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Interactive data analysis with iPython
- 'ipython' (http://ipython.org/) is an enhanced python shell for interactive use. Many of the examples here would work equally well with a 'regular' python shell.
- plotting is done with 'matplotlib' (http://matplotlib.sourceforge.net/)
- If you're looking for an IDE to work with, consider 'Spyder' (http://code.google.com/p/spyderlib/).
Interactive access to XTC data
An interactive framework based on ipython is currently being explored, but does not exist yet.
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This page tries to summarize how to read XTC files interactively
- How to read XTC files (LCLS's primary data format)?
Warning title The short answer Not yet possible!
Info title The long answer Several tools exist to read XTC files sequencially. Currently, to work interactively with data from XTC files you should read it with one of the tools, store the data in memory or file, and work with it interactively with your tool of choice (e.g. IPython).
Existing tools:- psana framework (C++)
- pyana framework (python)
- xtcreader (C++) / pyxtcreader (python)
- xtcscanner (python)
- xtcexplorer (python)
The rest of this page currently elaborates on The long answer, with a bias towards using python.Tip title Coming soon! We are currently working on better infrastructure for interactive analysis of XTC files. We welcome input from you if you think you may be one of the users of this .
- XTC files can be tranlated to HDF5 format on requst.
These may allow interactive analysis by outside tools (e.g. Matlab) or python (see How to access HDF5 data from Python
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- ). Be aware, though, that the framework (psana or pyana) does the job of syncronizing event data for you, and the lack of syncronization of arrays in the HDF5 files is the biggest drawback of working on datafiles outside of our framework.
The existing tools:
Python framework: pyana
The most pain-free way to access LCLS XTC data files from python is through LCLS's python framework, pyana. It is a non-interactive framework, but to some extent you can work interactively with the data it produces
All about pyana.
C++ framework: psana
The idea is the same as for pyana. Non-interactive. No interactive support as of yet.
All about psana - Original Documentation.
If you like GUIs:
The XTC Explorer - Old gives you an "interactive" way to configure your analysis.
If you like python (or IPython):
Python/IPython can be used to analyze data after you've saved them, or they can be embedded into a pyana module to give you interactive access to the data at regular intervals throughout your analysis.
'IPython' (http://ipython.org/) is an enhanced python shell for interactive use. Many of the examples here would work equally well with a 'regular' python shell.
Plotting is done with 'matplotlib' (http://matplotlib.sourceforge.net/)
If you're looking for an IDE to work with, consider 'Spyder' (http://code.google.com/p/spyderlib/).
Interactively exploring the XTC file.
Quick-start way to figure out what's in your xtc file is to run 'xtcscanner' or 'xtcexplorer'. The output can help you write a pyana module for further analysis. The explorer allows you to make some quick plots too.
xtcscanner
This tool also belongs to the XtcExplorer package, and is used by the GUI. But the tool can also be run directly from the command line:
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usage: xtcscanner [options] xtc-files ...
options:
-h, --help show this help message and exit
-n NDATAGRAMS, --ndatagrams=NDATAGRAMS
-v, --verbose
-l L1_OFFSET, --l1-offset=L1_OFFSET
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Example:
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Scanning....
Start parsing files:
['/reg/d/psdm/AMO/amo01509/xtc/e8-r0094-s00-c00.xtc', '/reg/d/psdm/AMO/amo01509/xtc/e8-r0094-s01-c00.xtc']
201 datagrams read in 0.070000 s . . . . . . .
-------------------------------------------------------------
XtcScanner information:
- 1 calibration cycles.
- Events per calib cycle:
[197]
Information from 0 control channels found:
Information from 9 devices found
BldInfo:EBeam: EBeamBld (197)
BldInfo:FEEGasDetEnergy: FEEGasDetEnergy (197)
DetInfo:AmoETof-0|Acqiris-0: (5 ch) AcqConfig_V1 (1) AcqWaveform_V1 (197)
DetInfo:AmoGasdet-0|Acqiris-0: (2 ch) AcqConfig_V1 (1) AcqWaveform_V1 (197)
DetInfo:AmoITof-0|Acqiris-0: (1 ch) AcqConfig_V1 (1) AcqWaveform_V1 (197)
DetInfo:AmoMbes-0|Acqiris-0: (1 ch) AcqConfig_V1 (1) AcqWaveform_V1 (197)
DetInfo:EpicsArch-0|NoDevice-0: Epics_V1 (688)
DetInfo:NoDetector-0|Evr-0: EvrConfig_V2 (1)
ProcInfo:: RunControlConfig_V1 (11)
XtcScanner is done!
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Benefits of XTC files is that they are available immediately, and you can start analyzing before the run is done collecting. The benefits of using the LCLS framework(s) is that each event is easily extracted and you don't have to worry about time-ordering or synchronizing data from different devices.
If you'd like to analyze XTC files with iPython, the options that exist are:
- ipython in combination with pyana. The XTC Explorer is an example of how this can be done (XtcExplorer#IPython).
Note that currently there is no way to run pyana from IPython, but you can run a pyana job and launch ipython at the end to play with the plots/arrays.
- Write your own application.
Data visualization with NumPy (arrays) and MatPlotLib (plots).
This is not meant to be documentation or a tutorial for matplotlib or numpy. Just a place to document stuff that I have a hard time finding explained elsewhere.
This is really just simple python. But since 'matplotlib' documentation can be frustratingly non-verbose about what functions and attributes are available for its various classes/objects, I found this is a useful way to inspect what an object knows about itself.Code Block 1 none title Inspecting objects for attr_name in dir(obj): attribute = getattr(obj, attr_name) print attr_name, ": ", attribute
...
import numpy as np
np.save("filename.npy", array)
array = np.load("filename.npy")
np.savetxt("filename.dat", array)
array = loadtxt("filename.dat")
...
import h5py
def beginjob(self,evt,env):
self.ofile = h5py.File("outputfile.hdf5", 'w') # open for writing (overwrites existing file)
self.shot_counter = 0
def event(self,evt,env)
# example: store several arrays from one shot in a group labeled with shot (event) number
self.shot_counter += 1
group = self.ofile.create_group("Shot%d" % self.shot_counter)
image1_source = "CxiSc1-0|TM6740-1"
image2_source = "CxiSc1-0|TM6740-2"
frame = evt.getFrameValue(image1_source)
image1 = frame.data()
frame = evt.getFrameValue(image2_source)
image2 = frame.data()
dataset1 = group.create_dataset("%s"%image1_source,data=image1)
dataset2 = group.create_dataset("%s"%image2_source,data=image2)
def endjob(self,env)
self.ofile.close()
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Examples from MATLAB
These examples are mostly python rewrites of matlab functions provided by XPP. Thanks to H. Lemke for matlab examples and advice.
- 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).
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[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.
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In [1]: from pymatlab import *
Pretend this is matlab
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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
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The XtcExplorer GUI.
With interactive python embedded, see: https://confluence.slac.stanford.edu/display/PCDS/XTC+Explorer#XTCExplorer-InteractiveplottingwithIPython
IPython used "like" MATLAB
Of course MATLAB is much more than this, but here's what we've started with. Here are some examples with IPython based on matlab functions provided by XPP. Thanks to H. Lemke for matlab examples and advice. A python module pymatlab.py defines a number of functions to use in this analysis example.
Starting an interactive session
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[ofte@psana0XXX myrelease]$ ipython -pylab 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. ? |
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-> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about |
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'object'. ?object also works, ?? prints more.
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In [1]: from pymatlab import *
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Generally, it is recommended to load library modules with 'import pymatlab' and access all its methods and classes with pyamatlab.function. In an interactive session it may be easier to have access to the contents of pymatlab in your immediate workspace by doing 'from pymatlab import *'.
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In [2]: who H5getobjnames ScanInput ScanOutput filtvec findmovingmotor getSTDMEANfrac_from_startpoint get_filter get_limits |
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get_limits_automatic get_limits_channelhist get_ |
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limits_correlation |
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get_limits_corrfrac h5py np plt rdXPPdata runexpNO2fina |
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scan |
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scaninput In [3]: whos Variable |
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Type |
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Data/Info -------------------------------------------------------- H5getobjnames |
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function <function |
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H5getobjnames at |
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0x2b57de8> |
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ScanInput type |
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<class ' |
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pymatlab.ScanInput'> |
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ScanOutput type |
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<class 'pymatlab.ScanOutput'> filtvec |
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function <function filtvec at 0x2b57f50> findmovingmotor |
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function <function findmovingmotor at 0x2b57d70> getSTDMEANfrac_from_startpoint |
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function <function |
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getSTDMEANfrac_<...>_startpoint at |
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0x2b581b8> get_filter function <function |
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get_filter at |
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0x2b57ed8> |
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get_limits function |
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<function get_limits at |
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0x2b58050> get_limits_automatic function <function get_limits_automatic at |
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0x2b58230> get_limits_channelhist function <function get_limits_channelhist at 0x2b582a8> get_limits_correlation |
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function |
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<function get_limits_correlation at |
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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.
Code Block | ||
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none | none | In [1]: from pymatlab import * Pretend this is matlab In [2]: whos Variable0x2b580c8> get_limits_corrfrac function <function get_limits_corrfrac at 0x2b58140> h5py module Type <module 'h5py' Data/Info ------------------------------------------------------ H5getobjnamesfrom '/reg<...>ython/h5py/__init__.pyc'> np function <function H5getobjnamesmodule at 0x1379fde8> ScanInput <module 'numpy' from '/re<...>thon/numpy/__init__.pyc'> plt type <class 'pymatlab.ScanInput'> ScanOutput module type <class 'pymatlab.ScanOutput<module 'matplotlib.pyplo<...>n/matplotlib/pyplot.pyc'> filtvecrdXPPdata function function <function filtvecrdXPPdata at 0x1379ff50>0x2b57c80> findmovingmotorrunexpNO2fina function <function findmovingmotorrunexpNO2fina at 0x1379fd70> getSTDMEANfrac_from_startpoint0x2b57e60> scan function <function getSTDMEANfrac_<...>startpoint at 0x137a01b8> get_filter ScanOutput function<pymatlab.ScanOutput <function get_filter object at 0x1379fed8>0x2b60536bee90> get_limitsscaninput function <function get_limits at 0x137a0050> get_limits_automatic ScanInput <pymatlab.ScanInput 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() |
Table of comparison (MATLAB vs MatPlotLib)
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MatLab
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MatPlotLib
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Comments
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Loglog plot of one array vs. another
Code Block |
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%
%
%
a1 = subplot(121);
loglog(channels(:,1),channels(:,2),'o')
xlabel('CH0')
ylabel('CH1')
a2 = subplot(122);
loglog(channels(:,3),channels(:,4),'o')
xlabel('CH2')
ylabel('CH3')
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Loglog plot of one array vs. another
Code Block |
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import matplotlib.pyplot as plt
import numpy as np
a1 = plt.subplot(221)
plt.loglog(channels[:,0],channels[:,1], 'o' )
plt.xlabel('CH0')
plt.ylabel('CH1')
a2 = plt.subplot(222)
plt.loglog(channels[:,2],channels[:,3], 'o' )
plt.xlabel('CH2')
plt.ylabel('CH3')
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...
]]></ac:plain-text-body></ac:structured-macro>
object at 0x2b60536b4e90>
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Like in MATLAB, who
gives you a short list of workspace contents, whos
gives you a longer list of workspace contents.
Plot filtered IPIMB data with limits from graphical input:
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.
Code Block | ||||
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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()
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Table of comparison (MATLAB vs MatPlotLib)
See also http://www.scipy.org/NumPy_for_Matlab_Users
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. | ||||
test | test | Test | ||||
array of limits from graphical input | array of limits from graphical input |
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| In MatLab, | ||||
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filter | filter |
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Writing Numpy and HDF5 files from python
You can store numpy arrays from a pyana job (reads XTC) and store them in simple numpy files or HDF5 files. Here are some examples:
Simple array to a NumPy file:
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import numpy as np
np.save("filename.npy", array)
array = np.load("filename.npy")
np.savetxt("filename.dat", array)
array = loadtxt("filename.dat")
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This example shows saving and loading of a binary numpy file (.npy) and an ascii file (.dat).
This only works with single arrays (max 2 dimensions).
If you need to save multiple events/shots in the same file you will need to do some tricks (e.g. flatten the array and stack 1d arrays into 2d arrays where axis2 represent event number). Or you could save as an HDF5 file.
Simple array to an HDF5 file
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import h5py
def beginjob(self,evt,env):
self.ofile = h5py.File("outputfile.hdf5", 'w') # open for writing (overwrites existing file)
self.shot_counter = 0
def event(self,evt,env)
# example: store several arrays from one shot in a group labeled with shot (event) number
self.shot_counter += 1
group = self.ofile.create_group("Shot%d" % self.shot_counter)
image1_source = "CxiSc1-0|TM6740-1"
image2_source = "CxiSc1-0|TM6740-2"
frame = evt.getFrameValue(image1_source)
image1 = frame.data()
frame = evt.getFrameValue(image2_source)
image2 = frame.data()
dataset1 = group.create_dataset("%s"%image1_source,data=image1)
dataset2 = group.create_dataset("%s"%image2_source,data=image2)
def endjob(self,env)
self.ofile.close()
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This example is shown in a pyana setting. The HDF5 file is declared and opened in beginjob, datasets created for each event, and the file is closed in the endjob method.
Or you can group your datasets any other way you find useful, of course.
Saving complex datasets to HDF5 file
Some more advanced examples (courtesy of Hubertus Bromberger):
Code Block |
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##############
# Create data set
##############
f = h5py.File('test.hdf5', 'w')
f.create_dataset('t-nonames', data = rand(30000), dtype='<f4')
f.create_dataset('t-names', data = np.array(rand(30000), dtype=[('ps', '<f4')]))
dt = np.dtype([
('Charge', '<f4'), ('Energy', '<f4'), ('PosX', '<f4'),
('PosY', '<f4'), ('AngX', '<f4'), ('AngY', '<f4'),
('PkCurrBC2', '<f4')])
f.create_dataset('eBeam-names', data =
np.array([tuple(i.tolist()) for i in rand(30000, 7)], dtype=dt))
f.create_dataset('eBeam-nonames', data = rand(30000, 7), dtype='<f4')
dt = np.dtype([('a', '<f4'), ('b', '<f4'), ('c', '<f4'), ('d', '<f4', (100,))])
f.create_dataset('dsSubset-names', data =
np.array([tuple((i[0], i[1], i[2], i[3:].tolist())) for i in rand(30000,103)], dtype=dt))
f.create_dataset('dsSubset-nonames', data = rand(30000,13))
f.close()
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Code Block |
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##############
# Load data and benchmark data access
##############
f = h5py.File('test.hdf5', 'r')
iterations = int(1e4)
#######
# Single col
#######
start = time.time()
for i in xrange(iterations):
a = f['t-names']['ps']/f['t-names']['ps'].max()
print "Single column as compound dataset: %.2fs" % (time.time() - start)
start = time.time()
for i in xrange(iterations):
a = f['t-nonames'][:]/f['t-nonames'][:].max()
print "Single column as dataset: %.2fs" % (time.time() - start)
start = time.time()
a = f['t-names']['ps']
for i in xrange(iterations):
b = a/a.max()
print "Single column from compound dataset prior assignment: %.2fs" % (time.time() - start)
start = time.time()
a = f['t-nonames'][:]
for i in xrange(iterations):
b = a/a.max()
print "Single column dataset and prior assignment: %.2fs\n" % (time.time() - start)
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#######
# Select single col from 2x2
#######
start = time.time()
for i in xrange(iterations):
a = f['eBeam-names']['Energy']/f['eBeam-names']['Energy'].max()
print "Single column as compound dataset: %.2fs" % (time.time() - start)
start = time.time()
for i in xrange(iterations):
a = f['eBeam-nonames'][:,1]/f['eBeam-nonames'][:,1].max()
print "Single column as dataset: %.2fs" % (time.time() - start)
start = time.time()
a = f['eBeam-names']['Energy']
for i in xrange(iterations):
b = a/a.max()
print "Single column from compound dataset prior assignment: %.2fs" % (time.time() - start)
start = time.time()
a = f['eBeam-nonames'][:,1]
for i in xrange(iterations):
b = a/a.max()
print "Single column dataset and prior assignment: %.2fs\n" % (time.time() - start)
#######
# Select columns from 2x2
#######
start = time.time()
for i in xrange(iterations/50):
for row in f['dsSubset-names']['d']:
a = row/row.max()
print "Columns as compound dataset: %.2fs" % (time.time() - start)
start = time.time()
for i in xrange(iterations/50):
for row in f['dsSubset-nonames'][:,3:103]:
a = row/row.max()
print "Columns as dataset '[:,3:103]': %.2fs" % (time.time() - start)
start = time.time()
for i in xrange(iterations/50):
for row in f['dsSubset-nonames'][:,3:]:
a = row/row.max()
print "Columns as dataset '[:,3:]': %.2fs" % (time.time() - start)
start = time.time()
a = f['dsSubset-names']['d']
for i in xrange(iterations/50):
for row in a:
b = row/row.max()
print "Columns as compound dataset and prior assignment: %.2fs" % (time.time() - start)
start = time.time()
a = f['dsSubset-nonames'][:,3:]
for i in xrange(iterations/50):
for row in a:
b = row/row.max()
print "Columns as dataset and prior assignment: %.2fs" % (time.time() - start)
f.close()
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test
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test
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Test
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array of limits from graphical input
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array of limits from graphical input
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Code Block |
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axes(a1)
hold on
lims(1:2,:) = ginput(2);
axes(a2)
hold on
lims(3:4,:) = ginput(2);
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...
Code Block |
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lims = np.zeros((4,2),dtype="float")
plt.axes(a1)
plt.hold(True)
lims[0:2,:] = plt.ginput(2)
plt.axes(a2)
plt.hold(True)
lims[2:4,:] = plt.ginput(2)
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In MatLab, lims
is an expandable array that holds limits as set by input from mouse click on the plot (ginput).
NumPy arrays cannot be expanded, so I've declared a 4x2 array of zeros to start with, then fill it with ginput().
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fbool1 = (channels(:,1)>min(lims(1:2,1)))&(channels(:,1)<max(lims(1:2,1)))
fbool2 = (channels(:,2)>min(lims(1:2,2)))&(channels(:,2)<max(lims(1:2,2)));
fbool = fbool1&fbool2
loglog(channels(fbool,1),channels(fbool,2),'or')
fbool3 = (channels(:,3)>min(lims(3:4,3)))&(channels(:,3)<max(lims(3:4,3)))
fbool4 = (channels(:,4)>min(lims(3:4,4)))&(channels(:,4)<max(lims(3:4,4)));
fbool = fbool3&fbool4
loglog(channels(fbool,3),channels(fbool,4),'or') |
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fbools0 = (channels[:,0]>lims[:,0].min())&(channels[:,0]<lims[:,0].max())
fbools1 = (channels[:,1]>lims[:,1].min())&(channels[:,1]<lims[:,1].max())
fbools = fbools0 & fbools1
fbools2 = (channels[:,2]>lims[:,2].min())&(channels[:,2]<lims[:,2].max())
fbools3 = (channels[:,3]>lims[:,3].min())&(channels[:,3]<lims[:,3].max())
fbools = fbools2&fbools3
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