Interface IRendersZptFile
An entry-point object for use by consuming logic. Renders ZPT documents from a filesystem file and returns the rendered document as a stream.
Namespace: ZptSharp
Assembly: ZptSharp.Abstractions.dll
Syntax
public interface IRendersZptFile
Remarks
Use this interface in your own application if you want to render ZPT documents from source files which can be described by file paths. The RenderAsync(String, Object, RenderingConfig, CancellationToken) method will use the filename/extension to detect which document provider is appropriate for the file and automatically select from those which have been registered.
Methods
| Improve this Doc View SourceRenderAsync(String, Object, RenderingConfig, CancellationToken)
Renders a specified ZPT document file using the specified model.
Declaration
Task<Stream> RenderAsync(string filePath, object model, RenderingConfig config = null, CancellationToken token = default(CancellationToken))
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | filePath | The relative or absolute file system path of the document to render. |
| System.Object | model | The model/data to be rendered by the document. |
| RenderingConfig | config | An optional rendering configuration. |
| System.Threading.CancellationToken | token | An optional token used to cancel/abort the operation whilst it is in-progress. |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<System.IO.Stream> | A stream containing the rendered document. |
Remarks
The filePath
parameter should be the disk or network share path
to the source document file which is to be rendered. This may be a an absolute
path or it may be relative to the current working directory.
The model
parameter is the model which will be rendered-by/bound-to
the document template. It will be available as the pre-defined TALES variable here
during the rendering process. This model may be any arbitrary object, as appropriate to
your application/use-case.
Passing a RenderingConfig in the config
parameter is
optional. If omitted or null then Default will be used,
which is likely to be suitable for simple usages.
The cancellation token parameter token
may be used during asynchronous
operations in order to abort/cancel the operation before completion. For more information
about task cancellation see
https://docs.microsoft.com/en-us/dotnet/api/system.threading.cancellationtokensource.
The return of this method is a Task
which exposes a Stream
. That stream is
the rendered document. If you wish to copy this to a TextWriter
, for example to save
it to a file, or to write it to another output, then consider using IWritesStreamToTextWriter.


What Kind Of Warrior Would You Be?
First tell us who you are and let us know how to contact you.
* Name:
* Email:
** Age:
If There's A Fight, What's Your First Move?
What do you do first?
Assess the situation
Enter the fight with back up
Enter the fight while barking orders
Charge in and get crazy
Hang back and see what happens
What is your primary role in a fight?
Apprentice
Fighter
Leader
Beserker
Strategist
Weapon Of Choice
Axe
Sword
Halberd
Fighting Style
Two Handed
Sword and Shield
Ranged Weapon
What Are You Willing To Fight For?
What is your primary reason for fighting?
Honor
Glory
Love
Patriotism
Necessity
Choose Your Accessories
War Horse
Trained Bird of Prey
Shield
Bow and a Quiver of Arrows
Lance or Spear
Homing Pigeon
Small Entourage Consisting of Five men
Wagon with Supplies
Spare Horses
Lead Scouts
A Pair of Trained Hounds
What Shall we call You?
Lord
Lady
Ruler of All!
Just (Your Name)
Is there anything you wish to add?
Submit


XTractor is an algorithmic text extractor from web pages written in Java. It builds upon the "commonly used web design practices" approach (from readability.js, goose and snacktory) to create a set of heuristics for fast article text extraction. It adds several features like
XTractor is built using JSoup and Apache HTTP Async Client
.Here are some sample links of extracted text from around the web:
Similar to the readability approach, XTractor uses heuristics based on common web design practices to extract text. It walks through the DOM of the HTML removing nodes that are unlikely to be the main article text (and fluff like ads, banners, shout outs etc.). It then assigns a score to the container elements to find the best possible match for article text. It also extracts one significant image representing that page. The resultant output is sanitized and tokenized into paragraphs and is returned to the caller.
XTractor is a weekend project/quick hack demo created by Saurav Mohapatra. If you like this, you might also like my other hack/weekend project TL;DRzr - an algorithmic summary generator.
I wrote this to improve the article detection of my algorithmic summarizer demo.


public class ImageWriterFactory
extends java.lang.Object
| Constructor and Description |
|---|
ImageWriterFactory() |
| Modifier and Type | Method and Description |
|---|---|
static java.lang.String[] |
getSupportedFormats()
Get the list of support format strings for this factory
|
static ImageWriter |
getWriterForFormat(java.lang.String format)
Get a Slick image writer for the given format
|
static void |
registerWriter(java.lang.String format,
ImageWriter writer)
Register an image writer with the factory.
|
public static void registerWriter(java.lang.String format, ImageWriter writer)
format
- The format (usually extension) of the files that will be written outwriter
- The writer to use for the given formatpublic static java.lang.String[] getSupportedFormats()
public static ImageWriter getWriterForFormat(java.lang.String format)
format
- The format of the image to write


Or, simply complete the form to the right and we'll be in touch very soon.
Alternatively, SMS text CALLME to 2222. We'll help with any questions you may have about opt-in mobile marketing and SMS text alerts and how it can revolutionise your business.


| CT SmokeS | A Main, from Truck |
| CT SmokeS | A Main, from Quad boxes |
| CT SmokeS | Vents, from Truck |
| CT SmokeS | B Halls, from Heaven |
| CT FlashF | A Main, from Truck Wall |
| CT FlashF | A Main, from Quad boxes |
| CT FlashF | A Main, from NBK |
| CT FlashF | B Halls, from Ventsroom |
| CT FlashF | B Hall, from New box |
| CT FlashF | Bombsite B, from Heaven |
| CT MollyM | EZ!Safe Plant, from Truck!! |
| CT MollyM | Boost, from Sandbags |
| CT FlashF | EZ!B Main, from Tripple |
| CT PopflashF |
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|
Name
|
Type
|
Description
|
| allowDataMovement | xsd:boolean |
Whether data movement is allowed for upcoming disk format conversion process.
If 'allowDataMovement' is set to False, disk format conversion flow will
check whether data movement is required. If disk format conversion need to
move data to complete the operation, the
VsanDisallowDataMovementIssue will fail
the disk format conversion task.
Since vSAN API 6.8.7
|
| dataEfficiencyConfig | VsanDataEfficiencyConfig |
Whether to enable or disable vSAN data efficiency
from disk format layer.
|
| dataEncryptionConfig | VsanDataEncryptionConfig |
vSAN encryption configuration parameters for
disk format layer.
Since vSAN API 6.6
|
| skipHostRemediation | xsd:boolean |
Do not perform host configuration remediation on hosts as part of the
Disk Format Conversion. Remediation is performed by default to ensure hosts
are consistent. This will prevent errors as part of Disk Format Conversion
due to inconsistent hosts. This field can be set to True when it is already
known that all hosts are already consistent and have the latest
configuration avoiding this extra step.
Unset means False.
Since vSAN API 6.6
|
|
Properties inherited from DynamicData |
| None |


Ilhan, a high school dropout, has a keen interest in starting her own business in the emerging technology sector.
Mari is looking for funding so he can start his own multimedia editing platform for kids in his community.
Chanelle is looking to start a program that integrates virtual reality with fashion design. This program will also help students in after school programs looking for a career in fashion or technology.


- A.A. Leino, A. Pulkkinen and T. Tarvainen, ValoMC: a Monte Carlo software and MATLAB toolbox for simulating light transport in biological tissue, OSA Continuum 2:957-972, 2019
- N. Hänninen, A. Pulkkinen and T. Tarvainen, Image reconstruction with reliability assessment in quantitative photoacoustic tomography, Journal of Imaging, 4:148, 2018.
- O. Lehtikangas, T. Tarvainen, A.D. Kim and S.R. Arridge, Finite element approximation of the radiative transport equation in a medium with piece-wise constant refractive index, Journal of Computational Physics, 282:345-359, 2015.
- A. Pulkkinen, V. Kolehmainen, J.P. Kaipio, B.T. Cox, S.R. Arridge and T. Tarvainen, Approximate marginalization of unknown scattering in quantitative photoacoustic tomography, Inverse Problems and Imaging, 8(3): 811-829, 2014.
- P.S. Mohan, T. Tarvainen, M. Schweiger, A. Pulkkinen and S.R. Arridge, Variable order spherical harmonics expansion scheme for the radiative transport equation using finite elements, Journal of Computational Physics, 230:7364–7383, 2011.
- T. Tarvainen, V. Kolehmainen, A. Pulkkinen, M. Vauhkonen, M. Schweiger, S.R. Arridge and J.P. Kaipio, An approximation error approach for compensating for modelling errors between the radiative transfer equation and diffusion approximation in diffuse optical tomography, Inverse Problems, 26:015005(18pp), 2010.
Literature on light transport modelling and Monte Carlo method is wide and thus we do not even try to give a complete reference list. The following books, articles and the references therein could serve as a starting point of a literature survey:
- A. Ishimaru, Wave Propagation and Scattering in Random Media, vol. 1 (Academic Press, New York, 1978).
- S.A. Prahl, M. Keijzer, S.L. Jacques, and A.J. Welch, A Monte Carlo model of light propagation in tissue,” in SPIE Proceedings of Dosimetry of Laser Radiation in Medicine and Biology, vol. IS 5, G. Müller and D. Sliney, eds., pp. 102–111 (1989).
- L. Wang, S.L. Jacques, and L. Zheng, MCML - Monte Carlo modeling of light transport in multi-layered tissues, Computer Methods and Programs in Biomedicine, 47:131–46, (1995).
- S.R. Arridge, Optical tomography in medical imaging, Inverse Problems, 15:R41–R93, (1999).
- A. Sassaroli and F. Martelli, Equivalence of four Monte Carlo methods for photon migration in turbid media, Journal of the Optical Society of America A, 29:2110–2117 (2012).
- C. Zhu and Q. Liu, Review of Monte Carlo modeling of light transport in tissues, Journal of Biomedical Optics, 18:050902 (2013).
- V. Periyasamy and M. Pramanik, Advances in Monte Carlo simulation for light propagation in tissue, IEEE Reviews in Biomedical Engineering, 10:122–135 (2017).


openssl-ecparam - EC parameter manipulation and generation
openssl ecparam [-help] [-inform DER|PEM] [-outform DER|PEM] [-in filename] [-out filename] [-noout] [-text] [-check] [-check_named] [-name arg] [-list_curves] [-conv_form arg] [-param_enc arg] [-no_seed] [-genkey] [-engine id] [-rand files] [-writerand file] [-provider name] [-provider-path path] [-propquery propq]
This command is used to manipulate or generate EC parameter files.
OpenSSL is currently not able to generate new groups and therefore this command can only create EC parameters from known (named) curves.
Print out a usage message.
The EC parameters input format; unspecified by default. See openssl-format-options(1) for details.
The EC parameters output format; the default is PEM. See openssl-format-options(1) for details.
Parameters are encoded as EcpkParameters as specified in IETF RFC 3279.
This specifies the input filename to read parameters from or standard input if this option is not specified.
This specifies the output filename parameters to. Standard output is used if this option is not present. The output filename should not be the same as the input filename.
This option inhibits the output of the encoded version of the parameters.
This option prints out the EC parameters in human readable form.
Validate the elliptic curve parameters.
Validate the elliptic name curve parameters by checking if the curve parameters match any built-in curves.
Use the EC parameters with the specified 'short' name. Use -list_curves to get a list of all currently implemented EC parameters.
Print out a list of all currently implemented EC parameters names and exit.
This specifies how the points on the elliptic curve are converted into octet strings. Possible values are: compressed, uncompressed (the default value) and hybrid. For more information regarding the point conversion forms please read the X9.62 standard. Note Due to patent issues the compressed option is disabled by default for binary curves and can be enabled by defining the preprocessor macro OPENSSL_EC_BIN_PT_COMP at compile time.
This specifies how the elliptic curve parameters are encoded. Possible value are: named_curve, i.e. the ec parameters are specified by an OID, or explicit where the ec parameters are explicitly given (see RFC 3279 for the definition of the EC parameters structures). The default value is named_curve. Note the implicitlyCA alternative, as specified in RFC 3279, is currently not implemented in OpenSSL.
This option inhibits that the 'seed' for the parameter generation is included in the ECParameters structure (see RFC 3279).
This option will generate an EC private key using the specified parameters.
See "Engine Options" in openssl(1). This option is deprecated.
See "Random State Options" in openssl(1) for details.
See "Provider Options" in openssl(1), provider(7), and property(7).
The openssl-genpkey(1) and openssl-pkeyparam(1) commands are capable of performing all the operations this command can, as well as supporting other public key types.
The documentation for the openssl-genpkey(1) and openssl-pkeyparam(1) commands contains examples equivalent to the ones listed here.
To create EC parameters with the group 'prime192v1':
openssl ecparam -out ec_param.pem -name prime192v1
To create EC parameters with explicit parameters:
openssl ecparam -out ec_param.pem -name prime192v1 -param_enc explicit
To validate given EC parameters:
openssl ecparam -in ec_param.pem -check
To create EC parameters and a private key:
openssl ecparam -out ec_key.pem -name prime192v1 -genkey
To change the point encoding to 'compressed':
openssl ecparam -in ec_in.pem -out ec_out.pem -conv_form compressed
To print out the EC parameters to standard output:
openssl ecparam -in ec_param.pem -noout -text
openssl(1), openssl-pkeyparam(1), openssl-genpkey(1), openssl-ec(1), openssl-dsaparam(1)
The -engine option was deprecated in OpenSSL 3.0.
The -C option was removed in OpenSSL 3.0.
Copyright 2003-2021 The OpenSSL Project Authors. All Rights Reserved.
Licensed under the Apache License 2.0 (the "License"). You may not use this file except in compliance with the License. You can obtain a copy in the file LICENSE in the source distribution or at https://www.openssl.org/source/license.html.


jtr.load package¶
Subpackages¶
Submodules¶
jtr.load.FB15K2jtr module¶
jtr converter for the fb15k dataset.
- METADATA: Training data: 483142 triples (subject, relation, object)
- 14951 different entities 1345 different relation types
data source: https://everest.hds.utc.fr/lib/exe/fetch.php?media=en:fb15k.tgz
webpage: https://everest.hds.utc.fr/doku.php?id=en:transe
paper reference: Bordes et al. 2013: Translating Embeddings for Modeling Multi-relational Data
-
jtr.load.FB15K2jtr.
compress_triples
(string_triples, unique_entities, unique_relations)¶
-
jtr.load.FB15K2jtr.
convert
(triples, neighbourhoods, unique_entities)¶ Converts into jtr format. :param triples: fact triples that should be converted. :param neighbourhoods: dictionary of supporting facts per triple :unique_entities: List of strings :return jtr formatted fb15k data.
-
jtr.load.FB15K2jtr.
extract_unique_entities_and_relations
(triples)¶ Identifies unique entities and relation types in collection of triples. :param triples: List of string triples. :return unique_entities: List of strings :return unique_relations: List of strings
-
jtr.load.FB15K2jtr.
get_fact_neighbourhoods
(triples, facts_per_entity, facts_per_relation, include_relations=False)¶ Extracts neighbouring facts for a collection of triples. neighbouring facts of fact f are such facts that share at least an entity with f. If relations are included, facts which share a relation are also considered neighbours. :param triples: list of facts triples :param facts_per_entity: dictionary; The facts an entity appears in :param facts_per_relation: dictionary; The facts a relation appears in :param include_relations: boolean. whether facts sharing the relation should
be considered neighbours as well.Return fact_neighbourhoods: dictionary mapping fact ID to set of fact IDs.
-
jtr.load.FB15K2jtr.
get_facts_per_entity
(triples)¶ obtain dictionary with all train fact ids that contain an entity. :param triples: List of fact triples :return Dictionary entity –> fact IDs it participates in
-
jtr.load.FB15K2jtr.
get_facts_per_relation
(triples)¶ obtain dictionary with all train fact ids that contain a relation type. :param triples: List of fact triples :return Dictionary relation type –> fact IDs it participates in
-
jtr.load.FB15K2jtr.
load_fb15k_triples
(path)¶ loads the raw data from file provided.
jtr.load.NYT2jtr module¶
This files allows creating a jtr train and test datafiles for the NYT corpus based on ../data/NYT/naacl2013.txt * train file: one jtr per train instance ; candidates = all entity pairs * test file: one jtr per test relation, with all correct answers in the valid answers list
usage: python3 NYT2jtr path/to/naacl2013.txt mode > naacl2013_mode.jtr.json in which mode = train or test
-
jtr.load.NYT2jtr.
create_test_jtr
(testfacts, tuples)¶ one jtr with all correct answers per relation in the test data
-
jtr.load.NYT2jtr.
create_train_jtr
(trainfacts, tuples)¶ one jtr per positive train fact
-
jtr.load.NYT2jtr.
load_naacl2013
(path, mode)¶
-
jtr.load.NYT2jtr.
main
()¶
jtr.load.SNLI2jtr_v1 module¶
This files allows creating a jtr datafile for the SNLI corpus, whereby each instance receives support under the form of ‘related’ instances
-
jtr.load.SNLI2jtr_v1.
convert_snli
(snli_file_jsonl)¶ convert SNLI files into jtr format. Data source: http://nlp.stanford.edu/projects/snli/snli_1.0.zip Files to be converted: snli_1.0_dev.jsonl, snli_1.0_train.jsonl, snli_1.0_test.jsonl (the *.txt files contain the same data in a different format)
- Format:
- support = the premise = ‘sentence1’ in original SNLI data (as id, use ‘captionID’, the id of sentence1)
- question = the hypothesis = ‘sentence2’ in original SNLI data
Notes
- instances with gold labels ‘-‘ are removed from the corpus
-
jtr.load.SNLI2jtr_v1.
main
()¶
jtr.load.SNLI2jtr_v2 module¶
This files allows creating a jtr datafile for the SNLI corpus, whereby - questions consist of premise + delim. + hypothesis - TODO: support facts can optinally be loaded from Wordnet / SNLItraining / PPDB
-
jtr.load.SNLI2jtr_v2.
convert_snli
(snli_file_jsonl, support=False)¶ convert SNLI files into jtr format. Data source: http://nlp.stanford.edu/projects/snli/snli_1.0.zip Files to be converted: snli_1.0_dev.jsonl, snli_1.0_train.jsonl, snli_1.0_test.jsonl (the *.txt files contain the same data in a different format)
- Format:
- question = the premise + CONJ + the hypothesis = ‘sentence1’ + CONJ + ‘sentence2’
- support instance = part1 + CONJ + part2 + CONJ + label
Notes
- instances with gold labels ‘-‘ are removed from the corpus
Parameters: - snli_file_json – input file
- support – False (no support), TODO: ‘WordNet’, ‘SNLItrain’, ‘PPDB’
-
jtr.load.SNLI2jtr_v2.
main
()¶
jtr.load.create_dummpy_scienceQA module¶
jtr.load.ls2jtr module¶
jtr.load.read_jtr module¶
-
class
jtr.load.read_jtr.
Answer
(adict)¶ Bases:
object
Class which holds the text or span of the answer.
-
class
jtr.load.read_jtr.
Question
(qdict)¶ Bases:
object
Class which holds answers and question as text.
-
class
jtr.load.read_jtr.
RichInstance
(instance)¶ Bases:
object
Holds lists of supports and questions and manages their iteration.
-
question_support_pairs
()¶ Yields nested pairs of question and support objects.
-
vocab
()¶ Gets the set of tokens from all questions and support objects.
-
-
class
jtr.load.read_jtr.
Support
(sdict)¶ Bases:
object
Holds a support which is a text or an interval between tokens (span).
-
token_from_char
(char_offset)¶ Returns string between zero and the char_offset.
-
-
jtr.load.read_jtr.
jtr_load
(path, max_count=None, **options)¶ General-purpose loader for jtr files Makes use of user-defined options for supports, questions, candidates, answers and only read in those things needed for model, e.g. if the dataset contains support, but the user defines support_alts == ‘none’ because they want to train a model that does not make use of support, support information in dataset is not read in
User options for jtr model/dataset attributes are: support_alts = {‘none’, ‘single’, ‘multiple’} question_alts = answer_alts = {‘single’, ‘multiple’} candidate_alts = {‘open’, ‘per-instance’, ‘fixed’}
-
jtr.load.read_jtr.
read_data
(data_filename)¶ Reads jtr JSON and returns the dictionary.
-
jtr.load.read_jtr.
read_rich_data
(data_filename)¶ Transforms a jtr JSON file into a list of RichInstances (S,Q(A)).
-
jtr.load.read_jtr.
token_pattern
= <_sre.SRE_Pattern object>¶ Loads jtr JSON files and manages the transformation into components.
jtr.load.read_semeval2017Task10 module¶
-
jtr.load.read_semeval2017Task10.
readAnn
(textfolder='../data/SemEval2017Task10/')¶ Read .ann files and look up corresponding spans in .txt files :param textfolder: :return:
jtr.load.sentihood2jtr module¶
-
jtr.load.sentihood2jtr.
convert_to_jtr
(sentihood_data, exhaustive=True)¶
-
jtr.load.sentihood2jtr.
main
()¶
-
jtr.load.sentihood2jtr.
wrap_into_jtr_global
(instances)¶
jtr.load.simpleQuestions2jtr module¶
-
jtr.load.simpleQuestions2jtr.
convert_simpleQuestions_to_jtr
(simpleQuestionFile)¶
-
jtr.load.simpleQuestions2jtr.
main
()¶


Select Tickets
STEP 1 Please select the match day you want to watch in the Afcon 2022
| Ticket | Match Day 1 | Match Day 2 | Match Day 3 |
|---|---|---|---|
| Date | Jan 10 to 15 | Jan 16 to 20 | Jan 21 to 25 |
| Normal | FCFA 10,000 |
FCFA 15,000 |
FCFA 20,000 |
| VIP | FCFA 20,000 |
FCFA 25,000 |
FCFA 50,000 |
Select Your Team
STEP 2 Please select the team you want to see play
| Group A |
|---|
| Group B |
|---|
| Group C |
|---|
| Group D |
|---|
| Group E |
|---|
| Group F |
|---|


Sprite
Icon
Text
Button
Form
Form Elements
Corner
Background
General
JavaScript is off. Please enable to view full site.


by Ed Blackburn / @ejblackburn
This code is awesome. This code is CRAP!
WCF, Windows Live Writer
Future Ticketing Programme
Part of a ticketing modernisation programme
Sending card taps to a back office affords the organisation to centralise behaviour and move hardware costs from the gates to the cloud.
A centralised back office ticketing system.
Permit high throughput cheap travel.
"Every journey matters"
What differentiates an organisation from their competitors, what is their USP?
This is where we tend to concentrate our efforts to best support an organisation to give it a competitive advantage.
Supporting the core domains. Commodity business process like invoicing, CRM, ERP.
Where to put that magic source?
Does your domain require a hi-tech solution?
 Do we care? Yes. Why?
Understand the why behind the problem
Appreciating good enough
Iterate
Putting the model at the heart of the problem
Facilitiate emergent design
Avoiding entropy
Have confidence to meet business demands
adjective
Using or based on what one feels to be true even without conscious reasoning;
instinctive: his intuitive understanding of the readers' real needs
Composition is explicit. Explicit code is more intuitive.
Abstractions require more reasoning and are not always intuitive
Never use an abstract class for code re-use


{%include 'django_registration/base.html'%} {% load crispy_forms_tags %} {% load bootstrap4 %} {% load static %} {% block content %}
{{neighbourhood.description}}
Police Department
{{neighbourhood.police_number}}
Hospital Department
{{neighbourhood.health_number}}
Lives at 345 Oak Court
13 Attending
recommended by Brian James
Lives at 345 Oak Court
{{ busines.description }}
Owner: {{ busines.user.user.username | title }}
{{ post.post }}
Posted {{ post.date_posted | timesince }} ago...


| 4 Nov 1969 |
Sean John Combs born, Harlem NY. |
| 1973 |
Father, Melvin Combs, murdered. |
| 1994 |
Sean, Justin Combs, born to Misa Hylton. |
| 1997 |
Son, Christian Casey Combs, born to Kim Porter. |
| 9 Mar 1997 |
Best friend Biggie Smalls murdered in a drive-by. |
| 1998 |
Begins dating Jennifer Lopez. |
| 27 Nov 1999 |
Sean Combs and two other gentlemen arrested after fleeing a shooting at Club NY (43rd and 8th, Manhattan), for criminal possession of a firearm. A fourth individual, singer Jennifer Lopez, is detained but not arrested. None of the four were actually involved in the shooting. |
| 22 Aug 2002 |
Bouncer at Combs' nightclub Justin allegedly throws Stephanie Gleason to the ground. She sues Combs for $5M. |


---
layout: post
title: The cloud billing mess
date: '2011-03-31T14:41:00.005+05:30'
author: Chandermani Arora
tags:
- Azure
- Billing
- Cloud
modified_time: '2011-03-31T15:10:20.558+05:30'
blogger_id: tag:blogger.com,1999:blog-20117988.post-8441526036741007650
blogger_orig_url: http://chandermani.blogspot.com/2011/03/cloud-billing-mess.html
---
"with great power comes great responsibility" - Spiderman Movie
Nothing can be more true than this statement when it comes to Cloud. Spinning up a new server is effortless, scaling up and down even easier. While building a cloud based solution for our client we too have made same mistakes. Since the impact of such mistakes are not immediate these thing many a times go unnoticed. It is not until we receive inflated bills that we start looking into such matters and realize our mistakes.
Cloud infrastructure should not cause an organization to abandon all server provisioning workflows but tweak them to take Cloud components into consideration.
Thanks,
Chandermani


Users upload the file to the system via File Scan in the Filter Management, and filter engine scans the file against the Sanction list. The system loads the alerts in the batch message database. This function is used to search those messages and make a decision on it.
The same message scan next time, if the sanction list does not change and user already has made the decision on this message, the system will keep the decision intact. If the sanction list changes, then the system will reset the decision status to New, which will force user to make decision again.
Click the "Message" in the left navigation menu under Filter management, a new "Messages" Tab folder will created in the right panel. There are 2 sub-Tab folders, Search Messages and Search Result Messages.
| MessageId | Unique record identifier. |
| Status | The status of this message. |
| Message | Message text. |
| Comments | Reserved field. |
| AppId | The Application Id the message belongs to. |
| Format | User who create this entry. |
| Created batch | The batch that first created this message. |
| Modified Batch | The last batch/user that modify the status of this message. |
| AND and OR | Select AND radio box if you want those criteria add together. Select OR radio box if you want to get the result of either criteria meet |
The top part is the table, shows the list of the messages that meet the search criteria. The bottom part is the details, alerts and status/comment history of the selected entry
A message has one to many alerts. An alert is a hit, that is the name or alias of the SDN list match all or part of text in the message.
User should to review each alerts, in order to make decision on the message.
Each message initial status is new, so is each alert of this message.
Double Click : the selected record in the table to update message status or add comments. To avoid 2 people working on the same message, the system will check whether someone else is working on this message already, if yes, the system will notify user that someone else is working on this message. Otherwise, the system will lock this message and allow the user to work on it.
A new Tab folder "Update/Delete Batch Messages" will be added, as show below.
In the top part, are the details of the message; in the bottom, are the alerts this message gets. The left side is a table, list entNum of all the alerts. The right side is the details of each alert Select each entNum in the left side table, the details of each alert will show in the right side.
In order to avoid 2 person works on the same message. When a user tries to working on the message by double click on the records, the system will check whether someone else is working on this message already, if yes, the system will notify user that someone else is working on this message. Otherwise, the system will lock this message and allow the user to work on it. When the user switch back to search result Batch Message Tab by click on Cancel or update message Status button. The system will unlock this message.
In the bottom, are the alerts this message gets. The left side is a table, list entNum of all the alerts. The right side is the details of each alert Select each entNum in the left side table, the details of each alert will show in the right side.


Content's background color is the same for the tab
We use the class nav-pills instead of nav-tabs which automatically creates a background color for the tab
We applied clearfix to the tab-content to rid of the gap between the tab and the content
We use css to change the background color of the content to be equal to the tab


| Home | Quem somos | Contato | |
Sobre a universidadeBut I must explainto you how all this mistaken idea of denouncing pleasure and praising pain was born and I will give you a complete account of the system, and expound the actual teachings of the great explorer of the truth, the master-builder of human happiness. No one rejects, dislikes, or avoids pleasure itself, because it is pleasure, but because those who do not know how to pursue pleasure rationally encounter consequences that are extremely painful. Nor again is there anyone who loves or pursues or desires to obtain pain of itself, because it is pain, but because occasionally circumstances occur in which toil and pain can procure him some great pleasure. To take a trivial example, which of us ever undertakes laborious physical exercise, except to obtain some advantage from it? But who has any right to find fault with a man who chooses to enjoy a pleasure that has no annoying consequences, or one who avoids a pain that produces no resultant pleasure? | |
Todos os direitos reservados |


Pair Stairs
Developers
Create a new developer
Developer Name:
Create
Delete a developer
Developer ID:
Delete
Update developer's name
Developer id:
Developer name:
Update
Pair stairs
Navigator
Driver


Projects
Graduate Projects
Fabrication and Performance analysis of InGaN/GaN based Blue Light Emitting Diode
Switching Characteristics of field effect transistor with different input parameters
Undergraduate Projects
Car Parking Using Microcontroller
RFID based attendance system
Motor Control Using Arduino


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Note: Many of the features in this lab (e.g. Browser Link) do not work in the ASP.NET 5 RC1 release. Support is being adding in Visual Studio web tools for ASP.NET Core, but is not available yet. You can complete this lab using a new ASP.NET MVC 5 application (File / New / ASP.NET 5) until they are supported in ASP.NET Core.
Visual Studio is an excellent development environment for .NET-based Windows and web projects. It includes a powerful text editor that can easily be used to edit standalone files without a project.
Visual Studio maintains a full-featured parse tree as you edit each file. This allows Visual Studio to provide unparalleled auto-completion and document-based actions while making the development experience much faster and more pleasant. These features are especially powerful in HTML and CSS documents.
All of this power is also available for extensions, making it simple to extend the editors with powerful new features to suit your needs. Web Essentials is a collection of (mostly) web-related enhancements to Visual Studio. It includes lots of new IntelliSense completions (especially for CSS), new Browser Link features, automatic JSHint for JavaScript files, new warnings for HTML and CSS, and many other features that are essential to modern web development.
In this hands-on lab, you will learn how to:
The following is required to complete this hands-on lab:
Note: You can take advantage of the Visual Studio Dev Essentials subscription in order to get everything you need to build and deploy your app on any platform.
In order to run the exercises in this hands-on lab, you will need to set up your environment first.
Note: Make sure you have checked all the dependencies for this lab before running the setup.
Throughout the lab document, you will be instructed to insert code blocks. For your convenience, most of this code is provided as Visual Studio Code Snippets, which you can access from within Visual Studio 2015 to avoid having to add it manually.
Note: Each exercise is accompanied by a starting solution located in the Begin folder of the exercise that allows you to follow each exercise independently of the others. Please be aware that the code snippets that are added during an exercise are missing from these starting solutions and may not work until you have completed the exercise. Inside the source code for an exercise, you will also find an End folder containing a Visual Studio solution with the code that results from completing the steps in the corresponding exercise. You can use these solutions as guidance if you need additional help as you work through this hands-on lab.
This hands-on lab includes the following exercises:
Estimated time to complete this lab: 45 minutes
Note: When you first start Visual Studio, you must select one of the predefined settings collections. Each predefined collection is designed to match a particular development style and determines window layouts, editor behavior, IntelliSense code snippets, and dialog box options. The procedures in this lab describe the actions necessary to accomplish a given task in Visual Studio when using the General Development Settings collection. If you choose a different settings collection for your development environment, there may be differences in the steps that you should take into account.
Web Essentials is a Visual Studio extension that adds a variety of useful features for modern web development, mostly focused on making the web development experience much faster and more pleasant. You can install Web Essentials from the Extension Gallery in Visual Studio.
Browser Link is a new feature included in Visual Studio 2013 and 2015 that provides a channel between the Visual Studio IDE and any open browser to exchange data between your web application and Visual Studio. Web Essentials extends Browser Link with tools to manipulate the DOM object model and the CSS styles of your web pages directly from the browser.
In this exercise, you will explore some of the features supported by Web Essentials and Browser Link to enhance a simple quiz page.
In this task, you will configure your web application to run in multiple browsers at once, which is useful for cross-browser testing.
Open Microsoft Visual Studio.
In the File menu, select Open | Project/Solution... and browse to Ex1-WorkingwithBrowserLinkandWebEssentials\Begin in the Source folder of the lab. Select GeekQuiz.sln and click Open.
In the Visual Studio toolbar, expand the browser menu and select Browse With....
Browse With menu option
In the Browse With dialog box, select both Google Chrome and Microsoft Edge by holding down the CTRL key and click Set as Default.
Selecting multiple default browsers
Both Google Chrome and Microsoft Edge should now appear as the default browsers. Click Cancel to close the dialog box.
Google Chrome and Microsoft Edge as default browsers
Note: After configuring the default browsers, the Multiple Browsers option is selected in the browser menu.
Multiple browsers
Press CTRL + F5 to run the application without debugging.
When both browser windows open, place one of them above the other in order to see the updates on both browsers simultaneously. The browsers should display a web page with a light-blue rectangle.
Placing one browser above the other
Do not close the browsers. You will use them in the next task.
Zen Coding is an editor plugin for high-speed HTML, XML, XSL (or any other structured code format) coding and editing. The core of this plugin is a powerful abbreviation engine which allows you to expand expressions -similar to CSS selectors- into HTML code. Zen Coding is a fast way to write HTML using a CSS style selector syntax.
In this exercise, you will use the Zen Coding feature provided by Web Essentials to generate the HTML buttons that represent the options of the question.
Switch back to Visual Studio.
Open the Index.cshtml file located in the Views | Home folder.
Replace the \ comment with the following code, and press TAB.
button.btn.btn-info.btn-lg.option{Answer $}*4
The code should be expanded to HTML.
Expanded HTML
Note: To learn more about Zen Coding syntax, see the following article.
Click the Refresh linked browsers button to update both browsers.
Refresh linked browsers
Microsoft Edge - Page updated with four buttons
Google Chrome - Page updated with four buttons
Switch back to Visual Studio.
You have added the buttons to the page, but you still need to add a template question. To do so, you will use a new feature in Web Essentials called Lorem Ipsum generator. Locate the div element with the class attribute front.
Add the following code as the first child element of the div, and press TAB.
p.lead>lorem5
The code should be expanded to HTML.
Lorem Ipsum autogenerated
Note: As part of Zen Coding, you can now generate Lorem Ipsum code directly in the HTML editor. Simply type lorem and hit TAB and a 30 word Lorem Ipsum text will be inserted. E.g. lorem10 inserts 10 Lorem Ipsum words.
You will add a logo at the top of the question by using another new feature in Web Essentials called Lorem Pixel generator. Add the following code as the first child element of the div element with container as class value, and press TAB.
div.row.header>pix-436x185-abstract
The code should expand to HTML.
Lorem Pixel autogenerated
Note: As part of Zen Coding, you can also generate Lorem Pixel code directly in the HTML editor. Simply type pix-200x200-animals and hit TAB and an img tag with a 200x200 image of an animal will be inserted. For more information, refer to Lorem Pixel.
Click the Refresh linked browsers button to update both browsers.
Microsoft Edge - Autogenerated image and text
Google Chrome - Autogenerated image and text
Note: Because the image is selected randomly when adding the code snippet, the image shown in the browsers may differ.
Do not close the browsers. You will use them in the next task.
In this task, you will use the Browser Link's Inspect Mode feature to detect the exact location where the specific DOM element is generated and then update the color property of that element using a color picker provided by Web Essentials.
In the Internet Explorer browser, press CTRL + ALT + I to enable Inspect Mode.
Move the pointer over the light blue border and click.
Moving the pointer over the light blue border
Switch back to Visual Studio. Notice how the HTML element that you selected in the browser is also selected in the Visual Studio HTML editor.
HTML element selected in the Visual Studio HTML editor
You will now update the front CSS class in order to change the styling of the selected element. To do so, press CTRL + , to open the Navigate To search box. Type site.css and press ENTER to open the file.
Opening file Site.css
Press CTRL + F and type .flip-container .front to find the CSS selector.
Click the light blue square in the border property of the class to open the Color Picker.
Opening the Color Picker
Expand the Color Picker by clicking the chevron button and select a new color.
Expanding the Color Picker
Press CTRL + ALT + ENTER to refresh linked browsers.
Switch to Microsoft Edge and notice how the color of the border has changed.
Microsoft Edge - Border color updated
Switch to Google Chrome and notice how the color of the border has changed.
Google Chrome - Border color updated
Switch back to Visual Studio.
Go to the end of the Site.css file and press CTRL + F to locate the .btn selector.
Notice that the -webkit-border-radius property is underlined in green.
-webkit-border-radius property of the btn selector
Place the caret in the -webkit-border-radius property. A blue line should appear under the first letter of the first word of the property. This is the smart tag.
Open the suggestions menu and click Add missing standard property (border-radius).
Add missing standard property suggestion
The border-radius property is automatically added to the CSS rule.
Missing standard property added
Move the pointer over the border-radius property to display the Browser matrix tooltip. The Browser matrix tooltip shows the availability of the property in each browser.
Browser matrix tooltip
Notice that the value of the border-radius property is still underlined. Move the pointer over the value to see the warning message.
Border-radius property value warning
Remove the unit of the border-radius property value as suggested by the tooltip.
Place the caret in the word-wrap property and notice that the smart tag also appears below.
Open the menu and click Add missing vendor specifics.
Add missing vendor specifics suggestion
The -ms-word-wrap property is automatically added to the CSS rule.
Vendor specific property added
In this task, you will use the Browser Link's Design Mode feature to edit the DOM object from the browser and transfer the changes to the HTML source file in Visual Studio.
In Google Chrome, press CTRL + ALT + D to enable Design Mode.
Move the pointer over the Lorem Ipsum dolor sit amet label and click.
Editing the question
A cursor should appear. Replace the original text with What does it look like when I write a longer question?, and then press ESC to exit Design Mode.
Question edited
Switch back to Visual Studio and open Index.cshtml, if not already opened. Notice that the inner text of the \ element has been updated.
Updated question in the HTML page
Search Engine Optimization (SEO) is the process of making a website rank higher on a search engine's list of results. The higher the site ranks and the more consistently it is listed, the more visitors the site will get from that search engine. Web Essentials incorporates an analytical tool that examines HTML, reports the issues found and provides assistance to fix them.
Go to the View menu and click Error List to open the Error List window.
Error List in View menu
Notice that there is an SEO warning notifying that a \ tag for the page description is missing. Double-click the SEO warning entry to fix it.
Error List window
In the Web Essentials dialog box, click Yes to insert a description \ tag.
Web Essentials dialog box
The editor for _Layout.cshtml opens and the \ tag is automatically added to the head section of the HTML file.
Meta tag automatically added to _Layout page
Change the value of the content attribute to GeekQuiz and save the file.
With Web Essentials, the HTML editor has been extended with extra functionality. In this exercise, you will see some new features that are helpful when developing web applications.
The first new feature you will see in this task is called Dynamic IntelliSense. Dynamic IntelliSense reads other tags and attributes to infer the possible ids you will use.
In this task, you will create a new HTML form element which contains a label and an input field. Then you will add a for attribute to the label to bind it to the input, and you will see IntelliSense suggestions based on the ids of the inputs in scope.
Open Visual Studio and the GeekQuiz.sln solution located in the Source/Ex2-TakingAdvantageofCodeSnippetsandIntelliSense/Begin folder. Alternatively, you can continue with the solution that you obtained in the previous exercise.
In Solution Explorer, open the Index.cshtml file located in the Views | Home folder.
Add the following form inside the div element with container as class value.
(Code Snippet - VSWebTooling - Ex2 - Form)
<form>
<input type="text" id="name" />
</form>
The input tag should be preceded by a label with some description of the field. Add the following label before the input tag.
<form>
<label id="name">Name</label>
<input type="text" id="name" />
</form>
The for attribute of a \ specifies which form element a label is bound to. The attribute's value should be equal to the id of the related element. Add the for attribute to the \ element. As shown in the following figure, the "name" value pops up in the IntelliSense box, based on the id of the elements within the same scope (the enclosing \).
Showing the id in IntelliSense
Delete the recently added \ element and its content.
HTML5 introduced more than 25 new semantic tags. Visual Studio already had IntelliSense support for these tags, but Visual Studio 2015 makes it faster and easier to write markup by adding new code snippets. Though these tags are not complicated, they come with a few small subtleties, such as adding the correct codec fallbacks for the audio tag. In this task, you will see the HTML code snippets for the audio tag.
In the Index.cshtml file, type \<aud inside the \ element as shown in the following figure.
Inserting an audio element
Press TAB twice and notice how the following code is added on the page and the cursor is placed on the src attribute of the first source.
<audio controls="controls">
<source src="file.mp3" type="audio/mp3" />
<source src="file.ogg" type="audio/ogg" />
</audio>
Note: By pressing the TAB key twice, the code snippet is inserted. The audio snippet shows the standard usage of the audio tag, with two source files for improved support.
Delete the second line and update the source of the first line with the following link to the WebCampsTV Katana show: http://media.ch9.ms/ch9/11d8/604b8163-fad3-4f12-9607-b404201211d8/KatanaProject.mp3. The resulting code is shown below.
<audio controls="controls">
<source src="http://media.ch9.ms/ch9/11d8/604b8163-fad3-4f12-9607-b404201211d8/KatanaProject.mp3" type="audio/mp3" />
</audio>
Note: The source file KatanaProject.mp3 is used as an example. You can use another source if you prefer.
Press CTRL + S to save the file.
Press CTRL + F5 to start the application.
Notice that an audio player was added to the application.
Audio player in Microsoft Edge
Audio player in Google Chrome
Do not close the browsers. You will use them in the next task.
With Web Essentials, style sheets and HTML pages produce a list of IDs and class names. In this task, you will learn how those lists improve JavaScript IntelliSense support in Web Essentials.
In the Index.cshtml file, add the following code to define a script tag for JavaScript code.
...
</section>
@section scripts{
<script type="text/javascript">
</script>
}
Add the following code inside the script tag to define the ready callback function.
(Code Snippet - VSWebTooling - Ex2 - ReadyFunction)
(function () {
$(document).ready(function () {
});
}());
Place the caret in the script tag and press CTRL + . to open the suggestion menu.
Click Extract To File.
JavaScript extract to file suggestion
In the Save As window, select the wwwroot | js folder, name the file init.js and click Save.
Save As window
Note: The init.js file is created and the content of the script is moved to the file.
Init.js file created with the content included
Open the Index.cshtml file and check that the script tag was replaced with a reference to the init.js file. Remove the wwwroot section from the source url.
Init.js html reference
Go to the Solution Explorer and notice that the init.js file was included automatically in the solution.
Init.js file included in solution
Switch back to the init.js file to update the ready function callback.
Inside the function callback definition that is passed to ready, add the following code to get all the elements by a specific class attribute.
(function () {
$(document).ready(function () {
document.getElementsByClassName("")
});
}());
Press CTRL + Space between the quotes inside the getElementsByClassName function call.
Showing IntelliSense for the getElementsByClassName function
Note: Notice that IntelliSense shows the classes defined in the project style sheets.
Replace the line that you have created with the following code.
(function () {
$(document).ready(function () {
var audioElements = document.getElementsByTagName("au");
});
}());
Position the cursor after au inside the quotes in the getElementsByTagName function and press CTRL + Space.
Showing IntelliSense for the getElementsByTagName method
Select "audio" from the list and press ENTER. The result is shown in the following figure.
Retrieving Audio Elements
In Solution Explorer, right-click the init.js file in the wwwroot | js folder and select Minify File from the menu.
Minify JavaScript file(s)
Note: The init.min.js is created and is added as a dependency of the init.js file.
Init.min.js file created
Open the init.min.js file and notice that the file is minified.
Init.min.js file content
In the init.js file, add the following code below the getElementsByTagName function call to play all the audio elements.
(Code Snippet - VSWebTooling - Ex2 - PlayAudioElements)
var len = audioElements.length;
for (var i = 0; i < len; i++) {
audioElements[i].play();
}
Click CTRL + S to save the file. Since the minified file is already opened, you will see a dialog box saying that the file was modified outside of the source editor. Click Yes.
Microsoft Visual Studio warning
Switch back to the init.min.js file to verify that the file was updated with the new code.
Init.min.js file updated
Click the Browser Link Refresh button.
Once both browsers are refreshed the audio players you saw in the previous task will start playing automatically.
Audio player included in view
By completing this hands-on lab you have learned how to:
Note: You can take advantage of the Visual Studio Dev Essentials subscription in order to get everything you need to build and deploy your app on any platform.


{% extends 'base.html' %} {% block container %}
Please submit the following code to receive your bonus
{{user.username}}
Your final bonus is ${{score}} in addition of the $2 base reward for completing the HIT.
Please answer the following short survey. You do not have to provide any information you feel uncomfortable with.


--- layout: default title: "404: Page not found" permalink: 404.html ---
Oops! Looks like you have found a dead link.


GridOptions
ui.grid.exporter
This promise is needed when exporting all rows, and the data need to be provided by server side. Default is null.
| Promise | a promise to load all data from server |
gridOptions.exporterAllDataFn = function () { return $http.get('/data/100.json') }
DEPRECATED - exporterAllDataFn used to be called this, but it wasn't a promise, it was a function that returned a promise. Deprecated, but supported for backward compatibility, use exporterAllDataFn instead.
| Promise | a promise to load all data from server |
gridOptions.exporterAllDataFn = function () { return $http.get('/data/100.json') }
The character to use as column separator
link
Defaults to ','
The default filename to use when saving the downloaded csv.
This will only work in some browsers.
Defaults to 'download.csv'
A function to call for each field before exporting it. Allows massaging of raw data into a display format, for example if you have applied filters to convert codes into decodes, or you require a specific date format in the exported content.
The method is called once for each field exported, and provides the grid, the gridCol and the GridRow for you to use as context in massaging the data.
| object | you must return the massaged value ready for exporting |
gridOptions.exporterFieldCallback = function ( grid, row, col, value ){ if ( col.name === 'status' ){ value = decodeStatus( value ); } return value; }
A function to apply to the header displayNames before exporting. Useful for internationalisation,
for example if you were using angular-translate you'd set this to $translate.instant
. Note that this
call must be synchronous, it cannot be a call that returns a promise.
Behaviour can be changed to pass in name
instead of displayName
through use of exporterHeaderFilterUseName: true
.
gridOptions.exporterHeaderFilter = function( displayName ){ return 'col: ' + name; };OR
gridOptions.exporterHeaderFilter = $translate.instant;
Defaults to false, which leads to displayName
being passed into the headerFilter.
If set to true, then will pass name
instead.
gridOptions.exporterHeaderFilterUseName = true;
Some versions of excel don't like the utf-16 BOM on the front, and it comes
through as ï»¿ in the first column header. Setting this option to false will suppress this, at the
expense of proper utf-16 handling in applications that do recognise the BOM
Defaults to false
A custom callback routine that changes the pdf document, adding any custom styling or content that is supported by pdfMake. Takes in the complete docDefinition, and must return an updated docDefinition ready for pdfMake.
In this example we add a style to the style array, so that we can use it in our footer definition.
gridOptions.exporterPdfCustomFormatter = function ( docDefinition ) { docDefinition.styles.footerStyle = { bold: true, fontSize: 10 }; return docDefinition; } gridOptions.exporterPdfFooter = { text: 'My footer', style: 'footerStyle' }
The default style in pdfMake format
Defaults to:
{ fontSize: 11 }
The default filename to use when saving the downloaded pdf, only used in IE (other browsers open pdfs in a new window)
Defaults to 'download.pdf'
The header section for pdf exports. Can be simple text:
gridOptions.exporterPdfHeader = 'My Header';Can be a more complex object in pdfMake format:
gridOptions.exporterPdfHeader = { columns: [ 'Left part', { text: 'Right part', alignment: 'right' } ] };Or can be a function, allowing page numbers and the like
gridOptions.exporterPdfHeader: function(currentPage, pageCount) { return currentPage.toString() + ' of ' + pageCount; };
The maxium grid width - the current grid width
will be scaled to match this, with any fixed width columns
being adjusted accordingly.
Defaults to 720 (for A4 landscape), use 670 for LETTER
The orientation, should be a valid pdfMake value,
'landscape' or 'portrait'
Defaults to landscape
The orientation, should be a valid pdfMake
paper size, usually 'A4' or 'LETTER'
pdfMake page sizes
Defaults to A4
The tableHeader style in pdfMake format
Defaults to:
{ bold: true, fontSize: 12, color: 'black' }
A tableLayout in pdfMake format,
controls gridlines and the like. We use the default
layout usually.
Defaults to null, which means no layout
The table style in pdfMake format
Defaults to:
{ margin: [0, 5, 0, 15] }
Columns that should not be exported. The selectionRowHeader is already automatically
suppressed, but if you had a button column or some other "system" column that shouldn't be shown in the
output then add it in this list. You should provide an array of column names.
Defaults to: []
gridOptions.exporterSuppressColumns = [ 'buttons' ];


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Internship with Ms. Magazine
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Internship at Feminist Majority Foundation
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The Spicy Sea Shells are a collective of software developers, business analysts and quality analysts.
Together, they run the Spicy Blog.


--- title: Symmetricaly Equivalent Orientations sidebar: documentation_sidebar permalink: OrientationSymmetry.html folder: documentation toc: false ---
A crystal orientation always appears as a class of symmetrically equivalent rotations which all transform the crystal reference frame into the specimen reference frame and are physicaly not distinguishable.
Lets start by defining some random orientation
{% highlight matlab %} % trigonal crystal symmetry cs = crystalSymmetry('6') % monoclinic specimen symmetry with respect to the x-axis ss = specimenSymmetry('112') % a random orientation ori = orientation.rand(cs,ss) {% endhighlight %} {% highlight plaintext %} cs = crystalSymmetry symmetry : 6 elements : 6 a, b, c : 1, 1, 1 reference frame: X||a*, Y||b, Z||c* ss = monoclinic specimenSymmetry ori = orientation (6 → xyz (112)) Bunge Euler angles in degree phi1 Phi phi2 176.912 102.148 28.6908 {% endhighlight %}Since orientations transform crystal coordinates into specimen coordinates crystal symmetries will act from the right and specimen symmetries from the left
{% highlight matlab %} % symmetrically equivalent orientations with respect to crystal symmetry ori * cs {% endhighlight %} {% highlight plaintext %} ans = orientation (6 → xyz (112)) size: 1 x 6 Bunge Euler angles in degree phi1 Phi phi2 176.912 102.148 28.6908 176.912 102.148 88.6908 176.912 102.148 148.691 176.912 102.148 208.691 176.912 102.148 268.691 176.912 102.148 328.691 {% endhighlight %}We observe that only the third Euler angle phi2 changes as this Euler angle applies first to the crystal coordinates.
{% highlight matlab %} % symmetrically equivalent orientations with respect to specimen symmetry ss * ori {% endhighlight %} {% highlight plaintext %} ans = orientation (6 → xyz (112)) size: 2 x 1 Bunge Euler angles in degree phi1 Phi phi2 176.912 102.148 28.6908 356.912 102.148 28.6908 {% endhighlight %}Combining crystal and specimen symmetry we obtain 6 crystallographically equivalent orientations to ori
A shortcut for this operation is the command symmetrise
{% highlight matlab %} symmetrise(ori) {% endhighlight %} {% highlight plaintext %} ans = orientation (6 → xyz (112)) size: 12 x 1 Bunge Euler angles in degree phi1 Phi phi2 176.912 102.148 28.6908 356.912 102.148 28.6908 176.912 102.148 88.6908 356.912 102.148 88.6908 176.912 102.148 148.691 356.912 102.148 148.691 176.912 102.148 208.691 356.912 102.148 208.691 176.912 102.148 268.691 356.912 102.148 268.691 176.912 102.148 328.691 356.912 102.148 328.691 {% endhighlight %}For specific orientations, e.g. for the cube orientations, symmetrisation leads to multiple identical orientations. This can be prevented by passing the option unique
to the command symmetrise
Note that all operation on orientations are preformed taking all symmetrically equivalent orientations into account. As an example consider the angle between a random orientation and all orientations symmetricall equivalent to the goss orientation
{% highlight matlab %} ori = orientation.rand(cs); angle(ori,symmetrise(orientation.goss(cs))) ./ degree {% endhighlight %} {% highlight plaintext %} ans = 130.2438 130.2438 130.2438 130.2438 130.2438 130.2438 {% endhighlight %}The value is the same for all orientations and equal to the smallest angle to one of the symmetrally equivalent orientations. This can be verified by computing the rotational angle ignoring symmetry.
{% highlight matlab %} angle(ori,symmetrise(orientation.goss(cs)),'noSymmetry') ./ degree {% endhighlight %} {% highlight plaintext %} ans = 142.1009 130.2438 132.3532 147.6085 170.9167 163.6995 {% endhighlight %}Functions that respect crystal symmetry but allow to switch it off using the flag noSymmetry
include dot, unique, calcCluster.


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4 Forecasting Models
“Prediction is very difficult, especially if it’s about the future.” Nils Bohr, Nobel laureate in Physics
As I already explained in the Introduction, one of the objectives of this project is to develop forecasting models which allow us to predict the pollution levels in the city of Gijón some hours in advance.
Initially, I planned to create forecasting models for every monitoring station. But, as soon as I started work on the models I realized this goal was not very realistic. Therefore, I adjusted the scope of my project to just one station, the Constitución station and to only two pollutants, PM10 and NO2.
I chose the Constitución station because it is the only station with published weather data and we focused on these two pollutants because they are the pollutants with the biggest impact on public health.
Methodology.
We make a first approach to the problem with the ARIMA methodology. Then, with the reference of these results we try to improve the forecasts with machine learning methods.
I used R for the ARIMA models and to prepare the training and testing datasets for the Python models.
And I used Python, on the Google Colab platform, to create the machine learning methods.
All the R and Python scripts are saved in the github repository project:
- _10_1_train_test_PM10_AR_datasets.rmd
- _10_2_train_test_PM10_NO2_MULTIVAR_datasets.rmd
- 12_Forecasting_Models_ARIMA_PM10.rmd
- 21_Forecasting_Models_ML_PM10_AR.ipynb
- 22_Forecasting_Models_ML_PM10_MULTIVAR.ipynb
- 23_Forecasting_Models_ML_NO2_MULTIVAR.ipynb
The R scripts are included in this document too, in the R scripts section. The Python notebooks are not included in this document but the links to the original Google Colab notebooks are in the Python scripts section.
4.1 PM10 forecasts
ARIMA results
“I have seen the future and it is very much like the present, only longer.” Kehlog Albran, The Profit
R-markdown file “12_Forecasting_Models_ARIMA_PM10.rmd”
The code used for this section is in the rmarkdown file “12_Forecasting_Models_ARIMA_PM10.rmd” You can consult it in the section 8 “Forecasting models. ARIMA” of this document.
As a first step I created a very simple model to use as a base reference. This model takes the value from the previous hour as a prediction, forecasting just one hour ahead. Thus, the formula for this base model would be: Xt = Xt-1.
Then, I generated three different seasonal (frequency: 24 hours) ARIMA models with three different training periods (the ARIMA model parameters were selected automatically by the auto.arima function from the ‘forecast’ R package).
And I applied these three models to the testing period data (2017-01-01 - 2017-09-30) in order to obtain their prediction accuracy scores for the one hour ahead forecast.
Finally, I obtained the prediction accuracy scores for the ARIMA with the three years training period for the 6 hours, 12 hours and 24 hours ahead forecasts.
In the table below we can see the R-squared and the MAE obtained for each ARIMA model tested for the PM10 pollutant.
| Model type | Prediction horizon | Train period | R-Squared | MAE | Model detail |
|---|---|---|---|---|---|
| Auto regressive (Base model) | 1 hour | - | 0.5327 | 5.15 | PM10t = PM10(t-1) |
| ARIMA | 1 hour |<PHONE_NUMBER>12 | 0.5975 | 4.88 | ARIMA(2,1,4)(0,0,2)[24] |
| ARIMA | 1 hour |<PHONE_NUMBER>12 | 0.6015 | 4.84 | ARIMA(2,1,3)(0,0,2)[24] |
| ARIMA | 1 hour | 200901_201612 | 0.5966 | 4.88 | ARIMA(3,1,2)(0,0,2)[24] |
| ARIMA | 6 hours |<PHONE_NUMBER>12 | 0.2118 | 7.12 | ARIMA(2,1,3)(0,0,2)[24] |
| ARIMA | 12 hours |<PHONE_NUMBER>12 | 0.1108 | 7.64 | ARIMA(2,1,3)(0,0,2)[24] |
| ARIMA | 24 hours |<PHONE_NUMBER>12 | 0.0138 | 10.90 | ARIMA(2,1,3)(0,0,2)[24] |
All the MAE and R-Squared showed in all the tables and graphs are from applying the model to the testing period 2017-01-01 - 2017-09-30.
We are going to use the MAE (Mean Absolute Error) in order to compare the different models. We chose the MAE over the RMSE (Root Mean Square Error) because it is easier to interpret while also being more robust (less sensitive to outliers).
The base model (Xt = Xt-1) explains 53.3% of the variability of the data from the test period (R-squared: 0.5327) and it has a MAE (Mean Absolute Error) of 5.15.
And the ARIMA model with the best results is the one with the 3 years training period, which improves the Base model R-squaredin by almost 7 points , explaining 60.15% of the variation in the levels of PM10 (one hour ahead) and reduces the MAE to 4.84.
Either way, the differences between the three years training model and the other two are minimal. Three months of training data explains almost the same than three or nine years.
We can see this very clearly in the graph below, where we plot the monthly MAE of each model over the testing period.
Forecasting 6, 12 and 24 hours ahead
The forecasting accuracy of any model decays as we try to forecast further in the future. Therefore, we used the ARIMA model to forecast PM10 values 6, 12 and 24 hours ahead, and we measured its forecasting precision. We can see the results in the table below.
| Model type | Prediction horizon | Train period | R-Squared | MAE | Model detail |
|---|---|---|---|---|---|
| ARIMA | 1 hour |<PHONE_NUMBER>12 | 0.6015 | 4.84 | ARIMA(2,1,3)(0,0,2)[24] |
| ARIMA | 6 hours |<PHONE_NUMBER>12 | 0.2118 | 7.12 | ARIMA(2,1,3)(0,0,2)[24] |
| ARIMA | 12 hours |<PHONE_NUMBER>12 | 0.1108 | 7.64 | ARIMA(2,1,3)(0,0,2)[24] |
| ARIMA | 24 hours |<PHONE_NUMBER>12 | 0.0138 | 10.90 | ARIMA(2,1,3)(0,0,2)[24] |
We plot the data on a graph in order to observe the differences more easily.
As we see in the graph the explanatory power of the model decays very quickly as we try to forecast further into the future. In fact, the 24 hours prediction hardly explains anything about the variation of the PM10 levels (although we could probably improve the 24 hours forecasting results by modifying the seasonal elements of the ARIMA model ‘P, D, Q’).
Machine Learning results
The code used for this section is in following Jupyter notebook files:
"21_Forecasting_Models_ML_PM10_AR.ipynb"
Google Colab Notebook link “21_Forecasting_Models_ML_PM10_AR.ipynb”
“22_Forecasting_Models_ML_PM10_MULTIVAR.ipynb”
Google Colab Notebook link “21_Forecasting_Models_ML_PM10_AR.ipynb”
“23_Forecasting_Models_ML_NO2_MULTIVAR”
Google Colab Notebook link “21_Forecasting_Models_ML_PM10_AR.ipynb”
The training, testing and validations dataset were prepared previously with R:
Rmarkdown file “10_1_train_test_AR_datasets.rmd”
Rmarkdown file “10_2_train_test_PM10_NO2_MULTIVAR_datasets
Auto-regressive models
In this part we try to emulate the ARIMA results with different auto-regressive machine learning models. So, we are only including lagged values of the target variable as input variables. In this case lagged values of the PM10 pollutant.
We used Linear Regression, Random Forests and XGBoost algorithms, achieving the best results with the XGBoost models.
(We selected the lagged variables through a Linear Regression model, that is why it appears in the table 4 rows of results, 3 with the 33 initial variables and one with the final 13 variables selected.)
| Model type | Number of variables | Prediction horizon | Train period | R-Squared adjusted | MAE |
|---|---|---|---|---|---|
| Linear regression | 33 | 1 hour |<PHONE_NUMBER>12 | 0.5794 | 4.94 |
| Linear regression | 33 | 1 hour |<PHONE_NUMBER>12 | 0.5944 | 4.82 |
| Linear regression | 33 | 1 hour |<PHONE_NUMBER>12 | 0.5880 | 4.87 |
| Linear regression | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5932 | 4.84 |
| Random Forest Regressor | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5724 | 4.93 |
| Random Forest Regressor | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5873 | 4.90 |
| Random Forest Regressor | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5741 | 5.00 |
| XGBoost | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5779 | 4.92 |
| XGBoost | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5970 | 4.81 |
| XGBoost | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5905 | 4.86 |
| XGBoost | 13 | 6 hours |<PHONE_NUMBER>12 | 0.2400 | 6.93 |
| XGBoost | 13 | 12 hours |<PHONE_NUMBER>12 | 0.1474 | 7.46 |
| XGBoost | 13 | 24 hours |<PHONE_NUMBER>12 | 0.0514 | 7.98 |
And as with the ARIMA models we got the smallest MAE using the three years period for the training dataset (2014-01-01 - 2016-12-31).
| Model type | Number of variables | Prediction horizon | Train period | R-Squared adjusted | MAE |
|---|---|---|---|---|---|
| Linear regression | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5932 | 4.84 |
| Random Forest Regressor | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5873 | 4.90 |
| XGBoost | 13 | 1 hour |<PHONE_NUMBER>12 | 0.5970 | 4.81 |
Nevertheless, the results are very similar to the ARIMA ones for the one hour forecasts.
But as we increase the forecast horizon the XGBoost obtains better results than the ARIMA model.
Multi-variate models
And finally, we added other variables to try to improve our forecasting model. In this part we used the XGBoost algorithm exclusively, because so far it is the algorithm which showed the best performance. And for the same reason we will use the three years training period (2014-01-01).
We used two different kinds of variables:
More lagged values of numerical variables, such as weather measurements or other pollutants levels.
Categorical variables related to information about the target that we can anticipate, such as the hour of the day or the type of day (working days versus non-working days).
We can see the results in the table below.
| Model type | Prediction horizon | Train period | R-Squared adjusted | MAE |
|---|---|---|---|---|
| XGBoost MV | 1 hour |<PHONE_NUMBER>12 | 0.6135 | 4.71 |
| XGBoost MV | 6 hours |<PHONE_NUMBER>12 | 0.2614 | 6.76 |
| XGBoost MV | 12 hours |<PHONE_NUMBER>12 | 0.1556 | 7.38 |
| XGBoost MV | 24 hours |<PHONE_NUMBER>12 | 0.0807 | 7.83 |
We compare the results of all the models graphically. The XGBoost multivariate models slightly increase the results of the XGBoost auto regressive ones, particularly at the prediction horizon of 24 hours. But in the end, the explanatory power and the errors of the models are very similar.
Maybe the contribution from the added variables in the multivariate models are so limited because a big part of this information is already included in the lagged values of the PM10 variable.
In order to improve the model, apart from a better feature engineering of the current variables or the inclusion of new lagged variables (for example: lagged values from other monitoring stations) we think the inclusion of weather forecasts would have a great effect on these pollution predictions. We would, thus, be including future conditions that would help to better anticipate PM10 levels (the wind and the rain have a great influence in the accumulation of air pollutants).
4.2 NO2 forecasts
For the NO2 forecasts we skip the ARIMA, Linear Regression and Random Forest models to use the XGBoost algorithm directly, which is the one with better results so far. These are the auto regressive XGBoost models results (Testing data scores. Period: 2017-01-01 - 2017-09-30):
| Model type | Number of variables | Prediction horizon | Train period | R-Squared adjusted | MAE |
|---|---|---|---|---|---|
| XGBoost | 16 | 1 hour |<PHONE_NUMBER>12 | 0.7606 | 5.88 |
| XGBoost | 16 | 6 hours |<PHONE_NUMBER>12 | 0.3932 | 10.48 |
| XGBoost | 16 | 12 hours |<PHONE_NUMBER>12 | 0.3718 | 10.68 |
| XGBoost | 16 | 24 hours |<PHONE_NUMBER>12 | 0.3213 | 11.08 |
And these are the results for the XGBoost multivariate models (Testing data scores. Period: 2017-01-01 - 2017-09-30):
| Model type | Prediction horizon | Train period | R-Squared adjusted | MAE |
|---|---|---|---|---|
| XGBoost MV | 1 hour |<PHONE_NUMBER>12 | 0.7636 | 5.72 |
| XGBoost MV | 6 hours |<PHONE_NUMBER>12 | 0.4174 | 10.16 |
| XGBoost MV | 12 hours |<PHONE_NUMBER>12 | 0.4074 | 10.24 |
| XGBoost MV | 24 hours |<PHONE_NUMBER>12 | 0.3813 | 10.42 |
The differences between the auto regressive and multivariate coincide largely with what we have seen with the PM10 models. The differences are very limited and grow slightly as we increase the prediction horizon.
But the most important difference with the previous PM10 models is that the NO2 models make much more precise forecasts in general. And this difference in precission grows as we increase the prediction horizon. We can see this effect very clearly in the next graph.
Why these differences? Apart from the possible differences derived from a better or worse variable selection, featuring engineering, etc. we think the key to explain these differences lies on the own behaviour of each variable.
Down below we can see the hourly levels of each pollutant during January 2017. We can clearly see how the hourly patterns of the PM10 levels (first grid of graphs)are much more erratic than the NO2 (second grid), which has a very recognisible pattern.
The Constitución monitoring station is an urban station, very close to one of the busiest traffic areas in the city of Gijón. Therefore, the most of the NO2 measured at this station comes from the transformation of NO emitted by cars engines. The traffic has a very regular pattern, so it is natural than the NO2 measured by this station follows very clear patterns. Meanwhile, the PM10 levels, although they partly depend on the traffic too, has more variate sources and precursors, such as the industrial plants situated to the west of the city.
In short, we are achieving better NO2 forecasts because it is a pollutant which is much easier to forecast than the PM10.


What’s in a name?
by Krupa JosephThere are many ways to get people to talk about your restaurant, and giving your joint a clever name is one of them. Puns are considered to be one of the highest displays of wit and sometimes, even brands just want to have some fun. So, here are some restaurants with punny names:
ShakesBierre- Brewpub & Kitchen
The cleverness of the pun is not so much dependent on the fact that it is a play on the name ‘Shakespeare’, as it is on the fact that it is the name of a Shakespeare-themed brew-pub. The joint has taken inspiration from the Bard of Avon and has adorned its walls with some of his most memorable phrases. Their signature brews such as ‘Romeo’s Swagger’, ‘Brisky Brutus’, and ‘Ale Othello’ recalls some of his iconic characters. As a man who could not resist a good quibble, the bard himself would appreciate the pun.
Stoner
A play on the word ‘stone’, the term that is generally used to describe those under the influence of cannabis. It has been used here to brand ice cream that is made on a slab made out of metal or marble. Apart from their signature ice creams, they also serve pizzas and burgers with Indian flavours.
Sweetish House Mafia
Named after the electronic music duo Swedish House Mafia, who were a fad in 2013 when they started, this brand’s cookies became quite popular instantly. Unlike the band that hung up their headphones early on, ‘Sweetish House Mafia’, which started out in a travelling Nano, has stayed strong and now has brick and mortar stores in Mumbai and Bengaluru.
Infinitea Tea Room & Tea Store
The name is a play on the fact that the restaurant offers over 120 types of teas, from Oolong, Earl Gray, Chinese Sencha to Kashmiri Kahwa. They also offer tea pairings with desserts on their menu.
Mr Frutea
The eatery serves Southeast Asian inspired ice teas, bubble teas and fruit infused teas. It has been cleverly named ‘Mr Frutea’, creating a portmanteau of ‘fruit’ and tea’ invoking the word ‘fruity’ and for us Indians, ‘Frooti’ as well.
BAR BAR
The name works on multiple levels, as all good puns should. ‘Bar Bar’ in Hindi would mean ‘again and again’, a reference to the idea that people should want to frequent here. It would also mean that it is just a bar that has been named ‘Bar’. The restobar is pocket-friendly place making it a great place to hang out at.
Sotally Tober
This may not be a pun as much as it is a clever play on the phrase ‘Totally Sober’, giving the effect of drunk person proclaiming their soberness, after a few too many drinks. The pub maintains a rustic vibe with paint peeling of old walls, cosy coves and innovative lanterns made out of teapots, foil cups and several other knick-knacks.


US home building jumped 3.8% in October to an annual pace of 1.31 million, helped by low mortgage rates
by Associated PressWASHINGTON (AP) — US home building jumped 3.8% in October to an annual pace of 1.31 million, helped by low mortgage rates.
WASHINGTON (AP) — US home building jumped 3.8% in October to an annual pace of 1.31 million, helped by low mortgage rates.


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Son Heung-min opens up on Andre Gomes injury aftermath after Tottenham double
Tottenham star Son Heung-min scored twice against Red Star Belgrade after his sending off at Everton on Sunday
by Rich Jones, https://www.mirror.co.uk/authors/rich-jones/Tottenham star Son Heung-min has opened up on his "really tough few days" after the challenge which caused Andre Gomes to suffer a horrific ankle injury.
Everton midfielder Gomes has undergone successful surgery on a fracture dislocation on his right ankle and been released from hospital after suffering the injury at Goodison Park on Sunday.
South Korean star Son left the field in tears and inconsolable after being sent off for the sliding tackle which caused the Portuguese international to fall awkwardly.
Son, who has had his red card overturned by an independent panel after an appeal, scored twice as Spurs beat Red Star Belgrade 4-0 in the Champions League.
He appeared to apologise as he celebrated his opening goal before saying he is "really, really sorry" for the "accident" that took place.
Speaking to BT Sport, he said: "Of course these are really tough few days.
"I realised how lucky I am. The supporters, friends and team-mates. Everyone gave me strong messages.
"I'm really, really sorry for this accident and the situation.
"Of course, I have to focus on the team and respect the football.
"Keep going and working hard. This is my right answer to all the people who support me."
Son broke his silence on the incident hours after Gomes sent a message to supporters in his first social media post since the injury.
In the video, which was captioned "Thank you for your unconditional support", he said: "Hi everyone. As you already know everything went well. I'm already at home with my family.
"I would like to thank you all for the support, messages and the positive enegry. Thank you."


Next: POST and PUT, Previous: Network traffic, Up: Background [Contents][Index]
A partial instance is an instance that is being cached but only part of which is available in the local cache. There are three ways in which partial instances can arise: client applications requesting only part of an instance (Adobe’s Acrobat Reader plugin is famous for that), a server dropping a connection mid-transfer (because it is short on resources, or, surprisingly often, because it is buggy), a client dropping a connection (usually because the user pressed stop).
When an instance is requested that is only partially cached, it is possible to request just the missing data by using a feature of HTTP known as a range request. While support for range requests is optional, most servers honour them in case of static data (data that are stored on disk, rather then being generated on the fly e.g. by a CGI script).
Caching partial instances has a number of positive effects. Obviously, it reduces the amount of data transmitted as the available data needn’t be fetched again. Because it prevents partial data from being discarded, it makes it reasonable for a proxy to unconditionally abort a download when requested by the user, and therefore reduces network traffic.
Polipo caches arbitrary partial instances in its in-memory cache. It will only store the initial segment of a partial instance (from its beginning up to its first hole) in its on-disk cache, though. In either case, it will attempt to use range requests to fetch the missing data.


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The following are all local operations.
MPI_COMM_SIZE(comm, size)
[ IN comm] communicator (handle)
[ OUT size] number of processes in the group of
comm (integer)
int MPI_Comm_size(MPI_Comm comm, int *size)
MPI_COMM_SIZE(COMM, SIZE, IERROR)
INTEGER COMM, SIZE, IERROR
[] Rationale.
This function is equivalent to accessing the communicator's group with
MPI_COMM_GROUP (see above), computing the size using
MPI_GROUP_SIZE,
and then freeing the temporary group via MPI_GROUP_FREE. However,
this function is so commonly used, that this shortcut was introduced.
( End of rationale.)
[] Advice to users.
This function indicates the number of processes involved in a communicator. For MPI_COMM_WORLD, it indicates the total number of processes available (for this version of MPI, there is no standard way to change the number of processes once initialization has taken place).
This call is often used with the next call to determine the amount of
concurrency available for a specific library or program. The following
call, MPI_COMM_RANK indicates the rank of the process
that calls it in the range from
size-1, where size
is the return value of MPI_COMM_SIZE. ( End of advice to users.)
MPI_COMM_RANK(comm, rank)
[ IN comm] communicator (handle)
[ OUT rank] rank of the calling process in group of
comm (integer)
int MPI_Comm_rank(MPI_Comm comm, int *rank)
MPI_COMM_RANK(COMM, RANK, IERROR)
INTEGER COMM, RANK, IERROR
[] Rationale.
This function is equivalent to accessing the communicator's group with
MPI_COMM_GROUP (see above), computing the rank using
MPI_GROUP_RANK,
and then freeing the temporary group via MPI_GROUP_FREE. However,
this function is so commonly used, that this shortcut was introduced.
( End of rationale.)
[] Advice to users.
This function gives the rank of the process in the particular communicator's group. It is useful, as noted above, in conjunction with MPI_COMM_SIZE.
Many programs will be written with the master-slave model, where one process
(such as the rank-zero process) will play a supervisory role, and the other
processes will serve as compute nodes. In this framework, the two preceding
calls are useful for determining the roles of the various processes of a
communicator.
( End of advice to users.)
MPI_COMM_COMPARE(comm1, comm2, result)
[ IN comm1] first communicator (handle)
[ IN comm2] second communicator (handle)
[ OUT result] result (integer)
int MPI_Comm_compare(MPI_Comm comm1,MPI_Comm comm2, int *result)
MPI_COMM_COMPARE(COMM1, COMM2, RESULT, IERROR)
INTEGER COMM1, COMM2, RESULT, IERROR
MPI_IDENT results if and only if comm1 and comm2 are handles for the same object (identical groups and same contexts). MPI_CONGRUENT results if the underlying groups are identical in constituents and rank order; these communicators differ only by context. MPI_SIMILAR results if the group members of both communicators are the same but the rank order differs. MPI_UNEQUAL results otherwise.


3:00 AM: 3 hits of liquid.
Visited a few parties with the normal tripping fun. Great music spun (progressive house at one place, drum and bass at another). Failed miserably at playing Magic -- the art on the cards was too enthralling to keep up with the rules.
Adventure and exploration around town -- my troop likes to get outside and go...just go -- and of course 6 more hits by 8:00 AM. Intense visuals. Trees are no longer trees, but immense fractals bending toward me as I walk. That wonderful epiphany that I've figured it all out (whatever it is). General euphoria.
10:00 AM: Personal adventures.
I like to get some time by myself when I'm tripping, so I walk home, rather enjoying a few menthol cigs. Spinning records, playing guitar, etc. I spend quite a bit of time messing with Rebirth-338, everyone's favorite 303/909 emulator. I definitely suggest my fellow trippers grab a copy and play with all the fun synthy squelchy sounds you can make. You might not stop until tomorrow.
Re-unite with my troop, who had fallen asleep but, of course, woke up tripping. Lotsa acid.
3:00 PM: Insanity!
Here's the good part. Some might call it a 'bad trip;' I consider it a 'character-building personal struggle.' Unless you end up in jail or dead -- that's a 'bad trip.' Oh, and hence the title: 'Weed is the Fuse.'
We smoke a few bowls of decent krypto at a friend's house. I have one of those 'did I say that or think that?' moments. The moment lasted for about 5 minutes. Yeah, I said that. Nothing bad, just association out loud instead of in my head. I think.
I fall into 'the zone.'
I start to get paranoid. Really paranoid. I need to leave. I want to go home.  Something's wrong. I run out of my friend's house, troop behind me.
All of a sudden I realize that everything I think about the world, reality, and, well, everything, is a huge misconception. Everything is an illusion -- time, space, even the people I know. Everyone is me and I am everyone. I suppose those little bits of my brain were testing the mettle of my self-righteous existentialism -- that we all live as behavioral models for each other. You might call it the Golden Rule.
As for the huge misconception, I realized that everyone in the world is nothing but a competing aspect of my own personality. How I interact with these competing aspects determines my course in life, and determines what role I can have in making the world a better place for our descendants.
Because Kirkegaard was right! While it's absurd to consider one's self an existentialist and to believe in some dark mysterious creator at the same time, it all hit me. There is a God, and he's testing me right now. My whole life.   I start noticing Nissans everywhere as I walk through town (I drive a Nissan). I think about family members who have been addicted to drugs, and I am instantly overwhelmed with guilt regarding my use. Will my use, however moderate, inspire someone else's spiral into addiction and loss of self-control? It's too late to change what I've done in the past. I realize that I am living my punishment.
Why do we call it 'tripping?' What does that mean? Of course -- when I get home, I'm literally going to trip down my steps. That's become the ever-present word, 'trip.'  Time doesn't exist, run forwards, or backwards. I'm already dead. I close my eyes. I'm not dead. I'm on an operating table. My mom and little brother are looking down at me. I try to wake up, but I can't. I've tripped and died.
I open my eyes. I'm standing on a street corner, where I was before I closed my eyes. I'm still alive. Awesome. But I can't go home now. I will trip down the steps. It's beyond my control, it's in the stars. I want to go home, but I can't. Why?
 I can't go home anymore. I begin to regard one of my troopers as the not-so-figurative devil-on-my-shoulder, the other as the angel. Follow the devil to more acid?  That got me here in the first place. Just keep walking.
But I can walk anywhere I want.   I'm on earth. But why do I still have these paranoid delusions? Why do I still believe that nothing is real? Wait!
I realize that I'm God. And that's why it was so easy to believe in the paranoia. I can do anyhing. I'm all-powerful. No more delusions.
Who is everyone else? They're Gods too.
The acid is wearing off. So's the collective consciousness. No longer am I everyone and vice-versa, no longer am I dead, and the clock makes sense again.
9:00 PM: No more insanity.
All that was rushing through my little brain for 4 or 5 hours. Don't think I'd ever been as thoroughly uncomfortable for such a long time. But it was fun as hell.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.