|
| def | __init__ (self, all_model_parts, creator_destructor, dem_fem_search, parameters, procedures) |
| |
| def | TranslationalIntegrationSchemeTranslator (self, name) |
| |
| def | RotationalIntegrationSchemeTranslator (self, name_translational, name_rotational) |
| |
| def | CreateCPlusPlusStrategy (self) |
| |
| def | GetTranslationalSchemeInstance (self, class_name) |
| |
| def | GetRotationalSchemeInstance (self, class_name) |
| |
| def | GetHydrodynamicLawParametersIfItExists (self, properties) |
| |
| def | ModifyProperties (self, properties, param=0) |
| |
◆ __init__()
| def swimming_sphere_strategy.SwimmingStrategy.__init__ |
( |
|
self, |
|
|
|
all_model_parts, |
|
|
|
creator_destructor, |
|
|
|
dem_fem_search, |
|
|
|
parameters, |
|
|
|
procedures |
|
) |
| |
◆ CreateCPlusPlusStrategy()
| def swimming_sphere_strategy.SwimmingStrategy.CreateCPlusPlusStrategy |
( |
|
self | ) |
|
◆ CreateHydrodynamicLaw()
| def swimming_sphere_strategy.SwimmingStrategy.CreateHydrodynamicLaw |
( |
|
properties, |
|
|
|
hydrodynamic_law_parameters |
|
) |
| |
|
static |
◆ GetHydrodynamicLawParametersIfItExists()
| def swimming_sphere_strategy.SwimmingStrategy.GetHydrodynamicLawParametersIfItExists |
( |
|
self, |
|
|
|
properties |
|
) |
| |
◆ GetRotationalSchemeInstance()
| def swimming_sphere_strategy.SwimmingStrategy.GetRotationalSchemeInstance |
( |
|
self, |
|
|
|
class_name |
|
) |
| |
◆ GetTranslationalSchemeInstance()
| def swimming_sphere_strategy.SwimmingStrategy.GetTranslationalSchemeInstance |
( |
|
self, |
|
|
|
class_name |
|
) |
| |
◆ ModifyProperties()
| def swimming_sphere_strategy.SwimmingStrategy.ModifyProperties |
( |
|
self, |
|
|
|
properties, |
|
|
|
param = 0 |
|
) |
| |
◆ RotationalIntegrationSchemeTranslator()
| def swimming_sphere_strategy.SwimmingStrategy.RotationalIntegrationSchemeTranslator |
( |
|
self, |
|
|
|
name_translational, |
|
|
|
name_rotational |
|
) |
| |
◆ SDEMEvaluateString()
| def swimming_sphere_strategy.SwimmingStrategy.SDEMEvaluateString |
( |
|
name | ) |
|
|
static |
◆ TranslationalIntegrationSchemeTranslator()
| def swimming_sphere_strategy.SwimmingStrategy.TranslationalIntegrationSchemeTranslator |
( |
|
self, |
|
|
|
name |
|
) |
| |
◆ cplusplus_strategy
| swimming_sphere_strategy.SwimmingStrategy.cplusplus_strategy |
◆ project_parameters
| swimming_sphere_strategy.SwimmingStrategy.project_parameters |
The documentation for this class was generated from the following file:


Structs
The following structs are available globally.
-
A keyword identified by Speech to Text.
See moreDeclaration
Swift
public struct KeywordResult: JSONDecodable
-
The results of a Speech to Text recognition request.
See moreDeclaration
Swift
public struct SpeechRecognitionResults
-
Word alternatives produced by Speech to Text.
See moreDeclaration
Swift
public struct WordAlternativeResults: JSONDecodable
-
The settings associated with a Speech to Text recognition request. Any
nil
parameters will use a default value provided by the Watson Speech to Text service.For more information about the Speech to Text service parameters, visit: http://www.ibm.com/watson/developercloud/doc/speech-to-text/input.shtml
See moreDeclaration
Swift
public struct RecognitionSettings: JSONEncodable
-
The timestamp of a word in a Speech to Text transcription.
See moreDeclaration
Swift
public struct WordTimestamp: JSONDecodable
-
A result from a Speech to Text recognition request.
See moreDeclaration
Swift
public struct SpeechRecognitionResult: JSONDecodable
-
The confidence of a word in a Speech to Text transcription.
See moreDeclaration
Swift
public struct WordConfidence: JSONDecodable
-
A transcription alternative produced by Speech to Text.
See moreDeclaration
Swift
public struct SpeechRecognitionAlternative: JSONDecodable
-
Alternative word hypotheses from Speech to Text for a word in the audio input.
See moreDeclaration
Swift
public struct WordAlternativeResult: JSONDecodable


and get customers at least 2 times cheaper than from contextual search engines
that will provide you with a quick and cheap flow of potential customers to the website. Our system works through a special browser extension, advertising is displayed to visitors to different sites.
than contextual advertising in search engines. At the same time, it is not inferior to them in efficiency and conversion.
Our algorithms filter and then block users who artificially inflate your ad views. We will refund you for poor traffic from the websites.
We will select thematic sites that the target audience of your business visits to display your ads. This helps maximize ad conversion.
For example, if you sell expensive men’s watches, it will make sense to advertise on car websites, which are visited by a large number of men with average and above average income.
Our prices are several times lower than on similar advertising platforms
On average, 10 times cheaper than competitors, from $0.02
On average, 3 times cheaper than competitors, from $1.00 for 1000 visits
On average, 2 times cheaperthan competitors: 1000 views — from $1 for 20 seconds, and + $0.15 for every 5 seconds
average cost per click on the banner
average daily number of visits to customer websites
average number of banner views per day
Our platform provides the largest daily audience coverage in comparison with competitors in banner advertising.
Our advertising method allows you to get traffic 2 to 5 times cheaper than from contextual advertising in search engines.
You can order from 1 view or click by us! Even with a minimum bid, you still get views and clicks.
For all questions, you can contact support and get a prompt response. A live person will answer you, not a bot!
You can get 25 free clicks on your banner right now, right after registration.
Our system analyzes user behavior on sites and subtracts from the cost of the advertising campaign % corresponding to the % of bot traffic.
Sign up on our advertising platform right now
and get a nice bonus — 10% to the amount of the first replenishment
25 clicks on your ad banner for free
Website owners install our ad network code to their sites so that you can display banners on their pages. Internet users install our extension to their browsers.
Sign up on Surfe.be in just a few clicks and create a personal account in the system.
You activate the test clicks through
You can replenish your account by credit cards Visa, MasterCard, JCB, MIR or electronic wallets: Qiwi, Yandex, WebMoney, Payeer and AdvCash
Choose the type of ad you want, upload a banner (video, popup) and click on launch. Ads will be displayed until the advertising budget is spent.
Anyone who is interested in advertising will be able to click on it and go to your landing page, and after that order your product or service, subscribe, etc.


|
Dreamachine
1.0.0
flashy lights thing
|
Go to the source code of this file.
Macros | |
| #define | pgm_read_byte(addr) (*(const unsigned char *)(addr)) |
| PROGMEM workaround for non-AVR. More... | |
| #define | WIRE_MAX 32 |
| Use common Arduino core default. More... | |
| #define | ssd1306_swap(a, b) (((a) ^= (b)), ((b) ^= (a)), ((a) ^= (b))) |
| No-temp-var swap operation. More... | |
| #define | WIRE_WRITE wire->send |
| Wire write function in older Arduino lib. More... | |
| #define | SSD1306_SELECT digitalWrite(csPin, LOW); |
| Device select. More... | |
| #define | SSD1306_DESELECT digitalWrite(csPin, HIGH); |
| Device deselect. More... | |
| #define | SSD1306_MODE_COMMAND digitalWrite(dcPin, LOW); |
| Command mode. More... | |
| #define | SSD1306_MODE_DATA digitalWrite(dcPin, HIGH); |
| Data mode. More... | |
| #define | SETWIRECLOCK |
| Dummy stand-in define. More... | |
| #define | RESWIRECLOCK |
| keeps compiler happy More... | |
| #define | SPI_TRANSACTION_START |
| Dummy stand-in define. More... | |
| #define | SPI_TRANSACTION_END |
| keeps compiler happy More... | |
| #define | TRANSACTION_START |
| Wire, SPI or bitbang transfer setup. More... | |
| #define | TRANSACTION_END |
| Wire, SPI or bitbang transfer end. More... | |
| #define pgm_read_byte | ( | addr | ) | (*(const unsigned char *)(addr)) |
PROGMEM workaround for non-AVR.
Definition at line 12 of file Adafruit_SSD1306.cpp.
| #define RESWIRECLOCK |
keeps compiler happy
Definition at line 64 of file Adafruit_SSD1306.cpp.
| #define SETWIRECLOCK |
Dummy stand-in define.
Definition at line 63 of file Adafruit_SSD1306.cpp.
| #define SPI_TRANSACTION_END |
keeps compiler happy
Definition at line 72 of file Adafruit_SSD1306.cpp.
| #define SPI_TRANSACTION_START |
Dummy stand-in define.
Definition at line 71 of file Adafruit_SSD1306.cpp.
| #define SSD1306_DESELECT digitalWrite(csPin, HIGH); |
Device deselect.
Definition at line 54 of file Adafruit_SSD1306.cpp.
| #define SSD1306_MODE_COMMAND digitalWrite(dcPin, LOW); |
Command mode.
Definition at line 55 of file Adafruit_SSD1306.cpp.
| #define SSD1306_MODE_DATA digitalWrite(dcPin, HIGH); |
Data mode.
Definition at line 56 of file Adafruit_SSD1306.cpp.
| #define SSD1306_SELECT digitalWrite(csPin, LOW); |
Device select.
Definition at line 53 of file Adafruit_SSD1306.cpp.
| #define ssd1306_swap | ( | a, | |
| b | |||
| ) | (((a) ^= (b)), ((b) ^= (a)), ((a) ^= (b))) |
No-temp-var swap operation.
Definition at line 39 of file Adafruit_SSD1306.cpp.
| #define TRANSACTION_END |
Wire, SPI or bitbang transfer end.
Definition at line 110 of file Adafruit_SSD1306.cpp.
| #define TRANSACTION_START |
Wire, SPI or bitbang transfer setup.
Definition at line 97 of file Adafruit_SSD1306.cpp.
| #define WIRE_MAX 32 |
Use common Arduino core default.
Definition at line 34 of file Adafruit_SSD1306.cpp.
| #define WIRE_WRITE wire->send |
Wire write function in older Arduino lib.
Definition at line 44 of file Adafruit_SSD1306.cpp.


Provides a short description that will be included when generating bean information.
Provides a display name when generating bean information.
This annotation indicates that a JavaBean-compliant BeanInfo class should be generated for this annotated Scala class.
This annotation indicates that bean information should not be generated for the val, var, or def that it is attached to.
When attached to a field, this annotation adds a setter and a getter method following the Java Bean convention.
This annotation has the same functionality as
scala.
A ClassManifest[T]
is an opaque descriptor for type T
.
A OptManifest[T]
is an optional Manifest
.
Provides some simple runtime processing necessary to create JavaBean descriptors for Scala entities.
This object is used by the compiler and should not be used in client code.
no summary matey
One of the branches of an OptManifest


To insert this review on to a page, copy & paste the code from the required views below into the html where it is to be positioned.


Posted by LC on November 23, 2017
Posted by LC on September 3, 2017
Posted by LC on July 2, 2017
Posted by LC on June 10, 2017
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Posted by LC on December 23, 2016
Posted by LC on November 15, 2016


Anastasia (1956)
Grade: 73
Ingrid Bergman won the Oscar for Best Actress for her role in "Anastasia", which cemented her Hollywood comeback following her scandalous affair with Italian director Roberto Rosellini. The film was based on the Marcelle Maurette play, and also provided good roles for bald-headed, fiery-eyed Yul Brynner and noted stage actress Helen Hayes.
Anastasia, youngest daughter of Czar Nicholas II, was murdered by the Bolsheviks following the Russian Revolution. Or did she somehow survive? Someone apparently believes so, or they wouldn't have a ten million dollar trust fund waiting for her to show up to claim it.
It is the early 1920s, and Brynner is conspiring with Akim Tamiroff and Sacha Pitoeff to produce a credible Anastasia so that the account can be defrauded. Bergman, a woman without an identity who bears a physical resemblance to Anastasia, is rescued from a suicide attempt by Brynner. He then coldly trains her to pass as Her Majesty.
But there are many skeptics, and to succeed Bergman must convince Anastasia's still-living grandmother (Hayes). Along the way, it becomes obvious that Bergman really is Anastasia. She also must choose between two suitors, pleasant rogue Prince Paul (Ivan Desny) and imposing, abrupt Brynner.
The best thing about "Anastasia" is the dense script. There are many clever lines that reveal the idiosyncrasies of the characters. Bergman, Hayes and Brynner are well cast. The biggest problem with "Anastasia" is the ending; there is no romantic chemistry between Bergman and Brynner, and it is difficult to believe that Brynner would forgo his hard-won jackpot and that Bergman would pass up a chance to play Princess.
<EMAIL_ADDRESS>http://members.tripod.com/~Brian_Koller/movies.html
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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Man granted bail for inflicting wounds on a farmer
by Alex Ofori Agyekum, GNAAkropong-Akuapem (E/R), Nov. 25, GNA – The Akropong-Akuapem Circuit Court has granted bail in the sum of GH¢10,000.00 with two sureties to Michael Owusu, 24, for allegedly inflicting cutlass wounds on one Robert Yeboah, a farmer at Adamorobe in the Eastern Region.
The accused pleaded not guilty and would reappear in court on December 13, 2019.
Prosecuting, Chief Police Inspector Emmanuel Owusu told the court presided over by Madam Charity Asem that on November 16, this year, a misunderstanding arose between the victim and the accused after a family meeting at Adamorobe.
Inspector Owusu said at the heat of the exchanges between the two, Owusu inflicted cutlass wounds on the forehead and the left hand of the victim who was rushed to the Aburi Police Station where he was issued with medical report form to attend hospital.
The accused was later arrested by the Police.
GNA


Choose the alloy type, shape, and number of pieces and enter the dimensions to calculate the total weight.
Enter Size Information:


WITNESS LIST Subcommittee on Agriculture Committee February 25, 1999-1:00P SB 224 For: Combs, Susan (Commissioner of Agriculture), Austin Grimm, James (Texas Poultry Federation), Austin Hatley, Mike (Texas Economic Development Council), Sweetwater Reeves, Michael (Lubbock Chamber of Commerce), Lubbock Tucker, Durwood (Texas Farm Bureau), Austin Williams, Tony (Texas Cotton Ginners' Assocation), Austin Wilson, Ross (Texas Cattle Feeders Association), Amarillo On: Lavine, Richard (Center For Public Policy Priorities), Austin Registering, but not testifying: For: Small, Ed (Texas & Southwestern Cattle Raisers Association), Austin


Komodo is pleased to announce a new Strategic Technology Partnership with Coinbene, one of the world's largest exchanges. Learn more here.
t0sma12-Feb-19 08:46 AM
Hello, come on Txbit https://txbit.io/ and trade your KMD. This is new exchange from Solaris project, opened since 31th January 2019. They listed Komodo yesterday. This is a very nice exchange and it deserves more volumes (edited)


Avgrund
Avgrund is a jQuery plugin for your modal boxes and popups. It uses new concept showing depth between popup and page.It works in all modern browsers and gracefully degrade in those that do not support CSS transitions and transformations.
Avgrund is a jQuery plugin for your modal boxes and popups. It uses new concept showing depth between popup and page.It works in all modern browsers and gracefully degrade in those that do not support CSS transitions and transformations.
The simple jQuery Plugin for fully customizable modal windows. plainModal has basic functions only, and it does not style. It has no image files and no CSS files. Just one small file.


This was a semi-commercial project, The goal of this was to quickly create a compact controller for 4 stepper motors, which will drive them in response to physical buttons push, and additionally take care to not exceed allowed limits without use of physical limit switches, so soft limits were implemented.


clpAPI: R Interface to C API of COIN-OR Clp
R Interface to C API of COIN-OR Clp, depends on COIN-OR Clp Version >= 1.12.0.
| Version: |
1.2.9 |
| Depends: |
R (≥ 2.6.0) |
| Imports: |
methods |
| Published: |
2018-09-24 |
| Author: |
Mayo Roettger [cre],
Gabriel Gelius-Dietrich [aut],
C. Jonathan Fritzemeier [ctb] |
| Maintainer: |
Mayo Roettger <mayo.roettger at hhu.de> |
| License: |
GPL-3 | file LICENSE |
| NeedsCompilation: |
yes |
| SystemRequirements: |
COIN-OR Clp (>= 1.12.0) |
| Materials: |
README NEWS INSTALL |
| In views: |
Optimization |
| CRAN checks: |
clpAPI results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=clpAPI
to link to this page.


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100%
Customer Satisfaction
A wonderful serenity has taken possession of my entire soul, like these sweet mornings of spring.

A wonderful serenity has taken possession of my entire...

A wonderful serenity has taken possession of my entire soul, like these sweet mornings...
A wonderful serenity has taken possession of my entire soul, like these sweet mornings...
A wonderful serenity has taken possession of my entire soul, like these sweet mornings...
This project is made by Himanshu Khaitan as a clone of the PHLOX website. Thanks for Visiting
X


Snapchat is a popular social media service that allows you to send photos and videos that supposedly disappear once a certain period of time is elapsed. Of course, sometimes, you receive a snap that you want to save forever, so you screenshot it. However, screenshotting a snap sends a notification to the original sender, which notifies them of your screenshot. So how do you get around that limitation? You follow this guide, which uses a flaw in Snapchat's screenshot detection system.
This tutorial may seem like a lot to remember at first, but you can always come back here to make sure you remember everything. Also, it gets a lot easier once you've done it a lot, and you end up breaking it down to about 4 steps, which I've included at the end.
Open Snapchat, and determine which snap you want to screenshot
If you've already opened the snap you want to save, just let the time run out, then tap and hold on the snap to use a Replay on it. Do not open the replayed snap yet
Make sure the snap is loaded. You will know the snap is loaded when it says "Tap to view" under the contact name, instead of "Tap to load".
Turn on Airplane mode.
Open the snap and screenshot it.
Press and hold the power button until you see "Slide to power off". DO NOT POWER DOWN OR LOCK THE DEVICE, OR THE NOTIFICATION WILL GO THROUGH.
Press and hold the home button (as if you're trying to summon Siri) until Snapchat "crashes" to the homescreen. If it looks like it's about to "crash", but Snapchat opens immediately, repeat this step until it crashes to the homescreen
Following steps 5 and 6 in any app on iOS apparently clears the cache or the device's RAM.
Turn off Airplane mode, and wait for your Internet connection to reconnect.
Open Snapchat and verify that no screenshot has been sent. You should also be able to view the screenshotted snap as if you had never opened it (once everything loads). If you see that a screenshot has been sent, then you most likely messed up either step 5 or step 6.
Open Snapchat, and load the snap you want to save
Turn on Airplane mode.
Screenshot the snap.
Do the power button cache/RAM clear trick.
That's it, you've screenshotted a snap without anyone knowing! If you like these tutorials or you want to hear more from me, please follow me on Twitter @JustinAlexP.
This guide is heavily based on some of EverythingApplePro's videos, with some clarification added by me.


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A simple web-based interface demo for the truly awesome Pimoroni Pan-Tilt HAT!
Please use a browser that supports the Canvas Element, like Chrome, Firefox, Safari or Internet Explorer 10


Methods
Create a request string (header and body)
buildRequest(\triagens\ArangoDb\ConnectionOptions $options, string $method, string $url, string $body) : string
Static
Parameters
Returns
string
- assembled HTTP request string
Create a one-time HTTP connection by opening a socket to the server
createConnection(\triagens\ArangoDb\ConnectionOptions $options) : resource
Static
It is the caller's responsibility to close the socket
Parameters
Exceptions
Returns
resource
- socket with server connection, will throw when no connection can be established
Splits a http message into its header and body.
parseHttpMessage(string $httpMessage) : array
Static
Parameters
$httpMessage
string
The http message string.
Exceptions
Returns
array
Execute an HTTP request on an opened socket
transfer(resource $socket, string $request) : string
Static
It is the caller's responsibility to close the socket
Parameters
$socket
resource
- connection socket (must be open)
$request
string
- complete HTTP request as a string
Exceptions
Returns
string
- HTTP response string as provided by the server
Validate an HTTP request method name
validateMethod(string $method) : bool
Static
Parameters
Exceptions
Returns
bool
- always true, will throw if an invalid method name is supplied
Constants
Chunk size (number of bytes processed in one batch)
CHUNK_SIZE
End of line mark used in HTTP
EOL
HTTP DELETE string constant
METHOD_DELETE
HTTP GET string constant
METHOD_GET
HTTP HEAD string constant
METHOD_HEAD
HTTP PATCH string constant
METHOD_PATCH
HTTP POST string constant
METHOD_POST
HTTP PUT string constant
METHOD_PUT
HTTP protocol version used, hard-coded to version 1.1
MIME_BOUNDARY
HTTP protocol version used, hard-coded to version 1.1
PROTOCOL


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Mentry Programmer's Guide
The mentry::mentry Command
mentry::mentry
Multi-Entry Widgets for Date and Time
Multi-Entry Widgets for Real Numbers in Fixed-Point Format
Multi-Entry Widgets for IP Addresses
Multi-Entry Widgets for IPv6 Addresses
Commands Related to Tile Themes
Wcb Command Reference


Basic
Data Types............................................................................................................................................................. 1
Basic Data Types
Contents........................................................................................................................................ 1
Basic Data Types......................................................................................................................................................... 2
Coordinates and Twips...................................................................................................................................... 2
Integer Types and Byte
Order.......................................................................................................................... 2
Fixed Point Numbers........................................................................................................................................... 3
Bit Values............................................................................................................................................................. 3
Bit Values............................................................................................................................................................. 4
Comment............................................................................................................................................................... 4
Using Bit Values.................................................................................................................................................. 4
String Values........................................................................................................................................................ 5
RGB Color Record............................................................................................................................................... 5
RGBA Color with Alpha
Record....................................................................................................................... 6
Rectangle Record................................................................................................................................................ 6
Matrix Record...................................................................................................................................................... 6
Color Transform Record..................................................................................................................................... 7
Color Transform with
Alpha Record................................................................................................................ 8
This section describes the basic data types that make up the more complex data structures in the Macromedia Flash File Format (SWF). All other structures in Macromedia Flash File Format (SWF) are built on these fundamental types, so it is important to understand these types before moving on.
SWF stores all X-Y coordinates as integers, in a unit of measurement called a twip. Strictly speaking, a twip is defined as 1/1440th of an inch, or 1/20th of a point, a traditional measure in printing. However, in the context of the Macromedia Flash (SWF) format, it is easiest to think of a twip as 1/20th of a logical pixel.
A logical pixel is the same as a screen pixel when the movie is played at 100% – i.e. without scaling.
For example, a rectangle 800 twips wide by 400 twips high is rendered as 40 by 20 logical pixels. Fractional pixel sizes are approximated with antialiasing. A rectangle 790 by 390 twips (39.5 by 19.5 pixels) appears to have slightly blurred edges.
Twips are a good compromise between size and precision. They provide sub-pixel accuracy for zooming and precise placement of objects, while consuming very few bits per coordinate.
Macromedia Flash (SWF) supports 8-bit, 16-bit and 32-bit, signed and unsigned integer types. All integer values are stored in the Macromedia Flash (SWF) file using the little-endian byte order. That is, the least significant byte is stored first, and the most significant byte is stored last, in the same way as the Intel x86 architecture. The 32-bit value B1B2B3B4, is stored in the SWF file as: B4B3B2B1.
For example:
The 32-bit value 0x456e7120 is
stored as: 20 71 6e 45
The 16-bit value 0xe712 is
stored as: 12 e7
The Flash Format SDK isolates you from endian issues. The methods OutputByte, OutputWord, OutputDWord of the FSWFStream class, always write integer values in the little-endian format
All integer types must be byte-aligned. That is, the first bit of an integer value must be stored in the first bit of a byte in the Macromedia Flash (SWF) file.
|
Integer Types |
|
|
Type |
Comment |
|
SI8 |
Signed 8-bit integer value |
|
SI16 |
Signed 16-bit integer value |
|
SI32 |
Signed 32-bit integer value |
|
SI8[n] |
Signed 8-bit array – n is
number of array elements |
|
SI16[n] |
Signed 16-bit array - n is
number of array elements |
|
UI8 |
Unsigned 8-bit integer
value |
|
UI16 |
Unsigned 16-bit integer
value |
|
UI32 |
Unsigned 32-bit integer
value |
|
UI8[n] |
Unsigned 8-bit array - n is
number of array elements |
|
UI16[n] |
Unsigned 16-bit array - n
is number of array elements |
|
UI32[n] |
Unsigned 32-bit array - n
is number of array elements |
Fixed values are 32-bit 16.16 signed fixed-point numbers. That is, the high 16 bits represent the number before the decimal point, and the low 16 bits represent the number after the decimal point.
FIXED values are stored like 32 bit integers in the Macromedia Flash (SWF) file (using the little-endian format) and must be byte-aligned.
For example:
The real value 7.5 is equivalent to: 0007.8000
Which is equal to the 32-bit value: 0x00078000
Which is stored in the MACROMEDIA FLASH (SWF) file as: 00 80 07 00
|
FIXED |
|
|
Type |
Comment |
|
FIXED |
32-bit 16.16
fixed-point number |
Bit values are variable-length bit fields that can represent three types of numbers:
· Unsigned integers
· Signed integers
· Signed 16.16 fixed-point values.
Bit values do not have to be byte-aligned. A single bit value can spread across multiple bytes, and more than one bit value can be packed into a single byte. Other types (such as U8 an U16) are always byte-aligned. If a byte-aligned type follows a bit-value, the byte containing the bit-value is padded out with zeros.
Below is a stream 64 bits, made up of 9 variable-length bit values followed by a U16 value:
Byte1---Byte2---Byte3---Byte4---Byte5---Byte6---Byte7---Byte8---
0101101010010010010111100100011010111001100100000100110010101101
BV1---BV2--BV3---BV4-BV5-----BV6BV7--BV8BV9—pad-U16-------------
The bit stream begins with a 6-bit value (BV1) followed by a 5-bit value (BV2) which is spread across Byte1 and Byte2. BV3 is spread across Byte2 and Byte3, while BV4 is wholly contained within Byte3. Byte 5 contains two bit values; BV7 and BV8. BV9 is followed by a byte-aligned type (U16) so the last four bits of Byte6 are padded with zeros.
Bit
Values
|
|
|
Type |
Comment
|
|
SB[nBits] |
Signed
bit value (nBits is the number of
bits used to store the value) |
|
UB[nBits] |
Unsigned
bit value (nBits is the number of
bits used to store the value) |
|
FB[nBits] |
Signed
fixed-point bit value (nBits is the
number of bits used to store the value) |
When an unsigned bit value is expanded into a larger word size the leftmost bits are filled with zeros. When a signed bit value is expanded into a larger word size the high bit is copied to the left most bits.
This is called sign extension. For example, the 4-bit unsigned value UB[4] = 1110 would be expanded to a 16-bit value like this:<PHONE_NUMBER>001110 = 14. The same value interpreted as a signed value (SB[4] = 1110) would be expanded to<PHONE_NUMBER>111110 = –2.
Bit values are stored using the minimum number of bits possible. The number of bits required to store an unsigned bit value is determined by the position of the leftmost ‘1’ bit. For example, say the integer 35 was stored in a 16-bit word like this:<PHONE_NUMBER>100011. The leftmost ‘1’ is 6 bits from the end of the number, so this number can be represented as UB[6]. The leftmost 10 bits are ‘chopped off’ and discarded.
Signed bit values are similar but must take account of the sign bit. The signed value of 35 is represented as SB[7] = 0100011. The extra zero bit is required otherwise the high-bit would be sign-extended and the value would be interpreted as negative.
Fixed-point bit values are 32-bit 16.16 signed fixed-point numbers. That is, the high 16 bits represent the number before the decimal point, and the low 16 bits represent the number after the decimal point. A fixed-point bit value is identical to a signed bit value, but the interpretation is different. For example, a 19-bit signed bit value of 0x30000 is interpreted as 196608 decimal. The 19-bit fixed-point bit value 0x30000 is interpreted as 3.0. In effect, the value was stored as 3.16.
Whenever possible, bit values are packed into the smallest possible number of bits possible. Coordinates are commonly stored using variable-sized bit fields. A field indicates how many bits are used to represent subsequent values in the record. For example, take a look at the RECT structure below:
|
RECT |
||
|
Field |
Type |
Comment |
|
Nbits |
nBits = UB[5] |
Bits in each rect value
field |
|
Xmin |
SB[nBits] |
X minimum position for rect |
|
Xmax |
SB[nBits] |
X maximum position for rect |
|
Ymin |
SB[nBits] |
Y minimum position for rect |
|
Ymax |
SB[nBits] |
Y maximum position for rect |
The nBits field determines the number of bits used to store the coordinate values Xmin, Xmax, Ymin, and Ymax. Say the coordinates of the rectangle were as follows:
Xmin = 127 decimal = 1111111 binary
Xmax = 260 decimal = 10000100 binary
Ymin =
15 decimal = 1111 binary
Ymax = 514 decimal =<PHONE_NUMBER> binary
Nbits is calculated by finding the coordinate that requires the most bits to represent. In this case that value is 514<PHONE_NUMBER>0 binary) which requires 11 bits to represent. So the rectangle is stored as below:
|
RECT |
||
|
Field |
Type |
Comment |
|
Nbits |
UB[5] = 1011 |
Bits required (11) |
|
Xmin |
SB[11] =<PHONE_NUMBER>1 |
X minimum in twips (127) |
|
Xmax |
SB[11] =<PHONE_NUMBER>0 |
X maximum in twips (260) |
|
Ymin |
SB[11] =<PHONE_NUMBER>1 |
Y minimum in twips (15) |
|
Ymax |
SB[11] =<PHONE_NUMBER>0 |
Y maximum in twips (514) |
A string value represents a null terminated ASCII or multiple byte character string. The format for a string value is a sequential list of bytes terminated by the null character byte.
(See class FString in the Macromedia Flash File Format (SWF) SDK)
|
STRING |
||
|
Field |
Type |
Comment |
|
String |
UI8[zero or more] |
Non-null string character data |
|
StringEnd |
UI8 |
Marks end of string - always zero |
The RGB record represents a color as 24-bit red, green, and blue value.
(See class FRGB in the Macromedia Flash File Format (SWF) SDK)
|
RGB |
||
|
Field |
Type |
Comment |
|
Red |
UI8 |
Red color value |
|
Green |
UI8 |
Green color value |
|
Blue |
UI8 |
Blue color value |
The RGBA record represents a color as 32-bit red, green, blue and alpha value. An RGBA color with an alpha value of 255 is completely opaque. An RGBA color with an alpha value of zero is completely transparent. Alpha values between zero and 255 are partially transparent.
(See class FRGBA in the Macromedia Flash File Format (SWF) SDK)
|
RGBA |
||
|
Field |
Type |
Comment |
|
Red |
UI8 |
Red color value |
|
Green |
UI8 |
Green color value |
|
Blue |
UI8 |
Blue color value |
|
Alpha |
UI8 |
Transparency color value |
A rectangle value represents a rectangular region defined by a minimum X- and Y-coordinate position with a maximum X- and Y-coordinate position.
|
RECT |
||
|
Field |
Type |
Comment |
|
Nbits |
nBits = UB[5] |
Bits used for each
subsequent field |
|
Xmin |
SB[nBits] |
X minimum position for
rectangle in twips |
|
Xmax |
SB[nBits] |
X maximum position for
rectangle in twips |
|
Ymin |
SB[nBits] |
Y minimum position for
rectangle in twips |
|
Ymax |
SB[nBits] |
Y maximum position for
rectangle in twips |
(See class FRect in the Macromedia Flash File Format (SWF) SDK)
The MATRIX record represents a standard 2x3 transformation matrix. It is used to describe the scale, rotation and translation of a graphic object.
(See class FMatrix in the Macromedia Flash File Format (SWF) SDK)
|
MATRIX |
||
|
Field |
Type |
Comment |
|
HasScale |
hasScale = UB[1] |
Has scale values if equal
to 1 |
|
NScaleBits |
If hasScale nScaleBits =
UB[5] |
Bits in each scale value
field |
|
ScaleX |
If hasScale FB[nScaleBits] |
X scale value |
|
ScaleY |
If hasScale FB[nScaleBits] |
Y scale value |
|
HasRotate |
hasRotate = UB[1] |
Has rotate and skew values
if equal to 1 |
|
NRotateBits |
If hasRotate nRotateBits =
UB[5] |
Bits in each rotate value
field |
|
RotateSkew0 |
If hasRotate
FB[nRotateBits] |
First rotate and skew value |
|
RotateSkew1 |
If hasRotate
FB[nRotateBits] |
Second rotate and skew
value |
|
NTranslateBits |
nTranslateBits = UB[5] |
Bits in each translate
value field |
|
TranslateX |
SB[nTranslateBits] |
X translate value in twips |
|
TranslateY |
SB[nTranslateBits] |
Y translate value in twips |
The ScaleX, ScaleY, RotateSkew0 and RotateSkew1 fields are stored as 16.16 fixed-point values. The TranslateX and TranslateY values are stored as signed values in twips.
The MATRIX record is optimized for common cases such as matrix that performs a translation only. In this case the HasScale and HasRotate flags are zero, and the matrix only contains the TranslateX and TranslateY fields.
The mapping from the MATRIX fields to the 2x3 matrix is as follows:
| ScaleX RotateSkew0 |
| RotateSkew1 ScaleY |
| TranslateX TranslateY |
For any coordinates (x, y), the transformed coordinates (x', y') are calculated as follows:
x' = x * ScaleX + y * RotateSkew1 + TranslateX
y' = x * RotateSkew0 + y * ScaleY + TranslateY
The following table describes how the members of the matrix are used for each type of operation:
|
Operation |
ScaleX |
RotateSkew0 |
RotateSkew1 |
ScaleY |
|
Rotation |
Cosine |
Sine |
Negative sine |
Cosine |
|
Scaling |
Horizontal scaling
component |
Nothing |
Nothing |
Vertical Scaling Component |
|
Shear |
Nothing |
Horizontal Proportionality
Constant |
Vertical Proportionality
Constant |
Nothing |
|
Reflection |
Horizontal Reflection
Component |
Nothing |
Nothing |
Vertical Reflection
Component |
The CXFORM record defines a simple transform that can be applied to the color space of a graphic object. There are two types of transform possible:
1. Multiplication
Transforms
2. Addition
Transforms
Multiplication transforms multiply the red, green, and blue components by an 8.8 fixed-point multiplication term. The fixed-point representation of 1.0 is 0x100 or 256 decimal. Therefore, the result of a multiplication operation should be divided by 256.
For any color (R,G,B) the transformed color (R', G', B') is calculated as follows:
R' =
(R * RedMultTerm) / 256
G' =
(G * GreenMultTerm) / 256
B' =
(B * BlueMultTerm) / 256
Addition transforms simply add an addition term (positive or negative) to the red, green and blue components of the object being displayed. If the result is greater than 255, the result is clamped to 255. If the result is less than zero, the result is clamped to zero.
For any color (R,G,B) the transformed color (R', G', B') is calculated as follows:
R' =
max(0, min(R + RedAddTerm, 255))
G' =
max(0, min(G + GreenAddTerm, 255))
B' =
max(0, min(B + BlueAddTerm, 255))
Addition and Multiplication transforms can be combined as below. The multiplication operation is performed first.
R' = max(0, min(((R * RedMultTerm) / 256) + RedAddTerm, 255))
G' = max(0, min(((G *
GreenMultTerm) / 256) +
GreenAddTerm, 255))
B' = max(0, min(((B *
BlueMultTerm) / 256) +
BlueAddTerm, 255))
(See class FCXForm in the Macromedia Flash File Format (SWF) SDK)
|
CXFORM |
||
|
Field |
Type |
Comment |
|
HasAddTerms |
hasAdd = UB[1] |
Has color addition values
if equal to 1 |
|
HasMultTerms |
hasMult = UB[1] |
Has color multiply values
if equal to 1 |
|
Nbits |
nBits = UB[4] |
Bits in each value field |
|
RedMultTerm |
If hasMult SB[nBits] |
Red multiply value |
|
GreenMultTerm |
If hasMult SB[nBits] |
Green multiply value |
|
BlueMultTerm |
If hasMult SB[nBits] |
Blue multiply value |
|
RedAddTerm |
If hasAdd SB[nBits] |
Red addition value |
|
GreenAddTerm |
If hasAdd SB[nBits] |
Green addition value |
|
BlueAddTerm |
If hasAdd SB[nBits] |
Blue addition value |
The CXFORMWITHALPHA record extends the functionality of CXFORM by allowing color transforms to be applied to the alpha channel, as well as the red, green and blue channels.
There are two types of transform possible:
1. Multiplication
Transforms
2. Addition
Transforms
Multiplication transforms multiply the red, green, blue and alpha components by an 8.8 fixed-point value. The fixed-point representation of 1.0 is 0x100 or 256 decimal. Therefore, the result of a multiplication operation should be divided by 256.
For any color (R,G,B,A) the transformed color (R', G', B', A') is calculated as follows:
R' =
(R * RedMultTerm) / 256
G' =
(G * GreenMultTerm) / 256
B' =
(B * BlueMultTerm) / 256
A' =
(A * AlphaMultTerm) / 256
The CXFORMWITHALPHA record is most commonly used to display objects as partially transparent. This is achieved by multiplying the alpha channel by some value between zero and 256.
Addition transforms simply add a fixed value (positive or negative) to the red, green, blue and alpha components of the object being displayed. If the result is greater than 255, the result is clamped to 255. If the result is less than zero, the result is clamped to zero.
For any color (R,G,B,A) the transformed color (R', G', B', A') is calculated as follows:
R' =
max(0, min(R + RedAddTerm, 255))
G' =
max(0, min(G + GreenAddTerm, 255))
B' =
max(0, min(B + BlueAddTerm, 255))
A' =
max(0, min(A + AlphaAddTerm, 255))
Addition and Multiplication transforms can be combined as below. The multiplication operation is performed first.
R' = max(0, min(((R * RedMultTerm) / 256) + RedAddTerm, 255))
G' = max(0, min(((G *
GreenMultTerm) / 256) +
GreenAddTerm, 255))
B' = max(0, min(((B *
BlueMultTerm) / 256) +
BlueAddTerm, 255))
A' = max(0, min(((A *
AlphaMultTerm) / 256) +
AlphaAddTerm, 255))
(See class FCXFormWAlpha in the Macromedia Flash File Format (SWF) SDK)
|
CXFORMWITHALPHA |
||
|
Field |
Type |
Comment |
|
HasAddTerms |
HasAdd = UB[1] |
Has color addition values
if equal to 1 |
|
HasMultTerms |
HasMult = UB[1] |
Has color multiply values
if equal to 1 |
|
Nbits |
NBits = UB[4] |
Bits in each value field |
|
RedMultTerm |
If hasMult SB[nBits] |
Red multiply value |
|
GreenMultTerm |
If hasMult SB[nBits] |
Green multiply value |
|
BlueMultTerm |
If hasMult SB[nBits] |
Blue multiply value |
|
AlphaMultTerm |
If hasMult SB[nBits] |
Alpha multiply value |
|
RedAddTerm |
If hasAdd SB[nBits] |
Red addition value |
|
GreenAddTerm |
If hasAdd SB[nBits] |
Green addition value |
|
BlueAddTerm |
If hasAdd SB[nBits] |
Blue addition value |
|
AlphaAddTerm |
If hasAdd SB[nBits] |
Transparency addition value
|


You can log in as one of three different roles: administrator, teacher and students. As administrator you have acces to all the features including: calendar, list of students and teachers, creating accounts, statistics and many editing options.
StudentsCalendar allows to schedule all the lessons and important events. When logged in as administrator you can filter lessons by teacher, student or course, edit them, drag and resize. When logged as teacher or student you have acces only your lessons without editing options.
CalendarAs administrator you have access to different statistics about number of lessons, students, percentage of attending in each course and many more.
Diagrams


First up is extrac32. If do not know this command you can read more about it here: https://ss64.com/nt/extract.html
Basically what you use it for is to extract cab files. What I figured out was that you also can use this command to add alternate data streams. The PoC for doing this (including creating a CAB) looks like this:
echo "empty file" > c:\ADS\file.txt
makecab c:\ADS\procexp.exe c:\ADS\procexp.cab
extrac32 C:\ADS\procexp.cab c:\ADS\file.txt:procexp.exe
wmic process call create '"c:\ADS\file.txt:procexp.exe"'


PROJECT
Abstract
This paper provides a study of two deep reinforcement learning techniques for application in navigation of mobile robots, one of the techniques is the Soft Actor Critic (SAC) that is compared with the Deep Deterministic Policy Gradients (DDPG) algorithm in the same situation. In order to make a robot to arrive at a target in an environment, both networks have 10 laser range findings, the previous linear and angular velocity, and relative position and angle of the mobile robot to the target are used as the network inputs. As outputs, the networks have the linear and angular velocity of the mobile robot. The reward function created was designed in a way to only give a positive reward to the agent when it gets to the target and a negative reward when colliding with any object. The proposed architecture was applied successfully in two simulated environments, and a comparison between the two referred techniques was made using the results obtained as a basis and it was demonstrated that the SAC algorithm has a superior performance for the navigation of mobile robots than the DDPG algorithm (Code available at https://github.com/dranaju/project).


I'm excited! I wish Phase 1 could start right now. It is clear from the video that they are constantly trying new things and I want to see that in action. This is clearly an educational situation unlike anything I have experienced before and want to see what the heck is going on.
I'm nervous because of course I don't feel smart enough, or fast enough or experienced enough but I'm gonna show a little faith and keep grinding through Phase 0 and see what happens.
I'm excited! I wish Phase 1 could start right now. It is clear from the video that they are constantly trying new things and I want to see that in action. This is clearly an educational situation unlike anything I have experienced before and want to see what the heck is going on.
I'm nervous because of course I don't feel smart enough, or fast enough or experienced enough but I'm gonna show a little faith and keep grinding through Phase 0 and see what happens.


"Streak" of Satisfaction by Homer Yen (c) 1999
If you've seen "Beverly Hills Cop" or "Lethal Weapon" or any of a dozen other cop/buddy/comedy movies, then you probably know that all of these films follow a very similar recipe:
Add 1 overzealous and reckless character that doesn't quite fit in; Add 1 subdued or perpetually naive partner; Make sure one person is black and the other white; Include at least two scenes of cars traveling death-defyingly fast; Liberally sprinkle bullets during the finale; and Voila!
"Blue Streak" does not veer from this recipe, but despite the cookie-cutter plot, what really makes or breaks these kinds of movies for me is the energy level of the main character. And surprisingly (I say surprisingly because I had low expectations for this film), Martin Lawrence shows that he has the charisma and the vigor to keep this movie breezing along.
Lawrence actually isn't a police officer in this film. Well, not at first anyway. He's Miles Logan, who is quite an accomplished thief. With his team of fellow thieves and a plethora of high-tech equipment, he easily enters the most secure buildings while bypassing the most sophisticated alarm systems. The film's beginning is a fairly well-executed sequence where Logan scurries his way into a building with the ultimate goal of stealing a $17 million diamond. By disabling a key security monitor, stealthily hiding behind furniture, and utilizing an array of thieving tools that could probably also defuse a nuclear warhead, he successfully captures his prize. But his jubilation will be short-lived. Police have arrived and the driver of the getaway car has sped off. Meanwhile, one of his men, Deke ( ), turns on him. He wants the diamond for himself. Logan manages to barely escape from Deke to a nearby construction site. But the police are closing in. He quickly hides the diamond inside of an air conditioning duct before being arrested.
Two years elapse, and when he's finally released from prison, he revisits the site of his precious diamond. Unfortunately, he learns that the new building houses the Los Angeles Police Department. Security is tight and the place is teeming with officers. There's no way for him to just stroll in and to start crawling through the ventilation system. His plan is simple. Steal a security access pass; forge identification; and walk on in and retrieve the jewel. Well, two out of three ain't bad. Unfortunately, he is mistaken for a new transfer and is immediately dispatched on a case. To make matters worse, he is paired with a rookie partner (an affable Luke Wilson) who always drives the speed limit and stops when the traffic light changes yellow.
Logan will have to solve the case his own way, using his acquired street smarts and thieving background, in order to speed his return back to the police department. He does impressively well as a man of the law, and his unorthodox manner and tactics draw praise from his colleagues. But this attention can only endanger his true identity. Meanwhile, he is given more dangerous assignments and by now, Deke has reappeared to track Logan down for the diamond. "Blue Streak" may not break any new ground, but I was pleasantly surprised at how well the movie held together. This is one of those rare times where the actual film is much better than the previews had led me to believe. I also enjoyed Lawrence's performance who is likeably glib. The movie moves along at a brisk pace and despite Lawrence's reputation as a sometimes-crass comic, the film is appropriately rated PG-13 for some mild gunplay. With so many mediocre thrillers and dramas in the theatres, this film makes a fine Saturday afternoon choice.
Grade: B-
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The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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belongs to the author.
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Related links: index of all rec.arts.movies.reviews reviews


The effect of Toremifene on the expression of some genes in human mononuclear cells.
Abstract
Toremifene exerts multiple and varied effects on the gene expression of human peripheral mononuclear cells. After short-term, in vitro exposure to therapeutical levels, distinct changes in P-glycoprotein, steroid receptors, p53 and Bcl-2 expression take place. In view of the increasing use of antiestrogens in cancer therapy and prevention, there is obvious merit in long-term in vivo studies to be conducted.


This page focuses on actions / commands that are performed in a browser on Git repositories hosted in Team Foundation Server (TFS).
This page focuses on actions / commands that are performed in a browser on Git repositories hosted in Team Foundation Server (TFS).
Merge branches - to changes from <source_branch> to <target_branch>
Create new repository - logical container to store
Create new branch - <feature_branch> from a <base_branch>
Moving Repositories - between TFS Projects while retaining full history


London is the capital city of England.
Paris is the capital of France.
Tokyo is the capital of Japan.
Oslo is the capital of Norway.


{% extends "NYRP/base.html" %} {% load static %} {% block content %}
A new way to prepare for the regents exams! Whether it's Chemistry, Physics, or History, study at your own pace with practice questions in an easy to use format! Customize the years and units for each subject to get the practice that's custom tailored for your needs. Receive feedback about what areas you could use more work on!


This is the partial for AddItem.
Showing of 'interpolate' filter: {{ 'Current version is v%VERSION%.' | interpolate }}
This is the partial for List.Test


Step by Step Methods
Today's class was devoted to the numerical methods for the integration of the equation of motion in the time domain, that we collectively name Step by Step Methods.
We introduced the concept of the state of a linear or non linear system, and the idea of computing the state of the system at the end of a short time step as a mean to compute, step by step, the response of a SDOF system in an approximate manner.
The specific qualities and defects of SbS methods were discussed at some length, especially the cause of errors and the problem of numerical instability.
First of all, we discussed the Central Differences Method, an unstable method that appeals the numerical analyst with its semplicity. The method gives also a taste of the structure of step by step solutions, where the end state is expressed in terms of the initial state and the values of the loadings at the extremes of the step.
The central differences is an explicit method, next we introduced implicit methods, that can be collected under the name of Newmark's Beta Methods, and we studied in particular the Constant and the Linear Acceleration Methods, the Constant Acceleration being remarkable as it is a stable method of integration.
We ended introducing the modified Newton-Raphson method
for the solution of non linear problems, with the
discussion of an elasto-plastic system.
The homework assigned was about an elastoplastic
system subjected to an impulsive loading.
- the slides i used for my lesson
- a ready to print version of the slides,
- a Python script to do analytical integration of the EOM for the homework example,
- the short paper, that was shown in class, that documents the above script
- the notebooks that i used in class, both the
executables and a pdf version that can be read onscreen
or printed without the software the is needed for the
live notebooks
- exact response for piecewise linear excitation, notebook and PDF,
- constant acceleration algorithm, notebook and PDF,
- linear acceleration algorithm, notebook and PDF,
- exact integration of the EOM for the EP system of your exercise, notebook and PDF.
also something about your homework


Inschain mutual aid is an innovative platform focus on community projects, which is well known for its peer to peer risk sharing model. By joining the platform as a participated member, you need to contribute your kindness to assist another member in case of accidents, vice versa you will also receive mutual assistance fund donated evenly by other members.
This mutual aid project set up by Inschain, and on live in March 2018. The project based on the vision of creating a community with a strong ‘All for one, one for all’ spirit. With a further mission of protecting low-income member against natural disaster and severe illness with an escort service that not only physically looks after member but also emotionally. Eventually, mutual aid project will create a protection net and supplement the social welfare system.


You Won!
And Probably Learned Some Stuff Too!
Play Again?
Science Match
Where You Can Read And Learn About Each Match You Make


The number of unique materials used in the construction of public art installations on display in Houston. You can explore the source data over at the City of Houston's Data Portal


Applying the public health core functions and essential services model to climate change.
1.
Monitor health status
2.
Diagnose and investigate health risks
Identify climate impacts and assess vulnerabilities
Project disease and impact burden
Climate services for projecting health outcomes
Disease surveillance (required syndromic surveillance and health and climate specific monitoring)
Adaptive risk assessment for population health risks due to climate hazards
Vulnerability assessment, indexes, mapping
3.
Mobilize partnerships
4.
Inform, educate and empower
5.
Develop policies and plans
Identify appropriate public heath actions and governance b arrangements
Develop health-outcome focused climate action plan
Collaboration, coordination, partnerships across agencies, levels, actors, frameworks
Identifying health co-benefits (cost-benefit analysis)
Mitigation action planning for health sector
Adaptation intervention planning for population health outcomes
Communication on health risks of climate hazards, resilience promoting actions, health co-benefits
6.
Enforce laws and regulations
7.
Link people to health care
8.
Assure competent workforce
9.
Evaluate
10.
Research
Implement b health-focused climate action plan
Evaluate impact of interventions and dynamically adjust b based on lessons learned
Vector-borne disease control
Water and food safety monitoring
Early warning systems (heat, flooding, vector borne disease, air pollution)
Nature-based solutions to reduce heat, flood risks
Health impact assessment (integration of health outcomes in non-health sectors)
Hospital and health care extreme event preparedness
Climate change curriculum for public health, medical schools
‘Good practice’ city climate change networks with health focus
a Adapted from US CDC Building Resilience Against Climate Effects Framework (BRACE) and the EU Climate-ADAPT Framework; b Concepts that enhance the core functions and essential services model.


Nonparametric change point estimation for survival data based on p-values of exact binomial tests.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | survival (≥ 2.40-1), muhaz (≥ 1.2.6), parallel (≥ 3.2.3) |
| Suggests: | testthat (≥ 0.11.0) |
| Published: | 2017-03-04 |
| Author: | Stefanie Krügel [aut, cre], Alessandra R. Brazzale, [aut], Helmut Kuechenhoff [aut] |
| Maintainer: | Stefanie Krügel <stefanie.kruegel at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | CPsurv results |
| Reference manual: | CPsurv.pdf |
| Package source: | CPsurv_1.0.0.tar.gz |
| Windows binaries: | r-devel: CPsurv_1.0.0.zip, r-release: CPsurv_1.0.0.zip, r-oldrel: CPsurv_1.0.0.zip |
| OS X binaries: | r-release: CPsurv_1.0.0.tgz, r-oldrel: CPsurv_1.0.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=CPsurv to link to this page.


hw-05 - D3 Event Handling
1) When square is clicked, circle changes color.
2) When circle is double clicked, both shapes change color.
3) When either shape is hovered over, that shape will get a border.
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Acknowledgments
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From the above derivation, we know that the DST, Y k, is related to the DFT, Hk, by Y k = Hk+1∕(−i), and thus the meaning of Y k is in principle clear. Next, let us use the DST to reconstruct the original function from which the data are sampled. Since DST is only a special case of DFT, reconstructing the function using DST follows the same procedure used in DFT. In DFT, the function is reconstructed via Eq. (44) (changing to the positive exponent convention), i.e.,
|
| (70) |
where 2L is the length of the interval in which the samplings hj with j = 0,1,…,N − 1 are made. For our present case, i.e., an odd extension of the original n data, we have N = 2(n + 1) and H0 = 0 and HN∕2 = 0. Then Eq. (70) is written as
|
| (71) |
Using the odd symmetry of Hk, i.e., Hk = −HN−k, the above expansion is written as
|
| (72) |
Deﬁne k′ = 2n + 2 − k, and note that N = 2(n + 1), then the above expression is written as
|
| (73) |
i.e.,
|
| (74) |
which is simpliﬁed as
|
| (75) |
As a convention, we prefer that the summation index begins from 0 and ends at n − 1. Then expression (75) is written as
| h(x) = ∑ k=0n−12iH k+1 sin |
Using the relation between DST and DFT, i.e., Hk+1 = −iY k, the above equation is written as
|
| (76) |
This is the formula for constructing the continuous function using the DST data. This formula is an expansion over the basis functions sin[(k + 1)πx∕L] with the DST Y k acting as the expansion coeﬃcients. Therefore the direct meaning of the DST, Y k, is that they are the expansion coeﬃcients when using sin(kπx∕L) as the basis functions to approximate a function in the domain [0,L]. From Fig. 6, we know that the interval length L is given by L = (n + 1)Δ, where Δ is the uniform spacing between the original n sampling points.
Evaluate the function in Eq. (76) at xj = (j + 1)Δ with j = 0,1,…,n − 1, then we obtain
It can be proved that h(xj) in the above equation exactly recover hj used in deﬁning the DST Y k. Therefore Eq. (77) is the Inverse Discrete Sine Transform.


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This village sits at the western end of the bay of Zaljev Marina and is about 6nm from Trogir. It is an attractive small port to visit.
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A hackathon is a tech innovation marathon. This is where your dreams run wild. Solve a real-world problem with technology or build the next Iron Man suit.
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crnt4sbml.MassConservationApproach¶
-
class
crnt4sbml.
MassConservationApproach
(cgraph, get_physiological_range)[source]¶ Class for constructing variables and methods needed for the mass conservation approach.
-
__init__
(cgraph, get_physiological_range)[source]¶ Initialization of the MassConservationApproach class.
Methods
__init__
(cgraph, get_physiological_range)Initialization of the MassConservationApproach class. generate_report
()Prints out helpful details constructed by crnt4sbml.MassConservationApproach.run_optimization()
andcrnt4sbml.MassConservationApproach.run_continuity_analysis()
.get_conservation_laws
()Returns a string representation of the conservation laws. get_decision_vector
()Returns a list of SymPy variables that represent the decision vector of the optimization problem. get_objective_fun_params
()Returns a list of SymPy variables that represent those variables that may be contained in the G matrix, Jacobian of the equilibrium manifold with respect to the species, or objective function. get_concentration_vals
()Returns a list of SymPy expressions representing the species in terms of those variables present in the decision vector. get_concentration_solutions
()Returns a more readable string representation of the species defined in terms of the decision vector. get_concentration_funs
()Returns a list of lambda functions representing each of the species. get_concentration_bounds_species
()Returns a list of SymPy variables that represents the order of species for the concentration bounds provided to crnt4sbml.MassConservationApproach.run_optimization()
.get_w_nullspace
()Returns a list of SymPy column vectors representing . get_w_matrix
()Returns SymPy matrix , which we call the W matrix. get_dch_matrix
()Returns a SymPy matrix representing the Jacobian of the equilibrium manifold with respect to the species. get_lambda_dch_matrix
()Returns a lambda function representation of the Jacobian of the equilibrium manifold matrix. get_h_vector
()Returns a SymPy matrix representing the equilibrium manifold. get_g_matrix
()Returns a SymPy matrix representing the G matrix of the defined optimization problem. get_lambda_g_matrix
()Returns a lambda function representation of the G matrix. get_symbolic_objective_fun
()Returns SymPy expression for the objective function of the optimization problem. get_lambda_objective_fun
()Returns a lambda function representation of the objective function of the optimization problem. get_independent_odes
()Returns a SymPy Matrix where the rows represent the independent ODEs used in the numerical continuation routine. get_independent_species
()Returns a list of SymPy representations of the independent species used in the numerical continuation routine. get_optimization_bounds
()Builds all of the necessary physiological bounds for the optimization routine. get_my_rank
()Returns the rank assigned by mpi4py if it is initialized, otherwise None will be returned. get_comm
()Returns a mpi4py communicator if it has been initialized and None otherwise. run_optimization
([bounds, iterations, …])Function for running the optimization problem for the mass conservation approach. run_continuity_analysis
([species, …])Function for running the numerical continuation and bistability analysis portions of the mass conservation approach. run_greedy_continuity_analysis
([species, …])Function for running the greedy numerical continuation and bistability analysis portions of the mass conservation approach. run_direct_simulation
([response, signal, …])Function for running direct simulation to conduct bistability analysis of the mass conservation approach. -
generate_report
()[source]¶ Prints out helpful details constructed by
crnt4sbml.MassConservationApproach.run_optimization()
andcrnt4sbml.MassConservationApproach.run_continuity_analysis()
.Example
See Mass Conservation Approach Example and Mass Conservation Approach Walkthrough.
-
get_concentration_bounds_species
()[source]¶ Returns a list of SymPy variables that represents the order of species for the concentration bounds provided to
crnt4sbml.MassConservationApproach.run_optimization()
.Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_concentration_bounds_species()) [s1, s3, s7, s16]
-
get_concentration_funs
()[source]¶ Returns a list of lambda functions representing each of the species. Here the species are those expressions provided by
crnt4sbml.MassConservationApproach.get_concentration_vals()
where the arguments of each lambda function is provided bycrnt4sbml.MassConservationApproach.get_decision_vector()
.Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_concentration_funs()) [<function _lambdifygenerated at 0x135f8b4d0>, <function _lambdifygenerated at 0x135f72050>, <function _lambdifygenerated at 0x135f728c0>, <function _lambdifygenerated at 0x135f725f0>, <function _lambdifygenerated at 0x135f5f830>, <function _lambdifygenerated at 0x135fa0170>, <function _lambdifygenerated at 0x135fa04d0>]
-
get_concentration_solutions
()[source]¶ Returns a more readable string representation of the species defined in terms of the decision vector.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_concentration_solutions()) s1 = s15*(re5r + re6)/(re5*s6) s2 = s2 s3 = re1*s15*s2*(re5r + re6)/(re5*s6*(re1r + re2)) s6 = s6 s7 = -s15*(re5*re5r*s6*(re1r + re2)*(re3r + re4) - (re5r + re6)*(-re1*re1r*re3r*s2 - re1*re1r*re4*s2 + re1*re3r*s2*(re1r + re2) + re1*re4*s2*(re1r + re2) + re5*s6*(re1r + re2)*(re3r + re4)))/(re3*re4*re5*s6**2*(re1r + re2)) s16 = s15*(re1*re2*re5r*s2 + re1*re2*re6*s2 + re1r*re5*re6*s6 + re2*re5*re6*s6)/(re4*re5*s6*(re1r + re2)) s15 = s15
-
get_concentration_vals
()[source]¶ Returns a list of SymPy expressions representing the species in terms of those variables present in the decision vector. The order is that established in
crnt4sbml.Cgraph.get_species()
. Note that if only a single species is provided as an element in the list, this means the species is a free variable.Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_concentration_vals()) [s15*(re5r + re6)/(re5*s6), s2, re1*s15*s2*(re5r + re6)/(re5*s6*(re1r + re2)), s6, -s15*(re5*re5r*s6*(re1r + re2)*(re3r + re4) - (re5r + re6)*(-re1*re1r*re3r*s2 - re1*re1r*re4*s2 + re1*re3r*s2*(re1r + re2) + re1*re4*s2*(re1r + re2) + re5*s6*(re1r + re2)*(re3r + re4)))/(re3*re4*re5*s6**2* (re1r + re2)), s15*(re1*re2*re5r*s2 + re1*re2*re6*s2 + re1r*re5*re6*s6 + re2*re5*re6*s6)/(re4*re5*s6*(re1r + re2)), s15]
-
get_conservation_laws
()[source]¶ Returns a string representation of the conservation laws. Here the values on the left hand side of each equation are the constants of the conservation laws.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_conservation_laws()) C1 = 1.0*s16 + 1.0*s7 C2 = 1.0*s2 + 1.0*s3 C3 = 1.0*s1 + 2.0*s15 + 1.0*s16 + 1.0*s3 + 1.0*s6
-
get_dch_matrix
()[source]¶ Returns a SymPy matrix representing the Jacobian of the equilibrium manifold with respect to the species.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> import sympy >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> sympy.pprint(approach.get_dch_matrix()) ⎡-re₁⋅s₂ -re₁⋅s₁ re1r 0 0 0 0 ⎤ ⎢ ⎥ ⎢re₁⋅s₂ re₁⋅s₁ -re1r - re₂ 0 0 0 0 ⎥ ⎢ ⎥ ⎢ 0 0 0 -re₃⋅s₇ -re₃⋅s₆ re3r 0 ⎥ ⎢ ⎥ ⎢ 0 0 0 re₃⋅s₇ re₃⋅s₆ -re3r - re₄ 0 ⎥ ⎢ ⎥ ⎢-re₅⋅s₆ 0 0 -re₅⋅s₁ 0 0 re5r ⎥ ⎢ ⎥ ⎣re₅⋅s₆ 0 0 re₅⋅s₁ 0 0 -re5r - re₆⎦
-
get_decision_vector
()[source]¶ Returns a list of SymPy variables that represent the decision vector of the optimization problem.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_decision_vector()) [re1, re1r, re2, re3, re3r, re4, re5, re5r, re6, s2, s6, s15]
-
get_g_matrix
()[source]¶ Returns a SymPy matrix representing the G matrix of the defined optimization problem.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> import sympy >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> sympy.pprint(approach.get_g_matrix()) ⎡-re₁⋅s₂ -re₁⋅s₁ re1r 0 0 0 0 1 -1⎤ ⎢ ⎥ ⎢re₁⋅s₂ re₁⋅s₁ -re1r - re₂ 0 0 0 0 0 0 ⎥ ⎢ ⎥ ⎢ 0 0 0 -re₃⋅s₇ -re₃⋅s₆ re3r 0 1 0 ⎥ ⎢ ⎥ ⎢ 0 0 0 re₃⋅s₇ re₃⋅s₆ -re3r - re₄ 0 0 0 ⎥ ⎢ ⎥ ⎢-re₅⋅s₆ 0 0 -re₅⋅s₁ 0 0 re5r 0 1 ⎥ ⎢ ⎥ ⎢re₅⋅s₆ 0 0 re₅⋅s₁ 0 0 -re5r - re₆ 0 0 ⎥ ⎢ ⎥ ⎢ 0 0 0 0 1.0 1.0 0 0 0 ⎥ ⎢ ⎥ ⎢ 0 1.0 1.0 0 0 0 0 0 0 ⎥ ⎢ ⎥ ⎣ 1.0 0 1.0 1.0 0 1.0 2.0 0 0 ⎦
-
get_h_vector
()[source]¶ Returns a SymPy matrix representing the equilibrium manifold.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> import sympy >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> sympy.pprint(approach.get_h_vector()) ⎡a₁ - a₂ - re₁⋅s₁⋅s₂ + re1r⋅s₃⎤ ⎢ ⎥ ⎢re₁⋅s₁⋅s₂ + s₃⋅(-re1r - re₂) ⎥ ⎢ ⎥ ⎢ a₁ - re₃⋅s₆⋅s₇ + re3r⋅s₁₆ ⎥ ⎢ ⎥ ⎢re₃⋅s₆⋅s₇ + s₁₆⋅(-re3r - re₄)⎥ ⎢ ⎥ ⎢ a₂ - re₅⋅s₁⋅s₆ + re5r⋅s₁₅ ⎥ ⎢ ⎥ ⎣re₅⋅s₁⋅s₆ + s₁₅⋅(-re5r - re₆)⎦
-
get_independent_odes
()[source]¶ Returns a SymPy Matrix where the rows represent the independent ODEs used in the numerical continuation routine. Here the entries of the list correspond to the time derivatives of the corresponding species provided by
crnt4sbml.MassConservationApproach.get_independent_species()
. Note that the independent ODEs created are based on the species chosen for the numerical continuation. Thus, the continuation routine needs to be ran first. If this function is called before the numerical continuation routine then None will be returned.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/sbml_file.xml") >>> approach = network.get_mass_conservation_approach() >>> multistable_param_ind = approach.run_greedy_continuity_analysis(species="species", parameters=params_for_global_min, auto_parameters={'PrincipalContinuationParameter': "PCP"}) >>> odes = approach.get_independent_odes()
-
get_independent_species
()[source]¶ Returns a list of SymPy representations of the independent species used in the numerical continuation routine. Note that the independent species created are based on the species chosen for the numerical continuation. Thus, the continuation routine needs to be ran first. If this function is called before the numerical continuation routine then None will be returned.
Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/sbml_file.xml") >>> approach = network.get_mass_conservation_approach() >>> multistable_param_ind = approach.run_greedy_continuity_analysis(species="species", parameters=params_for_global_min, auto_parameters={'PrincipalContinuationParameter': "PCP"}) >>> species = approach.get_independent_species()
-
get_lambda_dch_matrix
()[source]¶ Returns a lambda function representation of the Jacobian of the equilibrium manifold matrix. Here the arguments of the lambda function are given by the values provided by
crnt4sbml.MassConservationApproach.get_objective_fun_params()
.Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_lambda_dch_matrix()) <function _lambdifygenerated at 0x131a06ea0>
-
get_lambda_g_matrix
()[source]¶ Returns a lambda function representation of the G matrix. Here the arguments of the lambda function are given by the values provided by
crnt4sbml.MassConservationApproach.get_objective_fun_params()
.Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_lambda_g_matrix()) <function _lambdifygenerated at 0x13248ac80>
-
get_lambda_objective_fun
()[source]¶ Returns a lambda function representation of the objective function of the optimization problem. Here the arguments of the lambda function are given by the values provided by
crnt4sbml.MassConservationApproach.get_objective_fun_params()
.Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_lambda_objective_fun()) <function _lambdifygenerated at 0x12f6f7ea0>
-
get_my_rank
()[source]¶ Returns the rank assigned by mpi4py if it is initialized, otherwise None will be returned.
-
get_objective_fun_params
()[source]¶ Returns a list of SymPy variables that represent those variables that may be contained in the G matrix, Jacobian of the equilibrium manifold with respect to the species, or objective function.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_objective_fun_params()) [re1, re1r, re2, re3, re3r, re4, re5, re5r, re6, s1, s2, s3, s6, s7, s16, s15]
-
get_optimization_bounds
()[source]¶ Builds all of the necessary physiological bounds for the optimization routine.
Fig1Ci.xml
for the provided example.Returns: - bounds (list of tuples) – List of tuples defining the upper and lower bounds for the decision vector variables based on physiological ranges.
- concentration_bounds (list of tuples) – List of tuples defining the upper and lower bounds for those concentrations not in the decision vector based on physiological ranges.
Examples
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> bounds, concentration_bounds = approach.get_optimization_bounds() >>> print(bounds) [(1e-08, 0.0001), (1e-05, 0.001), (0.001, 1.0), (1e-08, 0.0001), (1e-05, 0.001), (0.001, 1.0), (1e-08, 0.0001), (1e-05, 0.001), (0.001, 1.0), (0.5, 500000.0), (0.5, 500000.0), (0.5, 500000.0)]
>>> print(concentration_bounds) [(0.5, 500000.0), (0.5, 500000.0), (0.5, 500000.0), (0.5, 500000.0)]
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get_symbolic_objective_fun
()[source]¶ Returns SymPy expression for the objective function of the optimization problem. This is the determinant of the G matrix squared.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> print(approach.get_symbolic_objective_fun()) 1.0*re1**2*re2**2*re3**2*re4**2*re5**2*re6**2*s1**2*s6**2*s7**2*((1.0*s2/s7 - 1.0*s2*(-re3r - re4)/ (re3*s6*s7))/re4 + (1.0 + 1.0*re1r/(re1*s1))/re2 + 1.0/(re1*s1))**2*(-((1.0*s6*(-1.0*s1/s6 + 1.0)/s7 + 1.0 - (-re3r - re4)*(-1.0*s1/s6 + 1.0)/(re3*s7))/re4 + 1.0/re2)*(-1.0*re5r*s2/(re5*re6*s1*s6) - 1.0*s2*(1 + re5*s6/(re1*s2))/(re5*s1*s6) - (1.0 + 1.0*re1r/(re1*s1))/re2)/((1.0*s2/s7 - 1.0*s2* (-re3r - re4)/(re3*s6*s7))/re4 + (1.0 + 1.0*re1r/(re1*s1))/re2 + 1.0/(re1*s1)) + (2.0 + 1.0*re5r/(re5*s6))/ re6 + 1.0*(1 + re5*s6/(re1*s2))/(re5*s6) - 1.0/re2 - 1.0/(re1*s2))**2
-
get_w_matrix
()[source]¶ Returns SymPy matrix , which we call the W matrix.
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> import sympy >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> sympy.pprint(approach.get_w_matrix()) ⎡1 0 0 0 0 1 1 0 0⎤ ⎢ ⎥ ⎢1 0 1 0 0 0 0 0 0⎥ ⎢ ⎥ ⎢0 1 0 0 0 0 0 0 0⎥ ⎢ ⎥ ⎢0 0 1 1 0 0 1 0 2⎥ ⎢ ⎥ ⎢0 0 0 1 0 1 0 0 0⎥ ⎢ ⎥ ⎢0 0 0 0 1 0 0 0 0⎥ ⎢ ⎥ ⎢0 0 0 0 0 0 0 1 0⎥ ⎢ ⎥ ⎢1 1 1 0 0 0 0 0 0⎥ ⎢ ⎥ ⎢0 0 0 1 1 1 0 0 0⎥ ⎢ ⎥ ⎣0 0 0 0 0 0 1 1 1⎦
-
get_w_nullspace
()[source]¶ Returns a list of SymPy column vectors representing .
Fig1Ci.xml
for the provided example.Example
>>> import crnt4sbml >>> import sympy >>> network = crnt4sbml.CRNT("path/to/Fig1Ci.xml") >>> approach = network.get_mass_conservation_approach() Creating Equilibrium Manifold ... Elapsed time for creating Equilibrium Manifold: 2.060944
>>> sympy.pprint(approach.get_w_nullspace()) ⎡⎡-1⎤ ⎡1 ⎤⎤ ⎢⎢ ⎥ ⎢ ⎥⎥ ⎢⎢0 ⎥ ⎢0 ⎥⎥ ⎢⎢ ⎥ ⎢ ⎥⎥ ⎢⎢1 ⎥ ⎢-1⎥⎥ ⎢⎢ ⎥ ⎢ ⎥⎥ ⎢⎢-1⎥ ⎢0 ⎥⎥ ⎢⎢ ⎥ ⎢ ⎥⎥ ⎢⎢0 ⎥, ⎢0 ⎥⎥ ⎢⎢ ⎥ ⎢ ⎥⎥ ⎢⎢1 ⎥ ⎢0 ⎥⎥ ⎢⎢ ⎥ ⎢ ⎥⎥ ⎢⎢0 ⎥ ⎢-1⎥⎥ ⎢⎢ ⎥ ⎢ ⎥⎥ ⎢⎢0 ⎥ ⎢0 ⎥⎥ ⎢⎢ ⎥ ⎢ ⎥⎥ ⎣⎣0 ⎦ ⎣1 ⎦⎦
-
run_continuity_analysis
(species=None, parameters=None, dir_path='./num_cont_graphs', print_lbls_flag=False, auto_parameters=None, plot_labels=None)[source]¶ Function for running the numerical continuation and bistability analysis portions of the mass conservation approach.
Parameters: - species (string) – A string stating the species that is the y-axis of the bifurcation diagram.
- parameters (list of numpy arrays) – A list of numpy arrays corresponding to the decision vectors that produce a small objective function value.
- dir_path (string) – A string stating the path where the bifurcation diagrams should be saved.
- print_lbls_flag (bool) – If True the routine will print the special points found by AUTO 2000 and False will not print any special points.
- auto_parameters (dict) – Dictionary defining the parameters for the AUTO 2000 run. Please note that one should not set
‘SBML’ or ‘ScanDirection’ in these parameters as these are automatically assigned. It is absolutely
necessary to set PrincipalContinuationParameter in this dictionary. For more information on these
parameters refer to
AUTO parameters
. ‘NMX’ will default to 10000 and ‘ITMX’ to 100. - plot_labels (list of strings) – A list of strings defining the labels for the x-axis, y-axis, and title. Where the first element is the label for x-axis, second is the y-axis label, and the last element is the title label. If you would like to use the default settings for some of the labels, simply provide None for that element.
Returns: - multistable_param_ind (list of integers) – A list of those indices in ‘parameters’ that produce multistable plots.
- plot_specifications (list of lists) – A list whose elements correspond to the plot specifications of each element in multistable_param_ind. Each element is a list where the first element specifies the range used for the x-axis, the second element is the range for the y-axis, and the last element provides the x-y values and special point label for each special point in the plot.
Example
See Mass Conservation Approach Example and Mass Conservation Approach Walkthrough.
-
run_direct_simulation
(response=None, signal=None, params_for_global_min=None, dir_path='./dir_sim_graphs', change_in_relative_error=1e-06, parallel_flag=False, print_flag=False, left_multiplier=0.5, right_multiplier=0.5)[source]¶ Function for running direct simulation to conduct bistability analysis of the mass conservation approach.
Note: This routine is more expensive than the numerical continuation routines, but can provide solutions when the Jacobian of the ODE system is always singular. A parallel version of this routine is available.
Parameters: - response (string) – A string stating the response species of the bifurcation analysis.
- signal (string) – A string stating the signal of the bifurcation analysis. Can be any of the of the conservation laws.
- params_for_global_min (list of numpy arrays) – A list of numpy arrays corresponding to the input vectors that produce a small objective function value.
- dir_path (string) – A string stating the path where the bifurcation diagrams should be saved.
- change_in_relative_error (float) – A float value that determines how small the relative error should be in order for the solution of the ODE system to be considered at a steady state. Note: a smaller value will run faster, but may produce an ODE system that is not at a steady state.
- parallel_flag (bool) – If set to True a parallel version of direct simulation is ran. If False, a serial version of the routine is ran. See Parallel General Approach for further information.
- print_flag (bool) – If set to True information about the direct simulation routine will be printed. If False, no output will be provided.
- left_multiplier (float) – A float value that determines the percentage of the signal that will be searched to the left of the signal value. For example, the lowerbound for the signal range will be signal_value - signal_value*left_multiplier.
- right_multiplier (float) – A float value that determines the percentage of the signal that will be searched to the right of the signal value. For example, the upperbound for the signal range will be signal_value + signal_value*right_multiplier.
Example
-
run_greedy_continuity_analysis
(species=None, parameters=None, dir_path='./num_cont_graphs', print_lbls_flag=False, auto_parameters=None, plot_labels=None)[source]¶ Function for running the greedy numerical continuation and bistability analysis portions of the mass conservation approach. This routine uses the initial value of the principal continuation parameter to construct AUTO parameters and then tests varying fixed step sizes for the continuation problem. Note that this routine may produce jagged or missing sections in the plots provided. To produce better plots one should use the information provided by this routine to run
crnt4sbml.MassConservationApproach.run_continuity_analysis()
.Parameters: - species (string) – A string stating the species that is the y-axis of the bifurcation diagram.
- parameters (list of numpy arrays) – A list of numpy arrays corresponding to the decision vectors that produce a small objective function value.
- dir_path (string) – A string stating the path where the bifurcation diagrams should be saved.
- print_lbls_flag (bool) – If True the routine will print the special points found by AUTO 2000 and False will not print any special points.
- auto_parameters (dict) – Dictionary defining the parameters for the AUTO 2000 run. Please note that only the
PrincipalContinuationParameter in this dictionary should be defined, no other AUTO parameters should
be set. For more information on these parameters refer to
AUTO parameters
. - plot_labels (list of strings) – A list of strings defining the labels for the x-axis, y-axis, and title. Where the first element is the label for x-axis, second is the y-axis label, and the last element is the title label. If you would like to use the default settings for some of the labels, simply provide None for that element.
Returns: - multistable_param_ind (list of integers) – A list of those indices in ‘parameters’ that produce multistable plots.
- plot_specifications (list of lists) – A list whose elements correspond to the plot specifications of each element in multistable_param_ind. Each element is a list where the first element specifies the range used for the x-axis, the second element is the range for the y-axis, and the last element provides the x-y values and special point label for each special point in the plot.
Example
-
run_optimization
(bounds=None, iterations=10, sys_min_val=2.220446049250313e-16, seed=0, print_flag=False, numpy_dtype=<class 'numpy.float64'>, concentration_bounds=None, confidence_level_flag=False, change_in_rel_error=0.01, parallel_flag=False)[source]¶ Function for running the optimization problem for the mass conservation approach.
Parameters: - bounds (list of tuples) – A list defining the lower and upper bounds for each variable in the decision vector. Here the reactions are allowed to be set to a single value.
- iterations (int) – The number of iterations to run the feasible point method.
- sys_min_val (float) – The value that should be considered zero for the optimization problem.
- seed (int) – Seed for the random number generator. None should be used if a random generation is desired.
- print_flag (bool) – Should be set to True if the user wants the objective function values found in the optimization problem and False otherwise.
- numpy_dtype – The numpy data type used within the optimization routine. All variables in the optimization routine will be converted to this data type.
- concentration_bounds (list of tuples) – A list defining the lower and upper bounds for those species’ concentrations not in the decision vector.
The user is not allowed to set the species’ concentration to a single value. See also:
crnt4sbml.MassConservationApproach.get_concentration_bounds_species()
. - confidence_level_flag (bool) – If True a confidence level for the objective function will be given.
- change_in_rel_error (float) – The maximum relative error that should be allowed to consider in the neighborhood of .
- parallel_flag (bool) – If set to True a parallel version of the optimization routine is ran. If False, a serial version of the optimization routine is ran. See Parallel General Approach.
Returns: - params_for_global_min (list of numpy arrays) – A list of numpy arrays that correspond to the decision vectors of the problem.
- obj_fun_val_for_params (list of floats) – A list of objective function values produced by the corresponding decision vectors in params_for_global_min.
Examples
See Mass Conservation Approach Example and Mass Conservation Approach Walkthrough.
-


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