tag:blogger.com,1999:blog-81603514772887340082024-03-19T09:46:31.056+01:00Computational BiochemistryComputational molecular sciences and free software. Pirates and molecules!Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.comBlogger49125tag:blogger.com,1999:blog-8160351477288734008.post-28115534206968814252014-06-12T17:11:00.000+02:002014-06-12T17:11:08.441+02:00Slides from my PhD defence<br />
<br />
<a href="https://dl.dropboxusercontent.com/u/17435887/talks_and_posters/phd-def.pdf">https://dl.dropboxusercontent.com/u/17435887/talks_and_posters/phd-def.pdf</a>Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com49tag:blogger.com,1999:blog-8160351477288734008.post-24463823802261167972014-02-19T11:04:00.004+01:002014-02-19T11:04:56.776+01:00Automatic mounting of remote storage via SSHFS on Amazon EC2 instancesIn this blog I demonstrate how you can create an Amazon EC2 instance image that will automount a folder on a remote server via SSHFS.<br />
The purpose here is to fire up a EC2 compute server, run a program and save the output from that program on our local compute cluster at the university.<br /><br />Basically, you just need to a line to /etc/fstab and save the instance as an image (that's what I did).<br />
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<br />
What you need:<br />
<br />
<ul>
<li>An Amazon EC2 instance with sshfs installed.</li>
<li>A user with SSH keys properly setup to the remote system (the SSH keys cannot require a passphrase).</li>
</ul>
Your remote server has a folder that is named remote_folder and your instance has a folder named local_folder. The default username on Amazon is "Ubuntu" for Ubuntu instances, so I'm using this as an example. <br />
<br />
<b><span style="font-family: "Courier New",Courier,monospace;">sshfs#ubuntu@remoteserver:/home/ubuntu/remote_folder/ /home/ubuntu/local_folder/ fuse user,delay_connect,_netdev,reconnect,uid=1000,gid=1000,IdentityFile=/home/ubuntu/.ssh/id_rsa,idmap=user,allow_other,workaround=rename 0 0 </span></b><br />
<br />
Everything is one long line that goes into /etc/fstab. The IdentityFile points to your SSH key. You need the "_netdev" keywords to mount the SSHFS folder after network becomes available. The "reconnect" keyword does what it reads, so throw that in as well.<br />
I read a few posts from other people who had difficulties mounting SSHFS properly without the "delay_connect" and "workaround=rename" keywords, so I added those for good measure.<br />
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Note that you need the trailing / after the folder names! I won't work without (and I'm talking from bitter experience here). <br />
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Furthermore, you want to add the following line to /etc/ssh/ssh_config<br />
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<b><span style="font-family: "Courier New",Courier,monospace;">ServerAliveInterval 5</span></b><br />
<br />
This makes SSH send a keep alive signal every 5 seconds so you don't get disconnected due to being idle. <br />
<br />
Apart from that I think the above should be self-explanatory (for someone looking for this information).<br />
<br />Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com5tag:blogger.com,1999:blog-8160351477288734008.post-33290629516267256892014-02-14T15:50:00.000+01:002014-02-14T15:51:33.479+01:00Chemistry on the Amazon EC2<br />
<br />
We are trying out the Amazon EC2 compute cloud for running computations in the Jensen Group. This is a note on how things are going so far.<br />
<br />
It was actually extremely easy to set up. Within minutes of having created the Amazon Web Services (AWS) account, I had a free instance of Ubuntu 12.04.3 LTS up and running and was able to SSH into it <br />
You have access to one free virtual box and 750 free hours per month for the first year so it is free to get started. My free instance had some Intel processor, 0.5GB RAM and 8GB disk space (I think the spec change from time to time).<br />
<br />
I copied binaries for PHAISTOS (the program we are looking to run) over and they ran successfully, and things pretty much went without a hitch.<br />
After trying out the free instance, I just saved the image (you can do that via the web interface), and every other instance I just started from the same image so no configuration was needed after the first time.<br />
I mounted a folder located on the university server via SSHFS which I use to store output data from the instance directly to our server. This way I don't lose data if the instance is terminated, and I don't have to log in to the instance to check output or log-files.<br />
<br />
The biggest problem for me was the vast number of different types of instance. You can select everything form memory-optimized to CPU, storage, interconnect or GPU instances, and these come in several different types each. This takes a bit of research and there is a lot of fine print. E.g. Amazon doesn't specifiy the physical core count, but rather "vCPU" which may or may not include hyperthreading (i.e. the vCPU number may be twice what you actually get!) <br />
Also the price varies depending where the data center where you spawn your instances is located. I picked N. Virginia data center which was the cheapest. I don't know why I would pick one of their other data centers? The closest to me is located in Ireland, but it is about 15% more expensive. Asia seems to be even more expensive.<br />
<br />
Managing payment is also surprisingly easy. I had my own free account which I used in the beginning. <a class="g-profile" href="https://plus.google.com/113593600676820394169" target="_blank">+Jan Jensen</a> created an account using the university billing account number. From there we used the Consolidated Billing option to add my account to having the bill sent to Jan's account.<br />
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<br />
Our current project is pretty much only CPU-intensive and barely requires any storage or memory, so naturally I had to benchmark the instance types that are CPU optimized.<br />
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I tested out the largest (by CPU count) instances I could launch in the General Purpose (m3 tier), Compute Optimized (c3 tier) and Compute Optimized//previous generation (c1 tier). These are the m3.2xlarge, c3.2xlarge and c1.xlarge instances.<br />
<br />
In short these machines are: <br />
<br />
name = core count (processor type) ~ hourly price (geographical location of server)<br />
<br />
m3.2xlarge = 4 physical cores (Intel E5-2670 @ 2.60 GHz) ~ \$0.90/hour (N. Virginia)<br />
c3.2xlarge = 4 physical cores (E5-2680v2 @ 2.80 GHz) ~ \$0.60/hour (N. Virginia)<br />
c1.xlarge = 8 physical cores* (E5-2650 @ 2.00 GHz) ~ \$0.58/hour (N. Virginia) <br />
<br />
The c1.xlarge didn't support hyper threading from what I could gather. The m3.2xlarge is more expensive, because it has faster disks and more RAM. Initially, I thought the m3.2xlarge had 8 physical cores, but turns out I was merely fooled by the "vCPU" number and several pages of fine print in the pricing list.<br />
<br />
<br />
As a test, I launched a Metropolis-Hastings simulation in PHAISTOS starting from the native structure of Protein G with the PROFASI force field at 300K with the same seed (666) in all the tests, and noted the iteration speed as a function of cores.<br />
<div style="text-align: center;">
<br /></div>
<div style="text-align: left;">
The maximum number of total iterations (all threads, collectively) per day for the three instances was comparable (see below) maxing out at around 500-600 millions/day.</div>
<div style="text-align: left;">
A slight win for the quad core c3.2xlarge instance when it is hyperthreading on 8 cores.</div>
<div style="text-align: left;">
No real benefit to spawn more than 8 concurrent threads either. </div>
<div style="text-align: center;">
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" 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What is probably more important is the throughput for each USD you spend. Again, the c3.2xlarge wins (when hyperthreading on 8 cores) and is the cheapest for our purpose.</div>
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" 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Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com18tag:blogger.com,1999:blog-8160351477288734008.post-88319927658933058542014-01-02T17:09:00.000+01:002014-01-02T17:09:01.506+01:00FragBuilder: Setting up a scan of peptide conformations.This post covers the essentials of how to set up a scan of different peptide conformations. We have used this method to calculate more than 1.5M QM calculation on model peptides in our group.<br />
<br />
In this example we make a tri-glycine peptide and scan over the phi/psi torsion angles between -180 and 180 degrees with 60 degrees spacing.<br />
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For each conformation, the MMFF94 force field energy is printed and the structure is saved as an XYZ file.<br />
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===
<script src="https://gist.github.com/andersx/8221316.js"></script>
===Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com12tag:blogger.com,1999:blog-8160351477288734008.post-31149405398691355152014-01-02T16:25:00.000+01:002014-01-02T16:25:58.781+01:00FragBuilder: The paper is out (as preprint)!Our latest papers on the FragBuilder Python library is out as preprint from PeerJ, while it is undergoing peer-review.<br />
<br />
PeerJ is in my opinion awesome for this type of publications, demonstating the use of a Python library, since they even allow examples and code-bits in the paper.<br />
<br />
You can see the preprint here: <a href="https://peerj.com/preprints/169v2/">https://peerj.com/preprints/169v2/</a><br />
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We even got DOI and can be cited as:<br />
<blockquote class="tr_bq">
<span style="font-size: x-small;"><span class="self-citation-authors">Christensen AS, Hamelryck T, Jensen JH.</span> (<span class="self-citation-year">2013</span>) <span class="self-citation-title">FragBuilder: An efficient Python library to setup quantum chemistry calculations on peptides models</span>. <span class="self-citation-journal" itemprop="publisher">PeerJ PrePrints</span> <span class="self-citation-volume">1</span>:<span class="self-citation-elocation">e169v2</span> <a href="http://dx.doi.org/10.7287/peerj.preprints.169v2" itemprop="url">http://dx.doi.org/10.7287/peerj.preprints.169v2</a></span></blockquote>
The FragBuilder Python library can be found at GitHub: <a href="https://github.com/jensengroup/fragbuilder">https://github.com/jensengroup/fragbuilder</a><br />
<br />
I am in the process of making a couple of more blog-posts to demonstrate the use of FragBuilder, stay tuned!Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com3tag:blogger.com,1999:blog-8160351477288734008.post-72475758039580687342013-12-27T14:29:00.000+01:002013-12-27T14:29:00.840+01:00FragBuilder: InstallationWith the FragBuilder library out as a <a href="https://peerj.com/preprints/169v2/">preprint from PeerJ</a> and the library available at <a href="https://github.com/jensengroup/fragbuilder">https://github.com/jensengroup/fragbuilder</a>, I am going to make a couple of blog posts to get you started using FragBuilder. You can access the manual via the GitHub page linked above.<br />
<br />
This post will guide you through the installation of the FragBuilder library.<br />
<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://dl.dropboxusercontent.com/u/17435887/fragbuilder/model_angles_crop.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="228" src="https://dl.dropboxusercontent.com/u/17435887/fragbuilder/model_angles_crop.png" title=" " width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><b>Figure 1:</b> Example of peptide model made using the Fragbuilder library<u><br /></u></td></tr>
</tbody></table>
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<br />
<br />
FragBuilder will run about every Linux distribution with a recent Python 2.x interpreter.<br />
<br />
You need to havethree things installed before FragBuilder can run. Install of these can be found in the 2nd half of this post.<br />
<ol>
<li>NumPy </li>
<li>Open Babel with Python bindings</li>
<li>git (in order to get FragBuilder from GitHub)</li>
</ol>
To actually download FragBuilder you need to use git, as FragBuilder is stored and maintained through GitHub. To clone the latest version do this:<span style="font-family: "Courier New",Courier,monospace;"> </span><br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">$ git clone git://github.com/jensengroup/fragbuilder</span></blockquote>
This will download (clone) FragBuilder to a folder named "<span style="font-family: "Courier New",Courier,monospace;">fragbuilder</span>". There is nothing that needs to be compiled, so in principle, FragBuilder is now installed and works.<br />
<br />
To actually be able to use FragBuilder from a Python script, you need to export the path to the FragBuilder library to your $PYTHONPATH. For instance, on my laptop, I do the export like this:<span style="font-family: "Courier New",Courier,monospace;"> </span><br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">\$ export PYTHONPATH=/home/andersx/dev/fragbuilder:\$PYTHONPATH</span> </blockquote>
Now you should be able to import FragBuilder from Python. You can copy this line into your <span style="font-family: "Courier New",Courier,monospace;">~/.bashrc</span> file if you want don't want to export this every time you log in.<br />
<br />
To test your installation fire up Python and import FragBuilder:<span style="font-family: "Courier New",Courier,monospace;"> </span><br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">$ python</span> <span style="font-family: "Courier New",Courier,monospace;"> </span><br />
<br />
<span style="font-family: "Courier New",Courier,monospace;">Python 2.7.3 (default, Sep 26 2013, 20:03:06) <br />[GCC 4.6.3] on linux2<br />Type "help", "copyright", "credits" or "license" for more information.</span><br />
<span style="font-family: "Courier New",Courier,monospace;">>>> import fragbuilder<br />>>> print fragbuilder.__version__<br />1.0</span><span style="font-family: "Courier New",Courier,monospace;"><br /></span><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;">>>> </span></span></blockquote>
If you don't get any errors FragBuilder now works.<br />
<br />
<br />
<br />
<b>Installation of dependencies:</b><br />
<br />
<b>1) NumPy:</b><br />
<br />
<br />
In most Linux distributions, NumPy is can be obtained through a package manager. The commands are usually.<br />
<br />
For Ubuntu/Debian distributions:<br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">sudo apt-get install </span><span style="font-family: "Courier New",Courier,monospace;">python-numpy</span> </blockquote>
For Fedora/Red Hat distributions:<span style="font-family: "Courier New",Courier,monospace;"> </span><br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">su</span><br />
<span style="font-family: "Courier New",Courier,monospace;">yum install python-numpy</span></blockquote>
See this link, if you still have troubles installing NumPy: <a href="http://www.scipy.org/install.html">http://www.scipy.org/install.html</a> <br />
<br />
<br />
<b>2) Open Babel with Python bindings:</b><br />
<br />
In most Linux distributions, Open Babel is can be obtained through a package manager. The commands are usually.<br />
<br />
For Ubuntu/Debian distributions:<span style="font-family: "Courier New",Courier,monospace;"> </span><br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">sudo apt-get install python-openbabel</span> </blockquote>
For Fedora/Red Hat distributions:<span style="font-family: "Courier New",Courier,monospace;"> </span><br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">su</span><br />
<span style="font-family: "Courier New",Courier,monospace;">yum install python-openbabel</span> </blockquote>
Optionally, you can compile and install Open Babel yourself. This may be required if you get a certain problem with setting dihedral angles to 180 degrees.<br />
In older versions of Open Babel you are only allowed to set the dihedral angle to 179.96 degrees. I recently submitted a patch to fix this, but I don't know when the changes will go "live" onto repositories, etc.<br />
<br />
I made a short guide here:<br />
<a href="http://combichem.blogspot.dk/2013/12/compiling-open-babel-with-python.html">http://combichem.blogspot.dk/2013/12/compiling-open-babel-with-python.html</a><br />
<br />
There is some extra stuff here from the Open Babel manual:<br />
<a href="http://openbabel.org/docs/dev/Installation/install.html"> http://openbabel.org/docs/dev/Installation/install.html</a><br />
<br />
<br />
<b>3) Git:</b><br />
<br />
Git is pretty much the program you use to download FragBuilder from GitHub. You can also use Git to submit patches back to the repository. Git also does a million other things, but you don't need to know more at this point.<br />
<br />
In most Linux distributions, git is can be obtained through a package manager. The commands are usually.<br />
<br />
For Ubuntu/Debian distributions:<br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">sudo apt-get install git</span> </blockquote>
For Fedora/Red Hat distributions:<span style="font-family: "Courier New",Courier,monospace;"> </span><br />
<blockquote class="tr_bq">
<span style="font-family: "Courier New",Courier,monospace;">su</span><br />
<span style="font-family: "Courier New",Courier,monospace;">yum install git</span></blockquote>
<br />
I think that is all. If not, feel free to leave a message!<br />
<br />
<br />Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com5tag:blogger.com,1999:blog-8160351477288734008.post-64209798980077939602013-12-21T11:11:00.000+01:002013-12-21T11:20:35.593+01:00Putting preprints on arXiv increases your h-index<div style="text-align: justify;">
... but it's not the only reason why you should do so. Let me explain! </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
My upcoming paper on amide proton chemical shifts in proteins was accepted on November 11 by PLOS ONE. The tentative publication date is December 31. Pretty goddamn, horribly slow if you you ask me.</div>
<div style="text-align: justify;">
Their only job at this point is to apply fancy formatting to a LaTeX document. This apparently takes 7 flippin' weeks? </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
However, this is no problem (and enough rage). I have had a preprint available on arXiv since I submitted the first draft. Since then it has been peer reviewed and accepted, and I have uploaded the same version to arXiv as the one we have accepted in PLOS ONE </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Today I was even cited by a paper in a "real" journal (Journal of Chemical Theory and Computation) by the Ochsenfeld group in Munich. See here:</div>
<div style="text-align: justify;">
<a href="http://scholar.google.dk/citations?view_op=view_citation&hl=en&user=g3eVny8AAAAJ&citation_for_view=g3eVny8AAAAJ:qjMakFHDy7sC">http://scholar.google.dk/citations?view_op=view_citation&hl=en&user=g3eVny8AAAAJ&citation_for_view=g3eVny8AAAAJ:qjMakFHDy7sC</a></div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
This might eventually increase my h-index, give me an edge when applying for money, etc. I am even waiting for a review on another of my papers in PeerJ which cites this very same paper on arXiv once more. My h-index might increase by <strike>TWO</strike> one because of this.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
</div>
<div style="text-align: justify;">
But the most important thing here is: The information I had freely available on arXiv before the entire cycle of waiting, peer review, waiting, acceptance, waiting, publishing was over had found it leverage another study.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
This proves, that preprints are indeed useful. And of don't deposit preprints your are not only holding back everyone else, but also yourself. If a publisher doesn't allow preprints, tell them to change their policies or, alternatively, to continue their cooperating with the Devil.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
In conclusion, be this Good Guy Greg:</div>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://dl.dropboxusercontent.com/u/17435887/44064363.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://dl.dropboxusercontent.com/u/17435887/44064363.jpg" /></a></div>
<br />Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com0tag:blogger.com,1999:blog-8160351477288734008.post-19977830804744893852013-12-10T20:33:00.002+01:002013-12-10T20:37:41.643+01:00Beautiful rendereing of small molecules in Pymol<a href="http://www.blogger.com/blogger.g?blogID=8160351477288734008" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><br />
<div style="text-align: justify;">
Getting ready to submit a publication of my FragBuilder paper to PeerJ, I had to make several pictures of peptides and pictures to illustrate dihedral angles in the proteins. </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Personally, a little bit of me dies whenever I see a screenshot from GaussView in a publication. I used Pymol to make the ray-traced 3D models. One example is shown below, and I think it came out pretty great. Note that I added the torsion angles in LibreOffice Draw manually.</div>
<div style="text-align: justify;">
<br />
<img alt="" height="366" src="data:;base64,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" width="640" /><br />
<br />
<br /></div>
<br />
The Pymol commands to make the above figure are:<br />
<br />
load my_molecule.xyz<br />
color grey05, elem c<br />
bg_color white<br />
preset.ball_and_stick(selection='all', mode=1)<br />
set ray_trace_mode, 4<br />
<br />
Once you have oritented the molecule to the position you want in your picture, type in the console:<br />
<br />
ray<br />
<br />
Then export the resulting image to a .png file.<br />
<br />
Happy Pymol'ingAnonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com3tag:blogger.com,1999:blog-8160351477288734008.post-32373092385129789762013-12-05T15:50:00.001+01:002013-12-06T10:12:58.790+01:00Compiling Open Babel with Python bindings<b>Use the commands below to compile Open Babel with python bindings: </b><br />
<br />
git clone git://github.com/openbabel/openbabel.git<br />
cd openbabel<br />
mkdir build<br />
cd build<br />
cmake -DRUN_SWIG=ON -DPYTHON_BINDINGS=ON \<br />
-DCMAKE_INSTALL_PREFIX=/opt/openbabel -DENABLE_TESTS=ON ..<br />
make<br />
make test <br />
sudo make install<br />
<br />
<b>Export these variables to use your newly compiled Open Babel:</b><br />
<br />
export PYTHONPATH=/opt/openbabel/lib/python2.7/site-packages:\$PYTHONPATH
<br />
export LD_LIBRARY_PATH=/opt/openbabel/lib:\$LD_LIBRARY_PATH
<br />
export PATH=/opt/openbabel/bin:\$PATHAnonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com2tag:blogger.com,1999:blog-8160351477288734008.post-9482040563497197792013-12-04T10:36:00.000+01:002013-12-04T10:36:47.655+01:00Constraint optimization in Python with Open BabelThis post is just a quick post to show, how you can optimize molecules with harmonic constraints in Python with Open Babel. This requires Open Babel to be compiled with Python SWIG-bindings. <br />
<br />
There are three types of constraints, distances, angles and torsions. Here is a gist that might help get you started. <br />
<br />
<script src="https://gist.github.com/andersx/7784817.js"></script>
<br />
<br />
<b>Acknowledgements: </b>Kasper Thofte pretty much wrote the above gist back in the days.Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com1tag:blogger.com,1999:blog-8160351477288734008.post-52897822464167620582013-11-09T15:20:00.004+01:002013-11-10T12:41:51.617+01:00My best, fastest and awesomest way of including matplotlib in LatexIf you use this post you will accomplish the following:<br />
<ol>
<li>Be able Include loss-less vector graphics in your Latex document (good).</li>
<li>Be able to select text inside figures with mouse (awesome).</li>
<li>Compile your latex document with figures faster (probably).</li>
<li>Make your files smaller and of higher quality (mostly).</li>
<li>That's it. The rest is up to you!</li>
</ol>
I see many people still using .eps files from matplotlib in their otherwise nicely formatted Latex document. Forget this. From this post on we use matplotlib figures in .pdf format. It works exactly as with .eps or .png or what else you might be using:<br />
<br />
<script src="https://gist.github.com/andersx/7385833.js"></script>
Let's also use pdflatex for compiling (just replace the latex command):<br />
<br />
<pre><span style="font-family: inherit;"><span style="font-size: large;">andersx@awesome:~/my_paper$ pdflatex mypaper.tex</span></span>
</pre>
<br />
This will also compile much faster than standard latex (using, say, .png-files), since you are now including something that is already in pdf-format. <br />
<br />
Last two steps are (1) to save your file as a .pdf-file and (2) autocrop the white borders (loss-less). Step one is accomplished using standard matplotlib/pylab/etc. syntax. The 2nd part is done using a tool called pdfcrop (which is already included in you have latex installed). I prefer to call pdfcrop from within the Python script to avoid having to run more than one command each time I make a new figure.<br />
<script src="https://gist.github.com/andersx/7385821.js"></script>
The above is some boilerplate code. The result is:<br />
<br />
<pre><span style="font-family: inherit;"><span style="font-size: large;">andersx@awesome:~/my_paper$ python my_figure.py
PDFCROP 1.33, 2012/02/01 - Copyright (c) 2002-2012 by Heiko Oberdiek.
==> 1 page written on `my_file.pdf'.
andersx@awesome:~/my_paper$</span></span></pre>
<br />
Now you have a beautifully formatted pdf-file to include in your latex document.<br />
<br />
I might edit this post later with a bit of bling and YOLO/swag just to give it the attractive combination of vitality and glamour it deserves.<br />
<br />Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com6tag:blogger.com,1999:blog-8160351477288734008.post-10464933163140195112013-10-28T11:17:00.002+01:002013-10-28T11:17:47.609+01:00Hybrid RHF/MP2 geometry optimizations with the Effective Fragment Molecular Orbital Method<div style="text-align: justify;">
This is a short post on a paper we are submitting to PLOS ONE. It is of course already (and have been for quite some time) available from arXiv:</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
========================================================================</div>
<div style="text-align: justify;">
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<div class="title mathjax" style="text-align: justify;">
<a href="http://arxiv.org/abs/1305.0676"><b>Hybrid RHF/MP2 geometry optimizations with the Effective Fragment Molecular Orbital Method</b></a></div>
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Anders S. Christensen, Casper Steinmann, Dmitri G. Fedorov, Jan H. Jensen</div>
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<blockquote class="abstract mathjax">
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The frozen domain Effective Fragment Molecular Orbital method (<i>PLoS
ONE</i> (2013<span style="background-color: transparent; color: black; font-family: Arial; font-style: normal; font-variant: normal; font-weight: normal; text-decoration: none; vertical-align: baseline;"></span>) 8(4):e60602) is extended to allow for the treatment of a single fragment at
the MP2 level of theory. The approach is applied to the conversion of
chorismate to phrephenate by chorismate mutase, where the substrate is treated
at the MP2 level of theory while the rest of the system is treated at the RHF
level. MP2 geometry optimization is found to lower the barrier by up to 3.5
kcal/mol compared to RHF optimzations and ONIOM energy refinement and leads to
smoother convergence with respect to basis set for the reaction profile. For
double zeta basis sets the increase in CPU time relative to RHF is roughly a
factor of two.</span></blockquote>
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<a href="http://arxiv.org/abs/1305.0676">http://arxiv.org/abs/1305.0676</a> </div>
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<b>Short summary:</b></div>
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We present a new layering scheme for fragment based calculations in GAMESS using the Fragment Molecular Orbital (FMO) and Effective Fragment Molecular Orbital (EFMO), in which the molecular system is divided into four parts - see figure below.</div>
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We are then able to optimize the geometry of a reaction complex (H in Fig. 1) in chorismate mutase at the MP2 level while simultaneously optimizing the geometry of the surrounding environment (A in Fig. 1) which is in turn embedded into a polarizable domain (b in Fig. 1) with frozen geometry, which in turn is embedded inside a non-polarizable, frozen-geometry domain (F. in Fig. 1).</div>
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We are treating the system at the MP2 level on the reaction complex and Hartree-Fock level on the rest of the system. As an example we calculate the reaction path of conversion of chorismate to prephenate in chorismate mutase.</div>
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The increase in computational load is rougly a factor of two, compared to a Hartree-Fock-only calculation.</div>
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The method will be available with a new keyword, but the final name is yet to be decided. There will be a blog post on this later if/when I get this in the official GAMESS distribution.</div>
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" 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" width="400" /></a></div>
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<span style="font-size: x-small;"><b>Figure 1: </b>F denotes the frozen domain (green); b denotes the polarizable domain (blue); A denotes the active domain (red); H 2 A denotes fragment H, for which the MP2 energy and gradients are evaluated (yellow).</span></div>
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Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com0tag:blogger.com,1999:blog-8160351477288734008.post-44611452326166878612013-10-19T15:53:00.002+02:002013-10-19T18:10:17.910+02:00Energy minimization RMS gradient convergence criterion; GAMESS vs. Tinker.<div style="text-align: justify;">
As per the recommendations of <a class="g-profile" href="http://plus.google.com/113593600676820394169" target="_blank">+Jan Jensen</a> we have in our group been using the convergence criterion, in GAMESS, OPTTOL=0.0005 in the $STATPT input group for minimization of protein structures using various fast quantum mechanical methods ranging from correlated fragment based methods to dispersion corrected Hartree-Fock theory to semi-empirical methods.</div>
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Yesterday I was about to carry out a molecular mechanics energy minimization of a PDB structure in order to remove a couple seemingly spurious bond lengths in the structure before putting the coordinates through a full QM calculation. I started Tinker and I was immediately faced with a cryptic question to which I only knew the answer in terms of GAMESS' OPTTOL variable:</div>
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<br /></div>
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<div style="text-align: justify;">
<b>Enter RMS Gradient per Atom Criterion [0.01] : </b></div>
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<br /></div>
<div style="text-align: justify;">
Firstly, the question is cryptic since there is no mention of units. Secondly, my answer to the question was also not obvious, since I didn't know exactly what OPTTOL=0.0005 (which is in hartree/bohr) really meant. INPUT.DOC in the gamess/ directory was helpful:</div>
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<br /></div>
<div style="text-align: justify;">
<blockquote class="tr_bq">
OPTTOL = gradient convergence tolerance, in Hartree/Bohr.<br />
Convergence of a geometry search requires the<br />
largest component of the gradient to be less<br />
than OPTTOL, and the root mean square gradient<br />
less than 1/3 of OPTTOL. (default=0.0001)</blockquote>
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So actually, my convergence criterion of the RMS gradient in GAMESS has been 1/3 * 0.0005 hartree/bohr. </div>
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After a bit of detective work, it turns out that the units of the Tinker criterion is in kcal/mol/angstom. So the relevant conversion factors from OPTTOL in GAMESS to RMS gradient in Tinker are can be summed up as:</div>
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<br /></div>
<ul style="text-align: justify;">
<li>1/3 (GAMESS definition)</li>
<li>627.51 (hartree to kcal/mol)</li>
<li>1.8897 (angstrom to bohr)</li>
</ul>
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The total conversion factor becomes: 1/3 * 627.51 * 1.8897 = 395.27</div>
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All in all OPTTOL=0.0005 in "GAMESS units" (hartree/bohr) amounts to an RMS gradient of 0.2 in "Tinker units" (kcal/mol/angstom).</div>
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<i>Happy Tinkering!</i></div>
Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com3tag:blogger.com,1999:blog-8160351477288734008.post-24790766198072978912013-08-27T17:25:00.000+02:002013-08-27T17:25:22.503+02:00Aligning PDB structures with BiopythonYou can use the Bio.PDB module in Biopython to align PDB files. This is how I did it. The code should be pretty much self-explanatory.<br />
<br />
In this example I align the crystal structure of Ubiquitin (PDB code: 1UBQ) to the first structure of a corresponding NMR ensemble (PDB code: 1D3Z, see picture below).<br />
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<a href="http://www.rcsb.org/pdb/images/1D3Z_bio_r_250.jpg?bioNum=1" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://www.rcsb.org/pdb/images/1D3Z_bio_r_250.jpg?bioNum=1" /></a></div>
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<script src="https://gist.github.com/andersx/6354971.js"></script>Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com8tag:blogger.com,1999:blog-8160351477288734008.post-72126296808497301992013-08-16T10:52:00.003+02:002013-08-16T10:53:31.248+02:00Numpy vs. cPickles (Python, ofc)I've been using<b><span style="font-family: "Courier New",Courier,monospace;"> cPickels</span></b> for storing data into a Python-friendly format for some time. See my earlier blog post for more on <b><span style="font-family: "Courier New",Courier,monospace;">cPickles</span></b>.<br />
<a href="http://combichem.blogspot.dk/2013/02/saving-into-data-into-cpickle-format-in.html">http://combichem.blogspot.dk/2013/02/saving-into-data-into-cpickle-format-in.html</a><br />
<br />
I have also been using Numpy's save function to do the same thing. numpy.save() and <span style="font-family: "Courier New",Courier,monospace;"><b>numpy.load()</b></span> is so much simpler, however. I really recommend that people use <span style="font-family: "Courier New",Courier,monospace;"><b>numpy.save()</b></span> and <span style="font-family: "Courier New",Courier,monospace;"><b>numpy.load()</b></span> over <span style="font-family: "Courier New",Courier,monospace;"><b>cPickles</b></span> for most purposes. It is <i>so</i> much more simple.<br />
<br />
I always thought a <span style="font-family: "Courier New",Courier,monospace;"><b>cPickle</b></span> was much, much faster than Numpy, but I guess I was wrong, according to this stackoverflow I just saw. Below are loading and saving times for a large array. Practically no difference between Numpy and cPickles!<br />
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<a href="http://i.stack.imgur.com/gEWnv.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://i.stack.imgur.com/gEWnv.png" /></a></div>
<span style="font-size: x-small;">Source: <a href="http://stackoverflow.com/questions/16833124/pickle-faster-than-cpickle-with-numeric-data">http://stackoverflow.com/questions/16833124/pickle-faster-than-cpickle-with-numeric-data</a></span><br />
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To save an array, a list or dictionary or whatever called <span style="font-family: "Courier New",Courier,monospace;"><b>my_array</b></span> into <b><span style="font-family: "Courier New",Courier,monospace;">my_file.npy</span></b>:<br />
<br />
<b><span style="font-family: "Courier New",Courier,monospace;"> numpy.save("my_file", my_array)</span></b><br />
<br />
Note that Numpy appends .npy to the filename automatically.<br />
<br />
<br />
To load the stored data simply:<br />
<br />
<span style="font-family: "Courier New",Courier,monospace;"><b> my_array = numpy.load("my_file.npy")</b></span><br />
<br />
Really py-fragging-thonicly easy!Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com2tag:blogger.com,1999:blog-8160351477288734008.post-40008463603798625022013-08-10T13:53:00.000+02:002013-08-10T13:53:16.382+02:00You know what really grinds my gears? (In Python)I can never correctly remember when things are passed as references or copied as local variables inside functions.<br />
<br />
<br />
Take these two, innocuously looking functions. Because both do the same thing (namely set the contents of a vector, P, to [1, 1]) I call them 1 and a, respectively, since one is not better than the other.<br />
<br />
<br />
<b><span style="font-size: small;"><span style="font-family: "Courier New",Courier,monospace;">def implementation_1(P):<br /><br /> P = [1, 1]<br /><br /><br />def implementation_a(P):<br /><br /> P[0] = 1<br /> P[1] = 1</span></span></b><br />
<br />
<br />
What you would expect is one of the following two options<br />
<ol>
<li>Both functions change P to [1, 1] (permanently).</li>
<li>Both functions take a local copy of P and change it to [1, 1], and after the function returns, the local [1, 1] array is forgotten.</li>
</ol>
<br />
A simple test is to do this:<br />
<br />
<br />
<span style="font-family: "Courier New",Courier,monospace;"><b>P = [0, 0]<br />print P<br /><br />implementation_1(P)<br />print P<br /><br />implementation_a(P)<br />print P</b></span><br />
<br />
which prints:<br />
<br />
<span style="font-family: "Courier New",Courier,monospace;"><b>[0, 0]<br />[0, 0]<br />[1, 1]</b></span><br />
<br />
So clearly implementation_a() is different from implementation_1(), although they seemingly do the same.<br />
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<script src="https://gist.github.com/andersx/6200096.js"></script>Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com6tag:blogger.com,1999:blog-8160351477288734008.post-30238271711745937712013-05-28T16:36:00.002+02:002015-09-24T05:10:54.935+02:00Calculating Diffusion Coefficients from Random Walk Monte CarloThis is taken from a lecture I gave during the Molecular Statistics course at University of Copenhagen, which I co-teach with +Jan Jensen and +Jimmy Kromann. The students have to program different types of molecular simulations and are not expected to have any programming experience before the course. <br />
<br />
Random walk Monte Carlo seems a bit silly at first glance, since a simulation does not really contain a lot of information. As the name implies, the motion of the particle is completely random, much like Brownian motion of a particle suspended in a fluid. The behavior of a particle in a random walk simulation is very much like the movement of the average particle in a fluid, which randomly and constantly bounces into other particles, every time from a random direction.<br />
A collection on particles will then have average properties much like a liquid or (as we have looked a lot at in the course) the Lennar-Jones fluid. Do note, that there are no interactions between any particles.<br />
<br />
A simple one-particle random walk Monte Carlo simulation is shown here.<br />
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<iframe allowfullscreen='allowfullscreen' webkitallowfullscreen='webkitallowfullscreen' mozallowfullscreen='mozallowfullscreen' width='320' height='266' src='https://www.blogger.com/video.g?token=AD6v5dwvDrORF3nJXeBnLpvw_uwhbyaaEz7ueZmbcNJ7NwQL8GNDMlVeQGv_R4Lq6VyyBn6B3of9xst9jwuk6ZqZGw' class='b-hbp-video b-uploaded' frameborder='0'></iframe></div>
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The equations of motion are shown here: <br />
$$x(t + dt) = x(t) \pm \mathrm{random.random()} \cdot \mathrm{d}l $$<br />
$$y(t + dt) = y(t) \pm \mathrm{random.random()} \cdot \mathrm{d}l $$<br />
<br />
where random.random() is the Python default random number generator and $\mathrm{d}l$ is a scaling factor that determines the average step-length. The fact that the equation does not include any interaction terms makes it computationally cheap to simulate a lot of particles.<br />
<br />
<br />
In the last Molecular Statistics class we used random walk Monte Carlo to obtain the diffusion coefficient via Fick's second law, here in one dimension:<br />
$$\frac{\partial \phi}{\partial t} = D\ \frac{\partial^2 \phi}{\partial x^2}$$<br />
The solution of Fick's second law is a Gaussian function<br />
$$\phi (x, t) = \frac{1}{\sqrt{2\ \pi\ \sigma^2}} \exp \left( - \frac{x^2}{2 \sigma^2}\right)$$<br />
with $\sigma = \sqrt{2\ D\ t}$. So if we can calculate $\sigma$ from the distribution of particles, we can immediately get the diffusion coefficient. <br />
The simple approach is to start a random walk simulation with many particles and then fit the distribution of particles to a Gaussian.<br />
<br />
We used this extremely simple Python/Numpy program to simulate random walk and calculate $D$: <br />
<br />
<div style="font-family: "Courier New",Courier,monospace;">
<b>X = numpy.zeros(n_particles)</b></div>
<div style="font-family: "Courier New",Courier,monospace;">
<b><br /></b></div>
<div style="font-family: "Courier New",Courier,monospace;">
<b>for i in range(n_steps):</b></div>
<div style="font-family: "Courier New",Courier,monospace;">
<b> X += numpy.random.uniform(-dl, dl, n_particles)</b></div>
<div style="font-family: "Courier New",Courier,monospace;">
<b> sigma = X.std()</b></div>
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<br /></div>
<div style="font-family: "Courier New",Courier,monospace;">
<b> print "D =", sigma**2 / ( 2 * i)</b></div>
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The simulation looks like this in 2D (with 400 particles):<br />
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<iframe allowfullscreen='allowfullscreen' webkitallowfullscreen='webkitallowfullscreen' mozallowfullscreen='mozallowfullscreen' width='320' height='266' src='https://www.youtube.com/embed/QUVkMjoWkAI?feature=player_embedded' frameborder='0'></iframe></div>
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As you can see, the particles collectively look like they are diffusing through a medium. The diffusion constant is the derived (here only in the $X$-direction by fitting the distribution to a Gaussian function (which was simpy done via Numpy's .std() function). The time evolution of the distribution of particles (blue histogram) and the Gaussian fit (red curve) looks like this:<br />
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<iframe allowfullscreen='allowfullscreen' webkitallowfullscreen='webkitallowfullscreen' mozallowfullscreen='mozallowfullscreen' width='320' height='266' src='https://www.youtube.com/embed/HwR0bnpI2FI?feature=player_embedded' frameborder='0'></iframe></div>
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The time evolution of $D$ should converge pretty quickly. You can see $D$ for a simulation with 10,000 particles below. It took a matter of seconds on Jimmy's old laptop to run this. Imagine doing an long enough MD with 10,000 particles on your laptop!<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhqFtSYNZNdvmp7WYhlOavUNiQNyfhnYHjJsI_raUoSfDlF15fMu_8ZhD1X-wpZeD844pm8YI0_hpgvDEMb7l4ySlLVPamlHO0MZ1ixMZ6jC0rVbfx0lJF6-1G9dycY0kptLjz6FoQw2yHs/s1600/sigma.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhqFtSYNZNdvmp7WYhlOavUNiQNyfhnYHjJsI_raUoSfDlF15fMu_8ZhD1X-wpZeD844pm8YI0_hpgvDEMb7l4ySlLVPamlHO0MZ1ixMZ6jC0rVbfx0lJF6-1G9dycY0kptLjz6FoQw2yHs/s320/sigma.png" width="320" /></a></div>
Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com4tag:blogger.com,1999:blog-8160351477288734008.post-912988972789925012013-05-07T12:54:00.000+02:002013-05-08T10:57:58.748+02:00Visualizing Pointer Aliasing in Python<div class="separator" style="clear: both; text-align: center;">
<a href="http://ip-pig.com/wp-content/uploads/2013/04/Python-Programming-Language.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="108" src="http://ip-pig.com/wp-content/uploads/2013/04/Python-Programming-Language.png" width="320" /></a></div>
This is a concept that has annoyed many a newcomer to Python - including the author of this post. I will be talking briefly about this concept in my lecture next Thursday, and the excellent TA, <a class="g-profile" href="http://plus.google.com/111529974982597129664" target="_blank">+Jimmy Charnley Kromann</a>, pointed me to an example he saw on pythontutor. The Visualize tool on pythontutor is a very explicit way to illustrate what happens under the hood of python: <a code="a+%3D+%5B1.0,+2.0,+3.0,+4.0%5D%0Ab+%3D+a%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b%0A%0Aa%5B1%5D+%3D+99.0%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b&mode=display&cumulative=false&heapPrimitives=false&drawParentPointers=false&textReferences=false&showOnlyOutputs=false&py=2&curInstr=0" href="http://www.pythontutor.com/visualize.html%3C/a%3E%20%3Cbr%20/%3E%3Cbr%20/%3EEDIT:%20There%20seems%20be%20an%20issue%20with%20pythontutor%20at%20the%20moment.%20Click%20this%20link,%20if%20the%20embedded%20code%20below%20fails%20to%20run.%3Ca%20href=" http:="" visualize.html="" www.pythontutor.com="">http://www.pythontutor.com/visualize.html</a><br />
<br />
The Visualize tool on pythontutor is a very clever way to illustrate what happens under the hood of python: <a code="a+%3D+%5B1.0,+2.0,+3.0,+4.0%5D%0Ab+%3D+a%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b%0A%0Aa%5B1%5D+%3D+99.0%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b&mode=display&cumulative=false&heapPrimitives=false&drawParentPointers=false&textReferences=false&showOnlyOutputs=false&py=2&curInstr=0" href="http://www.pythontutor.com/visualize.html" http:="" visualize.html="" www.pythontutor.com="">http://www.pythontutor.com/visualize.html</a> <br />
<br />
EDIT: There seems to be a problem with python tutor at the moment. Click this link, if the embedded code below fails to load: <a code="a+%3D+%5B1.0,+2.0,+3.0,+4.0%5D%0Ab+%3D+a%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b%0A%0Aa%5B1%5D+%3D+99.0%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b&mode=display&cumulative=false&heapPrimitives=false&drawParentPointers=false&textReferences=false&showOnlyOutputs=false&py=2&curInstr=0" href="http://www.pythontutor.com/visualize.html#code=a+%3D+%5B1.0,+2.0,+3.0,+4.0%5D%0Ab+%3D+a%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b%0A%0Aa%5B1%5D+%3D+99.0%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b&mode=display&cumulative=true&heapPrimitives=true&drawParentPointers=true&textReferences=false&showOnlyOutputs=false&py=2&curInstr=0" http:="" visualize.html="" www.pythontutor.com="">Python Example</a> <br />
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<span style="font-size: large;"><br /></span>
<div style="text-align: center;">
<span style="font-size: large;"><i>EDIT: embedded code not working - click link:</i></span></div>
<div style="text-align: center;">
<span style="font-size: large;"><a code="a+%3D+%5B1.0,+2.0,+3.0,+4.0%5D%0Ab+%3D+a%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b%0A%0Aa%5B1%5D+%3D+99.0%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b&mode=display&cumulative=false&heapPrimitives=false&drawParentPointers=false&textReferences=false&showOnlyOutputs=false&py=2&curInstr=0" href="http://www.pythontutor.com/visualize.html#code=a+%3D+%5B1.0,+2.0,+3.0,+4.0%5D%0Ab+%3D+a%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b%0A%0Aa%5B1%5D+%3D+99.0%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b&mode=display&cumulative=true&heapPrimitives=true&drawParentPointers=true&textReferences=false&showOnlyOutputs=false&py=2&curInstr=0" http:="" visualize.html="" www.pythontutor.com="">Python Example</a></span></div>
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<!-- <iframe frameborder="0" height="500" src="http://pythontutor.com/iframe-embed.html#code=a+%3D+%5B1.0,+2.0,+3.0,+4.0%5D%0Ab+%3D+a%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b%0A%0Aa%5B1%5D+%3D+99.0%0A%0Aprint+%22a+%3D+%22,+a%0Aprint+%22b+%3D+%22,+b&cumulative=false&heapPrimitives=false&drawParentPointers=false&textReferences=false&showOnlyOutputs=false&py=2&curInstr=0" width="800"> </iframe> -->
If you want to avoid pointer aliasing, you usually want to do one of these three things in order to copy a to b: <br />
<br />
<span style="font-family: "Courier New",Courier,monospace;">1) </span><br />
<span style="font-family: "Courier New",Courier,monospace;"><br /></span>
<span style="font-family: "Courier New",Courier,monospace;">b = a[:] </span><br />
<br />
This takes a shallow copy. Personally, I don't like this option, because the slicing notation is not clear to everyone. But it is definitely the shorter notation. This will fail if you want to copy a list of lists. Being a shallow copy, it does not copy lists of lists. It will merely copy the list of pointers to other "sub"-lists.<br />
<br />
<br />
<span style="font-family: "Courier New",Courier,monospace;">2) </span><br />
<span style="font-family: "Courier New",Courier,monospace;"><br /></span>
<span style="font-family: "Courier New",Courier,monospace;">import copy</span><br />
<span style="font-family: "Courier New",Courier,monospace;">b = copy.copy(a)</span><br />
<br />
This takes a shallow copy. It is more clear from the code what this does, so this is usually my preferred approach. Still a shallow copy, however.<br />
<br />
<br />
<span style="font-family: "Courier New",Courier,monospace;">3)</span><br />
<span style="font-family: "Courier New",Courier,monospace;"> </span> <br />
<span style="font-family: "Courier New",Courier,monospace;">import copy </span><br />
<span style="font-family: "Courier New",Courier,monospace;">b = copy.deepcopy(a)</span><br />
<br />
<span style="font-family: inherit;">This is the fail-safe method and takes a copy of everything, so use this if you're in doubt. Works pretty much in every case. The down side is that it's usually slower to copy everything with deepcopy.</span><br />
<br />
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<span style="font-family: inherit; font-size: x-small;"><u>Figure 1 </u></span><span style="font-size: x-small;">: a and b contain to the </span></div>
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<span style="font-size: x-small;">word "banana" in two different ways.</span></div>
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</span><span style="font-family: "Courier New",Courier,monospace;"><br /></span>Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com0tag:blogger.com,1999:blog-8160351477288734008.post-29638149807150689882013-04-27T14:39:00.000+02:002013-04-27T14:43:21.687+02:00Fun with Numba, NumPy and F2PY<div style="text-align: justify;">
I've started using Numba to speed up MD simulations for our course in Molecular Statistics, which I teach together with <a class="g-profile" href="http://plus.google.com/113593600676820394169" target="_blank">+Jan Jensen</a> and <a class="g-profile" href="http://plus.google.com/111529974982597129664" target="_blank">+Jimmy Charnley Kromann</a>.</div>
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<span id="goog_507366928">One exercise is using the Velo-Verlet algorithm to simulate a Lennard-Jones gas/liquid with periodic boundary conditions. Since we do everything in Python in this course, running the actual simulation is quite slow. What we do is we supply the students with a compiled FORTRAN module, compiled with F2PY which they can use when they've written their own Lennard-Jones gradient code.</span></div>
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<span id="goog_507366928">The FORmula TRANslator module is extremely fast, compared to Python code. I rewrote the code from last year's course to a function I could use with Numba and compare directly to the F2PY module</span></div>
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<span id="goog_507366928"><br /></span></div>
<div style="text-align: justify;">
<span id="goog_507366928">For 100 particles:</span></div>
<br />
<ul>
<li><b>Numba: </b>0.146 ms/iteration (466x speed up)</li>
<li><b>F2PY: </b>0.236 ms/iteration (289x speed up)</li>
<li><b>Python:</b> 68.06 ms/iteration</li>
</ul>
Now, I'm pretty sure a pure FORTRAN implementation would be faster than the F2PY, but I am very impressed with the Numba, since it's standard Python code and MUCH more simple to read than FORTRAN -- in theory all you have to do is write<span style="font-family: "Courier New",Courier,monospace;"> @autojit</span> before a function definition. This is of course in theory ... I didn't do anything to improve on the readability of the Lennard-Jones code here. Before I posted, I had actually inserted a comment saying<span style="font-family: "Courier New",Courier,monospace;"> """This code block is unreadable"""</span>.<br />
<br />
Now, there were a few issues I discovered:<br />
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<b>1)<span style="font-family: "Courier New",Courier,monospace;"> @autojit vs. </span><span style="font-family: "Courier New",Courier,monospace;">@jit(argtypes=[double[:,:], double[:]])</span></b><br />
<br />
Since Numba has to decide what arguments a function takes before it's being compiled, it needs to know what possible type of arguments the function takes. If you're lucky, Numba decides on the right type and you can get away with <span style="font-family: "Courier New",Courier,monospace;">@autojit</span>, but sometimes autojitting makes the code SLOWER than standard, interpreted Python. In one case I got a factor of 10x slower with autojit, but explicitly stating the function argument types with <span style="font-family: "Courier New",Courier,monospace;">@jit(argtypes = .... )</span> I got a speed up of 20x on the same code compared to interpreted Python.<br />
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<br />
<b>2) Returning tuples in compiled code block</b><br />
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Numba does not allow a tuple to be returned inside a compiled code block. So don't do this.<br />
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<b>3) Use Numpy properly</b><br />
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<ul>
<li>Numba is NumPy aware, so code ran faster when numbers were stored in <span style="font-family: "Courier New",Courier,monospace;">numpy.array</span> types rather than just regular Python lists.</li>
<li>At first I was <span style="font-family: "Courier New",Courier,monospace;">numpy.round()</span> to round to nearest integer in the periodic boundary condition code. Switching three <span style="font-family: "Courier New",Courier,monospace;">numpy.round()</span> calls to<span style="font-family: "Courier New",Courier,monospace;"> numpy.rint()</span> gave a speed up of around 100x on the code execution.</li>
<li>Use <span style="font-family: "Courier New",Courier,monospace;">U[i, j]</span> instead of <span style="font-family: "Courier New",Courier,monospace;">U[i][j]</span> on NumPy arrays. Not much of a difference in the vanilla Python code, but MASSIVE speed gains in compiled code.</li>
</ul>
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Python Lennard-Jones code:<br />
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<script src="https://gist.github.com/andersx/5472867.js"></script>
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F2PY Lennard-Jones code:<br />
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<script src="https://gist.github.com/andersx/5472862.js"></script>
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Python Velo-Verlet solver:<br />
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<script src="https://gist.github.com/andersx/5472874.js"></script>
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<br />Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com8tag:blogger.com,1999:blog-8160351477288734008.post-42907545917828458462013-04-26T17:06:00.000+02:002013-04-26T17:39:14.300+02:00Introducing 32 students to Linux and Python via VirtualBox<div style="text-align: justify;">
I am currently teaching the Molecular Statistics course together with <a class="g-profile" href="http://plus.google.com/111529974982597129664" target="_blank">+Jimmy Charnley Kromann</a> and <a class="g-profile" href="http://plus.google.com/113593600676820394169" target="_blank">+Jan Jensen</a> at the University of Copenhagen. My part of the course is teaching the students how to program simple molecular simulation algorithms in Python. <a class="g-profile" href="http://plus.google.com/113593600676820394169" target="_blank">+Jan Jensen</a> does the theoretical lectures, and I give lectures in basic Python programming and how to implement the equations from Jan.</div>
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One of the challenges we faced before the start of the course was to just get Python, Numpy and Matplotlib up and running on every single student's laptop. Since the students run everything from Windows XP, 7 and 8 to Linux and Mac, this can pose quite a challenge for the students, some of which have barely ever even installed a program before. </div>
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This year, we opted to create a VirtualBox with Ubuntu on it, and make sure everything the need throughout the course was pre-installed. The students could then simply download the VirtualBox program and install our VirtualBox image. </div>
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<a class="g-profile" href="http://plus.google.com/111529974982597129664" target="_blank">+Jimmy Charnley Kromann</a> made this excellent video of how to get the VirtualBox going before the first class:</div>
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<iframe allowfullscreen='allowfullscreen' webkitallowfullscreen='webkitallowfullscreen' mozallowfullscreen='mozallowfullscreen' width='320' height='266' src='https://www.youtube.com/embed/wFUZjmnxTnU?feature=player_embedded' frameborder='0'></iframe></div>
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My biggest fear was that the students would be frightened and confused by their first meeting with a non-Microsoft system. But that (fortunately!) didn't turn out to be an issue at all.</div>
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These are my observation from the first lecture and first exercises class:</div>
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</div>
<ul style="text-align: justify;">
<li>To my surprise all of the students had managed to bring their laptop with a working VirtualBox to the first class. ALL students! I was pretty thrilled since that meant they all could follow my programming from the projector on their own laptop, which is what I want them to do during my lectures.</li>
<li>What was going on on the projector was the same as on their laptop during the lecture, which I think help lessen the confusion.</li>
<li>We didn't have to do ANY tech support during the interactive lectures or the following exercises class to get Python working. Only hitch was two students who wanted American keyboard layout (our Box came with a Danish keyboard layout by default). This was quickly (though some might say awkwardly) solved by "<span style="font-family: "Courier New",Courier,monospace;">setxkbmap us</span>" in the terminal. </li>
<li>Some of the groups of students wanted an extra module to make a video of the things they had plotted in Matplotlib. <a class="g-profile" href="http://plus.google.com/111529974982597129664" target="_blank">+Jimmy Charnley Kromann</a> then posted simple instructions and the relevant <span style="font-family: "Courier New",Courier,monospace;">"sudo apt-get install" </span>command on the course website -- so that went relatively easy as well.</li>
<li>Things which didn't work very well were the shared clipboard
and drag-and-drop of files between the host OS and the VirtualBox. That
only worked on 50% of the student's laptops. Must be a bug in VirtualBox
somewhere. </li>
<li>I only overheard the sentence <i>"Grrr, I hate linux!"</i> once.</li>
</ul>
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So far so good!</div>
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<br />Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com2tag:blogger.com,1999:blog-8160351477288734008.post-1270445136473567192013-04-22T17:23:00.000+02:002013-04-22T17:24:26.206+02:00Numba -- It's like Python on steroids ... on steroids.<div style="text-align: justify;">
I'm teaching a course in molecular simulations this year in which the students have to program simulations of various simple systems. We're trying to keep things as simple as possible and everything is done in Python.</div>
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The task of the second week of the course is to program a simple simulation of a 2D Lennard-Jones gas. The computationally intensive step in this exercise is calculating the energy and gradient. In the past we've (and by we, I mean <a class="g-profile" href="http://plus.google.com/103736230785278907829" target="_blank">+Casper Steinmann</a>) written a FORTRAN module and used F2PY to exploit the awesomeness of the good old FORmula TRANslator and have the students link their Python scripts with a precompiled .so module for this to run at an acceptable speed</div>
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<br /></div>
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This solution is of course completely fine, but not very general. I've been looking into Cython, pypy and various other methods to speed things up a bit. But they all seemed quite elaborate an unPythonic, and you might as well do the whole thing in C then.</div>
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<br /></div>
<div style="text-align: justify;">
Yesterday Numba was brought to my attention on <a href="http://jakevdp.github.io/blog/2012/08/24/numba-vs-cython/">this blog</a>. In short, Numba is a way to compile (at execution time) Python functions into C which normally makes your program run substantially faster. The blog has a very nice example where the code gets a 1000x speedup just by adding one function decorator. And the syntax couldn't be simpler.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
Let's try Numba on our Lennard-Jones program. Here is the naive function they have to implement:</div>
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<br /></div>
<div style="text-align: justify;">
<script src="https://gist.github.com/andersx/5435455.js"></script>The code is then executed via Python as one would normally execute Python code, specifying in the script that the <span style="font-family: "Courier New",Courier,monospace;">calc_energy_and_gradient()</span> function is to be compiled via Numba. This can be done simply by writing the the<span style="font-family: "Courier New",Courier,monospace;"> @autojit</span> decorator before the function definition. </div>
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<br /></div>
<div style="text-align: justify;">
You can also specify exactly which type of input the <i>just-in-time</i> compilation takes takes (an example is commented out). This makes the compilation of the function be type specific, but a bit faster. Make sure to <span style="font-family: "Courier New",Courier,monospace;">import numba</span> and the specific type of argument (here the arguments are of the type <span style="font-family: "Courier New",Courier,monospace;">double</span>) and the relevant decorators (<span style="font-family: "Courier New",Courier,monospace;">jit</span> or <span style="font-family: "Courier New",Courier,monospace;">autojit</span>).</div>
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<br /></div>
<div style="text-align: justify;">
<script src="https://gist.github.com/andersx/5435486.js"></script><b>Conclusion: </b>10000 gradient and energy evaluations take 16 seconds with the standard Python implementation. 2 seconds with <span style="font-family: "Courier New",Courier,monospace;">@autojit</span> and 1.5 seconds with the explicit <span style="font-family: "Courier New",Courier,monospace;">@jit</span>. Not too bad for something that doesn't take ANY code changes.<br />
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<br />Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com8tag:blogger.com,1999:blog-8160351477288734008.post-39027368914905862342013-03-01T11:11:00.004+01:002013-03-01T18:06:05.743+01:00Problem solved? (for single point energies)<div style="text-align: justify;">
I am looking at the conversion of chorismate to prephenate in Chorismate Mutase, to benchmark a hacky EFMO-RHF:MP2 method we've implemented in our group. Casper Steinmann did an adiabatic mapping of the reaction path, and we though it'd be cool to do coupled cluster single point energies on snapshots from the reaction path and some something ONIOM style. </div>
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj812lvujBvw3Y4w1EuFVrN3qFLlK9bIO5AsTLm5wdgGoRz_16LZN0XliebcL60iUjxy9dk7HELS0HLDitrT8gTBYrTlPUq2581sTO_bioDFRWG-wnGNGofF5dNugWR95M_Y9iNvJ1lkCwg/s1600/r15fd3_000.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="256" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj812lvujBvw3Y4w1EuFVrN3qFLlK9bIO5AsTLm5wdgGoRz_16LZN0XliebcL60iUjxy9dk7HELS0HLDitrT8gTBYrTlPUq2581sTO_bioDFRWG-wnGNGofF5dNugWR95M_Y9iNvJ1lkCwg/s320/r15fd3_000.jpg" width="320" /></a></div>
<div style="text-align: center;">
<span style="font-size: x-small;"><b>Figure 1: </b>Snapshot 1 from the reaction path; chorismate to prephanate</span></div>
<div style="text-align: center;">
<br /></div>
<div style="text-align: center;">
<br /></div>
<div style="text-align: justify;">
Anyways, the reaction complex contains 118 electrons, so we fired up MOLPRO2012 and started looking at the DF-LCCSD(T0)-F12a method. A name you should probably memorize from now on. </div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
The single point energy evaluation took 3:43 hours with the VDZ-F12 basis set! Mind you, this was on only a single CPU core with <strike>4 GB</strike> 16GB of RAM allocated, so no supercomputers were involved.</div>
<div style="text-align: justify;">
</div>
<div style="text-align: justify;">
First I though the calculation had crashed because it didn't show up in the queue. My first attempt with the 3-21G basis set didn't converge because I failed to set the charge to -2, but this time around Molpro actually gave me an energy!<br />
Next problem on my list is to calculate the good old MP2/cc-pVTZ energy I need to subtract in order to get my ONIOM results. Those are going to take about 5-10 hours on 8 cores. </div>
<div style="text-align: justify;">
This is what the input file looked like for anyone interested </div>
<blockquote class="tr_bq">
<span style="font-size: x-small;"><span style="font-family: "Courier New",Courier,monospace;">memory,2000,M<br />geomtyp=xyz<br />geometry<br />24<br />NS= 27 NA= 105 NF= 9 EFMO2-RHF:MP2 (this line i<span style="font-size: x-small;">s just a title<span style="font-size: x-small;"> btw</span>)</span><br />C 42.243912717439 56.985332654358 33.508219032291<br />O 44.083656765173 58.469818126062 33.547456767374<br />C 42.170657938422 56.969234127412 32.053802633762<br />O 43.705108088223 57.205919113255 35.368497584787<br />C 41.129420410826 56.416380137206 31.405612518418<br />O 36.855437867615 57.552564896356 32.240095659821<br />C 40.011578869479 55.731334232825 32.133332774369<br />O 37.952282803211 59.164070642440 31.158486994867<br />C 39.926225853593 56.110094283828 33.626395940165<br />O 40.227454849874 54.351343596304 31.951217705746<br />C 41.196604656028 56.578036178752 34.246569733982<br />O 38.910804760126 57.082428023235 33.866893577924<br />C 38.968148927395 58.213925767965 33.092045796028<br />C 39.831866096203 59.221137407117 33.269727518172<br />C 43.428858040104 57.602185825538 34.190487998445<br />C 37.845543926101 58.319486314173 32.113613710711<br />H 39.478451838017 53.847769952241 32.300919500400<br />H 39.060609728200 56.030078213449 31.676260590468<br />H 39.584331039092 55.233181854460 34.161137739114<br />H 41.108061134298 56.387696261961 30.326013340305<br />H 42.988978401897 57.425894863516 31.511012407792<br />H 41.233442365036 56.630502649395 35.323808064193<br />H 40.574407364526 59.218144574703 34.050005215454<br />H 39.764972282572 60.084246967681 32.627123054496<br />end<br /><br />set,charge=-2<br />set,spin=0<br />basis={<br />C=vdz-f12<br />H=vdz-f12<br />O=vdz-f12<br />}<br />DF-HF,DF_BASIS=vdz-f12<br />DF-LCCSD(T)-F12,DF_BASIS=vdz-f12</span></span></blockquote>
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<br />
<br />
<br />
<b><span style="font-size: x-small;">Further reading:</span></b><br />
<br />
<br />
<div class="authorList_entry">
<span style="font-size: x-small;"><span class="aqslistener looklikelink">Thomas B. Adler</span>, Hans-Joachim Werner (2011) "An explicitly correlated local coupled cluster method for calculations of large molecules close to the basis set limit" J. Chem. Phys. <b>135</b>, 144117; <a href="http://link.aip.org/link/doi/10.1063/1.3647565">http://dx.doi.org/10.1063/1.3647565</a></span></div>
<br />
<br />
<span style="font-size: x-small;">Molpro manual entry: <a href="http://www.molpro.net/info/2012.1/doc/update/node10.html">http://www.molpro.net/info/2012.1/doc/update/node10.html</a></span><br />
<br />Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com0tag:blogger.com,1999:blog-8160351477288734008.post-21932183988670098152013-02-21T14:39:00.000+01:002013-02-22T15:45:20.082+01:00Saving data into cPickle format (in Python)I recently created a python script which generated a huge-ass dictionary, which I wanted to save and use later in another program. The simple solution was simpy to print my dictionary and pipe it into txt file. However, the txt file ended up being almost 500MB large, and using eval() to parse the text string into a Python object took around 5 minutes. Enter the cPickle module. cPickle uses a C implementation of handling pickled stuff, which effectively means that it's faster. In my case loading time decreased to few seconds, and the size of the cPickle'd file was 50% of the plain .txt file.<br />
<br />
Here are a couple of snippets to get you started using cPickle.
First import cPickle. The cPickle module should be included in most Python distributions
<br />
<pre class="brush: python">import cPickle</pre>
In this example, we have an array called "<span style="font-family: "Courier New",Courier,monospace;">large_array</span>" created by the "<span style="font-family: "Courier New",Courier,monospace;">create_large_array()</span>" function. I want to store "<span style="font-family: "Courier New",Courier,monospace;">large_array</span>" in a file called "<span style="font-family: "Courier New",Courier,monospace;">large_array.cpickle</span>".
<br />
<pre class="brush: python">large_array = create_large_array()
output_filename = "large_array.cpickle"
f = open(output_filename,"wb")
cPickle.dump(large_array, f, protocol=2)
f.close()
</pre>
In this example "<span style="font-family: "Courier New",Courier,monospace;">open()</span>" is called with the second argument "<span style="font-family: "Courier New",Courier,monospace;">wb</span>", which tells Python to open the file in write/binary mode.
"<span style="font-family: "Courier New",Courier,monospace;">cPickle.dump()</span>" dumps "<span style="font-family: "Courier New",Courier,monospace;">large_array</span>" into "<span style="font-family: "Courier New",Courier,monospace;">f</span>" using "<span style="font-family: "Courier New",Courier,monospace;">protocol=2</span>". cPickle has several different modes which all do the same -- protocol 2 is the fastest.<br />
<br />
If you want to load the array from "<span style="font-family: "Courier New",Courier,monospace;">large_array.cpickle</span>" in another script, you can use something like this:<br />
<pre class="brush: python">def load_pickle(filename):
f = open(filename,"rb")
p = cPickle.load(f)
f.close()
return(p)
large_array = load_pickle("large_array.cpickle")
</pre>
Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com3tag:blogger.com,1999:blog-8160351477288734008.post-15376496836026932252012-09-07T12:57:00.000+02:002012-09-07T13:00:24.349+02:00The least restrictive open source license?<div style="text-align: justify;">
I am working on a program to predict protein chemical shifts and predict protein structural features from chemical shifts. I want my work to be available to anyone, for any purpose, free of charge. Would be nice if the work would remain attributed to the respective authors, and finally I don't want to get sued under any circumstances.</div>
<div style="text-align: justify;">
Although widely used, the least restrictive open source software license is certainly not the <a href="http://www.gnu.org/copyleft/gpl.html">GNU General Public License</a>. I wouldn't mind having my code used for commercial purposes, for instance.</div>
<div style="text-align: justify;">
<br /></div>
<div style="text-align: justify;">
I also considered something along the lines of what is suggested <a href="http://www.coderanch.com/t/132890/gc/least-restrictive-software-license">here</a>:</div>
<div style="text-align: left;">
<blockquote class="tr_bq" style="font-family: "Courier New",Courier,monospace;">
<span style="font-size: x-small;"><span class="postbody"> Steal this code and use it for whatever
you want. No support or guarantee is provided or implied - use at your
own risk. Attribution would be nice but not essential.</span></span></blockquote>
</div>
However, after searching around a bit, I settled for the "2-clause Simplified <a href="http://en.wikipedia.org/wiki/BSD_licenses">BSD License</a>":<br />
<blockquote class="tr_bq">
<pre style="overflow: auto; width: auto;">Copyright (c) <YEAR>, <OWNER>
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. </pre>
</blockquote>
Only question is why the 2nd half is written entirely in CAPS!Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com2tag:blogger.com,1999:blog-8160351477288734008.post-75107112068946909372012-08-28T17:19:00.000+02:002012-08-28T17:20:50.640+02:00Displaying disagreements in protein structures<div style="text-align: justify;">
I recently made a <a href="http://combichem.blogspot.dk/2012/06/coloring-residues-by-chemical-shift.html">post</a> on how to color a protein structure by
disagreements between experimental chemical shifts and chemical shifts
predicted from the structure.</div>
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<br /></div>
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Here is another way to display these kinds of errors, which I also find quite nice. Atoms radii are scaled proportionally the error in predicted chemical shifts.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjeYv2j_l77c9GpzCzBjQ-z7VlrGwdms9HYz6WrfPrQbP7MQ7wfTr2dfSYTeyP_UujJCOOVTjcFjFHuIuxMUKptNYR2tpG1URRYExrJujvpoSxU0jzp3OElyGPL1D03O0nVPCBL3vLZknfi/s1600/ct-2011-00913r_0015.gif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="336" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjeYv2j_l77c9GpzCzBjQ-z7VlrGwdms9HYz6WrfPrQbP7MQ7wfTr2dfSYTeyP_UujJCOOVTjcFjFHuIuxMUKptNYR2tpG1URRYExrJujvpoSxU0jzp3OElyGPL1D03O0nVPCBL3vLZknfi/s640/ct-2011-00913r_0015.gif" width="640" /></a></div>
<div style="text-align: center;">
<span style="font-size: xx-small;">Picture from <a href="http://dx.doi.org/10.1021/ct200913r">http://dx.doi.org/10.1021/ct200913r</a> (paywall)</span></div>
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In the above example, chemical shifts are determined from quantum chemical calculations on a 23 residue protein structure, and then compared to the corresponding experimental chemical shifts.</div>
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It's clear, that calculated chemical shifts from several side chains atom do not reproduce the experimental values as well as the backbone atoms chemical shifts. Definitely useful to identify possible errors in the protein structure</div>
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<b><span style="font-size: x-small;">References:</span></b></div>
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<span style="font-size: x-small;"><cite>Frank et al. J. Chem. Theory Comput.</cite>, <b><span class="citation_year">2012</span>,</b> <i><span class="citation_volume">8</span></i> (4), pp 1480–1492</span></div>
<div style="text-align: justify;">
<span style="font-size: x-small;"><a href="http://dx.doi.org/10.1021/ct200913r">http://dx.doi.org/10.1021/ct200913r</a></span></div>
Anonymoushttp://www.blogger.com/profile/18123073028209991396noreply@blogger.com0