Mailman 3 March 2015 - NumPy-Discussion

Reminder - Summer School "Advanced Scientific Programming in Python" in Munich, Germany
by Tiziano Zito 23 Mar '15

23 Mar '15

Reminder: Deadline for application is 23:59 UTC, March 31, 2015. Advanced Scientific Programming in Python ========================================= a Summer School by the G-Node, the Bernstein Center for Computational Neuroscience Munich and the Graduate School of Systemic Neurosciences Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques, incorporating theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. Basic knowledge of Python is assumed. Participants without any prior experience with Python should work through the proposed introductory materials before the course. Date and Location ================= August 31—September 5, 2015. Munich, Germany. Preliminary Program =================== Day 0 (Mon Aug 31) — Best Programming Practices • Best Practices for Scientific Computing • Version control with git and how to contribute to Open Source with github • Object-oriented programming & design patterns Day 1 (Tue Sept 1) — Software Carpentry • Test-driven development, unit testing & quality assurance • Debugging, profiling and benchmarking techniques • Advanced Python: generators, decorators, and context managers Day 2 (Wed Sept 2) — Scientific Tools for Python • Advanced NumPy • The Quest for Speed (intro): Interfacing to C with Cython • Contributing to Open Source Software/Programming in teams Day 3 (Thu Sept 3) — The Quest for Speed • Writing parallel applications in Python • Python 3: why should I care • Programming project Day 4 (Fri Sept 4) — Efficient Memory Management • When parallelization does not help: the starving CPUs problem • Programming project Day 5 (Sat Sept 5) — Practical Software Development • Programming project • The Pelita Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects. Applications ============ You can apply on-line at https://python.g-node.org Applications must be submitted before 23:59 UTC, March 31, 2015. Notifications of acceptance will be sent by May 1, 2015. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate is usually around 20%. Prerequisites: You are supposed to know the basics of Python to participate in the lectures Preliminary Faculty =================== • Pietro Berkes, Enthought Inc., UK • Marianne Corvellec, Plotly Technologies Inc., Montréal, Canada • Kathryn D. Huff, Department of Nuclear Engineering, University of California - Berkeley, USA • Zbigniew Jędrzejewski-Szmek, Krasnow Institute, George Mason University, USA • Eilif Muller, Blue Brain Project, École Polytechnique Fédérale de Lausanne, Switzerland • Juan Nunez-Iglesias, Victorian Life Sciences Computation Initiative, University of Melbourne, Australia • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Bartosz Teleńczuk, European Institute for Theoretical Neuroscience, CNRS, Paris, France • Nelle Varoquaux, Centre for Computational Biology Mines ParisTech, Institut Curie, U900 INSERM, Paris, France • Tiziano Zito, Forschungszentrum Jülich GmbH, Germany Organized by Tiziano Zito (head) and Zbigniew Jędrzejewski-Szmek for the German Neuroinformatics Node of the INCF Germany, Christopher Roppelt for the German Center for Vertigo and Balance Disorders (DSGZ) and the Graduate School of Systemic Neurosciences (GSN) of the Ludwig-Maximilians-Universität Munich Germany, Christoph Hartmann for the Frankfurt Institute for Advanced Studies (FIAS) and International Max Planck Research School (IMPRS) for Neural Circuits, Frankfurt Germany, and Jakob Jordan for the Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Jülich Research Centre and JARA. Additional funding provided by the Bernstein Center for Computational Neuroscience (BCCN) Munich. Website: https://python.g-node.org Contact: python-info(a)g-node.org

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ANN: pandas 0.16.0 released
by Jeff Reback 23 Mar '15

23 Mar '15

Hello, We are proud to announce v0.16.0 of pandas, a major release from 0.15.2. This release includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. This was 4 months of work by 60 authors encompassing 204 issues. We recommend that all users upgrade to this version. *Highlights:* - - *DataFrame.assign* method, see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-enh…> - *Series.to_coo/from_coo* methods to interact with *scipy.sparse*, see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-enh…> - Backwards incompatible change to *Timedelta* to conform the *.seconds* attribute with *datetime.timedelta*, see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-api…> - Changes to the *.loc* slicing API to conform with the behavior of *.ix* see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-api…> - Changes to the default for ordering in the *Categorical* constructor, see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-api…> - Enhancement to the *.str* accessor to make string operations easier, see here <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-enh…> - The *pandas.tools.rplot*, *pandas.sandbox.qtpandas* and *pandas.rpy* modules are deprecated. - We refer users to external packages like seaborn <http://stanford.edu/~mwaskom/software/seaborn/>, pandas-qt <https://github.com/datalyze-solutions/pandas-qt> and rpy2 <http://rpy.sourceforge.net/> for similar or equivalent functionality, see here for more detail <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#deprecations> See a full description of the Whatsnew for v0.16.0 <http://pandas.pydata.org/pandas-docs/stable/whatsnew.html> *What is it:* *pandas* is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. Documentation: http://pandas.pydata.org/pandas-docs/stable/ Source tarballs, windows wheels, macosx wheels are available on PyPI: https://pypi.python.org/pypi/pandas windows binaries are courtesy of Christoph Gohlke and are built on Numpy 1.9 macosx wheels are courtesy of Matthew Brett and are built on Numpy 1.7.1 Please report any issues here: https://github.com/pydata/pandas/issues Thanks The Pandas Development Team

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element-wise array segmental function operation?
by oyster 23 Mar '15

23 Mar '15

Hi, all I want to know wether there is a terse way to apply a function to every array element, where the function behaves according to the element value. for example [code] def fun(v): if 0<=v<60: return f1(v) #where f1 is a function elif 60<=v<70: return f2(v) elif 70<=v<80: return f3(v) ...and so on... [/code] for 'a=numpy.array([20,50,75])', I hope to get numpy.array([f1(20), f1(50), f3(75)]) thanks in advance Lee

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Installation on Windows
by Per Tunedal 23 Mar '15

23 Mar '15

Hi, how do I install Numpy on Windows? I've tried the setup.py file, but get an error message: setup.py install gives: No module named msvccompiler in numpy.distutils; trying from distutils error: Unable to find vcvarsall.bat Yours, Per Tunedal

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GSoC projects
by Lulu Li 23 Mar '15

23 Mar '15

My apology if I am posting to the wrong mailing list. I am interested in NumPy project ideas for Google Summer of Code 2015 as posted here https://github.com/scipy/scipy/wiki/GSoC-project-ideas. In particular, knowing C and Python, I am interested in porting parts of bumpy from C to Cython or pythonic types. I wonder if these projects are still looking for participants? If not I will be excited to put together a proposal and work on them these summer. Lulu

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Use of NameValidator in np.genfromtxt is inconsistent with the rules for naming structured array fields
by Alistair Muldal 21 Mar '15

21 Mar '15

Hi all, I originally posted this to the issue tracker (https://github.com/numpy/numpy/issues/5686), and am posting here as well at the request of charris. Currently, np.genfromtxt uses a numpy.lib._iotools.NameValidator which mangles field names by replacing spaces and stripping out certain non-alphanumeric characters etc.: import numpy as np from io import BytesIO s = 'name,name with spaces,2*(x-1)!\n1,2,3\n4,5,6' x = np.genfromtxt(BytesIO(s), delimiter=',', names=True) print(repr(x)) # array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)], # dtype=[('name', '<f8'), ('name_with_spaces', '<f8'), ('2x1', '<f8')]) This behaviour has been the cause of some confusion in the past, e.g. http://stackoverflow.com/q/29097917/1461210, http://stackoverflow.com/q/16020137/1461210. Part of the issue is that it's currently not very well covered by the documentation for np.genfromtext - at best, it's alluded to in the descriptions for some of the keyword arguments ('deletechars', 'autostrip', 'replace_space' etc.). However, I think the more fundamental problem is that this behaviour seems to be inconsistent with the rules for naming the fields in structured arrays. In the example above, all of the original field names are perfectly legal: names = ['name', 'name with spaces', '2*(x-1)!'] types = ('f',) * 3 dtype = zip(names, types) x2 = np.empty(2, dtype=dtype) x2[:] = [(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)] print(repr(x2)) # array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)], # dtype=[('name', '<f4'), ('name with spaces', '<f4'), ('2*(x-1)!', '<f4')]) print(x2['2*(x-1)!']) # [3. 6.] What is the rationale behind the use of NameValidator here? One possible reason would be to ensure that the field names would also be legal for an np.recarray. However, this doesn't make sense for several reasons: Firstly, the names above also seem to be legal field names for a recarray: xr = x2.view(np.recarray) print(repr(x2)) # rec.array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)], # dtype=[('name', '<f4'), ('name with spaces', '<f4'), ('2*(x-1)!', '<f4')]) Obviously if the field names aren't valid Python identifiers then it won't be possible to access them via 'xr.fieldname' syntax, but dict-style indexing is still fine, e.g. xr['2*(x-1)!']. Also, if the goal of NameValidator were to ensure that the field names were always valid Python identifiers then it currently fails at this anyway, since in my first example, '2x1' is not a valid Python identifier. What is perhaps most confusing is the fact that np.genfromtxt will even mangle field names that you pass in directly via the 'names' keyword argument. Suppose you wanted to specify field names that NameValidator doesn't like. You might try something like this: print(repr(np.genfromtxt(BytesIO(s), delimiter=',', names=names, skip_header=1))) # array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)], # dtype=[('name', '<f8'), ('name_with_spaces', '<f8'), ('2x1', '<f8')]) Or even this: print(repr(np.genfromtxt(BytesIO(s), delimiter=',', names=names, skip_header=1, deletechars=[], replace_space=False, excludelist=[], autostrip=False))) # array([(1.0, 2.0, 3.0), (4.0, 5.0, 6.0)], # dtype=[('name', '<f8'), ('name_with_spaces', '<f8'), ('2x1', '<f8')]) Still no luck! As far as I can tell, there is no option in np.genfromtxt that allows you to preserve field names that don't conform to NameValidator's seemingly arbitrary rules. What should be done about this? Personally, I think that either things like spaces and non-alphanumeric characters should be disallowed in structured array field names altogether (and my second example should raise an exception), or np.genfromtxt should leave field names alone by default. It would also be a good idea to raise a SyntaxWarning in a case where the user creates a recarray containing field names that are not valid Python identifiers (and are therefore incompatible with the dot indexing syntax). This is essentially what PyTables does for non-conforming HDF5 node names: https://github.com/PyTables/PyTables/blob/13047c897d28b7278cbeab732f12feadb…. Any thoughts on this? Alistair

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how to optimize numpy code for Markovian path
by Jianhong Wang 19 Mar '15

19 Mar '15

Below is a python function to generate Markov path (the travelling salesman problem). def generate_travel_path(markov_matrix, n): assert markov_matrix.shape[0] == markov_matrix.shape[1] assert n <= markov_matrix.shape[0] p = markov_matrix.copy() path = [0] * n for k in range(1, n): k1 = path[k-1] row_sums = 1 / (1 - p[:, k1]) p *= row_sums[:, np.newaxis] p[:, k1] = 0 path[k] = np.random.multinomial(1, p[k1, :]).argmax() assert len(set(path)) == n return path markov_matrix is a predefined Markov transition matrix. The code generates a path starting from node zero and visit every node once based on this matrix. However I feel the function is quite slow. Below is the line-by-line profile with a 53x53 markov_matrix: Timer unit: 3.49943e-07 s Total time: 0.00551195 sFile: <ipython-input-29-37e4c9b5469e>Function: generate_travel_path at line 1Line # Hits Time Per Hit % Time Line Contents============================================================== 1 def generate_travel_path(markov_matrix, n): 2 1 31 31.0 0.2 assert markov_matrix.shape[0] == markov_matrix.shape[1] 3 1 12 12.0 0.1 assert n <= markov_matrix.shape[0] 4 5 1 99 99.0 0.6 p = markov_matrix.copy() 6 1 12 12.0 0.1 path = [0] * n 7 53 416 7.8 2.6 for k in range(1, n): 8 52 299 5.8 1.9 k1 = path[k-1] 9 52 3677 70.7 23.3 row_sums = 1 / (1 - p[:, k1]) 10 52 4811 92.5 30.5 p = p * row_sums[:, np.newaxis] 11 52 1449 27.9 9.2 p[:, k1] = 0 12 52 4890 94.0 31.0 path[k] = np.random.multinomial(1, p[k1, :]).argmax() 13 14 1 51 51.0 0.3 assert len(set(path)) == n 15 1 4 4.0 0.0 return path If I ran this function 25000 times, it will take me more than 125 seconds. Any headroom to improve the speed? Below is a simple function to generate a Markov matrix. def initial_trans_matrix(n): x = np.ones((n, n)) / (n - 1) np.fill_diagonal(x, 0.0) return x

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Problem with _dotblas.pyd when using Matplotlib for 3d plot
by Geoffrey Mégardon 19 Mar '15

19 Mar '15

Hi, I am on Windows 8.1, 64-bit, using the usual version of Anaconda-64-bit for windows. I just come from the mailing list of matplotlib: my PC is not able to do 3D plot with matplotlib. Python crashes completely if I try to plot something in 3D and to show it: "Python.exe has stopped working..." The main problem in resolving my issue is that the python traceback and the faulthandler package do not succeed in catching the errors. Only the Windows crash report allows us to see what happened before the crash. The windows report tells that the file _dotblas.pyd from numpy is likely responsible for the crash. So I tried a simple: import numpy; numpy.test() numpy.test() does not work! I dont get any traceback from python. It just crashes as before: "Python.exe has stopped working..." And again, the Windows report blames _dotblas.pyd :) Here the new Windows report for the numpy.test(), I highlighted the lines which blame _dotblas.pyd: Version=1 EventType=APPCRASH EventTime=130608138227608275 ReportType=2 Consent=1 UploadTime=130608138229728384 ReportIdentifier=216b16d2-6f5c-11e4-bec3-48d22435da2b IntegratorReportIdentifier=216b16d1-6f5c-11e4-bec3-48d22435da2b NsAppName=python.exe Response.BucketId=398b7eee350a0fd8a7a96f705d3488f6 Response.BucketTable=4 Response.LegacyBucketId=85979911964 Response.type=4 Sig[0].Name=Application Name Sig[0].Value=python.exe Sig[1].Name=Application Version Sig[1].Value=0.0.0.0 Sig[2].Name=Application Timestamp Sig[2].Value=53b4679e *Sig[3].Name=Fault Module NameSig[3].Value=_dotblas.pyd* Sig[4].Name=Fault Module Version Sig[4].Value=0.0.0.0 Sig[5].Name=Fault Module Timestamp Sig[5].Value=545678cb Sig[6].Name=Exception Code Sig[6].Value=c000001d Sig[7].Name=Exception Offset Sig[7].Value=0000000000324022 DynamicSig[1].Name=OS Version DynamicSig[1].Value=6.3.9600.2.0.0.768.101 DynamicSig[2].Name=Locale ID DynamicSig[2].Value=2057 DynamicSig[22].Name=Additional Information 1 DynamicSig[22].Value=f32f DynamicSig[23].Name=Additional Information 2 DynamicSig[23].Value=f32feb95f950f918532aa47b4372840e DynamicSig[24].Name=Additional Information 3 DynamicSig[24].Value=9882 DynamicSig[25].Name=Additional Information 4 DynamicSig[25].Value=98823b6c7f579b24e92112ab827fe4a1 UI[2]=C:\Anaconda\python.exe UI[3]=python.exe has stopped working UI[4]=Windows can check online for a solution to the problem. UI[5]=Check online for a solution and close the program UI[6]=Check online for a solution later and close the program UI[7]=Close the program LoadedModule[0]=C:\Anaconda\python.exe LoadedModule[1]=C:\WINDOWS\SYSTEM32\ntdll.dll LoadedModule[2]=C:\WINDOWS\system32\KERNEL32.DLL LoadedModule[3]=C:\WINDOWS\system32\KERNELBASE.dll LoadedModule[4]=C:\Anaconda\python27.dll LoadedModule[5]=C:\WINDOWS\WinSxS\amd64_microsoft.vc90.crt_1fc8b3b9a1e18e3b_9.0.30729.8387_none_08e793bfa83a89b5\MSVCR90.dll LoadedModule[6]=C:\WINDOWS\system32\USER32.dll LoadedModule[7]=C:\WINDOWS\system32\ADVAPI32.dll LoadedModule[8]=C:\WINDOWS\system32\SHELL32.dll LoadedModule[9]=C:\WINDOWS\system32\GDI32.dll LoadedModule[10]=C:\WINDOWS\system32\msvcrt.dll LoadedModule[11]=C:\WINDOWS\SYSTEM32\sechost.dll LoadedModule[12]=C:\WINDOWS\system32\RPCRT4.dll LoadedModule[13]=C:\WINDOWS\SYSTEM32\combase.dll LoadedModule[14]=C:\WINDOWS\system32\SHLWAPI.dll LoadedModule[15]=C:\WINDOWS\system32\IMM32.DLL LoadedModule[16]=C:\WINDOWS\system32\MSCTF.dll LoadedModule[17]=C:\Anaconda\DLLs\_socket.pyd LoadedModule[18]=C:\WINDOWS\system32\WS2_32.dll LoadedModule[19]=C:\WINDOWS\system32\NSI.dll LoadedModule[20]=C:\Anaconda\DLLs\_ssl.pyd LoadedModule[21]=C:\Anaconda\DLLs\_ctypes.pyd LoadedModule[22]=C:\WINDOWS\system32\ole32.dll LoadedModule[23]=C:\WINDOWS\system32\OLEAUT32.dll LoadedModule[24]=C:\Anaconda\DLLs\_hashlib.pyd LoadedModule[25]=C:\WINDOWS\SYSTEM32\CRYPTSP.dll LoadedModule[26]=C:\WINDOWS\system32\rsaenh.dll LoadedModule[27]=C:\WINDOWS\SYSTEM32\bcrypt.dll LoadedModule[28]=C:\WINDOWS\SYSTEM32\CRYPTBASE.dll LoadedModule[29]=C:\WINDOWS\SYSTEM32\bcryptPrimitives.dll LoadedModule[30]=C:\Users\User\AppData\Roaming\Python-Eggs\faulthandler-2.4-py2.7-win-amd64.egg-tmp\faulthandler.pyd LoadedModule[31]=C:\Anaconda\lib\site-packages\numpy\core\multiarray.pyd LoadedModule[32]=C:\Anaconda\lib\site-packages\numpy\core\umath.pyd LoadedModule[33]=C:\Anaconda\lib\site-packages\numpy\core\_dotblas.pyd LoadedModule[34]=C:\Anaconda\lib\site-packages\numpy\core\libiomp5md.dll LoadedModule[35]=C:\Anaconda\lib\site-packages\numpy\core\scalarmath.pyd LoadedModule[36]=C:\Anaconda\lib\site-packages\numpy\lib\_compiled_base.pyd LoadedModule[37]=C:\Anaconda\lib\site-packages\numpy\linalg\lapack_lite.pyd LoadedModule[38]=C:\Anaconda\lib\site-packages\numpy\linalg\_umath_linalg.pyd LoadedModule[39]=C:\Anaconda\lib\site-packages\numpy\fft\fftpack_lite.pyd LoadedModule[40]=C:\Anaconda\lib\site-packages\numpy\random\mtrand.pyd LoadedModule[41]=C:\Anaconda\DLLs\_multiprocessing.pyd State[0].Key=Transport.DoneStage1 State[0].Value=1 FriendlyEventName=Stopped working ConsentKey=APPCRASH AppName=python.exe AppPath=C:\Anaconda\python.exe NsPartner=windows NsGroup=windows8 ApplicationIdentity=5B036AF1EC2E20F320DBF28D119DE93D Any idea, of what I can do? I try to reinstall anaconda, I also try conda remove numpy and conda install numpy. That does not seem to change anything. -- -- MEGARDON Geoffrey

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Porting C to Python
by Marcos . 18 Mar '15

18 Mar '15

Dear colleagues, My name is Marcos Chaves, I'm an undergraduate student from Brazil. I study at the State University of Campinas, currently at the end of my graduation. I'm deeply interested in applying to GSOC this year, specifically to work with NumPy. About some months ago me and my friends at work started porting some legacy C/C++ code to Python, a code that we used to evaluate some images using OpenCV. We made use of NumPy and I liked it. I believe I can be of some help in making it better, particularly in solving the issue with porting parts of it from C (while maintaining optimal performance). I've never contributed to NumPy in any way and I see that its a requirement for me to apply for such task. I would like to know if there's a way I can participate, because the end of student registration is coming soon and I'm not sure if there is enough time for me to do something like that, but I'm interested. Some input from the potential mentor, Nathaniel Smith, along with some extra info on this task would be very nice for me. Thank you!

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SciPy 2015 Call for Propsals Open - tutorial & talk submissions due April 1st
by Courtenay Godshall (Enthought) 18 Mar '15

18 Mar '15

**SciPy 2015 Conference (Scientific Computing with Python) Call for Proposals: Submit Your Tutorial and Talk Ideas by April 1, 2015 at http://scipy2015.scipy.org.** SciPy 2015, the fourteenth annual Scientific Computing with Python conference, will be held July 6-12, 2015 in Austin, Texas. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference brings together over 500 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The full program will consist of two days of tutorials by followed by three days of presentations, and concludes with two days of developer sprints. More info available on the conference website at http://scipy2015.scipy.org; you can also sign up on the website for mailing list updates or follow @scipyconf on Twitter. We hope you'll join us - early bird registration is open until May 15, 2015 at http://scipy2015.scipy.org We encourage you to submit tutorial or talk proposals in the categories below; please also share with others who you'd like to see participate! Submit via the conference website @ http://scipy2015.scipy.org. *SCIPY TUTORIAL SESSION PROPOSALS - DEADLINE EXTENDED TO WED APRIL 1, 2015* The SciPy experience kicks off with two days of tutorials. These sessions provide extremely affordable access to expert training, and consistently receive fantastic feedback from participants. We're looking for submissions on topics from introductory to advanced - we'll have attendees across the gamut looking to learn. Whether you are a major contributor to a scientific Python library or an expert-level user, this is a great opportunity to share your knowledge and stipends are available. Submit Your Tutorial Proposal on the SciPy 2015 website: http://scipy2015.scipy.org *SCIPY TALK AND POSTER SUBMISSIONS - DUE April 1, 2015* SciPy 2015 will include 3 major topic tracks and 7 mini-symposia tracks. Submit Your Talk Proposal on the SciPy 2015 website: http://scipy2015.scipy.org Major topic tracks include: - Scientific Computing in Python (General track) - Python in Data Science - Quantitative and Computational Social Sciences Mini-symposia will include the applications of Python in: - Astronomy and astrophysics - Computational life and medical sciences - Engineering - Geographic information systems (GIS) - Geophysics - Oceanography and meteorology - Visualization, vision and imaging If you have any questions or comments, feel free to contact us at: scipy-organizers(a)scipy.org.

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