--- prev_page: '/recipes/recipes_intro.html' next_page: '/recipes/recipes_graphing.html' --- {% raw %}
# HIDDEN
# Clear previously defined variables
%reset -f
# Set directory for data loading to work properly
import os
os.chdir(os.path.expanduser('~/docs/notebooks/recipes'))
nbinteract
¶nbinteract
comes with two options for exporting Jupyter notebooks into
standalone HTML pages: running nbi.publish()
in a
notebook cell and using the nbinteract
command line tool.
nbi.publish()
¶The nbi.publish()
method can be run inside a Jupyter notebook cell. It has
the following signature:
import nbinteract as nbi
nbi.publish(spec, nb_name, template='full', save_first=True)
'''
Converts nb_name to an HTML file. Preserves widget functionality.
Outputs a link to download HTML file after conversion if called in a
notebook environment.
Equivalent to running `nbinteract ${spec} ${nb_name}` on the command line.
Args:
spec (str): BinderHub spec for Jupyter image. Must be in the format:
`${username}/${repo}/${branch}`.
nb_name (str): Complete name of the notebook file to convert. Can be a
relative path (eg. './foo/test.ipynb').
template (str): Template to use for conversion. Valid templates:
- 'full': Outputs a complete standalone HTML page with default
styling. Automatically loads the nbinteract JS library.
- 'partial': Outputs an HTML partial that can be embedded in
another page. Automatically loads the nbinteract JS library.
- 'gitbook': Outputs an HTML partial used to embed in a Gitbook or
other environments where the nbinteract JS library is already
loaded.
save_first (bool): If True, saves the currently opened notebook before
converting nb_name. Used to ensure notebook is written to
filesystem before starting conversion. Does nothing if not in a
notebook environment.
Returns:
None
'''
For example, to convert a notebook called Hello.ipynb
using the Binder spec
calebs11/nbinteract-image/master
:
nbi.publish('calebs11/nbinteract-image/master', 'Hello.ipynb')
nbinteract
CLI tool¶Installing the nbinteract
package also installs a command-line tool for
converting notebooks into HTML pages. It work identically to nbi.publish()
but can be used on the command line.
Converts notebooks to interactive HTML pages or Gitbook pages.
Usage:
nbinteract SPEC NOTEBOOKS ...
nbinteract [options] SPEC NOTEBOOKS ...
nbinteract (-h | --help)
Arguments:
SPEC BinderHub spec for Jupyter image. Must be in the format:
`{username}/{repo}/{branch}`. For example:
'SamLau95/nbinteract-image/master'.
NOTEBOOKS List of notebooks or folders to convert. If folders are passed in,
all the notebooks in each folder are converted. The resulting HTML
files are created adjacent to their originating notebooks and will
clobber existing files of the same name.
By default, notebooks in subfolders will not be converted; use the
--recursive flag to recursively convert notebooks in subfolders.
Options:
-h --help Show this screen
-t TYPE --template TYPE Specifies the type of HTML page to generate. Valid
types: full (standalone page), partial (embeddable
page), or gitbook (embeddable page for GitBook).
[default: full]
-B --no-top-button If set, doesn't generate button at top of page.
-r --recursive Recursively convert notebooks in subdirectories.
-o FOLDER --output=FOLDER Outputs HTML files into FOLDER instead of
outputting files adjacent to their originating
notebooks. All files will be direct descendants of
the folder even if --recursive is set.
-i FOLDER --images=FOLDER Extracts images from HTML and writes into FOLDER
instead of encoding images in base64 in the HTML.
Requires -o option to be set as well.
For example, to convert a notebook called Hello.ipynb
using the Binder spec
calebs11/nbinteract-image/master
:
nbinteract calebs11/nbinteract-image/master Hello.ipynb
One advantage of the command line tool is that it can convert notebooks in folders as well as individual notebooks:
# Using the -r flag tells nbinteract to recursively search for .ipynb files
# in nb_folder
nbinteract -r calebs11/nbinteract-image/master nb_folder/


Gayot, Vanessa Rose N.
<EMAIL_ADDRESS>To take a position as a Web Designer.
Name : Vanessa Rose
Address : 52 Sta. Escolastica Village Baguio City
Date of Birth : March 28, 1999
Age : 17
Nationality : Filipino
Tertiary : BS Information Technology, University of the Cordelleras - Present
Secondary : Baguio City National High School - Hillside Annex S.Y.
Primary : Manuel L. Quezon Elementary School S.Y.


{{< layout}} {{$propositionHeader}}{{/propositionHeader}} {{$pageTitle}}Information about urgent passport{{/pageTitle}} {{$header}} {{/header}} {{$main-content}}
You'll need to attend an appointment at a passport office in the UK. You can choose an appointment online when you apply.


If plugin's 'responsive' property is not enabled, the element will not maintain its aspect ratio on resize.
If plugin's 'responsive' property is not enabled, the element will maintain its aspect ratio on resize like a normal image.
Back To Github


WeChatResult¶
-
class
WeChatResult
(resp)¶ -
-
success
¶ success is used to determine whether this request is successful
-
result
¶ result is a python dict which contains the response data that parsed from xml
-
error_msg
¶ error_msg contains the error message if anything went wrong
-
-
class
UnifiedorderResult
(WeChatResult)¶ -
trade_type
¶
-
prepay_id
¶ generate by WeChat for more operation (valid in 2 hours)
-
code_url
¶ Qr code url for payment
-
-
class
ErrorResult
(WeChatResult)¶ -
success
¶ in this case success is False
-
error_msg
¶ error message dict
-
-
class
OrderResult
(WeChatResult)¶ -
trade_state
¶ the state of this order
-
out_trade_no
¶ order id
-
total_fee
¶ amount of fee of this order
-
-
class
CloseOrderResult
(WeChatResult)¶ pass


{% extends "layout/default.html" %} {% block title %}Receive updates from Chameleon{% endblock %} {% block content %}
Please subscribe to our announce mailing list to receive the latest news from the Chameleon project.
You can also catch up on previous announcement by consulting the archives of the mailing list.
{% endblock %}


Daniel Craig insists he isn't grumpy but refuses to try and change perceptions
The actor, gearing up for his latest James Bond release, says he isn't a grouch - and he loves show business
by Sam Elliott, https://www.mirror.co.uk/authors/sam-elliott/Daniel Craig insists he is far grumpy, despite what people say - but isn't interesting in changing what they think.
The James Bond star says the impression some have of him couldn't be further from the truth.
But the actor admits he makes very little effort to try and change perceptions of him.
Craig told The Sunday Times : “I don’t do much to dispel it, because I’d just be chasing my tail to prove that I’m not the person people think I am.
“You know, I probably don’t have a particularly good public persona. I just grew up in an era when, if you were trying to be an artist, you didn’t look for approval. You didn’t look for likes. You just did your thing. And this? This is what I do.
“Some do. Some can go on talk shows and tell stories, but I’m just not wired that way. I don’t know what to say.
“I can try, but people would go, ‘What’s he doing?’ They’d go, ‘Where is the grumpy f*****?'
“But I’m not grumpy. Genuinely, I’m not. I love what I do.”
Grumpy or not, cinemas across the world will sell out when 007 returns to the big screen.
A teaser image for Bond's 25th cinematic outing shows the dashing spy feeling the wear and tear.
Daniel appears battered and bleary-eyed with a ripped top and bloodied forehead.
The snap was shared by the official 007 Instagram account, he caption reading: "Back in action. Daniel Craig as 007 in NO TIME TO DIE."
The film's on course for an April 2020 release, helmed by director Cary Fukunaga.
The account's been teasing the flick for some time now - a previous snap shows Daniel in Bond's classic tux posing up a storm with Cary.
The account wrote: "That's a wrap on #NoTimeToDie. See you in cinemas April 2020. #Bond25."


An assignment operator assigns a value to its left operand based on the value of its right operand.
The basic assignment operator is equal (=
), which assigns the value of its right operand to its left operand. That is, x = y
assigns the value of y
to x
. The other assignment operators are usually shorthand for standard operations, as shown in the following definitions and examples.
Simple assignment operator which assigns a value to a variable. Chaining the assignment operator is possible in order to assign a single value to multiple variables. See the example.
Operator: x = y
// Assuming the following variables // x = 5 // y = 10 // z = 25 x = y // x is 10 x = y = z // x, y and z are all 25
The addition assignment operator adds the value of the right operand to a variable and assigns the result to the variable. The types of the two operands determine the behavior of the addition assignment operator. Addition or concatenation is possible. See the addition operator for more details.
Operator: x += y Meaning: x = x + y
// Assuming the following variables // foo = "foo" // bar = 5 // baz = true // Number + Number -> addition bar += 2 // 7 // Boolean + Number -> addition baz += 1 // 2 // Boolean + Boolean -> addition baz += false // 0 // Number + String -> concatenation bar += "foo" // "5foo" // String + Boolean -> concatenation foo += false // "foofalse" // String + String -> concatenation foo += "bar" // "foobar"
The subtraction assignment operator subtracts the value of the right operand from a variable and assigns the result to the variable. See the subtraction operator for more details.
Operator: x -= y Meaning: x = x - y
// Assuming the following variable // bar = 5 bar -= 2 // 3 bar -= "foo" // NaN
The multiplication assignment operator multiplies a variable by the value of the right operand and assigns the result to the variable. See the multiplication operator for more details.
Operator: x *= y Meaning: x = x * y
// Assuming the following variable // bar = 5 bar *= 2 // 10 bar *= "foo" // NaN
The division assignment operator divides a variable by the value of the right operand and assigns the result to the variable. See the division operator for more details.
Operator: x /= y Meaning: x = x / y
// Assuming the following variable // bar = 5 bar /= 2 // 2.5 bar /= "foo" // NaN bar /= 0 // Infinity
The remainder assignment operator divides a variable by the value of the right operand and assigns the remainder to the variable. See the remainder operator for more details.
Operator: x %= y Meaning: x = x % y
// Assuming the following variable // bar = 5 bar %= 2 // 1 bar %= "foo" // NaN bar %= 0 // NaN
[ECMAScript 2016 (ES7) proposal]
The exponentiation assignment operator returns the result of raising first operand to the power second operand. See the exponentiation operator for more details.
Operator: x **= y Meaning: x = x ** y
// Assuming the following variable // bar = 5 bar **= 2 // 25 bar **= "foo" // NaN
The left shift assignment operator moves the specified amount of bits to the left and assigns the result to the variable. See the left shift operator for more details.
Operator: x <<= y Meaning: x = x << y
var bar = 5; //<PHONE_NUMBER>0000000000000000000101) bar <<= 2; // 20<PHONE_NUMBER>0000000000000000010100)
The right shift assignment operator moves the specified amount of bits to the right and assigns the result to the variable. See the right shift operator for more details.
Operator: x >>= y Meaning: x = x >> y
var bar = 5; //<PHONE_NUMBER>0000000000000000000101) bar >>= 2; // 1<PHONE_NUMBER>0000000000000000000001) var bar -5; // (-00000000000000000000000000000101) bar >>= 2; // -2 (-00000000000000000000000000000010)
The unsigned right shift assignment operator moves the specified amount of bits to the right and assigns the result to the variable. See the unsigned right shift operator for more details.
Operator: x >>>= y Meaning: x = x >>> y
var bar = 5; //<PHONE_NUMBER>0000000000000000000101) bar >>>= 2; // 1<PHONE_NUMBER>0000000000000000000001) var bar = -5; // (-00000000000000000000000000000101) bar >>>= 2; //<PHONE_NUMBER><PHONE_NUMBER>1111111111111111111110)
The bitwise AND assignment operator uses the binary representation of both operands, does a bitwise AND operation on them and assigns the result to the variable. See the bitwise AND operator for more details.
Operator: x &= y Meaning: x = x & y
var bar = 5; // 5:<PHONE_NUMBER>0000000000000000000101 // 2:<PHONE_NUMBER>0000000000000000000010 bar &= 2; // 0
The bitwise XOR assignment operator uses the binary representation of both operands, does a bitwise XOR operation on them and assigns the result to the variable. See the bitwise XOR operator for more details.
Operator: x ^= y Meaning: x = x ^ y
var bar = 5; bar ^= 2; // 7 // 5:<PHONE_NUMBER>0000000000000000000101 // 2:<PHONE_NUMBER>0000000000000000000010 // ----------------------------------- // 7:<PHONE_NUMBER>0000000000000000000111
The bitwise OR assignment operator uses the binary representation of both operands, does a bitwise OR operation on them and assigns the result to the variable. See the bitwise OR operator for more details.
Operator: x |= y Meaning: x = x | y
var bar = 5; bar |= 2; // 7 // 5:<PHONE_NUMBER>0000000000000000000101 // 2:<PHONE_NUMBER>0000000000000000000010 // ----------------------------------- // 7:<PHONE_NUMBER>0000000000000000000111
In unusual situations, the assignment operator (e.g. x += y
) is not identical to the meaning expression (here x = x + y
). When the left operand of an assignment operator itself contains an assignment operator, the left operand is evaluated only once. For example:
a[i++] += 5 // i is evaluated only once a[i++] = a[i++] + 5 // i is evaluated twice
Created by Mozilla Contributors, license: CC-BY-SA 2.5


Ryan Roat Code Portfolio
MCDM Launcher
A chrome extension for quick access to social media accounts, based off a Traversy Media tutorial, with JavaScript added to pick a random catchphrase from an array.
A chrome extension for quick access to social media accounts, based off a Traversy Media tutorial, with JavaScript added to pick a random catchphrase from an array.


- Maciek Małecki, 2020 -
"These computers did not come with operating systems. They didn't even come with file systems. What you got was an assembler."
Robert C. Martin, Clean Architecture: A Craftsman's Guide to Software Structure and Design
169 0 141 32 208 169 1 141 32 208 76 0 192
169 0 141 32 208 169 1 141 32 208 76 0 192
$A9 $00 $8D $20 $D0 $A9 $01 $8D $20 $D0 $4C $00 $C0
- human readable form of machine code
- translator (a program) from assembly into the machine code
LDA value
+----------------+-----------------------+---------+---------+----------+ | Addressing Mode| Assembly Language Form| OP CODE |No. Bytes|No. Cycles| +----------------+-----------------------+---------+---------+----------+ | Immediate | LDA #$FF | $A9 | 2 | 2 | | ZeroPage | LDA $FF | $A5 | 2 | 3 | | ZeroPage,X | LDA $FF,X | $B5 | 2 | 4 | | Absolute | LDA $FFFF | $AD | 3 | 4 | | Absolute,X | LDA $FFFF,X | $BD | 3 | 4* | | Absolute,Y | LDA $FFFF,Y | $B9 | 3 | 4* | | (Indirect,X) | LDA ($FF,X) | $A1 | 2 | 6 | | (Indirect),Y | LDA ($FF),Y | $B1 | 2 | 5* | +----------------+-----------------------+---------+---------+----------+ *add extra 1 cycle if crossing 256b page boundary
ADC Add Memory to Accumulator with Carry LDA Load Accumulator with Memory AND "AND" Memory with Accumulator LDX Load Index X with Memory ASL Shift Left One Bit (Memory or Accumulator) LDY Load Index Y with Memory LSR Shift Right One Bit (Memory or Accumulator) BCC Branch on Carry Clear BCS Branch on Carry Set NOP No Operation BEQ Branch on Result Zero BIT Test Bits in Memory with Accumulator ORA "OR" Memory with Accumulator BMI Branch on Result Minus BNE Branch on Result not Zero PHA Push Accumulator on Stack BPL Branch on Result Plus PHP Push Processor Status on Stack BRK Force Break PLA Pull Accumulator from Stack BVC Branch on Overflow Clear PLP Pull Processor Status from Stack BVS Branch on Overflow Set ROL Rotate One Bit Left (Memory or Accumulator) CLC Clear Carry Flag ROR Rotate One Bit Right (Memory or Accumulator) CLD Clear Decimal Mode RTI Return from Interrupt CLI Clear interrupt Disable Bit RTS Return from Subroutine CLV Clear Overflow Flag CMP Compare Memory and Accumulator SBC Subtract Memory from Accumulator with Borrow CPX Compare Memory and Index X SEC Set Carry Flag CPY Compare Memory and Index Y SED Set Decimal Mode SEI Set Interrupt Disable Status DEC Decrement Memory by One STA Store Accumulator in Memory DEX Decrement Index X by One STX Store Index X in Memory DEY Decrement Index Y by One STY Store Index Y in Memory INC Increment Memory by One TAX Transfer Accumulator to Index X INX Increment Index X by One TAY Transfer Accumulator to Index Y INY Increment Index Y by One TSX Transfer Stack Pointer to Index X TXA Transfer Index X to Accumulator JMP Jump to New Location TXS Transfer Index X to Stack Pointer JSR Jump to New Location Saving Return Address TYA Transfer Index Y to Accumulator
10 POKE 53280,0 20 POKE 53280,1 30 GOTO 10
loop: LDA #$00 STA $D020 LDA #$01 STA $D020 JMP loop
$C000 LDA #$00 $C002 STA $D020 $C005 LDA #$01 $C007 STA $D020 $C00A JMP $C000
$C000 $A9 $00 $C002 $8D $20 $D0 $C005 $A9 $01 $C007 $8D $20 $D0 $C00A $4C $00 $C0
49152 169 0 49154 141 32 208 49157 169 1 49159 141 32 208 49162 76 0 192
169 0 141 32 208 169 1 141 32 208 76 0 192
Size and speed of the program
size (b) cycles time* (µs) $C000 LDA #$00 2 2 2.03 $C002 STA $D020 3 4 4.06 $C005 LDA #$01 2 2 2.03 $C007 STA $D020 3 4 4.06 $C00A JMP $C000 3 3 3.05 *PAL version, 0.965MHz


[[getSummaryAriaLabel_(creditCard)]], [[getSummaryAriaSublabel_(creditCard)]]
[[creditCard.metadata.summaryLabel]]
[[getSummarySublabel_(creditCard, virtualCardEnrollmentEnabled_)]]
[[getPaymentsLabel_(creditCard.metadata)]]


Breaking News: Zylofon Media building is on fire
Information available to GhanaWeb indicates that the headquarters of Zylofon Media is on fire.
Sources say the Ghana National Fire Service Personnel are currently at the station trying to put out the fire in other to save lives and property.
More details soon!


|
|
Great experience. Impossible to improve, was perfect
10/11/2016 - Alisha Kapetown
This was great. I did all by email, representative answered almost instantly, gave great advice, was happy to go through as many options as possible, didn't put any pressure on my decision.. in a nutshell, perfect experience. Closed a few weeks ago and everything was smooth and swift.
Explained the process and requirements until we fully understood
7/5/2016 - Norman Teller
I saw HomeBuyingBuddy's referral options, you could select any lender you want to be contacted from, Embrace Housing Loans had very good reviews and the lower rates and I can't be happier, They must have a lean operation and that allows them to give lower rates, but this is really like going to a small town local bank and sit and have coffee with the loan officer.


Weirich - Beyond Production Telemetry
LnL - DevOps Handbook
Learn SQL - Intro
Learn SQL - Querying
Learn SQL - Updating
Learn SQL - Aggregating
Learn RSpec - Intro
Learn RSpec - Structure
Learn RSpec - Factories
Learn RSpec - Request Spec
Learn GraphQL - Intro
Learn GraphQL - Client Side Nomenclature
Learn GraphQL - Server Side Nomenclature
LnL - GraphQL and Chill
LnL - Your Friend, Pry


Modern interior
A full-Service residential & commercial interior design and staging company offering professional organizing & eco-services.
Read more
A full-Service residential & commercial interior design and staging company offering professional organizing & eco-services.
Aliza Webber
Interior Designer


Solidity Distributed BlockChain Application
Install bloc
Download from npm
`npm install blockapps-bloc`
Initialize a project
bloc init
Install npm dependencies
cd app_name && npm install
Generate a key
bloc genkey
You will see an out put something like
app/users/admin/6f29e2b2b6b922800fd959a94b88cd305790413a.json
Remember the username and password, you will need to enter this in visual studio.
Run Your local bloc keyserver
bloc start
bloc should now be listening on localhost:8000
Create a distributed application in Visual Studio
- Click new project
- From the template select `Solidity` & DecentralizedApplication
- Navigate to the Solution Explorer and right click on your project
- Select Properties
- Enter the password you used with the `bloc genkey` command in the BlockchainPassword field
- Click apply
Compile and deploy your first smart contract
- In Visual Studio in `samples` right click one of the `.sol` files and click `compile`
- Now right click and click `deploy`
- You should see your browser open with generated html page based on the methods in the solidity contract
- If you do not look in the visual studio console and you will see a link to the deployed contract
- Click this link and you will see and generated html page based on the methods in the contract


${generalCss}
Memory Usage:| Max Available: | ${maxAvailable} | Max memory that ${appName} may allocate. | ||
|---|---|---|---|---|
| Allocated | Total: | ${allocatedTotal} | Memory allocated by ${appName}. | |
| In use: | ${allocatedInUse} | Memory that is currently in use. | ||
| Free: | ${allocatedFree} | Memory allocated but not used currently. | ||
| Unallocated: | ${unallocated} | Unallocated but available memory (may be allocated if needed). | ||
| Total Free: | ${totalFree} | Total free memory that may be used if needed. |


[{{episodeDetailsCtrl.episode.rated}}] {{episodeDetailsCtrl.episode.runtime}} min - {{genre}}, - {{episodeDetailsCtrl.episode.releaseYear}}
{{episodeDetailsCtrl.episode.plot}}
Season: {{episodeDetailsCtrl.episode.season}}
Episode: {{episodeDetailsCtrl.episode.episode}}
| {{actor.name}} | ... | {{actor.character}} |


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object javax.xml.transform.Transformer
public abstract class Transformer
An instance of this abstract class can transform a source tree into a result tree.
An instance of this class can be obtained with the
TransformerFactory.newTransformer
method. This instance may then be used to process XML from a
variety of sources and write the transformation output to a
variety of sinks.
An object of this class may not be used in multiple threads running concurrently. Different Transformers may be used concurrently by different threads.
A Transformer
may be used multiple times. Parameters and
output properties are preserved across transformations.
| Constructor Summary | |
|---|---|
protected |
Transformer()
Default constructor is protected on purpose. |
| Method Summary | |
|---|---|
abstract void |
clearParameters()
Clear all parameters set with setParameter. |
abstract ErrorListener |
getErrorListener()
Get the error event handler in effect for the transformation. |
abstract Properties |
getOutputProperties()
Get a copy of the output properties for the transformation. |
abstract String |
getOutputProperty(String name)
Get an output property that is in effect for the transformer. |
abstract Object |
getParameter(String name)
Get a parameter that was explicitly set with setParameter. |
abstract URIResolver |
getURIResolver()
Get an object that will be used to resolve URIs used in document(). |
void |
reset()
Reset this Transformer to its original configuration. |
abstract void |
setErrorListener(ErrorListener listener)
Set the error event listener in effect for the transformation. |
abstract void |
setOutputProperties(Properties oformat)
Set the output properties for the transformation. |
abstract void |
setOutputProperty(String name,
String value)
Set an output property that will be in effect for the transformation. |
abstract void |
setParameter(String name,
Object value)
Add a parameter for the transformation. |
abstract void |
setURIResolver(URIResolver resolver)
Set an object that will be used to resolve URIs used in document(). |
abstract void |
transform(Source xmlSource,
Result outputTarget)
Transform the XML Source to a Result . |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
protected Transformer()
| Method Detail |
|---|
public void reset()
Reset this Transformer
to its original configuration.
Transformer
is reset to the same state as when it was created with
TransformerFactory.newTransformer()
,
TransformerFactory.newTransformer(Source source)
or
Templates.newTransformer()
.
reset()
is designed to allow the reuse of existing Transformer
s
thus saving resources associated with the creation of new Transformer
s.
The reset Transformer
is not guaranteed to have the same URIResolver
or ErrorListener
Object
s, e.g. Object.equals(Object obj)
.
It is guaranteed to have a functionally equal URIResolver
and ErrorListener
.
public abstract void transform(Source xmlSource, Result outputTarget) throws TransformerException
Transform the XML Source
to a Result
.
Specific transformation behavior is determined by the settings of the
TransformerFactory
in effect when the
Transformer
was instantiated and any modifications made to
the Transformer
instance.
An empty Source
is represented as an empty document
as constructed by DocumentBuilder.newDocument()
.
The result of transforming an empty Source
depends on
the transformation behavior; it is not always an empty
Result
.
xmlSource
- The XML input to transform.outputTarget
- The Result
of transforming the
xmlSource
.
TransformerException
- If an unrecoverable error occurs
during the course of the transformation.public abstract void setParameter(String name, Object value)
Pass a qualified name as a two-part string, the namespace URI enclosed in curly braces ({}), followed by the local name. If the name has a null URL, the String only contain the local name. An application can safely check for a non-null URI by testing to see if the first character of the name is a '{' character.
For example, if a URI and local name were obtained from an element defined with <xyz:foo xmlns:xyz="http://xyz.foo.com/yada/baz.html"/>, then the qualified name would be "{http://xyz.foo.com/yada/baz.html}foo". Note that no prefix is used.
name
- The name of the parameter, which may begin with a
namespace URI in curly braces ({}).value
- The value object. This can be any valid Java object. It is
up to the processor to provide the proper object coersion or to simply
pass the object on for use in an extension.
NullPointerException
- If value is null.public abstract Object getParameter(String name)
This method does not return a default parameter value, which cannot be determined until the node context is evaluated during the transformation process.
name
- of Object
to get
public abstract void clearParameters()
public abstract void setURIResolver(URIResolver resolver)
If the resolver argument is null, the URIResolver value will be cleared and the transformer will no longer have a resolver.
resolver
- An object that implements the URIResolver interface,
or null.public abstract URIResolver getURIResolver()
public abstract void setOutputProperties(Properties oformat)
If argument to this function is null, any properties previously set are removed, and the value will revert to the value defined in the templates object.
Pass a qualified property key name as a two-part string, the namespace URI enclosed in curly braces ({}), followed by the local name. If the name has a null URL, the String only contain the local name. An application can safely check for a non-null URI by testing to see if the first character of the name is a '{' character.
For example, if a URI and local name were obtained from an element defined with <xyz:foo xmlns:xyz="http://xyz.foo.com/yada/baz.html"/>, then the qualified name would be "{http://xyz.foo.com/yada/baz.html}foo". Note that no prefix is used.
AnIllegalArgumentException
is thrown if any of the
argument keys are not recognized and are not namespace qualified.
oformat
- A set of output properties that will be
used to override any of the same properties in affect
for the transformation.OutputKeys
,
Properties
public abstract Properties getOutputProperties()
Get a copy of the output properties for the transformation.
The properties returned should contain properties set by the user,
and properties set by the stylesheet, and these properties
are "defaulted" by default properties specified by
section 16 of the
XSL Transformations (XSLT) W3C Recommendation. The properties that
were specifically set by the user or the stylesheet should be in the base
Properties list, while the XSLT default properties that were not
specifically set should be the default Properties list. Thus,
getOutputProperties().getProperty(String key) will obtain any
property in that was set by setOutputProperty(java.lang.String, java.lang.String)
,
setOutputProperties(java.util.Properties)
, in the stylesheet, or the default
properties, while
getOutputProperties().get(String key) will only retrieve properties
that were explicitly set by setOutputProperty(java.lang.String, java.lang.String)
,
setOutputProperties(java.util.Properties)
, or in the stylesheet.
Note that mutation of the Properties object returned will not effect the properties that the transformer contains.
If any of the argument keys are not recognized and are not
namespace qualified, the property will be ignored and not returned.
In other words the behaviour is not orthogonal with
setOutputProperties
.
OutputKeys
,
Properties
,
XSL Transformations (XSLT) Version 1.0public abstract void setOutputProperty(String name, String value) throws IllegalArgumentException
Pass a qualified property name as a two-part string, the namespace URI enclosed in curly braces ({}), followed by the local name. If the name has a null URL, the String only contain the local name. An application can safely check for a non-null URI by testing to see if the first character of the name is a '{' character.
For example, if a URI and local name were obtained from an element defined with <xyz:foo xmlns:xyz="http://xyz.foo.com/yada/baz.html"/>, then the qualified name would be "{http://xyz.foo.com/yada/baz.html}foo". Note that no prefix is used.
The Properties object that was passed to setOutputProperties(java.util.Properties)
won't be effected by calling this method.
name
- A non-null String that specifies an output
property name, which may be namespace qualified.value
- The non-null string value of the output property.
IllegalArgumentException
- If the property is not supported, and is
not qualified with a namespace.OutputKeys
public abstract String getOutputProperty(String name) throws IllegalArgumentException
name
- A non-null String that specifies an output
property name, which may be namespace qualified.
IllegalArgumentException
- If the property is not supported.OutputKeys
public abstract void setErrorListener(ErrorListener listener) throws IllegalArgumentException
listener
- The new error listener.
IllegalArgumentException
- if listener is null.public abstract ErrorListener getErrorListener()
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CSS - Text
This tutorial explains how to use CSS text properties to edit and modify text in an HTML document. Use the following CSS text properties to edit text elements:
- 1. color property to set the color of a text.
- 2. direction property to set the text direction.
- 3. letter-spacing property to modify the amount of space between the letters in text.
- 4. word-spacing property to modify the amount of space between the words in text.
- 5. text-decoration property to overline, underline, and strikethrough text.
- 6. text-align property to align the text of a document.
- 7. text-indent property to indent the text of a document.
- 8. text-transform property to capitalize text, or convert it to uppercase or lowercase letters.
- 9. white-space property to control the formatting and flow of text.
- 10. text-shadow property to set the text shadow.


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John Dumelo and son hang out with former President John Dramani Mahama (Photo)
by Qwame BenedictGhanaian actor turned politician John Dumelo together with his son John Dumelo Jnr. has been spotted hanging out with the NDC flagbearer for the 2020 elections John Dramani Mahama.
Though much is not known about the visit, it is believed that the Johns’ met to look at the way forward in seizing power from the Akuffo Addo led NPP in the next elections which is just a year away.
See the photo below:
Actor John Dumelo is seeking to be elected as a Member of Parliament to represent the people of Ayawaso West Wuogon on the ticket of the NDC.
Former President John Dramani Mahama, on the other hand, is seeking to be elected for the second time to head the affairs of the country after losing massively to the Nana Akuffo Addo in the 2016 Elections.


The Semi-Supervised VAE¶
Introduction¶
Most of the models we’ve covered in the tutorials are unsupervised:
We’ve also covered a simple supervised model:
The semi-supervised setting represents an interesting intermediate case where some of the data is labeled and some is not. It is also of great practical importance, since we often have very little labeled data and much more unlabeled data. We’d clearly like to leverage unlabeled data to improve our models of the labeled data.
The semi-supervised setting is also well suited to generative models, where missing data can be accounted for quite naturally—at least conceptually. As we will see, in restricting our attention to semi-supervised generative models, there will be no shortage of different model variants and possible inference strategies. Although we’ll only be able to explore a few of these variants in detail, the reader is likely to come away from the tutorial with a greater appreciation for the abstractions and modularity offered by probabilistic programming.
So let’s go about building a generative model. We have a dataset \(\mathcal{D}\) with \(N\) datapoints,
where the \(\{ {\bf x}_i \}\) are always observed and the labels \(\{ {\bf y}_i \}\) are only observed for some subset of the data. Since we want to be able to model complex variations in the data, we’re going to make this a latent variable model with a local latent variable \({\bf z}_i\) private to each pair \(({\bf x}_i, {\bf y}_i)\). Even with this set of choices, a number of model variants are possible: we’re going to focus on the model variant depicted in Figure 1 (this is model M2 in reference [1]).
For convenience—and since we’re going to model MNIST in our experiments below—let’s suppose the \(\{ {\bf x}_i \}\) are images and the \(\{ {\bf y}_i \}\) are digit labels. In this model setup, the latent random variable \({\bf z}_i\) and the (partially observed) digit label jointly generate the observed image. The \({\bf z}_i\) represents everything but the digit label, possibly handwriting style or position. Let’s sidestep asking when we expect this particular factorization of \(({\bf x}_i, {\bf y}_i, {\bf z}_i)\) to be appropriate, since the answer to that question will depend in large part on the dataset in question (among other things). Let’s instead highlight some of the ways that inference in this model will be challenging as well as some of the solutions that we’ll be exploring in the rest of the tutorial.
The Challenges of Inference¶
For concreteness we’re going to continue to assume that the partially-observed \(\{ {\bf y}_i \}\) are discrete labels; we will also assume that the \(\{ {\bf z}_i \}\) are continuous.
- If we apply the general recipe for stochastic variational inference to our model (e.g. see SVI Part I) we’re going to be sampling the discrete (and thus non-reparameterizable) variable \({\bf y}_i\) whenever it’s unobserved. As discussed in SVI Part III this will generally lead to high-variance gradient estimates.
- A common way to ameliorate this problem—and one we’ll explore below—is to forego sampling and instead sum out all ten values of the class label \({\bf y}_i\) when we calculate the ELBO for an unlabeled datapoint \({\bf x}_i\). This is more expensive per step, but can help us reduce the variance of our gradient estimator and thereby take fewer steps
- Recall that the role of the guide is to ‘fill in’ latent random variables. Concretely, one component of our guide will be a digit classifier \(q_\phi({\bf y} | {\bf x})\) that will randomly ‘fill in’ labels \(\{ {\bf y}_i \}\) given an image \(\{ {\bf x}_i \}\). Crucially, this means that the only term in the ELBO that will depend on \(q_\phi(\cdot | {\bf x})\) is the term that involves a sum over unlabeled datapoints. This means that our classifier \(q_\phi(\cdot | {\bf x})\)—which in many cases will be the primary object of interest—will not be learning from the labeled datapoints (at least not directly)
- This seems like a potential problem. Luckily, various fixes are possible. Below we’ll follow the approach in reference [1], which involves introducing an additional objective function for the classifier to ensure that the classifier learns directly from the labeled data
We have our work cut out for us so let’s get started!
First Variant: Standard objective function, naive estimator¶
As discussed in the introduction, we’re considering the model depicted in Figure 1. In more detail, the model has the following structure:
$ p({:raw-latex:`\bf y`}) = Cat({:raw-latex:`\bf y`}|{:raw-latex:`\bf \pi`})\(</td> <td > multinomial (or categorical) prior for the class label </tr> <tr> <td>\) p({:raw-latex:`\bf z`}) = :raw-latex:`\mathcal{N}`({:raw-latex:`bf z`}|{:raw-latex:`\bf 0,I`})$ | unit normal prior for the latent code \(\bf z\) |
We structure the components of our guide \(q_{\phi}(.)\) as follows:
$ q_{:raw-latex:`\phi`}({:raw-latex:`\bf y`}|{:raw-latex:`\bf x`}) = Cat({:raw-latex:`\bf y`}|{:raw-latex:`\bf \alpha`}_{:raw-latex:`\phi`}:raw-latex:left`({:raw-latex:bf x`}:raw-latex:right))$ | parameterized multinomial (or categorical) distribution;
\({\bf \alpha}_{\phi}\left({\bf x}\right)\) corresponds to
|
$ q_{:raw-latex:`\phi`}({:raw-latex:`\bf z`}|{:raw-latex:`\bf x, y`}) = :raw-latex:`\mathcal{N}`({:raw-latex:`bf z`}|{:raw-latex:`\bf \mu`}{:raw-latex:`phi`}:raw-latex:`left`({:raw-latex:`bf x, y`}:raw-latex:`right`), {:raw-latex:`bf sigma^2`{:raw-latex:`\phi`}:raw-latex:left`(x, y:raw-latex:right`)})$ | parameterized normal distribution;
\({\bf \mu}_{\phi}\left({\bf x, y}\right)\) and
\({\bf \sigma^2_{\phi}\left(x, y\right)}\) correspond to the neural
digit classifier |
These choices reproduce the structure of model M2 and its corresponding inference network in reference [1].
We translate this model and guide pair into Pyro code below. Note that:
1. The labels ys
, which are represented with a one-hot encoding, are
only partially observed (None
denotes unobserved values). 2.
model()
handles both the observed and unobserved case. 3. The code
assumes that xs
and ys
are mini-batches of images and labels,
respectively, with the size of each batch denoted by batch_size
.
In [1]:
def model(xs, ys=None):
# sample z from the prior
prior_mu = Variable(torch.zeros([batch_size, z_dim]))
prior_sigma = Variable(torch.ones([batch_size, z_dim]))
zs = pyro.sample("z", dist.normal, prior_mu, prior_sigma)
# if the label y is observed, sample from the prior.
# otherwise, observe the value
alpha_prior = Variable(torch.ones([batch_size, 10]) / (10.))
if ys is None:
ys = pyro.sample("y", dist.categorical, alpha_prior)
else:
pyro.observe("y", dist.categorical, ys, alpha_prior)
# finally, score the image x against the
# parameterized distribution p(x|y,z) = bernoulli(nn_mu_x(y,z))
mu = nn_mu_x.forward([zs, ys])
pyro.observe("x", dist.bernoulli, xs, mu)
def guide(self, xs, ys=None):
# if the class label is not observed, sample
# with the variational distribution
# q(y|x) = categorical(alpha(x))
if ys is None:
alpha = nn_alpha_y.forward(xs)
ys = pyro.sample("y", dist.categorical, alpha)
# sample the latent z with the variational
# distribution q(z|x,y) = normal(mu(x,y),sigma(x,y))
mu, sigma = nn_mu_sigma_z.forward([xs, ys])
zs = pyro.sample("z", dist.normal, mu, sigma)
Network Definitions¶
In our experiments we use the same network configurations as used in
reference [1]. The encoder and decoder networks have one hidden layer
with \(500\) hidden units and softplus activation functions. We use
softmax as the activation function for the output of nn_alpha_y
,
sigmoid as the output activation function for nn_mu_x
and
exponentiation for the sigma part of the output of nn_mu_sigma_z
.
The latent dimension is 50.
MNIST Pre-Processing¶
We normalize the pixel values to the range \([0.0, 1.0]\). We use the MNIST data loader from the torchvision library. The testing set consists of \(10000\) examples. The default training set consists of \(60000\) examples. We use the first \(50000\) examples for training (divided into supervised and un-supervised parts) and the remaining \(10000\) images for validation. For our experiments, we use \(4\) configurations of supervision in the training set, i.e. we consider \(3000\), \(1000\), \(600\) and \(100\) supervised examples selected randomly (while ensuring that each class is balanced).
The Objective Function¶
The objective function for this model has the two terms (c.f. Eqn. 8 in reference [1]):
To implement this in Pyro, we setup a single instance of the SVI
class. The two different terms in the objective functions will emerge
automatically depending on whether we pass the step
method labeled
or unlabeled data. We will alternate taking steps with labeled and
unlabeled mini-batches, with the number of steps taken for each type of
mini-batch depending on the total fraction of data that is labeled. For
example, if we have 1,000 labeled images and 49,000 unlabeled ones, then
we’ll take 49 steps with unlabeled mini-batches for each labeled
mini-batch. (Note that there are different ways we could do this, but
for simplicity we only consider this variant.) The code for this setup
is given below:
In [2]:
from pyro.infer import SVI
from pyro.optim import Adam
# setup the optimizer
adam_params = {"lr": 0.0003}
optimizer = Adam(adam_params)
# setup the inference algorithm
svi = SVI(model, guide, optimizer, loss="ELBO")
When we run this inference in Pyro, the performance seen during test time is degraded by the noise inherent in the sampling of the categorical variables (see Figure 2 and Table 1 at the end of this tutorial). To deal with this we’re going to need a better ELBO gradient estimator.
Figure 2: Variant 1 (Left) Training losses for the case with 3000 supervised examples. (Right) Test and validation accuracies.
Interlude: Summing Out Discrete Latents¶
As highlighted in the introduction, when the discrete latent labels \({\bf y}\) are not observed, the ELBO gradient estimates rely on sampling from \(q_\phi({\bf y}|{\bf x})\). These gradient estimates can be very high-variance, especially early in the learning process when the guessed labels are often incorrect. A common approach to reduce variance in this case is to sum out discrete latent variables, replacing the Monte Carlo expectation
with an explicit sum
This sum is usually implemented by hand, as in [1], but Pyro can
automate this in many cases. To automatically sum out all discrete
latent variables (here only \({\bf y}\)), we simply pass the
enum_discrete=True
argument to SVI()
:
svi = SVI(model, guide, optim, loss="ELBO", enum_discrete=True)
In this mode of operation, each svi.step(...)
computes a gradient
term for each of the ten latent states of \(y\). Although each step
is thus \(10\times\) more expensive, we’ll see that the
lower-variance gradient estimate outweighs the additional cost.
Going beyond the particular model in this tutorial, Pyro supports
summing over arbitrarily many discrete latent variables. Beware that the
cost of summing is exponential in the number of discrete variables, but
is cheap(er) if multiple independent discrete variables are packed into
a single tensor (as in this tutorial, where the discrete labels for the
entire mini-batch are packed into the single tensor \({\bf y}\)). To
use this parallel form of enum_discrete
, we must inform Pyro that
the items in a minibatch are indeed independent by wrapping our
vectorized code in a with iarange("name")
block.
Second Variant: Standard Objective Function, Better Estimator¶
Now that we have the technology to sum out discrete latents, we can see
if doing so helps our performance. First, as we can see from Figure 3,
the test and validation accuracies now evolve much more smoothly over
the course of training. More importantly, this single modification
improved test accuracy from around 20%
to about 90%
for the case
of \(3000\) labeled examples. See Table 1 for the full results. This
is great, but can we do better?
Figure 3: Variant 2 (Left) Training losses for the case with 3000 supervised examples. (Right) Test and validation accuracies.
Third Variant: Adding a Term to the Objective¶
For the two variants we’ve explored so far, the classifier \(q_{\phi}({\bf y}~|~ {\bf x})\) doesn’t learn directly from labeled data. As we discussed in the introduction, this seems like a potential problem. One approach to addressing this problem is to add an extra term to the objective so that the classifier learns directly from labeled data. Note that this is exactly the approach adopted in reference [1] (see their Eqn. 9). The modified objective function is given by:
where \(\tilde{p_l}({\bf x,y})\) is the empirical distribution over the labeled (or supervised) data and \(\alpha' \equiv \frac{\alpha}{|\mathcal{D}_{supervised}|}\). Note that we’ve introduced an arbitrary hyperparameter \(\alpha\) that modulates the importance of the new term.
To learn using this modified objective in Pyro we do the following: 1.
We use a new model and guide pair (see the code snippet below) that
corresponds to scoring the observed label \({\bf y}\) for a given
image \({\bf x}\) against the predictive distribution
\(q_{\phi}({\bf y}~|~ {\bf x})\) 2. We specify the scaling factor
\(\alpha'\) (aux_loss_multiplier
in the code) in the
pyro.observe
call by making use of the log_pdf_mask
argument.
Note that log_pdf_mask
was used to similar effect in the Deep
Markov Model to implement KL
annealing. 3. We create a new SVI
object and use it to take gradient
steps on the new objective term
In [3]:
def model_classify(xs, ys):
# this yields the extra term in the objective function
alpha = nn_alpha_y.forward(xs)
pyro.observe("y_aux", dist.categorical, ys, alpha,
log_pdf_mask=aux_loss_multiplier)
def guide_classify(xs, ys):
# the guide is trivial, since there are no
# latent random variables
pass
svi_aux = SVI(model_classify, guide_classify, optimizer, loss="ELBO")
When we run inference in Pyro with the additional term in the objective,
we outperform both previous inference setups. For example, the test
accuracy for the case with \(3000\) labeled examples improves from
90%
to 96%
(see Figure 4 below and Table 1 in the next section).
Note that we used validation accuracy to select the hyperparameter
\(\alpha'\).
Figure 4: Variant 3 (Left) Training losses for the case with 3000 supervised examples. (Right) Test and validation accuracies.
Results¶
| Supervised data size | First variant | Second variant | Third variant | Baseline classifier |
|---|---|---|---|---|
| 100 | 0.2.17(0. 0353) | 0.2254(0. 0346) | 0.9319(0. 0060) | 0.7712(0.0159 ) |
| 600 | 0.1791(0. 0244) | 0.6939(0. 0345) | 0.9437(0. 0070) | 0.8716(0.0064 ) |
| 1000 | 0.2.16(0. 0295) | 0.7562(0. 0235) | 0.9487(0. 0038) | 0.8863(0.0025 ) |
| 3000 | 0.1982(0. 0522) | 0.8932(0. 0159) | 0.9582(0. 0012) | 0.9108(0.0015 ) |
Table 1: Result accuracies (with 95% confidence bounds) for different inference methods
Table 1 collects our results from the three variants explored in the tutorial. For comparison, we also show results from a simple classifier baseline, which only makes use of the supervised data (and no latent random variables). Reported are mean accuracies (with 95% confidence bounds in parentheses) across five random selections of supervised data.
We first note that the results for the third variant—where we summed out the discrete latent random variable \(\bf y\) and made use of the additional term in the objective function—reproduce the results reported in reference [1]. This is encouraging, since it means that the abstractions in Pyro proved flexible enough to accomodate the required modeling and inference setup. Significantly, this flexibility was evidently necessary to outperform the baseline. It’s also worth emphasizing that the gap between the baseline and third variant of our generative model setup increases as the number of labeled datapoints decreases (maxing out at about 15% for the case with only 100 labeled datapoints). This is a tantalizing result because it’s precisely in the regime where we have few labeled data points that semi-supervised learning is particularly attractive.
Latent Space Visualization¶
Figure 5: Latent space embedding for variant 3 with 3000 supervised examples
We use T-SNE to reduce the dimensionality of the latent \(\bf z\) from \(50\) to \(2\) and visualize the 10 digit classes in Figure 5. Note that the structure of the embedding is quite different than that in the VAE case, where the digits are clearly separated from one another in the embedding. This make sense, since for the semi-supervised case the latent \(\bf z\) is free to use its representational capacity to model, e.g., handwriting style, since the variation between digits is provided by the (partially observed) labels.
Conditional image generation¶
Figure 6: Conditional samples obtained by fixing the class label and varying \({\bf z}\) (for variant 3 with 3000 supervised examples)
We sampled \(100\) images for each class label (\(0\) to \(9\)) by sampling different values of the latent variable \({\bf z}\). The diversity of handwriting styles exhibited by each digit is consistent with what we saw in the T-SNE visualization, suggesting that the representation learned by \(\bf z\) is disentangled from the class labels.
Final thoughts¶
We’ve seen that generative models offer a natural approach to semi-supervised machine learning. One of the most attractive features of generative models is that we can explore a large variety of models in a single unified setting. In this tutorial we’ve only been able to explore a small fraction of the possible model and inference setups that are possible. There is no reason to expect that one variant is best; depending on the dataset and application, there will be reason to prefer one over another. And there a lot of variants (see Figure 7)!
Some of these variants clearly make more sense than others, but a priori it’s difficult to know which ones are worth trying out. This is especially true once we open the door to more complicated setups, like the two models at the bottom of the figure, which include an always latent random variable \(\tilde{\bf y}\) in addition to the partially observed label \({\bf y}\). (Incidentally, this class of models—see reference [2] for similar variants—offers another potential solution to the ‘no training’ problem that we identified above.)
The reader probably doesn’t need any convincing that a systematic exploration of even a fraction of these options would be incredibly time-consuming and error-prone if each model and each inference procedure were coded up by scratch. It’s only with the modularity and abstraction made possible by a probabilistic programming system that we can hope to explore the landscape of generative models with any kind of nimbleness—and reap any awaiting rewards.
See the full code on Github.
References¶
[1] Semi-supervised Learning with Deep Generative Models
,
Diederik P. Kingma, Danilo J. Rezende, Shakir Mohamed, Max Welling
[2]
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
,
N. Siddharth, Brooks Paige, Jan-Willem Van de Meent, Alban
Desmaison, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H.S.
Torr


--- layout: post title: Yield expression in C language date: 2014-02-14 04:25:31.000000000 -08:00 type: post published: true status: publish categories: - Programming - Python tags: [] meta: _edit_last: '53481042' _publicize_pending: '1' author: login: allenbo email<EMAIL_ADDRESS>display_name: allenbo first_name: '' last_name: '' ---
Yield expression in Python language provides a powerful interface that can avoiding memory consuming list allocation and save amount of time when executing. That's an interesting feature that C language just doesn't have. Normally anyone who learned a little C language would like to have this neat and wonderful feature to be added to c language. I tried with some macros to imitate the syntax and cover the semantic. It looks like pretty similar to the original Python source code and does provide sort of Python yield semantics.
This is a typical Python yield function. With this function, we could get a generator generating a list starting from 0 up to 9.
[code language="python"]
def test(x):
a = 0
while a < x:
yield a
a += 1
y = test(10) # y is a generator
[/code]
With a Python generator, we should be able to use every function that iterator supports.
Here is some tricks. We want to preserve the internal state of the function after we exit and return off it. Sounds familiar? That's right, a global variable is capable of doing that. A static local variable is better. For the above code snippet, we have to make variable a as a static variable. Beside. we need to pin a pointer to the statement after the yield a, and that's where we will start after we reenter the function. Also, we want the C code to look as close to the Python one as possible. So this is the code that I tailored for this purpose.
[code language="c"]
int test(int x) {
static int a = 0;
static int flag = 0;
switch (flag) {
case 0:
while ( a < x ) {
flag = 1;
return a;
case 1:
flag = 0;
a += 1;
}
}
}
[/code]
Well, for now, it doesn't look close. But I abstracted some necessary but distracted codes to user-defined syntax and made some macros to separate the codes.
[code language="c"]
#define BEGIN_YIELD static int flag = 0; \
switch (flag) { \
case 0:
#define YIELD(x) flag = 1; \
return (x); \
case 1: \
flag = 0;
#define END_YIELD } \
return -1;
[/code]
Now, if we merge everything together and that's how the final version looks like.
[code language="c"]
int test(int x) {
static int a = 0;
BEGIN_YIELD
while ( a < x) {
YIELD(a)
a += 1;
}
END_YIELD
}
[/code]
You can see, comparing the corresponding Python code snippet, the only syntax overhead is I add a BEGIN_YIELD after the variable definition and an END_YIELD before the end of the function. The other lines of codes , are same syntax in two different languages. This test function will mimic the behavior of yield function and generate a list of integers.
[code language="c"]
int main() {
printf("%d", test(10));
printf("%d", test(10));
printf("%d", test(10));
printf("%d", test(10));
}
[/code]
You will see a "0123" as output.
Even though the code snippet I wrote above could "act" like a yield expression, they are actually worlds apart. In Python code, we could do something like this.
[code language="python"]
y = test(10)
z = test(2)
print y.next() # print out 0
print z.next() # print out 0
[/code]
However the C code can not. The reason why they are different is in Python test function, variable a is not a global one so it belongs to the runtime stack. In C code, a is a variable that attached to this function, not stack. There is no way C language would create another stack with a different value a. Python, on the other hand, powered by a user-defined frame management, could preserve, technically every frame after it finishes the function invocation.
I should post another blog, explaining how Python manage the runtime frames, cause it's really a long story.


--- layout: compress ---
{% include head.html %} {% include nav.html %}While growing up on the rough streets of Newark, New Jersey, Rameck, George, and Sampson could easily have followed their childhood friends into drug dealing, gangs, and prison. But when a presentation at their school made the three boys aware of the opportunities available to them in the medical and dental professions, they made a pact among themselves that they would become doctors.
Why I chose this bookI chose this book because I thought it was interesting that 3 friends from a poor neighborhood were able to have their dream jobs as doctors.
historical stuffThis took place around 70s, and U.S. President Richard Nixon orders an invasion of Cambodia, widening the war in Vietnam and The Beatles break up


{% extends "base.html" %} {% block title %}{% trans "Edit your bio" %} |{% endblock %} {% block header%}
{% trans "No HTML allowed, but paragraph tags will be added automatically and URLs will be turned in to links." %}


Man vs. Beast: Freaky Lizard Scares Off Man
The frilled-necked lizard (Chlamydosaurus kingii)) is the most striking and mysterious representative of the Agamidae family. When the reptile becomes agitated or fleeing from danger, the lacquered lizard inflates the part of the body, from which it owes its name. A bizarre-shaped cloak or collar resembling an open parachute suddenly appears.
Outwardly, representatives of this sort of lizard are similar to their prehistoric ancestors the Triceratops, who lived 68 million years ago in what is now present-day North America.
In this video an angry frilled-necked lizard can be seen fighting off a man.


Tkinter messageboxThe messagebox module is used to display the message boxes in the python applications. There are the various functions which are used to display the relevant messages depending upon the application requirements. The syntax to use the messagebox is given below. SyntaxParameters
The two options that can be used are default and parent. 1. default The default option is used to mention the types of the default button, i.e. ABORT, RETRY, or IGNORE in the message box. 2. parent The parent option specifies the parent window on top of which, the message box is to be displayed. There is one of the following functions used to show the appropriate message boxes. All the functions are used with the same syntax but have the specific functionalities. 1. showinfo()The showinfo() messagebox is used where we need to show some relevant information to the user. ExampleOutput: 2. showwarning()This method is used to display the warning to the user. Consider the following example. ExampleOutput: 3. showerror()This method is used to display the error message to the user. Consider the following example. ExampleOutput: 4. askquestion()This method is used to ask some question to the user which can be answered in yes or no. Consider the following example. ExampleOutput: 5. askokcancel()This method is used to confirm the user's action regarding some application activity. Consider the following example. ExampleOutput: 6. askyesno()This method is used to ask the user about some action to which, the user can answer in yes or no. Consider the following example. ExampleOutput: 7. askretrycancel()This method is used to ask the user about doing a particular task again or not. Consider the following example. ExampleOutput:
Next TopicWeb Blocker Introduction
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Tkinter messageboxThe messagebox module is used to display the message boxes in the python applications. There are the various functions which are used to display the relevant messages depending upon the application requirements. The syntax to use the messagebox is given below. SyntaxParameters
The two options that can be used are default and parent. 1. default The default option is used to mention the types of the default button, i.e. ABORT, RETRY, or IGNORE in the message box. 2. parent The parent option specifies the parent window on top of which, the message box is to be displayed. There is one of the following functions used to show the appropriate message boxes. All the functions are used with the same syntax but have the specific functionalities. 1. showinfo()The showinfo() messagebox is used where we need to show some relevant information to the user. ExampleOutput: 2. showwarning()This method is used to display the warning to the user. Consider the following example. ExampleOutput: 3. showerror()This method is used to display the error message to the user. Consider the following example. ExampleOutput: 4. askquestion()This method is used to ask some question to the user which can be answered in yes or no. Consider the following example. ExampleOutput: 5. askokcancel()This method is used to confirm the user's action regarding some application activity. Consider the following example. ExampleOutput: 6. askyesno()This method is used to ask the user about some action to which, the user can answer in yes or no. Consider the following example. ExampleOutput: 7. askretrycancel()This method is used to ask the user about doing a particular task again or not. Consider the following example. ExampleOutput:
Next TopicWeb Blocker Introduction
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Tkinter messageboxThe messagebox module is used to display the message boxes in the python applications. There are the various functions which are used to display the relevant messages depending upon the application requirements. The syntax to use the messagebox is given below. SyntaxParameters
The two options that can be used are default and parent. 1. default The default option is used to mention the types of the default button, i.e. ABORT, RETRY, or IGNORE in the message box. 2. parent The parent option specifies the parent window on top of which, the message box is to be displayed. There is one of the following functions used to show the appropriate message boxes. All the functions are used with the same syntax but have the specific functionalities. 1. showinfo()The showinfo() messagebox is used where we need to show some relevant information to the user. ExampleOutput: 2. showwarning()This method is used to display the warning to the user. Consider the following example. ExampleOutput: 3. showerror()This method is used to display the error message to the user. Consider the following example. ExampleOutput: 4. askquestion()This method is used to ask some question to the user which can be answered in yes or no. Consider the following example. ExampleOutput: 5. askokcancel()This method is used to confirm the user's action regarding some application activity. Consider the following example. ExampleOutput: 6. askyesno()This method is used to ask the user about some action to which, the user can answer in yes or no. Consider the following example. ExampleOutput: 7. askretrycancel()This method is used to ask the user about doing a particular task again or not. Consider the following example. ExampleOutput:
Next TopicWeb Blocker Introduction
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Tkinter messageboxThe messagebox module is used to display the message boxes in the python applications. There are the various functions which are used to display the relevant messages depending upon the application requirements. The syntax to use the messagebox is given below. SyntaxParameters
The two options that can be used are default and parent. 1. default The default option is used to mention the types of the default button, i.e. ABORT, RETRY, or IGNORE in the message box. 2. parent The parent option specifies the parent window on top of which, the message box is to be displayed. There is one of the following functions used to show the appropriate message boxes. All the functions are used with the same syntax but have the specific functionalities. 1. showinfo()The showinfo() messagebox is used where we need to show some relevant information to the user. ExampleOutput: 2. showwarning()This method is used to display the warning to the user. Consider the following example. ExampleOutput: 3. showerror()This method is used to display the error message to the user. Consider the following example. ExampleOutput: 4. askquestion()This method is used to ask some question to the user which can be answered in yes or no. Consider the following example. ExampleOutput: 5. askokcancel()This method is used to confirm the user's action regarding some application activity. Consider the following example. ExampleOutput: 6. askyesno()This method is used to ask the user about some action to which, the user can answer in yes or no. Consider the following example. ExampleOutput: 7. askretrycancel()This method is used to ask the user about doing a particular task again or not. Consider the following example. ExampleOutput:
Next TopicWeb Blocker Introduction
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Use this app to keep up with which Treehouse modules you should complete each week. Course is broken up into 12 weeks with a "Week 0" for prerequisites.


Rank the following frameworks to your liking ...
Wicket
300 px (-150 - 150 px) with divisor as 10
Struts
300 px (-150 - 150 px) with divisor as 30
Tapestry
300 px (-150 - 150 px) with divisor as 30
JSF
300 px (-150 - 150 px) with divisor as 30


Amazon sues Pentagon over $10B contract awarded to Microsoft
WASHINGTON (AP) - Amazon is suing the Pentagon over a $10 billion cloud-computing contract awarded to Microsoft.
Amazon said it filed a legal complaint Friday with the U.S. Court of Federal Claims seeking to challenge the decision.
The complaint is sealed, but the company said earlier this month there was “unmistakable bias” on the government’s part.
Amazon’s competitive bid for the “war cloud” project drew criticism from President Donald Trump, who said over the summer that he wanted the Pentagon to take a closer look.
The Pentagon awarded the contract to Microsoft in late October.
U.S. Defense Secretary Mark Esper has said the procurement was done fairly without outside influence.
The contract, formally called the Joint Enterprise Defense Infrastructure, or JEDI, was sought by Microsoft, Amazon, Oracle and IBM.
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#include <mmu.h>
Public Member Functions | |
| MMU () | |
| ~MMU () | |
| bool | ReadMem (int addr, int size, int *value, bool is_instruction, int *fetch_time) |
| bool | WriteMem (int addr, int size, int value, int *write_time) |
| ExceptionType | Translate (int virtAddr, int *physAddr, int size, bool writing) |
Public Attributes | |
| TranslationTable * | translationTable |
|
|
Construction. Empty for now |
|
|
Destructor. Empty for now |
|
|
Read or write 1, 2, or 4 bytes of virtual memory (at addr). Return FALSE if a |
|
|
Translate an address, and check for alignment. Set the use and dirty bits in the translation entry appropriately, and return an exception code if the translation couldn't be completed. |
|
|
Write or write 1, 2, or 4 bytes of virtual memory (at addr). Return FALSE if a correct translation couldn't be found. |
|
|
Pointer to the translation table.
|


openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout azure_cert.pem -out azure_cert.pemgenerating azure_cert.cer
openssl x509 -inform pem -in azure_cert.pem -outform der -out azure_cert.cer
Generating certificate on Windows
The Windows Software Development Kit for Windows 8 (possibly you will need to install it) includes the Certificate Creation Tool, MakeCert.exe.
If you are using Windows 8.1, enter following command in the command prompt to generate a self-signed certificate.
The certificate name should be changed in both places:
"C:\Program Files (x86)\Windows Kits\8.1\bin\x86\makecert.exe" -sky exchange -r -n "CN=certificate_name" -pe -a sha256 -len 2048 -ss My "certificate_name.cer"
You can create validity periods for the certificate, using the -b (beginning) and -e (end) options.
You will see “Succeeded” message after completing this procedure.
Now when certificate is created, a .cer file for the certificate will be written to your current directory, and the certificate will be saved to the machine’s certificate store.
In order to add DigitalOcean IaaS you need to have a valid DigitalOcean account and API access token. DigitalOcean tokens can be created with read or read-write scope, latter is required if you want to manage your resources through Waldur.


The age for blog has already passed. Instead, it is the world of Facebook, Twitter, and Instagram. More and more people prefer to update a short status in a minute, rather than to spend couple of hours sitting down to write a paragraph. However, I still desire to have my own place on the Internet, neither a social media homepage, nor a community account, but a true home made by my own hand.
After getting everything ready in my website, I would like to share what I did to establish my website.
Things to Be Done
- Register a domain
- Set up Github and Github Pages
- Learn how to use Jekyll
- Web pages design
- Set up Table of Contents
- Set up Google Code Prettify
- Set up MathJax
- Set up full-text search
Design Your Website with a Beautiful Template
Font-end design is never easy. It is extremely time consuming to design a pretty website from the beginning to the end. However, thanks for the well designed open-source templates and font-end development tools, things get much easy.
My Criteria for website template are as following:
- Appearance: simple and elegant design;
- Compatibility: functional in all platforms;
- Stabilization: coded by HTML5 and CSS3;
- Customizability: easy to reproduce.
HTML5 Up is a great place to get free website templates. And also Pixelarity provides more commercial templates. The templates of both HTML5 Up and Pixelarity are developed based on the framework Skel.
Skel is a lightweight, low-level framework for building responsive sites and web apps. Inspired in part by its namesake (/etc/skel), it's designed to do just enough to make building responsively simpler, faster, and more effective, but not so much it gets in your way. --- Skel Document
For me, I chose the template Aerial to create my home page and Strata for the other pages.
Table of Contents
A table of contents of every blog is very necessary for readers to know what you are talking about and to quickly get to the right place. As you can see, I formed a table of contents for all my blogs by jekyll-table-of-contents, a simple JavaScript table of contents generator.
Follow the instructions on the Github site, and there is no much trouble to deal with it.
Google Code Prettify
Among all the code highlight tools, Google Code Prettify is most easy to use and support almost the programming languages.
You can just include the JavaScript and CSS provided by Google and write code snippets as the following format. And then, you can see you code is pretty printed.

...

# Or
...
I recommend you to disable the highlighter setting of Jekyll, by just adding one line in the configuration file to avoid conflicts between Jekyll and Google Code Prettify.
highlighter: false
You can also include to following script in you HTML template to automatically add class attribute to tag

, when editing your code by Markdown.

Another thing you should pay attention to is that the CSS style of your HTML template may conflict with Google Code Prettify CSS style. If Google Code Prettify does not work well, you can check whether the CSS style is correct.
MathJax
My blog usually involves some mathematics, so I need to figure out the way to render nice math expressions. And MathJax is my solution.
Since MathJax is very similar to LateX, it is very easy to use. For details of syntax, you can refer to MathJax basic tutorial and quick reference.
To enable MathJax, you should include the following JavaScript in you HTML file.

However, MathJax does not support displaying inline formulas with $...$
. To change the setting, you should include another snippet.

But right now you may find if you input formulas like $x_i^2+x_j^2$
, MathJax cannot correctly interpret it, because Markdown renderer will parse the contents between two underlines to be emphasis. To avoid this, you are supposed to change the Markdown setting in the configuration of Jekyll.
markdown: redcarpet
redcarpet:
extensions: ["no_intra_emphasis"]
Full-text Search
It is cool to set up a full-text search engine for my own blog. I tried to insert Google Custom Search to my blog. However, not all my web pages are embodied by Google, so some pages cannot be searched. Hence, I found jekyll-lunr-js-search and managed to establish my own search engine by it.
You can refer to the introduction for the tool which is very clear and make you website searchable step by step. I personally recommend to install jekyll-lunr-js-search by copying the plugin to you Jekyll site. I have tried both methods, but the first one did not work well.
Words at the End
I wish my blogs can help you establish your own website, and if you have any question or if there is anything wrong with what I said, please no hesitate to leave a comment.
The repository of my blog is available at GitHub, you can take it as a vivid example of what I have introduced.


National Assembly session to be held today
by https://www.facebook.com/dunyanewsISLAMABAD (Dunya News) – The National Assembly session will be held on Thursday (today) and a commotion between the treasury and opposition benches is likely to be witnessed over Jamiat Ulema-e-Islam-Fazal’s Azadi March and sit-in.
The opposition will presumably lodge protest against the National Accountability Bureau (NAB) operations, current political situation, inflation and issuance of presidential ordinance.
It is pertinent here to mention that Speaker National Assembly Asad Qaiser has not yet issued the production orders of the detained parliamentarians. Prime Minister Imran Khan is likely to address the National Assembly.
The House will approve a resolution thanking President Dr Arif Alvi for addressing the session. Furthermore, the Constitutional (Amendment) Bill and ordinance regarding mortgaged properties will be presented.
Minster for Human Rights Dr Shireen M Mazari will table the Islamabad Capital Territory Senior Citizens Welfare Bill 2019. The NA will also hold debate on the Medical Tribunal Ordinance.
Federal Minister for Communications Murad Saeed will present the National Highways Safety (Amendment) Bill and Interior Minister Ijaz Shah will table Anti-Terrorism (Amendment) Act. The session will also analyze reports by standing committees on various bills.
Let it be known that separate meetings of the opposition parties and the government and its allies are scheduled before the NA session during which the strategies will be finalized.


Tata Year-End Discounts Announced On Hexa, Nexon, Tiago, Harrier – Up to 1.65 Lakh
by Surendhar MTata Motors is offering lucrative discounts even for its popular models such as the Tiago, Nexon and Harrier to boost sales volume
Tata Motors has been hit by the industry sales slowdown in 2019 just as other manufacturers. Undeterred, the homegrown auto major is determined to garner sales volume during the closing stages of the year by offering a number of discounts and benefit offers almost across its range.
Ever since going on sale in early 2016, the Tiago has been a star performer for the brand. It often topped the sales charts within the lineup but the sales crisis has certainly taken a toll on its volume figures. The hatchback is offered with benefits of up to Rs. 75,000 under what Tata calls the ‘CAR-NIVAL’.
The discount packages are for limited period only and they are endorsed to be ‘Best Offer Of The Decade’. The Nexon subcompact SUV with five-star Global NCAP rating is another top-seller for the company but the arrival of new competitors like the Hyundai Venue and Mahindra XUV300 has been a hammer-blow as they have eaten into its sales.
| S.No | Tata Models | Discounts |
| 1. | Tata Tiago | Rs. 75,000 |
| 2. | Tata Nexon | Rs. 1,07,500 |
| 3. | Tata Tigor | Rs. 1,00,000 |
| 4. | Tata Hexa | Rs. 1,65,000 |
| 5. | Tata Harrier | Rs. 65,000 |
It is now retailed with attractive benefits of up to Rs. 1,07,500. The Tigor subcompact sedan based on the Tiago hatchback gets up to Rs. 1 lakh benefits as part of the year-end deal while the Hexa crossover is the most benefitted with benefits of up to Rs. 1.65 lakh domestically.
The sales of Hexa have certainly been mediocre in recent times despite being a capable family-oriented MPV/crossover and the benefit deal could help in the revival. The Harrier created huge buzz no other SUV had when it entered the market in January 2019 but its sales have not been too impressive in recent months.
Tata is offering benefits of up to Rs. 65,000 on the Harrier while the seven-seater version could go on sale sometime next year to strengthen the range. Tata is also working on introducing the facelifted Tiago, Tigor and Nexon next year and all of them have been spied testing several times.
The all-electric version of the Nexon will go on sale in the final quarter of this financial year and it will be priced between Rs. 15-17 lakh.


The Spanish Prisoner (1998)
Director: David Mamet Cast: Campbell Scott, Rebecca Pidgeon, Steve Martin, Ben Gazzara, Ricky Jay, Felicity Huffman Screenplay: David Mamet Producers: Jean Doumanian Runtime: 112 min. US Distribution: Sony Pictures Classics Rated PG: mild violence, language
By Nathaniel R. Atcheson<EMAIL_ADDRESS>David Mamet's The Spanish Prisoner is about a bunch of people who are so smart and witty that it seems they are infallible in both speech and action. The film appears to take place in an alternate reality, in which the people described above are strictly governed by laws that keep them from going outside of Mamet's jigsaw plotting. It's not what I'd call a realistic film (the circumstances behind the events that unfold are about as unlikely as, say, the secrets revealed in Fincher's The Game), though I suppose it's not any less-realistic than any film of this genre. But Mamet's plot is so twisted, convoluted, and complex that just about the only thing I could do during and after was scrutinize it.
I don't mind too much. You see, I respect any film that brandishes intelligence as if it knows it's the only smart film out there. Mamet has created a situation that, I believe, could not happen outside a movie set, but the story is so ingenious and compelling that I can't help but admire it. The film is also entertaining, which helps a lot, and the acting is unique in that the actors clearly understand that they are playing people who can not and never will exist in the real world.
The main character is Joe Ross, played valiantly by the ever-solid Campbell Scott. The film begins with Joe on vacation in the Caribbean; he's there for a business meeting, along with his friend and co-worker, George Lang (Ricky Jay), and boss, Klein (Ben Gazzara). He also meets Susan Ricci (Rebecca Pidgeon), who turns out to be an underling in his company; and Jimmy Dell (Steven Martin), who seems to be just a rich guy spending time in the Caribbean.
I've given you the setup, but I don't know how to summarize the rest of the plot without giving away things that you shouldn't know. I went into the film with no prior knowledge of it, except from what I got out of the preview (which is very little -- some marketing execs should take a look at the the trailer for this film and examine how it works), and I found that the film was far different from what I expected. The twists are big an unpredictable, and placed in parts when you probably won't expect a twist to occur.
A film like this basically comes down to a couple things -- acting and plot. As I said, Mamet's story is compelling and ingeniously conceived. Writers for big-budget Hollywood mysteries should take lessons from this film, for this is the way to create a complex story that seems at first glance to be so much more simple than it is, and then fully explain every turn and make it as believable as possible. (The problem with writing a review of a film like this is that I have to be vague and ambiguous while explaining these qualities, but I'm sure you understand.)
The acting is interesting, so much that I often just wanted to see what strange things the characters might do. This is the kind of film where every character speaks in riddles, enunciates each word perfectly, and then predicts what the other characters are going to say. The most fascinating character is Susan, played by Pidgeon (an actress with whom I'm not familiar). She's an enticing woman, and has striking facial features. Her dialogue is the sharpest in the film, and her demeanor is unlike most female characters in most films. Also very interesting is Steve Martin (who, I should point out, does not go for one single joke throughout the film); he plays his character much like Pidgeon plays hers -- straight-faced and alarmingly calm.
The Spanish Prisoner is not really a film to be taken seriously. I doubt that Mamet had any deeper intentions than what are obvious here; it seems clear that he simply wanted to dazzle audiences with a plot so complex and (ahem) contrived that they walk out of the theaters smiling with disbelief. As I said, not even for a glimpse of a second is this a realistic picture. But it succeeds so entirely in being an unpredictable and compelling puzzle of a film that I can't imagine not liking it just for this reason.
*** out of **** (7/10, B)
Visit FILM PSYCHOSIS at http://www.pyramid.net/natesmovies
Nathaniel R. Atcheson
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


The Risk Profile gives a quick overview about the complexity and risk structure of the code base. It tells you how much of the
code is placed in files associated with low/high risk in means of maintainability. The separation of code in risk classes like
low or high complexity is based on Java reference metric values and therefore may be inaccurate for other programming languages.
More information can be found here:
link to How-To article.
Calculations are always based on mcc (cyclomatic complexity)
{{ data.riskProfile.lowRisk }}% of overall code is in files with low complexity
{{ data.riskProfile.moderateRisk }}% of overall code is in files with moderate complexity
{{ data.riskProfile.highRisk }}% of overall code is in files with high complexity
{{ data.riskProfile.veryHighRisk }}% of overall code is in files with extreme complexity
{{ data.riskProfile.lowRisk }}%
{{ data.riskProfile.moderateRisk }}%
{{ data.riskProfile.highRisk }}%
{{ data.riskProfile.veryHighRisk }}%
Could not calculate Risk Profile. Metrics rloc(Real lines of code) and mcc(Cyclomatic complexity) not available.


"AHIHUD "brother (i.e., “friend”) of union. (1.)  Name different in Hebrew, meaning brother of Judah. Chief of the tribe of Asher; one of those appointed by Moses to superintend the division of Canaan among the tribe (Numbers 34:27).
"AHIHUD "brother (i.e., “friend”) of union. (1.)  Name different in Hebrew, meaning brother of Judah. Chief of the tribe of Asher; one of those appointed by Moses to superintend the division of Canaan among the tribe (Numbers 34:27).


localclient
¶
|
Client interface for local file system. |
|
Create directory tree if destination does not exist. |
LocalClient
¶
- class cottoncandy.localclient.LocalClient(path)¶
Bases:
cottoncandy.backend.CCBackEnd
Client interface for local file system.
Handle metadata in CouldStream objects by storing a json file (.meta.json).
- __init__(path)¶
- check_file_exists(cloud_name, bucket_name=None)¶
Checks whether a file exists on the cloud
- Parameters
cloud_name (str) – file on cloud
bucket_name (str) – For a local client, the bucket is just a directory path. If None, use self.path.
- Returns
- Return type
bool
- copy(source, destination, source_bucket, destination_bucket, overwrite)¶
Copies an object
- Parameters
source (str) – origin path
destination (str) – destination path
source_bucket (ignored) –
destination_bucket (ignored) –
overwrite (bool) – overwrite if destination exists
- Returns
- Return type
bool, copy success
- delete(cloud_name, recursive=False, delete=False)¶
Deletes an object
- Parameters
cloud_name (str) – name of cloud object to delete
recursive (bool) – recursively delete directory?
delete (bool) – ignored
- download_stream(cloud_name)¶
Downloads a object to an in-memory stream
- Parameters
cloud_name (str) – name of object to download
- Returns
- Return type
CloudStream object
- download_to_file(cloud_name, file_name)¶
Downloads an object directly to disk
- Parameters
cloud_name (str) – name of object to download
file_name (str) – name on disk to use
- Returns
- Return type
bool, download success
- get_object_metadata(object_name)¶
Get metadata associated with an object
- get_object_size(object_name)¶
Get the size in bytes of an object
- list_directory(path, limit)¶
Lists the content of a directory
- Parameters
path (str) – path on the cloud to get contents for
limit (ignored) –
- list_objects(**kwargs)¶
Gets all objects contained by backend
- move(source, destination, source_bucket, destination_bucket, overwrite)¶
Moves an object
- Parameters
source –
destination –
source_bucket –
destination_bucket –
overwrite –
- property size¶
Size of stored cloud items in bytes
- Returns
- Return type
int
- upload_file(file_name, cloud_name, permissions)¶
Uploads a file from disk
- Parameters
file_name (str) – name of file to upload
cloud_name (str) – name to use on the cloud
permissions (ignored) –
- Returns
- Return type
bool, upload success
- upload_multipart(stream, cloud_name, metadata, permissions, buffersize, verbose)¶
Multi-part upload for large stream objects
- Parameters
stream (stream) – streaming object
cloud_name (str) – name to use on cloud
metadata (dict) – custom metadata
permissions (str?) – permissions for this file
buffersize (int) – s3 uploading buffersize
verbose (bool) – s3 verbosity
- Returns
- Return type
bool, upload success
- upload_stream(stream, cloud_name, metadata, permissions)¶
Uploads a stream object with a .read() function
- Parameters
stream (stream) – streaming object, e.g. StringIO(json.dumps(my_dict)) (no metadata)
cloud_name (str) – name to use on cloud
metadata (dict) – custom metadata for this file
permissions (ignored) –
- Returns
- Return type
bool, upload success


| View source on GitHub |
Creates hook to stop if the given metric is lower than the threshold.
tf.compat.v1.estimator.experimental.stop_if_lower_hook
tf.compat.v2.estimator.experimental.stop_if_lower_hook
tf.estimator.experimental.stop_if_lower_hook( estimator, metric_name, threshold, eval_dir=None, min_steps=0, run_every_secs=60, run_every_steps=None )
estimator = ... # Hook to stop training if loss becomes lower than 100. hook = early_stopping.stop_if_lower_hook(estimator, "loss", 100) train_spec = tf.estimator.TrainSpec(..., hooks=[hook]) tf.estimator.train_and_evaluate(estimator, train_spec, ...)
Caveat: Current implementation supports early-stopping both training and
evaluation in local mode. In distributed mode, training can be stopped but
evaluation (where it's a separate job) will indefinitely wait for new model
checkpoints to evaluate, so you will need other means to detect and stop it.
Early-stopping evaluation in distributed mode requires changes in
train_and_evaluate
API and will be addressed in a future revision.
estimator
: A tf.estimator.Estimator
instance.metric_name
: str
, metric to track. "loss", "accuracy", etc.threshold
: Numeric threshold for the given metric.eval_dir
: If set, directory containing summary files with eval metrics. By
default, estimator.eval_dir()
will be used.min_steps
: int
, stop is never requested if global step is less than this
value. Defaults to 0.run_every_secs
: If specified, calls should_stop_fn
at an interval of
run_every_secs
seconds. Defaults to 60 seconds. Either this or
run_every_steps
must be set.run_every_steps
: If specified, calls should_stop_fn
every
run_every_steps
steps. Either this or run_every_secs
must be set.An early-stopping hook of type SessionRunHook
that periodically checks
if the given metric is lower than specified threshold and initiates
early stopping if true.


Education
- Indian Institute of Technology, Bombay (Mumbai, India)
Bachelor and Master of Technology in Electrical Engineering (2015-2020)- Thesis: A Proof of Reserves Protocol with Short Proofs and a Method to Estimate Amount Upper Bounds for MimbleWimble
- Cumulative Performance Index (CPI): 8.80 / 10.00
- Specialising in Communication and Signal Processing (Specialisation CPI: 9.61 / 10.00)
Notable Coursework:
| Applied Math | Signal Processing | Miscellaneous |
|---|---|---|
| Number Theory & Cryptography | Computer Vision | Machine Learning |
| Advanced Tools from Modern Cryptography† | Image Processing | Neuromorphic Engineering |
| Real Analysis in Engineering | Digital Signal Processing | Complex Analysis |
| Matrix Computations | Error Correcting Codes | Strategic Management |
† : Partially offline because of Covid-19 😓
- Teaching Assistantship at IIT Bombay:
- EE 720: Introduction to Number Theory and Cryptography (Spring 2020)
- EE 465: Cryptocurrency & Blockchain Technologies (Autumn 2019)
- HPT Arts and RYK Science College (Nashik, India)
High School Certification, Maharashtra Board (2013-2015)- Board Percentage Score: 86.15%
- Felicitated for being 1 out of the 3 who qualified for IIT (amongst 2000)
- Silver Oak Universal School (Nashik, India)
Secondary School Certification, Maharashtra Board (2002-2013)- Board Percentage Score: 96.18% (Ranked 1st in school among 150)
- Represented school in Inter-school District-level Cricket Competition
- Highest marks in Marathi language in the state of Maharashtra


Scope: client and server, all versions.
This configures a PostgreSQL store. There are several options, but most of them are optional. See the document on setting up the database and follow the instructions there before trying to connect with it. Note that there may be many stores for a configuration if each of them, e.g. requires different connection information.
| Attribute | Required? | Default | Description |
| username | Y | N/A | The name of the user to use |
| password | Y | N/A | The password for the user |
| host | N | localhost | The host. (localhost) |
| port | N | 5432 | The port for the connection. |
| driver | N | org.postgresql.Driver | The JDBC driver (current) |
| database | N | spec | The name of the database. |
| schema | N | spec | The schema for the database |
| tablename | N | spec | The name of the table for this. |
| prefix | N | (none) | An optional prefix for the table. |
| parameters | N | (none) | An optional extra set of parameters to pass to the JDBC driver. This is of the form key0=value0&key1=value1&key2=value2... Note that each key/value pair is separated with an ampersand. Note that OA4MP does all connections using UTF8 so that parameter is always added. |
| useSSL | N | false | Try to connect to the database using SSL support. This presumes you have configured the database to use SSL. |
The spec refers to the specification. Defaults are given there and
applied.
<config> <server address="http://localhost/oauth"> <postgresql username="foo" password="bar" useSSL="true"> <transactions/> <clients/> <clientApprovals/> </postgresql> </server> <config>
Dumps everything into postgresql, uses the defaults for myproxy and disables mail notifications. Note that all the defaults for schemas &c. are used. Also notice that the connection will run over SSL.
In this case, there are multiple users who need specific access to stores.
<config> <server address="https://localhost/oauth"> <postgresql username="client-admin" password="changeme"> <clients/> </postgresql> <postgresql usernname="approver-admin" password="changeme2"> <clientApprovals/> </postgresql> <!-- other configuration as needed --> </server> </config>
This will put the clients and approvals under two different accounts, neither of which requires SSL. The default MyProxy server is used. Since no store for the transactions are specified, they will reside in memory only.
Example 3. A really fancy one.
if you needed to specify a great deal of specific information about a table, here is how you would do it:
<postgresql username="foo" password="bar" driver="org.postgresql.Driver" port="4771" schema="atmos" database="oauth" tablename="trans" prefix="ocd"> <transactions/> </postgresql>
This specifies a (very) non-standard port for PostgreSQL as well as specific information about the database, schema and tablename. This looks in the database named oauth for the table atmos.ocd_trans and puts all transactions there.
In the next example, a fully functional PostgreSQL client asset store is configured.
<config> <client name="my-cfg"> <postgresql username="fionna" password="sekret" schema="oa4mp" database="client"> <assetStore/> </postgresql> <!-- other stuff --> </client> </config>


DG HEALTH DUAL ACTION COMPLETE- famotidine, magnesium hydroxide and calcium carbonate tablet, chewable
Dolgencorp Inc
Active ingredient (in each chewable tablet)
Famotidine 10 mg
Calcium carbonate 800 mg
Magnesium hydroxide 165 mg
Purpose
Acid reducer
Antacid
Uses
relieves heartburn associated with acid indigestion and sour stomach
Warnings
Allergy alert: Do not use if you are allergic to famotidine or other acid reducers
Do not use
- •
- if you have trouble or pain swallowing food, vomiting with blood, or bloody or black stools. These may be signs of a serious condition. See your doctor.
- •
- with other acid reducers
Ask a doctor before use if you have
- •
- had heartburn over 3 months. This may be a sign of a more serious condition.
- •
- heartburn with lightheadedness, sweating, or dizziness
- •
- chest pain or shoulder pain with shortness of breath; sweating; pain spreading to arms, neck or shoulders; or lightheadedness
- •
- frequent chest pain
- •
- frequent wheezing, particularly with heartburn
- •
- unexplained weight loss
- •
- nausea or vomiting
- •
- stomach pain
Ask a doctor or pharmacist before use if you are
presently taking a prescription drug. Antacids may interact with certain prescription drugs.
Stop use and ask a doctor if
- •
- your heartburn continues or worsens
- •
- you need to take this product for more than 14 days
If pregnant or breast-feeding,
ask a health professional before use.
Keep out of reach of children.
In case of overdose, get medical help or contact a Poison Control Center right away. (1-800-222-1222)
Directions
- •
- adults and children 12 years and over:
- •
- do not swallow tablet whole: chew completely
- •
- to relieve symptoms, chew 1 tablet before swallowing
- •
- do not use more than 2 chewable tablets in 24 hours
- •
- children under 12 years: ask a doctor
Other information
- •
- each tablet contains: calcium 320 mg; magnesium 70 mg
- •
- Phenylketonurics: Contains phenylalanine 2.2 mg per tablet
- •
- read the directions and warnings before use
- •
- read the bottle label. It contains important information.
- •
- store at 20-25°C (68-77°F)
- •
- protect from moisture
Inactive ingredients
anhydrous lactose, artificial berry flavor, aspartame, D&C red no. 7 calcium lake, dextrates, FD&C blue no. 1 aluminum lake, FD&C red no. 40 aluminum lake, glyceryl monostearate, lactose monohydrate, magnesium stearate, microcrystalline cellulose, polyacrylate dispersion, polysorbate 80, povidone, pregelatinized starch, sodium starch glycolate, talc
Questions or comments?
1-888-309-9030
Principal Display Panel
Dual Action
Complete
Famotidine 10mg/Calcium Carbonate 800 mg/Magnesium Hydroxide 165 mg Tablets
Acid Reducer + Antacid
Compare to active ingredients of Pepcid Complete®
Just One Tablet Relieves Heartburn Due to Acid Indigestion
Actual Size
25 Chewable Tablets
Berry Flavor


The GAMUT Network - GAMUT Fact Sheet
Gradients Along Mountain to Urban Transitions
GAMUT Background
iUTAH researchers developed and deployed an ecohydrologic observatory to study water in ‘Gradients Along Mountain to Urban Transitions’ (GAMUT). The GAMUT Network measures aspects of climate, hydrology, and water quality along a mountain-to-urban gradient in three watersheds that share common water sources (winter-derived precipitation) but differ in the human and biophysical nature of land-use transitions. See this published paper for more information on the design and implementation of GAMUT.
GAMUT Watersheds
There are three watersheds with sites in the GAMUT Network. Click on the links to preview data and learn more about each watershed.
GAMUT Monitoring Sites
GAMUT includes state-of-the-art sensors at aquatic and terrestrial sites for real-time monitoring of common meteorological variables, snow accumulation and melt, soil moisture, surface water flow, and surface water quality. These figures show a typical GAMUT climate site and a typical GAMUT water quality site.
Cyberinfrastructure
The collection and dissemination of data from the GAMUT Network is supported by various cyberinfrastructure applications illustrated by this workflow and described in this published paper. Access to real-time sensor data from the GAMUT network is available to researchers, educators, and other stakeholders via online visualization .
Applications
GAMUT data have been used as preliminary results for grant proposals and are included in manuscripts for publication. GAMUT data are also being used by other stakeholders. The Utah Department of Environmental Quality’s Division of Water Quality has used GAMUT sensor output to screen for water quality violations and has used to GAMUT as a guide for their own continuous monitoring and data management efforts. Several university faculty have used GAMUT data in teaching their classes. Synergy is also achieved with other climate and water monitoring being conducted within the GAMUT watersheds.