THREE COLORS: RED Evening Redness in the West A film review by K. Leung Copyright 1995 K. Leung
Director: Krzysztof Kieslowski
Perhaps the opening sequence could have been more subtle. That is the only criticism I have of Kieslowski's last film, a glorious celebration of the art of cinema and the vagaries of life, and a tantalizing meditation on intersection between the two. Elegiac, self-referential, and full of world-weary wisdom and irony, RED is widely considered an update of THE TEMPEST, with Kieslowski as stage-manager/magician conjuring up sea-wraths, weaving intricate webs of fate that link unsuspecting strangers and reconcile present to past. But the director, ever respectful of human freedom, would be loathe to stuff alien words into his characters' mouths; instead, the magic is mostly in the shades of light, the camera angles, the oblique story-telling--the stuff of pure cinema. Irene Jacob's character (Valentine) comes upon dark suspicions, and shadows obscure her face; Jean-Louis Trintigant's retired judge sets to unveal his secrets, and the afternoon sun breaks through into his mauloseum-mansion. Thunderbolts interrupt the "conversation in the theater," thus evoking the Judge's tumultuous past and prefiguring an impending catastrophy in one stroke, illuminating one brief moment in time against all eternity. The all-reflecting panes of glass that frame the characters' isolation have never been more pristine, more revealing; every coincidence, every glance out the window is a destiny missed, and every turn into a side street is an adventure bursting with significance--as if revelation is just around the corner. Kieslowski does not invent the conventions; the originality in RED lies in the exceptional power of the symbols and imagery, and the propriety with which they fit into the narrative, anticipating/recalling each other, until they seem to sustain and animate the film all by themselves. Indeed the color red, in the guise of a Landrover, is used prominently as the vector of narrative. Nominally about "fratenity," in the master's hand it also becomes the color of betrayal, of breathless expectations, a warning; very early in the film (following the first fashion show), the oppressive array of red objects and road-map of cautionary signs and flashing lights (paced by Zbigniew Preisner's breath-taking score) combine to establish a mood almost oracular in intensity that will never go away. The crafty director is not above throwing in red herrings either: the red jacket is on the *other* fashion model as the surprised camera pulls away; it tracks down Valentine as she glides down the runway and then--with characteristic Kieslowskian modesty--almost trips and falls on her high spiked heels. In fact the entire film has this disposition of a precisely choreographed fashion show graced with humanizing glitches; it is a cosmic fairy tale told with the power and certainty (as Dave Kehr suggested in "Film Comment") of planetary alignment, yet it is also one that finally repudiates design in favor of freedom and the chaos of life. Kieslowski has set out to create a masterpiece, and who can deny he has created the cinematic landmark of the 1990's. All in all, it seems like an appropriate note on which to end his brilliant career.
It is springtime in Geneva, but Joseph Kern's garden is full of fallen leaves. Tormented by guilt and sunk in irony and indifference, he is really more Camus' judge-penitent than Kafka's Joseph K. Trintigant gives a supremely controlled portrait of this black-hole of accumulated cynicism, a deeply withdrawn man incapable of giving anything away; only his still clear eyes reveal the madness that must have burnt away so much of his life. Having given up his worldly titles, the Judge holds the world in contempt and spends his days eavesdropping on his neighbors' phone-calls. He is an impressive wizard of technology, and his command of weather conditions, planetary motion, and daylight also seems downright supernatural; but mostly he is a haggard, bitter old man whose lightbulbs are running out. Valentine is his opposite: she is open and radiant, the poster-girl of red-blooded youthfulness that gives so much life and inspiration to the film. She meets Kern through an accident, when she runs over his German shepherd while she is distracted by interference on her car radio. Initially Kern sends woman and dog away, prompting her to call on him to "stop breathing." Later he sets her up to "discover" his voyeurism; the display of self-flagellation is evidently meant as revenge for his loss of idealism, to be wrecked upon the guileless of the world.  The subtle, poignant, and finely-nuanced portrayal of their emerging friendship is reminiscent of the story-telling technique of the best parts of BLUE. Valentines tells Kern about her troubled brother, her fears and admonitions; he gradually reveals his tribulations and guilt, and tells her the story of his unfaithful lover who died in a car-wreck. He says he dreams of her (Valentine), and sets out to free her from the cold comfort of her displaced, destructive love affair. Round the corner from her apartment lives the earnest Auguste, a sort of young, unspoiled "double" for Kern, whom Kern learns about while spying on phone calls. Not only is Auguste also involved in a ruinous relationship, but it is clear that he shares Kern's dubious taste for self-pity and futility. (Kieslowski's concise characterization of Auguste--using just scraps of dialog and a few short scenes--has the clarity of self-knowledge.) Only the fairy-tale maiden Valentine can save him from a fate like Kern's, but they stubbornly keep missing each other in the streets, as though they were ciphers in an Altman tale. It is finally up to the Judge to take drastic measures, to give them a much needed hand on their way to their manifest destiny.
RED is a fable about the mysterious, intangible links between human beings, and as in THE DOUBLE LIFE OF VERONIQUE, these connections are for the most part one-sided or blocked, if not missed completely until too late. There are the missed opportunities between Valentine and Auguste, always a few heartbeats apart, and the miscommunications between them and their respective ex-lovers. Among the incidental figures, Valentine's estranged brother Mark is always preying on her mind. Through the intercepted phone calls, we also learn about the chasm between the closet-homosexual and his wife, as well as the pathetic story of the attention-pandering elderly mother; these little everyday tragedies seem to be growing in the air, flourishing behind every closed door. Kern, acknowledging his limited powers, tells Valentine she can't live for others, but must just "be" (herself); meanwhile he is secretly cultivating a redeeming love in the ruins with the last summons of his powers. It is amusing that Kieslowski chooses the telephone as metaphor for both isolation and connectedness, considering the days of wire-taps and crossed-up telephone lines he must have endured in old communist Poland, where phone calls were anything but private and insular. Valentine, the crown jewel of a Western culture that values individual freedom above all else, pleads that "everyone deserves their privacy"; but when she calls at the gay husband's home to blow the Judge's cover, she is shocked to find the man's young daughter eavesdropping on the phone too, and seems none the worse for having learned the terrible secret. Perhaps people are more complex and resilient, more understanding of others' weaknesses, than the romantics and cynics would like to believe after all. RED, the most flamboyant and visually beautiful work among Kieslowski's films, also has the most voyeurism, the most decrepit old women running around with recycled bottles, as well as numerous other obscenities. Death, the greatest obscenity of them all, is present in particular abundance. It is as if beauty and its antithesis must exist side by side, if not exactly in harmony, then at least with respectful wariness, and a peculiar kind of grace. During the second fashion show, Valentine receives Kieslowski's coronation of light and is showered with every tricks of flattery the camera can muster, and yet she is completely distracted, scarcely thinking of herself at all, but is looking all over for the disreputable dirty old man she has invited to the show. It is a remarkable scene, one that rivals in conception and care the miraculous spinning shot from inside the tourist bus in THE DOUBLE LIFE OF VERONIQUE. It makes one wonder what divine grace has given the young Jacob the centerpiece to two such memorable scenes in a row.
The conclusion to RED is more daring than the ending in BLUE, more revelatory than that of WHITE, and far more subtle and mysterious than both of them combined. Valentine, Auguste, as well as Julie and her assistant in BLUE and Dominique and Karol of WHITE are all assembled on board the same ferry. The thunderstorm, gathering force all this time on (Auguste's unfaithful lover) Karin's computer screen, unleashes its fury, killing all one thousand people aboard--only the principle characters of the trilogy and an unknown English barman survive. Also perishing in the tragedy are Karin and her new beau yachting nearby, but Kieslowski would only let us sympathize with Auguste's drowned pet dog. The Judge impassively follows the rescue on television; just moments earlier he has been cradling his shepherd's new born puppies. Safely landed, Auguste at last meets Valentine, whom he already knows from her larger-than-life poster hoisted over Geneva. The likeness of that poster--Valentine's mystery-haunted profile caught in a blaze of red--momentarily regenerates before him in freeze-frame. The Judge looks out the window, a shade more weary now, having passed one more sentence too many; he allows himself a hint of softness in the eyes, betraying the fondness expressed in his farewell and his gift of life. He will not see her again. With that, Kieslowski's long procession of magicians and demigods--the camera buff transforming people and objects into celluloid, the puppeteer Alexander honing his telepathy--have worked their capricious deeds for the last time. It is probably moot to ask whether the Judge has condemned the many or spared the few. Some die, others live on, and life regenerates itself--these are the rules that the director's characters have always lived by. On these very terms, the dead Weronica sends warning to Veronique, her double, and Ulla (NO END) is saved by her husband's ghost; Julie consents to destroying the baby mice in her apartment in BLUE, and in return grants legitimacy to the unborn child of her late husband's mistress. Ignored by them all, the decrepit old woman follows her relentless recycling to all corners of the world. Kieslowski's mythicism, dismissed as "new age" by some frivolous critics, is ultimately as old as the Book of Ecclesiastes, just as the conclusion to the trilogy is as harsh and arbitary as the story of Eve. Still, it is a tremendously moving and fitting ending, the likes of which I doubt I'll ever see again. Like BLUE and WHITE before it, RED brilliantly subverts the ideal that the color represents, turning the abstraction of universal comradeship into the mutual congratulations of the lucky surviving few. But if life in our times is truly the uninterrupted aftermath of car accidents and shipwrecks that Kieslowski's vision seems to suggest, then I suppose we can all be doing a lot worse.
K.L., December 1994.
.
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--- layout: archive title: Zulip Chat Archive permalink: /stream/144837-PR-reviews/topic/.231222.20provide.20.60trace!.60.20and.20.60fail!.60.20and.20allow.20tactic.html ---
New macros for tracing and reporting errors: fail!"wrong type: {e} : {infer_type e}"
(similarly for trace!
). Uses pp
for expressions (and others) and allows tactic-typed expressions
Can we please merge this? @Rob Lewis @Mario Carneiro
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


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Tutorials: Behind the GUI¶
The following tutorials pick up where the crash course left you, answers a couple more questions, gives a peak behind the GUI and provide some more advanced scenarios.
You have seen how the GUI can be launched via the launch_allesfitter app. Alternatively, you can also launch the GUI from a Python console:
import allesfitter
allesfitter.GUI()
But now let’s actually take a look behind the GUI. You have seen that GUI can be launched Here, we explain how to allesfit your discovery transit using the python console.
Setting things up¶
allesfitter expects a folder with
- all your data files (Leonardo.csv),
- a settings file (settings.csv), and
- a parameters file (params.csv).
Then you let allesfitter run on that directory, and it does the rest. The GUI hides all this from you, so let’s have a look behind the scenes. Open the examples/crash_course folder. You will see the following:
- a file run.py,
- a folder allesfit_Leonardo, and
- a folder allesfit_all_TMNT (this is for later).
The run.py file¶
This file just contains the simple 3 lines you need to execute to run any of the examples below (after installation). For example, using Nested Sampling:
import allesfitter
allesfitter.ns_fit('allesfit_Leonardo')
allesfitter.ns_output('allesfit_Leonardo')
Or, if you’re the MCMC kind of person:
import allesfitter
allesfitter.mcmc_fit('allesfit_Leonardo')
allesfitter.mcmc_output('allesfit_Leonardo')
The working directory¶
The folder allesfit_Leonardo is an example working directory for fitting one simple data set: Leonardo.csv (discovery photometry). Time, flux and flux error are given in a comma-separated .csv file.
settings.csv. Open it, and you will see that its minimal content are the planet letter (“b”) and instrument (“Leonardo”). All of these must match the entries in params.csv and the names of any data .csv, here we only have Leonardo.csv files. To speed up the example, we also set the fast_fit option and run on 4 cores. There are dozens of other possible settings to give the user all freedom. These are explained further in Section X below. (todo)
params.csv. Open it, and you will see the parameters describing the model. There are dozens of possible parameters to freeze and fit. These are further explained in Section Y below. (todo)
Finally, when allesfitter runs, it creates the subfolder results. It contains log files, result tables, LaTex tables, plots of the inital guess and final fit, corner plots, trace plots (for Nested Sampling), chain plots (for MCMC) and an internal save file. Have a look!
Becoming advanced:¶
Brilliant! You got great transit parameters that allowed to schedule follow-up observations, and you are now the hero of your team! They even baked you a cake! And even better: they nicely prepared all the old and new data for you in a folder called allesfit_all_TMNT:
- Leonardo.csv (discovery photometry)
- Michelangelo.csv (follow-up photometry)
- Donatello.csv (decent RV data)
- Raphael.csv (good RV data)
Now let’s allesfit all that data! Explore the settings.csv and params.csv files to see how all this additional data is included into a global fit for TMNT-1b. And/or, move on to the next tutorials page and learn how to tackle this new data set from scratch!


🐍 Installation and Conda Environment¶
Option 1 (recommended): conda environment¶
If conda environments are new to you, I suggest you become familiar with managing conda environments.
I have provided a sample Anaconda environment.yml file that lists the minimum packages required plus some extras that might be useful when working with other types of weather data. Look at the bottom lines of that yaml file–there are two ways to install SynopticPy with pip.
For the latest version of the code, use:
- pip:
- git+https://github.com/blaylockbk/SynopticPy.git
or use this for the latest version published to PyPI
- pip:
- SynopticPy
First, create the virtual environment with
conda env create -f environment.yml
Then, activate the synoptic
environment. Don’t confuse this environment name with the package name.
conda activate synoptic
Occasionally, you might want to update all the packages in the environment.
conda env update -f environment.yml
Option 2: pip¶
SynopticPy is my first PyPI package. You may install the last published version from PyPI. This requires the following are already installed:numpy
, pandas
, requests
, matplotlib
, and cartopy
.
pip install SynopticPy
Note that installing with pip will not give you the latest version from github.
Option 3: Alternative “Install” Method¶
There are several other ways to “install” a python package so you can import them. One alternative is to clone the repository into any directory.
git clone https://github.com/blaylockbk/SynopticPy.git
To import the SynopticPy functions, you will need to update your PYTHONPATH
environment variable to find the directory you cloned the package into, or you can add the line the following line to the top of your python scripts:
sys.path.append("/path/to/SynotpicPy")


{% include _head.html %}
{% include _browser-upgrade.html %} {% include _navigation.html %} {% if page.image.feature %}We aim to design and develop robots that learn to interact socially with humans and bring benefits to the society we live in, for example in application areas such as education, e-learning, healthcare and assistive technology.


@import uk.gov.hmrc.agentsregfrontend.models.SessionData
@import uk.gov.hmrc.govukfrontend.views.html.components._
@this(layout: Layout, govukSummaryList : GovukSummaryList)
@(user: SessionData)(implicit request: Request[_], messages: Messages)
@layout(pageTitle = Some("Summary")) {
Summary
Below are the details you entered. Please ensure these are correct, as some details cannot be changed after submission.
If you are happy with your details press the Submit button or you can cancel your application with the Cancel button
@govukSummaryList(SummaryList(
rows = Seq(
SummaryListRow(
key = Key(
content = Text("Business Name")
),
value = Value(
content = Text(user.businessName.get)
),
actions = Some(Actions(
items = Seq(
ActionItem(
href = "/registration/business-name/true",
content = Text("Change"),
visuallyHiddenText = Some("name")
)
)
))
),
SummaryListRow(
key = Key(
content = Text("Email")
),
value = Value(
content = Text(user.email.get)
),
actions = Some(Actions(
items = Seq(
ActionItem(
href = "/registration/email/true",
content = Text("Change"),
visuallyHiddenText = Some("date of birth")
)
)
))
),
SummaryListRow(
key = Key(
content = Text("Contact Number")
),
value = Value(
content = Text(user.contactNumber.get.toString())
),
actions = Some(Actions(
items = Seq(
ActionItem(
href = "/registration/contact-number/true",
content = Text("Change"),
visuallyHiddenText = Some("contact information")
)
)
))
),
SummaryListRow(
key = Key(
content = Text("Correspondence")
),
value = Value(
content = Text(user.correspondence.get.mkString(", "))
),
actions = Some(Actions(
items = Seq(
ActionItem(
href = "/registration/correspondence/true",
content = Text("Change"),
visuallyHiddenText = Some("contact details")
)
)
))
),
SummaryListRow(
key = Key(
content = Text("Address")
),
value = Value(
content = Text(user.address.get.propertyNumber+", "+user.address.get.postcode)
),
actions = Some(Actions(
items = Seq(
ActionItem(
href = "/registration/address/true",
content = Text("Change"),
visuallyHiddenText = Some("contact details")
)
)
))
)
)
))
Cancel
}


Namespace node.process
The process
object is a global object and can be accessed from anywhere.
It is an instance of EventEmitter
.
- Defined in: node.process.js
| Field Attributes | Field Name and Description |
|---|---|
| static |
node.process.ARGV
|
| static |
node.process.argv
An array containing the command line arguments.
|
| static |
node.process.env
An object containing the user environment.
|
| static |
node.process.ENV
|
| static |
node.process.execPath
This is the absolute pathname of the executable that started the process.
|
| static |
node.process.installPrefix
A compiled-in property that exposes
NODE_PREFIX . |
| static |
node.process.mainModule
|
| static |
node.process.pid
The PID of the process.
|
| static |
node.process.platform
What platform you're running on.
|
| static |
node.process.stderr
A writable stream to stderr.
|
| static |
node.process.stdin
A
Readable Stream for stdin. |
| static |
node.process.stdout
A
Writable Stream to stdout . |
| static |
node.process.title
Getter/setter to set what is displayed in 'ps'.
|
| static |
node.process.version
A compiled-in property that exposes
NODE_VERSION . |
| static |
node.process.versions
|
Method Summary
| Method Attributes | Method Name and Description |
|---|---|
| static |
node.process.addListener(type, listener)
|
| static |
node.process.assert()
|
| static |
node.process.binding()
|
| static |
node.process.chdir()
Changes the current working directory of the process or throws an exception if that fails.
|
| static |
node.process.compile()
|
| static |
node.process.createChildProcess()
|
| static |
node.process.cwd()
Returns the current working directory of the process.
|
| static |
node.process.debug()
|
| static |
node.process.dlopen()
|
| static |
node.process.emit(type)
|
| static |
node.process.error()
|
| static |
node.process.exit(code)
Ends the process with the specified
code . |
| static |
node.process.getgid()
Gets the group identity of the process.
|
| static |
node.process.getuid()
Gets the user identity of the process.
|
| static |
node.process.inherits()
|
| static |
node.process.kill(pid, sig)
Send a signal to a process.
|
| static |
node.process.listeners(type)
|
| static |
node.process.memoryUsage()
Returns an object describing the memory usage of the Node process.
|
| static |
node.process.mixin()
|
| static |
node.process.nextTick(callback)
On the next loop around the event loop call this callback.
|
| static |
node.process.on(type, listener)
|
| static |
node.process.once(type, listener)
|
| static |
node.process.openStdin()
|
| static |
node.process.reallyExit()
|
| static |
node.process.removeAllListeners(type)
|
| static |
node.process.removeListener(type, listener)
|
| static |
node.process.setgid()
Sets the group identity of the process.
|
| static |
node.process.setMaxListeners(n)
|
| static |
node.process.setuid()
Sets the user identity of the process.
|
| static |
node.process.umask()
Sets or reads the process's file mode creation mask.
|
| static |
node.process.unwatchFile()
|
| static |
node.process.watchFile()
|
Field Detail
static
node.process.ARGV
static
node.process.argv
An array containing the command line arguments. The first element will be
'node', the second element will be the name of the JavaScript file. The
next elements will be any additional command line arguments.
// print process.argv process.argv.forEach(function (val, index, array) { console.log(index + ': ' + val); });This will generate:
$ node process-2.js one two=three four 0: node 1: /Users/mjr/work/node/process-2.js 2: one 3: two=three 4: four
static
node.process.env
An object containing the user environment. See environ(7).
static
node.process.ENV
static
node.process.execPath
This is the absolute pathname of the executable that started the process.
Example:
/usr/local/bin/node
static
node.process.installPrefix
A compiled-in property that exposes
NODE_PREFIX
.
console.log('Prefix: ' + process.installPrefix);
static
node.process.mainModule
static
node.process.pid
The PID of the process.
console.log('This process is pid ' + process.pid);
static
node.process.platform
What platform you're running on.
'linux2'
, 'darwin'
, etc.
console.log('This platform is ' + process.platform);
static
node.process.stderr
A writable stream to stderr. Writes on this stream are blocking.
static
node.process.stdin
A
Readable Stream
for stdin. The stdin stream is paused by default, so one
must call process.stdin.resume()
to read from it.
Example of opening standard input and listening for both events:
process.stdin.resume(); process.stdin.setEncoding('utf8'); process.stdin.on('data', function (chunk) { process.stdout.write('data: ' + chunk); }); process.stdin.on('end', function () { process.stdout.write('end'); });
static
node.process.stdout
A
Writable Stream
to stdout
.
Example: the definition of console.log
console.log = function (d) { process.stdout.write(d + '\n'); };
static
node.process.title
Getter/setter to set what is displayed in 'ps'.
static
node.process.version
A compiled-in property that exposes
NODE_VERSION
.
console.log('Version: ' + process.version);
static
node.process.versions
Method Detail
-
static node.process.addListener(type, listener)
- Parameters:
- {string} type
- {string} listener
-
static node.process.assert()
-
static node.process.binding()
-
static node.process.chdir()Changes the current working directory of the process or throws an exception if that fails.
console.log('Starting directory: ' + process.cwd()); try { process.chdir('/tmp'); console.log('New directory: ' + process.cwd()); } catch (err) { console.log('chdir: ' + err); }
-
static node.process.compile()
-
static node.process.createChildProcess()
-
static node.process.cwd()Returns the current working directory of the process.
console.log('Current directory: ' + process.cwd());
-
static node.process.debug()
-
static node.process.dlopen()
-
static node.process.emit(type)
- Parameters:
- {string} type
-
static node.process.error()
-
static node.process.exit(code)Ends the process with the specified
code
. If omitted, exit uses the 'success' code0
. To exit with a 'failure' code:process.exit(1);
The shell that executed node should see the exit code as 1.- Parameters:
- {string} code
-
static node.process.getgid()Gets the group identity of the process. (See getgid(2).) This is the numerical group id, not the group name.
console.log('Current gid: ' + process.getgid());
-
static node.process.getuid()Gets the user identity of the process. (See getuid(2).) This is the numerical userid, not the username.
console.log('Current uid: ' + process.getuid());
-
static node.process.inherits()
-
static node.process.kill(pid, sig)Send a signal to a process.
pid
is the process id andsignal
is the string describing the signal to send. Signal names are strings like 'SIGINT' or 'SIGUSR1'. If omitted, the signal will be 'SIGTERM'. See kill(2) for more information. Note that just because the name of this function isprocess.kill
, it is really just a signal sender, like thekill
system call. The signal sent may do something other than kill the target process. Example of sending a signal to yourself:process.on('SIGHUP', function () { console.log('Got SIGHUP signal.'); }); setTimeout(function () { console.log('Exiting.'); process.exit(0); }, 100); process.kill(process.pid, 'SIGHUP');
- Parameters:
- {string} pid
- {string} sig
-
static node.process.listeners(type)
- Parameters:
- {string} type
-
static node.process.memoryUsage()Returns an object describing the memory usage of the Node process.
var util = require('util'); console.log(util.inspect(process.memoryUsage()));
This will generate:{ rss: 4935680, vsize: 41893888, heapTotal: 1826816, heapUsed: 650472 }
heapTotal
andheapUsed
refer to V8's memory usage. -
static node.process.mixin()
-
static node.process.nextTick(callback)On the next loop around the event loop call this callback. This is *not* a simple alias to
setTimeout(fn, 0)
, it's much more efficient.process.nextTick(function () { console.log('nextTick callback'); });
- Parameters:
- {function(Error?|...[*]):undefined=} callback
-
static node.process.on(type, listener)
- Parameters:
- {string} type
- {string} listener
-
static node.process.once(type, listener)
- Parameters:
- {string} type
- {string} listener
-
static node.process.openStdin()
-
static node.process.reallyExit()
-
static node.process.removeAllListeners(type)
- Parameters:
- {string} type
-
static node.process.removeListener(type, listener)
- Parameters:
- {string} type
- {string} listener
-
static node.process.setgid()Sets the group identity of the process. (See setgid(2).) This accepts either a numerical ID or a groupname string. If a groupname is specified, this method blocks while resolving it to a numerical ID.
console.log('Current gid: ' + process.getgid()); try { process.setgid(501); console.log('New gid: ' + process.getgid()); } catch (err) { console.log('Failed to set gid: ' + err); }
-
static node.process.setMaxListeners(n)
- Parameters:
- {string} n
-
static node.process.setuid()Sets the user identity of the process. (See setuid(2).) This accepts either a numerical ID or a username string. If a username is specified, this method blocks while resolving it to a numerical ID.
console.log('Current uid: ' + process.getuid()); try { process.setuid(501); console.log('New uid: ' + process.getuid()); } catch (err) { console.log('Failed to set uid: ' + err); }
-
static node.process.umask()Sets or reads the process's file mode creation mask. Child processes inherit the mask from the parent process. Returns the old mask if
mask
argument is given, otherwise returns the current mask.var oldmask, newmask = 0644; oldmask = process.umask(newmask); console.log('Changed umask from: ' + oldmask.toString(8) + ' to ' + newmask.toString(8));
-
static node.process.unwatchFile()
-
static node.process.watchFile()


Project rubric: https://review.udacity.com/#!/rubrics/107/view
Submission: What to include in your submission
Data dictionary: https://www.kaggle.com/c/titanic/data
I've chosen the titanic dataset to work in this project and using the additional facts found on the internet [I've captured some of them below], I used this sample dataset to compare the survival rate against different grouping of the passengers. The goal would be to identify which passenger group have the highest chance to survive. Based on what I've read in a wiki article, I've divided the groups into men, women and child (age < 18) and also by the ticket class since this grouping makes logical sense to me.
import pandas as pd
# pointing to the data file folder
import os.path
mypath = '/home/nbuser/'
os.chdir(mypath)
# checking the file layout
filename = 'titanic-data.csv'
titanic_df = pd.read_csv(filename)
print 'sample size:', len(titanic_df)
titanic_df.head()
# Age data in the set consists of NaN
empty_values = len(titanic_df[titanic_df['Age'].isnull()])
# check for percentages of modified data
print float(empty_values) / float(len(titanic_df)) * 100
# get the average age for the data set
titanic_df['Age'].fillna(titanic_df['Age'].mean(), inplace=True)
# check again to ensure no more NaN value for age
titanic_df[titanic_df['Age'].isnull()]
# adding IsAdult flag column
titanic_df['IsAdult'] = titanic_df['Age'] > 18
titanic_df.head()
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style("whitegrid")
sns.set(color_codes=True)
%pylab inline
def autolabel(ax, values):
rects = ax.patches
# Now make some labels
labels = ["%d" % i for i in (values)]
for rect, label in zip(rects, labels):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2, height*1.01, label, ha='center', va='bottom')
def rename_xticks(ax, title):
# Overwrite the xticks value for survival
if title=='Survival':
labels = ['','Perished','', 'Survived']
ax.set_xticklabels(labels)
elif title=='Survived':
labels = ['Perished','Survived']
ax.set_xticklabels(labels)
elif title=='Ticket Class':
labels = ['','First','', 'Second', '', 'Third']
ax.set_xticklabels(labels)
elif title=='Pclass':
labels = ['First','Second', 'Third']
ax.set_xticklabels(labels)
def plothist(title, fig, position, df, color, bins=8):
ax = fig.add_subplot(2, 3, position)
ax.set_title(title + ' of Passengers', y=1.08)
ax.set(xlabel=title, ylabel="Count")
values = ax.hist(df, color=color, bins=bins);
autolabel(ax, values[0])
rename_xticks(ax, title)
fig = plt.figure(figsize=(15,5))
fig.subplots_adjust(hspace=.6)
plothist('Age', fig, 1, titanic_df['Age'], '#80ccff')
plothist('Ticket Class', fig, 2, titanic_df['Pclass'], '#ccebff', bins=[1,2,3,4])
plothist('# of siblings / spouses aboard the Titanic', fig, 3, titanic_df['SibSp'], '#33adff')
plothist('# of parents / children aboard the Titanic', fig, 4, titanic_df['Parch'], '#007acc')
plothist('Survival', fig, 5, titanic_df['Survived'], '#003d66', bins=[0,1,2])
plothist('Fare', fig, 6, titanic_df['Fare'], '#001f33')
survival_pct = 549/891. * 100
print round(survival_pct,2),'%'
Let's begin by analysing the data across a few of the categories. I've looked at all the categories which includes numerical values to start the analysis.
Before proceeding further, let's look at some limitation and assumptions that we have with the current data.
Let's continue by looking at multiple variables comparison. We are interested to see which category correlates closer to the survival rate.
def rename_yticks(ax, title):
if title=='Pclass':
labels = ['','First','','Second','', 'Third']
ax.set_yticklabels(labels)
elif title=='Age':
labels = ['','0','20','40','60','80', '100']
ax.set_yticklabels(labels)
elif title=='SibSp':
labels = ['','0','2','4','6','8', '10']
ax.set_yticklabels(labels)
elif title=='Parch':
labels = ['','0','1','2','3','4', '5','6','7']
ax.set_yticklabels(labels)
elif title=='Fare':
labels = ['','0','100','200','300','400', '500', '600']
ax.set_yticklabels(labels)
elif title=='Survived':
labels = ['','Perished','','','', '', 'Survived']
ax.set_yticklabels(labels)
def plot_violin(df, x, y):
f, ax = plt.subplots(figsize=(8, 8))
# Show each distribution with both violins and points
sns.violinplot(x=x, y=y, data=df, inner="box", palette="Set3", cut=0, linewidth=3)
sns.despine(left=True)
title = x + ' by ' + y
f.suptitle(title, fontsize=18, fontweight='bold')
ax.set_xlabel(x,size = 16,alpha=0.7)
ax.set_ylabel(y,size = 16,alpha=0.7);
#ax.set_ylim(0)
rename_xticks(ax, x)
rename_yticks(ax, y)
plot_violin(titanic_df,'Pclass','Fare')
plot_violin(titanic_df,'Pclass','Age')
plot_violin(titanic_df,'Survived','SibSp')
plot_violin(titanic_df,'Survived','Parch')
plot_violin(titanic_df,'Survived','Fare')
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
def plot_barplot(x, y, hue, df):
title = x + " by " + y + " according to " + hue
ax = sns.barplot(x=x, y=y, hue=hue, data=df);
ax.set_title(title);
plot_barplot("Sex", "Survived", "Pclass", titanic_df)
Let's dive deeper to get a closer understanding of the probabilities.
def plot_stats_count(attributes, rotate=0):
survival_tag = 'Survived'
attr_list = []
# check the parameter passed contains multiple values as this needs to be appended differently
if isinstance(attributes, str):
attr_list.append(attributes)
xtitle = "Survival by " + attributes
else:
attr_list.extend(attributes)
xtitle = "Survival by " + ','.join(attributes)
attr_list.append(survival_tag)
# group by the needed parameter and count no of rows and later the percentage by each sub group
survivor_by_attr = titanic_df.groupby(attr_list)[survival_tag].agg({'Survival Count':'count'})
survivor_by_attr_formatted = survivor_by_attr.unstack()
display(survivor_by_attr_formatted)
# Create a simple bar plot to show the stats
ax = survivor_by_attr_formatted.plot(kind='bar', title = xtitle, figsize=(10, 5), legend=True, fontsize=12, rot=rotate,
color=['#ff4d4d', '#1aa3ff'])
ax.set_ylabel("Survival count", fontsize=12)
ax.plot()
return survivor_by_attr
def plot_stats_percentage(attributes, survivor_by_attr, rotate=0):
survivor_pcts = survivor_by_attr.groupby(level=0).apply(lambda x:
100*x / float(x.sum())).unstack()['Survival Count']
display(survivor_pcts)
# Create a simple bar plot to show the stats
if isinstance(attributes, str):
xtitle = "Survival by " + attributes
else:
xtitle = "Survival by " + ','.join(attributes)
ax = survivor_pcts.plot(kind='bar', title = xtitle, figsize=(10, 5), legend=True, fontsize=12, rot=rotate,
color=['#ff4d4d', '#1aa3ff'])
ax.set_ylabel("Survival %", fontsize=12)
ax.plot()
return survivor_pcts
survivor_by_attr = plot_stats_count('Parch')
survivor_by_attAr_pct = plot_stats_percentage('Parch', survivor_by_attr)
survivor_by_attr = plot_stats_count('SibSp')
survivor_by_attAr_pct = plot_stats_percentage('SibSp', survivor_by_attr)
survivor_by_attr = plot_stats_count('Pclass')
survivor_by_attAr_pct = plot_stats_percentage('Pclass', survivor_by_attr)
survivor_by_attr = plot_stats_count('Sex')
survivor_by_attr_pct = plot_stats_percentage('Sex', survivor_by_attr)
survivor_by_attr = plot_stats_count('IsAdult')
survivor_by_attr_pct = plot_stats_percentage('IsAdult', survivor_by_attr)
factors1 = ['Sex','Pclass']
survivor_by_attr = plot_stats_count(factors1)
survivor_by_attr_pct = plot_stats_percentage(factors1, survivor_by_attr)
factors2 = ['Sex','Parch']
survivor_by_attr = plot_stats_count(factors2, 90)
survivor_by_attr_pct = plot_stats_percentage(factors2, survivor_by_attr, 90)
factors3 = ['Pclass','Parch']
survivor_by_attr = plot_stats_count(factors3, 90)
survivor_by_attr_pct = plot_stats_percentage(factors3, survivor_by_attr)
factors4 = ['Sex','IsAdult']
survivor_by_attr = plot_stats_count(factors3)
factors4 = ['Sex','Pclass','Parch']
survivor_by_attr = plot_stats_count(factors4, 90)
survivor_by_attr_pct = plot_stats_percentage(factors4, survivor_by_attr, 90)
factors5 = ['Sex','Pclass','IsAdult']
survivor_by_attr = plot_stats_count(factors5, 90)
survivor_by_attr_pct = plot_stats_percentage(factors5, survivor_by_attr, 90)
factors6 = ['Sex','Pclass','SibSp']
survivor_by_attr = plot_stats_count(factors6, 90)
survivor_by_attr_pct = plot_stats_percentage(factors6, survivor_by_attr, 90)
pct_surviving_ParCh = 20.06/.31 * 100
print pct_surviving_ParCh
pct_surviving_isAdult = 25.8/.64 * 100
print pct_surviving_isAdult
pct_surviving_SibSp = 15.29/.32 * 100
print pct_surviving_SibSp
So if the group with the highest chance of survival based on the analysis of the three factors of is number of parent or child aboard, gender and ticket class would be the female first class ticket holder with zero parent or child aboard group that has a 64.71 times more likely to survive than perished probability.


require.js: require Error in Callback Test
Error in require callback does not stop other resolution.
More info
.
Check console for messages


Preparation time
30 mins to 1 hour
Cooking time
1 to 2 hours
Serves
Serves 4
Cheat the pastry with shop-bought shortcrust if you don’t have time to make it yourself, while wild mushrooms up the ante in this chicken and mushroom pie.
For the shortcrust pastry, put the flour and salt in a large bowl and add the cubes of butter and lard.
Use your fingertips to rub the butter into the flour until you have a mixture that resembles coarse breadcrumbs with no large lumps of butter remaining. Try to work quickly so that it does not become greasy.
Measure out four tablespoons of cold water. Using a knife, stir in just enough of the cold water to bind the dough together.
Wrap the dough in cling film and chill in the fridge for 10-15 minutes before using.
Alternatively using a food processor, put the flour, butter and salt in the food processor and pulse until the fat is rubbed into the flour. With the motor running, gradually add the water through the funnel until the dough comes together. Only add enough water to bind it and then stop. Wrap the dough in cling film as before and chill for 10-15 minutes before using.
For the pie, preheat the oven to 220C/425F/Gas 7.
Place the chicken in a large, deep pan with half of the onions and the bay leaf. Season with a little salt and pepper and cover with water. Bring to the boil, then reduce the heat and simmer for 45 minutes, or until the chicken is cooked through.
Remove the chicken from the pan and set aside to cool. Return the pan to the hob and cook the liquid on a rapid boil for a further 30 minutes, or until it has reduced in volume by half. Meanwhile, remove the meat from the chicken legs, thighs and carcass, then place in a 20x30cm/8x12in pie dish.
Heat the olive oil and butter in a large frying pan. Add the remaining onions and cook over a low-medium heat, without browning, for about five minutes, or until softened, then add the flour. Turn up the heat and then add the mushrooms and cook for a further 3-4 minutes.
Strain the reduced stock, then add it to the mushrooms and cook for one minute. Pour in the wine and cream, then bring to the boil, reduce the heat and simmer for five minutes, or until nicely thickened.
Roll out a rectangle of pastry 2cm/¾in wider than the pie dish. Brush the edges of the dish with beaten egg and lay the pastry on top, crimping the edges with a fork or your fingertips. Trim away any excess pastry and brush the top of the pie with the remaining egg. Place on a baking tray and bake for 30-35 minutes, or until the pastry is crisp and golden-brown.
For the carrots and peas, heat a frying pan until hot. Add the butter, sugar and five tablespoons of water and cook until the volume of liquid has reduced slightly. Add the carrots and cook for a couple of minutes, then add the peas and continue to cook until they are tender and the liquid has reduced to a glaze.
To serve, remove the pie from the oven and allow to cool slightly before serving with the carrots and peas.


thomas
784-6173
XForms controls with lazy authoring and binding
Five xf:input bind to nodes which are not defined in the model's instance.
<xf:input ref="lazy" >
<xf:label>lazy value:</xf:label>
</xf:input>
<xf:input ref="lazy" >
<xf:label>lazy value:</xf:label>
</xf:input>
<xf:input ref="lazy-second" >
<xf:label>laxy-second value:</xf:label>
</xf:input>
<xf:input ref="lazy-second" >
<xf:label>lazy-second value:</xf:label>
</xf:input>
<xf:input ref="lazy" >
<xf:label>lazy value:</xf:label>
</xf:input>
lazy value:
lazy value:
lazy-second value:
lazy-second value:
lazy value:
Two xf:input, one binds to a valid node in the model and the other binds to an undefined node.
<xf:input model="m2" ref="name" >
<xf:label>Name:</xf:label>
</xf:input>
<xf:input model="m2" ref="cell" >
<xf:label>Cell Phone:</xf:label>
</xf:input>
<xf:output model="m2" ref="cell" >
</xf:output>
Name:
Cell Phone:


Hi there. I am Xiaochun (Claire) Wang. I am a Master of Financial Engineering student at UCLA Anderson School of Management. Prior to attending graduate school, I studied Actuarial Science at University of Illinois at Urbana-Champaign with a minor in Computer Science.
Having experienced a long-term passion for quantitative finance, I am determined to pursue a career in asset management, actuarial science, risk management, or other quantitative finance related areas upon graduation in Dec 2018.
My academic and professional background equipped me with solid understanding in portfolio management (quantitative equity, fixed income), derivatives, insurance (life & health, retirement) and data mining as well as strong programming skills (R, Python, MATLAB and C++).
Here, you can learn more about my ongoing research projects, past experiences and other aspects of me. If you are interested in making a connection, please feel free to reach out to me at<EMAIL_ADDRESS>Society of Actuaries
CFA Institute
Programming
Databases
Coming Soon
Coming Soon


--- layout: post status: publish published: true title: Outdated Blogroll author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 913 wordpress_url: http://winterdom.com/2003/05/outdatedblogroll date: '2003-05-03 20:15:19 +0000' date_gmt: '2003-05-03 20:15:19 +0000' categories: - Blogging tags: [] comments: [] ---
I haven't updated my blogroll in quite a while. This is, of course, Greg's fault for producing such a wonderful tool. I subscribe now to quite a few more feeds, including of course, much of the MS newcomers and a few others....


#include <BL3_OakGame_structs.hpp>
◆ CurrentTargetActor
| class AActor* CurrentTargetActor |
◆ DamageType
◆ DirectHitPct
◆ DistanceFromPoint
◆ DrawDebugProjectileLine
| bool DrawDebugProjectileLine |
◆ LightProjectileData
| class UClass* LightProjectileData |
◆ MaxAngle
◆ MaxPrediction
◆ MinDelayBetweenBurstWave
| float MinDelayBetweenBurstWave |
◆ MinDelayInsideBurstShot
| float MinDelayInsideBurstShot |
◆ NbBulletPerBurst
◆ ProjectileSpeed
◆ Range
◆ ShootingStyle
◆ SocketName
◆ SpreadAngle
◆ UnknownData00
| unsigned char UnknownData00[0x3] |
◆ UnknownData01
| unsigned char UnknownData01[0x3] |
◆ UnknownData02
| unsigned char UnknownData02[0x10] |


{% extends "timepiece/user/base.html" %} {% load static %} {% load timepiece_tags %} {% block title %}Dashboard{% endblock title %} {% block bodyid %}dashboard{% endblock bodyid %} {% block crumbs %} {{ block.super }}
| Project | From | To | Total Time | Activities |
|---|---|---|---|---|
| {{ day.grouper }} | {{ daily_total|humanize_seconds:"{hours:02d}:{minutes:02d}:{seconds:02d}" }} | |||
|
{{ entry.project }} Edit Delete |
{{ entry.start_time|time }} | {% if entry.end_time %} {{ entry.end_time|time }} {% else %} current {% endif %} | {{ entry.get_total_seconds|humanize_seconds:"{hours:02d}:{minutes:02d}:{seconds:02d}" }} | {{ entry.activities }} |
| Overall total for the period: | {{ hours|humanize_hours:"{hours:02d}:{minutes:02d}:{seconds:02d}" }} |
No entries exist for this week.
{% endif %}| Project | Hours |
|---|---|
| {{ project.project__name}} - {{ project.activities }} | {{ project.sum|humanize_hours:"{hours:02d}:{minutes:02d}:{seconds:02d}" }} |
No entries exist for this week.
{% endif %}| Last Name | First Name | SSN | Title | Hours |
|---|---|---|---|---|
| {{ entry.last_name }} | {{ entry.first_name }} | {{ entry.ssn }} | {{ entry.title }} | {{ entry.hours|humanize_hours:"{hours:02d}:{minutes:02d}" }} |
No entries exist for this week.
{% endif %}


To learn more about Predictive Insights, please fill out this Talk To Sales form to speak with a a VoiceBase rep.


German word of the day: Oha!
by The LocalFeeling surprised about something? We've got a German expression for you.
One of the most underrated parts of learning a new language are the small quirks and phrases that continue to surprise non-native speakers for years after reaching fluency.
Onomatopoeias are one of those strange and lovely avenues. From the German rooster who says Kikeriki instead of cock-a-doodle-doo to the comic strip where a gun goes Peng! instead of Bang!, this category also includes everyday colloquial phrases.
Which brings us to our word of the day: Oha!
READ ALSO: 12 colourful German expressions which will add swagger to your language skills
This can be used in any situation that brings up surprise. The positive or negative connotation, if any, is conveyed through your tone. Similar to the English "Whoa," it usually conveys surprise or amazement, and in some cases can be used to rebuke someone.
Suppose that your friend has just informed you that her boyfriend will only be wearing tweed pants from now on: Oha!
Suppose your mother-in-law has stopped by for an unannounced visit: Oha!
Here are some other example sentences:
“Oha. Hast du gesehen wie hübsch sie ist?”
“Wow. Did you see how pretty she is?”
“Oha. Das war aber unfair.”
“Wow. That was unfair.”
Anyways, enjoy this short and fun expression to make you sound like a true German.


| Total net profit | -83.79 | Gross profit | 19.59 | Gross loss | -103.38 |
| Profit factor | 0.19 | Expected payoff | -0.36 | ||
| Absolute drawdown | -83.79 | Maximal drawdown (%) | 83.68 (-76072.73%) | ||
| Total trades | 231 | Profit trades (% of total) | 34 (14.72%) | Loss trades (% of total) | 197 (85.28%) |
| Long positions (won %) | 128 (20.31%) | Short positions (won %) | 103 (7.77%) | ||
| Largest | profit trade | 1.82 | loss trade | -1.56 | |
| Average | profit trade | 0.58 | loss trade | -0.52 | |
| Minimum | profit trade | 0.02 | loss trade | -0.02 | |
| Maximum | take profit in pips | 207.6 | stop loss in pips | -220.3 | |
| Average | take profit in pips | 57.2 | stop loss in pips | -43.9 | |
| Minimum | take profit in pips | -45.1 | stop loss in pips | -20.1 | |
| Maximum | Reward-Risk | 7.5 | Duration in minutes | 801 | |
| Average | Reward-Risk | 1.7 | Duration in minutes | 40.9 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 3.0 (1.09) | consecutive losses (loss in money) | 25.0 (-12.0) | |
| Average | consecutive wins | 1.0 | consecutive losses | 6.0 |
| Total net profit | -47.16 | Gross profit | 15.35 | Gross loss | -62.51 |
| Profit factor | 0.25 | Expected payoff | -0.37 | ||
| Absolute drawdown | -47.16 | Maximal drawdown (%) | 47.45 (16362.07%) | ||
| Total trades | 128 | Profit trades (% of total) | 26 (20.31%) | Loss trades (% of total) | 102 (79.69%) |
| Largest | profit trade | 1.48 | loss trade | -1.56 | |
| Average | profit trade | 0.59 | loss trade | -0.61 | |
| Minimum | profit trade | 0.02 | loss trade | -0.02 | |
| Maximum | take profit in pips | 169.3 | stop loss in pips | -220.3 | |
| Average | take profit in pips | 41.1 | stop loss in pips | -53.4 | |
| Minimum | take profit in pips | -45.1 | stop loss in pips | -26.1 | |
| Maximum | Reward-Risk | 4.2 | Duration in minutes | 801.0 | |
| Average | Reward-Risk | 0.9 | Duration in minutes | 60.9 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0.0 | |
| Maximum | consecutive wins (profit in money) | 2.0 (0.91) | consecutive losses (loss in money) | 14.0 (-8.4) | |
| Average | consecutive wins | 1.0 | consecutive losses | 4.0 |
| Total net profit | -36.63 | Gross profit | 4.24 | Gross loss | -40.87 |
| Profit factor | 0.1 | Expected payoff | -0.36 | ||
| Absolute drawdown | -36.63 | Maximal drawdown (%) | 36.23 (-9057.5%) | ||
| Total trades | 103 | Profit trades (% of total) | 8 (7.77%) | Loss trades (% of total) | 95 (92.23%) |
| Largest | profit trade | 1.82 | loss trade | -1.41 | |
| Average | profit trade | 0.53 | loss trade | -0.43 | |
| Minimum | profit trade | 0.16 | loss trade | -0.02 | |
| Maximum | take profit in pips | 207.6 | stop loss in pips | -109.2 | |
| Average | take profit in pips | 77.2 | stop loss in pips | -32.0 | |
| Minimum | take profit in pips | -11.7 | stop loss in pips | -20.1 | |
| Maximum | Reward-Risk | 7.5 | Duration in minutes | 241.0 | |
| Average | Reward-Risk | 2.6 | Duration in minutes | 16.1 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 1.0 (0.25) | consecutive losses (loss in money) | 42.0 (-17.46) | |
| Average | consecutive wins | 1.0 | consecutive losses | 10.0 |


| Package | com.yahoo.astra.fl.charts |
| Class | public class Chart |
| Inheritance | Chart fl.core.UIComponent |
| Subclasses | CartesianChart, PieChart |
Chart
object
shouldn't be instantiated directly. Instead, a subclass with a concrete
implementation should be used. That subclass generally should implement the
IPlotArea
interface.
| Property | Defined by | ||
|---|---|---|---|
| dataProvider : Object
The data to be displayed by the chart.
| Chart | ||
| dataTipFunction : Function
If defined, the chart will call the input function to determine the
text displayed in the chart's data tip.
| Chart | ||
| defaultSeriesType : Object
When raw data (like an Array of Numbers) is encountered where an
ISeries instance is expected, it will be converted to this default
type.
| Chart | ||
| legend : ILegend
The component that will display a human-readable legend for the chart.
| Chart | ||
| legendLabelFunction : Function
If defined, the chart will call the input function to determine the text displayed in
in the chart's legend.
| Chart |
| Method | Defined by | ||
|---|---|---|---|
|
Chart()
Constructor.
| Chart | ||
|
indexToSeries(index:int):ISeries
Returns the ISeries object at the specified index.
| Chart | ||
|
seriesToIndex(series:ISeries):int
Returns the index within this plot area of the input ISeries object.
| Chart |
| Method | Defined by | ||
|---|---|---|---|
|
refreshSeries():void
Analyzes the input data and smartly converts it to the correct ISeries type
required for drawing.
| Chart |
| dataProvider | property |
dataProvider:Object
[read-write]The data to be displayed by the chart. Accepted data types include all of the following:
Note: When complex objects or XML is used in the data provider,
developers must define "fields" used to access data used by the chart.
For instance, CartesianChart exposes horizontalField
and
verticalField
properties. PieChart exposes dataField
and categoryField
properties.
The chart will automatically convert the input data to an Array of
ISeries objects. Don't access dataProvider
if you intend
to retreive the data in its original form.
Implementation
public function get dataProvider():Object
public function set dataProvider(value:Object):void
| dataTipFunction | property |
dataTipFunction:Function
[read-write]If defined, the chart will call the input function to determine the text displayed in the chart's data tip. The function uses the following signature:
function dataTipFunction(item:Object, index:int, series:ISeries):String
public function get dataTipFunction():Function
public function set dataTipFunction(value:Function):void
| defaultSeriesType | property |
defaultSeriesType:Object
[read-write]When raw data (like an Array of Numbers) is encountered where an ISeries instance is expected, it will be converted to this default type. Accepts either a Class instance or a String referencing a fully-qualified class name.
Implementation public function get defaultSeriesType():Object
public function set defaultSeriesType(value:Object):void
| legend | property |
legend:ILegend
[read-write]The component that will display a human-readable legend for the chart.
Implementation public function get legend():ILegend
public function set legend(value:ILegend):void
| legendLabelFunction | property |
legendLabelFunction:Function
[read-write]If defined, the chart will call the input function to determine the text displayed in in the chart's legend.
Implementation public function get legendLabelFunction():Function
public function set legendLabelFunction(value:Function):void
| Chart | () | constructor |
public function Chart()
Constructor.
| indexToSeries | () | method |
public function indexToSeries(index:int):ISeries
Returns the ISeries object at the specified index.
Parametersindex:int — the index of the series to return
|
ISeries —
the series that appears at the input index or null if out of bounds
|
| refreshSeries | () | method |
protected function refreshSeries():void
Analyzes the input data and smartly converts it to the correct ISeries type required for drawing. Adds new ISeries objects to the display list and removes unused series objects that no longer need to be drawn.
| seriesToIndex | () | method |
public function seriesToIndex(series:ISeries):int
Returns the index within this plot area of the input ISeries object.
Parametersseries:ISeries — a series that is displayed in this plot area.
|
int — the index of the input series
|


--- title: call_to_action: Contact background_image_path: large_header: true testimonials: - message: We use Dumun in all our endeavours. They offer an unparalleled service when it comes to painting and siding. image: "https://placekitten.com/g/60/60?a=.png" name: Joice Harold - message: Dumun are the best of the best. Being local, they care about people and have strong ties to their customers. image: "https://www.fillmurray.com/60/60?a=.png" name: Kyle Rail - message: Dumun were everything we could have hoped for when buying our first home and upgrading it. Highly recommended to all. image: "https://placebear.com/60/60?a=.png" name: D. and G. Gibbleston _options: image: width: 60 height: 60 ---
Dumun is professional construction team. Practicing for over 4 years, our team have the knowledge and skills to get you results. We do painting, tailing and siding
{% endfor %}


\[ \newcommand{\epe}{\text{EPE}} \newcommand{\E}{\text{E}} \newcommand{\mse}{\text{MSE}} \newcommand{\tdata}{\mathcal{T}} \newcommand{\bias}{\text{Bias}} \newcommand{\var}{\text{Var}} \newcommand{\rss}{\text{RSS}} \] Class of restricted estimators: here we briefly introduce three classes of estimators. Usually there are some parameters associated with the class which controls the effective size of the local neighborhood.
Penalized version of RSS.
\[ \text{PRSS}(f; \lambda) = \rss(f) + \lambda J(f) \] where \(J(f)\) is used defined and it is large if \(f\) varies rapidly over small region (not smooth).
One example, for 1-d input, \[\text{PRSS}(f; \lambda) = \sum_i (y_i - f(x_i))^2 + \lambda \int [f''(x)]^2 dx\]. And the solution to these problem is the cubic smoothing spline. And when \(\lambda \rightarrow \infty\), only linear function is permitted.
For additive model, we can have \(J(f) = \sum_j J(f_j)\). For projection pursuit regression, we have \(f(X) = \sum_{m = 1}^M g_m(\alpha_m' X)\) for adaptively chosen directions \(\alpha_m\) and we can have a roughness penalty (\(J(\cdot)\)) for each of \(g_m\).
Usually, the penalty function indicates our prior belief of the function type and they can be cast in a Bayesian framework. Here \(J\) corresponds to a log-prior and PRSS is the log-posterior. By minimizing PRSS, we are finding the posterior mode.
Here, we directly specify the local neighborhood by kernel function \(K_\lambda(x_0, x)\). It can be interpreted as the weight of \(x\) around \(x_0\). For example, the Gaussian kernel is, \[K_\lambda(x_0, x) = \frac{1}{\lambda} e^{-\frac{\|x - x_0\|^2}{2 \lambda}}\].
The simplest form of kernel estimate is Nadaraya-Watson weighted average: \[\hat{f}(x_0) = \frac{\sum_i K_\lambda(x_0, x_i) y_i}{\sum_i K_\lambda(x_0, x_i)}.\]
More generally, we can define the local regression estimate, \(f_{\hat\theta}(x_0)\) as follow: \[\begin{aligned} \hat\theta &= \arg\min_\theta \rss(f_\theta, x_0) \\ &= \arg\min_\theta \sum_i K_\lambda(x_0, x_i) (y_i - f_\theta(x_i))^2 \end{aligned}\] Here, this RSS is rather a definition than something we can derive from the its definition. Since intuitively, RSS of \(x_0\) should be \((y_0 - f(x_0))^2\).
The knn can be seen as a case of kernel method where the kernel function is defined as: \[ K_k(x_0, x) = I(\|x - x_0\| \le \|x - x_{(k)}\|) \] , where \(x_{(k)}\) is the \(k\)th nearest neighbor of \(x_0\).
And we should note that these kernel based approach needs modification to avoid curse of dimensionality.
We parameterize the model as \(f_\theta(x) = \sum_{m = 1}^M \theta_m h_m(x)\). Here, \(h_m\) is some kinds of function and these \(h_m\)’s are combined additively. Sometimes, \(h_m\) is prescribed before the training, e.g. the basis for polynomials in \(x\) of total degree \(M\). More specifically, the polynomial splines of degree \(K\) gives piecewise polynomial of \(K\) degree between knots (assuming 1-d input).
Another example is Radial basis functions \(f_\theta(x) = \sum_{m = 1}^M K_{\lambda_m}(\mu_m, x)\theta_m\). And centroids \(\mu_m\) and scales \(\lambda_m\) need to be determined (beforehand). Ideally, we want to have some data-driven approach to determine these parameters (also the knots in spline).
Inline with this, feed-forward neural net is another example \(f_\theta(x) = \sum_{m = 1}^M \beta_m \sigma(\alpha_m' x + b_m)\). Still, we want to determine \(\alpha_m\) and \(b_m\) and it is estimated along the way.
For methods choosing the basis functions adaptively, they are also called dictionary methods. In principle, we have infinite number of basis candidate and we use some of them to build the model via some search mechanism.


RECYCLE |
DON'T RECYCLE |
|---|---|
- Water BottlesWater bottles are recycleable!- Plastic Candy WrapperRecycleable as long as there's no candy left in it- CanEmpty then recycle! |
- Container of FoodYou can't recycle food! But you can recycle the container after it has been washed.- ChipYou can't recycle food!- Lolipop stickYou can't recycle products with food on them! |


View all the directives in action at https://metamagicglobal.github.io/ng5-xlib
Install above dependencies via npm.
Now install ng5-xlib
via:
npm install --save ng5-xlib
Note:If you are using
SystemJS
, you should adjust your configuration to point to the UMD bundle. In your systemjs config file,map
needs to tell the System loader where to look forng5-xlib
:map: { 'ng5-xlib': 'node_modules/ng5-xlib/bundles/ng5-xlib.umd.js', }
Once installed you need to import the main module:
import { LibModule } from 'ng5-xlib';
The only remaining part is to list the imported module in your application module. The exact method will be slightly
different for the root (top-level) module for which you should end up with the code similar to (notice LibModule .forRoot()
):
import { LibModule } from 'ng5-xlib';
@NgModule({
declarations: [AppComponent, ...],
imports: [LibModule.forRoot(), ...],
bootstrap: [AppComponent]
})
export class AppModule {
}
Other modules in your application can simply import LibModule
:
import { LibModule } from 'ng5-xlib';
@NgModule({
declarations: [OtherComponent, ...],
imports: [LibModule, ...],
})
export class OtherModule {
}
Copyright (c) 2017 meta-magic-global. Licensed under the MIT License (MIT)


|
This Software provided by G3 Links Australia is for training purposes only which provides customers with programming information regarding feature products. This software is supplied "AS IS". However, you can contact G3 Links for support and get information about pay products. G3 Links Australia assumes no responsibility or liability for the use of the software, conveys no license or title under any patent, copyright, or mask work right to the product. G3 Links Australia reserves the right to make changes in the software without notification. G3 Links Australia also make no representation or warranty that such application will be suitable for the specified use without further testing or modification.
Personal Data That We Collect
G3Links do not collects data or share any personal information. Your name and email are only used to identify yourself with team members. Some information are stored using technologies like cookies, and for receiving error reports.
G3Links do not use any information you say in projects and documents (email, chat, video calls or voice mail, or your documents, photos or other personal files). We may use your name and email to communicate with you, for example, informing you about your account, security updates and product information.
You can view, edit or delete your personal data online for within G3Links. You can also make choices about security, privacy and use of your data.
G3Links uses cookies (small text files placed on your device) and similar technologies to provide our websites and online services and to help access your data. Cookies allow us, among other things, to store your preferences and settings; enable you to sign-in; combat fraud.
G3Links products are intended for use in both personal and by organizations. Organizations are administered to you by your master office and may be subject to organization's policies To learn more about the social features and other functionality, please review documentation or help content specific to the G3Links product. G3Links is not responsible for the privacy or security practices of our customers, which may differ from those set forth in this privacy statement.
You must provide a valid and active email to register with G3LInks.


Fuzz Testing
Bang for our buck.
WHY BOTHER
Writing tests takes more time, and then we have to maintain the tests.
Large Projects
When projects become larger, they become much more difficult to guarantee correctness. Manual testing also becomes prohibitive.
Teams
With even one more person tests immediately become more valuable.
Refactoring
Coming back to some code after a significant time and changing parts of it can be challenging.
OUR GOAL
Minimum effort in writing and maintaining, yet still able to catch the bulk of common problems.
Whitebox Testing
Tests are written with knowledge of how the internals of the system function.
Blockbox testing
Ignores internal implmentation, only considers system inputs and outputs.
Whitebox testing
-
Thorough
tests, can make strong guarantees. -
Run-time efficiency
as we test only what we need. -
Fragile
tests will often break when implementation changes. -
Maintenance
is difficult, as there is a lot of code.
Blackbox testing
-
Easier to write
as they focus only on inputs and outputs. -
Easily maintainable
as they don't depend on internals. -
Not guaranteed
to catch all issues. -
Slower
run-time due to exploring parameter spaces.
OUR GOAL
Minimum effort in writing and maintaining, yet still able to catch the bulk of common problems.
Fuzz testing
Does the code we've written run under a variety of well chosen inputs, no assertions really necessary.
Property testing
With randomised inputs, what are the properties of the system that can still be tested with assertions?
Fuzz testing steps
-
01. Analyse System Inputs
What are the distinct values? What are valid data ranges? Can we limit values based on other values?
-
02. Generate Fuzzed Data
Generate a sequence of semi-randomised data that covers an intelligent set of inputs.
-
03. Execute
Run a part of the system to be tested.
-
04. Analyse Behavior
Did the system behave as expected? This can be as simple as just checking that no exceptions occurred.
The most basic
How would it look to fuzz test something like the billing contracts model?
No assertions required, just run the code and let exceptions be our checks. But how do we choose reasonable values for our inputs?
class BillingContractTestCase(TestCase):
def test_get_next_due(self):
# TODO: Create a few billing contracts with
# good test values, and for each one run ...
billingcontract.get_next_due()
A bit cleverer
How would it look to fuzz test something like the billing contracts model?
We can use Hypothesis to help us generate inputs for our tests, greatly reducing the difficulty in writing tests. But, what is "billingcontract_strategy"?
from hypothesis import given
from .strategies import billingcontract_strategy
class BillingContractTestCase(TestCase):
@given(billingcontract_strategy)
def test_get_next_due(self, billingcontract):
# This method will be called repeatedly with semi-
# random values for "billingcontract".
billingcontract.get_next_due()
Pretty smart
How would it look to fuzz test something like the billing contracts model?
Strategies tell Hypothesis how to generate sample test data. Hypothesis ships with a Django specific helper that does this automatically for us using a Model as an input.
from hypothesis.extra.django import from_model
from abas.apps.properties.tests.strategies import (
property_strategy
)
from ..models import BillingContract, BillingContractType
billingcontract_strategy = from_model(
BillingContract,
property=property_strategy,
type=from_model(
BillingContractType
)
)
A solid test
How can we add property testing to the billing contracts fuzz test?
An invariant of the function to calculate the next due date is that it will always increment the value by the the contract's period.
from hypothesis import given
from .strategies import billingcontract_strategy
class BillingContractTestCase(TestCase):
@given(billingcontract_strategy)
def test_get_next_due(self, billingcontract):
# This method will be called repeatedly with semi-
# random values for "billingcontract".
a = billingcontract.due
b = billingcontract.get_next_due()
p = billingcontract.period
assert((b - a) == p)


Enter your birthday on the top of the page! OR Click on a button lined below!


|
GLib Framework
1.0
|
Header file of the graph component of gLib. More...
Go to the source code of this file.
Data Structures | |
| struct | vertice_t |
| Representation of a node of a graph. More... | |
| struct | adjacency_list_node_t |
| Representation of an adjacency list. More... | |
| struct | graph_t |
| Representation of an oriented or unoriented graph. More... | |
Typedefs | |
| typedef struct vertice_t | vertice_t |
| Representation of a node of a graph. More... | |
| typedef struct adjacency_list_node_t | adjacency_list_node_t |
| Representation of an adjacency list. More... | |
| typedef struct graph_t | graph_t |
| Representation of an oriented or unoriented graph. More... | |
Functions | |
| vertice_t * | new_vertice (unsigned int x, unsigned int y) |
| Function to create a new graph node. More... | |
| adjacency_list_node_t * | new_adjacency_node (vertice_t *vertice) |
| Function to create a new node of adjacency list. More... | |
| graph_t * | create_graph (unsigned int width, unsigned int height) |
| Function to create a new graph from a 2D map. More... | |
| void | init_vertices (graph_t *graph) |
| Create all node corresponding to a 2D map and add the edges. More... | |
| void | add_edge (graph_t *graph, vertice_t *src, vertice_t *dest) |
| Add a edge between two node in a graph. In the case of a non-oriented graph, remember to call this function with the nodes in the other direction. More... | |
| adjacency_list_node_t * | get_neighbors (graph_t *graph, vertice_t *vertice) |
| Return all the neighbors of a node. More... | |
| void | free_graph (graph_t *graph) |
| Free the resources of a graph. More... | |
Header file of the graph component of gLib.
This file provides all the functions necessary for the manipulation of graphs representing a 2-dimensional map.
| typedef struct adjacency_list_node_t adjacency_list_node_t |
Representation of an adjacency list.
adjacency_list_node_t is a representation of an adjacency list.
Representation of an oriented or unoriented graph.
graph_t is a representation of an oriented or unoriented graph.
Representation of a node of a graph.
vertice_t is a representation of a node of a graph.
Add a edge between two node in a graph. In the case of a non-oriented graph, remember to call this function with the nodes in the other direction.
| graph | The graph. |
| src | The source node. |
| dest | The destination node. |
| graph_t* create_graph | ( | unsigned int | width, |
| unsigned int | height | ||
| ) |
Function to create a new graph from a 2D map.
| width | The width of the 2D map. |
| height | The height of the 2D map. |
| void free_graph | ( | graph_t * | graph | ) |
Free the resources of a graph.
| graph | The graph. |
| adjacency_list_node_t* get_neighbors | ( | graph_t * | graph, |
| vertice_t * | vertice | ||
| ) |
Return all the neighbors of a node.
| graph | The graph. |
| vertice | The node to which we want the neighbors. |
| void init_vertices | ( | graph_t * | graph | ) |
Create all node corresponding to a 2D map and add the edges.
| graph | The graph to initialize. |
| adjacency_list_node_t* new_adjacency_node | ( | vertice_t * | vertice | ) |
Function to create a new node of adjacency list.
| vertice | The node on which its neighbors will be added. |
| vertice_t* new_vertice | ( | unsigned int | x, |
| unsigned int | y | ||
| ) |
Function to create a new graph node.
| x | The position on the x-axis. |
| y | The position on the y-axis. |


Working remotely with sublime text.
There are two basic scenarios.
Edit random files
To do that, i'd recommend using rsub. There is a great tutorial by tedbot explaining how to do that.
Working on a remote project
In this case you want to work on a particular part of your file system. In order to archive this, you want to mount the remote directory over ssh. Here you'll find a comprehensive tutorial how to set up sshfs, which enables you to simply do something like:
$ sshfs username@yourhostname.com:/path/to/remote/directory /path/to/local/


Did you know that...
Urine is usually yellow or clear, depending upon a person's health and diet.
It usually has an ammonia-like odor due to the nitrogenous wastes
that make up about 5% of the fluid (the remaining 95% is water).
Hematuria, or blood in the urine , can be either gross (visible) or microscopic
(as defined by more than three to five red blood cells per high power
field when viewed under magnification). Gross hematuria can vary widely
in appearance, from light pink to deep red with clots.


Library hw07_induction
Require Import Nat.
Require Import PeanoNat.
Require Import Arith Psatz.
Require Import ZArith.
Require Import Znumtheory.
Require Import BinInt.
Require Import Int.
Require Import ZArith_dec.
Section Homework7_Problems.
Open Scope Z_scope.
Search rel_prime.
If 'a' is mutual prime to 'b' and 'c', then it is mutual prime to 'b*c'
Lemma sample7_5_1: forall a b c: Z,
(Zis_gcd a b 1) -> (Zis_gcd a c 1) -> (Zis_gcd a (b*c) 1).
Proof.
Admitted.
(Zis_gcd a b 1) -> (Zis_gcd a c 1) -> (Zis_gcd a (b*c) 1).
Proof.
Admitted.
If c=gcd(a,b), then c*c=gcd(a*a,b*b).
Lemma sample7_5_2: forall a b c: Z,
(Zis_gcd a b c) -> (Zis_gcd (a*a) (b*b) (c*c)).
Proof.
Admitted.
Close Scope Z_scope.
Open Scope nat_scope.
Definition divide x y := exists z, y=z*x.
Notation "( x | y )" := (divide x y) (at level 0) : nat_scope.
Fixpoint sumBy3 n := match n with
0 => 0
| S k => (sumBy3 k) + (k+1)*(k+2)*(k+3)
end.
Eval compute in (sumBy3 0).
Eval compute in (sumBy3 1).
Eval compute in (sumBy3 5).
(Zis_gcd a b c) -> (Zis_gcd (a*a) (b*b) (c*c)).
Proof.
Admitted.
Close Scope Z_scope.
Open Scope nat_scope.
Definition divide x y := exists z, y=z*x.
Notation "( x | y )" := (divide x y) (at level 0) : nat_scope.
Fixpoint sumBy3 n := match n with
0 => 0
| S k => (sumBy3 k) + (k+1)*(k+2)*(k+3)
end.
Eval compute in (sumBy3 0).
Eval compute in (sumBy3 1).
Eval compute in (sumBy3 5).
Prove by induction that 1*2*3 + 2*3*4 + ... + n*(n+1)*(n+2) =
n*(n+1)*(n+2)*(n+3)/4
Lemma Rosen2019_p351_p16: forall (n:nat), 4 * sumBy3 n = n*(n+1)*(n+2)*(n+3).
Proof.
Admitted.
Proof.
Admitted.
Prove by induction that n^5-n is always divisible by 5
(Little Fermat theorem for p=5
Lemma Rosen2019_p351_p33: forall (n:nat), (5 | n^5 - n).
Proof.
Admitted.
Proof.
Admitted.
Prove by induction that for n>4 we have 2^n > n^2.
Lemma Rosen2019_p351_p21: forall (n:nat), n>4 -> 2^n > n^2.
Proof.
Admitted.
Close Scope nat_scope.
End Homework7_Problems.
Proof.
Admitted.
Close Scope nat_scope.
End Homework7_Problems.


Author Andrea Cesaro, powered by: Reveal.js
The naive approach to parsing an input: extracting and validating by hand the values, has some shortcomings:
For example, let suppose we need to parse various csv documents
With the naive approach we create an algorithm for each different kind of csv document, a more abstract approach is to write an algorithm to read csv documents and to configure it for each document kind.
The idea is to refactor some commons pattern into some parser, an object that can be configured to parse any kind of csv.
What is the difference between different formats?
The idea is to create a framework that we can use to implement generalized parser for each kind of format we want
To make a parser works we need to configure it for the specific kind of input we want to parse
This configuration is a description and it can take various form
A description is by nature of declarative form since it declares some kind of shape
In opposite of imperative style where we list the actions we need to follows to parse something
We start with an example: parsing a csv line
The framework code follows the functional programming paradigm as far as it is possible
The framework avoid throwing exception, instead use the Try data structure


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Fundamentals of Ecology (BIO/ESS 148) :: Schedule, Fall 2020
< Back to Course Page
< Back to Syllabus
Please note that the schedule is subject to change at any time. Check back frequently for updates!
T = Tuesday
R = Thursday
L = Lecture
D = Discussion
| Date | Lect/Disc_# | Lecture_Topic | Description | Required Readings | Assign. |
|---|---|---|---|---|---|
| 8/27 | (R-L1) | Web of Life | Introduction & Roadmap | Bowman Ch. 1 | |
| No Disc | No Discussion section | ||||
| 9/1 | (T-L2) | Scales | Ecological questions across scales | Bowman Salmon case study: Chp. 2 beginning/end | |
| 9/3 | (R-L3) | Data to theory | Collecting and analyzing data to inform theory | Leopold; excerpts | |
| 9/4 | (F-D1) | Disc 1 | Investigating ecological problems with R | What does ecology have to do with me? | |
| 9/8 | (T-L4) | Biosphere | Distinguishing among the major Earth’s biomes | Bowman Ch. 3 | |
| 9/10 | (R-L5) | Energy 1 | Variation in temperature and water | Bowman Ch. 4 | |
| 9/11 | (F-D2) | Disc 2 | Temperature as an ecological constraint | Gunderson & Leal 2015 | |
| 9/15 | (T-L6) | Energy 2 | Variation in energy | Bowman Ch. 5 | HW-1 due |
| 9/17 | (R-L7) | Allometry | Allometry & Macroecology | West & Brown | |
| 9/18 | (F-D3) | Disc 3 | Macroecology | ||
| 9/22 | (T-L8) | Evolution 1 | Ecology as the driver of natural selection | Bowman Ch. 6 | HW-2 due |
| 9/24 | (R-L9) | Evolution 2 | |||
| 9/25 | (F-D4) | Disc 4 | Evolution by natural selection | ||
| 9/29 | (T) | EXAM 1 | |||
| 10/1 | (R-L10) | Life history | Life history diversity and tradeoffs | Bowman Ch. 7 | |
| 10/2 | (F-D5) | Disc 5 | Life history strategies | ||
| 10/6 | (T-L11) | Behavior | Behavioral ecology | Bowman Ch. 8, Sinervo | |
| 10/8 | (R-L12) | Games | Evolutionary Game Theory | Easley & Kleinberg | |
| 10/9 | (F-D6) | Disc 6 | Ecological Games | ||
| 10/13 | (T-L13) | Populations 1 | Density dependence and population dynamics | Bowman Ch. 10 | HW-3 due |
| 10/15 | (R-L14) | Populations 2 | Logistic Growth & Discrete dynamics | ||
| 10/16 | (F-D7) | Disc 7 | Exponential and Logistic Growth | ||
| 10/20 | (T-L15) | Populations 3 | Fluctuations | Bowman Ch. 11.1-11.3 | HW-4 due |
| 10/22 | (R-L16) | Populations 4 | Extinctions | ||
| 10/23 | (F-D8) | Disc 8 | Exponential and logistic growth 2 | ||
| 10/27 | (T) | EXAM 2 | |||
| 10/29 | (R-L17) | Competition 1 | Competition | Bowman Ch. 14 | |
| 10/30 | (F-D9) | Disc 9 | Population growth review | ||
| 11/3 | (T-L18) | Competition 2 | Competition dynamics | ||
| 11/5 | (R-L19) | Competition 3 | |||
| 11/6 | (F-D10) | Disc 10 | Lotka-Volterra competition dynamics | ||
| 11/10 | (T-L20) | Predation 1 | Herbivory and Predation | Bowman Ch. 12 | HW-5 due |
| 11/12 | (R-L21) | Predation 2 | |||
| 11/13 | (F-D11) | Disc 11 | Lotka-Volterra predation dynamics | ||
| 11/17 | (T-L22) | Predation 3 | Predation dynamics | ||
| 11/19 | (R-L23) | Parasitism | Parasites and Parasitoids | Bowman Chap. 13 | |
| 11/20 | (F-D12) | Disc 12 | LV dynamics review | HW-6 due | |
| 11/24 | (T) | EXAM III | |||
| 11/26 | No Class | Thanksgiving Break | |||
| 11/27 | No Disc | No Discussion Section | |||
| 12/1 | (T-L24) | Disease 1 | The dynamics of disease | Bowman 13.4-end, Blackwood to 2.2.2 | |
| 12/3 | (R-L25) | Disease 2 | Epidemics and pandemics | R0 is just an average | |
| 12/4 | (F-D13) | Disc 13 | Disease dynamics | ||
| 12/8 | (T-L26) | Interactions | Mutualisms & commensalisms | Bowman Chap. 15 | |
| 12/10 | (R-L27) | Food webs | Ecological networks | Bowman Chap. 16 | |
| 12/11 | (F-D14) | Disc 14 | Species interaction networks | HW-7 due | |
| 12/15 | (T) | FINAL EXAM | Comprehensive (3-6PM) |


--- layout: post authors: ["Greg Wilson"] title: "Instructor Training Stats" date: 2014-11-22 time: "07:00:00" tags: ["Instructor Training"] ---
The Software Carpentry Foundation's board asked me for some stats on instructor training, and I thought other people would find them interesting as well:
In the year Nov 1, 2013 - Nov 1, 2014, our numbers were:
| instructors: | 360 |
| helper: | 163 |
| learner: | 3914 |
That gives us an 9:1 ratio of learners to instructors, but the reality is probably closer to 10:1, since we have all the instructors in our database but are still waiting for some recent workshops to report headcount. Note that "instructor" counts every time a person taught a workshop, including repeats, and similarly for "helper" and "learner". Note also that this significantly under-counts helpers, since they're often not reported or recorded.
Since September 2013, but not including the recent live round at TGAC and University of Washington, or the current online round:
| started: | 356 | |
| completed: | 98 | (27.5%) |
| incomplete: | 163 | (45.7%) |
| in progress: | 95 | (26.7%) |
Note that half of those who drop out do so in the first four weeks. Note also that some people have been "in progress" for months, but even a few of those do eventually complete...
89 people who got their instructor badge after Sept 1, 2013 have taught for us, out of 126 who have been badged in that time. That's 70%, and the number will climb as the people who have been badged in the last three months (summer online training, TGAC, and U. Virginia) start teaching.
Of the people signed up for online instructor training in January 2015:
| 66 | US |
| 17 | UK |
| 15 | Canada |
| 12 | Spain |
| 12 | Australia |
| 5 | unknown |
| 3 | Brazil |
| 2 | Netherlands, South Africa, Switzerland |
| 1 | Chile, China, Colombia, Germany, Ghana, Hungary, Ireland, Israel, Italy, Japana, New Zealand, Poland, Saudia Arabia, Singapore, Turkey |
and:
| female | 38 | (25%) |
| male | 113 | (75%) |
That last statistic is better than the gender balance among our current instructors (20% female), but still much lower than where we were a year and a half ago (38% female).


Bug fix (Cmajor Code): Context menu for a solution explorer tree view node was showing in the wrong position when solution explorer tree view was scrolled.
Bug fix (Cmajor Code): System.Windows.Console.Write method did not update scroll info, so the contents of a console control could not be scrolled.
Bug fix (compiler) Arrays of arrays did not compile.
(Cmajor Code) Build settings:
(Cmajor Code) View intermediate code.
XML serialization. XML serialization support is implemented with the help of the xml attribute that is recognized by the compiler. System.Xml.Serialization library contains the library part of the XML serialization support.
The System.Windows library now supports double buffering. The Control.SetDoubleBuffered function enables double buffering for a control. Once double-buffered the control is directed to paint itself to a bitmap graphics context instead of normal window graphics context. Once the control has painted itself to a bitmap, the bitmap is then painted to the window. Double buffering reduces flicker considerably.
(Cmajor Code) Editor windows, the output window and the console window are now double-buffered to reduce flickering.
(tracing) Filter expressions.


Strongtalk is a true Smalltalk that also provides an optional static type system. It adheres as closely as possible to modern Smalltalk-80 syntax and semantics:
The standard method syntax is supported, with some enhancements so that type annotations are also allowed.
Full block closure semantics are supported, with local variables allowed in any block.
The only hard-wired messages are the critical Boolean messages, such as ifTrue:..., whileTrue, etc. Other Smalltalks also hard-wire these, as well as others that are not treated specially in Strongtalk.
The normal mixed-mode arithmetic semantics are supported, as well as infinite precision integer arithmetic.
The normal class/metaclass semantics are supported. However, inside the VM, all classes are actually constructed from mixins, which adds flexibility.
Global and class variables are supported, but class instance variables are not (although we had planned to add them). Pools are not supported. Shared variables that would be in Pools in other Smalltalks are just kept in class variables and accessed via class messages in Strongtalk. This does not entail a message-send performance penalty as it would in other Smalltalks, since messages to literal classes (or self) are constant messages and can be inlined away by the Strongtalk compiler.
Indexed instance variables support is provided for objects, bytes, and double-bytes, although they are not specified as part of the class constructor as in other Smalltalks. Instead, support for them is added to a class by mixing in one of several special system mixins.
#become: is not supported. The VM represents objects with direct pointers, rather than an object table, and while this increases system performance for most objects, it would make #become: prohibitively slow. So for example Collections that need to grow hold internal Arrays, etc.
Blocks support #ensure:, to provide important stack-unwind protection.
The library interfaces (instance and class interface semantics as well as concrete class names) conform as closely as possible to the Smalltalk-80 libraries in areas used by model code. In other words, code that uses the core Collection classes and other basic data structures should be able to run virtually unchanged. User access to processes appears almost the same, with fork, Semaphores, Processor, and SharedQueues all working as expected (the system uses native threads and the libraries are designed to work with preemptive scheduling, but Processes are currently scheduled non-preemptively).
Although the Strongtalk VM performs many fancy optimizations, it still supports the kind of interactive program creation and modification that helps make Smalltalk so unique. The intention is to make optimizations invisible to users (except for speed, of course!). This requires an enormous amount of sophistication and infrastructure in the VM, because all the different customized and inlined versions of a method must be found and fixed up dynamically as the code changes. The programming environment is bootstrapped in the same image as the user's code, as in other Smalltalks.
The class hierarchy is structured somewhat differently than in Smalltalk-80. This is for two reasons: 1) the core Strongtalk classes are strongly-typed, which requires more structured inheritance relationships, and 2) Strongtalk supports mixins, so for example strings are able to mix-in Magnitude support, even though they are not subclasses of Magnitude (since not all Collections are Magnitudes). However, concrete class names are the same, so that for example, while Dictionary is a subclass of HashedCollection rather than Set as in Smalltalk-80, Dictionary can still be used the same way it normally would in most user code. The stream instance protocols are pretty much the same, although the stream class names and hierarchy are very different. This should not matter much as most code does not create streams directly from Stream classes, but by asking some lower-level object (a collection, a file, etc) to create the stream instead.


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{% blocktrans %}
Prosperity4All - Ecosystem infrastructure for smart and personalised inclusion and PROSPERITY for ALL stakeholders
This project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement, No 610510
{% endblocktrans %}
2015 - {{ year }} Prosperity4All


The Akan people are found in present day Ghana.The Akan name their children according to the day they were born. The name given is also based on the gender of the child. Here you will be able to know your Akan name based on the day you were born!!
During the naming ceremony, the child is handed over to the elders of the clan who pour libation to the ancestors before the child is given a name according to the day they were born.Sacred beads are placed on the child's head and clay marks specifically for this ceremony are applied on the child and mother. This tradition was carried out for years. THe child was named after seven days of them being born. The Akan people waited seven days because they believed that after seven days of the child being alive they had come to be part of the community forever.
For more interesting facts about the naming ceremony of the Akan people visit wikipedia.