "Not so good times".
is the trust, hope and belief in the goodness of a person being the image of God.
is the deep conviction that God deeply cares for us and will always act with our best interest at heart.
is the drive and passion to become the best of who and what we are before God and human kind in all aspects of our life
is an attitude to sustain improvement at all times in all areas of importance in one's life at the best level.
is to do the right things at the right time, all time.
is reaching out to others to develop their capabilities and help them liberate themselves from the quagmire of social, cultural and economic scarcity and deficiencies.
is an act of kindness and generosity to sustain and dignify the lives of others.
Vision
---
is an act of kindness and generosity to sustain and dignify the lives of others.
Mission
---
We commit ourselves to:
1. Develop culture of appreciation, support and harmony and live with compassion and humility.
2. Grow in prayer and reflection, practice discernment and attain interior freedom.
3. Be open to global opportunities and trends to develop and maximize potentials and capabilities in order to become enterprising world class individuals who practice the values of Faith, Excellence and Service.
4. Advocate deep understanding of cultural diversity for peace and communion and promote environmental preservation.
5. Build up resources and sustain the ministry.
6. Consistently provide greater educational accessibility and opportunities to the poor.
If you have any concerns, give us a call or send us an email and we will get back to you as soon as possible!
123-456-6789


Members
_maxPool
Indicate the maximal size of the pool holding pending download tasks.
Can be set by setting the NETWORK_MAX_POOL_SIZE environment variable.
- Source:
_maxSlots
Indicate the maximal number of slots available
- Source:
_minPool
Indicate the minimal size of the pool holding pending download tasks.
Can be set by setting the NETWORK_MIN_POOL_SIZE environment variable.
- Source:
DATA_ADDED_TO_POOL
This event is thrown everytime data is added to the pool (not for every
entry added but for every update of the pool)
- Source:
NEW_NETWORK_DATA_AVAILABLE
This event is thrown everytime the downloader has finished a request.
Is used to notify the client, that new data is available to process
- Source:
POOL_LOW
This event is emitted as long as the pool is below its minimum defined
size. This can be used by a client to add new data to the pool.
One argument should be passed, indicating how many entries are available
to the pool max.
- Source:
sequelize
Passing logger.silly directly to sequelize results in a TypeError (since
the logger seems to be uninitialized at some point). This wrapper function
was suggested as workaround.
- Source:
SLOT_FREED_UP
This event is thrown everytime a slot is freed-up
- Source:
Type Definitions
DbResult
Type:
- object
Properties:
| Name | Type | Attributes | Description |
|---|---|---|---|
url |
string | The base url | |
baseUrlId |
UUIDv4 | The ID of the base url entry. | |
path |
string | The path of the entry | |
pathId |
UUIDv4 | The ID of the path entry. | |
depth |
number | The search depth at which this entry resides. | |
content |
string |
<nullable> |
If available or requested, it can contain the content of the entry |
contentId |
UUIDv4 |
<nullable> |
The ID of the content entry in the db. |
link |
Link |
<nullable> |
A Link object, indicating a link between two paths/sites. |
secure |
boolean | Indicate if http or https should be used |
- Source:
Link
Type:
- object
Properties:
| Name | Type | Description |
|---|---|---|
sourcePathId |
UUIDv4 | The ID of the path entry where the link originated. |
destinationPathId |
UUIDv4 | The ID of the target path entry. |
timestamp |
number | Indicates at which time the link was in place. This does not mean, that the target was reachable, it only indicates the existence of a link at that given point in time. To check for reachability please refer to the attached content entries. The contain a successful flag. |
- Source:
NetworkHandlerResponse
Type:
- object
Properties:
| Name | Type | Attributes | Description |
|---|---|---|---|
url |
string | The url of the returned request. | |
path |
string | Th path of the returned request. | |
body |
string |
<nullable> |
The body of a response. |
statusCode |
number | The HTTP status code of a response. | |
mimeType |
string |
<nullable> |
The MIME Type of a response. |
startTime |
number | The timestamp when the response started. | |
endTime |
number | The timestamp when the response ended. |
- Source:
ParseResult
Properties:
| Name | Type | Description |
|---|---|---|
fullUrl |
string | The full url, no splits. Mostly useful for logging or display. |
http |
boolean | Indicates the usage of http in the url string. |
secure |
boolean | Indicates whether https (true) was used or not (false). |
www |
boolean | Indicates whether the URL contains www (true) or not (false). |
baseUrl |
string | The base url, including any possible http, https, www or subdomains. |
path |
string | The path, including any parameters or hooks. |


The Matrix Revolutions
The Matrix Revolutions is a movie starring Keanu Reeves, Laurence Fishburne, and Carrie-Anne Moss. The human city of Zion defends itself against the massive invasion of the machines as Neo fights to end the war at another front...


[ht::head {Logging in and User Settings}]
<%nochildlist%>
If you have an account on a RAMADDA server you can login by following the
login link:
[ht::screenshot images/login.png {Login Page}]
If you have forgotten your user name or password and if the RAMADDA server has been configured to send email there will be links shown to send you a reminder of your user name and to reset your password.
Once logged in there is a link
in the upper right which will take you to your user settings page.
:heading User Settings
User Settings
This page allows you to change your user settings and your password.
The Page Template is a user interface skin and allows you to change
the overall look and feel of the user interface.
[ht::screenshot images/usersettings.png {User Settings}]
User Favorites
The user favorites are a list of entries.
These are kind of like bookmarks in a web browser and aid in navigating and
managing content.
When logged in you can add a favorite entry through the file menu.
[ht::screenshot images/userfavorites.png {User Favorites}]
[ht::foot]


ESPN Analyst Releases Score Prediction For Michigan-Indiana
by Tzvi MachlinRumors of Michigan’s season ending after the Penn State loss last month have been greatly exaggerated. Since then the Wolverines have gone on a tear, shredding Notre Dame, Maryland and Michigan State with increasingly impressive performances.
They now head to Memorial Stadium in Bloomington for a showdown with Indiana before their season-finale against Ohio State.
But the Hoosiers have proven to be a far more resilient team than recent years. At 7-3 they already have their highest win total in over a decade. And considering Indiana just took Penn State to the wire at Beaver Stadium, they’re not to be taken lightly.
ESPN analyst Bill Connelly has used the SP+ prediction model to determine the likely outcome of the game.
The SP+ model predicts that Michigan will beat Indiana, 28-25. Michigan has a 55-percent chance of winning the game.
That scoreline and win percentage is pretty small for a Michigan game. But it may also speak to how good Indiana has been at home this year.
The Wolverines currently boast a 23-game winning streak over the Hoosiers, and are 38-1 against them since 1968. As easy as some of those wins have been in the past, this one seems like it will be more competitive.
The game will be played at 3:30 p.m. EST and will air on ESPN.


Buhari Demands Probe Into Murder Of Kogi PDP Woman Leader
by Olugbenga IgePresident Muhammadu Buhari has called for a thorough investigation of the gruesome murder of Salome Abuh, the woman leader of the Peoples Democratic Party (PDP) in Kogi State.
Concise News had reported that the PDP chieftain in Wada Aro, Ochadamu Ward in Ofu Local Government of the state was roasted alive during the governorship election, when political thugs barricaded her house, doused it with petrol and set it ablaze.
However, in a statement issued on Sunday by his Media Aide, Femi Adesina, President Buahri said the evildoers of the act must be brought to justice irrespective of whatever allegiances they hold.
President Buhari expressed outrage at the gruesome murder, while also decrying whatever reason could spur anyone to take the life of another human being, particularly a woman.
He charged all security agencies involved in the investigation to do a thorough and expeditious job on the matter, so that justice could be served without fear or favour.
The statement read: “We must learn to behave decently, whether during elections or at other times. No life should be taken wantonly under any guise, and the fact that this reprehensible act occurred two days after voting shows primitive behavior, which should not be accepted in a decent society.”
He also sympathised with the family of the deceased, adding that the treatment visited on the her was pure criminality and bestiality that has gone out of the realm of politics.


Everyone has unique health needs and goals, which is why I take an individualized and personalized approach for each client. We do a deep dive into your health history, lifestyle, and eating patterns. As a problem-solver at heart, I take a holistic point of view to uncover the root cause of your health and weight struggles to eliminate them once and for all. We’ll explore your diet, fitness, mental health, relationship to food, and how it’s all interconnected.
This program is not a diet. It’s been proven that diets fail over 80% of the time, which is why I will never promote them or recommend them for my clients1. I help you reach your goals in a sustainable way by shifting your mindset and lifestyle and developing positive, self-sufficient, lifelong habits that you’ll always have in your back pocket.
This program is 100% online over video-chat which means you can do coaching calls with me from the comfort of your own home. We’ll meet once a week for two months, then have intermittent ongoing calls after that for however long you need. Ultimately, my goal is to set you up for lifelong success so that you’ll never need another program or coach again. Each week I send you the materials you’ll need to be successful. I support you every step of the way by answering questions and providing guidance whenever you need it.


Hey
Has anyone tried using an Arduino Mega to count multiple pulses? The Mega seems to have 6 inputs with interrupt capability.
I read on http://openenergymonitor.org/emon/buildingblocks/12-input-pulse-counting that Trystan had problems using the Ethernet shield because it blocked for up to 1700 ms, but would that be a problem when using interrupt based pulse detection?
Since running pulse counting with exact micro second timing makes it impossible to use sleep modes for the CPU, it's not possible to run off batteries anyway, so using a full blown Arduino doesn't matter I think.
Re: Multiple pulse counting with Arduino Mega?
No one who has any input?
Since we get three phases here in Sweden, and I'd like to monitor three different (to start with) fuse boxes (sub centrals?), I think it would be easier and cheaper to go with three DIN mounted three phase meters, and one Arduino. I would prefer to use the RFM12b to communicate with the Raspberry base, but it seems easier to get a WLAN shield.
Re: Multiple pulse counting with Arduino Mega?
You could just use an emonTx for that. Any pin can be used as an interrupt using interrupt-on-change. I'm currently using digital pin 7 for pulse counting. You just need to write the code to see which pin caused the interrupt.
Re: Multiple pulse counting with Arduino Mega?
Uhm yes but there's only one interrupt, I need three. The cost of three emontx is more than one Arduino Mega.
Re: Multiple pulse counting with Arduino Mega?
That was my point. I said any pin can be used as interrupt. You should read the section on pin change interrupts in the ATmega328 data sheet. The pins are in 2 groups of 8 and 1 group of 7 with 3 separate interrupts so if you pick your pins carefully you could use the 3 interrupts or you could just use one interrupt and just read the pins in the interrupt handler to see which one changed.
Re: Multiple pulse counting with Arduino Mega?
Ah thanks Martin, I didn't know about pin change interrupts. You are right, that could work, just need to figure out how much re-work the emontx would need...
Quite nice article about interrupts:
http://www.gammon.com.au/forum/?id=11488
Re: Multiple pulse counting with Arduino Mega?
Yes, that's a useful article - thanks.
If it's any help, this is what I did to use digital pin 7....
In setup replace
attachInterrupt(1, onPulse, FALLING);
with
bitSet(PCMSK2,7); // unmask pin change interrupt for selected pin
bitSet(PCICR,PCIE2); // enable pin change interrupt
then in the interrupt routine replace
void onPulse()
{
with
ISR(PCINT2_vect)
{
if(digitalRead(7)==HIGH) return; // we only want the falling edge
and that's it!
Re: Multiple pulse counting with Arduino Mega?
Nice, thanks! And where did you connect it physically? DIO7 to the right of the crystal?
Re: Multiple pulse counting with Arduino Mega?
Yes, that's right. I wired the detector to a 6-pin plug which can go on any of the headers P1-P4. Only P1, DIO4 is actually used by emonTx and that's only if you have a temperature sensor.
Re: Multiple pulse counting with Arduino Mega?
I think they are wired to the CT inputs, no? (Which I won't be using). Would I need to remove the CT components, or they won't interfere?
Re: Multiple pulse counting with Arduino Mega?
No, each 6-pin header has an analogue and a digital pin. The CTs are connected to the analogue inputs, the digital inputs aren't connected to anything (apart from the temperature sensor). You can pick up 3.3V power and ground from the adjacent pins. I'm using the CTs and the pulse detector at he same time.


Manager Material is an app that allows a manager to manage different projects on a team.
When a manager goes to the home page, the manager can see an overview of current projects, employees, and a calendar displaying all of the projects.
Slack Integration gives employees the ability to quickly and easily view and update projects via slack.
Automated Emails keeps customers in the loop without disrupting employee workflow.
Calendar Overview shows managers the big picture about what their teams are working on.


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{{title}}
You’ll need to enter the code of the agent you want to appoint and tell us whether they can do checks, challenges or both on behalf of your business, for this property.
My agent hasn’t given me a code
Please be aware that you'll be responsible for any information your agent submits to us about your property. You'll be liable for any penalty or the backdating of revised valuations if your agent submits false information.


Expert: Porn Site Made Over $1 Million From Jane Doe Videos
by BIANCA BRUNOSAN DIEGO (CN) – GirlsDoPorn made more than $1 million on videos it produced of 22 Jane Does who accuse the porn company of lying to them to get them to appear in the videos while its owner stands to make $4 million more from over 200 unpublished videos, a financial expert testified Monday.
Robert Taylor said Monday the present value of the projected net income GirlsDoPorn owner Michael Pratt stands to gain from his library of over 200 unpublished porn videos exceeds $4.6 million.
Taylor said the value of the unpublished videos is not tied to the continued operation of the GirlsDoPorn website or its parent company BLL Media and is a fair estimate of what another website operator could stand to make if the videos were sold.
He called the projected net income a “reasonable estimate of what they could expect to make from a subscriber base interested in these types of videos.”
The financial expert’s testimony comes as the plaintiffs wrap up their case this week in the months-long trial accusing GirlsDoPorn of inducing them to appear in videos by telling the women the videos would only be sold on DVDs overseas.
In reality, the videos were posted on the company’s subscription-based website with clips and some full-length videos posted for free on some of the most trafficked websites in the world, including PornHub.
Attorneys for both parties stipulated Monday to the amount of money the website has made off the videos of the 22 Jane Does: $1,025,831.50. At least 10 videos featuring some of the Jane Does were still posted on GirlsDoPorn’s site Monday but are in the process of being removed, according to GirlsDoPorn’s attorney Aaron Sadock.
But Taylor said Monday his projected net income estimate is not based on the most current financial data and did not include money Pratt made when he sold his Lamborghini and his home in the ritzy San Diego neighborhood of Rancho Santa Fe last year.
Valorie Moser, the administrative assistant who worked in GirlsDoPorn’s downtown San Diego office, testified during trial Pratt told her he was going to sell his car and home to duck potential damages should the plaintiffs win their case.
Moser, along with Pratt, videographer Matthew Wolfe and actor Andre Garcia, face human trafficking charges in a separate criminal case in federal court filed in the midst of the civil trial.
GirlsDoPorn’s attorney Daniel Kaplan referred to the criminal case Monday when asking Taylor if the charges would diminish the valuation he’d placed on the unpublished video library.
“Isn’t it reasonable to assume the fact the defendants who produced the 200 videos as under a federal criminal complaint would affect the value of those videos?” Kaplan asked.
“I do not know,” Taylor responded.
The deposition of Alicia McKay, who was used to assuage women’s skepticism about appearing in GirlsDoPorn film, also played in court Monday.
McKay appeared in two GirlsDoPorn videos, flying from Canada to San Diego in 2016 to film the porn flicks when she was 19. She later agreed to serve as a reference woman before finding out GirlsDoPorn posted her first film online when she found a screenshot of the video on her boyfriend’s cellphone.
But McKay said when she acted as a reference woman, she believed the videos would only be published on DVDs or sold overseas, as she was told when she agreed to appear in them herself.
McKay said she decided to record a phone conversation with Garcia without his knowledge because “I wasn’t getting any straight answers from them” about where the videos were distributed.
“Everything they said that was important was over the phone. Everything over text was very vague,” McKay said in the videotaped deposition.
“I had recorded the video because I believed they were falsifying information. They weren’t telling the full truth to women. I wanted proof of that because like they had done to me, they never gave me a full answer of where the videos would be distributed,” McKay said.
In the 7-minute phone recording played in court Monday, Garcia told McKay to “downplay it as much as possible” when women asked where the videos would be published.
He instructed McKay to talk to women over the phone or through video chat but not to have lengthy “back-and-forth” text message conversations.
“You’re just there to make sure she knows she’s not going to get raped, she’s not going to get killed and she’s going to get paid,” Garcia told McKay in the recording.
Copyright © 2019 courthousenews.com


About
What is this?
Justice Divided is an educational tool and resource repository meant to promote awareness of disproportionate minority contact (DMC), or the overrepresentation of black youth in the juvenile justice system.
Within the system, the first steps toward equitable justice are acknowledging DMC as a problem of race, accepting joint responsibility, and making a deliberate effort to address its root causes.
Outside the system, we can and should demand these changes from policy makers and practitioners.
This website is intended to facilitate all the above.
Use our data
The data behind this website are available for download here. Raw source data, as well as recipes for processing it and building a database, are available on GitHub.
Team and credits
DataMade built Justice Divided in partnership with the Illinois Justice Project, Adler University, and Project NIA, with funding from the Polk Bros. Foundation.
Working at the state and local levels, the Illinois Justice Project uses research findings to advance proven criminal justice reform policies and practices in order to make communities safer and make the justice system more equitable.
Adler University continues the pioneering work of the first community psychologist Alfred Adler by graduating socially responsible practitioners, engaging communities, and advancing social justice.
Project NIA offers a new way of thinking about crime and violence. We use the principles of participatory community justice – often called restorative or transformative justice – which has been shown to meet the needs of victims, reduce recidivism, and improve satisfaction with the legal system.
The Polk Bros. Foundation. seeks to improve the quality of life for the people of Chicago by partnering with organizations that work to reduce the impact of poverty and provide area residents with better access to quality education, preventive health care, and basic human services.
DataMade is a civic technology company in based in Chicago. We build open-source software and stories using public data to make communities, journalists, governments, and advocacy organizations more powerful.
We're open source!
All of the code, data, and analysis on this site is open source and available on GitHub under the open source MIT License.
The site was built with:
The data was processed and analyzed with:
Get in touch
Found a bug? Have a question about the data? Report it in our issue tracker.
Have other feedback or suggestions? Send us an email at info@datamade.us.


Come and visit my favorite grocery store closest to me and where I worked at when I was high school.
why listen to some tunes when going to get some snacks or going to work.


| 1167 | (A) | than sir Sabyn of Surry whils the assawte lastes |
| 1170 | (C) | Syr Sabyne of Surry . whyle þat sawte lastedde |
| 1153 | (D) | Sir Sabyn of Surrye whil þe assaute last |
| 1130 | (E) | ¶ Sere sabyn of surry . while þe saut laste |
| (Ex) | ---- no text ---- | |
| 1191 | (L) | sire sabyn of surrye whyle þe saute laste |
| (P) | ---- no text ---- | |
| 1138 | (U) | Sire sabyn of Surre / whils the sege lastetħ |
| 231 | (V) | Sir Sabynne of surry while þe saute laste |


gym-electric-motor
Gym Electric Motor Readme:
Readme File
Environments Contents:
Technical Background
Dashboard
Environments
Technical Models
Utils
gym-electric-motor
Docs
»
Welcome to gym-electric-motor’s documentation!
View page source
Welcome to gym-electric-motor’s documentation!
¶
Getting started
¶
Gym Electric Motor Readme:
Readme File
Environments Contents:
Technical Background
DC Motors
Permanent Magnet Synchronous Motor (PMSM)
Converter
Load Models
References
Dashboard
Environments
DC Motor Base Environment
DC Externally Excited Motor Environments
DC Series Motor Environments
DC Shunt Motor Environments
DC Permanently Excited Motor Environments
Permanent Magnet Synchronous Motor Environments
Technical Models
Introduction
DC Motor Models
PMSM Motor Model
Converters
Load Model
Utils
Indices and tables
¶
Index
Module Index
Search Page


Kheo is an agentless application dedicated to servers management, including softwares inventory, os and network informations.
Kheo relies on SSH to communicate with the servers to manage so it does not need any specific configuration. As a constraint, it needs a key to contact remote hosts.
To register servers, you have many solutions:
- Using an inventory file just like Ansible does.
- Using the API
- Using the webapp
Once your servers have been registered, you can obtain informations like:
- Network interfaces and IP
- OS type and version
- Users
- Resources (ram, disk, cpu, ...)
- Running processes
- Installed packages
Kheo is build with the following technologies:
MongoDB
MongoDB is an open-source document database, and the leading NoSQL database.
AngularJS
MVW framework to build client side single page applications.
Dropwizard
Dropwizard pulls together stable, mature libraries from the Java ecosystem into a simple, light-weight package that lets you focus on getting things done.


Employers benefit from the number of students who attend college, including those who could not otherwise attend without taking out student loans. "Credentialism", the continuing growth in employers' expectations of postsecondary education on the part of their employees, has been called a key driver of student loan debt [9.11.154]. In \(2020\text{,}\) earnings were stagnant for most workers, but declined rapidly for workers with no college education. This pressures people to seek a college education whether or not they can afford it.
in-context


Add a description here.
First step is to follow the link. Click here
This step requires you to add a value to the input
Text for the third step goes here. For this step, you need to scroll to the bottom of the page to move forward. Real life scenario is reading TOS? This also tests really long text.
Completing the other steps is the criteria to complete this step.


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A project by
@ryanmcdonough
, Code is available on
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@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.agentclientmanagementfrontend.util.Paginated @(model: Paginated[_])(implicit msgs: Messages) @if(!model.dataFitsOnOnePage) {


Take a look at kids toys and outdoor play, we have a variety of choices waiting for you! want toys? they do, hot & new Batman 12-inch Rebirth Action Figure, Blue’s Clues & You! Peek-A-Blue Plush, Kindi Kids Shiver ‘N’ Shake Rainbow Kate, Cave Club Dolls - Roaralai, JUNKBOTS – Trash Bin Assortment Kit, Care Bears 14” Medium Plush, plug into fun with electronics for kids, learning toys for preschoolers and big kids, dog-themed toys to get their tails waggin’
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| Uniplex 400 General Information
-
| Synonyms: |
Polypropylene glycol dibenzoate |
| CAS Registry No.: |
72245-46-6 |
| Chemical Category: |
Benzoates |
Usage and Performance Characteristics
-
| Manufacturer: |
Unitex Chemical Corporation |
| Outstanding Properties: |
Low volatility |
| Used for Polymeric Materials: |
PU, PVAc, PVC, acrylic & more |
| Used for Products: |
Sealants, adhesives, coatings, foam |
Physical Properties
-
| Specific Gravity: |
1.103 (25°C) |
| Aggregate State at 25°C: |
Liquid |
| Density, kg/m3: |
1,102.16 (25°C) |
Chemical Properties
Optical Properties
-
| Color, Platinum-Cobalt Scale: |
<250 |
| Refractive Index: |
1.518 (n20D) |
Hazard-Related Data
-
| OSHA Hazard Class: |
Non-hazardous 29 CFR 1910.1200 |
| Eye Irritation (human): |
May cause irritation, with pain |
| Flash Point Temperature, °C: |
241 |
| Skin Irritation: |
May cause irritation, redness & swelling |
Animal Toxicity
-
| Rabbit Dermal, LD50, mg/kg: |
2,000 |
| Rat Oral, LD50, mg/kg: |
5,000 |
Plasticizers Database (2nd Electronic Edition) Copyright © 2004 by ChemTec Publishing |


--- layout: default title: Internet Explorer weight: 99 ---
Like most d3.js sites, this does not work with Microsoft Internet Explorer.
It does work in Google Chrome (recommended), Mozilla Firefox, Apple's Safari, and even Opera. Please use one of these browsers and enjoy the site.


Experimenting with a data science task is usually done through the R console or an IDE by executing portions of a file or collection of files. Sometimes this is slightly generalized into notebooks, interactive sessions that record the history of the code you executed and its outputs, including plots, data, and summaries.
This kind of workflow works well in the old way, when you don't intend on returning to your analysis for any future use and are happy with a static image result.
Developers working on pure software engineering projects usually operate in a different way: they manipulate a single codebase and provide iterability and experimentability through some other approach that ultimately ends up reflecting what's in their codebase.
For example, a front-end web developer might change a JavaScript file in the deep innards of a web app, hit refresh on their browser, and test out the new functionality by clicking their mouse.
To achieve a better data science workflow we will mirror the effectiveness of the typical developer approach. We will rely on a shared codebase composed of modular and testable resources that allows us to go back in time using version control and have new components vetted using code review.
Let's try putting the pieces of the codebase together into a living breathing object, the same way a web browser brings a web application alive. We'll start with a stagerunner but in the future may end up with much more.
As mentioned earlier, a typical data science cycle begins with: importing data, munging (cleaning) it, running a statistical classifier, and exporting the model for validation and deployment.
A stagerunner is a single R object (specifically, an R6 object, for those familiar with the R6 package which augments R's object-oriented programming capabilities) that represents the full execution cycle of the data science process. In workflows with large data sets, this process is typically distributed across many machines in frameworks like Spark and Hadoop. Since there is no distributed implementation of the R interpreter (yet - shh ;) ) we focus for now on small and medium size data problems. Typically, productionizing several 80% solutions offers more business value than productionizing one 100% solution and anyway downsampling is often good enough.
A stagerunner is simply a tree structure whose terminal nodes are functions that operate on an R environment object. If you are unfamiliar with R environments but understand pointers from languages like C++, simply think of it as a pointer to a list. These functions are executed linearly on the R environment starting with the empty environment and culminating in side effects that export a model, such as storing it to a backend like a cloud storage service or a file. For the mathematically inclined, think of a stagerunner as an element from finite sequences on the monoid of endomorphisms of R environments with the additional meta-data of a hierarchical structure (a very fancy way to say a list of functions that take and return exactly one environment)
Some task scheduling frameworks like Luigi and Airflow take arbitrary directed graphs of tasks and this is certainly the correct generalization but can increase the difficulty of debugging failures in complex topologies of tasks. Staying within a console tends to be much cozier. Eventually Syberia may release this kind of hybrid in-console task scheduling and debugging but for now let's master stagerunners. (If you think hard enough about it, any directed graph can be decomposed into purely serial components.)
Before we jump into the convenient conventions set up by the modeling engine that ships with Syberia by default, let's review the basics of stagerunners.
r <- stagerunner$new(list2env(list(data = iris)), list(
"Double first columns" = function(env) { env$data[[1]] <- 2 * env$data[[1]] },
"Drop categorical columns" = function(env) { env$data <- Filter(Negate(is.factor), env$data) }
))
r$run()
Ok so you got me, I lied a little. Stagerunner functions do not need to return the environment they modify since this is one of the few R objects that is passed by reference and not by value, and hence any modification in another scope (like a function call) modifies the original environment object.
Observe that running the above code yields the desired changes:
str(r$context$data)
shows we have a modified
version of iris
with the first column doubled
and the only categorical column removed.
env <- list2env(list(data = iris))
r <- stagerunner$new(env, list(
"Double first columns" = function(env) { env$data[[1]] <- 2 * env$data[[1]] },
"Drop categorical columns" = function(env) { env$data <- Filter(Negate(is.factor), env$data) }
))
r$run(1); r$run(1); r$run(1)
Inspecting head(env$data[[1]])
shows that the first column of
iris
is octupled as expected. Now try re-executing all but
the last line and type r$run(2)
. The last column
was dropped, as expected.
This is an example of a stateless stagerunner: it has no memory about which stages ("Double first columns" versus "Drop categorical columns") were executed. Typically this matters as many feature engineering operations are not commutative: dropping correlated features and then imputing may yield different results than doing it the other way around.
To enforce the order of operation and gain an additional feature we enable stateful stagerunners. This unlocks the ability to replay any part of the data science process like checkpoints in a video game.
r <- stagerunner$new(list2env(list(data = iris)), list(
"Double first columns" = function(env) { env$data[[1]] <- 2 * env$data[[1]] },
"Drop categorical columns" = function(env) { env$data <- Filter(Negate(is.factor), env$data) }
), remember = TRUE)
r$run(2)
Note the remember = TRUE
flag at the end. The above
code will fail with "Cannot run this stage yet because some previous stages have not been executed."
This is because we are trying to run step two without first running step one.
Running r$run(1)
first will fix the problem.
env <- list2env(list(data = iris))
r <- stagerunner$new(env, list(
"Double first columns" = function(env) { env$data[[1]] <- 2 * env$data[[1]] },
"Drop categorical columns" = function(env) { env$data <- Filter(Negate(is.factor), env$data) }
), remember = TRUE)
r$run(1); r$run(1); r$run(1)
Inspecting head(env$data)
shows that the first column
of iris
was doubled, not octupled! This is because
with remembrance of state turned on our stagerunner object begins with
a cached copy of the original environment rather than operating on the
latest environment. This is powerful: with stateful stagerunners, we can simulate
going back in time to any step in the process!
The code behind stagerunner makes some space-time tradeoffs. With stateful runners, a full copy is made of the environment on each step. This is prohibitively memory expensive with large datasets. With small datasets, it is manageable and gives us fantastic interactive data analysis and debugging capabilities.
The typical workflow within the Syberia modeling engine is to use an R option or flag to import a small subset (a couple thousand rows) of the desired data set and develop a full-fledged prototype of a feature engineering pipeline and classifier. Any immediate errors or unexpected bugs in feature engineering can be rapidly debugged by effectively using stagerunners.
When the data scientist is satisfied with the end-to-end process, she simply flips the stagerunner to run in a stateless context on the full data set and gets to have her cake and eat it too; no more painful debugging of background batch jobs or digging through logs.
A final goodie: by using objectdiff, we can reduce the space-time tradeoff even further by only copying the elements of the environment that changed during each step. If only one column was doubled in a thousand-column dataframe, the stagerunner and objectdiff combo heuristically analyzes the output of the functions that are executed and rapidly detects only one column was changed, minimizing how much is stored in the "git-like patches" recording the differences in each stage. This retains the replay capability of stagerunners while minimizing memory overhead.
env <- objectdiff::tracked_environment(list2env(list(data = iris)))
r <- stagerunner$new(env, list(
"Double first columns" = function(env) { env$data[[1]] <- 2 * env$data[[1]] },
"Drop categorical columns" = function(env) { env$data <- Filter(Negate(is.factor), env$data) }
), remember = TRUE)
r$run()
Note that class(env) == c("tracked_environment", "environment")
.
Inspecting objectdiff::commits(env)
shows that two commits
have been made and running objectdiff::rollback(env, 1)
shows
that ncol(env$data) == 4
.
It's possible to nest stagerunners:
r <- stagerunner$new(new.env(), list(
"Import data" = function(env) { env$data <- get(get("DATASET", envir = globalenv())) },
"Munge data" = list(
"Double first columns" = function(env) { env$data[[1]] <- 2 * env$data[[1]] },
"Drop categorical columns" = function(env) { env$data <- Filter(Negate(is.factor), env$data) }
),
"Make a model" = function(env) { env$model <- stats::lm(Sepal.Length ~ ., data = env$data) }
))
DATASET <- "iris"
r$run() # Note that class(r$context$model) is an lm object.
The default Syberia engine provides a lot of syntactic sugar to make the above process less painful. In particular, we make the notion of stages primitive and parametrizable: a stage is simply one node of the full execution tree of a stagerunner. The top-level stages that come out-of-the-box are import, data, model, and export stage but it is trivial to create more.
The power of this approach lies in finding good parametrizations of the data science process. Syberia is a meta-framework that allows you to build DSLs customized to solving your specific data problem. To use it correctly you may have to create new abstractions (like finding the correct parametrization for your stages) in order to reap its full power.
Web frameworks like Rails discovered that the model-view-controller pattern works well for many web applications, but as data science is a field still in relative infancy, it is your and the Syberia commmunity's duty to explore new paradigms for rapidly iterating on data science solutions.
Enough hype. Let's see an example of an actual data science workflow with this approach.
# Open up the example.sy project from earlier and place this file in
# models/dev/example2.R
list(import = list(R = "iris"))
Under the hood, typing run('example2')
creates a stagerunner
that is roughly equivalent to the following code:
import_R <- function(params) {
get(params, envir = globalenv())
}
import <- function(params) {
adapter <- names(params)[1]
params <- params[[1]]
adapter <- get(paste0("import_", adapter))
adapter(params)
}
r <- stagerunner$new(new.env(), list(
"Import data" = function(env) { env$data <- import(list(R = "iris")) }
))
r$run()
That seems like a very complicated way of saying env$data <- iris
!
It is. However, we have gained something in the process. Note that our model file
is simply one line of code and clearly reads "import data from an R global variable
called 'iris'." If we replace it with the below code we would read from an Amazon S3
cloud storage key without any additional work.
list(import = list(s3 = "some/s3/key"))
It is possible to create custom import adapters that read from your production database, a data warehouse, a stream, etc.
The modeling engine includes a few very minimal adapters but we will later see how to create your own.
list(
import = list(R = "iris"),
data = list(
"Create a dependent variable" = list(renamer, c("Sepal.Length" = "dep_var")),
"Drop categorical features" = list(drop_variables, is.factor)
)
)
After run("example2")
take a look at
head(A)
and you will see that we have a
dep_var
column and Species has
been dropped. The above shows a parametrization of feature engineering:
instead of writing out the full code for renaming the variable and
describing in R code how to drop the factor columns, we have
used a shorthand for describing mungebits to achieve our
feature engineering pipeline.
list(
import = list(R = "iris"),
data = list(
"Create a dependent variable" = list(renamer, c("Sepal.Length" = "dep_var")),
"Drop categorical features" = list(drop_variables, is.factor)
)
)
It is trivial to add new import and export adapters, parametrize mungebits that do complex feature engineering tasks, and parametrize statistical classifiers using any R package from CRAN or GitHub.
list(
import = list(R = "iris"),
data = list(
"Create a dependent variable" = list(renamer, c("Sepal.Length" = "dep_var")),
"Create a primary key variable" = list(multi_column_transformation(seq_along), "dep_var", "id"),
"Drop categorical features" = list(drop_variables, is.factor)
),
model = list("lm", .id_var = "id"),
export = list(R = "model")
) # Try typing class(model) after run("example2")
The default implementation of model stage requires a primary key in the data set so we artificially create one. This is so we can easily remember train/test splits when making production models on real data sets later. The default model stage encourages storing train IDs on the model object by deisgn. You almost always want to remember the train IDs so you can perform validation correctly in the future and correctly compare refits against past models without predicting on training rows.
With the Syberia modeling engine you enter the realm of developers and are no longer playing in an interactive notebook but recording the fruits of your labor into a breathing codebase with the aid of a very interactive execution environment. In its ideal incarnation, Syberia deprecates the need for notebooks and other data science tools and replaces it with a simple process understood by all software engineers: write clean, modular, and testable code so that anything you want to create is at your feet. If you are smart enough to master the statistics behind data science then you can leave the IDE behind and learn the development tools that make software engineers so capable and productive. You'll also increase your salary.
The real power from the stagerunner approach lies in replay capability. The standard development cycle for a new model should proceed as follows.
B
and A
global variable helpers to examine what the data looks like
before and after running a munging step.This is a local development workflow. The last step should be executed on a remote build scheduling system that deploys instances capable of handling your large data sets (like a large Amazon AWS instance or a multi-instance operating system like Plan 9--just kidding, but that would be cool). Alternatively, you can SSH into a large instance and run models there directly but it will be cumbersome to try experiments that simultaneously train thousands of models.
For example, try the following commands.
run(, 1)
run(, "2/1")
run(, "data/2")
run(, "da/1", "da/2")
run(, "data")
run(, "model")
run(, 3)
Carefully inspect the structure of B
and
A
before and after each command.
Note that these helpers are really active bindings and
boil down to r$context$data
on the
underlying stagerunner that is being constructed on-the-fly
before and after calling r$run("2/1")
, etc.
The extra comma at the beginning indicates we wish to continue to
run the "example2" model. The difference is that we are calling the
run global helper which takes an additional first parameter rather than the
run method on the underlying stagerunner. Typing run("example1", to = 2)
indicates we wish to switch focus to the "example1" model and execute its
process up to the data munging stage.
Note the fuzzy matching in the above examples. Under the hood,
"da/1" means "look for a stage that matches the regular expression
.*d.*a.* in its top-level and grab the first node." What
happens when you try run(, "d/1")
?
An important note is that fuzzy matching can be ambiguous and
sometimes execute the wrong model. Fuzzy match results are ordered
in descending order by file modification time, so if you ever find
run
is being annoying and not executing
the correct model (e.g., if you have "example" and "example_foo"
and run("example")
is matching to
the incorrect one) simply make a tiny change like adding a space
and save the model file: run
will
pick up the change and execute the model whose file has been
most recently modified.
You now know enough about stagerunners to start being productive. The next step is to learn more about how the various stages are parametrized and learn how to extend them and write your own. For example, if you need to generate a report after the model is trained but before exporting the object, you could write a "report" stage that creates an appropriate R markdown file. Or maybe you want to deploy a web service that has a dashboard after you have exported the model: simply write a "dashboard" stage.
To summarize, we have learned that:
One last time, enough hype! Let's dive in to a few more features of the modeling engine.


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ESCAPE FROM L. A. A film review by Scott Renshaw Copyright 1996 Scott Renshaw
Starring: John Carpenter, Stacy Keach, George Corraface, A. J. Langer, Steve Buscemi, Cliff Robertson, Valeria Golino, Peter Fonda, Pam Grier. Screenplay: John Carpenter, Debra Hill, Kurt Russell. Director: John Carpenter. Reviewed by Scott Renshaw.
"Snake is Back" proclaim the posters for ESCAPE FROM L. A., and from multiplexes across the nation, I can hear the answering cry of, "Who?" Let's face it, this isn't exactly Indiana Jones we're talking about -- Kurt Russell's Snake Plissken last graced theater screens fifteen years ago in ESCAPE FROM NEW YORK, when a significant percentage of today's action film audience was somewhere between puberty and mastering solid food, and that film was not what one would call a classic of the genre. But Russell is a born again action hero after the success of STARGATE and EXECUTIVE DECISION, so he has once again donned the eye-patch (as well as the titles of co-writer and co-producer) for another futuristic adventure. The result is an attempt at self-satire which comes off as a tiresome exercise in pyrotechnics and cheap blue screen effects.
In ESCAPE FROM NEW YORK, Snake was forced to rescue the President of the United States after Air Force 1 crashes inside a prison colony which once was Manhattan. This time around, the location is Los Angeles, broken off from the continent after a massive earthquake and turned into a dumping round for undesirables by a morally authoritarian President (Cliff Robertson). But it isn't the president Snake needs to find; instead, it is the First Daughter (A. J. Langer), who has stolen an experimental weapon in an act of defiance. She intends to bring it to Cuervo Jones (George Corraface), an international criminal who rules Los Angeles and plans to invade the U.S. Snake is made an offer he can't refuse: bring back the weapon in ten hours, or die of a designer virus introduced into his blood.
It is a good thing Carpenter, Russell and company waited so long between NEW YORK and L. A., because even those who actually saw the first film probably have forgotten enough not to notice that ESCAPE FROM L. A. is virtually a point-for-point remake of the original. In New York, Snake meets Brain (Harry Dean Stanton), a former running buddy who once left Snake high and dry and whom Snake insists on calling by his real name; in L. A., Snake meets Hershe (Pam Grier), a transsexual former running buddy who once left Snake high and dry and whom Snake insists on calling by his/her real name. NEW YORK had snake on a time clock before something introduced into his body kills him; ditto for L. A. Both find Snake chatting with a sympathetic woman who is promptly blown away, and both find snake in an athletic contest for the amusement of the villains. The number of duplicated details is quite astonishing, actually. At least everyone involved did their homework.
The twist they try to place on ESCAPE FROM L. A. is that it is supposed to be funnier, almost a parody of the original. I say "supposed to" because John Carpenter may be one of the most humorless directors of the last twenty years, taking even bizarre material like BIG TROUBLE IN LITTLE CHINA and deadening it with his brooding style. His idea of a joke in ESCAPE FROM L. A. is showing every possible Southern California landmark (Hey, it's Universal Studios!...Hey, it's the Queen Mary!) or making a quick reference to every possible stereotype of Los Angeles living, and to show that he isn't taking anything seriously with some low-tech special effects, including a computer-generated submarine voyage which makes TRON look positively state-of-the-art. A few of the gags are clever, like a shot at L. A.'s front-running sports fans; others can't find a comfortable place between gruesome and silly, like a gang of cosmetic surgery victims always in search of fresh body parts. ESCAPE FROM NEW YORK was overly somber, but it was lean and efficient and quite good at creating its desolate urban landscape. ESCAPE FROM L. A. finds Carpenter making scattershot attempts at humor, and the experience is like being tickled by a robot.
There are a few simple pleasures to be found in the performances, including Kurt Russell's laconic Snake. It's one of the best (if unintentional) jokes that despite the fact that everyone in the world seems to know who Snake Plissken is, he has probably been able to escape capture because he has no discernible personality. Russell does a guttural Clint Eastwood impression, and makes a solid anti-hero. Steve Buscemi also has fun as Cuervo's sniveling assistant Maps-of-the-Stars'-Homes Eddie, and Bruce Campbell makes an almost unrecognizable appearance as the Surgeon General of Beverly Hills. Mostly, though, ESCAPE FROM L. A. is a collection of fragments and missed opportunities where it is expected that a multitude of sins will be hidden by big fireballs (motorcycles tend to explode as though hit by nuclear warheads upon contact with the ground in a John Carpenter film). Snake may be back, but he doesn't have much to say, and what he does have to say has been said before.
On the Renshaw scale of 0 to 10 California splits: 3.
-- Scott Renshaw Stanford University http://www-leland.stanford.edu/~srenshaw
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