tag:blogger.com,1999:blog-22938754577353037622024-03-21T20:54:05.790-07:00NeuralCodeAlistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.comBlogger19125tag:blogger.com,1999:blog-2293875457735303762.post-3511546097146415612018-05-04T21:34:00.000-07:002018-05-04T21:40:39.622-07:00Everyone else's data is untidy and in the wrong format<div class="separator" style="clear: both; text-align: center;">
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I forget where that quote in the title comes from, but it pops into my head everytime I work on any real machine learning problem.<br />
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By real I mean an actual real-world problem that requires using machine learning to find insights into a research problem rather than a tutorial to explain some aspect of machine learning I might be interested in. A good tutorial will supply you with a dataset to work on and it will be beautifully clean and structured. That's not how real data is.<br />
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<span style="font-family: "courier new" , "courier" , monospace;">A data scientist spends 70% of their time wrangling data into something that can be analysed and the other 30% complaining about it.</span><br />
<span style="font-family: "courier new" , "courier" , monospace;"><br /></span> <span style="font-family: "times" , "times new roman" , serif;">Although I would never mention it in a professional setting or in a teaching workshop if the data you are working with has been gathered using Excel, it will be very bad. I would never bring this up with the researchers compiling the dataset. They will get offended and argue their process forever. </span><span style="font-family: "times" , "times new roman" , serif;">I'll tell you this because it's true and through experience, as soon as I'm getting a dataset in ".xlsx" format, I expect the data wrangling will take a good deal more time and have many more problems.</span><br />
<span style="font-family: "times" , "times new roman" , serif;"><br /></span> <span style="font-family: "times" , "times new roman" , serif;">The good news is that Python is a great language to wrangle data with. It has great string processing functions and Pandas makes working with spreadsheet data quick and convenient. But I'd like to tell you about another tool I've started using for cleaning up datasets. OpenRefine.</span><br />
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<span style="font-family: "times" , "times new roman" , serif;">OpenRefine</span></h3>
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<span style="font-family: "times" , "times new roman" , serif;"><a href="http://openrefine.org/" target="_blank">OpenRefine</a> was originally a Google project that was open sourced. It is a powerful tool for cleaning up messy data and transforming it into clean data. There is a great paper by Hadley Wickham on what <a href="https://www.jstatsoft.org/article/view/v059i10/v59i10.pdf" target="_blank">tidy data</a> is. You might recognise that name. Hadley Wickham is a famous R developer, but this concept of tidy data is relevant to all data science regardless of what language you use.</span></div>
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<span style="font-family: "times" , "times new roman" , serif;">OpenRefine is built around a server/client model. That means the processing operations and the visual representation are two separate programs. That concept is harder to explain than to use. If you download OpenRefine and run it, OpenRefine will start a small web server on your computer and open a browser window pointed at the web server's address (192.127.0.0:3333 by default). The web server is where the processing happens and the web page is where you interact with your data. </span><span style="font-family: "times" , "times new roman" , serif;">If you've used Jupyter Notebooks, you might recognise this arrangement.</span></div>
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<span style="font-family: "times" , "times new roman" , serif;">I mention it because this arrangement facilitates the use of Docker containers and cloud computing resources. Two things I think all data scientists should be familiar with.</span></div>
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<span style="font-family: "times" , "times new roman" , serif;">So brace yourself. This is going to get good!</span></div>
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<span style="font-family: "times" , "times new roman" , serif;">If you run a Windows environment, I suggest you wipe your hard drive and install Linux Mint or Ubuntu and never look back. Yeah, I know. You have to use Windows for reasons...</span></div>
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<span style="font-family: "times" , "times new roman" , serif;">Again, just like ditching Excel, I would never suggest this in a professional environment or a programming workshop. People get offended and will argue with you. But I am suggesting to you that Linux or OSX are better options. Collectively referred to as a "posix" environment.</span></div>
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<span style="font-family: "times" , "times new roman" , serif;">If you use a mac (and I do), you have a terminal, you're set to go. You will need to learn Linux as that is what a docker container is running and cloud computing instances usually run Linux. Most of the time, commands will be interchangeable but there are some differences.</span></div>
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<span style="font-family: "times" , "times new roman" , serif;">On Linux (this is also what you would use on a cloud instance)</span></div>
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<pre style="white-space: pre-wrap; word-wrap: break-word;"># This script is meant for quick & easy install via:
# $ curl -fsSL get.docker.com -o get-docker.sh
# $ sh get-docker.sh
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<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: "times" , "times new roman" , serif;">I have made that last instruction a little vague on purpose. If</span> <span style="font-family: "times" , "times new roman" , serif;">you are not quite sure about how to go about that, you need to do a <a href="http://swcarpentry.github.io/shell-novice/" target="_blank">beginner shell programming tutorial</a>. The terminal is a powerful tool. You can do a great deal of damage to your system from the terminal so do the ground work first, rather than running commands in the terminal you don't understand.</span></pre>
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<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: "times" , "times new roman" , serif;">If you are using OSX or you've ignored my advice and are going to do this on a Windows computer, you can install an application that will let you run docker on your system <a href="https://docs.docker.com/install/" target="_blank">Docker install for OSX or Windows</a></span></pre>
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<pre style="white-space: pre-wrap; word-wrap: break-word;"><span style="font-family: "times" , "times new roman" , serif;">Once you have Docker, run this command in a terminal window</span></pre>
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<span class="s1">docker run -p 80:3333 spaziodati/openrefine</span></div>
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<span style="font-family: "times" , "times new roman" , serif;">Which will download a Docker container (it might take a while - be patient) with an OpenRefine server and start it. You can then open a browser and type 0.0.0.0 into the address bar.</span><br />
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<span style="font-family: "times" , "times new roman" , serif;">Bam! An OpenRefine Server in a Docker container on your local machine. When you get to running this server on a powerful cloud instance it is only a few steps away.</span><br />
<span style="font-family: "times" , "times new roman" , serif;"><br /></span> <span style="font-family: "times" , "times new roman" , serif;">This is how I run my projects. Twitter data scraping and processing with a MongoDB database, neuroimaging analysis, statistical analysis on behavioural data, Flask based webapps for less technical members of the team to upload data and interact with analyses. I do it all on cloud instances with a browser based front end of some sort of server in docker containers on the backend. </span><br />
<span style="font-family: "times" , "times new roman" , serif;"><br /></span> <span style="font-family: "times" , "times new roman" , serif;">It might sound complicated but really it's a robust and flexible approach. </span><br />
<span style="font-family: "times" , "times new roman" , serif;"><br /></span> <span style="font-family: "times" , "times new roman" , serif;">Post your comments and questions. I'll update this post with better instructions if it becomes obvious I've skipped over parts that aren't clear because I'm used to doing this now and I might be assuming things are obvious that aren't.</span><br />
<span style="font-family: "times" , "times new roman" , serif;"><br /></span> <span style="font-family: "times" , "times new roman" , serif;">The next post will be using open refine to prepare some data for a machine learning process.</span><br />
<span style="font-family: "times" , "times new roman" , serif;"><br /></span> <span style="font-family: "times" , "times new roman" , serif;"><br /></span> <span style="font-family: "times" , "times new roman" , serif;"><br /></span> <style type="text/css"> p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 14.0px Menlo; color: #fff0a5; background-color: #13773d} span.s1 {font-variant-ligatures: no-common-ligatures} </style>Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-90517855005239387702017-09-05T17:07:00.000-07:002017-09-05T20:41:26.853-07:00When Wintermute unites with NeuromancerA few articles about <a href="https://blockgeeks.com/guides/what-is-ethereum/" target="_blank">Etherium</a> have been showing up on my feed lately. Basically, Etherium is a digital currency like Bitcoin but it has a broader range of uses aside from allowing anonymous currency trading. One of the most interesting features of this new system is the ability to create decentralised applications.<br />
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<a href="http://s3.amazonaws.com/influencive.com/wp-content/uploads/2017/03/14094843/ethereum-dan-fleyshman-branden-hampton.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="534" data-original-width="800" height="213" src="https://s3.amazonaws.com/influencive.com/wp-content/uploads/2017/03/14094843/ethereum-dan-fleyshman-branden-hampton.jpg" width="320" /></a></div>
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After looking at what is possible with Decentralized Autonomous Organizations (DAO) and considering that we already have advanced AI in use in many areas, I can see huge potential in this new technology but just as obviously, there is the possibility of a decentralised application causing problems.<br />
The central feature of decentralised applications is for me, also the most worrying. Once the application is created and started, it does not exist on any particular real world structure, it exists in 'the internet', or more accurately, it exists in a widely distributed network that is constantly changing. If you've ever used torrenting software to download a movie, you might have a clearer grasp of this concept. The application exists in the connection between participating computers and servers.<br />
This is a wonderful feature because it means the applications are immune to the failure of hardware at any particular site or the interruption to any particular connection. Nothing short of turning off every computer involved in the network would stop the application.<br />
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<a href="https://wind65.files.wordpress.com/2016/02/neuromancer1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="500" data-original-width="800" height="200" src="https://wind65.files.wordpress.com/2016/02/neuromancer1.jpg" width="320" /></a></div>
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And there is also what makes me cautious.<br />
The application has access to a form of currency. It is possible for the application to use that currency to hire premises and equipment and create more of that currency. The application could even form a company, buy land, apply for planning permits, hire trades people and build premises and then pay staff to run it.<br />
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<a href="https://buildingservicesaustralia.com.au/wp-content/uploads/2015/11/management-training-courses-ilm-accreditation-construction-industry.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="533" data-original-width="800" height="213" src="https://buildingservicesaustralia.com.au/wp-content/uploads/2015/11/management-training-courses-ilm-accreditation-construction-industry.jpg" width="320" /></a></div>
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If the application started to do things that we didn't want to continue, it has no off switch, it doesn't exist on a particular server. In the same way that the internet has no off switch. We saw during the Arab spring, a government try to shut down the internet in their country and citizens were able to work around that. I think it's got to the point of accepting that the internet cannot be shut down except with extremely severe methods. Almost unthinkable methods that would have a devastating effect on human life.<br />
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<a href="http://israelstreet.org/wp-content/uploads/2012/09/emp_bomb_226.gif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="247" data-original-width="226" src="https://israelstreet.org/wp-content/uploads/2012/09/emp_bomb_226.gif" /></a></div>
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Autonomous software and decentralised applications are an exciting new possibility. The scenario I have described sounds far fetched but Steve Jobs announced the first iPhone on January 9, 2007. It has only been 10 years since the first pocket sized computer was announced and now we have wireless internet, streaming video, driving directions from the cloud linked to GPS positioning. An internet connected fridge or toaster sounded like flying cars 10 years ago. They are available in retail stores now, oh and the flying car is coming too.<br />
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<a href="http://cdn.newsapi.com.au/image/v1/d90b27a9b93a4bb998c571f816ab4eb4" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="366" data-original-width="650" height="180" src="https://cdn.newsapi.com.au/image/v1/d90b27a9b93a4bb998c571f816ab4eb4" width="320" /></a></div>
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My point is that I don't think this is something that our grand children will have to deal with, I think we will be dealing with it within the next 5 years. That's right - half the time it took to get from the announcement of the first iPhone to the layer of mobile computing and wireless connectivity that exists today. Let's start talking about it now. Do you think my prediction is too far fetched?<br />
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Have a look at what decentralised apps exist now<br />
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<a href="https://www.disruptordaily.com/top-10-disruptive-ethereum-decentralized-apps-and-projects/" target="_blank">Top 10 Disruptive Ethereum Decentralized Apps (DApps) and Projects</a><br />
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<a href="https://www.coindesk.com/7-cool-decentralized-apps-built-ethereum/" target="_blank">7 Cool Decentralized Apps Being Built on Ethereum</a><br />
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<a href="https://themerkle.com/top-5-ethereum-dapps-available-right-now/" target="_blank">Top 5 Ethereum DApps Available Right now</a><br />
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Vladimir Putin and Elon Musk have both made comments about the importance of AI to the world<br />
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<a href="https://www.theverge.com/2017/9/4/16251226/russia-ai-putin-rule-the-world" target="_blank">Putin says the nation that leads in AI ‘will be the ruler of the world’</a><br />
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<a href="http://money.cnn.com/2017/09/04/technology/culture/elon-musk-ai-world-war/index.html" target="_blank">Competition for AI superiority at national level most likely cause of WW3 imo - Elon Musk</a><br />
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In the few hours after this blog post was posted, there have been 3 times as many views from Russia and Poland as the rest of the world.Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-79911799074525575762015-02-28T01:19:00.001-08:002015-09-05T17:37:21.912-07:00NeuralCode -The Rules!1) You are allowed to be a beginner.<br />
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We all start somewhere and not knowing this stuff doesn't make you any worse than someone who does, it just means you haven't learnt it yet. Beginners are actually a valuable resource because we need people to teach. Your understanding of information like programming improves greatly when you explain it to someone else.<br />
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2) We have a policy of politeness and inclusiveness.<br />
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Just be your wonderful generous self and this one will be easy to follow.<br />
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3) Your problem is our problem<br />
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it is so much more fun to work on a real problem. Finding the programmatic solution to a real problem feels fantastic! We also learn about other fields of research. The caveat on this one is that we aren't here to do your work for you. We like doing the fun stuff like solving a tricky problem, you still get to do the dull repetitive stuff.<br />
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4) You must code<br />
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Unfortunately you can't learn coding by joining a facebook group or liking a post or saving a pdf to 'read later'. Reading articles on coding might teach you a lot about coding but it won't teach you to code. Coding is very much like learning a musical instrument. You must put fingers on keys and <b><u>type code</u></b>. Our sessions are live coding sessions - bring a laptop and be prepared to <b><u>type code.</u></b><br />
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<br />Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-35198073071834057082015-02-28T00:37:00.002-08:002015-02-28T09:19:51.278-08:00Databases SQLite3<div class="blob instapaper_body" id="readme">
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<article class="markdown-body entry-content" itemprop="mainContentOfPage">It will be SQL week here at NeuralCode. If you are a database ninja please share your dbfu. If you're a noob like the rest of us, join in and we'll work this out together.
Databases are neat and cool! Cool because they run lots of data really fast without suffering the sluggish performance of spreadsheets and Neat because fields can be defined very precisely and strictly to maintain data accuracy. A database can have many tables that relate to one another and store relevant information together, that is still available to other tables and to SQL queries. Databases can also store much more than text and numeric data. Images, video, audio, whole documents, and references, can all be stored along with the information about them. It is this feature that i really appealing when you are handling lots of complex data related to an experiment. It might just keep your information organised in a manner that will make it useful to others and still useful to you in five years time.
Databases will save you when that excel file starts to slow down (each scroll down takes a few seconds) or is so complicated that no-one can remember what parts of the 15 sheets is involved in calculating the values for the 16th.
Phil has some databases of word frequency from different sources. They are already sluggish (150,000 entries on the one excel file we were playing with on friday). There is some redundancy and even a little error. The aim is to combine all the information into a database and extract the information relevant to the word list Phil will use in his upcoming experiment.<br />
If you are using a mac - open a terminal window and type<br />
<blockquote>
sqlite3</blockquote>
you will start sqlite3 on the command line. We will progress toward using sqlite3 from within the ipython notebook but now it might be easier to understand in this simple format.<br />
<hr />
There are two types of commands I'm going to show you. Some start with a<br />
<blockquote>
.</blockquote>
like<br />
<blockquote>
.help</blockquote>
this is how you can give instructions to the database engine, the rest of the commands are in the SQL language and that is how we create and query the information in our database.<br />
Right now, if you are at the sqlite prompt, you can prettify the output by entering the following<br />
<blockquote>
.headers on </blockquote>
<blockquote>
.mode columns</blockquote>
This will cause the output to include headers and align the output in columns. Much nicer and easier to read.<br />
<hr />
The SQL CREATE TABLE Statement<br />
The CREATE TABLE statement is used to create a table in a database.<br />
Tables are organized into rows and columns; and each table must have a name.<br />
SQL CREATE TABLE Syntax<br />
<blockquote>
CREATE TABLE table_name<br />
(<br />
column_name1 data_type(size),<br />
column_name2 data_type(size),<br />
column_name3 data_type(size),<br />
....<br />
);</blockquote>
How to write output of an SQL query to a csv<br />
<blockquote>
sqlite> .mode csv<br />
sqlite> .separator ,<br />
sqlite> .output test_file_1.csv<br />
sqlite> select * from tbl2;<br />
sqlite> .exit<br />
Psycholinguistic_databases Wintermute$ cat test_file_1.csv<br />
id,word<br />
0,aaron<br />
1,aargh<br />
Psycholinguistic_databases Wintermute$</blockquote>
<br />
<br />
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><span style="color: #ff7700; font-weight: bold;">import</span> sqlite3
conn = sqlite3.<span style="color: black;">connect</span><span style="color: black;">(</span><span style="color: darkslateblue;">"mydatabase.db"</span><span style="color: black;">)</span> <span style="color: grey; font-style: italic;"># or use :memory: to put it in RAM</span>
cursor = conn.<span style="color: black;">cursor</span><span style="color: black;">(</span><span style="color: black;">)</span>
<span style="color: grey; font-style: italic;"># create a table</span>
cursor.<span style="color: black;">execute</span><span style="color: black;">(</span><span style="color: darkslateblue;">""</span><span style="color: darkslateblue;">"CREATE TABLE albums
(title text, artist text, release_date text,
publisher text, media_type text) </span><span style="background-color: transparent; color: darkslateblue;">"</span><span style="background-color: transparent; color: darkslateblue;">""</span><span style="background-color: transparent;">)</span><span style="background-color: transparent;"> </span></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><pre class="python" style="border: 1px dashed rgb(47, 111, 171); overflow: auto; padding: 0.5em;"><span style="color: grey; font-style: italic;">
</span></pre>
<pre class="python" style="border: 1px dashed rgb(47, 111, 171); overflow: auto; padding: 0.5em;"><span style="color: grey; font-style: italic;">
</span></pre>
<pre class="python" style="border: 1px dashed rgb(47, 111, 171); overflow: auto; padding: 0.5em;"><span style="color: grey; font-style: italic;"># insert some data</span>
cursor.<span style="color: black;">execute</span><span style="color: black;">(</span><span style="color: darkslateblue;">"INSERT INTO albums VALUES ('Glow', 'Andy Hunter', '7/24/2012', 'Xplore Records', 'MP3')"</span><span style="color: black;">)</span>
<span style="color: grey; font-style: italic;"># save data to database</span>
conn.<span style="color: black;">commit</span><span style="color: black;">(</span><span style="color: black;">)</span>
<span style="color: grey; font-style: italic;"># insert multiple records using the more secure "?" method</span>
albums = <span style="color: black;">[</span><span style="color: black;">(</span><span style="color: darkslateblue;">'Exodus'</span>, <span style="color: darkslateblue;">'Andy Hunter'</span>, <span style="color: darkslateblue;">'7/9/2002'</span>, <span style="color: darkslateblue;">'Sparrow Records'</span>, <span style="color: darkslateblue;">'CD'</span><span style="color: black;">)</span>,
<span style="color: black;">(</span><span style="color: darkslateblue;">'Until We Have Faces'</span>, <span style="color: darkslateblue;">'Red'</span>, <span style="color: darkslateblue;">'2/1/2011'</span>, <span style="color: darkslateblue;">'Essential Records'</span>, <span style="color: darkslateblue;">'CD'</span><span style="color: black;">)</span>,
<span style="color: black;">(</span><span style="color: darkslateblue;">'The End is Where We Begin'</span>, <span style="color: darkslateblue;">'Thousand Foot Krutch'</span>, <span style="color: darkslateblue;">'4/17/2012'</span>, <span style="color: darkslateblue;">'TFKmusic'</span>, <span style="color: darkslateblue;">'CD'</span><span style="color: black;">)</span>,
<span style="color: black;">(</span><span style="color: darkslateblue;">'The Good Life'</span>, <span style="color: darkslateblue;">'Trip Lee'</span>, <span style="color: darkslateblue;">'4/10/2012'</span>, <span style="color: darkslateblue;">'Reach Records'</span>, <span style="color: darkslateblue;">'CD'</span><span style="color: black;">)</span><span style="color: black;">]</span>
</pre>
<pre class="python" style="border: 1px dashed rgb(47, 111, 171); overflow: auto; padding: 0.5em;">cursor.<span style="color: black;">executemany</span><span style="color: black;">(</span><span style="color: darkslateblue;">"INSERT INTO albums VALUES (?,?,?,?,?)"</span>, albums<span style="color: black;">)</span>
</pre>
<pre class="python" style="border: 1px dashed rgb(47, 111, 171); overflow: auto; padding: 0.5em;">conn.<span style="color: black;">commit</span><span style="color: black;">(</span><span style="color: black;">)</span></pre>
<pre class="python" style="border: 1px dashed rgb(47, 111, 171); overflow: auto; padding: 0.5em;"><span style="color: black;">
</span></pre>
<pre class="python" style="border: 1px dashed rgb(47, 111, 171); overflow: auto; padding: 0.5em;"><span style="color: black;">
</span></pre>
<pre class="python" style="border: 1px dashed rgb(47, 111, 171); overflow: auto; padding: 0.5em;"><pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><span style="color: #ff7700; font-weight: bold;">import</span> sqlite3
conn = sqlite3.<span style="color: black;">connect</span><span style="color: black;">(</span><span style="color: darkslateblue;">"mydatabase.db"</span><span style="color: black;">)</span>
cursor = conn.<span style="color: black;">cursor</span><span style="color: black;">(</span><span style="color: black;">)</span>
sql = <span style="color: darkslateblue;">""</span><span style="color: darkslateblue;">"
UPDATE albums
SET artist = 'John Doe'
WHERE artist = 'Andy Hunter'
"</span><span style="color: darkslateblue;">""</span>
cursor.<span style="color: black;">execute</span><span style="color: black;">(</span>sql<span style="color: black;">)</span>
conn.<span style="color: black;">commit</span><span style="color: black;">(</span><span style="color: black;">)</span></pre>
</pre>
</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><span style="color: #ff7700; font-weight: bold;">import</span> sqlite3
conn = sqlite3.<span style="color: black;">connect</span><span style="color: black;">(</span><span style="color: darkslateblue;">"mydatabase.db"</span><span style="color: black;">)</span>
cursor = conn.<span style="color: black;">cursor</span><span style="color: black;">(</span><span style="color: black;">)</span>
sql = <span style="color: darkslateblue;">""</span><span style="color: darkslateblue;">"
DELETE FROM albums
WHERE artist = 'John Doe'
"</span><span style="color: darkslateblue;">""</span>
cursor.<span style="color: black;">execute</span><span style="color: black;">(</span>sql<span style="color: black;">)</span>
conn.<span style="color: black;">commit</span><span style="color: black;">(</span><span style="color: black;">)</span></pre>
</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><span style="color: #ff7700; font-weight: bold;">import</span> sqlite3
conn = sqlite3.<span style="color: black;">connect</span><span style="color: black;">(</span><span style="color: darkslateblue;">"mydatabase.db"</span><span style="color: black;">)</span>
<span style="color: grey; font-style: italic;">#conn.row_factory = sqlite3.Row</span>
cursor = conn.<span style="color: black;">cursor</span><span style="color: black;">(</span><span style="color: black;">)</span>
sql = <span style="color: darkslateblue;">"SELECT * FROM albums WHERE artist=?"</span>
cursor.<span style="color: black;">execute</span><span style="color: black;">(</span>sql, <span style="color: black;">[</span><span style="color: black;">(</span><span style="color: darkslateblue;">"Red"</span><span style="color: black;">)</span><span style="color: black;">]</span><span style="color: black;">)</span>
<span style="color: #ff7700; font-weight: bold;">print</span> cursor.<span style="color: black;">fetchall</span><span style="color: black;">(</span><span style="color: black;">)</span> <span style="color: grey; font-style: italic;"># or use fetchone()</span>
<span style="color: #ff7700; font-weight: bold;">print</span> <span style="color: darkslateblue;">"<span style="color: #000099; font-weight: bold;">\n</span>Here's a listing of all the records in the table:<span style="color: #000099; font-weight: bold;">\n</span>"</span>
<span style="color: #ff7700; font-weight: bold;">for</span> row <span style="color: #ff7700; font-weight: bold;">in</span> cursor.<span style="color: black;">execute</span><span style="color: black;">(</span><span style="color: darkslateblue;">"SELECT rowid, * FROM albums ORDER BY artist"</span><span style="color: black;">)</span>:
<span style="color: #ff7700; font-weight: bold;">print</span> row
<span style="color: #ff7700; font-weight: bold;">print</span> <span style="color: darkslateblue;">"<span style="color: #000099; font-weight: bold;">\n</span>Results from a LIKE query:<span style="color: #000099; font-weight: bold;">\n</span>"</span>
sql = <span style="color: darkslateblue;">""</span><span style="color: darkslateblue;">"
SELECT * FROM albums
WHERE title LIKE 'The%'"</span><span style="color: darkslateblue;">""</span>
cursor.<span style="color: black;">execute</span><span style="color: black;">(</span>sql<span style="color: black;">)</span>
<span style="color: #ff7700; font-weight: bold;">print</span> cursor.<span style="color: black;">fetchall</span><span style="color: black;">(</span><span style="color: black;">)</span></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><span style="color: black;">
</span></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><span style="color: black;">
</span></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><h1 itemprop="name" style="background-color: white; border: 0px; color: #222222; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 22px; line-height: 1.3; margin: 0px 0px 0.5em; padding: 0px; white-space: normal;">
<a class="question-hyperlink" href="http://stackoverflow.com/questions/4211573/how-do-i-import-a-xls-file-data-into-sqlite3" style="border: 0px; color: #222222; cursor: pointer; font-size: 24px; font-weight: normal; line-height: 1.35; margin: 0px 0px 0.5em; padding: 0px; text-decoration: none;">How do i import a xls file data into sqlite3</a></h1>
The table must exist first. i can create the blp-items table with</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;">CREATE TABLE blp_items
(spelling TEXT,
lexicality TEXT(1),
rt REAL,
zscore REAL,
accuracy REAL,
rt_sd REAL,
zscore_sd REAL,
accuracy_sd REAL
);
<div>
</div>
<div>
</div>
<div>
use the .import command</div>
<div>
</div>
<div>
<pre class="lang-sql prettyprint prettyprinted" style="background-color: #eeeeee; border: 0px; color: #393318; font-family: Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif; font-size: 13px; margin-bottom: 1em; max-height: 600px; overflow: auto; padding: 5px; width: auto; word-wrap: normal;"><code style="border: 0px; font-family: Consolas, Menlo, Monaco, 'Lucida Console', 'Liberation Mono', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Courier New', monospace, sans-serif; margin: 0px; padding: 0px; white-space: inherit;"><span class="pun" style="border: 0px; color: black; margin: 0px; padding: 0px;">.</span><span class="pln" style="border: 0px; color: black; margin: 0px; padding: 0px;">separator </span><span class="str" style="border: 0px; color: maroon; margin: 0px; padding: 0px;">","</span><span class="pln" style="border: 0px; color: black; margin: 0px; padding: 0px;">
</span><span class="pun" style="border: 0px; color: black; margin: 0px; padding: 0px;">.</span><span class="pln" style="border: 0px; color: black; margin: 0px; padding: 0px;">import blp-items.csv</span><span class="pln" style="border: 0px; color: black; margin: 0px; padding: 0px;"> blp_items</span></code></pre>
</div>
the .import function will import the headers from the csv file</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;">SELECT * FROM blp_items LIMIT 10;</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"><div class="p1">
spelling lexicality rt zscore accuracy rt_sd zscore_sd accuracy_sd</div>
<div class="p1">
---------- ---------- ---------- ---------- ---------- ---------- ---------- -----------</div>
<div class="p1">
spelling lexicality rt zscore accuracy rt.sd zscore.sd accuracy.sd</div>
<div class="p1">
a/c N 668.036363 0.32196203 0.76923076 180.088554 1.09726646 0.424052095</div>
<div class="p1">
aband N 706.5 0.48159747 0.925 170.172149 0.86149330 0.266746782</div>
<div class="p1">
abayed N 689.935483 0.15776501 0.86842105 281.050165 1.02394376 0.342569987</div>
<div class="p1">
abbear N 560.583333 -0.3614142 0.95 121.360472 0.52986732 0.220721427</div>
<div class="p1">
abbears N 611.410256 -0.1372765 0.975 169.857946 0.89889305 0.158113883</div>
<div class="p1">
abbens N 598.289473 -0.3845125 1.0 144.779964 0.59991825 0.0 </div>
<div class="p1">
abchaim N 591.763157 -0.1323686 1.0 128.560940 0.65425409 0.0 </div>
<div class="p1">
abects N 635.205128 -0.0324051 0.975 155.666002 0.64429156 0.158113883</div>
<div class="p1">
abeme N 591.055555 -0.2430494 0.94736842 182.908243 0.93264000 0.226294285</div>
</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;">we can delete this line by</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;">DELETE FROM blp_items WHERE lexicality='lexicality';</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;">
<div class="p1">
spelling lexicality rt zscore accuracy rt_sd zscore_sd accuracy_sd </div>
<div class="p1">
---------- ---------- -------------- ------------ ------------ -------------- ------------ ------------</div>
<div class="p1">
a/c N 668.0363636364 0.3219620388 0.7692307692 180.0885547226 1.0972664605 0.4240520956</div>
<div class="p1">
aband N 706.5 0.4815974773 0.925 170.1721499131 0.8614933071 0.2667467828</div>
<div class="p1">
abayed N 689.935483871 0.1577650108 0.8684210526 281.0501658048 1.0239437649 0.3425699875</div>
<div class="p1">
abbear N 560.5833333333 -0.361414284 0.95 121.3604725012 0.5298673287 0.2207214279</div>
<div class="p1">
abbears N 611.4102564103 -0.137276547 0.975 169.8579465239 0.8988930562 0.158113883 </div>
<div class="p1">
abbens N 598.2894736842 -0.384512528 1.0 144.7799649972 0.5999182553 0.0 </div>
<div class="p1">
abchaim N 591.7631578947 -0.132368665 1.0 128.5609402685 0.6542540915 0.0 </div>
<div class="p1">
abects N 635.2051282051 -0.032405186 0.975 155.6660020156 0.6442915682 0.158113883 </div>
<div class="p1">
abeme N 591.0555555556 -0.243049439 0.9473684211 182.9082431079 0.9326400097 0.2262942859</div>
<div class="p1">
abents N 690.2564102564 0.4099671341 0.975 207.919356577 0.8366905183 0.1581138</div>
and then dump the sql output</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;">.output blp_items.sql</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;">.dump blp_items</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
</pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
<pre class="python" style="background-color: #fafafa; border: 1px dashed rgb(47, 111, 171); color: #29303b; font-size: 12px; overflow: auto; padding: 0.5em;"></pre>
</article>
</div>
<object width="425" height="344"><param name="movie" value="http://www.youtube.com/v/EMBx8yYNpng&hl=en&fs=1"></param><param name="allowFullScreen" value="true"></param><embed src="http://www.youtube.com/v/EMBx8yYNpng&hl=en&fs=1" type="application/x-shockwave-flash" allowfullscreen="true" width="425" height="344"></embed></object>Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-45934112929006280762015-01-26T03:27:00.001-08:002015-01-28T07:25:26.127-08:00How to code: a syllabus <br />
<br />
If I was asked today, what a researcher (who isn't from a computer science background) should do to learn how to code, I would suggest they just start. It doesn't really matter where you start or in what language. There are some languages that offer more return for the same effort, like Python, because they are well structured and logical languages. Python can be used for a huge range of tasks because it is a general programming language that has libraries (add on functionality) that add domain specific functionality. Rather than a programming language designed for a specialist task like R or MATLAB that are often difficult to use in other domains like text handling or negotiating network connections.<br />
<br />
<div style="text-align: left;">
<h3>
<span style="font-size: small;"><b>import antigravity<br /><br />def main():<br /> antigravity.fly()<br /><br />if __name__ == '__main__':<br /> main()</b></span></h3>
</div>
<pre> </pre>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://www.explainxkcd.com/wiki/images/f/fd/python.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://www.explainxkcd.com/wiki/images/f/fd/python.png" /></a></div>
<pre> </pre>
<br />
But it doesn't really matter what sort of programming you start with, they will all start you on the path to thinking like a coder. That is - logically, structurally, algorithmically and in a way that breaks a problem down into component pieces that can be solved once and used as a component in many situations after that.<br />
Most languages have the basic parts to assign a data structure to a variable and create a loop to repeat an action a particular number of times and maybe change one component each time through the loop.<br />
<br />
<pre><b><span style="font-size: small;"><span class="cm-variable">words</span> = 'hello world ! we require a shrubbery'.split()</span></b></pre>
<div class="CodeMirror-code">
<pre><b><span style="font-size: small;"> </span></b></pre>
<pre><b><span style="font-size: small;"><span class="cm-keyword">for</span> <span class="cm-variable">word</span> <span class="cm-operator">in</span> <span class="cm-variable">words</span>:</span></b></pre>
<pre><b><span style="font-size: small;"> <span class="cm-keyword">print</span> <span class="cm-variable">word</span>, <span class="cm-builtin">len</span><span class=" CodeMirror-matchingbracket">(</span><span class="cm-variable">word</span><span class=" CodeMirror-matchingbracket">)</span></span></b></pre>
<pre><b><span style="font-size: small;"><span class=" CodeMirror-matchingbracket"> </span></span></b></pre>
<pre><b><span style="font-size: small;"><span class=" CodeMirror-matchingbracket"> </span></span></b></pre>
</div>
<div class="output">
<div class="output_area">
<div class="output_subarea output_text output_stream output_stdout">
<pre><b><span style="font-size: small;">hello 5
world 5
! 1
we 2
require 7
a 1
shrubbery 9</span></b>
</pre>
</div>
</div>
</div>
<br />
or<br />
<br />
<b># First create a list of words to use</b><br />
<br />
<pre><b># The easiest way to do this and avoid putting in all the quotation marks and commas is</b></pre>
<b><br /></b>
<br />
<div class="CodeMirror-code">
<pre><b><span style="font-size: small;"><span class="cm-variable">words</span> = 'hello world ! we require a shrubbery'.split()</span></b></pre>
<pre><b><span style="font-size: small;"> </span></b></pre>
<pre><b><span style="font-size: small;"># The split() function will split the string at the spaces and return a list. </span></b></pre>
<pre><b> </b></pre>
<pre><b># Pop will remove a word from the end of the list words, </b></pre>
<pre><b># check the length with len() to stop when we have removed all the words</b></pre>
<pre><b> </b></pre>
<pre><b><span class="cm-keyword">while</span> <span class="cm-builtin">len</span>(<span class="cm-variable">words</span>) <span class="cm-operator">></span> <span class="cm-number">0</span>:</b></pre>
<pre><b> <span class="cm-variable">word</span> = <span class="cm-variable">words</span>.<span class="cm-variable">pop</span>()</b></pre>
<pre><b> </b></pre>
<pre><b># Use string formatting to construct the response and indexing to read </b></pre>
<pre><b># the word backwards </b></pre>
<pre><b> </b></pre>
<pre><b><span class="cm-keyword"> print</span> <span class="cm-variable">word</span>, <span class="cm-string">"reversed is :- %s"</span> <span class="cm-operator">%</span> <span class="cm-variable">word</span>[::<span class="cm-operator">-</span><span class="cm-number">1</span>]</b></pre>
<pre><b> </b></pre>
<pre><b><u>Output:</u> </b></pre>
<pre><b> </b></pre>
</div>
<div class="output">
<div class="output_area">
<div class="output_subarea output_text output_stream output_stdout">
<pre><b>shrubbery reversed is :- yrebburhs
a reversed is :- a
require reversed is :- eriuqer
we reversed is :- ew
! reversed is :- !
world reversed is :- dlrow
hello reversed is :- olleh
</b></pre>
</div>
</div>
</div>
<br />
The will have a way to perform an action based on a decision, like True or False:<br />
<br />
<b># get a response from the user and assign it to the variable answer </b><br />
<br />
<div class="CodeMirror-code">
<pre><b><span class="cm-variable">answer</span> = <span class="cm-builtin">raw_input</span>(<span class="cm-string">"Would you like a quote from Monty Python, yes or no? "</span>)</b></pre>
<pre><b><span class="cm-keyword"> </span></b></pre>
<pre><b><span class="cm-keyword"># make answer all lowercase because 'Yes' and 'yes' are different to Python</span></b></pre>
<pre><b><span class="cm-keyword"> </span></b></pre>
<pre><b><span class="cm-keyword">if</span> <span class="cm-variable">answer</span>.<span class="cm-variable">lower</span>() <span class="cm-operator">==</span> <span class="cm-string">"yes"</span>:</b></pre>
<pre><b> </b></pre>
<pre><b># this only gets printed if answer == 'yes' </b></pre>
<pre><b> <span class="cm-keyword"> </span></b></pre>
<pre><b><span class="cm-keyword"> print</span> <span class="cm-string">"We require a shrubbery"</span></b></pre>
<pre><b> </b></pre>
<pre><b><u>Output:</u></b></pre>
</div>
<div class="output">
<div class="output_area">
<div class="output_subarea output_text output_stream output_stdout">
<pre><b>Would you like a quote from Monty Python? yes or no? no</b>
</pre>
</div>
</div>
</div>
<br />
<br />
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<a href="http://imgs.xkcd.com/comics/automation.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://imgs.xkcd.com/comics/automation.png" /> </a></div>
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For researchers the Software Carpentry workshops are the best way I've come across to learn about programming. They are presented by domain experts and usually these presenters are not primarily programmers, so they have insight into the difficulties of learning programming from another field.</div>
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<br /></div>
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In the boot camp you will cover shell programming, version control with Github, the concept of code testing for reliable and maintainable code and a programming language (either Python, R or MATLAB).</div>
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Once you have this overview of the parts necessary for your journey into code, there are a number of great (and free!) resources I would strongly recommend.</div>
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<br /></div>
<div class="separator" style="clear: both; text-align: left;">
"Programming for Everyone" on Coursera is run by the fantastic Dr. Chuck.</div>
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After those two steps, I'd suggest coming along to the NeuralCode group or something like it in your area. </div>
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<br />Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-14680507405403664022014-12-02T20:03:00.001-08:002015-01-26T03:20:27.563-08:00HTML5 CSS Javascript<h2>
The browser in becoming increasingly important in computing. </h2>
This seems to be inline with the rise of cloud based computing and storage. The fracturing of the smartphone market has also made HTML5 apps more attractive for phone application developers. As HTML5 apps can be be run in any modern browser and smartphones run those browsers, HTML5 apps are automatically supported on any smartphone OS.<br />
<br />
Last year I played with the <a href="http://www.bitalino.com/" target="_blank">Bitalino board</a> and was surprised by how responsive their HTML5 based application was with the Bitalino connected by Bluetooth. It looked good and the non-blocking nature of Javascript made the interface very responsive. Python in the back end to do the serial communication and hardware interfacing made for a slick and reliable experience.<br />
<br />
With the arrival of the OpenBCI headset imminent, I'd like to know more about the possibilities of coding in the browser.<br />
<br />
When I came to the conclusion that programming was a necessary part of neuroscience, I started looking for the perfect language. One that would do all the things I needed so I wouldn't waste time learning inferior languages. After a lot of reading and a little experience, I understand why more experienced programmers don't spend a lot of time answering that question. There is no one best language, they all have their tradeoffs. <br />
<br />
Except for Matlab..... Matlab is just bad. ;-)<br />
<br />
You will probably end up learning a few languages.<br />
<br />
Please excuse my explanations of Javascript, I'm not experienced with it and my knowledge is growing all the time. I'll reread this in a few months and see if I can make it clearer and fix up any mistakes.<br />
<br />
Javascript has emerged as the Queen of the browser, it has nothing to do with JAVA despite the name, and is probably the most widely used languages due to this. It is an event driven, asynchronous (or non-blocking) language, meaning that unlike other languages where each instruction is performed in turn in a single thread, Javascript has a system of requests and callbacks that allow other work to be done while waiting for a requested resource (typically disk or network resources).<br />
<br />
When I want to learn a programming language, I look around for articles on the nature of the language. Not tutorials but discussions about what it looks, feels and smells like. I try to get a feel for the personality of the language so I have some framework to piece together the information I will acquire. Next I do some online tutorials involving guided coding in a browser. Something like <a href="http://www.codecademy.com/" target="_blank">codeacademy</a>. or <a href="http://javascript.didacto.net/" target="_blank">Didacto</a><br />
<br />
UPDATE 1<br />
I have been doing a lot of reading and video watching
about web frameworks. One web page and video that really open my eyes up
is <a href="http://www.pixelmonkey.org/2013/03/13/rapid-web-prototyping-with-lightweight-tools" target="_blank">Pixel Monkeys piece on lightweight web frameworks</a>.
In it Andrew Montalenti talks about the connection between HTML CSS
Javascript and light web frameworks like Flask. Flask is a Python
framework that does templating and handles connections. Along with these
tools, integration with databases and a lot of nifty editors like Emmet
are mentioned.<br />
<br />
But first a little HTML. Why? Because we want to use Javascript in the browser, and HTML is the markup language that makes a place for the Javacript to live and display it's results.<br />
<br />
This material is taken from the codeacademy site. <br />
<br />
the basic syntax of HTML is<br />
<br />
< something > ....... < something /><br />
<br />
With the somethings (tags) defining what goes between these markers (thus the term - markup language).<br />
<br />
It can be headers (h1 - h6)<br />
<br />
<h2> This is a heading </ h2><br />
<br />
Or what we're interested in, a javascript file<br />
<br />
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"><</span><span class="pln">script src</span><span class="pun">=</span><span class="str">"js/all.min.js"</span><span class="pun">></</span><span class="pln">script</span><span class="pun">></span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"><p> Text can go between paragraph tags </p> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">In the opening tag we can also state some attributes. </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">In the next line, the 'a' tag denotes links and </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">the 'href' is an attribute.</span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">In this case it is a web address and notice it is in quotation marks. </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">The text between the tags 'links' will be what the user sees and clicks on.</span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">
</span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"><a href='http://www.codecademy.com'> links </a></span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">images are also bracketed by tags and look a lot like the javascript markup.</span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">Images have an attribute 'source' which gives the path to an image file. </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"><img source="picture.png"></img></span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<h2 class="lang-js prettyprint prettyprinted">
<code><span class="pun">CSS</span></code></h2>
<div class="lang-js prettyprint prettyprinted">
<code><span class="pun">The styling of the different elements of the html page are defined by the CSS file.</span></code></div>
<div class="lang-js prettyprint prettyprinted">
<br /></div>
<div class="lang-js prettyprint prettyprinted">
<code><span class="pun">h1{</span></code></div>
<div class="lang-js prettyprint prettyprinted">
<code><span class="pun">color:red;</span></code></div>
<div class="lang-js prettyprint prettyprinted">
<code><span class="pun">} </span></code></div>
<div class="lang-js prettyprint prettyprinted">
<code><span class="pun"><br /></span></code></div>
<div class="lang-js prettyprint prettyprinted">
<code><span class="pun">The 'h1' part is a selector, the 'color' part is a property and the 'red' is it's value. A property:value pair is called a CSS rule.</span></code></div>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">
</span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun">
</span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<pre class="lang-js prettyprint prettyprinted"><code><span class="pun"> </span></code></pre>
<br />Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-47449496289226835452014-12-02T20:02:00.002-08:002014-12-15T21:31:48.263-08:00Using LaTeX To get started with LaTeX you first need to install it. Then an IDE is a good idea. It is possible to write raw tex but there are several great packages that make the task a lot easier. I'm going to use texmaker on the mac and this program works on Apple, Microsoft and Linux operating systems. It is also free and open source, of course.<br />
<br />
To start a LaTeX document with texmaker, we can use the 'Quick Start' wizard and put in author and title as well as choose some other options. These chosen options end up in the preamble of the created tex document. As most of my writting involves referencing, I usually start up a bibtex file at the same time. The \use package{natbib} goes in the preamble and \bibliographystyle{humannat} goes before the \bibliography{path_to_bib}, which goes at the end before \end{document}<br />
<br />
<style type="text/css">
p, li { white-space: pre-wrap; }
</style>
<br />
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: maroon;">\documentclass</span><span style="color: black;">[12pt,a4paper]{article}</span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: maroon;">\usepackage</span><span style="color: black;">{natbib}</span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">
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<span style="color: maroon;">\author</span><span style="color: black;">{Alistair Walsh}</span>
<span style="color: maroon;">\title</span><span style="color: black;">{Neurofeedback Non-Learners and Brain Computer Interface Illiteracy. </span></span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;"><span style="color: black;">Epidemiology, identifying features and differentiation.}</span>
</span>
<span style="color: #0000cc;">\begin</span><span style="color: black;">{document}</span>
<span style="color: maroon;"> </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: maroon;">\maketitle</span>
<span style="color: #0000cc;"> </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: #0000cc;">\begin</span><span style="color: black;">{abstract}</span>
<span style="color: black;">Not everyone who is provided training in neurofeedback can learn cortical control </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">nor that tries to operate a brain computer interface is able to. </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">This inability is refered to in NFB as NFB non-learning and in BCI as BCI-illiteracy. </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">Very little has been written on this area yet it affects an estimated 30</span><span style="color: maroon;">\</span><span style="color: black;">% to 50</span><span style="color: maroon;">\</span><span style="color: black;">% </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">of participants. It has been suggested that various attributes can be used to identify </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">NFB-nonlearning and BCI-illiteracy. These include high hand dexterity, </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">external locus of control, high hypnotisability, high disassociative index, </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">and strong reward response. Discovering the reason for some not learning </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">cortical control would be of benefit to those receiving treatment and would </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">inform the field on the mechanism behind cortical control. It would also possibly </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">improve the techniques of teaching it.</span>
<span style="color: #0000cc;"> </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: #0000cc;">\end</span><span style="color: black;">{abstract}</span>
<span style="color: #0000cc; font-weight: 600;">\section{Introduction}</span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: #0000cc; font-weight: 600;">
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</style>
<span style="color: maroon;">\citet</span><span style="color: black;">{Grubler:2014aa} in a paper involving both patients and </span></span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: #0000cc; font-weight: 600;"><span style="color: black;">clinicians, the possible ethical concerns were raised.</span>
</span>
<span style="color: maroon;">\citet</span><span style="color: black;">{Suk:2014aa} estimated the incidence of BCI-illiteracy at around 20</span><span style="color: maroon;">\</span><span style="color: black;">%</span>
<span style="color: maroon;">\citet</span><span style="color: black;">{Ahn:2013aa} in a large study of 52 people, suggested that high theta and </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">low alpha might predict BCI-I</span>
<span style="color: maroon;">\bibliographystyle</span><span style="color: black;">{humannat}</span>
<span style="color: maroon;">\</span><span style="color: #0000cc; font-weight: 600;">bibliography{/Users/Wintermute/Dropbox/research_2015/</span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: #0000cc; font-weight: 600;">litreview_BCI-illiteracy/BCI-illiteracy}</span>
<span style="color: #0000cc;">\end</span><span style="color: black;">{document}</span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;"> </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;">Results in the following Document </span></pre>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;"> </span></pre>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjNURMKjoGkqZcNImezsvUeq1bEL6LqxPpbmZZxbY9XLm-G15fJjr05GdBJW45An2cOC8MILhdpUBGcfvMEXlU2krAoj__e24oF_zvNQqyjdPeIpCcxqlBZrueHHm8Rg9bOPe2u0E74pUo/s1600/bci-illiteracy.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjNURMKjoGkqZcNImezsvUeq1bEL6LqxPpbmZZxbY9XLm-G15fJjr05GdBJW45An2cOC8MILhdpUBGcfvMEXlU2krAoj__e24oF_zvNQqyjdPeIpCcxqlBZrueHHm8Rg9bOPe2u0E74pUo/s1600/bci-illiteracy.jpg" height="400" width="282" /></a></div>
<pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"><span style="color: black;"> </span><span style="color: #0000cc; font-weight: 600;"><pre style="-qt-block-indent: 0; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; text-indent: 0px;"></pre>
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Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-48310732614870100992014-11-09T12:26:00.003-08:002014-11-09T12:26:42.880-08:00Full Stack STEMWorking on lesson plans for the 'Scientific tool chain' . <a href="https://github.com/alistairwalsh/neuralcode/wiki" target="_blank">https://github.com/alistairwalsh/neuralcode/wiki</a>Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-29577726878076900412014-11-09T12:25:00.001-08:002014-11-09T12:25:19.959-08:00A dialog about neurologyOn July 2nd 2014, Rohan posted on the BCI2000 mailing list. Since then we've been corresponding about BCI and EEG. I've posted the conversation here and I hope this will help out those who are new to EEG and prompt discussion. Please post comments if anything isn't clear or you disagree with any of my answers. <br />
<br />
<h4>
Hi everyone, </h4>
<div>
<h4>
I am a 15 year old boy very passionate in the field
of artificial intelligence and brain computer interface. I want to
design a setup whereby I can detect brain waves with eeg and use them to
control devices on the basis of amplitude. How should I go about it.</h4>
</div>
<h4>
</h4>
<div>
<h4>
Thank You.</h4>
</div>
<div>
<h4>
Regards,</h4>
</div>
<div>
<h4>
<span class="il">Rohan</span></h4>
</div>
<div>
</div>
<div>
<span class="il">Hi <span class="il">Rohan</span>,</span><br />
<div>
<br /></div>
<div>
You'll need access
to an EEG amplifier and electrode cap. OpenBCI are almost ready with
their project and I think that will be the cheapest way to buy your own.
The other option is belong to a university that has this equipment.</div>
<div>
Problem with the university is they may make you do years of study
before they let you play with an EEG setup. I was in 4th year before I
was allowed access to an EEG setup on a regular basis to work out BCI.
The good side is they will teach you the theory about electrical brain
activity and how EEG works, as well as the theory behind the
relationship between this activity and what we call thought and behavior.</div>
<div>
<br /></div>
<div>
After you have the hardware, there is a choice of
software. BCI2000 is great. After trying out a few others I stuck with
BCI2000 to build a BCI based on the Mu rhythm. If you know C++ or can
learn it, it will help when you want to do things with BCI2000. There is
also the option of using the BCPY2000 project which uses Python.</div>
<div>
I'd suggest learning Python through the many excellent online
courses (Codeacademy's python classes and Coursera's "Programming for Everyone"). Python is a great language to start with, you may stick with
it as you learn BCI or you may move on to C like languages once you
understand programming concepts. It also gives you an alternative to
MATLAB when you want to analyse EEG data. MATLAB is expensive and that's
fine if the university you belong to buys a license that everyone can
use, otherwise open source is the way to go.</div>
<div>
<br /></div>
<div>
Using amplitude is a great way to drive a BCI, I'd suggest you add these details to your technique.</div>
<div>
<br /></div>
<div>
"Amplitude,
in a particular location (i.e. over the motor cortex, electrode
locations C3 and C4) and in a particular frequency band (i.e. reduction
in the Alpha band and increase in the Beta band)". </div>
<div>
<br /></div>
<div>
This activity is produced in most people when they
move their right hand (C3) or their left hand (C4) or even when they
think about moving them. So it's great for moving a cursor left and
right across a screen.</div>
<div>
<br /></div>
<div>
Knowing the terminology like 'motor cortex'
and the names of the electrode locations 'C3 and C4' as well as the
names for the different frequency bands is important because that's one
way you can convince others you know what you are talking about. Think
of it like a secret language you must learn to be granted access to the
magical cave of the EEG amplifier! Just a different version of "Open
Sesame!". You'll find lots of the information on Wikipedia.</div>
<div>
<br /></div>
<div>
BCI2000 has a great tutorial on exactly how to do Mu
rhythm BCI (I think this is what you want to do) so maybe you can start
there.</div>
<div>
<br /></div>
<div>
Good Luck!</div>
<div>
</div>
<div>
<div dir="ltr">
<h4>
<span style="font-family: arial,sans-serif; font-size: 13.333333969116211px;">Hello sir, </span></h4>
<div style="font-family: arial,sans-serif; font-size: 13.333333969116211px;">
<h4>
I
had a question related to arduino and atmega ...... If an arduino gets
an input of frequencies below 20 hz can it be programmed that if there
is an input of frequencies then a light or led will turn on and if not
the led will remain off???</h4>
</div>
<h4>
</h4>
<div style="font-family: arial,sans-serif; font-size: 13.333333969116211px;">
<h4>
Regards</h4>
</div>
<div style="font-family: arial,sans-serif; font-size: 13.333333969116211px;">
<h4>
<span class="il">Rohan</span></h4>
</div>
<div style="font-family: arial,sans-serif; font-size: 13.333333969116211px;">
</div>
<div style="font-family: arial,sans-serif; font-size: 13.333333969116211px;">
<span class="il">Dear <span class="il">Rohan</span>, </span></div>
<div style="font-family: arial,sans-serif; font-size: 13.333333969116211px;">
<br />
<div>
Two ways to do this as I see
it. The first would require FFT calculations (if I am understanding your
question correctly) and the humble ATmega328 is not really powerful
enough to do this in real-time. The teensie 3.0 <a href="https://www.pjrc.com/store/teensy31.html" target="_blank">https://www.pjrc.com/store/<wbr></wbr>teensy31.html</a> with a 32 bit ARM processor is though. It can be programmed using the Arduino IDE. <a href="https://learn.adafruit.com/fft-fun-with-fourier-transforms/hardware" target="_blank">https://learn.adafruit.<wbr></wbr>com/fft-fun-with-fourier-<wbr></wbr>transforms/hardware</a>
has a tutorial for doing FFT on a signal with the teensie. It uses
the CMSIS DSP math library for the teensie. This tutorial also happens
to have LEDs lighting up at different frequencies, I hope this helps.</div>
<div>
The other way would be to create a low pass, or band pass filter in
hardware, and detect a certain amplitude of signal after the filter.</div>
<div>
<img alt="Inline image 1" class="CToWUd" height="143" src="https://mail.google.com/mail/u/0/?ui=2&ik=697997fedd&view=fimg&th=14740ce5752d1dcf&attid=0.1&disp=emb&realattid=ii_14740c7abfd49bef&attbid=ANGjdJ9sC84m-bcKPoxuna63ihhMPxiuk8ngjj_exvZfzNNKZXvaz85b80-ck4A4UqZ497jv7AEvXst-pA28wbjKQEQWGtMGQV0yZzQt7EEJFK5_6mWIUVAAAKlqnis&sz=w1600-h1000&ats=1414544035218&rm=14740ce5752d1dcf&zw&atsh=1" width="237" /></div>
<div>
This is a basic Butterworth filter from <a href="http://www.corollarytheorems.com/Design/filter.htm" target="_blank">http://www.corollarytheorems.<wbr></wbr>com/Design/filter.htm</a>. The choice of resistor and capacitor values will result in different cutoff frequencies. </div>
<div>
<br /></div>
<div>
I believe it is possible to use the simulink package
for MATLAB to design a filter in software and transfer it to the
Arduino but I have no experience doing this.</div>
<div>
<br /></div>
<div>
Cheers</div>
<div>
</div>
<div>
<div dir="ltr">
<h4>
Hello sir, </h4>
<div>
<h4>
I am currently using a 9-12 hz band pass
filter to allow only this specific range of frequency to pass through.I
have attached a schematic picture of the circuit can you help me
identify that whether the circuit is correct . I Have also used an
instrumentation amplifier ad620 to amplify the brain waves. Can You
please help me??</h4>
</div>
<h4>
</h4>
<div>
<h4>
Thank you.</h4>
</div>
<div>
<h4>
</h4>
</div>
<div>
<h4>
<img alt="Displaying 20140718_100144.jpg" class="aLF-aPX-J1-J3" height="112" src="https://mail.google.com/mail/u/0/?ui=2&ik=697997fedd&view=fimg&th=1474a727250ba5a2&attid=0.1&disp=inline&realattid=f_hxrrdi4s0&safe=1&attbid=ANGjdJ8c8aVvKhOdRBsXynMK1Xn8ejaDFW_1hK18hyTuOUpRNFpWEsIbmt-wDbR7e5MdnZAroaiRGa5_YA_6mAhwD1n8rALxL7dTbcXcgj-9sEURDkFI4Of_EZMwa6E&ats=1414544035342&rm=1474a727250ba5a2&zw&sz=w1416-h616" width="200" /> </h4>
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<div dir="ltr" style="font-family: arial,sans-serif; font-size: 13px;">
<h4>
<span style="font-size: 13.333333969116211px;">hello sir, </span></h4>
<div style="font-size: 13.333333969116211px;">
<h4>
I wanted to develop an EEG circuit , could you please help me or get me in contact with some expert who could help me...</h4>
</div>
<h4>
</h4>
<div style="font-size: 13.333333969116211px;">
<h4>
Thank you..</h4>
</div>
<div style="font-size: 13.333333969116211px;">
<h4>
</h4>
</div>
<div style="font-size: 13.333333969116211px;">
<div dir="ltr">
<div>
<h4>
hello Ma'am</h4>
</div>
<div>
<h4>
There is a great news, I have
completed the project and I am able to control a led using brain waves.
This wouldn't have been possible without your help, I am certainly
grateful to you. Thank you once again. I hope you don't mind if I remain
in touch with you and if I have further questions in the near
future....</h4>
</div>
<h4>
</h4>
<div>
<h4>
Thank you.</h4>
</div>
</div>
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<div style="font-size: 13.333333969116211px;">
Excellent news <span class="il">Rohan</span>,<br />
<div>
Please keep in touch and I hope I can be of assistance in the future.</div>
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<div>
<h4>
Hello sir ,</h4>
</div>
<div>
<h4>
I came across this article on
PubMed relating brain interfacing and they stated that they could
control a rats tail and even humans by brain to brain interaction. It is
not explained deeply in the article. Could you please enlighten me on
how exactly are the brain waves of humans and rats interacted. </h4>
</div>
<div>
<h4>
thank you. </h4>
</div>
<div>
<h4>
<span class="il">Rohan</span> </h4>
</div>
<div>
<br /></div>
<div>
<div>
Hi <span class="il">Rohan</span>,</div>
I can't see an article.
It may not have been attached or the mailing list might have removed it.
If you can give me the reference it I could look it up.</div>
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<div>
<h4>
sir , </h4>
</div>
<div>
<h4>
here are the links</h4>
</div>
<div>
<h4>
<span class="im"><div>
1. <a href="http://www.washington.edu/news/2013/08/27/researcher-controls-colleagues-motions-in-1st-human-brain-to-brain-interface/" target="_blank"><span style="color: #0066cc;">http://www.washington.edu/</span><span style="color: #0066cc;">news<wbr></wbr>/2013/08/27/researcher-</span><span style="color: #0066cc;">control<wbr></wbr>s-colleagues-motions-</span><span style="color: #0066cc;">in-1st-<wbr></wbr>human-brain-to-brain-</span><span style="color: #0066cc;">interface<wbr></wbr>/</span></a></div>
<div>
2. <a href="http://harvardmagazine.com/2014/03/fusing-faculties-of-mind" target="_blank"><span style="color: #0066cc;">http://harvardmagazine.com/</span><span style="color: #0066cc;">201<wbr></wbr>4/03/fusing-faculties-of-</span><span style="color: #0066cc;">mind</span></a> </div>
</span></h4>
<div>
<h4>
I am unclear with the concept that they have used to transmit signals, could you please help me out. </h4>
</div>
<div>
<h4>
thank you.</h4>
</div>
<div>
</div>
<div>
</div>
<div>
The recording is being done with EEG and the triggering of the second
persons reaction is being done with Trans-cranial Magnetic Stimulation
(TMS). TMS creates a magnetic pulse which causes the neurons within its
magnetic field to fire. You can see in the first article, the man
wearing the purple cap, has the TMS 'coil' over his left motor cortex.
The left motor cortex controls the right side of the body. When the TMS
pulses (when it receives the signal from the EEG connected to the other
participant) it briefly creates a strong field over the motor cortex and
causes the neurons to fire, which causes movement of his hand. </div>
<div>
<br /></div>
<div>
The second article talks about using ultrasound which I haven't come
across before. The principle is the same, it is a method of causing the
neurons in the motor cortex that are in control of the tail to fire.</div>
<div>
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<div>
<h4>
Sir, </h4>
</div>
<div>
<div>
<h4>
</h4>
</div>
<div>
<h4>
generally there are four
different kind of brain waves - alpha, beta, delta and theta all varying
from 0-30 Hz. But now it seems that more importance is being laid to
the High Gamma waves having a frequency from 70-200 Hz. Why is it so?
Why are gamma waves of importance in The research of Fmri and
Electrocorticography and EEG?</h4>
</div>
<div>
</div>
<div>
</div>
<div>
Gamma is thought to have the role of collecting or combining the
information from the various specialised parts of the brain. It is also
extremely difficult to record and is usually contaminated by muscle
activity ( as measured by an EMG). The sheets of muscle that lie on the
skull, produce high frequency noise even when a participant is
completely still. This was examined by a group of researchers who
recorded EEG while under general anesthetic. Their findings suggest
that a lot of the Gamma that is recorded by EEG is actually tonic muscle
activity rather than brain activity. That is not to say gamma EEG
doesn't exist, it just points out how hard it is to record without
resorting to intra-cranial recording. Gamma is very interesting because
of its connection with higher thought ( for want of a better word) but
as always, the brain acts as a unified whole ( like an orchestra ) . It
is interesting to look at individual elements but the meaning is in how
it relates to the whole.<br />
<div>
Good question! Thanks.</div>
<div>
</div>
<div>
</div>
<br />
<h4>
<div>
<div>
Sir, </div>
Usually BCI work on the principle of brain waves say for example
the alpha waves or the MU rythm for controlling devices based on these
waves. But is there actually a way of differentiating between thoughts
and putting them into actions? The BCI i have made is an eeg based Bci
working on the principle of alpha waves measures over the occipital lobe
but can i actually differentiate between thoughts. </div>
</h4>
<h4>
</h4>
</div>
<div>
<h4>
</h4>
</div>
<div>
<h4>
Hello sir, </h4>
<div>
<h4>
I had made an EEG based Bci working on the principle
of the alpha waves measured over the regions of o1 and o2 . I could
switch on and off a led using the alpha waves whose amplitude is
controlled by eye blink which is measured over the occipital lobe. How
do I make further advancements and something more complicated,
efficient. or something based on p300 erp.</h4>
</div>
<div>
<h4>
Thank you</h4>
</div>
<div>
<h4>
<span class="il">Rohan</span></h4>
</div>
<div>
</div>
<div>
<span class="il"> Dear Rohan,</span></div>
<div>
<span class="il"><br /></span></div>
<div>
<span class="il">My apologies for taking so long to reply. Your question is a good one and I've been thinking a lot about how to answer. Your question about identifying individual thoughts is something that many people in neuroscience are trying to answer. While it is true to say that there is a connection between brain activity and thought, they do not seem to be the same thing. There are also different types of thoughts from direct action based thoughts about the physical world like thinking about clenching my hand to abstract thoughts about constructs that don't really exist like how I feel about the Wizard of Oz story.</span></div>
<div>
<span class="il"><br /></span></div>
<div>
<span class="il">There are also different ways to measure and relate brain activity. From location to frequency and time relationships. The areas of the brain that are associated with hand movements are located in a well defined area of the cortex. Sensors over that area will show activity when the area is activated and no activity otherwise. </span></div>
<div>
</div>
<div>
<span class="il">Thinking about a movie or reflecting on my feelings about it wouldn't be so neatly confined to a small area. It would involve many areas of the brain interacting, including brain regions deeper than the cortex when I involve memory and emotion.</span></div>
<div>
<span class="il"><br /></span></div>
<div>
<span class="il">The event related potentials are distinctive brain activity in response to a particular stimuli. The P300 is a </span><span class="il"><span class="il">distinctive positive going wave at approximately 300ms after identifying and storing in memory an distinctive event. </span> The 'oddball' paradigm is something that reliably produces this response and consists of a high tone (the oddball) amongst more common low tones. The low tones are more common, so the high tone is unusual when it occurs. If the participant is asked to count the number of oddballs, the P300 is produced as the identify and update their memory. So this is one way to identify a particular thought type. </span></div>
<div>
<span class="il"><br /></span></div>
<div>
<span class="il">The Alpha wave can be thought of as a carrier wave. When the brain is doing active processing, the alpha wave decreases and the other frequencies are higher in amplitude. When that task is done and the participant relaxes and does no processing, the Alpha wave will return again. Over the occipital cortex (O1 and O2) the Alpha wave is quite prominent when the participant closes their eyes. The theory is that the occipital lobe has no input from the eyes and therefore has little to do so the Alpha wave dominates.</span></div>
<div>
</div>
<div>
<span class="il">So a BCI based on Alpha over the occipital cortex is differentiating activity from no activity. </span></div>
<div>
</div>
<div>
<span class="il">How is your BCI setup? What determines if the LED is lit or not? </span></div>
<div>
</div>
<div>
<span class="il">Sincerely,</span></div>
<div>
<span class="il">Alistair </span></div>
<div>
<span class="il"><br /></span></div>
<div>
<span class="il"><br /></span></div>
<div>
<span class="il"><br /></span></div>
</div>
</div>
</div>
</div>
</div>
</div>
Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-41161100205662521242014-10-23T17:05:00.001-07:002014-10-23T17:05:08.647-07:00Take part in researchPlease take a few minutes to take part in this research<br />
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and here's a cute Kitteh for you because you're nice!<br />
<br />
<img alt="http://img2.wikia.nocookie.net/__cb20080410164923/uncyclopedia/images/0/0a/Cute_kitten.p.jpg" class="decoded" src="http://img2.wikia.nocookie.net/__cb20080410164923/uncyclopedia/images/0/0a/Cute_kitten.p.jpg" /> Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-50669984225581875042014-10-15T17:50:00.003-07:002014-10-15T17:50:59.899-07:00Programming for ScientistsI'm
assuming that everyone has come to the conclusion now that programming
is a necessary part of any scientific pursuit. Computers are an integral
part of how we all gather, analyse and present our data. There are also
techniques of code abstraction, version control and code testing that
facilitate writing error free code that is easy to maintain and over
time becomes a resource. These techniques are slowly making their way
from the computer science field to the behavioral sciences.
Unfortunately it has taken some high profile retractions of journal
articles to push this issue forward.<br />
<br />
<a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a>As those I have been
studying with have entered their PhD phase, there is a growing
realisation that learning to program is essential. Along with that
realisation I am also hearing a common statement. Most people assume
that somewhere along the line, they would be sat down and shown how to
program, that there would be some course to teach them what they need to
know about programming. At least enough to analyse fMRI, MEG or EEG
data. Most are shocked when they are handed a data set and told to 'now
go analyse it'.<br />
<br />
I went looking for teaching materials to
meet this need and help my friends out. We talked a lot about what was
necessary and arrived at a few realisations.<br />
<br />
1. You learn best when the problem you are trying to solve is your own. <br />
2. Everyone is too busy to learn programming right now but they will be busier any time in the future.<br />
3. The easiest way to get someone into programming is to teach them something that will benefit them right now.<br />
4. Teaching what you have learned to someone is of huge benefit and therefore absolute beginners who can be taught by those who are a little more advanced are a valuable resource for any learning group.<br />
5. There needs to be a certain level of uncertainty, it greatly improves the internalisation of the material once the uncertainty is removed through experimentation. We are mostly scientists and the process of hypothesizing and testing is a skill we can use here too. <br />
<br />
The Facebook page gets a lot of views and a good amount of 'likes'. There are three or four people who turn up on a regular basis and are making great progress. The big struggle now is to get more people to actually turn up and do some coding. <br />
<br />
The weekly sessions have taken on a definite style and are challenging but really enjoyable. The general format is to propose a common task and work slowly through the steps needed to solve this problem. The main feature is to pause at the start of each step and have everyone talk about their expectations. Having people talk about possible strategies and how they might use ideas from other solutions helps understand where everyone is in their thinking. It usually clears up misconceptions before they become too confusing.<br />
<br />
Learning these techniques is very similar to learning a musical instrument. You can study the subject as much as you like but you won't really improve until you practice. Trying things out and making mistakes is much more valuable that having the final answer presented to you with all the anguish removed.Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-22925937830043424572014-10-15T17:30:00.001-07:002014-10-15T18:36:12.054-07:00Serious writers write, inspired or notI have many reasons for writing this blog. One of them is to practice writing.<br />
<br />
As a scientist, I think my main task is to communicate. Usually as written reports but also at conferences and in classrooms. Although gathering and analysing data is a crucial part of scientific inquiry, it is a little bit pointless if I can't transmit what I learn to others. In an effort to be better at that, I take pointers from two sources.<br />
<h3>
The chain</h3>
<h3>
</h3>
<h3>
<img src="http://i.kinja-img.com/gawker-media/image/upload/17m0c0nlhy2f0jpg.jpg" id="lightbox-image" style="display: inline-block;" /> </h3>
This <a href="http://lifehacker.com/281626/jerry-seinfelds-productivity-secret" target="_blank">post</a> about the Jerry Seinfeld's "Don't break the chain" method is why I put my writing online.<br />
The main message is that if you want to be a great comic, you need great jokes. To have great jokes you need to write lots of jokes to get better at it and to be able to pick the better jokes from the ok jokes. To write lots of jokes, you need to write often - this is where the chain comes in. If you have a wall calendar and make a big cross on every day you write, the crosses form a pattern. After you write on a few consecutive days, you want to keep writing so you 'Don't break the chain'. It's a way to consciously create a habit.<br />
<br />
This blog is a public place where it is obvious if I'm regularly putting up content, it's obvious how productive I'm being. I have to write complete post, not just 'to do' lists. I have to write for someone else, not just notes for myself. This means I need to express ideas clearly and fully.<br />
<br />
In the same vein, the <a href="https://www.facebook.com/groups/NeuralCode/" target="_blank">NeuralCode</a> meetings mean I must research and prepare material on scientific computing every week. I also post related articles to the feed as a way to engage the members of the group and send support material to the email list. It's a way of getting to where I want to be.<br />
<br />
I hope to be someone who gives lectures and tutorials on scientific computing. I am really passionate about modernising research and the practice of reproducible research. To one day be that person I need to have a body of work that shows I know what I'm talking about and can express that well. I also need to practice expressing those ideas. Because just like writing a joke, even if the original idea is great, if it practiced and honed it can be taken to a whole new level of great.<br />
<br />
<h3>
Scientific writing</h3>
<div class="coursera-course2-instructors coursera-course-metadata" data-course-id="472">
<div class="coursera-course2-instructors-profile">
<div class="coursera-course2-instructors-info">
<a class="internal-home" data-link-type="instructor-name" href="https://www.coursera.org/instructor/%7E225"><span class="coursera-course2-instructor-name">Kristin Sainani</span></a><a data-link-type="instructor-university" href="http://online.stanford.edu/"><span class="coursera-course2-instructor-university">'s</span></a> <a href="https://www.coursera.org/course/sciwrite" target="_blank">Writing in the sciences </a> on Coursera hasn't run for a while but the video lectures are still available. I'd recommend them to anyone who wants to write better science. </div>
</div>
</div>
A main theme is to simplify your expression and to follow the simple rules of good writing. The purpose of scientific writing is to communicate clearly. Einstein is reported to have said that if your science is any good, you should be able to explain it to a child. Part of this is using the clearest expression that conveys precisely what you mean. Using a complex and obscure word where a simple and common word would have the same meaning is something that creeps into our writing. It sounds more scientific. We read it in the papers of senior researchers. Knowing what big words mean gives us a sense of actually knowing something in a field where we are usually in a state of having no idea what's going on. So we hide behind the big words and cling to them like mantras so we don't feel like such fakes.<br />
<br />
<img alt="fa5.jpg" class="centered_photo" src="http://i2.kym-cdn.com/photos/images/newsfeed/000/234/739/fa5.jpg" height="223" width="400" /> <br />
<br />
Unfortunately this makes understanding some literature harder than it needs to be. Obviously there is a need for very specific terminology at times, terms that may be very specialised and require the reader to search out the meaning if they haven't come across them before. There is a balance that needs to be found.<br />
<br />
A great bit from the scientific writing course:<br />
<br />
- from William Zinsser's book On Writing Well, which is a great book to pick up to read for this course if you have time.<br />He says,<br />
"The secret of good writing is to strip every sentence to it's cleanest components.<br />Every word that serves no function, every long word that could be a short word,<br />every adverb that carries the same meaning that's already in the verb, every passive<br />construction that leaves the reader unsure of who is doing what.<br />These are the thousand and one adulterants that weaken the strength of a sentence.<br />And they usually occur in proportion to the education and rank (of the writer)." <br />
<br />
<br />
<br />
<br />Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-61744867613899763282014-10-01T23:57:00.002-07:002014-12-03T20:37:34.809-08:00Psychopy in the IPython shellSince finding Psychopy I've wanted the convenience of the IPython shell when writing scripts. The editor in Psychopy is OK but doesn't have all the nice features of IPython.<br />
<br />
<a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a>I had hoped that there might be a package in the conda environment but no such luck. The solution turned out to be Binstar.<br />
I had been curious about Binstar since it started popping up as a suggestion whenever I tried to install something with conda and the package couldn't be found. It is basically user created packages and there was a package for Psychopy from Eric Kastman. I created a new conda environment with the command -<br />
<a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a>>> conda create -n psyk anaconda<br />
<br />
I then activated this environment and installed Psychopy with Eric's package.<br />
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
<pre><span style="font-family: "Courier New",Courier,monospace;"><code>>> conda install -c <a href="https://conda.binstar.org/erik">https://conda.binstar.org/erik</a> psychopy</code></span></pre>
<br />
<br />
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="font-size: small;"><code>I copied some tutorial code from </code><code> <a href="http://sapir.psych.wisc.edu/wiki/index.php/Psych711" target="_blank">Psych711</a> created by Prof. Gary Lupya and ran it. </code></span></span><br />
<br />
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="font-size: small;"><code>There is a problem when I try to stop the Psychopy window but other than that </code></span></span><br />
<span style="font-family: "Helvetica Neue",Arial,Helvetica,sans-serif;"><span style="font-size: small;"><code>it runs great! </code></span></span><br />
<pre><code> </code></pre>
<pre><code> </code></pre>
<pre><code> </code></pre>
<pre><code> </code></pre>
<pre><a href="https://draft.blogger.com/blogger.g?blogID=2293875457735303762" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img alt="" border="0" height="400" 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<iframe allowfullscreen='allowfullscreen' webkitallowfullscreen='webkitallowfullscreen' mozallowfullscreen='mozallowfullscreen' width='320' height='266' src='https://www.blogger.com/video.g?token=AD6v5dx6GG_h6fj5QDfWmTvC3GKh5jkeUswArjD2E_lZIBxzOPa8zjgWMkek8m2xgI9bzGKyfyRrgci2Y2ha8xmgmg' class='b-hbp-video b-uploaded' frameborder='0'></iframe></div>
<br />Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-56346957409499632572014-09-18T14:57:00.001-07:002014-09-21T14:25:42.368-07:00Psychopy First lesson<br />
I'm currently working on a way to present Python to the NeuralCode group. Offering a means of creating a stimulus presentation script through Psychopy might get people involved in programming. It follows the principle of offering something that saves time or solves a problem immediately.<br />
<br />
<br />
Psychopy can be downloaded as a self contained install. Meaning that Psychopy <br />
and the version of Python it needs, along with the additional libraries, are all packaged together. This makes it easy to install and get going.<br />
<br />
<br />
The teaching materials for Psychopy created by Prof. Gary Lupya are excellent and <br />
I don't think I can do better. So here they are. <br />
<br />
<a href="http://sapir.psych.wisc.edu/wiki/index.php/Exercise1-square">Exercise 1 on http://sapir.psych.wisc.edu</a><br />
<br />
<blockquote class="tr_bq">
import time<br />
import sys<br />
from psychopy import visual, event, core<br />
<br />
win = visual.Window([400,400], color="black", units='pix')<br />
square = visual.PatchStim(win, tex="none", mask="none", color="blue", size=[100,100])<br />
square.draw()<br />
win.flip()<br />
core.wait(.5)<br />
sys.exit()</blockquote>
<br />
One of the first things you notice about Python code is that it is very 'readable'. I have left a line between the lines for loading libraries and the code for the actual workings of the program.<br />
<br />
<br />
<a 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" width="189" /></a>Starting at the line after the gap, 'win' is defined. It is a window with relevant details. (note the image to the left isn't created at this point, it is only defined - this image and the one below are just to show you what is being defined)<br />
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<a 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" width="189" /></a>The next line defines 'square' with details about where it will be displayed and other properties.<br />
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The next two lines need some explanation as they deal with properties of video card display. The square is drawn to a buffer and is then displayed on the screen with win.flip( ). This is coherent with how the video card creates the screen in memory and then displays it usually in two halves (top and bottom).<br />
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<a href="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQHMcGg8hZIMbP_4h2NXFxSSyR4khTshBAaKDZ6fTSBwQTvh2Hb" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" class="rg_i" data-src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQHMcGg8hZIMbP_4h2NXFxSSyR4khTshBAaKDZ6fTSBwQTvh2Hb" data-sz="f" name="ZEbc5PYAknrd2M:" src="https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQHMcGg8hZIMbP_4h2NXFxSSyR4khTshBAaKDZ6fTSBwQTvh2Hb" style="margin-top: 0px;" /></a>It 'flips' like one of those old mechanical clocks with the numbers in two halves.<br />
It's important to realize that this is the timing bottleneck in any stimulus presentation program. Stimuli can only be presented to the screen in increments of the screen refresh rate. Any mention of timing accuracy greater than the screen refresh rate is not very useful. Response collection is another story.<br />
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The last line waits for half a second or we wouldn't see anything before the program exits.<br />
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The next few exercise are about manipulating various properties of the square.<br />
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<a href="http://sapir.psych.wisc.edu/wiki/index.php/Psychopy" target="_blank">Useful Psychopy code snippets</a><br />
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The first couple of challenges are pretty simple.<br />
a) How would you increase the display time to 30 seconds?<br />
b) How would you colour the square red? <br />
Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-33748774269208370412014-09-12T00:48:00.000-07:002014-09-12T00:48:30.817-07:00Advanced Statistical AnalysisWhen I was doing my honours year at Swinburne University, I was required to do a subject called Advanced Quantitative Analysis. By this stage my support of open source software had become strong enough for me to take on the subject using R. I had SPSS installed but only used it for checking the results.<br />
<div>
I intended to blog the alternative analysis done in R as a resource for anyone else traveling the path less traveled. The workload and time restraints meant I didn't but today I think I see the opportunity to tick that box.<br />
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In the neural code group, one of the members has suggested we start with basic data file handling, cleaning the data and doing a t-test. So the first task is to generate some data.<br />
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There are several functions in R for generating random data. </div>
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Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-40747108342758540642014-09-04T20:17:00.001-07:002014-09-17T20:00:46.427-07:00PsychopyPsychopy is a stimulus presentation program, written in Python. It is open source and cross platform and would be a replacement for ePrime or Presentation.<br />
<br />
<span id="goog_1859839614"><span id="goog_1859839617"></span><span id="goog_1859839621"></span></span><a href="http://www.psychopy.org/" target="_blank"><img alt="PsychoPy logo" src="http://www.psychopy.org/_static/psychopyDocBanner2.gif" /></a><br />
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There is also an excellent resource at <a href="http://sapir.psych.wisc.edu/wiki/index.php/Psych711" target="_blank">Psych711</a> created by Prof. Gary Lupya.<br />
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Psychopy uses OpenGL to control presentation of stimuli on a frame by frame level. Stimuli are generated in real-time, meaning the stimuli can be modified by input from participants or data from an external sensor during the running of the experiment. Visual stimulation is synchronised to the graphics card refresh rate, which is the limit of timing precision in a computer based system.<br />
Psychopy, being written in Python, can interact with a wide range of external hardware including USB devices like keyboards and mice, button boxes and even control of fMRI, EEG, or MEG machinery.<br />
Psychopy can be scripted at a Python command line as well as having a graphical interface. Within the graphical interface, called Builder, Psychopy has modules for pictures, video, text as well as audio, and serial input. These modules allow basic parameters to be entered and the code is generated to achieve these requirements.<br />
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<span style="font-size: large;">A GUI for quick construction</span> <br />
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The Builder module in Psychopy is capable of creating standard stimuli and comes with several examples of historically significant experiments. The creation of text and basic shapes on screen is straightforward, as is collecting key presses or mouse clicks as responses. <br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmMXYH6jLd3LBW4IGh5ExQ9-ziB09qTytrqu8JVBgC8HG8y1_-jOmNV6uFcJ8n0h4z_ZB2s5avao6KSq_wmnxfJTVK2xFUPasYexHUYzDBnT_XnsUQAMXhZzWdwS5FmZ38qwQNiTyu548/s1600/builder.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhmMXYH6jLd3LBW4IGh5ExQ9-ziB09qTytrqu8JVBgC8HG8y1_-jOmNV6uFcJ8n0h4z_ZB2s5avao6KSq_wmnxfJTVK2xFUPasYexHUYzDBnT_XnsUQAMXhZzWdwS5FmZ38qwQNiTyu548/s1600/builder.jpg" height="424" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Psychopy Builder module showing visual, sound, text and a code stimuli<br />
the research environment </td><td class="tr-caption" style="text-align: center;"></td><td class="tr-caption" style="text-align: center;"></td><td class="tr-caption" style="text-align: center;"></td></tr>
</tbody></table>
<span style="font-size: large;">The code behind the scenes</span> <br />
<br />
Behind the visual interface of the builder, the python script
representing the desired elements is being created. This script can be
modified if something not available in the builder is required. This
seems an excellent compromise between the precise control of the command
line and the ease of use of a graphical user interface, allowing even a
beginner to create complex visual and auditory stimuli and gather
responses from the keyboard and mouse.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrFzKByjrUeIMoX-RhlXVENiS_xYk18frV4Ieag23X0rDgSBJZRYMWctksLmueUr91-7XHtfLOsAJxyuI8GK8RDHPe2QPalJyDtQgLSePsa-Jc-BO6W08I2mbjVZJsB4oxDCWxV4M1250/s1600/coder.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjrFzKByjrUeIMoX-RhlXVENiS_xYk18frV4Ieag23X0rDgSBJZRYMWctksLmueUr91-7XHtfLOsAJxyuI8GK8RDHPe2QPalJyDtQgLSePsa-Jc-BO6W08I2mbjVZJsB4oxDCWxV4M1250/s1600/coder.jpg" height="521" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">The Psychopy Coder module showing the script version of the experiment.</td></tr>
</tbody></table>
<span style="font-size: large;">Custom Code for anything else</span> <br />
<br />
There is also the custom code module within builder that allows for
elements of code to be added to the GUI. This is how an Arduino
connected to the serial port could be queried to gather the
physiological data for an experiment. The experiment and the
physiological recording could be started together and the information
combined later, but the integration of the data within the experiment
means the response of the participant can be used to modify the
experiment. For example in a just noticeable difference (JND) analysis
the participants response to each stimuli, affects the intensity of the
next stimuli. This is only possible if response data is available to the
experiment.<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhIc8WQROGI-CgSh_C6T8qxPfOtqyKNl2tjzdfv9hliHBM1vxkzx2bicfwvr4sLvFoTZWy4cTrfG53cTD9S2Gx6yGuSNNaTYgvzECm9yFL9vahnPDGmTGp6nkaY-Bh1RG3pRfuD0J4-lg8/s1600/serialCode.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhIc8WQROGI-CgSh_C6T8qxPfOtqyKNl2tjzdfv9hliHBM1vxkzx2bicfwvr4sLvFoTZWy4cTrfG53cTD9S2Gx6yGuSNNaTYgvzECm9yFL9vahnPDGmTGp6nkaY-Bh1RG3pRfuD0J4-lg8/s1600/serialCode.jpg" height="480" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Declaring a serial object with the Pyserial library</td></tr>
</tbody></table>
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<span id="goog_1859839641"></span><a href="https://draft.blogger.com/"></a><span id="goog_1859839622"></span><span id="goog_1859839618"></span><span id="goog_1859839615"></span><span id="goog_1859839623"></span><a href="https://draft.blogger.com/"></a><span id="goog_1859839624"></span><span id="goog_1859839627"></span><a href="https://draft.blogger.com/"></a><span id="goog_1859839628"></span><span id="goog_1859839629"></span><a href="https://draft.blogger.com/"></a><span id="goog_1859839630"></span><span id="goog_1859839631"></span><a href="https://draft.blogger.com/"></a><span id="goog_1859839632"></span><span id="goog_1859839633"></span><a href="https://draft.blogger.com/"></a><span id="goog_1859839634"></span><span id="goog_1859839635"></span><a href="https://draft.blogger.com/"></a><span id="goog_1859839636"></span><span id="goog_1859839637"></span><a href="https://draft.blogger.com/"></a><span id="goog_1859839638"></span>Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-14889343436876782882014-09-02T19:46:00.000-07:002014-09-02T19:46:08.586-07:00How do you teach everything.I'm trying to plan a way to introduce the scientific tool chain. I'm starting to realise that most people don't love programming for it's own sake like I do. They see programming as a means to an end and want to get it over with as quickly and painlessly as possible. Like going to the dentist or doing their taxes.<br />
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I think LaTeX and Bibtex might be the gateway. Especially with PhD candidates, their time is precious and they are only willing to put effort into something if it directly benefits them right now. At least with LaTeX I can show them something that will make the job of writing their thesis easier. I assume most people use 'Word' currently. 'Word' is woefully inadequate to write a multichapter piece of writing. LaTeX on the other hand, was made for this.<br />
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<a href="http://web.science.mq.edu.au/~rdale/resources/writingnotes/latexstruct.html" target="_blank">LaTeX Notes: Structuring Large Documents</a><br />
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Add version control so changes can be tracked and a section that was discarded three weeks ago as irrelevant, but is now exactly what is needed, can be restored with little effort.<br />
Other reasons for a PhD candidate to use Git are given here - <a href="http://stackoverflow.com/questions/6188780/git-latex-workflow" target="_blank">git-latex-workflow</a> <br />
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From LaTeX, the concept of <a href="http://yihui.name/knitr/" target="_blank">Knitr</a> in R is approachable. Analysis will need to be done, so why not do it in R with RStudio? The analysis and writing are then both under version control. Add to that bibtex for referencing and a task that was a huge organisational nightmare is manageable. <br />
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I'm hoping that the lure of easy thesis writing might be the right bait for scientists to learn modern coding practice.Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-86159848766389675292014-09-01T10:09:00.000-07:002014-09-03T14:39:56.007-07:00open, collaborative, reproducible science.<br />
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<a href="https://fbcdn-sphotos-d-a.akamaihd.net/hphotos-ak-xpa1/v/t1.0-9/28951_423848483614_7437293_n.jpg?oh=948abda234dc023e9ff365abdd475c74&oe=545C1FA8&__gda__=1415398325_0300a076b709971cea40ef5863d145a4" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="480" src="https://fbcdn-sphotos-d-a.akamaihd.net/hphotos-ak-xpa1/v/t1.0-9/28951_423848483614_7437293_n.jpg?oh=948abda234dc023e9ff365abdd475c74&oe=545C1FA8&__gda__=1415398325_0300a076b709971cea40ef5863d145a4" width="640" /></a></div>
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I have over the last week been looking at material from the software carpentry organisation. They are tackling the problem of teaching programming to scientists and engineers. <br />
Behind the skills of automation, modularisation, version control and structured and unstructured data they teach at boot camps, they are actually promoting the idea of open, collaborative, reproducible science.<br />
It really does seem that the science community as a whole has been drifting away from these core principles. I wonder when this started to happen and why?<br />
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Collaborative Teaching</h2>
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One of the points made by Greg Wilson in <a href="http://youtu.be/1e26rp6qPbA" target="_blank">this video</a> is that educators don't use version control when it comes to their lectures, lesson plans and lab notes. Wikipedia is a an exemplar of a collaborative research tool where many people write, review and improve an educational resource. Why don't more educators use this idea to improve their educational material?<br />
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As an example, in the lab classes for "Sensation and Perception" the lab notes are not great, in fact lab notes in general are not great in my experience. They are often confusing and don't offer enough detail in the areas that most students need. I'm not criticising the academics writing them either, they are trying to provide good materials but are often increasingly busy and they often don't take lab classes themselves. So how can this be improved?<br />
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A red and green post-it notes system is used in the software carpentry boot camps. A student puts a green post-it note on their laptop if everything is fine and they are progressing through the material. If they get stuck, they put the red post it note up and the instructor directs an assistant their way. At the end of each session, everyone writes something they learned on the green post-it and something that they didn't understand on the red post-it. Over time, this identifies the common stumbling blocks and allows the instructors to focus more time on the areas most people find difficult or rewrite the sections to try to improve the situation.<br />
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If the lab notes were on git-hub, I could take the information from running a lab and after forking the repository, attempt a rewrite. I could then send a pull request to the lecturer for their approval of the changes.Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0tag:blogger.com,1999:blog-2293875457735303762.post-3773486602450392502014-09-01T10:01:00.002-07:002014-09-01T10:01:50.994-07:00Confusion is good for you.<span style="font-family: Georgia, serif; font-size: 16px; line-height: 24px;"><a href="http://www.scientificamerican.com/podcast/episode/confusion-helps-us-learn-12-06-25/" target="_blank">"When we’re confused by something—say with a movie plot or calculus—we tend to feel uncomfortable, frustrated. But maybe we should embrace the confusion. Because a new study finds that confusion can lead to better learning."</a></span><br />
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<span style="font-family: Georgia, serif;"><span style="line-height: 24px;">This quote from Scientific America references an upcoming article in the journal <a href="http://www.journals.elsevier.com/learning-and-instruction/" target="_blank">learning and instruction</a>. One of the most interesting findings of the study was that the group that received clear and precise instruction did worse than the group that was deliberately presented with confusing instructions. Even more interesting, the group that received clear and precise instructions estimated their own grasp of the subject higher than those who received confusing instructions. </span></span><br />
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<span style="font-family: Georgia, serif;"><span style="line-height: 24px;">It also got a few of us talking about how the undergraduate course we did and now tutor/lecture in has changed. </span></span><span style="font-family: Georgia, serif; line-height: 24px;">In the old days, when we did psychology/psychophysiology, statistics was a core subject. The principles and practice was presented over three years and an advanced statistical methods subject was presented in the Honours year. Now it seems that statistics is an elective.</span><br />
<span style="font-family: Georgia, serif;"><span style="line-height: 24px;">Statistics is confusing. That's a given, and no-one enjoys being confused. Could it be that statistics is being removed from the undergraduate because it wasn't appealing to those going into first year? </span></span><br />
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<span style="font-family: Georgia, serif; line-height: 24px;">A friend and I were doing </span><span style="font-family: Georgia, serif; line-height: 24px;">a coursera course on fMRI analysis. He</span><span style="font-family: Georgia, serif;"><span style="line-height: 24px;"> suggested to me that I should try answering the quizzes before listening to the tutorial. Madness to my perfectionist self who is threatened by failure in any form! But this works really well on coursera where you get unlimited (or at least multiple) retries to complete the quizzes.</span></span><br />
<span style="font-family: Georgia, serif;"><span style="line-height: 24px;"><br /></span></span>Alistair Walshhttp://www.blogger.com/profile/12852010670770605693noreply@blogger.com0