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Interactive data analysis with iPython
Here are some examples of how you can use interactive python to analyze your data. We use matplotlib to draw plots from numpy arrays. The numpy arrays can be read directly from an HDF5 file, or you can construct them in a pyana job and launch ipython at the end to play with the plots/arrays.
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These examples are mostly python rewrites of matlab functions provided by XPP. Thanks to H. Lemke for matlab examples and advice.
The components
- ipython
... an interactive python shell
External links:
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- numpy
... numeric python
External links:
The library
- pymatlab.py
... a module implementing in python some of the tools written by Henrik/XPP for matlab. For those familiar with the XPP matlab tools, the functions here should be intuitive to use. Only a few functions have been implemented thus far... (feel free to contribute).Code Block none none title Starting iPython borderStyle solid [ofte@psana0XXX myrelease]$ ipython Python 2.4.3 (#1, Nov 3 2010, 12:52:40) Type "copyright", "credits" or "license" for more information. IPython 0.9.1 -- An enhanced Interactive Python. ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object'. ?object also works, ?? prints more.
Loading the library. Normally 'import pymatlab' would be recommended, but if you do 'from pymatlab import *', all the functions defined in this module gets loaded in the current namespace, and you can see them in your workspace. This might be easier for interactive work.Code Block none none borderStyle solid In [1]: from pymatlab import * Pretend this is matlab
who
gives you a short list of workspace contentsCode Block none none borderStyle solid In [2]: who H5getobjnames ScanInput ScanOutput filtvec findmovingmotor getSTDMEANfrac_from_startpoint get_filter get_limits get_limits_automatic get_limits_channelhist get_limits_correlation get_limits_corrfrac h5py np plt rdXPPdata runexpNO2fina scan scaninput
whos
gives you a longer list of workspace contentsCode Block none none borderStyle solid In [3]: whos Variable Type Data/Info -------------------------------------------------------- H5getobjnames function <function H5getobjnames at 0x2b57de8> ScanInput type <class 'pymatlab.ScanInput'> ScanOutput type <class 'pymatlab.ScanOutput'> filtvec function <function filtvec at 0x2b57f50> findmovingmotor function <function findmovingmotor at 0x2b57d70> getSTDMEANfrac_from_startpoint function <function getSTDMEANfrac_<...>_startpoint at 0x2b581b8> get_filter function <function get_filter at 0x2b57ed8> get_limits function <function get_limits at 0x2b58050> get_limits_automatic function <function get_limits_automatic at 0x2b58230> get_limits_channelhist function <function get_limits_channelhist at 0x2b582a8> get_limits_correlation function <function get_limits_correlation at 0x2b580c8> get_limits_corrfrac function <function get_limits_corrfrac at 0x2b58140> h5py module <module 'h5py' from '/reg<...>ython/h5py/__init__.pyc'> np module <module 'numpy' from '/re<...>thon/numpy/__init__.pyc'> plt module <module 'matplotlib.pyplo<...>n/matplotlib/pyplot.pyc'> rdXPPdata function <function rdXPPdata at 0x2b57c80> runexpNO2fina function <function runexpNO2fina at 0x2b57e60> scan ScanOutput <pymatlab.ScanOutput object at 0x2b60536bee90> scaninput ScanInput <pymatlab.ScanInput object at 0x2b60536b4e90>
Examples
1) Select limits from graphical input and plot filtered IPIMB
Here's a log from a session that produces a loglog plot (blue dots) of two IPIMB channels, selects limits from graphical inpu (mouse click),
draws the selected events with red dots.
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In [1]: from pymatlab import * Pretend this is matlab In [2]: whos Variable Type Data/Info ------------------------------------------------------ H5getobjnames function <function H5getobjnames at 0x1379fde8> ScanInput type <class 'pymatlab.ScanInput'> ScanOutput type <class 'pymatlab.ScanOutput'> filtvec function <function filtvec at 0x1379ff50> findmovingmotor function <function findmovingmotor at 0x1379fd70> getSTDMEANfrac_from_startpoint function <function getSTDMEANfrac_<...>startpoint at 0x137a01b8> get_filter function <function get_filter at 0x1379fed8> get_limits function <function get_limits at 0x137a0050> get_limits_automatic function <function get_limits_automatic at 0x137a0230> get_limits_channelhist function <function get_limits_channelhist at 0x137a02a8> get_limits_correlation function <function get_limits_correlation at 0x137a00c8> get_limits_corrfrac function <function get_limits_corrfrac at 0x137a0140> h5py module <module 'h5py' from '/reg<...>ython/h5py/__init__.pyc'> np module <module 'numpy' from '/re<...>thon/numpy/__init__.pyc'> plt module <module 'matplotlib.pyplo<...>n/matplotlib/pyplot.pyc'> rdXPPdata function <function rdXPPdata at 0x1379fc80> runexpNO2fina function <function runexpNO2fina at 0x1379fe60> In [3]: scaninput = ScanInput() In [4]: scaninput.fina = "/reg/d/psdm/XPP/xpp23410/hdf5/xpp23410-r0107.h5" In [5]: scan = rdXPPdata(scaninput) Reading XPP data from /reg/d/psdm/XPP/xpp23410/hdf5/xpp23410-r0107.h5 Found pv control object fs2:ramp_angsft_target Found scan vector [ 2800120. 2800240. 2800360. 2800480. 2800600. 2800720. 2800840. 2800960. 2801080. 2801200. 2801320. 2801440. 2801560. 2801680. 2801800. 2801920. 2802040. 2802160. 2802280. 2802400. 2802520. 2802640. 2802760. 2802880. 2803000. 2803120. 2803240. 2803360. 2803480. 2803600. 2803720. 2803840. 2803960. 2804080. 2804200. 2804320. 2804440. 2804560. 2804680. 2804800. 2804920. 2805040. 2805160. 2805280.] Fetching data to correlate with motor ['IPM1', 'IPM2'] (44, 120, 4) In [6]: channels = np.concatenate(scan.scandata,axis=0) In [7]: channels.shape Out[7]: (5280, 4) In [8]: get_limits(channels,1,"correlation") 4 channels a 5280 events indexes that pass filter: (array([ 1, 5, 8, ..., 5266, 5272, 5273]),) Out[8]: array([[ 0.00086654, 0.01604564], [ 0.67172102, 0.71968567], [ 0.00194716, 0.01447819], [ 0.80365403, 0.73463468]]) In [9]: plt.draw() |
Data visualization with NumPy (arrays) and MatPlotLib (plots).
Saving (and loading) a numpy array (e.g. image) to (from) a file
If you want to save one array (max 2 dimensions), you can use binary numpy file or ascii file:
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Or you can group your datasets any other way you find useful, of course.
A comparison with MatLab.
MatLab | MatPlotLib | Comments | |||||
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Loglog plot of one array vs. another
| Loglog plot of one array vs. another
| channels is a 4xN array of floats, where N is the number of events. Each column corresponds to one out of four Ipimb channels. | ]]></ac:plain-text-body></ac:structured-macro> | ||||
test | test | Test | |||||
array of limits from graphical input | array of limits from graphical input |
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filter | filter |
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