Video: ‘Gervinho 2.0’ – These fans blast Arsenal’s £72m Pepe after error vs Southampton
by Jaikuran RandhawaArsenal star Nicolas Pepe has been labelled as ‘Gervinho 2.0’ by some football fans on social media after a clip emerged of a slight blunder from him during today’s 2-2 draw with Southampton.
The 24-year-old was through on goal but decided to cut the ball back towards Mesut Ozil, the ace’s pass was blocked by Southampton’s defender.
Unfortunately, the attacker’s unselfishness has left him open to criticism from fans of some of Arsenal’s rivals on social media. The Ivorian was compared to his fellow countryman Gervinho who had a mixed spell with the Gunners.
As per BBC Sport, the Gunners paid a hefty club-record fee of £72m to bring the forward to the Emirates.
Take a look at the Ivorian’s unfortunate moment below:
Here’s how some fans reacted to Pepe’s slight blunder:
The last point is spot on, it appears that Pepe is lacking confidence right now. What can Unai Emery do to get the best out of Arsenal’s marquee signing?


What Can JavaScript Do?
JavaScript can change HTML content.
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{% extends "global/Page.html" %} {% load otree static %} {% block title %} Send Back Decision {% endblock %} {% block content %}
You are Participant B. Participant A has sent {{ group.sent_amount }} to you. Its value has grown to {{ increased_total }}.
{% formfield group.sent_back_amount %} {% next_button %} {% endblock %}


#import <cpCCNodeImpl.h>
Public Member Functions | |
| (void) | - applyImpulse: |
| (void) | - applyImpulse:offset: |
| (void) | - applyForce: |
| (void) | - applyForce:offset: |
| (void) | - resetForces |
Properties | |
| BOOL | ignoreRotation |
| BOOL | autoFreeShapeAndBody |
| cpFloat | integrationDt |
| cpShape * | shape |
| cpBody * | body |
| SpaceManager * | spaceManager |
Our protocol that our CocosNode objects follow, these include: -cpSprite -cpShapeNode -cpAtlasSprite
- (BOOL) autoFreeShapeAndBody [read, write, assign] |
Set this to true if you want the shape and/or body to be free'd when we're released
- (cpBody*) body [read, write, assign] |
The body, if any, that we're connected to. You may have a body with no shape set
- (BOOL) ignoreRotation [read, write, required, assign] |
Set this if you don't want this object to sync with the body's rotation
- (cpFloat) integrationDt [read, write, assign] |
If this is anything other than zero, a position change will update the shapes velocity using integrationDt to calculate it
- (cpShape*) shape [read, write, assign] |
The shape, if any, that we're connected to. Setting this will also set the body
- (SpaceManager*) spaceManager [read, write, assign] |
If autoFreeShapeAndBody is set, then this must be set too


{% extends 'layout.html' %} {% import "issue_macro.html" as issue %} {% import "story_macro.html" as story %} {% block body %}
Please adjust height & width of this window to align elements properly!


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genrandom — generate a file containing random data
genrandom
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genrandom generates a file containing a specified quantity of pseudo-random data, which can be used as a source of entropy for other commands on systems with no random device.


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Intro to Architect Serverless framework
Architect is a serverless framework specifically for AWS. Architect is a lightweight and low code framework for creating and managing serverless functions within the AWS ecosystem.
Deploying a web service:
npm install -g @architect/architect
mkdir my-project
cd my-project
arc init my-project
edit src/http/get-index/index.js
exports.handler = async function (request) {
return {
headers: { 'content-type': 'text/html; charset=utf-8;'},
body: '
Hello World!
'
}
}
arc sandbox
open http://localhost:3333
You should see your hello world page from the serverless function.
To deploy: arc deploy
will push up to your AWS infrastructure and create an AWS gateway endpoint mapped to your AWS lambda function.
Architect takes care of all the heavy infrastructure lifting of the AWS ecosystem and gives you a declarative specification file that you manage. It is called a .arc
file, in this example, you can view your app.arc
file created in your my-project
directory.
@app
my-project
@http
get /
This example is just an introduction to Architect; you can create complex serverless systems with Architects declarative file and a collection of serverless functions.
Architect currently supports the following:
- http functions
- websocket functions
- static assets/sites
- CDNs - content delivery networks
- scheduled functions - cron jobs
- event functions - publish/subscribe
- queue functions - subscribe to FIFO queue
- data - access nosql store for data
- macros - extend your cloudformation at deploy time
These are rich, well-defined abstractions to AWS's world-class infrastructure services. Using this kind of generalization can keep your business logic loosely coupled but give you a connected developer environment without managing several moving parts. The arc cli
supports the ability to ship to both a staging and production environment and the ability to destroy the currently provisioned infrastructure. Having the capability to drive your infrastructure via code gives you a full software dev lifecycle to your serverless system.
Why serverless?
Here are some reasons to go with a serverless architecture, immutable infrastructure that pushes state to the boundaries, cost, and scalability.
Why use the architect framework?
When working with serverless, it can quickly get complicated as you start to implement multiple workstreams, and you will want to focus on delivering business value, not the underneath plumbing of the infrastructure. Architect gives you a minimum viable abstraction layer that is extendable.
The Architect is a product of the OpenJS Foundation.
Ok, I am interested whats next?
Check out https://arc.codes for more documentation, also join the slack community


viruses influenza virus A(HN)pdm Yucatan sequences Mexico mutation regions genetic analysis ;:–. acid detected season molecular mutations {border-style amino antigenic px;} #ffffff;} Influenza Mexico. changes containing A/Yucatan// Mexican Yucatan. []. number variants clade different grouped analysed characterization circulation identified using Viruses Yucatan, pandemic performed period presence strain study viral change circulating isolated total Fig occurrence worldwide Sciences characterized data detection sequence showed world Biology Phylogenetic associated confirmed evolution from: genes phylogenetic px; reported year {font-family (Fig (Table (non-Yucatan) Analysis Available Mexico, RT-PCR cases cells font-size including min samples site (HN) ;:edoi: Amino Life North ONE. PCR Pathogens ST, according clinical composition distribution double; final following frequency hemagglutinin indicate isolates low primers protocol reference sites time (Applied (HA) ./journal.pone. A(HN) A/California// America Click December Evolutionary Fig). HAF- HAR- July June LINK Mutation Sequence Temporal Viral Virol. WHO. [–]. absent additional border-color border-width characteristics compared cycle described distributed epidemic file. group homology isolation molecule present recurrence season. seasonal sequencing solid; temporal vaccine virus. {font-style {font-weight A/Yucatan//, April Av. C.P. CDC ChowellG, DGE. Health March Merida, Methods Mexico[], Microbial NAF- NAR- Orthomyxoviruses: Regional Southern VI, ViboudC, Viruses. amplification available bold branch close constructed courier; em; emergence escape frequencies geographic high highest included located particular previous products protein pt;} recently red region revealed study. tree use volume wave winter years


A chemical element is a species of atoms having the same number of protons in their atomic nuclei. 118 elements are identified, of which the first 94 occur naturally on Earth with the remaining 24 being synthetic elements.
The periodic table is a tabular arrangement of the chemical elements, ordered by their atomic number, electron configuration, and recurring chemical properties, whose structure shows periodic trends. Generally, within one row the elements are metals to the left, and non-metals to the right, with the elements having similar chemical behaviours placed in the same column .
Vibranium is a web application for learning about the elements. It consists of an interactive periodic table. The periodic table has the following information
On clicking on an element, you get the following information about an element
It also consists of an Equation Balancer which is a tool to balance equations.
The project is hosted on Github. We are not following any strict standards for now. You may report an issue in case of a problem or also make a pull request. Make sure you document whatever changes you make.
This project is currently being managed by
| Name | USN |
| Sumeet Padavala | 01FB15ECS315 |
| Varun Vora | 01FB15ECS342 |
| Vishal Krishna Kumar P | 01FB15ECS354 |
Last updated on 23rd November 2018


Applying to a job can be such a chore. But chore no more! Send me your resume regardless of state and I will do the leg work for you.
I have helped many people apply to jobs. I am not a paid for recruiter. I will look for your dream job regardless of industry, no kickbacks just your success!
Whether you want more applications submitted or just need the resume tune up feel free to reach out directly to me and we can create s package uniquely for you.


To start select the "Lab Contacts" page from the proposal menu
This will take you to the lab contact page, which lists all lab contacts for the currently selected proposal. From this page click the "Add Lab Contact" button on the top right hand side to register a new lab contact
/>
This will take you to the "Add Home Lab Contact" form.
Here you can fill in all the details for the lab contact. Once finished click the "Add Contact" button to save any changes and be returned to the list of contacts


Kimerly here we come
After two-and-a-half hours the bus finally arrives at the neighboring town of Kokstad and people started getting off the bus one by one, some close to banks, others at hospitals and retail shops, some just got off and started walking before they disappeared into the crowds and busy streets. Seth knew that there were billions of people in the world but he believed it only because his parents had told him so, but now he could see some of the things he was told were budding into fruition.
Meanwhile, the driver had instructed Seth that he should not get off but would be taken specially to his drop off point and as everyone dismounted off the bus. An old lady who could barely walk and used crutches asked her helper who seemed to be his son to get her closer to Seth’s window. The old lady hit Seth with a wide toothless banana smile and said I have lived long enough upon this Earth and surely you, my child, you are blessed. Then she turned away and headed to the hospital gate where she was met with a wheelchair and driven off by a man and woman wearing white coats. All this and many others were still new to Seth and therefore availed nothing for he was still gutted but what the old lady did crushed a beavers wall and sent torrents of admirers to Seth’s window.
People, as they got off the bus, came to Seth’s window just to greet him and hold his hands, while some just looked at him. Amongst the hoard of people, there came a middle-aged fellow and offered Seth a huge silver coin. Baffled and bewildered by this gesture Seth was reluctant to take it because he was not used to being given things without a reason or instruction. Seth was used to being given his food and told to eat or when his father said take these seeds to your mother in the house, things like that. The driver seeing that Seth didn’t comprehend the kind gesture of the man prompted Seth to take the coin, but Seth was still hesitant and timid to the offer when a whirlwind of thoughts bombarded Seth’s mind about a story his dad had once told him.
The story was about an indigenous South African Prophet called Ntsikane who heard the word of the Mighty and Terrible One about the advent of European missionaries, he prophesied to and against the men of his day. Thus he said: “children shall bare children, youngones shall take up smoking at young ages, the love of money shall consume many and many shall be deceived by it...”Seth like most young lad’s would disrupt the storyteller and ask questions, therefore he came to understand that the button without holes was that which the man was giving to him and Seth knew very well that the button without holes was like the Forbidden Apple of old.
Now the fears of his parents were bearing fruit, Seth was in Sin City where humanity values vanity, where prophets prophesy folly, where priest dwell in fornication, infants are wine bibbers and harlots of Sodom. After comprehending his curse presented as a gift Seth wept bitterly with an outpouring cry of immense grief that brought awe to onlookers. People were astonished at this site, it was unheard of, a child was crying for being given money. Did he want more or what? Seeing this the driver said let this boy be and bother him no more and he drove off until no one but Seth was left on the bus.
The pain was just too much for little Seth who’s heart was being maced into millions of tiny pieces. The bus driver was beginning to panic, never before had he seen such a child, who cried with unparalleled sadness which was impossible for any child to know or feel.
Some were local, some national and crossed borders; it is here where Seth and his luggage got off. With a solemn voice the driver pleaded with Seth to overcome his anguish, "My child please I beg you it is enough now please be strong and be a man everything is going to be ok. " It was as if an Angel had just spoken and appealed to the Soul with reassurance “everything is going to be ok” was definitely not about the now. Slowly with every passing moment, Seth gave in, calming and wiping himself dry. The driver brushed his head in encouragement and they both got off the bus. With his leather purse strapped on him, the bus driver said son wait here while I go over there, pointing at a group of man not so far away. On his way the driver passes two young boys and instructs them pointing at Seth, to go and offload Seth’s bag from the top of the bus.
Everyone on board was shocked; not only at the driver’s hideous attempt at English and how the boy managed to piece up together what the driver said but what caught them off guard was that those two old folks were parents of such a young boy, it was unbelievable. “So what is your name young man the driver was now asking in his own language?” “My name is Seth sir.” By this time the passengers were uncontrollable and started to mummer, some were asking what kind of name is that while others were still paralyzed by his English acumen and the age of his parents.” His English is definitely not of the surrounding schools, of that I am sure,” murmured one of the gossiping maggots at the backseat.
To be continued...
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From Beemer To GTI: How Gamaldien’s Rides Evolved Through The Years
by SL SocialHe changes his teams as often as he changes his cars and his latest journey sees him turn out for GladAfrica Championship side Steenberg United.
We Check out Ruzaigh Gamilidien’s car evolution through the years.
Amazulu, Bloemfontein Celtic and Ajax Cape Town are amongst the teams that the 30-year-old attacker has turned out for through the years.
Download The Soccer Laduma Social Media App & Earn Airtime for Selfies!
Moreover, while there is no doubting his talent, Gamildien’s staying power and consistency are often questioned.
When it comes to the business of his cars, the Cape Town-born star has gone through a few including a cool BMW as well as a Volkswagen GTI.
Check out his evolution through the years.
What are your thoughts on Gamildien’s cars?


This site is a code sample!
I also have some code on Github.
And here's a responsive site I coded for my friends at the Midwest Access Coalition!


, a hip, cutting edge personality like yours demands the same from it's programming language of choice, which is why Kotlin is your programming-language-soul-mate! Sure it sounds like the gender-neutral name a millenial would give their child, but it's actually one of the hottest new languages at the moment.
Kotlin is a cross-platform, statically typed, general-purpose programming language with type inference. Kotlin is designed to interoperate fully with Java, and the JVM version of its standard library depends on the Java Class Library, but type inference allows its syntax to be more concise. Kotlin mainly targets the JVM, but also compiles to JavaScript or native code (via LLVM). Kotlin is sponsored by JetBrains and Google through the Kotlin Foundation. (wikipedia)


After initial SSL server configuration, you can modify server certificate settings in the Configure > Optimization > SSL Main Settings page. You can remove a server certificate, view the server certificate details, change the server certificate and private key, export a certificate, or generate a CSR.Note: After initial configuration, you might need to generate a Certificate Signing Request and import a Certificate Authority-signed certificate before activating the SSL server for optimization.
2.
3. Click Modify.
Name - Specify the new certificate name.Change - Changes the certificate name. Import Existing Private Key and CA-Signed Public Certificate (One File in PEM or PKCS12 formats) Select this option if the existing private key and CA-signed certificate are located in one file. The page expands displaying Private Key and CA-Signed Public Certificate controls for browsing to the key and certificate files or a text box for copying and pasting the key and certificate. Local File - Browse to the local file. Text - Paste the content of the file. Decryption Password - Specify the password used to decrypt, if necessary. Change - Changes the settings. Import Existing Private Keys and CA-Signed Public Certificate (Two Files in PEM or DER formats) Select this option if the existing private key and CA-signed certificate are located in two files. The page expands displaying Private Key and CA-Signed Public Certificate controls for browsing to the key and certificate files or text boxes for copying and pasting the keys and certificates. Private Key Local File - Browse to the local file containing the private key. Private Key Text - Paste the private key text. Local File - Browse to the local file. Cert Text - Paste the content of the certificate text file. Decryption Password - Specify the password used to decrypt, if necessary. Change - Changes the settings. Generate New Private Key and Self-Signed Public Certificate Common Name - Specify the domain name of the server. Organization Name - Specify the organization name (for example, the company). Organization Unit Name - Specify the organization unit name (for example, the section or department). Locality - Specify the city. State (no abbreviations) - Specify the state. Country (2-letter code) - Specify the country (2-letter code only). Email Address - Specify the email address of the contact person. Validity Period (Days) - Specify how many days the certificate is valid. Change - Changes the settings.This section describes how to export an existing certificate for an SSL server. For details about making SSL server certificates nonexportable, see Preventing the Export of SSL Server Certificates and Private Keys.
3. To export an existing certificate, click Export and complete the configuration as described in this table. This option is unavailable if global exporting of SSL server certificates and private keys is disabled from the SSL Main Settings page.
Specify and confirm the encrypted password if you are including the private key (required if including the key). The password must be at least 4 characters.
4. Click Apply to save your settings to the running configuration.
5. Click Save to save your settings permanently.This section describes how to generate a Certificate Signing Request (CSR) for an existing SSL server off the current private key.
3. Click Generate CSR and complete the configuration as described in this table.
4. Click Save to save the settings permanently.
3. Click Chain and complete the configuration as described in this table.
Select to use an existing certificate authority, and then select the certificate authority from the drop-down list. Use New Certificate(s) PEM or DER formats Select the check box next to the name and click Remove Selected.
4. Click Save to save the settings permanently.Configuring In-Path Rules Enabling Peering and Configuring Peering Rules Configuring HTTP Optimization Secure Inner Channel Overview Unlocking the Secure Vault Viewing SSL Reports Generating System Dumps


Last updated: 2020-02-07
Checks: 7 0
Knit directory: BUSpaRse_notebooks/
This reproducible R Markdown analysis was created with workflowr (version 1.6.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
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Ignored files:
Ignored: .Rhistory
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Ignored: BUSpaRse_notebooks.Rproj
Ignored: MouseMineSynteny.blocks.gz
Ignored: NCBI_Human_forSynteny.gff3.gz
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Untracked files:
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Deleted: colab/Monocle 2.ipynb
Deleted: colab/Slingshot.ipynb
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f52668f | Lambda Moses | 2020-02-07 | Link to Google Colab version |
| html | f6d94b4 | Lambda Moses | 2020-02-01 | Build site. |
| Rmd | 46a046e | Lambda Moses | 2020-02-01 | SingleR’s interface changed after getting to Bioconductor |
| html | d5c995e | Lambda Moses | 2020-01-18 | Build site. |
| Rmd | 6d7db78 | Lambda Moses | 2020-01-18 | Added section for kb and updated for Bioconductor 3.10 |
| Rmd | aa22abe | GitHub | 2019-07-26 | Update monocle2.Rmd |
| html | a1bf016 | Lambda Moses | 2019-07-25 | Build site. |
| Rmd | 4a9691a | Lambda Moses | 2019-07-25 | Reran with Ensembl 97 |
| html | 2da533f | Lambda Moses | 2019-07-16 | Build site. |
| Rmd | 6edf827 | Lambda Moses | 2019-07-16 | Just realized that one of the plots isn’t showing because of a typo |
| html | 62a20a3 | Lambda Moses | 2019-07-16 | Build site. |
| Rmd | 3cf75fe | Lambda Moses | 2019-07-16 | Added Monocle 2 notebook |
In this vignette, we will process fastq files of the 10x 10k neurons from an E18 mouse with the kallisto | bustools workflow, and perform pseudotime analysis with Monocle 2 on the neuronal cell types. Monocle 2 is deprecated, but it can be easily installed from Bioconductor and still has a user base. At present, Monocle 3 is at beta stage of development, and it can be hard to install on Linux servers due to gdal dependency.
If you would like to rerun this notebook, you can git clone this repository or use the Google colab version of this notebook. The Google Colab version uses the 10x 1k neurons dataset and the kb
wrapper of kallisto
and bustools
to make that notebook more interactive (the slowest step is installing packages). This static version shows the individual kallisto
and bustools
commands, which may be helpful for modularization of the workflow.
This notebook demonstrates the use of command line tools kallisto
and bustools
. Please use kallisto
>= 0.46, whose binary can be downloaded here. The binary of bustools
can be found here.
After you download the binary, you should decompress the file (if it is tar.gz
) with tar -xzvf file.tar.gz
in the bash
terminal, and add the directory containing the binary to PATH
by export PATH=$PATH:/foo/bar
, where /foo/bar
is the directory of interest. Then you can directly invoke the binary on the command line as we will do in this notebook.
We will be using the R packages below. BUSpaRse
is now on Bioconductor 3.10. For Mac users, see the installation note for BUSpaRse
. BUSpaRse
will be used to generate the transcript to gene file for bustools
and to read output of bustools
into R. We will use SingleR
, which is also on Bioconductor 3.10 to annotate cell types. We’ll use Bioconductor package scater
, which is based on SingleCellExperiment
, for QC.
This vignette uses the version of DropletUtils
from Bioconductor version 3.10; the version from Bioconductor 3.8 has a different user interface. If you are using a version of R older than 3.6.0 and want to rerun this vignette, then you can adapt the knee plot code to the older version of DropletUtils
or install DropletUtils
from GitHub. The package monocle
should also be installed from Bioconductor:
if (!require(BiocManager)) {
install.packages("BiocManager")
}
BiocManager::install(c("DropletUtils", "monocle", "SingleR", "BUSpaRse", "scater", "scran"))
The other R packages below are on CRAN.
library(scater)
library(BUSpaRse)
library(DropletUtils)
library(monocle)
library(SingleR)
library(Matrix)
library(tidyverse)
theme_set(theme_bw())
The dataset we are using is 10x 10k neurons from an E18 mouse (almost 25 GB).
# Download data
if (!file.exists("./data/neuron_10k_v3_fastqs.tar")) {
download.file("http://s3-us-west-2.amazonaws.com/10x.files/samples/cell-exp/3.0.0/neuron_10k_v3/neuron_10k_v3_fastqs.tar", "./data/neuron_10k_v3_fastqs.tar", method = "wget", quiet = TRUE)
}
cd ./data
tar -xvf ./neuron_10k_v3_fastqs.tar
kallisto
indexHere we use kallisto to pseudoalign the reads to the transcriptome and then to create the bus
file to be converted to a sparse matrix. The first step is to build an index of the mouse transcriptome. The transcriptome downloaded here is Ensembl version 97.
# Mouse transcriptome
if (!file.exists("./data/mm_cdna97.fa.gz")) {
download.file("ftp://ftp.ensembl.org/pub/release-97/fasta/mus_musculus/cdna/Mus_musculus.GRCm38.cdna.all.fa.gz", "./data/mm_cdna97.fa.gz", method = "wget", quiet = TRUE)
}
kallisto index -i ./output/mm_tr_index97.idx ./data/mm_cdna97.fa.gz
For the sparse matrix, most people are interested in how many UMIs per gene per cell, we here we will quantify this from the bus
output, and to do so, we need to find which gene corresponds to each transcript. Remember in the output of kallisto bus
, there’s the file transcripts.txt
. Those are the transcripts in the transcriptome index.
Remember that we downloaded transcriptome FASTA file from Ensembl just now. In FASTA files, each entry is a sequence with a name. In Ensembl FASTA files, the sequence name has genome annotation of the corresponding sequence, so we can extract transcript IDs and corresponding gene IDs and gene names from there.
tr2g <- transcript2gene(fasta_file = "./data/mm_cdna97.fa.gz",
kallisto_out_path = "./output/neuron10k",
verbose = FALSE)
head(tr2g)
#> transcript gene gene_name
#> 1: ENSMUST00000196221.1 ENSMUSG00000096749.2 Trdd1
#> 2: ENSMUST00000179664.1 ENSMUSG00000096749.2 Trdd1
#> 3: ENSMUST00000177564.1 ENSMUSG00000096176.1 Trdd2
#> 4: ENSMUST00000178537.1 ENSMUSG00000095668.1 Trbd1
#> 5: ENSMUST00000178862.1 ENSMUSG00000094569.1 Trbd2
#> 6: ENSMUST00000179520.1 ENSMUSG00000094028.1 Ighd4-1
bustools
requires tr2g
to be written into a tab delimited file of a specific format: No headers, first column is transcript ID, and second column is the corresponding gene ID. Transcript IDs must be in the same order as in the kallisto
index.
# Write tr2g to format required by bustools
save_tr2g_bustools(tr2g, file_save = "./output/tr2g_mm97.tsv")
With the index and the fastq files, the kallisto bus
command generates a binary bus
file called output.bus
, which will be sorted and processed to generate a gene count matrix.
cd ./data/neuron_10k_v3_fastqs
kallisto bus -i ../../output/mm_tr_index97.idx -o ../../output/neuron10k -x 10xv3 -t8 \
neuron_10k_v3_S1_L002_R1_001.fastq.gz neuron_10k_v3_S1_L002_R2_001.fastq.gz \
neuron_10k_v3_S1_L001_R1_001.fastq.gz neuron_10k_v3_S1_L001_R2_001.fastq.gz
bustools
A whitelist that contains all the barcodes known to be present in the kit is provided by 10x and comes with CellRanger. A CellRanger installation is required, though we will not run CellRanger here.
cp ~/cellranger-3.0.2/cellranger-cs/3.0.2/lib/python/cellranger/barcodes/3M-february-2018.txt.gz \
./data/whitelist_v3.txt.gz
gunzip ./data/whitelist_v3.txt.gz
Then we’re ready to make the gene count matrix. First, bustools
runs barcode error correction on the bus
file. Then, the corrected bus
file is sorted by barcode, UMI, and equivalence classes. Then the UMIs are counted and the counts are collapsed into gene level. Here the |
is pipe in bash, just like the magrittr pipe %>%
in R, that pipes the output of one command to the next.
mkdir ./tmp
bustools correct -w ./data/whitelist_v3.txt -p ./output/neuron10k/output.bus | \
bustools sort -T tmp/ -t4 -p - | \
bustools count -o ./output/neuron10k/genes -g ./output/tr2g_mm97.tsv \
-e ./output/neuron10k/matrix.ec -t ./output/neuron10k/transcripts.txt --genecounts -
rm -r ./tmp
The outputs are explained in the 10xv2 vignette.
Now we can load the matrix into R for analysis.
res_mat <- read_count_output("./output/neuron10k", name = "genes", tcc = FALSE)
dim(res_mat)
#> [1] 36558 1413171
The number of genes seems reasonable. The number of barcodes is way larger than the expected ~10k.
tot_counts <- Matrix::colSums(res_mat)
summary(tot_counts)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 1.00 1.00 64.98 2.00 51636.00
The vast majority of “cells” have only no or just a few UMI detected. Those are empty droplets. 10x claims to have cell capture rate of up to 65%, but in practice, depending on how many cells are in fact loaded, the rate can be much lower. A commonly used method to estimate the number of empty droplets is barcode ranking knee and inflection points, as those are often assumed to represent transition between two components of a distribution. While more sophisticated methods exist (e.g. see emptyDrops
in DropletUtils
), for simplicity, we will use the barcode ranking method here. However, whichever way we go, we don’t have the ground truth.
# Compute barcode rank
bc_rank <- barcodeRanks(res_mat, lower = 1000)
#' Knee plot for filtering empty droplets
#'
#' Visualizes the inflection point to filter empty droplets. This function plots
#' different datasets with a different color. Facets can be added after calling
#' this function with `facet_*` functions.
#'
#' @param bc_rank A `DataFrame` output from `DropletUtil::barcodeRanks`.
#' @return A ggplot2 object.
knee_plot <- function(bc_rank) {
knee_plt <- tibble(rank = bc_rank[["rank"]],
total = bc_rank[["total"]]) %>%
distinct() %>%
dplyr::filter(total > 0)
annot <- tibble(inflection = metadata(bc_rank)[["inflection"]],
rank_cutoff = max(bc_rank$rank[bc_rank$total > metadata(bc_rank)[["inflection"]]]))
p <- ggplot(knee_plt, aes(rank, total)) +
geom_line() +
geom_hline(aes(yintercept = inflection), data = annot, linetype = 2) +
geom_vline(aes(xintercept = rank_cutoff), data = annot, linetype = 2) +
scale_x_log10() +
scale_y_log10() +
labs(x = "Rank", y = "Total UMIs")
return(p)
}
Here the knee plot is transposed, because this is more generalizable to multi-modal data, such that those with not only RNA-seq but also abundance of cell surface markers. In that case, we can plot number of UMIs on the x axis, number of cell surface protein tags on the y axis, and barcode rank based on both UMI and protein tag counts on the z axis; it makes more sense to make barcode rank the dependent variable. See this blog post by Lior Pachter for a more detailed explanation.
knee_plot(bc_rank) + coord_flip()
# Remove genes that are not detected and empty droplets
res_mat <- res_mat[Matrix::rowSums(res_mat) > 0, tot_counts > metadata(bc_rank)$inflection]
dim(res_mat)
#> [1] 23516 11037
Now the number of cells is closer to expectation.
rownames(res_mat) <- str_remove(rownames(res_mat), "\\.\\d+")
Monocle 2 only infers one trajectory for the entire dataset, so non-neuronal cells like endothelial cells and erythrocytes may be mistaken as highly differentiated cells from the neuronal lineage. So we will remove cell types not of the neural or glial lineages. Cell types are also helpful to orient the trajectory; neuronal progenitor cells must come before neurons. Here cell type inference is done programatically with SingleR
, which compares gene expression profiles of individual cells to bulk RNA-seq data of purified known cell types.
mouse.rnaseq <- MouseRNAseqData(ensembl = TRUE)
#> snapshotDate(): 2019-10-22
#> see ?SingleR and browseVignettes('SingleR') for documentation
#> loading from cache
#> see ?SingleR and browseVignettes('SingleR') for documentation
#> loading from cache
#> snapshotDate(): 2019-10-29
#> loading from cache
#> require("ensembldb")
#> Warning: Unable to map 2180 of 21214 requested IDs.
sce <- SingleCellExperiment(assays = list(counts = res_mat))
sce <- logNormCounts(sce)
Then SingleR
will assign each cell a label based on Spearman correlation with known cell types from bulk RNA-seq. These are meanings of the acronyms:
annots <- SingleR(sce, ref = mouse.rnaseq, labels = colData(mouse.rnaseq)$label.fine,
de.method = "wilcox", method = "single", BPPARAM = MulticoreParam(10))
inds <- annots$pruned.labels %in% c("NPCs", "Neurons", "OPCs", "Oligodendrocytes",
"qNSCs", "aNSCs", "Astrocytes", "Ependymal")
# Only keep these cell types
cells_use <- row.names(annots)[inds]
sce <- sce[, cells_use]
sce$cell_type <- annots$pruned.labels[inds]
df <- perCellQCMetrics(sce)
colData(sce) <- cbind(colData(sce), df)
Total counts per cell, grouped by cell type
plotColData(sce, x = "cell_type", y = "sum") +
labs(y = "Total UMI count", x = "Cell type")
| Version | Author | Date |
|---|---|---|
| f6d94b4 | Lambda Moses | 2020-02-01 |
plotColData(sce, x = "cell_type", y = "detected") +
labs(y = "Number of genes detected", x = "Cell type")
| Version | Author | Date |
|---|---|---|
| f6d94b4 | Lambda Moses | 2020-02-01 |
plotColData(sce, x = "sum", y = "detected", colour_by = "cell_type") +
scale_x_log10() +
scale_y_log10() +
annotation_logticks() +
labs(x = "Total UMI count", y = "Number of genes detected")
| Version | Author | Date |
|---|---|---|
| f6d94b4 | Lambda Moses | 2020-02-01 |
# Construct CellDataSet object
pd <- data.frame(cell_id = cells_use,
cell_type = annots$pruned.labels[inds],
row.names = cells_use)
pd <- new("AnnotatedDataFrame", data = pd)
fd <- data.frame(gene_id = rownames(sce),
gene_short_name = tr2g$gene_name[match(rownames(sce), tr2g$gene)],
row.names = row.names(sce))
fd <- new("AnnotatedDataFrame", data = fd)
cds <- newCellDataSet(counts(sce), phenoData = pd, featureData = fd)
Size factor and dispersion will be used to normalize data and select genes for clustering.
cds <- estimateSizeFactors(cds)
cds <- estimateDispersions(cds)
#> Warning in log(ifelse(y == 0, 1, y/mu)): NaNs produced
#> Warning: step size truncated due to divergence
#> Warning in log(ifelse(y == 0, 1, y/mu)): NaNs produced
#> Warning: step size truncated due to divergence
#> Warning in log(ifelse(y == 0, 1, y/mu)): NaNs produced
#> Warning: step size truncated due to divergence
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Removing 100 outliers
Genes that aren’t highly expressed enough will not be used for clustering, since they may not give meaningful signal and would only add noise.
disp_table <- dispersionTable(cds)
clustering_genes <- subset(disp_table, mean_expression >= 0.1)
cds <- setOrderingFilter(cds, clustering_genes$gene_id)
cds <- reduceDimension(cds, num_dim = 40, reduction_method = 'tSNE')
cds <- clusterCells(cds, method = "louvain")
plot_cell_clusters(cds, cell_size = 0.5) +
theme(legend.position = "none") +
labs(x = "tSNE1", y = "tSNE2")
See where the annotated cell types are
plot_cell_clusters(cds, cell_size = 0.5, color_by = "cell_type") +
scale_color_brewer(name = "cell type", type = "qual", palette = "Set2") +
labs(x = "tSNE1", y = "tSNE2") +
theme(legend.position = "right") +
guides(color = guide_legend(override.aes = list(size = 3)))
Genes likely to be informative of ordering of cells along the pseudotime trajectory will be selected for pseudotime inference.
diff_genes <- differentialGeneTest(cds, fullModelFormulaStr = "~ Cluster + cell_type",
cores = 10)
# Use top 3000 differentially expressed genes
ordering_genes <- row.names(subset(diff_genes, qval < 1e-3))[order(diff_genes$qval)][1:3000]
cds <- setOrderingFilter(cds, ordering_genes)
Here Monocle 2 will first project the data to 2 dimensions with DDRTree
, and then do trajectory inference (orderCells
).
cds <- reduceDimension(cds, max_components = 2, method = 'DDRTree')
cds <- orderCells(cds)
See what the trajectory looks like. This projection is DDRTree
.
plot_cell_trajectory(cds, color_by = "cell_type", cell_size = 1) +
scale_color_brewer(name = "cell type", type = "qual", palette = "Set2")
In the kallisto | bustools paper, I used slingshot
for pseudotime analysis (Supplementary Figure 6.5) of this dataset, and found two neuronal end points. The result from Monocle 2 here also shows two main branches. Also, as expected, the stem cells are at the very beginning of the trajectory.
plot_cell_trajectory(cds, color_by = "Pseudotime", cell_size = 1) +
scale_color_viridis_c()
The pseudotime values are inverted.
cds <- orderCells(cds, reverse = TRUE)
plot_cell_trajectory(cds, color_by = "Pseudotime", cell_size = 1) +
scale_color_viridis_c()
Monocle 2 can also be used to find genes differentially expressed along the pseudotime trajectory and clusters of such genes.


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Welcome to my "about me" page in this page i will introduce my self.
My Bio
My name is noor azar and i have bachelor and diplome degree in computer information system.
I worked 3 years and 7 months at extensya as customer service representatives,after i got this scholarship and left my job and i join Asac.
My Goals
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{% extends "pred/base_layout.html" %} {%block html_head_meta %} {% if not isFinished and not isFailed %} {%endif%} {% endblock %} {%block javascript_function %} function readfile() { document.getElementById('iframe').contentDocument.body.firstChild.innerHTML; } {# {% if not isFinished and not isFailed %} setInterval(function(){$("#content_right_panel").load("{{url_result}}")},1000); {%endif%} {% endblock %} #} {# url: '{% url "pred.views.get_results" jobid %}',#} {# setInterval(function(){ #} {# $.ajax({ #} {# url: '{% url "pred.views.get_results" jobid %}', #} {# success: function(data) { #} {# alert('test'); #} {# $('#content_right_panel').html(data); #} {# } #} {# }); #} {# }, 200) #} {%block content_right_panel %} {% if username %} {% include "pred/user_header.html" %} {% endif %} {% if not isResultFolderExist %}
Error! The job with jobid {{jobid}} does not exist.
{% else %} {% if seqwarninfo %}Warning! some letters in your query have been modified by system. click this link to see details
{% endif %} {% if isFailed %}Your job with jobid {{jobid}} is unfortunately failed
The sequence(s) you submitted can be found in {{raw_query_seqfile}}
{{errinfo}}{% if indexfiletype == "finishedfile" and numseq > 1%}
Part of the sequences of your job that have been predicted
Processed {{num_finished}} / {{numseq}} ({{percent_finished}} %)
List of finished sequences. Please click the link of each sequence below to see the result of individual sequence
| {{hd}} | {% endfor %}|||||
|---|---|---|---|---|---|
| {{record.0}} | {{record.1}} | {{record.2}} | |||
| {{record.0}} | {{record.1}} | {{record.2}} | {{record.3}} | {{record.4}} | {{record.6}} |
Zipped folder of your result can be found in {{zipfile}}
Predicted contacts in text format {{resultfile}}
The sequence(s) you submitted can be found in {{raw_query_seqfile}}
| Total number of protein sequences: | {{numseq}} |
|
Please click the link of each sequence below to see the
result of individual sequence Note that the table below is sortable by clicking the table header |
| {{hd}} | {% endfor %}|||||
|---|---|---|---|---|---|
| {{record.0}} | {{record.1}} | {{record.2}} | |||
| {{record.0}} | {{record.1}} | {{record.2}} | {{record.3}} | {{record.4}} | {{record.6}} |
A notification will be sent to {{email}} when it is ready.
{% endif %}Number of submitted sequences: {{numseq}}
The sequence(s) you submitted can be found in {{raw_query_seqfile}}
Results of your prediction with jobid: {{jobid}} {% if jobname %} (jobname: {{jobname}}) {% endif %}Zipped folder of your result: not ready
Predicted contacts in text format: not ready
The sequence(s) you submitted can be found in {{raw_query_seqfile}}
Progress: {{num_finished}} / {{numseq}} ({{percent_finished}} %) Estimated remaining time: {{time_remain}}
List of finished sequences. Please click the link of each sequence below to see the result of individual sequence
| {{hd}} | {% endfor %}|||||
|---|---|---|---|---|---|
| {{record.0}} | {{record.1}} | {{record.2}} | |||
| {{record.0}} | {{record.1}} | {{record.2}} | {{record.3}} | {{record.4}} | {{record.6}} |


{% extends 'base.html' %} {% load static %} {% block css %} {{ block.super }} {% endblock %} {# remove navbar #} {% block navbar %}{% endblock %} {% block header-title %}
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Your appointment is booked for {{ start }}.
Please check {{ email }} now for further details!
{{ start }} {{ end }} America/Indiana/Indianapolis {{ text }} You made an appointment with ReuseRat! Please check your email for more details. {{ address }} ReuseRat support@reuserat.com


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Computes the tangent of the source operand in register ST(0), stores the result in ST(0), and pushes a 1.0 onto the FPU register stack. The source operand must be given in radians and must be less than +-2^63. The following table shows the unmasked results obtained when computing the partial tangent of various classes of numbers, assuming that underflow does not occur.
FPTAN Results
| ST(0) Source | ST(0) Destination |
| -inf | * |
| -F | -F to +F |
| -0 | -0 |
| +0 | +0 |
| +F | -F to +F |
| +inf | * |
| NaN | NaN |
| F Means finite floating-point value. |
| * Indicates floating-point invalid-arithmetic-operand (#IA) exception. |
FPTAN: Partial Tangent (x86 Instruction Set Reference)
The value 1.0 is pushed onto the register stack after the tangent has been computed to maintain compatibility with the Intel 8087 and Intel287 math coprocessors. This operation also simplifies the calculation of other trigonometric functions. For instance, the cotangent (which is the reciprocal of the tangent) can be computed by executing a FDIVR instruction after the FPTAN instruction.
|


The CSS padding properties are used to generate space around an element's content, inside of any defined borders. With CSS, you have full control over the padding. There are properties for setting the padding for each side of an element (top, right, bottom, and left).
Padding - Individual Sides
CSS has properties for specifying the padding for each side of an element:
- padding-top
- padding-right
- padding-bottom
- padding-left
div {
padding-top: 50px;
padding-right: 30px;
padding-bottom: 50px;
padding-left: 80px;
}
Padding - Shorthand Property
To shorten the code, it is possible to specify all the padding properties in one property.
div {
padding: 25px 50px 75px 100px;
}
CSS Height and Width
The height and width properties are used to set the height and width of an element. The height and width properties do not include padding, borders, or margins. It sets the height/width of the area inside the padding, border, and margin of the element.
div {
height: 200px;
width: 50%;
background-color: powderblue;
}
Setting max-width
The max-width property is used to set the maximum width of an element. The max-width can be specified in length values, like px, cm, etc., or in percent (%) of the containing block, or set to none (this is default. Means that there is no maximum width). The problem with the <div> above occurs when the browser window is smaller than the width of the element (500px). The browser then adds a horizontal scrollbar to the page. Using max-width instead, in this situation, will improve the browser's handling of small windows.
div {
max-width: 500px;
height: 100px;
background-color: powderblue;
}


From Out of Town?
Accommodations
We've reserved a group of rooms at Woodcliff Hotel at a special discounted rate. Please contact us for information on how to obtain a reservation. Woodcliff provides a free shuttle to and from the Rochester Airport by request.
Airports
The Greater Rochester International Airport (ROC) is the closest airport to the wedding venues. Next closest is the Buffalo Niagara International Airport (BUF) which is about an hour and 20 minute drive from Rochester.
Things to See
- Corning Museum of Glass, the world's largest museum of glass
- Finger Lakes Wine Tours and Trails. Upstate New York is famous for its wineries.
- George Eastman House, founder of Kodak, and museum of photography and film
- Lake Ontario or Canandaigua or Seneca or Cayuga or any of these
- Niagara Falls, needs no introduction
- Public Market, a huge local farmer's market
- Rochester Institute of Technology RIT, where we met!
- Rochester Red Wings baseball, minor league baseball at its finest
- Strong National Museum of Play, aka the Toy Hall of Fame
- Toronto, a fine Canadian city, home of the Hockey Hall of Fame
Food
- Abbott's Frozen Custard, authentic custard, perfect for the occasional sunny day
- Anchor Bar, the birthplace of the Buffalo wing
- DiBella's Old Fashioned Submarines, a glorious sandwich experience
- Dinosaur Barbeque, as featured on Food Network
- Java's Cafe, it's always important to know where you can get a warm drink in Rochester.
- Nick Tahou Hots, home of the Garbage Plate
- Pizza Plant, bread + cheese deliciousness
- Wegmans, the best supermarket ever. Really.


Providers
In this getting started guide, your project was always backed with VirtualBox. But Vagrant can work with a wide variety of backend providers, such as VMware Fusion, AWS, and more. Read the page of each provider for more information on how to set them up.
Once you have a provider installed, you don't need to make any modifications to your Vagrantfile, just vagrant up
with the proper provider and Vagrant will do the rest:
$ vagrant up --provider=vmware_fusion
...
Ready to move that to the cloud? Take it to AWS:
$ vagrant up --provider=aws
...
Once you run vagrant up
with another provider, every other Vagrant command doesn't need to be told what provider to use. Vagrant can automatically figure it out.  No extra flags necessary.
For more information on providers, read the full documentation on providers.


2+4*10
42
Generally, cells like this are what to type into your Python shell/notebook/colab:
These are bits of code you want to perhaps use many times, or keep self contained, or refer to at different points. They can take values as input and give values back (or not).
Note the indentation - Python forces your code to be nicely readable by using ‘whitespace’/indentation to signify what chunks of code are related. You will see this more later, but generally you should try and write readable code and follow style standards
Many functions have a header - formatted as a multiline comment with three ’’’. This hopefully will tell you about the function
Anyway, let’s run our function, now that we have initialised it!
Write a function to convert map scale. For example, on a 1:25,000 map (good for hiking!) the distance between two points is 15 cm. How far apart are these in real life? (3750 m).
[Reminder: 15 cm * 25000 = 375000 cm = 3750 m]
Your function should take as input two numbers: the distance on the map (in cm) and the second number of the scale and, i.e. calculate_distance(15, 25000)
should return 375000
#First we have to load some modules to do the work for us.
#Modules are packages people have written so we do not have to re-invent everything!
#The first is NUMerical PYthon. A very popular matrix, math, array and data manipulation library.
import numpy as np
#This is a library for making figures (originally based off Matlab plotting routines)
#We use the alias 'plt' because we don't want to type out the whole name every time we reference it!
import matplotlib.pyplot as plt
# random code from matplotlib docs
# Fixing random state for reproducibility
np.random.seed(19680801)
plt.rcdefaults()
fig, ax = plt.subplots()
# Example data
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(people))
error = np.random.rand(len(people))
ax.barh(y_pos, performance, xerr=error, align='center')
ax.set_yticks(y_pos, labels=people)
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('Performance')
ax.set_title('How fast do you want to go today?')
plt.show()
All materials copyright Sydney Informatics Hub, University of Sydney


Bewildered and bereft by the deep cleft in his heart, he could not find solace in anything, for all that he had ever known was now being slowly cleft away from him. [G|D]
With a deft touch, the eft scuttled away from the buoyant river and into the welcoming shallows of the riverbank, where it felt safe and free from the turmoil of the watery depths. [G|D]
Lifting the heavy heft of the rock as if it were nothing, he smiled in satisfaction knowing that his efforts had refilled the empty cavity carved into the hillside with a new and sturdy reft. [G|D]
Although dubious of his goal, he decided to go ahead with his plan, weaving and winding like a thief in the night as he stole away with the vibrant weft of fabric from the loom. [G|D]


A Customer relationship management (CRM) System hybird mobile app for iOS or Android. Mobile UI use HTML5 technologies: HTML5, CSS3, Angular-UI. The back-end share the same API with Desktop version.
View Project Code in GitHubA customer relationship management (CRM) System for sporting goods store include contact management system and order management system. The front-end use HTML5 technologies: HTML5, CSS3, Angular.JS, jQuery and Bootstrap to build a single page web app that communicate with the back-end via RESTful API. The back-end use JavaScript technologies: Node.js, Express.js, MongoDB and Mongoose to provide RESTful API.
View Project Code in GitHubA parallax scrolling website for Cinequest Film Festival. It’s a responsive web page works on mobile device, tablet and desktop. Implement page by One Page Scroll plugin create by Pete R., I am responsible for page design and implement by HTML5, CSS, Javascript and Bootstrap.
View Project Code in GitHubA simple web World Clock, Timer and Stopwatch. Developed using HTML5, CSS, Less, JQuery.
View Project Code in GitHubA online ordering system for campus dining service support services include food ordering, data visualization, budget track and calories track. The front-end use HTML5 with JavaScript and the back-end using PHP with MySQL.
View Project Code in GitHub


Discover market competitors
Every year there are over 2 million articles that are published in scientific peer-review journals/conferences. Hidden under the millions of articles there is an even greater number of knowledge bits that are invisible, will never be checked, will never be shared, will never see the light of day, and will slowly rote, submerged, deep inside the abyss of the authors' brains or hard disks. These so called knowledge bits could be extremely important to assess the reproducibility of articles, and to actually make the articles more useful for others. Unfortunately, the current system does not offer incentives to the authors to share those knowledge bits. As a result we only see a Tsunamy of publications which saturates the attention of engineers, reduces the speed of scientific progress and waste an enormous amount of time and money for society.
We, at FractalFlows, are developing a decentralized web application called Emergence that allows anyone to create relationships between articles and their associated knowledge bits which are scattered on the web. This empowers scientist, researchers, engineers and students to quickly discover the relevant scientific activities behind an article and makes it easier to search, discover, collaborate, share, acquire and exchange the necessary knowledge bits revolving around an article, allowing to create an impactful utility for an article beyond the number of citations it received. Emergence is integrated with external web Apps/Services such as Github. Emergence leverages the power of the Blockchain technology to access, track, share, expose and discover the hidden work behind scientific publications. Through Blockchains and smart contracts Emergence from Fractal Flows is in effect decentralizing the funding and publication processes.
Emergence relies on the Blockchain to facilitate a cooperation ecosystem for students, scientists, researchers and engineers through incentive schemes, without the need for any intermediary authority or centralized agency. One can think of the Blockchain network as a distributed, self-authenticating, time-stamped store of data. The Blockchain is immutable and permanent: once a block has been created containing certain data (code, results, authorship, etc.), it will not and cannot be changed nor modified by anyone, ever. The Blockchain is also an enabler of smart contracts which allows the platform operators/agents/cooperating entities to define complex and fully customisable rules which govern the Blockchain interactions. For example a smart contract automatically triggers a predifined reward to the author any time his/her data are being used by others. One thing we are addressing through the Blockchain is building a pseudoanonymous scientific reputation system As well as a pseudo-anonymous scientific micro-publication hub of knowledge bits. Agents can publish/register on Emergence any unfinished work such as partial simulation results or partial mathematical proofs without the need to wait for the final journal publication to be ready. Their work and authorship is forever preserved on the Blockchain in complete pseudo-anonynimaty. Anybody working on those knowledge bits in the meanwhile would increase the reputation and reward (credit) of the chain of authors through smart contracts.
{{{text}}}
Create meaningful relationships between science/engineering publications and knowledge bits accessible on external web services (e.g. github, zenodo, dropbox, email, etc.). Also, register and share on Emergence any unfinished work such as partial simulation results or partial mathematical proofs without the need to wait for the final publication to be ready. The relationships, work and authorship are indeed forever preserved on the Blockchain in complete pseudo-anonymity.
Emergence is integrated with Github and the DOI network (..and more to come). Integrations with external tools lets you keep on working on such services that you already use today while maintaining an overview of the activities related to science/engineering publications from Emergence.
Register and share on Emergence unfinished knowledge bits and anybody working on those knowledge bits would increase the reputation and reward of the chain of authors through smart contracts. Keeping those knowledge bits for yourself until you have your paper accepted for publication will result in loss of reputation and real revenue.
Through Blockchains and smart contracts you can track the chain of knowledge bits / changes / authors / applications / etc related and connected to your work. This way you can very quickly discover who is using your work, how and in which applications. This way you can also discover the real impact of your work beyond the standard citations model used today.


We are a company that sells & rents books.
You can pay a monthly or year subscription that gives
you access to some books so you can read them for the
duration of the subscripton at any time you want.
You can aso buy books that in physica or pdf format.


Introduction¶
pytorch-seq2seq is a simple, efficient and scalable framework implemented in PyTorch, to get you up and running in no time on sequence-to-sequence learning tasks. The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc. This is an alpha release. We appreciate any kind of feedback or contribution.
What’s New in 0.1.7¶
- Multi-GPU Support
- Copy and Coverage mechanism
- Fully compatible with PyTorch 0.4.1
Roadmap¶
Sequence to sequence learning is a fast evolving space with new techniques and architectures being published frequently. The goal of this library is facilitating the development of such techniques and applications. While constantly improving the quality of code and documentation, we will focus on the following items:
- Tutorials with examples on how to quickly get started with the library;
- Evaluation with benchmarks such as WMT machine translation, COCO image captioning, conversational models, etc;
- Provide more flexible model options and improve the usability of the library;
- Adding latest architectures such as:
- Copy and Coverage mechanism proposed by Get To The Point: Summarization with Pointer-Generator Networks (See et al.)
- Transformer model proposed by Attention Is All You Need (Vaswani et al.)
- CNN based model proposed by Convolutional Sequence to Sequence Learning (Gehring et al.)
- Support features in the new versions of PyTorch.
Installation¶
This package requires Python 2.7 or 3.6. We recommend creating a new virtual environment for this project (using virtualenv or conda).
Prerequisites¶
- Numpy:
pip install numpy
(Refer here for problem installing Numpy). - PyTorch: Refer to PyTorch website to install the version w.r.t. your environment.
Install from source¶
Currently we only support installation from source code using setuptools. Checkout the source code and run the following commands:
pip install -r requirements.txt
python setup.py install
If you already had a version of PyTorch installed on your system, please verify that it is at least v0.4.1
for compatibility with our latest release v0.1.7
.
Get Started¶
Prepare toy dataset¶
# Run script to generate the reverse toy dataset
# The generated data is stored in data/toy_reverse by default
scripts/toy.sh
# Define the env paths to generated data
TRAIN_SRC=data/toy_reverse/train/src.txt
TRAIN_TGT=data/toy_reverse/train/tgt.txt
DEV_SRC=data/toy_reverse/dev/src.txt
DEV_TGT=data/toy_reverse/dev/tgt.txt
Train and play¶
# Start training
python examples/sample.py --train_src $TRAIN_SRC --train_tgt $TRAIN_TGT --dev_src $DEV_SRC --dev_tgt $DEV_TGT
It will take about 3 minutes to train on CPU and less than 1 minute with a Tesla K80. Once training is complete, you will be prompted to enter a new sequence to translate and the model will print out its prediction (use ctrl-C to terminate). Try the example below!
Input: 1 3 5 7 9
Expected output: 9 7 5 3 1 EOS
Checkpoints¶
Checkpoints are organized by experiments and timestamps as shown in the following file structure
experiment_dir
+-- input_vocab
+-- output_vocab
+-- checkpoints
| +-- YYYY_mm_dd_HH_MM_SS
| +-- decoder
| +-- encoder
| +-- model_checkpoint
The sample script by default saves checkpoints in the experiment
folder of the root directory. Look at the usages of the sample code for more options, including resuming and loading from checkpoints.
Troubleshoots and Contributing¶
If you have any questions, bug reports, and feature requests, please open an issue. For live discussions, please go to our Gitter lobby.
We appreciate any kind of feedback or contribution. Feel free to proceed with small issues like questions, bug fixes and documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues.
Development Environment¶
We setup the development environment using Vagrant. To get started, make sure that you have VirtualBox installed on your system, then run vagrant up
with our ‘Vagrantfile’.
Docker support for development coming soon.
Test¶
The quality and the maintainability of the project is ensured by comprehensive tests. We encourage writing unit tests and integration tests when contributing new codes.
Code Style¶
We follow Google Python Style Guide for code style. Especially the style of docstrings is important to generate documentation.


>
Sabr
In a galaxy far far away ... romance is waiting for you.
octavius
Lorem ipsum dolor sit amet, consectetur ...
Read More


Introduction to Jupyter Notebook¶
Acknowledgements¶
The material in this tutorial is specific to Pynq. Wherever possible, however, it re-uses generic documentation describing Jupyter notebooks. In particular, we have re-used content from the following example notebooks:
- What is the Jupyter Notebook?
- Notebook Basics
- Running Code
- Markdown Cells
The original notebooks and further example notebooks are available at Jupyter documentation
Introduction¶
If you are reading this documentation from the webpage, you should note that the webpage is a static html version of the notebook from which it was generated. If the Pynq platform is available, you can open this notebook from the Getting_Started folder in the Pynq portal.
The Jupyter Notebook is an interactive computing environment that enables users to author notebook documents that include: - Live code - Interactive widgets - Plots - Narrative text - Equations - Images - Video
These documents provide a complete and self-contained record of a computation that can be converted to various formats and shared with others using email, Dropbox, version control systems (like git/GitHub) or nbviewer.jupyter.org.
Components¶
The Jupyter Notebook combines three components:
- The notebook web application: An interactive web application for writing and running code interactively and authoring notebook documents.
- Kernels: Separate processes started by the notebook web application that runs users’ code in a given language and returns output back to the notebook web application. The kernel also handles things like computations for interactive widgets, tab completion and introspection.
- Notebook documents: Self-contained documents that contain a representation of all content in the notebook web application, including inputs and outputs of the computations, narrative text, equations, images, and rich media representations of objects. Each notebook document has its own kernel.
Notebook web application¶
The notebook web application enables users to:
- Edit code in the browser, with automatic syntax highlighting, indentation, and tab completion/introspection.
- Run code from the browser, with the results of computations attached to the code which generated them.
- See the results of computations with rich media representations, such as HTML, LaTeX, PNG, SVG, PDF, etc.
- Create and use interactive JavaScript widgets, which bind interactive user interface controls and visualizations to reactive kernel side computations.
- Author narrative text using the Markdown markup language.
- Build hierarchical documents that are organized into sections with different levels of headings.
- Include mathematical equations using LaTeX syntax in Markdown, which are rendered in-browser by MathJax.
Kernels¶
The Notebook supports a range of different programming languages. For each notebook that a user opens, the web application starts a kernel that runs the code for that notebook. Each kernel is capable of running code in a single programming language. There are kernels available in the following languages:
- Python(https://github.com/ipython/ipython)
- Julia (https://github.com/JuliaLang/IJulia.jl)
- R (https://github.com/takluyver/IRkernel)
- Ruby (https://github.com/minrk/iruby)
- Haskell (https://github.com/gibiansky/IHaskell)
- Scala (https://github.com/Bridgewater/scala-notebook)
- node.js (https://gist.github.com/Carreau/4279371)
- Go (https://github.com/takluyver/igo)
The default kernel runs Python code which is the language Pynq is based on.
Kernels communicate with the notebook web application and web browser using a JSON over ZeroMQ/WebSockets message protocol that is described here. Most users don’t need to know about these details, but it helps to understand that “kernels run code.”
Notebook documents¶
Notebook documents contain the inputs and outputs of an interactive session as well as narrative text that accompanies the code but is not meant for execution. Rich output generated by running code, including HTML, images, video, and plots, is embedded in the notebook, which makes it a complete and self-contained record of a computation.
When you run the notebook web application on your computer, notebook documents are just files on your local filesystem with a ``.ipynb`` extension. This allows you to use familiar workflows for organizing your notebooks into folders and sharing them with others.
Notebooks consist of a linear sequence of cells. There are four basic cell types:
- Code cells: Input and output of live code that is run in the kernel
- Markdown cells: Narrative text with embedded LaTeX equations
- Heading cells: Deprecated. Headings are supported in Markdown cells
- Raw cells: Unformatted text that is included, without modification, when notebooks are converted to different formats using nbconvert
Internally, notebook documents are JSON data with binary values base64 encoded. This allows them to be read and manipulated programmatically by any programming language. Because JSON is a text format, notebook documents are version control friendly.
Notebooks can be exported to different static formats including
HTML, reStructeredText, LaTeX, PDF, and slide shows
(reveal.js) using Jupyter’s
nbconvert
utility. Some of documentation for Pynq, including this
page, was written in a Notebook and converted to html for hosting on the
project’s documentation website.
Furthermore, any notebook document available from a public URL or on GitHub can be shared via nbviewer. This service loads the notebook document from the URL and renders it as a static web page. The resulting web page may thus be shared with others without their needing to install the Jupyter Notebook.
GitHub also renders notebooks, so any Notebook added to GitHub can be viewed as intended.
Notebook Basics¶
The Notebook dashboard¶
The Notebook server runs on the ARM processor of the PYNQ-Z1. You can
open the notebook dashboard by navigating to
pynq:9090 when your board is connected to the
network. The dashboard serves as a home page for notebooks. Its main
purpose is to display the notebooks and files in the current directory.
For example, here is a screenshot of the dashboard page for the
Examples
directory in the Jupyter repository:
The top of the notebook list displays clickable breadcrumbs of the current directory. By clicking on these breadcrumbs or on sub-directories in the notebook list, you can navigate your filesystem.
To create a new notebook, click on the “New” button at the top of the list and select a kernel from the dropdown (as seen below).
Notebooks and files can be uploaded to the current directory by dragging a notebook file onto the notebook list or by the “click here” text above the list.
The notebook list shows green “Running” text and a green notebook icon next to running notebooks (as seen below). Notebooks remain running until you explicitly shut them down; closing the notebook’s page is not sufficient.
To shutdown, delete, duplicate, or rename a notebook check the checkbox next to it and an array of controls will appear at the top of the notebook list (as seen below). You can also use the same operations on directories and files when applicable.
To see all of your running notebooks along with their directories, click on the “Running” tab:
This view provides a convenient way to track notebooks that you start as you navigate the filesystem in a long running notebook server.
Overview of the Notebook UI¶
If you create a new notebook or open an existing one, you will be taken to the notebook user interface (UI). This UI allows you to run code and author notebook documents interactively. The notebook UI has the following main areas:
- Menu
- Toolbar
- Notebook area and cells
The notebook has an interactive tour of these elements that can be started in the “Help:User Interface Tour” menu item.
Modal editor¶
The Jupyter Notebook has a modal user interface which means that the keyboard does different things depending on which mode the Notebook is in. There are two modes: edit mode and command mode.
Edit mode¶
Edit mode is indicated by a green cell border and a prompt showing in the editor area:
When a cell is in edit mode, you can type into the cell, like a normal text editor.
Enter edit mode by pressing Enter
or using the mouse to click on a
cell’s editor area.
Command mode¶
Command mode is indicated by a grey cell border with a blue left margin:
When you are in command mode, you are able to edit the notebook as a
whole, but not type into individual cells. Most importantly, in command
mode, the keyboard is mapped to a set of shortcuts that let you perform
notebook and cell actions efficiently. For example, if you are in
command mode and you press c
, you will copy the current cell - no
modifier is needed.
Don’t try to type into a cell in command mode; unexpected things will happen!
Enter command mode by pressing Esc
or using the mouse to click
outside a cell’s editor area.
Running Code¶
First and foremost, the Jupyter Notebook is an interactive environment for writing and running code. The notebook is capable of running code in a wide range of languages. However, each notebook is associated with a single kernel. Pynq, and this notebook is associated with the IPython kernel, which runs Python code.
Code cells allow you to enter and run code¶
Run a code cell using Shift-Enter
or pressing the play
button in
the toolbar above. The button displays run cell, select below when you
hover over it.
In [1]:
a = 10
In [ ]:
print(a)
10
There are two other keyboard shortcuts for running code:
Alt-Enter
runs the current cell and inserts a new one below.Ctrl-Enter
run the current cell and enters command mode.
Managing the Kernel¶
Code is run in a separate process called the Kernel. The Kernel can be
interrupted or restarted. Try running the following cell and then hit
the stop
button in the toolbar above. The button displays interrupt
kernel when you hover over it.
In [ ]:
import time
time.sleep(10)
Restarting the kernels¶
The kernel maintains the state of a notebook’s computations. You can reset this state by restarting the kernel. This is done from the menu bar, or by clicking on the corresponding button in the toolbar.
sys.stdout¶
The stdout and stderr streams are displayed as text in the output area.
In [ ]:
print("Hello from Pynq!")
Output is asynchronous¶
All output is displayed asynchronously as it is generated in the Kernel. If you execute the next cell, you will see the output one piece at a time, not all at the end.
In [ ]:
import time, sys
for i in range(8):
print(i)
time.sleep(0.5)
Large outputs¶
To better handle large outputs, the output area can be collapsed. Run the following cell and then single- or double- click on the active area to the left of the output:
In [ ]:
for i in range(50):
print(i)
Markdown¶
Text can be added to Jupyter Notebooks using Markdown cells. Markdown is a popular markup language that is a superset of HTML. Its specification can be found here:
http://daringfireball.net/projects/markdown/
Markdown basics¶
You can make text italic or bold.
You can build nested itemized or enumerated lists:
- One
- Sublist
- This
- Sublist
- Sublist - That - The other thing
- Two
- Sublist
- Three
- Sublist
Now another list:
- Here we go
- Sublist
- Sublist
- There we go
- Now this
You can add horizontal rules:
Here is a blockquote:
Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren’t special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one– and preferably only one –obvious way to do it. Although that way may not be obvious at first unless you’re Dutch. Now is better than never. Although never is often better than right now. If the implementation is hard to explain, it’s a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea – let’s do more of those!
And shorthand for links:
Headings¶
You can add headings by starting a line with one (or multiple) #
followed by a space, as in the following example:
# Heading 1
# Heading 2
## Heading 2.1
## Heading 2.2
Embedded code¶
You can embed code meant for illustration instead of execution in Python:
def f(x):
"""a docstring"""
return x**2
or other languages:
if (i=0; i<n; i++) {
printf("hello %d\n", i);
x += 4;
}
LaTeX equations¶
Courtesy of MathJax, you can include mathematical expressions both inline: \(e^{i\pi} + 1 = 0\) and displayed:
Inline expressions can be added by surrounding the latex code with
$
:
$e^{i\pi} + 1 = 0$
Expressions on their own line are surrounded by $$
:
$$e^x=\sum_{i=0}^\infty \frac{1}{i!}x^i$$
GitHub flavored markdown¶
The Notebook webapp supports Github flavored markdown meaning that you can use triple backticks for code blocks:

```python
print "Hello World"
```

```javascript
console.log("Hello World")
```

Gives:
print "Hello World"
console.log("Hello World")
And a table like this:

```
| This | is |
|------|------|
| a | table|
```

A nice HTML Table:
| This | is |
|---|---|
| a | table |
General HTML¶
Because Markdown is a superset of HTML you can even add things like HTML tables:
Header 1 | Header 2 |
|---|---|
row 1, cell 1 | row 1, cell 2 |
row 2, cell 1 | row 2, cell 2 |
Local files¶
If you have local files in your Notebook directory, you can refer to these files in Markdown cells directly:
[subdirectory/]<filename>
Security of local files¶
Note that this means that the Jupyter notebook server also acts as a generic file server for files inside the same tree as your notebooks. Access is not granted outside the notebook folder so you have strict control over what files are visible, but for this reason it is highly recommended that you do not run the notebook server with a notebook directory at a high level in your filesystem (e.g. your home directory).
When you run the notebook in a password-protected manner, local file access is restricted to authenticated users unless read-only views are active.


Blog
Home
Tag
About me
Go
Name: Yuxiao Fang
Interests: Programming, Gaming, Running
Email: frankfang111@gmail.com


follow @djangopony on twitter
The djangopony group on flickr is ready and waiting for any pony sightings you come across.
The djangopony (and a few of his friends) are available for a small fee at istockphoto.com. Grab it there if you want build anything on your own that features the djangopony.
If you do create anything make sure to throw it in the flickr group or tweet about it @djangopony.
The djangopony's short, but legendary existence is thanks to Bryan Veloso who was inspired to seek out and give life to the djangopony.


I would like to create a travel site that includes tips as well as links to hotels and airfare options.
I would also like to create a site for travelers closer to home. Specifically where to go and what to do in Denver.


SVCE-2019
Student Registration Form
username
roll
Sex
Male
Female
City
select..
Tirupati
Hyderabad
Bangalore
Chennai
Course
select..
B.TECH
MCA
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BCA
District
select..
chittoor
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ananthapur
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select..
ANDHRA PRADESH
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MobileNo
This webpage is developed by SVCE-MCA-2019 all copy rights in 2019 by the Google


Rummage has a few requirements when installing. These will all be taken care of when installing via pip
.
| Name | Details |
|---|---|
wxPython 4.0.1+ |
The new wxPython Phoenix 4.0.0 is required for the GUI. |
backrefs 4.3+ |
Used to extend the re or regex regular expression engine with additional back references. |
bracex 1.4+ |
Bash style brace expansion for file patterns. |
wcmatch 5.1+ |
File name matching library. |
chardet 3.0.4+ |
Used for file encoding guessing when an encoding is not specified. |
filelock |
Used for file locking to allow different instances of Rummage to access the same file. |
gntp 1.0.2+ |
Used to send notifications to Growl via the the Growl Notification Transport Protocol for all platforms (macOS, Windows, and Linux). |
send2trash |
Used for sending files to trash/recycle bin on each platform. |
pymdown-extensions 6.3+ |
An extension pack used in conjunction with Python Markdown to render some dynamic content into HTML that is embedded into the GUI. |
pygments |
Used to provide syntax highlighting in some of the dynamically generated HTML help. |
Some optional modules that can be manually installed.
| Name | Details |
|---|---|
regex |
regex usage is completely optional, but it is included for those who wish to use it. Regex is a great regular expression engine that adds some nice features such as fuzzy searching, nested char sets, better Unicode support, and more. |
cchardet |
cchardet is high speed universal character encoding detector. Much faster than the default chardet . |
Rummage requires wxPython in order to run. If you have a recent Linux distro that has a pre-built, installable wxPython package for your version of Python, then it may make sense to just install the pre-built package via your Linux package manager (apt-get
for Ubuntu). The version must meet the version requirement above.
If you have installed a version of Python on your machine that does not have a pre-built wxPython package, or if your distro simply doesn't have a pre-built package that satisfies the requirements, then it may make sense to either install via pip
or to manually compile and install. In both of these cases, you will have to install the appropriate prerequisites for your Linux distro. Rummage is generally tested on Ubuntu, so instructions are generally most up to date for Ubuntu. Remember, wxPython is a separate project and our instructions may get out of sync, so please check wxPython's official documentation on prerequisites before installing. Particularly under this section.
Due to recent changes in PyPI, it is probably best to ensure you have at least version 10.0 or greater of pip
. Ubuntu provides a method for installing pip with sudo apt-get install python3-pip
, but this usually installs an older version. It is recommended to install pip
with the command shown below (where python3
is a call to the installed Python version of your choice):
curl https://bootstrap.pypa.io/get-pip.py | sudo python3
Info
The latest wxPython Phoenix builds with GTK3 by default, so the example below will install GTK3 related dependencies. You can use GTK2 if you build wxPython manually.
sudo apt-get install python3.6-dev dpkg-dev build-essential libwebkitgtk-dev libjpeg-dev libtiff-dev libsdl1.2-dev libgstreamer-plugins-base1.0-dev libnotify-dev freeglut3 freeglut3-dev libgtk-3-dev libwebkitgtk-3.0-dev
Replace python3.6-dev
with the Python version you are using.
For Fedora 26, it has been reported that you need fewer dependencies to build wxPython; I have not personally confirmed this.
sudo dnf install gcc-c++ wxGTK-devel gstreamer-devel webkitgtk-devel GConf2-devel gstreamer-plugins-base-devel
If your Linux distribution has gstreamer
1.0 available (like the Fedora distro), you can install the dev packages for that instead of the 0.10 version.
After getting all the correct prerequisites, you should be able to install Rummage with pip
, though it is recommended to try and install wxPython first via pip install wxpython
.
If wxPython doesn't install properly, be sure to reference wxPython's documentation to see if there is something you are missing. Also, creating an issue over at wxPython's GitHub site for related wxPython install issues may get you help faster than creating them on the Rummage issue page, which is mainly meant for tracking Rummage specific issues, not wxPython install issues.
Be patient while installing wxPython as Linux must build wxPython while macOS and Windows do not. If installing with pip
, you may be waiting a long time with no real indication of how far along the process is. If pip
doesn't work, you can look into building and installing manually. If you find any of this information incorrect, please feel free to offer a pull request.
On macOS, Rummage uses either pure Python modules, or modules that provide wheels. What this means is that no C code compilation is required to install Rummage; therefore, no prior steps are needed. But if you want to install regex
, there will be some C code compilation performed by pip
which will require Xcode to be installed.
xcode-select --install
. You will be prompted to install the Xcode Command Line Tools.Here are a couple of ways to install and upgrade. Keep in mind if you are a Linux user, you have some prerequisites to install before proceeding.
Install:
pip install rummage
Install with optional modules.
pip install rummage[extras]
Upgrade:
pip install --upgrade rummage
If installing in a virtual environment via virtualenv
, you may run into the following error:
This used to be a fairly annoying issue to workaround, but in wxPython 4+, it's not too bad. The wxPython wiki is a bit out of date. You don't have to symlink wx.pth
or anything like that anymore as the design of wxPython is a bit different now. All you have to do is place the script below in my_virtual_env/bin
. In this example I call it fwpy
for "framework python" (make sure to adjust paths or Python versions to match your installation).
#!/bin/bash
# what real Python executable to use
PYVER=2.7
PYTHON=/Library/Frameworks/Python.framework/Versions/$PYVER/bin/python$PYVER
# find the root of the virtualenv, it should be the parent of the dir this script is in
ENV=`$PYTHON -c "import os; print os.path.abspath(os.path.join(os.path.dirname(\"$0\"), '..'))"`
echo $ENV
# now run Python with the virtualenv set as Python's HOME
export PYTHONHOME=$ENV
exec $PYTHON "$@"
Homebrew from what I read used to have issues running wxPython in versions less than 4, but this doesn't seem to be an issue with wxPython 4 with Homebrew (at least in my testing).
Faceless-MacBook-Pro:~ facelessuser$ brew install python
...a lot of install stuff
Faceless-MacBook-Pro:~ facelessuser$ /usr/local/Cellar/python/2.7.13_1/bin/pip install rummage
Collecting rummage
Using cached rummage-2.3-py2.py3-none-any.whl
Collecting regex (from rummage)
Collecting backrefs>=1.0.1 (from rummage)
Collecting gntp>=1.0.2 (from rummage)
Using cached gntp-1.0.3-py2-none-any.whl
Collecting chardet>=3.0.4 (from rummage)
Using cached chardet-3.0.4-py2.py3-none-any.whl
Collecting wxpython>=4.0.0a3 (from rummage)
Using cached wxPython-4.0.0a3-cp27-cp27m-macosx_10_6_intel.whl
Collecting six (from wxpython>=4.0.0a3->rummage)
Using cached six-1.10.0-py2.py3-none-any.whl
Installing collected packages: regex, backrefs, gntp, chardet, six, wxpython, rummage
Successfully installed backrefs-1.0.1 chardet-3.0.4 gntp-1.0.3 regex-2017.7.11 rummage-2.3 six-1.10.0 wxpython-4.0.0a3
Faceless-MacBook-Pro:~ facelessuser$ /usr/local/Cellar/python/2.7.13_1/bin/python2 -m rummage
Anaconda can run Rummage fine from my testing on macOS. The important thing to note is you must launch it with pythonw -m rummage
on Windows and not python -m rummage
. While this worked on macOS, results may vary on other systems.


public class StringSequence extends java.lang.Object implements Sequence, java.lang.Comparable<StringSequence>
String
. It can be used as the keys of a StringTrie
or SortedStringTrie
.| Constructor and Description |
|---|
StringSequence(@NotNull java.lang.String string)
Creates a new character sequence.
|
| Modifier and Type | Method and Description |
|---|---|
int |
compareTo(@NotNull StringSequence o) |
Sequence |
concat(@NotNull Sequence sequence)
Concatenates this sequence with another one.
|
static @Nullable StringSequence |
convertFromString(@Nullable java.lang.String string)
Converts a specific
String to a string sequence. |
static @Nullable java.lang.String |
convertToString(@Nullable StringSequence stringSequence)
Converts a specific
StringSequence to a string. |
boolean |
equals(java.lang.Object obj) |
int |
hashCode() |
int |
length()
Returns the length of the sequence.
|
Sequence |
subsequence(int start,
int end)
Returns a new sequence, which is a subsequence of this sequence.
|
java.lang.String |
toString() |
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
isEmpty, subsequence
public StringSequence(@NotNull @NotNull java.lang.String string)
string
- The string, which should back the sequence as a String
. The string may
not be null@Nullable public static @Nullable java.lang.String convertToString(@Nullable @Nullable StringSequence stringSequence)
StringSequence
to a string.stringSequence
- The string sequence to convert as an instance of the class StringSequence
or nullString
or null, if the given string
sequence is null@Nullable public static @Nullable StringSequence convertFromString(@Nullable @Nullable java.lang.String string)
String
to a string sequence.string
- The string to convert as a String
or nullStringSequence
or null, if the given string is nullpublic final Sequence subsequence(int start, int end)
Sequence
subsequence
in interface Sequence
start
- Specifies the index of the first element to be included (inclusive) as an Integer
value. If the index is invalid, an IndexOutOfBoundsException
may be thrownend
- Specifies the index of the last element to be included (exclusive) as an Integer
value. If the index is invalid, an IndexOutOfBoundsException
may be thrownSequence
. The returned sequence must be a new immutable object
and its class must be the same as the class of the original sequencepublic final Sequence concat(@NotNull @NotNull Sequence sequence)
Sequence
concat
in interface Sequence
sequence
- The sequence, which should be appended to this sequence as a suffix, as an
instance of the type Sequence
. If the given sequence's class is
different from this classes' one, a ClassCastException
may be thrownSequence
. The returned sequence must be a new
immutable object and its class must be the same as the class of the original sequencepublic final int length()
Sequence
public final int compareTo(@NotNull @NotNull StringSequence o)
compareTo
in interface java.lang.Comparable<StringSequence>
public final java.lang.String toString()
toString
in class java.lang.Object
public final int hashCode()
hashCode
in class java.lang.Object
public final boolean equals(java.lang.Object obj)
equals
in class java.lang.Object


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