The PyMca Application and Toolkit - SciPyconference.scipy.org/static/wiki/sole_pymca.pdf · Slide:...
Transcript of The PyMca Application and Toolkit - SciPyconference.scipy.org/static/wiki/sole_pymca.pdf · Slide:...
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V.A. Solé - European Synchrotron Radiation Facility2009 Python for Scientific Computing Conference
The PyMca Application and Toolkit
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The ESRF: Just an X-Ray Source
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(Partial) Synchrotron Chart
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X-Ray Fluorescence Basics
Energy of emitted X rays is element dependent
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Visualization via Multi-Channel Analyzers (MCA)
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PyMca?
For the end users and the specific developers
Set of programs and widgets for XRF analysis
Spectrum modeling
Quantification
ROI imaging
Fit imaging via batch processing
For the general developer
Set of python modules
Data visualization
Peak search
Function fitting
Imaging of 1D data
V.A. Solé, E. Papillon, M. Cotte, Ph. Walter, J. Susini, Spectrochimica Acta B 62 (2007) 63-68
PyMca is set of software tools on its way to become a reference in XRF
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- First release in 2004
- Source code SVN repository available at sourceforge since 2006
- Official releases aim to provide ready-to-use binaries to end users
- External dependencies on numpy, PyQt4 and PyQwt5
- If installed, Matplotlib is used to generate high quality output
- Provides high level functional widgets that can be embedded in your own application
Generalities
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XRF Spectrum Analysis
Typical procedure:
1. Calibration
2. Peak identification
3. Peak area extraction
Region of interest (ROI)
Deconvolution (FIT)
4. Quantification
Documentation at http://pymca.sourceforge.net/documentation.html
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1. Calibration
Detailed documentation at http://pymca.sourceforge.net/documentation.html
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2. IdentificationTheoretical database in ASCII format
IUPAC notation whenever possible
PyMca proposes, the user decidesPyMca proposes, the user decidesPyMca proposes, the user decidesPyMca proposes, the user decides
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3.1 Peak area via ROI
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3.2 Peak area via fitting
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3.2 Peak area via fitting
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Fit configuration Dialog (I)
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Fit Configuration Dialog (II)
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Emission Lines
Allowed transitions Implemented transitions
Any transition defined
KShellRates.datKShellRates.datKShellRates.datKShellRates.dat
LShellRates.datLShellRates.datLShellRates.datLShellRates.dat
MShellRates.datMShellRates.datMShellRates.datMShellRates.dat
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4. Quantification (I)
Parallel beam approximationParallel beam approximationParallel beam approximationParallel beam approximation
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4. Quantification (II)Nominal concentration 500 ppm
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XRF Analysis Integration in other Applications
Integration in mxCuBE (ESRF) Integration elsewhere
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Is it easy to embed?
from PyMca import McaAdvancedFit
fitWindow = McaAdvancedFit.McaAdvancedFit()
fitWindow.setData(x, y)
fitWindow.show()
For the previous examples, basically one just needs 4 lines of code:
It can be used from ipython just starting it as “ipython –q4thread”
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Advanced Fit Batch processing
Select the input files
Select the fit configuration
Select the output directory
Select the output options
Start
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Output
Images in ASCII and ESRF format
• Easy to import in other programs
Individual peak contributions in ASCII
• Use your own plotting program
Fully automated HTML report
• Browse your results!
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+……
One spectrum for each pixel
A sum spectrum for the whole image
XRF Imaging
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Sb
Pb
S
Ca
We wanted to know what pigments were
used in this section of the painting
Copyright C2RMF
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Based on the batch generated element distribution maps …
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… we were able to determine the possible presence of stibnite grains (Sb2S3) embedded in a lead containing matrix.
M. Cotte, E. Welcomme, V.A. Solé, M. Salomé, M. Menu, Ph. Walter, J. Susini, Anal. Chem. 79 (2007) 6988-6994
Sulfur and antimony correlated Lead and antimony not correlated
… and their correlations as shown by the program
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Generic Fitting Module
Levenberg-Marquardt algorithm with constraints on fitting parameters
It accepts user defined functions
The simplest form of user function:
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Automatic Peak Search Routines
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In this example:
Stack = 101x200x2000 numpy array
20200 spectra of 2000 channels
Pixel[i, j] = numpy.sum(Stack[i, j, :])
Pixel[i, j] = numpy.sum(Stack[i, j, ch0:ch1])
1D Stack ROI Imaging
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1D Stack ROI Imaging
We can generate new images by moving the cursors or defining new ROIs in the table
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Imaging and Principal Component Analysis
Subtract the average spectrum from each spectrum of the dataset and arrange it as NpixelsxNchannels
Calculate the covariance = Data.T * Data
Get the eigenvectors of the covariance
Add the average spectrum to each row and get the projections of that dataset on each eigenvector (Npixels scalars for each eigenvector)
Reshape the projections in the original map shape to obtain the ¨Eigenimages¨
Covariance method, other ways in http:// en.wikipedia.org/wiki/Principal_components_analysis
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Eigenimages and Eigenvectors
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Eigenimages and Eigenvectors
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Eigenimages and Eigenvectors
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Eigenimages and Eigenvectors
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Eigenimages and Eigenvectors
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Eigenimages and Eigenvectors
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Getting the actual information
We can select a set of pixels on any of the displayed images and display the cumulative spectrum associated to those pixels.
Here we can see the average spectrum associated to the hotter pixels of the Eigenimage 02 (in red) compared to the average spectrum of the map (in black).
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We could have easily missed the presence of one element if we would have just analyzed the sum spectrum via ROIs.
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What have we done?
We have used principal components analysis to know what sample regions were worth to take a closer look.
Not bad when you have a lot of data …
This data treatment is totally generic and applicable to other methods of analysis
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Current Developments – HDF5 Support
- Collaboration with D. Dale from Cornell High Energy Synchrotron Source (CHESS)
- HDF5 expected to become the file format of most European synchrotrons sources
- Additional dependency on h5py http://h5py.alfven.org
- Partial support already available in current SVN sources
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Current Developments – 3D and 4D
-The goal is to provide ways to visualize and interact with multiparametric data
-Already available at the ESRF through an additional PyMca set of modules
-Currently evaluating the pros and cons of using MayaVi
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Conclusion
PyMca is a program as well as an XRF analysis toolkit
- Open source and distributed under the conditions of the GPLv2+
- Can be used as a fitting and visualization tool (for up to 4-dimensional data)
- Provides high level widgets based on PyQt that can be used independently or integrated into your application
- Allows you to specify a physically meaningful model which can quantitatively determine element concentrations from energy dispersive X-ray spectra
- Is implemented at many synchrotron facilities and labs around the world
- Active development is funded by the ESRF
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Acknowledgements
My ESRF colleagues (mainly software group and microscopy beamlines ID21 and ID22)
Ph. Walter and M. Cotte (Centre de Recherche et de Restauration des Musées de France)
Darren Dale (Cornell High Energy Synchrotron Source)
The PyMca users, for their enthusiasm and their encouragements