Rounders (1998) Matt Damon, Edward Norton, Gretchen Mol, Martin Landau, John Malkovich, John Turturro, Michael Rispoli, Famke Janssen. Screenplay by David Levien and Brian Koppelman. Directed by John Dahl. 120 minutes. Rated R, 3 stars (out of five stars)
Review by Ed Johnson-Ott, NUVO Newsweekly www.nuvo-online.com/film/ Archive reviews at http://us.imdb.com/M/reviews_by?Edward+Johnson-ott To receive reviews by e-mail at no charge, send subscription requests to<EMAIL_ADDRESS>"Rounders" offers an interesting look into the world of high-stakes poker, as well as some very good acting from Matt Damon, Edward Norton and Martin Landau. Unfortunately, the film lacks a compelling story arc, credible interpersonal relationships and characters that grow or change. As a result it feels incomplete, as if we're watching a filmed version of the first or second draft of a story rather than the finished product.
Establishing a conspiratorial tone with low-key voice-over narration, professional card player Mike McDermott (Matt Damon) ushers us into the smoky netherland of illicit poker that flourishes in musty back rooms of old buildings, protected by thick doors and beefy security guards. According to Mike, success as a rounder comes as much from reading the body language of an opponent as it does from the cards he holds.
After losing a whopping $30,000 during one bad night, Mike swears off the game and focuses on his legal studies, to the great relief of his girlfriend Jo (Gretchen Mol). The future looks bright until nine months later, when Mike's best friend Worm (Edward Norton) emerges from prison with a rakish smile on his face and a heavy gambling debt on his back. Within a short period of time, Mike is in the game again, with his relationship and studies in shambles and a major portion of Worm's debt on his own shoulders. To save their collective asses and assets, Mike must play the game like he has never played before.
Matt Damon anchors "Rounders" with another rock solid performance. He possesses something magical that goes beyond his good looks and exceptional acting skills. When you study Damon's face you can instantly picture what he looked like as a child and what he will look like as an old man. There's an air of familiarity about him that's comforting; watching him onscreen is like visiting an old friend. The film's exploration of the gritty, exotic gaming underworld is even more engrossing with Damon as our tour guide.
The chameleon-like Edward Norton creates another riveting characterization in the jittery, impulsive Worm, who comes off like a younger version of "Midnight Cowboy's" Ratso Rizzo. But, as presented in this screenplay, Worm's relationship with Mike just doesn't ring true. We're told that they have been best friends since childhood, but together they're like oil and water. Worm isn't just reckless and compulsive; he is completely self-destructive and shows no hesitation in pulling Mike down with him. Surely his behavior would have destroyed their friendship long before the men reached their mid 20's.
To be sure, the incredible loyalty Mike displays for Worm doesn't extend to his girlfriend. He appears to have little problem breaking his promises to Jo, a stable woman who stuck with him even after he blew $30 grand, choosing instead to head out for a night of rounding with Worm. The relationships between all three characters are unconvincing, playing simply as contrivances of the screenwriters to get all the cast members into their proper positions for the card games.
Pity poor Gretchen Mol, stuck with an utterly thankless role. Her job is merely to simmer, cluck at her wayward boyfriend and look wounded. Although the acting credit will be nice for her résumé, the film would have played more honestly had it omitted the role completely and just cut to the chase.
As for the other supporting characters, Martin Landau gives a luminous performance as Mike's law professor and ersatz father figure. Projecting strength, grace and warmth beneath his frail, bird-like appearance, Landau is a wonder who gets better and better with each new film. John Turturro does solid work as a man who plays not for sport, but to support his family. And John Malkovich is fun as Mike's nemesis, employing a thick Russian accent in a hammy, but engaging performance.
Ultimately, "Rounders" is a minor pleasure. The glimpse into an exotic, edgy world is absorbing and the performances of Damon, Norton and Landau are rewarding, despite the shaky credibility of their characters' relationships. But "Rounders" is frustrating because it could have been so much more, had the writers only devoted more attention to the script.
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Eddie Supa Is Back With New Song "Game Set"
Listen to Eddie Supa's latest offering "Game Set."
by Kevin GoddardGame Set
Eddie Supa
Editor Rating: HOTTTTT
User Rating: very hottttt - 100% (1)
Coming off the success of his latest bop “No Slack,” multi-talented Chicago artist Eddie Supa decides to keep his foot on the gas and share his latest offering “Game Set.”
“Sonically, I wanted to created a manic beat that felt like your in a brawl super smash bros. Everyone goes all out and its fast paced through out the melee. I wanted the song to have that hype feeling,” he said of the song.
Take a listen to the self-produced heater and let us know what you think.


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Class PointOctree<T>
A Dynamic Octree for storing any objects that can be described as a single point. This is a thin wrapper around the PointOctree class from NetOctree (https://github.com/mcserep/NetOctree).
Inheritance
Inherited Members
Namespace: Elements.Search
Assembly: Hypar.Elements.dll
Syntax
public class PointOctree<T>
Type Parameters
| Name | Description |
|---|---|
| T | The content of the octree can be anything, since the bounds data is supplied separately. |
Remarks
Octree: An octree is a tree data structure which divides 3D space into smaller partitions (nodes) and places objects into the appropriate nodes. This allows fast access to objects in an area of interest without having to check every object.
Dynamic: The octree grows or shrinks as required when objects as added or removed. It also splits and merges nodes as appropriate. There is no maximum depth.
Constructors
PointOctree(Double, Vector3, Double)
Constructor for the point octree.
Declaration
public PointOctree(double initialWorldSize, Vector3 initialWorldPos, double minNodeSize)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Double | initialWorldSize | Size of the sides of the initial node. The octree will never shrink smaller than this. |
| Vector3 | initialWorldPos | Position of the center of the initial node. |
| System.Double | minNodeSize | Nodes will stop splitting if the new nodes would be smaller than this. |
Properties
Count
The total amount of objects currently in the tree
Declaration
public int Count { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
MaxBounds
Gets the bounding box that represents the whole octree
Declaration
public BBox3 MaxBounds { get; }
Property Value
| Type | Description |
|---|---|
| BBox3 | The bounding box of the root node. |
Methods
Add(T, Vector3)
Add an object.
Declaration
public void Add(T obj, Vector3 objPos)
Parameters
| Type | Name | Description |
|---|---|---|
| T | obj | Object to add. |
| Vector3 | objPos | Position of the object. |
GetAll()
Returns all objects in the tree. If none, returns an empty array (not null).
Declaration
public ICollection<T> GetAll()
Returns
| Type | Description |
|---|---|
| System.Collections.Generic.ICollection<T> | All objects. |
GetNearby(Ray, Double)
Returns objects that are within maxDistance
of the specified ray.
If none, returns an empty array (not null).
Declaration
public T[] GetNearby(Ray ray, double maxDistance)
Parameters
| Type | Name | Description |
|---|---|---|
| Ray | ray | The ray. |
| System.Double | maxDistance | Maximum distance from the ray to consider. |
Returns
| Type | Description |
|---|---|
| T[] | Objects within range. |
GetNearby(Vector3, Double)
Returns objects that are within maxDistance
of the specified position.
If none, returns an empty array (not null).
Declaration
public T[] GetNearby(Vector3 position, double maxDistance)
Parameters
| Type | Name | Description |
|---|---|---|
| Vector3 | position | The position. Passing as ref to improve performance since it won't have to be copied. |
| System.Double | maxDistance | Maximum distance from the position to consider. |
Returns
| Type | Description |
|---|---|
| T[] | Objects within range. |
Remove(T)
Remove an object. Makes the assumption that the object only exists once in the tree.
Declaration
public bool Remove(T obj)
Parameters
| Type | Name | Description |
|---|---|---|
| T | obj | Object to remove. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the object was removed successfully. |
Remove(T, Vector3)
Removes the specified object at the given position. Makes the assumption that the object only exists once in the tree.
Declaration
public bool Remove(T obj, Vector3 objPos)
Parameters
| Type | Name | Description |
|---|---|---|
| T | obj | Object to remove. |
| Vector3 | objPos | Position of the object. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if the object was removed successfully. |


used to get callbacks when objects change
More...
#include <index.hpp>
used to get callbacks when objects change
Definition at line 42 of file index.hpp.
◆ ~index_observer()
| virtual graphene::db::index_observer::~index_observer |
( |
| ) |
|
|
virtualdefault |
◆ on_add()
| virtual void graphene::db::index_observer::on_add |
( |
const object & |
obj | ) |
|
|
inlinevirtual |
called just after the object is added
Definition at line 47 of file index.hpp.
◆ on_modify()
| virtual void graphene::db::index_observer::on_modify |
( |
const object & |
obj | ) |
|
|
inlinevirtual |
called just after obj is modified with new value
Definition at line 51 of file index.hpp.
◆ on_remove()
| virtual void graphene::db::index_observer::on_remove |
( |
const object & |
obj | ) |
|
|
inlinevirtual |
called just before obj is removed
Definition at line 49 of file index.hpp.
The documentation for this class was generated from the following file:


Development Version (1.0.0) 20k Source + Comments
Production Version (1.0.0) 2.2k Minified + Gzipped.
You can also clone the project with Git by running:
$ git clone git://github.com/timrwood/moment
npm install moment
Moment was designed to work in both the browser and in NodeJS. All code will work in both environments. All unit tests are run in both environments.
var moment = require('moment'); moment().add('hours', 1).fromNow(); // "1 hour ago"
<script src="moment.min.js"></script> moment().add('hours', 1).fromNow(); // "1 hour ago"
Instead of modifying the native Date.prototype
, Moment.js creates a wrapper for the Date
object.
Note: The Moment.js prototype is exposed through moment.fn
. If you want to add your own functions, that is where you would put them.
To get this wrapper object, simply call moment()
with one of the supported input types
A native Javascript Date object.
var day = new Date(2011, 9, 16); var dayWrapper = moment(day); var otherDay = moment(new Date(2020, 3, 7));
This is the fastest way to get a Moment.js wrapper.
An integer value representing the number of milliseconds since 1 January 1970 00:00:00 UTC.
var day = moment(1318781876406);
A string that can be parsed by Date.parse
.
var day = moment("Dec 25, 1995");
Browser support for this is somewhat inconsistent. If you are not getting consistent results, you can try using String + Format
An input string and a format string
var day = moment("12-25-1995", "MM-DD-YYYY");
The format parsing tokens are similar to the tokens for moment.fn.format
.
The parser ignores non-alphanumeric characters, so both moment("12-25-1995", "MM-DD-YYYY")
and
moment("12\25\1995", "MM-DD-YYYY")
will return the same thing.
| Input | Output |
|---|---|
| M or MM | Month |
| D or DD | Day of month |
| DDD or DDDD | Day of year |
| YY | 2 digit year (if greater than 70, will return 1900's, else 2000's) |
| YYYY | 4 digit year |
| a or A | AM/PM |
| H, HH, h, or hh | 24 hour (for 12 hour time, use in conjunction with a or A) |
| m or mm | Minutes |
| s or ss | Seconds |
Important: Parsing a string with a format is by far the slowest method of creating a date. If you have the ability to change the input, it is much faster (~15x) to use Unix timestamps.
An input string and an array of format strings.
This is the same as String + Format, only it will try to match the input to multiple formats.
var day = moment("12-25-1995", ["MM-DD-YYYY", "YYYY-MM-DD"]);
This approach is fundamentally problematic in cases like the following.
var day = moment("05-06-1995", ["MM-DD-YYYY", "DD-MM-YYYY"]); // June 5th or May 6th?
Important: THIS IS SLOW. This should only be used as a last line of defense.
To get the current time, just call moment()
with no parameters.
var now = moment();
This is essentially the same as calling moment(new Date())
.
An array mirroring the parameters passed into Date.UTC().
// [year, month = 0, date = 1, hours = 0, minutes = 0, seconds = 0, milliseconds = 0] var day = moment([2010, 1, 14, 15, 25, 50, 125]); // February 14th, 3:25:50.125 PM
Any value past the year is optional, and will default to the lowest possible number.
var day = moment([2010]); // January 1st var day = moment([2010, 6]); // July 1st var day = moment([2010, 6, 10]); // July 10th
Once you have a Moment.js wrapper object, you may want to manipulate it in some way. There are a number of moment.fn
methods to help with this.
All manipulation methods are chainable, so you can do crazy things like this.
moment().add('days', 7).subtract('months', 1).year(2009).hours(0).minutes(0).seconds(0);
This is a pretty robust function for adding time to an existing date. To add time, pass the key of what time you want to add, and the amount you want to add.
moment().add('days', 7);
There are some shorthand keys as well if you're into that whole brevity thing.
moment().add('d', 7);
| Key | Shorthand |
|---|---|
| years | y |
| months | M |
| days | d |
| hours | h |
| minutes | m |
| seconds | s |
| milliseconds | ms |
If you want to add multiple different keys at the same time, you can pass them in as an object literal.
moment().add('days', 7).add('months', 1); // with chaining moment().add({days:7,months:1}); // with object literal
There are no upper limits for the amounts, so you can overload any of the parameters.
moment().add('milliseconds', 1000000); // a million milliseconds moment().add('days', 360); // 360 days
If the day of the month on the original date is greater than the number of days in the final month, the day of the month will change to the last day in the final month.
Example:
moment([2010, 0, 31]); // January 31 moment([2010, 0, 31]).add('months', 1); // February 28
This is exactly the same as moment.fn.add
, only instead of adding time, it subtracts time.
moment().subtract('days', 7);
There are a number of shortcut getter and setter functions. Much like in jQuery, calling the function without paremeters causes it to function as a getter, and calling with a parameter causes it to function as a setter.
These map to the corresponding function on the native Date
object.
moment().seconds(30) === new Date().setSeconds(30); moment().seconds() === new Date().getSeconds();
moment().minutes(30); // set the minutes to 30
moment().hours(12); // set the hours to 12
moment().day(5); // set the day to 5
moment().month(5); // set the month to June
moment().year(1984); // set the year to 1984
Once parsing and manipulation are done, you need some way to display the moment. Moment.js offers many ways of doing that.
The most robust display option is moment.fn.format
. It takes a string of tokens and replaces them with their corresponding values from the Date object.
var date = new Date(2010, 1, 14, 15, 25, 50, 125); moment(date).format("dddd, MMMM Do YYYY, h:mm:ss a"); // "Sunday, February 14th 2010, 3:25:50 pm" moment(date).format("ddd, hA"); // "Sun, 3PM"
| Token | Output |
|---|---|
| Month | |
| M | 1 2 ... 11 12 |
| Mo | 1st 2nd ... 11th 12th |
| MM | 01 02 ... 11 12 |
| MMM | Jan Feb ... Nov Dec |
| MMMM | January February ... November December |
| Day of Month | |
| D | 1 2 ... 30 30 |
| Do | 1st 2nd ... 30th 31st |
| DD | 01 02 ... 30 31 |
| Day of Year | |
| DDD | 1 2 ... 364 365 |
| DDDo | 1st 2nd ... 364th 365th |
| DDDD | 001 002 ... 364 365 |
| Day of Week | |
| d | 0 1 ... 5 6 |
| do | 0th 1st ... 5th 6th |
| ddd | Sun Mon ... Fri Sat |
| dddd | Sunday Monday ... Friday Saturday |
| Week of Year | |
| w | 1 2 ... 52 53 |
| wo | 1st 2nd ... 52nd 53rd |
| ww | 01 02 ... 52 53 |
| Year | |
| YY | 70 71 ... 29 30 |
| YYYY | 1970 1971 ... 2029 2030 |
| AM/PM | |
| A | AM PM |
| a | am pm |
| Hour | |
| H | 0 1 ... 22 23 |
| HH | 00 01 ... 22 23 |
| h | 1 2 ... 11 12 |
| hh | 01 02 ... 11 12 |
| Minute | |
| m | 0 1 ... 58 59 |
| mm | 00 01 ... 58 59 |
| Second | |
| s | 0 1 ... 58 59 |
| ss | 00 01 ... 58 59 |
| Timezone | |
| z | EST CST ... MST PST |
Another common way of displaying time, sometimes called timeago, is handled by moment.fn.from
.
var a = moment([2007, 0, 29]); var b = moment([2007, 0, 28]); a.from(b) // "a day ago"
The first parameter is anything you can pass to moment()
or a Moment.js object.
var a = moment([2007, 0, 29]); var b = moment([2007, 0, 28]); a.from(b); // "a day ago" a.from([2007, 0, 28]); // "a day ago" a.from(new Date(2007, 0, 28)); // "a day ago" a.from("1-28-2007"); // "a day ago"
NOTE: Because it only accepts one parameter to pass in the date info, if you need to use String + Format or String + Formats, you should create a Moment.js object first and then call moment.fn.from
var a = moment(); var b = moment("10-10-1900", "MM-DD-YYYY"); a.from(b);
If you pass true
as the second parameter, you can get the value without the suffix. This is useful wherever you need to have a human readable length of time.
var start = moment([2007, 0, 5]); var end = moment([2007, 0, 10]); start.from(end); // "in 5 days" start.from(end, true); // "5 days"
The base strings are customized by moment.lang
or by modifying the values directly using moment.relativeTime
.
The breakdown of which string is displayed when is outlined in the table below.
| Range | Key | Sample Output |
|---|---|---|
| 0 to 45 seconds | s | seconds ago |
| 45 to 90 seconds | m | a minute ago |
| 90 seconds to 45 minutes | mm | 2 minutes ago ... 45 minutes ago |
| 45 to 90 minutes | h | an hour ago |
| 90 minutes to 22 hours | hh | 2 hours ago ... 22 hours ago |
| 22 to 36 hours | d | a day ago |
| 36 hours to 25 days | dd | 2 days ago ... 25 days ago |
| 25 to 45 days | M | a month ago |
| 45 to 345 days | MM | 2 months ago ... 11 months ago |
| 345 to 547 days (1.5 years) | y | a year ago |
| 548 days+ | yy | 2 years ago ... 20 years ago |
This is just a map to moment.fn.from(new Date())
moment([2007, 0, 29]).fromNow(); // 4 years ago
Like moment.fn.from
, if you pass true
as the second parameter, you can get the value without the suffix.
moment([2007, 0, 29]).fromNow(); // 4 years ago moment([2007, 0, 29]).fromNow(true); // 4 years
To get the difference in milliseconds, use moment.fn.diff
like you would use moment.fn.from
.
var a = moment([2007, 0, 29]); var b = moment([2007, 0, 28]); a.diff(b) // 86400000
To get the native Date object that Moment.js wraps, use moment.fn.native
.
var a = moment([2007, 0, 29]); var b = moment([2007, 0, 28]); a.diff(b) // 86400000
moment.fn.valueOf
simply outputs the unix timestamp.
moment(1318874398806).valueOf(); //<PHONE_NUMBER>806
These are the getters mentioned in the Manipulation section above.
These map to the corresponding function on the native Date
object.
moment().seconds() === new Date().getSeconds();
moment().minutes(); // get the minutes
moment().hours(); // get the hours
moment().day(); // get the day
moment().month(); // get the month
moment().year(); // get the year
moment.fn.isLeapYear
returns true if that year is a leap year, and false if it is not.
moment([2000]).isLeapYear() // true moment([2001]).isLeapYear() // false moment([2100]).isLeapYear() // false
Moment.js has pretty robust support for internationalization. You can load multiple languages onto the same instance and easily switch between them.
By default, Moment.js comes with English language strings. If you need other languages, you can load them into Moment.js for later use.
To load a language, pass the key and the string values to moment.lang
.
Note: More details on each of the parts of the language bundle can be found in the customization section.
moment.lang('fr', { months : "Janvier_Février_Mars_Avril_Mai_Juin_Juillet_Aout_Septembre_Octobre_Novembre_Décembre".split("_"), monthsShort : "Jan_Fev_Mar_Avr_Mai_Juin_Juil_Aou_Sep_Oct_Nov_Dec".split("_"), weekdays : "Dimanche_Lundi_Mardi_Mercredi_Jeudi_Vendredi_Samedi".split("_"), weekdaysShort : "Dim_Lun_Mar_Mer_Jeu_Ven_Sam".split("_"), relativeTime : { future : "in %s", past : "il y a %s", s : "secondes", m : "une minute", mm : "%d minutes", h : "une heure", hh : "%d heures", d : "un jour", dd : "%d jours", M : "un mois", MM : "%d mois", y : "une année", yy : "%d années" }, ordinal : function (number) { return (~~ (number % 100 / 10) === 1) ? 'er' : 'ème'; } });
Once you load a language, it becomes the active language. To change active languages, simply call moment.lang
with the key of a loaded language.
moment.lang('fr'); moment(1316116057189).fromNow() // il y a une heure moment.lang('en'); moment(1316116057189).fromNow() // an hour ago
Loading languages in NodeJS is super easy. If there is a language file in moment/lang/
named after that key, the first call to moment.lang
will load it.
var moment = require('moment'); moment.lang('fr'); moment(1316116057189).fromNow(); // il y a une heure
Right now, there is only support for English, French, Italian, and Portuguese. If you want your language supported, create a pull request or send me an email with the required files.
Loading languages in the browser just requires you to include the language files.
<script src="moment.min.js"></script> <script src="lang/fr.js"></script> <script src="lang/pt.js"></script>
There are minified versions of each of these languages. There is also a minified version of all of the languages bundled together.
<script src="moment.min.js"></script> <script src="lang/all.min.js"></script>
Ideally, you would bundle all the files you need into one file to minimize http requests.
<script src="moment-fr-it.min.js"></script>
To add your language to Moment.js, submit a pull request with both a language file and a test file. You can find examples in moment/lang/fr.js
and moment/test/lang/fr.js
To run the tests, do node build
.
If there are no errors building, then do node test
or open moment/test/index.html
.
If all the tests pass, submit that pull request, and thank you for contributing!
If you don't need i18n support, you can manually override the customization values. However, any calls to moment.lang
will override them. It is probably safer to create a language for your specific customizations than to override these values manually.
moment.months
should be an array of the month names.
moment.months = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"];
moment.monthsShort
should be an array of the month abbreviations.
moment.monthsShort = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"];
moment.weekdays
should be an array of the weekdays names.
moment.weekdays = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"];
moment.weekdaysShort
should be an array of the weekdays abbreviations.
moment.weekdaysShort = ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"];
moment.relativeTime
should be an object of the replacement strings for moment.fn.from
.
moment.relativeTime = { future: "in %s", past: "%s ago", s: "seconds", m: "a minute", mm: "%d minutes", h: "an hour", hh: "%d hours", d: "a day", dd: "%d days", M: "a month", MM: "%d months", y: "a year", yy: "%d years" };
future
refers to the prefix/suffix for future dates, and past
refers to the prefix/suffix for past dates. For all others, a single character refers to the singular, and an double character refers to the plural.
moment.ordinal
should be a function that returns the ordinal for a given number.
moment.ordinal = function (number) { var b = number % 10; return (~~ (number % 100 / 10) === 1) ? 'th' : (b === 1) ? 'st' : (b === 2) ? 'nd' : (b === 3) ? 'rd' : 'th'; };
For more information on ordinal numbers, see wikipedia


Details on JEDI's task based workload management can be found in the JEDI docs.
The task status shown in the monitor is in JEDI terms the simplified 'superstatus' the task is in. In ATLAS, these are in sync with DEFT states.
JEDI uses internally a more complex set of states, shown on task detail pages as 'Detailed JEDI status' (when it differs from the superstatus);
this is the 'status' field in the JEDI task.
{% if show == 'all' or task.superstatus == 'registered' %} - Status registered indicates the task has been received by JEDI and the task information is inserted to the JEDI_Tasks table.{% endif %}
{% if show == 'all' or task.superstatus == 'defined' %}
- Status defined indicates all task parameters are properly defined.{% endif %}
{% if show == 'all' or task.superstatus == 'assigning' %}
- Status assigning indicates the task brokerage is assigning the task to a cloud.{% endif %}
{% if show == 'all' or task.superstatus == 'ready' %}
- Status ready indicates the task is ready to generate jobs. New jobs are generated once computing resources become available.{% endif %}
{% if show == 'all' or task.superstatus == 'pending' %}
- Status pending indicates the task has a temporary problem, e.g. there is no sites to assign jobs.{% endif %}
{% if show == 'all' or task.superstatus == 'scouting' %}
- Status scouting indicates the task is running scout jobs to collect job data.{% endif %}
{% if show == 'all' or task.superstatus == 'scouted' %}
- Status scouted indicates all scout jobs were successfully finished.{% endif %}
{% if show == 'all' or task.superstatus == 'submitting' %}
- Status submitting indicates the task is progressing through the steps preparing it to be run, including scouting (sending scout jobs to validate the task and measure its parameters).{% endif %}
{% if show == 'all' or task.superstatus == 'running' %}
- Status running indicates the task has successfully validated and is running.{% endif %}
{% if show == 'all' or task.superstatus == 'prepared' %}
- Status prepared indicates the task is ready to go to a final status{% endif %}
{% if show == 'all' or task.superstatus == 'finishing' %}
- Status finishing indicates the task is forced to get finished{% endif %}
{% if show == 'all' or task.superstatus == 'paused' %}
- Status paused indicates the task is paused and doesn't do anything until it is resumed{% endif %}
{% if show == 'all' or task.superstatus == 'throttled' %}
- Status throttled indicates the task is throttled to generate jobs since the largest attemptNr in the task is a multiple of 5. The throttled period is 120 x int(max(attemptNr)/5)**2 minites{% endif %}
{% if show == 'all' or task.superstatus == 'exhausted' %}
- Status exhausted for production tasks indicates, all reattempts were done but some inputs were still unsuccessful, and the task is configured to go to this state instead of finished to wait for manual intervention or timeout of 10 days. Also, if cpuTime of scouts jobs > 2x cpuTime of task definition, the task goes to exhausted. For analysis tasks, if there are more than 5 scout jobs with short execution time (< 4 min) and more than 1000 jobs are expected, they will go to exhausted to prevent automatic avalanche since so many short jobs are problematic at grid sites. Analysis tasks in exhausted state can be retried using pbook but it is better to change some parameters like nFilesPerJob to have longer execution time. Also both production and analysis tasks will go to exhausted if they are timed-out while pending in the brokerage.{% endif %}
{% if show == 'all' or task.superstatus == 'tobroken' %}
- Status tobroken indicates the task is going to broken{% endif %}
{% if show == 'all' or task.superstatus == 'aborting' %}
- Status aborting indicates the task is being killed{% endif %}
{% if show == 'all' or task.superstatus == 'finished' %}
- Status finished indicates that some inputs of the task were successfully processed but others were failed or not processed (e.g. because files were unavailable) when the task was terminated. It can be re-tried by resubmitting the same command again, in order to try again the files not yet processed. It is possible to set/unset --site, --cloud, and --excludedSite when re-submitting the task.{% endif %}
{% if show == 'all' or task.superstatus == 'done' %}
- Status done indicates that all inputs of the task were successfully processed.{% endif %}
{% if show == 'all' or task.superstatus == 'failed' %}
- Status failed indicates the task has failed, all inputs have failed, in a way that retry is possible (if retry is judged to be impossible the task goes to status 'broken'). If some inputs succeeded while others failed and the class is terminated, the task goes to 'finished' status.{% endif %}
{% if show == 'all' or task.superstatus == 'aborted' %}
- Status aborted indicates the task has been killed. Aborted tasks can be re-executed.{% endif %}
{% if show == 'all' or task.superstatus == 'broken' %}
- Status broken indicates the task has failed in a way that makes retry very likely impossible. Tasks are declared as broken only in very limited circumstances, e.g. when all scout jobs failed, build jobs failed at multiple sites, etc. Users can force retry from broken state at their own risk see the pathena/prun documentation.{% endif %}
{% if show == 'all' or task.superstatus == 'toretry' %}
- Status toretry indicates the retry command was received for the task.{% endif %}
{% if show == 'all' or task.superstatus == 'toincexec' %}
- Status toincexec indicates the incexec command was received for the task.{% endif %}
{% if show == 'all' or task.superstatus == 'rerefine' %}
- Status rerefine indicates task parameters are going to be changed for incremental execution{% endif %}
{% if show == 'all' or task.superstatus == 'topreprocess' %}
- Status topreprocess indicates preprocess job is ready for the task{% endif %}
{% if show == 'all' or task.superstatus == 'preprocessing' %}
- Status preprocessing indicates preprocess job is running for the task{% endif %}
{% if show == 'all' or task.superstatus == 'passed' %}
- Status passed indicates the task is ready to go to a final state after manual intervention or timeout{% endif %}
The JEDI documentation has details on JEDI task
state transitions and detailed internal states.
Task state names in the monitor are links to this documentation.
{% if viewParams.MON_VO == 'ATLAS' %}
Prodsys, DEFT, and JEDI task states, transitions and relationships are
described here on the Prodsys twiki.
{% endif %}
Finished/failed progress of the task is indicated by the percentage and counts of input files that are finished/failed.
Click on the failed file percentage or count to see a summary of the errors.
In the JEDI system, task progress cannot be monitored in terms of jobs completed/failed because jobs are defined dynamically.
The input files associated with a task are an invariant of the task, so measuring their progress is a valid metric of task progress.
The front end clients -- JEDI based analysis and prodsys2 -- define only the task: a transformation to be applied to a set of inputs.
PanDA's mission in this system is to process those inputs, and the metric is progress and success in processing those input files.
Jobs are dynamically created and re-created at PanDA's discretion to process and as necessary retry those inputs,
shaping the jobs optimally for the resources available at a given time.
So jobs are shapeshifting and fluid, it is the inputs that really define the task and are the measure of its completion.
For JEDI based analysis and production, monitoring tasks and the processing of their input files is the best means of assessing processing status.
Further information that can be viewed via the task detail page:
- Task extra info:
- ProdSys2 UI to manage task
- Task and its job brokerage at ES-ATLAS Kibana
- Actions and retries logger at ES-ATLAS Kibana
- Error summary: job error summary page for the jobs in this task
- Child tasks: tasks can have parent/child relationships. This lists child tasks of the present task
- Show jobs:
- Jobs including retries
- Jobs excluding retries
- Scout jobs if there is any
- Active and ended jobs
- Task parameters and help
- Task status log - on demand option to see history of states a task went through
- Anchor links to different parts of the page
- Memory and walltime usage
- Task jobs consumption plots - on demand option to see a few histograms showing the following metrics distribution: PSS, walltime, CPU time
- Link to dashboard at Kibana@Chicago
- Other plots
- Task chain diagrams (tree and Gantt) to see child/parent tasks
- Task profile - to observe a task progress profile
API
Task data can be retrieved in JSON format with curl
so that it can be used programmatically.
See API section in complete help page how to do it from either lxplus or outside CERN.
Use this carefully, do not for example put scripts in crons that do heavy retrieval of job data this way.
If you have systematic needs for PanDA job data, tell us what they are. Do *not* use web page scraping.
By default you will 3 dictionaries: "task", "taskparams" and "datasets" that are corresponds to key tables in the page.


Constructor
new JobRepository(table)
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
table |
string | Jobs | the name of the table for wish to perform the job operations on. defaults to 'Jobs'. |
- Source:
Requires:
- module:aws-sdk
Requires
- module:aws-sdk
Methods
all(user) → {Promise.<PromiseResult.<D, E>>}
fetches all persisted jobs related to a single user.
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
user |
number | 1 | the user id you wish get all the jobs for. |
- Source:
Returns:
- returns a promise for the operation.
- Type
- Promise.<PromiseResult.<D, E>>
delete(user, job) → {Promise.<PromiseResult.<D, E>>}
deletes a single persisted job related to a single user based off of the input id.
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
user |
number | 1 | the user id you wish find the job to delete for. |
job |
number | 1 | the job id of the job you wish to delete from datastore. |
- Source:
Returns:
- returns a promise for the operation.
- Type
- Promise.<PromiseResult.<D, E>>
get(user, job) → {Promise.<PromiseResult.<D, E>>}
fetches a single persisted job related to a single user based off of the input id.
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
user |
number | 1 | the user id you wish get the job for. |
job |
number | 1 | the job id of the job you wish to get from datastore. |
- Source:
Returns:
- returns a promise for the operation.
- Type
- Promise.<PromiseResult.<D, E>>
save(job) → {Promise.<PromiseResult.<D, E>>}
persists a job in the data store.
Parameters:
| Name | Type | Description |
|---|---|---|
job |
object | the job information you wish to persist to the data store. required fields in object are jobId and userId. |
- Source:
Returns:
- returns a promise for the operation.
- Type
- Promise.<PromiseResult.<D, E>>


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Problem Description
A long time ago, the Egyptians figured out that a triangle with sides of length 3, 4, and 5 had a right angle as its largest angle. You must determine if other triangles have a similar property.
Input
Input represents several test cases, followed by a line containing 0 0 0. Each test case has three positive integers, less than 30,000, denoting the lengths of the sides of a triangle.
Output
For each test case, a line containing "right" if the triangle is a right triangle, and a line containing "wrong" if the triangle is not a right triangle.
Sample Input
6 8 10 25 52 60 5 12 13 0 0 0
Sample Output
right wrong right
一道验证是否直角三角形的题目= =
#include <iostream> #include <algorithm> using namespace std; int main() { int a[3]; while(cin >> a[0] >> a[1] >> a[2]) { if(a[0] == 0 && a[1] == 0 && a[2] == 0) break; sort(a,a+3); if(a[0]*a[0] + a[1]*a[1] == a[2]*a[2]) cout << "right" << endl; else cout << "wrong" << endl; } return 0; }
版权声明：本文为博主原创文章，未经博主允许不得转载。
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配合***食用更加


Vector Autoregressions tsa.vector_ar
¶
statsmodels.tsa.vector_ar
contains methods that are useful
for simultaneously modeling and analyzing multiple time series using
Vector Autoregressions (VAR) and
Vector Error Correction Models (VECM).
VAR(p) processes¶
We are interested in modeling a \(T \times K\) multivariate time series \(Y\), where \(T\) denotes the number of observations and \(K\) the number of variables. One way of estimating relationships between the time series and their lagged values is the vector autoregression process:
where \(A_i\) is a \(K \times K\) coefficient matrix.
We follow in large part the methods and notation of Lutkepohl (2005), which we will not develop here.
Model fitting¶
Note
The classes referenced below are accessible via the
statsmodels.tsa.api
module.
To estimate a VAR model, one must first create the model using an ndarray of homogeneous or structured dtype. When using a structured or record array, the class will use the passed variable names. Otherwise they can be passed explicitly:
# some example data
In [1]: import numpy as np
In [2]: import pandas
In [3]: import statsmodels.api as sm
In [4]: from statsmodels.tsa.api import VAR, DynamicVAR
In [5]: mdata = sm.datasets.macrodata.load_pandas().data
# prepare the dates index
In [6]: dates = mdata[['year', 'quarter']].astype(int).astype(str)
In [7]: quarterly = dates["year"] + "Q" + dates["quarter"]
In [8]: from statsmodels.tsa.base.datetools import dates_from_str
In [9]: quarterly = dates_from_str(quarterly)
In [10]: mdata = mdata[['realgdp','realcons','realinv']]
In [11]: mdata.index = pandas.DatetimeIndex(quarterly)
In [12]: data = np.log(mdata).diff().dropna()
# make a VAR model
In [13]: model = VAR(data)
Note
The VAR
class assumes that the passed time series are
stationary. Non-stationary or trending data can often be transformed to be
stationary by first-differencing or some other method. For direct analysis of
non-stationary time series, a standard stable VAR(p) model is not
appropriate.
To actually do the estimation, call the fit method with the desired lag order. Or you can have the model select a lag order based on a standard information criterion (see below):
In [14]: results = model.fit(2)
In [15]: results.summary()
Out[15]:
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sun, 24, Nov, 2019
Time: 07:54:40
--------------------------------------------------------------------
No. of Equations: 3.00000 BIC: -27.5830
Nobs: 200.000 HQIC: -27.7892
Log likelihood: 1962.57 FPE: 7.42129e-13
AIC: -27.9293 Det(Omega_mle): 6.69358e-13
--------------------------------------------------------------------
Results for equation realgdp
==============================================================================
coefficient std. error t-stat prob
------------------------------------------------------------------------------
const 0.001527 0.001119 1.365 0.172
L1.realgdp -0.279435 0.169663 -1.647 0.100
L1.realcons 0.675016 0.131285 5.142 0.000
L1.realinv 0.033219 0.026194 1.268 0.205
L2.realgdp 0.008221 0.173522 0.047 0.962
L2.realcons 0.290458 0.145904 1.991 0.047
L2.realinv -0.007321 0.025786 -0.284 0.776
==============================================================================
Results for equation realcons
==============================================================================
coefficient std. error t-stat prob
------------------------------------------------------------------------------
const 0.005460 0.000969 5.634 0.000
L1.realgdp -0.100468 0.146924 -0.684 0.494
L1.realcons 0.268640 0.113690 2.363 0.018
L1.realinv 0.025739 0.022683 1.135 0.257
L2.realgdp -0.123174 0.150267 -0.820 0.412
L2.realcons 0.232499 0.126350 1.840 0.066
L2.realinv 0.023504 0.022330 1.053 0.293
==============================================================================
Results for equation realinv
==============================================================================
coefficient std. error t-stat prob
------------------------------------------------------------------------------
const -0.023903 0.005863 -4.077 0.000
L1.realgdp -1.970974 0.888892 -2.217 0.027
L1.realcons 4.414162 0.687825 6.418 0.000
L1.realinv 0.225479 0.137234 1.643 0.100
L2.realgdp 0.380786 0.909114 0.419 0.675
L2.realcons 0.800281 0.764416 1.047 0.295
L2.realinv -0.124079 0.135098 -0.918 0.358
==============================================================================
Correlation matrix of residuals
realgdp realcons realinv
realgdp 1.000000 0.603316 0.750722
realcons 0.603316 1.000000 0.131951
realinv 0.750722 0.131951 1.000000
Several ways to visualize the data using matplotlib are available.
Plotting input time series:
In [16]: results.plot()
Out[16]: <Figure size 1000x1000 with 3 Axes>
Plotting time series autocorrelation function:
In [17]: results.plot_acorr()
Out[17]: <Figure size 1000x1000 with 9 Axes>
Lag order selection¶
Choice of lag order can be a difficult problem. Standard analysis employs
likelihood test or information criteria-based order selection. We have
implemented the latter, accessible through the VAR
class:
In [18]: model.select_order(15)
Out[18]: <statsmodels.tsa.vector_ar.var_model.LagOrderResults at 0x7faa543d65d0>
When calling the fit function, one can pass a maximum number of lags and the order criterion to use for order selection:
In [19]: results = model.fit(maxlags=15, ic='aic')
Forecasting¶
The linear predictor is the optimal h-step ahead forecast in terms of mean-squared error:
We can use the forecast function to produce this forecast. Note that we have to specify the “initial value” for the forecast:
In [20]: lag_order = results.k_ar
In [21]: results.forecast(data.values[-lag_order:], 5)
Out[21]:
array([[ 0.0062, 0.005 , 0.0092],
[ 0.0043, 0.0034, -0.0024],
[ 0.0042, 0.0071, -0.0119],
[ 0.0056, 0.0064, 0.0015],
[ 0.0063, 0.0067, 0.0038]])
The forecast_interval function will produce the above forecast along with asymptotic standard errors. These can be visualized using the plot_forecast function:
In [22]: results.plot_forecast(10)
Out[22]: <Figure size 1000x1000 with 3 Axes>
Class Reference¶
|
Fit VAR(p) process and do lag order selection |
|
Class represents a known VAR(p) process |
|
Estimate VAR(p) process with fixed number of lags |
Post-estimation Analysis¶
Several process properties and additional results after estimation are available for vector autoregressive processes.
|
Results class for choosing a model’s lag order. |
Results class for hypothesis tests. |
|
Results class for the Jarque-Bera-test for nonnormality. |
|
Results class for the Portmanteau-test for residual autocorrelation. |
Impulse Response Analysis¶
Impulse responses are of interest in econometric studies: they are the estimated responses to a unit impulse in one of the variables. They are computed in practice using the MA(\(\infty\)) representation of the VAR(p) process:
We can perform an impulse response analysis by calling the irf function on a VARResults object:
In [23]: irf = results.irf(10)
These can be visualized using the plot function, in either orthogonalized or non-orthogonalized form. Asymptotic standard errors are plotted by default at the 95% significance level, which can be modified by the user.
Note
Orthogonalization is done using the Cholesky decomposition of the estimated error covariance matrix \(\hat \Sigma_u\) and hence interpretations may change depending on variable ordering.
In [24]: irf.plot(orth=False)
Out[24]: <Figure size 1000x1000 with 9 Axes>
Note the plot function is flexible and can plot only variables of interest if so desired:
In [25]: irf.plot(impulse='realgdp')
Out[25]: <Figure size 1000x1000 with 3 Axes>
The cumulative effects \(\Psi_n = \sum_{i=0}^n \Phi_i\) can be plotted with the long run effects as follows:
In [26]: irf.plot_cum_effects(orth=False)
Out[26]: <Figure size 1000x1000 with 9 Axes>
Reference¶
|
Impulse response analysis class. |
Forecast Error Variance Decomposition (FEVD)¶
Forecast errors of component j on k in an i-step ahead forecast can be decomposed using the orthogonalized impulse responses \(\Theta_i\):
These are computed via the fevd function up through a total number of steps ahead:
In [27]: fevd = results.fevd(5)
In [28]: fevd.summary()
FEVD for realgdp
realgdp realcons realinv
0 1.000000 0.000000 0.000000
1 0.864889 0.129253 0.005858
2 0.816725 0.177898 0.005378
3 0.793647 0.197590 0.008763
4 0.777279 0.208127 0.014594
FEVD for realcons
realgdp realcons realinv
0 0.359877 0.640123 0.000000
1 0.358767 0.635420 0.005813
2 0.348044 0.645138 0.006817
3 0.319913 0.653609 0.026478
4 0.317407 0.652180 0.030414
FEVD for realinv
realgdp realcons realinv
0 0.577021 0.152783 0.270196
1 0.488158 0.293622 0.218220
2 0.478727 0.314398 0.206874
3 0.477182 0.315564 0.207254
4 0.466741 0.324135 0.209124
They can also be visualized through the returned FEVD
object:
In [29]: results.fevd(20).plot()
Out[29]: <Figure size 1000x1000 with 3 Axes>
Reference¶
|
Compute and plot Forecast error variance decomposition and asymptotic standard errors |
Statistical tests¶
A number of different methods are provided to carry out hypothesis tests about the model results and also the validity of the model assumptions (normality, whiteness / “iid-ness” of errors, etc.).
Granger causality¶
One is often interested in whether a variable or group of variables is “causal”
for another variable, for some definition of “causal”. In the context of VAR
models, one can say that a set of variables are Granger-causal within one of the
VAR equations. We will not detail the mathematics or definition of Granger
causality, but leave it to the reader. The VARResults
object has the
test_causality method for performing either a Wald (\(\chi^2\)) test or an
F-test.
In [30]: results.test_causality('realgdp', ['realinv', 'realcons'], kind='f')
Out[30]: <statsmodels.tsa.vector_ar.hypothesis_test_results.CausalityTestResults at 0x7faa543176d0>
Normality¶
Whiteness of residuals¶
Reference¶
Results class for hypothesis tests. |
|
Results class for Granger-causality and instantaneous causality. |
|
Results class for the Jarque-Bera-test for nonnormality. |
|
Results class for the Portmanteau-test for residual autocorrelation. |
Vector Error Correction Models (VECM)¶
Vector Error Correction Models are used to study short-run deviations from
one or more permanent stochastic trends (unit roots). A VECM models the
difference of a vector of time series by imposing structure that is implied
by the assumed number of stochastic trends. VECM
is used to
specify and estimate these models.
A VECM(\(k_{ar}-1\)) has the following form
where
as described in chapter 7 of 1.
A VECM(\(k_{ar} - 1\)) with deterministic terms has the form
In \(D^{co}_{t-1}\) we have the deterministic terms which are inside
the cointegration relation (or restricted to the cointegration relation).
\(\eta\) is the corresponding estimator. To pass a deterministic term
inside the cointegration relation, we can use the exog_coint argument.
For the two special cases of an intercept and a linear trend there exists
a simpler way to declare these terms: we can pass "ci"
and "li"
respectively to the deterministic argument. So for an intercept inside
the cointegration relation we can either pass "ci"
as deterministic
or np.ones(len(data)) as exog_coint if data is passed as the
endog argument. This ensures that \(D_{t-1}^{co} = 1\) for all
\(t\).
We can also use deterministic terms outside the cointegration relation.
These are defined in \(D_t\) in the formula above with the
corresponding estimators in the matrix \(C\). We specify such terms by
passing them to the exog argument. For an intercept and/or linear trend
we again have the possibility to use deterministic alternatively. For
an intercept we pass "co"
and for a linear trend we pass "lo"
where
the o stands for outside.
The following table shows the five cases considered in 2. The last column indicates which string to pass to the deterministic argument for each of these cases.
Case |
Intercept |
Slope of the linear trend |
deterministic |
|---|---|---|---|
I |
0 |
0 |
|
II |
\(- \alpha \beta^T \mu\) |
0 |
|
III |
\(\neq 0\) |
0 |
|
IV |
\(\neq 0\) |
\(- \alpha \beta^T \gamma\) |
|
V |
\(\neq 0\) |
\(\neq 0\) |
|
Reference¶
|
Class representing a Vector Error Correction Model (VECM). |
|
Perform the Johansen cointegration test for determining the cointegration rank of a VECM. |
|
Compute lag order selections based on each of the available information criteria. |
|
Calculate the cointegration rank of a VECM. |
|
Class for holding estimation related results of a vector error correction model (VECM). |
|
A class for holding the results from testing the cointegration rank. |


new Highlight(element, tag, type, colors)
Highlights text in an element
by wrapping the matched string passed via Highlight#apply
method into a tag
.
Example:
var hi = new Highlight({
element: [].slice.call(document.querySelectorAll('.nodeForHightlight')),
type: 'open'
});
hi.apply('highlight this');
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
element |
HTMLElement |
document.body
|
Element in which the search and highlighting will happen. A valid Array ( |
tag |
String |
EM
|
The tag used by default to apply highlighting to matched words is the EM (emphasis) tag. We can change that if we want by passing a second parameter with a different tag name. You can use any tag that renders inline - so not a DIV or similar block element which would break the layout. |
type |
String |
left+right
|
Type of match. |
colors |
Array.<String> | = ["#ff6", "#a0ffff", "#9f9", "#f99", "#f6f"] - Color palette to iterate within when highlighting. Specify only one to make color static |
Methods
apply(input)
Calling this method passes the words to be highlighted to the just instantiated object which traverses the DOM inside the selected node looking for text that matches any of the keywords.
Parameters:
| Name | Type | Description |
|---|---|---|
input |
String | query string |
getRegex()
Decompose a RegExp
remove(nodeopt)
Removes highlighting
Parameters:
| Name | Type | Attributes | Description |
|---|---|---|---|
node |
HTMLElement |
<optional> |
an node (if not passed then calculated) that might have highlighting that needs to be removed |
setMatchType(type)
Sets a type of search
Parameters:
| Name | Type | Default | Description |
|---|---|---|---|
type |
String |
left+right
|
Type of match. |
setRegex(input) → {Boolean}
Creates a regular expression
Parameters:
| Name | Type | Description |
|---|---|---|
input |
String | a search query |
Returns:
- whether the regex has been successfully set
- Type
- Boolean


Resume
Oleg Sheludko
Interests
Drawing
Photography
Design
Programming
Surf
Skills
Web Design, Ui/Ux basic, HTML5, CSS, Java script.
Education
Technical university
Codeacademy
W3schools
Codebabes
Experience
freelance projects
QA web sites
sigma projects


Amazon smile redirect plugin
Amazon smile redirector
This plugin adds a navigationbar to every amazon.* page and suggests to visit smile.amazon.* insted.
Get more information


Hi I'm Alice Alsford. I am currently studing with Enspiral Dev Academy. I am a mother of two small children and I am hoping that this course can help with my goal to change careers and become a software developer. Follow me on the website to see my learnings in both technical and core skills during the course.


If you have an ArrayList of 5 items and a for each loop iterating over the ArrayList, how many times would the code in the body of the loop execute?
Think about what would get returned from running the .size() method on an ArrayList of 5 items


404.
Sorry, that URL seems to be invalid :(
Are you sure it points to a valid YouTube video?
Made with ❤ by
xrisk
and
rudradevroy
•
Github


PARENTHOOD A film review by Steve Rhodes Copyright 1997 Steve Rhodes
RATING (0 TO ****): *** 1/2
When they are told that their son Kevin (Jasen Fisher) must go to a special school next year due to his behavior problems, his parents, Gil (Steve Martin) and Karen (Mary Steenburgen), start blaming each other and then they come up with various crackpot ideas. "We'll throw away the TV," Gil blurts out.
Although Ron Howard's 1989 film PARENTHOOD is a master at the verbal humor, the physical humor has its own special, subtle beauty. When the lights go out at the big family dinner, everyone goes looking for a flashlight. When power is restored Gil ends up holding the pulsating vibrator of his grown sister Helen, played in a knowing, Academy Award nominated performance by Dianne Wiest. In another scene, Helen flips through the graphic sexual pictures of her daughter Julie (Martha Plimpton) and Julie's boyfriend Tod (Keanu Reeves). Never letting us see the pictures, Helen laughs in horror as she interrogates Julie about how the pictures came to be made.
Interlaced with the humor is a multigenerational parenthood story. Frank (Jason Robards) is the family's patriarch, and Gil is one of his children. Most shows concentrate on the kids, but this show stays true to its title and focuses almost exclusively on parenthood itself, including relationship of adults to their parents.
Gil flashes back with daydreams about his relationship with his father when he was young and forward to a dream of his son's being valedictorian and thanking him in the commencement speech. When things don't go quite as Gil wishes, the dreams turn to nightmares of Kevin being as a campus sniper.
Lowell Ganz, Ron Howard and Babaloo Mandel's intelligent and profoundly wise script manages to be serious and delicious funny at the same time. Martin, in his prime, is the Dad who remains willing to do anything for his son, right down to impersonating Cowboy Dan at his son's birthday party. (The real Cowboy Dan was beaten to a pulp at an adult male's birthday party when a stripper and Cowboy Dan got their addresses mixed up.)
Gil resents the way his father ignored him when he was young. Gil doesn't understand Kevin either although he certainly tries harder than his dad did. "Where does he get this excessive behavior?" Gil asks about Kevin, while Gil obsessively washes his hands.
Most films are advised not to include too many featured parts lest none be adequately developed, but director Ron Howard is one of the few who can and does bring this off. Among the many minor players, Tom Hulce appears as Frank's ne'er-do-well son and compulsive gambler, Larry. Frank feeds Larry money which only exacerbates Larry's troubles.
Besides coping with her daughter Julie, Helen has other difficulties. One day, Helen uncovers the big secret of her 9-year-old son Garry, played by Joaquin Phoenix (then known as Leaf Phoenix). She fears it's drugs he's been keeping in his carefully guarded brown paper bag, but it's porno tapes instead. Relieved, she takes a charitable view, saying he must be "interested in sex -- or filmmaking."
My personal favorite scene is when Nathan (Rick Moranis) drills his 4-year-old daughter with flashcards of the chemical elements. It's never too soon to get ready for the best universities.
Gil loses his big promotion to partner to a guy who is so dedicated to his job that his boss doesn't even know if the guy has children. "Women have choices, and men have responsibilities," an angry Gil explodes to his wife when overcome by the pressures of both being a good father to his kids and keeping an adequate job. When his wife points out the fallacies in his argument, he backs down, but feels even more lost.
After being detestable in most of the movie, Frank finally becomes sympathetic toward the conclusion. "It never ends," Frank tells Gil about fatherhood. Frank's grown son Larry, being threatened with extinction by gangsters wanting their money, is still more than Frank can handle. And like a good father, he comes up with what he views as an eminently reasonable resolution of the problem, complete with an installment scheme payoff. Larry, never able to grow up, of course, does not see it that way and wants to chase another get-rich-quick scheme, this time with platinum.
Leave it to Grandma to explain it all. Although some people like merry-go-rounds, she says she has always preferred the roller coaster. Gil realizes his life is a roller coaster, and he comes to relish it. The marvelously life-affirming film ends in a massive baby birthing scene with enough joy for every member of the audience.
PARENTHOOD runs 2:04. It is rated PG-13 for profanity and sexual humor and situations. The film would be fine for kids 10 and up.
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


mlVajra
A framework or best practices to develop end to end machine learning pipeline (also has some tips for ML-management people )
Why we need mlvajra?
- There was no standard Design patterns exists for reasearching fields like Machine learning
Disclaimer : - For past few months I have been spending my time reading different Design patterns (The take way was simple - Layer to Layer comunication should happen without depending on the underlying technology stack. - for example : Data layer should abstract the underlying databases)
- I am not an expert in this space still learning (for more than 2.5 years) so take this guidelines with pinch of salt and I am open to change any principles discussed here if it has some drawback.
components of mlvajra:
- Data
- preprocess
- model
- model repository
- evaluation
- visualization
- Dashboarding
- Monitoring
- explanations
- schedulers
- Active learning
- modAL
- serving
- tf serving
- Experiment tracking
- Deployment strategies
- Microservices
- clipper.ai
- Documentation
- mkdocs
-
Test cases
- doc test
- pytest
-
Bonus (for Management people)
Data :
In this data layer ,plugins for connecting with different data sources are placed ranging from
local csv,json ,parquet to Hadoop filesystems (orc,parquet,avro)
Also reading from Different databases should be added here,ranging from sqlite ,ealsticsearch,hive ,iginite etc
Also Streaming plugins will be helpful.
for training the data from Hadoop file system, a python package called *`petastorm`* may be useful
Above things should be written in Deployment percespective
For Experimenting :
see python-intake package to get the feel of how version/maintain/abstract data
preprocess :
I have Deployed few models in production (sklearn and keras models) and preprocessing was an essential step or pivotal step in Deployment .
How we can handle the preprocessing in production ?
- so far:
- we are have different system/place for preprocessing i.e we treat preprocessing as a separate system, and we will speak to it or trigger it based on Rest api or schedulers.
- we save the intermediate preprocessed output in separate
databases and take the preprocessed output from the database and run prediction on that data.
- Is this a Bad practice ? no !!
- (some people claim this has a efficient way of deployment since we can use the preprocessed data later.)
Take away :
- Try to write a pipeline which consists of preprocessing ,feature Engineering and model prediction and deploy it as a single standlone service in Production .
pros:
- Low Latency
- No need of extra database to store intermediate data
- workflow complexity will be reduced
cons :
- yet to be identified
Is this Pipeline possible for all frameworks, can we serialize ,deserialize the pipeline in all framework ??
mlvajra to rescue ....!
Model
- First mandatory rule was to retrain the model on the new data to preserver model freshness
- Models can be built with any open source package - only catch was, the package should support pipelining with preprocessing step natively or we have to implement the our own pipeline.
One model vs Model repository
To illustrate this further ,lets consider the case, where we trained the model and productionized the model, The model was doing good, as days progress the incoming data distribuition changes and our model performance was slowly decreasing (losing freshness)
To Avoid this we planned to train the model whenever we feel the model was not performing upto the level. - we have two options We use active learning (kind of online learning) startegy to implement the retraining (i.e the same model was retrained with new data and serialized to the disk)
- In other hand we will train the old model with new data and compare the model performance with pervious(old) model and deploy the best model according to our metrics (this is because sometimes our new model's performance was slightly lower than the old model) (we will maintain model repository)
which one is best startegy??
Thing to consider here , the new input data was hand annotated by the SME in their respective dashboards, now considering the our two cases which is the preferred startegy ?! when we retrain the model with hand annotated data if the trained model's performance was low, we will rollback to the previous model (if you notice here we will be losing valuable hand annotated data in this case)
In future we are also planning to integerate with Active learning libraries like modAL
so One vs Model repository has to be used carefully (without losing any data)
Evalution
Whenever we start with Data science project, we start with the scope of the Data product which is some value add given to the customer.
This scope should be effectively converted into a script which is useful to evaluate our model in future (we will optimize our model for this metrics)
For example : - keeping F1 score as standard for imbalanced class data - keeping Detection Time as a metric for Time series Anomaly detection


{% extends 'blog/base/content.html' %} {% block title %} {{ object.username }} -{{ block.super }} {% endblock title %} {% block content %}
This user has is_editor set as true.
{% else %}This user has is_editor set as false.
{% endif %} {% else %}User has not a defined profile. That usually happens when you create a user through the createsuperuser command. Try changing its profile in the django admin (for example, change its is_editor to True).
{% endif %} {% endblock content %}


/* rules:
* id
* href + name
* href
* title
*/
link2 forwards to the link2.htm
link3 id + href, no conflicts
link4 href, no conflicts
link5 href, conflicts with link6
link6 href, conflicts with link5
link7 href and name, no conflicts
link8 href and name, conflicts with link9
link9 href and name, conflicts with link8
link10 href as link8, but other name, no conflicts
link11 name as link8, but other href, no conflicts
link20 id + href, same id as link21, conflicting
link21 id + href, same id as link20, conflicting
linkTitle1 title
linkTitle2 href + title
linkTitle2 href + title, same as LinkTitle2
linkTitle4 href + title, same as LinkTitle2
linkTitle4 href + title, same as LinkTitle2


{% extends '../../layout-email.html' %} {% block pageTitle %} Prior notification email {% endblock %} {% block content %}
Patients that are not reviewed or actioned within 4 weeks will be invited to be screened on their next test due date. View patients due to be invited to screening
Patients that are not reviewed or actioned will be removed from the list after 8 weeks. View patients who have not responded to invitations
If you currently receive notifications but need to stop them you can manage emails


We’re more than happy to contact. Give us a call or just drop a message & we’ll get in touch with you soon!


|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.apache.tools.ant.ProjectComponent org.apache.tools.ant.Task net.sf.antcontrib.net.PostTask
This task does an http post. Name/value pairs for the post can be set in either or both of two ways, by nested Prop elements and/or by a file containing properties. Nested Prop elements are automatically configured by Ant. Properties from a file are configured by code borrowed from Property so all Ant property constructs (like ${somename}) are resolved prior to the post. This means that a file can be set up in advance of running the build and the appropriate property values will be filled in at run time.
| Nested Class Summary | |
class |
PostTask.Cookie
Represents a cookie. |
| Field Summary |
| Fields inherited from class org.apache.tools.ant.Task |
description, location, target, taskName, taskType, wrapper |
| Fields inherited from class org.apache.tools.ant.ProjectComponent |
project |
| Constructor Summary | |
PostTask()
|
| Method Summary | |
void |
addConfiguredProp(Prop p)
Adds a name/value pair to post. |
void |
addText(java.lang.String text)
Adds a feature to the Text attribute of the PostTask object |
void |
execute()
Do the post. |
void |
setAppend(boolean b)
Should the log file be appended to or overwritten? Default is true, append to the file. |
void |
setEncoding(java.lang.String encoding)
Sets the encoding of the outgoing properties, default is UTF-8. |
void |
setFailonerror(boolean fail)
Should the build fail if the post fails? |
void |
setFile(java.io.File f)
Set the name of a file to read a set of properties from. |
void |
setLogfile(java.io.File f)
Set the name of a file to save the response to. |
void |
setMaxwait(int wait)
How long to wait on the remote server. |
void |
setProperty(java.lang.String name)
Set the name of a property to save the response to. |
void |
setTo(java.net.URL name)
Set the url to post to. |
void |
setVerbose(boolean b)
If true, progress messages and returned data from the post will be displayed. |
void |
setWantresponse(boolean b)
Default is true, get the response from the post. |
| Methods inherited from class org.apache.tools.ant.Task |
getDescription, getLocation, getOwningTarget, getRuntimeConfigurableWrapper, getTaskName, getTaskType, getWrapper, handleErrorFlush, handleErrorOutput, handleFlush, handleInput, handleOutput, init, isInvalid, log, log, maybeConfigure, perform, reconfigure, setDescription, setLocation, setOwningTarget, setRuntimeConfigurableWrapper, setTaskName, setTaskType |
| Methods inherited from class org.apache.tools.ant.ProjectComponent |
getProject, setProject |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
public PostTask()
| Method Detail |
public void setTo(java.net.URL name)
name
- the url to post to.public void setFile(java.io.File f)
f
- the filepublic void setLogfile(java.io.File f)
f
- the filepublic void setAppend(boolean b)
b
- append or notpublic void setVerbose(boolean b)
b
- true = verbosepublic void setWantresponse(boolean b)
b
- print/log server responsepublic void setProperty(java.lang.String name)
name
- the name to use for the propertypublic void setEncoding(java.lang.String encoding)
encoding
- The new encoding valuepublic void setMaxwait(int wait)
The default wait period is 3 minutes (180 seconds).
wait
- time to wait in seconds, set to 0 to wait forever.public void setFailonerror(boolean fail)
fail
- true = fail the build, default is falsepublic void addConfiguredProp(Prop p) throws org.apache.tools.ant.BuildException
p
- A property pair to send as part of the post.
org.apache.tools.ant.BuildException
- When name and/or value are missing.public void addText(java.lang.String text)
text
- The feature to be added to the Text attributepublic void execute() throws org.apache.tools.ant.BuildException
org.apache.tools.ant.BuildException
- On any error.
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Demo REST-Call with the ngResource Service
This is just one method how you can interact with the api!
Log:
{{entry}}


domestic | environment | fuel storage – containment | 2007
Every
building
must be designed and
constructed
in such a way that:
(a)
an oil storage installation, incorporating oil storage tanks
used solely to serve a fixed combustion appliance installation
providing space heating or cooking facilities in a
building
will: reduce the risk of oil escaping from the
installation; contain any oil spillage likely to contaminate any
water supply, ground water, watercourse, drain or sewer; and
permit any spill to be disposed of safely;
(b)
the volume of woody biomass fuel storage allows the number
of journeys by delivery vehicles to be minimised.
Limitation:
This standard does not apply to portable containers.
3.24.0 Introduction
Oil is a common and highly visible form of water pollution. Because of the
way it spreads, even a small quantity can cause a lot of harm to the aquatic
environment. Oil can pollute rivers, lochs, groundwater and coastal waters
killing wildlife and removing vital oxygen from the water.
Groundwater directive
Oil is a ‘List I’ substance within the meaning of the EC Groundwater Directive
(80/68/EEC). The UK government is required by this directive to prevent List I
substances from entering groundwater and to prevent groundwater pollution
by List II substances.
Storage of oil
authorisation
The storage of oil is a controlled activity under the Water Environment
(Controlled Activities)(Scotland) Regulations 2005 and will be deemed to be
authorised if it complies with The Water Environment (Oil Storage)(Scotland)
Regulations 2006. Enforcement is by SEPA.
Explanation of terms
The following terms are included below to provide clarity to their meaning in
this Technical Handbook.
Catchpit
means a pit, without a drain, which is capable of containing 110% of
the containers storage capacity with base and walls that are impermeable to
water and oil.
Integrally bunded tank
means a tank together with a catchpit manufactured
as a self-contained unit.
Woody biomass storage
Woody biomass fuel is unlikely to be locally sourced, except for chopped
firewood, and for large installations is likely to be delivered in bulk. If the
storage container is too small, the number of journeys by delivery vehicles
will make unnecessary use of diesel fuel. Wood chips tend to be used in
large boilers supplying heat to district heating systems rather than to
individual
houses
. Information on woody biomass fuel can be found on
the BSD website under: ‘Storage of woody biomass fuel for heating
equipment’.
In the case of
conversions
, as specified in regulation 4, the
building
as
converted
shall meet the requirement of this standard (regulation 12,
schedule 6).
3.24
—
3.24.0
domestic | environment | fuel storage – containment | 2007
3.24.1 Construction of oil storage tanks
Fixed oil storage tanks between 90 and 2500 litres and the fuel feed system
connecting them to a combustion appliance should be strong enough to
resist physical damage and corrosion so that the risk of oil spillage is
minimised. Tanks should be
constructed
in accordance with:
a. a. the recommendations of BS 799: Part 5: 1987, for a steel tank; or
b. the recommendations of OFTEC Technical Standard OFS T200, for a
steel tank, with or without integral bunding; or
c. the recommendations of OFTEC Technical Standard OFS T100, for a
polyethylene tank with or without integral bunding; or
d. a European harmonised product standard and assessed by a
notified
body
.
Large tanks
Tanks of more than 2500 litres, and their associated pipework must be
installed in accordance with the requirements of Regulation 6 of The Water
Environment (Oil Storage)(Scotland) Regulations 2006. Oil storage
containers up to 2500 litres serving
domestic buildings
will be deemed to be
authorised if they comply with the building regulations.
Small tanks
Tanks with a capacity of more than 90 litres but not more than 2500 litres and
the fuel feed system connecting them to a combustion appliance should be
installed in accordance with the recommendations of BS 5410: Part 1: 1997.
Underground tanks
Reference should be made to the Scottish Executive Code of Practice,
Underground Storage Tanks for Liquid Hydrocarbons (2003/27). This CoP
provide guidance on underground and partially buried oil storage tanks. The
CoP is currently being updated to be consistent with The Water Environment
(Controlled Activities)(Scotland) Regulations 2005 and will be issued by
SEPA. SEPA also provide guidance in PPG 27, (Installation,
Decommissioning and Removal of Underground Storage Tanks).
Care should be taken to prevent leakage from pipework. Pipework should be
run so as to provide the most direct route possible from the tank to the
burner. Joints should be kept to a minimum and the use of plastic coated
malleable copper pipe is recommended. Pipework should be installed in
accordance with the recommendations in BS 5410: Parts 1: 1997 and Part 2:
1978 and OFTEC Technical Book 3.
3.24.3 Secondary containment
Externally located, above ground, oil tanks with a capacity of not more than
2500 litres serving a
domestic building
should be provided with a catchpit or
be integrally bunded if subject to any of the hazards described below:
•
tank located within 10 m of the water environment (i.e. rivers, lochs,
coastal waters);
•
tank located where spillage could run into an open drain or to a loose
fitting manhole cover;
•
tank over ground where conditions are such that oil spillage could run-off
into a watercourse;
•
tank located in a position where the vent pipe outlet is not visible from the
fill point;
•
any other potential hazard individual to the
site
.
3.24.1
—
3.24.3
•
tank within 50 m of a borehole or spring;
domestic | environment | fuel storage – containment | 2007
OFTEC Technical Book 3 provides a simple and helpful crosscheck to the
above list.
A catchpit or integrally bunded tank should be provided in accordance with
the recommendations of OFTEC Standard OFS T100 and OFS T200; and
Clause 6.5 of BS 5410: Part 1: 1997.
Secondary containment should also be provided where a tank is within a
building
or wholly below ground.
3.24.4 Storage containers for solid woody biomass fuel
In order to best exploit the advantages achieved through the use of woody
biomass as low carbon technology it is recommended that wood fuel storage
provision is of a size that will ensure deliveries need not be made at intervals
of less that 3 months for bulk storage and 6 months for small installations.
Deliveries of wood pellets may be less frequent than deliveries of wood chips
because pellets can have three times the calorific value of dry wood chips.
Alternatively, the storage volume can be smaller for the same energy
capacity.
The following table provides recommended size of storage for a variety of
different
dwelling
types that will permit a large enough volume to be delivered
whilst minimising vehicle movements. Advice on the sizing of storage for
woody biomass fuel for larger
buildings
is given in the non-domestic
Technical Handbook.
Bulk woody biomass fuel storage: 100% heating (primary) and DHW
Dwelling size
Wood pellets
Wood chips
Logs - stacked
< 80 m²
1.5 m³
3.5 m³
3 m³
80 -160 m²
2 m³
5 m³
4 m³
> 160 m²
3 m³
6 m³
5 m³
Notes:
1.
The figures in the table relate to deliveries made every 3 months.
The guidance to standard 6.2 suggests that to achieve the carbon emissions
target, designers and developers may use on-
site
renewable energy
technologies to supply up to 10% of the annual heating demand, rather than
adopting slightly more demanding insulation standards for walls. A small
woody biomass stove or boiler could provide this level of heating.
Secondary heating
The following table provides recommended size of storage for secondary
heating for a variety of
dwelling
types:
Woody biomass fuel storage: secondary heating
Dwelling size
Wood pellets
Wood chips
Logs - stacked
< 80 m²
0.3 m³ (9 bags)
1 m³
0.5 m³
80 - 160 m²
0.5 m³ (13 bags)
1.5 m³
1 m³
> 160 m²
0.7 m³ (16 bags)
2 m³
1 m³
Notes:
1.
The figures in the table relate to deliveries made every 6 months.
3.24.3
—
3.24.4


The LiveSplit overlay is now ready to use.
Check out these resources to get you started:
LiveSplit Website
LiveSplit Tutorial
FAQ
GitHub


Bosch BMA421¶
this driver does not exist, so it has been created. Still work in progress ….
west build -p -b pinetime samples/gui/lvaccel
Overview¶
BMA421 is not a part number available to the general public, and therefore all the supporting documentation and design resources are neither discussed in public forums, nor disclosed on GitHub.
CHIP_ID=0X11 (so the Bosch BMA423 drivers need to be adapted)
The Bosch documentation on the bma423 seems to apply to the bma421.
Requirements¶
for this sensor does not exist any driver, so here’s what I did to create one under zephyr
adapt CMakeLists.txt¶
~/zephyrproject-2/zephyr/drivers/sensor add_subdirectory_ifdef(CONFIG_BMA280 bma280) add_subdirectory_ifdef(CONFIG_BMA421 bma421)
adapt Kconfig¶
~/zephyrproject-2/zephyr/drivers/sensor
add yaml file¶
~/zephyrproject-2/zephyr/dts/bindings/sensor cp bosch,bma280-i2c.yaml bosch,bma421-i2c.yaml
edit KConfig¶
- source “drivers/sensor/bma280/Kconfig”
source “drivers/sensor/bma421/Kconfig”
source “drivers/sensor/bmc150_magn/Kconfig”
source “drivers/sensor/bme280/Kconfig”
create driver¶
see under drivers/sensor/bma421
complement the pinetime.dts file with the following (under samples/sensor/bma280)
&i2c1 {
bma421@18 {
compatible = "bosch,bma421";
reg = <0x18>;
label = "BMA421";
int1-gpios = <&gpio0 8 0>;
};
};
Create a file: /dts/bindings/sensor/bosch,bma421-i2c.yaml. Which contains:
compatible: "bosch,bma421"
include: i2c-device.yaml
properties:
int1-gpios:
type: phandle-array
required: false
Building and Running¶
Todo¶
the driver is interrupt driven as well – need to test software
the sensor has algorithm for steps – read out register
temperature some attempt has been made, but … (OK, temp can be read)
References¶
Bosch has documented the BMA423 very well. I kind of hope it will apply to the bma421.
A mechanism to adapt the 0x5E register is provided. (burst read/write)
All kind of parameters can be set to trigger an interrupt. (e.g. number of steps taken : think of the 10000 steps threshold)


| 1122 | (A) | whare to bere in the burgħ the bodyes þat were dede |
| 1126 | (C) | where to burye in þe cyte . þe bodyes þat were dede |
| 1108 | (D) | wher to birie in þe borow the bodies that deyde |
| 1089 | (E) | where to byrye in þe town . þe bodyes þat deyde . |
| (Ex) | ---- no text ---- | |
| 1146 | (L) | where to burie in þe burwe þe bodies þat were ded |
| (P) | ---- no text ---- | |
| 1098 | (U) | where to bury in the burgħ / the bodys that there dyed |
| 187 | (V) | where to berien in þe burgȝ bodies þat were dede |


WITNESS LIST
Special Purpose Districts Committee
April 27, 2017 - 8:00 AM
For:
Calderon, Bill (Self; Harris County Improvement district No. 17)
Fogarty, Joe (Self)
Registering, but not testifying:
For:
Kelly, Bill (Mayor's Office, City of Houston)
For:
Martin, Ross (Winstead PC)
Registering, but not testifying:
For:
Wilson, Kirk (Self; Developer Landowner)
For:
Green, Timothy (Eland energy)
McGuire, Robim (Eland Energy, Inc)
For:
Barrett, Andy (Mountain City 150 LP)
Registering, but not testifying:
For:
Richardson, Jody (Plum creek Development Partners)
Shannon, Megan (Plum Creek Development Partners)
For:
Gerhardt, Carl (Westwood Magnolia Parkway Improvement District)
Grayson, Thomas (Westwood Magnolia Parkway Improvement District)
Registering, but not testifying:
For:
Eissler, Rob (Westwood Magnolia Parkway Improvement District)
For:
Calderon, Bill (Self)
1
WITNESS LIST
For:
Hawes, David (Stadium Park Redevelopment Authority)
Registering, but not testifying:
For:
Ford, Crystal (Houston Livestock Show and Rodeo)
Kelly, Bill (Mayor's Office, City of Houston)
Perkins, Val (City of Houston)
For:
Martin, Ross (Winstead PC)
For:
Perkins, Val (City of Sugar Land)
Zimmerman, Joe R. (Mayor of sugar land)
For:
Perkins, Val (22803 Schiel Partners, LLC)
Registering, but not testifying:
For:
Kahn, Joshua (Gulf Coast Land Trust)
Kelly, Bill (Mayor's Office, City of Houston)
For:
Barrett, Andy (Driftwood, llc)
On:
Rogers, Linda Kaye (Hays Trinity GCD)
For:
Martin, Ross (Winstead PC)
For:
Barrett, Andy (Damien Mandola)
On:
Rogers, Linda Kaye (Hays Trinity GCD)
For:
Barrett, Andy (Lasalle Holding, lp)
2
WITNESS LIST
Registering, but not testifying:
For:
Schroeder, Michael (Self)
For:
Barrett, Andy (Lasalle Holding, lp)
Registering, but not testifying:
For:
Schroeder, Michael (Self)
For:
Barrett, Andy (Lasalle Holding, lp)
Registering, but not testifying:
For:
Schroeder, Michael (Self)
For:
Barrett, Andy (Lasalle Holding, lp)
Registering, but not testifying:
For:
Schroeder, Michael (Self)
For:
Barrett, Andy (Lasalle Holding, lp)
Registering, but not testifying:
For:
Schroeder, Michael (Self)
For:
Lary, Trey (Home Assets Inc. dba NewQuest Properties)
Registering, but not testifying:
For:
Alvis, Steve (Home Assets, Inc. dba NewQuest Properties)
3
WITNESS LIST
For:
Roach, Jonathan (Roach & Mitchell, PLLC)
Registering, but not testifying:
For:
Mitchell, Frank (Roach & Mitchell, PLLC)
Registering, but not testifying:
For:
Lary, Trey (WBH Ranches, LP)
Mearig, Seth (William harrison)
Registering, but not testifying:
For:
Lary, Trey (Parkside NEC Grand Parkway\I-10, Ltd.)
Moody, Dan (Parkside NEC I-10/GP, Ltd.)
Registering, but not testifying:
For:
Lary, Trey (Westside Ventures, Ltd.)
Mccormack, John (Westside Ventures, Ltd.)
For:
Allen, Don (Self)
Green, Timothy (Aberdeen land company)
4


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There are different types of simulation experiments. In a simple experiment, a simulation scenario is run repeatedly by defining a number of replications (iterations) for being able to compute average statistics.
In a parameter variation experiment, several variants of a simulation scenario (called experiment scenarios), are defined by defining value sets for certain model parameters (the experiment parameters), such that a parameter variation experiment run consists of a set of experiment scenario runs, one for each combination of parameter values.
An experiment type is defined for a given simulation model and an experiment of that type is run on top of a given simulation scenario for that model.
When running an experiment, the resulting statistics data are stored in a database, which allows looking them up later on or exporting them to data analysis tools (such as Microsoft Excel or RStudio)
A simple experiment type is defined with a
sim.experimentType
record on top of a model by defining (1) the
number of replications and (2) possibly a list of seed
values, one for each replication. The following code shows an example
of a simple experiment type definition:
sim.experimentType = { title: "Simple Experiment with 10 replications, each running for 1000 time units (days)", nmrOfReplications: 10, seeds: [123, 234, 345, 456, 567, 678, 789, 890, 901, 1012] };
Running this simple experiment means running the underlying scenario 10 times, each time with another random seed, as specified by the list of seeds. The resulting statistics are composed of the user-defined statistics and the generic statistics (per activity type) for each replication complemented with a summary statistics listing averages, standard deviations, min/max values and confidence intervals.
When no seeds are defined, the experiment is run with implicit random seeds using JavaScript's built-in random number generator, which implies that experiment runs are not reproducible.
The following table shows the experiment results of a simple experiment defined for the Medical-Department-1c model.
| Replication | Statistics per activity type | |||||
|---|---|---|---|---|---|---|
| Examination | ||||||
| enqu | start | compl | qLen | wTime | cTime | |
| 1 | 284 | 283 | 282 | 5 | 12.36 | 20.06 |
| 2 | 314 | 314 | 314 | 6 | 13.28 | 22.66 |
| 3 | 297 | 296 | 294 | 8 | 20.77 | 29.25 |
| 4 | 298 | 297 | 296 | 8 | 15.28 | 22.66 |
| 5 | 296 | 295 | 292 | 5 | 14.44 | 21.92 |
| 6 | 318 | 318 | 316 | 16 | 40.64 | 50.23 |
| 7 | 299 | 299 | 298 | 8 | 18.21 | 25.75 |
| 8 | 334 | 327 | 325 | 12 | 30.8 | 40.35 |
| 9 | 296 | 295 | 292 | 7 | 16.82 | 25.79 |
| 10 | 302 | 301 | 299 | 6 | 13.12 | 22.1 |
| Average | 303.8 | 302.5 | 300.8 | 8.1 | 19.57 | 28.08 |
| Std.dev. | 14.29 | 13.13 | 13.25 | 3.45 | 9.18 | 9.72 |
| Minimum | 284 | 283 | 282 | 5 | 12.36 | 20.06 |
| Maximum | 334 | 327 | 325 | 16 | 40.64 | 50.23 |
| CI Lower | 294.8 | 294.6 | 292.7 | 5.9 | 13.67 | 21.51 |
| CI Upper | 311.5 | 309.7 | 308.2 | 9.9 | 24.25 | 32.99 |
A parameter variation experiment is defined with (1) a number of replications, (2) a list of seed values (one for each replication), and (3) one or more experiment parameters.
An
experiment parameter must have the same name as the model parameter to which
it refers. It defines a set of values for this model variable, either using
a values
field or a combination of a startValue
and endValue
field (and stepSize
for a non-default
increment value) as in the following example.
The following code shows an example of a parameter variation experiment definition (on top of the Inventory-Management simulation model):
sim.experimentTypes[1] = { id: 1, title: "Parameter variation experiment for exploring reorderInterval and targetInventory", nmrOfReplications: 10, seeds: [123, 234, 345, 456, 567, 678, 789, 890, 901, 1012], parameterDefs: [ {name:"reviewPolicy", values:["periodic"]}, {name:"reorderInterval", values:[2,3,4]}, {name:"targetInventory", startValue:80, endValue:100, stepSize:10}, ] };
Notice that this experiment definition defines 9 experiment scenarios resulting from the combinations of the values 2/3/4 and 80/90/100 for the parameters reorderInterval and targetInventory. Running this parameter variation experiment means running each of the 9 experiment scenarios 10 times (each time with another random seed, as specified by the list of seeds). The resulting statistics, as shown in the following table, is computed by averaging all statistics variables defined for the given model.
| Experiment Results | ||||
|---|---|---|---|---|
| Experiment scenario | Parameter values | Statistics | ||
| nmrOfStockOuts | lostSales | serviceLevel | ||
| 0 | periodic,2,80 | 21.8 | 180.7 | 97.82 |
| 1 | periodic,2,90 | 7.4 | 55.9 | 99.26 |
| 2 | periodic,2,100 | 2.1 | 15.8 | 99.79 |
| 3 | periodic,3,80 | 86.6 | 855.6 | 91.34 |
| 4 | periodic,3,90 | 40.6 | 377.5 | 95.94 |
| 5 | periodic,3,100 | 16.3 | 139.8 | 98.37 |
| 6 | periodic,4,80 | 171.5 | 2067.5 | 82.85 |
| 7 | periodic,4,90 | 110.6 | 1238.3 | 88.94 |
| 8 | periodic,4,100 | 63.8 | 661.4 | 93.62 |
In OESjs-Core1, an experiment's output statistics data is stored in a browser-managed database using JavaScript's IndexedDB technology. The name of this database is the same as the name of the simulation model. It can be inspected with the help of the browser's developer tools, which are typically activated with the key combination [Shift]+[Ctrl]+[I]. For instance, in Google's Chrome browser, one has to go to Application/Storage/IndexedDB.
The experiment statistics database consists of three tables containing data about (1) experiment runs, (2) experiment scenarios, and (3) experiment scenario runs, which can be exported to a CSV file.


We have already mentioned that paragraphs of text can be represented
with the

tag. Here are a few other ways to organize
text:
There are three types of lists in HTML: unordered lists, ordered lists, and definition lists. Of these, only the first two are commonly used, so we won't mention definition lists again.
Unordered lists are lists where the order of the content is not particularly important. These are represented as bullet lists, such as the one above the beginning of this section.
Unordered lists are surrounded by a

opening tag and
a

closing tag. ("ul" is short for "unordered
list".) Each list item is surrounded by a

opening
tag and

closing tag. ("li" is short for "list
item".)
Here is an example:
Ordered lists, on the other hand, are lists where the order of the content is important. These are represented as numbered lists.
Ordered lists are surrounded by a

opening tag and
a

closing tag. (Think "ol" equals "ordered list".)
Each list item is surrounded by a

opening tag and

closing tag, just like in unordered lists.
Here is an example:
There are six different header tags, each representing a different level of heading for your content:

The

tag represents a main-level header. h2 is a
subheader, and higher numbers are even deeper subheaders. Visually,

is biggest and

is smallest.
You can use headers to group text together, and even to put groups in a
larger group. In fact, we are using

through

on this page to group all of the content together!
Here is an example of how you can use headers to group content:
Which would look something like this in a browser:
Key ingredients in apple juice:
How to make apple juice:
Everything we have done so far has helped us write content onto a single page. However, most web sites separate their content into multiple pages and provide users a means of moving those pages in order to view the content they are interested in.
In order to allow users to move from one page to another, we can use
hyperlinks, or links for short. We can create hyperlinks
using the anchor tag (
).
Let's start with an example:
Google!
The first thing you might have noticed is the extra text in the opening

tag. That text is an attribute, which tells
web browsers more about this particular element. One thing to note
about attributes is that they never are used in a closing tag: they are
only used in an opening tag.
In this case, the href attribute tells a web browser what page it should show the user next if the user clicks on the hyperlink. The text of a hyperlink is completely irrelevant in determining the page the user sees when he or she clicks on the link. For example, the link below will not take the user to the Trustworthy Bank:
Building hyperlinks that go to unexpected websites is one of the more basic techniques cyber-criminals use to try to trick people into sharing private information. Make sure your friends and family know to hover over links and read the web address in the corner of the browser before clicking!
We've seen that the href attribute of an anchor tag is how we direct users to other pages. But how can we specify those pages? We use one of three different kinds of URLs. (URL stands for "uniform resource locator" and is a way to represent a location on the World Wide Web.)
Here are the three kinds of URLs:
http://github.com/platinumazure
)
https://github.com/weegeekps
, a link with href of
/platinumazure
would take you to the
https://github.com/platinumazure
page. Basically, everything
after the hostname is replaced by a virtual URL.
It depends on the situation.
An external link, which is a hyperlink going to a different website, must use an absolute URL.
When you don't need to use an absolute URL, either a virtual URL or a relative URL will work just fine. In most cases, it is possible to convert between virtual and relative URLs interchangeably.
Absolute URL
Virtual URL
Relative URL (same path)
Relative URL (deeper path)
Relative
URL (up a path level, then down another
path)
We can render images on our web pages using the
tag. The
tag requires a src
attribute
(meaning source). The value of the src
attribute is a
URL, meaning any of the URL types we learned about earlier will work here.
Here is an example:
Which looks something like this:

) with "My Favorite Music"


Ticket {{ticket.uid}}
{{ticket.date | date: 'short'}}
Ticket #{{ticket.uid}}
{{ticket.owner.fullname}}
{{ticket.group.name}}
{{ticket.assignee.fullname}}
Subject
{{ticket.subject}}
Issue
{{comment.owner.fullname}}
{{comment.date | date: 'short'}}
NOTE


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Time and Location: Fridays starting at 5pm in (Library Lobby - TBA)
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fun <TModule : Module> instanceUnsafe(type: RegisteredModuleType): TModule
Provides an instance of the module type given (for Java interoperability).
TL;DR: Java users -- beware! This function implements instance for Java callers, but unlike the Kotlin equivalent, provides no guarantee that a ClassCastException will not occur.
This method exists to aid Java interoperability: the instance method cannot be used from Java as it is both inlined and makes use of reified generics. With this method, Java users can query for RegisteredModuleType instances.
The reason it is unsafe is that there is no guarantee that the cast will be correct: if the type requested can be cast to Module then the cast will succeed and a ClassCastException will be given where the call is made (i.e. the function will return with no error until an assignment type is checked). With the normal (and safe) instance function, the invalid cast can be caught before the function returns and dealt with appropriately.
type
- The type of of module to get an instance of.
MissingModuleException
- When no builder has been registered for the type of module requested.
Return
An instance of the module registered for the given module type cast as TModule.


---
layout: post
title: ZOMGWTFBBQ11G!
date: '2007-07-11T22:39:00.000-05:00'
author: Don Seiler
tags:
- 11g
- oracle
- hype
modified_time: '2010-11-19T22:48:46.271-06:00'
blogger_id: tag:blogger.com,1999:blog-7032512792942232766.post-9176638126285071557
blogger_orig_url: http://www.seiler.us/2007/07/zomgwtfbbq11g.html
---
I didn't want to be the only Oracle blogger who didn't observe the holy day of HOLY CRAP ORACLE 11G IS COMING!!!!!11
Sarcastic excitement aside, I'm looking forward to Arup Nanda's 11g features series (with handy rss feed).


{% extends "main.html" %} {% load humanize %} {% block suggestionbox %}
Submitted {{ suggestion.created_at|naturaltime }} by {% if suggestion.author %}{{ suggestion.author }}{% else %}Anonymous{% endif %}{% if suggestion.in_reply_to %} in reply to this{% endif %}
{{ suggestion.description|linebreaksbr }}
| Title | Submitter | Most recent commented by | Number of comments |
|---|---|---|---|
| {{ comment.title }} | {% if comment.author %}{{ comment.author }}{% else %}Anonymous{% endif %} @ {{ comment.created_at|naturaltime }} | {% if comment.most_recent_comment %}{% if comment.most_recent_comment.author %}{{ comment.most_recent_comment.author }}{% else %}Anonymous{% endif %} @ {{ comment.most_recent_comment.created_at|naturaltime }}{% endif %} | {{ comment.descendant_count }} |


{% extends "base_template.html" %} {% block content %}
Browse - To browse the files that are availiable in the server.