Class ScopedContainer
Extends the Container to implement lifetime implicit scoping through the Scope that's created along with it.
Implementation of the IScopedContainer interface.
Implements
Inherited Members
Namespace: Rezolver
Assembly: Rezolver.dll
Syntax
public sealed class ScopedContainer : Container, IRootTargetContainer, ITargetContainer, ICovariantTypeIndex, IScopedContainer, IContainer, IScopeFactory, IServiceProvider, IDisposable
Remarks
If you want your root container to act as a lifetime scope, then you should use this class instead of using Container.
Note that this class does NOT implement the IContainerScope interface because the two interfaces are not actually compatible with each other, thanks to identical sets of extension methods.
Constructors
| Improve this Doc View SourceScopedContainer(IRootTargetContainer, IContainerConfig)
Constructs a new instance of the ScopedContainer class.
Declaration
public ScopedContainer(IRootTargetContainer targets = null, IContainerConfig config = null)
Parameters
| Type | Name | Description |
|---|---|---|
| IRootTargetContainer | targets | Optional. Contains the targets that will be used as the source of registrations for the container, ultimately being passed to the Rezolver.Targets property. If not provided, then a new TargetContainer will be created. |
| IContainerConfig | config | Can be null. A configuration to apply to this container (and, potentially its Rezolver.Targets). If not provided, then the DefaultConfig will be used |
Properties
| Improve this Doc View SourceScope
Gets the scope for this scoped container.
Note that this is used automatically by the container for Resolve(IResolveContext) operations where the Scope property is not already set.
Declaration
public IContainerScope Scope { get; }
Property Value
| Type | Description |
|---|---|
| IContainerScope |
Methods
| Improve this Doc View SourceCreateScope()
Overrides the base method's implementation of CreateScope() to pass the call to the Scope.
Declaration
public override IContainerScope CreateScope()
Returns
| Type | Description |
|---|---|
| IContainerScope |
Overrides
| Improve this Doc View SourceDispose()
Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.
Declaration
public void Dispose()


Performance boost in Clojure 1.3 (alpha4)
As release 1.3 of Clojure is on its way, I decided to give the currently available alpha4 a try and see some of the good stuff it brings. First thing first, I wanted to experiment myself with the performance gain that the extended support for native types would bring. I ran into an old post from which I got the inspiration for the specific test to run: the Takeuchi function.
The Java implementation for Tak provided me with a bottom line for performance comparison:
Then I used the Clojure version provided in the original blog post run to test against Clojure 1.2:
Finally, I leveraged the new syntax for the enhanced primitives support to test against Clojure 1.3 (thanks to neotyk to point that out to me):
As you can see, timing is tracked from the application code, as various accessory overhead like JVM and Clojure runtime bootstrap are not in the scope of this post.
As this benchmark is "just for fun", I won't pretent I did an extensive benchmark, or that I engineered a bullet proof benchmark strategy and the like. I just ran the above code some tens of times and here follows the average running time for the three versions:
NOTE: an updated benchmark is provided down below NOTE2: to have a more fair performance comparison, keep reading
The results tells of a ~4.5x speed gain, getting close to match plain Java code performance.
Now, even if such a benchmark won't be any news to the Clojure community, it's still absolutely awesome to see such a performance gain in the next release of this beautiful Lispy language :-)
Update: following the advice by Jürgen Hötzel in his comment, I slightly modified my Clojure sources to change clojure.core/- with clojure.core/unchecked-subtract and re-run the test. Here's the final results, which are way better for Clojure, especially for version 1.2:
Update 2: the following graph shows the impact of the direct use of the recur special form tested against plain recursive invocation of Tak, as per requested in some comments
Update 3: even if this all started as a quick&dirty, amatorial benchmark, it attracted quite some attentions, demanding more fair and precise benchmarks, especially on the Java vs Clojure comparison. As in the Java version of Tak I used Integers and not primitive types, there is an unfair burden Java had to carry along the computation. The following is the result of a re-run of the test for Java (using primitive long) and Clojure 1.3:
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{:lang("demand communication, sketching")}
{:lang("user interaction, high fidelity prototype")}
{:lang("vision and UI design")}
{:lang("frontend and backend development")}
{:lang("system design, system development, two development")}
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When I ask the Developers I use to work with about recursion, the always come back with this joke: "In order to understand recursion, you need to understand recursion." Well I don't understand recursion but this blog will be about researching on this fascinating Computer Science topic. The most basic definition of recursion is a function that calls itself.
Usually in a program, a function is declared and defined at the beginning of a file. Please see the simple function below.
def print_name(name)
print "Hello, #{name} how's it going? Have a good day, buddy."
end
In order to get the function to work, we need to call it using the function name of:
print_name("Leonard)
Recursion breaks down a large, complex problem into smaller versions of itself. This post on stackoverflow gave a pretty good explanation on recursion with the following example:
How do you empty a vase containing three flowers?  How do you empty a vase containing two flowers? Answer: if the vase is not empty, you take out one flower and then you empty a vase containing one flower. How do you empty a vase containing one flower?  How do you empty a vase containing no flowers? Answer: if the vase is not empty, you take out one flower but the vase is empty so you're done. That's repetitive. Let's generalize it: How do you empty a vase containing N flowers?
This is just a general simple explanation. In fact, with that example we could have used a for loop. But with more complex problems where you can break it down to smaller, recursive steps, a recursive function would be better. When at Devbootcamp, I hope to learn more and understand recursion so that I can now make the joke to others who ask me about it!


{% extends 'base.html' %} {% block content %}
written by {{ details.author }}
currently in the possesion of: {{ details.possessor }}


Finance Dashboard
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Free Cash Flow ($B)
Forward P/E
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{{ "{%" }}extends "layout.html"{{ "%}" }} {{ "{%" }}block body{{ "%}" }} {# Splash Page #}
Sleek, intuitive, and powerful mobile-first front-end framework for faster and easier web development.
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| Interface | Description |
|---|---|
| FeedManager |
Responsible for downloading Gfycat items.
|
| GetGfycatsObservableFactory |
Generate observable from
GfycatAPI with FeedIdentifier . |
| GetMoreGfycatsObservableFactory |
Create Observable from api by feedIdentifier and digestOrCursor.
|
| Class | Description |
|---|---|
| CategoriesCache |
Created by dekalo on 02.02.17.
|
| FeedData |
Contains current
FeedDescription and list of feed gfycats. |
| FeedDescription |
Contains basic feed data.
|
| FeedManagerImpl |
Created by dekalo on 20.01.16.
|
| GetGfycatsObservableFactoryMap |
NetworkClient support class.
|
| GetMoreGfycatsObservableFactoryMap |
* NetworkClient support class.
|
| NetworkStateReceiver |
Created by Andrew Khloponin
|
| PrevRequest |
Created by Andrew Khloponin
|
| Exception | Description |
|---|---|
| FeedManager.InternalGfycatException |
Indicate that there is an internal unhandled error inside Gfycat SDK.
|
| FeedManager.NoSearchResultException |
Indicates that there are no [more] results for requested search.
|
| NotAvailableInTheAppException |
Means that application is not able to show mentioned gfycat item.
|


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GOB files are a repository for many other files, and are by far the best way to distribute add-on levels.
They contain a header with a signature, a data part and an index part.
GOB_Header IS
{
| GOB_MAGIC | char[4] | // 'GOB' followed by 0x0A |
| MASTERX | long | // offset to MASTERN |
}
The embedded files follow, then comes the index.
GOB_Index IS
{
| MASTERN | long | // number of files in the GOB |
| INDEXES | GOB_Ix_Entry[n] | // one index entry per file |
}
Where:
GOB_Ix_Entry IS
{
| IX | long | // pointer to start of the file |
| LEN | long | // length of the file |
| NAME | char[13] | // name of the file, // null terminated |
}


| Parameter | Value |
|---|---|
| hashtag | #gi2020 |
| start_day | 2020-09-14 |
| end_day | 2020-09-16 |
| timezone | Europe/London |
| theme | theme_light |
| accent | #e75152 |
| accent2 | #F3A8A8 |
| kcore | 2 |
| topics_k | 6 |
| bigram_filter | 3 |
| fixed | TRUE |
| seed | 1 |
An analysis of tweets for the query #gi2020 related to Genome Informatics Conference 2020 from 2020-09-14 to 2020-09-16 (online due to the COVID-19 pandemic).
A total of 359 tweets from 203 users were collected using the {rtweet} R package.
Filtered for dates 2020-09-14 - 2020-09-16 in the Europe/London timezone.
The “replies network”, composed from users who reply directly to one another, coloured by PageRank.
The “mentions network”, where users mention other users in their tweets. Filtered for a k-core of 2. Node colour and size adjusted according to PageRank score.
| screen_name | text | retweet_count |
|---|---|---|
| aaronquinlan | Here is a link to our #GI2020 poster on the D4 format and associated toolset. D4 is a new format (yes I know) for representing quantitative genomics data in a more compact and faster to query manner than say, BigWig. Huge speed ups. Paper coming soon. https://t.co/YI6BmlopFZ | 26 |
| JonathanGoeke | There is a whole world of RNA modifications to be explored @ewanbirney at #GI2020. Have a look at the poster by Christopher Hendra on m6aNet to predict RNA modifications from a single sample of direct RNA-Seq, code here: https://t.co/Yuj12paUh7 | 17 |
| AliciaOshlack |
#GI2020 starts today. This is our 20th Genome Informatics meeting. What were you doing in 2001? I was working on my PhD in astrophysics on the central structure of quasars. I’m pretty sure I had no real concept of the genome 🤓 |
16 |
| ACSCevents |
#GI2020 starts today! We’re celebrating 20 years of Genome Informatics this week with over 350 participants from 42 countries. Please use the hashtag #GI2020 when discussing highlights from the meeting. Here’s a throwback of our 2001 #GenomeInformatics abstract book👇 https://t.co/NL9YQiwZ5n |
16 |
| pathogenomenick | The full Genome Informatics 2020 programme is now up, looking like a very tasty agenda indeed. About to do our technical rehearsal which I assume means loading up Zoom. #gi2020 https://t.co/yAzofXKTCH | 13 |
| Loic_Lnlg |
If you are attending Genome Informatics 2020, you can stop by our poster (P57) to learn more about the Green Algorithms project and ask questions! #GI2020 The app: https://t.co/HObEYM4fmi The preprint: https://t.co/vtqsQzh9vM https://t.co/oYlA2f3PHe |
13 |
| ctitusbrown | #GI2020 karen miga @khmiga gave great talk on sequencing, assembly, and analysis results from the telomere-to-telomere consortium! github repo here: https://t.co/C9w3bI5sPS | 10 |
| ACSCevents |
🚨FINAL CALL! Register for #GI2020 now: https://t.co/TvNUnkHKKZ Bringing researchers together to: ✅Discuss recent advances ✅Focus on approaches incl. variant-calling, #transcriptomic analysis & variant interpretation ✅Network online 📄 AGENDA: https://t.co/MMmmGEeDeM https://t.co/8Ho3nH6Hj2 |
9 |
| thesteinegger | PenguiN is a ProtEiN Guided Nucleotide assembler. It recovers many fold more viruses from metatranscriptomes than state of the art assemblers. Here is our #GI2020 Flash talk: https://t.co/I7Iawn9fMT Poster: https://t.co/WTEcdssJgq | 7 |
| hdashnow | Here’s my #GI2020 lightning talk and poster. It describes the STRling algorithm and a first look at running it on 2504 genomes from the 1000g project Talk: https://t.co/I1cYFrGHdc Poster: https://t.co/l71hgLvLzt | 7 |
| screen_name | text | favorite_count |
|---|---|---|
| AliciaOshlack |
#GI2020 starts today. This is our 20th Genome Informatics meeting. What were you doing in 2001? I was working on my PhD in astrophysics on the central structure of quasars. I’m pretty sure I had no real concept of the genome 🤓 |
152 |
| aaronquinlan | Here is a link to our #GI2020 poster on the D4 format and associated toolset. D4 is a new format (yes I know) for representing quantitative genomics data in a more compact and faster to query manner than say, BigWig. Huge speed ups. Paper coming soon. https://t.co/YI6BmlopFZ | 79 |
| ACSCevents |
#GI2020 starts today! We’re celebrating 20 years of Genome Informatics this week with over 350 participants from 42 countries. Please use the hashtag #GI2020 when discussing highlights from the meeting. Here’s a throwback of our 2001 #GenomeInformatics abstract book👇 https://t.co/NL9YQiwZ5n |
56 |
| pathogenomenick | Just kicking back with a beer in the Red Lion at the end of the 20th Genome Informatics, and the 1st virtual Genome Informatics #GI2020 - how was it for you? https://t.co/6tbBvWCuIV | 48 |
| JonathanGoeke | There is a whole world of RNA modifications to be explored @ewanbirney at #GI2020. Have a look at the poster by Christopher Hendra on m6aNet to predict RNA modifications from a single sample of direct RNA-Seq, code here: https://t.co/Yuj12paUh7 | 45 |
| nameluem |
New challenge (for me): present our latest paper in a one (1!) minute video.🎞 Great format for lightning talks, as part of the 2020 (virtual) edition of the Genome Informatics meeting. #GI2020 paper: https://t.co/S5YDiJEqqz data/browser/etcetera: https://t.co/8owz00vfK1 https://t.co/1U69g8LwC9 |
44 |
| JonathanGoeke | bambu generates a curated set of transcript annotations from long read RNA-Seq data and quantifies their expression across all samples of interest. You can install bambu from github (soon on bioconductor!): https://t.co/JoemORGQoP #GI2020 | 44 |
| pathogenomenick | The full Genome Informatics 2020 programme is now up, looking like a very tasty agenda indeed. About to do our technical rehearsal which I assume means loading up Zoom. #gi2020 https://t.co/yAzofXKTCH | 40 |
| aarzalluz_ | I was 7 years old, had not even started to learn English and was nowhere near knowing what DNA was 🙈 Kind of amazing to think that today I’ll be presenting at #GI2020, first Genome Informatics meeting for me (hopefully, with many more to come). https://t.co/vD2i4cLli3 | 33 |
| pathogenomenick | Conference chairing, post-COVID edition! Listening to @ewanbirney keynote defining genome informatics. Sadly missed the photo of his early century crew cut. #GI2020 https://t.co/ZXyeDjpx9q | 31 |
| screen_name | text | quote_count |
|---|---|---|
| aarzalluz_ | I was 7 years old, had not even started to learn English and was nowhere near knowing what DNA was 🙈 Kind of amazing to think that today I’ll be presenting at #GI2020, first Genome Informatics meeting for me (hopefully, with many more to come). https://t.co/vD2i4cLli3 | 2 |
| JoanaFFPViana |
In a more positive tweet, I’m really looking forward to #GI2020. The programme looks so good! PS: I was 12 in 2001 😬 https://t.co/MR2F4w6d3o |
2 |
| minouye271 | Be sure to check out Youwen (Owen) Qin’s talk at #GI2020 Wed (16th) at 130pm UK. The preprint’s just gone up! 👇 https://t.co/JzvaYuLSBP | 1 |
| MScBioinformat | #GI2020 - a very useful hashtag to watch for the next 4 days #genome #informatics #bioinformatics . Thanks @GeneFiddler ! https://t.co/VZl0Q8Gnsz | 1 |
| ACSCevents | Don’t miss this! 👇 #GI2020 https://t.co/VOYkHDnNA8 | 1 |
| Loic_Lnlg |
And if you are not at #GI2020, I got you covered! You can find my 1min lightning talk here: https://t.co/5PI91II216 and the full poster here: https://t.co/j8SO3ou8oB And you can ask your questions on Twitter! https://t.co/HObEYM4fmi https://t.co/O6ccX5sdny |
1 |
| metricausa | How green is your algorithm? What’s the carbon footprint of your GWAS, RNA read alignment, protein docking and any other computational analysis? Loïc Lannelongue @Cambridge_Uni and colleagues have an an app for that: https://t.co/Q12aw7Dn91 #Gi2020 https://t.co/3lzx3i2mSx | 1 |
| aaronquinlan | @jon_belyeu has developed a nice new tool for phasing de novo variants to gamete of origin. It uses “extended” read-backed phasing to increase the yield. #GI2020 https://t.co/UfDXo8Yp8u | 1 |
| SejModha | Really enjoyed participating in #GI2020 https://t.co/aZkhmSFE4H | 1 |
| screen_name | text | favorite_count |
|---|---|---|
| ACSCevents |
#GI2020 starts today! We’re celebrating 20 years of Genome Informatics this week with over 350 participants from 42 countries. Please use the hashtag #GI2020 when discussing highlights from the meeting. Here’s a throwback of our 2001 #GenomeInformatics abstract book👇 https://t.co/NL9YQiwZ5n |
56 |
| pathogenomenick | Just kicking back with a beer in the Red Lion at the end of the 20th Genome Informatics, and the 1st virtual Genome Informatics #GI2020 - how was it for you? https://t.co/6tbBvWCuIV | 48 |
| nameluem |
New challenge (for me): present our latest paper in a one (1!) minute video.🎞 Great format for lightning talks, as part of the 2020 (virtual) edition of the Genome Informatics meeting. #GI2020 paper: https://t.co/S5YDiJEqqz data/browser/etcetera: https://t.co/8owz00vfK1 https://t.co/1U69g8LwC9 |
44 |
| pathogenomenick | Conference chairing, post-COVID edition! Listening to @ewanbirney keynote defining genome informatics. Sadly missed the photo of his early century crew cut. #GI2020 https://t.co/ZXyeDjpx9q | 31 |
| aarzalluz_ | Excited to present my work today at Genome Informatics #GI2020 -tune in for some single-cell isoform fun! Great line up of speakers at the transcriptomics session this afternoon 👇🏼 https://t.co/EyD72r7RbD | 27 |
| Loic_Lnlg |
If you are attending Genome Informatics 2020, you can stop by our poster (P57) to learn more about the Green Algorithms project and ask questions! #GI2020 The app: https://t.co/HObEYM4fmi The preprint: https://t.co/vtqsQzh9vM https://t.co/oYlA2f3PHe |
19 |
| thesteinegger | Great talk from @johnlees6. He presented two tools: (1) pp-sketchlib a fast genome sketching library and (2) popPUNK a tool to cluster and visualize genomes. #GI2020. Code: https://t.co/v9v9F2idIM https://t.co/iCjFiurLXs | 16 |
| pmelsted | Handy trick to remember to look into the camera while chairing #GI2020 https://t.co/g8zXT3n4aH | 16 |
| thesteinegger | @ctitusbrown is talking about their work on spacegraphcats, a graph index structures to query diversity in metagenomes at #Gi2020. Code: https://t.co/o9FyIofTHK https://t.co/8MYOOtEtsn | 12 |
| JavierHerrero7 |
@pmelsted talking about kallisto and bustools to process scRNA-seq data. Try at home with pip install kb-python (https://t.co/oceR6woGjy) Love the slides inspired by the classic Pink Floyd’s “The Dark Side of the Moon” cover #GI2020 https://t.co/5caGEBnmFw |
12 |
The top 100 words used 3 or more times.
Words that were tweeted next to each other at least 3 times.
Top 10 words associated with 6 topics identified by LDA.
Most representative tweets for each topic
| screen_name | text | gamma |
|---|---|---|
| Diptavo | Really appreciate the immense effort the organizers have put into Genome Informatics 2020 to make it as much fun and engaging in the new virtual setup. My poster today on “Aggregative trans-eQTL associations detect trait-specific target genes in whole blood” #GI2020 https://t.co/13P7qptYOJ | 0.9953113 |
| GenomeRIK | #GI2020 #TAMAtools For anyone interested I have a poster for today’s session “P56 - Identifying novel genes in the human genome using TAMA long read RNA sequencing analysis”. It’s a bit short but I’ll be available for any TAMA questions during the poster session. | 0.9950782 |
| JonathanGoeke | There is a whole world of RNA modifications to be explored @ewanbirney at #GI2020. Have a look at the poster by Christopher Hendra on m6aNet to predict RNA modifications from a single sample of direct RNA-Seq, code here: https://t.co/Yuj12paUh7 | 0.9945349 |
| lal_avantika | After a great series of #transcriptomics and #epigenomics talks, on to the poster session at #GI2020 . #SingleCell researchers - check out @cjnolet’s poster (P80) on using @rapidsai to make single-cell analysis orders of magnitude faster! | 0.9945349 |
| GenomeRIK | #GI2020 @krsahlin What are your thoughts on NanoSplicer? Is this similar in use case as Ultra? Genome Informatics poster P127 - Accurate identification of mRNA splice sites using Oxford Nanopore sequencing | 0.9938569 |
| Loic_Lnlg |
If you are attending Genome Informatics 2020, you can stop by our poster (P57) to learn more about the Green Algorithms project and ask questions! #GI2020 The app: https://t.co/HObEYM4fmi The preprint: https://t.co/vtqsQzh9vM https://t.co/oYlA2f3PHe |
0.9938569 |
| lal_avantika | Excellent start to #GI2020 with @ewanbirney’s keynote followed by Smita Krishnaswamy. Now listening to my own recorded talk being played and feeling incredibly weird. Anyone else know this feeling? #VirtualConference | 0.9934505 |
| AliciaOshlack |
#GI2020 starts today. This is our 20th Genome Informatics meeting. What were you doing in 2001? I was working on my PhD in astrophysics on the central structure of quasars. I’m pretty sure I had no real concept of the genome 🤓 |
0.9934505 |
| pathogenomenick | Just kicking back with a beer in the Red Lion at the end of the 20th Genome Informatics, and the 1st virtual Genome Informatics #GI2020 - how was it for you? https://t.co/6tbBvWCuIV | 0.9918292 |
| JavierHerrero7 | Stick around for the last poster session #GI2020 and listen to my two PhD students (@MarkTranHS and @ChulingD) poster lightning talks. | 0.9910944 |
| screen_name | text | gamma |
|---|---|---|
| JavierHerrero7 | Almost ready for 2nd day of #GI2020: ✔morning run ✔London-style paella (AKA “rice with things”) for lunch ✔laptop ready for notes/social interaction ✔raspberry pi streaming talks on my TV ❌Solid internet connection 😯 | 0.9958945 |
| ACSCevents |
🚨FINAL CALL! Register for #GI2020 now: https://t.co/TvNUnkHKKZ Bringing researchers together to: ✅Discuss recent advances ✅Focus on approaches incl. variant-calling, #transcriptomic analysis & variant interpretation ✅Network online 📄 AGENDA: https://t.co/MMmmGEeDeM https://t.co/8Ho3nH6Hj2 |
0.9957169 |
| JavierHerrero7 | Redwood (Sequoia sempervirens) genome (50+Gbps) resolved in 3 days? Also assembly of the strawberry genome (octoploid) (Note: I am missing some details as my neighbours have called in the tree surgeons to trim their largely overgrown garden) #GI2020 | 0.9953113 |
| nomburg | Happy to be presenting my poster on non-canonical subgenomic RNAs generated by SARS-CoV-2 at #GI2020 (P81)! There’s a zoom link on the poster if you want to chat during the poster session. Also, feel free to check out the updated preprint - https://t.co/DVnkfZs0re | 0.9953113 |
| Digrigor | St George’s University at #Gi2020 Amazing work by @alan_michael_p and @SGUL_Genetics on creating the Middle Eastern Genome Exome Variant Browser Database. Come check his poster: Session 5, P91 @StGeorgesUni | 0.9945349 |
| Digrigor | St George’s University at #Gi2020 Come and check my poster on PQuery: A web application for visualisation and filtering of multi-sample DNA variant datasets! Session 3, P45 @StGeorgesUni @SGUL_Genetics | 0.9945349 |
| JavierHerrero7 | @MarkTranHS (P108) will be presenting MAPSCE. Following on our previous work in LOHHLA (https://t.co/V9pdRG5rLS), MAPSCE includes new features to improve mapping sub-clonal events on a tumour clonal tree. #GI2020 | 0.9938569 |
| JavierHerrero7 | @ChulingD (P31) will be presenting our recent effort to combine multi-sample EPIC data to identify clonal and sub-clonal CN events and derive all sub-clones. #GI2020 | 0.9938569 |
| aaronquinlan | @jon_belyeu has developed a nice new tool for phasing de novo variants to gamete of origin. It uses “extended” read-backed phasing to increase the yield. #GI2020 https://t.co/UfDXo8Yp8u | 0.9934505 |
| minouye271 | Be sure to check out Youwen (Owen) Qin’s talk at #GI2020 Wed (16th) at 130pm UK. The preprint’s just gone up! 👇 https://t.co/JzvaYuLSBP | 0.9910944 |
| screen_name | text | gamma |
|---|---|---|
| JavierHerrero7 |
@pmelsted talking about kallisto and bustools to process scRNA-seq data. Try at home with pip install kb-python (https://t.co/oceR6woGjy) Love the slides inspired by the classic Pink Floyd’s “The Dark Side of the Moon” cover #GI2020 https://t.co/5caGEBnmFw |
0.9957169 |
| aaronquinlan | Here is a link to our #GI2020 poster on the D4 format and associated toolset. D4 is a new format (yes I know) for representing quantitative genomics data in a more compact and faster to query manner than say, BigWig. Huge speed ups. Paper coming soon. https://t.co/YI6BmlopFZ | 0.9955233 |
| GeneFiddler |
Excellent talk by Angeles Arzalluz-Luque at #GI2020 describing isoform co-expression analysis in scRNA. Really like the idea of using long-read seq to identify isoforms and then short-read seq to quantify them. |
0.9948208 |
| JonathanGoeke | bambu generates a curated set of transcript annotations from long read RNA-Seq data and quantifies their expression across all samples of interest. You can install bambu from github (soon on bioconductor!): https://t.co/JoemORGQoP #GI2020 | 0.9945349 |
| JavierHerrero7 |
Attending my first virtual meeting today. #GI2020 started with @ewanbirney’s keynote address. Finding weird not to clap at the end of the talk… (would find even weirder to clap while seating alone at my dinning table) |
0.9938569 |
| JavierHerrero7 |
Smita Krishnaswamy talking about PHATE (https://t.co/mBPn2GNjBs), applied to COVID-19 data Incidentally, @FerranC96 presented the PHATE mss at our BLIC journal club last week. #GI2020 |
0.9934505 |
| pathogenomenick | Chair @pmelsted giving a cracking run down of the BUS file format and toolbox for UMI-seq data at #GI2020 - makes me wish I had some UMI data to play with! | 0.9934505 |
| GenomeRIK | @tendersombrero @drsarahdoom @andreaswallberg Just watched @khmiga talk at #GI2020 and there was a good slide showing the complementation of hifi with ont. I believe it made a good point for doing both. Sorry I think we aren’t supposed to tweet slides images for the conference. | 0.9934505 |
| AliciaOshlack | Hope you all enjoy the final day of #GI2020. It has been a lot of fun organising this conference over the last 3 years and I hope to return as a participant in future years. | 0.9918292 |
| MetaMoritz | Hanging-out awkwardly at conference coffee break where you know no one has become way less awkward at an online event! I am curious how this will work #GI2020 #geeksinthereelement #covidlife | 0.9910944 |
| screen_name | text | gamma |
|---|---|---|
| Rosy_Zh | Happy to present a poster at my very first conference #GI2020, on de novo prediction of phage-host relationships via CRISPR spacers using our tool SpacePHARER. Check out our poster: https://t.co/2DJgsWbkFG and the preprint: https://t.co/QGGp1dGHHa | 0.9945349 |
| thesteinegger | Great talk from @johnlees6. He presented two tools: (1) pp-sketchlib a fast genome sketching library and (2) popPUNK a tool to cluster and visualize genomes. #GI2020. Code: https://t.co/v9v9F2idIM https://t.co/iCjFiurLXs | 0.9942157 |
| JavierHerrero7 | Currently, Haoyu Cheng from @lh3lh3’s lab is talking about an de novo assembly using phased assembly graphs. GH: https://t.co/CfKno34gEz arXiv: https://t.co/rhmnbXqUG8 #GI2020 | 0.9934505 |
| jonathon_mifsud | Excited to present my poster (P73) tonight at Genome Infomatics #GI2020 - explorations of the plant virosphere. Looking forward to another late night of excellent talks. https://t.co/7MvWLktMHW | 0.9934505 |
| ctitusbrown | #GI2020 karen miga @khmiga gave great talk on sequencing, assembly, and analysis results from the telomere-to-telomere consortium! github repo here: https://t.co/C9w3bI5sPS | 0.9929866 |
| GenomeRIK | #GI2020 Genome Informatics starts today! Starting it right with transcriptomics. Looking forward to @aarzalluz_ @anaconesa talk on neural single cell isoform expression! https://t.co/L0MUH2iXL6 | 0.9929866 |
| aarzalluz_ | Excited to present my work today at Genome Informatics #GI2020 -tune in for some single-cell isoform fun! Great line up of speakers at the transcriptomics session this afternoon 👇🏼 https://t.co/EyD72r7RbD | 0.9929866 |
| pathogenomenick | And now for the second part of the session: @alicecarolyn starting with the CAMI challenge, followed by @johnlees6 on fast bacterial genome sketching and then @acritschristoph on SARS-CoV-2 strains in sewage! #gi2020 | 0.9929866 |
| can_pyong | Poster session is only 30min? 😲 Ok that’s no problem I’ll ask a question in person! 💨 I hope there is any notification to a presenter if I post a question from a website. #GI2020 | 0.9924520 |
| pathogenomenick | The full Genome Informatics 2020 programme is now up, looking like a very tasty agenda indeed. About to do our technical rehearsal which I assume means loading up Zoom. #gi2020 https://t.co/yAzofXKTCH | 0.9918292 |
| screen_name | text | gamma |
|---|---|---|
| Ahmed_Microbes | Just got introduced to The Green Algorithm by @Loic_Lnlg at #GI2020 that quantifies the carbon footprint of genomics. The calculator (https://t.co/X928bQ4lJx) is easy to use and encourages scientists to report/watch/reduce their computational carbon footprint. #GlobalWarming | 0.9948208 |
| CangeneCanvar |
@ACSCevents @ewanbirney @AliciaOshlack @pathogenomenick @rafalab @OliverStegle @TADG_Creavin @UCL_IHI @sangerinstitute @ConnectingSci Our Higher Scientific Officer @LovedayChey has been enjoying the diverse range of talks at #GI2020 today - roll on day 2! You can read about the teams #WES and #GWAS work in #TesticularCancer in our blog: https://t.co/iny7mr9f1q https://t.co/eODY1IJXBI |
0.9948208 |
| metricausa | How green is your algorithm? What’s the carbon footprint of your GWAS, RNA read alignment, protein docking and any other computational analysis? Loïc Lannelongue @Cambridge_Uni and colleagues have an an app for that: https://t.co/Q12aw7Dn91 #Gi2020 https://t.co/3lzx3i2mSx | 0.9948208 |
| A_L_Roberts | End of Day 2 of my first virtual conference #Gi2020 - it’s been briiant so far! It’s run to time thanks to pre-recorded talks. Live Q+As at the end of each sessions where all speakers are present have been engaging. Here’s hoping virtual conferences increase inclusivity too! | 0.9945349 |
| ctitusbrown | @thesteinegger my talk slides - https://t.co/f5I0jjHztJ - work with @BlairDSullivan @domoritz @ReiterTaylor w/Michael O’Brien and Felix Reidl (whose twitter handle I can’t remember). #gi2020 | 0.9924520 |
| thesteinegger | @ctitusbrown is talking about their work on spacegraphcats, a graph index structures to query diversity in metagenomes at #Gi2020. Code: https://t.co/o9FyIofTHK https://t.co/8MYOOtEtsn | 0.9910944 |
| AliciaOshlack | Checkout the poster P90 from @BelindaPhipson and @JovMaksimovic from my group on gene set testing methods for methylation arrays #GI2020 | 0.9902143 |
| SpyrosLytras |
One of the best-organised virtual conferences I’ve been to so far! lots of new ideas and concepts at #GI2020 Many thanks to the organisers! @pathogenomenick kudos to @SejModha for her excellent poster! 😁 |
0.9902143 |
| Ahmed_Microbes | Great talk by @ctitusbrown about spacegraphcats, a graph index structures to facilitate querying diversity in metagenomes #Gi2020. | 0.9891411 |
| TheFirstNuomics | Find out more about the technology and data behind Coronavirus Network Explorer in this just released preprint #GI2020 https://t.co/GCemnxosop | 0.9878036 |
| screen_name | text | gamma |
|---|---|---|
| can_pyong | Thank you very much for listening my talk about a meta chromatin accessibility analysis. Although a preprint hasn’t been published yet, I’m happy to exchange the ideas with ATAC-people! I also get excited to join a poster with being free from pressure☺ #GI2020 | 0.9950782 |
| craigandersn |
If you’re at #gi2020 then check out my poster: “Insights into strand-asymmetric mutational processes using segregating lesions in cancer genomes” https://t.co/rIdb9h7HPD I’ll be online to chat about #lesionsegregation and mutational processes in various asymmetric contexts. |
0.9948208 |
| acritschristoph | And we encourage questions / comments / constructive criticism / savage takedowns here or in medrxiv comments - many of us are newcomers to the viral world! Also you can see my short talk on this project on Wednesday at #GI2020, I will try to wake up for the London times | 0.9948208 |
| pathogenomenick | Evolution session up at #GI2020 - starting with @Kelley__Harris on mutation spectrum variation across the tree of life: how’s the virtual conference format going for you? I will be opening a beer at 6pm around the time of @ctitusbrown’s talk. | 0.9942157 |
| thesteinegger | PenguiN is a ProtEiN Guided Nucleotide assembler. It recovers many fold more viruses from metatranscriptomes than state of the art assemblers. Here is our #GI2020 Flash talk: https://t.co/I7Iawn9fMT Poster: https://t.co/WTEcdssJgq | 0.9938569 |
| camerongenomics | @khmiga Great #GI2020 talk! I’m really interested in your take on how we’ve gone about resolving somatic centromeric SVs (poster 17). We’re matching ~50% using hg38 repeats but surely we’d do better with a T2T reference. What approach would you recommend? | 0.9934505 |
| hdashnow | Here’s my #GI2020 lightning talk and poster. It describes the STRling algorithm and a first look at running it on 2504 genomes from the 1000g project Talk: https://t.co/I1cYFrGHdc Poster: https://t.co/l71hgLvLzt | 0.9929866 |
| SejModha | If you’ve ever wondered about #unknowns of human metagenomes, checkout my poster (P74) in today’s session at #GI2020. A short introductory talk video here: https://t.co/ejkmgJoPPk | 0.9929866 |
| ctitusbrown | Now, #GI2020 @johanneskoester talking about varlociraptor, an approach that enables a unified statistical approach to false discovery rate in variant calling. | 0.9924520 |
| pathogenomenick | Final day of #GI2020 today, starting with my favourite session: Microbes and Metagenomics, chaired by @alicecarolyn featuring @RafalMostowy, @GraceBlackwel1 and Youwen Quin ! | 0.9924520 |
Links to GitHub, GitLab, BitBucket, Bioconductor or CRAN mentioned in Tweets.
| Name | Tweets | Retweets | Type | Link |
|---|---|---|---|---|
| bambu | 1 | 0 | GitHub | goekelab/bambu |
| chm13 | 1 | 0 | GitHub | nanopore-wgs-consortium/chm13 |
| hifiasm | 1 | 0 | GitHub | chhylp123/hifiasm |
| m6anet | 1 | 0 | GitHub | goekelab/m6anet |
| pp-sketchlib | 1 | 0 | GitHub | johnlees/pp-sketchlib |
| spacegraphcats | 1 | 0 | GitHub | spacegraphcats/spacegraphcats |
## R version 4.0.0 (2020-04-24)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] fs_1.5.0 here_0.1 kableExtra_1.2.1 knitr_1.29
## [5] magick_2.4.0 webshot_0.5.2 viridis_0.5.1 viridisLite_0.3.0
## [9] wordcloud_2.6 RColorBrewer_1.1-2 ggtext_0.1.0 ggraph_2.0.3
## [13] ggrepel_0.8.2 ggplot2_3.3.2 emo_<IP_ADDRESS>00 rvest_0.3.6
## [17] xml2_1.3.2 topicmodels_0.2-11 tidytext_0.2.5 igraph_1.2.5
## [21] stringr_1.4.0 purrr_0.3.4 forcats_0.5.0 lubridate_1.7.9
## [25] tidyr_1.1.1 dplyr_1.0.1 rtweet_0.7.0 clamour_0.1.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 usethis_1.6.1 httr_1.4.2 rprojroot_1.3-2
## [5] SnowballC_0.7.0 tools_4.0.0 backports_1.1.8 utf8_1.1.4
## [9] R6_2.4.1 colorspace_1.4-1 withr_2.2.0 tidyselect_1.1.0
## [13] gridExtra_2.3 processx_3.4.3 curl_4.3 compiler_4.0.0
## [17] cli_2.0.2 NLP_0.2-0 labeling_0.3 slam_0.1-47
## [21] scales_1.1.1 tm_0.7-7 callr_3.4.3 askpass_1.1
## [25] digest_0.6.25 rmarkdown_2.3 pkgconfig_2.0.3 htmltools_0.5.0
## [29] highr_0.8 rlang_0.4.7 rstudioapi_0.11 farver_2.0.3
## [33] generics_0.0.2 jsonlite_1.7.0 tokenizers_0.2.1 RCurl_1.98-1.2
## [37] magrittr_1.5 modeltools_0.2-23 Matrix_1.2-18 Rcpp_1.0.5
## [41] munsell_0.5.0 fansi_0.4.1 lifecycle_0.2.0 stringi_1.4.6
## [45] yaml_2.2.1 MASS_7.3-51.6 plyr_1.8.6 grid_4.0.0
## [49] parallel_4.0.0 crayon_1.3.4 lattice_0.20-41 graphlayouts_0.7.0
## [53] gridtext_0.1.1 ps_1.3.3 pillar_1.4.6 markdown_1.1
## [57] reshape2_1.4.4 stats4_4.0.0 glue_1.4.1 evaluate_0.14
## [61] vctrs_0.3.2 png_0.1-7 tweenr_1.0.1 selectr_0.4-2
## [65] gtable_0.3.0 openssl_1.4.2 polyclip_1.10-0 assertthat_0.2.1
## [69] xfun_0.16 ggforce_0.3.2 tidygraph_1.2.0 janeaustenr_0.1.5
## [73] tibble_3.0.3 ellipsis_0.3.1


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---
layout: post
status: publish
published: true
title: "很讽刺的恶搞：Microsoft Firefox"
author: Leask
author_login: leask
author_email<EMAIL_ADDRESS>author_url: https://leaskh.com
wordpress_id: 439
wordpress_url: http://leaskh.wordpress.com/2006/10/28/%e5%be%88%e8%ae%bd%e5%88%ba%e7%9a%84%e6%81%b6%e6%90%9e%ef%bc%9amicrosoft-firefox
date: '2006-10-28 15:34:33 +0800'
date_gmt: '2006-10-28 15:34:33 +0800'
categories:
- Computers and Internet
tags: []
comments: []
---
Microsoft(R) Firefox 2007 Professional
The new award-winning browser from Microsoft is now faster, securer and quicker anything else on the market. Why use anything else?
Enjoy a Better Web Experience
MS Firefox 2007 delivers a dramatic boost in the download speeds of online pornography. By utilising a proprietary dynamic algorithm - anything that remotely resembles a tit or a boob will download up to 10 times faster. Fun at the speed of light!
Stay Secure on the Web
Firefox 2007's new TakeOver(TM) technology protects the Windows Kernel by automatically recognizing all McAfee and Symantec programs as viruses. Downloading has never been safer.
Interactive Multimedia
Microsoft's AKobe Phlash(TM) Plugin delivers awesome online interactive content; anything from music, videos to games. (Lawsuit from Adobe pending)
Personalize Your Browser
Choose from three excellent browsing styles! Minimized, Maximized and Fullscreen!
System Requirements
The following bare minimums are essential for the optimum operation of MS Firefox 2007
A Credit Card
Microsoft(R) Windows(TM)/Vista Operating System
Microsoft(R) Mouse & Keyboard (Hyperlinks will NOT work without these)
Live(TM) Messenger (Required API to maintain dangerous CPU temperatures)
Microsoft(R) Office 2007 (Provides the ability to scroll webpages)
Quad Core Processor (For loading Hotmail.com)
Any SLI Configured Graphics Card > 512mb (TO Display High Definition GIF Images)
1024 MB of RAM (Compensation for Frequent Memory Leaks)
483MB Disk Space
Warning
Visiting the following sites using MS Firefox 2007 will cause your computer to shut down unexpectedly
www.google.com
www.gmail.com
www.apple.com
www.itunes.com
www.yahoo.com
Any sites with the phrase: microsoft/windows/explorer/vista/bill sucks
Donations
The Microsoft(R) Corporation is an international non-profit organisation exempt from Federal income taxation under IRC 501(c)(3). It is governed by its Board of Directors.
Please consider supporting the Microsoft(R) organsiation by making a contribution. Donations are tax deductible.
We thank the millions individuals and organizations who have already made a donation.
Quoted from:
http://www.msfirefox.com


All DIDComm protocols share common assumptions about how communication happens, how security and privacy guarantees are achieved, and how the duties of each participant are described and enforced. What follows here is a summary; see the body of work in Hyperledger Aries for more information.
This method associates security guarantees with the message envelope rather than the transport, and is aligned philosophically with the approach of IETF's Message Level Security initiative. However, the first version of this spec predates MLS's maturity, so the wire format uses JWEs ([[RFC7516]]) with some extensions, instead. The extensions allow the same message to be encrypted once for multiple recipients and sent to them all in an efficient manner:
These extensions are under review by the maintainers of JWE/JWT and are likely to be mainstreamed eventually. The particulars are detailed in a doc that explains how agents in the Hyperledger Aries ecosystem have implemented the format - but the format is easy to implement without any Aries dependencies, and its design has no connection to any particular blockchain. See this implementation in pure javascript, and this one in pure python, in addition to Indy's C-callable implementation in Rust.


Symbol Index for "Activity Type"
Symbol Index for diagram
"Activity Type"
Activity (Class)
Activity Qualifier Type (Class)
Activity Type (Class)
Agent's Activity Type (Class)
classifier (Association)
classifier (Association)
Contact Method (Class)
DC/Describing (Generalization Node)
Descriptive Category (Class)
Incident Type (Class)
Intention Type (Class)
Level (Class)
Property Qualifier Type (Class)
Reason For Abandoning (Class)
Reason For Rejection (Class)
Reason Not Required (Class)
Resource Type (Class)
State Type (Class)
Subject Characteristic Type (Class)


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Noise and Random Values¶
An important part of generating realistic images is to add appropriate levels of noise. When simulating objects, you may also want the actual objects being drawn to be random in some way. GalSim has a number of classes to help inject these kinds of randomness into your simulations.
Random Variates¶
-
class
galsim.
BaseDeviate
(seed=None)[source]¶ Base class for all the various random deviates.
This holds the essential random number generator that all the other classes use.
All deviates take an initial
seed
argument that is used to seed the underlying random number generator. It has three different kinds of behavior.An integer value can be provided to explicitly seed the random number generator with a particular value. This is useful to have deterministic behavior. If you seed with an integer value, the subsequent series of “random” values will be the same each time you run the program.
A seed of 0 or None means to pick some arbitrary value that will be different each time you run the program. Currently, this tries to get a seed from /dev/urandom if possible. If that doesn’t work, then it creates a seed from the current time. You can also get this behavior by omitting the seed argument entirely. (i.e. the default is None.)
Providing another BaseDeviate object as the seed will make the new Deviate share the same underlying random number generator as the other Deviate. So you can make one Deviate (of any type), and seed it with a particular deterministic value. Then if you pass that Deviate to any other one you make, they will both be using the same RNG and the series of “random” values will be deterministic.
Usage:
There is not much you can do with something that is only known to be a BaseDeviate rather than one of the derived classes other than construct it and change the seed, and use it as an argument to pass to other Deviate constructors.:
>>> rng = galsim.BaseDeviate(215324) >>> rng() Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'BaseDeviate' object is not callable >>> ud = galsim.UniformDeviate(rng) >>> ud() 0.58736140513792634 >>> ud2 = galsim.UniformDeviate(215324) >>> ud2() 0.58736140513792634
There are a few methods that are common to all BaseDeviate classes, so we describe them here.
-
dev.seed(seed) Set a new (integer) seed value for the underlying RNG.
-
dev.reset(seed) Sever the connection to the current RNG and seed a new one (either
creating a new RNG if seed is an integer or connecting to an existing RNG if seed is a BaseDeviate instance)
-
dev.clearCache() Clear the internal cache of the Deviate, if there is any.
-
dev.duplicate() Create a duplicate of the current Deviate, which will produce an
identical series of values as the original.
-
add_generate
(array)[source]¶ Generate many pseudo-random values, adding them to the values of a numpy array.
-
clearCache
()[source]¶ Clear the internal cache of the Deviate, if any. This is currently only relevant for GaussianDeviate, since it generates two values at a time, saving the second one to use for the next output value.
-
duplicate
()[source]¶ Create a duplicate of the current Deviate object. The subsequent series from each copy of the Deviate will produce identical values.
Example
>>> u = galsim.UniformDeviate(31415926) >>> u() 0.17100770119577646 >>> u2 = u.duplicate() >>> u() 0.49095047544687986 >>> u() 0.10306670609861612 >>> u2() 0.49095047544687986 >>> u2() 0.10306670609861612 >>> u2() 0.13129289541393518 >>> u() 0.13129289541393518
-
generate
(array)[source]¶ Generate many pseudo-random values, filling in the values of a numpy array.
-
raw
()[source]¶ Generate the next pseudo-random number and rather than return the appropriate kind of random deviate for this class, just return the raw integer value that would have been used to generate this value.
-
reset
(seed=None)[source]¶ Reset the pseudo-random number generator, severing connections to any other deviates. Providing another BaseDeviate object as the seed connects this deviate with the other one, so they will both use the same underlying random number generator.
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using None means to generate a seed from the system. [default: None]
-
class
galsim.
UniformDeviate
(seed=None)[source]¶ Pseudo-random number generator with uniform distribution in interval [0.,1.).
Successive calls to u() generate pseudo-random values distributed uniformly in the interval [0., 1.).:
>>> u = galsim.UniformDeviate(31415926) >>> u() 0.17100770119577646 >>> u() 0.49095047544687986
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using 0 means to generate a seed from the system. [default: None]
-
class
galsim.
GaussianDeviate
(seed=None, mean=0.0, sigma=1.0)[source]¶ Pseudo-random number generator with Gaussian distribution.
See http://en.wikipedia.org/wiki/Gaussian_distribution for further details.
Successive calls to g() generate pseudo-random values distributed according to a Gaussian distribution with the provided
mean
,sigma
.:>>> g = galsim.GaussianDeviate(31415926) >>> g() 0.5533754000847082 >>> g() 1.0218588970190354
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using 0 means to generate a seed from the system. [default: None]
mean – Mean of Gaussian distribution. [default: 0.]
sigma – Sigma of Gaussian distribution. [default: 1.; Must be > 0]
-
class
galsim.
PoissonDeviate
(seed=None, mean=1.0)[source]¶ Pseudo-random Poisson deviate with specified
mean
.The input
mean
sets the mean and variance of the Poisson deviate. An integer deviate with this distribution is returned after each call. See http://en.wikipedia.org/wiki/Poisson_distribution for more details.Successive calls to p() generate pseudo-random integer values distributed according to a Poisson distribution with the specified
mean
.:>>> p = galsim.PoissonDeviate(31415926, mean=100) >>> p() 94 >>> p() 106
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using 0 means to generate a seed from the system. [default: None]
mean – Mean of the distribution. [default: 1; Must be > 0]
-
class
galsim.
BinomialDeviate
(seed=None, N=1, p=0.5)[source]¶ Pseudo-random Binomial deviate for
N
trials each of probabilityp
.N
is number of ‘coin flips,’p
is probability of ‘heads,’ and each call returns an integer value where 0 <= value <= N gives the number of heads. See http://en.wikipedia.org/wiki/Binomial_distribution for more information.Successive calls to b() generate pseudo-random integer values distributed according to a binomial distribution with the provided
N
,p
.:>>> b = galsim.BinomialDeviate(31415926, N=10, p=0.3) >>> b() 2 >>> b() 3
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using 0 means to generate a seed from the system. [default: None]
N – The number of ‘coin flips’ per trial. [default: 1; Must be > 0]
p – The probability of success per coin flip. [default: 0.5; Must be > 0]
-
class
galsim.
Chi2Deviate
(seed=None, n=1.0)[source]¶ Pseudo-random Chi^2-distributed deviate for degrees-of-freedom parameter
n
.See http://en.wikipedia.org/wiki/Chi-squared_distribution (note that k=n in the notation adopted in the Boost.Random routine called by this class). The Chi^2 distribution is a real-valued distribution producing deviates >= 0.
Successive calls to chi2() generate pseudo-random values distributed according to a chi-square distribution with the specified degrees of freedom,
n
.:>>> chi2 = galsim.Chi2Deviate(31415926, n=7) >>> chi2() 7.9182211987712385 >>> chi2() 6.644121724269535
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using 0 means to generate a seed from the system. [default: None]
n – Number of degrees of freedom for the output distribution. [default: 1; Must be > 0]
-
class
galsim.
GammaDeviate
(seed=None, k=1.0, theta=1.0)[source]¶ A Gamma-distributed deviate with shape parameter
k
and scale parametertheta
. See http://en.wikipedia.org/wiki/Gamma_distribution. (Note: we use the k, theta notation. If you prefer alpha, beta, use k=alpha, theta=1/beta.) The Gamma distribution is a real valued distribution producing deviates >= 0.Successive calls to p() generate pseudo-random values distributed according to a gamma distribution with the specified shape and scale parameters
k
andtheta
.:>>> gam = galsim.GammaDeviate(31415926, k=1, theta=2) >>> gam() 0.37508882726316 >>> gam() 1.3504199388358704
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using 0 means to generate a seed from the system. [default: None]
k – Shape parameter of the distribution. [default: 1; Must be > 0]
theta – Scale parameter of the distribution. [default: 1; Must be > 0]
-
class
galsim.
WeibullDeviate
(seed=None, a=1.0, b=1.0)[source]¶ Pseudo-random Weibull-distributed deviate for shape parameter
a
and scale parameterb
.The Weibull distribution is related to a number of other probability distributions; in particular, it interpolates between the exponential distribution (a=1) and the Rayleigh distribution (a=2). See http://en.wikipedia.org/wiki/Weibull_distribution (a=k and b=lambda in the notation adopted in the Wikipedia article) for more details. The Weibull distribution is real valued and produces deviates >= 0.
Successive calls to p() generate pseudo-random values distributed according to a Weibull distribution with the specified shape and scale parameters
a
andb
.:>>> w = galsim.WeibullDeviate(31415926, a=1.3, b=4) >>> w() 1.1038481241018219 >>> w() 2.957052966368049
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using 0 means to generate a seed from the system. [default: None]
a – Shape parameter of the distribution. [default: 1; Must be > 0]
b – Scale parameter of the distribution. [default: 1; Must be > 0]
-
class
galsim.
DistDeviate
(seed=None, function=None, x_min=None, x_max=None, interpolant=None, npoints=None)[source]¶ A class to draw random numbers from a user-defined probability distribution.
DistDeviate is a BaseDeviate class that can be used to draw from an arbitrary probability distribution. The probability distribution passed to DistDeviate can be given one of three ways: as the name of a file containing a 2d ASCII array of x and P(x), as a LookupTable mapping x to P(x), or as a callable function.
Once given a probability, DistDeviate creates a table of the cumulative probability and draws from it using a UniformDeviate. The precision of its outputs can be controlled with the keyword
npoints
, which sets the number of points DistDeviate creates for its internal table of CDF(x). To prevent errors due to non-monotonicity, the interpolant for this internal table is always linear.Two keywords,
x_min
andx_max
, define the support of the function. They must be passed if a callable function is given to DistDeviate, unless the function is a LookupTable, which has its own defined endpoints. If a filename or LookupTable is passed to DistDeviate, the use ofx_min
orx_max
will result in an error.If given a table in a file, DistDeviate will construct an interpolated LookupTable to obtain more finely gridded probabilities for generating the cumulative probability table. The default
interpolant
is linear, but any interpolant understood by LookupTable may be used. We caution against the use of splines because they can cause non-monotonic behavior. Passing theinterpolant
keyword next to anything but a table in a file will result in an error.Examples:
Some sample initialization calls:
>>> d = galsim.DistDeviate(function=f, x_min=x_min, x_max=x_max)
Initializes d to be a DistDeviate instance with a distribution given by the callable function
f(x)
fromx=x_min
tox=x_max
and seeds the PRNG using current time.:>>> d = galsim.DistDeviate(1062533, function=file_name, interpolant='floor')
Initializes d to be a DistDeviate instance with a distribution given by the data in file
file_name
, which must be a 2-column ASCII table, and seeds the PRNG using the integer seed 1062533. It generates probabilities fromfile_name
using the interpolant ‘floor’.:>>> d = galsim.DistDeviate(rng, function=galsim.LookupTable(x,p))
Initializes d to be a DistDeviate instance with a distribution given by P(x), defined as two arrays
x
andp
which are used to make a callable LookupTable, and links the DistDeviate PRNG to the already-existing random number generatorrng
.Successive calls to d() generate pseudo-random values with the given probability distribution.:
>>> d = galsim.DistDeviate(31415926, function=lambda x: 1-abs(x), x_min=-1, x_max=1) >>> d() -0.4151921102709466 >>> d() -0.00909781188974034
- Parameters
seed – Something that can seed a BaseDeviate: an integer seed or another BaseDeviate. Using 0 means to generate a seed from the system. [default: None]
function – A callable function giving a probability distribution or the name of a file containing a probability distribution as a 2-column ASCII table. [required]
x_min – The minimum desired return value (required for non-LookupTable callable functions; will raise an error if not passed in that case, or if passed in any other case) [default: None]
x_max – The maximum desired return value (required for non-LookupTable callable functions; will raise an error if not passed in that case, or if passed in any other case) [default: None]
interpolant – Type of interpolation used for interpolating a file (causes an error if passed alongside a callable function). Options are given in the documentation for LookupTable. [default: ‘linear’]
npoints – Number of points DistDeviate should create for its internal interpolation tables. [default: 256, unless the function is a non-log LookupTable, in which case it uses the table’s x values]
-
add_generate
(array)[source]¶ Generate many pseudo-random values, adding them to the values of a numpy array.
-
generate
(array)[source]¶ Generate many pseudo-random values, filling in the values of a numpy array.
-
val
(p)[source]¶ Return the value
x
of the input function to DistDeviate such thatp
= cdf(x), where cdf is the cumulattive probability distribution function:cdf(x) = int(pdf(t), t=0..x)
This function is typically called by self.__call__(), which generates a random p between 0 and 1 and calls
self.val(p)
.- Parameters
p – The desired cumulative probabilty p.
- Returns
the corresponding x such that p = cdf(x).
Noise Generators¶
-
class
galsim.
BaseNoise
(rng=None)[source]¶ Base class for all noise classes.
This class should not be constructed directly. Rather, you should instantiate one of the subclasses:
GaussianNoise PoissonNoise CCDNoise DeviateNoise VariableGaussianNoise
which define what kind of noise you want to implement. This base class mostly just serves as a way to check if an object is a valid noise object with:
>>> isinstance(noise, galsim.BaseNoise)
-
applyTo
(image)[source]¶ Add noise to an input Image.
e.g.:
>>> noise.applyTo(image)
On output the Image instance
image
will have been given additional noise according to the current noise model.Note: This is equivalent to the alternate syntax:
>>> image.addNoise(noise)
which may be more convenient or clearer.
-
withScaledVariance
(variance_ratio)[source]¶ Return a new noise object with the variance scaled up by the specified factor.
This is equivalent to noise * variance_ratio.
- Parameters
variance_ratio – The factor by which to scale the variance of the correlation function profile.
- Returns
a new Noise object whose variance has been scaled by the given amount.
-
-
class
galsim.
GaussianNoise
(rng=None, sigma=1.0)[source]¶ Class implementing simple Gaussian noise.
This is a simple noise model where each pixel in the image gets Gaussian noise with a given
sigma
.Example:
The following will add Gaussian noise to every element of an image:
>>> gaussian_noise = galsim.GaussianNoise(rng, sigma=1.0) >>> image.addNoise(gaussian_noise)
- Parameters
rng – A BaseDeviate instance to use for generating the random numbers.
sigma – The rms noise on each pixel. [default: 1.]
- Attributes
rng – The internal random number generator (read-only)
sigma – The value of the constructor parameter sigma (read-only)
-
class
galsim.
PoissonNoise
(rng=None, sky_level=0.0)[source]¶ Class implementing Poisson noise according to the flux (and sky level) present in the image.
The PoissonNoise class encapsulates a simple version of the noise model of a normal CCD image where each pixel has Poisson noise corresponding to the number of electrons in each pixel (including an optional extra sky level).
Note that if the image to which you are adding noise already has a sky level on it, then you should not provide the sky level here as well. The sky level here corresponds to a level that is taken to be already subtracted from the image, but that originally contributed to the addition of Poisson noise.
Example:
The following will add Poisson noise to every element of an image:
>>> poisson_noise = galsim.PoissonNoise(rng, sky_level=0.) >>> image.addNoise(poisson_noise)
- Parameters
rng – A BaseDeviate instance to use for generating the random numbers.
sky_level – The sky level in electrons per pixel that was originally in the input image, but which is taken to have already been subtracted off. [default: 0.]
- Attributes
rng – The internal random number generator (read-only)
sky_level – The value of the constructor parameter sky_level (read-only)
-
class
galsim.
CCDNoise
(rng=None, sky_level=0.0, gain=1.0, read_noise=0.0)[source]¶ Class implementing a basic CCD noise model.
The CCDNoise class encapsulates the noise model of a normal CCD image. The noise has two components: first, Poisson noise corresponding to the number of electrons in each pixel (including an optional extra sky level); second, Gaussian read noise.
Note that if the image to which you are adding noise already has a sky level on it, then you should not provide the sky level here as well. The sky level here corresponds to a level is taken to be already subtracted from the image, but which was present for the Poisson noise.
The units here are slightly confusing. We try to match the most common way that each of these quantities is usually reported. Note: ADU stands for Analog/Digital Units; they are the units of the numbers in the final image. Some places use the term “counts” for this.
sky_level is normally measured from the image itself, so it is normally quoted in ADU/pixel.
gain is a property of the detector and is normally measured in the laboratory. The units are normally e-/ADU. This is backwards what might be more intuitive, ADU/e-, but that’s how astronomers use the term gain, so we follow suit here.
read_noise is also a property of the detector and is usually quoted in e-/pixel.
If you are manually applying the quantum efficiency of the detector (e-/photon), then this would normally be applied before the noise. However, it is also fine to fold in the quantum efficiency into the gain to give units photons/ADU. Either way is acceptable. Just make sure you give the read noise in photons as well in this case.
Example:
The following will add CCD noise to every element of an image:
>>> ccd_noise = galsim.CCDNoise(rng, sky_level=0., gain=1., read_noise=0.) >>> image.addNoise(ccd_noise)
- Parameters
rng – A BaseDeviate instance to use for generating the random numbers.
sky_level – The sky level in ADU per pixel that was originally in the input image, but which is taken to have already been subtracted off. [default: 0.]
gain – The gain for each pixel in electrons per ADU; setting
gain<=0
will shut off the Poisson noise, and the Gaussian rms will take the valueread_noise
as being in units of ADU rather than electrons. [default: 1.]read_noise – The read noise on each pixel in electrons (gain > 0.) or ADU (gain <= 0.).
- Attributes
rng – The internal random number generator (read-only)
sky_level – The value of the constructor parameter sky_level (read-only)
gain – The value of the constructor parameter gain (read-only)
read_noise – The value of the constructor parameter read_noise (read-only)
-
class
galsim.
DeviateNoise
(dev)[source]¶ Class implementing noise with an arbitrary BaseDeviate object.
The DeviateNoise class provides a way to treat an arbitrary deviate as the noise model for each pixel in an image.
The following will add noise according to a given random deviate to every element of an image:
>>> dev_noise = galsim.DeviateNoise(dev) >>> image.addNoise(dev_noise)
- Parameters
dev – A BaseDeviate subclass to use as the noise deviate for each pixel.
- Attributes
rng – The internal random number generator (read-only)
-
class
galsim.
VariableGaussianNoise
(rng, var_image)[source]¶ Class implementing Gaussian noise that has a different variance in each pixel.
The following will add variable Gaussian noise to every element of an image:
>>> variable_noise = galsim.VariableGaussianNoise(rng, var_image) >>> image.addNoise(variable_noise)
- Parameters
rng – A BaseDeviate instance to use for generating the random numbers.
var_image – The variance of the noise to apply to each pixel. This image must be the same shape as the image for which you eventually call addNoise().
- Attributes
rng – The internal random number generator (read-only)
var_image – The value of the constructor parameter var_image (read-only)
-
applyTo
(image)[source]¶ Add noise to an input Image.
e.g.:
>>> noise.applyTo(image)
On output the Image instance
image
will have been given additional noise according to the current noise model.Note: This is equivalent to the alternate syntax:
>>> image.addNoise(noise)
which may be more convenient or clearer.


To make an angular component draggable, use the [ngxDraggable] directive
<my-cmp [ngxDraggable]="true">Drag me!</my-cmp>
Use [ngxDraggedClass] to attach css classes while drag is in progress
<my-cmp [ngxDraggable]="true"
[ngxDraggedClass]="'draggable'">Drag me!</my-cmp>


Introductory example that shows how you can disable emitting any form actions to the global store via the builtin
ActionsSubject.
Instead, you can listen for actions using the directive's output EventEmitter and route them to the component.
The component holds, updates and maintains a local form state, which does not taint the global store and
is automatically cleaned up once the component is destroyed.


STOP✋
Tangram Differ
Compare this .json test file or .yaml scene file:
Using this Tangram library:
(look for prerendered images)
to this this .json test file or .yaml scene file:
Using this Tangram library:
(look for prerendered images)
GO👍
STOP✋
Export contact sheet
Export info json
Save new images


--- layout: post title: Last Minute Sewing - Birthday Skirt date: 2012-11-21 08:47:46.000000000 -05:00 categories: - Craft tags: - Easy sewing - Last minute Craft - Quick skirt status: publish type: post published: true meta: aktt_notify_twitter: 'no' _edit_last: '1' dsq_thread_id: '1188576098' author: login: cint email<EMAIL_ADDRESS>display_name: Jacinta first_name: Jacinta last_name: '' ---
Tenneille had a birthday to go to recently and of course, in my usual fashion, had avoided the shops all week. I thought I had found time to make the top from a version of this dress pattern I had used for Tenneille's birthday dress earlier in the year, but of course mothering duties called and it got to an hour before the party before I could get near my sewing machine. This called for some quick thinking and I decided I could put a skirt together, without a pattern as such, in an hour, ahem! Ok, so it took me an hour and a half. Thankfully Greg took Tenneille to the party and I picked her up and delivered the present, talk about cutting it fine!
 Jacinta x


The dnd
plugin enables drag'n'drop support for jstree, also using foreign nodes and drop targets.
The special key used to make a drag copy instead of move ("ctrl"
, "shift"
, "alt"
, "meta"
).
The number of milliseconds to wait before checking if a move is valid upon hovering a node (while dragging). 200
is a reasonable value - a higher number means better performance but slow feedback to the user, a lower number means lower performance (possibly) but the user will get feedback faster.
The number of milliseconds to wait before opening a hovered if it has children (while dragging). This means that the user has to stop over the node for half a second in order to trigger the open operation. Keep in mind that a low value in combination with async data could mean a lot of unneeded traffic, so 500
is quite reasonable.
A jquery selector matching all drop targets (you can also use the comma ,
in the string to specify multiple valid targets). If set to false
drop targets are disabled.
Return false
to mark the move as invalid, otherwise return true
. The data
parameter is as follows:
data.o
- the object being dragged
data.r
- the drop target
Gets executed after a valid drop, you get one parameter, which is as follows:
data.o
- the object being dragged
data.r
- the drop target
A jquery selector matching all foreign nodes that can be dropped on the tree (you can also use the comma ,
in the string to specify multiple valid foreign nodes). If set to false
dragging foreign nodes is disabled.
Return a boolean for each position. The data
parameter is as follows:
data.o
- the foreign object being dragged
data.r
- the hovered node
Gets executed after a dropping a foreign element on a tree item, you get one parameter, which is as follows:
data.o
- the foreign object being dragged
data.r
- the target node
Drag stuff around!
All those functions are used internally only. If you want more information - examine the source code.


Class: Aws::AutoScaling::Types::PredictiveScalingMetricSpecification
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-autoscaling/lib/aws-sdk-autoscaling/types.rb |
Overview
This structure specifies the metrics and target utilization settings for a predictive scaling policy.
You must specify either a metric pair, or a load metric and a scaling metric individually. Specifying a metric pair instead of individual metrics provides a simpler way to configure metrics for a scaling policy. You choose the metric pair, and the policy automatically knows the correct sum and average statistics to use for the load metric and the scaling metric.
Example
You create a predictive scaling policy and specify
ALBRequestCount
as the value for the metric pair and1000.0
as the target value. For this type of metric, you must provide the metric dimension for the corresponding target group, so you also provide a resource label for the Application Load Balancer target group that is attached to your Auto Scaling group.The number of requests the target group receives per minute provides the load metric, and the request count averaged between the members of the target group provides the scaling metric. In CloudWatch, this refers to the
RequestCount
andRequestCountPerTarget
metrics, respectively.For optimal use of predictive scaling, you adhere to the best practice of using a dynamic scaling policy to automatically scale between the minimum capacity and maximum capacity in response to real-time changes in resource utilization.
Amazon EC2 Auto Scaling consumes data points for the load metric over the last 14 days and creates an hourly load forecast for predictive scaling. (A minimum of 24 hours of data is required.)
After creating the load forecast, Amazon EC2 Auto Scaling determines when to reduce or increase the capacity of your Auto Scaling group in each hour of the forecast period so that the average number of requests received by each instance is as close to 1000 requests per minute as possible at all times.
For information about using custom metrics with predictive scaling,
see Advanced predictive scaling policy configurations using custom
metrics in the Amazon ::Aws::EC2
Auto Scaling User Guide.
Constant Summary
Instance Attribute Summary
-
#customized_capacity_metric_specification ⇒ Types::PredictiveScalingCustomizedCapacityMetric
rw
The customized capacity metric specification.
-
#customized_load_metric_specification ⇒ Types::PredictiveScalingCustomizedLoadMetric
rw
The customized load metric specification.
-
#customized_scaling_metric_specification ⇒ Types::PredictiveScalingCustomizedScalingMetric
rw
The customized scaling metric specification.
-
#predefined_load_metric_specification ⇒ Types::PredictiveScalingPredefinedLoadMetric
rw
The predefined load metric specification.
-
#predefined_metric_pair_specification ⇒ Types::PredictiveScalingPredefinedMetricPair
rw
The predefined metric pair specification from which Amazon EC2 Auto Scaling determines the appropriate scaling metric and load metric to use.
-
#predefined_scaling_metric_specification ⇒ Types::PredictiveScalingPredefinedScalingMetric
rw
The predefined scaling metric specification.
-
#target_value ⇒ Float
rw
Specifies the target utilization.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Structure#to_h. |
Instance Attribute Details
#customized_capacity_metric_specification ⇒ Types::PredictiveScalingCustomizedCapacityMetric (rw)
The customized capacity metric specification.
# File 'gems/aws-sdk-autoscaling/lib/aws-sdk-autoscaling/types.rb', line 5467
class PredictiveScalingMetricSpecification < Struct.new( :target_value, :predefined_metric_pair_specification, :predefined_scaling_metric_specification, :predefined_load_metric_specification, :customized_scaling_metric_specification, :customized_load_metric_specification, :customized_capacity_metric_specification) SENSITIVE = [] include Aws::Structure end
#customized_load_metric_specification ⇒ Types::PredictiveScalingCustomizedLoadMetric (rw)
The customized load metric specification.
# File 'gems/aws-sdk-autoscaling/lib/aws-sdk-autoscaling/types.rb', line 5467
class PredictiveScalingMetricSpecification < Struct.new( :target_value, :predefined_metric_pair_specification, :predefined_scaling_metric_specification, :predefined_load_metric_specification, :customized_scaling_metric_specification, :customized_load_metric_specification, :customized_capacity_metric_specification) SENSITIVE = [] include Aws::Structure end
#customized_scaling_metric_specification ⇒ Types::PredictiveScalingCustomizedScalingMetric (rw)
The customized scaling metric specification.
# File 'gems/aws-sdk-autoscaling/lib/aws-sdk-autoscaling/types.rb', line 5467
class PredictiveScalingMetricSpecification < Struct.new( :target_value, :predefined_metric_pair_specification, :predefined_scaling_metric_specification, :predefined_load_metric_specification, :customized_scaling_metric_specification, :customized_load_metric_specification, :customized_capacity_metric_specification) SENSITIVE = [] include Aws::Structure end
#predefined_load_metric_specification ⇒ Types::PredictiveScalingPredefinedLoadMetric (rw)
The predefined load metric specification.
# File 'gems/aws-sdk-autoscaling/lib/aws-sdk-autoscaling/types.rb', line 5467
class PredictiveScalingMetricSpecification < Struct.new( :target_value, :predefined_metric_pair_specification, :predefined_scaling_metric_specification, :predefined_load_metric_specification, :customized_scaling_metric_specification, :customized_load_metric_specification, :customized_capacity_metric_specification) SENSITIVE = [] include Aws::Structure end
#predefined_metric_pair_specification ⇒ Types::PredictiveScalingPredefinedMetricPair (rw)
The predefined metric pair specification from which Amazon EC2 Auto Scaling determines the appropriate scaling metric and load metric to use.
# File 'gems/aws-sdk-autoscaling/lib/aws-sdk-autoscaling/types.rb', line 5467
class PredictiveScalingMetricSpecification < Struct.new( :target_value, :predefined_metric_pair_specification, :predefined_scaling_metric_specification, :predefined_load_metric_specification, :customized_scaling_metric_specification, :customized_load_metric_specification, :customized_capacity_metric_specification) SENSITIVE = [] include Aws::Structure end
#predefined_scaling_metric_specification ⇒ Types::PredictiveScalingPredefinedScalingMetric (rw)
The predefined scaling metric specification.
# File 'gems/aws-sdk-autoscaling/lib/aws-sdk-autoscaling/types.rb', line 5467
class PredictiveScalingMetricSpecification < Struct.new( :target_value, :predefined_metric_pair_specification, :predefined_scaling_metric_specification, :predefined_load_metric_specification, :customized_scaling_metric_specification, :customized_load_metric_specification, :customized_capacity_metric_specification) SENSITIVE = [] include Aws::Structure end
#target_value ⇒ Float
(rw)
Specifies the target utilization.
::Aws::SQS
queue. If the scaling policy specifies one of
these metrics, specify the target utilization as the optimal average
request or message count per instance during any one-minute
interval.
# File 'gems/aws-sdk-autoscaling/lib/aws-sdk-autoscaling/types.rb', line 5467
class PredictiveScalingMetricSpecification < Struct.new( :target_value, :predefined_metric_pair_specification, :predefined_scaling_metric_specification, :predefined_load_metric_specification, :customized_scaling_metric_specification, :customized_load_metric_specification, :customized_capacity_metric_specification) SENSITIVE = [] include Aws::Structure end


Get all triples contained by the graph.
The behaviour of the Stream is not specified if add, remove, or clear,
are called on the Stream before it terminates.
Implementations may throw ConcurrentModificationException from Stream
methods if they detect a conflict while the Stream is active.


#include <polyclip.h>
Public Member Functions | |
| Point () | |
| Default constructor. | |
| Point (Fixed _x, Fixed _y) | |
| Constructor with given X and Y coordinates values. | |
Public Attributes | |
| Fixed | x |
| X coordinate. | |
| Fixed | y |
| Y coordinate. |
|
|
Constructor with given X and Y coordinates values.
|


GSF is based on mimetypes. Each GSF language plugin is associated
with a particular "mime type". For example, Ruby files have the
mime type text/x-ruby
, JavaScript files have the mime
type text/javascript
, and so on.
In order for GSF to add its editing services to particular files, you have to teach the IDE how to recognize the mime type for your files. You do that using the "mime resolver" support in NetBeans. This is not particular to GSF, but it's a prerequisite.
Basically, all you have to do is write a simple XML file which
tells NetBeans for example that files with the extension .foo
have the mimetype text/foo
. Once you've done that,
you can go to the registration
section and register editing services for the text/foo
mime type. GSF will provide DataLoaders, DataObjects etc. for
files of the mimetypes as soon as you do that.
When you create a new NetBeans plugin, it will typically create an empty layer.xml file for you. This is an XML file where you can register implementations of various IDE services. In particular, to register a mime resolver, you'll need to add something like this:
<filesystem> <folder name="Services"> <folder name="MIMEResolver"> <file name="javascript.xml" url="jsresolver.xml"> <attr name="SystemFileSystem.localizingBundle" stringvalue="org.netbeans.modules.javascript.editing.Bundle"/> <attr name="position" intvalue="190"/> </file> </folder> </folder> <filesystem>Here, the file name
javascript.xml
should be something unique that no other
mime resolver might have picked. The url
attribute points to a filename
in the same directory as the layer.xml file. I'll cover that file next.
Finally, the attributes let you create a properly localized user visible description
of files of this file type.
For the example above, you would create a Bundle.properties
file in the same
package as the layer file (again, when you create a plugin you usually get one of these
created for you already), and it should contain something like this:
Services/MIMEResolver/javascript.xml=JavaScript Files
Finally, you need to go and write the actual mime resolver file, jsresolver.xml
(we could have named it anything, including javascript.xml
).
<!DOCTYPE MIME-resolver PUBLIC "-//NetBeans//DTD MIME Resolver 1.0//EN" "http://www.netbeans.org/dtds/mime-resolver-1_0.dtd"> <MIME-resolver> <file> <ext name="json"/> <ext name="js"/> <resolver mime="text/javascript"/> </file> </MIME-resolver>Here we're saying that files of extensions
json
and js
should
be treated as JavaScript files. The DTD for mime resolvers allow more complex
analysis, such as analyzing file headers and such. Take a look
at the DTD for
the full details. You can also write a custom class to do mime resolvers
if you have really specific needs. Avoid doing that if you can, since with a custom
mime resolver the IDE can't figure out what you're doing and do special optimizations.
There is an example of how to do this in the ruby editing module - look for RubyMimeResolver
- but
again try to avoid this if you don't strictly have to do it.
Finally, note that there is a position attribute on the registration of the mime resolver above. The position attributes are used to order the mime resolvers. In case there are overlaps in registrations, order matters. For that reason, pick some number. NetBeans integration unit tests will check all the layers and make sure there aren't inconsistencies into folder orderings and will generate unit test failures if there are.
Once you register your mime type with GSF (described in the registration document, files shown in the Files and Projects views will be handled by GSF. You can also assign a custom icon to file as well as context menu actions. To do this, you need a few more registration steps. Add something like the following to your layer:
<folder name="Loaders"> <folder name="text"> <folder name="javascript"> <attr name="SystemFileSystem.icon" urlvalue="nbresloc:/org/netbeans/modules/javascript/editing/javascript.png"/> <attr name="iconBase" stringvalue="org/netbeans/modules/javascript/editing/javascript.png"/> <folder name="Actions"> <file name="OpenAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.OpenAction"/> <attr name="position" intvalue="100"/> </file> <file name="Separator1.instance"> <attr name="instanceClass" stringvalue="javax.swing.JSeparator"/> <attr name="position" intvalue="200"/> </file> <file name="CutAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.CutAction"/> <attr name="position" intvalue="300"/> </file> <file name="CopyAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.CopyAction"/> <attr name="position" intvalue="400"/> </file> <file name="PasteAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.PasteAction"/> <attr name="position" intvalue="500"/> </file> <file name="Separator2.instance"> <attr name="instanceClass" stringvalue="javax.swing.JSeparator"/> <attr name="position" intvalue="600"/> </file> <file name="NewAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.NewAction"/> <attr name="position" intvalue="700"/> </file> <file name="DeleteAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.DeleteAction"/> <attr name="position" intvalue="800"/> </file> <file name="RenameAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.RenameAction"/> <attr name="position" intvalue="900"/> </file> <file name="Separator3.instance"> <attr name="instanceClass" stringvalue="javax.swing.JSeparator"/> <attr name="position" intvalue="1000"/> </file> <file name="SaveAsTemplateAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.SaveAsTemplateAction"/> <attr name="position" intvalue="1100"/> </file> <file name="Separator4.instance"> <attr name="instanceClass" stringvalue="javax.swing.JSeparator"/> <attr name="position" intvalue="1200"/> </file> <file name="FileSystemAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.FileSystemAction"/> <attr name="position" intvalue="1300"/> </file> <file name="Separator5.instance"> <attr name="instanceClass" stringvalue="javax.swing.JSeparator"/> <attr name="position" intvalue="1400"/> </file> <file name="ToolsAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.ToolsAction"/> <attr name="position" intvalue="1500"/> </file> <file name="PropertiesAction.instance"> <attr name="instanceClass" stringvalue="org.openide.actions.PropertiesAction"/> <attr name="position" intvalue="1600"/> </file> </folder> </folder> </folder> </folder>Here, the highlighted section shows how an icon is assigned to this type. You need to place an icon in the directory shown in the attribute. The rest of this section registers various common actions you might want on your files. These are all standard actions defined by NetBeans so you don't have to write these on your own.


The Hypertext Transfer Protocol (HTTP) is an application layer protocol for distributed, collaborative, hypermedia information systems. HTTP is the foundation of data communication for the World Wide Web, where hypertext documents include hyperlinks to other resources that the user can easily access, for example by a mouse click or by tapping the screen in a web browser.. HTTP uses Hypertext structured text which establishes the logical link between nodes containing text. It is also known as "stateless protocol" as each command is executed separately, without using reference of previous run command.
Whereas, HTTPS uses the port no. 443 for Data Communication. It allows the secure transactions by encrypting the entire communication with SSL. It is a combination of SSL/TLS protocol and HTTP. It provides encrypted and secure identification of a network server.
In HTTPS protocol, SSL transactions are negotiated with the help of key-based encryption algorithm. This key is generally either 40 or 128 bits in strength.
(Image Credits: https://vpnclientapp.com/)
(Image Credits: https://code.tutplus.com/)
Types of SSL/TLS certificate used with HTTPS:
Key Differences between HTTP and HTTPS are:
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We provide best deals and offers on the latest products. We offer coupon codes on various products where the customers can view the coupon code from our websites and apply when they want to buy the products. The website offers customers to have a promocode that are valid, verified and trusted by the authorities. The website offers deals and promocodes on food, clothing. groceries and appliances where the common people rely on. It attracts the users to buy more and more by using the promocodes where they can get more discounts on the products. We value our users to have an easy interface with the website.
Mission and VIssion
At BestBuy, we help you save money through our comprehensive listing of coupons, offers, deals and discounts from top online brands and websites. Our aim is to create a generation of smart shoppers by empowering customers to make the best possible choices. We sweeten everyday deals and makes online shopping more rewarding.
Our Objective
Help shoppers make the most informed shopping decisions
With lakhs of shoppers from all over India rating and discussing every deal; suggesting better bargains; and exchanging expert shopping advice, tips and tricks; the BestBuy forums aim to help every shopper find the right things, at the right price and right time
About us
Mission and VIssion
Our Objective


{% extends 'vesper/base.html' %} {% block head %}
Adds the specified tag to the specified clips.
The clips are specified as a set of station/mic pairs, a range of dates, a set of detectors, an optional classification, and a tag. Only clips that do not already have the specified tag are considered for tagging.
You can optionally specify the number of clips to tag in the
Clip count
field. The clips to tag are selected at
random from those specified, with all clips equally likely to be
selected.


QFITS Reference Manual Data Structures
Here are the data structures with brief descriptions:
_keytuple_
_qfits_cache_cell_


Good for defense against players and a some monsters you're going to face. And it can be upgraded, which is great. The only flaw is not having a card slot.
- -10% damage from DemiHuman race
- Defense: 1
- Magick Defense: 1
- Weight: 60
This is a full support focused on reduced cast time and delay. I really hate slow cast time and love to tank mobs. This is not intended for WoE or PvP but might have some fun there anyways. This guy is an all-round slave that fits my own style of playing, and is not interested in killing with his own hands, leaving this job for others.
Good for defense against players and a some monsters you're going to face. And it can be upgraded, which is great. The only flaw is not having a card slot.
Using this only for the extra DEX. Might change for something with slot or that increases resistance.
Cute accessory that improves resistance against DemiHuman.
Card: Pecopeco Card
From the Sprint Set(*), this helps decrease Delay - yet to be tested to verify its effectiveness.
Yes, a BGM weapon (100 Valor Badges from Flavius BG), just for its reduced delay.
Card: Thara Frog Card
From the Orleans Set(*), that decreases cast time.
Card: Raydric Card
Gotta catch 'em all. The drops, that is. Gotta make some money, babe. Not +7 or +9. YET!
Card: Verit Card
From the Sprint Set(*), this helps decrease Delay - yet to be tested to verify its effectiveness.
Card: Zerom Card
From the Orleans Set(*), that decreases cast time and increases DEX. I might get a Orlean's Glove with Phen Card - if this combo and the Sprint combo is really worth it.
From the Sprint Set(*), this helps decrease Delay - yet to be tested to verify its effectiveness.
Your main skill for sure. Use it to heal your allies and yourself. Or use it to kill Undead monsters.
A must-have buff. It increases +12 AGI (attack speed and flee) and movement speed.
Use this skill to heal from special conditions: Blind, Confusion, Silence.
Reduces damage from Undead and Demon races based on Skill Level and Base Level. Not necessary for a support build but to fill the requirements for Blessing and Angelus.
A great buff that increases +10 STR, DEX and INT. It also heals from Curse condition.
Reveals hiding and cloacking players and monsters in the skill's range. It lands a holy magic damage on those it revealed.
Teleport to a random place on the map or back to your save point. Great to save some money, and a requirement to get Warp Portal and Pneuma.
Open a portal at the selected cell to a memorized map or to your save point. It remains opened for the skill duration or until you enter it, and while it is open, anyone can go into it. You can memorize a warping spot using the
/memocommand. You can memorize up to 3 spots, the fourth will erase the first one.
A great buff that increases DEF from VIT of all party members on your screen.
Requirement for Aspersio. Create Holy Water when standing inside water. This item heals from Curse.


Deploys an application revision through the specified deployment group.
See also: AWS API Documentation
create-deployment
--application-name <value>
[--deployment-group-name <value>]
[--revision <value>]
[--deployment-config-name <value>]
[--description <value>]
[--ignore-application-stop-failures | --no-ignore-application-stop-failures]
[--target-instances <value>]
[--auto-rollback-configuration <value>]
[--update-outdated-instances-only | --no-update-outdated-instances-only]
[--s3-location <value>]
[--github-location <value>]
[--cli-input-json <value>]
[--generate-cli-skeleton <value>]
The name of an AWS CodeDeploy application associated with the applicable IAM user or AWS account.
--deployment-group-name (string)
The name of the deployment group.
The type and location of the revision to deploy.
Shorthand Syntax:
revisionType=string,s3Location={bucket=string,key=string,bundleType=string,version=string,eTag=string},gitHubLocation={repository=string,commitId=string}
JSON Syntax:
{
"revisionType": "S3"|"GitHub",
"s3Location": {
"bucket": "string",
"key": "string",
"bundleType": "tar"|"tgz"|"zip",
"version": "string",
"eTag": "string"
},
"gitHubLocation": {
"repository": "string",
"commitId": "string"
}
}
--deployment-config-name (string)
The name of a deployment configuration associated with the applicable IAM user or AWS account.
If not specified, the value configured in the deployment group will be used as the default. If the deployment group does not have a deployment configuration associated with it, then CodeDeployDefault.OneAtATime will be used by default.
A comment about the deployment.
--ignore-application-stop-failures | --no-ignore-application-stop-failures (boolean)
If set to true, then if the deployment causes the ApplicationStop deployment lifecycle event to an instance to fail, the deployment to that instance will not be considered to have failed at that point and will continue on to the BeforeInstall deployment lifecycle event.
If set to false or not specified, then if the deployment causes the ApplicationStop deployment lifecycle event to fail to an instance, the deployment to that instance will stop, and the deployment to that instance will be considered to have failed.
--target-instances (structure)
Information about the instances that will belong to the replacement environment in a blue/green deployment.
Shorthand Syntax:
tagFilters=[{Key=string,Value=string,Type=string},{Key=string,Value=string,Type=string}],autoScalingGroups=string,string
JSON Syntax:
{
"tagFilters": [
{
"Key": "string",
"Value": "string",
"Type": "KEY_ONLY"|"VALUE_ONLY"|"KEY_AND_VALUE"
}
...
],
"autoScalingGroups": ["string", ...]
}
--auto-rollback-configuration (structure)
Configuration information for an automatic rollback that is added when a deployment is created.
Shorthand Syntax:
enabled=boolean,events=string,string
JSON Syntax:
{
"enabled": true|false,
"events": ["DEPLOYMENT_FAILURE"|"DEPLOYMENT_STOP_ON_ALARM"|"DEPLOYMENT_STOP_ON_REQUEST", ...]
}
--update-outdated-instances-only | --no-update-outdated-instances-only (boolean)
Indicates whether to deploy to all instances or only to instances that are not running the latest application revision.
--s3-location (structure) Information about the location of the application revision in Amazon S3. You must specify the bucket, the key, and bundleType. Optionally, you can also specify an eTag and version.
Shorthand Syntax:
bundleType=string,eTag=string,bucket=string,version=string,key=string
JSON Syntax:
{
"bundleType": "tar"|"tgz"|"zip",
"eTag": "string",
"bucket": "string",
"version": "string",
"key": "string"
}
--github-location (structure) Information about the location of the application revision in GitHub. You must specify the repository and commit ID that references the application revision. For the repository, use the format GitHub-account/repository-name or GitHub-org/repository-name. For the commit ID, use the SHA1 Git commit reference.
Shorthand Syntax:
commitId=string,repository=string
JSON Syntax:
{
"commitId": "string",
"repository": "string"
}
--cli-input-json (string) Performs service operation based on the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, the CLI values will override the JSON-provided values.
--generate-cli-skeleton (string) Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command.
To create a deployment
This example creates a deployment and associates it with the user's AWS account.
Command:
aws deploy create-deployment --application-name WordPress_App --deployment-config-name CodeDeployDefault.OneAtATime --deployment-group-name WordPress_DG --description "My demo deployment" --s3-location bucket=CodeDeployDemoBucket,bundleType=zip,eTag=dd56cfd59d434b8e768f9d77fEXAMPLE,key=WordPressApp.zip
Output:
{
"deploymentId": "d-N65YI7Gex"
}


Test Application with Materialize and JQuery
Hello! Welcome to test application
Test Upload
Page Setting Test
This is testing setting button
Please wait a moment while we load the awesome stuffs


Lifting the Lou’s Big Hairy Audacious Goal (BHAG) is to see the entire city of St. Louis become one big, integrated “Island of Prosperity”.
We welcome you to join us to make that BHAG into a reality.
Safe and Vibrant
Neighborhoods
Every St. Louis neighborhood is safe to live in and vibrant in that it has viable/usable housing options and the local services residents need (grocery, medical, banking, and other businesses) to live there.
Educate
Every Child
Every child has access to good education from K through 12 and is adequately educated and equipped to fully participate in our 21st-century economy when they graduate high school (either through going on to college or entering a trade).
Housing
the Unhoused
All of our unhoused/homeless have access to housing and an opportunity to get back into the workforce.
2nd Chances to Men
in the Justice System
Adult men who are or have been involved with the justice system have access to programs that allow them to choose a new non-criminal path and empowerment options to support them in staying on their new non-criminal path.


Black Marketeer of Mammon:
I thank you for what you did for me. If I can be of any service to you, please come and see me.