From 10c532165a0bad200953e095cb85191f2a467594 Mon Sep 17 00:00:00 2001 From: Jerome Kieffer Date: Mon, 6 Jun 2016 14:03:49 +0200 Subject: [PATCH] Version 0.12: * Continuous integration on linux, windows using Python 2.7 and 3.4+ * Drop support of Python 2.6, 3.2, 3.3 and debian6 packaging * New radial output units: Reciprocal spacing squared and log(q) **ID02** * GPU accelerate version of ai.separate (Bragg & amorphous) **ID13** * Quantile filtering in pyFAI-average **ID02** * New graphical application for diffraction imaging **ID21** * Migrate to a common structure with *silx* (reorganize tests, benchmarks, ...) * Extensions (binary sub-modules) have all been moved to *ext* directory * Many improvements multigeometry integrators * Compatibility with the copy module (copy.deepcopy) for azimuthal integrator **ID02** * Distortion correction works also for non-contiguous detectors * Update documentation and provide advanced tutorials: - Introduction to pyFAI using the jupyter notebook - detector calibration **ID15, BM02** - Correction of detector distortion, examples of pixel detectors. - calibrant calculation **ID30** - error handling **ID02, BM29** * pyFAI-integrate can now be used with or without GUI * Many new detectors (ADSC, Pilatus CdTe, Apex II, Pixium): - support for non-flat/curved detectors (Aarhus) - non-contiguous detectors (WOS Xpad) * Include tests and benchmarking tools as part of the library * Better testing. --- MANIFEST.in | 2 + doc/build/_images/2Dhistogram.png | Bin 0 -> 172906 bytes doc/build/_images/2DwithSplit.png | Bin 0 -> 165756 bytes doc/build/_images/PONI.png | Bin 0 -> 37820 bytes doc/build/_images/benchmark.png | Bin 0 -> 94791 bytes doc/build/_images/diffmap_integrate.png | Bin 0 -> 64703 bytes doc/build/_images/diffmap_main.png | Bin 0 -> 72236 bytes doc/build/_images/diffmap_running.png | Bin 0 -> 47217 bytes doc/build/_images/drawMask.png | Bin 0 -> 214148 bytes doc/build/_images/fabio_viewer.png | Bin 0 -> 331769 bytes 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4 major steps: + +Pre-processing of images: +------------------------- +The typical pre-processing consists of the averaging (or better median filtering) of darks images. +Dark current images are then subtracted from data and corrected for flat. + +If saturated pixels exists, the are likely to be treated like peaks but their positions +will be wrong. +It is advised to either mask them out or to desaturate them (pyFAI provides an option, +but it is expensive in calculation time). + +Peak-picking +------------ + + +The Peak-picking consists in the identification of peaks and groups of peaks +belonging to same ring. It can be performed by 4 methods described hereafter. + +Massif detection +................ + +This method consists in making the difference of the original image and a blurred +image. Then look for a large contiguous region of positives values, corresponding +to a single group of peak. +The blurring parameter can be adjusted using the *"-g"* option in pyFAI-calib. + +Blob detection +.............. + +The approach is based on difference of gaussians (DoGs) as described in the +blob_detection_ article of wikipedia. + +.. _blob_detection: http://en.wikipedia.org/wiki/Blob_detection + +It consists in blurring the image by convolution with a 2D gaussian kernel and making +differences between two successive blurs (called Difference Of Gaussian or DoGs). +In theses DoGs, keypoints are defined as the maxima in the 3D space (y,x,size of +the gaussian). After their localization, keypoints are refined by Savitzky Golay +algorithm or by an interpolation at the second order which is equivalent but uses +less points. At this step, if the estimation of the maximum is too far from the maximum, +the keypoint will be considered as a fake maximum and removed. + +Steepest ascent +............... + +This is very naive implementation which looks for the nearest local maximum. +Subsequently a sub-pixel optimization is performed based on a second order expansion +using the local gradient and hessian. + +Monte-Carlo sampling +.................... + +Series of peaks can be extracted using the Steepest Ascent on randomly selected seeds. +This method can be biased towards an already known geometry by starting from +points which are supposed to be on the ring. + +Refinement of the parameters +---------------------------- + +After selecting groups of peaks, each of them is assigned to a Debye-Scherrer ring number +and associated to a d-spacing value hence a theoritcal 2\theta value. +A supervised least-squares refinement, performed on the difference of peak position's +2-theta values versus the expected ones from calibrant provides the 6-geometry parameters +fitted. + +The default optimization procedure is the Sequential Least SQuares Programming +implemented in *scipy.optimize.slsqp*. +The cost function is the sum of the square of the difference between the expected and +calculated 2\theta values for the various peaks. This sum is dependent on the number +of control-points. + + +Validation of the calibration +----------------------------- + +Validation by an human being of the geometry is an essential step: +pyFAI will overlaps to the diffraction image, the lines corresponding to the various diffraction +rings expected from the calibrant. +Those lines should be in pretty good agreement with the rings +of the scattering image. +The average error per control point (delta 2\theta error in radian) is printed out +and offers a quantitative measurement of the relative quality of the fit for similar +setups/experiment. +Neverthless its absolute value has no meaning, except the lower, the better. + +Subsequently, pyFAI offers some validation options in to check the quality of the fit. +some of them global, some of them limited to given rings. + diff --git a/doc/build/_sources/changelog.txt b/doc/build/_sources/changelog.txt new file mode 100644 index 000000000..e3d697bb8 --- /dev/null +++ b/doc/build/_sources/changelog.txt @@ -0,0 +1,209 @@ +:Author: Jérôme Kieffer +:Date: 02/06/2015 +:Keywords: changelog + +ChangeLog of Versions +===================== + +0.12.0: 06/2016 +--------------- +* Continuous integration on linux, windows using Python 2.7 and 3.4+ +* Drop support of Python 2.6, 3.2, 3.3 and debian6 packaging +* New radial output units: Reciprocal spacing squared and log(q) **ID02** +* GPU accelerate version of ai.separate (Bragg & amorphous) **ID13** +* Quantile filtering in pyFAI-average **ID02** +* New graphical application for diffraction imaging **ID21** +* Migrate to a common structure with *silx* (reorganize tests, benchmarks, ...) +* Extensions (binary sub-modules) have all been moved to *ext* directory +* Many improvements multigeometry integrators +* Compatibility with the copy module (copy.deepcopy) for azimuthal integrator **ID02** +* Distortion correction works also for non-contiguous detectors +* Update documentation and provide advanced tutorials: + - Introdution to pyFAI using the juyter notebook + - detector calibration **ID15, BM02** + - Correction of detector distortion, examples of pixel detectors. + - calibrant calculation **ID30** + - error handling **ID02, BM29** +* pyFAI-integrate can now be used with or without GUI +* Many new detectors (ADSC, Pilatus CdTe, Apex II, Pixium): + - support for non-flat/curved detectors (Aarhus) + - non-contiguous detectors (WOS Xpad) +* Include tests and benchmarking tools as part of the library +* Better testing. + +0.11.0: 07/2015 +--------------- +* All calibrant from NIST are now available, + Nickel, Aluminum, ... with bibliographic references +* The Cell class helps defining new calibrants. +* OpenCL Bitonic sort (to be integrated into Bragg/Amorphous separation) +* Calib is available from the Python interface (procedural API), not only from the shell script. +* Many new options in calib for reset/assign/delete/validate/validate2/chiplot. + - reset: set the detector, orthogonal, centered and at 10cm + - assign: checks the assignment of groups of points to rings + - delete: remove a group of peaks + - validate: autocorrelation of images: error on the center + - validate2: autocorrelation of patterns at 180° apart: error on the center function of chi + - chiplot: assesses the quality of control points of one/multiple rings. +* Fix the regression of the initial guess in calib (Thanks Jon Wright) +* New peak picking algorithm named "watershed" and based on inverse watershed for ridge recognition +* start factorizing cython regridding engines (work ongoing) +* Add "--poni" option for pyFAI-calib (Thanks Vadim Dyakin) +* Improved "guess_binning", especially for Perkin Elmer flat panel detectors. +* Support for non planar detectors like Curved Imaging plate developped at Aarhus +* Support for Multi-geometry experiments (tested) +* Speed improvement for detector initialization +* better isotropy in peak picking (add penalization term) +* enhanced documentation on http://pyfai.readthedocs.org + +0.10.3: 03/2015 +--------------- +* Image segmentation based on inverse watershed (only for recalib, not for calib) +* Python3 compatibility +* include testimages into distribution + + +0.10.2: 11/2014 +--------------- +* Update documentation +* Packaging for debian 8 + +0.10.1: 10/2014 +--------------- +* Fix issue in peak-picking +* Improve doc & manpages +* Compatibility with PyMca5 + +0.10.0: 10/2014 +--------------- +* Correct Caglioti's formula +* Update tests and OpenCL -> works with Beignet and pocl open source drivers +* Compatibility with MacOSX and windows + +0.9.4: 06/2014 +--------------- +* include spec of Maxwell GPU +* fix issues with intel OpenCL icd v4.4 +* introduce shape & max_shape in detectors +* work on marchingsquares/sorted controurplot for calibration +* Enforce the use the Qt4Agg for Matplotlib and other GUI stuff. +* Update shape of detector in case of binning +* unified distortion class: merge OpenCL & OpenMP implementation #108 +* Benchmarks for distortion +* Raise the level to warning when inverting the mask +* set of new ImXpad detectors Related issue #111 +* Fix issue with recalib within MX-calibrate +* saving detector description in Nexus files issue #110 +* Update some calibrants: gold +* about to make peak-picking more user-friendly +* test for bragg separation +* work on PEP8 compliance +* Do not re-cythonize: makes debian package generation able to benefit from ccache +* conversion to SPD (rotation is missing) +* pixelwise worker +* correct both LUT & OCL for memory error +* replace os.linsep with "\n" when file file opened in text mode (not binary) +* rework the Extension part to be explicit instead of "black magic" :) +* implement Kahan summation in Cython (default still use Doubles: faster) +* Preprocessing kernel containing all cast to float kernels #120 +* update setup for no-openmp option related to issue #127 +* Add read-out mode for mar345 as "guess_binning" method for detector. Also for MAR and Rayonix #125 +* tool to benchmark HDF5 writing +* try to be compatible with both PySide and PyQt4 ... the uic stuff is untested and probably buggy #130 +* Deactivate the automatic saturation correction by default. now it is opt-in #131 + +0.9.3: 02/2014 +--------------- +* Better control for peak-picking (Contribution from Gero Flucke, Desy) +* Precise Rayonix detectors description thanks to Michael Blum +* Start integrating blob-detection algorithm for peak-picking: #70 +* Switch fron OptParse to ArgPrse: #83 +* Provide some calibrant by default: #91 +* Description of Mar345 detector + mask#92 +* Auto-registration of detectors: #97 +* Recalib and check-calib can be called from calib: #99 +* Fake diffraction image from calibrant: #101 +* Implementation of the CSR matrix representation to replace LUT +* Tight pixel splitting: #43 +* Update documentation + +0.9.2: (01/2014) +---------------- +* Fix memory leak in Cython part of the look-up table generation +* Benchmarks with memory profiling + +0.9: 10/2013 +------------ +* Add detector S140 from ImXpad, Titan from Agilent, Rayonix +* Fix issues: 61, 62, 68, 76, 81, 82, 85, 86, 87 +* Enhancement in LImA plugins (better structure) +* IO module with Ascii/EDF/HDF5 writers +* Switch some GUI to pyQtGraph in addition to Qt +* Correction for solid-angle formula + +0.8: 10/2012 +------------ +* Detector object is member of the geometry +* Binning of the detector, propagation to the spline if needed +* Detector object know about their masks. +* Automatic mask for some detectors like Pilatus or XPad +* Implementation of sub-pixel position correction for Pilatus detectors +* LUT implementation in 1D & 2D (fully tested) both with OpenMP and with OpenCL +* Switch from C++/Cython OpenCL framework to PyOpenCL +* Port opencl code to both Windows 32/64 bits and MacOSX +* Add polarization corrections +* Use fast-CRC checksum on x86 using SSE4 (when available) to track array change on GPU buffers +* Support for flat 7*8 modules Xpad detectors. +* Benchmark with live graphics (still a memory issue with python2.6) +* Fat source distribution (python setup.py sdist --with-test-images) for debian +* Enhanced tests, especially for Saxs and OpenCL +* Recalibration tool for refining automatically parameters +* Enhancement of peak picking (much faster, recoded in pure Cython) +* Easy calibration for pixel detector (reconstruction of inter-module space) +* Error-bar generation using Poisson law +* Unified programming interface for all integration methods in 2theta, q or radius unit +* Graphical interface for azimuthal integration (pyFAI-integrate) +* Lots of test to prevent non regression +* Tool for merging images using various method (mean, median) and with outlayer rejection +* LImA plugin which can perform azimuthal integration live during the acquisition +* Distortion correction is available alone and as LImA plugin +* Recalibration can refine the wavelength in addition to 6 other parameters +* Calibration always done vs calibrant's ring number, lots of new calibrant are available +* Selection by hand of single peaks for calibration +* New detectors: Dexela and Perkin-Elmer flat panel +* Automatic refinement of multiple images at various geometries (for MX) +* Many improvements requested by ID11 and ID13 + +0.7.2: 08/2012 +-------------- +* Add diff_tomo script +* Geometry calculation optimized in (parallel) cython + +0.7: 07/2012 +------------ +Implementation of look-up table based integration and OpenCL version of it + +0.6: 07/2012 +------------ +* OpenCL flavor works well on GPU in double precision with device selection + +0.5: 06/2012 +------------ +* Include OpenCL version of azimuthal integration (based on histograms) + +0.4: 06/2012 +------------ +* Global clean up of the code regarding options from command line and better design +* Correct the orientation of the azimuthal angle chi +* Rename scripts in pyFAI-calib, pyFAI-saxs and pyFAI-waxs + +0.3: 11/2011 +------------ +* Azimuthal integration splits pixels like fit2d + +0.2: 07/2011 +------------ +* Azimuthal integration using cython histogramming is working + +0.1: 05/2011 +------------ + * Geometry is OK diff --git a/doc/build/_sources/coverage.txt b/doc/build/_sources/coverage.txt new file mode 100644 index 000000000..810239839 --- /dev/null +++ b/doc/build/_sources/coverage.txt @@ -0,0 +1,38 @@ +Test coverage report for pyFAI +============================== + +Measured on *pyFAI* version 0.12.0, 06/06/2016 + +.. csv-table:: Test suite coverage + :header: "Name", "Stmts", "Exec", "Cover" + :widths: 35, 8, 8, 8 + + "pyFAI/__init__", "35", "20", "57.1 %" + "pyFAI/_version", "34", "32", "94.1 %" + "pyFAI/azimuthalIntegrator", "1258", "909", "72.3 %" + "pyFAI/blob_detection", "517", "196", "37.9 %" + "pyFAI/calibrant", "338", "244", "72.2 %" + "pyFAI/decorators", "33", "33", "100.0 %" + "pyFAI/detectors", "1314", "1028", "78.2 %" + "pyFAI/directories", "33", "20", "60.6 %" + "pyFAI/distortion", "544", "162", "29.8 %" + "pyFAI/ext/__init__", "0", "0", "0.0 %" + "pyFAI/geometry", "961", "761", "79.2 %" + "pyFAI/geometryRefinement", "481", "174", "36.2 %" + "pyFAI/gui_utils", "69", "33", "47.8 %" + "pyFAI/integrate_widget", "493", "273", "55.4 %" + "pyFAI/io", "465", "250", "53.8 %" + "pyFAI/massif", "201", "132", "65.7 %" + "pyFAI/multi_geometry", "75", "65", "86.7 %" + "pyFAI/ocl_azim", "269", "191", "71.0 %" + "pyFAI/ocl_azim_csr", "240", "184", "76.7 %" + "pyFAI/ocl_azim_lut", "230", "177", "77.0 %" + "pyFAI/ocl_sort", "177", "136", "76.8 %" + "pyFAI/opencl", "204", "144", "70.6 %" + "pyFAI/peak_picker", "756", "195", "25.8 %" + "pyFAI/spline", "432", "165", "38.2 %" + "pyFAI/units", "70", "65", "92.9 %" + "pyFAI/utils", "778", "431", "55.4 %" + "pyFAI/worker", "340", "131", "38.5 %" + + "pyFAI total", "10347", "6151", "59.4 %" diff --git a/doc/build/_sources/design/ai.txt b/doc/build/_sources/design/ai.txt new file mode 100644 index 000000000..6cdf67706 --- /dev/null +++ b/doc/build/_sources/design/ai.txt @@ -0,0 +1,154 @@ +Design of the Python Fast Azimuthal Integrator +============================================== + +Author: Jérôme Kieffer + +Date: 20/03/2015 + +Keywords: Design + +Target: Developers interested in using the library + +Reference: API documentation + +Abstract +-------- + +The core part of pyFAI is the AzimuthalIntegator objects, named +*ai* hereafter. +This document describes the two importants methods of the class, +how it is related to Detector, Geometry, and integration engines. + +One of the core idea is to have a complete representation of the geometry +and perform the azimuthal integration as a single geometrical re-binning +which take into account all effects like: + +* Detector distortion +* Polar transformation +* assignment to the output space + + +This document focuses on the core of pyFAI while peripheral code +dealing with graphical user interfaces, image analysis online data +analysis integration are not covered. + +AzimuthalIntegrator +------------------- + +This class is the core of pyFAI, and it is the only one likely to be used by +external developers/users. It is usually instantiated via a function of the +module to load a poni-file: + +.. code-block:: python + >>> import pyFAI + >>> ai = pyFAI.load("Pilatus1M.poni") + >>> print(ai) + + Detector Detector Spline= None PixelSize= 1.720e-04, 1.720e-04 m + + SampleDetDist= 1.583231e+00m PONI= 3.341702e-02, 4.122778e-02m rot1=0.006487 rot2= 0.007558 rot3= 0.000000 rad + + DirectBeamDist= 1583.310mm Center: x=179.981, y=263.859 pix Tilt=0.571 deg tiltPlanRotation= 130.640 deg + +As one can see, the *ai* contains the detector geometry (type, pixel size, +distortion) as well as the +geometry of the experimental setup. The geometry is given in two equivalent +forms: the internal representation of pyFAI (second line) and the one used by +FIT2D. + +The *ai* is responsible for azimuthal integration, either the integration along +complete ring, called full-integration, obtained via *ai.integrate1d* method. +The sector-wise integration is obtained via the *ai.integrate2d* method. +The options for those two methods are really similar and differ only by the +parameters related to the azimuthal dimension of the averaging for *ai.integrate2d*. + +Azimuthal integration methods +_____________________________ + +Both integration method take as first argument the image coming from the detector +as a numpy array. This is the only mandatory parameter. + +Important parameters are the number of bins in radial and azimuthal dimensions. +Other parameters are the pre-processing information like dark and flat pixel wise +correction (as array), the polarization factor and the solid-angle correction to +be applied. + +Because multiple radial output space are possible (q, r, 2\theta) each with multiple +units, if one wants to avoid interpolation, it is important to export directly the data +in the destination space, specifying the unit="2th_deg" or "q_nm^-1" + +Many more option exists, please refer to the documentation of AzimuthalIntegration integrate_ + +The AzimuthalIntegration class inherits from the Geometry class and hold +references to configured rebinning engines. + +Geometry +-------- +The Geometry class contains a reference to the detector (composition) +and the logic to calculate the position in space of the various pixels. +All arrays in the class are cached and calculated on demand. + +The Geometry class relies on the detector to provide the pixel position in space +and subsequently transforms it in 2\theta coordinates, or q, \chi, r ... +This can either be performed in the class itself or by calling +function in the parallel implemented Cython module _geometry. +Those transformation could be GPU-ized in the future. + +Detector +-------- +PyFAI deals only with area detector, indexed in 2 dimension but can +handle pixel located in a 3D space. + +The *pyFAI.detectors* module contains the master *Detector* class +which is capable of describing any detector. +About 40 types of detectors, inheriting and specializing the *Detector* +class are provided, offering convienient access to most commercial detectors. +A factory is provided to easily instantiate a detector from its name. + +A detector class is responsible for two main tasks: + +- provide the coordinate in space of any pixel position (center, corner, ...) +- Handle the mask: some detector feature automatic mask calculation (i.e. module based detectors). + +The disortion of the detector is handled here and could be GPU-ized in the future. + +Rebinning engines +----------------- + +Once the geometry (radial and azimuthal coordinates) calculated for every pixel +on the detector, the image from the detector is rebinned into the output space. +Two types of rebinning engines exists: + +Histograms + They take each single pixel from the image and transfer it to the destination bin, like histograms do. + This family of algorithms is rather easy to implement and provides good single threaded performances, + but it is hard to parallelize (efficiently) due to the need of atomic operations. + +Sparse matrix multiplication + By recording where every single ends one can transform the previous histogram into a + large sparse matrix multiplication which is either stored as a Look-Up Table (actually an array of struct, also called LIL) + or more efficiently in the CSR_ format. + Those rebinning engines are trivially parallel and provide the best performances. + +Pixel splitting +--------------- + +Three levels of pixel splitting schemes are available within pyFAI: + +No splitting + The whole intensity is assigned to the center of the pixel and rebinned using a simple histogram + +Bounding box pixel splitting + The pixel is abstracted by a box surrounding it with, making calculation easier but blurring a bit the image + +Tight pixel splitting + The pixel is represented by its actual corner position, offering a very precise positionning in space. + +The main issue with pixel splitting arose from 2D integration and the habdling of pixel laying on the chi-discontinuity. + +References: +----------- + +:: _integrate: http://pythonhosted.org/pyFAI/api/pyFAI.html#pyFAI.azimuthalIntegrator.AzimuthalIntegrator.integrate1d + +:: _CSR: http://en.wikipedia.org/wiki/Sparse_matrix \ No newline at end of file diff --git a/doc/build/_sources/design/index.txt b/doc/build/_sources/design/index.txt new file mode 100644 index 000000000..7a75b3b1d --- /dev/null +++ b/doc/build/_sources/design/index.txt @@ -0,0 +1,17 @@ +Design of the Python Fast Azimuthal Integration library +======================================================= + +Author: Jérôme Kieffer + +Date: 18/12/2014 + +Keywords: Design + +Target: Developers interested in using the library + +Reference: API documentation + +.. toctree:: + :maxdepth: 2 + + ai diff --git a/doc/build/_sources/detector.txt b/doc/build/_sources/detector.txt new file mode 100644 index 000000000..189abdaf5 --- /dev/null +++ b/doc/build/_sources/detector.txt @@ -0,0 +1,108 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2016 +:Keywords: detector +:Target: General audiance +Simple detector +=============== + +Like most other diffraction processing packages, pyFAI allows the definition of +2D detectors with a constant pixel size and recoded in S.I.. +Typical pixel size are 50e-6 m (50 microns) and will be used as example in the +numerical application. + +Pixels of the detector are indexed from the *origin* located at the **lower left corner**. +The pixel's center is located at half integer index: + +* pixel 0 goes from position 0 m to 50e-6m and is centered at 25e-6 m. +* pixel 1 goes from position 50e-6 m to 100e-6 m and is centered at 75e-6 m + +**Nota**: +Most of the time you will need to pass the optional argument *origin="lower"* to +matplotlib imshow function when displaying the image to avoid confusion + +Complex detectors +================= + +The *simple detector* approach reaches its limits +with several detector types, such as multi-module and fiber optic taper coupled +detectors (most CCDs). +Large area pixel detectors are often composed of smaller modules (i.e. Pilatus +from Dectris, Maxipix from ESRF, ...). + +By construction, such detectors exhibit gaps between modules along with +pixels of various sizes within a single module, hence they require specific +data masks. +Optically coupled detectors need also to be corrected +for small spatial displacements, often called geometric distortion. +This is why detectors need more complex definitions than just that of a pixel +size. +To avoid complicated and error-prone sets of parameters, two tools have been introduced: +either *detector* classes define programatically detector or Nexus saved detector setup. + +Detectors classes +----------------- +They are used to define families of detectors. +In order to take the specificities of each detector into account, pyFAI +contains about 55 detector class definitions (and twice a much with aliases) +which contain a mask (invalid pixels, +gaps, ...) and a method to calculate the pixel positions in Cartesian +coordinates. Available detectors can be printed using: + +.. code-block:: python + + import pyFAI + print(pyFAI.detectors.ALL_DETECTORS) + +For optically coupled CCD detectors, the geometrical distortion is often +described by a bi-dimensional cubic spline which can be imported into +the detector instance and be used to calculate the actual pixel position in space. + +Nexus Detectors +--------------- + +Any detector object in pyFAI, can be saved into a HDF5 file following the NeXus +convention (http://nexusformat.org). +Detector objects can subsequently be restored from the disk, making +complex detector definitions less error-prone. +Pixels of an area detector are saved as a 4D dataset: i.e. a 2D +array of vertices pointing to every corner of each pixel, generating +an array of shape: (*Ny*, *Nx*, *Nc*, 3) where *Nx* and *Ny* are the dimensions of the +detector, *Nc* is the number of corners of each pixel, usually 4, and the last +entry contains the coordinates of the vertex itself (z,y,x). +This kind of definitions, while relying on large description files, +can address some of the most complex detector layouts: + +* hexagonal pixels (i.e. Pixirad detectors, still under development) +* curved/bent imaging plates (i.e. Rigaku, Aarhus detector) +* pixel detectors with tiled modular (i.e. Xpad detectors from ImXpad) +* semi-cylindrical pixel detectors (i.e. Pilatus12M from Dectris). + +The detector instance can be saved as HDF5, either programmatically, either +on the command line. + +.. code-block:: python + + from pyFAI import detectors + frelon = detectors.FReLoN("halfccd.spline") + print(frelon) + frelon.save("halfccd.h5") + +Using the *detector2nexus* script to convert a complex detector definition +(multiple modules, possibly in 3D) into +a single NeXus detector definition together with the mask: + + detector2nexus -s halfccd.spline -o halfccd.h5 + +Conclusion +========== + +Detector definition in pyFAI is very versatile. +Fortunately, most detectors are already described, making the usage transparent +for most users. +There are a couple of :ref:`tutorials` on detector definition which will help +you understanding the mechanisme : + +* Distortion which explains how to correct images for geometric distortion +* CCD-calibration which explains how to calibrate a detector for geometric + distortion. + diff --git a/doc/build/_sources/ecosystem.txt b/doc/build/_sources/ecosystem.txt new file mode 100644 index 000000000..10bd14c93 --- /dev/null +++ b/doc/build/_sources/ecosystem.txt @@ -0,0 +1,168 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2015 +:Keywords: Other software related to pyFAI + +PyFAI Ecosystem +=============== + +Software pyFAI is relying on +---------------------------- + +PyFAI is relying on the full Python scientific stack which includes [NumPy]_, +[SciPy]_, [Matplotlib]_, [PyOpenCL]_ but also on some ESRF-developped code: + +FabIO +..... + +PyFAI is using FabIO everywhere access to a 2D images is needed. +The *fabio_viewer* is also a lightweight convenient viewer for diffraction images. +It has been described in `doi:10.1107/S0021889813000150 `_ + +PyMca +..... + +The X-ray Fluorescence Toolkit provides convenient tools for +HDF5 file browsing and mask drawing. +It has been described in `doi:10.1016/j.sab.2006.12.002 `_ + +Silx +.... + +`The silx toolkit `_ is currently onging development. +Future releases of pyFAI will use its input/output and graphical visualization capabilities + +.. _ecosystem: + +Program using pyFAI as a library +-------------------------------- + +Bubble +...... +Client-server program to perform azimuthal integration online. +Developed for the SNBL and Dubble beamlines by Vadim DIADKIN and available from this `mercurial repository `_. + +Dahu +.... + +Dahu is a lightweight plugin based framework available from this +`git repository `_. +Lighter then EDNA, it is technically a JSON-RPC server over Tango. +Used on TRUSAXS beamline at ESRF (ID02), ID15 and ID31, +dahu uses pyFAI to process data up to the kHz range. + +Dioptas +....... + +Graphical user interface for high-pressure diffraction, developed at the +APS synchrotron by C. Prescher and described in: +`doi:10.1080/08957959.2015.1059835 `_ + +The amount of data collected during synchrotron X-ray diffraction (XRD) +experiments is constantly increasing. Most of the time, the data are +collected with image detectors, which necessitates the use of image +reduction/integration routines to extract structural information from measured XRD patterns. +This step turns out to be a bottleneck in the data processing procedure due to a lack of suitable software packages. +In particular, fast-running synchrotron experiments require online data reduction and analysis +in real time so that experimental parameters can be adjusted interactively. +Dioptas is a Python-based program for on-the-fly data processing and exploration of two-dimensional +X-ray diffraction area detector data, specifically designed for the large amount of data collected at +XRD beamlines at synchrotrons. Its fast data reduction algorithm and graphical data exploration capabilities +make it ideal for online data processing during XRD experiments and batch post-processing of large numbers of images. + +Dpdak +..... + +Graphical user interface for small angle diffusion, developed at the +Petra III synchrotron by G. Benecke and co-workers and described in +`doi:10.1107/S1600576714019773 `_ + +X-ray scattering experiments at synchrotron sources are characterized by large and constantly increasing amounts of data. +The great number of files generated during a synchrotron experiment is often a limiting factor in the analysis of the data, +since appropriate software is rarely available to perform fast and tailored data processing. +Furthermore, it is often necessary to perform online data reduction and analysis during the experiment in order +to interactively optimize experimental design. +This article presents an open-source software package developed to process +large amounts of data from synchrotron scattering experiments. +These data reduction processes involve calibration and correction of raw data, +one- or two-dimensional integration, as well as fitting and further analysis of the data, +including the extraction of certain parameters. +The software, DPDAK (directly programmable data analysis kit), is based on +a plug-in structure and allows individual extension in accordance with the +requirements of the user. +The article demonstrates the use of DPDAK for on- and offline analysis of +scanning small-angle X-ray scattering (SAXS) data on biological samples and +microfluidic systems, as well as for a comprehensive analysis of +grazing-incidence SAXS data. +In addition to a comparison with existing software packages, +the structure of DPDAK and the possibilities and limitations are discussed. + +EDNA +.... + +EDNA is a framework for developing plugin-based applications especially +for online data analysis in the X-ray experiments field (http://edna-site.org) +A EDNA data analysis server is using pyFAI as an integration engine (on the GPU) +on the ESRF BioSaxs beamline, BM29. +The server is running 24x7 with a processing frequency from 0.1 to 10 Hz. + +LImA +.... +The `Library for Image Acquisition `_, +developped at the European synchrotron is used worldwide to control any types of +cameras. +A pyFAI plugin has been written to integrate images on the fly without saving them. +(no more tested). + + +NanoPeakCell +............ +NanoPeakCell (NPC) is a python-software intended to pre-process your serial +crystallography raw-data into ready-to-be-inedexed images with CrystFEL, +cctbx.xfel and nXDS. +NPC is able to process data recorded at SACLA and LCLS XFELS, as well as data +recorded at any synchrotron beamline. +A graphical interface is deployed to visualize your raw and pre-processed data. + +Developed at `IBS (Grenoble) by N. Coquelle `_ + +pygix +..... + +A Python library for reduction of 2D grazing-incidence X-ray scattering +data developped at ESRF (ID13) by Thomas DANE. + +Grazing-incidence X-ray scattering techniques (GISAXS, GIWAXS/GID) +allow the study of thin films on surfaces that would otherwise be +unmeasurable in standard transmission geometry experiments. The fixed +incident X-ray angle gives rise to a distortion in the diffraction +patterns, which is extreme at wide-angles. The pygix library provides +routines for projecting 2D detector images into corrected reciprocal +space maps, radial transformations and line profile extraction using +pyFAI's regrouping functions. + + +PySAXS +...... +Python for Small Angle X-ray Scattering data acquisition, treatment and computation +of model SAXS intensities. + +Developed at CEA Saclay by O. Taché and available on `PyPI `_. + +xPDFsuite +......... + +Developed by the Billinge Group, this commercial software is described in +`arXiv 1402.3163 (2014) `_ + +xPDFsuite is an application that facilitates the workflow of atomic pair +distribution function analysis of x-ray diffraction measurements from +complex materials. It is specially designed to help the scientist +visualize, handle and process large numbers of datasets that is common +when working with high throughput modern synchrotron sources. It has a +full-featured interactive graphical user interface (GUI) with 3D and 3D +graphics for plotting data and it incorporates a number of powerful +packages for integrating 2D powder diffraction images, analyzing the +curves to obtain PDFs and then tools for assessing the data and modeling +it. It is available from `diffpy.org `_. + + diff --git a/doc/build/_sources/geometry.txt b/doc/build/_sources/geometry.txt new file mode 100644 index 000000000..e916c8791 --- /dev/null +++ b/doc/build/_sources/geometry.txt @@ -0,0 +1,108 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2016 +:Keywords: generic description of the geometry +:Target: General audiance + +PyFAI uses a 6-parameter geometry definition similar, while not rigorously +identical to SPD: +One distance, 2 coordinates to define the point of normal incidence and 3 rotations +around the main axis; these parameters are saved in text files usually +with the *.poni* extension. +In addition, the *poni-file* may contain the wavelength and the detector definition. + +Image representation in Python +------------------------------ + +PyFAI takes diffraction images as 2D numpy arrays, those are usually read +using the FabIO library: + +.. code-block:: python + + import fabio + data = fabio.open("image.edf").data + +But data can also be extracted from HDF5 files with h5py and displayed using matplotlib: + +.. code-block:: python + + %pylab + imshow(data, origin="lower") + +Because Python is written in C language, data are stored lines by lines, this means to go +from a pixel to the on its right, one offets the position by the pixel width. +To go the pixel above the current one, on needs to offset by the length of the line. +This is why, if one considers the pixel at position (x,y), its value can be retrieved by data[y,x] +(note the order y,x, this is not a bug!). +We usually refer the *x* axis as the fast dimension (because pixels are adjacent) and the *y* axis +as the slow axis (as pixel are appart from each other by a line length). +More information on how `numpy array are stored can be found at here `_ + +Like most scientific packages, the origin of the image is considered +to be at the **lower-left corner** of the image to have the polar angle growing +from 0 along the x axis to 90 deg along the y axis. +This is why we pass the *origin="lower"* option to *imshow* (from the matplotlib library). +Axis 1 and 2 on the image (like in poni1 & poni2) +refer to the slow and fast dimension of the image, so usually to the y and x axis +(and not the opposite) + +Position of the observer +------------------------ + +There are two (main) conventions when representing images: + +* In imaging application, one can can replace the camera by the eye, the camera looks at the scene. In this convention, the origin is usually at the top of the image. +* In diffraction application, the observer is situated at the sample position and looks + at the detector, hence on the other side of the detector. + +Because we measure (signed) angles, the origin is ideally situated at the lower left of the image. +PyFAI being a diffraction application, it uses the later description. +Changing the point of view behind the detector changes the sign of the azimuthal angle. + +Default geometry in pyFAI +------------------------- + +In the (most common) case of *transmission diffraction setup* on synchrotrons (like ESRF, Soleil, Petra3, SLS...) this makes looks like: + +* Observer looking at the detector from the sample position: +* Origin at the lower left of the detector +* Axis 1 (i.e. *y*) being vertical, pointing upwards +* Axis 2 (i.e. *x*) being horizontal, pointing to the center of the storage ring +* Axis 3 (i.e. *z*) being horizontal, along the transmitted beam + +Axis 3 is built in such a way to be orthogonal and (1,2,3) is a direct orientation. +This makes the sample position negative (at z<0). + + +Detector position +----------------- + +In pyFAI, the experiment geometry is defined by the position of the detector in +space, the origin being located at the sample position, more precisely where the +X-ray beam crosses the diffractometer main axis. + +.. figure:: img/PONI.png + :align: center + :alt: The geometry used by pyFAI is inspired by SPD + +With the detector being a rigid body, its position in space is described by +six parameters: 3 translations and 3 rotations. +In pyFAI, the beam center is not directly used as it is ill-defined with +highly tilted detectors. +Like SPD, we use the orthogonal projection of origin on +the detector surface called PONI (for Point Of Normal Incidence). +For non planar detectors, the PONI is defined in the plan z=0 in detector's +coordinate system. + +Poni1 and Poni2 are distances in meter (along the *y* and *x* axis of the detector), +like the sample-detector distance, letting the calibration parameters be +independent of the pixel size hence stable regarding the binning factor of the detector. + +In the same idea rot1, rot2 and rot3 are rotation along axis 1, 2 and 3, always expressed in radians. +Rotations applied in the same order: *rot1* then *rot2* and finally *rot3*. +Due to the axial symmetry of the Debye-Scherrer cones, *rot3* cannot be optimized but can be adjusted manually in some cases like if +the detector is not mounted horizontally and/or one cares about polarization correction. + +When all rotations are zero, the detector is in transmission mode with the +incident beam orthogonal to the detector's surface. + + diff --git a/doc/build/_sources/index.txt b/doc/build/_sources/index.txt new file mode 100644 index 000000000..32ee96ad5 --- /dev/null +++ b/doc/build/_sources/index.txt @@ -0,0 +1,68 @@ +.. pyFAI documentation master file, created by + sphinx-quickstart on Mon Nov 19 13:19:53 2012. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Fast Azimuthal Integration using Python +======================================= + +PyFAI is a python libary for azimuthal integration of diffraction data acquired +with 2D detectors, providing high performance thanks to GPU computing. +Most concepts have been presented at EuroScipy 2014 in this +`video `_ +as well as the `proceedings `_. + +The documentation starts with a general descriptions of the pyFAI library. +This first chapter contains an introduction to pyFAI, what it is, what it aims at +and how it works (from the scientists point of view). +Especially, geometry, calibration, azimuthal integration algorithms are described +and pixel splitting schemes are explained. + +Follows cookbook and tutorials on how to use pyFAI: +Cookbooks focus on how to use pyFAI using the various graphical interfaces or +from scripts on the command linee. +Tutorials use the *Jupyter* notebook (formerly ipython) and present the Python interface. +The first tutorial start with a quick general introduction of the notebooks, +how to process data in the general case, subsequent tutorials are more advanced +and require already a good knowledge both in Python and pyFAI.. +After the tutorials, all manual pages of pyFAI programs, both graphical interfaces +and scripts are described in the documentation. + +The design of the programming interface (API) is then exposed before a +comprehensive description of most modules contained in pyFAI. +Some minor submodules as well as the documentation of the Cython sub-modules are +not included for concision purposes. + +Installation procedures for Windows, MacOSX and Linux operating systems are then +described. + +Finally other programs/projects relying on pyFAI are presented and the project is +summarized from a developer's point of view. + +In appendix there are some figures about the project and its management and a list +of publication on pyFAI. + +.. toctree:: + :maxdepth: 1 + + + pyFAI + usage/cookbook/index + usage/tutorial/index + man/scripts + design/index + api/modules + operations/index + ecosystem + project + changelog + publications + biblio + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + diff --git a/doc/build/_sources/man/MX-calibrate.txt b/doc/build/_sources/man/MX-calibrate.txt new file mode 100644 index 000000000..1b23ed848 --- /dev/null +++ b/doc/build/_sources/man/MX-calibrate.txt @@ -0,0 +1,114 @@ +Calibration tool: MX-calibrate +============================== + +Purpose +------- + +Calibrate automatically a set of frames taken at various sample-detector distance. + +This tool has been developed for ESRF MX-beamlines where an acceptable calibration is +usually present is the header of the image. PyFAI reads it and does a "recalib" on +each of them before exporting a linear regression of all parameters versus this distance. + +Most standard calibrants are directly installed together with pyFAI. +If you prefer using your own, you can provide a "d-spacing" file +containing the spacing of Miller plans in Angstrom (in decreasing order). +Most crystal powders used for calibration are available in the American Mineralogist +database [AMD]_ or in the [COD]_. + + +Usage: +------ + +MX-Calibrate -w 1.54 -c CeO2 file1.cbf file2.cbf ... + +Options: +-------- +usage: MX-Calibrate -w 1.54 -c CeO2 file1.cbf file2.cbf ... + +Calibrate automatically a set of frames taken at various sample-detector +distance. Return the linear regression of the fit in funtion of the sample- +setector distance. + +positional arguments: + FILE List of files to calibrate + +optional arguments: + -h, --help show this help message and exit + -V, --version show program's version number and exit + -v, --verbose switch to debug/verbose mode + -c FILE, --calibrant FILE + file containing d-spacing of the calibrant reference + sample (MANDATORY) + -w WAVELENGTH, --wavelength WAVELENGTH + wavelength of the X-Ray beam in Angstrom + -e ENERGY, --energy ENERGY + energy of the X-Ray beam in keV (hc=12.398419292keV.A) + -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR + polarization factor, from -1 (vertical) to +1 + (horizontal), default is 0, synchrotrons are around + 0.95 + -b BACKGROUND, --background BACKGROUND + Automatic background subtraction if no value are + provided + -d DARK, --dark DARK list of dark images to average and subtract + -f FLAT, --flat FLAT list of flat images to average and divide + -s SPLINE, --spline SPLINE + spline file describing the detector distortion + -p PIXEL, --pixel PIXEL + size of the pixel in micron + -D DETECTOR_NAME, --detector DETECTOR_NAME + Detector name (instead of pixel size+spline) + -m MASK, --mask MASK file containing the mask (for image reconstruction) + --filter FILTER select the filter, either mean(default), max or median + --saturation SATURATION + consider all pixel>max*(1-saturation) as saturated and + reconstruct them + -r MAX_RINGS, --ring MAX_RINGS + maximum number of rings to extract + --weighted weight fit by intensity + -l DISTANCE, --distance DISTANCE + sample-detector distance in millimeter + --tilt Allow initially detector tilt to be refined (rot1, + rot2, rot3). Default: Activated + --no-tilt Deactivated tilt refinement and set all rotation to 0 + --dist DIST sample-detector distance in meter + --poni1 PONI1 poni1 coordinate in meter + --poni2 PONI2 poni2 coordinate in meter + --rot1 ROT1 rot1 in radians + --rot2 ROT2 rot2 in radians + --rot3 ROT3 rot3 in radians + --fix-dist fix the distance parameter + --free-dist free the distance parameter + --fix-poni1 fix the poni1 parameter + --free-poni1 free the poni1 parameter + --fix-poni2 fix the poni2 parameter + --free-poni2 free the poni2 parameter + --fix-rot1 fix the rot1 parameter + --free-rot1 free the rot1 parameter + --fix-rot2 fix the rot2 parameter + --free-rot2 free the rot2 parameter + --fix-rot3 fix the rot3 parameter + --free-rot3 free the rot3 parameter + --fix-wavelength fix the wavelength parameter + --free-wavelength free the wavelength parameter + --no-gui force the program to run without a Graphical interface + --gui force the program to run with a Graphical interface + --no-interactive force the program to run and exit without prompting + for refinements + --interactive force the program to prompt for refinements + --peak-picker PEAKPICKER + Uses the 'massif', 'blob' or 'watershed' peak-picker + algorithm (default: blob) + +This tool has been developed for ESRF MX-beamlines where an acceptable +calibration is usually present is the header of the image. PyFAI reads it and +does a "recalib" on each of them before exporting a linear regression of all +parameters versus this distance. + +Example: +-------- + + +.. command-output:: MX-calibrate --help + :nostderr: diff --git a/doc/build/_sources/man/check_calib.txt b/doc/build/_sources/man/check_calib.txt new file mode 100644 index 000000000..84379e57f --- /dev/null +++ b/doc/build/_sources/man/check_calib.txt @@ -0,0 +1,13 @@ +Calibration tool: check_calib +============================= + +Purpose +------- + +Check_calib is a deprecated tool aiming at validating both the geometric +calibration and everything else like flat-field correction, distortion +correction, at a sub-pixel level. Pleas use the `validate`, `validate2` and +`chiplot` commands in pyFAI-calib, during the refinement process to obtain the same output with more options. + +.. command-output:: check_calib --help + :nostderr: diff --git a/doc/build/_sources/man/detector2nexus.txt b/doc/build/_sources/man/detector2nexus.txt new file mode 100644 index 000000000..173cdda93 --- /dev/null +++ b/doc/build/_sources/man/detector2nexus.txt @@ -0,0 +1,45 @@ +Preprocessing tool: detector2nexus +================================== + +Convert a complex detector definition (multiple modules, possibly in 3D) into +a single NeXus detector definition together with the mask (and much more in +the future) + + +Purpose +------- + +Convert a detector to NeXus detector definition for pyFAI. + +Usage: +------ + +detector2nexus [options] [options] -o nxs.h5 + + +options: +-------- + + -h, --help show this help message and exit + -V, --version show program's version number and exit + -o OUTPUT, --output OUTPUT + Output nexus file, unless detector_name.h5 + -n NAME, --name NAME name of the detector + -m MASK, --mask MASK mask corresponding to the detector + -D DETECTOR, --detector DETECTOR + Base detector name (see documentation of + pyFAI.detectors + -s SPLINEFILE, --splinefile SPLINEFILE + Geometric distortion file from FIT2D + -dx DX, --x-corr DX Geometric correction for pilatus + -dy DY, --y-corr DY Geometric correction for pilatus + -p PIXEL, --pixel PIXEL + pixel size (comma separated): x,y + -S SHAPE, --shape SHAPE + shape of the detector (comma separated): x,y + -d DARK, --dark DARK Dark noise to be subtracted + -f FLAT, --flat FLAT Flat field correction + -v, --verbose switch to verbose/debug mode + +.. command-output:: detector2nexus --help + :nostderr: \ No newline at end of file diff --git a/doc/build/_sources/man/diff_map.txt b/doc/build/_sources/man/diff_map.txt new file mode 100644 index 000000000..2ce9fd016 --- /dev/null +++ b/doc/build/_sources/man/diff_map.txt @@ -0,0 +1,100 @@ +Integration tool: diff_map +========================== + +Purpose +------- + +Azimuthal integration for diffraction imaging + +Diffraction mapping is an imaging experiment where 2D diffraction patterns are +recorded while performing a 2D scan along two axes, one slower and the other fastest. + +Diff_map provides a Graphical User Interface (based on top of PyQt, pyFAI and h5py) +which allows the reduction of this 4D dataset into a 3D dataset containing the +two dimension of movement and the many diffraction angles (the output can be q-space for PDF-CT). + +.. figure:: diffmap_main.png + :align: center + :alt: image + +On the left-hand side, the user can select a bunch of files to be processed. +For now, any image format supported by FabIO is possible, including multi-frame +images, but not ye NeXus files (work ongoing). + +On the right-hand side, the motor range can be specified togeather with their +names. The diffraction parameters can be setup in a similar way to *pyFAI-integrate*. +The output name can also be specified. + +.. figure:: diffmap_integrate.png + :align: center + :alt: image + +The processing is launched using the *Run* button which opens a matplotlib figure +to display the plot of the diffraction pattern and of the map image under +construction. +During this processing, one can select a scatering range to highlight the regions +of interest. + +.. figure:: diffmap_running.png + :align: center + :alt: image + +Further analysis can be performed on the resulting dataset using the PyMca roitool +where the 1d dataset has to be selected as last dimension. +The result file aims at NeXus compliance. + +This tool can be used for tomography experiments if one considers the slow scan +direction as the rotation. + +The user interface can be disabled to allow scripting interface and providing a +JSON configuration file (or all the options). + +Usage: +------ + +diff_map [options] imagefiles* + +positional arguments: + FILE List of files to integrate + +optional arguments: + -h, --help show this help message and exit + -V, --version show program's version number and exit + -o FILE, --output FILE + HDF5 File where processed map will be saved + -v, --verbose switch to verbose/debug mode, defaut: quiet + -P FILE, --prefix FILE + Prefix or common base for all files + -e EXTENSION, --extension EXTENSION + Process all files with this extension + -t FAST, --fast FAST number of points for the fast motion. Mandatory + without GUI + -r SLOW, --slow SLOW number of points for slow motion. Mandatory without + GUI + -c NPT_RAD, --npt NPT_RAD + number of points in diffraction powder pattern, + Mandatory without GUI + -d FILE, --dark FILE list of dark images to average and subtract + -f FILE, --flat FILE list of flat images to average and divide + -m FILE, --mask FILE file containing the mask, no mask by default + -p FILE, --poni FILE file containing the diffraction parameter (poni-file), + Mandatory without GUI + -O OFFSET, --offset OFFSET + do not process the first files + -g, --gpu process using OpenCL on GPU + -S, --stats show statistics at the end + --gui Use the Graphical User Interface + --no-gui Do not use the Graphical User Interface + --config CONFIG provide a JSON configuration file + +Bugs: +..... +#. If the number of files is too large, use double + quotes "*.edf" +#. There is a known bug on Debian7 where importing a large + number of file can take much longer than the integration itself: consider + passing files in the command line + + +.. command-output:: diff_map --help + :nostderr: diff --git a/doc/build/_sources/man/diff_tomo.txt b/doc/build/_sources/man/diff_tomo.txt new file mode 100644 index 000000000..1fb6030fa --- /dev/null +++ b/doc/build/_sources/man/diff_tomo.txt @@ -0,0 +1,63 @@ +Integration tool: diff_tomo +=========================== + +Purpose +------- + +Azimuthal integration for diffraction tomography. + +Diffraction tomography is an experiment where 2D diffraction patterns are recorded +while performing a 2D scan, one (the slowest) in rotation around the sample center +and the other (the fastest) along a translation through the sample. +Diff_tomo is a script (based on pyFAI and h5py) which allows the reduction of this +4D dataset into a 3D dataset containing the rotations angle (hundreds), the translation step (hundreds) +and the many diffraction angles (thousands). The resulting dataset can be opened using PyMca roitool +where the 1d dataset has to be selected as last dimension. This file is not (yet) NeXus compliant. + +This tool can be used for mapping experiments if one considers the slow scan +direction as the rotation, but a tool named *diff_map* operates in a similar way, + provides a Graphical interface and is more flexible + +tips: If the number of files is too large, use double quotes around "*.edf" + + +Usage: +------ + +diff_tomo [options] -p ponifile imagefiles* + +Options: +-------- + + -h, --help show this help message and exit + -V, --version show program's version number and exit + -o FILE, --output FILE + HDF5 File where processed sinogram was saved, by + default diff_tomo.h5 + -v, --verbose switch to verbose/debug mode, defaut: quiet + -P FILE, --prefix FILE + Prefix or common base for all files + -e EXTENSION, --extension EXTENSION + Process all files with this extension + -t NTRANS, --nTrans NTRANS + number of points in translation. Mandatory + -r NROT, --nRot NROT number of points in rotation. Mandatory + -c NDIFF, --nDiff NDIFF + number of points in diffraction powder pattern, + Mandatory + -d FILE, --dark FILE list of dark images to average and subtract + -f FILE, --flat FILE list of flat images to average and divide + -m FILE, --mask FILE file containing the mask, no mask by default + -p FILE, --poni FILE file containing the diffraction parameter (poni-file), + Mandatory + -O OFFSET, --offset OFFSET + do not process the first files + -g, --gpu process using OpenCL on GPU + -S, --stats show statistics at the end + + +Most of those options are mandatory to define the structure of the dataset. + + +.. command-output:: diff_tomo --help + :nostderr: diff --git a/doc/build/_sources/man/drawMask_pymca.txt b/doc/build/_sources/man/drawMask_pymca.txt new file mode 100644 index 000000000..d05b4aa66 --- /dev/null +++ b/doc/build/_sources/man/drawMask_pymca.txt @@ -0,0 +1,32 @@ +Mask generation tool: drawMask_pymca +==================================== + +Purpose +------- + +Draw a mask, i.e. an image containing the list of pixels which are considered invalid (no scintillator, module gap, beam stop shadow, ...). + +.. figure:: ../img/drawMask.png + :align: center + :alt: image + + +This will open a PyMca window and let you draw on the first image (provided) with different tools (brush, rectangle selection, ...). +When you are finished, come back to the console and press enter. +The mask image is saved into file1-masked.edf. +Optionally the script will print the number of pixel +masked and the intensity masked (as well on other files provided in input) + + +Usage: drawMask_pymca [options] file1.edf file2.edf ... + +Options: +-------- + + --version show program's version number and exit + -h, --help show help message and exit + +Optionally the script will print the number of pixel masked and the intensity masked (as well on other files provided in input) + +.. command-output:: drawMask_pymca --help + :nostderr: diff --git a/doc/build/_sources/man/pyFAI-average.txt b/doc/build/_sources/man/pyFAI-average.txt new file mode 100644 index 000000000..cac45efd0 --- /dev/null +++ b/doc/build/_sources/man/pyFAI-average.txt @@ -0,0 +1,44 @@ +Preprocessing tool: pyFAI-average +================================= + +Purpose +------- + +This tool is used to average out a set of dark current images using +mean or median filter (along the image stack). One can also reject outliers +be specifying a cutoff (remove cosmic rays / zingers from dark) + +It can also be used to merge many images from the same sample when using a small beam +and reduce the spotty-ness of Debye-Scherrer rings. In this case the "max-filter" is usually +recommended. + +Options: +-------- + +Usage: pyFAI-average [options] -o output.edf file1.edf file2.edf ... + +positional arguments: + FILE Files to be processed + +optional arguments: + -h, --help show this help message and exit + -V, --version show program's version number and exit + -o OUTPUT, --output OUTPUT + Output/ destination of average image + -m METHOD, --method METHOD + Method used for averaging, can be 'mean'(default) or + 'min', 'max', 'median', 'sum', 'quantiles' + -c CUTOFF, --cutoff CUTOFF + Take the mean of the average +/- cutoff * std_dev. + -F FORMAT, --format FORMAT + Output file/image format (by default EDF) + -d DARK, --dark DARK Dark noise to be subtracted + -f FLAT, --flat FLAT Flat field correction + -v, --verbose switch to verbose/debug mode + -q QUANTILES, --quantiles QUANTILES + average out between two quantiles -q 0.20-0.90 + + +.. command-output:: pyFAI-average --help + :nostderr: + diff --git a/doc/build/_sources/man/pyFAI-calib.txt b/doc/build/_sources/man/pyFAI-calib.txt new file mode 100644 index 000000000..e925535c3 --- /dev/null +++ b/doc/build/_sources/man/pyFAI-calib.txt @@ -0,0 +1,186 @@ +Calibration tool: pyFAI-calib +============================= + +Purpose +------- + +Calibrate the diffraction setup geometry based on Debye-Sherrer rings images +without a priori knowledge of your setup. +You will need to provide a calibrant or a "d-spacing" file containing the spacing of Miller plans in +Angstrom (in decreasing order). + +If you are using a standard calibrant, look at +https://github.com/kif/pyFAI/tree/master/calibration +or search in the American Mineralogist database: +[AMD]_ or in the [COD]_. +The --calibrant option is mandatory ! + +Calibrants available: Ni, CrOx, NaCl, Si_SRM640e, +Si_SRM640d, Si_SRM640a, Si_SRM640b, Cr2O3, AgBh, Si_SRM640, CuO, PBBA, +alpha_Al2O3, SI_SRM640c, quartz, C14H30O, cristobaltite, Si, LaB6, CeO2, +LaB6_SRM660a, LaB6_SRM660b, LaB6_SRM660c, TiO2, ZnO, Al, Au + +You will need in addition: + * The radiation energy (in keV) or its wavelength (in A) + * The description of the detector: + - it name or + - it's pixel size or + - the spline file describing its distortion or + - the NeXus file describing the distortion + +Many option are available among those: + * dark-current / flat field corrections + * Masking of bad regions + * reconstruction of missing region (module based detectors), see option -r + * Polarization correction + * Automatic desaturation (time consuming!) + * Intensity weighted least-squares refinements + +The output of this program is a "PONI" file containing the detector +description and the 6 refined parameters (distance, center, rotation) and +wavelength. An 1D and 2D diffraction patterns are also produced. (.dat and +.azim files) + + +Usage: +------ +pyFAI-calib [options] -w 1 -D detector -c calibrant imagefile.edf + + +Options: +-------- + + -h, --help show this help message and exit + -V, --version show program's version number and exit + -o FILE, --out FILE Filename where processed image is saved + -v, --verbose switch to debug/verbose mode + -c FILE, --calibrant FILE + Calibrant name or file containing d-spacing of the + reference sample (MANDATORY, case sensitive !) + -w WAVELENGTH, --wavelength WAVELENGTH + wavelength of the X-Ray beam in Angstrom. Mandatory + -e ENERGY, --energy ENERGY + energy of the X-Ray beam in keV + (hc=12.398419292keV.A). + -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR + polarization factor, from -1 (vertical) to +1 + (horizontal), default is None (no correction), + synchrotrons are around 0.95 + -i FILE, --poni FILE file containing the diffraction parameter (poni-file). + MANDATORY for pyFAI-recalib! + -b BACKGROUND, --background BACKGROUND + Automatic background subtraction if no value are + provided + -d DARK, --dark DARK list of comma separated dark images to average and + subtract + -f FLAT, --flat FLAT list of comma separated flat images to average and + divide + -s SPLINE, --spline SPLINE + spline file describing the detector distortion + -D DETECTOR_NAME, --detector DETECTOR_NAME + Detector name (instead of pixel size+spline) + -m MASK, --mask MASK file containing the mask (for image reconstruction) + -n NPT, --pt NPT file with datapoints saved. Default: basename.npt + --filter FILTER select the filter, either mean(default), max or median + -l DISTANCE, --distance DISTANCE + sample-detector distance in millimeter. Default: 100mm + --dist DIST sample-detector distance in meter. Default: 0.1m + --poni1 PONI1 poni1 coordinate in meter. Default: center of detector + --poni2 PONI2 poni2 coordinate in meter. Default: center of detector + --rot1 ROT1 rot1 in radians. default: 0 + --rot2 ROT2 rot2 in radians. default: 0 + --rot3 ROT3 rot3 in radians. default: 0 + --fix-dist fix the distance parameter + --free-dist free the distance parameter. Default: Activated + --fix-poni1 fix the poni1 parameter + --free-poni1 free the poni1 parameter. Default: Activated + --fix-poni2 fix the poni2 parameter + --free-poni2 free the poni2 parameter. Default: Activated + --fix-rot1 fix the rot1 parameter + --free-rot1 free the rot1 parameter. Default: Activated + --fix-rot2 fix the rot2 parameter + --free-rot2 free the rot2 parameter. Default: Activated + --fix-rot3 fix the rot3 parameter + --free-rot3 free the rot3 parameter. Default: Activated + --fix-wavelength fix the wavelength parameter. Default: Activated + --free-wavelength free the wavelength parameter. Default: Deactivated + --tilt Allow initially detector tilt to be refined (rot1, + rot2, rot3). Default: Activated + --no-tilt Deactivated tilt refinement and set all rotation to 0 + --saturation SATURATION + consider all pixel>max*(1-saturation) as saturated and + reconstruct them, default: 0 (deactivated) + --weighted weight fit by intensity, by default not. + --npt NPT_1D Number of point in 1D integrated pattern, Default: + 1024 + --npt-azim NPT_2D_AZIM + Number of azimuthal sectors in 2D integrated images. + Default: 360 + --npt-rad NPT_2D_RAD Number of radial bins in 2D integrated images. + Default: 400 + --unit UNIT Valid units for radial range: 2th_deg, 2th_rad, + q_nm^-1, q_A^-1, r_mm. Default: 2th_deg + --no-gui force the program to run without a Graphical interface + --no-interactive force the program to run and exit without prompting + for refinements + -r, --reconstruct Reconstruct image where data are masked or <0 (for + Pilatus detectors or detectors with modules) + -g GAUSSIAN, --gaussian GAUSSIAN + Size of the gaussian kernel. Size of the gap (in + pixels) between two consecutive rings, by default 100 + Increase the value if the arc is not complete; + decrease the value if arcs are mixed together. + --square Use square kernel shape for neighbor search instead of + diamond shape + -p PIXEL, --pixel PIXEL + size of the pixel in micron + + +Tips & Tricks +------------- + +PONI-files are ASCII files and each new refinement adds an entry in the file. +So if you are unhappy with the last step, just edit this file and remove the last +entry (time-stamps will help you). + + + +Example of usage: +----------------- + + +.. command-output:: pyFAI-calib --help + :nostderr: + +Pilatus 1M image of Silver Behenate taken at ESRF-BM26: +....................................................... + +:: + + pyFAI-calib -D Pilatus1M -c AgBh -r -w 1.0 test/testimages/Pilatus1M.edf + +We use the parameter -r to reconstruct the missing part between the modules of the +Pilatus detector. + + +Half a FReLoN CCD image of Lantanide hexaboride taken at ESRF-ID11: +................................................................... + +:: + + pyFAI-calib -s test/testimages/halfccd.spline -c LaB6 -w 0.3 test/testimages/halfccd.edf -g 250 + + +This image is rather spotty. We need to blur a lot to get the continuity of the rings. +This is achieved by the -g parameter. +While the sample is well diffracting and well known, the wavelength has been guessed. +One should refine the wavelength when the peaks extracted are correct + + +All those images are part of the test-suite of pyFAI. To download them from internet, run + +:: + + python setup.py build test + +Downloaded test images are located in tests/testimages diff --git a/doc/build/_sources/man/pyFAI-integrate.txt b/doc/build/_sources/man/pyFAI-integrate.txt new file mode 100644 index 000000000..5e0e901bf --- /dev/null +++ b/doc/build/_sources/man/pyFAI-integrate.txt @@ -0,0 +1,45 @@ +Integration tool: pyFAI-integrate +================================= + +Purpose +------- + +PyFAI-integrate is a graphical interface (based on Python/Qt4) to perform azimuthal integration +on a set of files. It exposes most of the important options available within pyFAI and allows you +to select a GPU (or an openCL platform) to perform the calculation on. + +.. figure:: ../img/integrate.png + :align: center + :alt: image + + +Usage +----- + +pyFAI-integrate [options] file1.edf file2.edf ... + +Options: +-------- + + --version show program's version number and exit + -h, --help show help message and exit + -v, --verbose switch to verbose/debug mode + -o OUTPUT, --output=OUTPUT + Directory or file where to store the output data + +Tips & Tricks: +-------------- + +PyFAI-integrate saves all parameters in a .azimint.json (hidden) file. This JSON file +is an ascii file which can be edited and used to configure online data analysis using +the LImA plugin of pyFAI. + +Nota: there is bug in debian6 making the GUI crash (to be fixed inside pyqt) +http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=697348 + +Example: +-------- + + +.. command-output:: pyFAI-integrate --help + :nostderr: diff --git a/doc/build/_sources/man/pyFAI-recalib.txt b/doc/build/_sources/man/pyFAI-recalib.txt new file mode 100644 index 000000000..856817074 --- /dev/null +++ b/doc/build/_sources/man/pyFAI-recalib.txt @@ -0,0 +1,7 @@ +Calibration tool: pyFAI-recalib +=============================== + +pyFAI-recalib is now obsolete. All feature provided by it are now available as part of pyFAI-calib. + +.. command-output:: pyFAI-recalib --help + :nostderr: \ No newline at end of file diff --git a/doc/build/_sources/man/pyFAI-saxs.txt b/doc/build/_sources/man/pyFAI-saxs.txt new file mode 100644 index 000000000..06798a28e --- /dev/null +++ b/doc/build/_sources/man/pyFAI-saxs.txt @@ -0,0 +1,52 @@ +Integration tool: pyFAI-saxs +============================ + +Purpose +------- + +Azimuthal integration for SAXS users. + +pyFAI-saxs is the SAXS script of pyFAI that allows data reduction (azimuthal integration) for +Small Angle Scattering with output axis in q space. + +Usage: +------ + +pyFAI-saxs -p=param.poni -w1.54 file.edf file2.edf file3.edf + + +Options: + -h, --help show this help message and exit + -v, --version show program's version number and exit + -p PONIFILE PyFAI parameter file (.poni) + -n NPT, --npt NPT Number of points in radial dimension + -w WAVELENGTH, --wavelength WAVELENGTH + wavelength of the X-Ray beam in Angstrom + -e ENERGY, --energy ENERGY + energy of the X-Ray beam in keV (hc=12.398419292keV.A) + -u DUMMY, --dummy DUMMY + dummy value for dead pixels + -U DELTA_DUMMY, --delta_dummy DELTA_DUMMY + delta dummy value + -m MASK, --mask MASK name of the file containing the mask image + -d DARK, --dark DARK name of the file containing the dark current + -f FLAT, --flat FLAT name of the file containing the flat field + -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR + Polarization factor, from -1 (vertical) to +1 + (horizontal), default is None for no correction, + synchrotrons are around 0.95 + --error-model ERROR_MODEL + Error model to use. Currently on 'poisson' is + implemented + --unit UNIT unit for the radial dimension: can be q_nm^-1, q_A^-1, + 2th_deg, 2th_rad or r_mm + --ext EXT extension of the regrouped filename (.dat) + --method METHOD Integration method + + +Example: +-------- + + +.. command-output:: pyFAI-saxs --help + :nostderr: diff --git a/doc/build/_sources/man/pyFAI-waxs.txt b/doc/build/_sources/man/pyFAI-waxs.txt new file mode 100644 index 000000000..64f40daa5 --- /dev/null +++ b/doc/build/_sources/man/pyFAI-waxs.txt @@ -0,0 +1,58 @@ +Integration tool: pyFAI-waxs +============================ + +Purpose +------- + +Azimuthal integration for powder diffraction. + +pyFAI-waxs is the script of pyFAI that allows data reduction (azimuthal integration) for +Wide Angle Scattering to produce X-Ray Powder Diffraction Pattern with output axis in 2-theta space. + +Usage: +------ +pyFAI-waxs -p param.poni [options] file1.edf file2.edf ... + +Options: +-------- + -h, --help show this help message and exit + -v, --version show program's version number and exit + -p PONIFILE PyFAI parameter file (.poni) + -n NPT, --npt NPT Number of points in radial dimension + -w WAVELENGTH, --wavelength WAVELENGTH + wavelength of the X-Ray beam in Angstrom + -e ENERGY, --energy ENERGY + energy of the X-Ray beam in keV (hc=12.398419292keV.A) + -u DUMMY, --dummy DUMMY + dummy value for dead pixels + -U DELTA_DUMMY, --delta_dummy DELTA_DUMMY + delta dummy value + -m MASK, --mask MASK name of the file containing the mask image + -d DARK, --dark DARK name of the file containing the dark current + -f FLAT, --flat FLAT name of the file containing the flat field + -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR + Polarization factor, from -1 (vertical) to +1 + (horizontal), default is None for no correction, + synchrotrons are around 0.95 + --error-model ERROR_MODEL + Error model to use. Currently on 'poisson' is + implemented + --unit UNIT unit for the radial dimension: can be q_nm^-1, q_A^-1, + 2th_deg, 2th_rad or r_mm + --ext EXT extension of the regrouped filename (.xy) + --method METHOD Integration method + --multi Average out all frame in a file before integrating + --average AVERAGE Method for averaging out: can be 'mean' (default), + 'min', 'max' or 'median + --do-2D Perform 2D integration in addition to 1D + + +pyFAI-waxs is the script of pyFAI that allows data reduction (azimuthal integration) for Wide Angle Scattering +to produce X-Ray Powder Diffraction Pattern with output axis in 2-theta space. + +Example: +-------- + + +.. command-output:: pyFAI-waxs --help + :nostderr: diff --git a/doc/build/_sources/man/scripts.txt b/doc/build/_sources/man/scripts.txt new file mode 100644 index 000000000..4262bed94 --- /dev/null +++ b/doc/build/_sources/man/scripts.txt @@ -0,0 +1,49 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2016 +:Keywords: manpage +:Target: Scientists + +.. _manpage: + +pyFAI scripts manual +==================== + +While pyFAI is first and foremost a Python library to be used by developers, a set of scripts is provided to process +a full diffraction experiment on the command line without knowing anything about Python. +Those scripts can be divided into 3 categories: pre-processing tools which prepare the dataset for the calibration tool. +The calibration is the determination of the geometry of the experimental setup using Debye-Scherrer rings of a reference compound (or calibrant). +Finally a full dataset can be integrated using different tools targeted at different experiments. + +Pre-processing tools: + * drawMask_pymca: tool for drawing a mask on top of an image + * pyFAI-average: tool for averaging/median/... filtering images (i.e. for dark current) + +Calibration tools: + * pyFAI-calib: manually select the rings and refine the geometry + * pyFAI-recalib: automatic ring extraction to refine the geometry (deprecated: see "recalib" option in pyFAI-calib) + * MX-calibrate: Calibrate automatically a set of images taken at various detector distances + * check_calib: checks the calibration of an image at the sub-pixel level (deprecated: see "validate" option in pyFAI-calib) + +Azimuthal integration tools: + * pyFAI-integrate: the graphical interface for integration (GUI) + * pyFAI-saxs: command line interface for small-angle scattering + * pyFAI-waxs: command line interface for powder difration + * diff_map: diffraction mapping & tomography tool (command line and GUI) + * diff_tomo: diffraction tomography tool (command line only) + +.. toctree:: + :maxdepth: 1 + + pyFAI-average + drawMask_pymca + detector2nexus + pyFAI-calib + pyFAI-recalib + check_calib + MX-calibrate + pyFAI-integrate + diff_map + diff_tomo + pyFAI-saxs + pyFAI-waxs + diff --git a/doc/build/_sources/operations/index.txt b/doc/build/_sources/operations/index.txt new file mode 100644 index 000000000..eb2c76624 --- /dev/null +++ b/doc/build/_sources/operations/index.txt @@ -0,0 +1,109 @@ +:Author: Jérôme Kieffer +:Date: 06/06/2016 +:Keywords: Installation procedure +:Target: System administrators + + +Installation of Python Fast Azimuthal Integration library +========================================================= + + +Abstract +-------- + +Installation procedure for all main operating systems + +Hardware requirement +-------------------- + +PyFAI has been tested on various hardware: i386, x86_64, PPC64le, ARM. +The main constrain may be the memory requirement: 2GB of memory is a minimal requirement to run the tests. +The program may run with less but "MemoryError" are expected (appearing sometimes as segmentation faults). +As a consequence, a 64-bits operating system with enough memory is strongly advised. + +Dependencies +------------ + +PyFAI is a Python library which relies on the scientific stack (numpy, scipy, matplotlib) + +* Python: version 2.7, 3.4 and 3.5. + Support for 2.6, 3.2 and 3.3 has been dropped in v0.12 +* NumPy: version 1.4 or newer +* SciPy: version 0.7 or newer +* Matplotlib: verson 0.99 or newer +* FabIO: version 0.08 or newer +* h5py (to access HDF5 files) + +There are plenty of optional dependencies which will not prevent pyFAI from working +by may impair performances or prevent tools from properly working: + + +* pyopencl (for GPU computing) +* fftw (for image analysis) +* pymca (for mask drawing) +* PyQt4 or PySide (for the graphical user interface) + +Build dependencies: +------------------- + +In addition to the run dependencies, pyFAI needs a C compiler to build extensions. + +C files are generated from cython_ source and distributed. +The distributed version correspond to OpenMP version. +Non-OpenMP version needs to be built from cython source code (especially on MacOSX). +If you want to generate your own C files, make sure your local Cython version +is sufficiently recent (>0.20). + +.. _cython: http://cython.org + + +Building procedure +------------------ + +.. code :: + + python setup.py build + pip install . --upgrade + +There are few specific options to ``setup.py``: + +* ``--no-cython``: Prevent Cython (even if present) to re-generate the C source code. Use the one provided by the development team. +* ``--no-openmp``: Recompiles the Cython code without OpenMP support (default under MacOSX). +* ``--openmp``: Recompiles the Cython code with OpenMP support (Default under Windows and Linux). +* ``--with-testimages``: build the source distribution including all test images. Download 200MB of test images to create a self consistent tar-ball. + +Detailed installation procedure on different operating systems +-------------------------------------------------------------- + +.. toctree:: + :maxdepth: 2 + + linux + macosx + windows + + +Test suites +----------- + +PyFAI comes with a test suite to ensure all core functionalities are working as expected and numerical results are correct: + +.. code:: + + python setup.py build test + +**Nota:** to run the test, an internet connection is needed as 200MB of test images need to be download. +You may have to set the environment variable *http_proxy* and *https_proxy* +according to the networking environment you are in. +Specifically at ESRF, please phone the hotline at 24-24 to get those information. + +Environment variables +--------------------- + +PyFAI can use a certain number of environment variable to modify its default behavior: + +* PYFAI_OPENCL: set to "0" to disable the use of OpenCL +* PYFAI_DATA: path with gui, calibrant, ... +* PYFAI_TESTIMAGES: path wit test images (if absent, they get downloaded from the internet) + + diff --git a/doc/build/_sources/operations/linux.txt b/doc/build/_sources/operations/linux.txt new file mode 100644 index 000000000..63111c642 --- /dev/null +++ b/doc/build/_sources/operations/linux.txt @@ -0,0 +1,118 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2015 +:Keywords: Installation procedure on Linux +:Target: System administrators + +Installation procedure on Linux +=============================== + +We cover first Debian-like distribution, then a generic recipie for all other +version is given. + +Installation procedure on Debian/Ubuntu +--------------------------------------- + +PyFAI has been designed and originally developed on Ubuntu 10.04 and debian6. +Now, the pyFAI library is included into debian7, 8 and any recent Ubuntu and +Mint distribution. +To install the package provided by the distribution, use: + +.. code:: + + sudo apt-get install pyfai + +The issue with distribution based installation is the obsolescence of the version +available. + +Debian7 and Ubuntu 12.04 +........................ + +To build a more recent version, pyFAI provides you a small scripts which builds a *debian* package and installs it. +It relies on *stdeb* and provides a single package with everything inside. +You will be prompted for your password to gain root access in order to be able to install the freshly built package. + +.. code:: + + sudo apt-get install python-stdeb cython python-fabio + wget https://github.com/pyFAI/pyFAI/archive/master.zip + unzip master.zip + cd pyFAI-master + ./build-deb7.sh + +Debian8 and newer +................. + +Thanks to the work of Frédéric-Emmanuel Picca, the debian package of pyFAI +provides a pretty good template which allows continuous builds. + +From silx repository +++++++++++++++++++++ + +You can automatically install the latest nightly built of pyFAI with: + +.. code:: + + wget http://www.silx.org/pub/debian/silx.list + wget http://www.silx.org/pub/debian/silx.pref + sudo mv silx.list /etc/apt/sources.list.d/ + sudo mv silx.pref /etc/apt/preferences.d/ + sudo apt-get update + sudo apt-get install pyfai + +**Nota:** The nightly built packages are not signed, hence you will be prompted +to install non-signed packages. + +Build from sources +++++++++++++++++++ + +One can also built from sources: + +.. code:: + + sudo apt-get install cython cython-dbg cython3 cython3-dbg debhelper dh-python \ + python-all-dev python-all-dbg python-fabio python-fabio-dbg python-fftw python-h5py \ + python-lxml python-lxml-dbg python-matplotlib python-matplotlib-dbg python-numpy\ + python-numpy-dbg python-qt4 python-qt4-dbg python-scipy python-scipy-dbg python-sphinx \ + python-sphinxcontrib.programoutput python-tk python-tk-dbg python3-all-dev python3-all-dbg \ + python3-fabio python3-fabio-dbg python3-lxml python3-lxml-dbg python3-matplotlib \ + python3-matplotlib-dbg python3-numpy python3-numpy-dbg python3-pyqt4 python3-pyqt4-dbg \ + python3-scipy python3-scipy-dbg python3-sphinx python3-sphinxcontrib.programoutput \ + python3-tk python3-tk-dbg + wget https://github.com/pyFAI/pyFAI/archive/master.zip + unzip master.zip + cd pyFAI-master + ./build-deb8.sh + + +The first line is really long and defines all the dependence tree for building +*debian* package, including debug and documentation. +The build procedure last for a few minutes and you will be prompted for your +password in order to install the freshly built packages. +The *deb-*files, available in the *package* directory are backports for your local +installation. + +Installation procedure on other linux distibution +------------------------------------------------- + +If your distribution does not provide you pyFAI packages, using the **PIP** way +is advised, via wheels packages. First install *pip* and *wheel*: + +.. code:: + + wget https://bootstrap.pypa.io/get-pip.py + sudo python get-pip.py + sudo pip install pyFAI + +Or you can install pyFAI from the sources: + +.. code:: + + wget https://github.com/pyFAI/pyFAI/archive/master.zip + unzip master.zip + cd pyFAI-master + python setup.py build test + sudo pip install . --upgrade + +**Nota:** The usage of "python setup.py install" is now deprecated. +It causes much more trouble as there is no installed file tracking, +hence no way to de-install properly the package. diff --git a/doc/build/_sources/operations/macosx.txt b/doc/build/_sources/operations/macosx.txt new file mode 100644 index 000000000..7f7ed748c --- /dev/null +++ b/doc/build/_sources/operations/macosx.txt @@ -0,0 +1,93 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2015 +:Keywords: Installation procedure on MacOSX +:Target: System administrators + +Installation procedure on MacOSX +================================ + +Using PIP +--------- + +To install pyFAI on an *Apple* computer you will need a scientific Python stack. +MacOSX provides by default Python2.7 with Numpy which is a good basis. + +.. code:: + + sudo pip install matplotlib --upgrade + sudo pip install scipy --upgrade + sudo pip install fabio --upgrade + sudo pip install h5py --upgrade + sudo pip install cython --upgrade + sudo pip install pyFAI --upgrade + +If you get an error about the local "UTF-8", try to: + +.. code:: + + export LC_ALL=C + +Before the installation. + +Installation from sources +------------------------- + +Get the sources from Github: + +.. code:: + + wget https://github.com/pyFAI/pyFAI/archive/master.zip + unzip master.zip + cd pyFAI-master + + +About OpenMP +............ + +OpenMP is a way to write multi-threaded code, running on multiple processors +simultaneously. +PyFAI makes heavy use of OpenMP, but there is an issue with recent versions of +MacOSX (>v10.6) where the default compiler of Apple, *Xcode*, dropped the +support for OpenMP. + +There are two ways to compile pyFAI on MacOSX: + +* Using *Xcode* and de-activating OpenMP in pyFAI +* Using another compiler which supports OpenMP + +Using Xcode +........... + +To build pyFAI from sources, a C-compiler is needed. +On an *Apple* computer, the default compiler is +`Xcode `_, and it is available for free on +the **AppStore**. +As pyFAI has by default OpenMP activated, and it needs to be de-activated, +one needs to regenerate all Cython files without OpenMP. + +.. code:: + + sudo pip install cython --upgrade + rm pyFAI/ext/*.c + python setup.py build --no-openmp + python setup.py bdist_wheel + sudo pip install --find-links=dist/ --pre --no-index --upgrade pyFAI + +Using **gcc** or **clang** +.......................... + +If you want to keep the OpenMP feature (which makes the processing slightly faster), +the alternative is to install another compiler like `gcc `_ +or `clang `_ on your *Apple* computer. +As gcc & clang support OpenMP, there is no need to re-generate the cython files. + +.. code:: + + CC=gcc python setup.py build --openmp + python setup.py bdist_wheel + sudo pip install --find-links=dist/ --pre --no-index --upgrade pyFAI + + +**Nota:** The usage of "python setup.py install" is now deprecated. +It causes much more trouble as there is no installed file tracking, +hence no way to de-install properly a package. diff --git a/doc/build/_sources/operations/windows.txt b/doc/build/_sources/operations/windows.txt new file mode 100644 index 000000000..bff2c0a74 --- /dev/null +++ b/doc/build/_sources/operations/windows.txt @@ -0,0 +1,201 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2015 +:Keywords: Installation procedure +:Target: System administrators under windows + + +Installation procedure on Windows +================================= + +PyFAI is a Python library. Even if you are only interested in some tool like +pyFAI-calib or pyFAI-integrate, you need to install the complete library (for now). +This is usually performed in 3 steps: + +#. install Python, +#. install the scientific Python stack +#. install pyFAI itself. + +Get Python +---------- + +Unlike on Unix computers, Python is not available by default on Windows computers. +We recommend you to install the 64 bit version of `Python `_, +preferably the latest 64 bits version from the +`2.7 series `_. +But Python 3.4 and 3.5 are also very good candidates. +Python 2.6, 3.2 and 3.3 are no more supported since pyFAI v0.12. + +The 64 bits version is strongly advised if your hardware and operating system +supports it, as the 32 bits versions is +limited to 2 GB of memory, hence unable to treat large images (like 4096 x 4096). +The test suite is not passing on Windows 32 bits due to the limited amount of +memory available to the Python process, +nevertheless, pyFAI is running on Winodws 32 bits (but not as well). + +Alternative Scientific Python stacks exists, like +`Enthought Python Distribution `_ , +`Canopy `_, +`Anaconda `_, +`PythonXY `_ or +`WinPython `_. +They all offer most of the scientific packages already installed which makes +the installation of +dependencies much easier. +On the other hand, they all offer different packaging system and we cannot +support all of them. +Moreover, distribution from *Enthought* and *Continuum* are not free so you +should be able to get support from those companies. + +**Nota:** any flavor of those Python distribution is probably incompatible with +any other due to change in compiler or Python compilation options. +Mixing them is really looking for trouble, hence strongly discouraged. +If you want an advice on which scientific python distribution for Windows to use, +I would recommend `WinPython `_. + +Install PIP +----------- + +**PIP** is the package management system for Python, it connects to +`the Python Package Index `_, +download and install software packages from there. + +PIP has revolutionize the way Python libraries are installed as it is able to +select the right build for your system, or compile them from the sources, +which could be extremely tricky otherwise. +If you installed python 2.7.10, 3.4 or newer, PIP is already installed. +If **pip** is not yet installed on your system, download +`get_pip.py `_ and run it: + +.. code:: + + python get-pip.py + +Assuming python.exe is already in your PATH. + +**Nota:** Because PIP connects to the network, the *http_proxy* and *https_proxy* +environment variable may need to be set-up properly. +At ESRF, please get in contact with the hotline (24-24) to retrive those information. + + +Install the scientific stack +---------------------------- + +The strict dependencies for pyFAI are: + +* NumPy +* SciPy +* matplotlib +* FabIO +* h5py + +Recommended dependencies are: + +* cython +* h5py +* pyopencl +* PyQt4 +* pymca +* rfoo +* pyfftw3 +* lxml + +Using PIP +......... + +Most of the dependencies are available via PIP: + +.. code:: + + pip install numpy --upgrade + pip install scipy --upgrade + pip install matplotlib --upgrade + pip install fabio --upgrade + pip install PyQt4 --upgrade + +Note that numpy/scipy/matplotlib are already installed in most "Scientific Python distribution" + +If one of the dependency is not available as a Wheel (i.e. binary package) but +only as a source package, a compiler will be required. +In this case, see the next paragraph. +The generalization of Wheel packages should help and the installation of binary +modules should become easier. + +**Nota:** This requires a network access and correct proxy settings. +At ESRF, please get in contact with the hotline (24-24) to retrive those information. + +.. code:: + + set http_proxy=http://proxy.site.com:3128 + set https_proxy=http://proxy.site.com:3128 + + +Using Christoph Gohlke repository +................................. + +Christoph Gohlke is a researcher at Laboratory for Fluorescence Dynamics, University of California, Irvine. +He is maintaining a `large repository Python extension `_ (actually, all we need :) for Windows. +Check twice the Python version and the Windows version (win32 or win_amd64) before downloading. + +Moreover the libraries he provides are linked against the MKL library from Intel which +makes his packages faster then what you would get by simply recompiling them. + +Christoph now provides packages as wheels. +To install them, download the wheels and use PIP: + +.. code:: + + pip install numpy*.whl + +Alternatively, you can use the wheelhouse of the silx project: + +.. code:: + + pip install --trusted-host www.silx.org --find-links http://www.silx.org/pub/wheelhouse/ numpy scipy matplotlib fabio PyQt4 + +Install pyFAI via PIP +--------------------- + +The latest stable release of pyFAI should also be PIP-installable (starting at version 0.10.3): + +.. code:: + + pip install pyFAI --upgrade + + + +Install pyFAI from sources +-------------------------- + +The sources of pyFAI are available at https://github.com/pyFAI/pyFAI/releases +the development is performed on https://github.com/kif/pyFAI + +In addition to the Python interpreter, you will need *the* C compiler compatible +with your Python interpreter, for example you can find the one for Python2.7 at: +http://aka.ms/vcpython27 + +To upgrade the C-code in pyFAI, one needs in addition Cython: + +.. code:: + + pip install cython --upgrade + python setup.py bdist_wheel + pip install --pre --no-index --find-links dist/ pyFAI + +Troubleshooting +--------------- + +This section contains some tips on windows. + +Side-by-side error +.................. +When starting pyFAI you get a side-by-side error like:: + + ImportError: DLL load failed: The application has failed to start because its + side-by-side configuration is incorrect. Please see the application event log or + use the command-line sxstrace.exe tool for more detail. + +This means you are using a version of pyFAI which was compiled using the MSVC compiler +(maybe not on your computer) but the Microsoft Visual C++ Redistributable Package is missing. +For Python2.7, 64bits the missing DLL can be downloaded from:: + http://www.microsoft.com/en-us/download/confirmation.aspx?id=2092 + diff --git a/doc/build/_sources/project.txt b/doc/build/_sources/project.txt new file mode 100644 index 000000000..46f8b6540 --- /dev/null +++ b/doc/build/_sources/project.txt @@ -0,0 +1,289 @@ +:Author: Jérôme Kieffer +:Date: 02/06/2016 +:Keywords: Project management description +:Target: developers + +Project +======= + +PyFAI is a library to deal with diffraction images for data reduction. +This chapter describes the project from the computer engineering point of view. + +PyFAI is an open source project licensed under the GPL (+ MIT code) mainly +written in Python (v2.7 or newer, 3.4 or newer) and heavily relying on the +Python scientific ecosystem: numpy, scipy and matplotlib. +It provides high performances image treatment thanks to cython and +OpenCL... but only a C-compiler is needed to build it. + +Programming language +-------------------- + +PyFAI is a Python project but uses many programming languages: + +* 23000 lines of Python (plus 5000 for the test) +* 8000 lines of Cython which are converted into ... C (about half a million lines) +* 5000 lines of OpenCL kernels + +The OpenCL code has been tested using: + +* Nvidia OpenCL v1.1 and v1.2 on Linux, Windows (GPU device) +* Intel OpenCL v1.2 on Linux and Windows (CPU and ACC (Phi) devices) +* AMD OpenCL v1.2 on Linux and Windows (CPU and GPU device) +* Apple OpenCL v1.2 on MacOSX (CPU and GPU) +* Beignet OpenCL v1.2 on Linux (GPU device) +* Pocl OpenCL v1.2 on Linux (CPU device) + +Repository: +----------- + +The project is hosted on GitHub: +https://github.com/pyFAI/pyFAI + +Which provides the `issue tracker `_ in +addition to Git hosting. +Collaboration is done via Pull-Requests in github's web interface: + +Everybody is welcome to `fork the project `_ +and adapt it to his own needs: +CEA-Saclay, Synchrotrons Soleil, Desy and APS have already done so. +Collaboration is encouraged and new developments can be submitted and merged +into the main branch via pull-requests. + +Getting help +------------ + +A mailing list: pyfai@esrf.fr is publicly available, it is the best place to ask +your questions: the author and many advanced users are there and willing to help you. +To subscribe to this mailing list, send an email to +`sympa@esrf.fr with "subscribe pyfai" as subject `_. + +On this mailing list, you will have information about release of the software, +new features available and meet experts to help you solve issues related to +azimuthal integration and diffraction in general. +It also provides a knowledge-base of most frequently asked question which needs +to be integrated into this documentation. + +The volume of email on the list remains low, so you can subscribe without being +too much spammed. As the mailing list is archived, and can be consulted at: +`http://www.edna-site.org/lurker `_, +you can also check the volume of the list. + +If you think you are facing a bug, the best is to +`create a new issue on the GitHub page `_ +(you will need a GitHub account for that). + +Direct contact with authors is discouraged: +pyFAI is open source software that we develop to aid the research +community in doing what they do best. +While we do enjoy doing this, we +would not be able to dream of spending nearly as much time with +pyFAI as we do if it wasn't for your support. +Interest of the scientific community (via a lively mailing list) and citation in +scientific publication for our `software `_ +is one of the main criterion for ESRF management when deciding if they should +continue funding development. + +Run dependencies +---------------- + +* Python version 2.7, 3.4, 3.5 +* NumPy +* SciPy +* Matplotlib +* FabIO +* h5py +* pyopencl (optional) +* fftw (optional) +* pymca (optional) +* PyQt4 or PySide (for the graphical user interface) + +Build dependencies: +------------------- + +In addition to the run dependencies, pyFAI needs a C compiler. + +There is an issue with MacOSX (v10.8 onwards) where the default compiler +(Xcode5 or 6) switched from gcc 4.2 to clang which +dropped the support for OpenMP (clang v3.5 supports OpenMP under linux but not +directly under MacOSX). +Multiple solution exist, pick any of those: + +* Install a recent version of GCC (>=4.2) +* Use Xcode without OpenMP, using the --no-openmp flag for setup.py. + You will need to remove the *pyFAI/ext/histogram.c* file and regenerate it, + cython needs to be installed. + +C files are generated from cython source and distributed. Cython is only needed +for developing new binary modules. +If you want to generate your own C files, make sure your local Cython version +is recent enough (v0.21 and newer), +unless your Cython files will not be translated to C, nor used. + +Building procedure +------------------ + +As most of the Python projects: +............................... + +.. code:: + + python setup.py build bdist_wheel + pip install dist/pyFAI-0.12.0*.whl --upgrade + + +There are few specific options to setup.py: + +* --no-cython: do not use cython (even if present) and use the C source code + provided by the development team +* --no-openmp: if you compiler lacks OpenMP support, like Xcode on MacOSX. + Delete also *pyFAI/ext/histogram.c* and install cython to regenerate C-files. +* --with-testimages: build the source distribution including all test images. + Downloads 200MB of test images to create a self consistent tar-ball. + + +Test suites +----------- + +To test the installed version of pyFAI: + +.. code:: + + python run_test.py + +or from python: + +.. code-block:: python + + import pyFAI + pyFAI.tests() + + +It is normal to get few (<20) **Warning** messages as the test procedure also +tests corner cases. + +To run the test an internet connection is needed as 200MB of test images will be downloaded. +............................................................................................ + +Setting the environment variable http_proxy can be necessary (depending on your network): + +.. code:: + + export http_proxy=http://proxy.site.org:3128 + +Especially at ESRF, the configuration of the network proxy can be obtained +by phoning on the hotline: 24-24. + +To test the development version (built but not yet installed): + +.. code:: + + python setup.py build test + +or + +.. code:: + + python setup.py build + python run_test.py -i + + +PyFAI comes with 34 test-suites (223 tests in total) representing a coverage of 60%. +This ensures both non regression over time and ease the distribution under different platforms: +pyFAI runs under Linux, MacOSX and Windows (in each case in 32 and 64 bits). +Test may not pass on computer featuring less than 2GB of memory or 32 bit architectures. + +**Note:**: The test coverage tool does not count lines of Cython, nor those of OpenCL. +`Anyway test coverage is a tool for developers about possible weaknesses of their +code and not a management indicator. `_ + +.. toctree:: + :maxdepth: 1 + + coverage + + +Continuous integration +---------------------- + +This software engineering practice consists in merging all developer working copies +to a shared mainline several times a day and build the whole project for multiple +targets. + +On Debian 8 - Jessie +.................... +Continuous integration is made by a home-made scripts which checks out the latest release and builds and runs the test every night. +`Nightly builds `_ are available for debian8-64 bits. To install them: + +.. code:: + + + sudo apt-get update + sudo apt-get install pyfai + +You have to accept non-signed packages because they are automatically built. + +In addition some "cloud-based" tools are used to ensure a larger coverage of operating systems/environment. +They rely on a `"local wheelhouse" `_. + +Those wheels are optimized for Travis-CI, AppVeyor and ReadTheDocs, using them is not recommended as your Python configuration may differ +(and those libraries could even crash your system). + +Linux +..... + + +`Travis provides continuous integration on Linux `_, 64 bits computer with Python 2.7, 3.4 and 3.5. + +The builds cannot be retrieved with Travis-CO. + +AppVeyor +........ + +`AppVeyor provides continuous integration on Windows `_, 64 bits computer with Python 2.7 and 3.4. +Successful builds provide installers for pyFAI as *wheels* and *msi*, they are anonymously available as *artifacts*. +Due to the limitation of AppVeyor's build system, those installers have openMP disabled. + +List of contributors in code +---------------------------- + +:: + + $ git log --pretty='%aN##%s' | grep -v 'Merge pull' | grep -Po '^[^#]+' | sort | uniq -c | sort -rn + +As of 06/2016: + * Jérôme Kieffer (ESRF) + * Frédéric-Emmanuel Picca (Soleil) + * Aurore Deschildre (ESRF) + * Giannis Ashiotis (ESRF) + * Dimitrios Karkoulis (ESRF) + * Valentin Valls (ESRF) + * Jon Wright (ESRF) + * Zubair Nawaz (Sesame) + * Amund Hov (ESRF) + * Dodogerstlin @github + * Gunthard Benecke (Desy) + * Gero Flucke (Desy) + * Vadim Dyadkin (ESRF) + * Sigmund Neher (GWDG) + * Thomas Vincent (ESRF) + + +List of other contributors (ideas or code) +------------------------------------------ + +* Peter Boesecke (geometry) +* Manuel Sanchez del Rio (histogramming) +* Armando Solé (masking widget + PyMca plugin) +* Sebastien Petitdemange (Lima plugin) + +List of supporters +------------------ + +* LinkSCEEM project: porting to OpenCL +* ESRF ID11: Provided manpower in 2012 and 2013 and beamtime +* ESRF ID13: Provided manpower in 2012, 2013, 2014, 2015, 2016 and beamtime +* ESRF ID29: provided manpower in 2013 (MX-calibrate) +* ESRF ID02: provided manpower 2014, 2016 +* ESRF ID15: provide manpower 2015, 2016 +* ESRF ID21: provide manpower 2015, 2016 +* ESRF ID31: provide manpower 2016 diff --git a/doc/build/_sources/publications.txt b/doc/build/_sources/publications.txt new file mode 100644 index 000000000..5467362ef --- /dev/null +++ b/doc/build/_sources/publications.txt @@ -0,0 +1,28 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2015 +:Keywords: List of publications + + +List of publication about pyFAI +=============================== + +* *PyFAI, a versatile library for azimuthal regrouping*, + J Kieffer & D Karkoulis; + **Journal of Physics: Conference Series** (2013) 425 (20), pp202012 + +* *PyFAI: a Python library for high performance azimuthal integration on GPU* + J Kieffer & J.P. Wright; + **Powder Diffraction** (2013) 28 (S2), pp339-350 + +* *PyFAI: a Python library for high performance azimuthal integration on GPU* + Jérôme Kieffer, Giannis Ashiotis + **PROC. OF THE 7th EUR. CONF. ON PYTHON IN SCIENCE** (EUROSCIPY 2014) + +* *The fast azimuthal integration Python library: pyFAI* + J. Kieffer, G. Ashiotis, A. Deschildre, Z. Nawaz, J. P. Wright, D. Karkoulis, F. E. Picca + **Journal of Applied Crystallography** (2015) 48 (2), 510-519 + +The later publication provides a nice overview of the features introduced in version +0.11 and further developed in v0.12 and should be the one cited in publication using pyFAI. +There are already 55 publication referring to pyFAI, some of them in the most prestigious +scientific journals (Nature, PNAS, ...). \ No newline at end of file diff --git a/doc/build/_sources/pyFAI.txt b/doc/build/_sources/pyFAI.txt new file mode 100644 index 000000000..70a79d0d3 --- /dev/null +++ b/doc/build/_sources/pyFAI.txt @@ -0,0 +1,297 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2016 +:Keywords: generic description of the software +:Target: General audiance +General introduction to PyFAI +============================= + +Python Fast Azimuthal Integration +--------------------------------- + +PyFAI is implemented in Python_ programming language, which is open +source and already very popular for scientific data analysis ([PyMca]_, +[PyNX]_, …). It relies on the scientific stack of python composed of [NumPy]_, +[SciPy]_ and [Matplotlib]_ plus the [OpenCL]_ binding [PyOpenCL]_ for performances. + +.. _Python: http://python.org + +:math:`2D` area detectors like CCD or pixel detectors have become +popular in the last 15 years for diffraction experiments (e.g. for WAXS, +SAXS, single crystal and powder diffraction). +These detectors +have a large sensitive area of millions of pixels with high spatial +resolution. The software package pyFAI ([SRI2012]_, [EPDIC13]_) +has been designed to reduce SAXS, +WAXS and XRPD images taken with those detectors into :math:`1D` curves +(azimuthal integration) usable by other software for in-depth analysis +such as Rietveld refinement, or :math:`2D` images (a radial +transformation named *caking* in [FIT2D]_). +As a library, the aim of pyFAI is to be +integrated into other tools like [PyMca]_ or [EDNA]_ or [LImA]_ with a clean pythonic +interface. +However pyFAI features also command line and graphical tools for batch +processing, converting data into *q-space* (q being the momentum +transfer) or 2\ :math:`\theta`-space (:math:`\theta` being the Bragg +angle) and a calibration graphical interface for optimizing the geometry +of the experiment using the Debye-Scherrer rings of a reference sample. +PyFAI shares the geometry definition of SPD but can directly import +geometries determined by the software FIT2D. PyFAI has been designed to +work with any kind of detector and geometry (transmission or reflection) +and relies on FabIO, a library able to read more than 20 image formats +produced by detectors from 12 different manufacturers. During the +transformation from cartesian space :math:`(x,y)` to polar space +:math:`(2\theta, \chi )`, both local and total intensities are conserved +in order to obtain accurate quantitative results. Technical details on +how this integration is implemented and how it has been ported to native +code and parallelized on graphic cards are quickly presented but you can refer +to this `publications `_ for further details. + +Introduction +------------ + +With the advent of hyperspectral experiments like diffraction tomography +in the world of synchrotron radiation, existing software tools for +azimuthal integration like [FIT2D]_ and [SPD]_ reached their performance +limits owing to the fast data rate needed by such experiments. Even when +integrated into massively parallel frameworks like [EDNA]_ , such +stand-alone programs, due to their monolithic nature, cannot keep the +pace with the data flow of new detectors. Therefore we decided to +implemente from scratch a novel azimuthal integration tool which is +designed to take advantage of modern parallel hardware features. +PyFAI assumes the setup does not change during the experiment and tries to reuse +a maximum number of data (using memoization_), moreover those calculation are performed +only when needed (lazy_evaluation_). + +.. _memoization: http://en.wikipedia.org/wiki/Memoization +.. _lazy_evaluation: http://en.wikipedia.org/wiki/Lazy_evaluation + +Experiment description +---------------------- + +In pyFAI, the basic experiment is defined by a description of an area-detector whose +position in space is defined through the sample position and the incident X-ray +beam, and can be calibrated using Debye-Scherrer rings of a reference compound. + +Detector +........ + +.. toctree:: + :maxdepth: 4 + + detector + +Geometry +........ + +.. toctree:: + :maxdepth: 4 + + geometry + +Calibration +........... + +.. toctree:: + :maxdepth: 4 + + calibration + + +PyFAI executables +................. + +PyFAI was designed to be used by scientists needing a simple and +effective tool for azimuthal integration. Two command line programs +*pyFAI-waxs* and *pyFAI-saxs* are provided with pyFAI for performing the +integration of one or more images on the command line. +The waxs version outputs result in +:math:`2\theta /I`, whereas the saxs version outputs result in +:math:`q/I(/\sigma)`. Options for these programs are parameter file +(*poni-file*) +describing the geometry and the mask file. They can also do some +pre-processing like dark-noise subtraction and flat-field correction +(solid-angle correction is done by default). + +A new Graphical interface based on Qt called *pyFAI-integrate* is now available, +offers all options possible for azimuthal integration (dark/flat/polarization, +....) in addition to a finer tuning for the computing device selection (CPU/GPU). + +Finally couple of specialized tool called diff_tomo and diff_map are available to +reduce a 2D/3D-mapping experiment of 2D images into a 3D volume +(:math:`x, y, 2\theta` for mapping or :math:`rot, trans, 2\theta` for tomography) + +There are cookbooks on these scripts in :ref:`cookbook` and their complete +manual pages are available in the :ref:`manpage` section. + +Python library +.............. + +PyFAI is first and foremost a library: a tool of the scientific toolbox +built around [IPython]_ and [NumPy]_ to perform data analysis either +interactively or via scripts. Figure [notebook] shows an interactive +session where an integrator is created, and an image loaded and +integrated before being plotted. + +.. figure:: img/notebook.png + :align: center + :alt: image + +The :ref:`tutorial` section makes heavy use of *ipython*, now *jupyter* +to process data using pyFAI. +The first tutorial also explains a bit how Python and Jupyter works to be +able to perform basic processing efficiently with pyFAI. + + +Regrouping mechanism +-------------------- + +In pyFAI, regrouping is performed using a histogram-like algorithm. +Each pixel of the image is associated to its polar coordinates +:math:`(2\theta , \chi )` or :math:`(q, \chi )`, then a pair of +histograms versus :math:`2\theta` (or :math:`q`) are built, one non +weighted for measuring the number of pixels falling in each bin and +another weighted by pixel intensities (after dark-current subtraction, +and corrections for flat-field, solid-angle and polarization). +The division of the weighted histogram by the number of pixels per bin gives +the average signal over the given corona which provides the diffraction pattern. +:math:`2D` regrouping (called *caking* in +FIT2D) is obtained in the same way using two-dimensional histograms over +radial (:math:`2\theta` or :math:`q`) and azimuthal angles +(:math:`\chi`). + +Pixel splitting algorithm +......................... + +Powder diffraction patterns obtained by histogramming have a major +weakness where pixel statistics are low. A manifestation of this +weakness becomes apparent in the :math:`2D`-regrouping where most of the +bins close to the beam-stop are not populated by any pixel. In this figure, +many pixels are missing in the low :math:`2\theta` region, due +to the arbitrary discretization of the space in pixels as intensities +were assigned to each pixel center which does not reflect the physical +reality of the scattering experiment. + +.. figure:: img/2Dhistogram.png + :align: center + :alt: image + +PyFAI solves this problem by pixel +splitting : in addition to the pixel position, its +spatial extension is calculated and each pixel is then split and +distributed over the corresponding bins, the intensity being considered +as homogeneous within a pixel and spread accordingly. +The drawback of this is the correlation introduced between two adjacent bins. +To simplify +calculations, this was initially done by abstracting the pixel shape +with a bounding box that circumscribes the pixel. In an effort to better +the quality of the results this method was dropped in favoor of a full +pixel splitting scheme that actually uses the actual pixel geometry +for its calculations. + +.. figure:: img/2DwithSplit.png + :align: center + :alt: image + +Performances and migration to native code +......................................... + +Originally, regrouping was implemented using the histogram provided by +[NumPy]_, then re-implemented in [Cython]_ with pixel splitting to achieve a +four-fold speed-up. The computation time scales like O(N) with the size +of the input image. The number of output bins shows only little +influence; overall the single threaded [Cython]_ implementation has been +stated at 30 Mpix/s (on a 3.4 GHz Intel core i7-2600). + + +Parallel implementation +....................... + +The method based on histograms works well on a single processor but runs +into problems requiring so called "atomic operations" when run in parallel. +Processing pixels in the input data order causes write access conflicts which +become less efficient with the increase of number of computing units (need of atomic_operation_). +This is the main limit of the method exposed previously; +especially on GPU where thousands of threads are executed simultaneously. + +.. _atomic_operation: http://en.wikipedia.org/wiki/Atomic_operation + +To overcome this limitation; instead of looking at where input pixels GO TO +in the output image, we instead look at where the output pixels COME FROM +in the input image. +This transformation is called a "scatter to gather" transformation in parallel programming. + +The correspondence between pixels and output bins can be stored in a +look-up table (LUT) together with the pixel weight which make the integration +look like a simple (if large and sparse) matrix vector product. +This look-up table size depends on whether pixels are split over multiple +bins and to exploit the sparse structure, both index and weight of the pixel +have to be stored. +We measured that 500 Mbytes are needed to store the LUT to integrate a 16 +megapixels image, which fits onto a reasonable quality graphics card nowadays +but can still be too large to fit on an entry-level graphics card. + +By making this change we switched from a “linear read / random write” forward algorithm +to a “random read / linear write” backward algorithm which is more suitable for parallelization. +As a farther improvement on the algorithm, the use of compressed sparse row (CSR) format was +introduced, to store the LUT data. +This algorithm was implemented both in [Cython]_-OpenMP and OpenCL. +The CSR approach has a double benefit: +first, it reduces the size of the storage needed compared to the LUT by a factor two to three, +offering the opportunity of working with larger images on the same hardware. +Secondly, the CSR implementation in OpenCL is using an algorithm based on multiple parallel +reductions where many execution threads are collaborating to calculate +the content of a single bin. +This makes it very well suited to run on GPUs and accelerators +where hundreds to thousands of simultaneous threads are available. + +When using OpenCL for the GPU we used a compensated (or Kahan_summation_), to reduce +the error accumulation in the histogram summation (at the cost of more operations to be done). +This allows accurate results to be obtained on cheap hardware that performs calculations +in single precision floating-point arithmetic (32 bits) which are available on consumer +grade graphic cards. +Double precision operations are currently limited to high price and performance computing dedicated GPUs. +The additional cost of Kahan summation, 4x more arithmetic operations, is hidden by smaller data types, +the higher number of single precision units and that the GPU is usually limited by the memory bandwidth anyway. + +.. _Kahan_summation: http://en.wikipedia.org/wiki/Kahan_summation_algorithm + +The performances of the parallel implementation based on a LUT, stored in CSR format, can reach 750 MPix/s +on recent multi-core computer with a mid-range graphics card. +On multi-socket server featuring high-end GPUs like Tesla cards, the performances are similar with +the additional capability to work on multiple detector simultaneously. + +.. figure:: img/benchmark.png + :align: center + :alt: benchmark performed on a 2014 single-socket workstation + + +Related Work +------------ + +There are many projects which are already relying on pyFAI: Dioptas, NanoPeakCell, +Dpdak, PySAXS, xPDFSuite ... There is a list of :ref:`ecosystem` on the ecosystem +page. + +Conclusion +---------- + +The library pyFAI was developed with two main goals: + +- Performing azimuthal integration with a clean programming interface. + +- No compromise on the quality of the results is accepted: a careful + management of the geometry and precise pixel splitting ensures total + and local intensity preservation. + +PyFAI is the first implementation of an azimuthal integration algorithm +on a GPUs as far as we are aware of, and the stated twenty-fold speed up +opens the door to a new kind of analysis, not even considered before. +With a good interface close to the camera, we believe PyFAI is able to +sustain the data streams from the next generation high-speed detectors. + +Acknowledgments +............... + +Porting pyFAI to GPU would have not been possible without +the financial support of LinkSCEEM-2 (RI-261600). + diff --git a/doc/build/_sources/usage/cookbook/calibrate.txt b/doc/build/_sources/usage/cookbook/calibrate.txt new file mode 100644 index 000000000..1d50bbb51 --- /dev/null +++ b/doc/build/_sources/usage/cookbook/calibrate.txt @@ -0,0 +1,204 @@ +:Author: Jérôme Kieffer +:Date: 27/10/2015 +:Keywords: Tutorials +:Target: Scientists + +Calibration of a diffraction setup +================================== + +The files needed for this cookbook can be downloaded on: +http://www.silx.org/pub/pyFAI/cookbook/calibration/ +You can download them to practice: + +.. code-block:: shell + + $ mkdir calibration + $ cd calibration + $ wget http://www.silx.org/pub/pyFAI/cookbook/calibration/LaB6_29.4keV.tif + $ wget http://www.silx.org/pub/pyFAI/cookbook/calibration/F_K4320T_Cam43_30012013_distorsion.spline + $ ls + F_K4320T_Cam43_30012013_distorsion.spline LaB6_29.4keV.tif + + +`The associated video is here `_ + + +Review your calibration image +----------------------------- + +As viewer, try fabio_viewer from the FabIO package + +.. code-block:: shell + + $ fabio_viewer LaB6_29.4keV.tif + +.. figure:: fabio_viewer.png + :align: center + :alt: image + +You may need to pre-process your data, *pyFAI-average* is a tool to perform pixel-wise +transformation. +In this example, the image file was obtained using a "max filter" over 20 frames +with pyFAI-average: + +.. code-block:: shell + + $ pyFAI-average -m max -F tif -o LaB6_29.4keV.tif ref_lab6_00??.edf + + + +Get all additional data +----------------------- + + * calibrant used: LaB6 according to the name of the file + * the energy or the wavelength, 29.4keV according to the name of the file + * detector geometry: there is a spline file along with the file + * masks, dark, flat ... are not going to be used to keep things simple. + +Start pyFAI-calib +----------------- + +Use the man page (or --help) to see all options, or go to the :ref:`manpage` section. + +Here we just provide the energy, the detector disortion file and the calibrant +options in addition to the image file. + +.. code-block:: shell + + $ pyFAI-calib -e 29.4 -s F_K4320T_Cam43_30012013_distorsion.spline -c LaB6 LaB6_29.4keV.tif + FixedParameters(['wavelength']) + ERROR:pyFAI.peak_picker:ControlPoint.load: No such file LaB6_29.4keV.npt + INFO:pyFAI.massif:Image size is (2048, 2048) + INFO:pyFAI.massif:Binning size is [2, 2] + INFO:pyFAI.massif:Labeling found 7272 massifs. + INFO:pyFAI.massif:Labeling found 7272 massifs. + INFO:root:Please select rings on the diffraction image. In parenthesis, some modified shortcuts for single button mouse (Apple): + * Right-click (click+n): try an auto find for a ring + * Right-click + Ctrl (click+b): create new group with one point + * Right-click + Shift (click+v): add one point to current group + * Right-click + m (click+m): find more points for current group + * Center-click or (click+d): erase current group + * Center-click + 1 or (click+1): erase closest point from current group + Please press enter when you are happy with your selection + + +Pick peaks +---------- + +To perform the calibration one needs to create control points and assign them to rings. + + * Right click on a few (5) points in the inner-most ring which has the index + number #0 + * Increase the counter on the top right to change the ring number and pick a few + more points on the corresponding ring (again with right click). + +.. figure:: pyFAI-calib_1.png + :align: center + :alt: image + + +Review the group of peaks +------------------------- + +Press Enter in the terminal to do so... +and check the ring assignment + +Once done with all groups, the position of the expected rings is overlaid to the +diffraction image. You may need to unzoom to view them ! + +.. code-block:: shell + + Now fill in the ring number. Ring number starts at 0, like point-groups. + Point group # a (4 points) (1315.9,1090.1) [default=0] Ring# + Point group # b (5 points) (1302.0, 926.0) [default=0] Ring# + Point group # c (2 points) (1085.3,1268.1) [default=0] Ring# + Point group # d (2 points) ( 850.1,1083.3) [default=0] Ring# + Point group # e (5 points) ( 965.1, 825.7) [default=0] Ring# + Point group # f (4 points) ( 898.2,1315.9) [default=1] Ring# + Point group # g (2 points) (1244.6, 733.6) [default=1] Ring# + Point group # h (2 points) (1350.1, 821.9) [default=1] Ring# + Optimization terminated successfully. (Exit mode 0) + +.. figure:: pyFAI-calib_2.png + :align: center + :alt: image + + +Acquire some more control points +-------------------------------- + + * Use *recalib* to extract a new set of control points, specify the number of rings, first a few of them then more + * You may want to free/fix/bound some parameter then *refine* again + +.. code-block:: shell + + Fixed: wavelength + Modify parameters (or ? for help)? recalib 6 + [...] + Fixed: wavelength + Modify parameters (or ? for help)? recalib 15 + [...] + Fixed: wavelength + Modify parameters (or ? for help)? recalib 25 + +.. figure:: pyFAI-calib_3.png + :align: center + :alt: image + + +Visualize the integrated patterns +--------------------------------- + + * integrate to view the integrated pattern + * then extract a few extra rings ... + * the geometry is displayed on the screen, and saved automatically in the poni-file + +.. code-block:: shell + + Fixed: wavelength + Modify parameters (or ? for help)? integrate + + +.. figure:: pyFAI-calib_4.png + :align: center + :alt: image + + +Quit +---- + +.. code-block:: shell + + Fixed: wavelength + Modify parameters (or ? for help)? quit + $ + +.. code-block:: shell + ls + F_K4320T_Cam43_30012013_distorsion.spline LaB6_29.4keV.npt LaB6_29.4keV.tif + LaB6_29.4keV.azim LaB6_29.4keV.poni LaB6_29.4keV.xy + + +All different geometries have been saved into the LaB6_29.4keV.poni file and +can directly be used with *pyFAI-integrate*. +All control points are saved into LaB6_29.4keV.npt. + +**Final notes:** +In this case the calibration is far from being good and it is likely the +distortion file applied is not the proper one according to many the waves on the 2D +integration pattern. +If one flips up-down the spline file prior to the calibration, the residual error +is twice lower but the goes far beyond this cookbook. +Here is the diffraction pattern from a properly calibrated setup: + +.. figure:: pyFAI-calib_5.png + :align: center + :alt: image + +.. code-block:: python + + from pyFAI.spline import Spline + s = Spline("F_K4320T_Cam43_30012013_distorsion.spline") + ud = s.flipud() + ud.write("flipud.spline") + diff --git a/doc/build/_sources/usage/cookbook/index.txt b/doc/build/_sources/usage/cookbook/index.txt new file mode 100644 index 000000000..4066550e8 --- /dev/null +++ b/doc/build/_sources/usage/cookbook/index.txt @@ -0,0 +1,23 @@ +:Author: Jérôme Kieffer +:Date: 27/10/2015 +:Keywords: Cookbook +:Target: Scientists + +.. _cookbook: + +Cookbook recipes +================ + +Cookbook are short tutorials: 1 page, 5 minutes to read. + + +.. toctree:: + :maxdepth: 1 + + calibrate + integration_with_the_gui + integration_with_scripts + integration_with_python + + +For more in depth explanation, see the :ref:`tutorials` section. \ No newline at end of file diff --git a/doc/build/_sources/usage/cookbook/integration_with_python.txt b/doc/build/_sources/usage/cookbook/integration_with_python.txt new file mode 100644 index 000000000..88e44cfad --- /dev/null +++ b/doc/build/_sources/usage/cookbook/integration_with_python.txt @@ -0,0 +1,7 @@ +:Author: Jérôme Kieffer +:Date: 27/10/2015 +:Keywords: Cookbook +:Target: Scientists + +This cookbook is now available a a tutorial using `jupyter `_ +on the :ref:`tutorial` section. \ No newline at end of file diff --git a/doc/build/_sources/usage/cookbook/integration_with_scripts.txt b/doc/build/_sources/usage/cookbook/integration_with_scripts.txt new file mode 100644 index 000000000..6629c386b --- /dev/null +++ b/doc/build/_sources/usage/cookbook/integration_with_scripts.txt @@ -0,0 +1,6 @@ +:Author: Jérôme Kieffer +:Date: 27/10/2015 +:Keywords: Cookbook +:Target: Scientists + + diff --git a/doc/build/_sources/usage/cookbook/integration_with_the_gui.txt b/doc/build/_sources/usage/cookbook/integration_with_the_gui.txt new file mode 100644 index 000000000..8c99eb259 --- /dev/null +++ b/doc/build/_sources/usage/cookbook/integration_with_the_gui.txt @@ -0,0 +1,72 @@ +:Author: Jérôme Kieffer +:Date: 27/10/2015 +:Keywords: Cookbook +:Target: Scientists + +Azimuthal integration using the graphical user interface +======================================================== + +`Associated video `_ + +Look at your integrated patterns +-------------------------------- +PyFAI can perform 1D or 2D integration. +To view 1D patterns, I will use *grace* +Let's look at the integrated patterns +obtained during calibration. + +As you can see, only the 10 rings used for +calibration are well defined. + +This file is a text file containing as header +all metadata needed to determine the geometry. + +2D integrated pattern (aka cake images) +are EDF images. Try *fabio_viewer* to see them. + +Once again check the header of the file and the +associated metadata. + +Integrate a bunch of images +--------------------------- +We will work with the 20 images used for the calibration. + +Start pyFAI-intgrate +-------------------- +Either select files to process using the file-dialog or provide +them on the command line. + +Set the geometry +---------------- +Simply load the PONI file and check the populated fields. + + +Azimuthal integration options +----------------------------- +Check the dark/flat/ ... options +Use the check-box to activate the option. + +Do *NOT* forget to specify the number of radial bins ! + +Select the device for processing +-------------------------------- +Unless the processing will be done on the +CPU using OpenMP. + +Press OK to start the processing. + +The generation of the Look-Up table takes a few seconds +then all files get processed quickly + +Run it again to perform caking +------------------------------ +Same a previously ... but +provide a number of azimuthal bins ! + + +Visualize the integrated patterns +--------------------------------- +Once again I used *grace* and *fabio_viewer* +to display the result. + +That's all. diff --git a/doc/build/_sources/usage/tutorial/CCD_Calibration/CCD_calibration.txt b/doc/build/_sources/usage/tutorial/CCD_Calibration/CCD_calibration.txt new file mode 100644 index 000000000..8930abb0f --- /dev/null +++ b/doc/build/_sources/usage/tutorial/CCD_Calibration/CCD_calibration.txt @@ -0,0 +1,937 @@ + +CCD calibration +=============== + +This tutorial presents how to calibrate the distortion from a CCD camera +coupled with a taper of optic fibers. If your camera is already +calibrated using Fit2D and you have access to the corresponding *spline* +file, this tutorial is not for you: simply create your detector object +like this **pyFAI.detectors.Detector(splineFile="example.spline")** and +you are done. This tutorial uses the image of a regular grid on the +detector. + +It uses a procedure described in: "Calibration and correction of spatial +distortions in 2D detector systems" from Hammersley, A. P.; Svensson, S. +O.; Thompson, A. published in Nuclear Instruments and Methods in Physics +Research Section A, Volume 346, Issue 1-2, p. 312-321. +DOI:10.1016/0168-9002(94)90720-X + +The procedure is performed in 4 steps: 1. peak picking 2. grid +assignment 3. distortion fitting 4. interpolation of the fitted data 5. +saving into a detector definition file + +The picture used is the one of a regular metallic grid of holes (spaced +by 5mm), just in front of the detector. We will assume holes are +circular what looks correct in first apploximation. Paralax error will +be ignored in a first time. + +Peak picking +------------ + +Lets start with peak picking, for this, we will use the *FabIO* library +able to read the image and *matplotlib* to display the image. The +disortion is assumed to be minimal in the middle of the detector, so we +first focus on one spot in the middle: + +.. code:: python + + #For final rendering switch from nbagg -> inline + #%pylab nbagg + %pylab inline + # + # search for the image containing the grid + + +.. parsed-literal:: + + Populating the interactive namespace from numpy and matplotlib + + +.. code:: python + + ls + + +.. parsed-literal:: + + 03jun102d.edf* ccd2_g3.edf* CCD_calibration.ipynb grilHiCap_04jund.edf* + ccd2_g1_chk.edf ccd2_g6_1.edf corrected.edf testdetector.h5 + ccd2_g2.edf* ccd2_g6_2.edf flm-4-1.edf* + ccd2_g2g.edf ccd2_g6_3.edf frelonID22_grid.edf + ccd2_g3_chk.edf* ccd2_g6_chk.edf grid.edf* + + +.. code:: python + + fname = "frelonID22_grid.edf" + #fname = "corrected.edf" + import fabio + img = fabio.open(fname).data + + +.. parsed-literal:: + + WARNING:xsdimage:lxml library is probably not part of your python installation: disabling xsdimage format + + +.. parsed-literal:: + + WARNING: Not using BLAS, PCA calculation will be slower + WARNING: Not using BLAS/ATLAS, PCA calculation will be slower + + +.. code:: python + + imshow(img, interpolation="nearest", origin="lower") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_4_1.png + + +.. code:: python + + #Zoom into a spot in the middle of the image, where the distortion is expected to be minimal + imshow(img[1060:1100,1040:1080], interpolation="nearest", origin="lower") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_5_1.png + + +.. code:: python + + #Look at the profile of the peak to measure the width (it is expected to be a crenel) + plot(img[1060+25,1040:1060]) + + + + +.. parsed-literal:: + + [] + + + + +.. image:: output_6_1.png + + +Let's look at one spot, in the center of the image: it is circular and +is slightly larger than 10 pixels. We will define a convolution kernel +of size 11x11 of circular shape with sharp borders as this is what a +perfect spot is expected to look like. The kernel is normalized in such +a way it does not modify the average intensity of the image + +Now convolve the image with this circular kernel using scipy.signal (in +direct space: the kernel is small and performance does not really matter +here). + +It is important to have an odd size for the kernel for convolution as an +even shape would induce an offset of 1/2 pixel in the located +peak-position. + +.. code:: python + + size = 11 #Odd of course + center = (size-1)//2 + y, x = numpy.ogrid[-center:center+1,-center:center+1] + r2 = x*x + y*y + kernel = (r2<=(center+0.5)**2).astype(float) + kernel /= kernel.sum() + imshow(kernel, interpolation="nearest", origin="lower") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_8_1.png + + +.. code:: python + + from scipy import ndimage, signal + +.. code:: python + + cnv = signal.convolve2d(img, kernel, mode="same") + +.. code:: python + + #Check that size is unchanged. + print(img.shape) + print(cnv.shape) + + +.. parsed-literal:: + + (2048, 2048) + (2048, 2048) + + +.. code:: python + + #Check the image still looks the same. it is just supposed to be smoother. + imshow(cnv, origin="lower", interpolation="nearest") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_12_1.png + + +.. code:: python + + #Zoom into the very same spot to ensure it is smoother + imshow(cnv[1060:1100,1040:1080], interpolation="nearest", origin="lower") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_13_1.png + + +.. code:: python + + # and here again the same profile: + plot(cnv[1060+25,1030:1070]) + # the peak got broader (2x) but much smoother on the top: this is what we are interrested in. + + + + +.. parsed-literal:: + + [] + + + + +.. image:: output_14_1.png + + +After convolution with a pattern of the same shape as the hole, the peak +center is located with a sub-pixel resolution. The peak has a full size +of 30 pix in 1 dimension. + +All peak positions will be extracted using the pyFAI inverse watershed +algorithm. Once all regions are segmented, the ones too small are sieved +out and the remaining ones are classifies according to their peak +intensity using an histogram. As intensity vary a lot, this histogram it +is done on the log-scale of the intensity. + +.. code:: python + + mini = (kernel>0).sum() + print("Number of points in the kernel: %s"%mini) + + +.. parsed-literal:: + + Number of points in the kernel: 97 + + +.. code:: python + + try: #depends if the version of pyFAI you are using + from pyFAI.watershed import InverseWatershed + except: + from pyFAI.ext.watershed import InverseWatershed + #Version of pyFAI newer than feb 2016 + iw = InverseWatershed(cnv) + iw.init() + iw.merge_singleton() + all_regions = set(iw.regions.values()) + regions = [i for i in all_regions if i.size>mini] + + print("Number of region segmented: %s"%len(all_regions)) + print("Number of large enough regions : %s"%len(regions)) + + + +.. parsed-literal:: + + WARNING:pyFAI.utils:Exception No module named 'fftw3': FFTw3 not available. Falling back on Scipy + WARNING:pyFAI.timeit:init_labels took 2.123s + WARNING:pyFAI.timeit:init_borders took 0.066s + WARNING:pyFAI.timeit:init_regions took 0.646s + WARNING:pyFAI.timeit:init_pass took 0.229s + WARNING:pyFAI.timeit:merge_singleton took 0.054s + + +.. parsed-literal:: + + Number of region segmented: 79513 + Number of large enough regions : 8443 + + +.. code:: python + + s = [i.maxi for i in regions] + hist(numpy.log10(s), 20) + #Look for the maximum value in each region to be able to segment accordingly + + + + +.. parsed-literal:: + + (array([ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 1.00000000e+00, 2.85000000e+02, 5.99900000e+03, + 1.71900000e+03, 1.00000000e+00, 0.00000000e+00, + 1.00000000e+00, 3.00000000e+00, 2.30000000e+01, + 1.17000000e+02, 2.93000000e+02]), + array([ 2.05537045, 2.1621182 , 2.26886594, 2.37561369, 2.48236143, + 2.58910918, 2.69585692, 2.80260467, 2.90935241, 3.01610016, + 3.1228479 , 3.22959565, 3.33634339, 3.44309114, 3.54983888, + 3.65658663, 3.76333437, 3.87008212, 3.97682986, 4.08357761, + 4.19032535]), + ) + + + + +.. image:: output_18_1.png + + +There are clearly 3 groups of very different intensity, well segragated: + +- around 10^2.1 (~125), those are the peaks where no tapper brings + light +- around 10^3.4 (~2500), those are segmented region in the background +- above 10^3.9 (~8000), those are actual peaks, we are looking for. + +We retain all peaks >10^3.5 + +.. code:: python + + peaks = [(i.index//img.shape[-1], i.index%img.shape[-1]) for i in regions if (i.maxi)>10**3.5] + print("Number of remaining peaks: %s"%len(peaks)) + + +.. parsed-literal:: + + Number of remaining peaks: 438 + + +.. code:: python + + imshow(img, interpolation="nearest", origin="lower") + peaks_raw = numpy.array(peaks) + plot(peaks_raw[:,1], peaks_raw[:, 0], "or") + xlim(0,2048) + ylim(0,2048) + title("Extracted peak position (raw)") + print("Raw peak coordinate:") + print(peaks[:10]) + + +.. parsed-literal:: + + Raw peak coordinate: + [(1187, 654), (35, 155), (1082, 1447), (1082, 1545), (39, 62), (1083, 1250), (1083, 1349), (1943, 1925), (1084, 1151), (1190, 455)] + + + +.. image:: output_21_1.png + + +Precise peak extraction is performed using a second order tailor expansion +-------------------------------------------------------------------------- + +.. code:: python + + try: + from pyFAI.bilinear import Bilinear + except: + from pyFAI.ext.bilinear import Bilinear + bl = Bilinear(cnv) + +.. code:: python + + ref_peaks = [bl.local_maxi(p) for p in peaks] + imshow(img, interpolation="nearest", origin="lower") + peaks_ref = numpy.array(ref_peaks) + plot(peaks_raw[:,1], peaks_raw[:, 0], "or") + plot(peaks_ref[:,1],peaks_ref[:, 0], "ob") + xlim(0,2048) + ylim(0,2048) + title("Extracted peak position (red: raw, blue: refined)") + print("Refined peak coordinate:") + print(ref_peaks[:10]) + + +.. parsed-literal:: + + Refined peak coordinate: + [(1186.931446455419, 653.9433948583901), (34.4068917632103, 155.60163909196854), (1081.7659440785646, 1447.2154052853584), (1081.409073472023, 1545.5145089030266), (39.49280175566673, 62.08068184554577), (1083.1883269548416, 1249.924985870719), (1082.5653080940247, 1348.6598246097565), (1943.0012932673562, 1925.4793396294117), (1083.641106903553, 1150.8036927580833), (1189.607285887003, 455.1422745883465)] + + + +.. image:: output_24_1.png + + +At this stage, a visual inspection of the grid confirms all peaks have +been properly segmented. If this is not the case, one can adapt: + +- the size of the kernel +- the threshold comming out of the histogramming + +Pair-wise distribution function +------------------------------- + +We will now select the (4-) first neighbours for every single peak. For +this we calculate the distance\_matrix from any point to any other: + +.. code:: python + + # Nota, pyFAI uses **C-coordinates** so they come out as (y,x) and not the usual (x,y). + # This notation helps us to remind the order + yx = numpy.array(ref_peaks) + +.. code:: python + + # pairwise distance calculation using scipy.spatial.distance_matrix + from scipy.spatial import distance_matrix + dist = distance_matrix(peaks_ref, peaks_ref) + +Let's have a look at the pairwise distribution function for the first +neighbors + +.. code:: python + + hist(dist.ravel(), 200, range=(0,200)) + title("Pair-wise distribution function") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_29_1.png + + +This histogram provides us: + +- At 0, the 438 peaks with 0-distance to themselves. +- between 85 and 105 the first neighbours +- between 125 and 150 the second neighbours. +- ... and so on. + +We now focus on the first neighbours which are all located between 70 +and 110 pixel appart. + +.. code:: python + + #We define here a data-type for each peak (called center) with 4 neighbours (called north, east, south and west). + point_type = np.dtype([('center_y', float), ('center_x', float), + ('east_y', float), ('east_x', float), + ('west_y', float), ('west_x', float), + ('north_y', float), ('north_x', float), + ('south_y', float), ('south_x', float)]) + + neig = np.logical_and(dist>70.0, dist<110.0) + valid = (neig.sum(axis=-1)==4).sum() + print("There are %i control point with exactly 4 first neigbours"%valid) + point = numpy.zeros(valid, point_type) + # This initializes an empty structure to be populated + + + +.. parsed-literal:: + + There are 359 control point with exactly 4 first neigbours + + +.. code:: python + + #Populate the structure: we use a loop as it loops only over 400 points + h=-1 + for i, center in enumerate(peaks_ref): + if neig[i].sum()!=4: continue + h+=1 + point[h]["center_y"],point[h]["center_x"] = center + for j in ((0,1),(0,-1),(1,0),(-1,0)): + tmp = [] + for k in numpy.where(neig[i]): + curr = yx[k] + tmp.append(dot(curr-center,j)) + l = argmax(tmp) + y, x = peaks_ref[numpy.where(neig[i])][l] + if j==(0,1):point[h]["east_y"], point[h]["east_x"] = y, x + elif j==(0,-1):point[h]["west_y"], point[h]["west_x"] = y, x + elif j==(1,0): point[h]["north_y"],point[h]["north_x"] = y, x + elif j==(-1,0):point[h]["south_y"],point[h]["south_x"] = y, x + + +We will need to define an *origin* but taking it on the border of the +image is looking for trouble as this is where distortions are likely to +be the most important. The center of the detector is an option but we +prefer to take the peak the nearest to the centroid of all other peaks. + +.. code:: python + + #Select the initial guess for the center: + + #Most intense peak: + #m = max([i for i in regions], key=lambda i:i.maxi) + #Cx, Cy = m.index%img.shape[-1],m.index//img.shape[-1] + #Cx, Cy = point["center_x"].mean(), point["center_y"].mean() #Centroid of all points + Cx, Cy = 734, 1181 #beam center + #Cx, Cy = tuple(i//2 for i in cnv.shape) #detector center + print("The guessed center is at (%s, %s)"%(Cx, Cy)) + + #Get the nearest point from centroid: + d2 = ((point["center_x"]-Cx)**2+(point["center_y"]-Cy)**2) + best = d2.argmin() + Op = point[best] + Ox, Oy = Op["center_x"], Op["center_y"] + + print("The center is at (%s, %s)"%(Ox, Oy)) + #Calculate the average vector along the 4 main axes + Xx = (point[:]["east_x"] - point[:]["center_x"]).mean() + Xy = (point[:]["east_y"] - point[:]["center_y"]).mean() + Yx = (point[:]["north_x"] - point[:]["center_x"]).mean() + Yy = (point[:]["north_y"] - point[:]["center_y"]).mean() + + print("The X vector is is at (%s, %s)"%(Xx, Xy)) + print("The Y vector is is at (%s, %s)"%(Yx, Yy)) + + + +.. parsed-literal:: + + The guessed center is at (734, 1181) + The center is at (753.703500152, 1186.18798503) + The X vector is is at (97.7197301826, -0.787977117653) + The Y vector is is at (1.38218579497, 97.0826990758) + + +.. code:: python + + print("X has an angle of %s deg"%rad2deg(arctan2(Xy, Xx))) + print("Y has an angle of %s deg"%rad2deg(arctan2(Yy, Yx))) + print("The XY angle is %s deg"%rad2deg(arctan2(Yy, Yx)-arctan2(Xy, Xx))) + + + +.. parsed-literal:: + + X has an angle of -0.462002756355 deg + Y has an angle of 89.1843236418 deg + The XY angle is 89.6463263982 deg + + +.. code:: python + + x = point[:]["center_x"] - Ox + y = point[:]["center_y"] - Oy + xy = numpy.vstack((x,y)) + R = numpy.array([[Xx,Yx],[Xy,Yy]]) + iR = numpy.linalg.inv(R) + IJ = dot(iR,xy).T + +.. code:: python + + Xmin = IJ[:,0].min() + Xmax = IJ[:,0].max() + Ymin = IJ[:,1].min() + Ymax = IJ[:,1].max() + print("Xmin/max",Xmin, Xmax) + print("Ymin/max",Ymin,Ymax) + print("Maximum error versus integrer: %s * pitch size (5mm)"%(abs(IJ-IJ.round()).max())) + + +.. parsed-literal:: + + Xmin/max -6.07394212848 12.060721056 + Ymin/max -11.0890545732 7.04060363671 + Maximum error versus integrer: 0.117211354675 * pitch size (5mm) + + +At this point it is important to check the correct rounding to integers: +The maximum error should definitely be better than 0.2\*pitch ! If not, +try to change the origin (Cx and Cy). This criteria will be used for the +optimization later on. + +.. code:: python + + plot(IJ[:,0],IJ[:,1],"or") + idx = numpy.round(IJ).astype(int) + plot(idx[:,0],IJ[:,1],"og") + xlim(floor(Xmin), ceil(Xmax)) + ylim(floor(Ymin), ceil(Ymax)) + title("Red: measured peaks, Green: Expected position") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_39_1.png + + +Estimation of the pixel size: +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The pixel size is obtained from the pitch of the grid, in vectorial: + +pitch^2 = (Px\*Xx)^2 + (Py Xy)^2 + +pitch^2 = (Px\*Yx)^2 + (Py Yy)^2 + +.. code:: python + + pitch = 5e-3 #mm distance between holes + Py = pitch*sqrt((Yx**2-Xx**2)/((Xy*Yx)**2-(Xx*Yy)**2)) + Px = sqrt((pitch**2-(Xy*Py)**2)/Xx**2) + print("Pixel size in average: x:%.3f micron, y: %.3f microns"%(Px*1e6, Py*1e6)) + + +.. parsed-literal:: + + Pixel size in average: x:51.165 micron, y: 51.497 microns + + +At this stage, we have: + +- A list of control points placed on a regular grid with a sub-pixel + precision +- The center of the image, located on a control point +- the average X and Y vector to go from one control point to another + +Optimization of the pixel position +---------------------------------- + +The optimization is obtained by minimizing the mis-placement of the +control points on the regular grid. For a larger coverage we include now +the peaks on the border with less than 4 neighbours. + +.. code:: python + + #Measured peaks (all!), needs to flip x<->y + peaks_m = numpy.empty_like(peaks_ref) + peaks_m[:,1] = peaks_ref[:,0] + peaks_m[:,0] = peaks_ref[:,1] + + #parameter set for optimization: + P0 = [Ox, Oy, Xx, Yx, Xy, Yy] + + P = numpy.array(P0) + + def to_hole(P, pixels): + "Translate pixel -> hole" + T = numpy.atleast_2d(P[:2]) + R = P[2:].reshape((2,2)) + + #Transformation matrix from pixel to holes: + hole = dot(numpy.linalg.inv(R), (pixels - T).T).T + return hole + + def to_pix(P, holes): + "Translate hole -> pixel" + T = numpy.atleast_2d(P[:2]) + R = P[2:].reshape((2,2)) + #Transformation from index points (holes) to pixel coordinates: + pix = dot(R,holes.T).T + T + return pix + + def error(P): + "Error function" + hole_float = to_hole(P, peaks_m) + hole_int = hole_float.round() + delta = hole_float-hole_int + delta2 = (delta**2).sum() + return delta2 + + print("Total inital error ", error(P), P0) + holes = to_hole(P, peaks_m) + print("Maximum initial error versus integrer: %s * pitch size (5mm)"%(abs(holes-holes.round()).max())) + from scipy.optimize import minimize + res = minimize(error, P) + print(res) + print("total Final error ", error(res.x),res.x) + holes = to_hole(res.x, peaks_m) + print("Maximum final error versus integrer: %s * pitch size (5mm)"%(abs(holes-holes.round()).max())) + + + +.. parsed-literal:: + + Total inital error 2.5995763607 [753.70350015163422, 1186.1879850327969, 97.719730182623479, 1.3821857949656571, -0.78797711765336542, 97.082699075794565] + Maximum initial error versus integrer: 0.199838456433 * pitch size (5mm) + jac: array([ -1.49011612e-07, -2.08616257e-07, -2.68220901e-07, + 1.19209290e-07, 1.19209290e-07, 3.87430191e-07]) + nfev: 160 + nit: 15 + message: 'Optimization terminated successfully.' + status: 0 + njev: 20 + success: True + x: array([ 7.53021133e+02, 1.18519692e+03, 9.81143527e+01, + 1.47509463e+00, -8.04478515e-01, 9.73166896e+01]) + fun: 2.1237728421683415 + hess_inv: array([[ 1.42100651e+01, 5.92064797e-01, -8.65311650e-01, + 5.71616778e-01, -2.93157223e-02, 3.36330120e-02], + [ 5.92064797e-01, 1.42890834e+01, -3.28993124e-02, + 1.71968068e-02, -8.57363816e-01, 5.83878215e-01], + [ -8.65311650e-01, -3.28993124e-02, 2.99126185e-01, + -4.37220702e-03, 4.01788293e-03, -2.74807907e-03], + [ 5.71616778e-01, 1.71968068e-02, -4.37220702e-03, + 3.02142044e-01, -1.06852612e-03, 2.67366996e-03], + [ -2.93157223e-02, -8.57363816e-01, 4.01788293e-03, + -1.06852612e-03, 2.95805859e-01, -4.27421580e-03], + [ 3.36330120e-02, 5.83878215e-01, -2.74807907e-03, + 2.67366996e-03, -4.27421580e-03, 2.97301584e-01]]) + total Final error 2.12377284217 [ 7.53021133e+02 1.18519692e+03 9.81143527e+01 1.47509463e+00 + -8.04478515e-01 9.73166896e+01] + Maximum final error versus integrer: 0.234645010209 * pitch size (5mm) + + +.. code:: python + + clf() + peaks_c = to_pix(res.x,to_hole(res.x,peaks_m).round()) + imshow(img, interpolation="nearest", origin="lower") + plot(peaks_m[:,0],peaks_m[:, 1], "or") + plot(peaks_c[:,0], peaks_c[:, 1], "og") + xlim(0,2048) + ylim(0,2048) + title("Peak position: measured (red) and expected (Green)") + + + + + +.. parsed-literal:: + + + + + + +.. image:: output_45_1.png + + +.. code:: python + + pitch = 5e-3 #mm distance between holes + Ox, Oy, Xx, Yx, Xy, Yy = res.x + Py = pitch*sqrt((Yx**2-Xx**2)/((Xy*Yx)**2-(Xx*Yy)**2)) + Px = sqrt((pitch**2-(Xy*Py)**2)/Xx**2) + print("Optimized pixel size in average: x:%.3f micron, y: %.3f microns"%(Px*1e6, Py*1e6)) + + +.. parsed-literal:: + + Optimized pixel size in average: x:50.959 micron, y: 51.373 microns + + +Few comments: + +- The maximum error grow during optimization without explainations +- The outer part of the detector is the most distorted + +Interpolation of the fitted data +-------------------------------- + +Multivariate data interpolation (griddata) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Correction arrays are built slightly larger (+1) to be able to +manipulate corners instead of centers of pixels As coordinates are +needed as y,x (and not x,y) we use p instead of peaks\_m + +.. code:: python + + from scipy.interpolate import griddata + grid_x, grid_y = np.mgrid[0:img.shape[0]+1, 0:img.shape[1]+1] + delta = peaks_c - peaks_m + #we use peaks_res instead of peaks_m to be in y,x coordinates, not x,y + delta_x = griddata(peaks_ref, delta[:,0], (grid_x, grid_y), method='cubic') + delta_y = griddata(peaks_ref, delta[:,1], (grid_x, grid_y), method='cubic') + + figure(figsize=(12,5)) + subplot(1,2,1) + imshow(delta_x,origin="lower", interpolation="nearest") + title(r"$\delta$ x") + colorbar() + subplot(1,2,2) + imshow(delta_y, origin="lower", interpolation="nearest") + title(r"$\delta$ y") + colorbar() + #Nota: the arrays are filled with "NaN" outside the convex Hull + + + + +.. parsed-literal:: + + + + + + +.. image:: output_48_1.png + + +.. code:: python + + #From http://stackoverflow.com/questions/3662361/fill-in-missing-values-with-nearest-neighbour-in-python-numpy-masked-arrays + def fill(data, invalid=None): + """ + Replace the value of invalid 'data' cells (indicated by 'invalid') + by the value of the nearest valid data cell + + Input: + data: numpy array of any dimension + invalid: a binary array of same shape as 'data'. True cells set where data + value should be replaced. + If None (default), use: invalid = np.isnan(data) + + Output: + Return a filled array. + """ + + if invalid is None: + invalid = numpy.isnan(data) + + ind = ndimage.distance_transform_edt(invalid, return_distances=False, return_indices=True) + return data[tuple(ind)] + + +.. code:: python + + figure(figsize=(12,5)) + subplot(1,2,1) + imshow(fill(delta_x),origin="lower", interpolation="nearest") + title(r"$\delta$ x") + colorbar() + subplot(1,2,2) + imshow(fill(delta_y), origin="lower", interpolation="nearest") + title(r"$\delta$ y") + colorbar() + + + + + +.. parsed-literal:: + + + + + + +.. image:: output_50_1.png + + +It is important to understand the extrapolation outside the convex hull +has no justification, it is there just to prevent numerical bugs. + +Saving the distortion correction arrays to a detector +----------------------------------------------------- + +.. code:: python + + from pyFAI.detectors import Detector + detector = Detector(Py,Px) + detector.max_shape = detector.shape = img.shape + detector.set_dx(fill(delta_x)) + detector.set_dy(fill(delta_y)) + detector.mask = numpy.isnan(delta_x).astype(numpy.int8) + detector.save("testdetector.h5") + + +Validation of the distortion correction +--------------------------------------- + +.. code:: python + + from pyFAI.distortion import Distortion + dis = Distortion(detector) + cor = dis.correct(img) + figure(figsize=(12,5)) + subplot(1,2,1) + imshow(img, interpolation="nearest", origin="lower") + title("Original") + subplot(1,2,2) + imshow(cor, origin="lower", interpolation="nearest") + title("Corrected") + fabio.edfimage.EdfImage(data=cor).save("corrected.edf") + + +.. parsed-literal:: + + WARNING:pyFAI.timeit:calc_pos took 0.747s + WARNING:pyFAI.timeit:calc_size took 0.944s + WARNING:pyFAI.timeit:calc_LUT took 2.907s + + + +.. image:: output_54_1.png + + +Conclusion +========== +This procedure describes how to measure the detector +distortion and how to create a detector description file directly +useable in pyFAI. Only the region inside the convex hull of the grid +data-points is valid and the region of the detector which is not +calibrated has been masked out to preven accidental use of it. + +The distortion corrected image can now be used to check how "good" the +calibration actually is. This file can be injected in the third cell, +and follow the same procedure (left as exercise). This gives a maximum +mis-placement of 0.003, the average error is then of 0.0006 and +correction-map exhibit a displacement of pixels in the range +/- 0.2 +pixels which is acceptable and validates the whole procedure. + diff --git a/doc/build/_sources/usage/tutorial/Calibrant/index.txt b/doc/build/_sources/usage/tutorial/Calibrant/index.txt new file mode 100644 index 000000000..eb90ba841 --- /dev/null +++ b/doc/build/_sources/usage/tutorial/Calibrant/index.txt @@ -0,0 +1,236 @@ + +Selection of a calibrant +======================== + +In this tutorial we will see how to select a calibrant for a given +experimental setup. + +Experimental setup +------------------ + +The experimental setup is a classical protein crystallography setup +with: + +- Large Pilatus 6M detector on a translation table +- The small and intense beam of ~50 microns in size has a wavelength of + 1 Angstrom +- The detector is in *normal* condition: orthogonal to the beam and + centered in the middle of the detector. + +The scientist in charge of this beamline want to ensure all encoders are +working properly and needs to validate the setup for distances between +10cm and 80cm. He will buy reference material from NIST but he does not +know which calibrant is best suited for his setup. We will assume all +reference material sold by NIST are equally suitable for ray position +(no issue with grain size, ...). + +The calibration works best in pyFAI if more than one Debye-Scherrer ring +is seen on the detector. + +Define the detector +------------------- + +.. code:: python + + import pyFAI + dete = pyFAI.detectors.Pilatus6M() + print(dete) + +.. parsed-literal:: + + Detector Pilatus6M PixelSize= 1.720e-04, 1.720e-04 m + + +Select reference materials +-------------------------- + +The NIST sells different `Standard Refrence +Materials `__, among +them Silicon (SRM640), Lanthanum hexaboride (SRM660), Alumina (SRM676) +and Ceria (SRM674) are commonly used. Many other exists: Cr203, TiO2, +Zn0, SiO2, ... Evaluating them is left as an exercise. + +.. code:: python + + import pyFAI.calibrant + print(pyFAI.calibrant.ALL_CALIBRANTS) + +.. parsed-literal:: + + Calibrants available: Ni, CrOx, NaCl, Si_SRM640e, Si_SRM640d, Si_SRM640a, Si_SRM640c, alpha_Al2O3, Cr2O3, AgBh, Si_SRM640, CuO, PBBA, Si_SRM640b, quartz, C14H30O, cristobaltite, Si, LaB6, CeO2, LaB6_SRM660a, LaB6_SRM660b, LaB6_SRM660c, TiO2, ZnO, Al, Au + + +You may wonder where the names of the calibrant came from and how they +have been established. + +The name of all calibrant available in your version of pyFAI can be +listed by just printing out the content of ALL\_CALIBRANTS. New +calibrant may have been added in pyFAI in more recent releases, just +have a look at the `developent web +page `__. + +Most of those calibrant files, which contain the *d-spacing* in Angstrom +between Miller plans, have been prepared from the unit cell of the +compount, found in publication. This publication is referenced in the +header of the file. If one wishes to regenerate those files, the +*pyFAI.calibrant.Cell* class may be used for. + +We will now focus on a subset of calibrant, instanciate them and put +them into a dictionnary. The Python construct used here is called +*dict-comprehension* and allows the creation and population of a +dictionnary in a single line. + +.. code:: python + + cals = dict((name,pyFAI.calibrant.ALL_CALIBRANTS(name)) for name in ("Si", "LaB6", "CeO2", "alpha_Al2O3")) + print(cals) + +.. parsed-literal:: + + {'Si': Si Calibrant , 'alpha_Al2O3': alpha_Al2O3 Calibrant , 'LaB6': LaB6 Calibrant , 'CeO2': CeO2 Calibrant } + + +To be able to use those calibrants, one needs to define the wavelength +used, here 1 Angstrom. + +.. code:: python + + wl = 1e-10 + for cal in cals.values(): + cal.wavelength = wl + print(cals) + +.. parsed-literal:: + + {'Si': Si Calibrant at wavelength 1e-10, 'alpha_Al2O3': alpha_Al2O3 Calibrant at wavelength 1e-10, 'LaB6': LaB6 Calibrant at wavelength 1e-10, 'CeO2': CeO2 Calibrant at wavelength 1e-10} + + +Short distance images +--------------------- + +The shortest the detector can come to the sample is about 10cm (to leave +space for the beamstop). We will generate images of diffraction at this +distance. + +For the display of images we will use *matplotlib* inlined. + +.. code:: python + + p1, p2, p3 = dete.calc_cartesian_positions() + poni1 = p1.mean() + poni2 = p2.mean() + print("Detector center at %s, %s"%(poni1, poni2)) + ai_short = pyFAI.AzimuthalIntegrator(dist=0.1, poni1=poni1, poni2=poni2,detector=dete) + print(ai_short) + +.. parsed-literal:: + + Detector center at 0.217322000001, 0.211818 + Detector Pilatus6M PixelSize= 1.720e-04, 1.720e-04 m + SampleDetDist= 1.000000e-01m PONI= 2.173220e-01, 2.118180e-01m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=1231.500, y=1263.500 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + + +.. code:: python + + %pylab inline + +.. parsed-literal:: + + Populating the interactive namespace from numpy and matplotlib + + +.. code:: python + + fig = figure(figsize=(10,10)) + for idx, key in enumerate(cals): + cal = cals[key] + subplot(2,2,idx+1) + img = cal.fake_calibration_image(ai_short) + imshow(img) + title(key) + + +.. image:: output_11_0.png + + +As one can see, there are plenty of rings on the image: it should be +easy to calibrate. By moving further away the detector, the number of +rings will decrease. + +Long distance images +-------------------- + +To keep good calibration one should have at lease two rings for the +calibration. The longest distance from sample to the detector is 80cm. + +.. code:: python + + ai_long = pyFAI.AzimuthalIntegrator(dist=0.8, poni1=poni1, poni2=poni2,detector=dete) + print(ai_long) + +.. parsed-literal:: + + Detector Pilatus6M PixelSize= 1.720e-04, 1.720e-04 m + SampleDetDist= 8.000000e-01m PONI= 2.173220e-01, 2.118180e-01m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 800.000mm Center: x=1231.500, y=1263.500 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + + +.. code:: python + + fig = figure(figsize=(10,10)) + for idx, key in enumerate(cals): + cal = cals[key] + subplot(2,2,idx+1) + img = cal.fake_calibration_image(ai_long) + imshow(img) + title(key) + + +.. image:: output_14_0.png + + +The most adapted calibrant is probably the *LaB6* as 2 rings are still +visible at 80 cm from the detector. + +Integration of the pattern for the two extreme cases +---------------------------------------------------- + +We can integrate the image for the two extreme cases: + +.. code:: python + + lab6 = cals["LaB6"] + ai_short.wavelength = ai_long.wavelength = wl + + fig = figure(figsize=(10,10)) + ax = subplot(2,2,1) + img_short = lab6.fake_calibration_image(ai_short) + ax.imshow(img_short) + ax = subplot(2,2,2) + ax.plot(*ai_short.integrate1d(img_short,1000)) + ax.set_xlabel("q nm^-1") + ax = subplot(2,2,3) + img_long = lab6.fake_calibration_image(ai_long) + ax.imshow(img_long) + ax = subplot(2,2,4) + ax.plot(*ai_long.integrate1d(img_long,1000)) + ax.set_xlabel("q nm^-1") + + + +.. parsed-literal:: + + + + + + +.. image:: output_16_1.png + + +Conclusion +---------- + +The best calibrant in this case is probably LaB6. + diff --git a/doc/build/_sources/usage/tutorial/Distortion/Distortion.txt b/doc/build/_sources/usage/tutorial/Distortion/Distortion.txt new file mode 100644 index 000000000..20eb29dc1 --- /dev/null +++ b/doc/build/_sources/usage/tutorial/Distortion/Distortion.txt @@ -0,0 +1,489 @@ + +Detector distortion corrections +=============================== + +This tutorial shows how to correct images for spatial distortion. Some +tutorial examples rely on files available in +http://www.silx.org/pub/pyFAI/testimages/ and will be downloaded during +this tutorial. The requiered minimum version of pyFAI is 0.12.0 +(currently dev5) + +Detector definitions +-------------------- + +PyFAI features an impressive list of 55 detector definitions contributed +often by manufacturers and some other reverse engineerd by scientists. +Each of them is defined as an invividual class which contains a way to +calculate the mask (invalid pixels, gaps,…) and a method to calculate +the pixel positions in Cartesian coordinates. + +.. code:: python + + import pyFAI + all_detectors = list(set(pyFAI.detectors.ALL_DETECTORS.values())) + #Sort detectors according to their name + all_detectors.sort(key=lambda i:i.__name__) + nb_det = len(all_detectors) + print("Number of detectors registered:", nb_det) + for i in all_detectors: + print(i()) + + +.. parsed-literal:: + + WARNING:xsdimage:lxml library is probably not part of your python installation: disabling xsdimage format + WARNING:pyFAI.utils:Exception No module named 'fftw3': FFTw3 not available. Falling back on Scipy + + +.. parsed-literal:: + + Number of detectors registered: 55 + Detector Quantum 210 Spline= None PixelSize= 5.100e-05, 5.100e-05 m + Detector Quantum 270 Spline= None PixelSize= 6.480e-05, 6.480e-05 m + Detector Quantum 315 Spline= None PixelSize= 5.100e-05, 5.100e-05 m + Detector Quantum 4 Spline= None PixelSize= 8.200e-05, 8.200e-05 m + Detector Aarhus Spline= None PixelSize= 2.500e-05, 2.500e-05 m + Detector ApexII PixelSize= 6.000e-05, 6.000e-05 m + Detector aca1300 PixelSize= 3.750e-06, 3.750e-06 m + Undefined detector + Detector Dexela 2923 PixelSize= 7.500e-05, 7.500e-05 m + Detector Eiger16M PixelSize= 7.500e-05, 7.500e-05 m + Detector Eiger1M PixelSize= 7.500e-05, 7.500e-05 m + Detector Eiger4M PixelSize= 7.500e-05, 7.500e-05 m + Detector Eiger9M PixelSize= 7.500e-05, 7.500e-05 m + Detector Fairchild PixelSize= 1.500e-05, 1.500e-05 m + Detector HF-130k Spline= None PixelSize= 1.500e-04, 1.500e-04 m + Detector HF-1M Spline= None PixelSize= 1.500e-04, 1.500e-04 m + Detector HF-262k Spline= None PixelSize= 1.500e-04, 1.500e-04 m + Detector HF-2.4M Spline= None PixelSize= 1.500e-04, 1.500e-04 m + Detector HF-4M Spline= None PixelSize= 1.500e-04, 1.500e-04 m + Detector HF-9.4M Spline= None PixelSize= 1.500e-04, 1.500e-04 m + Detector Imxpad S10 PixelSize= 1.300e-04, 1.300e-04 m + Detector Imxpad S140 PixelSize= 1.300e-04, 1.300e-04 m + Detector Imxpad S70 PixelSize= 1.300e-04, 1.300e-04 m + Detector MAR 345 PixelSize= 1.000e-04, 1.000e-04 m + Detector Pixium 4700 detector PixelSize= 1.540e-04, 1.540e-04 m + Detector Perkin detector PixelSize= 2.000e-04, 2.000e-04 m + Detector Pilatus100k PixelSize= 1.720e-04, 1.720e-04 m + Detector Pilatus1M PixelSize= 1.720e-04, 1.720e-04 m + Detector Pilatus200k PixelSize= 1.720e-04, 1.720e-04 m + Detector Pilatus2M PixelSize= 1.720e-04, 1.720e-04 m + Detector Pilatus300k PixelSize= 1.720e-04, 1.720e-04 m + Detector Pilatus300kw PixelSize= 1.720e-04, 1.720e-04 m + Detector Pilatus6M PixelSize= 1.720e-04, 1.720e-04 m + Detector PilatusCdTe1M PixelSize= 1.720e-04, 1.720e-04 m + Detector PilatusCdTe2M PixelSize= 1.720e-04, 1.720e-04 m + Detector PilatusCdTe300k PixelSize= 1.720e-04, 1.720e-04 m + Detector PilatusCdTe300kw PixelSize= 1.720e-04, 1.720e-04 m + Detector Rayonix PixelSize= 3.200e-05, 3.200e-05 m + Detector MAR133 PixelSize= 6.400e-05, 6.400e-05 m + Detector Rayonix lx170 PixelSize= 4.427e-05, 4.427e-05 m + Detector Rayonix lx255 PixelSize= 4.427e-05, 4.427e-05 m + Detector Rayonix mx170 PixelSize= 4.427e-05, 4.427e-05 m + Detector Rayonix mx225 PixelSize= 7.324e-05, 7.324e-05 m + Detector Rayonix mx225hs PixelSize= 7.813e-05, 7.813e-05 m + Detector Rayonix mx300 PixelSize= 7.324e-05, 7.324e-05 m + Detector Rayonix mx300hs PixelSize= 7.813e-05, 7.813e-05 m + Detector Rayonix mx325 PixelSize= 7.935e-05, 7.935e-05 m + Detector Rayonix mx340hs PixelSize= 8.854e-05, 8.854e-05 m + Detector Rayonix mx425hs PixelSize= 4.427e-05, 4.427e-05 m + Detector MAR165 PixelSize= 3.950e-05, 3.950e-05 m + Detector Rayonix sx200 PixelSize= 4.800e-05, 4.800e-05 m + Detector Rayonix Sx30hs PixelSize= 1.563e-05, 1.563e-05 m + Detector Rayonix Sx85hs PixelSize= 4.427e-05, 4.427e-05 m + Detector Titan 2k x 2k PixelSize= 6.000e-05, 6.000e-05 m + Detector Xpad S540 flat PixelSize= 1.300e-04, 1.300e-04 m + + +Defining a detector from a spline file +-------------------------------------- + +For optically coupled CCD detectors, the geometrical distortion is often +described by a two-dimensional cubic spline (as in FIT2D) which can be +imported into the relevant detector instance and used to calculate the +actual pixel position in space (and masked pixels). + +At the ESRF, mainly FReLoN detectors [J.-C. Labiche, ESRF Newsletter 25, +41 (1996)] are used with spline files describing the distortion of the +fiber optic taper. + +Let's download such a file and create a detector from it. + +.. code:: python + + import os, sys + os.environ["http_proxy"] = "http://proxy.site.com:3128" + def download(url): + """download the file given in URL and return its local path""" + if sys.version_info[0]<3: + from urllib2 import urlopen, ProxyHandler, build_opener + else: + from urllib.request import urlopen, ProxyHandler, build_opener + dictProxies = {} + if "http_proxy" in os.environ: + dictProxies['http'] = os.environ["http_proxy"] + dictProxies['https'] = os.environ["http_proxy"] + if "https_proxy" in os.environ: + dictProxies['https'] = os.environ["https_proxy"] + if dictProxies: + proxy_handler = ProxyHandler(dictProxies) + opener = build_opener(proxy_handler).open + else: + opener = urlopen + target = os.path.split(url)[-1] + with open(target,"wb") as dest, opener(url) as src: + dest.write(src.read()) + return target + + spline_file = download("http://www.silx.org/pub/pyFAI/testimages/halfccd.spline") + +.. code:: python + + hd = pyFAI.detectors.FReLoN(splineFile=spline_file) + print(hd) + print("Shape: %i, %i"% hd.shape) + + +.. parsed-literal:: + + Detector FReLoN Spline= /users/kieffer/workspace-400/pyFAI/doc/source/usage/tutorial/Distortion/halfccd.spline PixelSize= 4.842e-05, 4.684e-05 m + Shape: 1025, 2048 + + +*Note* the unusual shape of this detector. This is probably a human +error when calibrating the detector distortion in FIT2D. + +Visualizing the mask +~~~~~~~~~~~~~~~~~~~~ + +Every detector object contains a mask attribute, defining pixels which +are invalid. For FReLoN detector (a spline-files-defined detectors), all +pixels having an offset such that the pixel falls out of the initial +detector are considered as invalid. + +Masked pixel have non-null values can be displayed like this: + +.. code:: python + + %pylab inline + imshow(hd.mask, origin="lower", interpolation="nearest") + + +.. parsed-literal:: + + Populating the interactive namespace from numpy and matplotlib + + + + +.. parsed-literal:: + + + + + + +.. image:: output_6_2.png + + +Detector definition files as NeXus files +---------------------------------------- + +Any detector object in pyFAI can be saved into an HDF5 file following +the NeXus convention [Könnecke et al., 2015, J. Appl. Cryst. 48, +301-305.]. Detector objects can subsequently be restored from disk, +making complex detector definitions less error prone. + +.. code:: python + + h5_file = "halfccd.h5" + hd.save(h5_file) + new_det = pyFAI.detector_factory(h5_file) + print(new_det) + print("Mask is the same: ", numpy.allclose(new_det.mask, hd.mask)) + print("Pixel positions are the same: ", numpy.allclose(new_det.get_pixel_corners(), hd.get_pixel_corners())) + print("Number of masked pixels", new_det.mask.sum()) + + +.. parsed-literal:: + + FReLoN detector from NeXus file: halfccd.h5 PixelSize= 4.842e-05, 4.684e-05 m + Mask is the same: True + Pixel positions are the same: True + Number of masked pixels 34382 + + +Pixels of an area detector are saved as a four-dimensional dataset: i.e. +a two-dimensional array of vertices pointing to every corner of each +pixel, generating an array of dimension (Ny, Nx, Nc, 3), where Nx and Ny +are the dimensions of the detector, Nc is the number of corners of each +pixel, usually four, and the last entry contains the coordinates of the +vertex itself (in the order: Z, Y, X). + +This kind of definition, while relying on large description files, can +address some of the most complex detector layouts. They will be +presented a bit later in this tutorial. + +.. code:: python + + print("Size of Spline-file:", os.stat('halfccd.spline').st_size) + print("Size of Nexus-file:", os.stat('halfccd.h5').st_size) + + +.. parsed-literal:: + + Size of Spline-file: 1183 + Size of Nexus-file: 21451707 + + +The HDF5 file is indeed much larger than the spline file. + +Modify a detector and saving +---------------------------- + +One may want to define a new mask (or flat-field) for its detector and +save the mask with the detector definition. Here, we create a copy of +the detector and reset its mask to enable all pixels in the detector and +save the new detector instance into another file. + +.. code:: python + + import copy + nomask_file = "nomask.h5" + nomask = copy.deepcopy(new_det) + nomask.mask = numpy.zeros_like(new_det.mask) + nomask.save(nomask_file) + nomask = pyFAI.detector_factory("nomask.h5") + print("No pixels are masked",nomask.mask.sum()) + + +.. parsed-literal:: + + No pixels are masked 0 + + +**Wrap up** + +In this section we have seen how detectors are defined in pyFAI, how +they can be created, either from the list of the parametrized ones, or +from spline files, or from NeXus detector files. We have also seen how +to save and subsequently restore a detector instance, preserving the +modifications made. + +Distortion correction +--------------------- + +Once the position of every single pixel in space is known, one can +benefit from the regridding engine of pyFAI adapted to image distortion +correction tasks. The *pyFAI.distortion.Distortion* class is the +equivalent of the *pyFAI.AzimuthalIntegrator* for distortion. Provided +with a detector definition, it enables the correction of a set of images +by using the same kind of look-up tables as for azimuthal integration. + +.. code:: python + + from pyFAI.distortion import Distortion + dis = Distortion(nomask) + print(dis) + + +.. parsed-literal:: + + Distortion correction lut on device None for detector shape (1025, 2048): + NexusDetector detector from NeXus file: nomask.h5 PixelSize= 4.842e-05, 4.684e-05 m + + +FReLoN detector +~~~~~~~~~~~~~~~ + +First load the image to be corrected, then correct it for geometric +distortion. + +.. code:: python + + halfccd_img = download("http://www.silx.org/pub/pyFAI/testimages/halfccd.edf") + import fabio + raw = fabio.open(halfccd_img).data + cor = dis.correct(raw) + + #Then display images side by side + numpy.seterr(divide="ignore") #remove warning messages from numpy + figure(figsize=(12,6)) + subplot(1,2,1) + imshow(numpy.log(raw), interpolation="nearest", origin="lower") + subplot(1,2,2) + imshow(numpy.log(cor), interpolation="nearest", origin="lower") + + +.. parsed-literal:: + + ERROR:pyFAI.distortion:The image shape ((1024, 2048)) is not the same as the detector ((1025, 2048)). Adapting shape ... + + + + +.. parsed-literal:: + + + + + + +.. image:: output_17_2.png + + +**Nota:** in this case the image size (1024 lines) does not match the +detector's number of lines (1025) hence pyFAI complains about it. Here, +pyFAI patched the image on an empty image of the right size so that the +processing can occur. + +In this example, the size of the pixels and the shape of the detector +are preserved, discarding all pixels falling outside the detector's +grid. + +One may want all pixels' intensity to be preserved in the +transformation. By allowing the output array to be large enough to +accomodate all pixels, the total intensity can be kept. For this, just +enable the "resize" option in the constructor of *Distortion*: + +.. code:: python + + dis1 = Distortion(hd, resize=True) + print(dis1) + cor = dis1.correct(raw) + print(dis1) + print("After correction, the image has a different shape", cor.shape) + + +.. parsed-literal:: + + ERROR:pyFAI.distortion:The image shape ((1024, 2048)) is not the same as the detector ((1025, 2048)). Adapting shape ... + + +.. parsed-literal:: + + Distortion correction lut on device None for detector shape None: + Detector FReLoN Spline= /users/kieffer/workspace-400/pyFAI/doc/source/usage/tutorial/Distortion/halfccd.spline PixelSize= 4.842e-05, 4.684e-05 m + Distortion correction lut on device None for detector shape (1045, 2052): + Detector FReLoN Spline= /users/kieffer/workspace-400/pyFAI/doc/source/usage/tutorial/Distortion/halfccd.spline PixelSize= 4.842e-05, 4.684e-05 m + After correction, the image has a different shape (1045, 2052) + + +Example of Pixel-detectors: +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +XPad Flat detector +^^^^^^^^^^^^^^^^^^ + +There is a striking example in the cover image of this article: +http://scripts.iucr.org/cgi-bin/paper?S1600576715004306 where a detector +made of multiple modules is *eating up* some rings. The first example +will be about the regeneration of an "eyes friendly" version of this +image. + +.. code:: python + + xpad_url = "http://www.silx.org/pub/pyFAI/testimages/LaB6_18.57keV_frame_13.edf" + xpad_file = download(xpad_url) + xpad = pyFAI.detector_factory("Xpad_flat") + print(xpad) + xpad_dis = Distortion(xpad, resize=True) + + raw = fabio.open(xpad_file).data + cor = xpad_dis.correct(raw) + print("Shape as input and output:", raw.shape, cor.shape) + + #then display images side by side + figure(figsize=(12,10)) + subplot(1,2,1) + imshow(numpy.log(raw), interpolation="nearest", origin="lower") + subplot(1,2,2) + imshow(numpy.log(cor), interpolation="nearest", origin="lower") + + print("Conservation of the total intensity:", raw.sum(), cor.sum()) + + +.. parsed-literal:: + + Detector Xpad S540 flat PixelSize= 1.300e-04, 1.300e-04 m + Shape as input and output: (960, 560) (1174, 578) + Conservation of the total intensity: 11120798 1.11208e+07 + + + +.. image:: output_21_1.png + + +WOS XPad detector +^^^^^^^^^^^^^^^^^ + +This is a new **WAXS opened for SAXS** pixel detector from ImXPad +(available at ESRF-BM02/D2AM CRG beamline). It looks like two of +*XPad\_flat* detectors side by side with some modules shifted in order +to create a hole to accomodate a flight-tube which gathers the SAXS +photons to a second detector further away. + +The detector definition for this specific detector has directly been put +down using the metrology informations from the manufacturer and saved as +a NeXus detector definition file. + +.. code:: python + + wos_det = download("http://www.silx.org/pub/pyFAI/testimages/WOS.h5") + wos_img = download("http://www.silx.org/pub/pyFAI/testimages/WOS.edf") + wos = pyFAI.detector_factory(wos_det) + print(wos) + wos_dis = Distortion(wos, resize=True) + + raw = fabio.open(wos_img).data + cor = wos_dis.correct(raw) + print("Shape as input and output:", raw.shape, cor.shape) + + #then display images side by side + figure(figsize=(12,10)) + subplot(1,2,1) + imshow(numpy.log(raw), interpolation="nearest", origin="lower") + subplot(1,2,2) + imshow(numpy.log(cor), interpolation="nearest", origin="lower") + + print("Conservation of the total intensity:", raw.sum(), cor.sum()) + + +.. parsed-literal:: + + NexusDetector detector from NeXus file: WOS.h5 PixelSize= 1.300e-04, 1.300e-04 m + Shape as input and output: (598, 1154) (710, 1302) + Conservation of the total intensity: 444356428 4.44363e+08 + + +.. parsed-literal:: + + /scisoft/users/jupyter/jupy34/lib/python3.4/site-packages/ipykernel/__main__.py:14: RuntimeWarning: invalid value encountered in log + + + +.. image:: output_23_2.png + + +**Nota:** Do not use this detector definition file to process data from +the WOS@D2AM as it has not (yet) been fully validated and may contain +some errors in the pixel positioning. + +Conclusion +---------- + +PyFAI provides a very comprehensive list of detector definitions, is +versatile enough to address most area detectors on the market, and +features a powerful regridding engine, both combined together into the +distortion correction tool which ensures the conservation of the signal +during the transformation (the number of photons counted is preserved +during the transformation) + +Distortion correction should not be used for pre-processing images prior +to azimuthal integration as it re-bins the image, thus induces a +broadening of the peaks. The AzimuthalIntegrator object performs all +this together with integration, it has hence a better precision. + +This tutorial did not answer the question *how to calibrate the +distortion of a given detector ?* which is addressed in another tutorial +called **detector calibration**. diff --git a/doc/build/_sources/usage/tutorial/Introduction/introduction.txt b/doc/build/_sources/usage/tutorial/Introduction/introduction.txt new file mode 100644 index 000000000..881fec981 --- /dev/null +++ b/doc/build/_sources/usage/tutorial/Introduction/introduction.txt @@ -0,0 +1,511 @@ + +Introduction to the tutorials +============================= + +The iPython/Jupyter notebook +---------------------------- + +The document you are seeing it may be a PDF or a web page or another +format, but what is important is how it has been made ... + +According to this article in +`Nature `__ +the Notebook, invented by iPython, and now part of the Jupyter project, +is the revolution for data analysis which will allow reproducable +science. + +This tutorial will introduce you to how to access to the notebook, how +to use it and perform some basic data analysis with it and the pyFAI +library. + +Getting access to the notebook +------------------------------ + +There are many cases ... + +Inside ESRF +~~~~~~~~~~~ + +The simplest case as the data analysis unit offers you a notebook +server: just connect your web browser to http://scisoft13:8000 and +authenticate with your ESRF credentials. + +Outside ESRF with an ESRF account. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The JupyterHub server is not directly available on the internet, but you +can +``login into the firewall``\ \_ +to forward the web server: + +ssh -XC -p5022 -L8000:scisoft13:8000 user@firewall.esrf.fr + +Once logged in ESRF, keep the terminal opened and browse on your local +computer to http://localhost:8000 to authenticate with your ESRF +credentials. Do not worry about confidentiality as the connection from +your computer to the ESRF firewall is encrypted by the SSH connection. + +Other cases +~~~~~~~~~~~ + +In the most general case you will need to install the notebook on your +local computer in addition to pyFAI and FabIO to follow the tutorial. +WinPython provides it under windows. Please refer to the installation +procedure of pyFAI to install locally pyFAI on your computer + +Getting trained in using the notebook +------------------------------------- + +There are plenty of good tutorials on how to use the notebook. `This +one `__ +presents a quick overview of the Python programming language and +explains how to use the notebook. Reading it is strongly encouraged +before proceeding to the pyFAI itself. + +Anyway, the most important information is to use **Control-Enter** to +evaluate a cell. + +In addition to this, we will need to download some files from the +internet. The following cell contains a piece of code to download files. +You do not need to understand what it does, but you may have to adjust +the proxy settings to be able to connect to internet, especially at +ESRF. + +.. code:: python + + import os, sys + os.environ["http_proxy"] = "http://proxy.site.com:3128" + + def download(url): + """download the file given in URL and return its local path""" + if sys.version_info[0]<3: + from urllib2 import urlopen, ProxyHandler, build_opener + else: + from urllib.request import urlopen, ProxyHandler, build_opener + dictProxies = {} + if "http_proxy" in os.environ: + dictProxies['http'] = os.environ["http_proxy"] + dictProxies['https'] = os.environ["http_proxy"] + if "https_proxy" in os.environ: + dictProxies['https'] = os.environ["https_proxy"] + if dictProxies: + proxy_handler = ProxyHandler(dictProxies) + opener = build_opener(proxy_handler).open + else: + opener = urlopen + target = os.path.split(url)[-1] + with open(target,"wb") as dest, opener(url) as src: + dest.write(src.read()) + return target + +Introduction to diffraction image analysis using the notebook +------------------------------------------------------------- + +All the tutorials in pyFAI are based on the notebook and if you wish to +practice the exercises, you can download the notebook files (.ipynb) +from +`Github `__ + +Load and display diffraction images +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First of all we will download an image and display it. Displaying it the +right way is important as the orientation of the image imposes the +azimuthal angle sign. + +.. code:: python + + #initializes the visualization module + %pylab inline + + +.. parsed-literal:: + + Populating the interactive namespace from numpy and matplotlib + + +.. code:: python + + moke = download("http://www.silx.org/pub/pyFAI/testimages/moke.tif") + print(moke) + + +.. parsed-literal:: + + moke.tif + + +The *moke.tif* image we just downloaded is not a real diffraction image +but it is a test pattern used in the tests of pyFAI. + +Prior to displaying it, we will use the Fable Input/Output library to +read the content of the file: + +.. code:: python + + import fabio + img = fabio.open(moke).data + imshow(img, origin="lower", cmap="gray") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_6_1.png + + +As you can see, the image looks like an archery target. The option +*origin="lower"* of *imshow* allows to display the image with the origin +at the **lower left** of the image. + +Displaying the image without this option ends with having the azimuthal +angle (which angles are displayed in degrees on the image) to turn +clockwise, so the inverse of the trigonometric order. + +.. code:: python + + imshow(img, cmap="gray") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_8_1.png + + +**Nota:** Displaying the image properly or not does not change the +content of the image or its representation in memory, it only changes +its representation, which is important only for the user. **DO NOT USE** +*numpy.flipud* or other array-manipulation which changes the memory +representation of the image. This is likely to mess-up all your +subsequent calculation. + +1D azimuthal integration +~~~~~~~~~~~~~~~~~~~~~~~~ + +To perform an azimuthal integration of this image, we need to create an +**AzimuthalIntegrator** object we will call *ai*. Fortunately, the +geometry is explained on the image. + +.. code:: python + + import pyFAI + ai = pyFAI.AzimuthalIntegrator(dist=0.1, pixel1=1e-4, pixel2=1e-4) + print(ai) + + +.. parsed-literal:: + + Detector Detector Spline= None PixelSize= 1.000e-04, 1.000e-04 m + SampleDetDist= 1.000000e-01m PONI= 0.000000e+00, 0.000000e+00m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=0.000, y=0.000 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + + +Printing the *ai* object displays 3 lines: + +- The detector definition, here a simple detector with square, regular + pixels with the right size +- The detector position in space using the *pyFAI* coordinate system +- The detector position in space using the *FIT2D* coordinate system + +Right now, the geometry in the *ai* object is wrong. It may be easier to +define it correctly using the *FIT2D* geometry which uses pixels for the +center coordinates (but the sample-detector distance is in millimeters). + +.. code:: python + + help(ai.setFit2D) + + +.. parsed-literal:: + + Help on method setFit2D in module pyFAI.geometry: + + setFit2D(directDist, centerX, centerY, tilt=0.0, tiltPlanRotation=0.0, pixelX=None, pixelY=None, splineFile=None) method of pyFAI.azimuthalIntegrator.AzimuthalIntegrator instance + Set the Fit2D-like parameter set: For geometry description see + HPR 1996 (14) pp-240 + + Warning: Fit2D flips automatically images depending on their file-format. + By reverse engineering we noticed this behavour for Tiff and Mar345 images (at least). + To obtaine correct result you will have to flip images using numpy.flipud. + + @param direct: direct distance from sample to detector along the incident beam (in millimeter as in fit2d) + @param tilt: tilt in degrees + @param tiltPlanRotation: Rotation (in degrees) of the tilt plan arround the Z-detector axis + * 0deg -> Y does not move, +X goes to Z<0 + * 90deg -> X does not move, +Y goes to Z<0 + * 180deg -> Y does not move, +X goes to Z>0 + * 270deg -> X does not move, +Y goes to Z>0 + + @param pixelX,pixelY: as in fit2d they ar given in micron, not in meter + @param centerX, centerY: pixel position of the beam center + @param splineFile: name of the file containing the spline + + + +.. code:: python + + ai.setFit2D(100, 300, 300) + print(ai) + + +.. parsed-literal:: + + Detector Detector Spline= None PixelSize= 1.000e-04, 1.000e-04 m + SampleDetDist= 1.000000e-01m PONI= 3.000000e-02, 3.000000e-02m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=300.000, y=300.000 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + + +With the *ai* object properly setup, we can perform the azimuthal +integration using the *intergate1d* method. This methods takes only 2 +mandatory parameters: the image to integrate and the number of bins. We +will provide a few other to enforce the calculations to be performed in +2theta-space and in degrees: + +.. code:: python + + tth, I = ai.integrate1d(img, 300, unit="2th_deg") + plot(tth, I, label="moke") + xlabel("2theta (deg)") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_15_1.png + + +As you can see, the 9 rings gave 9 sharp peaks at 2theta position +regularly ranging from 4 to 12 degrees as expected from the image +annotation. + +**Nota:** the default unit is "q\_nm^1", so the scattering vector length +expressed in inverse nanometers. To be able to calculate *q*, one needs +to specify the wavelength used (here we didn't). For example: +ai.wavelength = 1e-10 + +To save the content of the integrated pattern into a 2 column ASCII +file, one can either save the (tth, I) arrays, or directly ask pyFAI to +do it by providing an output filename: + +.. code:: python + + ai.integrate1d(img, 30, unit="2th_deg", filename="moke.dat") + !cat moke.dat + + +.. parsed-literal:: + + # == pyFAI calibration == + # SplineFile: None + # PixelSize: 1.000e-04, 1.000e-04 m + # PONI: 3.000e-02, 3.000e-02 m + # Distance Sample to Detector: 0.1 m + # Rotations: 0.000000 0.000000 0.000000 rad + # + # == Fit2d calibration == + # Distance Sample-beamCenter: 100.000 mm + # Center: x=300.000, y=300.000 pix + # Tilt: 0.000 deg TiltPlanRot: 0.000 deg + # + # Polarization factor: None + # Normalization factor: 1.0 + # --> moke.dat + # 2th_deg I + 3.831631e-01 6.384597e+00 + 1.149489e+00 1.240657e+01 + 1.915815e+00 1.222277e+01 + 2.682141e+00 1.170348e+01 + 3.448468e+00 9.964798e+00 + 4.214794e+00 8.913503e+00 + 4.981120e+00 9.104074e+00 + 5.747446e+00 9.242975e+00 + 6.513772e+00 6.136262e+00 + 7.280098e+00 9.039030e+00 + 8.046424e+00 9.203415e+00 + 8.812750e+00 9.324570e+00 + 9.579076e+00 6.470130e+00 + 1.034540e+01 7.790757e+00 + 1.111173e+01 9.410036e+00 + 1.187805e+01 9.464832e+00 + 1.264438e+01 7.749060e+00 + 1.341071e+01 1.151200e+01 + 1.417703e+01 1.324891e+01 + 1.494336e+01 1.038730e+01 + 1.570969e+01 1.069764e+01 + 1.647601e+01 1.056094e+01 + 1.724234e+01 1.286720e+01 + 1.800866e+01 1.323239e+01 + 1.877499e+01 1.548398e+01 + 1.954132e+01 2.364553e+01 + 2.030764e+01 2.537154e+01 + 2.107397e+01 2.512984e+01 + 2.184029e+01 2.191267e+01 + 2.260662e+01 7.605135e+00 + + +Here the excalmation mark indicates the notebook to call the *cat* +command from UNIX to print the content of the file. This "moke.dat" file +contains in addition to the 2th/I value, a header commented with "#" +with the geometry used to perform the calculation. + +**Nota: ** The *ai* object has initialized the geometry on the first +call and re-uses it on subsequent calls. This is why it is important to +re-use the geometry in performance critical applications. + +2D integration or Caking +~~~~~~~~~~~~~~~~~~~~~~~~ + +One can perform the 2D integration which is called caking in FIT2D by +simply calling the *intrgate2d* method with 3 mandatroy parameters: the +data to integrate, the number of radial bins and the number of azimuthal +bins. + +.. code:: python + + I, tth, chi = ai.integrate2d(img, 300, 360, unit="2th_deg") + imshow(I, origin="lower", extent=[tth.min(), tth.max(), chi.min(), chi.max()], aspect="auto") + xlabel("2 theta (deg)") + ylabel("Azimuthal angle chi (deg)") + + + + +.. parsed-literal:: + + + + + + +.. image:: output_19_1.png + + +The displayed image presents the "caked" image with the radial and +azimuthal angles properly set on the axes. Search for the -180, -90, +360/0 and 180 mark on the transformed image. + +Like *integrate1d*, *integrate2d* offers the ability to save the +intgrated image into an image file (EDF format by default) with again +all metadata in the headers. + +Radial integration +~~~~~~~~~~~~~~~~~~ + +Radial integration can directly be obtained from Caked images: + +.. code:: python + + target = 8 #degrees + #work on fewer radial bins in order to have an actual averaging: + I, tth, chi = ai.integrate2d(img, 100, 90, unit="2th_deg") + column = argmin(abs(tth-target)) + print("Column number %s"%column) + plot(chi, I[:,column]) + xlabel("Azimuthal angle") + + +.. parsed-literal:: + + Column number 34 + + + + +.. parsed-literal:: + + + + + + +.. image:: output_21_2.png + + +**Nota:** the pattern with higher noise along the diagonals is typical +from the pixel splitting scheme employed. Here this scheme is a +"bounding box" which makes digonal pixels look a bit larger (+40%) than +the ones on the horizontal and vertical axis, explaining the variation +of the noise. + +Integration of a bunch of files using pyFAI +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Once the processing for one file is established, one can loop over a +bunch of files. A convienient way to get the list of files matching a +pattern is with the *glob* module. + +Most of the time, the azimuthal integrator is obtained by simply loading +the *poni-file* into pyFAI and use it directly. + +.. code:: python + + import glob + all_files = glob.glob("al2o*.edf.bz2") + all_files.sort() + print(len(all_files)) + + +.. parsed-literal:: + + 51 + + +.. code:: python + + ai = pyFAI.load("al2o3_00_max_51_frames.poni") + print(ai) + + +.. parsed-literal:: + + Detector Detector Spline= /users/kieffer/workspace-400/pyFAI/doc/source/usage/tutorial/Introduction/distorsion_2x2.spline PixelSize= 1.034e-04, 1.025e-04 m + Wavelength= 7.084811e-11m + SampleDetDist= 1.168599e-01m PONI= 5.295653e-02, 5.473342e-02m rot1=0.015821 rot2= 0.009404 rot3= 0.000000 rad + DirectBeamDist= 116.880mm Center: x=515.795, y=522.995 pix Tilt=1.055 deg tiltPlanRotation= 149.271 deg + + +.. code:: python + + %%time + for one_file in all_files: + destination = os.path.splitext(one_file)[0]+".dat" + image = fabio.open(one_file).data + ai.integrate1d(image, 1000, filename=destination) + + +.. parsed-literal:: + + CPU times: user 35.6 s, sys: 272 ms, total: 35.8 s + Wall time: 22 s + + +This was a simple integration of 50 files, saving the result into 2 +column ASCII files. + +Conclusion +---------- + +Using the notebook is rather simple as it allows to mix comments, code, +and images for visualization of scientific data. + +The basic use pyFAI's AzimuthalIntgrator has also been presented and may +be adapted to you specific needs. diff --git a/doc/build/_sources/usage/tutorial/MakeCalibrant/make_calibrant.txt b/doc/build/_sources/usage/tutorial/MakeCalibrant/make_calibrant.txt new file mode 100644 index 000000000..fa2803798 --- /dev/null +++ b/doc/build/_sources/usage/tutorial/MakeCalibrant/make_calibrant.txt @@ -0,0 +1,361 @@ + +Creation of a calibrant file +============================ + +In this tutorial we will see how to generate a file describing a +*reference material* used as calibrant to refine the geometry of the +experimental setup, especially the position of the detector. + +In pyFAI, calibrant are defined in a bunch of files available from +`github `__. Those +files are automatically installed with pyFAI and readily available from +the programming interface, as described in the **Calibrant** tutorial. + +This tutorials focuses on the content of the file and how to generate +other calibrant files and how to exended existing ones. + +Content of the file +------------------- + +Calibrant files from *pyFAI* are heavily inspired from the ones used in +*Fit2D*: they simply store the *d-spacing* between Miller plans and are +simply loaded using the *numpy.loadtxt* function. In pyFAI we have +improved with plent of comment (using **#**) to provide in addition the +familly of Miller plan associated and the multiplicity, which, while not +proportionnal to *Fobs* is somewhat related. + +One may think it would have been better/simpler to describe the cell of +the material. Actually some calibrant, especially the ones used in SAXS +like silver behenate (AgBh), but not only, are not well crystallized +compounds and providing the *d-spacing* allows to use them as calibrant. +Neverthless this tutorial will be focused on how to generate calibrant +files fron crystal structures. + +Finally the *calibrant file* has a few lines of headers containing the +name, the crystal cell parameters (if available) and quite importantly a +reference to the source of information like a publication to allow the +re-generation of the file if needed. + +:: + + # Calibrant: Silver Behenate (AgBh) + # Pseudocrystal a=inf b=inf c=58.380 + # Ref: doi:10.1107/S0021889899012388 + 5.83800000e+01 # (0,0,1) + 2.91900000e+01 # (0,0,2) + 1.94600000e+01 # (0,0,3) + 1.45950000e+01 # (0,0,4) + 1.16760000e+01 # (0,0,5) + 9.73000000e+00 # (0,0,6) + 8.34000000e+00 # (0,0,7) + 7.29750000e+00 # (0,0,8) + 6.48666667e+00 # (0,0,9) + (...) + +The Cell class +-------------- + +To generate a *calibrant file* from a crystal structure, the easiest is +to use the **pyFAI.calibrant.Cell** class. + +.. code:: python + + from pyFAI.calibrant import Cell + print(Cell.__doc__) + print(Cell.__init__.__doc__) + + +.. parsed-literal:: + + + This is a cell object, able to calculate the volume and d-spacing according to formula from: + + http://geoweb3.princeton.edu/research/MineralPhy/xtalgeometry.pdf + + Constructor of the Cell class: + + Crystalographic units are Angstrom for distances and degrees for angles ! + + @param a,b,c: unit cell length in Angstrom + @param alpha, beta, gamma: unit cell angle in degrees + @param lattice: "cubic", "tetragonal", "hexagonal", "rhombohedral", "orthorhombic", "monoclinic", "triclinic" + @param lattice_type: P, I, F, C or R + + + +The constructor of the class is used to build and well suited to +triclinic crystal. + +Specific constructors +~~~~~~~~~~~~~~~~~~~~~ + +| Nevertheless, most used calibrants are of much higher symmetry, like +cubic which takes only **one** parameter. +| Here is an example for defining +`Polonium `__ which +is a simple cubic cell (Primitive) with a cell parameter of 335.9pm. +This example was chosen as Polonium is apparently the only element with +such primitive cubic packing. + +.. code:: python + + Po = Cell.cubic(3.359) + print(Po) + +.. parsed-literal:: + + Primitive cubic cell a=3.3590 b=3.3590 c=3.3590 alpha=90.000 beta=90.000 gamma=90.000 + + +.. code:: python + + print(Po.volume) + +.. parsed-literal:: + + 37.899197279 + + +.. code:: python + + print("Po.d_spacing?") + print(Po.d_spacing.__doc__) + print("Po.save?") + print(Po.save.__doc__) + +.. parsed-literal:: + + Po.d_spacing? + Calculate all d-spacing down to dmin + + applies selection rules + + @param dmin: minimum value of spacing requested + @return: dict d-spacing as string, list of tuple with Miller indices + preceded with the numerical value + + Po.save? + Save informations about the cell in a d-spacing file, usable as Calibrant + + @param name: name of the calibrant + @param doi: reference of the publication used to parametrize the cell + @param dmin: minimal d-spacing + @param dest_dir: name of the directory where to save the result + + + +To generate a *d-spacing* file usable as calibrant, one simply has to +call the *save* method of the *Cell* instance. + +**Nota:** the ".D" suffix is automatically appended. + +.. code:: python + + Po.save("Po",doi="http://www.periodictable.com/Elements/084/data.html", dmin=1) +.. code:: python + + !cat Po.D + +.. parsed-literal:: + + # Calibrant: Po + # Primitive cubic cell a=3.3590 b=3.3590 c=3.3590 alpha=90.000 beta=90.000 gamma=90.000 + # Ref: http://www.periodictable.com/Elements/084/data.html + 3.35900000 # (1, 0, 0) 6 + 2.37517168 # (1, 1, 0) 12 + 1.93931955 # (1, 1, 1) 8 + 1.67950000 # (2, 0, 0) 6 + 1.50219047 # (2, 1, 0) 24 + 1.37130601 # (2, 1, 1) 24 + 1.18758584 # (2, 2, 0) 12 + 1.11966667 # (3, 0, 0) 30 + 1.06220907 # (3, 1, 0) 24 + 1.01277661 # (3, 1, 1) 24 + + +Other Examples: LaB6 +~~~~~~~~~~~~~~~~~~~~ + +Lantanide Hexaboride, probably my favorite calibrant as it has a +primitive cubic cell hence all reflections are valid and intense. The +cell parameter is available from the +`NIST `__ at +a=4.156826. + +The number of reflections in a file is controled by the *dmin* +parameter. The lower it is, the more lines there are and the more +time-consuming this will be: + +.. code:: python + + LaB6 = Cell.cubic(4.156826) + %time d=LaB6.d_spacing(0.1) + print("Number of reflections: %s"%len(d)) + +.. parsed-literal:: + + CPU times: user 976 ms, sys: 76 ms, total: 1.05 s + Wall time: 964 ms + Number of reflections: 1441 + + +.. code:: python + + LaB6.save("LaB6",doi="https://www-s.nist.gov/srmors/certificates/660C.pdf", dmin=0.1) +.. code:: python + + !head LaB6.D + +.. parsed-literal:: + + # Calibrant: LaB6 + # Primitive cubic cell a=4.1568 b=4.1568 c=4.1568 alpha=90.000 beta=90.000 gamma=90.000 + # Ref: https://www-s.nist.gov/srmors/certificates/660C.pdf + 4.15682600 # (1, 0, 0) 6 + 2.93931985 # (1, 1, 0) 12 + 2.39994461 # (1, 1, 1) 8 + 2.07841300 # (2, 0, 0) 6 + 1.85898910 # (2, 1, 0) 24 + 1.69701711 # (2, 1, 1) 24 + 1.46965993 # (2, 2, 0) 12 + + +Other Examples: Silicon +~~~~~~~~~~~~~~~~~~~~~~~ + +Silicon is easy to obtain **pure** thanks to microelectronics industry. +Its cell is a face centered cubic with a diamond like structure. The +cell parameter is available from the +`NIST `__: +a=5.431179 A. + +Let's compare the difference between the silicon structure and the +equivalent FCC structure: + +.. code:: python + + Si = Cell.diamond(5.431179) + print(Si) + +.. parsed-literal:: + + Face centered cubic cell a=5.4312 b=5.4312 c=5.4312 alpha=90.000 beta=90.000 gamma=90.000 + + +.. code:: python + + FCC = Cell.cubic(5.431179,"F") + print(FCC) + +.. parsed-literal:: + + Face centered cubic cell a=5.4312 b=5.4312 c=5.4312 alpha=90.000 beta=90.000 gamma=90.000 + + +Apparently, there is not difference. But to check it, let's generate all +lines down to 1A and compare them: + +.. code:: python + + sid = Si.d_spacing(1) + for key, val in sid.items(): + print("%s: %s"%(sorted(val[1:][-1]),key)) + +.. parsed-literal:: + + [2, 2, 4]: 1.10863477e+00 + [1, 1, 3]: 1.63756208e+00 + [0, 2, 2]: 1.92021175e+00 + [1, 1, 1]: 3.13569266e+00 + [1, 1, 5]: 1.04523089e+00 + [1, 3, 3]: 1.24599792e+00 + [0, 0, 4]: 1.35779475e+00 + + +.. code:: python + + fccd = FCC.d_spacing(1) + for key, val in fccd.items(): + print("%s: %s"%(sorted(val[1:][-1]),key)) + +.. parsed-literal:: + + [2, 2, 4]: 1.10863477e+00 + [1, 1, 3]: 1.63756208e+00 + [0, 2, 2]: 1.92021175e+00 + [1, 1, 1]: 3.13569266e+00 + [1, 1, 5]: 1.04523089e+00 + [0, 2, 4]: 1.21444854e+00 + [2, 2, 2]: 1.56784633e+00 + [1, 3, 3]: 1.24599792e+00 + [0, 0, 2]: 2.71558950e+00 + [0, 0, 4]: 1.35779475e+00 + + +So there are many more reflection in the FCC structure compared to the +diamond-like structure: (4 2 0), (2 2 2) are extinct as h+k+l=4n and all +even. + +Selection rules +~~~~~~~~~~~~~~~ + +Cell object contain *selection\_rules* which tells if a reflection is +allowed or not. Those *selection\_rules* can be introspected: + +.. code:: python + + print(Si.selection_rules) + print(FCC.selection_rules) + +.. parsed-literal:: + + [ at 0x7f8080fad848>, at 0x7f8080fad938>, at 0x7f8080fad9b0>] + [ at 0x7f8080fadaa0>, at 0x7f8080fada28>] + + +The *Si* object has one additionnal selection rule which explains the +difference: A specific rule telling that reflection with h+k+l=4n is +forbidden when (h,k,l) are all even. + +We will now have a look at the source code of those selection rules +using the *inspect* module. + +**Nota:** This is advanced Python hacking but useful for the +demonstration + +.. code:: python + + import inspect + for rule in Si.selection_rules: + print(inspect.getsource(rule)) + +.. parsed-literal:: + + self.selection_rules = [lambda h, k, l: not(h == 0 and k == 0 and l == 0)] + + self.selection_rules.append(lambda h, k, l: (h % 2 + k % 2 + l % 2) in (0, 3)) + + lambda h, k, l:not((h % 2 == 0) and (k % 2 == 0) and (l % 2 == 0) and ((h + k + l) % 4 != 0)) + + + +Actually the last line correspond to an anonymous function (lambda) +which implements this rule. + +As we have seen previously one can simply adapt the Cell instance by +simply appending extra selection rules to this list. + +A selection rule is a function taking 3 integers as input and returning +*True* if the reflection is allowed and *False* when it is forbidden by +symmetry. By this way one can simply adapt existing object to generate +the right *calibrant file*. + +Conclusion +---------- + +In this tutorial we have seen the structure of a *calibrant file*, how +to generate crystal structure cell object to write such calibrant files +automatically, including all metadata needed for redistribution. Most +advanced programmers can now modify the selection rules to remove +forbidden reflection for a given cell. + diff --git a/doc/build/_sources/usage/tutorial/MultiGeometry/index.txt b/doc/build/_sources/usage/tutorial/MultiGeometry/index.txt new file mode 100644 index 000000000..acedcc7b1 --- /dev/null +++ b/doc/build/_sources/usage/tutorial/MultiGeometry/index.txt @@ -0,0 +1,558 @@ +:Author: Jérôme Kieffer +:Date: 27/10/2015 +:Keywords: Tutorials +:Target: Advanced users + +Demo of usage of the MultiGeometry class of pyFAI +================================================= + +For this tutorial, we will use the ipython notebook, now known as +*Jypyter*, an take advantage of the integration of matplotlib with the +*inline*: + +.. code:: python + + %pylab inline + +.. parsed-literal:: + + Populating the interactive namespace from numpy and matplotlib + + +The multi\_geometry module of pyFAI allows you to integrate multiple +images taken at various image position, all togeather. This tutorial +will explain you how to perform azimuthal integration in three use-case: +translation of the detector, rotation of the detector around the sample +and finally how to fill gaps of a pixel detector. But before, we need to +know how to generate fake diffraction image. + +Generation of diffraction images +-------------------------------- + +PyFAI knows about 20 different reference sample called calibrants. We +will use them to generate fake diffraction images knowing the detector +and its position in space + +.. code:: python + + import pyFAI + import pyFAI.calibrant + print("Number of known calibrants: %s"%len(pyFAI.calibrant.ALL_CALIBRANTS)) + print(" ".join(pyFAI.calibrant.ALL_CALIBRANTS.keys())) + +.. parsed-literal:: + + Number of known calibrants: 27 + Ni CrOx NaCl Si_SRM640e Si_SRM640d Si_SRM640a Si_SRM640c alpha_Al2O3 Cr2O3 AgBh Si_SRM640 CuO PBBA Si_SRM640b quartz C14H30O cristobaltite Si LaB6 CeO2 LaB6_SRM660a LaB6_SRM660b LaB6_SRM660c TiO2 ZnO Al Au + + +.. code:: python + + wl = 1e-10 + LaB6 = pyFAI.calibrant.ALL_CALIBRANTS("LaB6") + LaB6.set_wavelength(wl) + print(LaB6) + +.. parsed-literal:: + + LaB6 Calibrant at wavelength 1e-10 + + +We will start with a "simple" detector called *Titan* (build by *Oxford +Diffraction* but now sold by *Agilent*). It is a CCD detector with +scintilator and magnifying optics fibers. The pixel size is constant: +60µm + +.. code:: python + + det = pyFAI.detectors.Titan() + print(det) + p1, p2, p3 = det.calc_cartesian_positions() + print("Detector is flat, P3= %s"%p3) + poni1 = p1.mean() + poni2 = p2.mean() + print("Center of the detector: poni1=%s poni2=%s"%(poni1, poni2)) + +.. parsed-literal:: + + Detector Titan 2k x 2k PixelSize= 6.000e-05, 6.000e-05 m + Detector is flat, P3= None + Center of the detector: poni1=0.06144 poni2=0.06144 + + +The detector is placed orthogonal to the beam at 10cm. This geometry is +saved into an *AzimuthalIntegrator* instance: + +.. code:: python + + ai = pyFAI.AzimuthalIntegrator(dist=0.1, poni1=poni1, poni2=poni2, detector=det) + ai.set_wavelength(wl) + print(ai) + +.. parsed-literal:: + + Detector Titan 2k x 2k PixelSize= 6.000e-05, 6.000e-05 m + Wavelength= 1.000000e-10m + SampleDetDist= 1.000000e-01m PONI= 6.144000e-02, 6.144000e-02m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=1024.000, y=1024.000 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + + +Knowing the calibrant, the wavelength, the detector and the geometry, +one can simulate the 2D diffraction pattern: + +.. code:: python + + img = LaB6.fake_calibration_image(ai) + imshow(img, origin="lower") + + + +.. parsed-literal:: + + + + + + +.. image:: output_10_1.png + + +This image can be integrated in q-space and plotted: + +.. code:: python + + plot(*ai.integrate1d(img, 1000, unit="q_A^-1")) + + + +.. parsed-literal:: + + [] + + + + +.. image:: output_12_1.png + + +Note pyFAI does now about the ring position but nothing about relative +intensities of rings. + +Translation of the detector along the vertical axis +--------------------------------------------------- + +The vertical axis is defined along the *poni1*. If one moves the +detector higher, the poni will appear at lower coordinates. So lets +define 5 upwards verical translations of half the detector size. + +For this we will duplicate 5x the AzimuthalIntegrator object, but +instances of *AzimuthalIntegrator* are mutable, so it is important to +create an actual *copy* and not an *view* on them. In Python, one can +use the *copy* function of the *copy* module: + +.. code:: python + + import copy +We will now offset the *poni1* value of each AzimuthalIntegratoe which +correspond to a vertical translation. Each subsequent image is offsetted +by half a detector width (stored as *poni1*). + +.. code:: python + + ais = [] + imgs = [] + fig, plots = subplots(1,5) + for i in range(5): + my_ai = copy.deepcopy(ai) + my_ai.poni1 -= i*poni1 + my_img = LaB6.fake_calibration_image(my_ai) + plots[i].imshow(my_img, origin="lower") + ais.append(my_ai) + imgs.append(my_img) + print(my_ai) + + +.. parsed-literal:: + + Detector Titan 2k x 2k PixelSize= 6.000e-05, 6.000e-05 m + Wavelength= 1.000000e-10m + SampleDetDist= 1.000000e-01m PONI= 6.144000e-02, 6.144000e-02m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=1024.000, y=1024.000 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + Detector Titan 2k x 2k PixelSize= 6.000e-05, 6.000e-05 m + Wavelength= 1.000000e-10m + SampleDetDist= 1.000000e-01m PONI= 0.000000e+00, 6.144000e-02m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=1024.000, y=0.000 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + Detector Titan 2k x 2k PixelSize= 6.000e-05, 6.000e-05 m + Wavelength= 1.000000e-10m + SampleDetDist= 1.000000e-01m PONI= -6.144000e-02, 6.144000e-02m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=1024.000, y=-1024.000 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + Detector Titan 2k x 2k PixelSize= 6.000e-05, 6.000e-05 m + Wavelength= 1.000000e-10m + SampleDetDist= 1.000000e-01m PONI= -1.228800e-01, 6.144000e-02m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=1024.000, y=-2048.000 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + Detector Titan 2k x 2k PixelSize= 6.000e-05, 6.000e-05 m + Wavelength= 1.000000e-10m + SampleDetDist= 1.000000e-01m PONI= -1.843200e-01, 6.144000e-02m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=1024.000, y=-3072.000 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + + + +.. image:: output_16_1.png + + +MultiGeometry integrator +------------------------ + +The *MultiGeometry* instance can be created from any list of +*AzimuthalIntegrator* instances or list of *poni-files*. Here we will +use the former method. + +The main difference of a *MultiIntegrator* with a "normal" +*AzimuthalIntegrator* comes from the definition of the output space in +the constructor of the object. One needs to specify the unit and the +integration range. + +.. code:: python + + from pyFAI.multi_geometry import MultiGeometry +.. code:: python + + mg = MultiGeometry(ais, unit="q_A^-1", radial_range=(0, 10)) + print(mg) + +.. parsed-literal:: + + MultiGeometry integrator with 5 geometries on (0, 10) radial range (q_A^-1) and (-180, 180) azimuthal range (deg) + + +*MultiGeometry* integrators can be used in a similar way to "normal" +*AzimuthalIntegrator*\ s. Keep in mind the output intensity is always +scaled to absolute solid angle. + +.. code:: python + + plot(*mg.integrate1d(imgs, 10000)) + + + +.. parsed-literal:: + + [] + + + + +.. image:: output_21_1.png + + +.. code:: python + + for i, a in zip(imgs, ais): + plot(*a.integrate1d(i, 1000, unit="q_A^-1")) + + +.. image:: output_22_0.png + + +Rotation of the detector +------------------------ + +The strength of translating the detector is that it simulates a larger +detector, but this approach reaches its limit quikly as the higher the +detector gets, the smallest the solid angle gets and induces artificial +noise. One solution is to keep the detector at the same distance and +rotate the detector. + +Creation of diffraction images +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this example we will use a Pilatus 200k with 2 modules. It has a gap +in the middle of the two detectors and we will see how the +*MultiGeometry* can help. + +As previously, we will use LaB6 but instead of translating the images, +we will rotate them along the second axis: + +.. code:: python + + det = pyFAI.detectors.detector_factory("pilatus200k") + p1, p2, p3 = det.calc_cartesian_positions() + print(p3) + poni1 = p1.mean() + poni2 = p2.mean() + print(poni1) + print(poni2) + +.. parsed-literal:: + + None + 0.035002 + 0.041882 + + +.. code:: python + + ai = pyFAI.AzimuthalIntegrator(dist=0.1, poni1=poni1, poni2=poni2, detector=det) + img = LaB6.fake_calibration_image(ai) + imshow(img, origin="lower") + #imshow(log(ai.integrate2d(img, 500, 360, unit="2th_deg")[0])) + + + +.. parsed-literal:: + + + + + + +.. image:: output_25_1.png + + +.. code:: python + + plot(*ai.integrate1d(img, 500,unit="2th_deg")) + + + +.. parsed-literal:: + + [] + + + + +.. image:: output_26_1.png + + +We will rotate the detector with a step size of 15 degrees + +.. code:: python + + step = 15*pi/180 + ais = [] + imgs = [] + fig, plots = subplots(1,5) + for i in range(5): + my_ai = copy.deepcopy(ai) + my_ai.rot2 -= i*step + my_img = LaB6.fake_calibration_image(my_ai) + plots[i].imshow(my_img, origin="lower") + ais.append(my_ai) + imgs.append(my_img) + print(my_ai) + + +.. parsed-literal:: + + Detector Pilatus200k PixelSize= 1.720e-04, 1.720e-04 m + SampleDetDist= 1.000000e-01m PONI= 3.500200e-02, 4.188200e-02m rot1=0.000000 rot2= 0.000000 rot3= 0.000000 rad + DirectBeamDist= 100.000mm Center: x=243.500, y=203.500 pix Tilt=0.000 deg tiltPlanRotation= 0.000 deg + Detector Pilatus200k PixelSize= 1.720e-04, 1.720e-04 m + SampleDetDist= 1.000000e-01m PONI= 3.500200e-02, 4.188200e-02m rot1=0.000000 rot2= -0.261799 rot3= 0.000000 rad + DirectBeamDist= 103.528mm Center: x=243.500, y=47.716 pix Tilt=15.000 deg tiltPlanRotation= -90.000 deg + Detector Pilatus200k PixelSize= 1.720e-04, 1.720e-04 m + SampleDetDist= 1.000000e-01m PONI= 3.500200e-02, 4.188200e-02m rot1=0.000000 rot2= -0.523599 rot3= 0.000000 rad + DirectBeamDist= 115.470mm Center: x=243.500, y=-132.169 pix Tilt=30.000 deg tiltPlanRotation= -90.000 deg + Detector Pilatus200k PixelSize= 1.720e-04, 1.720e-04 m + SampleDetDist= 1.000000e-01m PONI= 3.500200e-02, 4.188200e-02m rot1=0.000000 rot2= -0.785398 rot3= 0.000000 rad + DirectBeamDist= 141.421mm Center: x=243.500, y=-377.895 pix Tilt=45.000 deg tiltPlanRotation= -90.000 deg + Detector Pilatus200k PixelSize= 1.720e-04, 1.720e-04 m + SampleDetDist= 1.000000e-01m PONI= 3.500200e-02, 4.188200e-02m rot1=0.000000 rot2= -1.047198 rot3= 0.000000 rad + DirectBeamDist= 200.000mm Center: x=243.500, y=-803.506 pix Tilt=60.000 deg tiltPlanRotation= -90.000 deg + + + +.. image:: output_28_1.png + + +.. code:: python + + for i, a in zip(imgs, ais): + plot(*a.integrate1d(i, 1000, unit="2th_deg")) + + +.. image:: output_29_0.png + + +Creation of the MultiGeometry +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This time we will work in 2theta angle using degrees: + +.. code:: python + + mg = MultiGeometry(ais, unit="2th_deg", radial_range=(0, 90)) + print(mg) + plot(*mg.integrate1d(imgs, 10000)) + +.. parsed-literal:: + + MultiGeometry integrator with 5 geometries on (0, 90) radial range (2th_deg) and (-180, 180) azimuthal range (deg) + area_pixel=1.32053624453 area_sum=2.69418873745, Error= -1.04022324159 + + + + +.. parsed-literal:: + + [] + + + + +.. image:: output_31_2.png + + +.. code:: python + + I,tth, chi = mg.integrate2d(imgs, 1000,360) + imshow(I, origin="lower",extent=[tth.min(), tth.max(), chi.min(), chi.max()], aspect="auto") + xlabel("2theta") + ylabel("chi") + + + +.. parsed-literal:: + + + + + + +.. image:: output_32_1.png + + +How to fill-up gaps in arrays of pixel detectors during 2D integration +---------------------------------------------------------------------- + +We will use ImXpad detectors which exhibits large gaps. + +.. code:: python + + det = pyFAI.detectors.detector_factory("Xpad_flat") + p1, p2, p3 = det.calc_cartesian_positions() + print(p3) + poni1 = p1.mean() + poni2 = p2.mean() + print(poni1) + print(poni2) + +.. parsed-literal:: + + None + 0.076457 + 0.0377653 + + +.. code:: python + + ai = pyFAI.AzimuthalIntegrator(dist=0.1, poni1=0, poni2=poni2, detector=det) + img = LaB6.fake_calibration_image(ai) + imshow(img, origin="lower") + + + +.. parsed-literal:: + + + + + + +.. image:: output_35_1.png + + +.. code:: python + + I, tth, chi=ai.integrate2d(img, 500, 360, azimuth_range=(0,180), unit="2th_deg", dummy=-1) + imshow(sqrt(I),origin="lower",extent=[tth.min(), tth.max(), chi.min(), chi.max()], aspect="auto") + xlabel("2theta") + ylabel("chi") + +.. parsed-literal:: + + -c:2: RuntimeWarning: invalid value encountered in sqrt + + + + +.. parsed-literal:: + + + + + + +.. image:: output_36_2.png + + +To observe textures, it is mandatory to fill the large empty space. This +can be done by tilting the detector by a few degrees to higher 2theta +angle (yaw 2x5deg) and turn the detector along the azimuthal angle (roll +2x5deg): + +.. code:: python + + step = 5*pi/180 + nb_geom = 3 + ais = [] + imgs = [] + for i in range(nb_geom): + for j in range(nb_geom): + my_ai = copy.deepcopy(ai) + my_ai.rot2 -= i*step + my_ai.rot3 -= j*step + my_img = LaB6.fake_calibration_image(my_ai) + ais.append(my_ai) + imgs.append(my_img) + mg = MultiGeometry(ais, unit="2th_deg", radial_range=(0, 60), azimuth_range=(0, 180), empty=-1) + print(mg) + I, tth, chi = mg.integrate2d(imgs, 1000, 360) + imshow(sqrt(I),origin="lower",extent=[tth.min(), tth.max(), chi.min(), chi.max()], aspect="auto") + xlabel("2theta") + ylabel("chi") + +.. parsed-literal:: + + MultiGeometry integrator with 9 geometries on (0, 60) radial range (2th_deg) and (0, 180) azimuthal range (deg) + + +.. parsed-literal:: + + -c:16: RuntimeWarning: invalid value encountered in sqrt + + + + +.. parsed-literal:: + + + + + + +.. image:: output_38_3.png + + +As on can see, the gaps have disapeared and the statistics is much +better, except on the border were only one image contributes to the +integrated image. + +Conclusion +========== + +The multi\_geometry module of pyFAI makes powder diffraction experiments +with small moving detectors much easier. + +Some people would like to stitch input images together prior to +integration. There are plenty of good tools to do this: generalist one +like `Photoshop `__ or +more specialized ones like `AutoPano `__. +More seriously this can be using the distortion module of a detector to +re-sample the signal on a regular grid but one will have to store on one +side the number of actual pixel contributing to a regular pixels and on +the other the total intensity contained in the regularized pixel. +Without the former information, doing science with a rebinned image is +as meaningful as using Photoshop. + diff --git a/doc/build/_sources/usage/tutorial/index.txt b/doc/build/_sources/usage/tutorial/index.txt new file mode 100644 index 000000000..89d6885fd --- /dev/null +++ b/doc/build/_sources/usage/tutorial/index.txt @@ -0,0 +1,27 @@ +:Author: Jérôme Kieffer +:Date: 31/05/2016 +:Keywords: Tutorials +:Target: Advanced users tutorials using jupyter notebooks + +.. _tutorials: + +Tutorials +========= + +Tutorials explain the Python interface of *pyFAI* and use the jupyter notebook +interface, formerly known as ipython notebooks. +The two first tutorials are an introduction to the usage of pyFAI from Python for +diffraction data reduction. +The subsequent tutorials are more in depth explanation and require +a good Python fluency and to a certain extent, of the pyFAI library. + +.. toctree:: + :maxdepth: 1 + + Introduction/introduction + Distortion/Distortion + Calibrant/index + multi-geometry + MakeCalibrant/make_calibrant + CCD_Calibration/CCD_calibration + diff --git a/doc/build/_sources/usage/tutorial/multi-geometry.txt b/doc/build/_sources/usage/tutorial/multi-geometry.txt new file mode 100644 index 000000000..fa076d020 --- /dev/null +++ b/doc/build/_sources/usage/tutorial/multi-geometry.txt @@ -0,0 +1,83 @@ +:Author: Jérôme Kieffer +:Date: 27/10/2015 +:Keywords: Tutorials +:Target: Advanced users + +==================================== +Multi-geometry azimuthal integration +==================================== + +Or how it is possible to perform an azimuthal regrouping using multiple detectors +or by moving a single (small) detector to cover more solid angle. + +Idea +==== + +Azimuthal integration or azimuthal regrouping is (roughly) the averaging of all +pixel intensities with the same Q value (or 2theta), as described in +`this publication `_ chapter 3.2 and 3.3. + +By taking multiple images at various places in space one covers more solid angle, +allowing either a better statistics or a larger Q-range coverage. + +As described in the publication, the average is calculated by the ratio of the +(intensity-) weighted histogram by the unweighted histogram. By enforcing a same +bin position over multiple geometries, one can create a combined weighted and +unweighted histograms by simply summing all partial histograms from each geometry. + +The resulting pattern is obtained as usual by the ration of weighted/unweighted + +How it works +============ + +Lets assume you are able to know where your detector is in space, either +calibrated, either calculated from the goniometer position. +A diffrection image (img_i) has been acquired using a geometry which is stored +in a poni-file (poni_i) useable by pyFAI. + +To define a multi-geometry integrator, one needs all poni-files and one needs +to define the output space so that all individual integrators use the same bins. + +.. code:: python + + import glob + import fabio + from pyFAI.multi_geometry import MultiGeometry + + img_files = glob.glob("*.cbf") + img_data = [fabio.open(i).data for i in img_files] + ais = [i[:-4]+".poni" for i in img_files] + mg = MultiGeometry(ais, unit="q_A^-1", radial_range=(0, 50), wavelength=1e-10) + q, I = mg.integrate1d(img_data, 10000) + +What is automatic +----------------- + +* MultiGeometry takes care of defining the same output space with the same bin position, +* It performs the normalization of the solid angle (absolute solid angle, unlike AzimuthalIntegrator !) +* It can handle polarization correction if needed +* It can normalize by a monitor (I1 normalization) or correct for exposure time + +What is not +----------- + +For PDF measurement, data needs to be properly prepared, especially: + +* Dark current subtraction +* Flat-field correction +* Exposure time correction (if all images are not taken with the same exposure time) + +Examples +======== + +.. toctree:: + :maxdepth: 2 + + MultiGeometry/index + +Conclusion +========== + +MultiGeometry is a unique feature of PyFAI ... +While extremely powerful, it need +careful understanding of the numerical treatement going on underneath. diff --git a/doc/build/_static/ajax-loader.gif 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this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated == 'undefined') + return string; + return (typeof translated == 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated == 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if 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+var jQuery = (function() { + +// Define a local copy of jQuery +var jQuery = function( selector, context ) { + // The jQuery object is actually just the init constructor 'enhanced' + return new jQuery.fn.init( selector, context, rootjQuery ); + }, + + // Map over jQuery in case of overwrite + _jQuery = window.jQuery, + + // Map over the $ in case of overwrite + _$ = window.$, + + // A central reference to the root jQuery(document) + rootjQuery, + + // A simple way to check for HTML strings or ID strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + quickExpr = /^(?:[^#<]*(<[\w\W]+>)[^>]*$|#([\w\-]*)$)/, + + // Check if a string has a non-whitespace character in it + rnotwhite = /\S/, + + // Used for trimming whitespace + trimLeft = /^\s+/, + trimRight = /\s+$/, + + // Match a standalone tag + rsingleTag = /^<(\w+)\s*\/?>(?:<\/\1>)?$/, + + // JSON RegExp + rvalidchars = /^[\],:{}\s]*$/, + rvalidescape = /\\(?:["\\\/bfnrt]|u[0-9a-fA-F]{4})/g, + rvalidtokens = /"[^"\\\n\r]*"|true|false|null|-?\d+(?:\.\d*)?(?:[eE][+\-]?\d+)?/g, + rvalidbraces = /(?:^|:|,)(?:\s*\[)+/g, + + // Useragent RegExp + rwebkit = /(webkit)[ \/]([\w.]+)/, + ropera = /(opera)(?:.*version)?[ \/]([\w.]+)/, + rmsie = /(msie) ([\w.]+)/, + rmozilla = /(mozilla)(?:.*? rv:([\w.]+))?/, + + // Matches dashed string for camelizing + rdashAlpha = /-([a-z]|[0-9])/ig, + rmsPrefix = /^-ms-/, + + // Used by jQuery.camelCase as callback to replace() + fcamelCase = function( all, letter ) { + return ( letter + "" ).toUpperCase(); + }, + + // Keep a UserAgent string for use with jQuery.browser + userAgent = navigator.userAgent, + + // For matching the engine and version of the browser + browserMatch, + + // The deferred used on DOM ready + readyList, + + // The ready event handler + DOMContentLoaded, + + // Save a reference to some core methods + toString = Object.prototype.toString, + hasOwn = Object.prototype.hasOwnProperty, + push = Array.prototype.push, + slice = Array.prototype.slice, + trim = String.prototype.trim, + indexOf = Array.prototype.indexOf, + + // [[Class]] -> type pairs + class2type = {}; + +jQuery.fn = jQuery.prototype = { + constructor: jQuery, + init: function( selector, context, rootjQuery ) { + var match, elem, ret, doc; + + // Handle $(""), $(null), or $(undefined) + if ( !selector ) { + return this; + } + + // Handle $(DOMElement) + if ( selector.nodeType ) { + this.context = this[0] = selector; + this.length = 1; + return this; + } + + // The body element only exists once, optimize finding it + if ( selector === "body" && !context && document.body ) { + this.context = document; + this[0] = document.body; + this.selector = selector; + this.length = 1; + return this; + } + + // Handle HTML strings + if ( typeof selector === "string" ) { + // Are we dealing with HTML string or an ID? + if ( selector.charAt(0) === "<" && selector.charAt( selector.length - 1 ) === ">" && selector.length >= 3 ) { + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = quickExpr.exec( selector ); + } + + // Verify a match, and that no context was specified for #id + if ( match && (match[1] || !context) ) { + + // HANDLE: $(html) -> $(array) + if ( match[1] ) { + context = context instanceof jQuery ? context[0] : context; + doc = ( context ? context.ownerDocument || context : document ); + + // If a single string is passed in and it's a single tag + // just do a createElement and skip the rest + ret = rsingleTag.exec( selector ); + + if ( ret ) { + if ( jQuery.isPlainObject( context ) ) { + selector = [ document.createElement( ret[1] ) ]; + jQuery.fn.attr.call( selector, context, true ); + + } else { + selector = [ doc.createElement( ret[1] ) ]; + } + + } else { + ret = jQuery.buildFragment( [ match[1] ], [ doc ] ); + selector = ( ret.cacheable ? jQuery.clone(ret.fragment) : ret.fragment ).childNodes; + } + + return jQuery.merge( this, selector ); + + // HANDLE: $("#id") + } else { + elem = document.getElementById( match[2] ); + + // Check parentNode to catch when Blackberry 4.6 returns + // nodes that are no longer in the document #6963 + if ( elem && elem.parentNode ) { + // Handle the case where IE and Opera return items + // by name instead of ID + if ( elem.id !== match[2] ) { + return rootjQuery.find( selector ); + } + + // Otherwise, we inject the element directly into the jQuery object + this.length = 1; + this[0] = elem; + } + + this.context = document; + this.selector = selector; + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || rootjQuery ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( jQuery.isFunction( selector ) ) { + return rootjQuery.ready( selector ); + } + + if ( selector.selector !== undefined ) { + this.selector = selector.selector; + this.context = selector.context; + } + + return jQuery.makeArray( selector, this ); + }, + + // Start with an empty selector + selector: "", + + // The current version of jQuery being used + jquery: "1.7.2", + + // The default length of a jQuery object is 0 + length: 0, + + // The number of elements contained in the matched element set + size: function() { + return this.length; + }, + + toArray: function() { + return slice.call( this, 0 ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + return num == null ? + + // Return a 'clean' array + this.toArray() : + + // Return just the object + ( num < 0 ? this[ this.length + num ] : this[ num ] ); + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems, name, selector ) { + // Build a new jQuery matched element set + var ret = this.constructor(); + + if ( jQuery.isArray( elems ) ) { + push.apply( ret, elems ); + + } else { + jQuery.merge( ret, elems ); + } + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + ret.context = this.context; + + if ( name === "find" ) { + ret.selector = this.selector + ( this.selector ? " " : "" ) + selector; + } else if ( name ) { + ret.selector = this.selector + "." + name + "(" + selector + ")"; + } + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + // (You can seed the arguments with an array of args, but this is + // only used internally.) + each: function( callback, args ) { + return jQuery.each( this, callback, args ); + }, + + ready: function( fn ) { + // Attach the listeners + jQuery.bindReady(); + + // Add the callback + readyList.add( fn ); + + return this; + }, + + eq: function( i ) { + i = +i; + return i === -1 ? + this.slice( i ) : + this.slice( i, i + 1 ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ), + "slice", slice.call(arguments).join(",") ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map(this, function( elem, i ) { + return callback.call( elem, i, elem ); + })); + }, + + end: function() { + return this.prevObject || this.constructor(null); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: [].sort, + splice: [].splice +}; + +// Give the init function the jQuery prototype for later instantiation +jQuery.fn.init.prototype = jQuery.fn; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[0] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + target = arguments[1] || {}; + // skip the boolean and the target + i = 2; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !jQuery.isFunction(target) ) { + target = {}; + } + + // extend jQuery itself if only one argument is passed + if ( length === i ) { + target = this; + --i; + } + + for ( ; i < length; i++ ) { + // Only deal with non-null/undefined values + if ( (options = arguments[ i ]) != null ) { + // Extend the base object + for ( name in options ) { + src = target[ name ]; + copy = options[ name ]; + + // Prevent never-ending loop + if ( target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject(copy) || (copyIsArray = jQuery.isArray(copy)) ) ) { + if ( copyIsArray ) { + copyIsArray = false; + clone = src && jQuery.isArray(src) ? src : []; + + } else { + clone = src && jQuery.isPlainObject(src) ? src : {}; + } + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend({ + noConflict: function( deep ) { + if ( window.$ === jQuery ) { + window.$ = _$; + } + + if ( deep && window.jQuery === jQuery ) { + window.jQuery = _jQuery; + } + + return jQuery; + }, + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Hold (or release) the ready event + holdReady: function( hold ) { + if ( hold ) { + jQuery.readyWait++; + } else { + jQuery.ready( true ); + } + }, + + // Handle when the DOM is ready + ready: function( wait ) { + // Either a released hold or an DOMready/load event and not yet ready + if ( (wait === true && !--jQuery.readyWait) || (wait !== true && !jQuery.isReady) ) { + // Make sure body exists, at least, in case IE gets a little overzealous (ticket #5443). + if ( !document.body ) { + return setTimeout( jQuery.ready, 1 ); + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.fireWith( document, [ jQuery ] ); + + // Trigger any bound ready events + if ( jQuery.fn.trigger ) { + jQuery( document ).trigger( "ready" ).off( "ready" ); + } + } + }, + + bindReady: function() { + if ( readyList ) { + return; + } + + readyList = jQuery.Callbacks( "once memory" ); + + // Catch cases where $(document).ready() is called after the + // browser event has already occurred. + if ( document.readyState === "complete" ) { + // Handle it asynchronously to allow scripts the opportunity to delay ready + return setTimeout( jQuery.ready, 1 ); + } + + // Mozilla, Opera and webkit nightlies currently support this event + if ( document.addEventListener ) { + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", DOMContentLoaded, false ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", jQuery.ready, false ); + + // If IE event model is used + } else if ( document.attachEvent ) { + // ensure firing before onload, + // maybe late but safe also for iframes + document.attachEvent( "onreadystatechange", DOMContentLoaded ); + + // A fallback to window.onload, that will always work + window.attachEvent( "onload", jQuery.ready ); + + // If IE and not a frame + // continually check to see if the document is ready + var toplevel = false; + + try { + toplevel = window.frameElement == null; + } catch(e) {} + + if ( document.documentElement.doScroll && toplevel ) { + doScrollCheck(); + } + } + }, + + // See test/unit/core.js for details concerning isFunction. + // Since version 1.3, DOM methods and functions like alert + // aren't supported. They return false on IE (#2968). + isFunction: function( obj ) { + return jQuery.type(obj) === "function"; + }, + + isArray: Array.isArray || function( obj ) { + return jQuery.type(obj) === "array"; + }, + + isWindow: function( obj ) { + return obj != null && obj == obj.window; + }, + + isNumeric: function( obj ) { + return !isNaN( parseFloat(obj) ) && isFinite( obj ); + }, + + type: function( obj ) { + return obj == null ? + String( obj ) : + class2type[ toString.call(obj) ] || "object"; + }, + + isPlainObject: function( obj ) { + // Must be an Object. + // Because of IE, we also have to check the presence of the constructor property. + // Make sure that DOM nodes and window objects don't pass through, as well + if ( !obj || jQuery.type(obj) !== "object" || obj.nodeType || jQuery.isWindow( obj ) ) { + return false; + } + + try { + // Not own constructor property must be Object + if ( obj.constructor && + !hasOwn.call(obj, "constructor") && + !hasOwn.call(obj.constructor.prototype, "isPrototypeOf") ) { + return false; + } + } catch ( e ) { + // IE8,9 Will throw exceptions on certain host objects #9897 + return false; + } + + // Own properties are enumerated firstly, so to speed up, + // if last one is own, then all properties are own. + + var key; + for ( key in obj ) {} + + return key === undefined || hasOwn.call( obj, key ); + }, + + isEmptyObject: function( obj ) { + for ( var name in obj ) { + return false; + } + return true; + }, + + error: function( msg ) { + throw new Error( msg ); + }, + + parseJSON: function( data ) { + if ( typeof data !== "string" || !data ) { + return null; + } + + // Make sure leading/trailing whitespace is removed (IE can't handle it) + data = jQuery.trim( data ); + + // Attempt to parse using the native JSON parser first + if ( window.JSON && window.JSON.parse ) { + return window.JSON.parse( data ); + } + + // Make sure the incoming data is actual JSON + // Logic borrowed from http://json.org/json2.js + if ( rvalidchars.test( data.replace( rvalidescape, "@" ) + .replace( rvalidtokens, "]" ) + .replace( rvalidbraces, "")) ) { + + return ( new Function( "return " + data ) )(); + + } + jQuery.error( "Invalid JSON: " + data ); + }, + + // Cross-browser xml parsing + parseXML: function( data ) { + if ( typeof data !== "string" || !data ) { + return null; + } + var xml, tmp; + try { + if ( window.DOMParser ) { // Standard + tmp = new DOMParser(); + xml = tmp.parseFromString( data , "text/xml" ); + } else { // IE + xml = new ActiveXObject( "Microsoft.XMLDOM" ); + xml.async = "false"; + xml.loadXML( data ); + } + } catch( e ) { + xml = undefined; + } + if ( !xml || !xml.documentElement || xml.getElementsByTagName( "parsererror" ).length ) { + jQuery.error( "Invalid XML: " + data ); + } + return xml; + }, + + noop: function() {}, + + // Evaluates a script in a global context + // Workarounds based on findings by Jim Driscoll + // http://weblogs.java.net/blog/driscoll/archive/2009/09/08/eval-javascript-global-context + globalEval: function( data ) { + if ( data && rnotwhite.test( data ) ) { + // We use execScript on Internet Explorer + // We use an anonymous function so that context is window + // rather than jQuery in Firefox + ( window.execScript || function( data ) { + window[ "eval" ].call( window, data ); + } )( data ); + } + }, + + // Convert dashed to camelCase; used by the css and data modules + // Microsoft forgot to hump their vendor prefix (#9572) + camelCase: function( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); + }, + + nodeName: function( elem, name ) { + return elem.nodeName && elem.nodeName.toUpperCase() === name.toUpperCase(); + }, + + // args is for internal usage only + each: function( object, callback, args ) { + var name, i = 0, + length = object.length, + isObj = length === undefined || jQuery.isFunction( object ); + + if ( args ) { + if ( isObj ) { + for ( name in object ) { + if ( callback.apply( object[ name ], args ) === false ) { + break; + } + } + } else { + for ( ; i < length; ) { + if ( callback.apply( object[ i++ ], args ) === false ) { + break; + } + } + } + + // A special, fast, case for the most common use of each + } else { + if ( isObj ) { + for ( name in object ) { + if ( callback.call( object[ name ], name, object[ name ] ) === false ) { + break; + } + } + } else { + for ( ; i < length; ) { + if ( callback.call( object[ i ], i, object[ i++ ] ) === false ) { + break; + } + } + } + } + + return object; + }, + + // Use native String.trim function wherever possible + trim: trim ? + function( text ) { + return text == null ? + "" : + trim.call( text ); + } : + + // Otherwise use our own trimming functionality + function( text ) { + return text == null ? + "" : + text.toString().replace( trimLeft, "" ).replace( trimRight, "" ); + }, + + // results is for internal usage only + makeArray: function( array, results ) { + var ret = results || []; + + if ( array != null ) { + // The window, strings (and functions) also have 'length' + // Tweaked logic slightly to handle Blackberry 4.7 RegExp issues #6930 + var type = jQuery.type( array ); + + if ( array.length == null || type === "string" || type === "function" || type === "regexp" || jQuery.isWindow( array ) ) { + push.call( ret, array ); + } else { + jQuery.merge( ret, array ); + } + } + + return ret; + }, + + inArray: function( elem, array, i ) { + var len; + + if ( array ) { + if ( indexOf ) { + return indexOf.call( array, elem, i ); + } + + len = array.length; + i = i ? i < 0 ? Math.max( 0, len + i ) : i : 0; + + for ( ; i < len; i++ ) { + // Skip accessing in sparse arrays + if ( i in array && array[ i ] === elem ) { + return i; + } + } + } + + return -1; + }, + + merge: function( first, second ) { + var i = first.length, + j = 0; + + if ( typeof second.length === "number" ) { + for ( var l = second.length; j < l; j++ ) { + first[ i++ ] = second[ j ]; + } + + } else { + while ( second[j] !== undefined ) { + first[ i++ ] = second[ j++ ]; + } + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, inv ) { + var ret = [], retVal; + inv = !!inv; + + // Go through the array, only saving the items + // that pass the validator function + for ( var i = 0, length = elems.length; i < length; i++ ) { + retVal = !!callback( elems[ i ], i ); + if ( inv !== retVal ) { + ret.push( elems[ i ] ); + } + } + + return ret; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var value, key, ret = [], + i = 0, + length = elems.length, + // jquery objects are treated as arrays + isArray = elems instanceof jQuery || length !== undefined && typeof length === "number" && ( ( length > 0 && elems[ 0 ] && elems[ length -1 ] ) || length === 0 || jQuery.isArray( elems ) ) ; + + // Go through the array, translating each of the items to their + if ( isArray ) { + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret[ ret.length ] = value; + } + } + + // Go through every key on the object, + } else { + for ( key in elems ) { + value = callback( elems[ key ], key, arg ); + + if ( value != null ) { + ret[ ret.length ] = value; + } + } + } + + // Flatten any nested arrays + return ret.concat.apply( [], ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // Bind a function to a context, optionally partially applying any + // arguments. + proxy: function( fn, context ) { + if ( typeof context === "string" ) { + var tmp = fn[ context ]; + context = fn; + fn = tmp; + } + + // Quick check to determine if target is callable, in the spec + // this throws a TypeError, but we will just return undefined. + if ( !jQuery.isFunction( fn ) ) { + return undefined; + } + + // Simulated bind + var args = slice.call( arguments, 2 ), + proxy = function() { + return fn.apply( context, args.concat( slice.call( arguments ) ) ); + }; + + // Set the guid of unique handler to the same of original handler, so it can be removed + proxy.guid = fn.guid = fn.guid || proxy.guid || jQuery.guid++; + + return proxy; + }, + + // Mutifunctional method to get and set values to a collection + // The value/s can optionally be executed if it's a function + access: function( elems, fn, key, value, chainable, emptyGet, pass ) { + var exec, + bulk = key == null, + i = 0, + length = elems.length; + + // Sets many values + if ( key && typeof key === "object" ) { + for ( i in key ) { + jQuery.access( elems, fn, i, key[i], 1, emptyGet, value ); + } + chainable = 1; + + // Sets one value + } else if ( value !== undefined ) { + // Optionally, function values get executed if exec is true + exec = pass === undefined && jQuery.isFunction( value ); + + if ( bulk ) { + // Bulk operations only iterate when executing function values + if ( exec ) { + exec = fn; + fn = function( elem, key, value ) { + return exec.call( jQuery( elem ), value ); + }; + + // Otherwise they run against the entire set + } else { + fn.call( elems, value ); + fn = null; + } + } + + if ( fn ) { + for (; i < length; i++ ) { + fn( elems[i], key, exec ? value.call( elems[i], i, fn( elems[i], key ) ) : value, pass ); + } + } + + chainable = 1; + } + + return chainable ? + elems : + + // Gets + bulk ? + fn.call( elems ) : + length ? fn( elems[0], key ) : emptyGet; + }, + + now: function() { + return ( new Date() ).getTime(); + }, + + // Use of jQuery.browser is frowned upon. + // More details: http://docs.jquery.com/Utilities/jQuery.browser + uaMatch: function( ua ) { + ua = ua.toLowerCase(); + + var match = rwebkit.exec( ua ) || + ropera.exec( ua ) || + rmsie.exec( ua ) || + ua.indexOf("compatible") < 0 && rmozilla.exec( ua ) || + []; + + return { browser: match[1] || "", version: match[2] || "0" }; + }, + + sub: function() { + function jQuerySub( selector, context ) { + return new jQuerySub.fn.init( selector, context ); + } + jQuery.extend( true, jQuerySub, this ); + jQuerySub.superclass = this; + jQuerySub.fn = jQuerySub.prototype = this(); + jQuerySub.fn.constructor = jQuerySub; + jQuerySub.sub = this.sub; + jQuerySub.fn.init = function init( selector, context ) { + if ( context && context instanceof jQuery && !(context instanceof jQuerySub) ) { + context = jQuerySub( context ); + } + + return jQuery.fn.init.call( this, selector, context, rootjQuerySub ); + }; + jQuerySub.fn.init.prototype = jQuerySub.fn; + var rootjQuerySub = jQuerySub(document); + return jQuerySub; + }, + + browser: {} +}); + +// Populate the class2type map +jQuery.each("Boolean Number String Function Array Date RegExp Object".split(" "), function(i, name) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); +}); + +browserMatch = jQuery.uaMatch( userAgent ); +if ( browserMatch.browser ) { + jQuery.browser[ browserMatch.browser ] = true; + jQuery.browser.version = browserMatch.version; +} + +// Deprecated, use jQuery.browser.webkit instead +if ( jQuery.browser.webkit ) { + jQuery.browser.safari = true; +} + +// IE doesn't match non-breaking spaces with \s +if ( rnotwhite.test( "\xA0" ) ) { + trimLeft = /^[\s\xA0]+/; + trimRight = /[\s\xA0]+$/; +} + +// All jQuery objects should point back to these +rootjQuery = jQuery(document); + +// Cleanup functions for the document ready method +if ( document.addEventListener ) { + DOMContentLoaded = function() { + document.removeEventListener( "DOMContentLoaded", DOMContentLoaded, false ); + jQuery.ready(); + }; + +} else if ( document.attachEvent ) { + DOMContentLoaded = function() { + // Make sure body exists, at least, in case IE gets a little overzealous (ticket #5443). + if ( document.readyState === "complete" ) { + document.detachEvent( "onreadystatechange", DOMContentLoaded ); + jQuery.ready(); + } + }; +} + +// The DOM ready check for Internet Explorer +function doScrollCheck() { + if ( jQuery.isReady ) { + return; + } + + try { + // If IE is used, use the trick by Diego Perini + // http://javascript.nwbox.com/IEContentLoaded/ + document.documentElement.doScroll("left"); + } catch(e) { + setTimeout( doScrollCheck, 1 ); + return; + } + + // and execute any waiting functions + jQuery.ready(); +} + +return jQuery; + +})(); + + +// String to Object flags format cache +var flagsCache = {}; + +// Convert String-formatted flags into Object-formatted ones and store in cache +function createFlags( flags ) { + var object = flagsCache[ flags ] = {}, + i, length; + flags = flags.split( /\s+/ ); + for ( i = 0, length = flags.length; i < length; i++ ) { + object[ flags[i] ] = true; + } + return object; +} + +/* + * Create a callback list using the following parameters: + * + * flags: an optional list of space-separated flags that will change how + * the callback list behaves + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible flags: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( flags ) { + + // Convert flags from String-formatted to Object-formatted + // (we check in cache first) + flags = flags ? ( flagsCache[ flags ] || createFlags( flags ) ) : {}; + + var // Actual callback list + list = [], + // Stack of fire calls for repeatable lists + stack = [], + // Last fire value (for non-forgettable lists) + memory, + // Flag to know if list was already fired + fired, + // Flag to know if list is currently firing + firing, + // First callback to fire (used internally by add and fireWith) + firingStart, + // End of the loop when firing + firingLength, + // Index of currently firing callback (modified by remove if needed) + firingIndex, + // Add one or several callbacks to the list + add = function( args ) { + var i, + length, + elem, + type, + actual; + for ( i = 0, length = args.length; i < length; i++ ) { + elem = args[ i ]; + type = jQuery.type( elem ); + if ( type === "array" ) { + // Inspect recursively + add( elem ); + } else if ( type === "function" ) { + // Add if not in unique mode and callback is not in + if ( !flags.unique || !self.has( elem ) ) { + list.push( elem ); + } + } + } + }, + // Fire callbacks + fire = function( context, args ) { + args = args || []; + memory = !flags.memory || [ context, args ]; + fired = true; + firing = true; + firingIndex = firingStart || 0; + firingStart = 0; + firingLength = list.length; + for ( ; list && firingIndex < firingLength; firingIndex++ ) { + if ( list[ firingIndex ].apply( context, args ) === false && flags.stopOnFalse ) { + memory = true; // Mark as halted + break; + } + } + firing = false; + if ( list ) { + if ( !flags.once ) { + if ( stack && stack.length ) { + memory = stack.shift(); + self.fireWith( memory[ 0 ], memory[ 1 ] ); + } + } else if ( memory === true ) { + self.disable(); + } else { + list = []; + } + } + }, + // Actual Callbacks object + self = { + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + var length = list.length; + add( arguments ); + // Do we need to add the callbacks to the + // current firing batch? + if ( firing ) { + firingLength = list.length; + // With memory, if we're not firing then + // we should call right away, unless previous + // firing was halted (stopOnFalse) + } else if ( memory && memory !== true ) { + firingStart = length; + fire( memory[ 0 ], memory[ 1 ] ); + } + } + return this; + }, + // Remove a callback from the list + remove: function() { + if ( list ) { + var args = arguments, + argIndex = 0, + argLength = args.length; + for ( ; argIndex < argLength ; argIndex++ ) { + for ( var i = 0; i < list.length; i++ ) { + if ( args[ argIndex ] === list[ i ] ) { + // Handle firingIndex and firingLength + if ( firing ) { + if ( i <= firingLength ) { + firingLength--; + if ( i <= firingIndex ) { + firingIndex--; + } + } + } + // Remove the element + list.splice( i--, 1 ); + // If we have some unicity property then + // we only need to do this once + if ( flags.unique ) { + break; + } + } + } + } + } + return this; + }, + // Control if a given callback is in the list + has: function( fn ) { + if ( list ) { + var i = 0, + length = list.length; + for ( ; i < length; i++ ) { + if ( fn === list[ i ] ) { + return true; + } + } + } + return false; + }, + // Remove all callbacks from the list + empty: function() { + list = []; + return this; + }, + // Have the list do nothing anymore + disable: function() { + list = stack = memory = undefined; + return this; + }, + // Is it disabled? + disabled: function() { + return !list; + }, + // Lock the list in its current state + lock: function() { + stack = undefined; + if ( !memory || memory === true ) { + self.disable(); + } + return this; + }, + // Is it locked? + locked: function() { + return !stack; + }, + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( stack ) { + if ( firing ) { + if ( !flags.once ) { + stack.push( [ context, args ] ); + } + } else if ( !( flags.once && memory ) ) { + fire( context, args ); + } + } + return this; + }, + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + + + +var // Static reference to slice + sliceDeferred = [].slice; + +jQuery.extend({ + + Deferred: function( func ) { + var doneList = jQuery.Callbacks( "once memory" ), + failList = jQuery.Callbacks( "once memory" ), + progressList = jQuery.Callbacks( "memory" ), + state = "pending", + lists = { + resolve: doneList, + reject: failList, + notify: progressList + }, + promise = { + done: doneList.add, + fail: failList.add, + progress: progressList.add, + + state: function() { + return state; + }, + + // Deprecated + isResolved: doneList.fired, + isRejected: failList.fired, + + then: function( doneCallbacks, failCallbacks, progressCallbacks ) { + deferred.done( doneCallbacks ).fail( failCallbacks ).progress( progressCallbacks ); + return this; + }, + always: function() { + deferred.done.apply( deferred, arguments ).fail.apply( deferred, arguments ); + return this; + }, + pipe: function( fnDone, fnFail, fnProgress ) { + return jQuery.Deferred(function( newDefer ) { + jQuery.each( { + done: [ fnDone, "resolve" ], + fail: [ fnFail, "reject" ], + progress: [ fnProgress, "notify" ] + }, function( handler, data ) { + var fn = data[ 0 ], + action = data[ 1 ], + returned; + if ( jQuery.isFunction( fn ) ) { + deferred[ handler ](function() { + returned = fn.apply( this, arguments ); + if ( returned && jQuery.isFunction( returned.promise ) ) { + returned.promise().then( newDefer.resolve, newDefer.reject, newDefer.notify ); + } else { + newDefer[ action + "With" ]( this === deferred ? newDefer : this, [ returned ] ); + } + }); + } else { + deferred[ handler ]( newDefer[ action ] ); + } + }); + }).promise(); + }, + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + if ( obj == null ) { + obj = promise; + } else { + for ( var key in promise ) { + obj[ key ] = promise[ key ]; + } + } + return obj; + } + }, + deferred = promise.promise({}), + key; + + for ( key in lists ) { + deferred[ key ] = lists[ key ].fire; + deferred[ key + "With" ] = lists[ key ].fireWith; + } + + // Handle state + deferred.done( function() { + state = "resolved"; + }, failList.disable, progressList.lock ).fail( function() { + state = "rejected"; + }, doneList.disable, progressList.lock ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( firstParam ) { + var args = sliceDeferred.call( arguments, 0 ), + i = 0, + length = args.length, + pValues = new Array( length ), + count = length, + pCount = length, + deferred = length <= 1 && firstParam && jQuery.isFunction( firstParam.promise ) ? + firstParam : + jQuery.Deferred(), + promise = deferred.promise(); + function resolveFunc( i ) { + return function( value ) { + args[ i ] = arguments.length > 1 ? sliceDeferred.call( arguments, 0 ) : value; + if ( !( --count ) ) { + deferred.resolveWith( deferred, args ); + } + }; + } + function progressFunc( i ) { + return function( value ) { + pValues[ i ] = arguments.length > 1 ? sliceDeferred.call( arguments, 0 ) : value; + deferred.notifyWith( promise, pValues ); + }; + } + if ( length > 1 ) { + for ( ; i < length; i++ ) { + if ( args[ i ] && args[ i ].promise && jQuery.isFunction( args[ i ].promise ) ) { + args[ i ].promise().then( resolveFunc(i), deferred.reject, progressFunc(i) ); + } else { + --count; + } + } + if ( !count ) { + deferred.resolveWith( deferred, args ); + } + } else if ( deferred !== firstParam ) { + deferred.resolveWith( deferred, length ? [ firstParam ] : [] ); + } + return promise; + } +}); + + + + +jQuery.support = (function() { + + var support, + all, + a, + select, + opt, + input, + fragment, + tds, + events, + eventName, + i, + isSupported, + div = document.createElement( "div" ), + documentElement = document.documentElement; + + // Preliminary tests + div.setAttribute("className", "t"); + div.innerHTML = "
a"; + + all = div.getElementsByTagName( "*" ); + a = div.getElementsByTagName( "a" )[ 0 ]; + + // Can't get basic test support + if ( !all || !all.length || !a ) { + return {}; + } + + // First batch of supports tests + select = document.createElement( "select" ); + opt = select.appendChild( document.createElement("option") ); + input = div.getElementsByTagName( "input" )[ 0 ]; + + support = { + // IE strips leading whitespace when .innerHTML is used + leadingWhitespace: ( div.firstChild.nodeType === 3 ), + + // Make sure that tbody elements aren't automatically inserted + // IE will insert them into empty tables + tbody: !div.getElementsByTagName("tbody").length, + + // Make sure that link elements get serialized correctly by innerHTML + // This requires a wrapper element in IE + htmlSerialize: !!div.getElementsByTagName("link").length, + + // Get the style information from getAttribute + // (IE uses .cssText instead) + style: /top/.test( a.getAttribute("style") ), + + // Make sure that URLs aren't manipulated + // (IE normalizes it by default) + hrefNormalized: ( a.getAttribute("href") === "/a" ), + + // Make sure that element opacity exists + // (IE uses filter instead) + // Use a regex to work around a WebKit issue. See #5145 + opacity: /^0.55/.test( a.style.opacity ), + + // Verify style float existence + // (IE uses styleFloat instead of cssFloat) + cssFloat: !!a.style.cssFloat, + + // Make sure that if no value is specified for a checkbox + // that it defaults to "on". + // (WebKit defaults to "" instead) + checkOn: ( input.value === "on" ), + + // Make sure that a selected-by-default option has a working selected property. + // (WebKit defaults to false instead of true, IE too, if it's in an optgroup) + optSelected: opt.selected, + + // Test setAttribute on camelCase class. If it works, we need attrFixes when doing get/setAttribute (ie6/7) + getSetAttribute: div.className !== "t", + + // Tests for enctype support on a form(#6743) + enctype: !!document.createElement("form").enctype, + + // Makes sure cloning an html5 element does not cause problems + // Where outerHTML is undefined, this still works + html5Clone: document.createElement("nav").cloneNode( true ).outerHTML !== "<:nav>", + + // Will be defined later + submitBubbles: true, + changeBubbles: true, + focusinBubbles: false, + deleteExpando: true, + noCloneEvent: true, + inlineBlockNeedsLayout: false, + shrinkWrapBlocks: false, + reliableMarginRight: true, + pixelMargin: true + }; + + // jQuery.boxModel DEPRECATED in 1.3, use jQuery.support.boxModel instead + jQuery.boxModel = support.boxModel = (document.compatMode === "CSS1Compat"); + + // Make sure checked status is properly cloned + input.checked = true; + support.noCloneChecked = input.cloneNode( true ).checked; + + // Make sure that the options inside disabled selects aren't marked as disabled + // (WebKit marks them as disabled) + select.disabled = true; + support.optDisabled = !opt.disabled; + + // Test to see if it's possible to delete an expando from an element + // Fails in Internet Explorer + try { + delete div.test; + } catch( e ) { + support.deleteExpando = false; + } + + if ( !div.addEventListener && div.attachEvent && div.fireEvent ) { + div.attachEvent( "onclick", function() { + // Cloning a node shouldn't copy over any + // bound event handlers (IE does this) + support.noCloneEvent = false; + }); + div.cloneNode( true ).fireEvent( "onclick" ); + } + + // Check if a radio maintains its value + // after being appended to the DOM + input = document.createElement("input"); + input.value = "t"; + input.setAttribute("type", "radio"); + support.radioValue = input.value === "t"; + + input.setAttribute("checked", "checked"); + + // #11217 - WebKit loses check when the name is after the checked attribute + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + fragment = document.createDocumentFragment(); + fragment.appendChild( div.lastChild ); + + // WebKit doesn't clone checked state correctly in fragments + support.checkClone = fragment.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Check if a disconnected checkbox will retain its checked + // value of true after appended to the DOM (IE6/7) + support.appendChecked = input.checked; + + fragment.removeChild( input ); + fragment.appendChild( div ); + + // Technique from Juriy Zaytsev + // http://perfectionkills.com/detecting-event-support-without-browser-sniffing/ + // We only care about the case where non-standard event systems + // are used, namely in IE. Short-circuiting here helps us to + // avoid an eval call (in setAttribute) which can cause CSP + // to go haywire. See: https://developer.mozilla.org/en/Security/CSP + if ( div.attachEvent ) { + for ( i in { + submit: 1, + change: 1, + focusin: 1 + }) { + eventName = "on" + i; + isSupported = ( eventName in div ); + if ( !isSupported ) { + div.setAttribute( eventName, "return;" ); + isSupported = ( typeof div[ eventName ] === "function" ); + } + support[ i + "Bubbles" ] = isSupported; + } + } + + fragment.removeChild( div ); + + // Null elements to avoid leaks in IE + fragment = select = opt = div = input = null; + + // Run tests that need a body at doc ready + jQuery(function() { + var container, outer, inner, table, td, offsetSupport, + marginDiv, conMarginTop, style, html, positionTopLeftWidthHeight, + paddingMarginBorderVisibility, paddingMarginBorder, + body = document.getElementsByTagName("body")[0]; + + if ( !body ) { + // Return for frameset docs that don't have a body + return; + } + + conMarginTop = 1; + paddingMarginBorder = "padding:0;margin:0;border:"; + positionTopLeftWidthHeight = "position:absolute;top:0;left:0;width:1px;height:1px;"; + paddingMarginBorderVisibility = paddingMarginBorder + "0;visibility:hidden;"; + style = "style='" + positionTopLeftWidthHeight + paddingMarginBorder + "5px solid #000;"; + html = "
" + + "" + + "
"; + + container = document.createElement("div"); + container.style.cssText = paddingMarginBorderVisibility + "width:0;height:0;position:static;top:0;margin-top:" + conMarginTop + "px"; + body.insertBefore( container, body.firstChild ); + + // Construct the test element + div = document.createElement("div"); + container.appendChild( div ); + + // Check if table cells still have offsetWidth/Height when they are set + // to display:none and there are still other visible table cells in a + // table row; if so, offsetWidth/Height are not reliable for use when + // determining if an element has been hidden directly using + // display:none (it is still safe to use offsets if a parent element is + // hidden; don safety goggles and see bug #4512 for more information). + // (only IE 8 fails this test) + div.innerHTML = "
t
"; + tds = div.getElementsByTagName( "td" ); + isSupported = ( tds[ 0 ].offsetHeight === 0 ); + + tds[ 0 ].style.display = ""; + tds[ 1 ].style.display = "none"; + + // Check if empty table cells still have offsetWidth/Height + // (IE <= 8 fail this test) + support.reliableHiddenOffsets = isSupported && ( tds[ 0 ].offsetHeight === 0 ); + + // Check if div with explicit width and no margin-right incorrectly + // gets computed margin-right based on width of container. For more + // info see bug #3333 + // Fails in WebKit before Feb 2011 nightlies + // WebKit Bug 13343 - getComputedStyle returns wrong value for margin-right + if ( window.getComputedStyle ) { + div.innerHTML = ""; + marginDiv = document.createElement( "div" ); + marginDiv.style.width = "0"; + marginDiv.style.marginRight = "0"; + div.style.width = "2px"; + div.appendChild( marginDiv ); + support.reliableMarginRight = + ( parseInt( ( window.getComputedStyle( marginDiv, null ) || { marginRight: 0 } ).marginRight, 10 ) || 0 ) === 0; + } + + if ( typeof div.style.zoom !== "undefined" ) { + // Check if natively block-level elements act like inline-block + // elements when setting their display to 'inline' and giving + // them layout + // (IE < 8 does this) + div.innerHTML = ""; + div.style.width = div.style.padding = "1px"; + div.style.border = 0; + div.style.overflow = "hidden"; + div.style.display = "inline"; + div.style.zoom = 1; + support.inlineBlockNeedsLayout = ( div.offsetWidth === 3 ); + + // Check if elements with layout shrink-wrap their children + // (IE 6 does this) + div.style.display = "block"; + div.style.overflow = "visible"; + div.innerHTML = "
"; + support.shrinkWrapBlocks = ( div.offsetWidth !== 3 ); + } + + div.style.cssText = positionTopLeftWidthHeight + paddingMarginBorderVisibility; + div.innerHTML = html; + + outer = div.firstChild; + inner = outer.firstChild; + td = outer.nextSibling.firstChild.firstChild; + + offsetSupport = { + doesNotAddBorder: ( inner.offsetTop !== 5 ), + doesAddBorderForTableAndCells: ( td.offsetTop === 5 ) + }; + + inner.style.position = "fixed"; + inner.style.top = "20px"; + + // safari subtracts parent border width here which is 5px + offsetSupport.fixedPosition = ( inner.offsetTop === 20 || inner.offsetTop === 15 ); + inner.style.position = inner.style.top = ""; + + outer.style.overflow = "hidden"; + outer.style.position = "relative"; + + offsetSupport.subtractsBorderForOverflowNotVisible = ( inner.offsetTop === -5 ); + offsetSupport.doesNotIncludeMarginInBodyOffset = ( body.offsetTop !== conMarginTop ); + + if ( window.getComputedStyle ) { + div.style.marginTop = "1%"; + support.pixelMargin = ( window.getComputedStyle( div, null ) || { marginTop: 0 } ).marginTop !== "1%"; + } + + if ( typeof container.style.zoom !== "undefined" ) { + container.style.zoom = 1; + } + + body.removeChild( container ); + marginDiv = div = container = null; + + jQuery.extend( support, offsetSupport ); + }); + + return support; +})(); + + + + +var rbrace = /^(?:\{.*\}|\[.*\])$/, + rmultiDash = /([A-Z])/g; + +jQuery.extend({ + cache: {}, + + // Please use with caution + uuid: 0, + + // Unique for each copy of jQuery on the page + // Non-digits removed to match rinlinejQuery + expando: "jQuery" + ( jQuery.fn.jquery + Math.random() ).replace( /\D/g, "" ), + + // The following elements throw uncatchable exceptions if you + // attempt to add expando properties to them. + noData: { + "embed": true, + // Ban all objects except for Flash (which handle expandos) + "object": "clsid:D27CDB6E-AE6D-11cf-96B8-444553540000", + "applet": true + }, + + hasData: function( elem ) { + elem = elem.nodeType ? jQuery.cache[ elem[jQuery.expando] ] : elem[ jQuery.expando ]; + return !!elem && !isEmptyDataObject( elem ); + }, + + data: function( elem, name, data, pvt /* Internal Use Only */ ) { + if ( !jQuery.acceptData( elem ) ) { + return; + } + + var privateCache, thisCache, ret, + internalKey = jQuery.expando, + getByName = typeof name === "string", + + // We have to handle DOM nodes and JS objects differently because IE6-7 + // can't GC object references properly across the DOM-JS boundary + isNode = elem.nodeType, + + // Only DOM nodes need the global jQuery cache; JS object data is + // attached directly to the object so GC can occur automatically + cache = isNode ? jQuery.cache : elem, + + // Only defining an ID for JS objects if its cache already exists allows + // the code to shortcut on the same path as a DOM node with no cache + id = isNode ? elem[ internalKey ] : elem[ internalKey ] && internalKey, + isEvents = name === "events"; + + // Avoid doing any more work than we need to when trying to get data on an + // object that has no data at all + if ( (!id || !cache[id] || (!isEvents && !pvt && !cache[id].data)) && getByName && data === undefined ) { + return; + } + + if ( !id ) { + // Only DOM nodes need a new unique ID for each element since their data + // ends up in the global cache + if ( isNode ) { + elem[ internalKey ] = id = ++jQuery.uuid; + } else { + id = internalKey; + } + } + + if ( !cache[ id ] ) { + cache[ id ] = {}; + + // Avoids exposing jQuery metadata on plain JS objects when the object + // is serialized using JSON.stringify + if ( !isNode ) { + cache[ id ].toJSON = jQuery.noop; + } + } + + // An object can be passed to jQuery.data instead of a key/value pair; this gets + // shallow copied over onto the existing cache + if ( typeof name === "object" || typeof name === "function" ) { + if ( pvt ) { + cache[ id ] = jQuery.extend( cache[ id ], name ); + } else { + cache[ id ].data = jQuery.extend( cache[ id ].data, name ); + } + } + + privateCache = thisCache = cache[ id ]; + + // jQuery data() is stored in a separate object inside the object's internal data + // cache in order to avoid key collisions between internal data and user-defined + // data. + if ( !pvt ) { + if ( !thisCache.data ) { + thisCache.data = {}; + } + + thisCache = thisCache.data; + } + + if ( data !== undefined ) { + thisCache[ jQuery.camelCase( name ) ] = data; + } + + // Users should not attempt to inspect the internal events object using jQuery.data, + // it is undocumented and subject to change. But does anyone listen? No. + if ( isEvents && !thisCache[ name ] ) { + return privateCache.events; + } + + // Check for both converted-to-camel and non-converted data property names + // If a data property was specified + if ( getByName ) { + + // First Try to find as-is property data + ret = thisCache[ name ]; + + // Test for null|undefined property data + if ( ret == null ) { + + // Try to find the camelCased property + ret = thisCache[ jQuery.camelCase( name ) ]; + } + } else { + ret = thisCache; + } + + return ret; + }, + + removeData: function( elem, name, pvt /* Internal Use Only */ ) { + if ( !jQuery.acceptData( elem ) ) { + return; + } + + var thisCache, i, l, + + // Reference to internal data cache key + internalKey = jQuery.expando, + + isNode = elem.nodeType, + + // See jQuery.data for more information + cache = isNode ? jQuery.cache : elem, + + // See jQuery.data for more information + id = isNode ? elem[ internalKey ] : internalKey; + + // If there is already no cache entry for this object, there is no + // purpose in continuing + if ( !cache[ id ] ) { + return; + } + + if ( name ) { + + thisCache = pvt ? cache[ id ] : cache[ id ].data; + + if ( thisCache ) { + + // Support array or space separated string names for data keys + if ( !jQuery.isArray( name ) ) { + + // try the string as a key before any manipulation + if ( name in thisCache ) { + name = [ name ]; + } else { + + // split the camel cased version by spaces unless a key with the spaces exists + name = jQuery.camelCase( name ); + if ( name in thisCache ) { + name = [ name ]; + } else { + name = name.split( " " ); + } + } + } + + for ( i = 0, l = name.length; i < l; i++ ) { + delete thisCache[ name[i] ]; + } + + // If there is no data left in the cache, we want to continue + // and let the cache object itself get destroyed + if ( !( pvt ? isEmptyDataObject : jQuery.isEmptyObject )( thisCache ) ) { + return; + } + } + } + + // See jQuery.data for more information + if ( !pvt ) { + delete cache[ id ].data; + + // Don't destroy the parent cache unless the internal data object + // had been the only thing left in it + if ( !isEmptyDataObject(cache[ id ]) ) { + return; + } + } + + // Browsers that fail expando deletion also refuse to delete expandos on + // the window, but it will allow it on all other JS objects; other browsers + // don't care + // Ensure that `cache` is not a window object #10080 + if ( jQuery.support.deleteExpando || !cache.setInterval ) { + delete cache[ id ]; + } else { + cache[ id ] = null; + } + + // We destroyed the cache and need to eliminate the expando on the node to avoid + // false lookups in the cache for entries that no longer exist + if ( isNode ) { + // IE does not allow us to delete expando properties from nodes, + // nor does it have a removeAttribute function on Document nodes; + // we must handle all of these cases + if ( jQuery.support.deleteExpando ) { + delete elem[ internalKey ]; + } else if ( elem.removeAttribute ) { + elem.removeAttribute( internalKey ); + } else { + elem[ internalKey ] = null; + } + } + }, + + // For internal use only. + _data: function( elem, name, data ) { + return jQuery.data( elem, name, data, true ); + }, + + // A method for determining if a DOM node can handle the data expando + acceptData: function( elem ) { + if ( elem.nodeName ) { + var match = jQuery.noData[ elem.nodeName.toLowerCase() ]; + + if ( match ) { + return !(match === true || elem.getAttribute("classid") !== match); + } + } + + return true; + } +}); + +jQuery.fn.extend({ + data: function( key, value ) { + var parts, part, attr, name, l, + elem = this[0], + i = 0, + data = null; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = jQuery.data( elem ); + + if ( elem.nodeType === 1 && !jQuery._data( elem, "parsedAttrs" ) ) { + attr = elem.attributes; + for ( l = attr.length; i < l; i++ ) { + name = attr[i].name; + + if ( name.indexOf( "data-" ) === 0 ) { + name = jQuery.camelCase( name.substring(5) ); + + dataAttr( elem, name, data[ name ] ); + } + } + jQuery._data( elem, "parsedAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each(function() { + jQuery.data( this, key ); + }); + } + + parts = key.split( ".", 2 ); + parts[1] = parts[1] ? "." + parts[1] : ""; + part = parts[1] + "!"; + + return jQuery.access( this, function( value ) { + + if ( value === undefined ) { + data = this.triggerHandler( "getData" + part, [ parts[0] ] ); + + // Try to fetch any internally stored data first + if ( data === undefined && elem ) { + data = jQuery.data( elem, key ); + data = dataAttr( elem, key, data ); + } + + return data === undefined && parts[1] ? + this.data( parts[0] ) : + data; + } + + parts[1] = value; + this.each(function() { + var self = jQuery( this ); + + self.triggerHandler( "setData" + part, parts ); + jQuery.data( this, key, value ); + self.triggerHandler( "changeData" + part, parts ); + }); + }, null, value, arguments.length > 1, null, false ); + }, + + removeData: function( key ) { + return this.each(function() { + jQuery.removeData( this, key ); + }); + } +}); + +function dataAttr( elem, key, data ) { + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + + var name = "data-" + key.replace( rmultiDash, "-$1" ).toLowerCase(); + + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = data === "true" ? true : + data === "false" ? false : + data === "null" ? null : + jQuery.isNumeric( data ) ? +data : + rbrace.test( data ) ? jQuery.parseJSON( data ) : + data; + } catch( e ) {} + + // Make sure we set the data so it isn't changed later + jQuery.data( elem, key, data ); + + } else { + data = undefined; + } + } + + return data; +} + +// checks a cache object for emptiness +function isEmptyDataObject( obj ) { + for ( var name in obj ) { + + // if the public data object is empty, the private is still empty + if ( name === "data" && jQuery.isEmptyObject( obj[name] ) ) { + continue; + } + if ( name !== "toJSON" ) { + return false; + } + } + + return true; +} + + + + +function handleQueueMarkDefer( elem, type, src ) { + var deferDataKey = type + "defer", + queueDataKey = type + "queue", + markDataKey = type + "mark", + defer = jQuery._data( elem, deferDataKey ); + if ( defer && + ( src === "queue" || !jQuery._data(elem, queueDataKey) ) && + ( src === "mark" || !jQuery._data(elem, markDataKey) ) ) { + // Give room for hard-coded callbacks to fire first + // and eventually mark/queue something else on the element + setTimeout( function() { + if ( !jQuery._data( elem, queueDataKey ) && + !jQuery._data( elem, markDataKey ) ) { + jQuery.removeData( elem, deferDataKey, true ); + defer.fire(); + } + }, 0 ); + } +} + +jQuery.extend({ + + _mark: function( elem, type ) { + if ( elem ) { + type = ( type || "fx" ) + "mark"; + jQuery._data( elem, type, (jQuery._data( elem, type ) || 0) + 1 ); + } + }, + + _unmark: function( force, elem, type ) { + if ( force !== true ) { + type = elem; + elem = force; + force = false; + } + if ( elem ) { + type = type || "fx"; + var key = type + "mark", + count = force ? 0 : ( (jQuery._data( elem, key ) || 1) - 1 ); + if ( count ) { + jQuery._data( elem, key, count ); + } else { + jQuery.removeData( elem, key, true ); + handleQueueMarkDefer( elem, type, "mark" ); + } + } + }, + + queue: function( elem, type, data ) { + var q; + if ( elem ) { + type = ( type || "fx" ) + "queue"; + q = jQuery._data( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !q || jQuery.isArray(data) ) { + q = jQuery._data( elem, type, jQuery.makeArray(data) ); + } else { + q.push( data ); + } + } + return q || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + fn = queue.shift(), + hooks = {}; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + } + + if ( fn ) { + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + jQuery._data( elem, type + ".run", hooks ); + fn.call( elem, function() { + jQuery.dequeue( elem, type ); + }, hooks ); + } + + if ( !queue.length ) { + jQuery.removeData( elem, type + "queue " + type + ".run", true ); + handleQueueMarkDefer( elem, type, "queue" ); + } + } +}); + +jQuery.fn.extend({ + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[0], type ); + } + + return data === undefined ? + this : + this.each(function() { + var queue = jQuery.queue( this, type, data ); + + if ( type === "fx" && queue[0] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + }); + }, + dequeue: function( type ) { + return this.each(function() { + jQuery.dequeue( this, type ); + }); + }, + // Based off of the plugin by Clint Helfers, with permission. + // http://blindsignals.com/index.php/2009/07/jquery-delay/ + delay: function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = setTimeout( next, time ); + hooks.stop = function() { + clearTimeout( timeout ); + }; + }); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, object ) { + if ( typeof type !== "string" ) { + object = type; + type = undefined; + } + type = type || "fx"; + var defer = jQuery.Deferred(), + elements = this, + i = elements.length, + count = 1, + deferDataKey = type + "defer", + queueDataKey = type + "queue", + markDataKey = type + "mark", + tmp; + function resolve() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + } + while( i-- ) { + if (( tmp = jQuery.data( elements[ i ], deferDataKey, undefined, true ) || + ( jQuery.data( elements[ i ], queueDataKey, undefined, true ) || + jQuery.data( elements[ i ], markDataKey, undefined, true ) ) && + jQuery.data( elements[ i ], deferDataKey, jQuery.Callbacks( "once memory" ), true ) )) { + count++; + tmp.add( resolve ); + } + } + resolve(); + return defer.promise( object ); + } +}); + + + + +var rclass = /[\n\t\r]/g, + rspace = /\s+/, + rreturn = /\r/g, + rtype = /^(?:button|input)$/i, + rfocusable = /^(?:button|input|object|select|textarea)$/i, + rclickable = /^a(?:rea)?$/i, + rboolean = /^(?:autofocus|autoplay|async|checked|controls|defer|disabled|hidden|loop|multiple|open|readonly|required|scoped|selected)$/i, + getSetAttribute = jQuery.support.getSetAttribute, + nodeHook, boolHook, fixSpecified; + +jQuery.fn.extend({ + attr: function( name, value ) { + return jQuery.access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each(function() { + jQuery.removeAttr( this, name ); + }); + }, + + prop: function( name, value ) { + return jQuery.access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + name = jQuery.propFix[ name ] || name; + return this.each(function() { + // try/catch handles cases where IE balks (such as removing a property on window) + try { + this[ name ] = undefined; + delete this[ name ]; + } catch( e ) {} + }); + }, + + addClass: function( value ) { + var classNames, i, l, elem, + setClass, c, cl; + + if ( jQuery.isFunction( value ) ) { + return this.each(function( j ) { + jQuery( this ).addClass( value.call(this, j, this.className) ); + }); + } + + if ( value && typeof value === "string" ) { + classNames = value.split( rspace ); + + for ( i = 0, l = this.length; i < l; i++ ) { + elem = this[ i ]; + + if ( elem.nodeType === 1 ) { + if ( !elem.className && classNames.length === 1 ) { + elem.className = value; + + } else { + setClass = " " + elem.className + " "; + + for ( c = 0, cl = classNames.length; c < cl; c++ ) { + if ( !~setClass.indexOf( " " + classNames[ c ] + " " ) ) { + setClass += classNames[ c ] + " "; + } + } + elem.className = jQuery.trim( setClass ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classNames, i, l, elem, className, c, cl; + + if ( jQuery.isFunction( value ) ) { + return this.each(function( j ) { + jQuery( this ).removeClass( value.call(this, j, this.className) ); + }); + } + + if ( (value && typeof value === "string") || value === undefined ) { + classNames = ( value || "" ).split( rspace ); + + for ( i = 0, l = this.length; i < l; i++ ) { + elem = this[ i ]; + + if ( elem.nodeType === 1 && elem.className ) { + if ( value ) { + className = (" " + elem.className + " ").replace( rclass, " " ); + for ( c = 0, cl = classNames.length; c < cl; c++ ) { + className = className.replace(" " + classNames[ c ] + " ", " "); + } + elem.className = jQuery.trim( className ); + + } else { + elem.className = ""; + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isBool = typeof stateVal === "boolean"; + + if ( jQuery.isFunction( value ) ) { + return this.each(function( i ) { + jQuery( this ).toggleClass( value.call(this, i, this.className, stateVal), stateVal ); + }); + } + + return this.each(function() { + if ( type === "string" ) { + // toggle individual class names + var className, + i = 0, + self = jQuery( this ), + state = stateVal, + classNames = value.split( rspace ); + + while ( (className = classNames[ i++ ]) ) { + // check each className given, space seperated list + state = isBool ? state : !self.hasClass( className ); + self[ state ? "addClass" : "removeClass" ]( className ); + } + + } else if ( type === "undefined" || type === "boolean" ) { + if ( this.className ) { + // store className if set + jQuery._data( this, "__className__", this.className ); + } + + // toggle whole className + this.className = this.className || value === false ? "" : jQuery._data( this, "__className__" ) || ""; + } + }); + }, + + hasClass: function( selector ) { + var className = " " + selector + " ", + i = 0, + l = this.length; + for ( ; i < l; i++ ) { + if ( this[i].nodeType === 1 && (" " + this[i].className + " ").replace(rclass, " ").indexOf( className ) > -1 ) { + return true; + } + } + + return false; + }, + + val: function( value ) { + var hooks, ret, isFunction, + elem = this[0]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && "get" in hooks && (ret = hooks.get( elem, "value" )) !== undefined ) { + return ret; + } + + ret = elem.value; + + return typeof ret === "string" ? + // handle most common string cases + ret.replace(rreturn, "") : + // handle cases where value is null/undef or number + ret == null ? "" : ret; + } + + return; + } + + isFunction = jQuery.isFunction( value ); + + return this.each(function( i ) { + var self = jQuery(this), val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( isFunction ) { + val = value.call( this, i, self.val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + } else if ( typeof val === "number" ) { + val += ""; + } else if ( jQuery.isArray( val ) ) { + val = jQuery.map(val, function ( value ) { + return value == null ? "" : value + ""; + }); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !("set" in hooks) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + }); + } +}); + +jQuery.extend({ + valHooks: { + option: { + get: function( elem ) { + // attributes.value is undefined in Blackberry 4.7 but + // uses .value. See #6932 + var val = elem.attributes.value; + return !val || val.specified ? elem.value : elem.text; + } + }, + select: { + get: function( elem ) { + var value, i, max, option, + index = elem.selectedIndex, + values = [], + options = elem.options, + one = elem.type === "select-one"; + + // Nothing was selected + if ( index < 0 ) { + return null; + } + + // Loop through all the selected options + i = one ? index : 0; + max = one ? index + 1 : options.length; + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Don't return options that are disabled or in a disabled optgroup + if ( option.selected && (jQuery.support.optDisabled ? !option.disabled : option.getAttribute("disabled") === null) && + (!option.parentNode.disabled || !jQuery.nodeName( option.parentNode, "optgroup" )) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + // Fixes Bug #2551 -- select.val() broken in IE after form.reset() + if ( one && !values.length && options.length ) { + return jQuery( options[ index ] ).val(); + } + + return values; + }, + + set: function( elem, value ) { + var values = jQuery.makeArray( value ); + + jQuery(elem).find("option").each(function() { + this.selected = jQuery.inArray( jQuery(this).val(), values ) >= 0; + }); + + if ( !values.length ) { + elem.selectedIndex = -1; + } + return values; + } + } + }, + + attrFn: { + val: true, + css: true, + html: true, + text: true, + data: true, + width: true, + height: true, + offset: true + }, + + attr: function( elem, name, value, pass ) { + var ret, hooks, notxml, + nType = elem.nodeType; + + // don't get/set attributes on text, comment and attribute nodes + if ( !elem || nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( pass && name in jQuery.attrFn ) { + return jQuery( elem )[ name ]( value ); + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + notxml = nType !== 1 || !jQuery.isXMLDoc( elem ); + + // All attributes are lowercase + // Grab necessary hook if one is defined + if ( notxml ) { + name = name.toLowerCase(); + hooks = jQuery.attrHooks[ name ] || ( rboolean.test( name ) ? boolHook : nodeHook ); + } + + if ( value !== undefined ) { + + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + + } else if ( hooks && "set" in hooks && notxml && (ret = hooks.set( elem, value, name )) !== undefined ) { + return ret; + + } else { + elem.setAttribute( name, "" + value ); + return value; + } + + } else if ( hooks && "get" in hooks && notxml && (ret = hooks.get( elem, name )) !== null ) { + return ret; + + } else { + + ret = elem.getAttribute( name ); + + // Non-existent attributes return null, we normalize to undefined + return ret === null ? + undefined : + ret; + } + }, + + removeAttr: function( elem, value ) { + var propName, attrNames, name, l, isBool, + i = 0; + + if ( value && elem.nodeType === 1 ) { + attrNames = value.toLowerCase().split( rspace ); + l = attrNames.length; + + for ( ; i < l; i++ ) { + name = attrNames[ i ]; + + if ( name ) { + propName = jQuery.propFix[ name ] || name; + isBool = rboolean.test( name ); + + // See #9699 for explanation of this approach (setting first, then removal) + // Do not do this for boolean attributes (see #10870) + if ( !isBool ) { + jQuery.attr( elem, name, "" ); + } + elem.removeAttribute( getSetAttribute ? name : propName ); + + // Set corresponding property to false for boolean attributes + if ( isBool && propName in elem ) { + elem[ propName ] = false; + } + } + } + } + }, + + attrHooks: { + type: { + set: function( elem, value ) { + // We can't allow the type property to be changed (since it causes problems in IE) + if ( rtype.test( elem.nodeName ) && elem.parentNode ) { + jQuery.error( "type property can't be changed" ); + } else if ( !jQuery.support.radioValue && value === "radio" && jQuery.nodeName(elem, "input") ) { + // Setting the type on a radio button after the value resets the value in IE6-9 + // Reset value to it's default in case type is set after value + // This is for element creation + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + }, + // Use the value property for back compat + // Use the nodeHook for button elements in IE6/7 (#1954) + value: { + get: function( elem, name ) { + if ( nodeHook && jQuery.nodeName( elem, "button" ) ) { + return nodeHook.get( elem, name ); + } + return name in elem ? + elem.value : + null; + }, + set: function( elem, value, name ) { + if ( nodeHook && jQuery.nodeName( elem, "button" ) ) { + return nodeHook.set( elem, value, name ); + } + // Does not return so that setAttribute is also used + elem.value = value; + } + } + }, + + propFix: { + tabindex: "tabIndex", + readonly: "readOnly", + "for": "htmlFor", + "class": "className", + maxlength: "maxLength", + cellspacing: "cellSpacing", + cellpadding: "cellPadding", + rowspan: "rowSpan", + colspan: "colSpan", + usemap: "useMap", + frameborder: "frameBorder", + contenteditable: "contentEditable" + }, + + prop: function( elem, name, value ) { + var ret, hooks, notxml, + nType = elem.nodeType; + + // don't get/set properties on text, comment and attribute nodes + if ( !elem || nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + notxml = nType !== 1 || !jQuery.isXMLDoc( elem ); + + if ( notxml ) { + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && (ret = hooks.set( elem, value, name )) !== undefined ) { + return ret; + + } else { + return ( elem[ name ] = value ); + } + + } else { + if ( hooks && "get" in hooks && (ret = hooks.get( elem, name )) !== null ) { + return ret; + + } else { + return elem[ name ]; + } + } + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + // elem.tabIndex doesn't always return the correct value when it hasn't been explicitly set + // http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + var attributeNode = elem.getAttributeNode("tabindex"); + + return attributeNode && attributeNode.specified ? + parseInt( attributeNode.value, 10 ) : + rfocusable.test( elem.nodeName ) || rclickable.test( elem.nodeName ) && elem.href ? + 0 : + undefined; + } + } + } +}); + +// Add the tabIndex propHook to attrHooks for back-compat (different case is intentional) +jQuery.attrHooks.tabindex = jQuery.propHooks.tabIndex; + +// Hook for boolean attributes +boolHook = { + get: function( elem, name ) { + // Align boolean attributes with corresponding properties + // Fall back to attribute presence where some booleans are not supported + var attrNode, + property = jQuery.prop( elem, name ); + return property === true || typeof property !== "boolean" && ( attrNode = elem.getAttributeNode(name) ) && attrNode.nodeValue !== false ? + name.toLowerCase() : + undefined; + }, + set: function( elem, value, name ) { + var propName; + if ( value === false ) { + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + // value is true since we know at this point it's type boolean and not false + // Set boolean attributes to the same name and set the DOM property + propName = jQuery.propFix[ name ] || name; + if ( propName in elem ) { + // Only set the IDL specifically if it already exists on the element + elem[ propName ] = true; + } + + elem.setAttribute( name, name.toLowerCase() ); + } + return name; + } +}; + +// IE6/7 do not support getting/setting some attributes with get/setAttribute +if ( !getSetAttribute ) { + + fixSpecified = { + name: true, + id: true, + coords: true + }; + + // Use this for any attribute in IE6/7 + // This fixes almost every IE6/7 issue + nodeHook = jQuery.valHooks.button = { + get: function( elem, name ) { + var ret; + ret = elem.getAttributeNode( name ); + return ret && ( fixSpecified[ name ] ? ret.nodeValue !== "" : ret.specified ) ? + ret.nodeValue : + undefined; + }, + set: function( elem, value, name ) { + // Set the existing or create a new attribute node + var ret = elem.getAttributeNode( name ); + if ( !ret ) { + ret = document.createAttribute( name ); + elem.setAttributeNode( ret ); + } + return ( ret.nodeValue = value + "" ); + } + }; + + // Apply the nodeHook to tabindex + jQuery.attrHooks.tabindex.set = nodeHook.set; + + // Set width and height to auto instead of 0 on empty string( Bug #8150 ) + // This is for removals + jQuery.each([ "width", "height" ], function( i, name ) { + jQuery.attrHooks[ name ] = jQuery.extend( jQuery.attrHooks[ name ], { + set: function( elem, value ) { + if ( value === "" ) { + elem.setAttribute( name, "auto" ); + return value; + } + } + }); + }); + + // Set contenteditable to false on removals(#10429) + // Setting to empty string throws an error as an invalid value + jQuery.attrHooks.contenteditable = { + get: nodeHook.get, + set: function( elem, value, name ) { + if ( value === "" ) { + value = "false"; + } + nodeHook.set( elem, value, name ); + } + }; +} + + +// Some attributes require a special call on IE +if ( !jQuery.support.hrefNormalized ) { + jQuery.each([ "href", "src", "width", "height" ], function( i, name ) { + jQuery.attrHooks[ name ] = jQuery.extend( jQuery.attrHooks[ name ], { + get: function( elem ) { + var ret = elem.getAttribute( name, 2 ); + return ret === null ? undefined : ret; + } + }); + }); +} + +if ( !jQuery.support.style ) { + jQuery.attrHooks.style = { + get: function( elem ) { + // Return undefined in the case of empty string + // Normalize to lowercase since IE uppercases css property names + return elem.style.cssText.toLowerCase() || undefined; + }, + set: function( elem, value ) { + return ( elem.style.cssText = "" + value ); + } + }; +} + +// Safari mis-reports the default selected property of an option +// Accessing the parent's selectedIndex property fixes it +if ( !jQuery.support.optSelected ) { + jQuery.propHooks.selected = jQuery.extend( jQuery.propHooks.selected, { + get: function( elem ) { + var parent = elem.parentNode; + + if ( parent ) { + parent.selectedIndex; + + // Make sure that it also works with optgroups, see #5701 + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + return null; + } + }); +} + +// IE6/7 call enctype encoding +if ( !jQuery.support.enctype ) { + jQuery.propFix.enctype = "encoding"; +} + +// Radios and checkboxes getter/setter +if ( !jQuery.support.checkOn ) { + jQuery.each([ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + get: function( elem ) { + // Handle the case where in Webkit "" is returned instead of "on" if a value isn't specified + return elem.getAttribute("value") === null ? "on" : elem.value; + } + }; + }); +} +jQuery.each([ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = jQuery.extend( jQuery.valHooks[ this ], { + set: function( elem, value ) { + if ( jQuery.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery(elem).val(), value ) >= 0 ); + } + } + }); +}); + + + + +var rformElems = /^(?:textarea|input|select)$/i, + rtypenamespace = /^([^\.]*)?(?:\.(.+))?$/, + rhoverHack = /(?:^|\s)hover(\.\S+)?\b/, + rkeyEvent = /^key/, + rmouseEvent = /^(?:mouse|contextmenu)|click/, + rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + rquickIs = /^(\w*)(?:#([\w\-]+))?(?:\.([\w\-]+))?$/, + quickParse = function( selector ) { + var quick = rquickIs.exec( selector ); + if ( quick ) { + // 0 1 2 3 + // [ _, tag, id, class ] + quick[1] = ( quick[1] || "" ).toLowerCase(); + quick[3] = quick[3] && new RegExp( "(?:^|\\s)" + quick[3] + "(?:\\s|$)" ); + } + return quick; + }, + quickIs = function( elem, m ) { + var attrs = elem.attributes || {}; + return ( + (!m[1] || elem.nodeName.toLowerCase() === m[1]) && + (!m[2] || (attrs.id || {}).value === m[2]) && + (!m[3] || m[3].test( (attrs[ "class" ] || {}).value )) + ); + }, + hoverHack = function( events ) { + return jQuery.event.special.hover ? events : events.replace( rhoverHack, "mouseenter$1 mouseleave$1" ); + }; + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + add: function( elem, types, handler, data, selector ) { + + var elemData, eventHandle, events, + t, tns, type, namespaces, handleObj, + handleObjIn, quick, handlers, special; + + // Don't attach events to noData or text/comment nodes (allow plain objects tho) + if ( elem.nodeType === 3 || elem.nodeType === 8 || !types || !handler || !(elemData = jQuery._data( elem )) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + events = elemData.events; + if ( !events ) { + elemData.events = events = {}; + } + eventHandle = elemData.handle; + if ( !eventHandle ) { + elemData.handle = eventHandle = function( e ) { + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && (!e || jQuery.event.triggered !== e.type) ? + jQuery.event.dispatch.apply( eventHandle.elem, arguments ) : + undefined; + }; + // Add elem as a property of the handle fn to prevent a memory leak with IE non-native events + eventHandle.elem = elem; + } + + // Handle multiple events separated by a space + // jQuery(...).bind("mouseover mouseout", fn); + types = jQuery.trim( hoverHack(types) ).split( " " ); + for ( t = 0; t < types.length; t++ ) { + + tns = rtypenamespace.exec( types[t] ) || []; + type = tns[1]; + namespaces = ( tns[2] || "" ).split( "." ).sort(); + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend({ + type: type, + origType: tns[1], + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + quick: selector && quickParse( selector ), + namespace: namespaces.join(".") + }, handleObjIn ); + + // Init the event handler queue if we're the first + handlers = events[ type ]; + if ( !handlers ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener/attachEvent if the special events handler returns false + if ( !special.setup || special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + // Bind the global event handler to the element + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle, false ); + + } else if ( elem.attachEvent ) { + elem.attachEvent( "on" + type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + // Nullify elem to prevent memory leaks in IE + elem = null; + }, + + global: {}, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var elemData = jQuery.hasData( elem ) && jQuery._data( elem ), + t, tns, type, origType, namespaces, origCount, + j, events, special, handle, eventType, handleObj; + + if ( !elemData || !(events = elemData.events) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = jQuery.trim( hoverHack( types || "" ) ).split(" "); + for ( t = 0; t < types.length; t++ ) { + tns = rtypenamespace.exec( types[t] ) || []; + type = origType = tns[1]; + namespaces = tns[2]; + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector? special.delegateType : special.bindType ) || type; + eventType = events[ type ] || []; + origCount = eventType.length; + namespaces = namespaces ? new RegExp("(^|\\.)" + namespaces.split(".").sort().join("\\.(?:.*\\.)?") + "(\\.|$)") : null; + + // Remove matching events + for ( j = 0; j < eventType.length; j++ ) { + handleObj = eventType[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !namespaces || namespaces.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || selector === "**" && handleObj.selector ) ) { + eventType.splice( j--, 1 ); + + if ( handleObj.selector ) { + eventType.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( eventType.length === 0 && origCount !== eventType.length ) { + if ( !special.teardown || special.teardown.call( elem, namespaces ) === false ) { + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + handle = elemData.handle; + if ( handle ) { + handle.elem = null; + } + + // removeData also checks for emptiness and clears the expando if empty + // so use it instead of delete + jQuery.removeData( elem, [ "events", "handle" ], true ); + } + }, + + // Events that are safe to short-circuit if no handlers are attached. + // Native DOM events should not be added, they may have inline handlers. + customEvent: { + "getData": true, + "setData": true, + "changeData": true + }, + + trigger: function( event, data, elem, onlyHandlers ) { + // Don't do events on text and comment nodes + if ( elem && (elem.nodeType === 3 || elem.nodeType === 8) ) { + return; + } + + // Event object or event type + var type = event.type || event, + namespaces = [], + cache, exclusive, i, cur, old, ontype, special, handle, eventPath, bubbleType; + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "!" ) >= 0 ) { + // Exclusive events trigger only for the exact event (no namespaces) + type = type.slice(0, -1); + exclusive = true; + } + + if ( type.indexOf( "." ) >= 0 ) { + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split("."); + type = namespaces.shift(); + namespaces.sort(); + } + + if ( (!elem || jQuery.event.customEvent[ type ]) && !jQuery.event.global[ type ] ) { + // No jQuery handlers for this event type, and it can't have inline handlers + return; + } + + // Caller can pass in an Event, Object, or just an event type string + event = typeof event === "object" ? + // jQuery.Event object + event[ jQuery.expando ] ? event : + // Object literal + new jQuery.Event( type, event ) : + // Just the event type (string) + new jQuery.Event( type ); + + event.type = type; + event.isTrigger = true; + event.exclusive = exclusive; + event.namespace = namespaces.join( "." ); + event.namespace_re = event.namespace? new RegExp("(^|\\.)" + namespaces.join("\\.(?:.*\\.)?") + "(\\.|$)") : null; + ontype = type.indexOf( ":" ) < 0 ? "on" + type : ""; + + // Handle a global trigger + if ( !elem ) { + + // TODO: Stop taunting the data cache; remove global events and always attach to document + cache = jQuery.cache; + for ( i in cache ) { + if ( cache[ i ].events && cache[ i ].events[ type ] ) { + jQuery.event.trigger( event, data, cache[ i ].handle.elem, true ); + } + } + return; + } + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data != null ? jQuery.makeArray( data ) : []; + data.unshift( event ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + eventPath = [[ elem, special.bindType || type ]]; + if ( !onlyHandlers && !special.noBubble && !jQuery.isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + cur = rfocusMorph.test( bubbleType + type ) ? elem : elem.parentNode; + old = null; + for ( ; cur; cur = cur.parentNode ) { + eventPath.push([ cur, bubbleType ]); + old = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( old && old === elem.ownerDocument ) { + eventPath.push([ old.defaultView || old.parentWindow || window, bubbleType ]); + } + } + + // Fire handlers on the event path + for ( i = 0; i < eventPath.length && !event.isPropagationStopped(); i++ ) { + + cur = eventPath[i][0]; + event.type = eventPath[i][1]; + + handle = ( jQuery._data( cur, "events" ) || {} )[ event.type ] && jQuery._data( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + // Note that this is a bare JS function and not a jQuery handler + handle = ontype && cur[ ontype ]; + if ( handle && jQuery.acceptData( cur ) && handle.apply( cur, data ) === false ) { + event.preventDefault(); + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( (!special._default || special._default.apply( elem.ownerDocument, data ) === false) && + !(type === "click" && jQuery.nodeName( elem, "a" )) && jQuery.acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name name as the event. + // Can't use an .isFunction() check here because IE6/7 fails that test. + // Don't do default actions on window, that's where global variables be (#6170) + // IE<9 dies on focus/blur to hidden element (#1486) + if ( ontype && elem[ type ] && ((type !== "focus" && type !== "blur") || event.target.offsetWidth !== 0) && !jQuery.isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + old = elem[ ontype ]; + + if ( old ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + elem[ type ](); + jQuery.event.triggered = undefined; + + if ( old ) { + elem[ ontype ] = old; + } + } + } + } + + return event.result; + }, + + dispatch: function( event ) { + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( event || window.event ); + + var handlers = ( (jQuery._data( this, "events" ) || {} )[ event.type ] || []), + delegateCount = handlers.delegateCount, + args = [].slice.call( arguments, 0 ), + run_all = !event.exclusive && !event.namespace, + special = jQuery.event.special[ event.type ] || {}, + handlerQueue = [], + i, j, cur, jqcur, ret, selMatch, matched, matches, handleObj, sel, related; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[0] = event; + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers that should run if there are delegated events + // Avoid non-left-click bubbling in Firefox (#3861) + if ( delegateCount && !(event.button && event.type === "click") ) { + + // Pregenerate a single jQuery object for reuse with .is() + jqcur = jQuery(this); + jqcur.context = this.ownerDocument || this; + + for ( cur = event.target; cur != this; cur = cur.parentNode || this ) { + + // Don't process events on disabled elements (#6911, #8165) + if ( cur.disabled !== true ) { + selMatch = {}; + matches = []; + jqcur[0] = cur; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + sel = handleObj.selector; + + if ( selMatch[ sel ] === undefined ) { + selMatch[ sel ] = ( + handleObj.quick ? quickIs( cur, handleObj.quick ) : jqcur.is( sel ) + ); + } + if ( selMatch[ sel ] ) { + matches.push( handleObj ); + } + } + if ( matches.length ) { + handlerQueue.push({ elem: cur, matches: matches }); + } + } + } + } + + // Add the remaining (directly-bound) handlers + if ( handlers.length > delegateCount ) { + handlerQueue.push({ elem: this, matches: handlers.slice( delegateCount ) }); + } + + // Run delegates first; they may want to stop propagation beneath us + for ( i = 0; i < handlerQueue.length && !event.isPropagationStopped(); i++ ) { + matched = handlerQueue[ i ]; + event.currentTarget = matched.elem; + + for ( j = 0; j < matched.matches.length && !event.isImmediatePropagationStopped(); j++ ) { + handleObj = matched.matches[ j ]; + + // Triggered event must either 1) be non-exclusive and have no namespace, or + // 2) have namespace(s) a subset or equal to those in the bound event (both can have no namespace). + if ( run_all || (!event.namespace && !handleObj.namespace) || event.namespace_re && event.namespace_re.test( handleObj.namespace ) ) { + + event.data = handleObj.data; + event.handleObj = handleObj; + + ret = ( (jQuery.event.special[ handleObj.origType ] || {}).handle || handleObj.handler ) + .apply( matched.elem, args ); + + if ( ret !== undefined ) { + event.result = ret; + if ( ret === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + // Includes some event props shared by KeyEvent and MouseEvent + // *** attrChange attrName relatedNode srcElement are not normalized, non-W3C, deprecated, will be removed in 1.8 *** + props: "attrChange attrName relatedNode srcElement altKey bubbles cancelable ctrlKey currentTarget eventPhase metaKey relatedTarget shiftKey target timeStamp view which".split(" "), + + fixHooks: {}, + + keyHooks: { + props: "char charCode key keyCode".split(" "), + filter: function( event, original ) { + + // Add which for key events + if ( event.which == null ) { + event.which = original.charCode != null ? original.charCode : original.keyCode; + } + + return event; + } + }, + + mouseHooks: { + props: "button buttons clientX clientY fromElement offsetX offsetY pageX pageY screenX screenY toElement".split(" "), + filter: function( event, original ) { + var eventDoc, doc, body, + button = original.button, + fromElement = original.fromElement; + + // Calculate pageX/Y if missing and clientX/Y available + if ( event.pageX == null && original.clientX != null ) { + eventDoc = event.target.ownerDocument || document; + doc = eventDoc.documentElement; + body = eventDoc.body; + + event.pageX = original.clientX + ( doc && doc.scrollLeft || body && body.scrollLeft || 0 ) - ( doc && doc.clientLeft || body && body.clientLeft || 0 ); + event.pageY = original.clientY + ( doc && doc.scrollTop || body && body.scrollTop || 0 ) - ( doc && doc.clientTop || body && body.clientTop || 0 ); + } + + // Add relatedTarget, if necessary + if ( !event.relatedTarget && fromElement ) { + event.relatedTarget = fromElement === event.target ? original.toElement : fromElement; + } + + // Add which for click: 1 === left; 2 === middle; 3 === right + // Note: button is not normalized, so don't use it + if ( !event.which && button !== undefined ) { + event.which = ( button & 1 ? 1 : ( button & 2 ? 3 : ( button & 4 ? 2 : 0 ) ) ); + } + + return event; + } + }, + + fix: function( event ) { + if ( event[ jQuery.expando ] ) { + return event; + } + + // Create a writable copy of the event object and normalize some properties + var i, prop, + originalEvent = event, + fixHook = jQuery.event.fixHooks[ event.type ] || {}, + copy = fixHook.props ? this.props.concat( fixHook.props ) : this.props; + + event = jQuery.Event( originalEvent ); + + for ( i = copy.length; i; ) { + prop = copy[ --i ]; + event[ prop ] = originalEvent[ prop ]; + } + + // Fix target property, if necessary (#1925, IE 6/7/8 & Safari2) + if ( !event.target ) { + event.target = originalEvent.srcElement || document; + } + + // Target should not be a text node (#504, Safari) + if ( event.target.nodeType === 3 ) { + event.target = event.target.parentNode; + } + + // For mouse/key events; add metaKey if it's not there (#3368, IE6/7/8) + if ( event.metaKey === undefined ) { + event.metaKey = event.ctrlKey; + } + + return fixHook.filter? fixHook.filter( event, originalEvent ) : event; + }, + + special: { + ready: { + // Make sure the ready event is setup + setup: jQuery.bindReady + }, + + load: { + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + + focus: { + delegateType: "focusin" + }, + blur: { + delegateType: "focusout" + }, + + beforeunload: { + setup: function( data, namespaces, eventHandle ) { + // We only want to do this special case on windows + if ( jQuery.isWindow( this ) ) { + this.onbeforeunload = eventHandle; + } + }, + + teardown: function( namespaces, eventHandle ) { + if ( this.onbeforeunload === eventHandle ) { + this.onbeforeunload = null; + } + } + } + }, + + simulate: function( type, elem, event, bubble ) { + // Piggyback on a donor event to simulate a different one. + // Fake originalEvent to avoid donor's stopPropagation, but if the + // simulated event prevents default then we do the same on the donor. + var e = jQuery.extend( + new jQuery.Event(), + event, + { type: type, + isSimulated: true, + originalEvent: {} + } + ); + if ( bubble ) { + jQuery.event.trigger( e, null, elem ); + } else { + jQuery.event.dispatch.call( elem, e ); + } + if ( e.isDefaultPrevented() ) { + event.preventDefault(); + } + } +}; + +// Some plugins are using, but it's undocumented/deprecated and will be removed. +// The 1.7 special event interface should provide all the hooks needed now. +jQuery.event.handle = jQuery.event.dispatch; + +jQuery.removeEvent = document.removeEventListener ? + function( elem, type, handle ) { + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle, false ); + } + } : + function( elem, type, handle ) { + if ( elem.detachEvent ) { + elem.detachEvent( "on" + type, handle ); + } + }; + +jQuery.Event = function( src, props ) { + // Allow instantiation without the 'new' keyword + if ( !(this instanceof jQuery.Event) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = ( src.defaultPrevented || src.returnValue === false || + src.getPreventDefault && src.getPreventDefault() ) ? returnTrue : returnFalse; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || jQuery.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +function returnFalse() { + return false; +} +function returnTrue() { + return true; +} + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// http://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + preventDefault: function() { + this.isDefaultPrevented = returnTrue; + + var e = this.originalEvent; + if ( !e ) { + return; + } + + // if preventDefault exists run it on the original event + if ( e.preventDefault ) { + e.preventDefault(); + + // otherwise set the returnValue property of the original event to false (IE) + } else { + e.returnValue = false; + } + }, + stopPropagation: function() { + this.isPropagationStopped = returnTrue; + + var e = this.originalEvent; + if ( !e ) { + return; + } + // if stopPropagation exists run it on the original event + if ( e.stopPropagation ) { + e.stopPropagation(); + } + // otherwise set the cancelBubble property of the original event to true (IE) + e.cancelBubble = true; + }, + stopImmediatePropagation: function() { + this.isImmediatePropagationStopped = returnTrue; + this.stopPropagation(); + }, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse +}; + +// Create mouseenter/leave events using mouseover/out and event-time checks +jQuery.each({ + mouseenter: "mouseover", + mouseleave: "mouseout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var target = this, + related = event.relatedTarget, + handleObj = event.handleObj, + selector = handleObj.selector, + ret; + + // For mousenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || (related !== target && !jQuery.contains( target, related )) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +}); + +// IE submit delegation +if ( !jQuery.support.submitBubbles ) { + + jQuery.event.special.submit = { + setup: function() { + // Only need this for delegated form submit events + if ( jQuery.nodeName( this, "form" ) ) { + return false; + } + + // Lazy-add a submit handler when a descendant form may potentially be submitted + jQuery.event.add( this, "click._submit keypress._submit", function( e ) { + // Node name check avoids a VML-related crash in IE (#9807) + var elem = e.target, + form = jQuery.nodeName( elem, "input" ) || jQuery.nodeName( elem, "button" ) ? elem.form : undefined; + if ( form && !form._submit_attached ) { + jQuery.event.add( form, "submit._submit", function( event ) { + event._submit_bubble = true; + }); + form._submit_attached = true; + } + }); + // return undefined since we don't need an event listener + }, + + postDispatch: function( event ) { + // If form was submitted by the user, bubble the event up the tree + if ( event._submit_bubble ) { + delete event._submit_bubble; + if ( this.parentNode && !event.isTrigger ) { + jQuery.event.simulate( "submit", this.parentNode, event, true ); + } + } + }, + + teardown: function() { + // Only need this for delegated form submit events + if ( jQuery.nodeName( this, "form" ) ) { + return false; + } + + // Remove delegated handlers; cleanData eventually reaps submit handlers attached above + jQuery.event.remove( this, "._submit" ); + } + }; +} + +// IE change delegation and checkbox/radio fix +if ( !jQuery.support.changeBubbles ) { + + jQuery.event.special.change = { + + setup: function() { + + if ( rformElems.test( this.nodeName ) ) { + // IE doesn't fire change on a check/radio until blur; trigger it on click + // after a propertychange. Eat the blur-change in special.change.handle. + // This still fires onchange a second time for check/radio after blur. + if ( this.type === "checkbox" || this.type === "radio" ) { + jQuery.event.add( this, "propertychange._change", function( event ) { + if ( event.originalEvent.propertyName === "checked" ) { + this._just_changed = true; + } + }); + jQuery.event.add( this, "click._change", function( event ) { + if ( this._just_changed && !event.isTrigger ) { + this._just_changed = false; + jQuery.event.simulate( "change", this, event, true ); + } + }); + } + return false; + } + // Delegated event; lazy-add a change handler on descendant inputs + jQuery.event.add( this, "beforeactivate._change", function( e ) { + var elem = e.target; + + if ( rformElems.test( elem.nodeName ) && !elem._change_attached ) { + jQuery.event.add( elem, "change._change", function( event ) { + if ( this.parentNode && !event.isSimulated && !event.isTrigger ) { + jQuery.event.simulate( "change", this.parentNode, event, true ); + } + }); + elem._change_attached = true; + } + }); + }, + + handle: function( event ) { + var elem = event.target; + + // Swallow native change events from checkbox/radio, we already triggered them above + if ( this !== elem || event.isSimulated || event.isTrigger || (elem.type !== "radio" && elem.type !== "checkbox") ) { + return event.handleObj.handler.apply( this, arguments ); + } + }, + + teardown: function() { + jQuery.event.remove( this, "._change" ); + + return rformElems.test( this.nodeName ); + } + }; +} + +// Create "bubbling" focus and blur events +if ( !jQuery.support.focusinBubbles ) { + jQuery.each({ focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler while someone wants focusin/focusout + var attaches = 0, + handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ), true ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + if ( attaches++ === 0 ) { + document.addEventListener( orig, handler, true ); + } + }, + teardown: function() { + if ( --attaches === 0 ) { + document.removeEventListener( orig, handler, true ); + } + } + }; + }); +} + +jQuery.fn.extend({ + + on: function( types, selector, data, fn, /*INTERNAL*/ one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { // && selector != null + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + this.on( type, selector, data, types[ type ], one ); + } + return this; + } + + if ( data == null && fn == null ) { + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return this; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return this.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + }); + }, + one: function( types, selector, data, fn ) { + return this.on( types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + if ( types && types.preventDefault && types.handleObj ) { + // ( event ) dispatched jQuery.Event + var handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? handleObj.origType + "." + handleObj.namespace : handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + // ( types-object [, selector] ) + for ( var type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each(function() { + jQuery.event.remove( this, types, fn, selector ); + }); + }, + + bind: function( types, data, fn ) { + return this.on( types, null, data, fn ); + }, + unbind: function( types, fn ) { + return this.off( types, null, fn ); + }, + + live: function( types, data, fn ) { + jQuery( this.context ).on( types, this.selector, data, fn ); + return this; + }, + die: function( types, fn ) { + jQuery( this.context ).off( types, this.selector || "**", fn ); + return this; + }, + + delegate: function( selector, types, data, fn ) { + return this.on( types, selector, data, fn ); + }, + undelegate: function( selector, types, fn ) { + // ( namespace ) or ( selector, types [, fn] ) + return arguments.length == 1? this.off( selector, "**" ) : this.off( types, selector, fn ); + }, + + trigger: function( type, data ) { + return this.each(function() { + jQuery.event.trigger( type, data, this ); + }); + }, + triggerHandler: function( type, data ) { + if ( this[0] ) { + return jQuery.event.trigger( type, data, this[0], true ); + } + }, + + toggle: function( fn ) { + // Save reference to arguments for access in closure + var args = arguments, + guid = fn.guid || jQuery.guid++, + i = 0, + toggler = function( event ) { + // Figure out which function to execute + var lastToggle = ( jQuery._data( this, "lastToggle" + fn.guid ) || 0 ) % i; + jQuery._data( this, "lastToggle" + fn.guid, lastToggle + 1 ); + + // Make sure that clicks stop + event.preventDefault(); + + // and execute the function + return args[ lastToggle ].apply( this, arguments ) || false; + }; + + // link all the functions, so any of them can unbind this click handler + toggler.guid = guid; + while ( i < args.length ) { + args[ i++ ].guid = guid; + } + + return this.click( toggler ); + }, + + hover: function( fnOver, fnOut ) { + return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver ); + } +}); + +jQuery.each( ("blur focus focusin focusout load resize scroll unload click dblclick " + + "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " + + "change select submit keydown keypress keyup error contextmenu").split(" "), function( i, name ) { + + // Handle event binding + jQuery.fn[ name ] = function( data, fn ) { + if ( fn == null ) { + fn = data; + data = null; + } + + return arguments.length > 0 ? + this.on( name, null, data, fn ) : + this.trigger( name ); + }; + + if ( jQuery.attrFn ) { + jQuery.attrFn[ name ] = true; + } + + if ( rkeyEvent.test( name ) ) { + jQuery.event.fixHooks[ name ] = jQuery.event.keyHooks; + } + + if ( rmouseEvent.test( name ) ) { + jQuery.event.fixHooks[ name ] = jQuery.event.mouseHooks; + } +}); + + + +/*! + * Sizzle CSS Selector Engine + * Copyright 2011, The Dojo Foundation + * Released under the MIT, BSD, and GPL Licenses. + * More information: http://sizzlejs.com/ + */ +(function(){ + +var chunker = /((?:\((?:\([^()]+\)|[^()]+)+\)|\[(?:\[[^\[\]]*\]|['"][^'"]*['"]|[^\[\]'"]+)+\]|\\.|[^ >+~,(\[\\]+)+|[>+~])(\s*,\s*)?((?:.|\r|\n)*)/g, + expando = "sizcache" + (Math.random() + '').replace('.', ''), + done = 0, + toString = Object.prototype.toString, + hasDuplicate = false, + baseHasDuplicate = true, + rBackslash = /\\/g, + rReturn = /\r\n/g, + rNonWord = /\W/; + +// Here we check if the JavaScript engine is using some sort of +// optimization where it does not always call our comparision +// function. If that is the case, discard the hasDuplicate value. +// Thus far that includes Google Chrome. +[0, 0].sort(function() { + baseHasDuplicate = false; + return 0; +}); + +var Sizzle = function( selector, context, results, seed ) { + results = results || []; + context = context || document; + + var origContext = context; + + if ( context.nodeType !== 1 && context.nodeType !== 9 ) { + return []; + } + + if ( !selector || typeof selector !== "string" ) { + return results; + } + + var m, set, checkSet, extra, ret, cur, pop, i, + prune = true, + contextXML = Sizzle.isXML( context ), + parts = [], + soFar = selector; + + // Reset the position of the chunker regexp (start from head) + do { + chunker.exec( "" ); + m = chunker.exec( soFar ); + + if ( m ) { + soFar = m[3]; + + parts.push( m[1] ); + + if ( m[2] ) { + extra = m[3]; + break; + } + } + } while ( m ); + + if ( parts.length > 1 && origPOS.exec( selector ) ) { + + if ( parts.length === 2 && Expr.relative[ parts[0] ] ) { + set = posProcess( parts[0] + parts[1], context, seed ); + + } else { + set = Expr.relative[ parts[0] ] ? + [ context ] : + Sizzle( parts.shift(), context ); + + while ( parts.length ) { + selector = parts.shift(); + + if ( Expr.relative[ selector ] ) { + selector += parts.shift(); + } + + set = posProcess( selector, set, seed ); + } + } + + } else { + // Take a shortcut and set the context if the root selector is an ID + // (but not if it'll be faster if the inner selector is an ID) + if ( !seed && parts.length > 1 && context.nodeType === 9 && !contextXML && + Expr.match.ID.test(parts[0]) && !Expr.match.ID.test(parts[parts.length - 1]) ) { + + ret = Sizzle.find( parts.shift(), context, contextXML ); + context = ret.expr ? + Sizzle.filter( ret.expr, ret.set )[0] : + ret.set[0]; + } + + if ( context ) { + ret = seed ? + { expr: parts.pop(), set: makeArray(seed) } : + Sizzle.find( parts.pop(), parts.length === 1 && (parts[0] === "~" || parts[0] === "+") && context.parentNode ? context.parentNode : context, contextXML ); + + set = ret.expr ? + Sizzle.filter( ret.expr, ret.set ) : + ret.set; + + if ( parts.length > 0 ) { + checkSet = makeArray( set ); + + } else { + prune = false; + } + + while ( parts.length ) { + cur = parts.pop(); + pop = cur; + + if ( !Expr.relative[ cur ] ) { + cur = ""; + } else { + pop = parts.pop(); + } + + if ( pop == null ) { + pop = context; + } + + Expr.relative[ cur ]( checkSet, pop, contextXML ); + } + + } else { + checkSet = parts = []; + } + } + + if ( !checkSet ) { + checkSet = set; + } + + if ( !checkSet ) { + Sizzle.error( cur || selector ); + } + + if ( toString.call(checkSet) === "[object Array]" ) { + if ( !prune ) { + results.push.apply( results, checkSet ); + + } else if ( context && context.nodeType === 1 ) { + for ( i = 0; checkSet[i] != null; i++ ) { + if ( checkSet[i] && (checkSet[i] === true || checkSet[i].nodeType === 1 && Sizzle.contains(context, checkSet[i])) ) { + results.push( set[i] ); + } + } + + } else { + for ( i = 0; checkSet[i] != null; i++ ) { + if ( checkSet[i] && checkSet[i].nodeType === 1 ) { + results.push( set[i] ); + } + } + } + + } else { + makeArray( checkSet, results ); + } + + if ( extra ) { + Sizzle( extra, origContext, results, seed ); + Sizzle.uniqueSort( results ); + } + + return results; +}; + +Sizzle.uniqueSort = function( results ) { + if ( sortOrder ) { + hasDuplicate = baseHasDuplicate; + results.sort( sortOrder ); + + if ( hasDuplicate ) { + for ( var i = 1; i < results.length; i++ ) { + if ( results[i] === results[ i - 1 ] ) { + results.splice( i--, 1 ); + } + } + } + } + + return results; +}; + +Sizzle.matches = function( expr, set ) { + return Sizzle( expr, null, null, set ); +}; + +Sizzle.matchesSelector = function( node, expr ) { + return Sizzle( expr, null, null, [node] ).length > 0; +}; + +Sizzle.find = function( expr, context, isXML ) { + var set, i, len, match, type, left; + + if ( !expr ) { + return []; + } + + for ( i = 0, len = Expr.order.length; i < len; i++ ) { + type = Expr.order[i]; + + if ( (match = Expr.leftMatch[ type ].exec( expr )) ) { + left = match[1]; + match.splice( 1, 1 ); + + if ( left.substr( left.length - 1 ) !== "\\" ) { + match[1] = (match[1] || "").replace( rBackslash, "" ); + set = Expr.find[ type ]( match, context, isXML ); + + if ( set != null ) { + expr = expr.replace( Expr.match[ type ], "" ); + break; + } + } + } + } + + if ( !set ) { + set = typeof context.getElementsByTagName !== "undefined" ? + context.getElementsByTagName( "*" ) : + []; + } + + return { set: set, expr: expr }; +}; + +Sizzle.filter = function( expr, set, inplace, not ) { + var match, anyFound, + type, found, item, filter, left, + i, pass, + old = expr, + result = [], + curLoop = set, + isXMLFilter = set && set[0] && Sizzle.isXML( set[0] ); + + while ( expr && set.length ) { + for ( type in Expr.filter ) { + if ( (match = Expr.leftMatch[ type ].exec( expr )) != null && match[2] ) { + filter = Expr.filter[ type ]; + left = match[1]; + + anyFound = false; + + match.splice(1,1); + + if ( left.substr( left.length - 1 ) === "\\" ) { + continue; + } + + if ( curLoop === result ) { + result = []; + } + + if ( Expr.preFilter[ type ] ) { + match = Expr.preFilter[ type ]( match, curLoop, inplace, result, not, isXMLFilter ); + + if ( !match ) { + anyFound = found = true; + + } else if ( match === true ) { + continue; + } + } + + if ( match ) { + for ( i = 0; (item = curLoop[i]) != null; i++ ) { + if ( item ) { + found = filter( item, match, i, curLoop ); + pass = not ^ found; + + if ( inplace && found != null ) { + if ( pass ) { + anyFound = true; + + } else { + curLoop[i] = false; + } + + } else if ( pass ) { + result.push( item ); + anyFound = true; + } + } + } + } + + if ( found !== undefined ) { + if ( !inplace ) { + curLoop = result; + } + + expr = expr.replace( Expr.match[ type ], "" ); + + if ( !anyFound ) { + return []; + } + + break; + } + } + } + + // Improper expression + if ( expr === old ) { + if ( anyFound == null ) { + Sizzle.error( expr ); + + } else { + break; + } + } + + old = expr; + } + + return curLoop; +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Utility function for retreiving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +var getText = Sizzle.getText = function( elem ) { + var i, node, + nodeType = elem.nodeType, + ret = ""; + + if ( nodeType ) { + if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + // Use textContent || innerText for elements + if ( typeof elem.textContent === 'string' ) { + return elem.textContent; + } else if ( typeof elem.innerText === 'string' ) { + // Replace IE's carriage returns + return elem.innerText.replace( rReturn, '' ); + } else { + // Traverse it's children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + } else { + + // If no nodeType, this is expected to be an array + for ( i = 0; (node = elem[i]); i++ ) { + // Do not traverse comment nodes + if ( node.nodeType !== 8 ) { + ret += getText( node ); + } + } + } + return ret; +}; + +var Expr = Sizzle.selectors = { + order: [ "ID", "NAME", "TAG" ], + + match: { + ID: /#((?:[\w\u00c0-\uFFFF\-]|\\.)+)/, + CLASS: /\.((?:[\w\u00c0-\uFFFF\-]|\\.)+)/, + NAME: /\[name=['"]*((?:[\w\u00c0-\uFFFF\-]|\\.)+)['"]*\]/, + ATTR: /\[\s*((?:[\w\u00c0-\uFFFF\-]|\\.)+)\s*(?:(\S?=)\s*(?:(['"])(.*?)\3|(#?(?:[\w\u00c0-\uFFFF\-]|\\.)*)|)|)\s*\]/, + TAG: /^((?:[\w\u00c0-\uFFFF\*\-]|\\.)+)/, + CHILD: /:(only|nth|last|first)-child(?:\(\s*(even|odd|(?:[+\-]?\d+|(?:[+\-]?\d*)?n\s*(?:[+\-]\s*\d+)?))\s*\))?/, + POS: /:(nth|eq|gt|lt|first|last|even|odd)(?:\((\d*)\))?(?=[^\-]|$)/, + PSEUDO: /:((?:[\w\u00c0-\uFFFF\-]|\\.)+)(?:\((['"]?)((?:\([^\)]+\)|[^\(\)]*)+)\2\))?/ + }, + + leftMatch: {}, + + attrMap: { + "class": "className", + "for": "htmlFor" + }, + + attrHandle: { + href: function( elem ) { + return elem.getAttribute( "href" ); + }, + type: function( elem ) { + return elem.getAttribute( "type" ); + } + }, + + relative: { + "+": function(checkSet, part){ + var isPartStr = typeof part === "string", + isTag = isPartStr && !rNonWord.test( part ), + isPartStrNotTag = isPartStr && !isTag; + + if ( isTag ) { + part = part.toLowerCase(); + } + + for ( var i = 0, l = checkSet.length, elem; i < l; i++ ) { + if ( (elem = checkSet[i]) ) { + while ( (elem = elem.previousSibling) && elem.nodeType !== 1 ) {} + + checkSet[i] = isPartStrNotTag || elem && elem.nodeName.toLowerCase() === part ? + elem || false : + elem === part; + } + } + + if ( isPartStrNotTag ) { + Sizzle.filter( part, checkSet, true ); + } + }, + + ">": function( checkSet, part ) { + var elem, + isPartStr = typeof part === "string", + i = 0, + l = checkSet.length; + + if ( isPartStr && !rNonWord.test( part ) ) { + part = part.toLowerCase(); + + for ( ; i < l; i++ ) { + elem = checkSet[i]; + + if ( elem ) { + var parent = elem.parentNode; + checkSet[i] = parent.nodeName.toLowerCase() === part ? parent : false; + } + } + + } else { + for ( ; i < l; i++ ) { + elem = checkSet[i]; + + if ( elem ) { + checkSet[i] = isPartStr ? + elem.parentNode : + elem.parentNode === part; + } + } + + if ( isPartStr ) { + Sizzle.filter( part, checkSet, true ); + } + } + }, + + "": function(checkSet, part, isXML){ + var nodeCheck, + doneName = done++, + checkFn = dirCheck; + + if ( typeof part === "string" && !rNonWord.test( part ) ) { + part = part.toLowerCase(); + nodeCheck = part; + checkFn = dirNodeCheck; + } + + checkFn( "parentNode", part, doneName, checkSet, nodeCheck, isXML ); + }, + + "~": function( checkSet, part, isXML ) { + var nodeCheck, + doneName = done++, + checkFn = dirCheck; + + if ( typeof part === "string" && !rNonWord.test( part ) ) { + part = part.toLowerCase(); + nodeCheck = part; + checkFn = dirNodeCheck; + } + + checkFn( "previousSibling", part, doneName, checkSet, nodeCheck, isXML ); + } + }, + + find: { + ID: function( match, context, isXML ) { + if ( typeof context.getElementById !== "undefined" && !isXML ) { + var m = context.getElementById(match[1]); + // Check parentNode to catch when Blackberry 4.6 returns + // nodes that are no longer in the document #6963 + return m && m.parentNode ? [m] : []; + } + }, + + NAME: function( match, context ) { + if ( typeof context.getElementsByName !== "undefined" ) { + var ret = [], + results = context.getElementsByName( match[1] ); + + for ( var i = 0, l = results.length; i < l; i++ ) { + if ( results[i].getAttribute("name") === match[1] ) { + ret.push( results[i] ); + } + } + + return ret.length === 0 ? null : ret; + } + }, + + TAG: function( match, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( match[1] ); + } + } + }, + preFilter: { + CLASS: function( match, curLoop, inplace, result, not, isXML ) { + match = " " + match[1].replace( rBackslash, "" ) + " "; + + if ( isXML ) { + return match; + } + + for ( var i = 0, elem; (elem = curLoop[i]) != null; i++ ) { + if ( elem ) { + if ( not ^ (elem.className && (" " + elem.className + " ").replace(/[\t\n\r]/g, " ").indexOf(match) >= 0) ) { + if ( !inplace ) { + result.push( elem ); + } + + } else if ( inplace ) { + curLoop[i] = false; + } + } + } + + return false; + }, + + ID: function( match ) { + return match[1].replace( rBackslash, "" ); + }, + + TAG: function( match, curLoop ) { + return match[1].replace( rBackslash, "" ).toLowerCase(); + }, + + CHILD: function( match ) { + if ( match[1] === "nth" ) { + if ( !match[2] ) { + Sizzle.error( match[0] ); + } + + match[2] = match[2].replace(/^\+|\s*/g, ''); + + // parse equations like 'even', 'odd', '5', '2n', '3n+2', '4n-1', '-n+6' + var test = /(-?)(\d*)(?:n([+\-]?\d*))?/.exec( + match[2] === "even" && "2n" || match[2] === "odd" && "2n+1" || + !/\D/.test( match[2] ) && "0n+" + match[2] || match[2]); + + // calculate the numbers (first)n+(last) including if they are negative + match[2] = (test[1] + (test[2] || 1)) - 0; + match[3] = test[3] - 0; + } + else if ( match[2] ) { + Sizzle.error( match[0] ); + } + + // TODO: Move to normal caching system + match[0] = done++; + + return match; + }, + + ATTR: function( match, curLoop, inplace, result, not, isXML ) { + var name = match[1] = match[1].replace( rBackslash, "" ); + + if ( !isXML && Expr.attrMap[name] ) { + match[1] = Expr.attrMap[name]; + } + + // Handle if an un-quoted value was used + match[4] = ( match[4] || match[5] || "" ).replace( rBackslash, "" ); + + if ( match[2] === "~=" ) { + match[4] = " " + match[4] + " "; + } + + return match; + }, + + PSEUDO: function( match, curLoop, inplace, result, not ) { + if ( match[1] === "not" ) { + // If we're dealing with a complex expression, or a simple one + if ( ( chunker.exec(match[3]) || "" ).length > 1 || /^\w/.test(match[3]) ) { + match[3] = Sizzle(match[3], null, null, curLoop); + + } else { + var ret = Sizzle.filter(match[3], curLoop, inplace, true ^ not); + + if ( !inplace ) { + result.push.apply( result, ret ); + } + + return false; + } + + } else if ( Expr.match.POS.test( match[0] ) || Expr.match.CHILD.test( match[0] ) ) { + return true; + } + + return match; + }, + + POS: function( match ) { + match.unshift( true ); + + return match; + } + }, + + filters: { + enabled: function( elem ) { + return elem.disabled === false && elem.type !== "hidden"; + }, + + disabled: function( elem ) { + return elem.disabled === true; + }, + + checked: function( elem ) { + return elem.checked === true; + }, + + selected: function( elem ) { + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + parent: function( elem ) { + return !!elem.firstChild; + }, + + empty: function( elem ) { + return !elem.firstChild; + }, + + has: function( elem, i, match ) { + return !!Sizzle( match[3], elem ).length; + }, + + header: function( elem ) { + return (/h\d/i).test( elem.nodeName ); + }, + + text: function( elem ) { + var attr = elem.getAttribute( "type" ), type = elem.type; + // IE6 and 7 will map elem.type to 'text' for new HTML5 types (search, etc) + // use getAttribute instead to test this case + return elem.nodeName.toLowerCase() === "input" && "text" === type && ( attr === type || attr === null ); + }, + + radio: function( elem ) { + return elem.nodeName.toLowerCase() === "input" && "radio" === elem.type; + }, + + checkbox: function( elem ) { + return elem.nodeName.toLowerCase() === "input" && "checkbox" === elem.type; + }, + + file: function( elem ) { + return elem.nodeName.toLowerCase() === "input" && "file" === elem.type; + }, + + password: function( elem ) { + return elem.nodeName.toLowerCase() === "input" && "password" === elem.type; + }, + + submit: function( elem ) { + var name = elem.nodeName.toLowerCase(); + return (name === "input" || name === "button") && "submit" === elem.type; + }, + + image: function( elem ) { + return elem.nodeName.toLowerCase() === "input" && "image" === elem.type; + }, + + reset: function( elem ) { + var name = elem.nodeName.toLowerCase(); + return (name === "input" || name === "button") && "reset" === elem.type; + }, + + button: function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && "button" === elem.type || name === "button"; + }, + + input: function( elem ) { + return (/input|select|textarea|button/i).test( elem.nodeName ); + }, + + focus: function( elem ) { + return elem === elem.ownerDocument.activeElement; + } + }, + setFilters: { + first: function( elem, i ) { + return i === 0; + }, + + last: function( elem, i, match, array ) { + return i === array.length - 1; + }, + + even: function( elem, i ) { + return i % 2 === 0; + }, + + odd: function( elem, i ) { + return i % 2 === 1; + }, + + lt: function( elem, i, match ) { + return i < match[3] - 0; + }, + + gt: function( elem, i, match ) { + return i > match[3] - 0; + }, + + nth: function( elem, i, match ) { + return match[3] - 0 === i; + }, + + eq: function( elem, i, match ) { + return match[3] - 0 === i; + } + }, + filter: { + PSEUDO: function( elem, match, i, array ) { + var name = match[1], + filter = Expr.filters[ name ]; + + if ( filter ) { + return filter( elem, i, match, array ); + + } else if ( name === "contains" ) { + return (elem.textContent || elem.innerText || getText([ elem ]) || "").indexOf(match[3]) >= 0; + + } else if ( name === "not" ) { + var not = match[3]; + + for ( var j = 0, l = not.length; j < l; j++ ) { + if ( not[j] === elem ) { + return false; + } + } + + return true; + + } else { + Sizzle.error( name ); + } + }, + + CHILD: function( elem, match ) { + var first, last, + doneName, parent, cache, + count, diff, + type = match[1], + node = elem; + + switch ( type ) { + case "only": + case "first": + while ( (node = node.previousSibling) ) { + if ( node.nodeType === 1 ) { + return false; + } + } + + if ( type === "first" ) { + return true; + } + + node = elem; + + /* falls through */ + case "last": + while ( (node = node.nextSibling) ) { + if ( node.nodeType === 1 ) { + return false; + } + } + + return true; + + case "nth": + first = match[2]; + last = match[3]; + + if ( first === 1 && last === 0 ) { + return true; + } + + doneName = match[0]; + parent = elem.parentNode; + + if ( parent && (parent[ expando ] !== doneName || !elem.nodeIndex) ) { + count = 0; + + for ( node = parent.firstChild; node; node = node.nextSibling ) { + if ( node.nodeType === 1 ) { + node.nodeIndex = ++count; + } + } + + parent[ expando ] = doneName; + } + + diff = elem.nodeIndex - last; + + if ( first === 0 ) { + return diff === 0; + + } else { + return ( diff % first === 0 && diff / first >= 0 ); + } + } + }, + + ID: function( elem, match ) { + return elem.nodeType === 1 && elem.getAttribute("id") === match; + }, + + TAG: function( elem, match ) { + return (match === "*" && elem.nodeType === 1) || !!elem.nodeName && elem.nodeName.toLowerCase() === match; + }, + + CLASS: function( elem, match ) { + return (" " + (elem.className || elem.getAttribute("class")) + " ") + .indexOf( match ) > -1; + }, + + ATTR: function( elem, match ) { + var name = match[1], + result = Sizzle.attr ? + Sizzle.attr( elem, name ) : + Expr.attrHandle[ name ] ? + Expr.attrHandle[ name ]( elem ) : + elem[ name ] != null ? + elem[ name ] : + elem.getAttribute( name ), + value = result + "", + type = match[2], + check = match[4]; + + return result == null ? + type === "!=" : + !type && Sizzle.attr ? + result != null : + type === "=" ? + value === check : + type === "*=" ? + value.indexOf(check) >= 0 : + type === "~=" ? + (" " + value + " ").indexOf(check) >= 0 : + !check ? + value && result !== false : + type === "!=" ? + value !== check : + type === "^=" ? + value.indexOf(check) === 0 : + type === "$=" ? + value.substr(value.length - check.length) === check : + type === "|=" ? + value === check || value.substr(0, check.length + 1) === check + "-" : + false; + }, + + POS: function( elem, match, i, array ) { + var name = match[2], + filter = Expr.setFilters[ name ]; + + if ( filter ) { + return filter( elem, i, match, array ); + } + } + } +}; + +var origPOS = Expr.match.POS, + fescape = function(all, num){ + return "\\" + (num - 0 + 1); + }; + +for ( var type in Expr.match ) { + Expr.match[ type ] = new RegExp( Expr.match[ type ].source + (/(?![^\[]*\])(?![^\(]*\))/.source) ); + Expr.leftMatch[ type ] = new RegExp( /(^(?:.|\r|\n)*?)/.source + Expr.match[ type ].source.replace(/\\(\d+)/g, fescape) ); +} +// Expose origPOS +// "global" as in regardless of relation to brackets/parens +Expr.match.globalPOS = origPOS; + +var makeArray = function( array, results ) { + array = Array.prototype.slice.call( array, 0 ); + + if ( results ) { + results.push.apply( results, array ); + return results; + } + + return array; +}; + +// Perform a simple check to determine if the browser is capable of +// converting a NodeList to an array using builtin methods. +// Also verifies that the returned array holds DOM nodes +// (which is not the case in the Blackberry browser) +try { + Array.prototype.slice.call( document.documentElement.childNodes, 0 )[0].nodeType; + +// Provide a fallback method if it does not work +} catch( e ) { + makeArray = function( array, results ) { + var i = 0, + ret = results || []; + + if ( toString.call(array) === "[object Array]" ) { + Array.prototype.push.apply( ret, array ); + + } else { + if ( typeof array.length === "number" ) { + for ( var l = array.length; i < l; i++ ) { + ret.push( array[i] ); + } + + } else { + for ( ; array[i]; i++ ) { + ret.push( array[i] ); + } + } + } + + return ret; + }; +} + +var sortOrder, siblingCheck; + +if ( document.documentElement.compareDocumentPosition ) { + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + if ( !a.compareDocumentPosition || !b.compareDocumentPosition ) { + return a.compareDocumentPosition ? -1 : 1; + } + + return a.compareDocumentPosition(b) & 4 ? -1 : 1; + }; + +} else { + sortOrder = function( a, b ) { + // The nodes are identical, we can exit early + if ( a === b ) { + hasDuplicate = true; + return 0; + + // Fallback to using sourceIndex (in IE) if it's available on both nodes + } else if ( a.sourceIndex && b.sourceIndex ) { + return a.sourceIndex - b.sourceIndex; + } + + var al, bl, + ap = [], + bp = [], + aup = a.parentNode, + bup = b.parentNode, + cur = aup; + + // If the nodes are siblings (or identical) we can do a quick check + if ( aup === bup ) { + return siblingCheck( a, b ); + + // If no parents were found then the nodes are disconnected + } else if ( !aup ) { + return -1; + + } else if ( !bup ) { + return 1; + } + + // Otherwise they're somewhere else in the tree so we need + // to build up a full list of the parentNodes for comparison + while ( cur ) { + ap.unshift( cur ); + cur = cur.parentNode; + } + + cur = bup; + + while ( cur ) { + bp.unshift( cur ); + cur = cur.parentNode; + } + + al = ap.length; + bl = bp.length; + + // Start walking down the tree looking for a discrepancy + for ( var i = 0; i < al && i < bl; i++ ) { + if ( ap[i] !== bp[i] ) { + return siblingCheck( ap[i], bp[i] ); + } + } + + // We ended someplace up the tree so do a sibling check + return i === al ? + siblingCheck( a, bp[i], -1 ) : + siblingCheck( ap[i], b, 1 ); + }; + + siblingCheck = function( a, b, ret ) { + if ( a === b ) { + return ret; + } + + var cur = a.nextSibling; + + while ( cur ) { + if ( cur === b ) { + return -1; + } + + cur = cur.nextSibling; + } + + return 1; + }; +} + +// Check to see if the browser returns elements by name when +// querying by getElementById (and provide a workaround) +(function(){ + // We're going to inject a fake input element with a specified name + var form = document.createElement("div"), + id = "script" + (new Date()).getTime(), + root = document.documentElement; + + form.innerHTML = ""; + + // Inject it into the root element, check its status, and remove it quickly + root.insertBefore( form, root.firstChild ); + + // The workaround has to do additional checks after a getElementById + // Which slows things down for other browsers (hence the branching) + if ( document.getElementById( id ) ) { + Expr.find.ID = function( match, context, isXML ) { + if ( typeof context.getElementById !== "undefined" && !isXML ) { + var m = context.getElementById(match[1]); + + return m ? + m.id === match[1] || typeof m.getAttributeNode !== "undefined" && m.getAttributeNode("id").nodeValue === match[1] ? + [m] : + undefined : + []; + } + }; + + Expr.filter.ID = function( elem, match ) { + var node = typeof elem.getAttributeNode !== "undefined" && elem.getAttributeNode("id"); + + return elem.nodeType === 1 && node && node.nodeValue === match; + }; + } + + root.removeChild( form ); + + // release memory in IE + root = form = null; +})(); + +(function(){ + // Check to see if the browser returns only elements + // when doing getElementsByTagName("*") + + // Create a fake element + var div = document.createElement("div"); + div.appendChild( document.createComment("") ); + + // Make sure no comments are found + if ( div.getElementsByTagName("*").length > 0 ) { + Expr.find.TAG = function( match, context ) { + var results = context.getElementsByTagName( match[1] ); + + // Filter out possible comments + if ( match[1] === "*" ) { + var tmp = []; + + for ( var i = 0; results[i]; i++ ) { + if ( results[i].nodeType === 1 ) { + tmp.push( results[i] ); + } + } + + results = tmp; + } + + return results; + }; + } + + // Check to see if an attribute returns normalized href attributes + div.innerHTML = ""; + + if ( div.firstChild && typeof div.firstChild.getAttribute !== "undefined" && + div.firstChild.getAttribute("href") !== "#" ) { + + Expr.attrHandle.href = function( elem ) { + return elem.getAttribute( "href", 2 ); + }; + } + + // release memory in IE + div = null; +})(); + +if ( document.querySelectorAll ) { + (function(){ + var oldSizzle = Sizzle, + div = document.createElement("div"), + id = "__sizzle__"; + + div.innerHTML = "

"; + + // Safari can't handle uppercase or unicode characters when + // in quirks mode. + if ( div.querySelectorAll && div.querySelectorAll(".TEST").length === 0 ) { + return; + } + + Sizzle = function( query, context, extra, seed ) { + context = context || document; + + // Only use querySelectorAll on non-XML documents + // (ID selectors don't work in non-HTML documents) + if ( !seed && !Sizzle.isXML(context) ) { + // See if we find a selector to speed up + var match = /^(\w+$)|^\.([\w\-]+$)|^#([\w\-]+$)/.exec( query ); + + if ( match && (context.nodeType === 1 || context.nodeType === 9) ) { + // Speed-up: Sizzle("TAG") + if ( match[1] ) { + return makeArray( context.getElementsByTagName( query ), extra ); + + // Speed-up: Sizzle(".CLASS") + } else if ( match[2] && Expr.find.CLASS && context.getElementsByClassName ) { + return makeArray( context.getElementsByClassName( match[2] ), extra ); + } + } + + if ( context.nodeType === 9 ) { + // Speed-up: Sizzle("body") + // The body element only exists once, optimize finding it + if ( query === "body" && context.body ) { + return makeArray( [ context.body ], extra ); + + // Speed-up: Sizzle("#ID") + } else if ( match && match[3] ) { + var elem = context.getElementById( match[3] ); + + // Check parentNode to catch when Blackberry 4.6 returns + // nodes that are no longer in the document #6963 + if ( elem && elem.parentNode ) { + // Handle the case where IE and Opera return items + // by name instead of ID + if ( elem.id === match[3] ) { + return makeArray( [ elem ], extra ); + } + + } else { + return makeArray( [], extra ); + } + } + + try { + return makeArray( context.querySelectorAll(query), extra ); + } catch(qsaError) {} + + // qSA works strangely on Element-rooted queries + // We can work around this by specifying an extra ID on the root + // and working up from there (Thanks to Andrew Dupont for the technique) + // IE 8 doesn't work on object elements + } else if ( context.nodeType === 1 && context.nodeName.toLowerCase() !== "object" ) { + var oldContext = context, + old = context.getAttribute( "id" ), + nid = old || id, + hasParent = context.parentNode, + relativeHierarchySelector = /^\s*[+~]/.test( query ); + + if ( !old ) { + context.setAttribute( "id", nid ); + } else { + nid = nid.replace( /'/g, "\\$&" ); + } + if ( relativeHierarchySelector && hasParent ) { + context = context.parentNode; + } + + try { + if ( !relativeHierarchySelector || hasParent ) { + return makeArray( context.querySelectorAll( "[id='" + nid + "'] " + query ), extra ); + } + + } catch(pseudoError) { + } finally { + if ( !old ) { + oldContext.removeAttribute( "id" ); + } + } + } + } + + return oldSizzle(query, context, extra, seed); + }; + + for ( var prop in oldSizzle ) { + Sizzle[ prop ] = oldSizzle[ prop ]; + } + + // release memory in IE + div = null; + })(); +} + +(function(){ + var html = document.documentElement, + matches = html.matchesSelector || html.mozMatchesSelector || html.webkitMatchesSelector || html.msMatchesSelector; + + if ( matches ) { + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9 fails this) + var disconnectedMatch = !matches.call( document.createElement( "div" ), "div" ), + pseudoWorks = false; + + try { + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( document.documentElement, "[test!='']:sizzle" ); + + } catch( pseudoError ) { + pseudoWorks = true; + } + + Sizzle.matchesSelector = function( node, expr ) { + // Make sure that attribute selectors are quoted + expr = expr.replace(/\=\s*([^'"\]]*)\s*\]/g, "='$1']"); + + if ( !Sizzle.isXML( node ) ) { + try { + if ( pseudoWorks || !Expr.match.PSEUDO.test( expr ) && !/!=/.test( expr ) ) { + var ret = matches.call( node, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || !disconnectedMatch || + // As well, disconnected nodes are said to be in a document + // fragment in IE 9, so check for that + node.document && node.document.nodeType !== 11 ) { + return ret; + } + } + } catch(e) {} + } + + return Sizzle(expr, null, null, [node]).length > 0; + }; + } +})(); + +(function(){ + var div = document.createElement("div"); + + div.innerHTML = "
"; + + // Opera can't find a second classname (in 9.6) + // Also, make sure that getElementsByClassName actually exists + if ( !div.getElementsByClassName || div.getElementsByClassName("e").length === 0 ) { + return; + } + + // Safari caches class attributes, doesn't catch changes (in 3.2) + div.lastChild.className = "e"; + + if ( div.getElementsByClassName("e").length === 1 ) { + return; + } + + Expr.order.splice(1, 0, "CLASS"); + Expr.find.CLASS = function( match, context, isXML ) { + if ( typeof context.getElementsByClassName !== "undefined" && !isXML ) { + return context.getElementsByClassName(match[1]); + } + }; + + // release memory in IE + div = null; +})(); + +function dirNodeCheck( dir, cur, doneName, checkSet, nodeCheck, isXML ) { + for ( var i = 0, l = checkSet.length; i < l; i++ ) { + var elem = checkSet[i]; + + if ( elem ) { + var match = false; + + elem = elem[dir]; + + while ( elem ) { + if ( elem[ expando ] === doneName ) { + match = checkSet[elem.sizset]; + break; + } + + if ( elem.nodeType === 1 && !isXML ){ + elem[ expando ] = doneName; + elem.sizset = i; + } + + if ( elem.nodeName.toLowerCase() === cur ) { + match = elem; + break; + } + + elem = elem[dir]; + } + + checkSet[i] = match; + } + } +} + +function dirCheck( dir, cur, doneName, checkSet, nodeCheck, isXML ) { + for ( var i = 0, l = checkSet.length; i < l; i++ ) { + var elem = checkSet[i]; + + if ( elem ) { + var match = false; + + elem = elem[dir]; + + while ( elem ) { + if ( elem[ expando ] === doneName ) { + match = checkSet[elem.sizset]; + break; + } + + if ( elem.nodeType === 1 ) { + if ( !isXML ) { + elem[ expando ] = doneName; + elem.sizset = i; + } + + if ( typeof cur !== "string" ) { + if ( elem === cur ) { + match = true; + break; + } + + } else if ( Sizzle.filter( cur, [elem] ).length > 0 ) { + match = elem; + break; + } + } + + elem = elem[dir]; + } + + checkSet[i] = match; + } + } +} + +if ( document.documentElement.contains ) { + Sizzle.contains = function( a, b ) { + return a !== b && (a.contains ? a.contains(b) : true); + }; + +} else if ( document.documentElement.compareDocumentPosition ) { + Sizzle.contains = function( a, b ) { + return !!(a.compareDocumentPosition(b) & 16); + }; + +} else { + Sizzle.contains = function() { + return false; + }; +} + +Sizzle.isXML = function( elem ) { + // documentElement is verified for cases where it doesn't yet exist + // (such as loading iframes in IE - #4833) + var documentElement = (elem ? elem.ownerDocument || elem : 0).documentElement; + + return documentElement ? documentElement.nodeName !== "HTML" : false; +}; + +var posProcess = function( selector, context, seed ) { + var match, + tmpSet = [], + later = "", + root = context.nodeType ? [context] : context; + + // Position selectors must be done after the filter + // And so must :not(positional) so we move all PSEUDOs to the end + while ( (match = Expr.match.PSEUDO.exec( selector )) ) { + later += match[0]; + selector = selector.replace( Expr.match.PSEUDO, "" ); + } + + selector = Expr.relative[selector] ? selector + "*" : selector; + + for ( var i = 0, l = root.length; i < l; i++ ) { + Sizzle( selector, root[i], tmpSet, seed ); + } + + return Sizzle.filter( later, tmpSet ); +}; + +// EXPOSE +// Override sizzle attribute retrieval +Sizzle.attr = jQuery.attr; +Sizzle.selectors.attrMap = {}; +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; +jQuery.expr[":"] = jQuery.expr.filters; +jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; + + +})(); + + +var runtil = /Until$/, + rparentsprev = /^(?:parents|prevUntil|prevAll)/, + // Note: This RegExp should be improved, or likely pulled from Sizzle + rmultiselector = /,/, + isSimple = /^.[^:#\[\.,]*$/, + slice = Array.prototype.slice, + POS = jQuery.expr.match.globalPOS, + // methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend({ + find: function( selector ) { + var self = this, + i, l; + + if ( typeof selector !== "string" ) { + return jQuery( selector ).filter(function() { + for ( i = 0, l = self.length; i < l; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + }); + } + + var ret = this.pushStack( "", "find", selector ), + length, n, r; + + for ( i = 0, l = this.length; i < l; i++ ) { + length = ret.length; + jQuery.find( selector, this[i], ret ); + + if ( i > 0 ) { + // Make sure that the results are unique + for ( n = length; n < ret.length; n++ ) { + for ( r = 0; r < length; r++ ) { + if ( ret[r] === ret[n] ) { + ret.splice(n--, 1); + break; + } + } + } + } + } + + return ret; + }, + + has: function( target ) { + var targets = jQuery( target ); + return this.filter(function() { + for ( var i = 0, l = targets.length; i < l; i++ ) { + if ( jQuery.contains( this, targets[i] ) ) { + return true; + } + } + }); + }, + + not: function( selector ) { + return this.pushStack( winnow(this, selector, false), "not", selector); + }, + + filter: function( selector ) { + return this.pushStack( winnow(this, selector, true), "filter", selector ); + }, + + is: function( selector ) { + return !!selector && ( + typeof selector === "string" ? + // If this is a positional selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + POS.test( selector ) ? + jQuery( selector, this.context ).index( this[0] ) >= 0 : + jQuery.filter( selector, this ).length > 0 : + this.filter( selector ).length > 0 ); + }, + + closest: function( selectors, context ) { + var ret = [], i, l, cur = this[0]; + + // Array (deprecated as of jQuery 1.7) + if ( jQuery.isArray( selectors ) ) { + var level = 1; + + while ( cur && cur.ownerDocument && cur !== context ) { + for ( i = 0; i < selectors.length; i++ ) { + + if ( jQuery( cur ).is( selectors[ i ] ) ) { + ret.push({ selector: selectors[ i ], elem: cur, level: level }); + } + } + + cur = cur.parentNode; + level++; + } + + return ret; + } + + // String + var pos = POS.test( selectors ) || typeof selectors !== "string" ? + jQuery( selectors, context || this.context ) : + 0; + + for ( i = 0, l = this.length; i < l; i++ ) { + cur = this[i]; + + while ( cur ) { + if ( pos ? pos.index(cur) > -1 : jQuery.find.matchesSelector(cur, selectors) ) { + ret.push( cur ); + break; + + } else { + cur = cur.parentNode; + if ( !cur || !cur.ownerDocument || cur === context || cur.nodeType === 11 ) { + break; + } + } + } + } + + ret = ret.length > 1 ? jQuery.unique( ret ) : ret; + + return this.pushStack( ret, "closest", selectors ); + }, + + // Determine the position of an element within + // the matched set of elements + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[0] && this[0].parentNode ) ? this.prevAll().length : -1; + } + + // index in selector + if ( typeof elem === "string" ) { + return jQuery.inArray( this[0], jQuery( elem ) ); + } + + // Locate the position of the desired element + return jQuery.inArray( + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[0] : elem, this ); + }, + + add: function( selector, context ) { + var set = typeof selector === "string" ? + jQuery( selector, context ) : + jQuery.makeArray( selector && selector.nodeType ? [ selector ] : selector ), + all = jQuery.merge( this.get(), set ); + + return this.pushStack( isDisconnected( set[0] ) || isDisconnected( all[0] ) ? + all : + jQuery.unique( all ) ); + }, + + andSelf: function() { + return this.add( this.prevObject ); + } +}); + +// A painfully simple check to see if an element is disconnected +// from a document (should be improved, where feasible). +function isDisconnected( node ) { + return !node || !node.parentNode || node.parentNode.nodeType === 11; +} + +jQuery.each({ + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return jQuery.dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, i, until ) { + return jQuery.dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return jQuery.nth( elem, 2, "nextSibling" ); + }, + prev: function( elem ) { + return jQuery.nth( elem, 2, "previousSibling" ); + }, + nextAll: function( elem ) { + return jQuery.dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return jQuery.dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, i, until ) { + return jQuery.dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, i, until ) { + return jQuery.dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return jQuery.sibling( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return jQuery.sibling( elem.firstChild ); + }, + contents: function( elem ) { + return jQuery.nodeName( elem, "iframe" ) ? + elem.contentDocument || elem.contentWindow.document : + jQuery.makeArray( elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var ret = jQuery.map( this, fn, until ); + + if ( !runtil.test( name ) ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + ret = jQuery.filter( selector, ret ); + } + + ret = this.length > 1 && !guaranteedUnique[ name ] ? jQuery.unique( ret ) : ret; + + if ( (this.length > 1 || rmultiselector.test( selector )) && rparentsprev.test( name ) ) { + ret = ret.reverse(); + } + + return this.pushStack( ret, name, slice.call( arguments ).join(",") ); + }; +}); + +jQuery.extend({ + filter: function( expr, elems, not ) { + if ( not ) { + expr = ":not(" + expr + ")"; + } + + return elems.length === 1 ? + jQuery.find.matchesSelector(elems[0], expr) ? [ elems[0] ] : [] : + jQuery.find.matches(expr, elems); + }, + + dir: function( elem, dir, until ) { + var matched = [], + cur = elem[ dir ]; + + while ( cur && cur.nodeType !== 9 && (until === undefined || cur.nodeType !== 1 || !jQuery( cur ).is( until )) ) { + if ( cur.nodeType === 1 ) { + matched.push( cur ); + } + cur = cur[dir]; + } + return matched; + }, + + nth: function( cur, result, dir, elem ) { + result = result || 1; + var num = 0; + + for ( ; cur; cur = cur[dir] ) { + if ( cur.nodeType === 1 && ++num === result ) { + break; + } + } + + return cur; + }, + + sibling: function( n, elem ) { + var r = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + r.push( n ); + } + } + + return r; + } +}); + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, keep ) { + + // Can't pass null or undefined to indexOf in Firefox 4 + // Set to 0 to skip string check + qualifier = qualifier || 0; + + if ( jQuery.isFunction( qualifier ) ) { + return jQuery.grep(elements, function( elem, i ) { + var retVal = !!qualifier.call( elem, i, elem ); + return retVal === keep; + }); + + } else if ( qualifier.nodeType ) { + return jQuery.grep(elements, function( elem, i ) { + return ( elem === qualifier ) === keep; + }); + + } else if ( typeof qualifier === "string" ) { + var filtered = jQuery.grep(elements, function( elem ) { + return elem.nodeType === 1; + }); + + if ( isSimple.test( qualifier ) ) { + return jQuery.filter(qualifier, filtered, !keep); + } else { + qualifier = jQuery.filter( qualifier, filtered ); + } + } + + return jQuery.grep(elements, function( elem, i ) { + return ( jQuery.inArray( elem, qualifier ) >= 0 ) === keep; + }); +} + + + + +function createSafeFragment( document ) { + var list = nodeNames.split( "|" ), + safeFrag = document.createDocumentFragment(); + + if ( safeFrag.createElement ) { + while ( list.length ) { + safeFrag.createElement( + list.pop() + ); + } + } + return safeFrag; +} + +var nodeNames = "abbr|article|aside|audio|bdi|canvas|data|datalist|details|figcaption|figure|footer|" + + "header|hgroup|mark|meter|nav|output|progress|section|summary|time|video", + rinlinejQuery = / jQuery\d+="(?:\d+|null)"/g, + rleadingWhitespace = /^\s+/, + rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([\w:]+)[^>]*)\/>/ig, + rtagName = /<([\w:]+)/, + rtbody = /]", "i"), + // checked="checked" or checked + rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i, + rscriptType = /\/(java|ecma)script/i, + rcleanScript = /^\s*", "" ], + legend: [ 1, "
", "
" ], + thead: [ 1, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + col: [ 2, "", "
" ], + area: [ 1, "", "" ], + _default: [ 0, "", "" ] + }, + safeFragment = createSafeFragment( document ); + +wrapMap.optgroup = wrapMap.option; +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// IE can't serialize and + + + + + + + + + +
+

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+ +
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+
+
+ +
+

pyFAI API

+

This chapter describes the programming interface of pyFAI, so what you can expect after having launched ipython and typed: +..

+
+
import pyFAI
+

The most important class is AzimuthalIntegrator which is an object containing both the geometry (it inherits from Geometry, another class) +and exposes important methods (functions) like integrate1d and integrate2d.

+
+ +
+
+ + +
+
+
+
+
+

Previous topic

+

Design of the Python Fast Azimuthal Integrator

+

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pyFAI Package

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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/api/pyFAI.html b/doc/build/api/pyFAI.html new file mode 100644 index 000000000..316c4421c --- /dev/null +++ b/doc/build/api/pyFAI.html @@ -0,0 +1,8971 @@ + + + + + + + + pyFAI Package — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

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+ +
+ +
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+

pyFAI Package

+
+
+pyFAI.__init__.benchmarks()
+

Run the benchmarks

+
+ +
+
+pyFAI.__init__.tests(deprecation=False)
+

Runs the test suite of the installed version

+ +++ + + + +
Parameters:deprecation – enable/disables deprecation warning in the tests
+
+ +
+
+

azimuthalIntegrator Module

+
+
+class pyFAI.azimuthalIntegrator.AzimuthalIntegrator(dist=1, poni1=0, poni2=0, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None)
+

Bases: pyFAI.geometry.Geometry

+

This class is an azimuthal integrator based on P. Boesecke’s +geometry and histogram algorithm by Manolo S. del Rio and V.A Sole

+

All geometry calculation are done in the Geometry class

+

main methods are:

+
>>> tth, I = ai.integrate1d(data, npt, unit="2th_deg")
+>>> q, I, sigma = ai.integrate1d(data, npt, unit="q_nm^-1", error_model="poisson")
+>>> regrouped = ai.integrate2d(data, npt_rad, npt_azim, unit="q_nm^-1")[0]
+
+
+
+
+DEFAULT_METHOD = 'splitbbox'
+
+ +
+
+create_mask(data, mask=None, dummy=None, delta_dummy=None, mode='normal')
+

Combines various masks into another one.

+ +++ + + + + + + + +
Parameters:
    +
  • data (ndarray) – input array of data
    • +
  • mask (ndarray) – input mask (if none, self.mask is used)
    • +
  • dummy (float) – value of dead pixels
    • +
  • delta_dumy – precision of dummy pixels
    • +
  • mode (str) – can be “normal” or “numpy” (inverted) or “where” applied to the mask
    • +
+
Returns:

the new mask

+
Return type:

ndarray of bool

+
+

This method combine two masks (dynamic mask from data & +dummy and mask) to generate a new one with the ‘or’ binary +operation. One can adjust the level, with the dummy and +the delta_dummy parameter, when you consider the data +values needs to be masked out.

+

This method can work in two different mode:

+
+
    +
  • “normal”: False for valid pixels, True for bad pixels
    • +
  • “numpy”: True for valid pixels, false for others
    • +
+
+

This method tries to accomodate various types of masks (like +valid=0 & masked=-1, ...) and guesses if an input mask needs +to be inverted.

+
+ +
+
+dark_correction(data, dark=None)
+

Correct for Dark-current effects. +If dark is not defined, correct for a dark set by “set_darkfiles”

+ +++ + + + + + +
Parameters:
    +
  • data – input ndarray with the image
    • +
  • dark – ndarray with dark noise or None
    • +
+
Returns:

2tuple: corrected_data, dark_actually used (or None)

+
+
+ +
+
+darkcurrent
+
+ +
+
+empty
+
+ +
+
+flat_correction(data, flat=None)
+

Correct for flat field. +If flat is not defined, correct for a flat set by “set_flatfiles”

+ +++ + + + + + +
Parameters:
    +
  • data – input ndarray with the image
    • +
  • dark – ndarray with dark noise or None
    • +
+
Returns:

2tuple: corrected_data, flat_actually used (or None)

+
+
+ +
+
+flatfield
+
+ +
+
+get_darkcurrent()
+
+ +
+
+get_empty()
+
+ +
+
+get_flatfield()
+
+ +
+
+integrate1d(data, npt, filename=None, correctSolidAngle=True, variance=None, error_model=None, radial_range=None, azimuth_range=None, mask=None, dummy=None, delta_dummy=None, polarization_factor=None, dark=None, flat=None, method='csr', unit=q_nm^-1, safe=True, normalization_factor=1.0, block_size=32, profile=False, all=False)
+

Calculate the azimuthal integrated Saxs curve in q(nm^-1) by default

+

Multi algorithm implementation (tries to be bullet proof), suitable for SAXS, WAXS, ... and much more

+ +++ + + + + + + + +
Parameters:
    +
  • data (ndarray) – 2D array from the Detector/CCD camera
    • +
  • npt (int) – number of points in the output pattern
    • +
  • filename (str) – output filename in 2/3 column ascii format
    • +
  • correctSolidAngle (bool) – correct for solid angle of each pixel if True
    • +
  • variance (ndarray) – array containing the variance of the data. If not available, no error propagation is done
    • +
  • error_model (str) – When the variance is unknown, an error model can be given: “poisson” (variance = I), “azimuthal” (variance = (I-<I>)^2)
    • +
  • radial_range ((float, float), optional) – The lower and upper range of the radial unit. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored.
    • +
  • azimuth_range ((float, float), optional) – The lower and upper range of the azimuthal angle in degree. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored.
    • +
  • mask (ndarray) – array (same size as image) with 1 for masked pixels, and 0 for valid pixels
    • +
  • dummy (float) – value for dead/masked pixels
    • +
  • delta_dummy (float) – precision for dummy value
    • +
  • polarization_factor (float) – polarization factor between -1 (vertical) and +1 (horizontal). 0 for circular polarization or random, None for no correction
    • +
  • dark (ndarray) – dark noise image
    • +
  • flat (ndarray) – flat field image
    • +
  • method (str) – can be “numpy”, “cython”, “BBox” or “splitpixel”, “lut”, “csr”, “nosplit_csr”, “full_csr”, “lut_ocl” and “csr_ocl” if you want to go on GPU. To Specify the device: “csr_ocl_1,2”
    • +
  • unit (pyFAI.units.Enum) – Output units, can be “q_nm^-1”, “q_A^-1”, “2th_deg”, “2th_rad”, “r_mm” for now
    • +
  • safe (bool) – Do some extra checks to ensure LUT/CSR is still valid. False is faster.
    • +
  • normalization_factor (float) – Value of a normalization monitor
    • +
  • block_size – size of the block for OpenCL integration (unused?)
    • +
  • profile – set to True to enable profiling in OpenCL
    • +
  • all – if true return a dictionary with many more parameters
    • +
+
Returns:

q/2th/r bins center positions and regrouped intensity (and error array if variance or variance model provided), uneless all==True.

+
Return type:

2 or 3-tuple of ndarrays

+
+
+ +
+
+integrate2d(data, npt_rad, npt_azim=360, filename=None, correctSolidAngle=True, variance=None, error_model=None, radial_range=None, azimuth_range=None, mask=None, dummy=None, delta_dummy=None, polarization_factor=None, dark=None, flat=None, method='bbox', unit=q_nm^-1, safe=True, normalization_factor=1.0, all=False)
+

Calculate the azimuthal regrouped 2d image in q(nm^-1)/chi(deg) by default

+

Multi algorithm implementation (tries to be bullet proof)

+ +++ + + + + + + + +
Parameters:
    +
  • data (ndarray) – 2D array from the Detector/CCD camera
    • +
  • npt_rad (int) – number of points in the radial direction
    • +
  • npt_azim (int) – number of points in the azimuthal direction
    • +
  • filename (str) – output image (as edf format)
    • +
  • correctSolidAngle (bool) – correct for solid angle of each pixel if True
    • +
  • variance (ndarray) – array containing the variance of the data. If not available, no error propagation is done
    • +
  • error_model (str) – When the variance is unknown, an error model can be given: “poisson” (variance = I), “azimuthal” (variance = (I-<I>)^2)
    • +
  • radial_range ((float, float), optional) – The lower and upper range of the radial unit. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored.
    • +
  • azimuth_range ((float, float), optional) – The lower and upper range of the azimuthal angle in degree. If not provided, range is simply (data.min(), data.max()). Values outside the range are ignored.
    • +
  • mask (ndarray) – array (same size as image) with 1 for masked pixels, and 0 for valid pixels
    • +
  • dummy (float) – value for dead/masked pixels
    • +
  • delta_dummy (float) – precision for dummy value
    • +
  • polarization_factor (float) – polarization factor between -1 (vertical) and +1 (horizontal). 0 for circular polarization or random, None for no correction
    • +
  • dark (ndarray) – dark noise image
    • +
  • flat (ndarray) – flat field image
    • +
  • method (str) – can be “numpy”, “cython”, “BBox” or “splitpixel”, “lut”, “csr; “lut_ocl” and “csr_ocl” if you want to go on GPU. To Specify the device: “csr_ocl_1,2”
    • +
  • unit (pyFAI.units.Enum) – Output units, can be “q_nm^-1”, “q_A^-1”, “2th_deg”, “2th_rad”, “r_mm” for now
    • +
  • safe (bool) – Do some extra checks to ensure LUT is still valid. False is faster.
    • +
  • normalization_factor (float) – Value of a normalization monitor
    • +
  • all – if true, return many more intermediate results as a dict.
    • +
+
Returns:

azimuthaly regrouped intensity, q/2theta/r pos. and chi pos.

+
Return type:

3-tuple of ndarrays (2d, 1d, 1d)

+
+
+ +
+
+makeHeaders(hdr='#', dark=None, flat=None, polarization_factor=None, normalization_factor=None)
+
+++ + + + + + + + +
Parameters:
    +
  • hdr (str) – string used as comment in the header
    • +
  • dark – save the darks filenames (default: no)
    • +
  • flat – save the flat filenames (default: no)
    • +
  • polarization_factor (float) – the polarization factor
    • +
+
Returns:

the header

+
Return type:

str

+
+
+ +
+
+reset()
+

Reset azimuthal integrator in addition to other arrays.

+
+ +
+
+save1D(filename, dim1, I, error=None, dim1_unit=2th_deg, dark=None, flat=None, polarization_factor=None, normalization_factor=None)
+
+++ + + + +
Parameters:
    +
  • filename (str) – the filename used to save the 1D integration
    • +
  • dim1 (numpy.ndarray) – the x coordinates of the integrated curve
    • +
  • I (numpy.mdarray) – The integrated intensity
    • +
  • error (numpy.ndarray or None) – the error bar for each intensity
    • +
  • dim1_unit (pyFAI.units.Unit) – the unit of the dim1 array
    • +
  • dark – save the darks filenames (default: no)
    • +
  • flat – save the flat filenames (default: no)
    • +
  • polarization_factor (float) – the polarization factor
    • +
  • normalization_factor (float) – the monitor value
    • +
+
+

This method save the result of a 1D integration.

+
+ +
+
+save2D(filename, I, dim1, dim2, error=None, dim1_unit=2th_deg, dark=None, flat=None, polarization_factor=None, normalization_factor=None)
+
+++ + + + +
Parameters:
    +
  • filename (str) – the filename used to save the 2D histogram
    • +
  • dim1 (numpy.ndarray) – the 1st coordinates of the histogram
    • +
  • dim1 – the 2nd coordinates of the histogram
    • +
  • I (numpy.mdarray) – The integrated intensity
    • +
  • error (numpy.ndarray or None) – the error bar for each intensity
    • +
  • dim1_unit (pyFAI.units.Unit) – the unit of the dim1 array
    • +
  • dark – save the darks filenames (default: no)
    • +
  • flat – save the flat filenames (default: no)
    • +
  • polarization_factor (float) – the polarization factor
    • +
  • normalization_factor (float) – the monitor value
    • +
+
+

This method save the result of a 2D integration.

+
+ +
+
+saxs(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+separate(data, npt_rad=1024, npt_azim=512, unit='2th_deg', method='splitpixel', percentile=50, mask=None, restore_mask=True)
+

Separate bragg signal from powder/amorphous signal using azimuthal integration, +median filering and projected back before subtraction.

+ +++ + + + + + +
Parameters:
    +
  • data – input image as numpy array
    • +
  • npt_rad – number of radial points
    • +
  • npt_azim – number of azimuthal points
    • +
  • unit – unit to be used for integration
    • +
  • method – pathway for integration and sort
    • +
  • percentile – which percentile use for cutting out
    • +
  • mask – masked out pixels array
    • +
  • restore_mask – masked pixels have the same value as input data provided
    • +
+
Returns:

bragg, amorphous

+
+
+ +
+
+set_darkcurrent(dark)
+
+ +
+
+set_darkfiles(files=None, method='mean')
+
+++ + + + +
Parameters:
    +
  • files (str or list(str) or None) – file(s) used to compute the dark.
    • +
  • method (str) – method used to compute the dark, “mean” or “median”
    • +
+
+

Set the dark current from one or mutliple files, avaraged +according to the method provided

+
+ +
+
+set_empty(value)
+
+ +
+
+set_flatfield(flat)
+
+ +
+
+set_flatfiles(files, method='mean')
+
+++ + + + +
Parameters:
    +
  • files (str or list(str) or None) – file(s) used to compute the dark.
    • +
  • method (str) – method used to compute the dark, “mean” or “median”
    • +
+
+

Set the flat field from one or mutliple files, averaged +according to the method provided

+
+ +
+
+setup_CSR(shape, npt, mask=None, pos0_range=None, pos1_range=None, mask_checksum=None, unit=2th_deg, split='bbox')
+

Prepare a look-up-table

+ +++ + + + +
Parameters:
    +
  • shape ((int, int)) – shape of the dataset
    • +
  • npt (int or (int, int)) – number of points in the the output pattern
    • +
  • mask (ndarray) – array with masked pixel (1=masked)
    • +
  • pos0_range ((float, float)) – range in radial dimension
    • +
  • pos1_range ((float, float)) – range in azimuthal dimension
    • +
  • mask_checksum (int (or anything else ...)) – checksum of the mask buffer
    • +
  • unit (pyFAI.units.Enum) – use to propagate the LUT object for further checkings
    • +
  • split – Splitting scheme: valid options are “no”, “bbox”, “full”
    • +
+
+

This method is called when a look-up table needs to be set-up. +The shape parameter, correspond to the shape of the original +datatset. It is possible to customize the number of point of +the output histogram with the npt parameter which can be +either an integer for an 1D integration or a 2-tuple of +integer in case of a 2D integration. The LUT will have a +different shape: (npt, lut_max_size), the later parameter +being calculated during the instanciation of the splitBBoxLUT +class.

+

It is possible to prepare the LUT with a predefine +mask. This operation can speedup the computation of the +later integrations. Instead of applying the patch on the +dataset, it is taken into account during the histogram +computation. If provided the mask_checksum prevent the +re-calculation of the mask. When the mask changes, its +checksum is used to reset (or not) the LUT (which is a very +time consuming operation !)

+

It is also possible to restrain the range of the 1D or 2D +pattern with the pos1_range and pos2_range.

+

The unit parameter is just propagated to the LUT integrator +for further checkings: The aim is to prevent an integration to +be performed in 2th-space when the LUT was setup in q space.

+
+ +
+
+setup_LUT(shape, npt, mask=None, pos0_range=None, pos1_range=None, mask_checksum=None, unit=2th_deg)
+

Prepare a look-up-table

+ +++ + + + +
Parameters:
    +
  • shape ((int, int)) – shape of the dataset
    • +
  • npt (int or (int, int)) – number of points in the the output pattern
    • +
  • mask (ndarray) – array with masked pixel (1=masked)
    • +
  • pos0_range ((float, float)) – range in radial dimension
    • +
  • pos1_range ((float, float)) – range in azimuthal dimension
    • +
  • mask_checksum (int (or anything else ...)) – checksum of the mask buffer
    • +
  • unit (pyFAI.units.Enum) – use to propagate the LUT object for further checkings
    • +
+
+

This method is called when a look-up table needs to be set-up. +The shape parameter, correspond to the shape of the original +datatset. It is possible to customize the number of point of +the output histogram with the npt parameter which can be +either an integer for an 1D integration or a 2-tuple of +integer in case of a 2D integration. The LUT will have a +different shape: (npt, lut_max_size), the later parameter +being calculated during the instanciation of the splitBBoxLUT +class.

+

It is possible to prepare the LUT with a predefine +mask. This operation can speedup the computation of the +later integrations. Instead of applying the patch on the +dataset, it is taken into account during the histogram +computation. If provided the mask_checksum prevent the +re-calculation of the mask. When the mask changes, its +checksum is used to reset (or not) the LUT (which is a very +time consuming operation !)

+

It is also possible to restrain the range of the 1D or 2D +pattern with the pos1_range and pos2_range.

+

The unit parameter is just propagated to the LUT integrator +for further checkings: The aim is to prevent an integration to +be performed in 2th-space when the LUT was setup in q space.

+
+ +
+
+xrpd(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd2(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd2_histogram(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd2_numpy(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd2_splitBBox(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd2_splitPixel(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd_CSR_OCL(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd_LUT(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd_LUT_OCL(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd_OpenCL(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd_cython(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd_numpy(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd_splitBBox(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+xrpd_splitPixel(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+ +
+
+

integrate_widget Module

+

pyFAI-integrate

+

A graphical tool (based on PyQt4) for performing azimuthal integration on series of files.

+
+
+class pyFAI.integrate_widget.AIWidget(input_data=None, output_path=None, output_format=None, slow_dim=None, fast_dim=None, json_file='.azimint.json')
+

Bases: PyQt4.QtGui.QWidget

+
+
+URL = 'http://pyfai.readthedocs.org/en/latest/man/pyFAI-integrate.html'
+
+ +
+
+assign_unit()
+

assign unit to the corresponding widget

+
+ +
+
+detector_changed()
+
+ +
+
+die()
+
+ +
+
+dump(filename=None)
+

Dump the status of the current widget to a file in JSON

+ +++ + + + + + +
Parameters:filename (string) – path where to save the config
Returns:dict with configuration
+
+ +
+
+get_config()
+

Read the configuration of the plugin and returns it as a dictionary

+ +++ + + + +
Returns:dict with all information.
+
+ +
+
+get_method()
+

Return the method name for azimuthal intgration

+
+ +
+
+help()
+
+ +
+
+openCL_changed()
+
+ +
+
+platform_changed()
+
+ +
+
+proceed()
+
+ +
+
+restore(filename='.azimint.json')
+

Restore from JSON file the status of the current widget

+ +++ + + + +
Parameters:filename (str) – path where the config was saved
+
+ +
+
+save_config()
+
+ +
+
+select_darkcurrent()
+
+ +
+
+select_flatfield()
+
+ +
+
+select_maskfile()
+
+ +
+
+select_ponifile()
+
+ +
+
+select_splinefile()
+
+ +
+
+setStackDataObject(stack, stack_name=None)
+
+ +
+
+set_config(dico)
+

Setup the widget from its description

+ +++ + + + +
Parameters:dico (dict) – dictionary with description of the widget
+
+ +
+
+set_input_data(stack, stack_name=None)
+
+ +
+
+set_ponifile(ponifile=None)
+
+ +
+
+set_validators()
+

Set all validators for text entries

+
+ +
+ +
+
+class pyFAI.integrate_widget.Browser(default_url='http://google.com')
+

Bases: PyQt4.QtGui.QMainWindow

+
+
+browse()
+

Make a web browse on a specific url and show the page on the +Webview widget.

+
+ +
+ +
+
+

geometry Module

+
+
+class pyFAI.geometry.Geometry(dist=1, poni1=0, poni2=0, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None)
+

Bases: object

+

This class is an azimuthal integrator based on P. Boesecke’s geometry and +histogram algorithm by Manolo S. del Rio and V.A Sole

+

Detector is assumed to be corrected from “raster orientation” effect. +It is not addressed here but rather in the Detector object or at read time. +Considering there is no tilt:

+
    +
  • Detector fast dimension (dim2) is supposed to be horizontal +(dimension X of the image)
    • +
  • Detector slow dimension (dim1) is supposed to be vertical, upwards +(dimension Y of the image)
    • +
  • The third dimension is chose such as the referential is +orthonormal, so dim3 is along incoming X-ray beam
    • +
+

Axis 1 is along first dimension of detector (when not tilted), +this is the slow dimension of the image array in C or Y +x1={1,0,0}

+

Axis 2 is along second dimension of detector (when not tilted), +this is the fast dimension of the image in C or X +x2={0,1,0}

+

Axis 3 is along the incident X-Ray beam +x3={0,0,1}

+

We define the 3 rotation around axis 1, 2 and 3:

+

rotM1 = RotationMatrix[rot1,x1] = {{1,0,0},{0,cos[rot1],-sin[rot1]},{0,sin[rot1],cos[rot1]}} +rotM2 = RotationMatrix[rot2,x2] = {{cos[rot2],0,sin[rot2]},{0,1,0},{-sin[rot2],0,cos[rot2]}} +rotM3 = RotationMatrix[rot3,x3] = {{cos[rot3],-sin[rot3],0},{sin[rot3],cos[rot3],0},{0,0,1}}

+

Rotations of the detector are applied first Rot around axis 1, +then axis 2 and finally around axis 3:

+

R = rotM3.rotM2.rotM1

+
+
R = {{cos[rot2] cos[rot3],cos[rot3] sin[rot1] sin[rot2]-cos[rot1] sin[rot3],cos[rot1] cos[rot3] sin[rot2]+sin[rot1] sin[rot3]},
+
{cos[rot2] sin[rot3],cos[rot1] cos[rot3]+sin[rot1] sin[rot2] sin[rot3],-cos[rot3] sin[rot1]+cos[rot1] sin[rot2] sin[rot3]}, +{-sin[rot2],cos[rot2] sin[rot1],cos[rot1] cos[rot2]}}
+
+

In Python notation:

+

R.x1 = [cos(rot2)*cos(rot3),cos(rot2)*sin(rot3),-sin(rot2)]

+

R.x2 = [cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3),cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3), cos(rot2)*sin(rot1)]

+

R.x3 = [cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3),-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3), cos(rot1)*cos(rot2)]

+
    +

  • Coordinates of the Point of Normal Incidence:

    +

    PONI = R.{0,0,L}

    +
    +
    PONI = [L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)),
    +

    L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)),L*cos(rot1)*cos(rot2)]

    +
    +
    +
    • +

  • Any pixel on detector plan at coordinate (d1, d2) in +meters. Detector is at z=L

    +

    P={d1,d2,L}

    +

    R.P = [t1, t2, t3] +t1 = R.P.x1 = d1*cos(rot2)*cos(rot3) + d2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) + L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)) +t2 = R.P.x2 = d1*cos(rot2)*sin(rot3) + d2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3)) + L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) +t3 = R.P.x3 = d2*cos(rot2)*sin(rot1) - d1*sin(rot2) + L*cos(rot1)*cos(rot2)

    +
    • +

  • Distance sample (origin) to detector point (d1,d2)

    +
    +
    |R.P| = sqrt(pow(Abs(L*cos(rot1)*cos(rot2) + d2*cos(rot2)*sin(rot1) - d1*sin(rot2)),2) +
    +

    pow(Abs(d1*cos(rot2)*cos(rot3) + d2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) + +L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3))),2) + +pow(Abs(d1*cos(rot2)*sin(rot3) + L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) + +d2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3))),2))

    +
    +
    +
    • +

  • cos(2theta) is defined as (R.P component along x3) over the distance from origin to data point |R.P|

    +
    • +
+

tth = ArcCos [-(R.P).x3/|R.P|]

+
+
tth = Arccos((-(L*cos(rot1)*cos(rot2)) - d2*cos(rot2)*sin(rot1) + d1*sin(rot2))/
+
+
sqrt(pow(Abs(L*cos(rot1)*cos(rot2) + d2*cos(rot2)*sin(rot1) - d1*sin(rot2)),2) +
+
+
pow(Abs(d1*cos(rot2)*cos(rot3) + d2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) +
+
+
L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3))),2) +
+
pow(Abs(d1*cos(rot2)*sin(rot3) + L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) +
+
+

d2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3))),2)))

+
+
+
+
+
    +
  • tan(2theta) is defined as sqrt(t1**2 + t2**2) / t3
    • +
+

tth = ArcTan2 [sqrt(t1**2 + t2**2) , t3 ]

+

Getting 2theta from it’s tangeant seems both more precise (around +beam stop very far from sample) and faster by about 25% Currently +there is a swich in the method to follow one path or the other.

+
    +
  • Tangeant of angle chi is defined as (R.P component along x1) +over (R.P component along x2). Arctan2 should be used in actual +calculation
    • +
+
+

chi = ArcTan[((R.P).x1) / ((R.P).x2)]

+
+
chi = ArcTan2(d1*cos(rot2)*cos(rot3) + d2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) +
+
+
L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)),
+
+
d1*cos(rot2)*sin(rot3) + L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) +
+
d2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3)))
+
+
+
+
+
+
+array_from_unit(shape=None, typ='center', unit=2th_deg)
+

Generate an array of position in different dimentions (R, Q, +2Theta)

+ +++ + + + + + + + +
Parameters:
    +
  • shape (ndarray.shape) – shape of the expected array
    • +
  • typ (str) – “center”, “corner” or “delta”
    • +
  • unit (pyFAI.units.Enum) – can be Q, TTH, R for now
    • +
+
Returns:

R, Q or 2Theta array depending on unit

+
Return type:

ndarray

+
+
+ +
+
+calc_pos_zyx(d0=None, d1=None, d2=None, param=None, corners=False, use_cython=True)
+

Calculate the position of a set of points in space in the sample’s centers referential.

+

This is usually used for calculating the pixel position in space.

+ +++ + + + +
Parameters:
    +
  • d0 – altitude on the point compared to the detector (i.e. z), may be None
    • +
  • d1 – position on the detector along the slow dimention (i.e. y)
    • +
  • d2 – position on the detector along the fastest dimention (i.e. x)
    • +
  • corners – return positions on the corners (instead of center)
    • +
+
+
+
:return 3-tuple of nd-array, with dim0=along the beam,
+
dim1=along slowest dimension +dim2=along fastest dimension
+
+
+ +
+
+calc_transmission(t0, shape=None)
+

Defines the absorption correction for a phosphor screen or a scintillator +from t0, the normal transmission of the screen.

+
+
Icor = Iobs(1-t0)/(1-exp(ln(t0)/cos(incidence)))
+
1-exp(ln(t0)/cos(incidence)
+
let t = —————————–
+
1 - t0
+
+

See reference on: +J. Appl. Cryst. (2002). 35, 356–359 G. Wu et al. CCD phosphor

+ +++ + + + + + +
Parameters:
    +
  • t0 – value of the normal transmission (from 0 to 1)
    • +
  • shape – shape of the array
    • +
+
Returns:

actual

+
+
+ +
+
+calcfrom1d(tth, I, shape=None, mask=None, dim1_unit=2th_deg, correctSolidAngle=True, dummy=0.0, polarization_factor=None, dark=None, flat=None)
+

Computes a 2D image from a 1D integrated profile

+ +++ + + + + + +
Parameters:
    +
  • tth – 1D array with radial unit
    • +
  • I – scattering intensity
    • +
  • shape – shape of the image (if not defined by the detector)
    • +
  • dim1_unit – unit for the “tth” array
    • +
  • correctSolidAngle –
    • +
  • dummy – value for masked pixels
    • +
  • polarization_factor – set to true to use previously used value
    • +
  • dark – dark current correction
    • +
  • flat – flatfield corrction
    • +
+
Returns:

2D image reconstructed

+
+
+ +
+
+center_array(shape=None, unit='2th')
+

Generate a 2D array of the given shape with (i,j) (radial +angle ) for all elements.

+ +++ + + + + + +
Parameters:shape (2-tuple of integer) – expected shape
Returns:3d array with shape=(*shape,4,2) the two elements are: +* dim3[0]: radial angle 2th, q, r, ... +* dim3[1]: azimuthal angle chi
+
+ +
+
+chi(d1, d2, path='cython')
+

Calculate the chi (azimuthal angle) for the centre of a pixel +at coordinate d1,d2 which in the lab ref has coordinate:

+

X1 = p1*cos(rot2)*cos(rot3) + p2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) - L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)) +X2 = p1*cos(rot2)*sin(rot3) - L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) + p2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3)) +X3 = -(L*cos(rot1)*cos(rot2)) + p2*cos(rot2)*sin(rot1) - p1*sin(rot2) +hence tan(Chi) = X2 / X1

+ +++ + + + + + +
Parameters:
    +
  • d1 (float or array of them) – pixel coordinate along the 1st dimention (C convention)
    • +
  • d2 (float or array of them) – pixel coordinate along the 2nd dimention (C convention)
    • +
  • path – can be “tan” (i.e via numpy) or “cython”
    • +
+
Returns:

chi, the azimuthal angle in rad

+
+
+ +
+
+chiArray(shape=None)
+

Generate an array of azimuthal angle chi(i,j) for all elements in the detector.

+

Azimuthal angles are in radians

+

Nota: Refers to the pixel centers !

+ +++ + + + + + +
Parameters:shape – the shape of the chi array
Returns:the chi array as numpy.ndarray
+
+ +
+
+chi_corner(d1, d2)
+

Calculate the chi (azimuthal angle) for the corner of a pixel +at coordinate d1,d2 which in the lab ref has coordinate:

+

X1 = p1*cos(rot2)*cos(rot3) + p2*(cos(rot3)*sin(rot1)*sin(rot2) - cos(rot1)*sin(rot3)) - L*(cos(rot1)*cos(rot3)*sin(rot2) + sin(rot1)*sin(rot3)) +X2 = p1*cos(rot2)*sin(rot3) - L*(-(cos(rot3)*sin(rot1)) + cos(rot1)*sin(rot2)*sin(rot3)) + p2*(cos(rot1)*cos(rot3) + sin(rot1)*sin(rot2)*sin(rot3)) +X3 = -(L*cos(rot1)*cos(rot2)) + p2*cos(rot2)*sin(rot1) - p1*sin(rot2) +hence tan(Chi) = X2 / X1

+ +++ + + + + + +
Parameters:
    +
  • d1 (float or array of them) – pixel coordinate along the 1st dimention (C convention)
    • +
  • d2 (float or array of them) – pixel coordinate along the 2nd dimention (C convention)
    • +
+
Returns:

chi, the azimuthal angle in rad

+
+
+ +
+
+chia
+

chi array in cache

+
+ +
+
+cornerArray(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+cornerQArray(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+cornerRArray(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+cornerRd2Array(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+corner_array(shape=None, unit='2th', use_cython=True)
+

Generate a 3D array of the given shape with (i,j) (radial +angle 2th, azimuthal angle chi ) for all elements.

+ +++ + + + + + +
Parameters:shape (2-tuple of integer) – expected shape
Returns:3d array with shape=(*shape,4,2) the two elements are: +* dim3[0]: radial angle 2th, q, r, ... +* dim3[1]: azimuthal angle chi
+
+ +
+
+correct_SA_spline
+
+ +
+
+cosIncidance(d1, d2, path='cython')
+

Calculate the incidence angle (alpha) for current pixels (P). +The poni being the point of normal incidence, +it’s incidence angle is ${alpha} = 0$ hence $cos({alpha}) = 1$

+ +++ + + + + + +
Parameters:
    +
  • d1 – 1d or 2d set of points in pixel coord
    • +
  • d2 – 1d or 2d set of points in pixel coord
    • +
+
Returns:

cosine of the incidence angle

+
+
+ +
+
+del_chia()
+
+ +
+
+del_dssa()
+
+ +
+
+del_qa()
+
+ +
+
+del_ra()
+
+ +
+
+del_ttha()
+
+ +
+
+delta2Theta(shape=None)
+

Generate a 3D array of the given shape with (i,j) with the max +distance between the center and any corner in 2 theta

+ +++ + + + + + +
Parameters:shape – The shape of the detector array: 2-tuple of integer
Returns:2D-array containing the max delta angle between a pixel center and any corner in 2theta-angle (rad)
+
+ +
+
+deltaChi(shape=None)
+

Generate a 3D array of the given shape with (i,j) with the max +distance between the center and any corner in chi-angle (rad)

+ +++ + + + + + +
Parameters:shape – The shape of the detector array: 2-tuple of integer
Returns:2D-array containing the max delta angle between a pixel center and any corner in chi-angle (rad)
+
+ +
+
+deltaQ(shape=None)
+

Generate a 2D array of the given shape with (i,j) with the max +distance between the center and any corner in q_vector unit +(nm^-1)

+ +++ + + + + + +
Parameters:shape – The shape of the detector array: 2-tuple of integer
Returns:array 2D containing the max delta Q between a pixel center and any corner in q_vector unit (nm^-1)
+
+ +
+
+deltaR(shape=None)
+

Generate a 2D array of the given shape with (i,j) with the max +distance between the center and any corner in radius unit (mm)

+ +++ + + + + + +
Parameters:shape – The shape of the detector array: 2-tuple of integer
Returns:array 2D containing the max delta Q between a pixel center and any corner in q_vector unit (nm^-1)
+
+ +
+
+deltaRd2(shape=None)
+

Generate a 2D array of the given shape with (i,j) with the max +distance between the center and any corner in unit: reciprocal spacing squarred (1/nm^2)

+ +++ + + + + + +
Parameters:shape – The shape of the detector array: 2-tuple of integer
Returns:array 2D containing the max delta (d*)^2 between a pixel center and any corner in reciprocal spacing squarred (1/nm^2)
+
+ +
+
+delta_array(shape=None, unit='2th')
+

Generate a 2D array of the given shape with (i,j) (delta-radial +angle) for all elements.

+ +++ + + + + + +
Parameters:shape (2-tuple of integer) – expected shape
Returns:3d array with shape=(*shape,4,2) the two elements are: +* dim3[0]: radial angle 2th, q, r, ... +* dim3[1]: azimuthal angle chi
+
+ +
+
+diffSolidAngle(d1, d2)
+

Calculate the solid angle of the current pixels (P) versus the PONI (C)

+
+
Omega(P) A cos(a) SC^2 3 SC^3
+
+
dOmega = ——— = ——— x ——— = cos (a) = ——
+
Omega(C) SP^2 A cos(0) SP^3
+
+

cos(a) = SC/SP

+ +++ + + + + + +
Parameters:
    +
  • d1 – 1d or 2d set of points
    • +
  • d2 – 1d or 2d set of points (same size&shape as d1)
    • +
+
Returns:

solid angle correction array

+
+
+ +
+
+dist
+
+ +
+
+dssa
+

solid angle array in cache

+
+ +
+
+getFit2D()
+

Export geometry setup with the geometry of Fit2D

+ +++ + + + +
Returns:dict with parameters compatible with fit2D geometry
+
+ +
+
+getPyFAI()
+

Export geometry setup with the geometry of PyFAI

+ +++ + + + +
Returns:dict with the parameter-set of the PyFAI geometry
+
+ +
+
+getSPD()
+

get the SPD like parameter set: For geometry description see +Peter Boesecke J.Appl.Cryst.(2007).40, s423–s427

+

Basically the main difference with pyFAI is the order of the axis which are flipped

+ +++ + + + +
Returns:dictionnary with those parameters: +SampleDistance: distance from sample to detector at the PONI (orthogonal projection) +Center_1, pixel position of the PONI along fastest axis +Center_2: pixel position of the PONI along slowest axis +Rot_1: rotation around the fastest axis (x) +Rot_2: rotation around the slowest axis (y) +Rot_3: rotation around the axis ORTHOGONAL to the detector plan +PSize_1: pixel size in meter along the fastest dimention +PSize_2: pixel size in meter along the slowst dimention +splineFile: name of the file containing the spline +BSize_1: pixel binning factor along the fastest dimention +BSize_2: pixel binning factor along the slowst dimention +WaveLength: wavelength used in meter
+
+ +
+
+get_chia()
+
+ +
+
+get_correct_solid_angle_for_spline()
+
+ +
+
+get_dist()
+
+ +
+
+get_dssa()
+
+ +
+
+get_mask()
+
+ +
+
+get_maskfile()
+
+ +
+
+get_pixel1()
+
+ +
+
+get_pixel2()
+
+ +
+
+get_poni1()
+
+ +
+
+get_poni2()
+
+ +
+
+get_qa()
+
+ +
+
+get_ra()
+
+ +
+
+get_rot1()
+
+ +
+
+get_rot2()
+
+ +
+
+get_rot3()
+
+ +
+
+get_shape(shape=None)
+

Guess what is the best shape .... +:param shape: force this value (2-tuple of int) +:return: 2-tuple of int

+
+ +
+
+get_spline()
+
+ +
+
+get_splineFile()
+
+ +
+
+get_ttha()
+
+ +
+
+get_wavelength()
+
+ +
+
+load(filename)
+

Load the refined parameters from a file.

+ +++ + + + +
Parameters:filename (string) – name of the file to load
+
+ +
+
+mask
+
+ +
+
+maskfile
+
+ +
+
+oversampleArray(myarray)
+
+ +
+
+pixel1
+
+ +
+
+pixel2
+
+ +
+
+polarization(shape=None, factor=None, axis_offset=0)
+

Calculate the polarization correction accoding to the +polarization factor:

+
    +
  • If the polarization factor is None, the correction is not applied (returns 1)
    • +
  • If the polarization factor is 0 (circular polarization), the correction correspond to (1+(cos2θ)^2)/2
    • +
  • If the polarization factor is 1 (linear horizontal polarization), there is no correction in the vertical plane and a node at 2th=90, chi=0
    • +
  • If the polarization factor is -1 (linear vertical polarization), there is no correction in the horizontal plane and a node at 2th=90, chi=90
    • +
  • If the polarization is elliptical, the polarization factor varies between -1 and +1.
    • +
+

The axis_offset parameter allows correction for the misalignement of the polarization plane (or ellipse main axis) and the the detector’s X axis.

+ +++ + + + + + +
Parameters:
    +
  • factor – (Ih-Iv)/(Ih+Iv): varies between 0 (no polarization) and 1 (where division by 0 could occure at 2th=90, chi=0)
    • +
  • axis_offset – Angle between the polarization main axis and detector X direction (in radians !!!)
    • +
+
Returns:

2D array with polarization correction array (intensity/polarisation)

+
+
+ +
+
+poni1
+
+ +
+
+poni2
+
+ +
+
+positionArray(shape=None, corners=False, dtype=<type 'numpy.float64'>)
+

Generate an array for the pixel position given the shape of the detector.

+

if corners is False, the coordinates of the center of the pixel +is returned in an array of shape: (shape[0], shape[1], 3) +where the 3 coordinates are: +* z: along incident beam, +* y: to the top/sky, +* x: towards the center of the ring

+

If is True, the corner of each pixels are then returned. +the output shape is then (shape[0], shape[1], 4, 3)

+ +++ + + + + + +
Parameters:
    +
  • shape – shape of the array expected
    • +
  • corners – set to true to receive a (...,4,3) array of corner positions
    • +
  • dtype – output format requested
    • +
+
Returns:

3D coodinates as nd-array of size (...,3) or (...,3) (default)

+
+

Nota: this value is not cached and actually generated on demand (costly)

+
+ +
+
+qArray(shape=None)
+

Generate an array of the given shape with q(i,j) for all +elements.

+
+ +
+
+qCornerFunct(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+qFunction(d1, d2, param=None, path='cython')
+

Calculates the q value for the center of a given pixel (or set +of pixels) in nm-1

+

q = 4pi/lambda sin( 2theta / 2 )

+ +++ + + + + + + + +
Parameters:
    +
  • d1 (scalar or array of scalar) – position(s) in pixel in first dimension (c order)
    • +
  • d2 (scalar or array of scalar) – position(s) in pixel in second dimension (c order)
    • +
+
Returns:

q in in nm^(-1)

+
Return type:

float or array of floats.

+
+
+ +
+
+qa
+

Q array in cache

+
+ +
+
+rArray(shape=None)
+

Generate an array of the given shape with r(i,j) for all elements; +The radius r being in meters.

+ +++ + + + + + +
Parameters:shape – expected shape of the detector
Returns:2d array of the given shape with radius in m from beam center on detector.
+
+ +
+
+rCornerFunct(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+rFunction(d1, d2, param=None, path='cython')
+

Calculates the radius value for the center of a given pixel +(or set of pixels) in m

+
+
r = distance to the incident beam
+ +++ + + + + + + + +
Parameters:
    +
  • d1 (scalar or array of scalar) – position(s) in pixel in first dimension (c order)
    • +
  • d2 (scalar or array of scalar) – position(s) in pixel in second dimension (c order)
    • +
+
Returns:

r in in m

+
Return type:

float or array of floats.

+
+
+ +
+
+ra
+

R array in cache

+
+ +
+
+rd2Array(shape=None)
+

Generate an array of the given shape with (d*(i,j))^2 for all pixels.

+

d*^2 is the reciprocal spacing squared in inverse nm squared

+ +++ + + + +
Parameters:shape – expected shape of the detector
+

:return:2d array of the given shape with reciprocal spacing squared

+
+ +
+
+read(filename)
+

Load the refined parameters from a file.

+ +++ + + + +
Parameters:filename (string) – name of the file to load
+
+ +
+
+reset()
+

reset most arrays that are cached: used when a parameter +changes.

+
+ +
+
+rot1
+
+ +
+
+rot2
+
+ +
+
+rot3
+
+ +
+
+save(filename)
+

Save the geometry parameters.

+ +++ + + + +
Parameters:filename (string) – name of the file where to save the parameters
+
+ +
+
+setChiDiscAtPi()
+

Set the position of the discontinuity of the chi axis between +-pi and +pi. This is the default behavour

+
+ +
+
+setChiDiscAtZero()
+

Set the position of the discontinuity of the chi axis between +0 and 2pi. By default it is between pi and -pi

+
+ +
+
+setFit2D(directDist, centerX, centerY, tilt=0.0, tiltPlanRotation=0.0, pixelX=None, pixelY=None, splineFile=None)
+

Set the Fit2D-like parameter set: For geometry description see +HPR 1996 (14) pp-240

+

Warning: Fit2D flips automatically images depending on their file-format. +By reverse engineering we noticed this behavour for Tiff and Mar345 images (at least). +To obtaine correct result you will have to flip images using numpy.flipud.

+ +++ + + + +
Parameters:
    +
  • direct – direct distance from sample to detector along the incident beam (in millimeter as in fit2d)
    • +
  • tilt – tilt in degrees
    • +
  • tiltPlanRotation – Rotation (in degrees) of the tilt plan arround the Z-detector axis +* 0deg -> Y does not move, +X goes to Z<0 +* 90deg -> X does not move, +Y goes to Z<0 +* 180deg -> Y does not move, +X goes to Z>0 +* 270deg -> X does not move, +Y goes to Z>0
    • +
  • pixelX,pixelY – as in fit2d they ar given in micron, not in meter
    • +
  • centerY (centerX,) – pixel position of the beam center
    • +
  • splineFile – name of the file containing the spline
    • +
+
+
+ +
+
+setOversampling(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+setPyFAI(**kwargs)
+

set the geometry from a pyFAI-like dict

+
+ +
+
+setSPD(SampleDistance, Center_1, Center_2, Rot_1=0, Rot_2=0, Rot_3=0, PSize_1=None, PSize_2=None, splineFile=None, BSize_1=1, BSize_2=1, WaveLength=None)
+

Set the SPD like parameter set: For geometry description see +Peter Boesecke J.Appl.Cryst.(2007).40, s423–s427

+

Basically the main difference with pyFAI is the order of the axis which are flipped

+ +++ + + + +
Parameters:SampleDistance – distance from sample to detector at the PONI (orthogonal projection)
+

:param Center_1, pixel position of the PONI along fastest axis +:param Center_2: pixel position of the PONI along slowest axis +:param Rot_1: rotation around the fastest axis (x) +:param Rot_2: rotation around the slowest axis (y) +:param Rot_3: rotation around the axis ORTHOGONAL to the detector plan +:param PSize_1: pixel size in meter along the fastest dimention +:param PSize_2: pixel size in meter along the slowst dimention +:param splineFile: name of the file containing the spline +:param BSize_1: pixel binning factor along the fastest dimention +:param BSize_2: pixel binning factor along the slowst dimention +:param WaveLength: wavelength used

+
+ +
+
+set_chia(_)
+
+ +
+
+set_correct_solid_angle_for_spline(value)
+
+ +
+
+set_dist(value)
+
+ +
+
+set_dssa(_)
+
+ +
+
+set_mask(mask)
+
+ +
+
+set_maskfile(maskfile)
+
+ +
+
+set_pixel1(pixel1)
+
+ +
+
+set_pixel2(pixel2)
+
+ +
+
+set_poni1(value)
+
+ +
+
+set_poni2(value)
+
+ +
+
+set_qa(_)
+
+ +
+
+set_ra(_)
+
+ +
+
+set_rot1(value)
+
+ +
+
+set_rot2(value)
+
+ +
+
+set_rot3(value)
+
+ +
+
+set_spline(spline)
+
+ +
+
+set_splineFile(splineFile)
+
+ +
+
+set_ttha(_)
+
+ +
+
+set_wavelength(value)
+
+ +
+
+classmethod sload(filename)
+

A static method combining the constructor and the loader from +a file

+ +++ + + + + + +
Parameters:filename (string) – name of the file to load
Returns:instance of Gerometry of AzimuthalIntegrator set-up with the parameter from the file.
+
+ +
+
+solidAngleArray(shape=None, order=3, absolute=False)
+

Generate an array for the solid angle correction +given the shape of the detector.

+

solid_angle = cos(incidence)^3

+ +++ + + + +
Parameters:
    +
  • shape – shape of the array expected
    • +
  • order – should be 3, power of the formula just obove
    • +
  • absolute – the absolute solid angle is calculated as:
    • +
+
+

SA = pix1*pix2/dist^2 * cos(incidence)^3

+
+ +
+
+spline
+
+ +
+
+splineFile
+
+ +
+
+tth(d1, d2, param=None, path='cython')
+

Calculates the 2theta value for the center of a given pixel +(or set of pixels)

+ +++ + + + + + + + +
Parameters:
    +
  • d1 (scalar or array of scalar) – position(s) in pixel in first dimension (c order)
    • +
  • d2 (scalar or array of scalar) – position(s) in pixel in second dimension (c order)
    • +
  • path – can be “cos”, “tan” or “cython”
    • +
+
Returns:

2theta in radians

+
Return type:

float or array of floats.

+
+
+ +
+
+tth_corner(*arg, **kw)
+

decorator that deprecates the use of a function

+
+ +
+
+ttha
+

2theta array in cache

+
+ +
+
+twoThetaArray(shape=None)
+

Generate an array of two-theta(i,j) in radians for each pixel in detector

+

the 2theta array values are in radians

+ +++ + + + + + +
Parameters:shape – shape of the detector
Returns:array of 2theta position in radians
+
+ +
+
+wavelength
+
+ +
+
+write(filename)
+

Save the geometry parameters.

+ +++ + + + +
Parameters:filename (string) – name of the file where to save the parameters
+
+ +
+ +
+
+

geometryRefinement Module

+
+
+class pyFAI.geometryRefinement.GeometryRefinement(data, dist=1, poni1=None, poni2=None, rot1=0, rot2=0, rot3=0, pixel1=None, pixel2=None, splineFile=None, detector=None, wavelength=None, calibrant=None)
+

Bases: pyFAI.azimuthalIntegrator.AzimuthalIntegrator

+
+
+anneal(maxiter=1000000)
+
+ +
+
+calc_2th(rings, wavelength=None)
+
+++ + + + +
Parameters:
    +
  • rings – indices of the rings. starts at 0 and self.dSpacing should be long enough !!!
    • +
  • wavelength – wavelength in meter
    • +
+
+
+ +
+
+chi2(param=None)
+
+ +
+
+chi2_wavelength(param=None)
+
+ +
+
+confidence(with_rot=True)
+

Confidence interval obtained from the second derivative of the error function +next to its minimum value.

+

Note the confidence interval increases with the number of points which is “surprizing”

+ +++ + + + + + +
Parameters:with_rot – if true include rot1 & rot2 in the parameter set.
Returns:std_dev, confidence
+
+ +
+
+curve_fit(with_rot=True)
+

Refine the geometry and provide confidence interval +Use curve_fit from scipy.optimize to not only refine the geometry (unconstrained fit)

+ +++ + + + + + +
Parameters:with_rot – include rotation intro error measurment
Returns:std_dev, confidence
+
+ +
+
+dist_max
+
+ +
+
+dist_min
+
+ +
+
+get_dist_max()
+
+ +
+
+get_dist_min()
+
+ +
+
+get_poni1_max()
+
+ +
+
+get_poni1_min()
+
+ +
+
+get_poni2_max()
+
+ +
+
+get_poni2_min()
+
+ +
+
+get_rot1_max()
+
+ +
+
+get_rot1_min()
+
+ +
+
+get_rot2_max()
+
+ +
+
+get_rot2_min()
+
+ +
+
+get_rot3_max()
+
+ +
+
+get_rot3_min()
+
+ +
+
+get_wavelength_max()
+
+ +
+
+get_wavelength_min()
+
+ +
+
+guess_poni()
+

Poni can be guessed by the centroid of the ring with lowest 2Theta

+
+ +
+
+poni1_max
+
+ +
+
+poni1_min
+
+ +
+
+poni2_max
+
+ +
+
+poni2_min
+
+ +
+
+refine1()
+
+ +
+
+refine2(maxiter=1000000, fix=['wavelength'])
+
+ +
+
+refine2_wavelength(maxiter=1000000, fix=['wavelength'])
+
+ +
+
+residu1(param, d1, d2, rings)
+
+ +
+
+residu1_wavelength(param, d1, d2, rings)
+
+ +
+
+residu2(param, d1, d2, rings)
+
+ +
+
+residu2_wavelength(param, d1, d2, rings)
+
+ +
+
+residu2_wavelength_weighted(param, d1, d2, rings, weight)
+
+ +
+
+residu2_weighted(param, d1, d2, rings, weight)
+
+ +
+
+roca()
+

run roca to optimise the parameter set

+
+ +
+
+rot1_max
+
+ +
+
+rot1_min
+
+ +
+
+rot2_max
+
+ +
+
+rot2_min
+
+ +
+
+rot3_max
+
+ +
+
+rot3_min
+
+ +
+
+set_dist_max(value)
+
+ +
+
+set_dist_min(value)
+
+ +
+
+set_poni1_max(value)
+
+ +
+
+set_poni1_min(value)
+
+ +
+
+set_poni2_max(value)
+
+ +
+
+set_poni2_min(value)
+
+ +
+
+set_rot1_max(value)
+
+ +
+
+set_rot1_min(value)
+
+ +
+
+set_rot2_max(value)
+
+ +
+
+set_rot2_min(value)
+
+ +
+
+set_rot3_max(value)
+
+ +
+
+set_rot3_min(value)
+
+ +
+
+set_tolerance(value=10)
+

Set the tolerance for a refinement of the geometry; in percent of the original value

+ +++ + + + +
Parameters:value – Tolerance as a percentage
+
+ +
+
+set_wavelength_max(value)
+
+ +
+
+set_wavelength_min(value)
+
+ +
+
+simplex(maxiter=1000000)
+
+ +
+
+wavelength_max
+
+ +
+
+wavelength_min
+
+ +
+ +
+
+

detectors Module

+

Description of all detectors with a factory to instantiate them

+
+
+class pyFAI.detectors.ADSC_Q210(pixel1=5.1e-05, pixel2=5.1e-05)
+

Bases: pyFAI.detectors.Detector

+

ADSC Quantum 210r detector, 2x2 chips

+

Informations from +http://www.adsc-xray.com/products/ccd-detectors/q210r-ccd-detector/

+

Question: how are the gaps handled ?

+
+
+MAX_SHAPE = (4096, 4096)
+
+ +
+
+aliases = ['Quantum 210']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.ADSC_Q270(pixel1=6.48e-05, pixel2=6.48e-05)
+

Bases: pyFAI.detectors.Detector

+

ADSC Quantum 270r detector, 2x2 chips

+

Informations from +http://www.adsc-xray.com/products/ccd-detectors/q270-ccd-detector/

+

Question: how are the gaps handled ?

+
+
+MAX_SHAPE = (4168, 4168)
+
+ +
+
+aliases = ['Quantum 270']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.ADSC_Q315(pixel1=5.1e-05, pixel2=5.1e-05)
+

Bases: pyFAI.detectors.Detector

+

ADSC Quantum 315r detector, 3x3 chips

+

Informations from +http://www.adsc-xray.com/products/ccd-detectors/q315r-ccd-detector/

+

Question: how are the gaps handled ?

+
+
+MAX_SHAPE = (6144, 6144)
+
+ +
+
+aliases = ['Quantum 315']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.ADSC_Q4(pixel1=8.2e-05, pixel2=8.2e-05)
+

Bases: pyFAI.detectors.Detector

+

ADSC Quantum 4r detector, 2x2 chips

+

Informations from +http://proteincrystallography.org/detectors/adsc.php

+

Question: how are the gaps handled ?

+
+
+MAX_SHAPE = (2304, 2304)
+
+ +
+
+aliases = ['Quantum 4']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Aarhus(pixel1=2.5e-05, pixel2=2.5e-05, radius=0.3)
+

Bases: pyFAI.detectors.Detector

+

Cylindrical detector made of a bent imaging-plate. +Developped at the Danish university of Aarhus +r = 1.2m or 0.3m

+

The image has to be laid-out horizontally

+

Nota: the detector is bending towards the sample, hence reducing the sample-detector distance. +This is why z<0 (or p3<0)

+

TODO: update cython code for 3d detectors +use expand2d instead of outer product with ones

+
+
+IS_FLAT = False
+
+ +
+
+MAX_SHAPE = (1000, 16000)
+
+ +
+
+calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)
+

Calculate the position of each pixel center in cartesian coordinate +and in meter of a couple of coordinates. +The half pixel offset is taken into account here !!! +Adapted to Nexus detector definition

+ +++ + + + + + + + +
Parameters:
    +
  • d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension)
    • +
  • d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
    • +
  • center – retrieve the coordinate of the center of the pixel
    • +
  • use_cython – set to False to test Python implementeation
    • +
+
Returns:

position in meter of the center of each pixels.

+
Return type:

ndarray

+
+

d1 and d2 must have the same shape, returned array will have +the same shape.

+
+ +
+
+force_pixel = True
+
+ +
+
+get_pixel_corners(use_cython=True)
+

Calculate the position of the corner of the pixels

+

This should be overwritten by class representing non-contiguous detector (Xpad, ...)

+ +++ + + + +
Returns:4D array containing: +pixel index (slow dimension) +pixel index (fast dimension) +corner index (A, B, C or D), triangles or hexagons can be handled the same way +vertex position (z,y,x)
+
+ +
+ +
+
+class pyFAI.detectors.Apex2(pixel1=0.00012, pixel2=0.00012)
+

Bases: pyFAI.detectors.Detector

+

BrukerApex2 detector

+

Actually a derivative from the Fairchild detector with higher binning

+
+
+DEFAULT_PIXEL1 = 6e-05
+
+ +
+
+DEFAULT_PIXEL2 = 6e-05
+
+ +
+
+MAX_SHAPE = (1024, 1024)
+
+ +
+
+aliases = ['ApexII', 'Bruker']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Basler(pixel=3.75e-06)
+

Bases: pyFAI.detectors.Detector

+

Basler camera are simple CCD camara over GigaE

+
+
+MAX_SHAPE = (966, 1296)
+
+ +
+
+aliases = ['aca1300']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Detector(pixel1=None, pixel2=None, splineFile=None, max_shape=None)
+

Bases: object

+

Generic class representing a 2D detector

+
+
+API_VERSION = '1.0'
+
+ +
+
+IS_CONTIGUOUS = True
+
+ +
+
+IS_FLAT = True
+
+ +
+
+aliases = []
+
+ +
+
+binning
+
+ +
+
+calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)
+

Calculate the position of each pixel center in cartesian coordinate +and in meter of a couple of coordinates. +The half pixel offset is taken into account here !!! +Adapted to Nexus detector definition

+ +++ + + + + + + + +
Parameters:
    +
  • d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension)
    • +
  • d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
    • +
  • center – retrieve the coordinate of the center of the pixel, unless gives one corner
    • +
  • use_cython – set to False to test Python implementation
    • +
+
Returns:

position in meter of the center of each pixels.

+
Return type:

3xndarray, the later being None if IS_FLAT

+
+

d1 and d2 must have the same shape, returned array will have +the same shape.

+

pos_z is None for flat detectors

+
+ +
+
+calc_mask()
+

Method calculating the mask for a given detector

+

Detectors with gaps should overwrite this method with +something actually calculating the mask!

+ +++ + + + + + +
Returns:the mask with valid pixel to 0
Return type:numpy ndarray of int8 or None
+
+ +
+
+classmethod factory(name, config=None)
+

A kind of factory...

+ +++ + + + + + + + +
Parameters:
    +
  • name (str) – name of a detector
    • +
  • config (dict or JSON representation of it.) – configuration of the detector
    • +
+
Returns:

an instance of the right detector, set-up if possible

+
Return type:

pyFAI.detectors.Detector

+
+
+ +
+
+force_pixel = False
+
+ +
+
+getFit2D()
+

Helper method to serialize the description of a detector using the Fit2d units

+ +++ + + + + + +
Returns:representation of the detector easy to serialize
Return type:dict
+
+ +
+
+getPyFAI()
+

Helper method to serialize the description of a detector using the pyFAI way +with everything in S.I units.

+ +++ + + + + + +
Returns:representation of the detector easy to serialize
Return type:dict
+
+ +
+
+get_binning()
+
+ +
+
+get_mask()
+
+ +
+
+get_maskfile()
+
+ +
+
+get_name()
+

Get a meaningful name for detector

+
+ +
+
+get_pixel1()
+
+ +
+
+get_pixel2()
+
+ +
+
+get_pixel_corners()
+

Calculate the position of the corner of the pixels

+

This should be overwritten by class representing non-contiguous detector (Xpad, ...)

+

Precision float32 is ok: precision of 1µm for a detector size of 1m

+ +++ + + + +
Returns:4D array containing: +pixel index (slow dimension) +pixel index (fast dimension) +corner index (A, B, C or D), triangles or hexagons can be handled the same way +vertex position (z,y,x)
+
+ +
+
+get_splineFile()
+
+ +
+
+guess_binning(data)
+

Guess the binning/mode depending on the image shape +:param data: 2-tuple with the shape of the image or the image with a .shape attribute.

+
+ +
+
+mask
+
+ +
+
+maskfile
+
+ +
+
+name
+

Get a meaningful name for detector

+
+ +
+
+pixel1
+
+ +
+
+pixel2
+
+ +
+
+registry = {'imxpads10': <class 'pyFAI.detectors.ImXPadS10'>, 'rayonix_mx225hs': <class 'pyFAI.detectors.RayonixMx225hs'>, 'd5': <class 'pyFAI.detectors.Xpad_flat'>, 'thaleselectronics': <class 'pyFAI.detectors.Pixium'>, 'imxpad_s10': <class 'pyFAI.detectors.ImXPadS10'>, 'rayonix_mx340hs': <class 'pyFAI.detectors.RayonixMx340hs'>, 'condor': <class 'pyFAI.detectors.Fairchild'>, 'pilatuscdte2m': <class 'pyFAI.detectors.PilatusCdTe2M'>, 'oxd_titan': <class 'pyFAI.detectors.Titan'>, 'dexela_2923': <class 'pyFAI.detectors.Dexela2923'>, 'pilatus6m': <class 'pyFAI.detectors.Pilatus6M'>, 'titan_2k_x_2k': <class 'pyFAI.detectors.Titan'>, 'hf_262k': <class 'pyFAI.detectors.HF_262k'>, 'rayonixmx225': <class 'pyFAI.detectors.RayonixMx225'>, 'rayonixsx200': <class 'pyFAI.detectors.RayonixSx200'>, 'eiger4m': <class 'pyFAI.detectors.Eiger4M'>, 'rayonix_lx170': <class 'pyFAI.detectors.RayonixLx170'>, 'pilatuscdte300kw': <class 'pyFAI.detectors.PilatusCdTe300kw'>, 'pilatus300k': <class 'pyFAI.detectors.Pilatus300k'>, 'quantum_270': <class 'pyFAI.detectors.ADSC_Q270'>, 'rayonixmx300': <class 'pyFAI.detectors.RayonixMx300'>, 'pilatuscdte1m': <class 'pyFAI.detectors.PilatusCdTe1M'>, 'perkin': <class 'pyFAI.detectors.Perkin'>, 'rayonixmx340hs': <class 'pyFAI.detectors.RayonixMx340hs'>, 'rayonix_mx325': <class 'pyFAI.detectors.RayonixMx325'>, 'adsc_q270': <class 'pyFAI.detectors.ADSC_Q270'>, 'oxdtitan': <class 'pyFAI.detectors.Titan'>, 'dexela2923': <class 'pyFAI.detectors.Dexela2923'>, 'xpads540flat': <class 'pyFAI.detectors.Xpad_flat'>, 'titan': <class 'pyFAI.detectors.Titan'>, 'aarhus': <class 'pyFAI.detectors.Aarhus'>, 'rayonix_mx425hs': <class 'pyFAI.detectors.RayonixMx425hs'>, 'hf_4m': <class 'pyFAI.detectors.HF_4M'>, 'pilatuscdte300k': <class 'pyFAI.detectors.PilatusCdTe300k'>, 'thales_electronics': <class 'pyFAI.detectors.Pixium'>, 'rayonixmx300hs': <class 'pyFAI.detectors.RayonixMx300hs'>, 'pixium4700detector': <class 'pyFAI.detectors.Pixium'>, 'agilenttitan': <class 'pyFAI.detectors.Titan'>, 'rayonixsx165': <class 'pyFAI.detectors.RayonixSx165'>, 'pilatus100k': <class 'pyFAI.detectors.Pilatus100k'>, 'perkin_elmer': <class 'pyFAI.detectors.Perkin'>, 'aca1300': <class 'pyFAI.detectors.Basler'>, 'quantum315': <class 'pyFAI.detectors.ADSC_Q315'>, 'rayonixsx85hs': <class 'pyFAI.detectors.RayonixSx85hs'>, 'imxpads140': <class 'pyFAI.detectors.ImXPadS140'>, 'rayonix_mx170': <class 'pyFAI.detectors.RayonixMx170'>, 'pilatus300kw': <class 'pyFAI.detectors.Pilatus300kw'>, 'hf-130k': <class 'pyFAI.detectors.HF_130K'>, 'rayonixsx30hs': <class 'pyFAI.detectors.RayonixSx30hs'>, 'mar345': <class 'pyFAI.detectors.Mar345'>, 'perkindetector': <class 'pyFAI.detectors.Perkin'>, 'rayonix_lx255hs': <class 'pyFAI.detectors.RayonixLx255'>, 'rayonix_mx225': <class 'pyFAI.detectors.RayonixMx225'>, 'eiger1m': <class 'pyFAI.detectors.Eiger1M'>, 'mar_345': <class 'pyFAI.detectors.Mar345'>, 'pilatus2m': <class 'pyFAI.detectors.Pilatus2M'>, 'hf-1m': <class 'pyFAI.detectors.HF_1M'>, 'fairchildcondor486:90': <class 'pyFAI.detectors.Fairchild'>, 'rayonixmx170': <class 'pyFAI.detectors.RayonixMx170'>, 'mar165': <class 'pyFAI.detectors.RayonixSx165'>, 'hf_2m': <class 'pyFAI.detectors.HF_2M'>, 'titan2kx2k': <class 'pyFAI.detectors.Titan'>, 'rayonix_mx300hs': <class 'pyFAI.detectors.RayonixMx300hs'>, 'hf-9.4m': <class 'pyFAI.detectors.HF_9M'>, 'fairchild_condor_486:90': <class 'pyFAI.detectors.Fairchild'>, 'hf-262k': <class 'pyFAI.detectors.HF_262k'>, 'xpad_flat': <class 'pyFAI.detectors.Xpad_flat'>, 'quantum_315': <class 'pyFAI.detectors.ADSC_Q315'>, 'pi': <class 'pyFAI.detectors.RaspberryPi'>, 'pilatus1m': <class 'pyFAI.detectors.Pilatus1M'>, 'xpad_s540_flat': <class 'pyFAI.detectors.Xpad_flat'>, 'pixium_4700_detector': <class 'pyFAI.detectors.Pixium'>, 'basler': <class 'pyFAI.detectors.Basler'>, 'rayonix_sx200': <class 'pyFAI.detectors.RayonixSx200'>, 'quantum270': <class 'pyFAI.detectors.ADSC_Q270'>, 'perkin_detector': <class 'pyFAI.detectors.Perkin'>, 'mar3450': <class 'pyFAI.detectors.Mar345'>, 'pixium': <class 'pyFAI.detectors.Pixium'>, 'fairchild': <class 'pyFAI.detectors.Fairchild'>, 'raspberry': <class 'pyFAI.detectors.RaspberryPi'>, 'rayonixmx225hs': <class 'pyFAI.detectors.RayonixMx225hs'>, 'apexii': <class 'pyFAI.detectors.Apex2'>, 'quantum_210': <class 'pyFAI.detectors.ADSC_Q210'>, 'bruker': <class 'pyFAI.detectors.Apex2'>, 'raspberrypi': <class 'pyFAI.detectors.RaspberryPi'>, 'rayonixmx425hs': <class 'pyFAI.detectors.RayonixMx425hs'>, 'eiger16m': <class 'pyFAI.detectors.Eiger16M'>, 'imxpads70': <class 'pyFAI.detectors.ImXPadS70'>, 'rayonix_sx30hs': <class 'pyFAI.detectors.RayonixSx30hs'>, 'hf_130k': <class 'pyFAI.detectors.HF_130K'>, 'agilent_titan': <class 'pyFAI.detectors.Titan'>, 'rayonixlx255hs': <class 'pyFAI.detectors.RayonixLx255'>, 'rayonix133': <class 'pyFAI.detectors.Rayonix133'>, 'rayonix_lx255': <class 'pyFAI.detectors.RayonixLx255'>, 'perkinelmer': <class 'pyFAI.detectors.Perkin'>, 'rayonix': <class 'pyFAI.detectors.Rayonix'>, 'rayonix_sx165': <class 'pyFAI.detectors.RayonixSx165'>, 'adsc_q4': <class 'pyFAI.detectors.ADSC_Q4'>, 'rayonixlx255': <class 'pyFAI.detectors.RayonixLx255'>, 'apex2': <class 'pyFAI.detectors.Apex2'>, 'hf-2.4m': <class 'pyFAI.detectors.HF_2M'>, 'detector': <class 'pyFAI.detectors.Detector'>, 'imxpad_s140': <class 'pyFAI.detectors.ImXPadS140'>, 'adsc_q210': <class 'pyFAI.detectors.ADSC_Q210'>, 'quantum4': <class 'pyFAI.detectors.ADSC_Q4'>, 'rayonixlx170': <class 'pyFAI.detectors.RayonixLx170'>, 'hf_9m': <class 'pyFAI.detectors.HF_9M'>, 'quantum210': <class 'pyFAI.detectors.ADSC_Q210'>, 'hf_1m': <class 'pyFAI.detectors.HF_1M'>, 'mar133': <class 'pyFAI.detectors.Rayonix133'>, 'pilatus200k': <class 'pyFAI.detectors.Pilatus200k'>, 'imxpad_s70': <class 'pyFAI.detectors.ImXPadS70'>, 'adsc_q315': <class 'pyFAI.detectors.ADSC_Q315'>, 'rayonixmx325': <class 'pyFAI.detectors.RayonixMx325'>, 'rayonix_sx85hs': <class 'pyFAI.detectors.RayonixSx85hs'>, 'eiger9m': <class 'pyFAI.detectors.Eiger9M'>, 'hf-4m': <class 'pyFAI.detectors.HF_4M'>, 'rayonix_mx300': <class 'pyFAI.detectors.RayonixMx300'>, 'quantum_4': <class 'pyFAI.detectors.ADSC_Q4'>}
+
+ +
+
+save(filename)
+

Saves the detector description into a NeXus file, adapted from: +http://download.nexusformat.org/sphinx/classes/base_classes/NXdetector.html +Main differences:

+
+
    +
  • differentiate pixel center from pixel corner offsets
    • +
  • store all offsets are ndarray according to slow/fast dimension (not x, y)
    • +
+
+ +++ + + + +
Parameters:filename – name of the file on the disc
+
+ +
+
+setFit2D(**kwarg)
+

Twin method of getFit2D: setup a detector instance according to a description

+ +++ + + + +
Parameters:kwarg – dictionary containing pixel1, pixel2 and splineFile
+
+ +
+
+setPyFAI(**kwarg)
+

Twin method of getPyFAI: setup a detector instance according to a description

+ +++ + + + +
Parameters:kwarg – dictionary containing detector, pixel1, pixel2 and splineFile
+
+ +
+
+set_binning(bin_size=(1, 1))
+

Set the “binning” of the detector,

+ +++ + + + +
Parameters:bin_size ((int, int)) – binning as integer or tuple of integers.
+
+ +
+
+set_config(config)
+

Sets the configuration of the detector. This implies: +- Orientation: integers +- Binning +- ROI

+

The configuration is either a python dictionary or a JSON string or a file containing this JSON configuration

+

keys in that dictionary are : +“orientation”: integers from 0 to 7 +“binning”: integer or 2-tuple of integers. If only one integer is provided, +“offset”: coordinate (in pixels) of the start of the detector

+
+ +
+
+set_dx(dx=None)
+

set the pixel-wise displacement along X (dim2):

+
+ +
+
+set_dy(dy=None)
+

set the pixel-wise displacement along Y (dim1):

+
+ +
+
+set_mask(mask)
+
+ +
+
+set_maskfile(maskfile)
+
+ +
+
+set_pixel1(value)
+
+ +
+
+set_pixel2(value)
+
+ +
+
+set_splineFile(splineFile)
+
+ +
+
+splineFile
+
+ +
+
+uniform_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.DetectorMeta(name, bases, dct)
+

Bases: type

+

Metaclass used to register all detector classes inheriting from Detector

+
+ +
+
+class pyFAI.detectors.Dexela2923(pixel1=7.5e-05, pixel2=7.5e-05)
+

Bases: pyFAI.detectors.Detector

+

Dexela CMOS family detector

+
+
+MAX_SHAPE = (3888, 3072)
+
+ +
+
+aliases = ['Dexela 2923']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Eiger(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)
+

Bases: pyFAI.detectors.Detector

+

Eiger detector: generic description containing mask algorithm

+
+
+MODULE_GAP = (37, 10)
+
+ +
+
+MODULE_SIZE = (514, 1030)
+
+ +
+
+calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)
+

Calculate the position of each pixel center in cartesian coordinate +and in meter of a couple of coordinates. +The half pixel offset is taken into account here !!!

+ +++ + + + + + + + +
Parameters:
    +
  • d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension)
    • +
  • d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
    • +
+
Returns:

p1, p2 position in meter of the center of each pixels.

+
Return type:

2-tuple of numpy.ndarray

+
+

d1 and d2 must have the same shape, returned array will have +the same shape.

+
+ +
+
+calc_mask()
+

Returns a generic mask for Pilatus detectors...

+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Eiger16M(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)
+

Bases: pyFAI.detectors.Eiger

+

Eiger 16M detector

+
+
+MAX_SHAPE = (4371, 4150)
+
+ +
+ +
+
+class pyFAI.detectors.Eiger1M(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)
+

Bases: pyFAI.detectors.Eiger

+

Eiger 1M detector

+
+
+MAX_SHAPE = (1065, 1030)
+
+ +
+ +
+
+class pyFAI.detectors.Eiger4M(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)
+

Bases: pyFAI.detectors.Eiger

+

Eiger 4M detector

+
+
+MAX_SHAPE = (2167, 2070)
+
+ +
+ +
+
+class pyFAI.detectors.Eiger9M(pixel1=7.5e-05, pixel2=7.5e-05, max_shape=None, module_size=None)
+

Bases: pyFAI.detectors.Eiger

+

Eiger 9M detector

+
+
+MAX_SHAPE = (3269, 3110)
+
+ +
+ +
+
+class pyFAI.detectors.FReLoN(splineFile=None)
+

Bases: pyFAI.detectors.Detector

+

FReLoN detector: +The spline is mandatory to correct for geometric distortion of the taper

+

TODO: create automatically a mask that removes pixels out of the “valid reagion”

+
+
+calc_mask()
+

Returns a generic mask for Frelon detectors... +All pixels which (center) turns to be out of the valid region are by default discarded

+
+ +
+ +
+
+class pyFAI.detectors.Fairchild(pixel1=1.5e-05, pixel2=1.5e-05)
+

Bases: pyFAI.detectors.Detector

+

Fairchild Condor 486:90 detector

+
+
+MAX_SHAPE = (4096, 4096)
+
+ +
+
+aliases = ['Fairchild', 'Condor', 'Fairchild Condor 486:90']
+
+ +
+
+force_pixel = True
+
+ +
+
+uniform_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.HF_130K(pixel1=0.00015, pixel2=0.00015)
+

Bases: pyFAI.detectors.Detector

+

ADSC HF-130K 1 module

+

Informations from +http://www.adsc-xray.com/products/pixel-array-detectors/hf-130k/

+
+
+MAX_SHAPE = (256, 512)
+
+ +
+
+aliases = ['HF-130k']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.HF_1M(pixel1=0.00015, pixel2=0.00015)
+

Bases: pyFAI.detectors.Detector

+

ADSC HF-1M 2x4 modules

+

Informations from +http://www.adsc-xray.com/products/pixel-array-detectors/hf-1m/

+

Nota: gaps between modules is not known/described

+
+
+MAX_SHAPE = (1024, 1024)
+
+ +
+
+aliases = ['HF-1M']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.HF_262k(pixel1=0.00015, pixel2=0.00015)
+

Bases: pyFAI.detectors.Detector

+

ADSC HF-262k 2 module

+

Informations from +http://www.adsc-xray.com/products/pixel-array-detectors/hf-262k/

+

Nota: gaps between modules is not known/described

+
+
+MAX_SHAPE = (512, 512)
+
+ +
+
+aliases = ['HF-262k']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.HF_2M(pixel1=0.00015, pixel2=0.00015)
+

Bases: pyFAI.detectors.Detector

+

ADSC HF-1M 3x6 modules

+

Informations from +http://www.adsc-xray.com/products/pixel-array-detectors/hf-2.4m/

+

Nota: gaps between modules is not known/described

+
+
+MAX_SHAPE = (1536, 1536)
+
+ +
+
+aliases = ['HF-2.4M']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.HF_4M(pixel1=0.00015, pixel2=0.00015)
+

Bases: pyFAI.detectors.Detector

+

ADSC HF-4M 4x8 modules

+

Informations from +http://www.adsc-xray.com/products/pixel-array-detectors/hf-4m/

+
+
+MAX_SHAPE = (2048, 2048)
+
+ +
+
+aliases = ['HF-4M']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.HF_9M(pixel1=0.00015, pixel2=0.00015)
+

Bases: pyFAI.detectors.Detector

+

ADSC HF-130K 1 module

+

Informations from +http://www.adsc-xray.com/products/pixel-array-detectors/hf-9-4m/

+
+
+MAX_SHAPE = (3072, 3072)
+
+ +
+
+aliases = ['HF-9.4M']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.ImXPadS10(pixel1=0.00013, pixel2=0.00013, max_shape=None, module_size=None)
+

Bases: pyFAI.detectors.Detector

+

ImXPad detector: ImXPad s10 detector with 1x1modules

+
+
+BORDER_SIZE_RELATIVE = 2.5
+
+ +
+
+MAX_SHAPE = (120, 80)
+
+ +
+
+MODULE_SIZE = (120, 80)
+
+ +
+
+PIXEL_SIZE = (0.00013, 0.00013)
+
+ +
+
+aliases = ['Imxpad S10']
+
+ +
+
+calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)
+

Calculate the position of each pixel center in cartesian coordinate +and in meter of a couple of coordinates. +The half pixel offset is taken into account here !!!

+ +++ + + + + + + + +
Parameters:
    +
  • d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension)
    • +
  • d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
    • +
+
Returns:

position in meter of the center of each pixels.

+
Return type:

ndarray

+
+

d1 and d2 must have the same shape, returned array will have +the same shape.

+
+ +
+
+calc_mask()
+

Calculate the mask

+
+ +
+
+calc_pixels_edges()
+

Calculate the position of the pixel edges

+
+ +
+
+force_pixel = True
+
+ +
+
+get_pixel_corners()
+

Calculate the position of the corner of the pixels

+

This should be overwritten by class representing non-contiguous detector (Xpad, ...)

+

Precision float32 is ok: precision of 1µm for a detector size of 1m

+ +++ + + + +
Returns:4D array containing: +pixel index (slow dimension) +pixel index (fast dimension) +corner index (A, B, C or D), triangles or hexagons can be handled the same way +vertex position (z,y,x)
+
+ +
+
+uniform_pixel = False
+
+ +
+ +
+
+class pyFAI.detectors.ImXPadS140(pixel1=0.00013, pixel2=0.00013)
+

Bases: pyFAI.detectors.ImXPadS10

+

ImXPad detector: ImXPad s140 detector with 2x7modules

+
+
+BORDER_PIXEL_SIZE_RELATIVE = 2.5
+
+ +
+
+MAX_SHAPE = (240, 560)
+
+ +
+
+MODULE_SIZE = (120, 80)
+
+ +
+
+PIXEL_SIZE = (0.00013, 0.00013)
+
+ +
+
+aliases = ['Imxpad S140']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.ImXPadS70(pixel1=0.00013, pixel2=0.00013)
+

Bases: pyFAI.detectors.ImXPadS10

+

ImXPad detector: ImXPad s70 detector with 1x7modules

+
+
+BORDER_SIZE_RELATIVE = 2.5
+
+ +
+
+MAX_SHAPE = (120, 560)
+
+ +
+
+MODULE_SIZE = (120, 80)
+
+ +
+
+PIXEL_EDGES = None
+
+ +
+
+PIXEL_SIZE = (0.00013, 0.00013)
+
+ +
+
+aliases = ['Imxpad S70']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Mar345(pixel1=0.0001, pixel2=0.0001)
+

Bases: pyFAI.detectors.Detector

+

Mar345 Imaging plate detector

+

In this detector, pixels are always square +The valid image size are 2300, 2000, 1600, 1200, 3450, 3000, 2400, 1800

+
+
+MAX_SHAPE = (3450, 3450)
+
+ +
+
+VALID_SIZE = {2000: 0.00015, 1600: 0.00015, 3000: 0.0001, 2400: 0.0001, 3450: 0.0001, 1200: 0.00015, 2300: 0.00015, 1800: 0.0001}
+
+ +
+
+aliases = ['MAR 345', 'Mar3450']
+
+ +
+
+calc_mask()
+
+ +
+
+force_pixel = True
+
+ +
+
+guess_binning(data)
+

Guess the binning/mode depending on the image shape +:param data: 2-tuple with the shape of the image or the image with a .shape attribute.

+
+ +
+ +
+
+class pyFAI.detectors.NexusDetector(filename=None)
+

Bases: pyFAI.detectors.Detector

+

Class representing a 2D detector loaded from a NeXus file

+
+
+getFit2D()
+

Helper method to serialize the description of a detector using the Fit2d units

+ +++ + + + + + +
Returns:representation of the detector easy to serialize
Return type:dict
+
+ +
+
+getPyFAI()
+

Helper method to serialize the description of a detector using the pyFAI way +with everything in S.I units.

+ +++ + + + + + +
Returns:representation of the detector easy to serialize
Return type:dict
+
+ +
+
+load(filename)
+

Loads the detector description from a NeXus file, adapted from: +http://download.nexusformat.org/sphinx/classes/base_classes/NXdetector.html

+ +++ + + + +
Parameters:filename – name of the file on the disk
+
+ +
+
+classmethod sload(filename)
+

Instantiate the detector description from a NeXus file, adapted from: +http://download.nexusformat.org/sphinx/classes/base_classes/NXdetector.html

+ +++ + + + + + +
Parameters:filename – name of the file on the disk
Returns:Detector instance
+
+ +
+ +
+
+class pyFAI.detectors.Perkin(pixel1=0.0002, pixel2=0.0002)
+

Bases: pyFAI.detectors.Detector

+

Perkin detector

+
+
+DEFAULT_PIXEL1 = 0.0002
+
+ +
+
+DEFAULT_PIXEL2 = 0.0002
+
+ +
+
+MAX_SHAPE = (4096, 4096)
+
+ +
+
+aliases = ['Perkin detector', 'Perkin Elmer']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Pilatus(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.Detector

+

Pilatus detector: generic description containing mask algorithm

+

Sub-classed by Pilatus1M, Pilatus2M and Pilatus6M

+
+
+MODULE_GAP = (17, 7)
+
+ +
+
+MODULE_SIZE = (195, 487)
+
+ +
+
+calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)
+

Calculate the position of each pixel center in cartesian coordinate +and in meter of a couple of coordinates. +The half pixel offset is taken into account here !!!

+ +++ + + + + + + + +
Parameters:
    +
  • d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension)
    • +
  • d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
    • +
+
Returns:

position in meter of the center of each pixels.

+
Return type:

ndarray

+
+

d1 and d2 must have the same shape, returned array will have +the same shape.

+
+ +
+
+calc_mask()
+

Returns a generic mask for Pilatus detectors...

+
+ +
+
+force_pixel = True
+
+ +
+
+get_splineFile()
+
+ +
+
+set_splineFile(splineFile=None)
+

In this case splinefile is a couple filenames

+
+ +
+
+splineFile
+
+ +
+ +
+
+class pyFAI.detectors.Pilatus100k(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.Pilatus

+

Pilatus 100k detector

+
+
+MAX_SHAPE = (195, 487)
+
+ +
+ +
+
+class pyFAI.detectors.Pilatus1M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.Pilatus

+

Pilatus 1M detector

+
+
+MAX_SHAPE = (1043, 981)
+
+ +
+ +
+
+class pyFAI.detectors.Pilatus200k(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.Pilatus

+

Pilatus 200k detector

+
+
+MAX_SHAPE = (407, 487)
+
+ +
+ +
+
+class pyFAI.detectors.Pilatus2M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.Pilatus

+

Pilatus 2M detector

+
+
+MAX_SHAPE = (1679, 1475)
+
+ +
+ +
+
+class pyFAI.detectors.Pilatus300k(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.Pilatus

+

Pilatus 300k detector

+
+
+MAX_SHAPE = (619, 487)
+
+ +
+ +
+
+class pyFAI.detectors.Pilatus300kw(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.Pilatus

+

Pilatus 300k-wide detector

+
+
+MAX_SHAPE = (195, 1475)
+
+ +
+ +
+
+class pyFAI.detectors.Pilatus6M(pixel1=0.000172, pixel2=0.000172)
+

Bases: pyFAI.detectors.Pilatus

+

Pilatus 6M detector

+
+
+MAX_SHAPE = (2527, 2463)
+
+ +
+ +
+
+class pyFAI.detectors.PilatusCdTe(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.Pilatus

+

Pilatus CdTe detector: Like the Pilatus with an extra 3 pixel in the middle +of every module (vertically)

+
+
+calc_mask()
+

Returns a generic mask for Pilatus detectors...

+
+ +
+ +
+
+class pyFAI.detectors.PilatusCdTe1M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.PilatusCdTe

+

Pilatus CdTe 1M detector

+
+
+MAX_SHAPE = (1043, 981)
+
+ +
+ +
+
+class pyFAI.detectors.PilatusCdTe2M(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.PilatusCdTe

+

Pilatus CdTe 2M detector

+
+
+MAX_SHAPE = (1679, 1475)
+
+ +
+ +
+
+class pyFAI.detectors.PilatusCdTe300k(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.PilatusCdTe

+

Pilatus CdTe 300k detector

+
+
+MAX_SHAPE = (619, 487)
+
+ +
+ +
+
+class pyFAI.detectors.PilatusCdTe300kw(pixel1=0.000172, pixel2=0.000172, max_shape=None, module_size=None, x_offset_file=None, y_offset_file=None)
+

Bases: pyFAI.detectors.PilatusCdTe

+

Pilatus CdTe 300k-wide detector

+
+
+MAX_SHAPE = (195, 1475)
+
+ +
+ +
+
+class pyFAI.detectors.Pixium(pixel1=0.000308, pixel2=0.000308)
+

Bases: pyFAI.detectors.Detector

+

PIXIUM 4700 detector

+

High energy X ray diffraction using the Pixium 4700 flat panel detector +J E Daniels, M Drakopoulos, et al.; Journal of Synchrotron Radiation 16(Pt 4):463-8 · August 2009

+
+
+DEFAULT_PIXEL1 = 0.000154
+
+ +
+
+DEFAULT_PIXEL2 = 0.000154
+
+ +
+
+MAX_SHAPE = (1910, 2480)
+
+ +
+
+aliases = ['Pixium 4700 detector', 'Thales Electronics']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RaspberryPi(pixel1=1.4e-06, pixel2=1.4e-06)
+

Bases: pyFAI.detectors.Detector

+

5 Mpix detector from Raspberry Pi

+
+
+MAX_SHAPE = (1944, 2592)
+
+ +
+
+aliases = ['Raspberry', 'Pi']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Rayonix(pixel1=3.2e-05, pixel2=3.2e-05)
+

Bases: pyFAI.detectors.Detector

+
+
+BINNED_PIXEL_SIZE = {1: 3.2e-05}
+
+ +
+
+MAX_SHAPE = (4096, 4096)
+
+ +
+
+binning
+
+ +
+
+force_pixel = True
+
+ +
+
+get_binning()
+
+ +
+
+guess_binning(data)
+

Guess the binning/mode depending on the image shape +:param data: 2-tuple with the shape of the image or the image with a .shape attribute.

+
+ +
+
+set_binning(bin_size=(1, 1))
+

Set the “binning” of the detector,

+ +++ + + + +
Parameters:bin_size (int or (int, int)) – set the binning of the detector
+
+ +
+ +
+
+class pyFAI.detectors.Rayonix133(pixel1=6.4e-05, pixel2=6.4e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix 133 2D CCD detector detector also known as mar133

+

Personnal communication from M. Blum

+

What should be the default binning factor for those cameras ?

+

Circular detector

+
+
+BINNED_PIXEL_SIZE = {8: 0.000256, 1: 3.2e-05, 2: 6.4e-05, 4: 0.000128}
+
+ +
+
+MAX_SHAPE = (4096, 4096)
+
+ +
+
+aliases = ['MAR133']
+
+ +
+
+calc_mask()
+

Circular mask

+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RayonixLx170(pixel1=4.42708e-05, pixel2=4.42708e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix lx170 2d CCD Detector (2x1 CCDs).

+

Nota: this is the same for lx170hs

+
+
+BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}
+
+ +
+
+MAX_SHAPE = (1920, 3840)
+
+ +
+
+aliases = ['Rayonix lx170']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RayonixLx255(pixel1=4.42708e-05, pixel2=4.42708e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix lx255 2d Detector (3x1 CCDs)

+

Nota: this detector is also called lx255hs

+
+
+BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}
+
+ +
+
+MAX_SHAPE = (1920, 5760)
+
+ +
+
+aliases = ['Rayonix lx255', 'Rayonix lx255hs']
+
+ +
+ +
+
+class pyFAI.detectors.RayonixMx170(pixel1=4.42708e-05, pixel2=4.42708e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix mx170 2d CCD Detector (2x2 CCDs).

+

Nota: this is the same for mx170hs

+
+
+BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}
+
+ +
+
+MAX_SHAPE = (3840, 3840)
+
+ +
+
+aliases = ['Rayonix mx170']
+
+ +
+ +
+
+class pyFAI.detectors.RayonixMx225(pixel1=7.3242e-05, pixel2=7.3242e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix mx225 2D CCD detector detector

+

Nota: this is the same definition for mx225he +Personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {8: 0.000292969, 1: 3.6621e-05, 2: 7.3242e-05, 3: 0.000109971, 4: 0.000146484}
+
+ +
+
+MAX_SHAPE = (6144, 6144)
+
+ +
+
+aliases = ['Rayonix mx225']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RayonixMx225hs(pixel1=7.8125e-05, pixel2=7.8125e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix mx225hs 2D CCD detector detector

+

Pixel size from a personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {1: 3.90625e-05, 2: 7.8125e-05, 3: 0.0001171875, 4: 0.00015625, 5: 0.0001953125, 6: 0.000234375, 8: 0.0003125, 10: 0.000390625}
+
+ +
+
+MAX_SHAPE = (5760, 5760)
+
+ +
+
+aliases = ['Rayonix mx225hs']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RayonixMx300(pixel1=7.3242e-05, pixel2=7.3242e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix mx300 2D detector (4x4 CCDs)

+

Pixel size from a personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {8: 0.000292969, 1: 3.6621e-05, 2: 7.3242e-05, 3: 0.000109971, 4: 0.000146484}
+
+ +
+
+MAX_SHAPE = (8192, 8192)
+
+ +
+
+aliases = ['Rayonix mx300']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RayonixMx300hs(pixel1=7.8125e-05, pixel2=7.8125e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix mx300hs 2D detector (4x4 CCDs)

+

Pixel size from a personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {1: 3.90625e-05, 2: 7.8125e-05, 3: 0.0001171875, 4: 0.00015625, 5: 0.0001953125, 6: 0.000234375, 8: 0.0003125, 10: 0.000390625}
+
+ +
+
+MAX_SHAPE = (7680, 7680)
+
+ +
+
+aliases = ['Rayonix mx300hs']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RayonixMx325(pixel1=7.9346e-05, pixel2=7.9346e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix mx325 and mx325he 2D detector (4x4 CCD chips)

+

Pixel size from a personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {8: 0.000317383, 1: 3.9673e-05, 2: 7.9346e-05, 3: 0.000119135, 4: 0.000158691}
+
+ +
+
+MAX_SHAPE = (8192, 8192)
+
+ +
+
+aliases = ['Rayonix mx325']
+
+ +
+ +
+
+class pyFAI.detectors.RayonixMx340hs(pixel1=8.85417e-05, pixel2=8.85417e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix mx340hs 2D detector (4x4 CCDs)

+

Pixel size from a personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}
+
+ +
+
+MAX_SHAPE = (7680, 7680)
+
+ +
+
+aliases = ['Rayonix mx340hs']
+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RayonixMx425hs(pixel1=4.42708e-05, pixel2=4.42708e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix mx425hs 2D CCD camera (5x5 CCD chip)

+

Pixel size from a personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}
+
+ +
+
+MAX_SHAPE = (9600, 9600)
+
+ +
+
+aliases = ['Rayonix mx425hs']
+
+ +
+ +
+
+class pyFAI.detectors.RayonixSx165(pixel1=3.95e-05, pixel2=3.95e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix sx165 2d Detector also known as MAR165.

+

Circular detector

+
+
+BINNED_PIXEL_SIZE = {8: 0.000316, 1: 3.95e-05, 2: 7.9e-05, 3: 0.000118616, 4: 0.000158}
+
+ +
+
+MAX_SHAPE = (4096, 4096)
+
+ +
+
+aliases = ['MAR165', 'Rayonix Sx165']
+
+ +
+
+calc_mask()
+

Circular mask

+
+ +
+
+force_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.RayonixSx200(pixel1=4.8e-05, pixel2=4.8e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix sx200 2d CCD Detector.

+

Pixel size are personnal communication from M. Blum.

+
+
+BINNED_PIXEL_SIZE = {8: 0.000384, 1: 4.8e-05, 2: 9.6e-05, 3: 0.000144, 4: 0.000192}
+
+ +
+
+MAX_SHAPE = (4096, 4096)
+
+ +
+
+aliases = ['Rayonix sx200']
+
+ +
+ +
+
+class pyFAI.detectors.RayonixSx30hs(pixel1=1.5625e-05, pixel2=1.5625e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix sx30hs 2D CCD camera (1 CCD chip)

+

Pixel size from a personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {1: 1.5625e-05, 2: 3.125e-05, 3: 4.6875e-05, 4: 6.25e-05, 5: 7.8125e-05, 6: 9.375e-05, 8: 0.000125, 10: 0.00015625}
+
+ +
+
+MAX_SHAPE = (1920, 1920)
+
+ +
+
+aliases = ['Rayonix Sx30hs']
+
+ +
+ +
+
+class pyFAI.detectors.RayonixSx85hs(pixel1=4.42708e-05, pixel2=4.42708e-05)
+

Bases: pyFAI.detectors.Rayonix

+

Rayonix sx85hs 2D CCD camera (1 CCD chip)

+

Pixel size from a personnal communication from M. Blum

+
+
+BINNED_PIXEL_SIZE = {1: 4.42708e-05, 2: 8.85417e-05, 3: 0.0001328125, 4: 0.0001770833, 5: 0.0002213542, 6: 0.000265625, 8: 0.0003541667, 10: 0.0004427083}
+
+ +
+
+MAX_SHAPE = (1920, 1920)
+
+ +
+
+aliases = ['Rayonix Sx85hs']
+
+ +
+ +
+
+class pyFAI.detectors.Titan(pixel1=6e-05, pixel2=6e-05)
+

Bases: pyFAI.detectors.Detector

+

Titan CCD detector from Agilent. Mask not handled

+
+
+MAX_SHAPE = (2048, 2048)
+
+ +
+
+aliases = ['Titan 2k x 2k', 'OXD Titan', 'Agilent Titan']
+
+ +
+
+force_pixel = True
+
+ +
+
+uniform_pixel = True
+
+ +
+ +
+
+class pyFAI.detectors.Xpad_flat(pixel1=0.00013, pixel2=0.00013, max_shape=None, module_size=None)
+

Bases: pyFAI.detectors.ImXPadS10

+

Xpad detector: generic description for +ImXPad detector with 8x7modules

+
+
+BORDER_PIXEL_SIZE_RELATIVE = 2.5
+
+ +
+
+IS_CONTIGUOUS = False
+
+ +
+
+MAX_SHAPE = (960, 560)
+
+ +
+
+MODULE_GAP = (0.00357, 0)
+
+ +
+
+MODULE_SIZE = (120, 80)
+
+ +
+
+PIXEL_SIZE = (0.00013, 0.00013)
+
+ +
+
+aliases = ['Xpad S540 flat', 'd5']
+
+ +
+
+calc_cartesian_positions(d1=None, d2=None, center=True, use_cython=True)
+

Calculate the position of each pixel center in cartesian coordinate +and in meter of a couple of coordinates. +The half pixel offset is taken into account here !!! +Adapted to Nexus detector definition

+ +++ + + + +
Parameters:
    +
  • d1 (ndarray (1D or 2D)) – the Y pixel positions (slow dimension)
    • +
  • d2 (ndarray (1D or 2D)) – the X pixel positions (fast dimension)
    • +
  • center – retrieve the coordinate of the center of the pixel
    • +
+
+

@parm use_cython: set to False to test Numpy implementation +:return: position in meter of the center of each pixels. +:rtype: ndarray

+

d1 and d2 must have the same shape, returned array will have +the same shape.

+
+ +
+
+calc_mask()
+

Returns a generic mask for Xpad detectors... +discards the first line and raw form all modules: +those are 2.5x bigger and often mis - behaving

+
+ +
+
+calc_pixels_edges()
+

Calculate the position of the pixel edges, specific to the S540, d5 detector

+
+ +
+
+force_pixel = True
+
+ +
+
+get_pixel_corners()
+

Calculate the position of the corner of the pixels

+ +++ + + + +
Returns:4D array containing: +pixel index (slow dimension) +pixel index (fast dimension) +corner index (A, B, C or D), triangles or hexagons can be handled the same way +vertex position (z,y,x)
+
+ +
+
+uniform_pixel = False
+
+ +
+ +
+
+

spline Module

+

This is piece of software aims at manipulating spline files +describing for geometric corrections of the 2D detectors using cubic-spline.

+

Mainly used at ESRF with FReLoN CCD camera.

+
+
+class pyFAI.spline.Spline(filename=None)
+

Bases: object

+

This class is a python representation of the spline file

+

Those file represent cubic splines for 2D detector distortions and +makes heavy use of fitpack (dierckx in netlib) — A Python-C +wrapper to FITPACK (by P. Dierckx). FITPACK is a collection of +FORTRAN programs for curve and surface fitting with splines and +tensor product splines. See +_http://www.cs.kuleuven.ac.be/cwis/research/nalag/research/topics/fitpack.html +or _http://www.netlib.org/dierckx/index.html

+
+
+array2spline(smoothing=1000, timing=False)
+

Calculates the spline coefficients from the displacements +matrix using fitpack.

+ +++ + + + +
Parameters:
    +
  • smoothing (float) – the greater the smoothing, the fewer the number of knots remaining
    • +
  • timing (bool) – print the profiling of the calculation
    • +
+
+
+ +
+
+bin(binning=None)
+

Performs the binning of a spline (same camera with different binning)

+ +++ + + + + + +
Parameters:binning – binning factor as integer or 2-tuple of integers
Type:int or (int, int)
+
+ +
+
+comparison(ref, verbose=False)
+

Compares the current spline distortion with a reference

+ +++ + + + + + + + +
Parameters:
    +
  • ref (Spline instance) – another spline file
    • +
  • verbose (bool) – print or not pylab plots
    • +
+
Returns:

True or False depending if the splines are the same or not

+
Return type:

bool

+
+
+ +
+
+correct(pos)
+
+ +
+
+fliplr()
+

Flip the spline +:return: new spline object

+
+ +
+
+fliplrud()
+

Flip the spline left-right and up-down +:return: new spline object

+
+ +
+
+flipud()
+

Flip the spline up-down +:return: new spline object

+
+ +
+
+getPixelSize()
+

Return the size of the pixel from as a 2-tuple of floats expressed +in meters.

+ +++ + + + + + +
Returns:the size of the pixel from a 2D detector
Return type:2-tuple of floats expressed in meter.
+
+ +
+
+read(filename)
+

read an ascii spline file from file

+ +++ + + + +
Parameters:filename (str) – file containing the cubic spline distortion file
+
+ +
+
+setPixelSize(pixelSize)
+

Sets the size of the pixel from a 2-tuple of floats expressed +in meters.

+ +++ + + + +
Param:pixel size in meter
+
+ +
+
+spline2array(timing=False)
+

Calculates the displacement matrix using fitpack +bisplev(x, y, tck, dx = 0, dy = 0)

+ +++ + + + + + + + +
Parameters:timing (bool) – profile the calculation or not
Returns:Nothing !
Return type:float or ndarray
+

Evaluate a bivariate B-spline and its derivatives. Return a +rank-2 array of spline function values (or spline derivative +values) at points given by the cross-product of the rank-1 +arrays x and y. In special cases, return an array or just a +float if either x or y or both are floats.

+
+ +
+
+splineFuncX(x, y, list_of_points=False)
+

Calculates the displacement matrix using fitpack for the X +direction on the given grid.

+ +++ + + + + + + + +
Parameters:
    +
  • x (ndarray) – points of the grid in the x direction
    • +
  • y (ndarray) – points of the grid in the y direction
    • +
  • list_of_points – if true, consider the zip(x,y) instead of the of the square array
    • +
+
Returns:

displacement matrix for the X direction

+
Return type:

ndarray

+
+
+ +
+
+splineFuncY(x, y, list_of_points=False)
+

calculates the displacement matrix using fitpack for the Y +direction

+ +++ + + + + + + + +
Parameters:
    +
  • x (ndarray) – points in the x direction
    • +
  • y (ndarray) – points in the y direction
    • +
  • list_of_points – if true, consider the zip(x,y) instead of the of the square array
    • +
+
Returns:

displacement matrix for the Y direction

+
Return type:

ndarray

+
+
+ +
+
+tilt(center=(0.0, 0.0), tiltAngle=0.0, tiltPlanRot=0.0, distanceSampleDetector=1.0, timing=False)
+

The tilt method apply a virtual tilt on the detector, the +point of tilt is given by the center

+ +++ + + + + + + + +
Parameters:
    +
  • center (2-tuple of floats) – position of the point of tilt, this point will not be moved.
    • +
  • tiltAngle (float in the range [-90:+90] degrees) – the value of the tilt in degrees
    • +
  • tiltPlanRot (Float in the range [-180:180]) – the rotation of the tilt plan with the Ox axis (0 deg for y axis invariant, 90 deg for x axis invariant)
    • +
  • distanceSampleDetector (float) – the distance from sample to detector in meter (along the beam, so distance from sample to center)
    • +
+
Returns:

tilted Spline instance

+
Return type:

Spline

+
+
+ +
+
+write(filename)
+

save the cubic spline in an ascii file usable with Fit2D or +SPD

+ +++ + + + +
Parameters:filename (str) – name of the file containing the cubic spline distortion file
+
+ +
+
+writeEDF(basename)
+

save the distortion matrices into a couple of files called +basename-x.edf and basename-y.edf

+ +++ + + + +
Parameters:basename (str) – base of the name used to save the data
+
+ +
+
+zeros(xmin=0.0, ymin=0.0, xmax=2048.0, ymax=2048.0, pixSize=None)
+

Defines a spline file with no ( zero ) displacement.

+ +++ + + + +
Parameters:
    +
  • xmin (float) – minimum coordinate in x, usually zero
    • +
  • xmax (float) – maximum coordinate in x (+1) usually 2048
    • +
  • ymin (float) – minimum coordinate in y, usually zero
    • +
  • ymax (float) – maximum coordinate y (+1) usually 2048
    • +
  • pixSize (float) – size of the pixel
    • +
+
+
+ +
+
+zeros_like(other)
+

Defines a spline file with no ( zero ) displacement with the +same shape as the other one given.

+ +++ + + + +
Parameters:other (Spline instance) – another Spline instance
+
+ +
+ +
+
+pyFAI.spline.main()
+

Some tests ....

+
+ +
+
+

opencl Module

+
+
+class pyFAI.opencl.Device(name='None', dtype=None, version=None, driver_version=None, extensions='', memory=None, available=None, cores=None, frequency=None, flop_core=None, idx=0, workgroup=1)
+

Bases: object

+

Simple class that contains the structure of an OpenCL device

+
+
+pretty_print()
+

Complete device description

+ +++ + + + +
Returns:string
+
+ +
+ +
+
+class pyFAI.opencl.OpenCL
+

Bases: object

+

Simple class that wraps the structure ocl_tools_extended.h

+

This is a static class. +ocl should be the only instance and shared among all python modules.

+
+
+comput_cap = (5, 0)
+
+ +
+
+context_cache = {}
+
+ +
+
+create_context(devicetype='ALL', useFp64=False, platformid=None, deviceid=None, cached=True)
+

Choose a device and initiate a context.

+

Devicetypes can be GPU,gpu,CPU,cpu,DEF,ACC,ALL. +Suggested are GPU,CPU. +For each setting to work there must be such an OpenCL device and properly installed. +E.g.: If Nvidia driver is installed, GPU will succeed but CPU will fail. The AMD SDK kit is required for CPU via OpenCL. +:param devicetype: string in [“cpu”,”gpu”, “all”, “acc”] +:param useFp64: boolean specifying if double precision will be used +:param platformid: integer +:param devid: integer +:return: OpenCL context on the selected device

+
+ +
+
+device_from_context(context)
+

Retrieves the Device from the context

+ +++ + + + + + +
Parameters:context – OpenCL context
Returns:instance of Device
+
+ +
+
+flop_core = 4
+
+ +
+
+get_platform(key)
+

Return a platform according

+ +++ + + + +
Parameters:key (int or str) – identifier for a platform, either an Id (int) or it’s name
+
+ +
+
+idd = 0
+
+ +
+
+idx = 3
+
+ +
+
+nb_devices = 5
+
+ +
+
+platforms = [NVIDIA CUDA, FreeOCL, Intel(R) OpenCL, AMD Accelerated Parallel Processing]
+
+ +
+
+select_device(dtype='ALL', memory=None, extensions=[], best=True, **kwargs)
+

Select a device based on few parameters (at the end, keep the one with most memory)

+ +++ + + + +
Parameters:
    +
  • type – “gpu” or “cpu” or “all” ....
    • +
  • memory – minimum amount of memory (int)
    • +
  • extensions – list of extensions to be present
    • +
  • best – shall we look for the
    • +
+
+
+ +
+
+workgroup = 1024
+
+ +
+ +
+
+class pyFAI.opencl.Platform(name='None', vendor='None', version=None, extensions=None, idx=0)
+

Bases: object

+

Simple class that contains the structure of an OpenCL platform

+
+
+add_device(device)
+

Add new device to the platform

+ +++ + + + +
Parameters:device – Device instance
+
+ +
+
+get_device(key)
+

Return a device according to key

+ +++ + + + +
Parameters:key (int or str) – identifier for a device, either it’s id (int) or it’s name
+
+ +
+ +
+
+pyFAI.opencl.allocate_cl_buffers(buffers, device=None, context=None)
+
+++ + + + + + + + +
Parameters:buffers – the buffers info use to create the pyopencl.Buffer
Returns:a dict containing the instanciated pyopencl.Buffer
Return type:dict(str, pyopencl.Buffer)
+

This method instanciate the pyopencl.Buffer from the buffers +description.

+
+ +
+
+pyFAI.opencl.release_cl_buffers(cl_buffers)
+
+++ + + + +
Parameters:cl_buffer (dict(str, pyopencl.Buffer)) – the buffer you want to release
+

This method release the memory of the buffers store in the dict

+
+ +
+
+

ocl_azim Module

+

C++ less implementation of Dimitris’ code based on PyOpenCL

+
+
TODO and trick from dimitris still missing:
+
    +
  • dark-current subtraction is still missing
    • +
  • In fact you might want to consider doing the conversion on the GPU when +possible. Think about it, you have a uint16 to float which for large arrays +was slow.. You load on the graphic card a uint16 (2x transfer speed) and +you convert to float inside so it should be blazing fast.
    • +
+
+
+
+
+class pyFAI.ocl_azim.Integrator1d(filename=None)
+

Bases: object

+

Attempt to implements ocl_azim using pyopencl

+
+
+BLOCK_SIZE = 128
+
+ +
+
+clean(preserve_context=False)
+

Free OpenCL related resources allocated by the library.

+

clean() is used to reinitiate the library back in a vanilla +state. It may be asked to preserve the context created by +init or completely clean up OpenCL. Guard/Status flags that +are set will be reset.

+ +++ + + + +
Parameters:preserve_context (bool) – preserves or destroys all OpenCL resources
+
+ +
+
+configure(kernel=None)
+

The method configure() allocates the OpenCL resources required +and compiled the OpenCL kernels. An active context must exist +before a call to configure() and getConfiguration() must have +been called at least once. Since the compiled OpenCL kernels +carry some information on the integration parameters, a change +to any of the parameters of getConfiguration() requires a +subsequent call to configure() for them to take effect.

+

If a configuration exists and configure() is called, the +configuration is cleaned up first to avoid OpenCL memory leaks

+ +++ + + + +
Parameters:kernel_path – is the path to the actual kernel
+
+ +
+
+execute(image)
+

Perform a 1D azimuthal integration

+

execute() may be called only after an OpenCL device is +configured and a Tth array has been loaded (at least once) It +takes the input image and based on the configuration provided +earlier it performs the 1D integration. Notice that if the +provided image is bigger than N then only N points will be +taked into account, while if the image is smaller than N the +result may be catastrophic. set/unset and loadTth methods +have a direct impact on the execute() method. All the rest of +the methods will require at least a new configuration via +configure().

+

Takes an image, integrate and return the histogram and weights

+ +++ + + + + + +
Parameters:image – image to be processed as a numpy array
Returns:tth_out, histogram, bins
+

TODO: to improve performances, the image should be casted to +float32 in an optimal way: currently using numpy machinery but +would be better if done in OpenCL

+
+ +
+
+getConfiguration(Nimage, Nbins, useFp64=None)
+

getConfiguration gets the description of the integrations +to be performed and keeps an internal copy

+ +++ + + + +
Parameters:
    +
  • Nimage – number of pixel in image
    • +
  • Nbins – number of bins in regrouped histogram
    • +
  • useFp64 – use double precision. By default the same as init!
    • +
+
+
+ +
+
+get_status()
+

return a dictionnary with the status of the integrator: for +compatibilty with former implementation

+
+ +
+
+init(devicetype='GPU', useFp64=True, platformid=None, deviceid=None)
+

Initial configuration: Choose a device and initiate a +context. Devicetypes can be GPU, gpu, CPU, cpu, DEF, ACC, +ALL. Suggested are GPU,CPU. For each setting to work there +must be such an OpenCL device and properly installed. E.g.: If +Nvidia driver is installed, GPU will succeed but CPU will +fail. The AMD SDK kit (AMD APP) is required for CPU via +OpenCL.

+ +++ + + + +
Parameters:
    +
  • devicetype – string in [“cpu”,”gpu”, “all”, “acc”]
    • +
  • useFp64 – boolean specifying if double precision will be used
    • +
  • platformid – integer
    • +
  • devid – integer
    • +
+
+
+ +
+
+loadTth(tth, dtth, tth_min=None, tth_max=None)
+

Load the 2th arrays along with the min and max value.

+

loadTth maybe be recalled at any time of the execution in +order to update the 2th arrays.

+

loadTth is required and must be called at least once after a +configure()

+
+ +
+
+log(**kwarg)
+

log in a file all opencl events

+
+ +
+
+setDummyValue(dummy, delta_dummy)
+

Enables dummy value functionality and uploads the value to the +OpenCL device.

+

Image values that are similar to the dummy value are set to 0.

+ +++ + + + +
Parameters:
    +
  • dummy – value in image of missing values (masked pixels?)
    • +
  • delta_dummy – precision for dummy values
    • +
+
+
+ +
+
+setMask(mask)
+

Enables the use of a Mask during integration. The Mask can be +updated by recalling setMask at any point.

+

The Mask must be a PyFAI Mask. Pixels with 0 are masked +out. TODO: check and invert!

+ +++ + + + +
Parameters:mask – numpy.ndarray of integer.
+
+ +
+
+setRange(lowerBound, upperBound)
+

Instructs the program to use a user - defined range for 2th +values

+

setRange is optional. By default the integration will use the +tth_min and tth_max given by loadTth() as integration +range. When setRange is called it sets a new integration range +without affecting the 2th array. All values outside that range +will then be discarded when interpolating. Currently, if the +interval of 2th (2th + -d2th) is not all inside the range +specified, it is discarded. The bins of the histogram are +RESCALED to the defined range and not the original tth_max - +tth_min range.

+

setRange can be called at any point and as many times required +after a valid configuration is created.

+ +++ + + + +
Parameters:
    +
  • lowerBound (float) – lower bound of the integration range
    • +
  • upperBound (float) – upper bound of the integration range
    • +
+
+
+ +
+
+setSolidAngle(solidAngle)
+

Enables SolidAngle correction and uploads the suitable array +to the OpenCL device.

+

By default the program will assume no solidangle correction +unless setSolidAngle() is called. From then on, all +integrations will be corrected via the SolidAngle array.

+

If the SolidAngle array needs to be changes, one may just call +setSolidAngle() again with that array

+ +++ + + + +
Parameters:solidAngle (ndarray) – the solid angle of the given pixel
+
+ +
+
+unsetDummyValue()
+

Disable a dummy value. +May be re-enabled at any time by setDummyValue

+
+ +
+
+unsetMask()
+

Disables the use of a Mask from that point. +It may be re-enabled at any point via setMask

+
+ +
+
+unsetRange()
+

Disable the use of a user-defined 2th range and revert to +tth_min,tth_max range

+

unsetRange instructs the program to revert to its default +integration range. If the method is called when no +user-defined range had been previously specified, no action +will be performed

+
+ +
+
+unsetSolidAngle()
+

Instructs the program to not perform solidangle correction from now on.

+

SolidAngle correction may be turned back on at any point

+
+ +
+ +
+
+

ocl_azim_lut Module

+
+
+class pyFAI.ocl_azim_lut.OCL_LUT_Integrator(lut, image_size, devicetype='all', platformid=None, deviceid=None, checksum=None, profile=False, empty=None)
+

Bases: object

+
+
+BLOCK_SIZE = 16
+
+ +
+
+integrate(data, dummy=None, delta_dummy=None, dark=None, flat=None, solidAngle=None, polarization=None, dark_checksum=None, flat_checksum=None, solidAngle_checksum=None, polarization_checksum=None, preprocess_only=False, safe=True, normalization_factor=1.0)
+

Before performing azimuthal integration, the preprocessing is :

+

data = (data - dark) / (flat*solidAngle*polarization)

+

Integration is performed using the CSR representation of the look-up table

+ +++ + + + +
Parameters:
    +
  • dark – array of same shape as data for pre-processing
    • +
  • flat – array of same shape as data for pre-processing
    • +
  • solidAngle – array of same shape as data for pre-processing
    • +
  • polarization – array of same shape as data for pre-processing
    • +
  • dark_checksum – CRC32 checksum of the given array
    • +
  • flat_checksum – CRC32 checksum of the given array
    • +
  • solidAngle_checksum – CRC32 checksum of the given array
    • +
  • polarization_checksum – CRC32 checksum of the given array
    • +
  • safe – if True (default) compares arrays on GPU according to their checksum, unless, use the buffer location is used
    • +
  • normalization_factor – divide raw signal by this value
    • +
  • preprocess_only – return the dark subtracted; flat field & solidAngle & polarization corrected image, else
    • +
+
+

:return averaged data, weighted histogram, unweighted histogram

+
+ +
+
+log_profile()
+

If we are in profiling mode, prints out all timing for every single OpenCL call

+
+ +
+ +
+
+

ocl_azim_csr Module

+
+
+class pyFAI.ocl_azim_csr.OCL_CSR_Integrator(lut, image_size, devicetype='all', block_size=32, platformid=None, deviceid=None, checksum=None, profile=False, empty=None)
+

Bases: object

+
+
+integrate(data, dummy=None, delta_dummy=None, dark=None, flat=None, solidAngle=None, polarization=None, dark_checksum=None, flat_checksum=None, solidAngle_checksum=None, polarization_checksum=None, preprocess_only=False, safe=True, normalization_factor=1.0)
+

Before performing azimuthal integration, the preprocessing is :

+

data = (data - dark) / (flat*solidAngle*polarization)

+

Integration is performed using the CSR representation of the look-up table

+ +++ + + + +
Parameters:
    +
  • dark – array of same shape as data for pre-processing
    • +
  • flat – array of same shape as data for pre-processing
    • +
  • solidAngle – array of same shape as data for pre-processing
    • +
  • polarization – array of same shape as data for pre-processing
    • +
  • dark_checksum – CRC32 checksum of the given array
    • +
  • flat_checksum – CRC32 checksum of the given array
    • +
  • solidAngle_checksum – CRC32 checksum of the given array
    • +
  • polarization_checksum – CRC32 checksum of the given array
    • +
  • safe – if True (default) compares arrays on GPU according to their checksum, unless, use the buffer location is used
    • +
  • preprocess_only – return the dark subtracted; flat field & solidAngle & polarization corrected image, else
    • +
  • normalization_factor – divide result by this value
    • +
+
+

:return averaged data, weighted histogram, unweighted histogram

+
+ +
+
+log_profile()
+

If we are in profiling mode, prints out all timing for every single OpenCL call

+
+ +
+ +
+
+

ocl_azim_csr_dis Module

+
+
+class pyFAI.ocl_azim_csr_dis.OCL_CSR_Integrator(lut, image_size, devicetype='all', block_size=32, platformid=None, deviceid=None, checksum=None, profile=False, empty=None)
+

Bases: object

+
+
+integrate(data, dummy=None, delta_dummy=None, dark=None, flat=None, solidAngle=None, polarization=None, dark_checksum=None, flat_checksum=None, solidAngle_checksum=None, polarization_checksum=None)
+
+ +
+
+log_profile()
+

If we are in profiling mode, prints out all timing for every +single OpenCL call

+
+ +
+ +
+
+

worker Module

+

This module contains the Worker class:

+

A tool able to perform azimuthal integration with: +additional saving capabilities like +- save as 2/3D structure in a HDF5 File +- read from HDF5 files

+

Aims at being integrated into a plugin like LImA or as model for the GUI

+

The configuration of this class is mainly done via a dictionary transmitted as a JSON string: +Here are the valid keys:

+
+

“dist”, +“poni1”, +“poni2”, +“rot1” +“rot3” +“rot2” +“pixel1” +“pixel2”

+

“splineFile” +“wavelength”

+

“poni” #path of the file

+

“chi_discontinuity_at_0” +“do_mask” +“do_dark” +“do_azimuthal_range” +“do_flat” +“do_2D” +“azimuth_range_min” +“azimuth_range_max”

+

“polarization_factor” +“nbpt_rad” +“do_solid_angle” +“do_radial_range” +“do_poisson” +“delta_dummy” +“nbpt_azim” +“flat_field” +“radial_range_min” +“dark_current” +“do_polarization” +“mask_file” +“detector” +“unit” +“radial_range_max” +“val_dummy” +“do_dummy” +“method”

+
+

}

+
+
+class pyFAI.worker.DistortionWorker(detector=None, dark=None, flat=None, solidangle=None, polarization=None, mask=None, dummy=None, delta_dummy=None, device=None)
+

Bases: object

+

Simple worker doing dark, flat, solid angle and polarization correction

+
+
+process(data, normalization=None)
+

Process the data and apply a normalization factor +:param data: input data +:param normalization: normalization factor +:return processed data

+
+ +
+ +
+
+class pyFAI.worker.PixelwiseWorker(dark=None, flat=None, solidangle=None, polarization=None, mask=None, dummy=None, delta_dummy=None, device=None)
+

Bases: object

+

Simple worker doing dark, flat, solid angle and polarization correction

+
+
+process(data, normalization=None)
+

Process the data and apply a normalization factor +:param data: input data +:param normalization: normalization factor +:return processed data

+
+ +
+ +
+
+class pyFAI.worker.Worker(azimuthalIntgrator=None, shapeIn=(2048, 2048), shapeOut=(360, 500), unit='r_mm', dummy=None, delta_dummy=None)
+

Bases: object

+
+
+do_2D()
+
+ +
+
+error_model
+
+ +
+
+get_config()
+

return configuration as a dictionary

+
+ +
+
+get_error_model()
+
+ +
+
+get_json_config()
+

return configuration as a JSON string

+
+ +
+
+get_normalization_factor()
+
+ +
+
+get_unit()
+
+ +
+
+normalization_factor
+
+ +
+
+process(data)
+

Process a frame +#TODO: +dark, flat, sa are missing

+ +++ + + + +
Param:data: numpy array containing the input image
+
+ +
+
+reconfig(shape=(2048, 2048), sync=False)
+

This is just to force the integrator to initialize with a given input image shape

+ +++ + + + +
Parameters:
    +
  • shape – shape of the input image
    • +
  • sync – return only when synchronized
    • +
+
+
+ +
+
+reset()
+

this is just to force the integrator to initialize

+
+ +
+
+save_config(filename=None)
+
+ +
+
+setDarkcurrentFile(imagefile)
+
+ +
+
+setExtension(ext)
+

enforce the extension of the processed data file written

+
+ +
+
+setFlatfieldFile(imagefile)
+
+ +
+
+setJsonConfig(jsonconfig)
+
+ +
+
+setSubdir(path)
+

Set the relative or absolute path for processed data

+
+ +
+
+set_error_model(value)
+
+ +
+
+set_normalization_factor(value)
+
+ +
+
+set_unit(value)
+
+ +
+
+unit
+
+ +
+
+warmup(sync=False)
+

Process a dummy image to ensure everything is initialized

+ +++ + + + +
Parameters:sync – wait for processing to be finished
+
+ +
+ +
+
+pyFAI.worker.make_ai(config)
+

Create an Azimuthal integrator from the configuration +Static method !

+ +++ + + + + + +
Parameters:config – dict with all parameters
Returns:configured (but uninitialized) AzimuthalIntgrator
+
+ +
+
+

io Module

+

Module for “high-performance” writing in either 1D with Ascii , +or 2D with FabIO or even nD with n varying from 2 to 4 using HDF5

+

Stand-alone module which tries to offer interface to HDF5 via H5Py and +capabilities to write EDF or other formats using fabio.

+

Can be imported without h5py but then limited to fabio & ascii formats.

+

TODO: +* add monitor to HDF5

+
+
+class pyFAI.io.AsciiWriter(filename=None, prefix='fai_', extension='.dat')
+

Bases: pyFAI.io.Writer

+

Ascii file writer (.xy or .dat)

+
+
+init(fai_cfg=None, lima_cfg=None)
+

Creates the directory that will host the output file(s)

+
+ +
+
+write(data, index=0)
+
+ +
+ +
+
+class pyFAI.io.FabioWriter(filename=None)
+

Bases: pyFAI.io.Writer

+

Image file writer based on FabIO

+

TODO !!!

+
+
+init(fai_cfg=None, lima_cfg=None)
+

Creates the directory that will host the output file(s)

+
+ +
+
+write(data, index=0)
+
+ +
+ +
+
+class pyFAI.io.HDF5Writer(filename, hpath='data', fast_scan_width=None)
+

Bases: pyFAI.io.Writer

+

Class allowing to write HDF5 Files.

+
+
+CONFIG = 'pyFAI'
+
+ +
+
+DATASET_NAME = 'data'
+
+ +
+
+close()
+
+ +
+
+flush(radial=None, azimuthal=None)
+

Update some data like axis units and so on.

+ +++ + + + +
Parameters:
    +
  • radial – position in radial direction
    • +
  • azimuthal – position in azimuthal direction
    • +
+
+
+ +
+
+init(fai_cfg=None, lima_cfg=None)
+

Initializes the HDF5 file for writing +:param fai_cfg: the configuration of the worker as a dictionary

+
+ +
+
+write(data, index=0)
+

Minimalistic method to limit the overhead. +:param data: array with intensities or tuple (2th,I) or (I,2th,chi)

+
+ +
+ +
+
+class pyFAI.io.Nexus(filename, mode='r')
+

Bases: object

+

Writer class to handle Nexus/HDF5 data +Manages: +entry

+
+
+
pyFAI-subentry
+
detector
+
+
+

#TODO: make it thread-safe !!!

+
+
+close()
+

close the filename and update all entries

+
+ +
+
+deep_copy(name, obj, where='/', toplevel=None, excluded=None, overwrite=False)
+

perform a deep copy: +create a “name” entry in self containing a copy of the object

+ +++ + + + +
Parameters:
    +
  • where – path to the toplevel object (i.e. root)
    • +
  • toplevel – firectly the top level Group
    • +
  • excluded – list of keys to be excluded
    • +
  • overwrite – replace content if already existing
    • +
+
+
+ +
+
+find_detector(all=False)
+

Tries to find a detector within a NeXus file, takes the first compatible detector

+ +++ + + + +
Parameters:all – return all detectors found as a list
+
+ +
+
+classmethod get_attr(dset, name, default=None)
+

Return the attribute of the dataset

+

Handles the ascii -> unicode issue in python3 #275

+ +++ + + + + + +
Parameters:
    +
  • dset – a HDF5 dataset (or a group)
    • +
  • name – name of the attribute
    • +
  • default – default value to be returned
    • +
+
Returns:

attribute value decoded in python3 or default

+
+
+ +
+
+get_class(grp, class_type='NXcollection')
+

return all sub-groups of the given type within a group

+ +++ + + + +
Parameters:
    +
  • grp – HDF5 group
    • +
  • class_type – name of the NeXus class
    • +
+
+
+ +
+
+get_data(grp, class_type='NXdata')
+

return all dataset of the the NeXus class NXdata

+ +++ + + + +
Parameters:
    +
  • grp – HDF5 group
    • +
  • class_type – name of the NeXus class
    • +
+
+
+ +
+
+get_entries()
+

retrieves all entry sorted the latest first.

+ +++ + + + +
Returns:list of HDF5 groups
+
+ +
+
+get_entry(name)
+

Retrieves an entry from its name

+ +++ + + + + + +
Parameters:name – name of the entry to retrieve
Returns:HDF5 group of NXclass == NXentry
+
+ +
+
+new_class(grp, name, class_type='NXcollection')
+

create a new sub-group with type class_type +:param grp: parent group +:param name: name of the sub-group +:param class_type: NeXus class name +:return: subgroup created

+
+ +
+
+new_detector(name='detector', entry='entry', subentry='pyFAI')
+

Create a new entry/pyFAI/Detector

+ +++ + + + +
Parameters:
    +
  • detector – name of the detector
    • +
  • entry – name of the entry
    • +
  • subentry – all pyFAI description of detectors should be in a pyFAI sub-entry
    • +
+
+
+ +
+
+new_entry(entry='entry', program_name='pyFAI', title='description of experiment', force_time=None)
+

Create a new entry

+ +++ + + + +
Parameters:
    +
  • entry – name of the entry
    • +
  • program_name – value of the field as string
    • +
  • title – value of the field as string
    • +
+
+

@force_time: enforce the start_time (as string!) +:return: the corresponding HDF5 group

+
+ +
+
+new_instrument(entry='entry', instrument_name='id00')
+

Create an instrument in an entry or create both the entry and the instrument if

+
+ +
+ +
+
+class pyFAI.io.Writer(filename=None, extension=None)
+

Bases: object

+

Abstract class for writers.

+
+
+CONFIG_ITEMS = ['filename', 'dirname', 'extension', 'subdir', 'hpath']
+
+ +
+
+flush(*arg, **kwarg)
+

To be implemented

+
+ +
+
+init(fai_cfg=None, lima_cfg=None)
+

Creates the directory that will host the output file(s) +:param fai_cfg: configuration for worker +:param lima_cfg: configuration for acquisition

+
+ +
+
+setJsonConfig(json_config=None)
+

Sets the JSON configuration

+
+ +
+
+write(data)
+

To be implemented

+
+ +
+ +
+
+pyFAI.io.from_isotime(text, use_tz=False)
+
+++ + + + +
Parameters:text – string representing the time is iso format
+
+ +
+
+pyFAI.io.get_isotime(forceTime=None)
+
+++ + + + + + + + +
Parameters:forceTime (float) – enforce a given time (current by default)
Returns:the current time as an ISO8601 string
Return type:string
+
+ +
+
+pyFAI.io.is_hdf5(filename)
+

Check if a file is actually a HDF5 file

+ +++ + + + +
Parameters:filename – this file has better to exist
+
+ +
+
+

calibration Module

+

pyFAI-calib

+

A tool for determining the geometry of a detector using a reference sample.

+
+
+class pyFAI.calibration.AbstractCalibration(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, wavelength=None, calibrant=None)
+

Bases: object

+

Everything that is common to Calibration and Recalibration

+
+
+HELP = {'reset': 'Reset the geometry to the initial guess (rotation to zero, distance to 0.1m, poni at the center of the image)', 'delete': 'delete a group of points, provide the letter.', 'set': "set the value of a parameter to the given value, i.e 'set wavelength 0.1 nm', units are optional", 'chiplot': 'plot control point radial error as function of azimuthal angle, optional parameters: the rings for which this need to be plotted', 'help': "Try to get the help of a given action, like 'refine?'. Use done when finished. Most command are composed of 'action parameter value' like 'set wavelength 1 A'.", 'weight': 'toggle from weighted to unweighted mode...', 'get': 'print he value of a parameter', 'fix': "fixes the value of a parameter so that its value will not be optimized, i.e. 'fix wavelength'", 'validate2': 'measures the offset of the center as function of azimuthal angle by cross-correlation of 2 plots, 180 deg appart. Option: number of azimuthal sliced, default: 36', 'show': "Just print out the current parameter set. Optional parameters are units for length, rotation and wavelength, i.e. 'show mm deg A'", 'bound': "sets the upper and lower bound of a parameter: 'bound dist 0.1 0.2'", 'free': "frees the parameter so that the value can be optimized, i.e. 'free wavelength'", 'bounds': 'sets the upper and lower bound of all parameters', 'abort': 'quit immediately, discarding any unsaved changes', 'done': 'finishes the processing, performs an integration and quits', 'refine': 'performs a new cycle of refinement', 'integrate': 'perform the azimuthal integration and display results', 'validate': 'plot the offset between the calibrated image and the back-projected image', 'recalib': 'extract a new set of rings and re-perform the calibration. One can specify how many rings to extract and the algorithm to use (blob, massif, watershed) and the nb_pts_per_deg in azimuth', 'assign': 'Change the assignment of a group of points to a rings', 'define': 'Re-define the value for a constant internal parameter of the program like max_iter, nPt_1D, nPt_2D_azim, nPt_2D_rad, integrator_method, error_model. Warning: attribute change may be harmful !'}
+
+ +
+
+PARAMETERS = ['dist', 'poni1', 'poni2', 'rot1', 'rot2', 'rot3', 'wavelength']
+
+ +
+
+PTS_PER_DEG = 0.3
+
+ +
+
+UNITS = {'poni1': 'meter', 'poni2': 'meter', 'rot1': 'radian', 'rot3': 'radian', 'rot2': 'radian', 'wavelength': 'meter', 'dist': 'meter'}
+
+ +
+
+VALID_URL = ['', 'file', 'hdf5', 'nxs', 'h5']
+
+ +
+
+analyse_options(options=None, args=None)
+

Analyzes options and arguments

+ +++ + + + +
Returns:option,arguments
+
+ +
+
+chiplot(rings=None)
+

plot delta_2theta/2theta = f(chi) and fit the curve.

+ +++ + + + +
Parameters:rings – list of rings to consider
+
+ +
+
+configure_parser(version='calibration from pyFAI version 0.12.0: 06/06/2016', usage='pyFAI-calib [options] input_image.edf', description=None, epilog=None)
+

Common configuration for parsers

+
+ +
+
+extract_cpt(method='massif', pts_per_deg=1.0)
+

Performs an automatic keypoint extraction: +Can be used in recalib or in calib after a first calibration has been performed.

+ +++ + + + +
Parameters:
    +
  • method – method for keypoint extraction
    • +
  • pts_per_deg – number of control points per azimuthal degree (increase for better precision)
    • +
+
+
+ +
+
+get_pixelSize(ans)
+

convert a comma separated sting into pixel size

+
+ +
+
+postProcess()
+

Common part: shows the result of the azimuthal integration in 1D and 2D

+
+ +
+
+preprocess()
+

Common part: +do dark, flat correction thresholding, ... +and read missing data from keyboard if needed

+
+ +
+
+prompt()
+

prompt for commands to guide the calibration process

+ +++ + + + +
Returns:True when the user is happy with what he has, False to request another refinement
+
+ +
+
+read_dSpacingFile(verbose=True)
+

Read the name of the calibrant / file with d-spacing

+
+ +
+
+read_pixelsSize()
+

Read the pixel size from prompt if not available

+
+ +
+
+read_wavelength()
+

Read the wavelength

+
+ +
+
+refine()
+

Contains the common geometry refinement part

+
+ +
+
+reset_geometry(how='center', refine=False)
+

Reset the geometry: no tilt in all cases

+ +++ + + + +
Parameters:how – multiple options +* center: set the PONI at the center of the detector +* ring: center the poni at the middle of the inner-most ring +//* best: try both option and keeps the best
+

//:param refine: launch the refinement

+
+ +
+
+set_data(data)
+

call-back function for the peak-picker

+
+ +
+
+validate_calibration()
+

Validate the calibration and calculate the offset in the diffraction image

+
+ +
+
+validate_center(slices=36)
+

Validate the position of the center by cross-correlating two spectra 180 deg appart. +Output values are in micron.

+

Designed for orthogonal setup with centered beam...

+ +++ + + + +
Parameters:slices – number of slices on which perform
+
+ +
+
+win_error = 'We are under windows with a 32 bit version of python, matplotlib is not able to display too many images without crashing, this is why the window showing the diffraction image is closed'
+
+ +
+ +
+
+class pyFAI.calibration.Calibration(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, gaussianWidth=None, wavelength=None, calibrant=None)
+

Bases: pyFAI.calibration.AbstractCalibration

+

class doing the calibration of frames

+
+
+gui_peakPicker()
+
+ +
+
+initgeoRef()
+

Tries to initialise the GeometryRefinement (dist, poni, rot) +Returns a dictionary of key value pairs

+
+ +
+
+parse()
+

parse options from command line

+
+ +
+
+preprocess()
+

do dark, flat correction thresholding, ...

+
+ +
+
+refine()
+

Contains the geometry refinement part specific to Calibration +Sets up the initial guess when starting pyFAI-calib

+
+ +
+ +
+
+class pyFAI.calibration.CheckCalib(poni=None, img=None, unit='2th_deg')
+

Bases: object

+
+
+get_1dsize()
+
+ +
+
+integrate()
+
+ +
+
+parse()
+
+ +
+
+rebuild()
+

Rebuild the diffraction image and measures the offset with the reference +:return: offset

+
+ +
+
+show()
+

Show the image with the the errors

+
+ +
+
+size1d
+
+ +
+
+smooth_mask(hwhm=5)
+

smooth out around the mask to avoid aligning on the mask

+
+ +
+ +
+
+class pyFAI.calibration.MultiCalib(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None)
+

Bases: object

+
+
+get_pixelSize(ans)
+

convert a comma separated sting into pixel size

+
+ +
+
+parse(exe=None, description=None, epilog=None)
+

parse options from command line +:param exe: name of the program (MX-calibrate) +:param description: Description of the program

+
+ +
+
+process()
+
+ +
+
+read_dSpacingFile()
+

Read the name of the calibrant or the file with d-spacing

+
+ +
+
+read_pixelsSize()
+

Read the pixel size from prompt if not available

+
+ +
+
+read_wavelength()
+

Read the wavelength

+
+ +
+
+regression()
+
+ +
+ +
+
+class pyFAI.calibration.Recalibration(dataFiles=None, darkFiles=None, flatFiles=None, pixelSize=None, splineFile=None, detector=None, wavelength=None, calibrant=None)
+

Bases: pyFAI.calibration.AbstractCalibration

+

class doing the re-calibration of frames

+
+
+parse()
+

parse options from command line

+
+ +
+
+preprocess()
+

do dark, flat correction thresholding, ...

+
+ +
+
+read_dSpacingFile()
+

Read the name of the file with d-spacing

+
+ +
+
+refine()
+

Contains the geometry refinement part specific to Recalibration

+
+ +
+ +
+
+pyFAI.calibration.calib(img, calibrant, detector, basename='from_ipython', reconstruct=False, dist=0.1, gaussian=None, interactive=True)
+

Procedural interfact for calibration

+ +++ + + + + + +
Parameters:
    +
  • img – 2d array representing the calibration image
    • +
  • calibrant – Instance of Calibrant, set-up with wavelength
    • +
  • detector – Detector instance containing the mask
    • +
  • basename – output file base
    • +
  • recontruct – perform image reconstruction of masked pixel ?
    • +
  • dist – initial distance
    • +
  • gaussian – width of the gaussian used for difference of gaussian in the “massif” peak-picking algorithm
    • +
  • interactive – set to False for testing
    • +
+
Returns:

AzimuthalIntegrator instance

+
+
+ +
+
+pyFAI.calibration.get_detector(detector, datafiles=None)
+

Detector factory taking into account the binning knowing the datafiles +:param detector: string or detector or other junk +:param datafiles: can be a list of images to be opened and their shape used. +:return pyFAI.detector.Detector instance

+
+ +
+
+

peak_picker Module

+
+
+class pyFAI.peak_picker.ControlPoints(filename=None, calibrant=None, wavelength=None)
+

Bases: object

+

This class contains a set of control points with (optionally) their ring number hence d-spacing and diffraction 2Theta angle ...

+
+
+append(points, ring=None, annotate=None, plot=None)
+
+++ + + + + + +
Parameters:
    +
  • point – list of points
    • +
  • ring – ring number
    • +
  • annotate – matplotlib.annotate reference
    • +
  • plot – matplotlib.plot reference
    • +
+
Returns:

PointGroup instance

+
+
+ +
+
+append_2theta_deg(points, angle=None, ring=None)
+
+++ + + + +
Parameters:
    +
  • point – list of points
    • +
  • angle – 2-theta angle in degrees
    • +
+
+
+ +
+
+check()
+

check internal consistency of the class

+
+ +
+
+dSpacing
+
+ +
+
+get(ring=None)
+

retireves the last set of points for a given ring (by default the last)

+ +++ + + + +
Parameters:ring – index of ring to search for
+
+ +
+
+getList()
+

Retrieve the list of control points suitable for geometry refinement with ring number

+
+ +
+
+getList2theta()
+

Retrieve the list of control points suitable for geometry refinement

+
+ +
+
+getListRing()
+

Retrieve the list of control points suitable for geometry refinement with ring number

+
+ +
+
+getWeightedList(image)
+

Retrieve the list of control points suitable for geometry refinement with ring number and intensities +:param image: +:return: a (x,4) array with pos0, pos1, ring nr and intensity

+

#TODO: refine the value of the intensity using 2nd order polynomia

+
+ +
+
+get_dSpacing()
+
+ +
+
+get_wavelength()
+
+ +
+
+load(filename)
+

load all control points from a file

+
+ +
+
+pop(ring=None, lbl=None)
+

Remove the set of points, either from its code or from a given ring (by default the last)

+ +++ + + + +
Parameters:
    +
  • ring – index of ring of which remove the last group
    • +
  • lbl – code of the ring to remove
    • +
+
+
+ +
+
+readRingNrFromKeyboard()
+

Ask the ring number values for the given points

+
+ +
+
+reset()
+

remove all stored values and resets them to default

+
+ +
+
+save(filename)
+

Save a set of control points to a file +:param filename: name of the file +:return: None

+
+ +
+
+setWavelength_change2th(value=None)
+
+ +
+
+setWavelength_changeDs(value=None)
+

This is probably not a good idea, but who knows !

+
+ +
+
+set_dSpacing(lst)
+
+ +
+
+set_wavelength(value=None)
+
+ +
+
+wavelength
+
+ +
+ +
+
+class pyFAI.peak_picker.PeakPicker(data, reconst=False, mask=None, pointfile=None, calibrant=None, wavelength=None, detector=None, method='massif')
+

Bases: object

+

This class is in charge of peak picking, i.e. find bragg spots in the image +Two methods can be used : massif or blob

+
+
+VALID_METHODS = ['massif', 'blob', 'watershed']
+
+ +
+
+closeGUI()
+
+ +
+
+contour(data, cmap='autumn', linewidths=2, linestyles='dashed')
+

Overlay a contour-plot

+ +++ + + + +
Parameters:data – 2darray with the 2theta values in radians...
+
+ +
+
+display_points(minIndex=0, reset=False)
+

display all points and their ring annotations +:param minIndex: ring index to start with +:param reset: remove all point before re-displaying them

+
+ +
+
+finish(filename=None, callback=None)
+

Ask the ring number for the given points

+ +++ + + + +
Parameters:filename – file with the point coordinates saved
+
+ +
+
+gui(log=False, maximize=False, pick=True)
+
+++ + + + +
Parameters:log – show z in log scale
+
+ +
+
+help = ['Please select rings on the diffraction image. In parenthesis, some modified shortcuts for single button mouse (Apple):', ' * Right-click (click+n): try an auto find for a ring', ' * Right-click + Ctrl (click+b): create new group with one point', ' * Right-click + Shift (click+v): add one point to current group', ' * Right-click + m (click+m): find more points for current group', ' * Center-click or (click+d): erase current group', ' * Center-click + 1 or (click+1): erase closest point from current group']
+
+ +
+
+init(method, sync=True)
+

Unified initializer

+
+ +
+
+load(filename)
+

load a filename and plot data on the screen (if GUI)

+
+ +
+
+massif_contour(data)
+

Overlays a mask over a diffraction image

+ +++ + + + +
Parameters:data – mask to be overlaid
+
+ +
+
+on_minus_pts_clicked(*args)
+

callback function

+
+ +
+
+on_option_clicked(*args)
+

callback function

+
+ +
+
+on_plus_pts_clicked(*args)
+

callback function

+
+ +
+
+on_refine_clicked(*args)
+

callback function

+
+ +
+
+onclick(event)
+

Called when a mouse is clicked

+
+ +
+
+peaks_from_area(**kwargs)
+

Return the list of peaks within an area

+ +++ + + + + + +
Parameters:
    +
  • mask – 2d array with mask.
    • +
  • Imin – minimum of intensity above the background to keep the point
    • +
  • keep – maximum number of points to keep
    • +
  • method – enforce the use of detection using “massif” or “blob” or “watershed”
    • +
  • ring – ring number to which assign the points
    • +
  • dmin – minimum distance between two peaks (in pixels)
    • +
  • seed – good starting points.
    • +
+
Returns:

list of peaks [y,x], [y,x], ...]

+
+
+ +
+
+remove_grp(lbl)
+

remove a group of points

+ +++ + + + +
Parameters:lbl – label of the group of points
+
+ +
+
+reset()
+

Reset control point and graph (if needed)

+
+ +
+
+sync_init()
+
+ +
+ +
+
+class pyFAI.peak_picker.PointGroup(points=None, ring=None, annotate=None, plot=None, force_label=None)
+

Bases: object

+

Class contains a group of points ... +They all belong to the same Debye-Scherrer ring

+
+
+code
+

Numerical value for the label: mainly for sorting

+
+ +
+
+classmethod get_label()
+

return the next label

+
+ +
+
+get_ring()
+
+ +
+
+label
+
+ +
+
+last_label = 0
+
+ +
+
+classmethod reset_label()
+

reset intenal counter

+
+ +
+
+ring
+
+ +
+
+classmethod set_label(label)
+

update the internal counter if needed

+
+ +
+
+set_ring(value)
+
+ +
+ +
+
+

massif Module

+
+
+class pyFAI.massif.Massif(data=None)
+

Bases: object

+

A massif is defined as an area around a peak, it is used to find neighboring peaks

+
+
+TARGET_SIZE = 1024
+
+ +
+
+calculate_massif(x)
+

defines a map of the massif around x and returns the mask

+
+ +
+
+delValleySize()
+
+ +
+
+find_peaks(x, nmax=200, annotate=None, massif_contour=None, stdout=<open file '<stdout>', mode 'w' at 0x7fceb9e2e150>)
+

All in one function that finds a maximum from the given seed (x) +then calculates the region extension and extract position of the neighboring peaks. +:param x: seed for the calculation, input coordinates +:param nmax: maximum number of peak per region +:param annotate: call back method taking number of points + coordinate as input. +:param massif_contour: callback to show the contour of a massif with the given index. +:param stdout: this is the file where output is written by default. +:return: list of peaks

+
+ +
+
+getBinnedData()
+

:return binned data

+
+ +
+
+getBluredData()
+
+++ + + + +
Returns:a blurred image
+
+ +
+
+getLabeledMassif(pattern=None)
+
+++ + + + +
Returns:an image composed of int with a different value for each massif
+
+ +
+
+getMedianData()
+
+++ + + + +
Returns:a spacial median filtered image
+
+ +
+
+getValleySize()
+
+ +
+
+initValleySize()
+
+ +
+
+nearest_peak(x)
+
+++ + + + +
Parameters:x – coordinates of the peak
+

:returns the coordinates of the nearest peak

+
+ +
+
+peaks_from_area(mask, Imin=None, keep=1000, dmin=0.0, seed=None, **kwarg)
+

Return the list of peaks within an area

+ +++ + + + + + +
Parameters:
    +
  • mask – 2d array with mask.
    • +
  • Imin – minimum of intensity above the background to keep the point
    • +
  • keep – maximum number of points to keep
    • +
  • kwarg – ignored parameters
    • +
  • dmin – minimum distance to another peak
    • +
  • seed – list of good guesses to start with
    • +
+
Returns:

list of peaks [y,x], [y,x], ...]

+
+
+ +
+
+setValleySize(size)
+
+ +
+
+valley_size
+

Defines the minimum distance between two massifs

+
+ +
+ +
+
+

blob_detection Module

+
+
+class pyFAI.blob_detection.BlobDetection(img, cur_sigma=0.25, init_sigma=0.5, dest_sigma=1, scale_per_octave=2, mask=None)
+

Bases: object

+

Performs a blob detection: +http://en.wikipedia.org/wiki/Blob_detection +using a Difference of Gaussian + Pyramid of Gaussians

+
+
+direction()
+

Perform and plot the two main directions of the peaks, considering their previously +calculated scale ,by calculating the Hessian at different sizes as the combination of +gaussians and their first and second derivatives

+
+ +
+
+nearest_peak(p, refine=True, Imin=None)
+

Return the nearest peak from a position

+ +++ + + + +
Parameters:
    +
  • p – input position (y,x) 2-tuple of float
    • +
  • refine – shall the position be refined on the raw data
    • +
  • Imin – minimum of intensity above the background
    • +
+
+
+ +
+
+peaks_from_area(mask, keep=None, refine=True, Imin=None, dmin=0.0, **kwargs)
+

Return the list of peaks within an area

+ +++ + + + + + +
Parameters:
    +
  • mask – 2d array with mask.
    • +
  • refine – shall the position be refined on the raw data
    • +
  • Imin – minimum of intensity above the background
    • +
  • kwarg – ignored parameters
    • +
+
Returns:

list of peaks [y,x], [y,x], ...]

+
+
+ +
+
+process(max_octave=None)
+

Perform the keypoint extraction for max_octave cycles or until all octaves have been processed. +:param max_octave: number of octave to process

+
+ +
+
+refine_Hessian(kpx, kpy, kps)
+

Refine the keypoint location based on a 3 point derivative, and delete uncoherent keypoints

+ +++ + + + +
Parameters:
    +
  • kpx – x_pos of keypoint
    • +
  • kpy – y_pos of keypoint
    • +
  • kps – s_pos of keypoint
    • +
+
+

:return arrays of corrected coordinates of keypoints, values and locations of keypoints

+
+ +
+
+refine_Hessian_SG(kpx, kpy, kps)
+

Savitzky Golay algorithm to check if a point is really the maximum +:param kpx: x_pos of keypoint +:param kpy: y_pos of keypoint +:param kps: s_pos of keypoint +:return array of corrected keypoints

+
+ +
+
+refinement()
+
+ +
+
+show_neighboor()
+
+ +
+
+show_stats()
+

Shows a window with the repartition of keypoint in function of scale/intensity

+
+ +
+
+tresh = 0.6
+
+ +
+ +
+
+pyFAI.blob_detection.image_test()
+
+ +
+
+pyFAI.blob_detection.local_max(dogs, mask=None, n_5=True)
+
+++ + + + +
Parameters:dogs – 3d array with (sigma,y,x) containing difference of gaussians
+

@parm mask: mask out keypoint next to the mask (or inside the mask) +:param n_5: look for a larger neighborhood

+
+ +
+
+pyFAI.blob_detection.make_gaussian(im, sigma, xc, yc)
+
+ +
+
+

calibrant Module

+

Calibrant

+

A module containing classical calibrant and also tools to generate d-spacing.

+

Interesting formula: +http://geoweb3.princeton.edu/research/MineralPhy/xtalgeometry.pdf

+
+
+class pyFAI.calibrant.Calibrant(filename=None, dSpacing=None, wavelength=None)
+

Bases: object

+

A calibrant is a reference compound where the d-spacing (interplanar distances) +are known. They are expressed in Angstrom (in the file)

+
+
+append_2th(value)
+
+ +
+
+append_dSpacing(value)
+
+ +
+
+dSpacing
+
+ +
+
+fake_calibration_image(ai, shape=None, Imax=1.0, U=0, V=0, W=0.0001)
+

Generates a fake calibration image from an azimuthal integrator

+ +++ + + + +
Parameters:
    +
  • ai – azimuthal integrator
    • +
  • Imax – maximum intensity of rings
    • +
  • V, W (U,) – width of the peak from Caglioti’s law (FWHM^2 = Utan(th)^2 + Vtan(th) + W)
    • +
+
+
+ +
+
+get_2th()
+
+ +
+
+get_2th_index(angle)
+

return the index in the 2theta angle index

+
+ +
+
+get_dSpacing()
+
+ +
+
+get_wavelength()
+
+ +
+
+load_file(filename=None)
+
+ +
+
+save_dSpacing(filename=None)
+

save the d-spacing to a file

+
+ +
+
+setWavelength_change2th(value=None)
+
+ +
+
+setWavelength_changeDs(value=None)
+

This is probably not a good idea, but who knows !

+
+ +
+
+set_dSpacing(lst)
+
+ +
+
+set_wavelength(value=None)
+
+ +
+
+wavelength
+
+ +
+ +
+
+class pyFAI.calibrant.Cell(a=1, b=1, c=1, alpha=90, beta=90, gamma=90, lattice='triclinic', lattice_type='P')
+

Bases: object

+

This is a cell object, able to calculate the volume and d-spacing according to formula from:

+

http://geoweb3.princeton.edu/research/MineralPhy/xtalgeometry.pdf

+
+
+classmethod cubic(a, lattice_type='P')
+

Factory for cubic lattices

+ +++ + + + +
Parameters:a – unit cell length
+
+ +
+
+d(hkl)
+

Calculate the actual d-spacing for a 3-tuple of integer representing a +family of Miller plans

+ +++ + + + + + +
Parameters:hkl – 3-tuple of integers
Returns:the inter-planar distance
+
+ +
+
+d_spacing(dmin=1.0)
+

Calculate all d-spacing down to dmin

+

applies selection rules

+ +++ + + + + + +
Parameters:dmin – minimum value of spacing requested
Returns:dict d-spacing as string, list of tuple with Miller indices +preceded with the numerical value
+
+ +
+
+classmethod diamond(a)
+

Factory for Diamond type FCC like Si and Ge

+ +++ + + + +
Parameters:a – unit cell length
+
+ +
+
+get_type()
+
+ +
+
+classmethod hexagonal(a, c, lattice_type='P')
+

Factory for hexagonal lattices

+ +++ + + + +
Parameters:
    +
  • a – unit cell length
    • +
  • c – unit cell length
    • +
+
+
+ +
+
+lattices = ['cubic', 'tetragonal', 'hexagonal', 'rhombohedral', 'orthorhombic', 'monoclinic', 'triclinic']
+
+ +
+
+classmethod monoclinic(a, b, c, beta, lattice_type='P')
+

Factory for hexagonal lattices

+ +++ + + + +
Parameters:
    +
  • a – unit cell length
    • +
  • b – unit cell length
    • +
  • c – unit cell length
    • +
  • beta – unit cell angle
    • +
+
+
+ +
+
+classmethod orthorhombic(a, b, c, lattice_type='P')
+

Factory for orthorhombic lattices

+ +++ + + + +
Parameters:
    +
  • a – unit cell length
    • +
  • b – unit cell length
    • +
  • c – unit cell length
    • +
+
+
+ +
+
+classmethod rhombohedral(a, alpha, lattice_type='P')
+

Factory for hexagonal lattices

+ +++ + + + +
Parameters:
    +
  • a – unit cell length
    • +
  • alpha – unit cell angle
    • +
+
+
+ +
+
+save(name, long_name=None, doi=None, dmin=1.0, dest_dir=None)
+

Save informations about the cell in a d-spacing file, usable as Calibrant

+ +++ + + + +
Parameters:
    +
  • name – name of the calibrant
    • +
  • doi – reference of the publication used to parametrize the cell
    • +
  • dmin – minimal d-spacing
    • +
  • dest_dir – name of the directory where to save the result
    • +
+
+
+ +
+
+selection_rules = None
+

contains a list of functions returning True(allowed)/False(forbiden)/None(unknown)

+
+ +
+
+set_type(lattice_type)
+
+ +
+
+classmethod tetragonal(a, c, lattice_type='P')
+

Factory for tetragonal lattices

+ +++ + + + +
Parameters:
    +
  • a – unit cell length
    • +
  • c – unit cell length
    • +
+
+
+ +
+
+type
+
+ +
+
+types = {'I': 'Body centered', 'P': 'Primitive', 'C': 'Side centered', 'R': 'Rhombohedral', 'F': 'Face centered'}
+
+ +
+
+volume
+
+ +
+ +
+
+class pyFAI.calibrant.calibrant_factory(basedir=None)
+

Bases: object

+

Behaves like a dict but is actually a factory: +Each time one retrieves an object it is a new geniune new calibrant (unmodified)

+
+
+get(what, notfound=None)
+
+ +
+
+has_key(k)
+
+ +
+
+items()
+
+ +
+
+keys()
+
+ +
+
+values()
+
+ +
+ +
+
+

distortion Module

+
+
+class pyFAI.distortion.Distortion(detector='detector', shape=None, resize=False, empty=0, mask=None, method='LUT', device=None, workgroup=8)
+

Bases: object

+

This class applies a distortion correction on an image.

+

New version compatible both with CSR and LUT...

+
+
+calc_LUT(use_common=True)
+

Calculate the Look-up table

+ +++ + + + +
Returns:look up table either in CSR or LUT format depending on serl.method
+
+ +
+
+calc_init()
+

Initialize all arrays

+
+ +
+
+calc_pos(use_cython=True)
+

Calculate the pixel boundary position on the regular grid

+ +++ + + + +
Returns:pixel corner positions (in pixel units) on the regular grid
+

@rtyep: ndarray of shape (nrow, ncol, 4, 2)

+
+ +
+
+calc_size(use_cython=True)
+

Calculate the number of pixels falling into every single bin and

+ +++ + + + +
Returns:max of pixel falling into a single bin
+

Considering the “half-CCD” spline from ID11 which describes a (1025,2048) detector, +the physical location of pixels should go from: +[-17.48634 : 1027.0543, -22.768829 : 2028.3689] +We chose to discard pixels falling outside the [0:1025,0:2048] range with a lose of intensity

+
+ +
+
+correct(image, dummy=None, delta_dummy=None)
+

Correct an image based on the look-up table calculated ...

+ +++ + + + + + +
Parameters:
    +
  • image – 2D-array with the image
    • +
  • dummy – value suggested for bad pixels
    • +
  • delta_dummy – precision of the dummy value
    • +
+
Returns:

corrected 2D image

+
+

#TODO: #225

+
+ +
+
+reset(method=None, device=None, workgroup=None, prepare=True)
+

reset the distortion correction and re-calculate the look-up table

+ +++ + + + +
Parameters:
    +
  • method – can be “lut” or “csr”, “lut” looks faster
    • +
  • device – can be None, “cpu” or “gpu” or the id as a 2-tuple of integer
    • +
  • worgroup – enforce the workgroup size for CSR.
    • +
  • prepare – set to false to only reset and not re-initialize
    • +
+
+
+ +
+
+shape_out
+

Calculate/cache the output shape

+

:return output shape

+
+ +
+
+uncorrect(image)
+

Take an image which has been corrected and transform it into it’s raw (with loss of information)

+ +++ + + + + + +
Parameters:image – 2D-array with the image
Returns:uncorrected 2D image and a mask (pixels in raw image
+
+ +
+ +
+
+class pyFAI.distortion.Quad(buffer)
+

Bases: object

+
+
+
+
+

+

+
+
+
xxxxxA
+
+
xxxxxxxI’xxxxxxxx x
+
+
+
+

xxxxxxxxIxxxxxx | x

+
+

Bxxxxxxxxxxxx | | x +x | | x +x | | x

+
+

x | | x +x | | x +x | | x +x | | x +x | | x

+
+
x | | x +x | | x +x | | x +x O| P A’ x
+
+
+
+
—————–J——————+——————————–L———————–
+

x | x +x | x +x | x

+
+

x | xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxD +CxxxxxxxxxxxxxxxxxKxxxxx

+
+
+

+

+

+

+
+
+
+
+
+
+
+calc_area()
+
+ +
+
+calc_area_AB(I1, I2)
+
+ +
+
+calc_area_BC(J1, J2)
+
+ +
+
+calc_area_CD(K1, K2)
+
+ +
+
+calc_area_DA(L1, L2)
+
+ +
+
+calc_area_old()
+
+ +
+
+calc_area_vectorial()
+
+ +
+
+get_box(i, j)
+
+ +
+
+get_box_size0()
+
+ +
+
+get_box_size1()
+
+ +
+
+get_idx(i, j)
+
+ +
+
+get_offset0()
+
+ +
+
+get_offset1()
+
+ +
+
+init_slope()
+
+ +
+
+integrateAB(start, stop, calc_area)
+
+ +
+
+populate_box()
+
+ +
+
+reinit(A0, A1, B0, B1, C0, C1, D0, D1)
+
+ +
+ +
+
+pyFAI.distortion.test()
+
+ +
+
+

worker Module

+

This module contains the Worker class:

+

A tool able to perform azimuthal integration with: +additional saving capabilities like +- save as 2/3D structure in a HDF5 File +- read from HDF5 files

+

Aims at being integrated into a plugin like LImA or as model for the GUI

+

The configuration of this class is mainly done via a dictionary transmitted as a JSON string: +Here are the valid keys:

+
+

“dist”, +“poni1”, +“poni2”, +“rot1” +“rot3” +“rot2” +“pixel1” +“pixel2”

+

“splineFile” +“wavelength”

+

“poni” #path of the file

+

“chi_discontinuity_at_0” +“do_mask” +“do_dark” +“do_azimuthal_range” +“do_flat” +“do_2D” +“azimuth_range_min” +“azimuth_range_max”

+

“polarization_factor” +“nbpt_rad” +“do_solid_angle” +“do_radial_range” +“do_poisson” +“delta_dummy” +“nbpt_azim” +“flat_field” +“radial_range_min” +“dark_current” +“do_polarization” +“mask_file” +“detector” +“unit” +“radial_range_max” +“val_dummy” +“do_dummy” +“method”

+
+

}

+
+
+class pyFAI.worker.DistortionWorker(detector=None, dark=None, flat=None, solidangle=None, polarization=None, mask=None, dummy=None, delta_dummy=None, device=None)
+

Bases: object

+

Simple worker doing dark, flat, solid angle and polarization correction

+
+
+process(data, normalization=None)
+

Process the data and apply a normalization factor +:param data: input data +:param normalization: normalization factor +:return processed data

+
+ +
+ +
+
+class pyFAI.worker.PixelwiseWorker(dark=None, flat=None, solidangle=None, polarization=None, mask=None, dummy=None, delta_dummy=None, device=None)
+

Bases: object

+

Simple worker doing dark, flat, solid angle and polarization correction

+
+
+process(data, normalization=None)
+

Process the data and apply a normalization factor +:param data: input data +:param normalization: normalization factor +:return processed data

+
+ +
+ +
+
+class pyFAI.worker.Worker(azimuthalIntgrator=None, shapeIn=(2048, 2048), shapeOut=(360, 500), unit='r_mm', dummy=None, delta_dummy=None)
+

Bases: object

+
+
+do_2D()
+
+ +
+
+error_model
+
+ +
+
+get_config()
+

return configuration as a dictionary

+
+ +
+
+get_error_model()
+
+ +
+
+get_json_config()
+

return configuration as a JSON string

+
+ +
+
+get_normalization_factor()
+
+ +
+
+get_unit()
+
+ +
+
+normalization_factor
+
+ +
+
+process(data)
+

Process a frame +#TODO: +dark, flat, sa are missing

+ +++ + + + +
Param:data: numpy array containing the input image
+
+ +
+
+reconfig(shape=(2048, 2048), sync=False)
+

This is just to force the integrator to initialize with a given input image shape

+ +++ + + + +
Parameters:
    +
  • shape – shape of the input image
    • +
  • sync – return only when synchronized
    • +
+
+
+ +
+
+reset()
+

this is just to force the integrator to initialize

+
+ +
+
+save_config(filename=None)
+
+ +
+
+setDarkcurrentFile(imagefile)
+
+ +
+
+setExtension(ext)
+

enforce the extension of the processed data file written

+
+ +
+
+setFlatfieldFile(imagefile)
+
+ +
+
+setJsonConfig(jsonconfig)
+
+ +
+
+setSubdir(path)
+

Set the relative or absolute path for processed data

+
+ +
+
+set_error_model(value)
+
+ +
+
+set_normalization_factor(value)
+
+ +
+
+set_unit(value)
+
+ +
+
+unit
+
+ +
+
+warmup(sync=False)
+

Process a dummy image to ensure everything is initialized

+ +++ + + + +
Parameters:sync – wait for processing to be finished
+
+ +
+ +
+
+pyFAI.worker.make_ai(config)
+

Create an Azimuthal integrator from the configuration +Static method !

+ +++ + + + + + +
Parameters:config – dict with all parameters
Returns:configured (but uninitialized) AzimuthalIntgrator
+
+ +
+
+

units Module

+
+
+class pyFAI.units.Enum
+

Bases: dict

+

Simple class half way between a dict and a class, behaving as an enum

+
+ +
+
+pyFAI.units.eq_2th(x, y, z, wavelength=None)
+

Calculates the 2theta aperture of the cone

+ +++ + + + +
Parameters:
    +
  • x – horizontal position, towards the center of the ring, from sample position
    • +
  • y – Vertical position, to the roof, from sample position
    • +
  • z – distance from sample along the beam
    • +
  • wavelength – in meter
    • +
+
+
+ +
+
+pyFAI.units.eq_logq(x, y, z, wavelength)
+

Calculates the log (decimal) of the scattering vector

+ +++ + + + +
Parameters:
    +
  • x – horizontal position, towards the center of the ring, from sample position
    • +
  • y – Vertical position, to the roof, from sample position
    • +
  • z – distance from sample along the beam
    • +
  • wavelength – in meter
    • +
+
+
+ +
+
+pyFAI.units.eq_q(x, y, z, wavelength)
+

Calculates the modulus of the scattering vector

+ +++ + + + +
Parameters:
    +
  • x – horizontal position, towards the center of the ring, from sample position
    • +
  • y – Vertical position, to the roof, from sample position
    • +
  • z – distance from sample along the beam
    • +
  • wavelength – in meter
    • +
+
+
+ +
+
+pyFAI.units.eq_r(x, y, z=None, wavelength=None)
+

Calculates the radius

+ +++ + + + +
Parameters:
    +
  • x – horizontal position, towards the center of the ring, from sample position
    • +
  • y – Vertical position, to the roof, from sample position
    • +
  • z – distance from sample along the beam
    • +
  • wavelength – in meter
    • +
+
+
+ +
+
+pyFAI.units.eq_rd2(x, y, z, wavelength)
+

Calculates the reciprocal spacing squared

+ +++ + + + +
Parameters:
    +
  • x – horizontal position, towards the center of the ring, from sample position
    • +
  • y – Vertical position, to the roof, from sample position
    • +
  • z – distance from sample along the beam
    • +
  • wavelength – in meter
    • +
+
+
+ +
+
+pyFAI.units.to_unit(obj, type_=(2th_deg, 2th_rad, q_nm^-1, q_A^-1, r_mm, r_m, d*2_A^-2, d*2_nm^-2, log(q)_m))
+
+ +
+
+

utils Module

+

Utilities, mainly for image treatment

+
+
+class pyFAI.utils.FixedParameters
+

Bases: set

+

Like a set, made for FixedParameters in geometry refinement

+
+
+add_or_discard(key, value=True)
+

Add a value to a set if value, else discard it +:param key: element to added or discared from set +:type value: boolean. If None do nothing ! +:return: None

+
+ +
+ +
+
+pyFAI.utils.averageDark(lstimg, center_method='mean', cutoff=None, quantiles=(0.5, 0.5))
+

Averages a serie of dark (or flat) images. +Centers the result on the mean or the median ... +but averages all frames within cutoff*std

+ +++ + + + + + +
Parameters:
    +
  • lstimg – list of 2D images or a 3D stack
    • +
  • center_method – is the center calculated by a “mean” or a “median”, or “quantile”
    • +
  • cutoff – keep all data where (I-center)/std < cutoff
    • +
  • quantiles – 2-tuple of floats average out data between the two quantiles
    • +
+
Returns:

2D image averaged

+
+
+ +
+
+pyFAI.utils.averageImages(listImages, output=None, threshold=0.1, minimum=None, maximum=None, darks=None, flats=None, filter_='mean', correct_flat_from_dark=False, cutoff=None, quantiles=None, fformat='edf')
+

Takes a list of filenames and create an average frame discarding all saturated pixels.

+ +++ + + + + + +
Parameters:
    +
  • listImages – list of string representing the filenames
    • +
  • output – name of the optional output file
    • +
  • threshold – what is the upper limit? all pixel > max*(1-threshold) are discareded.
    • +
  • minimum – minimum valid value or True
    • +
  • maximum – maximum valid value
    • +
  • darks – list of dark current images for subtraction
    • +
  • flats – list of flat field images for division
    • +
  • filter – can be “min”, “max”, “median”, “mean”, “sum”, “quantiles” (default=’mean’)
    • +
  • correct_flat_from_dark – shall the flat be re-corrected ?
    • +
  • cutoff – keep all data where (I-center)/std < cutoff
    • +
  • quantiles – 2-tuple containing the lower and upper quantile (0<q<1) to average out.
    • +
  • fformat – file format of the output image, default: edf
    • +
+
Returns:

filename with the data or the data ndarray in case format=None

+
+
+ +
+
+pyFAI.utils.binning(input_img, binsize, norm=True)
+
+++ + + + + + +
Parameters:
    +
  • input_img – input ndarray
    • +
  • binsize – int or 2-tuple representing the size of the binning
    • +
  • norm – if False, do average instead of sum
    • +
+
Returns:

binned input ndarray

+
+
+ +
+
+pyFAI.utils.boundingBox(img)
+

Tries to guess the bounding box around a valid massif

+ +++ + + + + + +
Parameters:img – 2D array like
Returns:4-typle (d0_min, d1_min, d0_max, d1_max)
+
+ +
+
+pyFAI.utils.calc_checksum(ary, safe=True)
+

Calculate the checksum by default (or returns its buffer location if unsafe)

+
+ +
+
+pyFAI.utils.center_of_mass(img)
+

Calculate the center of mass of of the array. +Like scipy.ndimage.measurements.center_of_mass +:param img: 2-D array +:return: 2-tuple of float with the center of mass

+
+ +
+
+pyFAI.utils.concatenate_cl_kernel(filenames)
+
+++ + + + +
Parameters:filenames – filenames containing the kernels
+

this method concatenates all the kernel from the list

+
+ +
+
+pyFAI.utils.convert_CamelCase(name)
+

convert a function name in CamelCase into camel_case

+
+ +
+
+pyFAI.utils.deg2rad(dd)
+

Convert degrees to radian in the range -pi->pi

+ +++ + + + +
Parameters:dd – angle in degrees
+

Nota: depending on the platform it could be 0<2pi +A branch is cheaper than a trigo operation

+
+ +
+
+pyFAI.utils.dog(s1, s2, shape=None)
+

2D difference of gaussian +typically 1 to 10 parameters

+
+ +
+
+pyFAI.utils.dog_filter(input_img, sigma1, sigma2, mode='reflect', cval=0.0)
+

2-dimensional Difference of Gaussian filter implemented with FFTw

+ +++ + + + +
Parameters:
    +
  • input_img (array-like) – input_img array to filter
    • +
  • sigma (scalar or sequence of scalars) – standard deviation for Gaussian kernel. +The standard deviations of the Gaussian filter are given for each axis as a sequence, +or as a single number, in which case it is equal for all axes.
    • +
  • mode – {‘reflect’,’constant’,’nearest’,’mirror’, ‘wrap’}, optional +The mode parameter determines how the array borders are +handled, where cval is the value when mode is equal to +‘constant’. Default is ‘reflect’
    • +
  • cval – scalar, optional +Value to fill past edges of input if mode is ‘constant’. Default is 0.0
    • +
+
+
+ +
+
+pyFAI.utils.expand(input_img, sigma, mode='constant', cval=0.0)
+

Expand array a with its reflection on boundaries

+ +++ + + + +
Parameters:
    +
  • a – 2D array
    • +
  • sigma – float or 2-tuple of floats.
    • +
+
+

:param mode:”constant”, “nearest”, “reflect” or mirror +:param cval: filling value used for constant, 0.0 by default

+

Nota: sigma is the half-width of the kernel. For gaussian convolution it is adviced that it is 4*sigma_of_gaussian

+
+ +
+
+pyFAI.utils.expand2d(vect, size2, vertical=True)
+

This expands a vector to a 2d-array.

+

The resul is the same as +if vertical:

+
+
numpy.outer(numpy.ones(size2), vect)
+
+
else:
+
numpy.outer(vect, numpy.ones( size2))
+
+

This is a ninja optimization: replace *1 with a memcopy, saves 50% of time at the ms level.

+ +++ + + + +
Parameters:
    +
  • vect – 1d vector
    • +
  • size2 – size
    • +
  • vertical – if False,
    • +
+
+
+ +
+
+pyFAI.utils.expand_args(args)
+

Takes an argv and expand it (under Windows, cmd does not convert *.tif into a list of files. +Keeps only valid files (thanks to glob)

+ +++ + + + + + +
Parameters:args – list of files or wilcards
Returns:list of actual args
+
+ +
+
+pyFAI.utils.float_(val)
+

Convert anything to a float ... or None if not applicable

+
+ +
+
+pyFAI.utils.gaussian(M, std)
+

Return a Gaussian window of length M with standard-deviation std.

+

This differs from the scipy.signal.gaussian implementation as: +- The default for sym=False (needed for gaussian filtering without shift) +- This implementation is normalized

+ +++ + + + +
Parameters:
    +
  • M – length of the windows (int)
    • +
  • std – standatd deviation sigma
    • +
+
+

The FWHM is 2*numpy.sqrt(2 * numpy.pi)*std

+
+ +
+
+pyFAI.utils.gaussian_filter(input_img, sigma, mode='reflect', cval=0.0)
+

2-dimensional Gaussian filter implemented with FFTw

+ +++ + + + +
Parameters:
    +
  • input_img (array-like) – input array to filter
    • +
  • sigma (scalar or sequence of scalars) – standard deviation for Gaussian kernel. +The standard deviations of the Gaussian filter are given for each axis as a sequence, +or as a single number, in which case it is equal for all axes.
    • +
  • mode – {‘reflect’,’constant’,’nearest’,’mirror’, ‘wrap’}, optional +The mode parameter determines how the array borders are +handled, where cval is the value when mode is equal to +‘constant’. Default is ‘reflect’
    • +
  • cval – scalar, optional +Value to fill past edges of input if mode is ‘constant’. Default is 0.0
    • +
+
+
+ +
+
+pyFAI.utils.get_calibration_dir()
+

get the full path of a calibration directory

+ +++ + + + +
Returns:the full path of the calibrant file
+
+ +
+
+pyFAI.utils.get_cl_file(filename)
+

get the full path of a openCL file

+ +++ + + + +
Returns:the full path of the openCL source file
+
+ +
+
+pyFAI.utils.get_ui_file(filename)
+

get the full path of a user-interface file

+ +++ + + + +
Returns:the full path of the ui
+
+ +
+
+pyFAI.utils.int_(val)
+

Convert anything to an int ... or None if not applicable

+
+ +
+
+pyFAI.utils.is_far_from_group(pt, lst_pts, d2)
+

Tells if a point is far from a group of points, distance greater than d2 (distance squared)

+ +++ + + + + + +
Parameters:
    +
  • pt – point of interest
    • +
  • lst_pts – list of points
    • +
  • d2 – minimum distance squarred
    • +
+
Returns:

True If the point is far from all others.

+
+
+ +
+
+class pyFAI.utils.lazy_property(fget)
+

Bases: object

+

meant to be used for lazy evaluation of an object attribute. +property should represent non-mutable data, as it replaces itself.

+
+ +
+
+pyFAI.utils.maximum_position(img)
+

Same as scipy.ndimage.measurements.maximum_position: +Find the position of the maximum of the values of the array.

+ +++ + + + + + +
Parameters:img – 2-D image
Returns:2-tuple of int with the position of the maximum
+
+ +
+
+pyFAI.utils.measure_offset(img1, img2, method='numpy', withLog=False, withCorr=False)
+

Measure the actual offset between 2 images +:param img1: ndarray, first image +:param img2: ndarray, second image, same shape as img1 +:param withLog: shall we return logs as well ? boolean +:return: tuple of floats with the offsets

+
+ +
+
+pyFAI.utils.readFloatFromKeyboard(text, dictVar)
+

Read float from the keyboard ....

+ +++ + + + +
Parameters:
    +
  • text – string to be displayed
    • +
  • dictVar – dict of this type: {1: [set_dist_min],3: [set_dist_min, set_dist_guess, set_dist_max]}
    • +
+
+
+ +
+
+pyFAI.utils.read_cl_file(filename)
+
+++ + + + + + +
Parameters:filename – read an OpenCL file and apply a preprocessor
Returns:preprocessed source code
+
+ +
+
+pyFAI.utils.relabel(label, data, blured, max_size=None)
+

Relabel limits the number of region in the label array. +They are ranked relatively to their max(I0)-max(blur(I0)

+ +++ + + + +
Parameters:
    +
  • label – a label array coming out of scipy.ndimage.measurement.label
    • +
  • data – an array containing the raw data
    • +
  • blured – an array containing the blured data
    • +
  • max_size – the max number of label wanted
    • +
+
+

:return array like label

+
+ +
+
+pyFAI.utils.removeSaturatedPixel(ds, threshold=0.1, minimum=None, maximum=None)
+
+++ + + + + + +
Parameters:
    +
  • ds – a dataset as ndarray
    • +
  • threshold – what is the upper limit? all pixel > max*(1-threshold) are discareded.
    • +
  • minimum – minumum valid value (or True for auto-guess)
    • +
  • maximum – maximum valid value
    • +
+
Returns:

another dataset

+
+
+ +
+
+pyFAI.utils.roundfft(N)
+

This function returns the integer >=N for which size the Fourier analysis is faster (fron the FFT point of view) +Credit: Alessandro Mirone, ESRF, 2012

+ +++ + + + + + +
Parameters:N – interger on which one would like to do a Fourier transform
Returns:integer with a better choice
+
+ +
+
+pyFAI.utils.shift(input_img, shift_val)
+

Shift an array like scipy.ndimage.interpolation.shift(input_img, shift_val, mode=”wrap”, order=0) but faster +:param input_img: 2d numpy array +:param shift_val: 2-tuple of integers +:return: shifted image

+
+ +
+
+pyFAI.utils.shiftFFT(input_img, shift_val, method='fftw')
+

Do shift using FFTs +Shift an array like scipy.ndimage.interpolation.shift(input, shift, mode=”wrap”, order=”infinity”) but faster +:param input_img: 2d numpy array +:param shift_val: 2-tuple of float +:return: shifted image

+
+ +
+
+pyFAI.utils.str_(val)
+

Convert anything to a string ... but None -> “”

+
+ +
+
+pyFAI.utils.unBinning(binnedArray, binsize, norm=True)
+
+++ + + + + + +
Parameters:
    +
  • binnedArray – input ndarray
    • +
  • binsize – 2-tuple representing the size of the binning
    • +
  • norm – if True (default) decrease the intensity by binning factor. If False, it is non-conservative
    • +
+
Returns:

unBinned input ndarray

+
+
+ +
+
+

gui_utils Module

+

gui_utils

+

Module to handle matplotlib and the Qt backend

+
+
+class pyFAI.gui_utils.Event(width, height)
+

Bases: object

+

Dummy class for dummy things

+
+ +
+
+pyFAI.gui_utils.maximize_fig(fig=None)
+

Try to set the figure fullscreen

+
+ +
+
+pyFAI.gui_utils.update_fig(fig=None)
+

Update a matplotlib figure with a Qt4 backend

+ +++ + + + +
Parameters:fig – pylab figure
+
+ +
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

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+

Next topic

+

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+

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+ + + +
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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/biblio.html b/doc/build/biblio.html new file mode 100644 index 000000000..c4b8ed2cc --- /dev/null +++ b/doc/build/biblio.html @@ -0,0 +1,255 @@ + + + + + + + + <no title> — pyFAI 0.12.0 documentation + + + + + + + + + + + + + +
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+ +
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+ + + + + + +
[SRI2012]PyFAI, a versatile library for azimuthal regrouping +J. Kieffer & D. Karkoulis +J. Phys.: Conf. Ser. 425 202012 +http://dx.doi.org/10.1088/1742-6596/425/20/202012
+ + + + + +
[EPDIC13]PyFAI: a Python library for high performance azimuthal integration on GPU +J. Kieffer & J. P. Wright, +Powder Diffraction / Volume 28 / Supplement S2 / September 2013, pp S339-S350 +http://dx.doi.org/10.1017/S0885715613000924
+ + + + + +
[FIT2D]Hammersley A. P., Svensson S. O., Hanfland M., Fitch A. N. and Hausermann D. +1996 High Press. Res. vol14 p235–248
+ + + + + +
[SPD]Bösecke P. 2007 J. Appl. Cryst. vol40 s423–s427
+ + + + + +
[EDNA]Incardona M. F., Bourenkov G. P., Levik K., Pieritz R. A., Popov A. N. and Svensson O. +2009 J. Synchrotron Rad. vol16 p872–879
+ + + + + +
[PyMca]Solé V. A., Papillon E., Cotte M., Walter P. and Susini J. +2007 Spectrochim. Acta Part B vol vol62 p63 – 68
+ + + + + +
[PyNX]Favre-Nicolin V., Coraux J., Richard M. I. and Renevier H. +2011 J. Appl. Cryst. vol44 p635–640
+ + + + + +
[IPython]Pérez F and Granger B E +2007 Comput. Sci. Eng. vol9 p21–29 URL http://ipython.org
+ + + + + +
[NumPy]Oliphant T E 2007 Comput. Sci. Eng. vol9 p10–20
+ + + + + +
[Cython]Behnel S, Bradshaw R, Citro C, Dalcin L, Seljebotn D and Smith K 2011 Comput. Sci. Eng. vol13 p31 –39
+ + + + + +
[OpenCL]Khronos OpenCL Working Group 2010 The OpenCL Specification, version 1.1 URL http://www.khronos.org/registry/cl/specs/opencl-1.1.pdf
+ + + + + +
[FabIO]Sorensen H O, Knudsen E, Wright J, Kieffer J et al. +2007–2013 FabIO: I/O library for images produced by 2D X-ray detectors URL http://fable.sf.net/
+ + + + + +
[Matplotlib]Hunter J D 2007 Comput. Sci. Eng. vol9 p90–95 ISSN 1521-9615
+ + + + + +
[SciPy]Jones E, Oliphant T, Peterson P et al. +2001– SciPy: Open source scientific tools for Python URL +http://www.scipy.org/
+ + + + + +
[FFTw]Frigo M and Johnson S G +2005 Proceedings of the IEEE 93 p 216–231
+ + + + + +
[LImA]The LIMA Project Update +S. Petitdemange, L. Claustre, A. Homs, R. Homs Regojo, E. Papillon +Proceedings of ICALEPCS2013 +http://accelconf.web.cern.ch/AccelConf/ICALEPCS2013/html/auth1084.htm
+ + + + + +
[PyOpenCL]PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation +Andreas Klöckner, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, Ahmed Fasih, +Parallel Computing Vol 38, 3, March 2012, Pages 157–174 +http://dx.doi.org/10.1016/j.parco.2011.09.001
+ + + + + +
[AMD]The American Mineralogist Crystal Structure Database. +Downs, R.T. and Hall-Wallace, M. (2003) +American Mineralogist 88, 247-250 +http://rruff.geo.arizona.edu/AMS/amcsd.php
+ + + + + +
[COD]Crystallography Open Database: an open-access collection of crystal structures and platform for world-wide collaboration +Saulius Grazulis et al. +Nucl. Acids Res. (2012) 40 (D1): D420-D427. +http://dx.doi.org/10.1093/nar/gkr900 +http://www.crystallography.net/
+ + + + + +
[Dpdak]A customizable software for fast reduction and analysis of large X-ray scattering data sets: applications of the new DPDAK package to small angle X-ray scattering and grazing-incidence small angle X-ray scattering, +Benecke, G. et al., (2014) +J. Appl. Cryst. 47, +http://dx.doi.org/10.1107/S1600576714019773
+ + +
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+ + \ No newline at end of file diff --git a/doc/build/calibration.html b/doc/build/calibration.html new file mode 100644 index 000000000..f63b27e1a --- /dev/null +++ b/doc/build/calibration.html @@ -0,0 +1,233 @@ + + + + + + + + Pre-processing of images: — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
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+ +

The determination of the geometry of the experimental setup for the diffraction pattern +of a reference sample is called calibration in pyFAI. +A geometry setup is composed of a detector, the six refined parameters like the distance +and fixed parameters like the wavelength (or the energy of the beam), they are all +saved together into a text files named ”.poni” (as a reference to the point of +normal incidence) which is subsequently used for processing the experiment.

+

The program pyFAI-calib is used for calibrating +the experimental setup using a constrained least squares optimization on +the Debye-Scherrer rings of a reference sample (\(LaB_6\), \(CeO_2\), silver +behenate \(AgBh\), …) and saves the results into a .poni file. +Alternatively, geometries calibrated using Fit2D can be +imported into pyFAI, including geometric distortions (i.e. optical-fiber +tapers distortion) described as spline-files. +For Fit2D compatibility, please refer to the Tutorials on basic usage of pyFAI.

+

By storing all parameters together in a single small file, the risk of mixing two +parameters is highly reduced and we believe this helps performing better +science with fewer mistakes.

+

While entering the geometry of the experiment in a poni-file is possible, it is +easier to perform a calibration, using the Debye-Sherrer rings of a reference +sample called calibrant. +About 30 calibrants are provided by pyFAI like \(LaB_6\), ceria \(CeO_2\), +silicon, corrundum or silver behenate. +Among other simple compound, all of the NIST Standard Reference Materials +have been are tabulated and are directly available as calibrant. +One can alternatively provide its own calibrant description files which is +a simple text-file containing the largest d-spacing (in Angstrom) for a set of +Miller plans. +A useful reference to generate this file is the American Mineralogist database [AMD] +or the Crystallographic Open database [COD].

+

The calibration procedure is divided into 4 major steps:

+
+

Pre-processing of images:

+

The typical pre-processing consists of the averaging (or better median filtering) of darks images. +Dark current images are then subtracted from data and corrected for flat.

+

If saturated pixels exists, the are likely to be treated like peaks but their positions +will be wrong. +It is advised to either mask them out or to desaturate them (pyFAI provides an option, +but it is expensive in calculation time).

+
+
+

Peak-picking

+

The Peak-picking consists in the identification of peaks and groups of peaks +belonging to same ring. It can be performed by 4 methods described hereafter.

+
+

Massif detection

+

This method consists in making the difference of the original image and a blurred +image. Then look for a large contiguous region of positives values, corresponding +to a single group of peak. +The blurring parameter can be adjusted using the “-g” option in pyFAI-calib.

+
+
+

Blob detection

+

The approach is based on difference of gaussians (DoGs) as described in the +blob_detection article of wikipedia.

+

It consists in blurring the image by convolution with a 2D gaussian kernel and making +differences between two successive blurs (called Difference Of Gaussian or DoGs). +In theses DoGs, keypoints are defined as the maxima in the 3D space (y,x,size of +the gaussian). After their localization, keypoints are refined by Savitzky Golay +algorithm or by an interpolation at the second order which is equivalent but uses +less points. At this step, if the estimation of the maximum is too far from the maximum, +the keypoint will be considered as a fake maximum and removed.

+
+
+

Steepest ascent

+

This is very naive implementation which looks for the nearest local maximum. +Subsequently a sub-pixel optimization is performed based on a second order expansion +using the local gradient and hessian.

+
+
+

Monte-Carlo sampling

+

Series of peaks can be extracted using the Steepest Ascent on randomly selected seeds. +This method can be biased towards an already known geometry by starting from +points which are supposed to be on the ring.

+
+
+
+

Refinement of the parameters

+

After selecting groups of peaks, each of them is assigned to a Debye-Scherrer ring number +and associated to a d-spacing value hence a theoritcal 2theta value. +A supervised least-squares refinement, performed on the difference of peak position’s +2-theta values versus the expected ones from calibrant provides the 6-geometry parameters +fitted.

+

The default optimization procedure is the Sequential Least SQuares Programming +implemented in scipy.optimize.slsqp. +The cost function is the sum of the square of the difference between the expected and +calculated 2theta values for the various peaks. This sum is dependent on the number +of control-points.

+
+
+

Validation of the calibration

+

Validation by an human being of the geometry is an essential step: +pyFAI will overlaps to the diffraction image, the lines corresponding to the various diffraction +rings expected from the calibrant. +Those lines should be in pretty good agreement with the rings +of the scattering image. +The average error per control point (delta 2theta error in radian) is printed out +and offers a quantitative measurement of the relative quality of the fit for similar +setups/experiment. +Neverthless its absolute value has no meaning, except the lower, the better.

+

Subsequently, pyFAI offers some validation options in to check the quality of the fit. +some of them global, some of them limited to given rings.

+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Image representation in Python

+

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+

Cookbook recipes

+

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+ + + +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/changelog.html b/doc/build/changelog.html new file mode 100644 index 000000000..f4e7f47e9 --- /dev/null +++ b/doc/build/changelog.html @@ -0,0 +1,429 @@ + + + + + + + + ChangeLog of Versions — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

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+ +
+ +
+
+
+
+ +
+

ChangeLog of Versions

+
+

0.12.0: 06/2016

+
    +

  • Continuous integration on linux, windows using Python 2.7 and 3.4+

    +
    • +

  • Drop support of Python 2.6, 3.2, 3.3 and debian6 packaging

    +
    • +

  • New radial output units: Reciprocal spacing squared and log(q) ID02

    +
    • +

  • GPU accelerate version of ai.separate (Bragg & amorphous) ID13

    +
    • +

  • Quantile filtering in pyFAI-average ID02

    +
    • +

  • New graphical application for diffraction imaging ID21

    +
    • +

  • Migrate to a common structure with silx (reorganize tests, benchmarks, ...)

    +
    • +

  • Extensions (binary sub-modules) have all been moved to ext directory

    +
    • +

  • Many improvements multigeometry integrators

    +
    • +

  • Compatibility with the copy module (copy.deepcopy) for azimuthal integrator ID02

    +
    • +

  • Distortion correction works also for non-contiguous detectors

    +
    • +
  • +
    Update documentation and provide advanced tutorials:
    +
      +
    • Introdution to pyFAI using the juyter notebook
      • +
    • detector calibration ID15, BM02
      • +
    • Correction of detector distortion, examples of pixel detectors.
      • +
    • calibrant calculation ID30
      • +
    • error handling ID02, BM29
      • +
    +
    +
    +
    • +

  • pyFAI-integrate can now be used with or without GUI

    +
    • +
  • +
    Many new detectors (ADSC, Pilatus CdTe, Apex II, Pixium):
    +
      +
    • support for non-flat/curved detectors (Aarhus)
      • +
    • non-contiguous detectors (WOS Xpad)
      • +
    +
    +
    +
    • +

  • Include tests and benchmarking tools as part of the library

    +
    • +

  • Better testing.

    +
    • +
+
+
+

0.11.0: 07/2015

+
    +

  • All calibrant from NIST are now available, + Nickel, Aluminum, ... with bibliographic references

    +
    • +

  • The Cell class helps defining new calibrants.

    +
    • +

  • OpenCL Bitonic sort (to be integrated into Bragg/Amorphous separation)

    +
    • +

  • Calib is available from the Python interface (procedural API), not only from the shell script.

    +
    • +
  • +
    Many new options in calib for reset/assign/delete/validate/validate2/chiplot.
    +
      +
    • reset: set the detector, orthogonal, centered and at 10cm
      • +
    • assign: checks the assignment of groups of points to rings
      • +
    • delete: remove a group of peaks
      • +
    • validate: autocorrelation of images: error on the center
      • +
    • validate2: autocorrelation of patterns at 180° apart: error on the center function of chi
      • +
    • chiplot: assesses the quality of control points of one/multiple rings.
      • +
    +
    +
    +
    • +

  • Fix the regression of the initial guess in calib (Thanks Jon Wright)

    +
    • +

  • New peak picking algorithm named “watershed” and based on inverse watershed for ridge recognition

    +
    • +

  • start factorizing cython regridding engines (work ongoing)

    +
    • +

  • Add “–poni” option for pyFAI-calib (Thanks Vadim Dyakin)

    +
    • +

  • Improved “guess_binning”, especially for Perkin Elmer flat panel detectors.

    +
    • +

  • Support for non planar detectors like Curved Imaging plate developped at Aarhus

    +
    • +

  • Support for Multi-geometry experiments (tested)

    +
    • +

  • Speed improvement for detector initialization

    +
    • +

  • better isotropy in peak picking (add penalization term)

    +
    • +

  • enhanced documentation on http://pyfai.readthedocs.org

    +
    • +
+
+
+

0.10.3: 03/2015

+
    +
  • Image segmentation based on inverse watershed (only for recalib, not for calib)
    • +
  • Python3 compatibility
    • +
  • include testimages into distribution
    • +
+
+
+

0.10.2: 11/2014

+
    +
  • Update documentation
    • +
  • Packaging for debian 8
    • +
+
+
+

0.10.1: 10/2014

+
    +
  • Fix issue in peak-picking
    • +
  • Improve doc & manpages
    • +
  • Compatibility with PyMca5
    • +
+
+
+

0.10.0: 10/2014

+
    +
  • Correct Caglioti’s formula
    • +
  • Update tests and OpenCL -> works with Beignet and pocl open source drivers
    • +
  • Compatibility with MacOSX and windows
    • +
+
+
+

0.9.4: 06/2014

+
    +
  • include spec of Maxwell GPU
    • +
  • fix issues with intel OpenCL icd v4.4
    • +
  • introduce shape & max_shape in detectors
    • +
  • work on marchingsquares/sorted controurplot for calibration
    • +
  • Enforce the use the Qt4Agg for Matplotlib and other GUI stuff.
    • +
  • Update shape of detector in case of binning
    • +
  • unified distortion class: merge OpenCL & OpenMP implementation #108
    • +
  • Benchmarks for distortion
    • +
  • Raise the level to warning when inverting the mask
    • +
  • set of new ImXpad detectors Related issue #111
    • +
  • Fix issue with recalib within MX-calibrate
    • +
  • saving detector description in Nexus files issue #110
    • +
  • Update some calibrants: gold
    • +
  • about to make peak-picking more user-friendly
    • +
  • test for bragg separation
    • +
  • work on PEP8 compliance
    • +
  • Do not re-cythonize: makes debian package generation able to benefit from ccache
    • +
  • conversion to SPD (rotation is missing)
    • +
  • pixelwise worker
    • +
  • correct both LUT & OCL for memory error
    • +
  • replace os.linsep with “n” when file file opened in text mode (not binary)
    • +
  • rework the Extension part to be explicit instead of “black magic” :)
    • +
  • implement Kahan summation in Cython (default still use Doubles: faster)
    • +
  • Preprocessing kernel containing all cast to float kernels #120
    • +
  • update setup for no-openmp option related to issue #127
    • +
  • Add read-out mode for mar345 as “guess_binning” method for detector. Also for MAR and Rayonix #125
    • +
  • tool to benchmark HDF5 writing
    • +
  • try to be compatible with both PySide and PyQt4 ... the uic stuff is untested and probably buggy #130
    • +
  • Deactivate the automatic saturation correction by default. now it is opt-in #131
    • +
+
+
+

0.9.3: 02/2014

+
    +
  • Better control for peak-picking (Contribution from Gero Flucke, Desy)
    • +
  • Precise Rayonix detectors description thanks to Michael Blum
    • +
  • Start integrating blob-detection algorithm for peak-picking: #70
    • +
  • Switch fron OptParse to ArgPrse: #83
    • +
  • Provide some calibrant by default: #91
    • +
  • Description of Mar345 detector + mask#92
    • +
  • Auto-registration of detectors: #97
    • +
  • Recalib and check-calib can be called from calib: #99
    • +
  • Fake diffraction image from calibrant: #101
    • +
  • Implementation of the CSR matrix representation to replace LUT
    • +
  • Tight pixel splitting: #43
    • +
  • Update documentation
    • +
+
+
+

0.9.2: (01/2014)

+
    +
  • Fix memory leak in Cython part of the look-up table generation
    • +
  • Benchmarks with memory profiling
    • +
+
+
+

0.9: 10/2013

+
    +
  • Add detector S140 from ImXpad, Titan from Agilent, Rayonix
    • +
  • Fix issues: 61, 62, 68, 76, 81, 82, 85, 86, 87
    • +
  • Enhancement in LImA plugins (better structure)
    • +
  • IO module with Ascii/EDF/HDF5 writers
    • +
  • Switch some GUI to pyQtGraph in addition to Qt
    • +
  • Correction for solid-angle formula
    • +
+
+
+

0.8: 10/2012

+
    +
  • Detector object is member of the geometry
    • +
  • Binning of the detector, propagation to the spline if needed
    • +
  • Detector object know about their masks.
    • +
  • Automatic mask for some detectors like Pilatus or XPad
    • +
  • Implementation of sub-pixel position correction for Pilatus detectors
    • +
  • LUT implementation in 1D & 2D (fully tested) both with OpenMP and with OpenCL
    • +
  • Switch from C++/Cython OpenCL framework to PyOpenCL
    • +
  • Port opencl code to both Windows 32/64 bits and MacOSX
    • +
  • Add polarization corrections
    • +
  • Use fast-CRC checksum on x86 using SSE4 (when available) to track array change on GPU buffers
    • +
  • Support for flat 7*8 modules Xpad detectors.
    • +
  • Benchmark with live graphics (still a memory issue with python2.6)
    • +
  • Fat source distribution (python setup.py sdist –with-test-images) for debian
    • +
  • Enhanced tests, especially for Saxs and OpenCL
    • +
  • Recalibration tool for refining automatically parameters
    • +
  • Enhancement of peak picking (much faster, recoded in pure Cython)
    • +
  • Easy calibration for pixel detector (reconstruction of inter-module space)
    • +
  • Error-bar generation using Poisson law
    • +
  • Unified programming interface for all integration methods in 2theta, q or radius unit
    • +
  • Graphical interface for azimuthal integration (pyFAI-integrate)
    • +
  • Lots of test to prevent non regression
    • +
  • Tool for merging images using various method (mean, median) and with outlayer rejection
    • +
  • LImA plugin which can perform azimuthal integration live during the acquisition
    • +
  • Distortion correction is available alone and as LImA plugin
    • +
  • Recalibration can refine the wavelength in addition to 6 other parameters
    • +
  • Calibration always done vs calibrant’s ring number, lots of new calibrant are available
    • +
  • Selection by hand of single peaks for calibration
    • +
  • New detectors: Dexela and Perkin-Elmer flat panel
    • +
  • Automatic refinement of multiple images at various geometries (for MX)
    • +
  • Many improvements requested by ID11 and ID13
    • +
+
+
+

0.7.2: 08/2012

+
    +
  • Add diff_tomo script
    • +
  • Geometry calculation optimized in (parallel) cython
    • +
+
+
+

0.7: 07/2012

+

Implementation of look-up table based integration and OpenCL version of it

+
+
+

0.6: 07/2012

+
    +
  • OpenCL flavor works well on GPU in double precision with device selection
    • +
+
+
+

0.5: 06/2012

+
    +
  • Include OpenCL version of azimuthal integration (based on histograms)
    • +
+
+
+

0.4: 06/2012

+
    +
  • Global clean up of the code regarding options from command line and better design
    • +
  • Correct the orientation of the azimuthal angle chi
    • +
  • Rename scripts in pyFAI-calib, pyFAI-saxs and pyFAI-waxs
    • +
+
+
+

0.3: 11/2011

+
    +
  • Azimuthal integration splits pixels like fit2d
    • +
+
+
+

0.2: 07/2011

+
    +
  • Azimuthal integration using cython histogramming is working
    • +
+
+
+

0.1: 05/2011

+
+
    +
  • Geometry is OK
    • +
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Test coverage report for pyFAI

+

Next topic

+

List of publication about pyFAI

+

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+ + + +
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+
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+
+ +
+

Test coverage report for pyFAI

+

Measured on pyFAI version 0.12.0, 06/06/2016

+ + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Test suite coverage
NameStmtsExecCover
pyFAI/__init__352057.1 %
pyFAI/_version343294.1 %
pyFAI/azimuthalIntegrator125890972.3 %
pyFAI/blob_detection51719637.9 %
pyFAI/calibrant33824472.2 %
pyFAI/decorators3333100.0 %
pyFAI/detectors1314102878.2 %
pyFAI/directories332060.6 %
pyFAI/distortion54416229.8 %
pyFAI/ext/__init__000.0 %
pyFAI/geometry96176179.2 %
pyFAI/geometryRefinement48117436.2 %
pyFAI/gui_utils693347.8 %
pyFAI/integrate_widget49327355.4 %
pyFAI/io46525053.8 %
pyFAI/massif20113265.7 %
pyFAI/multi_geometry756586.7 %
pyFAI/ocl_azim26919171.0 %
pyFAI/ocl_azim_csr24018476.7 %
pyFAI/ocl_azim_lut23017777.0 %
pyFAI/ocl_sort17713676.8 %
pyFAI/opencl20414470.6 %
pyFAI/peak_picker75619525.8 %
pyFAI/spline43216538.2 %
pyFAI/units706592.9 %
pyFAI/utils77843155.4 %
pyFAI/worker34013138.5 %
    
pyFAI total10347615159.4 %
+
+ + +
+
+
+
+
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/design/ai.html b/doc/build/design/ai.html new file mode 100644 index 000000000..35b0b11ab --- /dev/null +++ b/doc/build/design/ai.html @@ -0,0 +1,253 @@ + + + + + + + + Design of the Python Fast Azimuthal Integrator — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
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+ +
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+
+
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+ +
+

Design of the Python Fast Azimuthal Integrator

+

Author: Jérôme Kieffer

+

Date: 20/03/2015

+

Keywords: Design

+

Target: Developers interested in using the library

+

Reference: API documentation

+
+

Abstract

+

The core part of pyFAI is the AzimuthalIntegator objects, named +ai hereafter. +This document describes the two importants methods of the class, +how it is related to Detector, Geometry, and integration engines.

+

One of the core idea is to have a complete representation of the geometry +and perform the azimuthal integration as a single geometrical re-binning +which take into account all effects like:

+
    +
  • Detector distortion
    • +
  • Polar transformation
    • +
  • assignment to the output space
    • +
+

This document focuses on the core of pyFAI while peripheral code +dealing with graphical user interfaces, image analysis online data +analysis integration are not covered.

+
+
+

AzimuthalIntegrator

+

This class is the core of pyFAI, and it is the only one likely to be used by +external developers/users. It is usually instantiated via a function of the +module to load a poni-file:

+

As one can see, the ai contains the detector geometry (type, pixel size, +distortion) as well as the +geometry of the experimental setup. The geometry is given in two equivalent +forms: the internal representation of pyFAI (second line) and the one used by +FIT2D.

+

The ai is responsible for azimuthal integration, either the integration along +complete ring, called full-integration, obtained via ai.integrate1d method. +The sector-wise integration is obtained via the ai.integrate2d method. +The options for those two methods are really similar and differ only by the +parameters related to the azimuthal dimension of the averaging for ai.integrate2d.

+
+

Azimuthal integration methods

+

Both integration method take as first argument the image coming from the detector +as a numpy array. This is the only mandatory parameter.

+

Important parameters are the number of bins in radial and azimuthal dimensions. +Other parameters are the pre-processing information like dark and flat pixel wise +correction (as array), the polarization factor and the solid-angle correction to +be applied.

+

Because multiple radial output space are possible (q, r, 2theta) each with multiple +units, if one wants to avoid interpolation, it is important to export directly the data +in the destination space, specifying the unit=”2th_deg” or “q_nm^-1”

+

Many more option exists, please refer to the documentation of AzimuthalIntegration integrate_

+

The AzimuthalIntegration class inherits from the Geometry class and hold +references to configured rebinning engines.

+
+
+
+

Geometry

+

The Geometry class contains a reference to the detector (composition) +and the logic to calculate the position in space of the various pixels. +All arrays in the class are cached and calculated on demand.

+

The Geometry class relies on the detector to provide the pixel position in space +and subsequently transforms it in 2theta coordinates, or q, chi, r ... +This can either be performed in the class itself or by calling +function in the parallel implemented Cython module _geometry. +Those transformation could be GPU-ized in the future.

+
+
+

Detector

+

PyFAI deals only with area detector, indexed in 2 dimension but can +handle pixel located in a 3D space.

+

The pyFAI.detectors module contains the master Detector class +which is capable of describing any detector. +About 40 types of detectors, inheriting and specializing the Detector +class are provided, offering convienient access to most commercial detectors. +A factory is provided to easily instantiate a detector from its name.

+

A detector class is responsible for two main tasks:

+
    +
  • provide the coordinate in space of any pixel position (center, corner, ...)
    • +
  • Handle the mask: some detector feature automatic mask calculation (i.e. module based detectors).
    • +
+

The disortion of the detector is handled here and could be GPU-ized in the future.

+
+
+

Rebinning engines

+

Once the geometry (radial and azimuthal coordinates) calculated for every pixel +on the detector, the image from the detector is rebinned into the output space. +Two types of rebinning engines exists:

+
+
Histograms
+
They take each single pixel from the image and transfer it to the destination bin, like histograms do. +This family of algorithms is rather easy to implement and provides good single threaded performances, +but it is hard to parallelize (efficiently) due to the need of atomic operations.
+
Sparse matrix multiplication
+
By recording where every single ends one can transform the previous histogram into a +large sparse matrix multiplication which is either stored as a Look-Up Table (actually an array of struct, also called LIL) +or more efficiently in the CSR_ format. +Those rebinning engines are trivially parallel and provide the best performances.
+
+
+
+

Pixel splitting

+

Three levels of pixel splitting schemes are available within pyFAI:

+
+
No splitting
+
The whole intensity is assigned to the center of the pixel and rebinned using a simple histogram
+
Bounding box pixel splitting
+
The pixel is abstracted by a box surrounding it with, making calculation easier but blurring a bit the image
+
Tight pixel splitting
+
The pixel is represented by its actual corner position, offering a very precise positionning in space.
+
+

The main issue with pixel splitting arose from 2D integration and the habdling of pixel laying on the chi-discontinuity.

+
+ +
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Design of the Python Fast Azimuthal Integration library

+

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+

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+

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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/design/index.html b/doc/build/design/index.html new file mode 100644 index 000000000..a4e41821a --- /dev/null +++ b/doc/build/design/index.html @@ -0,0 +1,136 @@ + + + + + + + + Design of the Python Fast Azimuthal Integration library — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

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+ +
+ +
+
+
+
+ +
+

Design of the Python Fast Azimuthal Integration library

+

Author: Jérôme Kieffer

+

Date: 18/12/2014

+

Keywords: Design

+

Target: Developers interested in using the library

+

Reference: API documentation

+
+ +
+
+ + +
+
+
+
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+

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+

Integration tool: pyFAI-waxs

+

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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/detector.html b/doc/build/detector.html new file mode 100644 index 000000000..e60e9bbd0 --- /dev/null +++ b/doc/build/detector.html @@ -0,0 +1,223 @@ + + + + + + + + Simple detector — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

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+ +
+ +
+
+
+
+ +
+

Simple detector

+

Like most other diffraction processing packages, pyFAI allows the definition of +2D detectors with a constant pixel size and recoded in S.I.. +Typical pixel size are 50e-6 m (50 microns) and will be used as example in the +numerical application.

+

Pixels of the detector are indexed from the origin located at the lower left corner. +The pixel’s center is located at half integer index:

+
    +
  • pixel 0 goes from position 0 m to 50e-6m and is centered at 25e-6 m.
    • +
  • pixel 1 goes from position 50e-6 m to 100e-6 m and is centered at 75e-6 m
    • +
+

Nota: +Most of the time you will need to pass the optional argument origin=”lower” to +matplotlib imshow function when displaying the image to avoid confusion

+
+
+

Complex detectors

+

The simple detector approach reaches its limits +with several detector types, such as multi-module and fiber optic taper coupled +detectors (most CCDs). +Large area pixel detectors are often composed of smaller modules (i.e. Pilatus +from Dectris, Maxipix from ESRF, ...).

+

By construction, such detectors exhibit gaps between modules along with +pixels of various sizes within a single module, hence they require specific +data masks. +Optically coupled detectors need also to be corrected +for small spatial displacements, often called geometric distortion. +This is why detectors need more complex definitions than just that of a pixel +size. +To avoid complicated and error-prone sets of parameters, two tools have been introduced: +either detector classes define programatically detector or Nexus saved detector setup.

+
+

Detectors classes

+

They are used to define families of detectors. +In order to take the specificities of each detector into account, pyFAI +contains about 55 detector class definitions (and twice a much with aliases) +which contain a mask (invalid pixels, +gaps, ...) and a method to calculate the pixel positions in Cartesian +coordinates. Available detectors can be printed using:

+
import pyFAI
+print(pyFAI.detectors.ALL_DETECTORS)
+
+
+

For optically coupled CCD detectors, the geometrical distortion is often +described by a bi-dimensional cubic spline which can be imported into +the detector instance and be used to calculate the actual pixel position in space.

+
+
+

Nexus Detectors

+

Any detector object in pyFAI, can be saved into a HDF5 file following the NeXus +convention (http://nexusformat.org). +Detector objects can subsequently be restored from the disk, making +complex detector definitions less error-prone. +Pixels of an area detector are saved as a 4D dataset: i.e. a 2D +array of vertices pointing to every corner of each pixel, generating +an array of shape: (Ny, Nx, Nc, 3) where Nx and Ny are the dimensions of the +detector, Nc is the number of corners of each pixel, usually 4, and the last +entry contains the coordinates of the vertex itself (z,y,x). +This kind of definitions, while relying on large description files, +can address some of the most complex detector layouts:

+
    +
  • hexagonal pixels (i.e. Pixirad detectors, still under development)
    • +
  • curved/bent imaging plates (i.e. Rigaku, Aarhus detector)
    • +
  • pixel detectors with tiled modular (i.e. Xpad detectors from ImXpad)
    • +
  • semi-cylindrical pixel detectors (i.e. Pilatus12M from Dectris).
    • +
+

The detector instance can be saved as HDF5, either programmatically, either +on the command line.

+
from pyFAI import detectors
+frelon = detectors.FReLoN("halfccd.spline")
+print(frelon)
+frelon.save("halfccd.h5")
+
+
+

Using the detector2nexus script to convert a complex detector definition +(multiple modules, possibly in 3D) into +a single NeXus detector definition together with the mask:

+
+
detector2nexus -s halfccd.spline -o halfccd.h5
+
+
+
+

Conclusion

+

Detector definition in pyFAI is very versatile. +Fortunately, most detectors are already described, making the usage transparent +for most users. +There are a couple of Tutorials on detector definition which will help +you understanding the mechanisme :

+
    +
  • Distortion which explains how to correct images for geometric distortion
    • +
  • CCD-calibration which explains how to calibrate a detector for geometric +distortion.
    • +
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

General introduction to PyFAI

+

Next topic

+

Image representation in Python

+

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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/ecosystem.html b/doc/build/ecosystem.html new file mode 100644 index 000000000..b5c5bd05e --- /dev/null +++ b/doc/build/ecosystem.html @@ -0,0 +1,283 @@ + + + + + + + + PyFAI Ecosystem — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

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+ +
+ +
+
+
+
+ +
+

PyFAI Ecosystem

+
+

Software pyFAI is relying on

+

PyFAI is relying on the full Python scientific stack which includes [NumPy], +[SciPy], [Matplotlib], [PyOpenCL] but also on some ESRF-developped code:

+
+

FabIO

+

PyFAI is using FabIO everywhere access to a 2D images is needed. +The fabio_viewer is also a lightweight convenient viewer for diffraction images. +It has been described in doi:10.1107/S0021889813000150

+
+
+

PyMca

+

The X-ray Fluorescence Toolkit provides convenient tools for +HDF5 file browsing and mask drawing. +It has been described in doi:10.1016/j.sab.2006.12.002

+
+
+

Silx

+

The silx toolkit is currently onging development. +Future releases of pyFAI will use its input/output and graphical visualization capabilities

+
+
+
+

Program using pyFAI as a library

+
+

Bubble

+

Client-server program to perform azimuthal integration online. +Developed for the SNBL and Dubble beamlines by Vadim DIADKIN and available from this mercurial repository.

+
+
+

Dahu

+

Dahu is a lightweight plugin based framework available from this +git repository. +Lighter then EDNA, it is technically a JSON-RPC server over Tango. +Used on TRUSAXS beamline at ESRF (ID02), ID15 and ID31, +dahu uses pyFAI to process data up to the kHz range.

+
+
+

Dioptas

+

Graphical user interface for high-pressure diffraction, developed at the +APS synchrotron by C. Prescher and described in: +doi:10.1080/08957959.2015.1059835

+

The amount of data collected during synchrotron X-ray diffraction (XRD) +experiments is constantly increasing. Most of the time, the data are +collected with image detectors, which necessitates the use of image +reduction/integration routines to extract structural information from measured XRD patterns. +This step turns out to be a bottleneck in the data processing procedure due to a lack of suitable software packages. +In particular, fast-running synchrotron experiments require online data reduction and analysis +in real time so that experimental parameters can be adjusted interactively. +Dioptas is a Python-based program for on-the-fly data processing and exploration of two-dimensional +X-ray diffraction area detector data, specifically designed for the large amount of data collected at +XRD beamlines at synchrotrons. Its fast data reduction algorithm and graphical data exploration capabilities +make it ideal for online data processing during XRD experiments and batch post-processing of large numbers of images.

+
+
+

Dpdak

+

Graphical user interface for small angle diffusion, developed at the +Petra III synchrotron by G. Benecke and co-workers and described in +doi:10.1107/S1600576714019773

+

X-ray scattering experiments at synchrotron sources are characterized by large and constantly increasing amounts of data. +The great number of files generated during a synchrotron experiment is often a limiting factor in the analysis of the data, +since appropriate software is rarely available to perform fast and tailored data processing. +Furthermore, it is often necessary to perform online data reduction and analysis during the experiment in order +to interactively optimize experimental design. +This article presents an open-source software package developed to process +large amounts of data from synchrotron scattering experiments. +These data reduction processes involve calibration and correction of raw data, +one- or two-dimensional integration, as well as fitting and further analysis of the data, +including the extraction of certain parameters. +The software, DPDAK (directly programmable data analysis kit), is based on +a plug-in structure and allows individual extension in accordance with the +requirements of the user. +The article demonstrates the use of DPDAK for on- and offline analysis of +scanning small-angle X-ray scattering (SAXS) data on biological samples and +microfluidic systems, as well as for a comprehensive analysis of +grazing-incidence SAXS data. +In addition to a comparison with existing software packages, +the structure of DPDAK and the possibilities and limitations are discussed.

+
+
+

EDNA

+

EDNA is a framework for developing plugin-based applications especially +for online data analysis in the X-ray experiments field (http://edna-site.org) +A EDNA data analysis server is using pyFAI as an integration engine (on the GPU) +on the ESRF BioSaxs beamline, BM29. +The server is running 24x7 with a processing frequency from 0.1 to 10 Hz.

+
+
+

LImA

+

The Library for Image Acquisition, +developped at the European synchrotron is used worldwide to control any types of +cameras. +A pyFAI plugin has been written to integrate images on the fly without saving them. +(no more tested).

+
+
+

NanoPeakCell

+

NanoPeakCell (NPC) is a python-software intended to pre-process your serial +crystallography raw-data into ready-to-be-inedexed images with CrystFEL, +cctbx.xfel and nXDS. +NPC is able to process data recorded at SACLA and LCLS XFELS, as well as data +recorded at any synchrotron beamline. +A graphical interface is deployed to visualize your raw and pre-processed data.

+

Developed at IBS (Grenoble) by N. Coquelle

+
+
+

pygix

+

A Python library for reduction of 2D grazing-incidence X-ray scattering +data developped at ESRF (ID13) by Thomas DANE.

+

Grazing-incidence X-ray scattering techniques (GISAXS, GIWAXS/GID) +allow the study of thin films on surfaces that would otherwise be +unmeasurable in standard transmission geometry experiments. The fixed +incident X-ray angle gives rise to a distortion in the diffraction +patterns, which is extreme at wide-angles. The pygix library provides +routines for projecting 2D detector images into corrected reciprocal +space maps, radial transformations and line profile extraction using +pyFAI’s regrouping functions.

+
+
+

PySAXS

+

Python for Small Angle X-ray Scattering data acquisition, treatment and computation +of model SAXS intensities.

+

Developed at CEA Saclay by O. Taché and available on PyPI.

+
+
+

xPDFsuite

+

Developed by the Billinge Group, this commercial software is described in +arXiv 1402.3163 (2014)

+

xPDFsuite is an application that facilitates the workflow of atomic pair +distribution function analysis of x-ray diffraction measurements from +complex materials. It is specially designed to help the scientist +visualize, handle and process large numbers of datasets that is common +when working with high throughput modern synchrotron sources. It has a +full-featured interactive graphical user interface (GUI) with 3D and 3D +graphics for plotting data and it incorporates a number of powerful +packages for integrating 2D powder diffraction images, analyzing the +curves to obtain PDFs and then tools for assessing the data and modeling +it. It is available from diffpy.org.

+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Installation procedure on Windows

+

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+

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+

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+ + + +
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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/genindex.html b/doc/build/genindex.html new file mode 100644 index 000000000..c4dc06b90 --- /dev/null +++ b/doc/build/genindex.html @@ -0,0 +1,4281 @@ + + + + + + + + + Index — pyFAI 0.12.0 documentation + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ + +

Index

+ +
+ A + | B + | C + | D + | E + | F + | G + | H + | I + | K + | L + | M + | N + | O + | P + | Q + | R + | S + | T + | U + | V + | W + | X + | Z + +
+

A

+ + + +
+ +
Aarhus (class in pyFAI.detectors) +
+ + +
AbstractCalibration (class in pyFAI.calibration) +
+ + +
add_device() (pyFAI.opencl.Platform method) +
+ + +
add_or_discard() (pyFAI.utils.FixedParameters method) +
+ + +
ADSC_Q210 (class in pyFAI.detectors) +
+ + +
ADSC_Q270 (class in pyFAI.detectors) +
+ + +
ADSC_Q315 (class in pyFAI.detectors) +
+ + +
ADSC_Q4 (class in pyFAI.detectors) +
+ + +
AIWidget (class in pyFAI.integrate_widget) +
+ + +
aliases (pyFAI.detectors.ADSC_Q210 attribute) +
+ +
+ +
(pyFAI.detectors.ADSC_Q270 attribute) +
+ + +
(pyFAI.detectors.ADSC_Q315 attribute) +
+ + +
(pyFAI.detectors.ADSC_Q4 attribute) +
+ + +
(pyFAI.detectors.Apex2 attribute) +
+ + +
(pyFAI.detectors.Basler attribute) +
+ + +
(pyFAI.detectors.Detector attribute) +
+ + +
(pyFAI.detectors.Dexela2923 attribute) +
+ + +
(pyFAI.detectors.Fairchild attribute) +
+ + +
(pyFAI.detectors.HF_130K attribute) +
+ + +
(pyFAI.detectors.HF_1M attribute) +
+ + +
(pyFAI.detectors.HF_262k attribute) +
+ + +
(pyFAI.detectors.HF_2M attribute) +
+ + +
(pyFAI.detectors.HF_4M attribute) +
+ + +
(pyFAI.detectors.HF_9M attribute) +
+ + +
(pyFAI.detectors.ImXPadS10 attribute) +
+ + +
(pyFAI.detectors.ImXPadS140 attribute) +
+ + +
(pyFAI.detectors.ImXPadS70 attribute) +
+ + +
(pyFAI.detectors.Mar345 attribute) +
+ + +
(pyFAI.detectors.Perkin attribute) +
+ + +
(pyFAI.detectors.Pixium attribute) +
+ + +
(pyFAI.detectors.RaspberryPi attribute) +
+ + +
(pyFAI.detectors.Rayonix133 attribute) +
+ + +
(pyFAI.detectors.RayonixLx170 attribute) +
+ + +
(pyFAI.detectors.RayonixLx255 attribute) +
+ + +
(pyFAI.detectors.RayonixMx170 attribute) +
+ + +
(pyFAI.detectors.RayonixMx225 attribute) +
+ + +
(pyFAI.detectors.RayonixMx225hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx300 attribute) +
+ + +
(pyFAI.detectors.RayonixMx300hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx325 attribute) +
+ + +
(pyFAI.detectors.RayonixMx340hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx425hs attribute) +
+ + +
(pyFAI.detectors.RayonixSx165 attribute) +
+ + +
(pyFAI.detectors.RayonixSx200 attribute) +
+ + +
(pyFAI.detectors.RayonixSx30hs attribute) +
+ + +
(pyFAI.detectors.RayonixSx85hs attribute) +
+ + +
(pyFAI.detectors.Titan attribute) +
+ + +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
allocate_cl_buffers() (in module pyFAI.opencl) +
+ + +
analyse_options() (pyFAI.calibration.AbstractCalibration method) +
+ + +
anneal() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ +
+ +
Apex2 (class in pyFAI.detectors) +
+ + +
API_VERSION (pyFAI.detectors.Detector attribute) +
+ + +
append() (pyFAI.peak_picker.ControlPoints method) +
+ + +
append_2th() (pyFAI.calibrant.Calibrant method) +
+ + +
append_2theta_deg() (pyFAI.peak_picker.ControlPoints method) +
+ + +
append_dSpacing() (pyFAI.calibrant.Calibrant method) +
+ + +
array2spline() (pyFAI.spline.Spline method) +
+ + +
array_from_unit() (pyFAI.geometry.Geometry method) +
+ + +
AsciiWriter (class in pyFAI.io) +
+ + +
assign_unit() (pyFAI.integrate_widget.AIWidget method) +
+ + +
averageDark() (in module pyFAI.utils) +
+ + +
averageImages() (in module pyFAI.utils) +
+ + +
AzimuthalIntegrator (class in pyFAI.azimuthalIntegrator) +
+ +
+ +

B

+ + + +
+ +
Basler (class in pyFAI.detectors) +
+ + +
benchmarks() (in module pyFAI.__init__) +
+ + +
bin() (pyFAI.spline.Spline method) +
+ + +
BINNED_PIXEL_SIZE (pyFAI.detectors.Rayonix attribute) +
+ +
+ +
(pyFAI.detectors.Rayonix133 attribute) +
+ + +
(pyFAI.detectors.RayonixLx170 attribute) +
+ + +
(pyFAI.detectors.RayonixLx255 attribute) +
+ + +
(pyFAI.detectors.RayonixMx170 attribute) +
+ + +
(pyFAI.detectors.RayonixMx225 attribute) +
+ + +
(pyFAI.detectors.RayonixMx225hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx300 attribute) +
+ + +
(pyFAI.detectors.RayonixMx300hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx325 attribute) +
+ + +
(pyFAI.detectors.RayonixMx340hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx425hs attribute) +
+ + +
(pyFAI.detectors.RayonixSx165 attribute) +
+ + +
(pyFAI.detectors.RayonixSx200 attribute) +
+ + +
(pyFAI.detectors.RayonixSx30hs attribute) +
+ + +
(pyFAI.detectors.RayonixSx85hs attribute) +
+ +
+ +
binning (pyFAI.detectors.Detector attribute) +
+ +
+ +
(pyFAI.detectors.Rayonix attribute) +
+ +
+ +
binning() (in module pyFAI.utils) +
+ + +
BlobDetection (class in pyFAI.blob_detection) +
+ +
+ +
BLOCK_SIZE (pyFAI.ocl_azim.Integrator1d attribute) +
+ +
+ +
(pyFAI.ocl_azim_lut.OCL_LUT_Integrator attribute) +
+ +
+ +
BORDER_PIXEL_SIZE_RELATIVE (pyFAI.detectors.ImXPadS140 attribute) +
+ +
+ +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
BORDER_SIZE_RELATIVE (pyFAI.detectors.ImXPadS10 attribute) +
+ +
+ +
(pyFAI.detectors.ImXPadS70 attribute) +
+ +
+ +
boundingBox() (in module pyFAI.utils) +
+ + +
browse() (pyFAI.integrate_widget.Browser method) +
+ + +
Browser (class in pyFAI.integrate_widget) +
+ +
+ +

C

+ + + +
+ +
calc_2th() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
calc_area() (pyFAI.distortion.Quad method) +
+ + +
calc_area_AB() (pyFAI.distortion.Quad method) +
+ + +
calc_area_BC() (pyFAI.distortion.Quad method) +
+ + +
calc_area_CD() (pyFAI.distortion.Quad method) +
+ + +
calc_area_DA() (pyFAI.distortion.Quad method) +
+ + +
calc_area_old() (pyFAI.distortion.Quad method) +
+ + +
calc_area_vectorial() (pyFAI.distortion.Quad method) +
+ + +
calc_cartesian_positions() (pyFAI.detectors.Aarhus method) +
+ +
+ +
(pyFAI.detectors.Detector method) +
+ + +
(pyFAI.detectors.Eiger method) +
+ + +
(pyFAI.detectors.ImXPadS10 method) +
+ + +
(pyFAI.detectors.Pilatus method) +
+ + +
(pyFAI.detectors.Xpad_flat method) +
+ +
+ +
calc_checksum() (in module pyFAI.utils) +
+ + +
calc_init() (pyFAI.distortion.Distortion method) +
+ + +
calc_LUT() (pyFAI.distortion.Distortion method) +
+ + +
calc_mask() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.detectors.Eiger method) +
+ + +
(pyFAI.detectors.FReLoN method) +
+ + +
(pyFAI.detectors.ImXPadS10 method) +
+ + +
(pyFAI.detectors.Mar345 method) +
+ + +
(pyFAI.detectors.Pilatus method) +
+ + +
(pyFAI.detectors.PilatusCdTe method) +
+ + +
(pyFAI.detectors.Rayonix133 method) +
+ + +
(pyFAI.detectors.RayonixSx165 method) +
+ + +
(pyFAI.detectors.Xpad_flat method) +
+ +
+ +
calc_pixels_edges() (pyFAI.detectors.ImXPadS10 method) +
+ +
+ +
(pyFAI.detectors.Xpad_flat method) +
+ +
+ +
calc_pos() (pyFAI.distortion.Distortion method) +
+ + +
calc_pos_zyx() (pyFAI.geometry.Geometry method) +
+ + +
calc_size() (pyFAI.distortion.Distortion method) +
+ + +
calc_transmission() (pyFAI.geometry.Geometry method) +
+ + +
calcfrom1d() (pyFAI.geometry.Geometry method) +
+ + +
calculate_massif() (pyFAI.massif.Massif method) +
+ + +
calib() (in module pyFAI.calibration) +
+ + +
Calibrant (class in pyFAI.calibrant) +
+ + +
calibrant_factory (class in pyFAI.calibrant) +
+ + +
Calibration (class in pyFAI.calibration) +
+ + +
Cell (class in pyFAI.calibrant) +
+ + +
center_array() (pyFAI.geometry.Geometry method) +
+ + +
center_of_mass() (in module pyFAI.utils) +
+ + +
check() (pyFAI.peak_picker.ControlPoints method) +
+ + +
CheckCalib (class in pyFAI.calibration) +
+ + +
chi() (pyFAI.geometry.Geometry method) +
+ + +
chi2() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
chi2_wavelength() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ +
+ +
chi_corner() (pyFAI.geometry.Geometry method) +
+ + +
chia (pyFAI.geometry.Geometry attribute) +
+ + +
chiArray() (pyFAI.geometry.Geometry method) +
+ + +
chiplot() (pyFAI.calibration.AbstractCalibration method) +
+ + +
clean() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
close() (pyFAI.io.HDF5Writer method) +
+ +
+ +
(pyFAI.io.Nexus method) +
+ +
+ +
closeGUI() (pyFAI.peak_picker.PeakPicker method) +
+ + +
code (pyFAI.peak_picker.PointGroup attribute) +
+ + +
comparison() (pyFAI.spline.Spline method) +
+ + +
comput_cap (pyFAI.opencl.OpenCL attribute) +
+ + +
concatenate_cl_kernel() (in module pyFAI.utils) +
+ + +
confidence() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
CONFIG (pyFAI.io.HDF5Writer attribute) +
+ + +
CONFIG_ITEMS (pyFAI.io.Writer attribute) +
+ + +
configure() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
configure_parser() (pyFAI.calibration.AbstractCalibration method) +
+ + +
context_cache (pyFAI.opencl.OpenCL attribute) +
+ + +
contour() (pyFAI.peak_picker.PeakPicker method) +
+ + +
ControlPoints (class in pyFAI.peak_picker) +
+ + +
convert_CamelCase() (in module pyFAI.utils) +
+ + +
corner_array() (pyFAI.geometry.Geometry method) +
+ + +
cornerArray() (pyFAI.geometry.Geometry method) +
+ + +
cornerQArray() (pyFAI.geometry.Geometry method) +
+ + +
cornerRArray() (pyFAI.geometry.Geometry method) +
+ + +
cornerRd2Array() (pyFAI.geometry.Geometry method) +
+ + +
correct() (pyFAI.distortion.Distortion method) +
+ +
+ +
(pyFAI.spline.Spline method) +
+ +
+ +
correct_SA_spline (pyFAI.geometry.Geometry attribute) +
+ + +
cosIncidance() (pyFAI.geometry.Geometry method) +
+ + +
create_context() (pyFAI.opencl.OpenCL method) +
+ + +
create_mask() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
cubic() (pyFAI.calibrant.Cell class method) +
+ + +
curve_fit() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ +
+ +

D

+ + + +
+ +
d() (pyFAI.calibrant.Cell method) +
+ + +
d_spacing() (pyFAI.calibrant.Cell method) +
+ + +
dark_correction() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
darkcurrent (pyFAI.azimuthalIntegrator.AzimuthalIntegrator attribute) +
+ + +
DATASET_NAME (pyFAI.io.HDF5Writer attribute) +
+ + +
deep_copy() (pyFAI.io.Nexus method) +
+ + +
DEFAULT_METHOD (pyFAI.azimuthalIntegrator.AzimuthalIntegrator attribute) +
+ + +
DEFAULT_PIXEL1 (pyFAI.detectors.Apex2 attribute) +
+ +
+ +
(pyFAI.detectors.Perkin attribute) +
+ + +
(pyFAI.detectors.Pixium attribute) +
+ +
+ +
DEFAULT_PIXEL2 (pyFAI.detectors.Apex2 attribute) +
+ +
+ +
(pyFAI.detectors.Perkin attribute) +
+ + +
(pyFAI.detectors.Pixium attribute) +
+ +
+ +
deg2rad() (in module pyFAI.utils) +
+ + +
del_chia() (pyFAI.geometry.Geometry method) +
+ + +
del_dssa() (pyFAI.geometry.Geometry method) +
+ + +
del_qa() (pyFAI.geometry.Geometry method) +
+ + +
del_ra() (pyFAI.geometry.Geometry method) +
+ + +
del_ttha() (pyFAI.geometry.Geometry method) +
+ + +
delta2Theta() (pyFAI.geometry.Geometry method) +
+ + +
delta_array() (pyFAI.geometry.Geometry method) +
+ + +
deltaChi() (pyFAI.geometry.Geometry method) +
+ + +
deltaQ() (pyFAI.geometry.Geometry method) +
+ + +
deltaR() (pyFAI.geometry.Geometry method) +
+ + +
deltaRd2() (pyFAI.geometry.Geometry method) +
+ + +
delValleySize() (pyFAI.massif.Massif method) +
+ +
+ +
Detector (class in pyFAI.detectors) +
+ + +
detector_changed() (pyFAI.integrate_widget.AIWidget method) +
+ + +
DetectorMeta (class in pyFAI.detectors) +
+ + +
Device (class in pyFAI.opencl) +
+ + +
device_from_context() (pyFAI.opencl.OpenCL method) +
+ + +
Dexela2923 (class in pyFAI.detectors) +
+ + +
diamond() (pyFAI.calibrant.Cell class method) +
+ + +
die() (pyFAI.integrate_widget.AIWidget method) +
+ + +
diffSolidAngle() (pyFAI.geometry.Geometry method) +
+ + +
direction() (pyFAI.blob_detection.BlobDetection method) +
+ + +
display_points() (pyFAI.peak_picker.PeakPicker method) +
+ + +
dist (pyFAI.geometry.Geometry attribute) +
+ + +
dist_max (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
dist_min (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
Distortion (class in pyFAI.distortion) +
+ + +
DistortionWorker (class in pyFAI.worker), [1] +
+ + +
do_2D() (pyFAI.worker.Worker method), [1] +
+ + +
dog() (in module pyFAI.utils) +
+ + +
dog_filter() (in module pyFAI.utils) +
+ + +
dSpacing (pyFAI.calibrant.Calibrant attribute) +
+ +
+ +
(pyFAI.peak_picker.ControlPoints attribute) +
+ +
+ +
dssa (pyFAI.geometry.Geometry attribute) +
+ + +
dump() (pyFAI.integrate_widget.AIWidget method) +
+ +
+ +

E

+ + + +
+ +
Eiger (class in pyFAI.detectors) +
+ + +
Eiger16M (class in pyFAI.detectors) +
+ + +
Eiger1M (class in pyFAI.detectors) +
+ + +
Eiger4M (class in pyFAI.detectors) +
+ + +
Eiger9M (class in pyFAI.detectors) +
+ + +
empty (pyFAI.azimuthalIntegrator.AzimuthalIntegrator attribute) +
+ + +
Enum (class in pyFAI.units) +
+ + +
eq_2th() (in module pyFAI.units) +
+ + +
eq_logq() (in module pyFAI.units) +
+ + +
eq_q() (in module pyFAI.units) +
+ +
+ +
eq_r() (in module pyFAI.units) +
+ + +
eq_rd2() (in module pyFAI.units) +
+ + +
error_model (pyFAI.worker.Worker attribute), [1] +
+ + +
Event (class in pyFAI.gui_utils) +
+ + +
execute() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
expand() (in module pyFAI.utils) +
+ + +
expand2d() (in module pyFAI.utils) +
+ + +
expand_args() (in module pyFAI.utils) +
+ + +
extract_cpt() (pyFAI.calibration.AbstractCalibration method) +
+ +
+ +

F

+ + + +
+ +
FabioWriter (class in pyFAI.io) +
+ + +
factory() (pyFAI.detectors.Detector class method) +
+ + +
Fairchild (class in pyFAI.detectors) +
+ + +
fake_calibration_image() (pyFAI.calibrant.Calibrant method) +
+ + +
find_detector() (pyFAI.io.Nexus method) +
+ + +
find_peaks() (pyFAI.massif.Massif method) +
+ + +
finish() (pyFAI.peak_picker.PeakPicker method) +
+ + +
FixedParameters (class in pyFAI.utils) +
+ + +
flat_correction() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
flatfield (pyFAI.azimuthalIntegrator.AzimuthalIntegrator attribute) +
+ +
+ +
fliplr() (pyFAI.spline.Spline method) +
+ + +
fliplrud() (pyFAI.spline.Spline method) +
+ + +
flipud() (pyFAI.spline.Spline method) +
+ + +
float_() (in module pyFAI.utils) +
+ + +
flop_core (pyFAI.opencl.OpenCL attribute) +
+ + +
flush() (pyFAI.io.HDF5Writer method) +
+ +
+ +
(pyFAI.io.Writer method) +
+ +
+ +
force_pixel (pyFAI.detectors.Aarhus attribute) +
+ +
+ +
(pyFAI.detectors.ADSC_Q210 attribute) +
+ + +
(pyFAI.detectors.ADSC_Q270 attribute) +
+ + +
(pyFAI.detectors.ADSC_Q315 attribute) +
+ + +
(pyFAI.detectors.ADSC_Q4 attribute) +
+ + +
(pyFAI.detectors.Apex2 attribute) +
+ + +
(pyFAI.detectors.Basler attribute) +
+ + +
(pyFAI.detectors.Detector attribute) +
+ + +
(pyFAI.detectors.Dexela2923 attribute) +
+ + +
(pyFAI.detectors.Eiger attribute) +
+ + +
(pyFAI.detectors.Fairchild attribute) +
+ + +
(pyFAI.detectors.HF_130K attribute) +
+ + +
(pyFAI.detectors.HF_1M attribute) +
+ + +
(pyFAI.detectors.HF_262k attribute) +
+ + +
(pyFAI.detectors.HF_2M attribute) +
+ + +
(pyFAI.detectors.HF_4M attribute) +
+ + +
(pyFAI.detectors.HF_9M attribute) +
+ + +
(pyFAI.detectors.ImXPadS10 attribute) +
+ + +
(pyFAI.detectors.ImXPadS140 attribute) +
+ + +
(pyFAI.detectors.ImXPadS70 attribute) +
+ + +
(pyFAI.detectors.Mar345 attribute) +
+ + +
(pyFAI.detectors.Perkin attribute) +
+ + +
(pyFAI.detectors.Pilatus attribute) +
+ + +
(pyFAI.detectors.Pixium attribute) +
+ + +
(pyFAI.detectors.RaspberryPi attribute) +
+ + +
(pyFAI.detectors.Rayonix attribute) +
+ + +
(pyFAI.detectors.Rayonix133 attribute) +
+ + +
(pyFAI.detectors.RayonixLx170 attribute) +
+ + +
(pyFAI.detectors.RayonixMx225 attribute) +
+ + +
(pyFAI.detectors.RayonixMx225hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx300 attribute) +
+ + +
(pyFAI.detectors.RayonixMx300hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx340hs attribute) +
+ + +
(pyFAI.detectors.RayonixSx165 attribute) +
+ + +
(pyFAI.detectors.Titan attribute) +
+ + +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
FReLoN (class in pyFAI.detectors) +
+ + +
from_isotime() (in module pyFAI.io) +
+ +
+ +

G

+ + + +
+ +
gaussian() (in module pyFAI.utils) +
+ + +
gaussian_filter() (in module pyFAI.utils) +
+ + +
Geometry (class in pyFAI.geometry) +
+ + +
GeometryRefinement (class in pyFAI.geometryRefinement) +
+ + +
get() (pyFAI.calibrant.calibrant_factory method) +
+ +
+ +
(pyFAI.peak_picker.ControlPoints method) +
+ +
+ +
get_1dsize() (pyFAI.calibration.CheckCalib method) +
+ + +
get_2th() (pyFAI.calibrant.Calibrant method) +
+ + +
get_2th_index() (pyFAI.calibrant.Calibrant method) +
+ + +
get_attr() (pyFAI.io.Nexus class method) +
+ + +
get_binning() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.detectors.Rayonix method) +
+ +
+ +
get_box() (pyFAI.distortion.Quad method) +
+ + +
get_box_size0() (pyFAI.distortion.Quad method) +
+ + +
get_box_size1() (pyFAI.distortion.Quad method) +
+ + +
get_calibration_dir() (in module pyFAI.utils) +
+ + +
get_chia() (pyFAI.geometry.Geometry method) +
+ + +
get_cl_file() (in module pyFAI.utils) +
+ + +
get_class() (pyFAI.io.Nexus method) +
+ + +
get_config() (pyFAI.integrate_widget.AIWidget method) +
+ +
+ +
(pyFAI.worker.Worker method), [1] +
+ +
+ +
get_correct_solid_angle_for_spline() (pyFAI.geometry.Geometry method) +
+ + +
get_darkcurrent() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
get_data() (pyFAI.io.Nexus method) +
+ + +
get_detector() (in module pyFAI.calibration) +
+ + +
get_device() (pyFAI.opencl.Platform method) +
+ + +
get_dist() (pyFAI.geometry.Geometry method) +
+ + +
get_dist_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_dist_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_dSpacing() (pyFAI.calibrant.Calibrant method) +
+ +
+ +
(pyFAI.peak_picker.ControlPoints method) +
+ +
+ +
get_dssa() (pyFAI.geometry.Geometry method) +
+ + +
get_empty() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
get_entries() (pyFAI.io.Nexus method) +
+ + +
get_entry() (pyFAI.io.Nexus method) +
+ + +
get_error_model() (pyFAI.worker.Worker method), [1] +
+ + +
get_flatfield() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
get_idx() (pyFAI.distortion.Quad method) +
+ + +
get_isotime() (in module pyFAI.io) +
+ + +
get_json_config() (pyFAI.worker.Worker method), [1] +
+ + +
get_label() (pyFAI.peak_picker.PointGroup class method) +
+ + +
get_mask() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
get_maskfile() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
get_method() (pyFAI.integrate_widget.AIWidget method) +
+ + +
get_name() (pyFAI.detectors.Detector method) +
+ + +
get_normalization_factor() (pyFAI.worker.Worker method), [1] +
+ + +
get_offset0() (pyFAI.distortion.Quad method) +
+ + +
get_offset1() (pyFAI.distortion.Quad method) +
+ + +
get_pixel1() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
get_pixel2() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
get_pixel_corners() (pyFAI.detectors.Aarhus method) +
+ +
+ +
(pyFAI.detectors.Detector method) +
+ + +
(pyFAI.detectors.ImXPadS10 method) +
+ + +
(pyFAI.detectors.Xpad_flat method) +
+ +
+ +
get_pixelSize() (pyFAI.calibration.AbstractCalibration method) +
+ +
+ +
(pyFAI.calibration.MultiCalib method) +
+ +
+
+ +
get_platform() (pyFAI.opencl.OpenCL method) +
+ + +
get_poni1() (pyFAI.geometry.Geometry method) +
+ + +
get_poni1_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_poni1_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_poni2() (pyFAI.geometry.Geometry method) +
+ + +
get_poni2_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_poni2_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_qa() (pyFAI.geometry.Geometry method) +
+ + +
get_ra() (pyFAI.geometry.Geometry method) +
+ + +
get_ring() (pyFAI.peak_picker.PointGroup method) +
+ + +
get_rot1() (pyFAI.geometry.Geometry method) +
+ + +
get_rot1_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_rot1_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_rot2() (pyFAI.geometry.Geometry method) +
+ + +
get_rot2_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_rot2_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_rot3() (pyFAI.geometry.Geometry method) +
+ + +
get_rot3_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_rot3_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_shape() (pyFAI.geometry.Geometry method) +
+ + +
get_spline() (pyFAI.geometry.Geometry method) +
+ + +
get_splineFile() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.detectors.Pilatus method) +
+ + +
(pyFAI.geometry.Geometry method) +
+ +
+ +
get_status() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
get_ttha() (pyFAI.geometry.Geometry method) +
+ + +
get_type() (pyFAI.calibrant.Cell method) +
+ + +
get_ui_file() (in module pyFAI.utils) +
+ + +
get_unit() (pyFAI.worker.Worker method), [1] +
+ + +
get_wavelength() (pyFAI.calibrant.Calibrant method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ + +
(pyFAI.peak_picker.ControlPoints method) +
+ +
+ +
get_wavelength_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
get_wavelength_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
getBinnedData() (pyFAI.massif.Massif method) +
+ + +
getBluredData() (pyFAI.massif.Massif method) +
+ + +
getConfiguration() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
getFit2D() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.detectors.NexusDetector method) +
+ + +
(pyFAI.geometry.Geometry method) +
+ +
+ +
getLabeledMassif() (pyFAI.massif.Massif method) +
+ + +
getList() (pyFAI.peak_picker.ControlPoints method) +
+ + +
getList2theta() (pyFAI.peak_picker.ControlPoints method) +
+ + +
getListRing() (pyFAI.peak_picker.ControlPoints method) +
+ + +
getMedianData() (pyFAI.massif.Massif method) +
+ + +
getPixelSize() (pyFAI.spline.Spline method) +
+ + +
getPyFAI() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.detectors.NexusDetector method) +
+ + +
(pyFAI.geometry.Geometry method) +
+ +
+ +
getSPD() (pyFAI.geometry.Geometry method) +
+ + +
getValleySize() (pyFAI.massif.Massif method) +
+ + +
getWeightedList() (pyFAI.peak_picker.ControlPoints method) +
+ + +
guess_binning() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.detectors.Mar345 method) +
+ + +
(pyFAI.detectors.Rayonix method) +
+ +
+ +
guess_poni() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
gui() (pyFAI.peak_picker.PeakPicker method) +
+ + +
gui_peakPicker() (pyFAI.calibration.Calibration method) +
+ +
+ +

H

+ + + +
+ +
has_key() (pyFAI.calibrant.calibrant_factory method) +
+ + +
HDF5Writer (class in pyFAI.io) +
+ + +
HELP (pyFAI.calibration.AbstractCalibration attribute) +
+ + +
help (pyFAI.peak_picker.PeakPicker attribute) +
+ + +
help() (pyFAI.integrate_widget.AIWidget method) +
+ + +
hexagonal() (pyFAI.calibrant.Cell class method) +
+ +
+ +
HF_130K (class in pyFAI.detectors) +
+ + +
HF_1M (class in pyFAI.detectors) +
+ + +
HF_262k (class in pyFAI.detectors) +
+ + +
HF_2M (class in pyFAI.detectors) +
+ + +
HF_4M (class in pyFAI.detectors) +
+ + +
HF_9M (class in pyFAI.detectors) +
+ +
+ +

I

+ + + +
+ +
idd (pyFAI.opencl.OpenCL attribute) +
+ + +
idx (pyFAI.opencl.OpenCL attribute) +
+ + +
image_test() (in module pyFAI.blob_detection) +
+ + +
ImXPadS10 (class in pyFAI.detectors) +
+ + +
ImXPadS140 (class in pyFAI.detectors) +
+ + +
ImXPadS70 (class in pyFAI.detectors) +
+ + +
init() (pyFAI.io.AsciiWriter method) +
+ +
+ +
(pyFAI.io.FabioWriter method) +
+ + +
(pyFAI.io.HDF5Writer method) +
+ + +
(pyFAI.io.Writer method) +
+ + +
(pyFAI.ocl_azim.Integrator1d method) +
+ + +
(pyFAI.peak_picker.PeakPicker method) +
+ +
+ +
init_slope() (pyFAI.distortion.Quad method) +
+ + +
initgeoRef() (pyFAI.calibration.Calibration method) +
+ + +
initValleySize() (pyFAI.massif.Massif method) +
+ + +
int_() (in module pyFAI.utils) +
+ +
+ +
integrate() (pyFAI.calibration.CheckCalib method) +
+ +
+ +
(pyFAI.ocl_azim_csr.OCL_CSR_Integrator method) +
+ + +
(pyFAI.ocl_azim_csr_dis.OCL_CSR_Integrator method) +
+ + +
(pyFAI.ocl_azim_lut.OCL_LUT_Integrator method) +
+ +
+ +
integrate1d() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
integrate2d() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
integrateAB() (pyFAI.distortion.Quad method) +
+ + +
Integrator1d (class in pyFAI.ocl_azim) +
+ + +
IS_CONTIGUOUS (pyFAI.detectors.Detector attribute) +
+ +
+ +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
is_far_from_group() (in module pyFAI.utils) +
+ + +
IS_FLAT (pyFAI.detectors.Aarhus attribute) +
+ +
+ +
(pyFAI.detectors.Detector attribute) +
+ +
+ +
is_hdf5() (in module pyFAI.io) +
+ + +
items() (pyFAI.calibrant.calibrant_factory method) +
+ +
+ +

K

+ + +
+ +
keys() (pyFAI.calibrant.calibrant_factory method) +
+ +
+ +

L

+ + + +
+ +
label (pyFAI.peak_picker.PointGroup attribute) +
+ + +
last_label (pyFAI.peak_picker.PointGroup attribute) +
+ + +
lattices (pyFAI.calibrant.Cell attribute) +
+ + +
lazy_property (class in pyFAI.utils) +
+ + +
load() (pyFAI.detectors.NexusDetector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ + +
(pyFAI.peak_picker.ControlPoints method) +
+ + +
(pyFAI.peak_picker.PeakPicker method) +
+ +
+
+ +
load_file() (pyFAI.calibrant.Calibrant method) +
+ + +
loadTth() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
local_max() (in module pyFAI.blob_detection) +
+ + +
log() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
log_profile() (pyFAI.ocl_azim_csr.OCL_CSR_Integrator method) +
+ +
+ +
(pyFAI.ocl_azim_csr_dis.OCL_CSR_Integrator method) +
+ + +
(pyFAI.ocl_azim_lut.OCL_LUT_Integrator method) +
+ +
+
+ +

M

+ + + +
+ +
main() (in module pyFAI.spline) +
+ + +
make_ai() (in module pyFAI.worker), [1] +
+ + +
make_gaussian() (in module pyFAI.blob_detection) +
+ + +
makeHeaders() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
Mar345 (class in pyFAI.detectors) +
+ + +
mask (pyFAI.detectors.Detector attribute) +
+ +
+ +
(pyFAI.geometry.Geometry attribute) +
+ +
+ +
maskfile (pyFAI.detectors.Detector attribute) +
+ +
+ +
(pyFAI.geometry.Geometry attribute) +
+ +
+ +
Massif (class in pyFAI.massif) +
+ + +
massif_contour() (pyFAI.peak_picker.PeakPicker method) +
+ +
+ +
MAX_SHAPE (pyFAI.detectors.Aarhus attribute) +
+ +
+ +
(pyFAI.detectors.ADSC_Q210 attribute) +
+ + +
(pyFAI.detectors.ADSC_Q270 attribute) +
+ + +
(pyFAI.detectors.ADSC_Q315 attribute) +
+ + +
(pyFAI.detectors.ADSC_Q4 attribute) +
+ + +
(pyFAI.detectors.Apex2 attribute) +
+ + +
(pyFAI.detectors.Basler attribute) +
+ + +
(pyFAI.detectors.Dexela2923 attribute) +
+ + +
(pyFAI.detectors.Eiger16M attribute) +
+ + +
(pyFAI.detectors.Eiger1M attribute) +
+ + +
(pyFAI.detectors.Eiger4M attribute) +
+ + +
(pyFAI.detectors.Eiger9M attribute) +
+ + +
(pyFAI.detectors.Fairchild attribute) +
+ + +
(pyFAI.detectors.HF_130K attribute) +
+ + +
(pyFAI.detectors.HF_1M attribute) +
+ + +
(pyFAI.detectors.HF_262k attribute) +
+ + +
(pyFAI.detectors.HF_2M attribute) +
+ + +
(pyFAI.detectors.HF_4M attribute) +
+ + +
(pyFAI.detectors.HF_9M attribute) +
+ + +
(pyFAI.detectors.ImXPadS10 attribute) +
+ + +
(pyFAI.detectors.ImXPadS140 attribute) +
+ + +
(pyFAI.detectors.ImXPadS70 attribute) +
+ + +
(pyFAI.detectors.Mar345 attribute) +
+ + +
(pyFAI.detectors.Perkin attribute) +
+ + +
(pyFAI.detectors.Pilatus100k attribute) +
+ + +
(pyFAI.detectors.Pilatus1M attribute) +
+ + +
(pyFAI.detectors.Pilatus200k attribute) +
+ + +
(pyFAI.detectors.Pilatus2M attribute) +
+ + +
(pyFAI.detectors.Pilatus300k attribute) +
+ + +
(pyFAI.detectors.Pilatus300kw attribute) +
+ + +
(pyFAI.detectors.Pilatus6M attribute) +
+ + +
(pyFAI.detectors.PilatusCdTe1M attribute) +
+ + +
(pyFAI.detectors.PilatusCdTe2M attribute) +
+ + +
(pyFAI.detectors.PilatusCdTe300k attribute) +
+ + +
(pyFAI.detectors.PilatusCdTe300kw attribute) +
+ + +
(pyFAI.detectors.Pixium attribute) +
+ + +
(pyFAI.detectors.RaspberryPi attribute) +
+ + +
(pyFAI.detectors.Rayonix attribute) +
+ + +
(pyFAI.detectors.Rayonix133 attribute) +
+ + +
(pyFAI.detectors.RayonixLx170 attribute) +
+ + +
(pyFAI.detectors.RayonixLx255 attribute) +
+ + +
(pyFAI.detectors.RayonixMx170 attribute) +
+ + +
(pyFAI.detectors.RayonixMx225 attribute) +
+ + +
(pyFAI.detectors.RayonixMx225hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx300 attribute) +
+ + +
(pyFAI.detectors.RayonixMx300hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx325 attribute) +
+ + +
(pyFAI.detectors.RayonixMx340hs attribute) +
+ + +
(pyFAI.detectors.RayonixMx425hs attribute) +
+ + +
(pyFAI.detectors.RayonixSx165 attribute) +
+ + +
(pyFAI.detectors.RayonixSx200 attribute) +
+ + +
(pyFAI.detectors.RayonixSx30hs attribute) +
+ + +
(pyFAI.detectors.RayonixSx85hs attribute) +
+ + +
(pyFAI.detectors.Titan attribute) +
+ + +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
maximize_fig() (in module pyFAI.gui_utils) +
+ + +
maximum_position() (in module pyFAI.utils) +
+ + +
measure_offset() (in module pyFAI.utils) +
+ + +
MODULE_GAP (pyFAI.detectors.Eiger attribute) +
+ +
+ +
(pyFAI.detectors.Pilatus attribute) +
+ + +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
MODULE_SIZE (pyFAI.detectors.Eiger attribute) +
+ +
+ +
(pyFAI.detectors.ImXPadS10 attribute) +
+ + +
(pyFAI.detectors.ImXPadS140 attribute) +
+ + +
(pyFAI.detectors.ImXPadS70 attribute) +
+ + +
(pyFAI.detectors.Pilatus attribute) +
+ + +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
monoclinic() (pyFAI.calibrant.Cell class method) +
+ + +
MultiCalib (class in pyFAI.calibration) +
+ +
+ +

N

+ + + +
+ +
name (pyFAI.detectors.Detector attribute) +
+ + +
nb_devices (pyFAI.opencl.OpenCL attribute) +
+ + +
nearest_peak() (pyFAI.blob_detection.BlobDetection method) +
+ +
+ +
(pyFAI.massif.Massif method) +
+ +
+ +
new_class() (pyFAI.io.Nexus method) +
+ + +
new_detector() (pyFAI.io.Nexus method) +
+ +
+ +
new_entry() (pyFAI.io.Nexus method) +
+ + +
new_instrument() (pyFAI.io.Nexus method) +
+ + +
Nexus (class in pyFAI.io) +
+ + +
NexusDetector (class in pyFAI.detectors) +
+ + +
normalization_factor (pyFAI.worker.Worker attribute), [1] +
+ +
+ +

O

+ + + +
+ +
OCL_CSR_Integrator (class in pyFAI.ocl_azim_csr) +
+ +
+ +
(class in pyFAI.ocl_azim_csr_dis) +
+ +
+ +
OCL_LUT_Integrator (class in pyFAI.ocl_azim_lut) +
+ + +
on_minus_pts_clicked() (pyFAI.peak_picker.PeakPicker method) +
+ + +
on_option_clicked() (pyFAI.peak_picker.PeakPicker method) +
+ + +
on_plus_pts_clicked() (pyFAI.peak_picker.PeakPicker method) +
+ + +
on_refine_clicked() (pyFAI.peak_picker.PeakPicker method) +
+ +
+ +
onclick() (pyFAI.peak_picker.PeakPicker method) +
+ + +
OpenCL (class in pyFAI.opencl) +
+ + +
openCL_changed() (pyFAI.integrate_widget.AIWidget method) +
+ + +
orthorhombic() (pyFAI.calibrant.Cell class method) +
+ + +
oversampleArray() (pyFAI.geometry.Geometry method) +
+ +
+ +

P

+ + + +
+ +
PARAMETERS (pyFAI.calibration.AbstractCalibration attribute) +
+ + +
parse() (pyFAI.calibration.Calibration method) +
+ +
+ +
(pyFAI.calibration.CheckCalib method) +
+ + +
(pyFAI.calibration.MultiCalib method) +
+ + +
(pyFAI.calibration.Recalibration method) +
+ +
+ +
PeakPicker (class in pyFAI.peak_picker) +
+ + +
peaks_from_area() (pyFAI.blob_detection.BlobDetection method) +
+ +
+ +
(pyFAI.massif.Massif method) +
+ + +
(pyFAI.peak_picker.PeakPicker method) +
+ +
+ +
Perkin (class in pyFAI.detectors) +
+ + +
Pilatus (class in pyFAI.detectors) +
+ + +
Pilatus100k (class in pyFAI.detectors) +
+ + +
Pilatus1M (class in pyFAI.detectors) +
+ + +
Pilatus200k (class in pyFAI.detectors) +
+ + +
Pilatus2M (class in pyFAI.detectors) +
+ + +
Pilatus300k (class in pyFAI.detectors) +
+ + +
Pilatus300kw (class in pyFAI.detectors) +
+ + +
Pilatus6M (class in pyFAI.detectors) +
+ + +
PilatusCdTe (class in pyFAI.detectors) +
+ + +
PilatusCdTe1M (class in pyFAI.detectors) +
+ + +
PilatusCdTe2M (class in pyFAI.detectors) +
+ + +
PilatusCdTe300k (class in pyFAI.detectors) +
+ + +
PilatusCdTe300kw (class in pyFAI.detectors) +
+ + +
pixel1 (pyFAI.detectors.Detector attribute) +
+ +
+ +
(pyFAI.geometry.Geometry attribute) +
+ +
+ +
pixel2 (pyFAI.detectors.Detector attribute) +
+ +
+ +
(pyFAI.geometry.Geometry attribute) +
+ +
+ +
PIXEL_EDGES (pyFAI.detectors.ImXPadS70 attribute) +
+ + +
PIXEL_SIZE (pyFAI.detectors.ImXPadS10 attribute) +
+ +
+ +
(pyFAI.detectors.ImXPadS140 attribute) +
+ + +
(pyFAI.detectors.ImXPadS70 attribute) +
+ + +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
PixelwiseWorker (class in pyFAI.worker), [1] +
+ + +
Pixium (class in pyFAI.detectors) +
+ + +
Platform (class in pyFAI.opencl) +
+ + +
platform_changed() (pyFAI.integrate_widget.AIWidget method) +
+ + +
platforms (pyFAI.opencl.OpenCL attribute) +
+ + +
PointGroup (class in pyFAI.peak_picker) +
+ + +
polarization() (pyFAI.geometry.Geometry method) +
+ + +
poni1 (pyFAI.geometry.Geometry attribute) +
+ + +
poni1_max (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
poni1_min (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
poni2 (pyFAI.geometry.Geometry attribute) +
+ + +
poni2_max (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ +
+ +
poni2_min (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
pop() (pyFAI.peak_picker.ControlPoints method) +
+ + +
populate_box() (pyFAI.distortion.Quad method) +
+ + +
positionArray() (pyFAI.geometry.Geometry method) +
+ + +
postProcess() (pyFAI.calibration.AbstractCalibration method) +
+ + +
preprocess() (pyFAI.calibration.AbstractCalibration method) +
+ +
+ +
(pyFAI.calibration.Calibration method) +
+ + +
(pyFAI.calibration.Recalibration method) +
+ +
+ +
pretty_print() (pyFAI.opencl.Device method) +
+ + +
proceed() (pyFAI.integrate_widget.AIWidget method) +
+ + +
process() (pyFAI.blob_detection.BlobDetection method) +
+ +
+ +
(pyFAI.calibration.MultiCalib method) +
+ + +
(pyFAI.worker.DistortionWorker method), [1] +
+ + +
(pyFAI.worker.PixelwiseWorker method), [1] +
+ + +
(pyFAI.worker.Worker method), [1] +
+ +
+ +
prompt() (pyFAI.calibration.AbstractCalibration method) +
+ + +
PTS_PER_DEG (pyFAI.calibration.AbstractCalibration attribute) +
+ + +
pyFAI.__init__ (module) +
+ + +
pyFAI.azimuthalIntegrator (module) +
+ + +
pyFAI.blob_detection (module) +
+ + +
pyFAI.calibrant (module) +
+ + +
pyFAI.calibration (module) +
+ + +
pyFAI.detectors (module) +
+ + +
pyFAI.distortion (module) +
+ + +
pyFAI.geometry (module) +
+ + +
pyFAI.geometryRefinement (module) +
+ + +
pyFAI.gui_utils (module) +
+ + +
pyFAI.integrate_widget (module) +
+ + +
pyFAI.io (module) +
+ + +
pyFAI.massif (module) +
+ + +
pyFAI.ocl_azim (module) +
+ + +
pyFAI.ocl_azim_csr (module) +
+ + +
pyFAI.ocl_azim_csr_dis (module) +
+ + +
pyFAI.ocl_azim_lut (module) +
+ + +
pyFAI.opencl (module) +
+ + +
pyFAI.peak_picker (module) +
+ + +
pyFAI.spline (module) +
+ + +
pyFAI.units (module) +
+ + +
pyFAI.utils (module) +
+ + +
pyFAI.worker (module), [1] +
+ +
+ +

Q

+ + + +
+ +
qa (pyFAI.geometry.Geometry attribute) +
+ + +
qArray() (pyFAI.geometry.Geometry method) +
+ + +
qCornerFunct() (pyFAI.geometry.Geometry method) +
+ +
+ +
qFunction() (pyFAI.geometry.Geometry method) +
+ + +
Quad (class in pyFAI.distortion) +
+ +
+ +

R

+ + + +
+ +
ra (pyFAI.geometry.Geometry attribute) +
+ + +
rArray() (pyFAI.geometry.Geometry method) +
+ + +
RaspberryPi (class in pyFAI.detectors) +
+ + +
Rayonix (class in pyFAI.detectors) +
+ + +
Rayonix133 (class in pyFAI.detectors) +
+ + +
RayonixLx170 (class in pyFAI.detectors) +
+ + +
RayonixLx255 (class in pyFAI.detectors) +
+ + +
RayonixMx170 (class in pyFAI.detectors) +
+ + +
RayonixMx225 (class in pyFAI.detectors) +
+ + +
RayonixMx225hs (class in pyFAI.detectors) +
+ + +
RayonixMx300 (class in pyFAI.detectors) +
+ + +
RayonixMx300hs (class in pyFAI.detectors) +
+ + +
RayonixMx325 (class in pyFAI.detectors) +
+ + +
RayonixMx340hs (class in pyFAI.detectors) +
+ + +
RayonixMx425hs (class in pyFAI.detectors) +
+ + +
RayonixSx165 (class in pyFAI.detectors) +
+ + +
RayonixSx200 (class in pyFAI.detectors) +
+ + +
RayonixSx30hs (class in pyFAI.detectors) +
+ + +
RayonixSx85hs (class in pyFAI.detectors) +
+ + +
rCornerFunct() (pyFAI.geometry.Geometry method) +
+ + +
rd2Array() (pyFAI.geometry.Geometry method) +
+ + +
read() (pyFAI.geometry.Geometry method) +
+ +
+ +
(pyFAI.spline.Spline method) +
+ +
+ +
read_cl_file() (in module pyFAI.utils) +
+ + +
read_dSpacingFile() (pyFAI.calibration.AbstractCalibration method) +
+ +
+ +
(pyFAI.calibration.MultiCalib method) +
+ + +
(pyFAI.calibration.Recalibration method) +
+ +
+ +
read_pixelsSize() (pyFAI.calibration.AbstractCalibration method) +
+ +
+ +
(pyFAI.calibration.MultiCalib method) +
+ +
+ +
read_wavelength() (pyFAI.calibration.AbstractCalibration method) +
+ +
+ +
(pyFAI.calibration.MultiCalib method) +
+ +
+ +
readFloatFromKeyboard() (in module pyFAI.utils) +
+ + +
readRingNrFromKeyboard() (pyFAI.peak_picker.ControlPoints method) +
+ + +
rebuild() (pyFAI.calibration.CheckCalib method) +
+ + +
Recalibration (class in pyFAI.calibration) +
+ + +
reconfig() (pyFAI.worker.Worker method), [1] +
+ + +
refine() (pyFAI.calibration.AbstractCalibration method) +
+ +
+ +
(pyFAI.calibration.Calibration method) +
+ + +
(pyFAI.calibration.Recalibration method) +
+ +
+ +
refine1() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
refine2() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
refine2_wavelength() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ +
+ +
refine_Hessian() (pyFAI.blob_detection.BlobDetection method) +
+ + +
refine_Hessian_SG() (pyFAI.blob_detection.BlobDetection method) +
+ + +
refinement() (pyFAI.blob_detection.BlobDetection method) +
+ + +
registry (pyFAI.detectors.Detector attribute) +
+ + +
regression() (pyFAI.calibration.MultiCalib method) +
+ + +
reinit() (pyFAI.distortion.Quad method) +
+ + +
relabel() (in module pyFAI.utils) +
+ + +
release_cl_buffers() (in module pyFAI.opencl) +
+ + +
remove_grp() (pyFAI.peak_picker.PeakPicker method) +
+ + +
removeSaturatedPixel() (in module pyFAI.utils) +
+ + +
reset() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ +
+ +
(pyFAI.distortion.Distortion method) +
+ + +
(pyFAI.geometry.Geometry method) +
+ + +
(pyFAI.peak_picker.ControlPoints method) +
+ + +
(pyFAI.peak_picker.PeakPicker method) +
+ + +
(pyFAI.worker.Worker method), [1] +
+ +
+ +
reset_geometry() (pyFAI.calibration.AbstractCalibration method) +
+ + +
reset_label() (pyFAI.peak_picker.PointGroup class method) +
+ + +
residu1() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
residu1_wavelength() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
residu2() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
residu2_wavelength() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
residu2_wavelength_weighted() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
residu2_weighted() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
restore() (pyFAI.integrate_widget.AIWidget method) +
+ + +
rFunction() (pyFAI.geometry.Geometry method) +
+ + +
rhombohedral() (pyFAI.calibrant.Cell class method) +
+ + +
ring (pyFAI.peak_picker.PointGroup attribute) +
+ + +
roca() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
rot1 (pyFAI.geometry.Geometry attribute) +
+ + +
rot1_max (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
rot1_min (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
rot2 (pyFAI.geometry.Geometry attribute) +
+ + +
rot2_max (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
rot2_min (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
rot3 (pyFAI.geometry.Geometry attribute) +
+ + +
rot3_max (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
rot3_min (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
roundfft() (in module pyFAI.utils) +
+ +
+ +

S

+ + + +
+ +
save() (pyFAI.calibrant.Cell method) +
+ +
+ +
(pyFAI.detectors.Detector method) +
+ + +
(pyFAI.geometry.Geometry method) +
+ + +
(pyFAI.peak_picker.ControlPoints method) +
+ +
+ +
save1D() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
save2D() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
save_config() (pyFAI.integrate_widget.AIWidget method) +
+ +
+ +
(pyFAI.worker.Worker method), [1] +
+ +
+ +
save_dSpacing() (pyFAI.calibrant.Calibrant method) +
+ + +
saxs() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
select_darkcurrent() (pyFAI.integrate_widget.AIWidget method) +
+ + +
select_device() (pyFAI.opencl.OpenCL method) +
+ + +
select_flatfield() (pyFAI.integrate_widget.AIWidget method) +
+ + +
select_maskfile() (pyFAI.integrate_widget.AIWidget method) +
+ + +
select_ponifile() (pyFAI.integrate_widget.AIWidget method) +
+ + +
select_splinefile() (pyFAI.integrate_widget.AIWidget method) +
+ + +
selection_rules (pyFAI.calibrant.Cell attribute) +
+ + +
separate() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
set_binning() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.detectors.Rayonix method) +
+ +
+ +
set_chia() (pyFAI.geometry.Geometry method) +
+ + +
set_config() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.integrate_widget.AIWidget method) +
+ +
+ +
set_correct_solid_angle_for_spline() (pyFAI.geometry.Geometry method) +
+ + +
set_darkcurrent() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
set_darkfiles() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
set_data() (pyFAI.calibration.AbstractCalibration method) +
+ + +
set_dist() (pyFAI.geometry.Geometry method) +
+ + +
set_dist_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_dist_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_dSpacing() (pyFAI.calibrant.Calibrant method) +
+ +
+ +
(pyFAI.peak_picker.ControlPoints method) +
+ +
+ +
set_dssa() (pyFAI.geometry.Geometry method) +
+ + +
set_dx() (pyFAI.detectors.Detector method) +
+ + +
set_dy() (pyFAI.detectors.Detector method) +
+ + +
set_empty() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
set_error_model() (pyFAI.worker.Worker method), [1] +
+ + +
set_flatfield() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
set_flatfiles() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
set_input_data() (pyFAI.integrate_widget.AIWidget method) +
+ + +
set_label() (pyFAI.peak_picker.PointGroup class method) +
+ + +
set_mask() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
set_maskfile() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
set_normalization_factor() (pyFAI.worker.Worker method), [1] +
+ + +
set_pixel1() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
set_pixel2() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
set_poni1() (pyFAI.geometry.Geometry method) +
+ + +
set_poni1_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_poni1_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_poni2() (pyFAI.geometry.Geometry method) +
+ + +
set_poni2_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_poni2_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_ponifile() (pyFAI.integrate_widget.AIWidget method) +
+ + +
set_qa() (pyFAI.geometry.Geometry method) +
+ + +
set_ra() (pyFAI.geometry.Geometry method) +
+ + +
set_ring() (pyFAI.peak_picker.PointGroup method) +
+ + +
set_rot1() (pyFAI.geometry.Geometry method) +
+ + +
set_rot1_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_rot1_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_rot2() (pyFAI.geometry.Geometry method) +
+ + +
set_rot2_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_rot2_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ +
+ +
set_rot3() (pyFAI.geometry.Geometry method) +
+ + +
set_rot3_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_rot3_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_spline() (pyFAI.geometry.Geometry method) +
+ + +
set_splineFile() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.detectors.Pilatus method) +
+ + +
(pyFAI.geometry.Geometry method) +
+ +
+ +
set_tolerance() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_ttha() (pyFAI.geometry.Geometry method) +
+ + +
set_type() (pyFAI.calibrant.Cell method) +
+ + +
set_unit() (pyFAI.worker.Worker method), [1] +
+ + +
set_validators() (pyFAI.integrate_widget.AIWidget method) +
+ + +
set_wavelength() (pyFAI.calibrant.Calibrant method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ + +
(pyFAI.peak_picker.ControlPoints method) +
+ +
+ +
set_wavelength_max() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
set_wavelength_min() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
setChiDiscAtPi() (pyFAI.geometry.Geometry method) +
+ + +
setChiDiscAtZero() (pyFAI.geometry.Geometry method) +
+ + +
setDarkcurrentFile() (pyFAI.worker.Worker method), [1] +
+ + +
setDummyValue() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
setExtension() (pyFAI.worker.Worker method), [1] +
+ + +
setFit2D() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
setFlatfieldFile() (pyFAI.worker.Worker method), [1] +
+ + +
setJsonConfig() (pyFAI.io.Writer method) +
+ +
+ +
(pyFAI.worker.Worker method), [1] +
+ +
+ +
setMask() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
setOversampling() (pyFAI.geometry.Geometry method) +
+ + +
setPixelSize() (pyFAI.spline.Spline method) +
+ + +
setPyFAI() (pyFAI.detectors.Detector method) +
+ +
+ +
(pyFAI.geometry.Geometry method) +
+ +
+ +
setRange() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
setSolidAngle() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
setSPD() (pyFAI.geometry.Geometry method) +
+ + +
setStackDataObject() (pyFAI.integrate_widget.AIWidget method) +
+ + +
setSubdir() (pyFAI.worker.Worker method), [1] +
+ + +
setup_CSR() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
setup_LUT() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
setValleySize() (pyFAI.massif.Massif method) +
+ + +
setWavelength_change2th() (pyFAI.calibrant.Calibrant method) +
+ +
+ +
(pyFAI.peak_picker.ControlPoints method) +
+ +
+ +
setWavelength_changeDs() (pyFAI.calibrant.Calibrant method) +
+ +
+ +
(pyFAI.peak_picker.ControlPoints method) +
+ +
+ +
shape_out (pyFAI.distortion.Distortion attribute) +
+ + +
shift() (in module pyFAI.utils) +
+ + +
shiftFFT() (in module pyFAI.utils) +
+ + +
show() (pyFAI.calibration.CheckCalib method) +
+ + +
show_neighboor() (pyFAI.blob_detection.BlobDetection method) +
+ + +
show_stats() (pyFAI.blob_detection.BlobDetection method) +
+ + +
simplex() (pyFAI.geometryRefinement.GeometryRefinement method) +
+ + +
size1d (pyFAI.calibration.CheckCalib attribute) +
+ + +
sload() (pyFAI.detectors.NexusDetector class method) +
+ +
+ +
(pyFAI.geometry.Geometry class method) +
+ +
+ +
smooth_mask() (pyFAI.calibration.CheckCalib method) +
+ + +
solidAngleArray() (pyFAI.geometry.Geometry method) +
+ + +
Spline (class in pyFAI.spline) +
+ + +
spline (pyFAI.geometry.Geometry attribute) +
+ + +
spline2array() (pyFAI.spline.Spline method) +
+ + +
splineFile (pyFAI.detectors.Detector attribute) +
+ +
+ +
(pyFAI.detectors.Pilatus attribute) +
+ + +
(pyFAI.geometry.Geometry attribute) +
+ +
+ +
splineFuncX() (pyFAI.spline.Spline method) +
+ + +
splineFuncY() (pyFAI.spline.Spline method) +
+ + +
str_() (in module pyFAI.utils) +
+ + +
sync_init() (pyFAI.peak_picker.PeakPicker method) +
+ +
+ +

T

+ + + +
+ +
TARGET_SIZE (pyFAI.massif.Massif attribute) +
+ + +
test() (in module pyFAI.distortion) +
+ + +
tests() (in module pyFAI.__init__) +
+ + +
tetragonal() (pyFAI.calibrant.Cell class method) +
+ + +
tilt() (pyFAI.spline.Spline method) +
+ + +
Titan (class in pyFAI.detectors) +
+ + +
to_unit() (in module pyFAI.units) +
+ +
+ +
tresh (pyFAI.blob_detection.BlobDetection attribute) +
+ + +
tth() (pyFAI.geometry.Geometry method) +
+ + +
tth_corner() (pyFAI.geometry.Geometry method) +
+ + +
ttha (pyFAI.geometry.Geometry attribute) +
+ + +
twoThetaArray() (pyFAI.geometry.Geometry method) +
+ + +
type (pyFAI.calibrant.Cell attribute) +
+ + +
types (pyFAI.calibrant.Cell attribute) +
+ +
+ +

U

+ + + +
+ +
unBinning() (in module pyFAI.utils) +
+ + +
uncorrect() (pyFAI.distortion.Distortion method) +
+ + +
uniform_pixel (pyFAI.detectors.Detector attribute) +
+ +
+ +
(pyFAI.detectors.Fairchild attribute) +
+ + +
(pyFAI.detectors.ImXPadS10 attribute) +
+ + +
(pyFAI.detectors.Titan attribute) +
+ + +
(pyFAI.detectors.Xpad_flat attribute) +
+ +
+ +
unit (pyFAI.worker.Worker attribute), [1] +
+ + +
UNITS (pyFAI.calibration.AbstractCalibration attribute) +
+ + +
unsetDummyValue() (pyFAI.ocl_azim.Integrator1d method) +
+ +
+ +
unsetMask() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
unsetRange() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
unsetSolidAngle() (pyFAI.ocl_azim.Integrator1d method) +
+ + +
update_fig() (in module pyFAI.gui_utils) +
+ + +
URL (pyFAI.integrate_widget.AIWidget attribute) +
+ +
+ +

V

+ + + +
+ +
VALID_METHODS (pyFAI.peak_picker.PeakPicker attribute) +
+ + +
VALID_SIZE (pyFAI.detectors.Mar345 attribute) +
+ + +
VALID_URL (pyFAI.calibration.AbstractCalibration attribute) +
+ + +
validate_calibration() (pyFAI.calibration.AbstractCalibration method) +
+ +
+ +
validate_center() (pyFAI.calibration.AbstractCalibration method) +
+ + +
valley_size (pyFAI.massif.Massif attribute) +
+ + +
values() (pyFAI.calibrant.calibrant_factory method) +
+ + +
volume (pyFAI.calibrant.Cell attribute) +
+ +
+ +

W

+ + + +
+ +
warmup() (pyFAI.worker.Worker method), [1] +
+ + +
wavelength (pyFAI.calibrant.Calibrant attribute) +
+ +
+ +
(pyFAI.geometry.Geometry attribute) +
+ + +
(pyFAI.peak_picker.ControlPoints attribute) +
+ +
+ +
wavelength_max (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
wavelength_min (pyFAI.geometryRefinement.GeometryRefinement attribute) +
+ + +
win_error (pyFAI.calibration.AbstractCalibration attribute) +
+ +
+ +
Worker (class in pyFAI.worker), [1] +
+ + +
workgroup (pyFAI.opencl.OpenCL attribute) +
+ + +
write() (pyFAI.geometry.Geometry method) +
+ +
+ +
(pyFAI.io.AsciiWriter method) +
+ + +
(pyFAI.io.FabioWriter method) +
+ + +
(pyFAI.io.HDF5Writer method) +
+ + +
(pyFAI.io.Writer method) +
+ + +
(pyFAI.spline.Spline method) +
+ +
+ +
writeEDF() (pyFAI.spline.Spline method) +
+ + +
Writer (class in pyFAI.io) +
+ +
+ +

X

+ + + +
+ +
Xpad_flat (class in pyFAI.detectors) +
+ + +
xrpd() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd2() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd2_histogram() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd2_numpy() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd2_splitBBox() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd2_splitPixel() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd_CSR_OCL() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ +
+ +
xrpd_cython() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd_LUT() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd_LUT_OCL() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd_numpy() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd_OpenCL() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd_splitBBox() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ + +
xrpd_splitPixel() (pyFAI.azimuthalIntegrator.AzimuthalIntegrator method) +
+ +
+ +

Z

+ + + +
+ +
zeros() (pyFAI.spline.Spline method) +
+ +
+ +
zeros_like() (pyFAI.spline.Spline method) +
+ +
+ + + +
+
+
+
+
+ + + + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/geometry.html b/doc/build/geometry.html new file mode 100644 index 000000000..8bfc812b0 --- /dev/null +++ b/doc/build/geometry.html @@ -0,0 +1,211 @@ + + + + + + + + Image representation in Python — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +

PyFAI uses a 6-parameter geometry definition similar, while not rigorously +identical to SPD: +One distance, 2 coordinates to define the point of normal incidence and 3 rotations +around the main axis; these parameters are saved in text files usually +with the .poni extension. +In addition, the poni-file may contain the wavelength and the detector definition.

+
+

Image representation in Python

+

PyFAI takes diffraction images as 2D numpy arrays, those are usually read +using the FabIO library:

+
import fabio
+data = fabio.open("image.edf").data
+
+
+

But data can also be extracted from HDF5 files with h5py and displayed using matplotlib:

+
%pylab
+imshow(data, origin="lower")
+
+
+

Because Python is written in C language, data are stored lines by lines, this means to go +from a pixel to the on its right, one offets the position by the pixel width. +To go the pixel above the current one, on needs to offset by the length of the line. +This is why, if one considers the pixel at position (x,y), its value can be retrieved by data[y,x] +(note the order y,x, this is not a bug!). +We usually refer the x axis as the fast dimension (because pixels are adjacent) and the y axis +as the slow axis (as pixel are appart from each other by a line length). +More information on how numpy array are stored can be found at here

+

Like most scientific packages, the origin of the image is considered +to be at the lower-left corner of the image to have the polar angle growing +from 0 along the x axis to 90 deg along the y axis. +This is why we pass the origin=”lower” option to imshow (from the matplotlib library). +Axis 1 and 2 on the image (like in poni1 & poni2) +refer to the slow and fast dimension of the image, so usually to the y and x axis +(and not the opposite)

+
+
+

Position of the observer

+

There are two (main) conventions when representing images:

+
    +
  • In imaging application, one can can replace the camera by the eye, the camera looks at the scene. In this convention, the origin is usually at the top of the image.
    • +
  • In diffraction application, the observer is situated at the sample position and looks +at the detector, hence on the other side of the detector.
    • +
+

Because we measure (signed) angles, the origin is ideally situated at the lower left of the image. +PyFAI being a diffraction application, it uses the later description. +Changing the point of view behind the detector changes the sign of the azimuthal angle.

+
+
+

Default geometry in pyFAI

+

In the (most common) case of transmission diffraction setup on synchrotrons (like ESRF, Soleil, Petra3, SLS...) this makes looks like:

+
    +
  • Observer looking at the detector from the sample position:
    • +
  • Origin at the lower left of the detector
    • +
  • Axis 1 (i.e. y) being vertical, pointing upwards
    • +
  • Axis 2 (i.e. x) being horizontal, pointing to the center of the storage ring
    • +
  • Axis 3 (i.e. z) being horizontal, along the transmitted beam
    • +
+

Axis 3 is built in such a way to be orthogonal and (1,2,3) is a direct orientation. +This makes the sample position negative (at z<0).

+
+
+

Detector position

+

In pyFAI, the experiment geometry is defined by the position of the detector in +space, the origin being located at the sample position, more precisely where the +X-ray beam crosses the diffractometer main axis.

+
+The geometry used by pyFAI is inspired by SPD +
+

With the detector being a rigid body, its position in space is described by +six parameters: 3 translations and 3 rotations. +In pyFAI, the beam center is not directly used as it is ill-defined with +highly tilted detectors. +Like SPD, we use the orthogonal projection of origin on +the detector surface called PONI (for Point Of Normal Incidence). +For non planar detectors, the PONI is defined in the plan z=0 in detector’s +coordinate system.

+

Poni1 and Poni2 are distances in meter (along the y and x axis of the detector), +like the sample-detector distance, letting the calibration parameters be +independent of the pixel size hence stable regarding the binning factor of the detector.

+

In the same idea rot1, rot2 and rot3 are rotation along axis 1, 2 and 3, always expressed in radians. +Rotations applied in the same order: rot1 then rot2 and finally rot3. +Due to the axial symmetry of the Debye-Scherrer cones, rot3 cannot be optimized but can be adjusted manually in some cases like if +the detector is not mounted horizontally and/or one cares about polarization correction.

+

When all rotations are zero, the detector is in transmission mode with the +incident beam orthogonal to the detector’s surface.

+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Simple detector

+

Next topic

+

Pre-processing of images:

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/index.html b/doc/build/index.html new file mode 100644 index 000000000..0c557de46 --- /dev/null +++ b/doc/build/index.html @@ -0,0 +1,165 @@ + + + + + + + + Fast Azimuthal Integration using Python — pyFAI 0.12.0 documentation + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Fast Azimuthal Integration using Python

+

PyFAI is a python libary for azimuthal integration of diffraction data acquired +with 2D detectors, providing high performance thanks to GPU computing. +Most concepts have been presented at EuroScipy 2014 in this +video +as well as the proceedings.

+

The documentation starts with a general descriptions of the pyFAI library. +This first chapter contains an introduction to pyFAI, what it is, what it aims at +and how it works (from the scientists point of view). +Especially, geometry, calibration, azimuthal integration algorithms are described +and pixel splitting schemes are explained.

+

Follows cookbook and tutorials on how to use pyFAI: +Cookbooks focus on how to use pyFAI using the various graphical interfaces or +from scripts on the command linee. +Tutorials use the Jupyter notebook (formerly ipython) and present the Python interface. +The first tutorial start with a quick general introduction of the notebooks, +how to process data in the general case, subsequent tutorials are more advanced +and require already a good knowledge both in Python and pyFAI.. +After the tutorials, all manual pages of pyFAI programs, both graphical interfaces +and scripts are described in the documentation.

+

The design of the programming interface (API) is then exposed before a +comprehensive description of most modules contained in pyFAI. +Some minor submodules as well as the documentation of the Cython sub-modules are +not included for concision purposes.

+

Installation procedures for Windows, MacOSX and Linux operating systems are then +described.

+

Finally other programs/projects relying on pyFAI are presented and the project is +summarized from a developer’s point of view.

+

In appendix there are some figures about the project and its management and a list +of publication on pyFAI.

+
+ +
+
+
+

Indices and tables

+ +
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Next topic

+

General introduction to PyFAI

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/MX-calibrate.html b/doc/build/man/MX-calibrate.html new file mode 100644 index 000000000..7c6f9a598 --- /dev/null +++ b/doc/build/man/MX-calibrate.html @@ -0,0 +1,422 @@ + + + + + + + + Calibration tool: MX-calibrate — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Calibration tool: MX-calibrate

+
+

Purpose

+

Calibrate automatically a set of frames taken at various sample-detector distance.

+

This tool has been developed for ESRF MX-beamlines where an acceptable calibration is +usually present is the header of the image. PyFAI reads it and does a “recalib” on +each of them before exporting a linear regression of all parameters versus this distance.

+

Most standard calibrants are directly installed together with pyFAI. +If you prefer using your own, you can provide a “d-spacing” file +containing the spacing of Miller plans in Angstrom (in decreasing order). +Most crystal powders used for calibration are available in the American Mineralogist +database [AMD] or in the [COD].

+
+
+

Usage:

+

MX-Calibrate -w 1.54 -c CeO2 file1.cbf file2.cbf ...

+
+
+

Options:

+

usage: MX-Calibrate -w 1.54 -c CeO2 file1.cbf file2.cbf ...

+

Calibrate automatically a set of frames taken at various sample-detector +distance. Return the linear regression of the fit in funtion of the sample- +setector distance.

+
+
positional arguments:
+
FILE List of files to calibrate
+
optional arguments:
+
+++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+-h, --helpshow this help message and exit
+-V, --versionshow program’s version number and exit
+-v, --verboseswitch to debug/verbose mode
+-c FILE, --calibrant FILE
 file containing d-spacing of the calibrant reference +sample (MANDATORY)
+-w WAVELENGTH, --wavelength WAVELENGTH
 wavelength of the X-Ray beam in Angstrom
+-e ENERGY, --energy ENERGY
 energy of the X-Ray beam in keV (hc=12.398419292keV.A)
+-P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
 polarization factor, from -1 (vertical) to +1 +(horizontal), default is 0, synchrotrons are around +0.95
+-b BACKGROUND, --background BACKGROUND
 Automatic background subtraction if no value are +provided
+-d DARK, --dark DARK
 list of dark images to average and subtract
+-f FLAT, --flat FLAT
 list of flat images to average and divide
+-s SPLINE, --spline SPLINE
 spline file describing the detector distortion
+-p PIXEL, --pixel PIXEL
 size of the pixel in micron
+-D DETECTOR_NAME, --detector DETECTOR_NAME
 Detector name (instead of pixel size+spline)
+-m MASK, --mask MASK
 file containing the mask (for image reconstruction)
+--filter FILTER
 select the filter, either mean(default), max or median
+--saturation SATURATION
 consider all pixel>max*(1-saturation) as saturated and +reconstruct them
+-r MAX_RINGS, --ring MAX_RINGS
 maximum number of rings to extract
+--weightedweight fit by intensity
+-l DISTANCE, --distance DISTANCE
 sample-detector distance in millimeter
+--tiltAllow initially detector tilt to be refined (rot1, +rot2, rot3). Default: Activated
+--no-tiltDeactivated tilt refinement and set all rotation to 0
+--dist DISTsample-detector distance in meter
+--poni1 PONI1poni1 coordinate in meter
+--poni2 PONI2poni2 coordinate in meter
+--rot1 ROT1rot1 in radians
+--rot2 ROT2rot2 in radians
+--rot3 ROT3rot3 in radians
+--fix-distfix the distance parameter
+--free-distfree the distance parameter
+--fix-poni1fix the poni1 parameter
+--free-poni1free the poni1 parameter
+--fix-poni2fix the poni2 parameter
+--free-poni2free the poni2 parameter
+--fix-rot1fix the rot1 parameter
+--free-rot1free the rot1 parameter
+--fix-rot2fix the rot2 parameter
+--free-rot2free the rot2 parameter
+--fix-rot3fix the rot3 parameter
+--free-rot3free the rot3 parameter
+--fix-wavelength
 fix the wavelength parameter
+--free-wavelength
 free the wavelength parameter
+--no-guiforce the program to run without a Graphical interface
+--guiforce the program to run with a Graphical interface
+--no-interactive
 force the program to run and exit without prompting +for refinements
+--interactiveforce the program to prompt for refinements
+--peak-picker PEAKPICKER
 Uses the ‘massif’, ‘blob’ or ‘watershed’ peak-picker +algorithm (default: blob)
+
+
+

This tool has been developed for ESRF MX-beamlines where an acceptable +calibration is usually present is the header of the image. PyFAI reads it and +does a “recalib” on each of them before exporting a linear regression of all +parameters versus this distance.

+
+
+

Example:

+
$ MX-calibrate --help
+usage: MX-Calibrate -w 1.54 -c CeO2 file1.cbf file2.cbf ...
+
+Calibrate automatically a set of frames taken at various sample-detector
+distance. Return the linear regression of the fit in funtion of the sample-
+setector distance.
+
+positional arguments:
+  FILE                  List of files to calibrate
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version
+  -v, --verbose         switch to debug/verbose mode
+  -c FILE, --calibrant FILE
+                        file containing d-spacing of the calibrant reference
+                        sample (MANDATORY)
+  -w WAVELENGTH, --wavelength WAVELENGTH
+                        wavelength of the X-Ray beam in Angstrom
+  -e ENERGY, --energy ENERGY
+                        energy of the X-Ray beam in keV (hc=12.398419292keV.A)
+  -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
+                        polarization factor, from -1 (vertical) to +1
+                        (horizontal), default is 0, synchrotrons are around
+                        0.95
+  -b BACKGROUND, --background BACKGROUND
+                        Automatic background subtraction if no value are
+                        provided
+  -d DARK, --dark DARK  list of dark images to average and subtract
+  -f FLAT, --flat FLAT  list of flat images to average and divide
+  -s SPLINE, --spline SPLINE
+                        spline file describing the detector distortion
+  -p PIXEL, --pixel PIXEL
+                        size of the pixel in micron
+  -D DETECTOR_NAME, --detector DETECTOR_NAME
+                        Detector name (instead of pixel size+spline)
+  -m MASK, --mask MASK  file containing the mask (for image reconstruction)
+  --filter FILTER       select the filter, either mean(default), max or median
+  --saturation SATURATION
+                        consider all pixel>max*(1-saturation) as saturated and
+                        reconstruct them
+  -r MAX_RINGS, --ring MAX_RINGS
+                        maximum number of rings to extract
+  --weighted            weight fit by intensity
+  -l DISTANCE, --distance DISTANCE
+                        sample-detector distance in millimeter
+  --tilt                Allow initially detector tilt to be refined (rot1,
+                        rot2, rot3). Default: Activated
+  --no-tilt             Deactivated tilt refinement and set all rotation to 0
+  --dist DIST           sample-detector distance in meter
+  --poni1 PONI1         poni1 coordinate in meter
+  --poni2 PONI2         poni2 coordinate in meter
+  --rot1 ROT1           rot1 in radians
+  --rot2 ROT2           rot2 in radians
+  --rot3 ROT3           rot3 in radians
+  --fix-dist            fix the distance parameter
+  --free-dist           free the distance parameter
+  --fix-poni1           fix the poni1 parameter
+  --free-poni1          free the poni1 parameter
+  --fix-poni2           fix the poni2 parameter
+  --free-poni2          free the poni2 parameter
+  --fix-rot1            fix the rot1 parameter
+  --free-rot1           free the rot1 parameter
+  --fix-rot2            fix the rot2 parameter
+  --free-rot2           free the rot2 parameter
+  --fix-rot3            fix the rot3 parameter
+  --free-rot3           free the rot3 parameter
+  --fix-wavelength      fix the wavelength parameter
+  --free-wavelength     free the wavelength parameter
+  --no-gui              force the program to run without a Graphical interface
+  --gui                 force the program to run with a Graphical interface
+  --no-interactive      force the program to run and exit without prompting
+                        for refinements
+  --interactive         force the program to prompt for refinements
+  --peak-picker PEAKPICKER
+                        Uses the 'massif', 'blob' or 'watershed' peak-picker
+                        algorithm (default: blob)
+
+This tool has been developed for ESRF MX-beamlines where an acceptable
+calibration is usually present is the header of the image. PyFAI reads it and
+does a "recalib" on each of them before exporting a linear regression of all
+parameters versus this distance.
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Calibration tool: check_calib

+

Next topic

+

Integration tool: pyFAI-integrate

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/check_calib.html b/doc/build/man/check_calib.html new file mode 100644 index 000000000..f2e4a2266 --- /dev/null +++ b/doc/build/man/check_calib.html @@ -0,0 +1,161 @@ + + + + + + + + Calibration tool: check_calib — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Calibration tool: check_calib

+
+

Purpose

+

Check_calib is a deprecated tool aiming at validating both the geometric +calibration and everything else like flat-field correction, distortion +correction, at a sub-pixel level. Pleas use the validate, validate2 and +chiplot commands in pyFAI-calib, during the refinement process to obtain the same output with more options.

+
$ check_calib --help
+usage: check_calib [options] -p param.poni image.edf
+
+Check_calib is a research tool aiming at validating both the geometric
+calibration and everything else like flat-field correction, distortion
+correction, at a sub-pixel level. Note that `check_calib` program is obsolete
+as the same functionality is available from within pyFAI-calib, using the
+`validate` command in the refinement process.
+
+positional arguments:
+  FILE                  Image file to check calibration for
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version
+  -v, --verbose         switch to debug mode
+  -d FILE, --dark FILE  file containing the dark images to subtract
+  -f FILE, --flat FILE  file containing the flat images to divide
+  -m FILE, --mask FILE  file containing the mask
+  -p FILE, --poni FILE  file containing the diffraction parameter (poni-file)
+  -e ENERGY, --energy ENERGY
+                        energy of the X-Ray beam in keV (hc=12.398419292keV.A)
+  -w WAVELENGTH, --wavelength WAVELENGTH
+                        wavelength of the X-Ray beam in Angstrom
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Calibration tool: pyFAI-recalib

+

Next topic

+

Calibration tool: MX-calibrate

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/detector2nexus.html b/doc/build/man/detector2nexus.html new file mode 100644 index 000000000..61f869ce0 --- /dev/null +++ b/doc/build/man/detector2nexus.html @@ -0,0 +1,237 @@ + + + + + + + + Preprocessing tool: detector2nexus — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Preprocessing tool: detector2nexus

+

Convert a complex detector definition (multiple modules, possibly in 3D) into +a single NeXus detector definition together with the mask (and much more in +the future)

+
+

Purpose

+

Convert a detector to NeXus detector definition for pyFAI.

+
+
+

Usage:

+

detector2nexus [options] [options] -o nxs.h5

+
+
+

options:

+
+
+++ + + + + + + + + + + + + + + + + + + + + +
+-h, --helpshow this help message and exit
+-V, --versionshow program’s version number and exit
+-o OUTPUT, --output OUTPUT
 Output nexus file, unless detector_name.h5
+-n NAME, --name NAME
 name of the detector
+-m MASK, --mask MASK
 mask corresponding to the detector
+-D DETECTOR, --detector DETECTOR
 Base detector name (see documentation of +pyFAI.detectors
+-s SPLINEFILE, --splinefile SPLINEFILE
 Geometric distortion file from FIT2D
+

-dx DX, –x-corr DX Geometric correction for pilatus +-dy DY, –y-corr DY Geometric correction for pilatus +-p PIXEL, –pixel PIXEL

+
+
pixel size (comma separated): x,y
+ +++ + + + + + + + + + + + + +
+-S SHAPE, --shape SHAPE
 shape of the detector (comma separated): x,y
+-d DARK, --dark DARK
 Dark noise to be subtracted
+-f FLAT, --flat FLAT
 Flat field correction
+-v, --verboseswitch to verbose/debug mode
+
+
$ detector2nexus --help
+usage: detector2nexus [options] [options] -o nxs.h5
+
+Convert a complex detector definition (multiple modules, possibly in 3D) into
+a single NeXus detector definition together with the mask (and much more in
+the future)
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version         show program's version number and exit
+  -o OUTPUT, --output OUTPUT
+                        Output nexus file, unless detector_name.h5
+  -n NAME, --name NAME  name of the detector
+  -m MASK, --mask MASK  mask corresponding to the detector
+  -D DETECTOR, --detector DETECTOR
+                        Base detector name (see documentation of
+                        pyFAI.detectors
+  -s SPLINEFILE, --splinefile SPLINEFILE
+                        Geometric distortion file from FIT2D
+  -dx DX, --x-corr DX   Geometric correction for pilatus
+  -dy DY, --y-corr DY   Geometric correction for pilatus
+  -p PIXEL, --pixel PIXEL
+                        pixel size (comma separated): x,y
+  -S SHAPE, --shape SHAPE
+                        shape of the detector (comma separated): x,y
+  -d DARK, --dark DARK  Dark noise to be subtracted
+  -f FLAT, --flat FLAT  Flat field correction
+  -v, --verbose         switch to verbose/debug mode
+
+This summarizes detector2nexus
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Mask generation tool: drawMask_pymca

+

Next topic

+

Calibration tool: pyFAI-calib

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/diff_map.html b/doc/build/man/diff_map.html new file mode 100644 index 000000000..05cd3fb31 --- /dev/null +++ b/doc/build/man/diff_map.html @@ -0,0 +1,328 @@ + + + + + + + + Integration tool: diff_map — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Integration tool: diff_map

+
+

Purpose

+

Azimuthal integration for diffraction imaging

+

Diffraction mapping is an imaging experiment where 2D diffraction patterns are +recorded while performing a 2D scan along two axes, one slower and the other fastest.

+

Diff_map provides a Graphical User Interface (based on top of PyQt, pyFAI and h5py) +which allows the reduction of this 4D dataset into a 3D dataset containing the +two dimension of movement and the many diffraction angles (the output can be q-space for PDF-CT).

+
+image +
+

On the left-hand side, the user can select a bunch of files to be processed. +For now, any image format supported by FabIO is possible, including multi-frame +images, but not ye NeXus files (work ongoing).

+

On the right-hand side, the motor range can be specified togeather with their +names. The diffraction parameters can be setup in a similar way to pyFAI-integrate. +The output name can also be specified.

+
+image +
+

The processing is launched using the Run button which opens a matplotlib figure +to display the plot of the diffraction pattern and of the map image under +construction. +During this processing, one can select a scatering range to highlight the regions +of interest.

+
+image +
+

Further analysis can be performed on the resulting dataset using the PyMca roitool +where the 1d dataset has to be selected as last dimension. +The result file aims at NeXus compliance.

+

This tool can be used for tomography experiments if one considers the slow scan +direction as the rotation.

+

The user interface can be disabled to allow scripting interface and providing a +JSON configuration file (or all the options).

+
+
+

Usage:

+

diff_map [options] imagefiles*

+
+
positional arguments:
+
FILE List of files to integrate
+
optional arguments:
+
+++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+-h, --helpshow this help message and exit
+-V, --versionshow program’s version number and exit
+-o FILE, --output FILE
 HDF5 File where processed map will be saved
+-v, --verboseswitch to verbose/debug mode, defaut: quiet
+-P FILE, --prefix FILE
 Prefix or common base for all files
+-e EXTENSION, --extension EXTENSION
 Process all files with this extension
+-t FAST, --fast FAST
 number of points for the fast motion. Mandatory +without GUI
+-r SLOW, --slow SLOW
 number of points for slow motion. Mandatory without +GUI
+-c NPT_RAD, --npt NPT_RAD
 number of points in diffraction powder pattern, +Mandatory without GUI
+-d FILE, --dark FILE
 list of dark images to average and subtract
+-f FILE, --flat FILE
 list of flat images to average and divide
+-m FILE, --mask FILE
 file containing the mask, no mask by default
+-p FILE, --poni FILE
 file containing the diffraction parameter (poni-file), +Mandatory without GUI
+-O OFFSET, --offset OFFSET
 do not process the first files
+-g, --gpuprocess using OpenCL on GPU
+-S, --statsshow statistics at the end
+--guiUse the Graphical User Interface
+--no-guiDo not use the Graphical User Interface
+--config CONFIG
 provide a JSON configuration file
+
+
+
+

Bugs:

+
    +
  1. +
    If the number of files is too large, use double
    +

    quotes “*.edf”

    +
    +
    +
    2. +

  2. There is a known bug on Debian7 where importing a large +number of file can take much longer than the integration itself: consider +passing files in the command line

    +
    3. +
+
$ diff_map --help
+usage: diff_map [options] -p ponifile imagefiles*
+If the number of files is too large, use double quotes like "*.edf" 
+
+Azimuthal integration for diffraction imaging. Diffraction mapping is an
+experiment where 2D diffraction patterns are recorded while performing a 2D
+scan. Diff_map is a graphical application (based on pyFAI and h5py) which
+allows the reduction of this 4D dataset into a 3D dataset containing the two
+motion dimensions and the many diffraction angles (thousands). The resulting
+dataset can be opened using PyMca roitool where the 1d dataset has to be
+selected as last dimension. This result file aims at being NeXus compliant.
+This tool can be used for diffraction tomography experiment as well,
+considering the slow scan direction as the rotation.
+
+positional arguments:
+  FILE                  List of files to integrate. Mandatory without GUI
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version
+  -o FILE, --output FILE
+                        HDF5 File where processed map will be saved. Mandatory
+                        without GUI
+  -v, --verbose         switch to verbose/debug mode, default: quiet
+  -P FILE, --prefix FILE
+                        Prefix or common base for all files
+  -e EXTENSION, --extension EXTENSION
+                        Process all files with this extension
+  -t FAST, --fast FAST  number of points for the fast motion. Mandatory
+                        without GUI
+  -r SLOW, --slow SLOW  number of points for slow motion. Mandatory without
+                        GUI
+  -c NPT_RAD, --npt NPT_RAD
+                        number of points in diffraction powder pattern.
+                        Mandatory without GUI
+  -d FILE, --dark FILE  list of dark images to average and subtract (comma
+                        separated list)
+  -f FILE, --flat FILE  list of flat images to average and divide (comma
+                        separated list)
+  -m FILE, --mask FILE  file containing the mask, no mask by default
+  -p FILE, --poni FILE  file containing the diffraction parameter (poni-file),
+                        Mandatory without GUI
+  -O OFFSET, --offset OFFSET
+                        do not process the first files
+  -g, --gpu             process using OpenCL on GPU
+  -S, --stats           show statistics at the end
+  --gui                 Use the Graphical User Interface
+  --no-gui              Do not use the Graphical User Interface
+  --config CONFIG       provide a JSON configuration file
+
+Bugs: Many, see hereafter: 1)If the number of files is too large, use double
+quotes "*.edf" 2)There is a known bug on Debian7 where importing a large
+number of file can take much longer than the integration itself: consider
+passing files in the command line
+
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Integration tool: pyFAI-integrate

+

Next topic

+

Integration tool: diff_tomo

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/diff_tomo.html b/doc/build/man/diff_tomo.html new file mode 100644 index 000000000..eea32f1fd --- /dev/null +++ b/doc/build/man/diff_tomo.html @@ -0,0 +1,273 @@ + + + + + + + + Integration tool: diff_tomo — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Integration tool: diff_tomo

+
+

Purpose

+

Azimuthal integration for diffraction tomography.

+

Diffraction tomography is an experiment where 2D diffraction patterns are recorded +while performing a 2D scan, one (the slowest) in rotation around the sample center +and the other (the fastest) along a translation through the sample. +Diff_tomo is a script (based on pyFAI and h5py) which allows the reduction of this +4D dataset into a 3D dataset containing the rotations angle (hundreds), the translation step (hundreds) +and the many diffraction angles (thousands). The resulting dataset can be opened using PyMca roitool +where the 1d dataset has to be selected as last dimension. This file is not (yet) NeXus compliant.

+

This tool can be used for mapping experiments if one considers the slow scan +direction as the rotation, but a tool named diff_map operates in a similar way,

+
+
provides a Graphical interface and is more flexible
+

tips: If the number of files is too large, use double quotes around “*.edf”

+
+
+

Usage:

+

diff_tomo [options] -p ponifile imagefiles*

+
+
+

Options:

+
+
+++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+-h, --helpshow this help message and exit
+-V, --versionshow program’s version number and exit
+-o FILE, --output FILE
 HDF5 File where processed sinogram was saved, by +default diff_tomo.h5
+-v, --verboseswitch to verbose/debug mode, defaut: quiet
+-P FILE, --prefix FILE
 Prefix or common base for all files
+-e EXTENSION, --extension EXTENSION
 Process all files with this extension
+-t NTRANS, --nTrans NTRANS
 number of points in translation. Mandatory
+-r NROT, --nRot NROT
 number of points in rotation. Mandatory
+-c NDIFF, --nDiff NDIFF
 number of points in diffraction powder pattern, +Mandatory
+-d FILE, --dark FILE
 list of dark images to average and subtract
+-f FILE, --flat FILE
 list of flat images to average and divide
+-m FILE, --mask FILE
 file containing the mask, no mask by default
+-p FILE, --poni FILE
 file containing the diffraction parameter (poni-file), +Mandatory
+-O OFFSET, --offset OFFSET
 do not process the first files
+-g, --gpuprocess using OpenCL on GPU
+-S, --statsshow statistics at the end
+
+

Most of those options are mandatory to define the structure of the dataset.

+
$ diff_tomo --help
+usage: diff_tomo [options] -p ponifile imagefiles*
+
+Azimuthal integration for diffraction tomography. Diffraction tomography is an
+experiment where 2D diffraction patterns are recorded while performing a 2D
+scan, one (the slowest) in rotation around the sample center and the other
+(the fastest) along a translation through the sample. Diff_tomo is a script
+(based on pyFAI and h5py) which allows the reduction of this 4D dataset into a
+3D dataset containing the rotations angle (hundreds), the translation step
+(hundreds) and the many diffraction angles (thousands). The resulting dataset
+can be opened using the PyMca ROItool where the 1d dataset has to be selected
+as last dimension. The output file aims at being NeXus compliant. This tool
+can be used for mapping experiments if one considers the slow scan direction
+as the rotation. but the *diff_map* tool provides in addition a graphical user
+interface.
+
+positional arguments:
+  FILE                  List of files to calibrate
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version         show program's version number and exit
+  -o FILE, --output FILE
+                        HDF5 File where processed sinogram was saved, by
+                        default diff_tomo.h5
+  -v, --verbose         switch to verbose/debug mode, defaut: quiet
+  -P FILE, --prefix FILE
+                        Prefix or common base for all files
+  -e EXTENSION, --extension EXTENSION
+                        Process all files with this extension
+  -t NTRANS, --nTrans NTRANS
+                        number of points in translation. Mandatory
+  -r NROT, --nRot NROT  number of points in rotation. Mandatory
+  -c NDIFF, --nDiff NDIFF
+                        number of points in diffraction powder pattern,
+                        Mandatory
+  -d FILE, --dark FILE  list of dark images to average and subtract
+  -f FILE, --flat FILE  list of flat images to average and divide
+  -m FILE, --mask FILE  file containing the mask, no mask by default
+  -p FILE, --poni FILE  file containing the diffraction parameter (poni-file),
+                        Mandatory
+  -O OFFSET, --offset OFFSET
+                        do not process the first files
+  -g, --gpu             process using OpenCL on GPU
+  -S, --stats           show statistics at the end
+
+If the number of files is too large, use double quotes "*.edf"
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Integration tool: diff_map

+

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+

Integration tool: pyFAI-saxs

+

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+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/drawMask_pymca.html b/doc/build/man/drawMask_pymca.html new file mode 100644 index 000000000..effe12d9d --- /dev/null +++ b/doc/build/man/drawMask_pymca.html @@ -0,0 +1,182 @@ + + + + + + + + Mask generation tool: drawMask_pymca — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Mask generation tool: drawMask_pymca

+
+

Purpose

+

Draw a mask, i.e. an image containing the list of pixels which are considered invalid (no scintillator, module gap, beam stop shadow, ...).

+
+image +
+

This will open a PyMca window and let you draw on the first image (provided) with different tools (brush, rectangle selection, ...). +When you are finished, come back to the console and press enter. +The mask image is saved into file1-masked.edf. +Optionally the script will print the number of pixel +masked and the intensity masked (as well on other files provided in input)

+

Usage: drawMask_pymca [options] file1.edf file2.edf ...

+
+
+

Options:

+
+
+++ + + + + + +
+--versionshow program’s version number and exit
+-h, --helpshow help message and exit
+
+

Optionally the script will print the number of pixel masked and the intensity masked (as well on other files provided in input)

+
$ drawMask_pymca --help
+API 1 -> Console widget not available
+usage: drawMask_pymca file1.edf file2.edf ...
+
+Draw a mask, i.e. an image containing the list of pixels which are considered
+invalid (no scintillator, module gap, beam stop shadow, ...). This will open a
+PyMca window and let you draw on the first image (provided) with different
+tools (brush, rectangle selection, ...). When you are finished, come back to
+the console and press enter.
+
+positional arguments:
+  FILE           Files to be processed
+
+optional arguments:
+  -h, --help     show this help message and exit
+  -v, --version  show program's version number and exit
+
+The mask image is saved into file1-masked.edf. Optionally the script will
+print the number of pixel masked and the intensity masked (as well on other
+files provided in input)
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Preprocessing tool: pyFAI-average

+

Next topic

+

Preprocessing tool: detector2nexus

+

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+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/pyFAI-average.html b/doc/build/man/pyFAI-average.html new file mode 100644 index 000000000..2c006bb59 --- /dev/null +++ b/doc/build/man/pyFAI-average.html @@ -0,0 +1,226 @@ + + + + + + + + Preprocessing tool: pyFAI-average — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Preprocessing tool: pyFAI-average

+
+

Purpose

+

This tool is used to average out a set of dark current images using +mean or median filter (along the image stack). One can also reject outliers +be specifying a cutoff (remove cosmic rays / zingers from dark)

+

It can also be used to merge many images from the same sample when using a small beam +and reduce the spotty-ness of Debye-Scherrer rings. In this case the “max-filter” is usually +recommended.

+
+
+

Options:

+

Usage: pyFAI-average [options] -o output.edf file1.edf file2.edf ...

+
+
positional arguments:
+
FILE Files to be processed
+
optional arguments:
+
+++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+-h, --helpshow this help message and exit
+-V, --versionshow program’s version number and exit
+-o OUTPUT, --output OUTPUT
 Output/ destination of average image
+-m METHOD, --method METHOD
 Method used for averaging, can be ‘mean’(default) or +‘min’, ‘max’, ‘median’, ‘sum’, ‘quantiles’
+-c CUTOFF, --cutoff CUTOFF
 Take the mean of the average +/- cutoff * std_dev.
+-F FORMAT, --format FORMAT
 Output file/image format (by default EDF)
+-d DARK, --dark DARK
 Dark noise to be subtracted
+-f FLAT, --flat FLAT
 Flat field correction
+-v, --verboseswitch to verbose/debug mode
+-q QUANTILES, --quantiles QUANTILES
 average out between two quantiles -q 0.20-0.90
+
+
+
$ pyFAI-average --help
+usage: pyFAI-average [options] [options] -o output.edf file1.edf file2.edf ...
+
+This tool can be used to average out a set of dark current images using mean
+or median filter (along the image stack). One can also reject outliers be
+specifying a cutoff (remove cosmic rays / zingers from dark)
+
+positional arguments:
+  FILE                  Files to be processed
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version         show program's version number and exit
+  -o OUTPUT, --output OUTPUT
+                        Output/ destination of average image
+  -m METHOD, --method METHOD
+                        Method used for averaging, can be 'mean'(default) or
+                        'min', 'max', 'median', 'sum', 'quantiles'
+  -c CUTOFF, --cutoff CUTOFF
+                        Take the mean of the average +/- cutoff * std_dev.
+  -F FORMAT, --format FORMAT
+                        Output file/image format (by default EDF)
+  -d DARK, --dark DARK  Dark noise to be subtracted
+  -f FLAT, --flat FLAT  Flat field correction
+  -v, --verbose         switch to verbose/debug mode
+  -q QUANTILES, --quantiles QUANTILES
+                        average out between two quantiles -q 0.20-0.90
+  --quiet               Only error messages are printed out
+
+It can also be used to merge many images from the same sample when using a
+small beam and reduce the spotty-ness of Debye-Sherrer rings. In this case the
+"max-filter" is usually recommended.
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

pyFAI scripts manual

+

Next topic

+

Mask generation tool: drawMask_pymca

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/pyFAI-calib.html b/doc/build/man/pyFAI-calib.html new file mode 100644 index 000000000..e909cdcdf --- /dev/null +++ b/doc/build/man/pyFAI-calib.html @@ -0,0 +1,537 @@ + + + + + + + + Calibration tool: pyFAI-calib — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Calibration tool: pyFAI-calib

+
+

Purpose

+

Calibrate the diffraction setup geometry based on Debye-Sherrer rings images +without a priori knowledge of your setup. +You will need to provide a calibrant or a “d-spacing” file containing the spacing of Miller plans in +Angstrom (in decreasing order).

+

If you are using a standard calibrant, look at +https://github.com/kif/pyFAI/tree/master/calibration +or search in the American Mineralogist database: +[AMD] or in the [COD]. +The –calibrant option is mandatory !

+

Calibrants available: Ni, CrOx, NaCl, Si_SRM640e, +Si_SRM640d, Si_SRM640a, Si_SRM640b, Cr2O3, AgBh, Si_SRM640, CuO, PBBA, +alpha_Al2O3, SI_SRM640c, quartz, C14H30O, cristobaltite, Si, LaB6, CeO2, +LaB6_SRM660a, LaB6_SRM660b, LaB6_SRM660c, TiO2, ZnO, Al, Au

+
+
You will need in addition:
+
    +
  • The radiation energy (in keV) or its wavelength (in A)
    • +
  • The description of the detector:
    • +
+
+
    +
  • it name or
    • +
  • it’s pixel size or
    • +
  • the spline file describing its distortion or
    • +
  • the NeXus file describing the distortion
    • +
+
+
+
Many option are available among those:
+
    +
  • dark-current / flat field corrections
    • +
  • Masking of bad regions
    • +
  • reconstruction of missing region (module based detectors), see option -r
    • +
  • Polarization correction
    • +
  • Automatic desaturation (time consuming!)
    • +
  • Intensity weighted least-squares refinements
    • +
+
+
+

The output of this program is a “PONI” file containing the detector +description and the 6 refined parameters (distance, center, rotation) and +wavelength. An 1D and 2D diffraction patterns are also produced. (.dat and +.azim files)

+
+
+

Usage:

+

pyFAI-calib [options] -w 1 -D detector -c calibrant imagefile.edf

+
+
+

Options:

+
+
+++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+-h, --helpshow this help message and exit
+-V, --versionshow program’s version number and exit
+-o FILE, --out FILE
 Filename where processed image is saved
+-v, --verboseswitch to debug/verbose mode
+-c FILE, --calibrant FILE
 Calibrant name or file containing d-spacing of the +reference sample (MANDATORY, case sensitive !)
+-w WAVELENGTH, --wavelength WAVELENGTH
 wavelength of the X-Ray beam in Angstrom. Mandatory
+-e ENERGY, --energy ENERGY
 energy of the X-Ray beam in keV +(hc=12.398419292keV.A).
+-P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
 polarization factor, from -1 (vertical) to +1 +(horizontal), default is None (no correction), +synchrotrons are around 0.95
+-i FILE, --poni FILE
 file containing the diffraction parameter (poni-file). +MANDATORY for pyFAI-recalib!
+-b BACKGROUND, --background BACKGROUND
 Automatic background subtraction if no value are +provided
+-d DARK, --dark DARK
 list of comma separated dark images to average and +subtract
+-f FLAT, --flat FLAT
 list of comma separated flat images to average and +divide
+-s SPLINE, --spline SPLINE
 spline file describing the detector distortion
+-D DETECTOR_NAME, --detector DETECTOR_NAME
 Detector name (instead of pixel size+spline)
+-m MASK, --mask MASK
 file containing the mask (for image reconstruction)
+-n NPT, --pt NPT
 file with datapoints saved. Default: basename.npt
+--filter FILTER
 select the filter, either mean(default), max or median
+-l DISTANCE, --distance DISTANCE
 sample-detector distance in millimeter. Default: 100mm
+--dist DISTsample-detector distance in meter. Default: 0.1m
+--poni1 PONI1poni1 coordinate in meter. Default: center of detector
+--poni2 PONI2poni2 coordinate in meter. Default: center of detector
+--rot1 ROT1rot1 in radians. default: 0
+--rot2 ROT2rot2 in radians. default: 0
+--rot3 ROT3rot3 in radians. default: 0
+--fix-distfix the distance parameter
+--free-distfree the distance parameter. Default: Activated
+--fix-poni1fix the poni1 parameter
+--free-poni1free the poni1 parameter. Default: Activated
+--fix-poni2fix the poni2 parameter
+--free-poni2free the poni2 parameter. Default: Activated
+--fix-rot1fix the rot1 parameter
+--free-rot1free the rot1 parameter. Default: Activated
+--fix-rot2fix the rot2 parameter
+--free-rot2free the rot2 parameter. Default: Activated
+--fix-rot3fix the rot3 parameter
+--free-rot3free the rot3 parameter. Default: Activated
+--fix-wavelength
 fix the wavelength parameter. Default: Activated
+--free-wavelength
 free the wavelength parameter. Default: Deactivated
+--tiltAllow initially detector tilt to be refined (rot1, +rot2, rot3). Default: Activated
+--no-tiltDeactivated tilt refinement and set all rotation to 0
+--saturation SATURATION
 consider all pixel>max*(1-saturation) as saturated and +reconstruct them, default: 0 (deactivated)
+--weightedweight fit by intensity, by default not.
+--npt NPT_1DNumber of point in 1D integrated pattern, Default: +1024
+--npt-azim NPT_2D_AZIM
 Number of azimuthal sectors in 2D integrated images. +Default: 360
+--npt-rad NPT_2D_RAD
 Number of radial bins in 2D integrated images. +Default: 400
+--unit UNITValid units for radial range: 2th_deg, 2th_rad, +q_nm^-1, q_A^-1, r_mm. Default: 2th_deg
+--no-guiforce the program to run without a Graphical interface
+--no-interactive
 force the program to run and exit without prompting +for refinements
+-r, --reconstruct
 Reconstruct image where data are masked or <0 (for +Pilatus detectors or detectors with modules)
+-g GAUSSIAN, --gaussian GAUSSIAN
 Size of the gaussian kernel. Size of the gap (in +pixels) between two consecutive rings, by default 100 +Increase the value if the arc is not complete; +decrease the value if arcs are mixed together.
+--squareUse square kernel shape for neighbor search instead of +diamond shape
+-p PIXEL, --pixel PIXEL
 size of the pixel in micron
+
+
+
+

Tips & Tricks

+

PONI-files are ASCII files and each new refinement adds an entry in the file. +So if you are unhappy with the last step, just edit this file and remove the last +entry (time-stamps will help you).

+
+
+

Example of usage:

+
$ pyFAI-calib --help
+usage: pyFAI-calib [options] -w 1 -D detector -c calibrant.D imagefile.edf
+
+Calibrate the diffraction setup geometry based on Debye-Sherrer rings images
+without a priori knowledge of your setup. You will need to provide a calibrant
+or a "d-spacing" file containing the spacing of Miller plans in Angstrom (in
+decreasing order). Calibrants available: Ni, CrOx, NaCl, Si_SRM640e,
+Si_SRM640d, Si_SRM640a, Si_SRM640c, alpha_Al2O3, Cr2O3, AgBh, Si_SRM640, CuO,
+PBBA, Si_SRM640b, quartz, C14H30O, cristobaltite, Si, LaB6, CeO2, moke,
+LaB6_SRM660a, LaB6_SRM660b, LaB6_SRM660c, TiO2, ZnO, Al, Au or search in the
+American Mineralogist database: http://rruff.geo.arizona.edu/AMS/amcsd.php The
+--calibrant option is mandatory !
+
+positional arguments:
+  FILE                  List of files to calibrate
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version
+  -o FILE, --out FILE   Filename where processed image is saved
+  -v, --verbose         switch to debug/verbose mode
+  -c FILE, --calibrant FILE
+                        Calibrant name or file containing d-spacing of the
+                        reference sample (MANDATORY, case sensitive !)
+  -w WAVELENGTH, --wavelength WAVELENGTH
+                        wavelength of the X-Ray beam in Angstrom. Mandatory
+  -e ENERGY, --energy ENERGY
+                        energy of the X-Ray beam in keV
+                        (hc=12.398419292keV.A).
+  -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
+                        polarization factor, from -1 (vertical) to +1
+                        (horizontal), default is None (no correction),
+                        synchrotrons are around 0.95
+  -i FILE, --poni FILE  file containing the diffraction parameter (poni-file).
+                        MANDATORY for pyFAI-recalib!
+  -b BACKGROUND, --background BACKGROUND
+                        Automatic background subtraction if no value are
+                        provided
+  -d DARK, --dark DARK  list of comma separated dark images to average and
+                        subtract
+  -f FLAT, --flat FLAT  list of comma separated flat images to average and
+                        divide
+  -s SPLINE, --spline SPLINE
+                        spline file describing the detector distortion
+  -D DETECTOR_NAME, --detector DETECTOR_NAME
+                        Detector name (instead of pixel size+spline)
+  -m MASK, --mask MASK  file containing the mask (for image reconstruction)
+  -n NPT, --pt NPT      file with datapoints saved. Default: basename.npt
+  --filter FILTER       select the filter, either mean(default), max or median
+  -l DISTANCE, --distance DISTANCE
+                        sample-detector distance in millimeter. Default: 100mm
+  --dist DIST           sample-detector distance in meter. Default: 0.1m
+  --poni1 PONI1         poni1 coordinate in meter. Default: center of detector
+  --poni2 PONI2         poni2 coordinate in meter. Default: center of detector
+  --rot1 ROT1           rot1 in radians. default: 0
+  --rot2 ROT2           rot2 in radians. default: 0
+  --rot3 ROT3           rot3 in radians. default: 0
+  --fix-dist            fix the distance parameter
+  --free-dist           free the distance parameter. Default: Activated
+  --fix-poni1           fix the poni1 parameter
+  --free-poni1          free the poni1 parameter. Default: Activated
+  --fix-poni2           fix the poni2 parameter
+  --free-poni2          free the poni2 parameter. Default: Activated
+  --fix-rot1            fix the rot1 parameter
+  --free-rot1           free the rot1 parameter. Default: Activated
+  --fix-rot2            fix the rot2 parameter
+  --free-rot2           free the rot2 parameter. Default: Activated
+  --fix-rot3            fix the rot3 parameter
+  --free-rot3           free the rot3 parameter. Default: Activated
+  --fix-wavelength      fix the wavelength parameter. Default: Activated
+  --free-wavelength     free the wavelength parameter. Default: Deactivated
+  --tilt                Allow initially detector tilt to be refined (rot1,
+                        rot2, rot3). Default: Activated
+  --no-tilt             Deactivated tilt refinement and set all rotation to 0
+  --saturation SATURATION
+                        consider all pixel>max*(1-saturation) as saturated and
+                        reconstruct them, default: 0 (deactivated)
+  --weighted            weight fit by intensity, by default not.
+  --npt NPT_1D          Number of point in 1D integrated pattern, Default:
+                        1024
+  --npt-azim NPT_2D_AZIM
+                        Number of azimuthal sectors in 2D integrated images.
+                        Default: 360
+  --npt-rad NPT_2D_RAD  Number of radial bins in 2D integrated images.
+                        Default: 400
+  --unit UNIT           Valid units for radial range: 2th_deg, 2th_rad,
+                        q_nm^-1, q_A^-1, r_mm. Default: 2th_deg
+  --no-gui              force the program to run without a Graphical interface
+  --no-interactive      force the program to run and exit without prompting
+                        for refinements
+  -r, --reconstruct     Reconstruct image where data are masked or <0 (for
+                        Pilatus detectors or detectors with modules)
+  -g GAUSSIAN, --gaussian GAUSSIAN
+                        Size of the gaussian kernel. Size of the gap (in
+                        pixels) between two consecutive rings, by default 100
+                        Increase the value if the arc is not complete;
+                        decrease the value if arcs are mixed together.
+  --square              Use square kernel shape for neighbor search instead of
+                        diamond shape
+  -p PIXEL, --pixel PIXEL
+                        size of the pixel in micron
+
+The output of this program is a "PONI" file containing the detector
+description and the 6 refined parameters (distance, center, rotation) and
+wavelength. An 1D and 2D diffraction patterns are also produced. (.dat and
+.azim files)
+
+
+
+

Pilatus 1M image of Silver Behenate taken at ESRF-BM26:

+
pyFAI-calib -D Pilatus1M -c AgBh -r -w 1.0 test/testimages/Pilatus1M.edf
+
+
+

We use the parameter -r to reconstruct the missing part between the modules of the +Pilatus detector.

+
+
+

Half a FReLoN CCD image of Lantanide hexaboride taken at ESRF-ID11:

+
pyFAI-calib -s test/testimages/halfccd.spline -c LaB6 -w 0.3 test/testimages/halfccd.edf -g 250
+
+
+

This image is rather spotty. We need to blur a lot to get the continuity of the rings. +This is achieved by the -g parameter. +While the sample is well diffracting and well known, the wavelength has been guessed. +One should refine the wavelength when the peaks extracted are correct

+

All those images are part of the test-suite of pyFAI. To download them from internet, run

+
python setup.py build test
+
+
+

Downloaded test images are located in tests/testimages

+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Preprocessing tool: detector2nexus

+

Next topic

+

Calibration tool: pyFAI-recalib

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/pyFAI-integrate.html b/doc/build/man/pyFAI-integrate.html new file mode 100644 index 000000000..d1241d6fb --- /dev/null +++ b/doc/build/man/pyFAI-integrate.html @@ -0,0 +1,217 @@ + + + + + + + + Integration tool: pyFAI-integrate — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Integration tool: pyFAI-integrate

+
+

Purpose

+

PyFAI-integrate is a graphical interface (based on Python/Qt4) to perform azimuthal integration +on a set of files. It exposes most of the important options available within pyFAI and allows you +to select a GPU (or an openCL platform) to perform the calculation on.

+
+image +
+
+
+

Usage

+

pyFAI-integrate [options] file1.edf file2.edf ...

+
+
+

Options:

+
+
+++ + + + + + + + + + + +
+--versionshow program’s version number and exit
+-h, --helpshow help message and exit
+-v, --verboseswitch to verbose/debug mode
+-o OUTPUT, --output=OUTPUT
 Directory or file where to store the output data
+
+
+
+

Tips & Tricks:

+

PyFAI-integrate saves all parameters in a .azimint.json (hidden) file. This JSON file +is an ascii file which can be edited and used to configure online data analysis using +the LImA plugin of pyFAI.

+

Nota: there is bug in debian6 making the GUI crash (to be fixed inside pyqt) +http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=697348

+
+
+

Example:

+
$ pyFAI-integrate --help
+usage: pyFAI-integrate [options] file1.edf file2.edf ...
+
+PyFAI-integrate is a graphical interface (based on Python/Qt4) to perform
+azimuthal integration on a set of files. It exposes most of the important
+options available within pyFAI and allows you to select a GPU (or an openCL
+platform) to perform the calculation on.
+
+positional arguments:
+  FILE                  Files to be integrated
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version         show program's version number and exit
+  -v, --verbose         switch to verbose/debug mode
+  -o OUTPUT, --output OUTPUT
+                        Directory or file where to store the output data
+  -f FORMAT, --format FORMAT
+                        output data format (can be HDF5)
+  -s SLOW, --slow-motor SLOW
+                        Dimension of the scan on the slow direction (makes
+                        sense only with HDF5)
+  -r RAPID, --fast-motor RAPID
+                        Dimension of the scan on the fast direction (makes
+                        sense only with HDF5)
+  --no-gui              Process the dataset without showing the user
+                        interface.
+  -j JSON, --json JSON  Configuration file containing the processing to be
+                        done
+
+PyFAI-integrate saves all parameters in a .azimint.json (hidden) file. This
+JSON file is an ascii file which can be edited and used to configure online
+data analysis using the LImA plugin of pyFAI. Nota: there is bug in debian6
+making the GUI crash (to be fixed inside pyqt) http://bugs.debian.org/cgi-
+bin/bugreport.cgi?bug=697348
+
+
+
+
+ + +
+
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+

Table Of Contents

+ + +

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Calibration tool: MX-calibrate

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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/pyFAI-recalib.html b/doc/build/man/pyFAI-recalib.html new file mode 100644 index 000000000..d215ac59f --- /dev/null +++ b/doc/build/man/pyFAI-recalib.html @@ -0,0 +1,231 @@ + + + + + + + + Calibration tool: pyFAI-recalib — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

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+ +
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+
+ +
+

Calibration tool: pyFAI-recalib

+

pyFAI-recalib is now obsolete. All feature provided by it are now available as part of pyFAI-calib.

+
$ pyFAI-recalib --help
+usage: pyFAI-recalib [options] -i ponifile -w 1 -c calibrant.D imagefile.edf
+
+Calibrate the diffraction setup geometry based on Debye-Sherrer rings images
+with a priori knowledge of your setup (an input PONI-file). You will need to
+provide a calibrant or a "d-spacing" file containing the spacing of Miller
+plans in Angstrom (in decreasing order). Calibrants available: Ni, CrOx, NaCl,
+Si_SRM640e, Si_SRM640d, Si_SRM640a, Si_SRM640c, alpha_Al2O3, Cr2O3, AgBh,
+Si_SRM640, CuO, PBBA, Si_SRM640b, quartz, C14H30O, cristobaltite, Si, LaB6,
+CeO2, moke, LaB6_SRM660a, LaB6_SRM660b, LaB6_SRM660c, TiO2, ZnO, Al, Au or
+search in the American Mineralogist database:
+http://rruff.geo.arizona.edu/AMS/amcsd.php The --calibrant option is mandatory
+!
+
+positional arguments:
+  FILE                  List of files to calibrate
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -V, --version
+  -o FILE, --out FILE   Filename where processed image is saved
+  -v, --verbose         switch to debug/verbose mode
+  -c FILE, --calibrant FILE
+                        Calibrant name or file containing d-spacing of the
+                        reference sample (MANDATORY, case sensitive !)
+  -w WAVELENGTH, --wavelength WAVELENGTH
+                        wavelength of the X-Ray beam in Angstrom. Mandatory
+  -e ENERGY, --energy ENERGY
+                        energy of the X-Ray beam in keV
+                        (hc=12.398419292keV.A).
+  -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
+                        polarization factor, from -1 (vertical) to +1
+                        (horizontal), default is None (no correction),
+                        synchrotrons are around 0.95
+  -i FILE, --poni FILE  file containing the diffraction parameter (poni-file).
+                        MANDATORY for pyFAI-recalib!
+  -b BACKGROUND, --background BACKGROUND
+                        Automatic background subtraction if no value are
+                        provided
+  -d DARK, --dark DARK  list of comma separated dark images to average and
+                        subtract
+  -f FLAT, --flat FLAT  list of comma separated flat images to average and
+                        divide
+  -s SPLINE, --spline SPLINE
+                        spline file describing the detector distortion
+  -D DETECTOR_NAME, --detector DETECTOR_NAME
+                        Detector name (instead of pixel size+spline)
+  -m MASK, --mask MASK  file containing the mask (for image reconstruction)
+  -n NPT, --pt NPT      file with datapoints saved. Default: basename.npt
+  --filter FILTER       select the filter, either mean(default), max or median
+  -l DISTANCE, --distance DISTANCE
+                        sample-detector distance in millimeter. Default: 100mm
+  --dist DIST           sample-detector distance in meter. Default: 0.1m
+  --poni1 PONI1         poni1 coordinate in meter. Default: center of detector
+  --poni2 PONI2         poni2 coordinate in meter. Default: center of detector
+  --rot1 ROT1           rot1 in radians. default: 0
+  --rot2 ROT2           rot2 in radians. default: 0
+  --rot3 ROT3           rot3 in radians. default: 0
+  --fix-dist            fix the distance parameter
+  --free-dist           free the distance parameter. Default: Activated
+  --fix-poni1           fix the poni1 parameter
+  --free-poni1          free the poni1 parameter. Default: Activated
+  --fix-poni2           fix the poni2 parameter
+  --free-poni2          free the poni2 parameter. Default: Activated
+  --fix-rot1            fix the rot1 parameter
+  --free-rot1           free the rot1 parameter. Default: Activated
+  --fix-rot2            fix the rot2 parameter
+  --free-rot2           free the rot2 parameter. Default: Activated
+  --fix-rot3            fix the rot3 parameter
+  --free-rot3           free the rot3 parameter. Default: Activated
+  --fix-wavelength      fix the wavelength parameter. Default: Activated
+  --free-wavelength     free the wavelength parameter. Default: Deactivated
+  --tilt                Allow initially detector tilt to be refined (rot1,
+                        rot2, rot3). Default: Activated
+  --no-tilt             Deactivated tilt refinement and set all rotation to 0
+  --saturation SATURATION
+                        consider all pixel>max*(1-saturation) as saturated and
+                        reconstruct them, default: 0 (deactivated)
+  --weighted            weight fit by intensity, by default not.
+  --npt NPT_1D          Number of point in 1D integrated pattern, Default:
+                        1024
+  --npt-azim NPT_2D_AZIM
+                        Number of azimuthal sectors in 2D integrated images.
+                        Default: 360
+  --npt-rad NPT_2D_RAD  Number of radial bins in 2D integrated images.
+                        Default: 400
+  --unit UNIT           Valid units for radial range: 2th_deg, 2th_rad,
+                        q_nm^-1, q_A^-1, r_mm. Default: 2th_deg
+  --no-gui              force the program to run without a Graphical interface
+  --no-interactive      force the program to run and exit without prompting
+                        for refinements
+  -r MAX_RINGS, --ring MAX_RINGS
+                        maximum number of rings to extract. Default: all
+                        accessible
+  -k, --keep            Keep existing control point and append new
+
+The main difference with pyFAI-calib is the way control-point hence Debye-
+Sherrer rings are extracted. While pyFAI-calib relies on the contiguity of a
+region of peaks called massif; pyFAI-recalib knows approximatly the geometry
+and is able to select the region where the ring should be. From this region it
+selects automatically the various peaks; making pyFAI-recalib able to run
+without graphical interface and without human intervention (--no-gui and --no-
+interactive options). Note that `pyFAI-recalib` program is obsolete as the
+same functionality is available from within pyFAI-calib, using the `recalib`
+command in the refinement process. Two option are available for recalib: the
+numbe of rings to extract (similar to the -r option of this program) and a new
+option which lets you choose between the original `massif` algorithm and newer
+ones like `blob` and `watershed` detection.
+
+
+
+ + +
+
+
+
+
+

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Calibration tool: pyFAI-calib

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Calibration tool: check_calib

+

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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/pyFAI-saxs.html b/doc/build/man/pyFAI-saxs.html new file mode 100644 index 000000000..8283413ae --- /dev/null +++ b/doc/build/man/pyFAI-saxs.html @@ -0,0 +1,256 @@ + + + + + + + + Integration tool: pyFAI-saxs — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Integration tool: pyFAI-saxs

+
+

Purpose

+

Azimuthal integration for SAXS users.

+

pyFAI-saxs is the SAXS script of pyFAI that allows data reduction (azimuthal integration) for +Small Angle Scattering with output axis in q space.

+
+
+

Usage:

+

pyFAI-saxs -p=param.poni -w1.54 file.edf file2.edf file3.edf

+
+
Options:
+
+++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+-h, --helpshow this help message and exit
+-v, --versionshow program’s version number and exit
+-p PONIFILEPyFAI parameter file (.poni)
+-n NPT, --npt NPT
 Number of points in radial dimension
+-w WAVELENGTH, --wavelength WAVELENGTH
 wavelength of the X-Ray beam in Angstrom
+-e ENERGY, --energy ENERGY
 energy of the X-Ray beam in keV (hc=12.398419292keV.A)
+-u DUMMY, --dummy DUMMY
 dummy value for dead pixels
+-U DELTA_DUMMY, --delta_dummy DELTA_DUMMY
 delta dummy value
+-m MASK, --mask MASK
 name of the file containing the mask image
+-d DARK, --dark DARK
 name of the file containing the dark current
+-f FLAT, --flat FLAT
 name of the file containing the flat field
+-P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
 Polarization factor, from -1 (vertical) to +1 +(horizontal), default is None for no correction, +synchrotrons are around 0.95
+--error-model ERROR_MODEL
 Error model to use. Currently on ‘poisson’ is +implemented
+--unit UNITunit for the radial dimension: can be q_nm^-1, q_A^-1, +2th_deg, 2th_rad or r_mm
+--ext EXTextension of the regrouped filename (.dat)
+--method METHOD
 Integration method
+
+
+
+
+

Example:

+
$ pyFAI-saxs --help
+usage: pyFAI-saxs [options] -n 1000 -p ponifile file1.edf file2.edf ...
+
+Azimuthal integration for SAXS users.
+
+positional arguments:
+  FILE                  Image files to integrate
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -v, --version         show program's version number and exit
+  -p PONIFILE           PyFAI parameter file (.poni)
+  -n NPT, --npt NPT     Number of points in radial dimension
+  -w WAVELENGTH, --wavelength WAVELENGTH
+                        wavelength of the X-Ray beam in Angstrom
+  -e ENERGY, --energy ENERGY
+                        energy of the X-Ray beam in keV (hc=12.398419292keV.A)
+  -u DUMMY, --dummy DUMMY
+                        dummy value for dead pixels
+  -U DELTA_DUMMY, --delta_dummy DELTA_DUMMY
+                        delta dummy value
+  -m MASK, --mask MASK  name of the file containing the mask image
+  -d DARK, --dark DARK  name of the file containing the dark current
+  -f FLAT, --flat FLAT  name of the file containing the flat field
+  -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
+                        Polarization factor, from -1 (vertical) to +1
+                        (horizontal), default is None for no correction,
+                        synchrotrons are around 0.95
+  --error-model ERROR_MODEL
+                        Error model to use. Currently on 'poisson' is
+                        implemented
+  --unit UNIT           unit for the radial dimension: can be q_nm^-1, q_A^-1,
+                        2th_deg, 2th_rad or r_mm
+  --ext EXT             extension of the regrouped filename (.dat)
+  --method METHOD       Integration method
+
+pyFAI-saxs is the SAXS script of pyFAI that allows data reduction (azimuthal
+integration) for Small Angle Scattering with output axis in q space.
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Integration tool: diff_tomo

+

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Integration tool: pyFAI-waxs

+

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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/pyFAI-waxs.html b/doc/build/man/pyFAI-waxs.html new file mode 100644 index 000000000..fdc3fd805 --- /dev/null +++ b/doc/build/man/pyFAI-waxs.html @@ -0,0 +1,276 @@ + + + + + + + + Integration tool: pyFAI-waxs — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Integration tool: pyFAI-waxs

+
+

Purpose

+

Azimuthal integration for powder diffraction.

+

pyFAI-waxs is the script of pyFAI that allows data reduction (azimuthal integration) for +Wide Angle Scattering to produce X-Ray Powder Diffraction Pattern with output axis in 2-theta space.

+
+
+

Usage:

+

pyFAI-waxs -p param.poni [options] file1.edf file2.edf ...

+
+
+

Options:

+
+
+++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+-h, --helpshow this help message and exit
+-v, --versionshow program’s version number and exit
+-p PONIFILEPyFAI parameter file (.poni)
+-n NPT, --npt NPT
 Number of points in radial dimension
+-w WAVELENGTH, --wavelength WAVELENGTH
 wavelength of the X-Ray beam in Angstrom
+-e ENERGY, --energy ENERGY
 energy of the X-Ray beam in keV (hc=12.398419292keV.A)
+-u DUMMY, --dummy DUMMY
 dummy value for dead pixels
+-U DELTA_DUMMY, --delta_dummy DELTA_DUMMY
 delta dummy value
+-m MASK, --mask MASK
 name of the file containing the mask image
+-d DARK, --dark DARK
 name of the file containing the dark current
+-f FLAT, --flat FLAT
 name of the file containing the flat field
+-P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
 Polarization factor, from -1 (vertical) to +1 +(horizontal), default is None for no correction, +synchrotrons are around 0.95
+--error-model ERROR_MODEL
 Error model to use. Currently on ‘poisson’ is +implemented
+--unit UNITunit for the radial dimension: can be q_nm^-1, q_A^-1, +2th_deg, 2th_rad or r_mm
+--ext EXTextension of the regrouped filename (.xy)
+--method METHOD
 Integration method
+--multiAverage out all frame in a file before integrating
+--average AVERAGE
 Method for averaging out: can be ‘mean’ (default), +‘min’, ‘max’ or ‘median
+--do-2DPerform 2D integration in addition to 1D
+
+

pyFAI-waxs is the script of pyFAI that allows data reduction (azimuthal integration) for Wide Angle Scattering +to produce X-Ray Powder Diffraction Pattern with output axis in 2-theta space.

+
+
+

Example:

+
$ pyFAI-waxs --help
+usage: pyFAI-waxs [options] -p ponifile file1.edf file2.edf ...
+
+Azimuthal integration for powder diffraction.
+
+positional arguments:
+  FILE                  Image files to integrate
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -v, --version         show program's version number and exit
+  -p PONIFILE           PyFAI parameter file (.poni)
+  -n NPT, --npt NPT     Number of points in radial dimension
+  -w WAVELENGTH, --wavelength WAVELENGTH
+                        wavelength of the X-Ray beam in Angstrom
+  -e ENERGY, --energy ENERGY
+                        energy of the X-Ray beam in keV (hc=12.398419292keV.A)
+  -u DUMMY, --dummy DUMMY
+                        dummy value for dead pixels
+  -U DELTA_DUMMY, --delta_dummy DELTA_DUMMY
+                        delta dummy value
+  -m MASK, --mask MASK  name of the file containing the mask image
+  -d DARK, --dark DARK  name of the file containing the dark current
+  -f FLAT, --flat FLAT  name of the file containing the flat field
+  -P POLARIZATION_FACTOR, --polarization POLARIZATION_FACTOR
+                        Polarization factor, from -1 (vertical) to +1
+                        (horizontal), default is None for no correction,
+                        synchrotrons are around 0.95
+  --error-model ERROR_MODEL
+                        Error model to use. Currently on 'poisson' is
+                        implemented
+  --unit UNIT           unit for the radial dimension: can be q_nm^-1, q_A^-1,
+                        2th_deg, 2th_rad or r_mm
+  --ext EXT             extension of the regrouped filename (.xy)
+  --method METHOD       Integration method
+  --multi               Average out all frame in a file before integrating
+  --average AVERAGE     Method for averaging out: can be 'mean' (default),
+                        'min', 'max' or 'median
+  --do-2D               Perform 2D integration in addition to 1D
+
+pyFAI-waxs is the script of pyFAI that allows data reduction (azimuthal
+integration) for Wide Angle Scattering to produce X-Ray Powder Diffraction
+Pattern with output axis in 2-theta space.
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Integration tool: pyFAI-saxs

+

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+

Design of the Python Fast Azimuthal Integration library

+

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+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/man/scripts.html b/doc/build/man/scripts.html new file mode 100644 index 000000000..6fc9c3369 --- /dev/null +++ b/doc/build/man/scripts.html @@ -0,0 +1,163 @@ + + + + + + + + pyFAI scripts manual — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

pyFAI scripts manual

+

While pyFAI is first and foremost a Python library to be used by developers, a set of scripts is provided to process +a full diffraction experiment on the command line without knowing anything about Python. +Those scripts can be divided into 3 categories: pre-processing tools which prepare the dataset for the calibration tool. +The calibration is the determination of the geometry of the experimental setup using Debye-Scherrer rings of a reference compound (or calibrant). +Finally a full dataset can be integrated using different tools targeted at different experiments.

+
+
Pre-processing tools:
+
    +
  • drawMask_pymca: tool for drawing a mask on top of an image
    • +
  • pyFAI-average: tool for averaging/median/... filtering images (i.e. for dark current)
    • +
+
+
Calibration tools:
+
    +
  • pyFAI-calib: manually select the rings and refine the geometry
    • +
  • pyFAI-recalib: automatic ring extraction to refine the geometry (deprecated: see “recalib” option in pyFAI-calib)
    • +
  • MX-calibrate: Calibrate automatically a set of images taken at various detector distances
    • +
  • check_calib: checks the calibration of an image at the sub-pixel level (deprecated: see “validate” option in pyFAI-calib)
    • +
+
+
Azimuthal integration tools:
+
    +
  • pyFAI-integrate: the graphical interface for integration (GUI)
    • +
  • pyFAI-saxs: command line interface for small-angle scattering
    • +
  • pyFAI-waxs: command line interface for powder difration
    • +
  • diff_map: diffraction mapping & tomography tool (command line and GUI)
    • +
  • diff_tomo: diffraction tomography tool (command line only)
    • +
+
+
+
+ +
+
+ + +
+
+
+
+
+

Previous topic

+

CCD calibration

+

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+

Preprocessing tool: pyFAI-average

+

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+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
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zZvjZi9nZbu$XsRXb%86H90*+!AOD936V~K~HaZh)MS08i5|GXk%{AT&N~7W1oSoH63QF;XruMU32A^K`0L`Ic!+Zz4nTI z8}UK&SomU66x>kk$R$-LOHdZs!8S>)h!CL{*7R&&wO8wwFo@Z_w4JbK9zqvTg~@Vv z35H?C8|1U7h-c`d_;LxT;Na+DIVM{!wme*+=ypaLx + + + + + + + Installation of Python Fast Azimuthal Integration library — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Installation of Python Fast Azimuthal Integration library

+
+

Abstract

+

Installation procedure for all main operating systems

+
+
+

Hardware requirement

+

PyFAI has been tested on various hardware: i386, x86_64, PPC64le, ARM. +The main constrain may be the memory requirement: 2GB of memory is a minimal requirement to run the tests. +The program may run with less but “MemoryError” are expected (appearing sometimes as segmentation faults). +As a consequence, a 64-bits operating system with enough memory is strongly advised.

+
+
+

Dependencies

+

PyFAI is a Python library which relies on the scientific stack (numpy, scipy, matplotlib)

+
    +
  • Python: version 2.7, 3.4 and 3.5. +Support for 2.6, 3.2 and 3.3 has been dropped in v0.12
    • +
  • NumPy: version 1.4 or newer
    • +
  • SciPy: version 0.7 or newer
    • +
  • Matplotlib: verson 0.99 or newer
    • +
  • FabIO: version 0.08 or newer
    • +
  • h5py (to access HDF5 files)
    • +
+

There are plenty of optional dependencies which will not prevent pyFAI from working +by may impair performances or prevent tools from properly working:

+
    +
  • pyopencl (for GPU computing)
    • +
  • fftw (for image analysis)
    • +
  • pymca (for mask drawing)
    • +
  • PyQt4 or PySide (for the graphical user interface)
    • +
+
+
+

Build dependencies:

+

In addition to the run dependencies, pyFAI needs a C compiler to build extensions.

+

C files are generated from cython source and distributed. +The distributed version correspond to OpenMP version. +Non-OpenMP version needs to be built from cython source code (especially on MacOSX). +If you want to generate your own C files, make sure your local Cython version +is sufficiently recent (>0.20).

+
+
+

Building procedure

+
python setup.py build
+pip install . --upgrade
+
+
+

There are few specific options to setup.py:

+
    +
  • --no-cython: Prevent Cython (even if present) to re-generate the C source code. Use the one provided by the development team.
    • +
  • --no-openmp: Recompiles the Cython code without OpenMP support (default under MacOSX).
    • +
  • --openmp: Recompiles the Cython code with OpenMP support (Default under Windows and Linux).
    • +
  • --with-testimages: build the source distribution including all test images. Download 200MB of test images to create a self consistent tar-ball.
    • +
+
+ +
+

Test suites

+

PyFAI comes with a test suite to ensure all core functionalities are working as expected and numerical results are correct:

+
python setup.py build test
+
+
+

Nota: to run the test, an internet connection is needed as 200MB of test images need to be download. +You may have to set the environment variable http_proxy and https_proxy +according to the networking environment you are in. +Specifically at ESRF, please phone the hotline at 24-24 to get those information.

+
+
+

Environment variables

+

PyFAI can use a certain number of environment variable to modify its default behavior:

+
    +
  • PYFAI_OPENCL: set to “0” to disable the use of OpenCL
    • +
  • PYFAI_DATA: path with gui, calibrant, ...
    • +
  • PYFAI_TESTIMAGES: path wit test images (if absent, they get downloaded from the internet)
    • +
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

pyFAI Package

+

Next topic

+

Installation procedure on Linux

+

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+ + + +
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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/operations/linux.html b/doc/build/operations/linux.html new file mode 100644 index 000000000..9c9719e42 --- /dev/null +++ b/doc/build/operations/linux.html @@ -0,0 +1,229 @@ + + + + + + + + Installation procedure on Linux — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Installation procedure on Linux

+

We cover first Debian-like distribution, then a generic recipie for all other +version is given.

+
+

Installation procedure on Debian/Ubuntu

+

PyFAI has been designed and originally developed on Ubuntu 10.04 and debian6. +Now, the pyFAI library is included into debian7, 8 and any recent Ubuntu and +Mint distribution. +To install the package provided by the distribution, use:

+
sudo apt-get install pyfai
+
+
+

The issue with distribution based installation is the obsolescence of the version +available.

+
+

Debian7 and Ubuntu 12.04

+

To build a more recent version, pyFAI provides you a small scripts which builds a debian package and installs it. +It relies on stdeb and provides a single package with everything inside. +You will be prompted for your password to gain root access in order to be able to install the freshly built package.

+
sudo apt-get install python-stdeb cython python-fabio
+wget https://github.com/pyFAI/pyFAI/archive/master.zip
+unzip master.zip
+cd pyFAI-master
+./build-deb7.sh
+
+
+
+
+

Debian8 and newer

+

Thanks to the work of Frédéric-Emmanuel Picca, the debian package of pyFAI +provides a pretty good template which allows continuous builds.

+
+

From silx repository

+

You can automatically install the latest nightly built of pyFAI with:

+
wget http://www.silx.org/pub/debian/silx.list
+wget http://www.silx.org/pub/debian/silx.pref
+sudo mv silx.list /etc/apt/sources.list.d/
+sudo mv silx.pref /etc/apt/preferences.d/
+sudo apt-get update
+sudo apt-get install pyfai
+
+
+

Nota: The nightly built packages are not signed, hence you will be prompted +to install non-signed packages.

+
+
+

Build from sources

+

One can also built from sources:

+
sudo apt-get install cython cython-dbg cython3 cython3-dbg debhelper dh-python \
+python-all-dev python-all-dbg python-fabio python-fabio-dbg python-fftw python-h5py \
+python-lxml python-lxml-dbg python-matplotlib python-matplotlib-dbg python-numpy\
+python-numpy-dbg python-qt4 python-qt4-dbg python-scipy python-scipy-dbg python-sphinx \
+python-sphinxcontrib.programoutput python-tk python-tk-dbg python3-all-dev python3-all-dbg \
+python3-fabio python3-fabio-dbg python3-lxml python3-lxml-dbg python3-matplotlib \
+python3-matplotlib-dbg python3-numpy python3-numpy-dbg python3-pyqt4 python3-pyqt4-dbg \
+python3-scipy python3-scipy-dbg python3-sphinx python3-sphinxcontrib.programoutput \
+python3-tk python3-tk-dbg
+wget https://github.com/pyFAI/pyFAI/archive/master.zip
+unzip master.zip
+cd pyFAI-master
+./build-deb8.sh
+
+
+

The first line is really long and defines all the dependence tree for building +debian package, including debug and documentation. +The build procedure last for a few minutes and you will be prompted for your +password in order to install the freshly built packages. +The deb-*files, available in the *package directory are backports for your local +installation.

+
+
+
+
+

Installation procedure on other linux distibution

+

If your distribution does not provide you pyFAI packages, using the PIP way +is advised, via wheels packages. First install pip and wheel:

+
wget https://bootstrap.pypa.io/get-pip.py
+sudo python get-pip.py
+sudo pip install pyFAI
+
+
+

Or you can install pyFAI from the sources:

+
wget https://github.com/pyFAI/pyFAI/archive/master.zip
+unzip master.zip
+cd pyFAI-master
+python setup.py build test
+sudo pip install . --upgrade
+
+
+

Nota: The usage of “python setup.py install” is now deprecated. +It causes much more trouble as there is no installed file tracking, +hence no way to de-install properly the package.

+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

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+

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+ +
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+ +
+

Installation procedure on MacOSX

+
+

Using PIP

+

To install pyFAI on an Apple computer you will need a scientific Python stack. +MacOSX provides by default Python2.7 with Numpy which is a good basis.

+
sudo pip install matplotlib --upgrade
+sudo pip install scipy --upgrade
+sudo pip install fabio --upgrade
+sudo pip install h5py --upgrade
+sudo pip install cython --upgrade
+sudo pip install pyFAI --upgrade
+
+
+

If you get an error about the local “UTF-8”, try to:

+
export LC_ALL=C
+
+
+

Before the installation.

+
+
+

Installation from sources

+

Get the sources from Github:

+
wget https://github.com/pyFAI/pyFAI/archive/master.zip
+unzip master.zip
+cd pyFAI-master
+
+
+
+

About OpenMP

+

OpenMP is a way to write multi-threaded code, running on multiple processors +simultaneously. +PyFAI makes heavy use of OpenMP, but there is an issue with recent versions of +MacOSX (>v10.6) where the default compiler of Apple, Xcode, dropped the +support for OpenMP.

+

There are two ways to compile pyFAI on MacOSX:

+
    +
  • Using Xcode and de-activating OpenMP in pyFAI
    • +
  • Using another compiler which supports OpenMP
    • +
+
+
+

Using Xcode

+

To build pyFAI from sources, a C-compiler is needed. +On an Apple computer, the default compiler is +Xcode, and it is available for free on +the AppStore. +As pyFAI has by default OpenMP activated, and it needs to be de-activated, +one needs to regenerate all Cython files without OpenMP.

+
sudo pip install cython --upgrade
+rm pyFAI/ext/*.c
+python setup.py build --no-openmp
+python setup.py bdist_wheel
+sudo pip install --find-links=dist/ --pre --no-index --upgrade pyFAI
+
+
+
+
+

Using gcc or clang

+

If you want to keep the OpenMP feature (which makes the processing slightly faster), +the alternative is to install another compiler like gcc +or clang on your Apple computer. +As gcc & clang support OpenMP, there is no need to re-generate the cython files.

+
CC=gcc python setup.py build --openmp
+python setup.py bdist_wheel
+sudo pip install --find-links=dist/ --pre --no-index --upgrade pyFAI
+
+
+

Nota: The usage of “python setup.py install” is now deprecated. +It causes much more trouble as there is no installed file tracking, +hence no way to de-install properly a package.

+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Installation procedure on Linux

+

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Installation procedure on Windows

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+ +
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+ + \ No newline at end of file diff --git a/doc/build/operations/windows.html b/doc/build/operations/windows.html new file mode 100644 index 000000000..075a47e20 --- /dev/null +++ b/doc/build/operations/windows.html @@ -0,0 +1,302 @@ + + + + + + + + Installation procedure on Windows — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

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+ +
+ +
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+
+
+ +
+

Installation procedure on Windows

+

PyFAI is a Python library. Even if you are only interested in some tool like +pyFAI-calib or pyFAI-integrate, you need to install the complete library (for now). +This is usually performed in 3 steps:

+
    +
  1. install Python,
    2. +
  2. install the scientific Python stack
    3. +
  3. install pyFAI itself.
    4. +
+
+

Get Python

+

Unlike on Unix computers, Python is not available by default on Windows computers. +We recommend you to install the 64 bit version of Python, +preferably the latest 64 bits version from the +2.7 series. +But Python 3.4 and 3.5 are also very good candidates. +Python 2.6, 3.2 and 3.3 are no more supported since pyFAI v0.12.

+

The 64 bits version is strongly advised if your hardware and operating system +supports it, as the 32 bits versions is +limited to 2 GB of memory, hence unable to treat large images (like 4096 x 4096). +The test suite is not passing on Windows 32 bits due to the limited amount of +memory available to the Python process, +nevertheless, pyFAI is running on Winodws 32 bits (but not as well).

+

Alternative Scientific Python stacks exists, like +Enthought Python Distribution , +Canopy, +Anaconda, +PythonXY or +WinPython. +They all offer most of the scientific packages already installed which makes +the installation of +dependencies much easier. +On the other hand, they all offer different packaging system and we cannot +support all of them. +Moreover, distribution from Enthought and Continuum are not free so you +should be able to get support from those companies.

+

Nota: any flavor of those Python distribution is probably incompatible with +any other due to change in compiler or Python compilation options. +Mixing them is really looking for trouble, hence strongly discouraged. +If you want an advice on which scientific python distribution for Windows to use, +I would recommend WinPython.

+
+
+

Install PIP

+

PIP is the package management system for Python, it connects to +the Python Package Index, +download and install software packages from there.

+

PIP has revolutionize the way Python libraries are installed as it is able to +select the right build for your system, or compile them from the sources, +which could be extremely tricky otherwise. +If you installed python 2.7.10, 3.4 or newer, PIP is already installed. +If pip is not yet installed on your system, download +get_pip.py and run it:

+
python get-pip.py
+
+
+

Assuming python.exe is already in your PATH.

+

Nota: Because PIP connects to the network, the http_proxy and https_proxy +environment variable may need to be set-up properly. +At ESRF, please get in contact with the hotline (24-24) to retrive those information.

+
+
+

Install the scientific stack

+

The strict dependencies for pyFAI are:

+
    +
  • NumPy
    • +
  • SciPy
    • +
  • matplotlib
    • +
  • FabIO
    • +
  • h5py
    • +
+

Recommended dependencies are:

+
    +
  • cython
    • +
  • h5py
    • +
  • pyopencl
    • +
  • PyQt4
    • +
  • pymca
    • +
  • rfoo
    • +
  • pyfftw3
    • +
  • lxml
    • +
+
+

Using PIP

+

Most of the dependencies are available via PIP:

+
pip install numpy --upgrade
+pip install scipy --upgrade
+pip install matplotlib --upgrade
+pip install fabio --upgrade
+pip install PyQt4 --upgrade
+
+
+

Note that numpy/scipy/matplotlib are already installed in most “Scientific Python distribution”

+

If one of the dependency is not available as a Wheel (i.e. binary package) but +only as a source package, a compiler will be required. +In this case, see the next paragraph. +The generalization of Wheel packages should help and the installation of binary +modules should become easier.

+

Nota: This requires a network access and correct proxy settings. +At ESRF, please get in contact with the hotline (24-24) to retrive those information.

+
set http_proxy=http://proxy.site.com:3128
+set https_proxy=http://proxy.site.com:3128
+
+
+
+
+

Using Christoph Gohlke repository

+

Christoph Gohlke is a researcher at Laboratory for Fluorescence Dynamics, University of California, Irvine. +He is maintaining a large repository Python extension (actually, all we need :) for Windows. +Check twice the Python version and the Windows version (win32 or win_amd64) before downloading.

+

Moreover the libraries he provides are linked against the MKL library from Intel which +makes his packages faster then what you would get by simply recompiling them.

+

Christoph now provides packages as wheels. +To install them, download the wheels and use PIP:

+
pip install numpy*.whl
+
+
+

Alternatively, you can use the wheelhouse of the silx project:

+
pip install --trusted-host www.silx.org --find-links http://www.silx.org/pub/wheelhouse/ numpy scipy matplotlib fabio PyQt4
+
+
+
+
+
+

Install pyFAI via PIP

+

The latest stable release of pyFAI should also be PIP-installable (starting at version 0.10.3):

+
pip install pyFAI --upgrade
+
+
+
+
+

Install pyFAI from sources

+

The sources of pyFAI are available at https://github.com/pyFAI/pyFAI/releases +the development is performed on https://github.com/kif/pyFAI

+

In addition to the Python interpreter, you will need the C compiler compatible +with your Python interpreter, for example you can find the one for Python2.7 at: +http://aka.ms/vcpython27

+

To upgrade the C-code in pyFAI, one needs in addition Cython:

+
pip install cython --upgrade
+python setup.py bdist_wheel
+pip install --pre --no-index --find-links dist/ pyFAI
+
+
+
+
+

Troubleshooting

+

This section contains some tips on windows.

+
+

Side-by-side error

+

When starting pyFAI you get a side-by-side error like:

+
ImportError: DLL load failed: The application has failed to start because its
+side-by-side configuration is incorrect. Please see the application event log or
+use the command-line sxstrace.exe tool for more detail.
+
+
+

This means you are using a version of pyFAI which was compiled using the MSVC compiler +(maybe not on your computer) but the Microsoft Visual C++ Redistributable Package is missing. +For Python2.7, 64bits the missing DLL can be downloaded from:

+
http://www.microsoft.com/en-us/download/confirmation.aspx?id=2092
+
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Installation procedure on MacOSX

+

Next topic

+

PyFAI Ecosystem

+

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+ + + +
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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/project.html b/doc/build/project.html new file mode 100644 index 000000000..49c28e460 --- /dev/null +++ b/doc/build/project.html @@ -0,0 +1,391 @@ + + + + + + + + Project — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Project

+

PyFAI is a library to deal with diffraction images for data reduction. +This chapter describes the project from the computer engineering point of view.

+

PyFAI is an open source project licensed under the GPL (+ MIT code) mainly +written in Python (v2.7 or newer, 3.4 or newer) and heavily relying on the +Python scientific ecosystem: numpy, scipy and matplotlib. +It provides high performances image treatment thanks to cython and +OpenCL... but only a C-compiler is needed to build it.

+
+

Programming language

+

PyFAI is a Python project but uses many programming languages:

+
    +
  • 23000 lines of Python (plus 5000 for the test)
    • +
  • 8000 lines of Cython which are converted into ... C (about half a million lines)
    • +
  • 5000 lines of OpenCL kernels
    • +
+

The OpenCL code has been tested using:

+
    +
  • Nvidia OpenCL v1.1 and v1.2 on Linux, Windows (GPU device)
    • +
  • Intel OpenCL v1.2 on Linux and Windows (CPU and ACC (Phi) devices)
    • +
  • AMD OpenCL v1.2 on Linux and Windows (CPU and GPU device)
    • +
  • Apple OpenCL v1.2 on MacOSX (CPU and GPU)
    • +
  • Beignet OpenCL v1.2 on Linux (GPU device)
    • +
  • Pocl OpenCL v1.2 on Linux (CPU device)
    • +
+
+
+

Repository:

+

The project is hosted on GitHub: +https://github.com/pyFAI/pyFAI

+

Which provides the issue tracker in +addition to Git hosting. +Collaboration is done via Pull-Requests in github’s web interface:

+

Everybody is welcome to fork the project +and adapt it to his own needs: +CEA-Saclay, Synchrotrons Soleil, Desy and APS have already done so. +Collaboration is encouraged and new developments can be submitted and merged +into the main branch via pull-requests.

+
+
+

Getting help

+

A mailing list: pyfai@esrf.fr is publicly available, it is the best place to ask +your questions: the author and many advanced users are there and willing to help you. +To subscribe to this mailing list, send an email to +sympa@esrf.fr with "subscribe pyfai" as subject.

+

On this mailing list, you will have information about release of the software, +new features available and meet experts to help you solve issues related to +azimuthal integration and diffraction in general. +It also provides a knowledge-base of most frequently asked question which needs +to be integrated into this documentation.

+

The volume of email on the list remains low, so you can subscribe without being +too much spammed. As the mailing list is archived, and can be consulted at: +http://www.edna-site.org/lurker, +you can also check the volume of the list.

+

If you think you are facing a bug, the best is to +create a new issue on the GitHub page +(you will need a GitHub account for that).

+

Direct contact with authors is discouraged: +pyFAI is open source software that we develop to aid the research +community in doing what they do best. +While we do enjoy doing this, we +would not be able to dream of spending nearly as much time with +pyFAI as we do if it wasn’t for your support. +Interest of the scientific community (via a lively mailing list) and citation in +scientific publication for our software +is one of the main criterion for ESRF management when deciding if they should +continue funding development.

+
+
+

Run dependencies

+
    +
  • Python version 2.7, 3.4, 3.5
    • +
  • NumPy
    • +
  • SciPy
    • +
  • Matplotlib
    • +
  • FabIO
    • +
  • h5py
    • +
  • pyopencl (optional)
    • +
  • fftw (optional)
    • +
  • pymca (optional)
    • +
  • PyQt4 or PySide (for the graphical user interface)
    • +
+
+
+

Build dependencies:

+

In addition to the run dependencies, pyFAI needs a C compiler.

+

There is an issue with MacOSX (v10.8 onwards) where the default compiler +(Xcode5 or 6) switched from gcc 4.2 to clang which +dropped the support for OpenMP (clang v3.5 supports OpenMP under linux but not +directly under MacOSX). +Multiple solution exist, pick any of those:

+
    +
  • Install a recent version of GCC (>=4.2)
    • +
  • Use Xcode without OpenMP, using the –no-openmp flag for setup.py. +You will need to remove the pyFAI/ext/histogram.c file and regenerate it, +cython needs to be installed.
    • +
+

C files are generated from cython source and distributed. Cython is only needed +for developing new binary modules. +If you want to generate your own C files, make sure your local Cython version +is recent enough (v0.21 and newer), +unless your Cython files will not be translated to C, nor used.

+
+
+

Building procedure

+
+

As most of the Python projects:

+
python setup.py build bdist_wheel
+pip install dist/pyFAI-0.12.0*.whl --upgrade
+
+
+

There are few specific options to setup.py:

+
    +
  • –no-cython: do not use cython (even if present) and use the C source code +provided by the development team
    • +
  • –no-openmp: if you compiler lacks OpenMP support, like Xcode on MacOSX. +Delete also pyFAI/ext/histogram.c and install cython to regenerate C-files.
    • +
  • –with-testimages: build the source distribution including all test images. +Downloads 200MB of test images to create a self consistent tar-ball.
    • +
+
+
+
+

Test suites

+

To test the installed version of pyFAI:

+
python run_test.py
+
+
+

or from python:

+
import pyFAI
+pyFAI.tests()
+
+
+

It is normal to get few (<20) Warning messages as the test procedure also +tests corner cases.

+
+

To run the test an internet connection is needed as 200MB of test images will be downloaded.

+

Setting the environment variable http_proxy can be necessary (depending on your network):

+
export http_proxy=http://proxy.site.org:3128
+
+
+

Especially at ESRF, the configuration of the network proxy can be obtained +by phoning on the hotline: 24-24.

+

To test the development version (built but not yet installed):

+
python setup.py build test
+
+
+

or

+
python setup.py build
+python run_test.py -i
+
+
+

PyFAI comes with 34 test-suites (223 tests in total) representing a coverage of 60%. +This ensures both non regression over time and ease the distribution under different platforms: +pyFAI runs under Linux, MacOSX and Windows (in each case in 32 and 64 bits). +Test may not pass on computer featuring less than 2GB of memory or 32 bit architectures.

+

Note:: The test coverage tool does not count lines of Cython, nor those of OpenCL. +Anyway test coverage is a tool for developers about possible weaknesses of their +code and not a management indicator.

+
+ +
+
+
+
+

Continuous integration

+

This software engineering practice consists in merging all developer working copies +to a shared mainline several times a day and build the whole project for multiple +targets.

+
+

On Debian 8 - Jessie

+

Continuous integration is made by a home-made scripts which checks out the latest release and builds and runs the test every night. +Nightly builds are available for debian8-64 bits. To install them:

+
sudo apt-get update
+sudo apt-get install pyfai
+
+
+

You have to accept non-signed packages because they are automatically built.

+

In addition some “cloud-based” tools are used to ensure a larger coverage of operating systems/environment. +They rely on a “local wheelhouse”.

+

Those wheels are optimized for Travis-CI, AppVeyor and ReadTheDocs, using them is not recommended as your Python configuration may differ +(and those libraries could even crash your system).

+
+
+

Linux

+

Travis provides continuous integration on Linux, 64 bits computer with Python 2.7, 3.4 and 3.5.

+

The builds cannot be retrieved with Travis-CO.

+
+
+

AppVeyor

+

AppVeyor provides continuous integration on Windows, 64 bits computer with Python 2.7 and 3.4. +Successful builds provide installers for pyFAI as wheels and msi, they are anonymously available as artifacts. +Due to the limitation of AppVeyor’s build system, those installers have openMP disabled.

+
+
+
+

List of contributors in code

+
$ git log  --pretty='%aN##%s' | grep -v 'Merge pull' | grep -Po '^[^#]+' | sort | uniq -c | sort -rn
+
+
+
+
As of 06/2016:
+
    +
  • Jérôme Kieffer (ESRF)
    • +
  • Frédéric-Emmanuel Picca (Soleil)
    • +
  • Aurore Deschildre (ESRF)
    • +
  • Giannis Ashiotis (ESRF)
    • +
  • Dimitrios Karkoulis (ESRF)
    • +
  • Valentin Valls (ESRF)
    • +
  • Jon Wright (ESRF)
    • +
  • Zubair Nawaz (Sesame)
    • +
  • Amund Hov (ESRF)
    • +
  • Dodogerstlin @github
    • +
  • Gunthard Benecke (Desy)
    • +
  • Gero Flucke (Desy)
    • +
  • Vadim Dyadkin (ESRF)
    • +
  • Sigmund Neher (GWDG)
    • +
  • Thomas Vincent (ESRF)
    • +
+
+
+
+
+

List of other contributors (ideas or code)

+
    +
  • Peter Boesecke (geometry)
    • +
  • Manuel Sanchez del Rio (histogramming)
    • +
  • Armando Solé (masking widget + PyMca plugin)
    • +
  • Sebastien Petitdemange (Lima plugin)
    • +
+
+
+

List of supporters

+
    +
  • LinkSCEEM project: porting to OpenCL
    • +
  • ESRF ID11: Provided manpower in 2012 and 2013 and beamtime
    • +
  • ESRF ID13: Provided manpower in 2012, 2013, 2014, 2015, 2016 and beamtime
    • +
  • ESRF ID29: provided manpower in 2013 (MX-calibrate)
    • +
  • ESRF ID02: provided manpower 2014, 2016
    • +
  • ESRF ID15: provide manpower 2015, 2016
    • +
  • ESRF ID21: provide manpower 2015, 2016
    • +
  • ESRF ID31: provide manpower 2016
    • +
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

PyFAI Ecosystem

+

Next topic

+

Test coverage report for pyFAI

+

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+ +
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/publications.html b/doc/build/publications.html new file mode 100644 index 000000000..053628890 --- /dev/null +++ b/doc/build/publications.html @@ -0,0 +1,142 @@ + + + + + + + + List of publication about pyFAI — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

List of publication about pyFAI

+
    +

  • PyFAI, a versatile library for azimuthal regrouping, +J Kieffer & D Karkoulis; +Journal of Physics: Conference Series (2013) 425 (20), pp202012

    +
    • +

  • PyFAI: a Python library for high performance azimuthal integration on GPU +J Kieffer & J.P. Wright; +Powder Diffraction (2013) 28 (S2), pp339-350

    +
    • +
  • +
    PyFAI: a Python library for high performance azimuthal integration on GPU
    +

    Jérôme Kieffer, Giannis Ashiotis +PROC. OF THE 7th EUR. CONF. ON PYTHON IN SCIENCE (EUROSCIPY 2014)

    +
    +
    +
    • +

  • The fast azimuthal integration Python library: pyFAI +J. Kieffer, G. Ashiotis, A. Deschildre, Z. Nawaz, J. P. Wright, D. Karkoulis, F. E. Picca +Journal of Applied Crystallography (2015) 48 (2), 510-519

    +
    • +
+

The later publication provides a nice overview of the features introduced in version +0.11 and further developed in v0.12 and should be the one cited in publication using pyFAI. +There are already 55 publication referring to pyFAI, some of them in the most prestigious +scientific journals (Nature, PNAS, ...).

+
+ + +
+
+
+
+
+

Previous topic

+

ChangeLog of Versions

+

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+

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+ +
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Python Module Index

+ +
+ p +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ p
+ pyFAI +
    + pyFAI.__init__ +
    + pyFAI.azimuthalIntegrator +
    + pyFAI.blob_detection +
    + pyFAI.calibrant +
    + pyFAI.calibration +
    + pyFAI.detectors +
    + pyFAI.distortion +
    + pyFAI.geometry +
    + pyFAI.geometryRefinement +
    + pyFAI.gui_utils +
    + pyFAI.integrate_widget +
    + pyFAI.io +
    + pyFAI.massif +
    + pyFAI.ocl_azim +
    + pyFAI.ocl_azim_csr +
    + pyFAI.ocl_azim_csr_dis +
    + pyFAI.ocl_azim_lut +
    + pyFAI.opencl +
    + pyFAI.peak_picker +
    + pyFAI.spline +
    + pyFAI.units +
    + pyFAI.utils +
    + pyFAI.worker +
+ + +
+
+
+
+
+ + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/pyFAI.html b/doc/build/pyFAI.html new file mode 100644 index 000000000..3149122c8 --- /dev/null +++ b/doc/build/pyFAI.html @@ -0,0 +1,412 @@ + + + + + + + + General introduction to PyFAI — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

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+ +
+ +
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+
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+ +
+

General introduction to PyFAI

+
+

Python Fast Azimuthal Integration

+

PyFAI is implemented in Python programming language, which is open +source and already very popular for scientific data analysis ([PyMca], +[PyNX], …). It relies on the scientific stack of python composed of [NumPy], +[SciPy] and [Matplotlib] plus the [OpenCL] binding [PyOpenCL] for performances.

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\(2D\) area detectors like CCD or pixel detectors have become +popular in the last 15 years for diffraction experiments (e.g. for WAXS, +SAXS, single crystal and powder diffraction). +These detectors +have a large sensitive area of millions of pixels with high spatial +resolution. The software package pyFAI ([SRI2012], [EPDIC13]) +has been designed to reduce SAXS, +WAXS and XRPD images taken with those detectors into \(1D\) curves +(azimuthal integration) usable by other software for in-depth analysis +such as Rietveld refinement, or \(2D\) images (a radial +transformation named caking in [FIT2D]). +As a library, the aim of pyFAI is to be +integrated into other tools like [PyMca] or [EDNA] or [LImA] with a clean pythonic +interface. +However pyFAI features also command line and graphical tools for batch +processing, converting data into q-space (q being the momentum +transfer) or 2\(\theta\)-space (\(\theta\) being the Bragg +angle) and a calibration graphical interface for optimizing the geometry +of the experiment using the Debye-Scherrer rings of a reference sample. +PyFAI shares the geometry definition of SPD but can directly import +geometries determined by the software FIT2D. PyFAI has been designed to +work with any kind of detector and geometry (transmission or reflection) +and relies on FabIO, a library able to read more than 20 image formats +produced by detectors from 12 different manufacturers. During the +transformation from cartesian space \((x,y)\) to polar space +\((2\theta, \chi )\), both local and total intensities are conserved +in order to obtain accurate quantitative results. Technical details on +how this integration is implemented and how it has been ported to native +code and parallelized on graphic cards are quickly presented but you can refer +to this publications for further details.

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+

Introduction

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With the advent of hyperspectral experiments like diffraction tomography +in the world of synchrotron radiation, existing software tools for +azimuthal integration like [FIT2D] and [SPD] reached their performance +limits owing to the fast data rate needed by such experiments. Even when +integrated into massively parallel frameworks like [EDNA] , such +stand-alone programs, due to their monolithic nature, cannot keep the +pace with the data flow of new detectors. Therefore we decided to +implemente from scratch a novel azimuthal integration tool which is +designed to take advantage of modern parallel hardware features. +PyFAI assumes the setup does not change during the experiment and tries to reuse +a maximum number of data (using memoization), moreover those calculation are performed +only when needed (lazy_evaluation).

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Experiment description

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In pyFAI, the basic experiment is defined by a description of an area-detector whose +position in space is defined through the sample position and the incident X-ray +beam, and can be calibrated using Debye-Scherrer rings of a reference compound.

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PyFAI executables

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PyFAI was designed to be used by scientists needing a simple and +effective tool for azimuthal integration. Two command line programs +pyFAI-waxs and pyFAI-saxs are provided with pyFAI for performing the +integration of one or more images on the command line. +The waxs version outputs result in +\(2\theta /I\), whereas the saxs version outputs result in +\(q/I(/\sigma)\). Options for these programs are parameter file +(poni-file) +describing the geometry and the mask file. They can also do some +pre-processing like dark-noise subtraction and flat-field correction +(solid-angle correction is done by default).

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A new Graphical interface based on Qt called pyFAI-integrate is now available, +offers all options possible for azimuthal integration (dark/flat/polarization, +....) in addition to a finer tuning for the computing device selection (CPU/GPU).

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Finally couple of specialized tool called diff_tomo and diff_map are available to +reduce a 2D/3D-mapping experiment of 2D images into a 3D volume +(\(x, y, 2\theta\) for mapping or \(rot, trans, 2\theta\) for tomography)

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There are cookbooks on these scripts in Cookbook recipes and their complete +manual pages are available in the pyFAI scripts manual section.

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Python library

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PyFAI is first and foremost a library: a tool of the scientific toolbox +built around [IPython] and [NumPy] to perform data analysis either +interactively or via scripts. Figure [notebook] shows an interactive +session where an integrator is created, and an image loaded and +integrated before being plotted.

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+image +
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The tutorial section makes heavy use of ipython, now jupyter +to process data using pyFAI. +The first tutorial also explains a bit how Python and Jupyter works to be +able to perform basic processing efficiently with pyFAI.

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Regrouping mechanism

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In pyFAI, regrouping is performed using a histogram-like algorithm. +Each pixel of the image is associated to its polar coordinates +\((2\theta , \chi )\) or \((q, \chi )\), then a pair of +histograms versus \(2\theta\) (or \(q\)) are built, one non +weighted for measuring the number of pixels falling in each bin and +another weighted by pixel intensities (after dark-current subtraction, +and corrections for flat-field, solid-angle and polarization). +The division of the weighted histogram by the number of pixels per bin gives +the average signal over the given corona which provides the diffraction pattern. +\(2D\) regrouping (called caking in +FIT2D) is obtained in the same way using two-dimensional histograms over +radial (\(2\theta\) or \(q\)) and azimuthal angles +(\(\chi\)).

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+

Pixel splitting algorithm

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Powder diffraction patterns obtained by histogramming have a major +weakness where pixel statistics are low. A manifestation of this +weakness becomes apparent in the \(2D\)-regrouping where most of the +bins close to the beam-stop are not populated by any pixel. In this figure, +many pixels are missing in the low \(2\theta\) region, due +to the arbitrary discretization of the space in pixels as intensities +were assigned to each pixel center which does not reflect the physical +reality of the scattering experiment.

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+image +
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PyFAI solves this problem by pixel +splitting : in addition to the pixel position, its +spatial extension is calculated and each pixel is then split and +distributed over the corresponding bins, the intensity being considered +as homogeneous within a pixel and spread accordingly. +The drawback of this is the correlation introduced between two adjacent bins. +To simplify +calculations, this was initially done by abstracting the pixel shape +with a bounding box that circumscribes the pixel. In an effort to better +the quality of the results this method was dropped in favoor of a full +pixel splitting scheme that actually uses the actual pixel geometry +for its calculations.

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+image +
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Performances and migration to native code

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Originally, regrouping was implemented using the histogram provided by +[NumPy], then re-implemented in [Cython] with pixel splitting to achieve a +four-fold speed-up. The computation time scales like O(N) with the size +of the input image. The number of output bins shows only little +influence; overall the single threaded [Cython] implementation has been +stated at 30 Mpix/s (on a 3.4 GHz Intel core i7-2600).

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Parallel implementation

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The method based on histograms works well on a single processor but runs +into problems requiring so called “atomic operations” when run in parallel. +Processing pixels in the input data order causes write access conflicts which +become less efficient with the increase of number of computing units (need of atomic_operation). +This is the main limit of the method exposed previously; +especially on GPU where thousands of threads are executed simultaneously.

+

To overcome this limitation; instead of looking at where input pixels GO TO +in the output image, we instead look at where the output pixels COME FROM +in the input image. +This transformation is called a “scatter to gather” transformation in parallel programming.

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The correspondence between pixels and output bins can be stored in a +look-up table (LUT) together with the pixel weight which make the integration +look like a simple (if large and sparse) matrix vector product. +This look-up table size depends on whether pixels are split over multiple +bins and to exploit the sparse structure, both index and weight of the pixel +have to be stored. +We measured that 500 Mbytes are needed to store the LUT to integrate a 16 +megapixels image, which fits onto a reasonable quality graphics card nowadays +but can still be too large to fit on an entry-level graphics card.

+

By making this change we switched from a “linear read / random write” forward algorithm +to a “random read / linear write” backward algorithm which is more suitable for parallelization. +As a farther improvement on the algorithm, the use of compressed sparse row (CSR) format was +introduced, to store the LUT data. +This algorithm was implemented both in [Cython]-OpenMP and OpenCL. +The CSR approach has a double benefit: +first, it reduces the size of the storage needed compared to the LUT by a factor two to three, +offering the opportunity of working with larger images on the same hardware. +Secondly, the CSR implementation in OpenCL is using an algorithm based on multiple parallel +reductions where many execution threads are collaborating to calculate +the content of a single bin. +This makes it very well suited to run on GPUs and accelerators +where hundreds to thousands of simultaneous threads are available.

+

When using OpenCL for the GPU we used a compensated (or Kahan_summation), to reduce +the error accumulation in the histogram summation (at the cost of more operations to be done). +This allows accurate results to be obtained on cheap hardware that performs calculations +in single precision floating-point arithmetic (32 bits) which are available on consumer +grade graphic cards. +Double precision operations are currently limited to high price and performance computing dedicated GPUs. +The additional cost of Kahan summation, 4x more arithmetic operations, is hidden by smaller data types, +the higher number of single precision units and that the GPU is usually limited by the memory bandwidth anyway.

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The performances of the parallel implementation based on a LUT, stored in CSR format, can reach 750 MPix/s +on recent multi-core computer with a mid-range graphics card. +On multi-socket server featuring high-end GPUs like Tesla cards, the performances are similar with +the additional capability to work on multiple detector simultaneously.

+
+benchmark performed on a 2014 single-socket workstation +
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+

Conclusion

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The library pyFAI was developed with two main goals:

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    +
  • Performing azimuthal integration with a clean programming interface.
    • +
  • No compromise on the quality of the results is accepted: a careful +management of the geometry and precise pixel splitting ensures total +and local intensity preservation.
    • +
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PyFAI is the first implementation of an azimuthal integration algorithm +on a GPUs as far as we are aware of, and the stated twenty-fold speed up +opens the door to a new kind of analysis, not even considered before. +With a good interface close to the camera, we believe PyFAI is able to +sustain the data streams from the next generation high-speed detectors.

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+

Acknowledgments

+

Porting pyFAI to GPU would have not been possible without +the financial support of LinkSCEEM-2 (RI-261600).

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\ No newline at end of file diff --git a/doc/build/usage/cookbook/calibrate.html b/doc/build/usage/cookbook/calibrate.html new file mode 100644 index 000000000..d477c99a9 --- /dev/null +++ b/doc/build/usage/cookbook/calibrate.html @@ -0,0 +1,301 @@ + + + + + + + + Calibration of a diffraction setup — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Calibration of a diffraction setup

+

The files needed for this cookbook can be downloaded on: +http://www.silx.org/pub/pyFAI/cookbook/calibration/ +You can download them to practice:

+
$ mkdir calibration
+$ cd calibration
+$ wget http://www.silx.org/pub/pyFAI/cookbook/calibration/LaB6_29.4keV.tif
+$ wget http://www.silx.org/pub/pyFAI/cookbook/calibration/F_K4320T_Cam43_30012013_distorsion.spline
+$ ls
+F_K4320T_Cam43_30012013_distorsion.spline  LaB6_29.4keV.tif
+
+
+

The associated video is here

+
+

Review your calibration image

+

As viewer, try fabio_viewer from the FabIO package

+
$ fabio_viewer LaB6_29.4keV.tif
+
+
+
+image +
+

You may need to pre-process your data, pyFAI-average is a tool to perform pixel-wise +transformation. +In this example, the image file was obtained using a “max filter” over 20 frames +with pyFAI-average:

+
$ pyFAI-average -m max -F tif -o LaB6_29.4keV.tif ref_lab6_00??.edf
+
+
+
+
+

Get all additional data

+
+
    +
  • calibrant used: LaB6 according to the name of the file
    • +
  • the energy or the wavelength, 29.4keV according to the name of the file
    • +
  • detector geometry: there is a spline file along with the file
    • +
  • masks, dark, flat ... are not going to be used to keep things simple.
    • +
+
+
+
+

Start pyFAI-calib

+

Use the man page (or –help) to see all options, or go to the pyFAI scripts manual section.

+

Here we just provide the energy, the detector disortion file and the calibrant +options in addition to the image file.

+
$ pyFAI-calib -e 29.4 -s F_K4320T_Cam43_30012013_distorsion.spline -c LaB6 LaB6_29.4keV.tif
+FixedParameters(['wavelength'])
+ERROR:pyFAI.peak_picker:ControlPoint.load: No such file LaB6_29.4keV.npt
+INFO:pyFAI.massif:Image size is (2048, 2048)
+INFO:pyFAI.massif:Binning size is [2, 2]
+INFO:pyFAI.massif:Labeling found 7272 massifs.
+INFO:pyFAI.massif:Labeling found 7272 massifs.
+INFO:root:Please select rings on the diffraction image. In parenthesis, some modified shortcuts for single button mouse (Apple):
+ * Right-click (click+n):         try an auto find for a ring
+ * Right-click + Ctrl (click+b):  create new group with one point
+ * Right-click + Shift (click+v): add one point to current group
+ * Right-click + m (click+m):     find more points for current group
+ * Center-click or (click+d):     erase current group
+ * Center-click + 1 or (click+1): erase closest point from current group
+Please press enter when you are happy with your selection
+
+
+
+
+

Pick peaks

+

To perform the calibration one needs to create control points and assign them to rings.

+
+
    +
  • Right click on a few (5) points in the inner-most ring which has the index +number #0
    • +
  • Increase the counter on the top right to change the ring number and pick a few +more points on the corresponding ring (again with right click).
    • +
+
+
+image +
+
+
+

Review the group of peaks

+

Press Enter in the terminal to do so... +and check the ring assignment

+

Once done with all groups, the position of the expected rings is overlaid to the +diffraction image. You may need to unzoom to view them !

+
Now fill in the ring number. Ring number starts at 0, like point-groups.
+Point group # a (4 points)  (1315.9,1090.1)   [default=0] Ring#
+Point group # b (5 points)  (1302.0, 926.0)   [default=0] Ring#
+Point group # c (2 points)  (1085.3,1268.1)   [default=0] Ring#
+Point group # d (2 points)  ( 850.1,1083.3)   [default=0] Ring#
+Point group # e (5 points)  ( 965.1, 825.7)   [default=0] Ring#
+Point group # f (4 points)  ( 898.2,1315.9)   [default=1] Ring#
+Point group # g (2 points)  (1244.6, 733.6)   [default=1] Ring#
+Point group # h (2 points)  (1350.1, 821.9)   [default=1] Ring#
+Optimization terminated successfully.    (Exit mode 0)
+
+
+
+image +
+
+
+

Acquire some more control points

+
+
    +
  • Use recalib to extract a new set of control points, specify the number of rings, first a few of them then more
    • +
  • You may want to free/fix/bound some parameter then refine again
    • +
+
+
Fixed: wavelength
+Modify parameters (or ? for help)?   recalib 6
+[...]
+Fixed: wavelength
+Modify parameters (or ? for help)?   recalib 15
+[...]
+Fixed: wavelength
+Modify parameters (or ? for help)?   recalib 25
+
+
+
+image +
+
+
+

Visualize the integrated patterns

+
+
    +
  • integrate to view the integrated pattern
    • +
  • then extract a few extra rings ...
    • +
  • the geometry is displayed on the screen, and saved automatically in the poni-file
    • +
+
+
Fixed: wavelength
+Modify parameters (or ? for help)?   integrate
+
+
+
+image +
+
+
+

Quit

+
Fixed: wavelength
+Modify parameters (or ? for help)?   quit
+$
+
+
+

All different geometries have been saved into the LaB6_29.4keV.poni file and +can directly be used with pyFAI-integrate. +All control points are saved into LaB6_29.4keV.npt.

+

Final notes: +In this case the calibration is far from being good and it is likely the +distortion file applied is not the proper one according to many the waves on the 2D +integration pattern. +If one flips up-down the spline file prior to the calibration, the residual error +is twice lower but the goes far beyond this cookbook. +Here is the diffraction pattern from a properly calibrated setup:

+
+image +
+
from pyFAI.spline import Spline
+s = Spline("F_K4320T_Cam43_30012013_distorsion.spline")
+ud = s.flipud()
+ud.write("flipud.spline")
+
+
+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

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+

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+

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+

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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/cookbook/index.html b/doc/build/usage/cookbook/index.html new file mode 100644 index 000000000..d6810b3d2 --- /dev/null +++ b/doc/build/usage/cookbook/index.html @@ -0,0 +1,125 @@ + + + + + + + + Cookbook recipes — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + +
+

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Cookbook recipes

+

Cookbook are short tutorials: 1 page, 5 minutes to read.

+
+ +
+

For more in depth explanation, see the Tutorials section.

+
+ + +
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+

Pre-processing of images:

+

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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/cookbook/integration_with_python.html b/doc/build/usage/cookbook/integration_with_python.html new file mode 100644 index 000000000..5790f65b5 --- /dev/null +++ b/doc/build/usage/cookbook/integration_with_python.html @@ -0,0 +1,119 @@ + + + + + + + + <no title> — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
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+ +
+ +
+
+
+
+ +

This cookbook is now available a a tutorial using jupyter +on the tutorial section.

+ + +
+
+
+
+
+

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+

<no title>

+

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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/cookbook/integration_with_scripts.html b/doc/build/usage/cookbook/integration_with_scripts.html new file mode 100644 index 000000000..b6b7fd81f --- /dev/null +++ b/doc/build/usage/cookbook/integration_with_scripts.html @@ -0,0 +1,117 @@ + + + + + + + + <no title> — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
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Azimuthal integration using the graphical user interface

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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/cookbook/integration_with_the_gui.html b/doc/build/usage/cookbook/integration_with_the_gui.html new file mode 100644 index 000000000..0eff824b2 --- /dev/null +++ b/doc/build/usage/cookbook/integration_with_the_gui.html @@ -0,0 +1,189 @@ + + + + + + + + Azimuthal integration using the graphical user interface — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

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+ +
+ +
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+ +
+

Azimuthal integration using the graphical user interface

+

Associated video

+
+

Look at your integrated patterns

+

PyFAI can perform 1D or 2D integration. +To view 1D patterns, I will use grace +Let’s look at the integrated patterns +obtained during calibration.

+

As you can see, only the 10 rings used for +calibration are well defined.

+

This file is a text file containing as header +all metadata needed to determine the geometry.

+

2D integrated pattern (aka cake images) +are EDF images. Try fabio_viewer to see them.

+

Once again check the header of the file and the +associated metadata.

+
+
+

Integrate a bunch of images

+

We will work with the 20 images used for the calibration.

+
+
+

Start pyFAI-intgrate

+

Either select files to process using the file-dialog or provide +them on the command line.

+
+
+

Set the geometry

+

Simply load the PONI file and check the populated fields.

+
+
+

Azimuthal integration options

+

Check the dark/flat/ ... options +Use the check-box to activate the option.

+

Do NOT forget to specify the number of radial bins !

+
+
+

Select the device for processing

+

Unless the processing will be done on the +CPU using OpenMP.

+

Press OK to start the processing.

+

The generation of the Look-Up table takes a few seconds +then all files get processed quickly

+
+
+

Run it again to perform caking

+

Same a previously ... but +provide a number of azimuthal bins !

+
+
+

Visualize the integrated patterns

+

Once again I used grace and fabio_viewer +to display the result.

+

That’s all.

+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

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+

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+

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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/tutorial/CCD_Calibration/CCD_calibration.html b/doc/build/usage/tutorial/CCD_Calibration/CCD_calibration.html new file mode 100644 index 000000000..fec03168e --- /dev/null +++ b/doc/build/usage/tutorial/CCD_Calibration/CCD_calibration.html @@ -0,0 +1,821 @@ + + + + + + + + CCD calibration — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

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+ +
+ +
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+
+ +
+

CCD calibration

+

This tutorial presents how to calibrate the distortion from a CCD camera +coupled with a taper of optic fibers. If your camera is already +calibrated using Fit2D and you have access to the corresponding spline +file, this tutorial is not for you: simply create your detector object +like this pyFAI.detectors.Detector(splineFile=”example.spline”) and +you are done. This tutorial uses the image of a regular grid on the +detector.

+

It uses a procedure described in: “Calibration and correction of spatial +distortions in 2D detector systems” from Hammersley, A. P.; Svensson, S. +O.; Thompson, A. published in Nuclear Instruments and Methods in Physics +Research Section A, Volume 346, Issue 1-2, p. 312-321. +DOI:10.1016/0168-9002(94)90720-X

+

The procedure is performed in 4 steps: 1. peak picking 2. grid +assignment 3. distortion fitting 4. interpolation of the fitted data 5. +saving into a detector definition file

+

The picture used is the one of a regular metallic grid of holes (spaced +by 5mm), just in front of the detector. We will assume holes are +circular what looks correct in first apploximation. Paralax error will +be ignored in a first time.

+
+

Peak picking

+

Lets start with peak picking, for this, we will use the FabIO library +able to read the image and matplotlib to display the image. The +disortion is assumed to be minimal in the middle of the detector, so we +first focus on one spot in the middle:

+
#For final rendering switch from nbagg -> inline
+#%pylab nbagg
+%pylab inline
+#
+# search for the image containing the grid
+
+
+
Populating the interactive namespace from numpy and matplotlib
+
+
+
ls
+
+
+
03jun102d.edf*    ccd2_g3.edf*     CCD_calibration.ipynb  grilHiCap_04jund.edf*
+ccd2_g1_chk.edf   ccd2_g6_1.edf    corrected.edf          testdetector.h5
+ccd2_g2.edf*      ccd2_g6_2.edf    flm-4-1.edf*
+ccd2_g2g.edf      ccd2_g6_3.edf    frelonID22_grid.edf
+ccd2_g3_chk.edf*  ccd2_g6_chk.edf  grid.edf*
+
+
+
fname = "frelonID22_grid.edf"
+#fname = "corrected.edf"
+import fabio
+img = fabio.open(fname).data
+
+
+
WARNING:xsdimage:lxml library is probably not part of your python installation: disabling xsdimage format
+
+
+
WARNING: Not using BLAS, PCA calculation will be slower
+WARNING: Not using BLAS/ATLAS, PCA calculation will be slower
+
+
+
imshow(img, interpolation="nearest", origin="lower")
+
+
+
<matplotlib.image.AxesImage at 0x7f0c4e70e588>
+
+
+../../../_images/output_4_1.png +
#Zoom into a spot in the middle of the image, where the distortion is expected to be minimal
+imshow(img[1060:1100,1040:1080], interpolation="nearest", origin="lower")
+
+
+
<matplotlib.image.AxesImage at 0x7f0c4eae6908>
+
+
+../../../_images/output_5_1.png +
#Look at the profile of the peak to measure the width (it is expected to be a crenel)
+plot(img[1060+25,1040:1060])
+
+
+
[<matplotlib.lines.Line2D at 0x7f0c4ec89c50>]
+
+
+../../../_images/output_6_1.png +

Let’s look at one spot, in the center of the image: it is circular and +is slightly larger than 10 pixels. We will define a convolution kernel +of size 11x11 of circular shape with sharp borders as this is what a +perfect spot is expected to look like. The kernel is normalized in such +a way it does not modify the average intensity of the image

+

Now convolve the image with this circular kernel using scipy.signal (in +direct space: the kernel is small and performance does not really matter +here).

+

It is important to have an odd size for the kernel for convolution as an +even shape would induce an offset of 1/2 pixel in the located +peak-position.

+
size = 11 #Odd of course
+center = (size-1)//2
+y, x = numpy.ogrid[-center:center+1,-center:center+1]
+r2 = x*x + y*y
+kernel = (r2<=(center+0.5)**2).astype(float)
+kernel /= kernel.sum()
+imshow(kernel, interpolation="nearest", origin="lower")
+
+
+
<matplotlib.image.AxesImage at 0x7f0c49f72828>
+
+
+../../../_images/output_8_1.png +
from scipy import ndimage, signal
+
+
+
cnv = signal.convolve2d(img, kernel, mode="same")
+
+
+
#Check that size is unchanged.
+print(img.shape)
+print(cnv.shape)
+
+
+
(2048, 2048)
+(2048, 2048)
+
+
+
#Check the image still looks the same. it is just supposed to be smoother.
+imshow(cnv, origin="lower", interpolation="nearest")
+
+
+
<matplotlib.image.AxesImage at 0x7f0c3cf93e80>
+
+
+../../../_images/output_12_1.png +
#Zoom into the very same spot to ensure it is smoother
+imshow(cnv[1060:1100,1040:1080], interpolation="nearest", origin="lower")
+
+
+
<matplotlib.image.AxesImage at 0x7f0c3cef3160>
+
+
+../../../_images/output_13_1.png +
# and here again the same profile:
+plot(cnv[1060+25,1030:1070])
+# the peak got broader (2x) but much smoother on the top: this is what we are interrested in.
+
+
+
[<matplotlib.lines.Line2D at 0x7f0c3cedc320>]
+
+
+../../../_images/output_14_1.png +

After convolution with a pattern of the same shape as the hole, the peak +center is located with a sub-pixel resolution. The peak has a full size +of 30 pix in 1 dimension.

+

All peak positions will be extracted using the pyFAI inverse watershed +algorithm. Once all regions are segmented, the ones too small are sieved +out and the remaining ones are classifies according to their peak +intensity using an histogram. As intensity vary a lot, this histogram it +is done on the log-scale of the intensity.

+
mini = (kernel>0).sum()
+print("Number of points in the kernel: %s"%mini)
+
+
+
Number of points in the kernel: 97
+
+
+
try: #depends if the version of pyFAI you are using
+    from pyFAI.watershed import InverseWatershed
+except:
+    from pyFAI.ext.watershed import InverseWatershed
+    #Version of pyFAI newer than feb 2016
+iw = InverseWatershed(cnv)
+iw.init()
+iw.merge_singleton()
+all_regions = set(iw.regions.values())
+regions = [i for i in all_regions if i.size>mini]
+
+print("Number of region segmented: %s"%len(all_regions))
+print("Number of large enough regions : %s"%len(regions))
+
+
+
WARNING:pyFAI.utils:Exception No module named 'fftw3': FFTw3 not available. Falling back on Scipy
+WARNING:pyFAI.timeit:init_labels took 2.123s
+WARNING:pyFAI.timeit:init_borders took 0.066s
+WARNING:pyFAI.timeit:init_regions took 0.646s
+WARNING:pyFAI.timeit:init_pass took 0.229s
+WARNING:pyFAI.timeit:merge_singleton took 0.054s
+
+
+
Number of region segmented: 79513
+Number of large enough regions : 8443
+
+
+
s = [i.maxi for i in regions]
+hist(numpy.log10(s), 20)
+#Look for the maximum value in each region to be able to segment accordingly
+
+
+
(array([  1.00000000e+00,   0.00000000e+00,   0.00000000e+00,
+          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,
+          0.00000000e+00,   0.00000000e+00,   0.00000000e+00,
+          1.00000000e+00,   2.85000000e+02,   5.99900000e+03,
+          1.71900000e+03,   1.00000000e+00,   0.00000000e+00,
+          1.00000000e+00,   3.00000000e+00,   2.30000000e+01,
+          1.17000000e+02,   2.93000000e+02]),
+ array([ 2.05537045,  2.1621182 ,  2.26886594,  2.37561369,  2.48236143,
+         2.58910918,  2.69585692,  2.80260467,  2.90935241,  3.01610016,
+         3.1228479 ,  3.22959565,  3.33634339,  3.44309114,  3.54983888,
+         3.65658663,  3.76333437,  3.87008212,  3.97682986,  4.08357761,
+         4.19032535]),
+ <a list of 20 Patch objects>)
+
+
+../../../_images/output_18_1.png +

There are clearly 3 groups of very different intensity, well segragated:

+
    +
  • around 10^2.1 (~125), those are the peaks where no tapper brings +light
    • +
  • around 10^3.4 (~2500), those are segmented region in the background
    • +
  • above 10^3.9 (~8000), those are actual peaks, we are looking for.
    • +
+

We retain all peaks >10^3.5

+
peaks = [(i.index//img.shape[-1], i.index%img.shape[-1]) for i in regions if (i.maxi)>10**3.5]
+print("Number of remaining peaks: %s"%len(peaks))
+
+
+
Number of remaining peaks: 438
+
+
+
imshow(img, interpolation="nearest", origin="lower")
+peaks_raw = numpy.array(peaks)
+plot(peaks_raw[:,1], peaks_raw[:, 0], "or")
+xlim(0,2048)
+ylim(0,2048)
+title("Extracted peak position (raw)")
+print("Raw peak coordinate:")
+print(peaks[:10])
+
+
+
Raw peak coordinate:
+[(1187, 654), (35, 155), (1082, 1447), (1082, 1545), (39, 62), (1083, 1250), (1083, 1349), (1943, 1925), (1084, 1151), (1190, 455)]
+
+
+../../../_images/output_21_1.png +
+
+

Precise peak extraction is performed using a second order tailor expansion

+
try:
+    from pyFAI.bilinear import Bilinear
+except:
+    from pyFAI.ext.bilinear import Bilinear
+bl = Bilinear(cnv)
+
+
+
ref_peaks = [bl.local_maxi(p) for p in peaks]
+imshow(img, interpolation="nearest", origin="lower")
+peaks_ref = numpy.array(ref_peaks)
+plot(peaks_raw[:,1], peaks_raw[:, 0], "or")
+plot(peaks_ref[:,1],peaks_ref[:, 0], "ob")
+xlim(0,2048)
+ylim(0,2048)
+title("Extracted peak position (red: raw, blue: refined)")
+print("Refined peak coordinate:")
+print(ref_peaks[:10])
+
+
+
Refined peak coordinate:
+[(1186.931446455419, 653.9433948583901), (34.4068917632103, 155.60163909196854), (1081.7659440785646, 1447.2154052853584), (1081.409073472023, 1545.5145089030266), (39.49280175566673, 62.08068184554577), (1083.1883269548416, 1249.924985870719), (1082.5653080940247, 1348.6598246097565), (1943.0012932673562, 1925.4793396294117), (1083.641106903553, 1150.8036927580833), (1189.607285887003, 455.1422745883465)]
+
+
+../../../_images/output_24_1.png +

At this stage, a visual inspection of the grid confirms all peaks have +been properly segmented. If this is not the case, one can adapt:

+
    +
  • the size of the kernel
    • +
  • the threshold comming out of the histogramming
    • +
+
+
+

Pair-wise distribution function

+

We will now select the (4-) first neighbours for every single peak. For +this we calculate the distance_matrix from any point to any other:

+
# Nota, pyFAI uses **C-coordinates** so they come out as (y,x) and not the usual (x,y).
+# This notation helps us to remind the order
+yx = numpy.array(ref_peaks)
+
+
+
# pairwise distance calculation using scipy.spatial.distance_matrix
+from scipy.spatial import distance_matrix
+dist = distance_matrix(peaks_ref, peaks_ref)
+
+
+

Let’s have a look at the pairwise distribution function for the first +neighbors

+
hist(dist.ravel(), 200, range=(0,200))
+title("Pair-wise distribution function")
+
+
+
<matplotlib.text.Text at 0x7f0bff0c5dd8>
+
+
+../../../_images/output_29_1.png +

This histogram provides us:

+
    +
  • At 0, the 438 peaks with 0-distance to themselves.
    • +
  • between 85 and 105 the first neighbours
    • +
  • between 125 and 150 the second neighbours.
    • +
  • ... and so on.
    • +
+

We now focus on the first neighbours which are all located between 70 +and 110 pixel appart.

+
#We define here a data-type for each peak (called center) with 4 neighbours (called north, east, south and west).
+point_type = np.dtype([('center_y', float), ('center_x', float),
+                        ('east_y', float), ('east_x', float),
+                        ('west_y', float), ('west_x', float),
+                        ('north_y', float), ('north_x', float),
+                        ('south_y', float), ('south_x', float)])
+
+neig = np.logical_and(dist>70.0, dist<110.0)
+valid = (neig.sum(axis=-1)==4).sum()
+print("There are %i control point with exactly 4 first neigbours"%valid)
+point = numpy.zeros(valid, point_type)
+# This initializes an empty structure to be populated
+
+
+
There are 359 control point with exactly 4 first neigbours
+
+
+
#Populate the structure: we use a loop as it loops only over 400 points
+h=-1
+for i, center in enumerate(peaks_ref):
+    if neig[i].sum()!=4: continue
+    h+=1
+    point[h]["center_y"],point[h]["center_x"] = center
+    for j in ((0,1),(0,-1),(1,0),(-1,0)):
+        tmp = []
+        for k in numpy.where(neig[i]):
+            curr = yx[k]
+            tmp.append(dot(curr-center,j))
+            l = argmax(tmp)
+            y, x = peaks_ref[numpy.where(neig[i])][l]
+            if j==(0,1):point[h]["east_y"], point[h]["east_x"] = y, x
+            elif j==(0,-1):point[h]["west_y"], point[h]["west_x"] = y, x
+            elif j==(1,0): point[h]["north_y"],point[h]["north_x"] = y, x
+            elif j==(-1,0):point[h]["south_y"],point[h]["south_x"] = y, x
+
+
+

We will need to define an origin but taking it on the border of the +image is looking for trouble as this is where distortions are likely to +be the most important. The center of the detector is an option but we +prefer to take the peak the nearest to the centroid of all other peaks.

+
#Select the initial guess for the center:
+
+#Most intense peak:
+#m = max([i for i in regions], key=lambda i:i.maxi)
+#Cx, Cy = m.index%img.shape[-1],m.index//img.shape[-1]
+#Cx, Cy = point["center_x"].mean(), point["center_y"].mean() #Centroid of all points
+Cx, Cy = 734, 1181 #beam center
+#Cx, Cy = tuple(i//2 for i in cnv.shape) #detector center
+print("The guessed center is at (%s, %s)"%(Cx, Cy))
+
+#Get the nearest point from centroid:
+d2 = ((point["center_x"]-Cx)**2+(point["center_y"]-Cy)**2)
+best = d2.argmin()
+Op = point[best]
+Ox, Oy = Op["center_x"], Op["center_y"]
+
+print("The center is at (%s, %s)"%(Ox, Oy))
+#Calculate the average vector along the 4 main axes
+Xx = (point[:]["east_x"] - point[:]["center_x"]).mean()
+Xy = (point[:]["east_y"] - point[:]["center_y"]).mean()
+Yx = (point[:]["north_x"] - point[:]["center_x"]).mean()
+Yy = (point[:]["north_y"] - point[:]["center_y"]).mean()
+
+print("The X vector is is at (%s, %s)"%(Xx, Xy))
+print("The Y vector is is at (%s, %s)"%(Yx, Yy))
+
+
+
The guessed center is at (734, 1181)
+The center is at (753.703500152, 1186.18798503)
+The X vector is is at (97.7197301826, -0.787977117653)
+The Y vector is is at (1.38218579497, 97.0826990758)
+
+
+
print("X has an angle of %s deg"%rad2deg(arctan2(Xy, Xx)))
+print("Y has an angle of %s deg"%rad2deg(arctan2(Yy, Yx)))
+print("The XY angle is %s deg"%rad2deg(arctan2(Yy, Yx)-arctan2(Xy, Xx)))
+
+
+
X has an angle of -0.462002756355 deg
+Y has an angle of 89.1843236418 deg
+The XY angle is 89.6463263982 deg
+
+
+
x = point[:]["center_x"] - Ox
+y = point[:]["center_y"] - Oy
+xy = numpy.vstack((x,y))
+R = numpy.array([[Xx,Yx],[Xy,Yy]])
+iR = numpy.linalg.inv(R)
+IJ = dot(iR,xy).T
+
+
+
Xmin = IJ[:,0].min()
+Xmax = IJ[:,0].max()
+Ymin = IJ[:,1].min()
+Ymax = IJ[:,1].max()
+print("Xmin/max",Xmin, Xmax)
+print("Ymin/max",Ymin,Ymax)
+print("Maximum error versus integrer: %s * pitch size (5mm)"%(abs(IJ-IJ.round()).max()))
+
+
+
Xmin/max -6.07394212848 12.060721056
+Ymin/max -11.0890545732 7.04060363671
+Maximum error versus integrer: 0.117211354675 * pitch size (5mm)
+
+
+

At this point it is important to check the correct rounding to integers: +The maximum error should definitely be better than 0.2*pitch ! If not, +try to change the origin (Cx and Cy). This criteria will be used for the +optimization later on.

+
plot(IJ[:,0],IJ[:,1],"or")
+idx = numpy.round(IJ).astype(int)
+plot(idx[:,0],IJ[:,1],"og")
+xlim(floor(Xmin), ceil(Xmax))
+ylim(floor(Ymin), ceil(Ymax))
+title("Red: measured peaks, Green: Expected position")
+
+
+
<matplotlib.text.Text at 0x7f0bff096390>
+
+
+../../../_images/output_39_1.png +
+

Estimation of the pixel size:

+

The pixel size is obtained from the pitch of the grid, in vectorial:

+

pitch^2 = (Px*Xx)^2 + (Py Xy)^2

+

pitch^2 = (Px*Yx)^2 + (Py Yy)^2

+
pitch = 5e-3 #mm distance between holes
+Py = pitch*sqrt((Yx**2-Xx**2)/((Xy*Yx)**2-(Xx*Yy)**2))
+Px = sqrt((pitch**2-(Xy*Py)**2)/Xx**2)
+print("Pixel size in average: x:%.3f micron, y: %.3f microns"%(Px*1e6, Py*1e6))
+
+
+
Pixel size in average: x:51.165 micron, y: 51.497 microns
+
+
+

At this stage, we have:

+
    +
  • A list of control points placed on a regular grid with a sub-pixel +precision
    • +
  • The center of the image, located on a control point
    • +
  • the average X and Y vector to go from one control point to another
    • +
+
+
+
+

Optimization of the pixel position

+

The optimization is obtained by minimizing the mis-placement of the +control points on the regular grid. For a larger coverage we include now +the peaks on the border with less than 4 neighbours.

+
#Measured peaks (all!), needs to flip x<->y
+peaks_m = numpy.empty_like(peaks_ref)
+peaks_m[:,1] = peaks_ref[:,0]
+peaks_m[:,0] = peaks_ref[:,1]
+
+#parameter set for optimization:
+P0 = [Ox, Oy, Xx, Yx, Xy, Yy]
+
+P = numpy.array(P0)
+
+def to_hole(P, pixels):
+    "Translate pixel -> hole"
+    T = numpy.atleast_2d(P[:2])
+    R = P[2:].reshape((2,2))
+
+    #Transformation matrix from pixel to holes:
+    hole = dot(numpy.linalg.inv(R), (pixels - T).T).T
+    return hole
+
+def to_pix(P, holes):
+    "Translate hole -> pixel"
+    T = numpy.atleast_2d(P[:2])
+    R = P[2:].reshape((2,2))
+    #Transformation from index points (holes) to pixel coordinates:
+    pix = dot(R,holes.T).T + T
+    return pix
+
+def error(P):
+    "Error function"
+    hole_float = to_hole(P, peaks_m)
+    hole_int = hole_float.round()
+    delta = hole_float-hole_int
+    delta2 = (delta**2).sum()
+    return delta2
+
+print("Total inital error ", error(P), P0)
+holes = to_hole(P, peaks_m)
+print("Maximum initial error versus integrer: %s * pitch size (5mm)"%(abs(holes-holes.round()).max()))
+from scipy.optimize import minimize
+res = minimize(error, P)
+print(res)
+print("total Final error ", error(res.x),res.x)
+holes = to_hole(res.x, peaks_m)
+print("Maximum final error versus integrer: %s * pitch size (5mm)"%(abs(holes-holes.round()).max()))
+
+
+
Total inital error  2.5995763607 [753.70350015163422, 1186.1879850327969, 97.719730182623479, 1.3821857949656571, -0.78797711765336542, 97.082699075794565]
+Maximum initial error versus integrer: 0.199838456433 * pitch size (5mm)
+      jac: array([ -1.49011612e-07,  -2.08616257e-07,  -2.68220901e-07,
+         1.19209290e-07,   1.19209290e-07,   3.87430191e-07])
+     nfev: 160
+      nit: 15
+  message: 'Optimization terminated successfully.'
+   status: 0
+     njev: 20
+  success: True
+        x: array([  7.53021133e+02,   1.18519692e+03,   9.81143527e+01,
+         1.47509463e+00,  -8.04478515e-01,   9.73166896e+01])
+      fun: 2.1237728421683415
+ hess_inv: array([[  1.42100651e+01,   5.92064797e-01,  -8.65311650e-01,
+          5.71616778e-01,  -2.93157223e-02,   3.36330120e-02],
+       [  5.92064797e-01,   1.42890834e+01,  -3.28993124e-02,
+          1.71968068e-02,  -8.57363816e-01,   5.83878215e-01],
+       [ -8.65311650e-01,  -3.28993124e-02,   2.99126185e-01,
+         -4.37220702e-03,   4.01788293e-03,  -2.74807907e-03],
+       [  5.71616778e-01,   1.71968068e-02,  -4.37220702e-03,
+          3.02142044e-01,  -1.06852612e-03,   2.67366996e-03],
+       [ -2.93157223e-02,  -8.57363816e-01,   4.01788293e-03,
+         -1.06852612e-03,   2.95805859e-01,  -4.27421580e-03],
+       [  3.36330120e-02,   5.83878215e-01,  -2.74807907e-03,
+          2.67366996e-03,  -4.27421580e-03,   2.97301584e-01]])
+total Final error  2.12377284217 [  7.53021133e+02   1.18519692e+03   9.81143527e+01   1.47509463e+00
+  -8.04478515e-01   9.73166896e+01]
+Maximum final error versus integrer: 0.234645010209 * pitch size (5mm)
+
+
+
clf()
+peaks_c = to_pix(res.x,to_hole(res.x,peaks_m).round())
+imshow(img, interpolation="nearest", origin="lower")
+plot(peaks_m[:,0],peaks_m[:, 1], "or")
+plot(peaks_c[:,0], peaks_c[:, 1], "og")
+xlim(0,2048)
+ylim(0,2048)
+title("Peak position: measured (red) and expected (Green)")
+
+
+
<matplotlib.text.Text at 0x7f0c04a54518>
+
+
+../../../_images/output_45_1.png +
pitch = 5e-3 #mm distance between holes
+Ox, Oy, Xx, Yx, Xy, Yy = res.x
+Py = pitch*sqrt((Yx**2-Xx**2)/((Xy*Yx)**2-(Xx*Yy)**2))
+Px = sqrt((pitch**2-(Xy*Py)**2)/Xx**2)
+print("Optimized pixel size in average: x:%.3f micron, y: %.3f microns"%(Px*1e6, Py*1e6))
+
+
+
Optimized pixel size in average: x:50.959 micron, y: 51.373 microns
+
+
+

Few comments:

+
    +
  • The maximum error grow during optimization without explainations
    • +
  • The outer part of the detector is the most distorted
    • +
+
+
+

Interpolation of the fitted data

+
+

Multivariate data interpolation (griddata)

+

Correction arrays are built slightly larger (+1) to be able to +manipulate corners instead of centers of pixels As coordinates are +needed as y,x (and not x,y) we use p instead of peaks_m

+
from scipy.interpolate import griddata
+grid_x, grid_y = np.mgrid[0:img.shape[0]+1, 0:img.shape[1]+1]
+delta = peaks_c - peaks_m
+#we use peaks_res instead of peaks_m to be in y,x coordinates, not x,y
+delta_x = griddata(peaks_ref, delta[:,0], (grid_x, grid_y), method='cubic')
+delta_y = griddata(peaks_ref, delta[:,1], (grid_x, grid_y), method='cubic')
+
+figure(figsize=(12,5))
+subplot(1,2,1)
+imshow(delta_x,origin="lower", interpolation="nearest")
+title(r"$\delta$ x")
+colorbar()
+subplot(1,2,2)
+imshow(delta_y, origin="lower", interpolation="nearest")
+title(r"$\delta$ y")
+colorbar()
+#Nota: the arrays are filled with "NaN" outside the convex Hull
+
+
+
<matplotlib.colorbar.Colorbar at 0x7f0bfd478240>
+
+
+../../../_images/output_48_1.png +
#From http://stackoverflow.com/questions/3662361/fill-in-missing-values-with-nearest-neighbour-in-python-numpy-masked-arrays
+def fill(data, invalid=None):
+    """
+    Replace the value of invalid 'data' cells (indicated by 'invalid')
+    by the value of the nearest valid data cell
+
+    Input:
+        data:    numpy array of any dimension
+        invalid: a binary array of same shape as 'data'. True cells set where data
+                 value should be replaced.
+                 If None (default), use: invalid  = np.isnan(data)
+
+    Output:
+        Return a filled array.
+    """
+
+    if invalid is None:
+        invalid = numpy.isnan(data)
+
+    ind = ndimage.distance_transform_edt(invalid, return_distances=False, return_indices=True)
+    return data[tuple(ind)]
+
+
+
figure(figsize=(12,5))
+subplot(1,2,1)
+imshow(fill(delta_x),origin="lower", interpolation="nearest")
+title(r"$\delta$ x")
+colorbar()
+subplot(1,2,2)
+imshow(fill(delta_y), origin="lower", interpolation="nearest")
+title(r"$\delta$ y")
+colorbar()
+
+
+
<matplotlib.colorbar.Colorbar at 0x7f0bfc1f8f60>
+
+
+../../../_images/output_50_1.png +

It is important to understand the extrapolation outside the convex hull +has no justification, it is there just to prevent numerical bugs.

+
+
+
+

Saving the distortion correction arrays to a detector

+
from pyFAI.detectors import Detector
+detector = Detector(Py,Px)
+detector.max_shape = detector.shape = img.shape
+detector.set_dx(fill(delta_x))
+detector.set_dy(fill(delta_y))
+detector.mask = numpy.isnan(delta_x).astype(numpy.int8)
+detector.save("testdetector.h5")
+
+
+
+
+

Validation of the distortion correction

+
from pyFAI.distortion import Distortion
+dis = Distortion(detector)
+cor = dis.correct(img)
+figure(figsize=(12,5))
+subplot(1,2,1)
+imshow(img, interpolation="nearest", origin="lower")
+title("Original")
+subplot(1,2,2)
+imshow(cor, origin="lower", interpolation="nearest")
+title("Corrected")
+fabio.edfimage.EdfImage(data=cor).save("corrected.edf")
+
+
+
WARNING:pyFAI.timeit:calc_pos took 0.747s
+WARNING:pyFAI.timeit:calc_size took 0.944s
+WARNING:pyFAI.timeit:calc_LUT took 2.907s
+
+
+../../../_images/output_54_1.png +
+
+
+

Conclusion

+

This procedure describes how to measure the detector +distortion and how to create a detector description file directly +useable in pyFAI. Only the region inside the convex hull of the grid +data-points is valid and the region of the detector which is not +calibrated has been masked out to preven accidental use of it.

+

The distortion corrected image can now be used to check how “good” the +calibration actually is. This file can be injected in the third cell, +and follow the same procedure (left as exercise). This gives a maximum +mis-placement of 0.003, the average error is then of 0.0006 and +correction-map exhibit a displacement of pixels in the range +/- 0.2 +pixels which is acceptable and validates the whole procedure.

+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Creation of a calibrant file

+

Next topic

+

pyFAI scripts manual

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/tutorial/Calibrant/index.html b/doc/build/usage/tutorial/Calibrant/index.html new file mode 100644 index 000000000..00df42e05 --- /dev/null +++ b/doc/build/usage/tutorial/Calibrant/index.html @@ -0,0 +1,312 @@ + + + + + + + + Selection of a calibrant — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Selection of a calibrant

+

In this tutorial we will see how to select a calibrant for a given +experimental setup.

+
+

Experimental setup

+

The experimental setup is a classical protein crystallography setup +with:

+
    +
  • Large Pilatus 6M detector on a translation table
    • +
  • The small and intense beam of ~50 microns in size has a wavelength of +1 Angstrom
    • +
  • The detector is in normal condition: orthogonal to the beam and +centered in the middle of the detector.
    • +
+

The scientist in charge of this beamline want to ensure all encoders are +working properly and needs to validate the setup for distances between +10cm and 80cm. He will buy reference material from NIST but he does not +know which calibrant is best suited for his setup. We will assume all +reference material sold by NIST are equally suitable for ray position +(no issue with grain size, ...).

+

The calibration works best in pyFAI if more than one Debye-Scherrer ring +is seen on the detector.

+
+
+

Define the detector

+
import pyFAI
+dete = pyFAI.detectors.Pilatus6M()
+print(dete)
+
+
+
Detector Pilatus6M   PixelSize= 1.720e-04, 1.720e-04 m
+
+
+
+
+

Select reference materials

+

The NIST sells different Standard Refrence +Materials, among +them Silicon (SRM640), Lanthanum hexaboride (SRM660), Alumina (SRM676) +and Ceria (SRM674) are commonly used. Many other exists: Cr203, TiO2, +Zn0, SiO2, ... Evaluating them is left as an exercise.

+
import pyFAI.calibrant
+print(pyFAI.calibrant.ALL_CALIBRANTS)
+
+
+
Calibrants available: Ni, CrOx, NaCl, Si_SRM640e, Si_SRM640d, Si_SRM640a, Si_SRM640c, alpha_Al2O3, Cr2O3, AgBh, Si_SRM640, CuO, PBBA, Si_SRM640b, quartz, C14H30O, cristobaltite, Si, LaB6, CeO2, LaB6_SRM660a, LaB6_SRM660b, LaB6_SRM660c, TiO2, ZnO, Al, Au
+
+
+

You may wonder where the names of the calibrant came from and how they +have been established.

+

The name of all calibrant available in your version of pyFAI can be +listed by just printing out the content of ALL_CALIBRANTS. New +calibrant may have been added in pyFAI in more recent releases, just +have a look at the developent web +page.

+

Most of those calibrant files, which contain the d-spacing in Angstrom +between Miller plans, have been prepared from the unit cell of the +compount, found in publication. This publication is referenced in the +header of the file. If one wishes to regenerate those files, the +pyFAI.calibrant.Cell class may be used for.

+

We will now focus on a subset of calibrant, instanciate them and put +them into a dictionnary. The Python construct used here is called +dict-comprehension and allows the creation and population of a +dictionnary in a single line.

+
cals = dict((name,pyFAI.calibrant.ALL_CALIBRANTS(name)) for name in ("Si", "LaB6", "CeO2", "alpha_Al2O3"))
+print(cals)
+
+
+
{'Si': Si Calibrant , 'alpha_Al2O3': alpha_Al2O3 Calibrant , 'LaB6': LaB6 Calibrant , 'CeO2': CeO2 Calibrant }
+
+
+

To be able to use those calibrants, one needs to define the wavelength +used, here 1 Angstrom.

+
wl = 1e-10
+for cal in cals.values():
+    cal.wavelength = wl
+print(cals)
+
+
+
{'Si': Si Calibrant at wavelength 1e-10, 'alpha_Al2O3': alpha_Al2O3 Calibrant at wavelength 1e-10, 'LaB6': LaB6 Calibrant at wavelength 1e-10, 'CeO2': CeO2 Calibrant at wavelength 1e-10}
+
+
+
+
+

Short distance images

+

The shortest the detector can come to the sample is about 10cm (to leave +space for the beamstop). We will generate images of diffraction at this +distance.

+

For the display of images we will use matplotlib inlined.

+
p1, p2, p3 = dete.calc_cartesian_positions()
+poni1 = p1.mean()
+poni2 = p2.mean()
+print("Detector center at %s, %s"%(poni1, poni2))
+ai_short = pyFAI.AzimuthalIntegrator(dist=0.1, poni1=poni1, poni2=poni2,detector=dete)
+print(ai_short)
+
+
+
Detector center at 0.217322000001, 0.211818
+Detector Pilatus6M   PixelSize= 1.720e-04, 1.720e-04 m
+SampleDetDist= 1.000000e-01m        PONI= 2.173220e-01, 2.118180e-01m       rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=1231.500, y=1263.500 pix      Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+
+
+
%pylab inline
+
+
+
Populating the interactive namespace from numpy and matplotlib
+
+
+
fig = figure(figsize=(10,10))
+for idx, key in enumerate(cals):
+    cal = cals[key]
+    subplot(2,2,idx+1)
+    img = cal.fake_calibration_image(ai_short)
+    imshow(img)
+    title(key)
+
+
+../../../_images/output_11_0.png +

As one can see, there are plenty of rings on the image: it should be +easy to calibrate. By moving further away the detector, the number of +rings will decrease.

+
+
+

Long distance images

+

To keep good calibration one should have at lease two rings for the +calibration. The longest distance from sample to the detector is 80cm.

+
ai_long = pyFAI.AzimuthalIntegrator(dist=0.8, poni1=poni1, poni2=poni2,detector=dete)
+print(ai_long)
+
+
+
Detector Pilatus6M   PixelSize= 1.720e-04, 1.720e-04 m
+SampleDetDist= 8.000000e-01m        PONI= 2.173220e-01, 2.118180e-01m       rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 800.000mm   Center: x=1231.500, y=1263.500 pix      Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+
+
+
fig = figure(figsize=(10,10))
+for idx, key in enumerate(cals):
+    cal = cals[key]
+    subplot(2,2,idx+1)
+    img = cal.fake_calibration_image(ai_long)
+    imshow(img)
+    title(key)
+
+
+../../../_images/output_14_0.png +

The most adapted calibrant is probably the LaB6 as 2 rings are still +visible at 80 cm from the detector.

+
+
+

Integration of the pattern for the two extreme cases

+

We can integrate the image for the two extreme cases:

+
lab6 = cals["LaB6"]
+ai_short.wavelength = ai_long.wavelength = wl
+
+fig = figure(figsize=(10,10))
+ax = subplot(2,2,1)
+img_short = lab6.fake_calibration_image(ai_short)
+ax.imshow(img_short)
+ax = subplot(2,2,2)
+ax.plot(*ai_short.integrate1d(img_short,1000))
+ax.set_xlabel("q nm^-1")
+ax = subplot(2,2,3)
+img_long = lab6.fake_calibration_image(ai_long)
+ax.imshow(img_long)
+ax = subplot(2,2,4)
+ax.plot(*ai_long.integrate1d(img_long,1000))
+ax.set_xlabel("q nm^-1")
+
+
+
<matplotlib.text.Text at 0x7f1b7da2de50>
+
+
+../../../_images/output_16_1.png +
+
+

Conclusion

+

The best calibrant in this case is probably LaB6.

+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Detector distortion corrections

+

Next topic

+

Multi-geometry azimuthal integration

+

This Page

+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/tutorial/Distortion/Distortion.html b/doc/build/usage/tutorial/Distortion/Distortion.html new file mode 100644 index 000000000..3eca43be5 --- /dev/null +++ b/doc/build/usage/tutorial/Distortion/Distortion.html @@ -0,0 +1,530 @@ + + + + + + + + Detector distortion corrections — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Detector distortion corrections

+

This tutorial shows how to correct images for spatial distortion. Some +tutorial examples rely on files available in +http://www.silx.org/pub/pyFAI/testimages/ and will be downloaded during +this tutorial. The requiered minimum version of pyFAI is 0.12.0 +(currently dev5)

+
+

Detector definitions

+

PyFAI features an impressive list of 55 detector definitions contributed +often by manufacturers and some other reverse engineerd by scientists. +Each of them is defined as an invividual class which contains a way to +calculate the mask (invalid pixels, gaps,…) and a method to calculate +the pixel positions in Cartesian coordinates.

+
import pyFAI
+all_detectors = list(set(pyFAI.detectors.ALL_DETECTORS.values()))
+#Sort detectors according to their name
+all_detectors.sort(key=lambda i:i.__name__)
+nb_det = len(all_detectors)
+print("Number of detectors registered:", nb_det)
+for i in all_detectors:
+    print(i())
+
+
+
WARNING:xsdimage:lxml library is probably not part of your python installation: disabling xsdimage format
+WARNING:pyFAI.utils:Exception No module named 'fftw3': FFTw3 not available. Falling back on Scipy
+
+
+
Number of detectors registered: 55
+Detector Quantum 210         Spline= None    PixelSize= 5.100e-05, 5.100e-05 m
+Detector Quantum 270         Spline= None    PixelSize= 6.480e-05, 6.480e-05 m
+Detector Quantum 315         Spline= None    PixelSize= 5.100e-05, 5.100e-05 m
+Detector Quantum 4   Spline= None    PixelSize= 8.200e-05, 8.200e-05 m
+Detector Aarhus      Spline= None    PixelSize= 2.500e-05, 2.500e-05 m
+Detector ApexII      PixelSize= 6.000e-05, 6.000e-05 m
+Detector aca1300     PixelSize= 3.750e-06, 3.750e-06 m
+Undefined detector
+Detector Dexela 2923         PixelSize= 7.500e-05, 7.500e-05 m
+Detector Eiger16M    PixelSize= 7.500e-05, 7.500e-05 m
+Detector Eiger1M     PixelSize= 7.500e-05, 7.500e-05 m
+Detector Eiger4M     PixelSize= 7.500e-05, 7.500e-05 m
+Detector Eiger9M     PixelSize= 7.500e-05, 7.500e-05 m
+Detector Fairchild   PixelSize= 1.500e-05, 1.500e-05 m
+Detector HF-130k     Spline= None    PixelSize= 1.500e-04, 1.500e-04 m
+Detector HF-1M       Spline= None    PixelSize= 1.500e-04, 1.500e-04 m
+Detector HF-262k     Spline= None    PixelSize= 1.500e-04, 1.500e-04 m
+Detector HF-2.4M     Spline= None    PixelSize= 1.500e-04, 1.500e-04 m
+Detector HF-4M       Spline= None    PixelSize= 1.500e-04, 1.500e-04 m
+Detector HF-9.4M     Spline= None    PixelSize= 1.500e-04, 1.500e-04 m
+Detector Imxpad S10  PixelSize= 1.300e-04, 1.300e-04 m
+Detector Imxpad S140         PixelSize= 1.300e-04, 1.300e-04 m
+Detector Imxpad S70  PixelSize= 1.300e-04, 1.300e-04 m
+Detector MAR 345     PixelSize= 1.000e-04, 1.000e-04 m
+Detector Pixium 4700 detector        PixelSize= 1.540e-04, 1.540e-04 m
+Detector Perkin detector     PixelSize= 2.000e-04, 2.000e-04 m
+Detector Pilatus100k         PixelSize= 1.720e-04, 1.720e-04 m
+Detector Pilatus1M   PixelSize= 1.720e-04, 1.720e-04 m
+Detector Pilatus200k         PixelSize= 1.720e-04, 1.720e-04 m
+Detector Pilatus2M   PixelSize= 1.720e-04, 1.720e-04 m
+Detector Pilatus300k         PixelSize= 1.720e-04, 1.720e-04 m
+Detector Pilatus300kw        PixelSize= 1.720e-04, 1.720e-04 m
+Detector Pilatus6M   PixelSize= 1.720e-04, 1.720e-04 m
+Detector PilatusCdTe1M       PixelSize= 1.720e-04, 1.720e-04 m
+Detector PilatusCdTe2M       PixelSize= 1.720e-04, 1.720e-04 m
+Detector PilatusCdTe300k     PixelSize= 1.720e-04, 1.720e-04 m
+Detector PilatusCdTe300kw    PixelSize= 1.720e-04, 1.720e-04 m
+Detector Rayonix     PixelSize= 3.200e-05, 3.200e-05 m
+Detector MAR133      PixelSize= 6.400e-05, 6.400e-05 m
+Detector Rayonix lx170       PixelSize= 4.427e-05, 4.427e-05 m
+Detector Rayonix lx255       PixelSize= 4.427e-05, 4.427e-05 m
+Detector Rayonix mx170       PixelSize= 4.427e-05, 4.427e-05 m
+Detector Rayonix mx225       PixelSize= 7.324e-05, 7.324e-05 m
+Detector Rayonix mx225hs     PixelSize= 7.813e-05, 7.813e-05 m
+Detector Rayonix mx300       PixelSize= 7.324e-05, 7.324e-05 m
+Detector Rayonix mx300hs     PixelSize= 7.813e-05, 7.813e-05 m
+Detector Rayonix mx325       PixelSize= 7.935e-05, 7.935e-05 m
+Detector Rayonix mx340hs     PixelSize= 8.854e-05, 8.854e-05 m
+Detector Rayonix mx425hs     PixelSize= 4.427e-05, 4.427e-05 m
+Detector MAR165      PixelSize= 3.950e-05, 3.950e-05 m
+Detector Rayonix sx200       PixelSize= 4.800e-05, 4.800e-05 m
+Detector Rayonix Sx30hs      PixelSize= 1.563e-05, 1.563e-05 m
+Detector Rayonix Sx85hs      PixelSize= 4.427e-05, 4.427e-05 m
+Detector Titan 2k x 2k       PixelSize= 6.000e-05, 6.000e-05 m
+Detector Xpad S540 flat      PixelSize= 1.300e-04, 1.300e-04 m
+
+
+
+
+

Defining a detector from a spline file

+

For optically coupled CCD detectors, the geometrical distortion is often +described by a two-dimensional cubic spline (as in FIT2D) which can be +imported into the relevant detector instance and used to calculate the +actual pixel position in space (and masked pixels).

+

At the ESRF, mainly FReLoN detectors [J.-C. Labiche, ESRF Newsletter 25, +41 (1996)] are used with spline files describing the distortion of the +fiber optic taper.

+

Let’s download such a file and create a detector from it.

+
import os, sys
+os.environ["http_proxy"] = "http://proxy.site.com:3128"
+def download(url):
+    """download the file given in URL and return its local path"""
+    if sys.version_info[0]<3:
+        from urllib2 import urlopen, ProxyHandler, build_opener
+    else:
+        from urllib.request import urlopen, ProxyHandler, build_opener
+    dictProxies = {}
+    if "http_proxy" in os.environ:
+        dictProxies['http'] = os.environ["http_proxy"]
+        dictProxies['https'] = os.environ["http_proxy"]
+    if "https_proxy" in os.environ:
+        dictProxies['https'] = os.environ["https_proxy"]
+    if dictProxies:
+        proxy_handler = ProxyHandler(dictProxies)
+        opener = build_opener(proxy_handler).open
+    else:
+        opener = urlopen
+    target = os.path.split(url)[-1]
+    with open(target,"wb") as dest, opener(url) as src:
+        dest.write(src.read())
+    return target
+
+spline_file = download("http://www.silx.org/pub/pyFAI/testimages/halfccd.spline")
+
+
+
hd = pyFAI.detectors.FReLoN(splineFile=spline_file)
+print(hd)
+print("Shape: %i, %i"% hd.shape)
+
+
+
Detector FReLoN      Spline= /users/kieffer/workspace-400/pyFAI/doc/source/usage/tutorial/Distortion/halfccd.spline  PixelSize= 4.842e-05, 4.684e-05 m
+Shape: 1025, 2048
+
+
+

Note the unusual shape of this detector. This is probably a human +error when calibrating the detector distortion in FIT2D.

+
+

Visualizing the mask

+

Every detector object contains a mask attribute, defining pixels which +are invalid. For FReLoN detector (a spline-files-defined detectors), all +pixels having an offset such that the pixel falls out of the initial +detector are considered as invalid.

+

Masked pixel have non-null values can be displayed like this:

+
%pylab inline
+imshow(hd.mask, origin="lower", interpolation="nearest")
+
+
+
Populating the interactive namespace from numpy and matplotlib
+
+
+
<matplotlib.image.AxesImage at 0x7f2304a8cd68>
+
+
+../../../_images/output_6_2.png +
+
+
+

Detector definition files as NeXus files

+

Any detector object in pyFAI can be saved into an HDF5 file following +the NeXus convention [Könnecke et al., 2015, J. Appl. Cryst. 48, +301-305.]. Detector objects can subsequently be restored from disk, +making complex detector definitions less error prone.

+
h5_file = "halfccd.h5"
+hd.save(h5_file)
+new_det = pyFAI.detector_factory(h5_file)
+print(new_det)
+print("Mask is the same: ", numpy.allclose(new_det.mask, hd.mask))
+print("Pixel positions are the same: ", numpy.allclose(new_det.get_pixel_corners(), hd.get_pixel_corners()))
+print("Number of masked pixels", new_det.mask.sum())
+
+
+
FReLoN detector from NeXus file: halfccd.h5  PixelSize= 4.842e-05, 4.684e-05 m
+Mask is the same:  True
+Pixel positions are the same:  True
+Number of masked pixels 34382
+
+
+

Pixels of an area detector are saved as a four-dimensional dataset: i.e. +a two-dimensional array of vertices pointing to every corner of each +pixel, generating an array of dimension (Ny, Nx, Nc, 3), where Nx and Ny +are the dimensions of the detector, Nc is the number of corners of each +pixel, usually four, and the last entry contains the coordinates of the +vertex itself (in the order: Z, Y, X).

+

This kind of definition, while relying on large description files, can +address some of the most complex detector layouts. They will be +presented a bit later in this tutorial.

+
print("Size of Spline-file:", os.stat('halfccd.spline').st_size)
+print("Size of Nexus-file:", os.stat('halfccd.h5').st_size)
+
+
+
Size of Spline-file: 1183
+Size of Nexus-file: 21451707
+
+
+

The HDF5 file is indeed much larger than the spline file.

+
+
+

Modify a detector and saving

+

One may want to define a new mask (or flat-field) for its detector and +save the mask with the detector definition. Here, we create a copy of +the detector and reset its mask to enable all pixels in the detector and +save the new detector instance into another file.

+
import copy
+nomask_file = "nomask.h5"
+nomask = copy.deepcopy(new_det)
+nomask.mask = numpy.zeros_like(new_det.mask)
+nomask.save(nomask_file)
+nomask = pyFAI.detector_factory("nomask.h5")
+print("No pixels are masked",nomask.mask.sum())
+
+
+
No pixels are masked 0
+
+
+

Wrap up

+

In this section we have seen how detectors are defined in pyFAI, how +they can be created, either from the list of the parametrized ones, or +from spline files, or from NeXus detector files. We have also seen how +to save and subsequently restore a detector instance, preserving the +modifications made.

+
+
+

Distortion correction

+

Once the position of every single pixel in space is known, one can +benefit from the regridding engine of pyFAI adapted to image distortion +correction tasks. The pyFAI.distortion.Distortion class is the +equivalent of the pyFAI.AzimuthalIntegrator for distortion. Provided +with a detector definition, it enables the correction of a set of images +by using the same kind of look-up tables as for azimuthal integration.

+
from pyFAI.distortion import Distortion
+dis = Distortion(nomask)
+print(dis)
+
+
+
Distortion correction lut on device None for detector shape (1025, 2048):
+NexusDetector detector from NeXus file: nomask.h5    PixelSize= 4.842e-05, 4.684e-05 m
+
+
+
+

FReLoN detector

+

First load the image to be corrected, then correct it for geometric +distortion.

+
halfccd_img = download("http://www.silx.org/pub/pyFAI/testimages/halfccd.edf")
+import fabio
+raw = fabio.open(halfccd_img).data
+cor = dis.correct(raw)
+
+#Then display images side by side
+numpy.seterr(divide="ignore") #remove warning messages from numpy
+figure(figsize=(12,6))
+subplot(1,2,1)
+imshow(numpy.log(raw), interpolation="nearest", origin="lower")
+subplot(1,2,2)
+imshow(numpy.log(cor), interpolation="nearest", origin="lower")
+
+
+
ERROR:pyFAI.distortion:The image shape ((1024, 2048)) is not the same as the detector ((1025, 2048)). Adapting shape ...
+
+
+
<matplotlib.image.AxesImage at 0x7f22fb6d48d0>
+
+
+../../../_images/output_17_2.png +

Nota: in this case the image size (1024 lines) does not match the +detector’s number of lines (1025) hence pyFAI complains about it. Here, +pyFAI patched the image on an empty image of the right size so that the +processing can occur.

+

In this example, the size of the pixels and the shape of the detector +are preserved, discarding all pixels falling outside the detector’s +grid.

+

One may want all pixels’ intensity to be preserved in the +transformation. By allowing the output array to be large enough to +accomodate all pixels, the total intensity can be kept. For this, just +enable the “resize” option in the constructor of Distortion:

+
dis1 = Distortion(hd, resize=True)
+print(dis1)
+cor = dis1.correct(raw)
+print(dis1)
+print("After correction, the image has a different shape", cor.shape)
+
+
+
ERROR:pyFAI.distortion:The image shape ((1024, 2048)) is not the same as the detector ((1025, 2048)). Adapting shape ...
+
+
+
Distortion correction lut on device None for detector shape None:
+Detector FReLoN      Spline= /users/kieffer/workspace-400/pyFAI/doc/source/usage/tutorial/Distortion/halfccd.spline  PixelSize= 4.842e-05, 4.684e-05 m
+Distortion correction lut on device None for detector shape (1045, 2052):
+Detector FReLoN      Spline= /users/kieffer/workspace-400/pyFAI/doc/source/usage/tutorial/Distortion/halfccd.spline  PixelSize= 4.842e-05, 4.684e-05 m
+After correction, the image has a different shape (1045, 2052)
+
+
+
+
+

Example of Pixel-detectors:

+
+

XPad Flat detector

+

There is a striking example in the cover image of this article: +http://scripts.iucr.org/cgi-bin/paper?S1600576715004306 where a detector +made of multiple modules is eating up some rings. The first example +will be about the regeneration of an “eyes friendly” version of this +image.

+
xpad_url = "http://www.silx.org/pub/pyFAI/testimages/LaB6_18.57keV_frame_13.edf"
+xpad_file = download(xpad_url)
+xpad = pyFAI.detector_factory("Xpad_flat")
+print(xpad)
+xpad_dis = Distortion(xpad, resize=True)
+
+raw = fabio.open(xpad_file).data
+cor = xpad_dis.correct(raw)
+print("Shape as input and output:", raw.shape, cor.shape)
+
+#then display images side by side
+figure(figsize=(12,10))
+subplot(1,2,1)
+imshow(numpy.log(raw), interpolation="nearest", origin="lower")
+subplot(1,2,2)
+imshow(numpy.log(cor), interpolation="nearest", origin="lower")
+
+print("Conservation of the total intensity:", raw.sum(), cor.sum())
+
+
+
Detector Xpad S540 flat      PixelSize= 1.300e-04, 1.300e-04 m
+Shape as input and output: (960, 560) (1174, 578)
+Conservation of the total intensity: 11120798 1.11208e+07
+
+
+../../../_images/output_21_11.png +
+
+

WOS XPad detector

+

This is a new WAXS opened for SAXS pixel detector from ImXPad +(available at ESRF-BM02/D2AM CRG beamline). It looks like two of +XPad_flat detectors side by side with some modules shifted in order +to create a hole to accomodate a flight-tube which gathers the SAXS +photons to a second detector further away.

+

The detector definition for this specific detector has directly been put +down using the metrology informations from the manufacturer and saved as +a NeXus detector definition file.

+
wos_det = download("http://www.silx.org/pub/pyFAI/testimages/WOS.h5")
+wos_img = download("http://www.silx.org/pub/pyFAI/testimages/WOS.edf")
+wos = pyFAI.detector_factory(wos_det)
+print(wos)
+wos_dis = Distortion(wos, resize=True)
+
+raw = fabio.open(wos_img).data
+cor = wos_dis.correct(raw)
+print("Shape as input and output:", raw.shape, cor.shape)
+
+#then display images side by side
+figure(figsize=(12,10))
+subplot(1,2,1)
+imshow(numpy.log(raw), interpolation="nearest", origin="lower")
+subplot(1,2,2)
+imshow(numpy.log(cor), interpolation="nearest", origin="lower")
+
+print("Conservation of the total intensity:", raw.sum(), cor.sum())
+
+
+
NexusDetector detector from NeXus file: WOS.h5       PixelSize= 1.300e-04, 1.300e-04 m
+Shape as input and output: (598, 1154) (710, 1302)
+Conservation of the total intensity: 444356428 4.44363e+08
+
+
+
/scisoft/users/jupyter/jupy34/lib/python3.4/site-packages/ipykernel/__main__.py:14: RuntimeWarning: invalid value encountered in log
+
+
+../../../_images/output_23_2.png +

Nota: Do not use this detector definition file to process data from +the WOS@D2AM as it has not (yet) been fully validated and may contain +some errors in the pixel positioning.

+
+
+
+
+

Conclusion

+

PyFAI provides a very comprehensive list of detector definitions, is +versatile enough to address most area detectors on the market, and +features a powerful regridding engine, both combined together into the +distortion correction tool which ensures the conservation of the signal +during the transformation (the number of photons counted is preserved +during the transformation)

+

Distortion correction should not be used for pre-processing images prior +to azimuthal integration as it re-bins the image, thus induces a +broadening of the peaks. The AzimuthalIntegrator object performs all +this together with integration, it has hence a better precision.

+

This tutorial did not answer the question how to calibrate the +distortion of a given detector ? which is addressed in another tutorial +called detector calibration.

+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Introduction to the tutorials

+

Next topic

+

Selection of a calibrant

+

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+ + + +
+
+
+
+
+

Navigation

+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/tutorial/Introduction/introduction.html b/doc/build/usage/tutorial/Introduction/introduction.html new file mode 100644 index 000000000..de757cf8b --- /dev/null +++ b/doc/build/usage/tutorial/Introduction/introduction.html @@ -0,0 +1,525 @@ + + + + + + + + Introduction to the tutorials — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Introduction to the tutorials

+
+

The iPython/Jupyter notebook

+

The document you are seeing it may be a PDF or a web page or another +format, but what is important is how it has been made ...

+

According to this article in +Nature +the Notebook, invented by iPython, and now part of the Jupyter project, +is the revolution for data analysis which will allow reproducable +science.

+

This tutorial will introduce you to how to access to the notebook, how +to use it and perform some basic data analysis with it and the pyFAI +library.

+
+
+

Getting access to the notebook

+

There are many cases ...

+
+

Inside ESRF

+

The simplest case as the data analysis unit offers you a notebook +server: just connect your web browser to http://scisoft13:8000 and +authenticate with your ESRF credentials.

+
+
+

Outside ESRF with an ESRF account.

+

The JupyterHub server is not directly available on the internet, but you +can +login into the firewall<http://www.esrf.eu/Infrastructure/Computing/Networks/InternetAndTheFirewall/UsersManual/SSH>_ +to forward the web server:

+

ssh -XC -p5022 -L8000:scisoft13:8000 user@firewall.esrf.fr

+

Once logged in ESRF, keep the terminal opened and browse on your local +computer to http://localhost:8000 to authenticate with your ESRF +credentials. Do not worry about confidentiality as the connection from +your computer to the ESRF firewall is encrypted by the SSH connection.

+
+
+

Other cases

+

In the most general case you will need to install the notebook on your +local computer in addition to pyFAI and FabIO to follow the tutorial. +WinPython provides it under windows. Please refer to the installation +procedure of pyFAI to install locally pyFAI on your computer

+
+
+
+

Getting trained in using the notebook

+

There are plenty of good tutorials on how to use the notebook. This +one +presents a quick overview of the Python programming language and +explains how to use the notebook. Reading it is strongly encouraged +before proceeding to the pyFAI itself.

+

Anyway, the most important information is to use Control-Enter to +evaluate a cell.

+

In addition to this, we will need to download some files from the +internet. The following cell contains a piece of code to download files. +You do not need to understand what it does, but you may have to adjust +the proxy settings to be able to connect to internet, especially at +ESRF.

+
import os, sys
+os.environ["http_proxy"] = "http://proxy.site.com:3128"
+
+def download(url):
+    """download the file given in URL and return its local path"""
+    if sys.version_info[0]<3:
+        from urllib2 import urlopen, ProxyHandler, build_opener
+    else:
+        from urllib.request import urlopen, ProxyHandler, build_opener
+    dictProxies = {}
+    if "http_proxy" in os.environ:
+        dictProxies['http'] = os.environ["http_proxy"]
+        dictProxies['https'] = os.environ["http_proxy"]
+    if "https_proxy" in os.environ:
+        dictProxies['https'] = os.environ["https_proxy"]
+    if dictProxies:
+        proxy_handler = ProxyHandler(dictProxies)
+        opener = build_opener(proxy_handler).open
+    else:
+        opener = urlopen
+    target = os.path.split(url)[-1]
+    with open(target,"wb") as dest, opener(url) as src:
+        dest.write(src.read())
+    return target
+
+
+
+
+

Introduction to diffraction image analysis using the notebook

+

All the tutorials in pyFAI are based on the notebook and if you wish to +practice the exercises, you can download the notebook files (.ipynb) +from +Github

+
+

Load and display diffraction images

+

First of all we will download an image and display it. Displaying it the +right way is important as the orientation of the image imposes the +azimuthal angle sign.

+
#initializes the visualization module
+%pylab inline
+
+
+
Populating the interactive namespace from numpy and matplotlib
+
+
+
moke = download("http://www.silx.org/pub/pyFAI/testimages/moke.tif")
+print(moke)
+
+
+
moke.tif
+
+
+

The moke.tif image we just downloaded is not a real diffraction image +but it is a test pattern used in the tests of pyFAI.

+

Prior to displaying it, we will use the Fable Input/Output library to +read the content of the file:

+
import fabio
+img = fabio.open(moke).data
+imshow(img, origin="lower", cmap="gray")
+
+
+
<matplotlib.image.AxesImage at 0x7fd7aaa975c0>
+
+
+../../../_images/output_6_11.png +

As you can see, the image looks like an archery target. The option +origin=”lower” of imshow allows to display the image with the origin +at the lower left of the image.

+

Displaying the image without this option ends with having the azimuthal +angle (which angles are displayed in degrees on the image) to turn +clockwise, so the inverse of the trigonometric order.

+
imshow(img, cmap="gray")
+
+
+
<matplotlib.image.AxesImage at 0x7fd7aaa70b00>
+
+
+../../../_images/output_8_11.png +

Nota: Displaying the image properly or not does not change the +content of the image or its representation in memory, it only changes +its representation, which is important only for the user. DO NOT USE +numpy.flipud or other array-manipulation which changes the memory +representation of the image. This is likely to mess-up all your +subsequent calculation.

+
+
+

1D azimuthal integration

+

To perform an azimuthal integration of this image, we need to create an +AzimuthalIntegrator object we will call ai. Fortunately, the +geometry is explained on the image.

+
import pyFAI
+ai = pyFAI.AzimuthalIntegrator(dist=0.1, pixel1=1e-4, pixel2=1e-4)
+print(ai)
+
+
+
Detector Detector    Spline= None    PixelSize= 1.000e-04, 1.000e-04 m
+SampleDetDist= 1.000000e-01m        PONI= 0.000000e+00, 0.000000e+00m       rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=0.000, y=0.000 pix    Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+
+
+

Printing the ai object displays 3 lines:

+
    +
  • The detector definition, here a simple detector with square, regular +pixels with the right size
    • +
  • The detector position in space using the pyFAI coordinate system
    • +
  • The detector position in space using the FIT2D coordinate system
    • +
+

Right now, the geometry in the ai object is wrong. It may be easier to +define it correctly using the FIT2D geometry which uses pixels for the +center coordinates (but the sample-detector distance is in millimeters).

+
help(ai.setFit2D)
+
+
+
Help on method setFit2D in module pyFAI.geometry:
+
+setFit2D(directDist, centerX, centerY, tilt=0.0, tiltPlanRotation=0.0, pixelX=None, pixelY=None, splineFile=None) method of pyFAI.azimuthalIntegrator.AzimuthalIntegrator instance
+    Set the Fit2D-like parameter set: For geometry description see
+    HPR 1996 (14) pp-240
+
+    Warning: Fit2D flips automatically images depending on their file-format.
+    By reverse engineering we noticed this behavour for Tiff and Mar345 images (at least).
+    To obtaine correct result you will have to flip images using numpy.flipud.
+
+    @param direct: direct distance from sample to detector along the incident beam (in millimeter as in fit2d)
+    @param tilt: tilt in degrees
+    @param tiltPlanRotation: Rotation (in degrees) of the tilt plan arround the Z-detector axis
+            * 0deg -> Y does not move, +X goes to Z<0
+            * 90deg -> X does not move, +Y goes to Z<0
+            * 180deg -> Y does not move, +X goes to Z>0
+            * 270deg -> X does not move, +Y goes to Z>0
+
+    @param pixelX,pixelY: as in fit2d they ar given in micron, not in meter
+    @param centerX, centerY: pixel position of the beam center
+    @param splineFile: name of the file containing the spline
+
+
+
ai.setFit2D(100, 300, 300)
+print(ai)
+
+
+
Detector Detector    Spline= None    PixelSize= 1.000e-04, 1.000e-04 m
+SampleDetDist= 1.000000e-01m        PONI= 3.000000e-02, 3.000000e-02m       rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=300.000, y=300.000 pix        Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+
+
+

With the ai object properly setup, we can perform the azimuthal +integration using the intergate1d method. This methods takes only 2 +mandatory parameters: the image to integrate and the number of bins. We +will provide a few other to enforce the calculations to be performed in +2theta-space and in degrees:

+
tth, I = ai.integrate1d(img, 300, unit="2th_deg")
+plot(tth, I, label="moke")
+xlabel("2theta (deg)")
+
+
+
<matplotlib.text.Text at 0x7fd7aab3e908>
+
+
+../../../_images/output_15_1.png +

As you can see, the 9 rings gave 9 sharp peaks at 2theta position +regularly ranging from 4 to 12 degrees as expected from the image +annotation.

+

Nota: the default unit is “q_nm^1”, so the scattering vector length +expressed in inverse nanometers. To be able to calculate q, one needs +to specify the wavelength used (here we didn’t). For example: +ai.wavelength = 1e-10

+

To save the content of the integrated pattern into a 2 column ASCII +file, one can either save the (tth, I) arrays, or directly ask pyFAI to +do it by providing an output filename:

+
ai.integrate1d(img, 30, unit="2th_deg", filename="moke.dat")
+!cat moke.dat
+
+
+
# == pyFAI calibration ==
+# SplineFile: None
+# PixelSize: 1.000e-04, 1.000e-04 m
+# PONI: 3.000e-02, 3.000e-02 m
+# Distance Sample to Detector: 0.1 m
+# Rotations: 0.000000 0.000000 0.000000 rad
+#
+# == Fit2d calibration ==
+# Distance Sample-beamCenter: 100.000 mm
+# Center: x=300.000, y=300.000 pix
+# Tilt: 0.000 deg  TiltPlanRot: 0.000 deg
+#
+# Polarization factor: None
+# Normalization factor: 1.0
+# --> moke.dat
+#       2th_deg             I
+  3.831631e-01    6.384597e+00
+  1.149489e+00    1.240657e+01
+  1.915815e+00    1.222277e+01
+  2.682141e+00    1.170348e+01
+  3.448468e+00    9.964798e+00
+  4.214794e+00    8.913503e+00
+  4.981120e+00    9.104074e+00
+  5.747446e+00    9.242975e+00
+  6.513772e+00    6.136262e+00
+  7.280098e+00    9.039030e+00
+  8.046424e+00    9.203415e+00
+  8.812750e+00    9.324570e+00
+  9.579076e+00    6.470130e+00
+  1.034540e+01    7.790757e+00
+  1.111173e+01    9.410036e+00
+  1.187805e+01    9.464832e+00
+  1.264438e+01    7.749060e+00
+  1.341071e+01    1.151200e+01
+  1.417703e+01    1.324891e+01
+  1.494336e+01    1.038730e+01
+  1.570969e+01    1.069764e+01
+  1.647601e+01    1.056094e+01
+  1.724234e+01    1.286720e+01
+  1.800866e+01    1.323239e+01
+  1.877499e+01    1.548398e+01
+  1.954132e+01    2.364553e+01
+  2.030764e+01    2.537154e+01
+  2.107397e+01    2.512984e+01
+  2.184029e+01    2.191267e+01
+  2.260662e+01    7.605135e+00
+
+
+

Here the excalmation mark indicates the notebook to call the cat +command from UNIX to print the content of the file. This “moke.dat” file +contains in addition to the 2th/I value, a header commented with “#” +with the geometry used to perform the calculation.

+

**Nota: ** The ai object has initialized the geometry on the first +call and re-uses it on subsequent calls. This is why it is important to +re-use the geometry in performance critical applications.

+
+
+

2D integration or Caking

+

One can perform the 2D integration which is called caking in FIT2D by +simply calling the intrgate2d method with 3 mandatroy parameters: the +data to integrate, the number of radial bins and the number of azimuthal +bins.

+
I, tth, chi = ai.integrate2d(img, 300, 360, unit="2th_deg")
+imshow(I, origin="lower", extent=[tth.min(), tth.max(), chi.min(), chi.max()], aspect="auto")
+xlabel("2 theta (deg)")
+ylabel("Azimuthal angle chi (deg)")
+
+
+
<matplotlib.text.Text at 0x7fd7aaa0aa58>
+
+
+../../../_images/output_19_1.png +

The displayed image presents the “caked” image with the radial and +azimuthal angles properly set on the axes. Search for the -180, -90, +360/0 and 180 mark on the transformed image.

+

Like integrate1d, integrate2d offers the ability to save the +intgrated image into an image file (EDF format by default) with again +all metadata in the headers.

+
+
+

Radial integration

+

Radial integration can directly be obtained from Caked images:

+
target = 8 #degrees
+#work on fewer radial bins in order to have an actual averaging:
+I, tth, chi = ai.integrate2d(img, 100, 90, unit="2th_deg")
+column = argmin(abs(tth-target))
+print("Column number %s"%column)
+plot(chi, I[:,column])
+xlabel("Azimuthal angle")
+
+
+
Column number 34
+
+
+
<matplotlib.text.Text at 0x7fd7a833dcc0>
+
+
+../../../_images/output_21_2.png +

Nota: the pattern with higher noise along the diagonals is typical +from the pixel splitting scheme employed. Here this scheme is a +“bounding box” which makes digonal pixels look a bit larger (+40%) than +the ones on the horizontal and vertical axis, explaining the variation +of the noise.

+
+
+

Integration of a bunch of files using pyFAI

+

Once the processing for one file is established, one can loop over a +bunch of files. A convienient way to get the list of files matching a +pattern is with the glob module.

+

Most of the time, the azimuthal integrator is obtained by simply loading +the poni-file into pyFAI and use it directly.

+
import glob
+all_files = glob.glob("al2o*.edf.bz2")
+all_files.sort()
+print(len(all_files))
+
+
+
51
+
+
+
ai = pyFAI.load("al2o3_00_max_51_frames.poni")
+print(ai)
+
+
+
Detector Detector    Spline= /users/kieffer/workspace-400/pyFAI/doc/source/usage/tutorial/Introduction/distorsion_2x2.spline         PixelSize= 1.034e-04, 1.025e-04 m
+Wavelength= 7.084811e-11m
+SampleDetDist= 1.168599e-01m        PONI= 5.295653e-02, 5.473342e-02m       rot1=0.015821  rot2= 0.009404  rot3= 0.000000 rad
+DirectBeamDist= 116.880mm   Center: x=515.795, y=522.995 pix        Tilt=1.055 deg  tiltPlanRotation= 149.271 deg
+
+
+
%%time
+for one_file in all_files:
+    destination = os.path.splitext(one_file)[0]+".dat"
+    image = fabio.open(one_file).data
+    ai.integrate1d(image, 1000, filename=destination)
+
+
+
CPU times: user 35.6 s, sys: 272 ms, total: 35.8 s
+Wall time: 22 s
+
+
+

This was a simple integration of 50 files, saving the result into 2 +column ASCII files.

+
+
+
+

Conclusion

+

Using the notebook is rather simple as it allows to mix comments, code, +and images for visualization of scientific data.

+

The basic use pyFAI’s AzimuthalIntgrator has also been presented and may +be adapted to you specific needs.

+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Tutorials

+

Next topic

+

Detector distortion corrections

+

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+ + + +
+
+
+
+
+

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+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/tutorial/MakeCalibrant/make_calibrant.html b/doc/build/usage/tutorial/MakeCalibrant/make_calibrant.html new file mode 100644 index 000000000..5b43d132a --- /dev/null +++ b/doc/build/usage/tutorial/MakeCalibrant/make_calibrant.html @@ -0,0 +1,425 @@ + + + + + + + + Creation of a calibrant file — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Creation of a calibrant file

+

In this tutorial we will see how to generate a file describing a +reference material used as calibrant to refine the geometry of the +experimental setup, especially the position of the detector.

+

In pyFAI, calibrant are defined in a bunch of files available from +github. Those +files are automatically installed with pyFAI and readily available from +the programming interface, as described in the Calibrant tutorial.

+

This tutorials focuses on the content of the file and how to generate +other calibrant files and how to exended existing ones.

+
+

Content of the file

+

Calibrant files from pyFAI are heavily inspired from the ones used in +Fit2D: they simply store the d-spacing between Miller plans and are +simply loaded using the numpy.loadtxt function. In pyFAI we have +improved with plent of comment (using #) to provide in addition the +familly of Miller plan associated and the multiplicity, which, while not +proportionnal to Fobs is somewhat related.

+

One may think it would have been better/simpler to describe the cell of +the material. Actually some calibrant, especially the ones used in SAXS +like silver behenate (AgBh), but not only, are not well crystallized +compounds and providing the d-spacing allows to use them as calibrant. +Neverthless this tutorial will be focused on how to generate calibrant +files fron crystal structures.

+

Finally the calibrant file has a few lines of headers containing the +name, the crystal cell parameters (if available) and quite importantly a +reference to the source of information like a publication to allow the +re-generation of the file if needed.

+
# Calibrant: Silver Behenate (AgBh)
+# Pseudocrystal a=inf b=inf c=58.380
+# Ref: doi:10.1107/S0021889899012388
+5.83800000e+01 # (0,0,1)
+2.91900000e+01 # (0,0,2)
+1.94600000e+01 # (0,0,3)
+1.45950000e+01 # (0,0,4)
+1.16760000e+01 # (0,0,5)
+9.73000000e+00 # (0,0,6)
+8.34000000e+00 # (0,0,7)
+7.29750000e+00 # (0,0,8)
+6.48666667e+00 # (0,0,9)
+(...)
+
+
+
+
+

The Cell class

+

To generate a calibrant file from a crystal structure, the easiest is +to use the pyFAI.calibrant.Cell class.

+
from pyFAI.calibrant import Cell
+print(Cell.__doc__)
+print(Cell.__init__.__doc__)
+
+
+
    This is a cell object, able to calculate the volume and d-spacing according to formula from:
+
+    http://geoweb3.princeton.edu/research/MineralPhy/xtalgeometry.pdf
+
+Constructor of the Cell class:
+
+        Crystalographic units are Angstrom for distances and degrees for angles !
+
+        @param a,b,c: unit cell length in Angstrom
+        @param alpha, beta, gamma: unit cell angle in degrees
+        @param lattice: "cubic", "tetragonal", "hexagonal", "rhombohedral", "orthorhombic", "monoclinic", "triclinic"
+        @param lattice_type: P, I, F, C or R
+
+
+

The constructor of the class is used to build and well suited to +triclinic crystal.

+
+

Specific constructors

+
+
Nevertheless, most used calibrants are of much higher symmetry, like
+
+

cubic which takes only one parameter. +| Here is an example for defining +Polonium which +is a simple cubic cell (Primitive) with a cell parameter of 335.9pm. +This example was chosen as Polonium is apparently the only element with +such primitive cubic packing.

+
Po = Cell.cubic(3.359)
+print(Po)
+
+
+
Primitive cubic cell a=3.3590 b=3.3590 c=3.3590 alpha=90.000 beta=90.000 gamma=90.000
+
+
+
print(Po.volume)
+
+
+
37.899197279
+
+
+
print("Po.d_spacing?")
+print(Po.d_spacing.__doc__)
+print("Po.save?")
+print(Po.save.__doc__)
+
+
+
Po.d_spacing?
+Calculate all d-spacing down to dmin
+
+        applies selection rules
+
+        @param dmin: minimum value of spacing requested
+        @return: dict d-spacing as string, list of tuple with Miller indices
+                preceded with the numerical value
+
+Po.save?
+Save informations about the cell in a d-spacing file, usable as Calibrant
+
+        @param name: name of the calibrant
+        @param doi: reference of the publication used to parametrize the cell
+        @param dmin: minimal d-spacing
+        @param dest_dir: name of the directory where to save the result
+
+
+

To generate a d-spacing file usable as calibrant, one simply has to +call the save method of the Cell instance.

+

Nota: the ”.D” suffix is automatically appended.

+
Po.save("Po",doi="http://www.periodictable.com/Elements/084/data.html", dmin=1)
+
+
+
!cat Po.D
+
+
+
# Calibrant: Po
+# Primitive cubic cell a=3.3590 b=3.3590 c=3.3590 alpha=90.000 beta=90.000 gamma=90.000
+# Ref: http://www.periodictable.com/Elements/084/data.html
+3.35900000 # (1, 0, 0) 6
+2.37517168 # (1, 1, 0) 12
+1.93931955 # (1, 1, 1) 8
+1.67950000 # (2, 0, 0) 6
+1.50219047 # (2, 1, 0) 24
+1.37130601 # (2, 1, 1) 24
+1.18758584 # (2, 2, 0) 12
+1.11966667 # (3, 0, 0) 30
+1.06220907 # (3, 1, 0) 24
+1.01277661 # (3, 1, 1) 24
+
+
+
+
+

Other Examples: LaB6

+

Lantanide Hexaboride, probably my favorite calibrant as it has a +primitive cubic cell hence all reflections are valid and intense. The +cell parameter is available from the +NIST at +a=4.156826.

+

The number of reflections in a file is controled by the dmin +parameter. The lower it is, the more lines there are and the more +time-consuming this will be:

+
LaB6 = Cell.cubic(4.156826)
+%time d=LaB6.d_spacing(0.1)
+print("Number of reflections: %s"%len(d))
+
+
+
CPU times: user 976 ms, sys: 76 ms, total: 1.05 s
+Wall time: 964 ms
+Number of reflections: 1441
+
+
+
LaB6.save("LaB6",doi="https://www-s.nist.gov/srmors/certificates/660C.pdf", dmin=0.1)
+
+
+
!head LaB6.D
+
+
+
# Calibrant: LaB6
+# Primitive cubic cell a=4.1568 b=4.1568 c=4.1568 alpha=90.000 beta=90.000 gamma=90.000
+# Ref: https://www-s.nist.gov/srmors/certificates/660C.pdf
+4.15682600 # (1, 0, 0) 6
+2.93931985 # (1, 1, 0) 12
+2.39994461 # (1, 1, 1) 8
+2.07841300 # (2, 0, 0) 6
+1.85898910 # (2, 1, 0) 24
+1.69701711 # (2, 1, 1) 24
+1.46965993 # (2, 2, 0) 12
+
+
+
+
+

Other Examples: Silicon

+

Silicon is easy to obtain pure thanks to microelectronics industry. +Its cell is a face centered cubic with a diamond like structure. The +cell parameter is available from the +NIST: +a=5.431179 A.

+

Let’s compare the difference between the silicon structure and the +equivalent FCC structure:

+
Si = Cell.diamond(5.431179)
+print(Si)
+
+
+
Face centered cubic cell a=5.4312 b=5.4312 c=5.4312 alpha=90.000 beta=90.000 gamma=90.000
+
+
+
FCC = Cell.cubic(5.431179,"F")
+print(FCC)
+
+
+
Face centered cubic cell a=5.4312 b=5.4312 c=5.4312 alpha=90.000 beta=90.000 gamma=90.000
+
+
+

Apparently, there is not difference. But to check it, let’s generate all +lines down to 1A and compare them:

+
sid = Si.d_spacing(1)
+for key, val in sid.items():
+    print("%s: %s"%(sorted(val[1:][-1]),key))
+
+
+
[2, 2, 4]: 1.10863477e+00
+[1, 1, 3]: 1.63756208e+00
+[0, 2, 2]: 1.92021175e+00
+[1, 1, 1]: 3.13569266e+00
+[1, 1, 5]: 1.04523089e+00
+[1, 3, 3]: 1.24599792e+00
+[0, 0, 4]: 1.35779475e+00
+
+
+
fccd = FCC.d_spacing(1)
+for key, val in fccd.items():
+    print("%s: %s"%(sorted(val[1:][-1]),key))
+
+
+
[2, 2, 4]: 1.10863477e+00
+[1, 1, 3]: 1.63756208e+00
+[0, 2, 2]: 1.92021175e+00
+[1, 1, 1]: 3.13569266e+00
+[1, 1, 5]: 1.04523089e+00
+[0, 2, 4]: 1.21444854e+00
+[2, 2, 2]: 1.56784633e+00
+[1, 3, 3]: 1.24599792e+00
+[0, 0, 2]: 2.71558950e+00
+[0, 0, 4]: 1.35779475e+00
+
+
+

So there are many more reflection in the FCC structure compared to the +diamond-like structure: (4 2 0), (2 2 2) are extinct as h+k+l=4n and all +even.

+
+
+

Selection rules

+

Cell object contain selection_rules which tells if a reflection is +allowed or not. Those selection_rules can be introspected:

+
print(Si.selection_rules)
+print(FCC.selection_rules)
+
+
+
[<function <lambda> at 0x7f8080fad848>, <function <lambda> at 0x7f8080fad938>, <function <lambda> at 0x7f8080fad9b0>]
+[<function <lambda> at 0x7f8080fadaa0>, <function <lambda> at 0x7f8080fada28>]
+
+
+

The Si object has one additionnal selection rule which explains the +difference: A specific rule telling that reflection with h+k+l=4n is +forbidden when (h,k,l) are all even.

+

We will now have a look at the source code of those selection rules +using the inspect module.

+

Nota: This is advanced Python hacking but useful for the +demonstration

+
import inspect
+for rule in Si.selection_rules:
+    print(inspect.getsource(rule))
+
+
+
self.selection_rules = [lambda h, k, l: not(h == 0 and k == 0 and l == 0)]
+
+    self.selection_rules.append(lambda h, k, l: (h % 2 + k % 2 + l % 2) in (0, 3))
+
+    lambda h, k, l:not((h % 2 == 0) and (k % 2 == 0) and (l % 2 == 0) and ((h + k + l) % 4 != 0))
+
+
+

Actually the last line correspond to an anonymous function (lambda) +which implements this rule.

+

As we have seen previously one can simply adapt the Cell instance by +simply appending extra selection rules to this list.

+

A selection rule is a function taking 3 integers as input and returning +True if the reflection is allowed and False when it is forbidden by +symmetry. By this way one can simply adapt existing object to generate +the right calibrant file.

+
+
+
+

Conclusion

+

In this tutorial we have seen the structure of a calibrant file, how +to generate crystal structure cell object to write such calibrant files +automatically, including all metadata needed for redistribution. Most +advanced programmers can now modify the selection rules to remove +forbidden reflection for a given cell.

+
+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

Previous topic

+

Demo of usage of the MultiGeometry class of pyFAI

+

Next topic

+

CCD calibration

+

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+ + + +
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+
+
+
+

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+ +
+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
+ + \ No newline at end of file diff --git a/doc/build/usage/tutorial/MultiGeometry/index.html b/doc/build/usage/tutorial/MultiGeometry/index.html new file mode 100644 index 000000000..a42e90afa --- /dev/null +++ b/doc/build/usage/tutorial/MultiGeometry/index.html @@ -0,0 +1,521 @@ + + + + + + + + Demo of usage of the MultiGeometry class of pyFAI — pyFAI 0.12.0 documentation + + + + + + + + + + + + + + + +
+

Navigation

+ +
+ +
+
+
+
+ +
+

Demo of usage of the MultiGeometry class of pyFAI

+

For this tutorial, we will use the ipython notebook, now known as +Jypyter, an take advantage of the integration of matplotlib with the +inline:

+
%pylab inline
+
+
+
Populating the interactive namespace from numpy and matplotlib
+
+
+

The multi_geometry module of pyFAI allows you to integrate multiple +images taken at various image position, all togeather. This tutorial +will explain you how to perform azimuthal integration in three use-case: +translation of the detector, rotation of the detector around the sample +and finally how to fill gaps of a pixel detector. But before, we need to +know how to generate fake diffraction image.

+
+

Generation of diffraction images

+

PyFAI knows about 20 different reference sample called calibrants. We +will use them to generate fake diffraction images knowing the detector +and its position in space

+
import pyFAI
+import pyFAI.calibrant
+print("Number of known calibrants: %s"%len(pyFAI.calibrant.ALL_CALIBRANTS))
+print(" ".join(pyFAI.calibrant.ALL_CALIBRANTS.keys()))
+
+
+
Number of known calibrants: 27
+Ni CrOx NaCl Si_SRM640e Si_SRM640d Si_SRM640a Si_SRM640c alpha_Al2O3 Cr2O3 AgBh Si_SRM640 CuO PBBA Si_SRM640b quartz C14H30O cristobaltite Si LaB6 CeO2 LaB6_SRM660a LaB6_SRM660b LaB6_SRM660c TiO2 ZnO Al Au
+
+
+
wl = 1e-10
+LaB6 = pyFAI.calibrant.ALL_CALIBRANTS("LaB6")
+LaB6.set_wavelength(wl)
+print(LaB6)
+
+
+
LaB6 Calibrant at wavelength 1e-10
+
+
+

We will start with a “simple” detector called Titan (build by Oxford +Diffraction but now sold by Agilent). It is a CCD detector with +scintilator and magnifying optics fibers. The pixel size is constant: +60µm

+
det = pyFAI.detectors.Titan()
+print(det)
+p1, p2, p3 = det.calc_cartesian_positions()
+print("Detector is flat, P3= %s"%p3)
+poni1 = p1.mean()
+poni2 = p2.mean()
+print("Center of the detector: poni1=%s poni2=%s"%(poni1, poni2))
+
+
+
Detector Titan 2k x 2k       PixelSize= 6.000e-05, 6.000e-05 m
+Detector is flat, P3= None
+Center of the detector: poni1=0.06144 poni2=0.06144
+
+
+

The detector is placed orthogonal to the beam at 10cm. This geometry is +saved into an AzimuthalIntegrator instance:

+
ai = pyFAI.AzimuthalIntegrator(dist=0.1, poni1=poni1, poni2=poni2, detector=det)
+ai.set_wavelength(wl)
+print(ai)
+
+
+
Detector Titan 2k x 2k       PixelSize= 6.000e-05, 6.000e-05 m
+Wavelength= 1.000000e-10m
+SampleDetDist= 1.000000e-01m        PONI= 6.144000e-02, 6.144000e-02m       rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=1024.000, y=1024.000 pix      Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+
+
+

Knowing the calibrant, the wavelength, the detector and the geometry, +one can simulate the 2D diffraction pattern:

+
img = LaB6.fake_calibration_image(ai)
+imshow(img, origin="lower")
+
+
+
<matplotlib.image.AxesImage at 0x7fbf946e68d0>
+
+
+../../../_images/output_10_1.png +

This image can be integrated in q-space and plotted:

+
plot(*ai.integrate1d(img, 1000, unit="q_A^-1"))
+
+
+
[<matplotlib.lines.Line2D at 0x7fbf92da9d90>]
+
+
+../../../_images/output_12_11.png +

Note pyFAI does now about the ring position but nothing about relative +intensities of rings.

+
+
+

Translation of the detector along the vertical axis

+

The vertical axis is defined along the poni1. If one moves the +detector higher, the poni will appear at lower coordinates. So lets +define 5 upwards verical translations of half the detector size.

+

For this we will duplicate 5x the AzimuthalIntegrator object, but +instances of AzimuthalIntegrator are mutable, so it is important to +create an actual copy and not an view on them. In Python, one can +use the copy function of the copy module:

+
import copy
+
+
+

We will now offset the poni1 value of each AzimuthalIntegratoe which +correspond to a vertical translation. Each subsequent image is offsetted +by half a detector width (stored as poni1).

+
ais = []
+imgs = []
+fig, plots = subplots(1,5)
+for i in range(5):
+    my_ai = copy.deepcopy(ai)
+    my_ai.poni1 -= i*poni1
+    my_img = LaB6.fake_calibration_image(my_ai)
+    plots[i].imshow(my_img, origin="lower")
+    ais.append(my_ai)
+    imgs.append(my_img)
+    print(my_ai)
+
+
+
Detector Titan 2k x 2k       PixelSize= 6.000e-05, 6.000e-05 m
+Wavelength= 1.000000e-10m
+SampleDetDist= 1.000000e-01m        PONI= 6.144000e-02, 6.144000e-02m       rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=1024.000, y=1024.000 pix      Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+Detector Titan 2k x 2k       PixelSize= 6.000e-05, 6.000e-05 m
+Wavelength= 1.000000e-10m
+SampleDetDist= 1.000000e-01m        PONI= 0.000000e+00, 6.144000e-02m       rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=1024.000, y=0.000 pix Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+Detector Titan 2k x 2k       PixelSize= 6.000e-05, 6.000e-05 m
+Wavelength= 1.000000e-10m
+SampleDetDist= 1.000000e-01m        PONI= -6.144000e-02, 6.144000e-02m      rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=1024.000, y=-1024.000 pix     Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+Detector Titan 2k x 2k       PixelSize= 6.000e-05, 6.000e-05 m
+Wavelength= 1.000000e-10m
+SampleDetDist= 1.000000e-01m        PONI= -1.228800e-01, 6.144000e-02m      rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=1024.000, y=-2048.000 pix     Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+Detector Titan 2k x 2k       PixelSize= 6.000e-05, 6.000e-05 m
+Wavelength= 1.000000e-10m
+SampleDetDist= 1.000000e-01m        PONI= -1.843200e-01, 6.144000e-02m      rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=1024.000, y=-3072.000 pix     Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+
+
+../../../_images/output_16_11.png +
+
+

MultiGeometry integrator

+

The MultiGeometry instance can be created from any list of +AzimuthalIntegrator instances or list of poni-files. Here we will +use the former method.

+

The main difference of a MultiIntegrator with a “normal” +AzimuthalIntegrator comes from the definition of the output space in +the constructor of the object. One needs to specify the unit and the +integration range.

+
from pyFAI.multi_geometry import MultiGeometry
+
+
+
mg = MultiGeometry(ais, unit="q_A^-1", radial_range=(0, 10))
+print(mg)
+
+
+
MultiGeometry integrator with 5 geometries on (0, 10) radial range (q_A^-1) and (-180, 180) azimuthal range (deg)
+
+
+

MultiGeometry integrators can be used in a similar way to “normal” +AzimuthalIntegrators. Keep in mind the output intensity is always +scaled to absolute solid angle.

+
plot(*mg.integrate1d(imgs, 10000))
+
+
+
[<matplotlib.lines.Line2D at 0x7fbf90a4a210>]
+
+
+../../../_images/output_21_12.png +
for i, a in zip(imgs, ais):
+    plot(*a.integrate1d(i, 1000, unit="q_A^-1"))
+
+
+../../../_images/output_22_0.png +
+
+

Rotation of the detector

+

The strength of translating the detector is that it simulates a larger +detector, but this approach reaches its limit quikly as the higher the +detector gets, the smallest the solid angle gets and induces artificial +noise. One solution is to keep the detector at the same distance and +rotate the detector.

+
+

Creation of diffraction images

+

In this example we will use a Pilatus 200k with 2 modules. It has a gap +in the middle of the two detectors and we will see how the +MultiGeometry can help.

+

As previously, we will use LaB6 but instead of translating the images, +we will rotate them along the second axis:

+
det = pyFAI.detectors.detector_factory("pilatus200k")
+p1, p2, p3 = det.calc_cartesian_positions()
+print(p3)
+poni1 = p1.mean()
+poni2 = p2.mean()
+print(poni1)
+print(poni2)
+
+
+
None
+0.035002
+0.041882
+
+
+
ai = pyFAI.AzimuthalIntegrator(dist=0.1, poni1=poni1, poni2=poni2, detector=det)
+img = LaB6.fake_calibration_image(ai)
+imshow(img, origin="lower")
+#imshow(log(ai.integrate2d(img, 500, 360, unit="2th_deg")[0]))
+
+
+
<matplotlib.image.AxesImage at 0x7fbf90923790>
+
+
+../../../_images/output_25_1.png +
plot(*ai.integrate1d(img, 500,unit="2th_deg"))
+
+
+
[<matplotlib.lines.Line2D at 0x7fbf90847490>]
+
+
+../../../_images/output_26_1.png +

We will rotate the detector with a step size of 15 degrees

+
step = 15*pi/180
+ais = []
+imgs = []
+fig, plots = subplots(1,5)
+for i in range(5):
+    my_ai = copy.deepcopy(ai)
+    my_ai.rot2 -= i*step
+    my_img = LaB6.fake_calibration_image(my_ai)
+    plots[i].imshow(my_img, origin="lower")
+    ais.append(my_ai)
+    imgs.append(my_img)
+    print(my_ai)
+
+
+
Detector Pilatus200k         PixelSize= 1.720e-04, 1.720e-04 m
+SampleDetDist= 1.000000e-01m        PONI= 3.500200e-02, 4.188200e-02m       rot1=0.000000  rot2= 0.000000  rot3= 0.000000 rad
+DirectBeamDist= 100.000mm   Center: x=243.500, y=203.500 pix        Tilt=0.000 deg  tiltPlanRotation= 0.000 deg
+Detector Pilatus200k         PixelSize= 1.720e-04, 1.720e-04 m
+SampleDetDist= 1.000000e-01m        PONI= 3.500200e-02, 4.188200e-02m       rot1=0.000000  rot2= -0.261799  rot3= 0.000000 rad
+DirectBeamDist= 103.528mm   Center: x=243.500, y=47.716 pix Tilt=15.000 deg  tiltPlanRotation= -90.000 deg
+Detector Pilatus200k         PixelSize= 1.720e-04, 1.720e-04 m
+SampleDetDist= 1.000000e-01m        PONI= 3.500200e-02, 4.188200e-02m       rot1=0.000000  rot2= -0.523599  rot3= 0.000000 rad
+DirectBeamDist= 115.470mm   Center: x=243.500, y=-132.169 pix       Tilt=30.000 deg  tiltPlanRotation= -90.000 deg
+Detector Pilatus200k         PixelSize= 1.720e-04, 1.720e-04 m
+SampleDetDist= 1.000000e-01m        PONI= 3.500200e-02, 4.188200e-02m       rot1=0.000000  rot2= -0.785398  rot3= 0.000000 rad
+DirectBeamDist= 141.421mm   Center: x=243.500, y=-377.895 pix       Tilt=45.000 deg  tiltPlanRotation= -90.000 deg
+Detector Pilatus200k         PixelSize= 1.720e-04, 1.720e-04 m
+SampleDetDist= 1.000000e-01m        PONI= 3.500200e-02, 4.188200e-02m       rot1=0.000000  rot2= -1.047198  rot3= 0.000000 rad
+DirectBeamDist= 200.000mm   Center: x=243.500, y=-803.506 pix       Tilt=60.000 deg  tiltPlanRotation= -90.000 deg
+
+
+../../../_images/output_28_1.png +
for i, a in zip(imgs, ais):
+    plot(*a.integrate1d(i, 1000, unit="2th_deg"))
+
+
+../../../_images/output_29_0.png +
+
+

Creation of the MultiGeometry

+

This time we will work in 2theta angle using degrees:

+
mg = MultiGeometry(ais, unit="2th_deg", radial_range=(0, 90))
+print(mg)
+plot(*mg.integrate1d(imgs, 10000))
+
+
+
MultiGeometry integrator with 5 geometries on (0, 90) radial range (2th_deg) and (-180, 180) azimuthal range (deg)
+area_pixel=1.32053624453 area_sum=2.69418873745, Error= -1.04022324159
+
+
+
[<matplotlib.lines.Line2D at 0x7fbf903e2650>]
+
+
+../../../_images/output_31_2.png +
I,tth, chi = mg.integrate2d(imgs, 1000,360)
+imshow(I, origin="lower",extent=[tth.min(), tth.max(), chi.min(), chi.max()], aspect="auto")
+xlabel("2theta")
+ylabel("chi")
+
+
+
<matplotlib.text.Text at 0x7fbf90330350>
+
+
+../../../_images/output_32_1.png +
+
+
+

How to fill-up gaps in arrays of pixel detectors during 2D integration

+

We will use ImXpad detectors which exhibits large gaps.

+
det = pyFAI.detectors.detector_factory("Xpad_flat")
+p1, p2, p3 = det.calc_cartesian_positions()
+print(p3)
+poni1 = p1.mean()
+poni2 = p2.mean()
+print(poni1)
+print(poni2)
+
+
+
None
+0.076457
+0.0377653
+
+
+
ai = pyFAI.AzimuthalIntegrator(dist=0.1, poni1=0, poni2=poni2, detector=det)
+img = LaB6.fake_calibration_image(ai)
+imshow(img, origin="lower")
+
+
+
<matplotlib.image.AxesImage at 0x7fbf909b8210>
+
+
+../../../_images/output_35_1.png +
I, tth, chi=ai.integrate2d(img, 500, 360, azimuth_range=(0,180), unit="2th_deg", dummy=-1)
+imshow(sqrt(I),origin="lower",extent=[tth.min(), tth.max(), chi.min(), chi.max()], aspect="auto")
+xlabel("2theta")
+ylabel("chi")
+
+
+
-c:2: RuntimeWarning: invalid value encountered in sqrt
+
+
+
<matplotlib.text.Text at 0x7fbf90a67850>
+
+
+../../../_images/output_36_2.png +

To observe textures, it is mandatory to fill the large empty space. This +can be done by tilting the detector by a few degrees to higher 2theta +angle (yaw 2x5deg) and turn the detector along the azimuthal angle (roll +2x5deg):

+
step = 5*pi/180
+nb_geom = 3
+ais = []
+imgs = []
+for i in range(nb_geom):
+    for j in range(nb_geom):
+        my_ai = copy.deepcopy(ai)
+        my_ai.rot2 -= i*step
+        my_ai.rot3 -= j*step
+        my_img = LaB6.fake_calibration_image(my_ai)
+        ais.append(my_ai)
+        imgs.append(my_img)
+mg = MultiGeometry(ais, unit="2th_deg", radial_range=(0, 60), azimuth_range=(0, 180), empty=-1)
+print(mg)
+I, tth, chi = mg.integrate2d(imgs, 1000, 360)
+imshow(sqrt(I),origin="lower",extent=[tth.min(), tth.max(), chi.min(), chi.max()], aspect="auto")
+xlabel("2theta")
+ylabel("chi")
+
+
+
MultiGeometry integrator with 9 geometries on (0, 60) radial range (2th_deg) and (0, 180) azimuthal range (deg)
+
+
+
-c:16: RuntimeWarning: invalid value encountered in sqrt
+
+
+
<matplotlib.text.Text at 0x7fbf92c74710>
+
+
+../../../_images/output_38_3.png +

As on can see, the gaps have disapeared and the statistics is much +better, except on the border were only one image contributes to the +integrated image.

+
+
+
+

Conclusion

+

The multi_geometry module of pyFAI makes powder diffraction experiments +with small moving detectors much easier.

+

Some people would like to stitch input images together prior to +integration. There are plenty of good tools to do this: generalist one +like Photoshop or +more specialized ones like AutoPano. +More seriously this can be using the distortion module of a detector to +re-sample the signal on a regular grid but one will have to store on one +side the number of actual pixel contributing to a regular pixels and on +the other the total intensity contained in the regularized pixel. +Without the former information, doing science with a rebinned image is +as meaningful as using Photoshop.

+
+ + +
+
+
+
+
+

Table Of Contents

+ + +

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+

Multi-geometry azimuthal integration

+

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+

Creation of a calibrant file

+

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+

Tutorials

+

Tutorials explain the Python interface of pyFAI and use the jupyter notebook +interface, formerly known as ipython notebooks. +The two first tutorials are an introduction to the usage of pyFAI from Python for +diffraction data reduction. +The subsequent tutorials are more in depth explanation and require +a good Python fluency and to a certain extent, of the pyFAI library.

+
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+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
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+ +
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+

Multi-geometry azimuthal integration

+

Or how it is possible to perform an azimuthal regrouping using multiple detectors +or by moving a single (small) detector to cover more solid angle.

+
+

Idea

+

Azimuthal integration or azimuthal regrouping is (roughly) the averaging of all +pixel intensities with the same Q value (or 2theta), as described in +this publication chapter 3.2 and 3.3.

+

By taking multiple images at various places in space one covers more solid angle, +allowing either a better statistics or a larger Q-range coverage.

+

As described in the publication, the average is calculated by the ratio of the +(intensity-) weighted histogram by the unweighted histogram. By enforcing a same +bin position over multiple geometries, one can create a combined weighted and +unweighted histograms by simply summing all partial histograms from each geometry.

+

The resulting pattern is obtained as usual by the ration of weighted/unweighted

+
+
+

How it works

+

Lets assume you are able to know where your detector is in space, either +calibrated, either calculated from the goniometer position. +A diffrection image (img_i) has been acquired using a geometry which is stored +in a poni-file (poni_i) useable by pyFAI.

+

To define a multi-geometry integrator, one needs all poni-files and one needs +to define the output space so that all individual integrators use the same bins.

+
import glob
+import fabio
+from pyFAI.multi_geometry import MultiGeometry
+
+img_files = glob.glob("*.cbf")
+img_data = [fabio.open(i).data for i in img_files]
+ais = [i[:-4]+".poni" for i in img_files]
+mg = MultiGeometry(ais, unit="q_A^-1", radial_range=(0, 50), wavelength=1e-10)
+q, I = mg.integrate1d(img_data, 10000)
+
+
+
+

What is automatic

+
    +
  • MultiGeometry takes care of defining the same output space with the same bin position,
    • +
  • It performs the normalization of the solid angle (absolute solid angle, unlike AzimuthalIntegrator !)
    • +
  • It can handle polarization correction if needed
    • +
  • It can normalize by a monitor (I1 normalization) or correct for exposure time
    • +
+
+
+

What is not

+

For PDF measurement, data needs to be properly prepared, especially:

+
    +
  • Dark current subtraction
    • +
  • Flat-field correction
    • +
  • Exposure time correction (if all images are not taken with the same exposure time)
    • +
+
+
+ +
+

Conclusion

+

MultiGeometry is a unique feature of PyFAI ... +While extremely powerful, it need +careful understanding of the numerical treatement going on underneath.

+
+
+ + +
+
+
+
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+ + +

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+

Selection of a calibrant

+

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+

Demo of usage of the MultiGeometry class of pyFAI

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+
+ © Copyright 2012-2016, Jerome Kieffer. + Created using Sphinx 1.2.3. +
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zd>zT1XyUYWjvn*CarkUxL2MuD8@aRp8v@{eu0($f;VJP+_pESTwkIW!1wkLB6UT?{ z=Hve_0q}oA$^TTxFGH0.20). +is sufficiently recent (>0.20). + +.. _cython: http://cython.org + Building procedure ------------------ @@ -104,8 +107,3 @@ PyFAI can use a certain number of environment variable to modify its default beh * PYFAI_TESTIMAGES: path wit test images (if absent, they get downloaded from the internet) - -References: ------------ - -:: _cython: http://cython.org diff --git a/openCL/for_eclipse.h b/openCL/for_eclipse.h index 547c44c7a..ef85400ff 100644 --- a/openCL/for_eclipse.h +++ b/openCL/for_eclipse.h @@ -4,7 +4,7 @@ * * Copyright (C) 2015 European Synchrotron Radiation Facility, Grenoble, France * - * Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu) + * Principal author: Jerome Kieffer (Jerome.Kieffer@ESRF.eu) * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal diff --git a/openCL/separate.cl b/openCL/separate.cl index 800366886..10c892b6f 100644 --- a/openCL/separate.cl +++ b/openCL/separate.cl @@ -4,7 +4,7 @@ * * Copyright (C) 2015 European Synchrotron Radiation Facility, Grenoble, France * - * Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu) + * Principal author: Jerome Kieffer (Jerome.Kieffer@ESRF.eu) * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal diff --git a/setup.py b/setup.py index bef1952d6..3cd3d5c3d 100644 --- a/setup.py +++ b/setup.py @@ -28,7 +28,7 @@ __contact__ = "Jerome.Kieffer@ESRF.eu" __license__ = "GPLv3+" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" -__date__ = "13/05/2016" +__date__ = "06/06/2016" __status__ = "stable" install_warning = True @@ -539,7 +539,7 @@ def get_readme(): long_description = fp.read().decode("utf-8") return long_description - +# double check classifiers on https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers = ["Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Programming Language :: Python :: 2", @@ -547,7 +547,7 @@ def get_readme(): "Programming Language :: Cython", "Environment :: Console", "Environment :: X11 Applications :: Qt", - "Intented Audience :: Science/Research", + "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License (GPL)", "License :: OSI Approved :: MIT License", "Topic :: Software Development :: Libraries :: Python Modules", diff --git a/version.py b/version.py index 753e59077..1b870361c 100644 --- a/version.py +++ b/version.py @@ -33,7 +33,7 @@ __contact__ = "Jerome.Kieffer@ESRF.eu" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" -__date__ = "03/06/2016" +__date__ = "06/06/2016" __status__ = "production" __docformat__ = 'restructuredtext' __doc__ = """ @@ -73,8 +73,8 @@ MAJOR = 0 MINOR = 12 MICRO = 0 -RELEV = "rc" # <16 -SERIAL = 1 # <16 +RELEV = "final" # <16 +SERIAL = 0 # <16 date = __date__

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