The new python parameter exploration toolkit: pypet manages exploration of the parameter space of any numerical simulation in python, thereby storing your data into HDF5 files for you. Moreover, pypet offers a new data container which lets you access all your parameters and results from a single source. Data I/O of your simulations and analyses becomes a piece of cake!
Latest version: 0.1b.8
The program is currently under development, please keep that in mind and use it very carefully.
Before publishing the official 0.1.0 release I will integrate pypet first in my own research project. Thus, I have a more profound testing environment than only using unittests. Accordingly, you still have to deal with the naming 0.1b.X for a little while. However, unless it is really, really, really necessary I do not plan to change the API anymore. So feel free to use this beta version and feel free to give feedback, suggestions, and report bugs. Use github (https://github.com/SmokinCaterpillar/pypet) issues or write to the pypet Google Group :-)
Thanks!
Python 2.6, 2.7, 3.3, 3.41
- tables >= 2.3.1
- pandas >= 0.12.02
- numpy >= 1.6.1
- scipy >= 0.9.0
If you use Python 2.6 you also need
- ordereddict >= 1.1
For git integration you additionally need
To utilize the cap feature for more-on-multiprocessing you need
- psutil >= 2.0.0
To utilize the continuing of crashed trajectories you need
- dill >= 0.2.1
Automatic sumatra records are supported for
- Sumatra >= 0.6.0
Footnotes
introduction tutorial cookbook examples faqs code
There is a poster about pypet that was shown at the Berlin Brain Days 2013 and the EuroPython 2014.
Download:
CLICK ME for PDF DOWNLOAD <./bbd_2013_poster/meyer_bbd_2013.pdf>CLICK ME for PNG DOWNLOAD <./bbd_2013_poster/meyer_bbd_2013.png>
Thanks to Robert Pröpper and Philipp Meier for answering all my python questions
You might wanna check out their SpykeViewer tool for visualization of MEA recordings and NEO data
Thanks to Owen Mackwood for his SNEP toolbox which provided the initial ideas for this project
Thanks to the BCCN Berlin, the Research Training Group GRK 1589/1, and the Neural Information Processing Group for support
Tests can be found in pypet/tests. Note that they involve heavy file IO and you need privileges to write files to a temporary folder. The tests suite will make use of the tempfile.gettempdir() function to create such a temporary folder.
You can run all tests with $ python all_tests.py which can also be found under pypet/tests. You can pass additional arguments as $ python all_tests.py -k --folder=myfolder/ with -k to keep the HDF5 files created by the tests (if you want to inspect them, otherwise they will be deleted after the completed tests), and --folder= to specify a folder where to store the HDF5 files instead of the temporary one. If the folder cannot be created the program defaults to tempfile.gettempdir().
If you do not want to browse to your installation folder, you can also download the all_tests.py <../../pypet/tests/all_tests.py> script.
Running all tests can take up to 15 minutes and might temporarily take up to 8 GB of disk space. The test suite encompasses more than 400 tests (including the BRIAN based tests) and has a code coverage of about 90%!
Robert Meyer
robert.meyer (at) ni.tu-berlin.de
Marchstr. 23
MAR 5.046
D-10587 Berlin
genindex
../../LICENSE
pypet might also work under python 3.0-3.2 but has not been tested.↩
Preferably use pandas 0.14.1 or 0.12.0 since there are some upcasting issues with version 0.13.x (see pandas-dev/pandas#6526). pypet works under 0.13.x but not all features are fully supported. For instance, these upcasting issues may prevent you from storing Trajectories containing ArrayParameters to disk. These unwanted upcastings did not happen in previous pandas versions and will be, or more precisely, have already been removed in the next pandas version. So please up or downgrade your pandas distribution if your current installation is 0.13.x.↩
Keep in mind that GitPython currently does not support python 3.↩