CSVeed Compared
Disclaimer: this page is the personal opinion of Robert Bor
I have taken a look at the following Java CSV libraries:
- Commons CSV; Apache Commons project
- Flatpack; extensive tool, much more than just CSV
- JavaCSV; two-class solution
- opencsv; very popular Csv-to-Bean transformer
- Super CSV; extensively details Csv-to-Bean transformer
- jcsv; Csv parser experimenting with annotations
- JSefa; XML/CSV/FLR reader/writer with annotations
I checked out all the documentation and sites I could find on the tooling. After that I downloaded the latest know versions of the libraries and proceeded to run a couple of tests against them. The combination of these two activities gave me a pretty good feeling of the quality of the products. Enough to make a quality appraisal on each of them. Check out the Comparison Matrix to see the differences.
Commons CSV
Apache Commons CSV set out with a noble goal:
There are three pre-existing BSD compatible CSV parsers which this component will hopefully make redundant
The project is still in the sandbox phase and has no releases in Maven Central yet. Given the time that has passed, it is doubtful it will ever grow to see its goal fulfilled. JARs have to be manually retrieved and added to the Maven dependencies.
Its primary focus is on the tokenizing process at which it excels. There is no support for Bean conversions. The documentation is extremely poor and mostly outdated, although the project is still being maintained.
Flatpack
Flatpack is obviously a lot more than just CSV handling, which a glance at the website shows. Configuring the instructions is a convoluted and disassociated, using old-school XML configuration files.
Flatpack can be a bit mean by failing silently. You will have to check the errors logs, because situations can become very nasty by its apparent willingness to gobble it all.
I dislike how you are forced to read the entire file in a DataSet and then have to iterate over that set.
Though the project has Maven Central entries (albeit without source), it is outdated (last update: 2008) and offers no support for Bean conversion. The documentation of the project is quite good.
JavaCSV
The first thing that is noteworthy about this library is that it has only two classes; CsvClient and CsvWriter. The ease of its interface is clamored and has been a huge incentive for me to add the Facade layer to CSVeed.
The project has releases in Maven Central, although without source. The process lets you read a line one by one, there seems to be no way to get them all at once. There is no support for bean conversion. Documentation is somewhat poor since the website seems to have been used for another purpose as well.
opencsv
Adorable statement in the author's blog:
For reasons I’ll never understand, my little Apache2 open source CSV parser, opencsv has recently trucked through the 20,000 download mark.
And understandably so. The library was far ahead of its time with rudimentary Bean conversion support. The coupling is slightly tedious, but it works. Documentation is fine, albeit a bit on the thin side. The library is registered in Maven Central. Active community with speedy bug fixing.
Very poor in its error feedback, a definitive negative. Also does NOT support all basic Objects out of the box. No support for comment and empty line skipping. Nevertheless, disruptive technology at that time and much needed, given its success.
Super CSV
I really have a soft spot for Super CSV. What the author says and does and the quality of his documentation and code, references to RFC 4180, it all fits. Just have a look at the landing page and see the motivation and the author's background. Super CSV is nothing, if not ambitious.
The project is extremely well documented and releases are available in Maven Central.
Mapping must be done by column name mapping, there does not seem to be a way to do this by column index. There are no annotations and conversions to other classes have to be set manually with a disassociated mapping table (the "CellProcessor" approach). Advertisedly, Super CSV also supports deep conversions, although I haven't tried those.
Personally, I do not find the interface very good. It is irksome and counter-intuitive. The CellProcessor approach is the wrong way to go about in my humble opinion. Error handling is better than opencsv's, but still not the way it should be.
jcsv
I mainly adopted this library into my comparison because of its usage of annotations. The project is obviously short-lived and has not been refined much. Interestingly enough it has some nice documentation, including examples and reference to RFC 4180.
The error feedback of jcsv is extremely poor and its bean conversion is error-ridden. Immature technology, not suitable for usage.
JSefa
A colleague pointed me to JSefa as a reliable CSV reader. The first thing that caught my eye is that it aims to provide for XML, CSV and FLR. This approach has up and downsides, it is more versatile, but it is also harder to cater to the special requirements of a file structure, be they technical or process-wise.
Of the above libraries this one cost me the most trouble to get to the intricacies. Documentation on the API is scarce, though a number of useful sample projects have been provided. The library works with the concept of high- and low-level interfaces, high being similar to the bean reader and low the row reader. Its killer feature is that it supports deep conversion.
What irks me most about this interface is that one must define all properties with annotations, even having to tell it what converter it must use, for example for converting text to Long. This is not very convenient. I find error feedback lacking, although the line and position are reported, you cannot see the cell that went wrong.
Comparison matrix
Check out the different attributes of the afore-mentioned Java CSV libraries and CSVeed in the Comparison Matrix.


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This was my first independent project which entails a restaurant website,i used Html and Css as the main languanges.
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This was a group project where we worked with my team members and came up with a complete website, we used Html ,Css, JavaScript and Bootstrap. Here is the link https://github.com/Ken-muigai/Tesla
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--- layout: post title: Autumn is the Best Season date: 2014-10-12 17:13:09.000000000 -05:00 categories: - Personal tags: - autumn - gateway park - hiking - liswamírë - personal - season - season affective disorder status: publish type: post published: true author: image: /assets/images/img_0196.jpg --- {% include figure.html src="/assets/images/img_0196.jpg" caption="Gateway Park" align="center" %}
Liswamírë and I took a trip to Gateway Park today and went on a nice four-mile hike with Merry and Pippin.
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rorty: “to know is to represent accurately what is outside the mind”
but he’s really just ranting about the social construction of science. and he’s mad because that corresponds to a top-down projection of normative knowledge on the craft of architecture.
tl;dr: rational technicality isn’t always the path to knowledge. designers also have it, but it’s much more internalized (or not yet formalized, which might be the same thing).
yes because if design knowledge is personal, the problem of programming is communicating that personal knowledge


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And we just had our first Python 101 class in the Architecture Fine Arts building :D
Didn't manage to get through all of it but we'll be continuing where we left off next week.
Here are the slides:
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[dscrd-hush] <dukeleto> @tedy what exactly do you mean by "immature blocks", do you mean "transactions in the mempool" i.e. "transactions with 0 confirmations" ?
[dscrd-hush] <dukeleto> The snapshot is calculated from the output of the getsnapshot RPC command, and it only looks at things with at least 1 confirmation, transactions that are inside a block. The mempool is dropped, in any snapshot/airdrop, because it's not part of blockchain history yet
[dscrd-hush] <tedy> immature block rewards
[dscrd-hush] <dukeleto> @tedy ok, you mean immature coinbase mining rewards
[dscrd-hush] <tedy> immature block rewards don't show up with "getbalance" so i guess they didn't show up on getsnapshot?
[dscrd-hush] <dukeleto> this is a good question, about edge cases
[dscrd-hush] <ComputerGenie - ChickenPool> @tedy you running your own pool?
[dscrd-hush] <dukeleto> @ComputerGenie - ChickenPool you mined the next to last block, right?
[dscrd-hush] <ComputerGenie - ChickenPool> just before the last
[dscrd-hush] <ComputerGenie - ChickenPool> I got block #499999
[dscrd-hush] <dukeleto> detective time
[dscrd-hush] <ComputerGenie - ChickenPool> ya
[dscrd-hush] <dukeleto> $ ./contrib/convert_address.py t1gD24o2JBYA8tWZgHbhQmNqcg5LYmqGj4W
RXcc8yVSp3B7MssypLsQj64uBgc4dXUDYr
[dscrd-hush] <dukeleto> that address is in the snapshot, but it has lots of funds, so it's not clear if that xtn was included
[dscrd-hush] <tedy> It is clear
[dscrd-hush] <tedy> Just check the difference
[dscrd-hush] <tedy> RXcc8yVSp3B7MssypLsQj64uBgc4dXUDYr":4962.58235991
[dscrd-hush] <tedy> Final Balance
4,987.58245991 HUSH
[dscrd-hush] <ComputerGenie - ChickenPool> gimme a bit to get dressed and pull out my scripts
[dscrd-hush] <dukeleto> @tedy i am pretty sure immature coinbase is included, because they have >= confirmations
[dscrd-hush] <dukeleto> getsnapshot doesn't really look at maturity
[dscrd-hush] <tedy> No, confirmations doesn't have anything to do with immature block rewards
[dscrd-hush] <dukeleto> @ComputerGenie - ChickenPool airdrop hasn't happen yet
[dscrd-hush] <tedy> for example, if I check balance for 1+ confirmations, immature bloc krewards are not included
[dscrd-hush] <dukeleto> @tedy the RPC calls you are asking are not what getsnapshot uses
[dscrd-hush] <dukeleto> getsnapshot talks to the low level LevelDB database directly
[dscrd-hush] <ComputerGenie - ChickenPool> airdrop hasn't happen yet well, there's @tedy's answer
[dscrd-hush] <tedy> I know but I'm assuming since none of my last 100 blocks were migrated
[dscrd-hush] <tedy> @ComputerGenie - ChickenPool I'm checking balance on airdrop bash script
[dscrd-hush] <dukeleto> so what you see for getbalance is not exactly the same logic as getsnapshot uses, it doesn't look at maturity as far as I know.
[dscrd-hush] <tedy> Apparently it does.. it's not huge difference and i can live without it, just saying since I have noticed it and it's not fair for the last 100 block miners
[dscrd-hush] <ComputerGenie - ChickenPool> @tedy have you checked on the chain/explorer that the blocks you're looking for weren't orphaned?
[dscrd-hush] <ComputerGenie - ChickenPool> because it captured all of mine and I literally had the next to last block
[dscrd-hush] <tedy> No it didn't.. just check it yourself
[dscrd-hush] <tedy> You will get airdropped RXcc8yVSp3B7MssypLsQj64uBgc4dXUDYr":4962.58235991
[dscrd-hush] <dukeleto> @tedy i am willing to look into this, because I was just about to do a testnet airdrop and prod airdrop, and I want everybody to be happy
[dscrd-hush] <tedy> And your balance on old chain is: 4,987.58245991 HUSH
[dscrd-hush] <dukeleto> looks like 2ish blocks were orhpaned maybe?
[dscrd-hush] <tedy> No, he mined 2 of the ladt 100 blocks
[dscrd-hush] <tedy> I have mined 12 and none of them will be airdroped
[dscrd-hush] <dukeleto> And your balance on old chain is: 4,987.58245991 HUSH
[dscrd-hush] <dukeleto> where is that coming from?
[dscrd-hush] <dukeleto> Quote all datasources please
[dscrd-hush] <dukeleto> if you are looking at balances of a legacy Hush wallet right now, it probably contains UTXOs from past block 500,000
[dscrd-hush] <dukeleto> and those are not part of the snapshot
[dscrd-hush] <ComputerGenie - ChickenPool> the last 3 blocks showing on explorer for me cover 82 blocks, so if it didn't cover 100 unconfirmed, then it would be off by more than the 25.0001
(edited)
[dscrd-hush] <ComputerGenie - ChickenPool> will pull out his scripts and load his chain in a bit and check where he's showing he's at
[dscrd-hush] <dukeleto> @tedy how about, tell me an address, and tell me what balance you think it should have
[dscrd-hush] <dukeleto> and I can do a little digging
[dscrd-hush] <tedy> Oh, wait a sec, is zelcore balance showing orphaned blocks as final balance?
[dscrd-hush] <dukeleto> fuck me
[dscrd-hush] <dukeleto> ZEL is unsupported closed source software
[dscrd-hush] <tedy> I meant the explorer
[dscrd-hush] <tedy> since their explorer is still on old chain
[dscrd-hush] <ComputerGenie - ChickenPool> who dafuq knows what they show, the chain is what is real
[dscrd-hush] <dukeleto> yes, their explorer for legacy chain should be correct
[dscrd-hush] <ComputerGenie - ChickenPool> "should be"
[dscrd-hush] <dukeleto> @tedy which address are you looking at?
[dscrd-hush] <tedy> Final Balance<PHONE_NUMBER>7 HUSH
[dscrd-hush] <tedy> and I'll get airdroped RBecgbAbDx9WCGmD4yAst92pEDW4bn9z4c":112.50034460


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CLEAR AND PRESENT DANGER A film review by Adam Taylor Copyright 1994 Adam Taylor
Well, what can I say? Harrison Ford still hasn't done a bad movie; he must be one of the most successful actors ever. The movie CLEAR AND PRESENT DANGER is a complicated political action/thriller. The main plot is that the US wants to get revenge on a Columbian who killed a US businessman, and also would like to stop drug smuggling from Columbia into the US.
Do not expect to go to this movie and just sit back and not think. CLEAR AND PRESENT DANGER in some parts is hard to follow so if you are expecting a easy-to-follow, "full-of-action" movie you may not want to see this one. It deals with more legal and political subjects than action, but this movie has a great finish and the acting is superb.
Although this film is mostly serious, some humour has been slipped into it to make it a bit more entertaining during the "slow" parts of the film. Harrison Ford plays Jack Ryan, who works for US intelligence and is forced to take over James Earl Jones's job as Deputy when James Earl Jones finds out he has cancer. Harrison Ford then falls into a deadly trap since he has to fly down to Columbia under the President of the US's orders to apprehend a man who is wanted for the murder of a woman working for him. Ford finds that this flight becomes a lot more dangerous than he expected since he barely gets out alive after many Columbians (from a drug smuggling clan) begin a small war by shooting at Ford's car and other vehicles around him. Ford does get out alive, but just barely.
The movie starts off quite slow but by the end you are into it since the action and suspense is classic (sort of like that of the previous Harrison Ford/Tom Clancy movie PATRIOT GAMES). This is not one of Ford's best movies, but I would say it is worth the 8 bucks to go see it since it is fit for the big screen, and it is a nice lengthy entertaining yet complicated movie.
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These sections are shown by default. Simply becuase there is no hidden style applied.


@(message: String) @Main("Laird Hamilton") {
Laird was born Laird John Zerfas in San Francisco on March 2, 1964, in an experimental salt-water sphere at UCSF Medical Center designed to ease the mother's labor. His Greek birth father, L. G. Zerfas, left the family before his first birthday. While he was an infant, Laird and his mother, Joann, moved to Hawaii. While still a young boy living on Oahu, Laird met with 1960s surfer Bill Hamilton, a bachelor at the time, on Pūpūkea beach on the North Shore. Bill Hamilton was a surfboard shaper and glasser on Oahu in the 1960s and 1970s and owned a small business handmaking custom, high-performance surfboards for the Oahu North Shore big wave riders of the era. The two became immediate companions. The young Laird invited Bill Hamilton home to meet his mother. Bill Hamilton married Laird's then-single mother, becoming Laird's adoptive father.
The family later moved to a remote valley on Kauaʻi island. Joann and Bill had a second son, Lyon, Laird's half-brother, who also became a surfer.
Hamilton had a reputation for an aggressive demeanor around others of his age. This hostile attitude was in part due to Laird and his brother Lyon being bigger than their classmates, fair-skinned, and blonde: unusual in their predominantly Hawaiian-populated neighborhood. The role of the outsider profoundly affected Laird through to his teen years and early adult life. He became used to this role and was uncomfortable being in the center of anything. He was also known for his physical and mental toughness. Young Laird is shown in early video footage jumping off a 60-foot cliff into deep water at 7 years of age.


Copyright (c) 1994? Laymen Allen
Meditation is played on a 7x7 board, with the following setup:
|
As I present this game, things may tend to become a bit confusing, when setup stones are replaced by dropped stones it is possible to use small pieces of paper to differentiate setup stones from dropped ones). The original game uses a board with colored squares on the same positions presented on the initial setup.
More information about the game and other Allen's inventions, check here.


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Other ways to spread the word...
Start by joining our Givology Facebook group to receive our updates and connect to team members and fellow supporters of Givology.
Follow our daily updates on Givology's Twitter Page to catch tidbits of exciting Givology news.


Youthful Dimensions recommends several monthly treatments, so the desired result can be achieved, followed by maintenance treatments 3-6 monthly.
Please avoid taking ibuprofen, blood thinners, herbal supplements, vitamin E and fish oil for at least one week prior to the PRP facial to minimise bruising.
Immediately following the procedure, the most commonly reported side effects are redness, swelling, bruising, lumpiness and feeling the pressure of fullness at the injection sites or treated areas. Cold gel packs/ice may be applied immediately after treatment to reduce swelling.
Avoid scrubbing or exfoliation over the injection sites for at least 4 hours. For the next 7 days after treatment wash you face gently with a mild cleanser once to twice daily. Then you may return to your regular skincare routine. Avoid make up for at least 24 hours and until healed.


$petList.size() Pets on Sale!
We are proud to offer these fine pets at these amazing prices.
This month only, choose from: #set( $count = 1 )
#foreach( $pet in $petList )
$count)
$pet.name
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#set( $count = $count + 1 ) #end
Call Today!


| pl |
Entry point for loading all PL libraries only on demand. |
| pl.Date |
Date and Date Format classes. |
| pl.List |
Python-style list class. |
| pl.Map |
A Map class. |
| pl.MultiMap |
MultiMap, a Map which has multiple values per key. |
| pl.OrderedMap |
OrderedMap, a map which preserves ordering. |
| pl.Set |
A Set class. |
| pl.app |
Application support functions. |
| pl.array2d |
Operations on two-dimensional arrays. |
| pl.class |
Provides a reuseable and convenient framework for creating classes in Lua. |
| pl.comprehension |
List comprehensions implemented in Lua. |
| pl.config |
Reads configuration files into a Lua table. |
| pl.data |
Reading and querying simple tabular data. |
| pl.dir |
Useful functions for getting directory contents and matching them against wildcards. |
| pl.file |
File manipulation functions: reading, writing, moving and copying. |
| pl.func |
Functional helpers like composition, binding and placeholder expressions. |
| pl.input |
Iterators for extracting words or numbers from an input source. |
| pl.lapp |
Simple command-line parsing using human-readable specification. |
| pl.lexer |
Lexical scanner for creating a sequence of tokens from text. |
| pl.luabalanced |
Extract delimited Lua sequences from strings. |
| pl.operator |
Lua operators available as functions. |
| pl.path |
Path manipulation and file queries. |
| pl.permute |
Permutation operations. |
| pl.pretty |
Pretty-printing Lua tables. |
| pl.seq |
Manipulating iterators as sequences. |
| pl.sip |
Simple Input Patterns (SIP). |
| pl.strict |
Checks uses of undeclared global variables. |
| pl.stringio |
Reading and writing strings using file-like objects. |
| pl.stringx |
Python-style extended string library. |
| pl.tablex |
Extended operations on Lua tables. |
| pl.template |
A template preprocessor. |
| pl.test |
Useful test utilities. |
| pl.text |
Text processing utilities. |
| pl.utils |
Generally useful routines. |
| pl.xml |
XML LOM Utilities. |


Id: 103
Name: Doofus Rick
Status: unknown
Species: Human
Type:
Gender: Male
OriginName: Earth (J19ζ7)
OriginUrl: https://rickandmortyapi.com/api/location/31
LocationName: Earth (Replacement Dimension)
LocationUrl: https://rickandmortyapi.com/api/location/20
Image: https://rickandmortyapi.com/api/character/avatar/103.jpeg
Episode: ['https://rickandmortyapi.com/api/episode/10', 'https://rickandmortyapi.com/api/episode/22']
Url: https://rickandmortyapi.com/api/character/103
Created: 2017-12-01T12:29:27.984Z


The Young Offenders will return to our screens for a third series next year
Episodes for the third series were shot back-to-back with season two
by Ian ManganFans of The Young Offenders can rejoice as it has today been confirmed that the hit comedy show will be getting a third series.
Loveable teen troublemakers Conor and Jock are set to return to screens for more of their unruly antics in their native Cork in the new year.
The coming of age series which stars Alex Murphy, Chris Walley and Hilary Rose has been commissioned by the BBC for a third season, the broadcaster today announced.
Show creator Peter Foott revealed that extra episodes for the third season had been filmed back-to-back with season two.
Foott said he was "thrilled" that The Young Offenders would be returning and that they "had a lot of fun" shooting the two seasons over the summer in Cork.
He said: "We can't wait for everyone to see more of Conor, Jock, Mairead and all the characters next year in our third series. We've been really amazed at the huge reaction to series two so far, and we're so happy that we can officially announce that there's more on the way for fans of the show."
It's no surprise that the comedy series which also airs on RTE Two is getting another series considering the success it has seen.
Over 370,000 viewers tuned into RTE for the first episode of season two upon the show's return earlier this month.
Kate Daughton, Head of BBC Comedy, said: “Young Offenders is spit-your-tea out funny and heartbreakingly emotional.
Daughton hailed the "passion" of the "very special cast, crew and writing team," saying that the broadcaster "can't wait" to share season three with fans.
How to follow the Irish Mirror on social media
You can like our main Facebook page here.
Our Irish Mirror Sports page - which brings you all your Irish and UK sports news - can be found here.
You can also check out our Irish Mirror GAA sports page.
The Irish Mirror Twitter account is @IrishMirror while our sports account is @MirrorSportIE.
Our Instagram account can be found here.
She added: "Cork’s finest have never looked finer.”
Fiona Campbell, Controller BBC Three said: “At BBC Three, we love championing local voices and The Young Offenders are a brilliant success story for us .
"With great characters and on point writing that is hugely relatable to our audience, we’re delighted they’re coming back for another series.”
The Young Offenders continues on RTE Two on Monday night.


VexFlow JSON Examples
Simple Chord
Simple Chord (SVG)
Simple Chord Scaled 75%
Simple Chord with Octaves
Simple Chord with Duration
Simple List of Notes
Simple Sequence of Chords
Explicit Sequence of Chords
Chord Across Treble & Bass Clefs
Explicit Voices
Explicit Voices with Beaming
TODO


Pep Lijnders comments on Liverpool’s ‘Only Pressure’ are beautiful
by Jordan ChamberlainLiverpool will be in safe hands after Jurgen Klopp eventually departs.
That’s if Pep Lijnders’ management is half as good as his ability to portray our team’s identity in his interviews.
There’s obviously no confirmation he’ll take the reigns, but the manner in which he has risen up to coaching ranks under Klopp’s guidance suggest he’ll be a great candidate at the very least.
Lijnders has been discussing the team’s mentality and the pressure of the title race, wonderfully suggesting that the only ‘pressure’ the side feels is the pressure to help your team-mate.
The Dutchman told the official Liverpool FC magazine: “Purely the mentality of the boys in the hard, hard moments, never giving up and never giving up our playing style. Never complaining or making excuses on the training pitch.
“I love how we tell our team every day that we are capable of becoming really great as long as we focus and we don’t drop concentration – we need to maintain 100 per cent tactical discipline in our way.
“That’s why I think these last few games were incredible in all aspects. We have to keep the same hunger and passion to win but at the same time understand that it’s much more important that we stay focused on how we play and play our way, whatever the scoreline, wherever we play and whoever we play against.
“We must never leave someone alone and need to help the player who is chasing from all directions. That should be the only pressure: to help your teammate.”
The attitude of this Liverpool team really is second to none.
It’s been shown not only in our many unbelievable comebacks and late goals – but also in our relentless consistency.
it takes a truly special team to win the Champions League in the same season as racking up 97 points domestically, then to start the next campaign in a similar vein with no new first-team arrivals.
The Reds face Crystal Palace on Saturday afternoon, perhaps without Mo Salah and Andy Robertson – both of who are carrying knocks.
Hopefully James Milner and Divock Origi, the likely replacements, will be able to replace them.


Clara Gonzalez Sueyro
Anthropologist and UX Researcher based in Oakland, California
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@main("Welcome to Play") {
Connect 4 is a two player, turnbased strategy game.

Your Goal is to build up consecutive lines of tokens to ultimately get 4 tokens in a row and win the game.
These rows can be vertical, horizontal or even diagonal! But beware, your enemy can and will disrupt your plans.
At the start of the Game each of you picks a color, you'll get 21 Tokens of that color to try and win the game with.
If it's your turn you choose a Column to put in your token, the token then falls down until it either reaches the edge of the board or another Token
After your token has fallen down your opponent's turn begins.
The Game ends as soon there is a consecutive row of 4 Tokens in the same color. This can happen either horizontally
Or vertically
or even diagonally!
The Game ends in a draw if both players happen to run out of Tokens and all 42 Fields in the Board are filled without 4 of the same color in a row.
Well, let's get started shall we? Check out the Lobby


Practical information: Annapurna Circuit
by Monika Suchoszek
As always before a trip, I did my homework and researched what we’d need to take with us. This was no different for our trip to Nepal. The internet is full of information about the Annapurna circuit, however the set of things you take really depends on the style of your hike. We decided to do the Annapurna Circuit without any help of porters or guides, carrying our backpacks and using MAPS.ME app for navigation. We did not have any major problems but there are many things which will make your trip more pleasant and easier, so is why I want to point them out again in this post. We spent the first 24 days of a March in Nepal and we had very sunny but cold weather above 2800m and a bit of rain in the afternoons in the lower parts. There was also a hard snow surface when crossing the Thorong La pass which was pretty slippery to walk on. March is also a perfect month to admire a beautiful rhododendrons flowering around Poon Hill, turning the entire valley into a stunning, colorful landscape!
Accommodation
In each village there are plenty of options to sleep (hotels and guesthouses, from very simple wooden constructions to more fancy ones), all of them also serve as restaurants. Usually we did not pay for a room or we got a great discount when promise to buy a dinner and breakfast from our hosts (we only paid at the beginning not knowing about this system :p). You just simply need to ask the host if you can stay a night for free when buying all food (dinner and breakfast the next morning) there.
The rooms never have a heating system so take this into consideration when choosing sleeping bag which should have a comfort zone around zero (above 3600m we used warm hats and down jackets during the night). A good sleeping bag is necessary, however higher up there were additional blankets or duvets in the room which we always used on the top of the sleeping bag what was extremely helpful to keep us warm. Chame was the first village where we could warm up next to the stove in a dining room after a sunset when the temperature drops immediately. It is also a perfect opportunity to sit with a cup of tea and to talk with other hikers during long evening hours.
Equipment
We were quite happy about our equipment but still some things were missing and others were not very useful. Clothespins are definitely something we would recommend to take. You can easily wash your clothes in the guesthouses and hang them to dry in the sun, though it was difficult to ensure our clothes wouldn’t fly off the string :p On the other hand we did not use our cotton slipping bag liners in Nepal but they were very useful during next part of our trip in SE Asia. Don’t forget earplugs (very thin walls between the rooms!) and vitamin A ointment (perfect to protect lips and hands from the strong sun, wind and low temperatures!).
Water
Most of the safe drinking water stations were closed (we used only one in Tal, just to check the quality of the water and it was fine, 40 rupees per liter though this price goes up to as you go up the mountains). We had with us Care Plus Water Filter and water purification tablets. Mainly we used the first one, connected to big 1.5l Fanta bottles. We reused these bottles for the entire duration of the circuit, we considered this very important for the environment since we noticed several times that plastic garbage was being burned or dumped in the streams. Important to buy them before you start a trek because there were only 0.5l bottles available during a trekking which really contributes to the big problem about plastic waste. In Manang or higher it is good to fill your bottles with water in the evening because it happened to us that in the morning the water in the pipes was frozen and we had to wait until sun was higher on horizon. We also did not have a chance to get a warm shower above Manang… at this point baby wipes became very useful :p Sometimes we had to pay for a warm shower (between 100-150 rupees) so better ask your host at the beginning.
Electricity and internet
On average we had a chance to charge our batteries every other day or so, though usually just for a few hours per day since there were many, long electricity supply interruptions. Our power bank was helping us to keep our phones and GoPro charged. Be prepared to spend many nights with a candle light and remember to take a headlamp with extra batteries. Our european plug adapter worked but did not fit nicely to all sockets.
Last years access to the internet has improved and you can enjoy it almost along the whole trek. However, few days before we started Annapurna Circuit there was a failure in internet service provider (at least that was always the explanation we got :p) and we did not have access to the internet most of the time. Last time we had an internet in Dharapani and then after 8 days in Mukintinah when we finished first part of a trek. Therefore, my kindle was my best friend during these days since it doesn’t require to be charged frequently. We have never paid any extra fees for internet or electricity but we have seen sometimes in other places extra fees for internet. As for cell phone reception, note that in many areas it may not be possible to get a signal.
Food
The quality of the food and the size of a portions depends on a guesthouse where you stay. In general, the food had a really nice flavour taking in consideration how little ingredients were used to prepare our meals. Of course, the higher we were, the less vegetables were used for cooking. Surprisingly the price of a breakfast was the same or even higher than price of a lunch, often sets are sold at a discount (oat porridge, fried potatoes, bread and coffee). My favourite breakfast was oat porridge with apple and Tibetan bread with jam and it kept me full for a long time. You can buy two types of bread, chapati (flat bread from the Indian Subcontinent, similar to tortilla) and Tibetan bread (deep-fried and taste like doughnut).
Most menus consists of rice dishes, noodle soups, spring rolls, pizzas from a pan (sometimes surprisingly good) and a big variety of a hot beverages, our favourite was hot lemon. You can always buy a “big pot” what is basically a 1L flask of tea you can drink slowly during the evening to stay constantly warm. Tip to safe money: take your own favourite tea or buy it in a local shop and just order hot water what was always possible. Relatively expensive are snacks like candy bars, chocolate, chips or sodas and they are very often expired. We took our favourite protein bars, chocolate covered nuts and other sweets with us. One of our favourite dishes were steamed or fried momo (dumplings) with vegetable or potatoes and cheese stuffing. It was very practical to order breakfast in the evening so we didn’t have to wait long in the morning (it was always possible to order breakfast at 7am) and leave early for a hike.
Altitude sickness
We started our trekking in Bhulbhule (840m) and went through Thorong La pass (5416m), often climbing 500m to 1000m up a day. Since we moved every day higher, the high atlitude disease was a real danger. To avoid it we decided firstly to stay hydrated (4L of water every day for each of us) and don’t ignore early symptoms. Severe headache, sleeplessness, dizziness, nausea, vomiting, loss of appetite, lots of coughing etc are next, more severe problems which are occurring with the progression of an illness. You should not ignore even a mild headache and stay an extra day on the same altitude (we had a mild headache in Manang, easily manageable with an over the counter pain killer (ibuprofen), but stayed an extra day there to avoid worse later) or even go lower when it is not over. In Manang there is a doctor where you can go every day during a certain hours and ask about his suggestions. We brought Diamox as well, though we didn’t need it (we did take half a tablet to check if we’d experience any side-effects). We also didn’t have any problems to sleep, even at 4800m but we know from other people that they have many problems and had to stay few days extra in one village so it’s good to have few extra days when you plan this hike. We spent our backup days in Pokhara, chilling out :D
Permits
When we arrived to Kathmandu there was a Holi festival and all offices had shorter opening hours. That was something we didn’t check before our trip and it was not the last time we suddenly joined the national holiday. Luckily, the office of Nepal Tourism Board in Kathmandu (Pradarshani Marg, Kathmandu 44617, Nepal) where we obtained our permits was still open in the afternoon when we got there (open daily between 9am and 3pm, on public holidays 9am to 12am). First you need to get TIMS (Trekkers’ Information Management Systems) card and ACAP (Annapurna Conservation Area Project) permit which allowed us for combined trek of Annapurna Circuit and Poon Hill (for each document you need 2 passport-sized photos). Remember to have with you the names of your entry and exit points during a trek and dates when you want to do this. You also need to provide a insurance policy number and emergency contact. Each document costs 20$ and allows them to keep track of trekkers and improve their safety. You’ll have to show these permits and sign the log at each check-point along the circuit.
Environment
Unfortunately, a large numbers of hikers visiting Himalayas every year and contribute to the waste problem and pollution. We saw huge piles of plastic bottle behind the lodges, therefore we decided to collect all plastic waste we produced and bring it back to Pokhara. At least we didn’t leave them in the mountains. The more extra bottles you buy, the more plastic will end up in the rivers or being burned behind someone’s guesthouse.
Money
Make sure you have enough cash with you for the whole trek and some dollars in case of emergency. We simply exchanged euro to rupees in Pokhara. There are many exchange offices with almost the same rates. Better to have small denominations but usually we paid at the end of a stay in each guesthouse so 1000 rupees note was not a problem to pay with. We spend daily around 3800 rupees for both of us (max 5200 rupees in High Camp).
Transportation
During our trip in nepal we always used local buses. In view of spiral and damaged roads, it always took us few times longer than in Europe for the same distance (usually you should count 30km/h average speed). Best ask in the guesthouse where you are staying about the bus options. Almost always they can sell you a ticket or call someone responsible for buses in a certain village. Usually, it is not so obvious where is a bus stop, you just need to ask locals or bus driver and finally someone will point you a right direction. It can get really crowded inside, with all backpacks and bags with local products for sale. It happened once that we had to stand 4,5h on a bus to Besisahar because they sold approximately double number of tickets for the bus and decided to squize all of us inside of one instead of orginizing a second bus. For a bus tickets we paid between 300-800 rupees, depends on the distance. Buses are old and really dusty, we saw people getting motion sickness.
Some expenses:
- Visa to Nepal for 30 days - 35 euro/person
- Taxi from the airport to Thamel district - 700 rupees (prepaid), 500 rupees when we negotiated the price with the drivers waiting outside of the airport (you need to pass by the prepaid taxis zone)
- Permits - 40$/person (TIMS + Annapurna Conservatory Area)
- Pokhara to Besishahar bus - 400 rupees/person (4.5h)
- Muktinath to Old Jamson bus - 300 rupees/person (1.5h)
- Marpha to Tatopani bus - 800 rupees/person (5h)
- Ghandruk to Pokhara bus - 400 rupees/person
- Paragliding - 7000 rupees/person (Frontiers Paragliding)
- Pokhara to Kathmandu bus - 700 rupees/person
- Swayambhunath temple - 200 rupees/person
List of our equippement for Annapurna Circuit trekking:
- Hiking shoes (Salewa Wildfire GTX - Monika and Salomon Speedcross 4 GTX - Sebastian)
- Flip-flops
- Gaiters
- Sleeping bag
- Sleeping bag liner
- Trekking poles
- Thermal underwear (leggings plus long-sleeved t-shirt)
- Pyjamas (t-shirt plus shorts or leggings)
- Waterproof jacket
- Dawn jacket
- Hiking trousers
- 3 technical t-shirts
- Softshell
- Underwear (3xhiking socks, 3xpanties, 2xsport bra)
- Warm hat
- Buff (warm and thin)
- Gloves (warm and thin)
- Cap
- Fast drying towels
- Duck tape
- Head lamp + batteries
- Pocket knife
- Padlock
- Trash bags
- Metal cups
- Spork
- Piece of paracord
- Soap
- Shampoo
- Toothpaste
- Toothbrush
- Blistex
- Sunscreen spf. 50
- Hairbrush
- Nail file
- Razor
- Shaving cream
- Baby wipes
- Hand sanitizer
- Cotton buds
- Ear plugs
- Toilet paper
- Sunglasses
- Water purification tablets
- Power-bank
- Camera
- GoPro
- Kindle
- Passport
- Passport photos for permits and visa (5!)
- Notebook with pen
- Copy of passport
- Health insurance
- Cash (25euro per day per person during the hike)
- Yellow book
- Snacks: protein bars, snickers, M&M, nuts, dry fruits
- 2 x 1.5l bottle to refill every day
- Isotonic powder to dissolve in water
- Imodium
- Fenistil
- Compeed plasters
- Diamox
- Peroxygel
- Ibum (ibuprofen)
- Paracetamol
- Vitamin A ointment
- Vitamines
- Probiotics
- Melatonine
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{% set title = 'Components › Statistic' %} {% set page = 'components-statistic.html' %} {% extends 'layouts/master.html' %} {% block plugins_css %} {% endblock %} {% block content %} Statistic Dashboard Components Statistic Statistics The chat component and is equipped with a JavaScript API, making it easy for you to integrate with Back-end. Summary Chart Text $1,858 Sold 4 items on 2 customers View All Item List (4 Items) $805 iBook Noob by Ahmad Sutisna Sunday $405 Headphone Blitz by Hasan Basri Sunday $499 RocketZ by Hasan Basri Sunday $149 Xiaomay Readme 4.0 by Kusnaedi Tuesday Statistics Week Month Year 7% $243 Today 23% $2,902 This Week 9% $12,821 This Month 19% $92,142 This Year Popular Browser Chrome 48% Firefox 26% Safari 14% Opera 7% IE 5% Visitors Indonesia 8,400 Palestine 7,328 Syiria 5,830 Malaysia 4,763 Referral URL 558 Google 338 Facebook 238 Bing 190 Yahoo {% endblock %} {% block plugins_js %} {% endblock %} {% block page_js %} {% endblock %}
The chat component and is equipped with a JavaScript API, making it easy for you to integrate with Back-end.


Who we are
NHS Digital was set up by the Department of Health and Social Care in April 2013 and is an executive non-departmental public body that provides national information, data and IT systems for health and care services. We exist to help patients, clinicians, commissioners, analysts and researchers. Our goal is to improve health and social care in England by making better use of technology, data and information.
Find out more about NHS Digital.
Mentions of "us" and "we" mean NHS Digital and "you" means anyone using NHS login.
We have developed a platform for you to prove your I.D. and create your own NHS login account. With this, you can use “Connected Services” such as health related websites and apps, provided by the NHS or by third parties on behalf of the NHS “Connecting Parties”. To create your NHS login, we need to take some personal information from you.
We are the data controller of the personal information you provide to us when:
- we verify (check) your identity
- you use NHS login to access Connecting Services
- you contact us
We do not have access to any of your medical records or the product provided by the Connected Services.
What personal information we collect about you
NHS login provides access to online health services. The different items of your personal information which you choose to provide to conduct ID checks will determine the level of identity verification you achieve. The levels of identity verification are low-level , medium-level, and high-level. The level of identity verification which you have will affect which Connected Services you are able to access using NHS login. Once you have established a low or medium-level you may at any time provide the further personal information required to move to a higher level.
Identity and contact information
We collect your basic personal details needed to process your NHS login application, including:
- For the low-level identity verification, NHS login will process your email address and mobile phone number.
- For the medium-level identity verification, NHS login will process your email address, mobile phone number and, depending on the user journey selected either 'first name, middle name, last name, date of birth and postcode' or 'NHS number and date of birth'.
- For the high-level identity verification, NHS login will process all of the data items in the medium-level identity verification plus the special category information detailed below.
Special category information (sensitive personal data)
For high-level identity verification we also collect special category information needed to verify your identity, including:
- a short video of you
- biometric data of your face that will include ethnicity
- information from your I.D. document
Technical information
In all cases we also collect technical information needed for security and to set up and manage your account to a Connected Service. This includes:
- linkage key information (GP Account I.D., GP ODS code, GP system Linkage key)
- audit data and event logs
- online identifiers, like your Internet Protocol (IP) addresses
Why we collect your personal information
We collect personal information from you to:
- verify who you are
- provide you with an NHS login so that you can access Connecting Services
- provide Connecting Parties with data you explicitly agree to us sharing
- to inform you of new Connected Services which you can access using NHS login
Collecting this information also allows us to manage our service, so that we can:
- improve our manual and automated I.D. checking function by monitoring the rates of successful and unsuccessful I.D. verification submissions
- provide data in support of the NHS Digital protective monitoring plan to improve the security of the service
- improve the accuracy of the data held within our NHS systems
We put analytic cookies on your device to collect information about how people use NHS login and help us make improvements to it.
Our legal basis for using your information
We are directed by NHS England, under its powers conferred by the Health and Social Care Act 2012 and regulation 32 of the National Institute for Health and Care Excellence (Constitution and Functions) and the Health and Social Care Information Centre (Functions) Regulations 2013, to establish and operate the NHS login Services.
As NHS Digital processes personal data under the legal direction from NHS England, then the organisations are joint data controllers for data protection purposes. The legal bases under the Data Protection Act 2018 / General Data Protection Regulation (GDPR) for the processing is explained here.
NHS Digital processing of personal data
GDPR Article 6(1)(a) - ‘consent of the individual’ (solely for the purposes of non-essential cookies, which comprise personal data, which are placed on the platform).
GDPR Article 6(1)(c) – the ‘processing is necessary for compliance with a legal obligation to which the controller (NHS Digital) is subject’.
NHS Digital processing of special category data (sensitive personal data)
GDPR Article 9(2) (h) – ‘processing is necessary for the purposes of preventive or occupational medicine, for the assessment of the working capacity of the employee, medical diagnosis, the provision of health or social care or treatment or the management of health or social care systems and services’.
Data Protection Act 2018, schedule 1, part 1, paragraph 2, sub paragraph (2), sub paragraph (f) – ‘the management of health care systems or services or social care systems or services’.
NHS England processing of personal data
GDPR Article (6)(1)(e) – ‘processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller’.
We only collect, use and share your information when we have an appropriate legal basis to do so.
How we process your personal information
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Page last reviewed: 07 May 2019


This is a collection of graphs of output from the 6581 SID emulation. There are several sets of graphs spread across six pages.
Each of the graphs covers 25,000 processor cycles, plotting the audio level at each cycle. Some points will of course be culled (unless you're viewing on a display that's at least 25,000 pixels wide), but this is by far the most accurate waveform views that I've been able to produce.
| Type | Description |
|---|---|
| Waveform generator | These are outputs from the waveform generator alone at several settings. Each combination of waveforms is available at two frequencies, A4 (440 Hz) and A7 (3520 Hz). No combinations of waveforms with noise are shown because those combinations always output a steady 0. |
| Sync and ring modulation | Additional waveform generator outputs, but these are produced by synching two generators. Three sync graphs are available per waveform (except noise, which isn't going to look much different if synched), one at A4 synched to C4 (261.6 Hz), one at A7 synched to C4, and one at A7 synched to C7 (2093 Hz). A set of three similar graphs are shown for ring-modulated triangle waves (only triangle can be ring-modulated). |
| Envelope generator | Outputs directly from the envelope generator. There is a small variety of different settings for attack, decay, sustain, and release shown. All of them are very low settings as even an attack of 3 will last longer than 25,000 clock cycles. The graph at the bottom demonstrates the wrapping bug that happens when an attack is gated and the gate is immediately turned off; the level should stay at 0, but it instead wraps around to 255 and drops as a normal release. |
| Voice | Outputs from the amplitude modulator that combines the waveform and envelope generator outputs. These graphs are all generated from the same envelope settings: attack 0, decay 0, sustain 8, and release 0. |
| Filter | Outputs directly from the internal filter/mixer. The top three graphs show unmixed, unfiltered voices; the rest of the graphs show the same three waveforms combined and filtered in various ways. The low, band, and high pass filters are used with three different cutoff frequencies. |
| Full chip test |
The same graphs as with the filter, but including the external filter. This is also the
only set of graphs that's produced fully through the regular pin interface without the
benefit of accessing individual internal parts of the 6581. The output shown is what comes
off the AUDIO pin.
|


vignettes/AI4M_C1_W1_lecture_ex_01.rmd
AI4M_C1_W1_lecture_ex_01.rmd
%load_ext rpy2.ipython
# Import necessary packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import os
import seaborn as sns
set()
sns.
import os
= os.getcwd()
cwd cwd
'/Users/zero/Desktop/AI4Medicine/vignettes'
# Read csv file containing training datadata
= pd.read_csv("../analysis/notebooks/Diagnosis/M1/nih/train-small.csv")
train_df # Print first 5 rows
print(f'There are {train_df.shape[0]} rows and {train_df.shape[1]} columns in this data frame')
train_df.head()
There are 1000 rows and 16 columns in this data frame
| Image | Atelectasis | Cardiomegaly | Consolidation | Edema | Effusion | Emphysema | Fibrosis | Hernia | Infiltration | Mass | Nodule | PatientId | Pleural_Thickening | Pneumonia | Pneumothorax | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 00008270_015.png | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8270 | 0 | 0 | 0 |
| 1 | 00029855_001.png | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 29855 | 0 | 0 | 0 |
| 2 | 00001297_000.png | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1297 | 1 | 0 | 0 |
| 3 | 00012359_002.png | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12359 | 0 | 0 | 0 |
| 4 | 00017951_001.png | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 17951 | 0 | 0 | 0 |
%%R
library(tidyverse)
<- read_csv("../analysis/notebooks/Diagnosis/M1/nih/train-small.csv")
train_df str_glue('There are {nrow(train_df)} rows and {ncol(train_df)} columns in this data frame')
%>% glimpse() train_df
[36m──[39m [1m[1mColumn specification[1m[22m [36m────────────────────────────────────────────────────────[39m
cols(
Image = [31mcol_character()[39m,
Atelectasis = [32mcol_double()[39m,
Cardiomegaly = [32mcol_double()[39m,
Consolidation = [32mcol_double()[39m,
Edema = [32mcol_double()[39m,
Effusion = [32mcol_double()[39m,
Emphysema = [32mcol_double()[39m,
Fibrosis = [32mcol_double()[39m,
Hernia = [32mcol_double()[39m,
Infiltration = [32mcol_double()[39m,
Mass = [32mcol_double()[39m,
Nodule = [32mcol_double()[39m,
PatientId = [32mcol_double()[39m,
Pleural_Thickening = [32mcol_double()[39m,
Pneumonia = [32mcol_double()[39m,
Pneumothorax = [32mcol_double()[39m
)
Rows: 1,000
Columns: 16
$ Image [3m[90m<chr>[39m[23m "00008270_015.png", "00029855_001.png", "00001297_0…
$ Atelectasis [3m[90m<dbl>[39m[23m 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
$ Cardiomegaly [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Consolidation [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Edema [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Effusion [3m[90m<dbl>[39m[23m 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Emphysema [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Fibrosis [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Hernia [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Infiltration [3m[90m<dbl>[39m[23m 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Mass [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Nodule [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, …
$ PatientId [3m[90m<dbl>[39m[23m 8270, 29855, 1297, 12359, 17951, 1232, 17135, 27235…
$ Pleural_Thickening [3m[90m<dbl>[39m[23m 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Pneumonia [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ Pneumothorax [3m[90m<dbl>[39m[23m 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
# Look at the data type of each column and whether null values are present
train_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 16 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Image 1000 non-null object
1 Atelectasis 1000 non-null int64
2 Cardiomegaly 1000 non-null int64
3 Consolidation 1000 non-null int64
4 Edema 1000 non-null int64
5 Effusion 1000 non-null int64
6 Emphysema 1000 non-null int64
7 Fibrosis 1000 non-null int64
8 Hernia 1000 non-null int64
9 Infiltration 1000 non-null int64
10 Mass 1000 non-null int64
11 Nodule 1000 non-null int64
12 PatientId 1000 non-null int64
13 Pleural_Thickening 1000 non-null int64
14 Pneumonia 1000 non-null int64
15 Pneumothorax 1000 non-null int64
dtypes: int64(15), object(1)
memory usage: 125.1+ KB
%%R
%>%
train_df ::plot_missing(.) DataExplorer
“PatientId” has an identification number for each patient.
print(f"The total patient ids are {train_df['PatientId'].count()}, from those the unique ids are {train_df['PatientId'].value_counts().shape[0]} ")
The total patient ids are 1000, from those the unique ids are 928
%%R
str_glue("The total patient ids are {train_df$PatientId %>% length}, from those the unique ids are {train_df %>% count(PatientId) %>% nrow()} ")
The total patient ids are 1000, from those the unique ids are 928
FALSE
%%R
%>%
train_df ::rowid_to_column(var = "row_id") %>%
tibble::get_dupes(PatientId) %>%
janitorarrange(dupe_count %>% desc) %>%
head()
[90m# A tibble: 6 x 18[39m
PatientId dupe_count row_id Image Atelectasis Cardiomegaly Consolidation Edema
[3m[90m<dbl>[39m[23m [3m[90m<int>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m [3m[90m<dbl>[39m[23m
[90m1[39m [4m2[24m[4m0[24m213 6 198 0002… 0 0 0 0
[90m2[39m [4m2[24m[4m0[24m213 6 297 0002… 1 0 1 0
[90m3[39m [4m2[24m[4m0[24m213 6 441 0002… 0 0 1 0
[90m4[39m [4m2[24m[4m0[24m213 6 790 0002… 0 0 1 0
[90m5[39m [4m2[24m[4m0[24m213 6 841 0002… 1 0 0 0
[90m6[39m [4m2[24m[4m0[24m213 6 986 0002… 0 0 1 0
[90m# … with 10 more variables: Effusion <dbl>, Emphysema <dbl>, Fibrosis <dbl>,[39m
[90m# Hernia <dbl>, Infiltration <dbl>, Mass <dbl>, Nodule <dbl>,[39m
[90m# Pleural_Thickening <dbl>, Pneumonia <dbl>, Pneumothorax <dbl>[39m
%%R
%>%
train_df ::rowid_to_column(var = "row_id") %>%
tibble::get_dupes(PatientId) %>%
janitordistinct(PatientId, .keep_all = TRUE) %>%
pull(dupe_count) %>%
::plot_histogram() DataExplorer
= train_df.keys()
columns = list(columns)
columns print(columns)
# Remove unnecesary elements
'Image')
columns.remove('PatientId')
columns.remove(# Get the total classes
print(f"There are {len(columns)} columns of labels for these conditions: {columns}")
# Print out the number of positive labels for each class
for column in columns:
print(f"The class {column} has {train_df[column].sum()} samples")
['Image', 'Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion', 'Emphysema', 'Fibrosis', 'Hernia', 'Infiltration', 'Mass', 'Nodule', 'PatientId', 'Pleural_Thickening', 'Pneumonia', 'Pneumothorax']
There are 14 columns of labels for these conditions: ['Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Effusion', 'Emphysema', 'Fibrosis', 'Hernia', 'Infiltration', 'Mass', 'Nodule', 'Pleural_Thickening', 'Pneumonia', 'Pneumothorax']
The class Atelectasis has 106 samples
The class Cardiomegaly has 20 samples
The class Consolidation has 33 samples
The class Edema has 16 samples
The class Effusion has 128 samples
The class Emphysema has 13 samples
The class Fibrosis has 14 samples
The class Hernia has 2 samples
The class Infiltration has 175 samples
The class Mass has 45 samples
The class Nodule has 54 samples
The class Pleural_Thickening has 21 samples
The class Pneumonia has 10 samples
The class Pneumothorax has 38 samples
%%R
%>% colnames()
train_df
<-
columns %>%
train_df select(-Image, -PatientId) %>%
colnames()
str_glue(
"There are {
columns %>%
length()
} columns of labels for these conditions: {
columns %>%
str_c(collapse = ', ')
}"
)
%>%
train_df select(columns) %>%
# head() %>%
map_df(~ sum(.x)) %>%
t() %>% as.data.frame() %>%
set_names("n_samples") %>%
::rownames_to_column("class_name") %>%
tibblerowwise() %>%
mutate(res = str_glue("The class {class_name} has {n_samples} samples")) %>%
select(res)
Note: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(columns)` instead of `columns` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
[90m# A tibble: 14 x 1[39m
[90m# Rowwise: [39m
res
[3m[90m<glue>[39m[23m
[90m 1[39m The class Atelectasis has 106 samples
[90m 2[39m The class Cardiomegaly has 20 samples
[90m 3[39m The class Consolidation has 33 samples
[90m 4[39m The class Edema has 16 samples
[90m 5[39m The class Effusion has 128 samples
[90m 6[39m The class Emphysema has 13 samples
[90m 7[39m The class Fibrosis has 14 samples
[90m 8[39m The class Hernia has 2 samples
[90m 9[39m The class Infiltration has 175 samples
[90m10[39m The class Mass has 45 samples
[90m11[39m The class Nodule has 54 samples
[90m12[39m The class Pleural_Thickening has 21 samples
[90m13[39m The class Pneumonia has 10 samples
[90m14[39m The class Pneumothorax has 38 samples
# Extract numpy values from Image column in data frame
#images = train_df['Image'].values
= ["00016189_001.png", "00016508_040.png", "00017417_008.png", "00017470_002.png", "00017503_003.png", "00017504_059.png", "00017508_000.png", "00017524_018.png", "00017541_032.png", "00017549_001.png", "00017587_004.png", "00017645_002.png", "00017691_003.png", "00017702_000.png"]
images
# Extract 9 random images from it
= [np.random.choice(images) for i in range(9)]
random_images
# Location of the image dir
= '../analysis/notebooks/Diagnosis/M1/nih/images-small/'
img_dir
print('Display Random Images')
# Adjust the size of your images
=(20,10))
plt.figure(figsize
# Iterate and plot random images
for i in range(9):
3, 3, i + 1)
plt.subplot(= plt.imread(os.path.join(img_dir, random_images[i]))
img ='gray')
plt.imshow(img, cmap'off')
plt.axis(
# Adjust subplot parameters to give specified padding
plt.tight_layout()
Display Random Images
# Get the first image that was listed in the train_df dataframe
= train_df.Image[1]
sample_img = plt.imread(os.path.join(img_dir, sample_img))
raw_image ='gray')
plt.imshow(raw_image, cmap
plt.colorbar()'Raw Chest X Ray Image')
plt.title(
plt.show()print(f"The dimensions of the image are {raw_image.shape[0]} pixels width and {raw_image.shape[1]} pixels height, one single color channel")
print(f"The maximum pixel value is {raw_image.max():.4f} and the minimum is {raw_image.min():.4f}")
print(f"The mean value of the pixels is {raw_image.mean():.4f} and the standard deviation is {raw_image.std():.4f}")
The dimensions of the image are 1024 pixels width and 1024 pixels height, one single color channel
The maximum pixel value is 1.0000 and the minimum is 0.0000
The mean value of the pixels is 0.4669 and the standard deviation is 0.2492
sample_img
'00029855_001.png'
Run the cell below to plot up the distribution of pixel values in the image shown above.
# Plot a histogram of the distribution of the pixels
sns.distplot(raw_image.ravel(), =f'Pixel Mean {np.mean(raw_image):.4f} & Standard Deviation {np.std(raw_image):.4f}', kde=False)
label='upper center')
plt.legend(loc'Distribution of Pixel Intensities in the Image')
plt.title('Pixel Intensity')
plt.xlabel('# Pixels in Image') plt.ylabel(
/Users/zero/anaconda3/envs/AI4Medicine/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
Text(0, 0.5, '# Pixels in Image')
Before training, you’ll first modify your images to be better suited for training a convolutional neural network. For this task you’ll use the Keras ImageDataGenerator function to perform data preprocessing and data augmentation.
Run the next two cells to import this function and create an image generator for preprocessing.
# Import data generator from keras
from keras.preprocessing.image import ImageDataGenerator
# Normalize images
= ImageDataGenerator(
image_generator =True, #Set each sample mean to 0.
samplewise_center= True # Divide each input by its standard deviation
samplewise_std_normalization )
The image_generator
you created above will act to adjust your image data such that the new mean of the data will be zero, and the standard deviation of the data will be 1.
In other words, the generator will replace each pixel value in the image with a new value calculated by subtracting the mean and dividing by the standard deviation.
\[ \frac{x_{i}-\mu}{\sigma} \]
Run the next cell to pre-process your data using the image_generator
. In this step you will also be reducing the image size down to 320x320 pixels.
%%R -o train_df
<-
images_names ::dir_ls("../analysis/notebooks/Diagnosis/M1/nih/images-small/") %>%
fsbasename()
<-
train_df %>%
train_df filter(Image %in% images_names)
# Flow from directory with specified batch size and target image size
= image_generator.flow_from_dataframe(
generator =train_df,
dataframe="../analysis/notebooks/Diagnosis/M1/nih/images-small/",
directory="Image", # features
x_col# Let's say we build a model for mass detection
= ['Mass'], # labels
y_col="raw", # 'Mass' column should be in train_df
class_mode= 1, # images per batch
batch_size=False, # shuffle the rows or not
shuffle=(320,320) # width and height of output image
target_size )
Found 776 validated image filenames.
Run the next cell to plot up an example of a pre-processed image
# Plot a processed image
"white")
sns.set_style(= generator.__getitem__(0)
generated_image, label 0], cmap='gray')
plt.imshow(generated_image[
plt.colorbar()'Raw Chest X Ray Image')
plt.title(print(f"The dimensions of the image are {generated_image.shape[1]} pixels width and {generated_image.shape[2]} pixels height")
print(f"The maximum pixel value is {generated_image.max():.4f} and the minimum is {generated_image.min():.4f}")
print(f"The mean value of the pixels is {generated_image.mean():.4f} and the standard deviation is {generated_image.std():.4f}")
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
The dimensions of the image are 320 pixels width and 320 pixels height
The maximum pixel value is 2.1455 and the minimum is -1.8822
The mean value of the pixels is -0.0000 and the standard deviation is 1.0000
Run the cell below to see a comparison of the distribution of pixel values in the new pre-processed image versus the raw image.
# Include a histogram of the distribution of the pixels
set()
sns.=(10, 7))
plt.figure(figsize
# Plot histogram for original iamge
sns.distplot(raw_image.ravel(), =f'Original Image: mean {np.mean(raw_image):.4f} - Standard Deviation {np.std(raw_image):.4f} \n '
labelf'Min pixel value {np.min(raw_image):.4} - Max pixel value {np.max(raw_image):.4}',
='blue',
color=False)
kde
# Plot histogram for generated image
0].ravel(),
sns.distplot(generated_image[=f'Generated Image: mean {np.mean(generated_image[0]):.4f} - Standard Deviation {np.std(generated_image[0]):.4f} \n'
labelf'Min pixel value {np.min(generated_image[0]):.4} - Max pixel value {np.max(generated_image[0]):.4}',
='red',
color=False)
kde
# Place legends
plt.legend()'Distribution of Pixel Intensities in the Image')
plt.title('Pixel Intensity')
plt.xlabel('# Pixel') plt.ylabel(
/Users/zero/anaconda3/envs/AI4Medicine/lib/python3.7/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
warnings.warn(msg, FutureWarning)
Text(0, 0.5, '# Pixel')
type(raw_image)
numpy.ndarray
image download url: https://wwlhugij.labs.coursera.org/files/M1/nih/images-small/00000003_001.png
%%R
library(readr)
<-
image_names read_csv("../analysis/notebooks/Diagnosis/M1/nih/images_name.csv") %>% pull(Field1)
<-
urls str_glue("https://wwlhugij.labs.coursera.org/files/M1/nih/images-small/{image_names}")
%>% clipr::write_clip() urls
[36m──[39m [1m[1mColumn specification[1m[22m [36m────────────────────────────────────────────────────────[39m
cols(
Field1 = [31mcol_character()[39m
)


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Oshirabe exemplifies the expressive use of intermedia at its minimum. The spectacle begins with only music sounding from behind the curtain. The instruments are heard sequentially, starting with the nohkan followed by the kotsuzumi, the ōtsuzumi, and the taiko, completing the hayashi. There is no singing, and the percussionists do not utter any kakegoe. After a minute or two, the oshirabe concludes as the musicians drop out, one after the other, in reverse order. Music performed invisibly is at its most fleeting. The audience hears a mere ‘ghost’ of it. The absence of other elements intensifies the anticipation. As the viewers fall into silence, they get to appreciate the visual aspects of the stage in the atmosphere evoked by the music. This minimalistic beginning is a perfect starting point for a slow and gradual intermedia development -the essence of a Noh play.


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