Based on a HN comment.
There is now a very entertaining python3 script at this gist, author 4lhc.
The idea is that there is an analogy between the historical events after the invention of the printing press and the recent events after the ARPANET.
Based on empirical observations, we propose the hypothesis:
where the Δ t-historic is the time in decades from the invention of the printing press and Δ t-today is the time in decades from the opening of the ARPANET.
Here is a list of interesting correspondences:
Predictions: if there is anything serious about this analogy then we should observe some other analogies


Shows how to measure progress with UpscalerJS.
Click the upscale button to upscale the photo.
Progress:


How
it works
Step
1
Before
you start
come closer to the camera
Too
far away!
Too
close!
Perfect!
Step
2
For
Liveness Detection
nod your head slightly
Please don't move the device!
Uploading...
Uploading...
Uploading...
Uploading...


--- layout: post title: BizTalk complaining about Orchestration instances still existing date: 2008-12-11 20:27:41.000000000 +01:00 categories: [] tags: - BizTalk 2006 R2 - Orchestration - SQL Server 2005 - Visual Studio 2005 status: publish type: post published: true meta: {} author: login: admin email<EMAIL_ADDRESS>display_name: Philippe first_name: Philippe last_name: Vlérick excerpt: !ruby/object:Hpricot::Doc options: {} ---
As I was studying BizTalk 2006 R2, I came across a strange error. This error was reported by Visual Studio 2005 when trying to redeploy.
Could not change the bindings for orchestration 'xxx', Version=<IP_ADDRESS>,Culture=neutral, PublicKeyToken=c380e04620d206e8' as one or more instances of the orchestration still exist.
Trying to act on the orchestration itself or even the solution in BizTalk Administration Tool was giving the same error.
The fix was found in this blog post: One or more instances of the orchestration still exist.
So, there are two solutions to fix this issue:
As pointed out in one of the comments of the abode post, the bts_CleanupMsgbox comes empty at BizTalk install. This means that it executes when you call it, but nothing happens. The orchestrations were still in the list of running orchestrations in BizTalk Administration Tool.
However, the script to create the working procedure is located in the "C:\Program Files\Microsoft BizTalk Server 2006\Schema\msgbox_cleanup_logic.sql" file. Once this has been executed, the stored procedure will correctly clean BizTalk's messages database.


Numbers
This button simply creates an ordered list from 5-15
Language Names
The Langage Names button will launch a list of some of the most common programming languages that are used all around.
Language Popularity
By clicking this button, an ordered list of the most popular languages will launch.


Mixly Lite
Make Programming Easier
Mithon CC
Experiment with Mithon CC
BBC micro:bit
Build cool projects with micro:bit
Python 3
Code with Python 3


Please confirm your identity by logging into one of your social media accounts ...
What is your current gender identity?

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Are you Hispanic or Latino?
Age
Month
Year


New Year is here, So let’s celebrate, Come over to my place, And please don’t be late. The smell of the turkey, And lights on the way – Be lovely and happy This wonderful day!


Hello, my name is Mohsin. I was born and raised in Oklahoma. I moved to Austin a little over six years ago to work in tech. I am currently a student at Code Fellows and look forward to becoming a Software Engineer.
- Code Fellows | Software Engineering - Python | 2021
- University of Central Oklahoma | Coursework completed in Accounting
- The University of Oklahoma | Coursework completed in Interdispinciary Studies
- Manager, Business Intelligence, Global Analytics | Juul Labs, Inc.
- Implementation Consultant | PDI Software
I have a background in data analytics and was most recently in a Business Intelligence and Reporting Manager role at Juul Labs. I would like to pursue training from Code Fellows to pivot into backend software development. I believe I can leverage my data analytics background in this specialization to quickly become an asset for any company. I am pursuing the career change because I am interested in software engineering, and I believe my opportunities are greater in this career path than the previous.
Top Ten Cities to Live in the U.S.A for Quality of Life
- Boulder, Colorado
- Ann Arbor, Michigan
- McAllen, Texas
- Brownsville, Texas
- Madison, Wisconsin
- Green Bay, Wisconsin
- Santa Barbara, California
- San Jose, California
- Grand Rapids, Michigan
- Fort Collins, Colorado


GETSOCKNAME
Section: Linux Programmer's Manual (2)
Updated: 2008-12-03
Index
Return to Main Contents
NAME
getsockname - get socket name
SYNOPSIS
#include <sys/socket.h>
int getsockname(int sockfd, struct sockaddr *addr, socklen_t *addrlen);
DESCRIPTION
getsockname()
returns the current address to which the socket
sockfd
is bound, in the buffer pointed to by
addr.
The
addrlen
argument should be initialized to indicate
the amount of space (in bytes) pointed to by
addr.
On return it contains the actual size of the socket address.
The returned address is truncated if the buffer provided is too small;
in this case,
addrlen
will return a value greater than was supplied to the call.
RETURN VALUE
On success, zero is returned.
On error, -1 is returned, and
errno
is set appropriately.
ERRORS
- EBADF
-
The argument
sockfd
is not a valid descriptor.
- EFAULT
-
The
addr
argument points to memory not in a valid part of the
process address space.
- EINVAL
-
addrlen
is invalid (e.g., is negative).
- ENOBUFS
-
Insufficient resources were available in the system
to perform the operation.
- ENOTSOCK
-
The argument
sockfd
is a file, not a socket.
CONFORMING TO
SVr4, 4.4BSD (the
getsockname()
function call appeared in 4.2BSD), POSIX.1-2001.
NOTES
The third argument of
getsockname()
is in reality an
int *
(and this is what 4.x BSD and libc4 and libc5 have).
Some POSIX confusion resulted in the present
socklen_t,
also used by glibc.
See also
accept(2).
SEE ALSO
bind(2),
socket(2),
getifaddrs(3),
ip(7),
socket(7),
unix(7)
Index
- NAME
-
- SYNOPSIS
-
- DESCRIPTION
-
- RETURN VALUE
-
- ERRORS
-
- CONFORMING TO
-
- NOTES
-
- SEE ALSO
-
This document was created by
man2html,
using the manual pages.
Time: 02:54:48 GMT, September 18, 2014


--- layout: post title: Ocean heaves date: 2013-04-03 18:31:08.000000000 -07:00 type: post background: '/img/posts/bg.jpg' parent_id: '0' published: true password: '' status: publish categories: - travel tags: [] meta: _edit_last: '2' _thumbnail_id: '615' permalink: "/travel/ocean-heaves/" ---
A quick update on the adventures and frustrations that alternate on this journey:
Adventure - riding a 3 foot wave on a 7' 6" fiberglass board
Frustration - trying to file for an extension on taxes for over two hours
Adventure - eating seaweed flavored Lays potato chips on the beach at 3am with Aaron, discussing love, life, and motorbike accidents
Frustration - temporarily losing motorbike keys on the 3am beach and searching by remarkably dim cell phones until keys located in the ignition
Adventure - eating strange foods
Frustration - getting a bad case of who-knows and spending a day horizontal or in the bathroom
Adventure - puking on a surfboard after a day of ick
Frustration - getting a boo-boo while surfing on one of the only exposed surfaces left in my Spidey costume
Adventure - having boo-boos from trying to surf!
Adventure - drinking local palm sap liquor with locals
Frustration - day number two puking on surf board, this time from drinking the night before
Frustration - getting email that birthday package from Thailand requires a custom's agent to be hired in the US and might cost upwards of $300 to receive??????
Adventure - being on the phone about birthday big package and hearing brother unpack small package that just arrived from Thailand
Here's a short catalog of boo-boos and more news forthcoming!


Teens tame shrew
10 Things I Hate About You A Film Review By Michael Redman Copyright 1999 By Michael Redman
**1/2 (Out of ****)
Love and lust, however they might be combined, are probably the most fascinating of human endeavors. People will do the strangest things when obsessed with either. A combination of the two emotions is almost deadly. With the stars in the eyes, rational thought goes out the window and a beautiful delirium takes over.
Lovers will reveal their innermost selves to each other and often it's a person that the outside world never sees. The hard-edged and cruel can turn into puppy dogs. On the other hand, the nicest person in the world might become a maelstrom. It's a fascinating thing to watch from the outside and an alternate dimension from the inside.
Kat Stratford (Julia Stiles) and Heath Ledger (Patrick Verona) are seemingly a couple made in hell. She's an hot blooded opinionated ill-tempered high school beauty who has sworn off men. He's the local bad boy, drilling holes in text books during shop class.
Kat's younger sister Bianca (Larisa Oleynik) is the opposite of her sibling. She desperately wants to be popular and has her sights set on pretty boy male model Joey Donner (Andrew Keegan). Her problem is that her dad won't let her date until Kat does. Kat has no intentions of hanging out with the local male riff-raff.
Nerdish Cameron James (Joseph Gordon-Levitt) is smitten with Bianca and hatches a plot to toss Kat into the dating pool. He schemes with Joey and they approach Heath with an offer he can't refuse. They offer him money to take out the unapproachable Kat.
Loosely based on Shakespeare's "The Taming Of The Shrew", this is a notch above current teen films.  There's a definite chemistry between Stiles and Verona and both could become box office draws.
An aberration for high school films, the adults also have decent roles. Daryl "Chill" Mitchell has a small but entertaining part as a literature teacher. ("I know Shakespeare is a dead white guy, but he knows his shit.") Larry Miller is great as the girls' father. Unfortunately he is saddled with lame dialog.
There are other problems with the film. In any good story, the characters eventually go through changes. Here they appear to transform instantaneously. Bianca is shallow and self-centered and suddenly she's not. Heath is the school bully but after 30 seconds with Kat, it turns out he's really an angel.
The biggest flaw is a plot device that must have been old even in the Bard's time. After being paid to date Kat, Heath falls for her. It doesn't take an olde English scholar to know that she's going to find out about the conspiracy and break up with him.
This isn't a perfect film, but after the recent horrendous movies aimed at the younger market, it's a tiny ray of hope.
(Michael Redman has written this column for over 575 fortnights and decided that he'd write another one even though the perfect light-hearted film remains elusive. Email tales of love and lust to<EMAIL_ADDRESS>[This appeared in the 4/15/99 "Bloomington Independent", Bloomington, Indiana. Michael Redman can be contacted at<EMAIL_ADDRESS>--<EMAIL_ADDRESS>This week's film review: http://www.indepen.com/ Film reviews archive: http://us.imdb.com/M/reviews_by?Michael%20Redman Y2K articles: http://www.indepen.com/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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© Erik Konijn og Mikkel Damgaard, 2015. Website version UNDER DEVELOPMENT 0.0.7


- The LinkDialog plugin is an 'example' style plugin that shows how to insert basic web links as well as
image tags. This plugin is intended to guide users in writing their own, more complex, link and image handlers
while still providing useful function.
RTL Editor:
- The LinkDialog plugin is an 'example' style plugin that shows how to insert basic web links as well as
image tags. This plugin is intended to guide users in writing their own, more complex, link and image handlers
while still providing useful function.


MoovCheckout Workbench
Reload Page
MC iFrame
Disable Actions
Get Selector
Map Widget
Export Widget
Choose an option above to start working
Pick a Widget
is Modal
common Properties
only Mapped
only Error fields
Selectors
Settings
x
Here is your Widget
only Fields
Don't worry about tabs, it's already formated.
Copy to Clipboard
x
Selector Picker
Just click on any element of the original site
Copy to Clipboard


Click here to fade a monkey in or out.
Click here to toggle a rainbow.
Click here to slide a lollipop up and down.


--- layout: post authors: ["Raniere Silva"] title: "Community Calendar Available" date: 2015-12-10 time: "10:00:00" tags: ["Community"] ---
During this year we host many events for the community including lab meetings, sub-committees and de/briefing sessions. Unfortunately we fail to let everyone know of the events and to try solve this problem next year we are launching a community calendar. If you want to add the community calendar into your agenda you should use https://calendar.google.com/calendar/ical/oseuuoht0tvjbokgg3noh8c47g%40group.calendar.google.com/public/basic.ics.


EDMA3 RM Instance Specific Configuration Structure. More...
#include <edma3resmgr.h>
Data Fields | |
| EDMA3_RM_Param | initParam |
| EDMA3_CCRL_ShadowRegs * | shadowRegs |
| EDMA3_RM_Obj * | pResMgrObjHandle |
| unsigned int | avlblDmaChannels [EDMA3_MAX_DMA_CHAN_DWRDS] |
| unsigned int | avlblQdmaChannels [EDMA3_MAX_QDMA_CHAN_DWRDS] |
| unsigned int | avlblPaRAMSets [EDMA3_MAX_PARAM_DWRDS] |
| unsigned int | avlblTccs [EDMA3_MAX_TCC_DWRDS] |
| unsigned int | paramInitRequired |
| unsigned int | regModificationRequired |
| EDMA3_RM_mapXbarEvtToChan | mapXbarToChan |
| EDMA3_RM_xbarConfigScr | configScrMapXbarToEvt |
| EDMA3_RM_GblXbarToChanConfigParams | rmXbarToEvtMapConfig |
EDMA3 RM Instance Specific Configuration Structure.
Used to maintain information of the EDMA3 Res Mgr instances. One such storage exists for each instance of the EDMA3 Res Mgr.
Maximum EDMA3_MAX_RM_INSTANCES instances are allowed for each EDMA3 hardware instance, for same or different shadow regions.
Configuration such as region id, IsMaster, Callback function This configuration is passed to the "Open" API. For a single EDMA3 HW controller, there can be EDMA3_MAX_REGIONS different instances tied to different regions.
| EDMA3_CCRL_ShadowRegs* EDMA3_RM_Instance::shadowRegs |
Pointer to appropriate Shadow Register region of CC Registers
Pointer to the EDMA3 RM Object (HW specific) opened by RM instance.
| unsigned int EDMA3_RM_Instance::avlblDmaChannels[EDMA3_MAX_DMA_CHAN_DWRDS] |
Available DMA Channels to the RM Instance
| unsigned int EDMA3_RM_Instance::avlblQdmaChannels[EDMA3_MAX_QDMA_CHAN_DWRDS] |
Available QDMA Channels to the RM Instance
| unsigned int EDMA3_RM_Instance::avlblPaRAMSets[EDMA3_MAX_PARAM_DWRDS] |
Available PaRAM Sets to the RM Instance
| unsigned int EDMA3_RM_Instance::avlblTccs[EDMA3_MAX_TCC_DWRDS] |
Available TCCs to the RM Instance
| unsigned int EDMA3_RM_Instance::paramInitRequired |
Sometimes, PaRAM clearing is not required for some particular RM Instances. In that case, PaRAM Sets allocated will NOT be cleared before allocating to any particular user. It is the responsibility of user to program it accordingly, without assuming anything for a specific field because the PaRAM Set might contain junk values. Not programming it fully might result in erroneous behavior. On the other hand, RM instances can also use this variable to get the PaRAM Sets cleared before allocating them to the specific user. User can program only the selected fields in this case.
Value '0' : PaRAM Sets will NOT be cleared during their allocation. Value '1' : PaRAM Sets will be cleared during their allocation.
This value can be modified using the IOCTL commands.
| unsigned int EDMA3_RM_Instance::regModificationRequired |
Sometimes, global EDMA3 registers (DCHMAP/QCHMAP) and PaRAM Sets should not be modified during EDMA3_RM_allocLogicalChannel (), for some particular RM Instances. In that case, it is the responsibility of user to program them accordingly, when needed, without assuming anything because they might contain junk values. Not programming the registers/PaRAMs fully might result in erroneous behavior. On the other hand, RM instances can also use this variable to get the global registers and PaRAM Sets minimally programmed before allocating them to the specific user. User can program only the remaining fields in this case.
Value '0' : EDMA3 registers (DCHMAP/QCHMAP) and PaRAM Sets will NOT be programmed during their allocation. Value '1' : EDMA3 registers (DCHMAP/QCHMAP) and PaRAM Sets will be programmed during their allocation.
This value can be modified using the IOCTL commands.
Pointer to the user defined function for mapping cross bar events to channel.
Pointer to the user defined function for configuring the cross bar events to appropriate channel in the Control Config TPCC Event Config registers.
Pointer to the configuration data structure for mapping cross bar events to channel.


Rackspace CDN and the Cloud Control Panel¶
When you log in to the Cloud Control Panel, your session begins with information about your Cloud Servers. To see your Rackspace CDN information, click Storage and then click CDN.
If your list of services is not empty, then for each service you can see:
- The name of the service
- Its domain name
- Its origin
Note
Because the Cloud Control Panel uses APIs, watching the Cloud Control Panel work can help you learn how to work with APIs.
To see how the Cloud Control Panel gets its information:
- Launch the Chrome browser.
- In Chrome, log in to your Cloud Control Panel account.
- For Mac, from the Chrome menu, select View > Developer > Developer Tools. For Windows, from the Chrome menu, select More tools > Developer Tools.
- Experiment in the Cloud Control Panel and observe the actions in Chrome’s developer view.


PyQt5 signals and slots
Thu 12 October 2017 by Little Captainsignals and slots
Graphical applications (GUI) are event-driven, unlike console or terminal applications. A users action like clicks a button or selecting an item in a list is called an event.
If an event takes place, each PyQt5 widget can emit a signal. A signal does not execute any action, that is done by a slot.
- 基本格式
# The button click (signal) is connected to the action (slot)
button.clicked.connect(self.slot_method)
# This principle of connecting slots methods or function to a widget, applies to all widgets
widget.signal.connect(slot_method)
- 显式的定义信号
QtCore.QObject.connect(widget, QtCore.SIGNAL(‘signalname’), slot_function)
PyQt supports many type of signals, not just clicks. A slot is any callable function or method.
from PyQt5.QtWidgets import (QApplication, QComboBox, QDialog,\
QDialogButtonBox, QFormLayout, QGridLayout, QGroupBox, QHBoxLayout,\
QLabel, QLineEdit, QMenu, QMenuBar, QPushButton, QSpinBox, QTextEdit,\
QVBoxLayout)
import sys
class Dialog(QDialog):
def slot_method(self):
print('slot method called')
def __init__(self):
super(Dialog, self).__init__()
button = QPushButton('Click')
button.clicked.connect(self.slot_method)
mainLayout = QVBoxLayout()
mainLayout.addWidget(button)
self.setLayout(mainLayout)
self.setWindowTitle('Button Example - pythonspot.com')
if __name__ == '__main__':
app = QApplication(sys.argv)
dialog = Dialog()
sys.exit(dialog.exec_())


{% extends "blog/base.html" %} {% load staticfiles %} {% block content %}
We've emailed you instructions for setting your password, you should receive them shortly.
If you don't receive an email, please try again after some time , and check your spam folder.
{% endblock %}


A transparent dependency injection library based on file based dependency config. Handles initialization, bootstrapping and dependency injection for you.
var Core = Object.create(require("nCore")).constructor({
server: {
controller: "hello-world"
}
}),
http = require("http")
Core.use("hello-world", {
print: function (res) {
res.end("hello world")
}
})
Core.use("server", {
init: function () {
http.createServer(this.handleRequest).listen(8080)
},
handleRequest: function (req, res) {
this.controller.print(res)
}
});
Core.init()
Or using the file based loader
// ./hello-world.js
module.exports = {
print: function (res) {
res.end("hello world")
}
}
// ./server.js
var http = require("http")
module.exports = {
init: function () {
http.createServer(this.handleRequest).listen(8080)
},
handleRequest: function (req, res) {
this.controller.print(res)
}
}
// dependency.json
{
"./server.js": {
"controller": "./hello-world.js"
}
}
nCore is a dependency injection framework.
nCore gives you a CLI to build a browser bundle using browserify with the same syntax as the moduleLoader
This means you can set up your modules folder and you dependency.json file directly inside your modules folder then just
$ ncore ./modules/ -o /build/bundle.js
And that's it. Everything just works. All the modules are loaded, bundled, attached to the core, nCore is loaded, the core is initialized.
See test/modules for an example
A module has a few public properties that are used by the Core.
A module can define it's dependencies through expose
and the dependencies are mixed into the module.
A module also has an init
method which is invoked when the core is done initializing and a setup
method which is invoked before init to do asynchronous setup logic for that module
Modules are handled in four phases,
setup
phase, where modules can do asynchronous setup up logic. This happens after all dependencies are injected into all modulesinit
phase, this happens after every module says it's done it's setup. This also happens after the callback on Core.init
. This phase is meant to start your application like starting your HTTP server.The optional setup method allows a module to setup logic
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("db", {
setup: function (done) {
db.openConnection(done)
}
})
Core.init()
Instead of defining an interface you can say what parts of the module should be exposed. Expose should contain an array containing the propertyNames that should become part of the interface
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("name", {
public: function () {
this.private();
},
private: function () {
// this is also an event emitter
this.emit("somethingHappened");
},
expose: ["public"]
})
Core.interfaces.name.on("somethingHappened", function () {
assert(true);
})
Core.interfaces.name.public();
assert(!Core.interfaces.name.private)
``` javascript var Core = Object.create(require("ncore")).constructor(), assert = require("assert")
Core.use("name", { public: function () { this.private(); }, otherPublic: function () { // this is also an event emitter this.emit("somethingHappened"); } })
Core.interfaces.name.on("somethingHappened", function () { assert(true); }) Core.interfaces.name.public(); assert(Core.interfaces.name.otherPublic) ```
A module exposes an init method which is invoked when the core is done initializing.
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("name", {
setup: function () {
assert("happens first");
},
init: function () {
assert("happens third");
}
})
Core.init(function () {
assert("happens second")
})
The destroy method is invoked when the module is removed from the core. This is the cleanup method, here your supposed to clean up any coupling to other modules and destroy any state/objects that still exist
var Core = Object.create(require("ncore")).constructor(),
bigObject = new BigObject();
Core.use("name", {
destroy: function () {
// clean objects from memory
bigObject = null;
}
})
Core.remove("name");
The Core is an object you attach modules to. It keeps a record of the dependency mapping between modules and initializes multiple modules with their correct dependencies.
The Core has a public property named interfaces that contains all the public interfaces attached to the core.
A public interface is a proxy of the internal interface (the one passed to define) which means it has all the same properties / methods, it just has a thin proxy that invokes them indirectly. This proxy exist for easy module hot reloading.
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert");
Core.use("name", {
method: function () { },
expose: ["method"]
})
assert(Core.interfaces.name.method);
The Core exposes it's dependency mapping as Core.dependencies
. This can be manipulated after the constructor is invoked
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("name", {
init: function () {
assert(this.a);
}
})
Core.use("a", moduleA);
Core.dependencies.name = {
a: "a"
};
Core.init();
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
var name = Core.use("name", {
foo: function () {
return this.bars;
},
expose: ["foo"]
})
Core.use("a", moduleA)
Core.use("b", moduleB)
Core.dependencies.name = {
bars: ["a", "b"]
}
Core.init
assert(name.foo().length === 2)
Instantiate an instance of the core. You need to pass the dependency mapping to the core so it knows what to inject into what module. You can also optionally pass in EventEmitter prototype which will be mixed into every interface.
var ncore = require("ncore");
// create an instance
var Core = Object.create(ncore).constructor();
Core.use(...);
var ncore = require("ncore"),
assert = require("assert");
var Core = Object.create(ncore).constructor({
// The dependencies for foo are
foo: {
// an object called bar that contains the interface baz
bar: "baz"
}
});
var bar = Core.use("baz", bazObject);
Core.use("foo", {
init: function () {
// For module foo the deps have been mixed in
// so this has a bar property
// that is the interface of bazObject
assert.equal(this.bar, bar);
}
});
Core.init()
var ncore = require("ncore");
// constructor also accepts strings as arguments
var Core = Object.create(ncore).constructor(require("./dependency.json"));
var ncore = require("ncore"),
EventEmitter = require("eventemitter2").EventEmitter.prototype;
var Core = Object.create(ncore).constructor(null, EventEmitter);
Core.use("bar", {});
Core.init()
// The bar interface is now an EventEmitter2 instance
assert(Core.interfaces.bar.many)
Use a module to attach it to the Core. The core will ask the module to define it's public interface.
Core.use returns the public interface
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("name", {
prop: true,
method: function () {
// this === module
this.private()
},
private: function () {
...
},
expose: ["prop", "method"]
})
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("name", {
// define the interface as an object
expose: {
interface_method: function () {
}
}
})
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("name", {
public: function () {
...
},
private: function () {
},
expose: ["public"]
})
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
var name = Core.use("name", {});
assert.equal(name, Core.interfaces.name);
init will inject dependencies into modules. It also invokes the init method on modules after they are done with dependency injection.
The optional callback to init will be called after all modules are done with the setup.
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("bar", barObject);
Core.use("name", {
setup: function (done) {
assert(this.bar);
assert("happens first");
done();
},
init: function () {
assert("happens third");
}
})
Core.dependencies.name = {
bar: "bar"
};
Core.init(function () {
assert("happens second");
})
Remove the module by that name. This will invoke the destroy method on the module
var Core = Object.create(require("ncore")).constructor(),
assert = require("assert")
Core.use("name", {
destroy: function () {
...
}
})
Core.remove("name");
removes all modules
Core.purge();
// Core is clean now
nCore comes with a set of default modules implemented to do specific tasks
The moduleLoader exposes an interface to autoLoad modules from a folder. It also exposes an API to write your dependency map based on files.
var Core = Object.create(require("ncore")).constructor()
moduleLoader = Core.use("moduleLoader",
require("ncore/modules/moduleLoader")),
path = require("path")
moduleLoader.load({
uri: path.join(__dirname, "./modules"),
dependencies: require("./dependencies.json"),
core: Core,
callback: init
})
Call load on the moduleLoader to load all the modules in a folder, moduleLoader also takes a callback that's called when it's done loading and it also takes a file based dependencies object.
var Core = Object.create(require("ncore")).constructor()
moduleLoader = Core.use("moduleLoader",
require("ncore/modules/moduleLoader")),
path = require("path")
moduleLoader.load({
uri: path.join(__dirname, "./modules"),
dependencies: require("./dependencies.json"),
core: Core,
callback: init
});
function init(err) {
if (err) {
return console.log("error loading", err, err.stack);
}
Core.init();
}
// dependencies.json format
{
// bar and foo will be injected into module located at foo.js
"./foo.js": {
// value of bar will be /bar/bar.js
"bar": "./bar/bar.js",
// value of foo will be the module inside folder /bar/ that matches the same fileName as this module
// in this case that would be foo.js
"foo": "./bar/"
},
// foo, bars and foobar will be injected into all modules located directly under folder bar
"./bar/": {
// value of foo will be fine /foo.js
"foo": "./foo.js",
// value of bars will be an (unsorted) array containing all modules directly under folder bar
"bars": ["./bar/"],
// value of foobar will be the module matching the same fileName under bar as the current
// module fileName (i.e. itself, for each module under bar)
"foobar": "./bar/"
}
}
$ npm install ncore
$ make test


Samples Tutorial
1. Logging in
You need to log in to MISO LIMS in order to make changes to any LIMS entities. Logging in lets MISO record any changes you make on entities you have access to. If you are a new user and have not been given a username and password, you will first need to get a username and password by following the steps in the Adding Users section.
Try to log in now:
- Click on http://miso.gsi.oicr.on.ca.
- Enter your username (e.g. jdoe) and password and click the Login button.
If all goes well, you should see the MISO Dashboard and see a message at the top right: "Logged in as: jdoe".
2. Receiving samples
A Sample contains information about the material upon which the sequencing experiments are to be based. Samples can be used in any number of sequencing Experiments in the form of a Library that is processed further into single or pooled Dilutions.
2.1 Receiving a Sample
Samples are most easily entered into MISO using the bulk entry screen.
- On the left hand menu under Tracking, click Samples.
- Click the Create button at the top left of the table.
- Select Sample Class Tissue and Quantity 1.
- Click Create.
- Enter or select the following in the table:
-
Sample Name: leave blank. This will be auto-generated after save.
- Sample Alias: enter a sample alias like
PRO1_S#_#
. Replace the#
symbol with numbers of your choosing. -
Description:
Wheat
- Date of Receipt: Select a date
- Sample Type: select
GENOMIC
from the drop down. - Scientific Name:
Triticum aestivum
.
-
-
Project: Select the project you created in the last exercise.
- QC Passed?:
True
- QC Passed?:
- At the upper right hand side, click Save.
Like in Excel, you can fill down a column by double-clicking the square at the lower right hand side of a selected cell. You can also click and drag to only fill in a certain number of cells.
Copy and pasting from Excel and Word is supported. Press Ctrl+V (Windows & Linux) or Command+V (Mac) on your keyboard to paste.
2.2 Scanning tissues and stocks into a Box
In this section we will add the stocks and tissues you just received into your inbox for further work.
Boxes hold samples, libraries, and pools. They do not track reagents or primers. Boxes are separated into uses for different kinds of storage (e.g., tissue samples versus extracted DNA), but there is no check that items in a box match. Each box also has a size that includes the physical dimensions of the box as well as whether the box is compatible with the VisionMate scanner. Every position in the box is identified by a standard row letter + column number format (e.g., C05).
In this exercise, we are going to take tissues/samples/libraries from the previous team/collaborator by scanning them out of the previous box and into a newly created 'inbox'.
First, create a new box. This box will be your 'inbox'.
- From the navigation panel, choose Boxes and then Add Box.
- On the Create Box page, enter the information:
- Alias: A short name for the box. Record this name in your worksheet.
- Description: a human description of the box purpose “gDNA ready for
library prep for
”. - Use: The contents of the box. Select an appropriate use for your entities.
- Size: Select
8 x 12 scannable
. - Click Save.
Upon clicking save, a graphic of the box will appear.
You can now fill the box with the tissues, stocks, aliquots, libraries, or pools that were made in previous steps. Collect the appropriate matrix tubes/barcodes now.
- Click on a position in the displayed Contents grid.
- Enter a matrix barcode into the Search box on the right and click Lookup. Normally a hand scanner would be used. You can also search for items by their name or alias
- Click Update Position.
- ⚠ The Save button at the top of the page does not work for individual positions, only for Box Information.
- Repeat for as many samples/libraries/pools as you want.
The table below the box diagram shows the position and information for the currently selected sample. If you would like to see all of the samples in the table, click the List all Box Contents button at the top right of the table.
In the lab, it is possible to use Options > Scan Box to use the plate scanner and update all positions at once, but that will not be covered in this tutorial.
Using Boxes
Boxes can be found either from the Sample or Library page or the Boxes page.
- On the Sample page, search for one of your sample or library aliases and click on the alias to load the page.
- The Box and position is listed under Location near the top of the Sample
Information section. Click on the link to go to the Box (e.g.
PROJ_OUTBOX, A03
).
Boxes can be used to store Samples, Libraries and Pools and one box can store all three types.
2.3 Bulk Editing
Samples can be edited in bulk. Assume that we wish to update the description of the samples.
- Select the sample you wish to edit:
- If you are continuing from the end of section 2.1 Receiving a Sample, do not
navigate away from the page. At the top left of the table after saving samples, click
the Edit button. Continue to step 2. Otherwise, select samples using the following:
- On the Samples page, enter your project name in the search box.
- Check the boxes for the samples that you created in section 2.1.
- If you are continuing from the end of section 2.1 Receiving a Sample, do not
navigate away from the page. At the top left of the table after saving samples, click
the Edit button. Continue to step 2. Otherwise, select samples using the following:
- Click the Edit button at the top left of the table.
- Change the Description value: any value
- Click Save.
3. Sample QCs
Several QC methods are supported in MISO.
3.1 Adding Bulk Sample QCs
- Select the samples you wish to edit:
- If you are continuing from the end of section 2.3 do not navigate away from the page. At the top left of the table after saving samples, click the Add QCs button. Continue to step 2. Otherwise, select samples using the following:
- On the Samples page, enter your project name in the search box.
- Check the boxes for the samples that you created in section 3.2.
- Click Add QCs at the top left of the table.
- Enter
1
QC per sample and click Add. - Date: enter today's date
- Type:
Qubit
- Result:
33
- Click Save.
4. Scanning samples into outboxes
Finally, scan the samples you made into the outbox so the libraries team can take over.
Finally, update the TUTORIAL_OUTBOX with your samples/libraries/pools to pass them on to the next team or to put them into storage.
Removing and discarding tubes
When you have a position selected, you can also either remove the tube from the box (setting its location to "Unknown") or discard the tube, meaning it has been used up. Discarding the tube sets the volume of the sample to 0 and marks it as "discarded", and removes it from its box.
- Click on one of the positions in the box with a tube. The position, alias, and barcode will appear to the right or below the box graphic. Right-click on the alias and pick "Open in new tab" to load the details page.
- Go back to the Box page in the other tab. Make sure the tube is still selected in the Box and click Discard Tube. Click "OK" in the pop-up.
- Go to the other tab with the Details page and click the browser refresh button. The Location field will show as blank, Volume will be set to 0.0, and the Discarded box will be ticked.
Moving items around in boxes
An item can only exist in one box. If assigned to a new box, it will disappear from the original.
- In the current tab, go to the Boxes page, find the TUTORIAL_OUTBOX box. This box was previously created by the MISO developers for the tutorial.
- Choose an empty position and enter one of your barcodes.
- Click Search and Update Position.
- Go back to your own Box and refresh the page. The sample was removed.
- Repeat this process for the rest of the samples/libraries that you are transferring to the next team.
| < Project coordination tutorial | Home | Libraries tutorial > |


Your First Session
We’ll chat things through, to make sure I understand exactly what you want, and what your child needs. I will carefully assess your child’s strengths, and also identify areas where they need support and extension. Then I create a tailored teaching programme for your child and will review this with you, and answer any questions you may have.
As your child learns and develops, I adjust and reshape the programme to ensure they continue to progress.


Antharas Watchman Gilmore:
Could it be? If my eyes aren't deceiving me this used to belong to a friend of mine...
"Tell me more, Gilmore."


--- layout: post title: Setting up Wordpress-MU for an organization date: 2008-08-11 10:37:57.000000000 +05:30 categories: - Nexenta - tech tags: [] status: publish type: post published: true meta: {} author: login: admin email<EMAIL_ADDRESS>display_name: admin first_name: '' last_name: '' ---
I figured I'd write this guide for those who have certain needs to setup a blogging infrastructure for an organization.
I've joined the Nexenta project and one of my first tasks was to setup blogs and a planet. The requirements were thus:
My first choice was Wordpress Mu, given that I already had experience with WP. Setting it up was easy.. below is a quick overview of what I did:
Now to setup the planet.. install the following
Set FeedWordpress to post feeds directly, and check for new content every 10-20 minutes. When you give it feed links of services like blogspot, be sure to pass RSS feeds rather than ATOM.
Add a few themes and your blogging platform is ready to serve.


LabVIEW 2012
The Continuous Measurement and Logging (NI-DAQmx) sample project acquires measurements continuously and logs them to disk. It executes five loops in parallel:
This sample project also features a Settings dialog box (Settings.lvlib) you can use to configure the application.
This sample project is based on the Queued Message Handler template. Refer to the Queued Message Handler template and its documentation, available from the Create Project dialog box, for information about how this template works.
Refer to ni.com for a developer walkthrough of the sample projects.
LabVIEW Base Package and NI-DAQmx driver software.
The Continuous Measurement and Logging (NI-DAQmx) sample project is designed for a continuous measurement application that requires a responsive user interface; that is, users should be able to click buttons even while the application is executing another command.
If the default logging behavior does not meet the needs of your application, you can modify this sample project in the following ways:
Copyright
© 2012 National Instruments. All rights reserved.
Under the copyright laws, this publication may not be reproduced or transmitted in any form, electronic or mechanical, including photocopying, recording, storing in an information retrieval system, or translating, in whole or in part, without the prior written consent of National Instruments Corporation.
National Instruments respects the intellectual property of others, and we ask our users to do the same. NI software is protected by copyright and other intellectual property laws. Where NI software may be used to reproduce software or other materials belonging to others, you may use NI software only to reproduce materials that you may reproduce in accordance with the terms of any applicable license or other legal restriction.
End-User License Agreements and Third-Party Legal Notices
You can find end-user license agreements (EULAs) and third-party legal notices in the following locations:
Trademarks
LabVIEW, National Instruments, NI, ni.com, the National Instruments corporate logo, and the Eagle logo are trademarks of National Instruments Corporation. Refer to the Trademark Information at ni.com/trademarks for other National Instruments trademarks.
Other product and company names mentioned herein are trademarks or trade names of their respective companies.
Patents
For patents covering the National Instruments products/technology, refer to the appropriate location: Help»Patents in your software, the patents.txt file on your media, or the National Instruments Patent Notice at ni.com/patents.


Bayesian Hierarchical Linear Regression¶
Author: Carlos Souza
Probabilistic Machine Learning models can not only make predictions about future data, but also model uncertainty. In areas such as personalized medicine, there might be a large amount of data, but there is still a relatively small amount of data for each patient. To customize predictions for each person it becomes necessary to build a model for each person — with its inherent uncertainties — and to couple these models together in a hierarchy so that information can be borrowed from other similar people [1].
The purpose of this tutorial is to demonstrate how to implement a Bayesian Hierarchical Linear Regression model using NumPyro. To motivate the tutorial, I will use OSIC Pulmonary Fibrosis Progression competition, hosted at Kaggle.
1. Understanding the task¶
Pulmonary fibrosis is a disorder with no known cause and no known cure, created by scarring of the lungs. In this competition, we were asked to predict a patient’s severity of decline in lung function. Lung function is assessed based on output from a spirometer, which measures the forced vital capacity (FVC), i.e. the volume of air exhaled.
In medical applications, it is useful to evaluate a model’s confidence in its decisions. Accordingly, the metric used to rank the teams was designed to reflect both the accuracy and certainty of each prediction. It’s a modified version of the Laplace Log Likelihood (more details on that later).
Let’s explore the data and see what’s that all about:
[1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
[2]:
train = pd.read_csv('https://gist.githubusercontent.com/ucals/'
'2cf9d101992cb1b78c2cdd6e3bac6a4b/raw/'
'43034c39052dcf97d4b894d2ec1bc3f90f3623d9/'
'osic_pulmonary_fibrosis.csv')
train.head()
[2]:
| Patient | Weeks | FVC | Percent | Age | Sex | SmokingStatus | |
|---|---|---|---|---|---|---|---|
| 0 | ID00007637202177411956430 | -4 | 2315 | 58.253649 | 79 | Male | Ex-smoker |
| 1 | ID00007637202177411956430 | 5 | 2214 | 55.712129 | 79 | Male | Ex-smoker |
| 2 | ID00007637202177411956430 | 7 | 2061 | 51.862104 | 79 | Male | Ex-smoker |
| 3 | ID00007637202177411956430 | 9 | 2144 | 53.950679 | 79 | Male | Ex-smoker |
| 4 | ID00007637202177411956430 | 11 | 2069 | 52.063412 | 79 | Male | Ex-smoker |
In the dataset, we were provided with a baseline chest CT scan and associated clinical information for a set of patients. A patient has an image acquired at time Week = 0 and has numerous follow up visits over the course of approximately 1-2 years, at which time their FVC is measured. For this tutorial, I will use only the Patient ID, the weeks and the FVC measurements, discarding all the rest. Using only these columns enabled our team to achieve a competitive score, which shows the power of Bayesian hierarchical linear regression models especially when gauging uncertainty is an important part of the problem.
Since this is real medical data, the relative timing of FVC measurements varies widely, as shown in the 3 sample patients below:
[3]:
def chart(patient_id, ax):
data = train[train['Patient'] == patient_id]
x = data['Weeks']
y = data['FVC']
ax.set_title(patient_id)
ax = sns.regplot(x, y, ax=ax, ci=None, line_kws={'color':'red'})
f, axes = plt.subplots(1, 3, figsize=(15, 5))
chart('ID00007637202177411956430', axes[0])
chart('ID00009637202177434476278', axes[1])
chart('ID00010637202177584971671', axes[2])
/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
On average, each of the 176 provided patients made 9 visits, when FVC was measured. The visits happened in specific weeks in the [-12, 133] interval. The decline in lung capacity is very clear. We see, though, they are very different from patient to patient.
We were are asked to predict every patient’s FVC measurement for every possible week in the [-12, 133] interval, and the confidence for each prediction. In other words: we were asked fill a matrix like the one below, and provide a confidence score for each prediction:
The task was perfect to apply Bayesian inference. However, the vast majority of solutions shared by Kaggle community used discriminative machine learning models, disconsidering the fact that most discriminative methods are very poor at providing realistic uncertainty estimates. Because they are typically trained in a manner that optimizes the parameters to minimize some loss criterion (e.g. the predictive error), they do not, in general, encode any uncertainty in either their parameters or the subsequent predictions. Though many methods can produce uncertainty estimates either as a by-product or from a post-processing step, these are typically heuristic based, rather than stemming naturally from a statistically principled estimate of the target uncertainty distribution [2].
2. Modelling: Bayesian Hierarchical Linear Regression with Partial Pooling¶
The simplest possible linear regression, not hierarchical, would assume all FVC decline curves have the same \(\alpha\) and \(\beta\). That’s the pooled model. In the other extreme, we could assume a model where each patient has a personalized FVC decline curve, and these curves are completely unrelated. That’s the unpooled model, where each patient has completely separate regressions.
Here, I’ll use the middle ground: Partial pooling. Specifically, I’ll assume that while \(\alpha\)’s and \(\beta\)’s are different for each patient as in the unpooled case, the coefficients all share similarity. We can model this by assuming that each individual coefficient comes from a common group distribution. The image below represents this model graphically:
Mathematically, the model is described by the following equations:
\begin{align} \mu_{\alpha} &\sim \mathcal{N}(0, 100) \\ \sigma_{\alpha} &\sim |\mathcal{N}(0, 100)| \\ \mu_{\beta} &\sim \mathcal{N}(0, 100) \\ \sigma_{\beta} &\sim |\mathcal{N}(0, 100)| \\ \alpha_i &\sim \mathcal{N}(\mu_{\alpha}, \sigma_{\alpha}) \\ \beta_i &\sim \mathcal{N}(\mu_{\beta}, \sigma_{\beta}) \\ \sigma &\sim \mathcal{N}(0, 100) \\ FVC_{ij} &\sim \mathcal{N}(\alpha_i + t \beta_i, \sigma) \end{align}
where t is the time in weeks. Those are very uninformative priors, but that’s ok: our model will converge!
Implementing this model in NumPyro is pretty straightforward:
[4]:
import numpyro
from numpyro.infer import MCMC, NUTS, Predictive
import numpyro.distributions as dist
from jax import random
[5]:
def model(PatientID, Weeks, FVC_obs=None):
μ_α = numpyro.sample("μ_α", dist.Normal(0., 100.))
σ_α = numpyro.sample("σ_α", dist.HalfNormal(100.))
μ_β = numpyro.sample("μ_β", dist.Normal(0., 100.))
σ_β = numpyro.sample("σ_β", dist.HalfNormal(100.))
unique_patient_IDs = np.unique(PatientID)
n_patients = len(unique_patient_IDs)
with numpyro.plate("plate_i", n_patients):
α = numpyro.sample("α", dist.Normal(μ_α, σ_α))
β = numpyro.sample("β", dist.Normal(μ_β, σ_β))
σ = numpyro.sample("σ", dist.HalfNormal(100.))
FVC_est = α[PatientID] + β[PatientID] * Weeks
with numpyro.plate("data", len(PatientID)):
numpyro.sample("obs", dist.Normal(FVC_est, σ), obs=FVC_obs)
That’s all for modelling!
3. Fitting the model¶
A great achievement of Probabilistic Programming Languages such as NumPyro is to decouple model specification and inference. After specifying my generative model, with priors, condition statements and data likelihood, I can leave the hard work to NumPyro’s inference engine.
Calling it requires just a few lines. Before we do it, let’s add a numerical Patient ID for each patient code. That can be easily done with scikit-learn’s LabelEncoder:
[6]:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
train['PatientID'] = le.fit_transform(train['Patient'].values)
FVC_obs = train['FVC'].values
Weeks = train['Weeks'].values
PatientID = train['PatientID'].values
[16]:
numpyro.set_host_device_count(4)
Now, calling NumPyro’s inference engine:
[7]:
nuts_kernel = NUTS(model)
mcmc = MCMC(nuts_kernel, num_samples=2000, num_warmup=2000)
rng_key = random.PRNGKey(0)
mcmc.run(rng_key, PatientID, Weeks, FVC_obs=FVC_obs)
posterior_samples = mcmc.get_samples()
sample: 100%|██████████| 4000/4000 [00:27<00:00, 144.62it/s, 63 steps of size 1.09e-01. acc. prob=0.88]
4. Checking the model¶
4.1. Inspecting the learned parameters¶
First, let’s inspect the parameters learned. To do that, I will use ArviZ, which perfectly integrates with NumPyro:
[8]:
import arviz as az
data = az.from_numpyro(mcmc)
az.plot_trace(data, compact=True);
/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.
data = yaml.load(f.read()) or {}
Looks like our model learned personalized alphas and betas for each patient!
4.2. Visualizing FVC decline curves for some patients¶
Now, let’s visually inspect FVC decline curves predicted by our model. We will completely fill in the FVC table, predicting all missing values. The first step is to create a table to fill:
[9]:
pred_template = []
for i in range(train['Patient'].nunique()):
df = pd.DataFrame(columns=['PatientID', 'Weeks'])
df['Weeks'] = np.arange(-12, 134)
df['PatientID'] = i
pred_template.append(df)
pred_template = pd.concat(pred_template, ignore_index=True)
Predicting the missing values in the FVC table and confidence (sigma) for each value becomes really easy:
[10]:
PatientID = pred_template['PatientID'].values
Weeks = pred_template['Weeks'].values
predictive = Predictive(model, posterior_samples,
return_sites=['σ', 'obs'])
samples_predictive = predictive(random.PRNGKey(0),
PatientID, Weeks, None)
Let’s now put the predictions together with the true values, to visualize them:
[11]:
df = pd.DataFrame(columns=['Patient', 'Weeks', 'FVC_pred', 'sigma'])
df['Patient'] = le.inverse_transform(pred_template['PatientID'])
df['Weeks'] = pred_template['Weeks']
df['FVC_pred'] = samples_predictive['obs'].T.mean(axis=1)
df['sigma'] = samples_predictive['obs'].T.std(axis=1)
df['FVC_inf'] = df['FVC_pred'] - df['sigma']
df['FVC_sup'] = df['FVC_pred'] + df['sigma']
df = pd.merge(df, train[['Patient', 'Weeks', 'FVC']],
how='left', on=['Patient', 'Weeks'])
df = df.rename(columns={'FVC': 'FVC_true'})
df.head()
[11]:
| Patient | Weeks | FVC_pred | sigma | FVC_inf | FVC_sup | FVC_true | |
|---|---|---|---|---|---|---|---|
| 0 | ID00007637202177411956430 | -12 | 2220.162598 | 159.290878 | 2060.871826 | 2379.453369 | NaN |
| 1 | ID00007637202177411956430 | -11 | 2210.083496 | 157.518021 | 2052.565430 | 2367.601562 | NaN |
| 2 | ID00007637202177411956430 | -10 | 2213.199951 | 154.847916 | 2058.352051 | 2368.047852 | NaN |
| 3 | ID00007637202177411956430 | -9 | 2209.025391 | 153.300079 | 2055.725342 | 2362.325439 | NaN |
| 4 | ID00007637202177411956430 | -8 | 2203.191895 | 156.085449 | 2047.106445 | 2359.277344 | NaN |
Finally, let’s see our predictions for 3 patients:
[12]:
def chart(patient_id, ax):
data = df[df['Patient'] == patient_id]
x = data['Weeks']
ax.set_title(patient_id)
ax.plot(x, data['FVC_true'], 'o')
ax.plot(x, data['FVC_pred'])
ax = sns.regplot(x, data['FVC_true'], ax=ax, ci=None,
line_kws={'color':'red'})
ax.fill_between(x, data["FVC_inf"], data["FVC_sup"],
alpha=0.5, color='#ffcd3c')
ax.set_ylabel('FVC')
f, axes = plt.subplots(1, 3, figsize=(15, 5))
chart('ID00007637202177411956430', axes[0])
chart('ID00009637202177434476278', axes[1])
chart('ID00011637202177653955184', axes[2])
/Users/pdh21/anaconda3/envs/xidplus/lib/python3.6/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
The results are exactly what we expected to see! Highlight observations: - The model adequately learned Bayesian Linear Regressions! The orange line (learned predicted FVC mean) is very inline with the red line (deterministic linear regression). But most important: it learned to predict uncertainty, showed in the light orange region (one sigma above and below the mean FVC line) - The model predicts a higher uncertainty where the data points are more disperse (1st and 3rd patients). Conversely, where the points are closely grouped together (2nd patient), the model predicts a higher confidence (narrower light orange region) - Finally, in all patients, we can see that the uncertainty grows as the look more into the future: the light orange region widens as the # of weeks grow!
4.3. Computing the modified Laplace Log Likelihood and RMSE¶
As mentioned earlier, the competition was evaluated on a modified version of the Laplace Log Likelihood. In medical applications, it is useful to evaluate a model’s confidence in its decisions. Accordingly, the metric is designed to reflect both the accuracy and certainty of each prediction.
For each true FVC measurement, we predicted both an FVC and a confidence measure (standard deviation \(\sigma\)). The metric was computed as:
\begin{align} \sigma_{clipped} &= max(\sigma, 70) \\ \delta &= min(|FVC_{true} - FVC_{pred}|, 1000) \\ metric &= -\dfrac{\sqrt{2}\delta}{\sigma_{clipped}} - \ln(\sqrt{2} \sigma_{clipped}) \end{align}
The error was thresholded at 1000 ml to avoid large errors adversely penalizing results, while the confidence values were clipped at 70 ml to reflect the approximate measurement uncertainty in FVC. The final score was calculated by averaging the metric across all (Patient, Week) pairs. Note that metric values will be negative and higher is better.
Next, we calculate the metric and RMSE:
[13]:
y = df.dropna()
rmse = ((y['FVC_pred'] - y['FVC_true']) ** 2).mean() ** (1/2)
print(f'RMSE: {rmse:.1f} ml')
sigma_c = y['sigma'].values
sigma_c[sigma_c < 70] = 70
delta = (y['FVC_pred'] - y['FVC_true']).abs()
delta[delta > 1000] = 1000
lll = - np.sqrt(2) * delta / sigma_c - np.log(np.sqrt(2) * sigma_c)
print(f'Laplace Log Likelihood: {lll.mean():.4f}')
RMSE: 122.1 ml
Laplace Log Likelihood: -6.1375
What do these numbers mean? It means if you adopted this approach, you would outperform most of the public solutions in the competition. Curiously, the vast majority of public solutions adopt a standard deterministic Neural Network, modelling uncertainty through a quantile loss. Most of the people still adopt a frequentist approach.
Uncertainty for single predictions becomes more and more important in machine learning and is often a requirement. Especially when the consequenses of a wrong prediction are high, we need to know what the probability distribution of an individual prediction is. For perspective, Kaggle just launched a new competition sponsored by Lyft, to build motion prediction models for self-driving vehicles. “We ask that you predict a few trajectories for every agent and provide a confidence score for each of them.”
Finally, I hope the great work done by Pyro/NumPyro developers help democratize Bayesian methods, empowering an ever growing community of researchers and practitioners to create models that can not only generate predictions, but also assess uncertainty in their predictions.
References¶
Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 521, 452–459 (2015). https://doi.org/10.1038/nature14541
Rainforth, Thomas William Gamlen. Automating Inference, Learning, and Design Using Probabilistic Programming. University of Oxford, 2017.
[ ]:


Pizza Hawt
To order a pizza, choose a size, toppings and voila!
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[gecode-users] Reified max/min
Mikael Zayenz Lagerkvist
zayenz at gmail.com
Wed Apr 18 17:39:35 CEST 2007
Hi,
There is no reified version of min available. However you can solve
this easily by introducing an auxiliary variable.
Say that you want to express
b <=> min(x_0,..x_n)=y
This can be modeled as follows
min(x_0,...,x_n)=t
b <=> t=y
where t is a otherwise unconstrained variable with an initial domain
including all possible values for x_0,...,x_n.
Hope this helps,
Mikael
On 4/18/07, Jonathan MÃ¶rndal <Jonathan.Morndal.7625 at student.uu.se> wrote:
> Hello all!
>
> I would like to use a reified version of the max/min functions in
> Gecode/J. As far as I can see there are none. Is this true, and if so,
> are there any "quick fix"?
>
> One idea I had was to model it as numerous inequalities and equalities
> instead, but I guess the propagation will not be very good in that case.
>
>
> Thanks in advance for any help!
> /Jonathan
>
> Clarification:
>
> What I want to model exactly:
>
> x_0 = min{x_i,...,x_j}
> or
> x_1 = min{x_k,...,x_l}
> or
> ...
> or
> x_p = min{x_m,...,x_n}
>
> My (probably pretty bad) solution would then be
>
> ((x_0 \leq x_i and ... and x_0 \leq x_j)
> and
> (x_0 = x_i or ... or x_0 = x_j))
>
> or
>
> ((x_1 \leq x_k and ... and x_1 \leq x_l)
> and
> (x_1 = x_k or ... or x_1 = x_l))
>
> or
>
> ...
>
> or
>
> ((x_p \leq x_m and ... and x_p \leq x_n)
> and
> (x_p = x_m or ... or x_p = x_n))
>
>
>
> _______________________________________________
> Gecode users mailing list
> users at gecode.org
> https://www.gecode.org/mailman/listinfo/gecode-users
>
--
Mikael Zayenz Lagerkvist, http://www.ict.kth.se/~zayenz/
More information about the gecode-users
mailing list


4 days ago friend of mine showed up to my house telling me he wanted to try making this Tea out of these white/yellow bell looking flowers, he told me the tea would make me see things, I laughed, then humored him , and boiled the flowers to make enough to fill 2 cups.
The drink didnít taste bad at all, actually didnít need sugar or anything. We each drank our cup full, and rolled out for while to visit friends. This was at about 2pm. By 2:30 I noticed the effects, my vision became framed, each movement was jerky, I noticed the sparkles that we moving from my peripherals to the center, I thought it was wonderful... best trip ever... then worst trip ever.
I had to drop the other back at the house an hour after, not really understanding what he was going through. A bit earlier he was trying to smoke a bowl, using his thumb as the lighter, had me rollin!  He asked us 100x where the pipe, lighter, and weed was, each time having it right in his hand, or his lap.
 As he was getting out of the back seat of my car he got stuck on STUPID, took him at least 5 mins to get out of the car as me and a few people outside the car laughed. At this point I had no idea that his LEGS DIDNT WORK.   Straight to the E.R.  And was rushed to the hospital.
Now MY TRIP.
[Erowid Note: Driving while intoxicated or tripping is dangerous and irresponsible because it endangers other people. Don't do it!]
Things a still a little blurry still it has only been 4 days for me. I remember driving, but not seeing anything... Yes I drove on it... Very very very very stupid, and dangerous for me and my passengers. I remember not seeing the car in front of my stop, and me having to slam on my brakes, barely missing by millimeters, I should have crashed. As soon as I slammed on the brakes, I was lost again. Then I heard my buddy in the seat next to make a noise, I nearly freaked, forgetting I wasnít here alone.  Then I almost crashed again into the back of a stopped car. Donít know how I missed that car.
I found my self saying my thoughts out loud, it trip my passengers out for real, one asking me if he could drive, and me saying HELL NO. I thought I was in total control. I was wrong.
I cant remember too much right now, but I do remember getting out of my car at 11pm, and not being able to move my legs, like I didnít have any, I had to concentrate on which leg I was moving so I didnít fall over.
Before bed each night I read a little to help fall asleep, I couldnít, everything was blurred and double vision, I had to cross my eyes to read one side of the blurred writing, that gave me a bad head rush, got dizzy from it so I just went to sleep.
I didnít trip while I was a sleep, only got 5 hours sleep, woke up fine. Barely any lingering effect... a little confused about the last night, possible still tripping, but no invisible people, donít even remember any pictures, or distant images or ghosts from the previous night... unfortunately my friend wasnít so lucky.

In a couple days they are going to transfer him to the Psych ward at [Erowid Note: Name of Institution expurgated], where he may be deemed FUCKED UP FOR LIFE!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Class for half-idempotent symmetric polynomials.
More...
#include <Half_Idempotent.hpp>
|
| | TwistedChernBasis (int n) |
| | Constructs the generators and the relation set given \(n\) in \(x_1,...,x_n,y_1,...,y_n\). More...
|
| |
| const std::vector< chern_poly_t > & | relations () const |
| | The relations are \(\gamma_{s,i}\gamma_{t,j}\) for \(0<s<=t<=s+i\) and \(i,j>0\). More...
|
| |
| const xy_poly_t & | generator (int s, int j) const |
| | The generator \(\gamma_{s,j}\). More...
|
| |
| chern_poly_t | operator() (xy_poly_t a) const |
| | Transform a polynomial on the original variables to one on the generating basis. More...
|
| |
| xy_poly_t | operator() (const chern_poly_t &a) const |
| | Transform a polynomial on the generating basis into a polynomial on the original variables. More...
|
| |
| | PolynomialBasis (int num) |
| | Constructor given number of variables. More...
|
| |
| const std::vector< xy_poly_t > & | generators () const |
| | Returns vector containing the generating basis. More...
|
| |
| const std::vector< typename new_poly_t::deg_t > & | dimensions () const |
| | Returns vector containing the dimensions of the generating basis (can be empty!) More...
|
| |
| const std::vector< std::string > & | names () const |
| | Returns vector containing the names of the generating basis (can be empty!) More...
|
| |
|
| const int | number_of_variables |
| | The number of (the original) variables of the polynomial ring. More...
|
| |
| std::vector< xy_poly_t > | _generators |
| | The generators of the polynomial basis, constructed in the inheriting class. More...
|
| |
| std::vector< typename new_poly_t::deg_t > | generator_dimensions |
| | The dimensions of the generators, optionally constructed in the inheriting class. More...
|
| |
| std::vector< std::string > | generator_names |
| | The names of the generators, optionally constructed in the inheriting class. More...
|
| |
template<class xy_poly_t, class chern_poly_t>
class symmp::TwistedChernBasis< xy_poly_t, chern_poly_t >
Class for half-idempotent symmetric polynomials.
Facilitates transformation from \(x_i,y_i\) variables to \(\gamma_{s,i}\) variables and vice-versa.
- Template Parameters
-
◆ TwistedChernBasis()
Constructs the generators and the relation set given \(n\) in \(x_1,...,x_n,y_1,...,y_n\).
- Parameters
-
| n | Half(!) the number of variables |
- Attention
- The parameter
n
is half(!) the number of variables (the \(n\) in \(BU(n)\))
◆ relations()
| const std::vector<chern_poly_t>& relations |
( |
| ) |
const |
The relations are \(\gamma_{s,i}\gamma_{t,j}\) for \(0<s<=t<=s+i\) and \(i,j>0\).
- Returns
- const& to vector containing the relation Polynomials \(\gamma_{s,i}\gamma_{t,j}\)
◆ generator()
| const xy_poly_t& generator |
( |
int |
s, |
|
|
int |
j |
|
) |
| const |
The generator \(\gamma_{s,j}\).
- Parameters
-
| s | The index of \(s\) of \(\gamma_{s,j}\). |
| j | The index of \(j\) of \(\gamma_{s,j}\). |
- Returns
- const& to the polynomial \(\gamma_{s,j}\) on the \(x_i,y_i\) variables
◆ PolynomialBasis< TwistedChernBasis< xy_poly_t, chern_poly_t >, xy_poly_t, chern_poly_t >
Befriending parent for CRTP.
The documentation for this class was generated from the following file:


:: com :: sun :: star :: rendering ::
|
| unpublished |
constants group RenderingIntent |
|
|
- Usage Restrictions
- not published
- Description
- The rendering intent for a color space.
The rendering intent for a color space mostly determines how
out-of-gamut color is treated. See Wikipedia
for a thorough explanation.
- Since
- OOo 2.0
|
| Constants |
| PERCEPTUAL |
Also known as the image intent, this rendering intent
aims to preserve the visual relationship between
colors in a way that is perceived as natural to the
human eye, although the color values themselves may
change. This intent is most suitable for photographic
images.
|
| SATURATION |
The rendering intent for business graphics that
maintains vivid color at the expense of accurate
color. It scales the source gamut to the destination
gamut but preserves relative saturation instead of
hue, so when scaling to a smaller gamut, hues may
shift. This rendering intent is primarily designed for
business graphics, where bright saturated colors are
more important than the exact relationship between
colors (such as in a photographic image).
|
| RELATIVE_COLORIMETRIC |
The rendering intent almost identical to Absolute
Colorimetric except for the following difference:
Relative Colorimetric compares the white point
(extreme highlight) of the source color space to that
of the destination color space and shifts all colors
accordingly.
|
| ABSOLUTE_COLORIMETRIC |
The rendering intent that leaves colors that fall
inside the destination gamut unchanged. Out of gamut
colors are clipped. No scaling of colors to
destination white point is performed. This intent aims
to maintain color accuracy at the expense of
preserving relationships between colors, and is useful
for seeing how output will look on a non-neutral
substrate.
|
| Constants' Details |
PERCEPTUAL
const byte PERCEPTUAL =0;
- Description
- Also known as the image intent, this rendering intent
aims to preserve the visual relationship between
colors in a way that is perceived as natural to the
human eye, although the color values themselves may
change. This intent is most suitable for photographic
images.
|
|
SATURATION
const byte SATURATION =1;
- Description
- The rendering intent for business graphics that
maintains vivid color at the expense of accurate
color. It scales the source gamut to the destination
gamut but preserves relative saturation instead of
hue, so when scaling to a smaller gamut, hues may
shift. This rendering intent is primarily designed for
business graphics, where bright saturated colors are
more important than the exact relationship between
colors (such as in a photographic image).
|
|
RELATIVE_COLORIMETRIC
const byte RELATIVE_COLORIMETRIC =2;
- Description
- The rendering intent almost identical to Absolute
Colorimetric except for the following difference:
Relative Colorimetric compares the white point
(extreme highlight) of the source color space to that
of the destination color space and shifts all colors
accordingly.
|
|
ABSOLUTE_COLORIMETRIC
const byte ABSOLUTE_COLORIMETRIC =3;
- Description
- The rendering intent that leaves colors that fall
inside the destination gamut unchanged. Out of gamut
colors are clipped. No scaling of colors to
destination white point is performed. This intent aims
to maintain color accuracy at the expense of
preserving relationships between colors, and is useful
for seeing how output will look on a non-neutral
substrate.
|
|
Top of Page
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


| EasyRoads3D V3 Manual |
Package Contents
Folders
/EasyRoads3D/Editor/ This folder is used for editor scripts
/EasyRoads3D/html/ and /EasyRoads3D/images/ These are used for the manual which is also available online through the most right Help tab in the Inspector: http://unityterraintools.com/EasyRoads3D/v3/manualv3.html
/EasyRoads3D/lib/ This folder is used for scripts
/EasyRoads3D/Materials/ It is recommended to use the material folders mentioned further below. This folder includes additional material setups. Currently included is a decal material properly setup (without textures) to be used for start / end decals.
/EasyRoads3D/prefab meshes/ The generated mesh assets for custom crossing / connection prefabs will be stored in this folder
/EasyRoads3D/prefab sources/ Crossing prefabs generated with the provisional crossing creator utility will be stored in this folder
/EasyRoads3D/Resources/custom prefabs/ Custom crossing / connection prefabs based on mesh models will be stored in this folder. All prefabs will be listed in the crossing / connection tab (3rd from the left) in the Inspector.
/EasyRoads3D/Resources/dynamic prefabs/ Dynamic crossing / connection prefabs generator through the built-in crossing system will be stored here. All prefabs will be listed in the crossing / connection tab (3rd from the left) in the Inspector.
/EasyRoads3D/Resources/Materials/crossings/ Any crossing material stored here will be listed in the various material dropdowns related to assigning materials to crossing connections. By storing these materials in this folder you will be able to select them quickly from the dropdowns. This is optional, you can also manually assign materials to crossings.
/EasyRoads3D/Resources/Materials/roads/ Any road material stored here will be listed in the various material dropdowns related to assigning materials to roads objects. Jus like for crossings, by storing these materials in this folder you will be able to select them quickly from the dropdowns. This is also optional, you can also manually assign materials to crossings.
/EasyRoads3D/Resources/Materials/roundabouts/ The same as above but for roundabouts
/EasyRoads3D/Resources/Materials/sidewalks/ The same as above but for sidewalks
/EasyRoads3D/Shaders/ You can use your own materials and shaders. The package itself comes with a variety of shaders showing how to take advantage of UV and vertex color options defined in both the road settings and side object settings. Visit the Materials & Shaders tutorial section on our website for more info. Some shaders for blending textures are especially useful on I Connectors.
/EasyRoads3D/Textures/editor/ The textures in this folder are used inside the Unity Editor, they are required.
/EasyRoads3D/Textures/roads/ These are the default road textures.
Additional Assets The tool itself does not include additional road and side object assets to keep the import clean and package updates from the asset store fast. Additional assets can be imported from the demo project, the demo project package itself could also be used as a starting point.
|
|


WITNESS LIST Jurisprudence Committee March 19, 2001 - 1:30P SB 478 AGAINST: Hawkins, Jr., William J. Assistant District Attorney (Harris County District Attorney's Office) ON: Childress, Charles G. Attorney (Texas Department of Protective and Regulatory Services), Austin, TX Registering, but not testifying: For: Hampton, Keith S. Law (Texas Criminal Defense Lawyers Association) SB 698 FOR: Fletcher, Derek L. Attorney (Self), Dallas, TX Roberts, Michelle Attorney (Trust Financial Services Division - Texas Bankers Association), Austin, TX AGAINST: Saunders, Stephen Attorney (Self), Austin, TX Registering, but not testifying: Against: McGehee, Jack E. Lawyer (TTLA), Houston, TX SB 720 FOR: Wood, Mike Judge, Harris County Probate Court No. 2 (Judicial Council Committee on Campaign Finance), Houston, TX Registering, but not testifying: For: Woodford, Suzy Executive Director (Common Cause Texas), Austin, TX On: Phillips, Tom Chief Justice, Supreme Court (self and Texas Judicial Council), Austin, TX SB 822 FOR: Wood, Mike Judge, Harris County Probate Court No. 2 (Judicial Council Committee on Campaign Finance), Houston, TX Registering, but not testifying: For: Woodford, Suzy Executive Director (Common Cause Texas), Austin, TX On: Phillips, Tom Chief Justice, Supreme Court (self and Texas Judicial Council), Austin, TX SB 823 FOR: Wood, Mike Judge, Harris County Probate Court No. 2 (Judicial Council Committee on Campaign Finance), Houston, TX AGAINST: Cunnigham, Sr., Vickers L. Judge, Dallas County Criminal Court No.8 (Dallas County Judiciary), Dallas, TX Pruitt, Jim Judge, County Criminal Court No. 2 (Self), Dallas, TX Registering, but not testifying: For: Woodford, Suzy Executive Director (Common Cause Texas), Austin, TX Against: Clauncy, Danny Judge, County Criminal Court No.1 (County Criminal Court Judges, Dallas County), Dallas, TX On: Phillips, Tom Chief Justice, Supreme Court (self and Texas Judicial Council), Austin, TX SB 1192 AGAINST: Aguirre, Fernando Physician, Internal Medicine (self and parents, Mauriel and Merciol), San Antonio, TX Anderson, Ken District Attorney, 26th Judicial Dt. (Self) Bradley, John First Assistant DA, Williamson County (Self) Esparza, Jaime District Attorney, 34th Judicial Dt. (Self) Halbert, Ellen Director, Victim/Witness Division (Crime Victims), Austin, TX Hill, Bill Dallas County District Attorney (Dallas County District Attorney's Office), Dallas, TX Isaacks, Bruce Criminal District Attorney (Self) Kirkendall, W.C. District Attorney (Self) Macha, Barry Wichita County District Attorney (Self), Wichita Falls, TX Rosenthal, Chuck District Attorney (Harris County District Attorney) Turner, Bill District Attorney, Brazos County (Self), Bryan, TX Weeks, David P. Walker County Criminal District Attorney (Self), Huntsville, TX ON: Cornyn, John Attorney General of Texas (Office of the Attorney General), Austin, TX Registering, but not testifying: Against: de Leon, Yolanda County and District Attorney (Cameron County Distict Attorney's Office) McCracken, Mindy Montford Assistant District Attorney (Travis County District Attorney's Office), Austin, TX SJR 26 FOR: Fletcher, Derek L. Attorney (Self), Dallas, TX Roberts, Michelle Attorney (Trust Financial Services Division, Texas Bankers Association), Austin, TX AGAINST: Saunders, Stephen Attorney (Self), Austin, TX


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Cameron Instruments Inc.
Mailing Address:
103-173 Woolwich StGUELPH, Ontario
N1H 3V4
Location Address:
103-173 Woolwich StGUELPH, Ontario
N1H 3V4
Tel.:<PHONE_NUMBER>
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Logo
Company Profile
Cameron Instruments Inc. is dedicated to supplying the best measurement, calibration and test equipment from some of the world's leading manufacturers. By providing top quality products, along with superior service and support, we work with you to find the best solutions for your measurement needs. Our success depends on your success.
Providing quality products for pressure, temperature, flow, calibration, measurement, and test for over 30 years.
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
Gauge/Absolute Pressure Datalogger to 15,000psi
Differential Pressure with up to 0.025% of reading accuracy
Switch, Voltage, Current and Temperature
500,000 data points - 10 reading/sec. from 2 independent modules
Ultra rugged, intrinsically safe, digital test gauge with 0.1% or reading accuracy.
Now available in ranges up to 15,000psi
Marine grade enclosure resists corrosion
Accuracy not affected from -10oC to 50oC
Replaces 5 or more digital test gauges
Fast pressure safety valve (PSV) mode
Define custom pressure units
Easy to use-No Menu. No Manual? No Problem!
Wide pressure range can measure vacuum and pressure up to 5000psi (300 bar)
Indicates milliamps to calibrate transmitters
Ultra compact and light
Very easy to learn and use
Battery operated
Brings laboratory accuracy to outdoor field conditions
Pressure to 15,000psi/1000 bar/100 MPa
Vacuum to -12.5 psi/ -0.08 bar/ -86 kPa
Faster than any deadweight tester and most automated pressure controllers
http://cameroninstruments.com/crystal-pressure.htm#gaugecalhp
http://cameroninstruments.com/process.htm
A spring-loaded piston/magnet sensor is magnetically coupled to a pointer to indicate a pressure difference. Sensing is completely separated from the indicator, switches, relay or transmitter. A high pressure rated housing is machined from a single block making these a natural for high pressure applications. Includes the 1201, 1203 & 1303 series.
Diaphragm Sensor for Gases & Liquids
For air or gas applications and no bypass, consider the diaphragm sensor design. Built on a design similar to the piston units, a diaphragm replaces the piston and separates the high and low pressure ports. A variety of diaphragm designs are available; small for more common DP measurements, large for more sensitive applications and a rolling diaphragm for high DP measurements. The gauges can be accompanied by a switch, relay or transmitter. Includes the 1502, 1516, 1518 & 1831 series.
Hazardous Location Models
Switches, relays and transmitters are enclosed in UL, CSA, FM and CENELEC approved enclosures that have a gauge option for local indication. Choose one or two switches, SPST, SPDT or a DPDT relay, available in a variety of voltages. Transmitters allow for continuous remote location monitoring. These enclosures meet Class I, Div. I specifications along with Nema-3, 4X, 7, 9 and 12. Both piston and diaphragm sensor models are available to make this the most comprehensive offering in the industry. Includes the 1204, 1504 & 1514 series.
http://cameroninstruments.com/orange-research.htm
The HPGV & XHGV models are available to generate and set pressures as high as 5000 psi. A convenient volume adjust provides fine pressure control. The generous fluid reservoir ensures plenty of fill capacity. This pump can be used with hydraulic oil, water or methanol. The HPGV has two connections one with a micro bore hose fitting and the other with a 1/4" female npt swivel fitting. Crystal calibrators and XP2i's can be direct mounted. Comes complete with one 3' micro bore hose and one 1/4" npt adaptor
A simple reliable pneumatic pump with volume adjustment for pressures to 220psi. The APGV pump is supplied with two connections, one with a micro bore hose fitting and the other a full swivel " female npt. Can be used with any calibrator or test gauge. Crystal calibrators and test gauges can be direct mounted. Ideal for quick test and calibrations of gauges, pressure switches and relief valves. Supplied with one 3' Micro Bore hose and 1/4" male npt adaptor.
When it comes to locating underground pipes and cables, precision is key. The more precise the measurement, the lower the risk of digging up the wrong spot. Performance, practical handling and simple operation are just as important for fast and efficient work. Likewise, reliability, versatility and a sturdy design are also essential for obtaining reliable measurement in difficult conditions and inaccessible environments.
The UT 9000 is a cutting-edge location system, which fulfills these requirements with a pioneering design and sets new standards in pipe location.
Cutting-edge technology for outstanding performance
With the UT 9000, the UT 9000 R receiver is best combined with the UT 9012 TX generator, the most powerful transmitter in its class. The system features automatic frequency selection, extremely long battery life, amazingly simple operation and, above all, versatility - enabling you to rise to any work challenge.
Find the optimal frequency immediately, connect two pipes simultaneously or locate extra long pipe sections. Accurately locate pipes in difficult environments and all weathers, or reliably determine the depth of the pipe - just work more quickly, more accurately and thus more efficiently!
CPF Fittings and Hoses are a new line of quick-test pressure fittings for calibration and test applications - designed with an emphasis on safety.  It can also be used as a bleed point.
Each fitting is machined in 316 Stainless Steel and laser markings indicate the fitting type, size, part number, and Maximum Allowable Working Pressure to prevent a mismatched connection.
An industry-first patented o-ring connection allows CPF Fittings to maintain a leak-free seal up to 10 000 psi with only finger-tightening. This offers an alternative to commonly-used NPT Fittings, which require technicians to wrap their fittings in thread tape and use a wrench to tighten each installation. Unlike other quick-change fittings, the o-rings in CPF Fittings are also easily replaceable.
uses laminar flow element and sonic nozzle technology to deliver fast, repeatable calibrations in the range of less than 1 sccm to over 5,000 slm. Customers can choose from two different measurement terminals and multiple flow element ranges and calibration options to allow measurements with uncertainty as low as ±0.125% of reading in several gases. Measurements are traceable through DHI’s GFS2102 primary gravimetric flow system.
All molbloc elements allow precise real-time readings to enable quick testing and easy adjustment of instruments under test. The system is ideal for testing mass flow meters and controllers, rotameters and other gas flow devices. Many calibrations can be fully automated and adjustments for the effect of varying ambient conditions on these devices can be automatically performed using COMPASS for Flow software.
Calibrate a wide range of gauges and sensors with a single instrument
Modular configuration makes this a versatile and economical solution
Easy to operate
Easy to maintain
Wide measurement range - vacuum to 20MPa (3000 psi)
Two levels of accuracy - 0.02% F.S. or 0.01% reading - let you balance accuracy and budget
High speed, stable pressure control
Localized graphical user interface in choice of nin languages
Can be fully automated with COMPASS for Pressure software
Optional contamination prevention system helps keep valves clean and free from debris
Easy integration with a PC makes Fluke's PPC and RPM4 the instruments of choice for calibrating portable pressure calibrators, high accuracy pressure transmitters and test.
A precision load cell supports the differential piston assembly and serves to quickly and accurately zero the standard at elevated line pressures. Until now, this has been a time consuming and technique dependent process. Supplied with Windows based menu driven software to control the piston gauge and provide the full range of corrections and calculations, the 2482 is the ideal standard for calibrating transmitters used in applications such as turbine efficiency testing and custody transfer measurements.
This low-cost indicator uses the flow of liquid to agitate multi-colored balls in a clear Lexan® dome. The solid brass alloy body is designed to be easily installed in the pipe line or to be panel mounted for observing multi-line applications. The MPC SFI operates in all planes and positions, vertical and horizontal, providing visibility from virtually any angle.
The user can follow the validation process in real time on his PC screen and can stop a faulty process immediately, if necessary, saving much time and effort during process monitoring or validation.
The wireless real time monitoring of hot processes up to +400°C is suitable for many medical, pharmaceutical, laboratory and industrial applications:
With the EBI 100 data loggers, Ebro offers reliable measurment devices for routine control according to German guidelines and ISO 15883.
The precision data loggers are available in different versions including temperature or temperature/pressure loggers with and without external probes. They record the temperature and pressure variation over time during the washer-disinfector or sterilizer process and can easily be read out on a computer using the Winlog-med software. This software will make the test evaluation easy and automatically calculates the A0 value.
The easy to use data loggers with USB connection monitor the temperature and/or humidity during transport and storage of sensitive goods like medicine, food, serums etc. Measurement reports are created automatically as PDF files when you connect the logger to a PC.
The EBI 300 and EBI 310 USB Data Loggers are suitable for multi-use, the EBI 330 Data Loggers are single-use versions which can be ordered preconfigured and used especially when returning a more expensive multi-use logger to the sender after a shipment is difficult.
Sector information
Key / Major clients:
Maritime IndustryPharmaceutical
Oil & Gas
Petrochemical and Refinery
Food & Beverage
Power and Energy
Water & Wastewater
Manufacturing
Pulp & Paper
Calibration Laboratories
Gas Leak Survey
Biogas
Healthcare
Public Health
Gas Distribution/Transmission
Aerospace
Construction-Infrastructure
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Gauge/Absolute Pressure Datalogger to 15,000psi
Differential Pressure with up to 0.025% of reading accuracy
Switch, Voltage, Current and Temperature
500,000 data points - 10 reading/sec. from 2 independent modules
Ultra rugged, intrinsically safe, digital test gauge with 0.1% or reading accuracy.
Now available in ranges up to 15,000psi
Marine grade enclosure resists corrosion
Accuracy not affected from -10oC to 50oC
Replaces 5 or more digital test gauges
Fast pressure safety valve (PSV) mode
Define custom pressure units
Easy to use-No Menu. No Manual? No Problem!
Wide pressure range can measure vacuum and pressure up to 5000psi (300 bar)
Indicates milliamps to calibrate transmitters
Ultra compact and light
Very easy to learn and use
Battery operated
Brings laboratory accuracy to outdoor field conditions
Pressure to 15,000psi/1000 bar/100 MPa
Vacuum to -12.5 psi/ -0.08 bar/ -86 kPa
Faster than any deadweight tester and most automated pressure controllers
http://cameroninstruments.com/crystal-pressure.htm#gaugecalhp
http://cameroninstruments.com/process.htm
A spring-loaded piston/magnet sensor is magnetically coupled to a pointer to indicate a pressure difference. Sensing is completely separated from the indicator, switches, relay or transmitter. A high pressure rated housing is machined from a single block making these a natural for high pressure applications. Includes the 1201, 1203 & 1303 series.
Diaphragm Sensor for Gases & Liquids
For air or gas applications and no bypass, consider the diaphragm sensor design. Built on a design similar to the piston units, a diaphragm replaces the piston and separates the high and low pressure ports. A variety of diaphragm designs are available; small for more common DP measurements, large for more sensitive applications and a rolling diaphragm for high DP measurements. The gauges can be accompanied by a switch, relay or transmitter. Includes the 1502, 1516, 1518 & 1831 series.
Hazardous Location Models
Switches, relays and transmitters are enclosed in UL, CSA, FM and CENELEC approved enclosures that have a gauge option for local indication. Choose one or two switches, SPST, SPDT or a DPDT relay, available in a variety of voltages. Transmitters allow for continuous remote location monitoring. These enclosures meet Class I, Div. I specifications along with Nema-3, 4X, 7, 9 and 12. Both piston and diaphragm sensor models are available to make this the most comprehensive offering in the industry. Includes the 1204, 1504 & 1514 series.
http://cameroninstruments.com/orange-research.htm
The HPGV & XHGV models are available to generate and set pressures as high as 5000 psi. A convenient volume adjust provides fine pressure control. The generous fluid reservoir ensures plenty of fill capacity. This pump can be used with hydraulic oil, water or methanol. The HPGV has two connections one with a micro bore hose fitting and the other with a 1/4" female npt swivel fitting. Crystal calibrators and XP2i's can be direct mounted. Comes complete with one 3' micro bore hose and one 1/4" npt adaptor
A simple reliable pneumatic pump with volume adjustment for pressures to 220psi. The APGV pump is supplied with two connections, one with a micro bore hose fitting and the other a full swivel " female npt. Can be used with any calibrator or test gauge. Crystal calibrators and test gauges can be direct mounted. Ideal for quick test and calibrations of gauges, pressure switches and relief valves. Supplied with one 3' Micro Bore hose and 1/4" male npt adaptor.
When it comes to locating underground pipes and cables, precision is key. The more precise the measurement, the lower the risk of digging up the wrong spot. Performance, practical handling and simple operation are just as important for fast and efficient work. Likewise, reliability, versatility and a sturdy design are also essential for obtaining reliable measurement in difficult conditions and inaccessible environments.
The UT 9000 is a cutting-edge location system, which fulfills these requirements with a pioneering design and sets new standards in pipe location.
Cutting-edge technology for outstanding performance
With the UT 9000, the UT 9000 R receiver is best combined with the UT 9012 TX generator, the most powerful transmitter in its class. The system features automatic frequency selection, extremely long battery life, amazingly simple operation and, above all, versatility - enabling you to rise to any work challenge.
Find the optimal frequency immediately, connect two pipes simultaneously or locate extra long pipe sections. Accurately locate pipes in difficult environments and all weathers, or reliably determine the depth of the pipe - just work more quickly, more accurately and thus more efficiently!
CPF Fittings and Hoses are a new line of quick-test pressure fittings for calibration and test applications - designed with an emphasis on safety.  It can also be used as a bleed point.
Each fitting is machined in 316 Stainless Steel and laser markings indicate the fitting type, size, part number, and Maximum Allowable Working Pressure to prevent a mismatched connection.
An industry-first patented o-ring connection allows CPF Fittings to maintain a leak-free seal up to 10 000 psi with only finger-tightening. This offers an alternative to commonly-used NPT Fittings, which require technicians to wrap their fittings in thread tape and use a wrench to tighten each installation. Unlike other quick-change fittings, the o-rings in CPF Fittings are also easily replaceable.
uses laminar flow element and sonic nozzle technology to deliver fast, repeatable calibrations in the range of less than 1 sccm to over 5,000 slm. Customers can choose from two different measurement terminals and multiple flow element ranges and calibration options to allow measurements with uncertainty as low as ±0.125% of reading in several gases. Measurements are traceable through DHI’s GFS2102 primary gravimetric flow system.
All molbloc elements allow precise real-time readings to enable quick testing and easy adjustment of instruments under test. The system is ideal for testing mass flow meters and controllers, rotameters and other gas flow devices. Many calibrations can be fully automated and adjustments for the effect of varying ambient conditions on these devices can be automatically performed using COMPASS for Flow software.
Calibrate a wide range of gauges and sensors with a single instrument
Modular configuration makes this a versatile and economical solution
Easy to operate
Easy to maintain
Wide measurement range - vacuum to 20MPa (3000 psi)
Two levels of accuracy - 0.02% F.S. or 0.01% reading - let you balance accuracy and budget
High speed, stable pressure control
Localized graphical user interface in choice of nin languages
Can be fully automated with COMPASS for Pressure software
Optional contamination prevention system helps keep valves clean and free from debris
Easy integration with a PC makes Fluke's PPC and RPM4 the instruments of choice for calibrating portable pressure calibrators, high accuracy pressure transmitters and test.
A precision load cell supports the differential piston assembly and serves to quickly and accurately zero the standard at elevated line pressures. Until now, this has been a time consuming and technique dependent process. Supplied with Windows based menu driven software to control the piston gauge and provide the full range of corrections and calculations, the 2482 is the ideal standard for calibrating transmitters used in applications such as turbine efficiency testing and custody transfer measurements.
This low-cost indicator uses the flow of liquid to agitate multi-colored balls in a clear Lexan® dome. The solid brass alloy body is designed to be easily installed in the pipe line or to be panel mounted for observing multi-line applications. The MPC SFI operates in all planes and positions, vertical and horizontal, providing visibility from virtually any angle.
The user can follow the validation process in real time on his PC screen and can stop a faulty process immediately, if necessary, saving much time and effort during process monitoring or validation.
The wireless real time monitoring of hot processes up to +400°C is suitable for many medical, pharmaceutical, laboratory and industrial applications:
With the EBI 100 data loggers, Ebro offers reliable measurment devices for routine control according to German guidelines and ISO 15883.
The precision data loggers are available in different versions including temperature or temperature/pressure loggers with and without external probes. They record the temperature and pressure variation over time during the washer-disinfector or sterilizer process and can easily be read out on a computer using the Winlog-med software. This software will make the test evaluation easy and automatically calculates the A0 value.
The easy to use data loggers with USB connection monitor the temperature and/or humidity during transport and storage of sensitive goods like medicine, food, serums etc. Measurement reports are created automatically as PDF files when you connect the logger to a PC.
The EBI 300 and EBI 310 USB Data Loggers are suitable for multi-use, the EBI 330 Data Loggers are single-use versions which can be ordered preconfigured and used especially when returning a more expensive multi-use logger to the sender after a shipment is difficult.
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Key / Major clients:
Maritime IndustryPharmaceutical
Oil & Gas
Petrochemical and Refinery
Food & Beverage
Power and Energy
Water & Wastewater
Manufacturing
Pulp & Paper
Calibration Laboratories
Gas Leak Survey
Biogas
Healthcare
Public Health
Gas Distribution/Transmission
Aerospace
Construction-Infrastructure