{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Integration with Python\n", "\n", "This cookbook explains you how to perform azimuthal integration using the Python interpreter.\n", "It is divided in two parts, the first part uses purely Python while the second uses some advanced features of the Jupyter notebook.\n", "\n", "We will re-use the same files as in the other tutorials.\n", "\n", "## Performing azimuthal integration with pyFAI of a bunch of images\n", "\n", "To be able to perform the azimuthal integration of some images, one needs:\n", "\n", "* The diffraction images themselves, in this example they are stored as EDF files\n", "* The geometry of the experimental setup as obtained from the calibration and stored as a PONI-file\n", "* other files like flat-field, dark current images or detector distortion file (spline-file).\n", "\n", "Image file: http://www.silx.org/pub/pyFAI/cookbook/calibration/Eiger4M_Al2O3_13.45keV.edf\n", "\n", "Geometry file: http://www.silx.org/pub/pyFAI/cookbook/calibration/alpha-Al2O3.poni\n", "\n", "The calibration has been performed in the previous cookbook.\n", "\n", "### Basic usage of pyFAI\n", "To perform azimuthal averaging, one can use pyFAI to load the geometry and FabIO to read the image:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "image_file: /tmp/pyFAI_testdata_jerome/Eiger4M_Al2O3_13.45keV.edf\n", "poni_file: /tmp/pyFAI_testdata_jerome/alpha-Al2O3.poni\n" ] }, { "data": { "text/plain": [ "['alpha-Al2O3.poni',\n", " 'F_K4320T_Cam43_30012013_distorsion.spline',\n", " 'integration_with_scripts.ipynb',\n", " 'integration_with_python.ipynb',\n", " 'LaB6_29.4keV.tif',\n", " 'integration_with_the_gui.rst',\n", " '.ipynb_checkpoints',\n", " 'calib-gui',\n", " 'pyFAI-integrate.png',\n", " 'Eiger4M_Al2O3_13.45keV.edf',\n", " 'LaB6_29.4keV.dat',\n", " 'index.rst',\n", " 'calib-cli',\n", " 'LaB6_29.4keV.poni']" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This cell is just to download the files to perform the analysis:\n", "import time\n", "import shutil, os\n", "from silx.resources import ExternalResources\n", "\n", "t0 = time.perf_counter()\n", "\n", "downloader = ExternalResources(\"pyFAI\", \"http://www.silx.org/pub/pyFAI/cookbook/calibration/\", \"PYFAI_DATA\")\n", "image_file = downloader.getfile(\"Eiger4M_Al2O3_13.45keV.edf\")\n", "poni_file = downloader.getfile(\"alpha-Al2O3.poni\")\n", "\n", "print(\"image_file:\", image_file)\n", "print(\"poni_file:\", poni_file)\n", "\n", "# Copy all files locally\n", "shutil.copy(poni_file, \".\")\n", "shutil.copy(image_file, \".\")\n", "# clean-up files from previous run:\n", "for result in ('integrated.edf', \"integrated.dat\", \"Eiger4M_Al2O3_13.45keV.dat\"):\n", " if os.path.exists(result):\n", " os.unlink(result)\n", "\n", "os.listdir()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pyFAI version: 0.20.0-beta1\n", "Image: \n", "\n", "Integrator: \n", " Detector Eiger 4M\t PixelSize= 7.500e-05, 7.500e-05 m\n", "Wavelength= 9.218156e-11m\n", "SampleDetDist= 1.625467e-01m\tPONI= 9.636511e-02, 8.600623e-02m\trot1=0.002605 rot2= 0.000641 rot3= 0.000000 rad\n", "DirectBeamDist= 162.547mm\tCenter: x=1141.104, y=1286.257 pix\tTilt=0.154 deg tiltPlanRotation= 166.178 deg\n" ] } ], "source": [ "import pyFAI, fabio\n", "print(\"pyFAI version:\", pyFAI.version)\n", "img = fabio.open(\"Eiger4M_Al2O3_13.45keV.edf\")\n", "print(\"Image:\", img)\n", "\n", "ai = pyFAI.load(\"alpha-Al2O3.poni\")\n", "print(\"\\nIntegrator: \\n\", ai)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Azimuthal averaging using pyFAI\n", "\n", "\n", "One needs first to retrieve the image as a numpy array. This allows to use other libraries than FabIO for image reading, for example HDF5 files.\n", "\n", "This shows how to perform the azimuthal integration of one image over 1000 bins:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "img_array: (2167, 2070) float32\n" ] } ], "source": [ "img_array = img.data\n", "print(\"img_array:\", type(img_array), img_array.shape, img_array.dtype)\n", "mask = img_array>4e9\n", "\n", "res = ai.integrate1d_ng(img_array, \n", " 1000, \n", " mask=mask,\n", " unit=\"2th_deg\", \n", " filename=\"integrated.dat\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note:* There are 2 mandatory parameters for this method: the 2D-numpy array with the image and the number of bins. In addition, we specified the name of the file where to save the data and the unit for performing the integration.\n", "\n", "There are many other options of `integrate1d`. The difference between the `legacy` and the `ng` flavor is mostly on the way error is propagated:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on method integrate1d_ng in module pyFAI.azimuthalIntegrator:\n", "\n", "integrate1d_ng(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, metadata=None) method of pyFAI.azimuthalIntegrator.AzimuthalIntegrator instance\n", " Calculate the azimuthal integration (1d) of a 2D image.\n", " \n", " Multi algorithm implementation (tries to be bullet proof), suitable for SAXS, WAXS, ... and much more\n", " Takes extra care of normalization and performs proper variance propagation.\n", " \n", " :param ndarray data: 2D array from the Detector/CCD camera\n", " :param int npt: number of points in the output pattern\n", " :param str filename: output filename in 2/3 column ascii format\n", " :param bool correctSolidAngle: correct for solid angle of each pixel if True \n", " :param ndarray variance: array containing the variance of the data. \n", " :param str error_model: When the variance is unknown, an error model can be given: \"poisson\" (variance = I), \"azimuthal\" (variance = (I-)^2)\n", " :param radial_range: The lower and upper range of the radial unit. If not provided, range is simply (min, max). Values outside the range are ignored.\n", " :type radial_range: (float, float), optional\n", " :param azimuth_range: The lower and upper range of the azimuthal angle in degree. If not provided, range is simply (min, max). Values outside the range are ignored.\n", " :type azimuth_range: (float, float), optional\n", " :param ndarray mask: array with 0 for valid pixels, all other are masked (static mask)\n", " :param float dummy: value for dead/masked pixels (dynamic mask)\n", " :param float delta_dummy: precision for dummy value \n", " :param float polarization_factor: polarization factor between -1 (vertical) and +1 (horizontal).\n", " 0 for circular polarization or random,\n", " None for no correction,\n", " True for using the former correction \n", " :param ndarray dark: dark noise image\n", " :param ndarray flat: flat field image\n", " :param IntegrationMethod method: IntegrationMethod instance or 3-tuple with (splitting, algorithm, implementation)\n", " :param Unit unit: Output units, can be \"q_nm^-1\" (default), \"2th_deg\", \"r_mm\" for now.\n", " :param bool safe: Perform some extra checks to ensure LUT/CSR is still valid. False is faster.\n", " :param float normalization_factor: Value of a normalization monitor \n", " :param metadata: JSON serializable object containing the metadata, usually a dictionary.\n", " :return: Integrate1dResult namedtuple with (q,I,sigma) +extra informations in it.\n", "\n" ] } ], "source": [ "help(ai.integrate1d_ng)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result file contains the integrated data with some headers as shown:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# == pyFAI calibration ==\n", "# Distance Sample to Detector: 0.16254673947902704 m\n", "# PONI: 9.637e-02, 8.601e-02 m\n", "# Rotations: 0.002605 0.000641 0.000000 rad\n", "#\n", "# == Fit2d calibration ==\n", "# Distance Sample-beamCenter: 162.547 mm\n", "# Center: x=1141.104, y=1286.257 pix\n", "# Tilt: 0.154 deg TiltPlanRot: 166.178 deg\n", "#\n", "# Detector Eiger 4M\t PixelSize= 7.500e-05, 7.500e-05 m\n", "# Detector has a mask: True\n", "# Detector has a dark current: False\n", "# detector has a flat field: False\n", "#\n", "# Wavelength: 9.218156017338309e-11 m\n", "# Mask applied: user provided\n", "# Dark current applied: False\n", "# Flat field applied: False\n", "# Polarization factor: None\n", "# Normalization factor: 1.0\n", "# --> integrated.dat\n", "# 2th_deg I\n", "1.919453e-02 1.069850e+02\n", "5.758360e-02 1.369700e+02\n", "9.597267e-02 1.055188e+03\n", "1.343617e-01 7.239056e+03\n", "1.727508e-01 0.000000e+00\n", "2.111399e-01 2.046833e+04\n", "2.495289e-01 1.767070e+04\n", "2.879180e-01 1.170921e+04\n", "3.263071e-01 7.144741e+03\n", "3.646961e-01 4.495773e+03\n", "4.030852e-01 2.918518e+03\n", "4.414743e-01 1.955721e+03\n", "4.798634e-01 1.355407e+03\n", "5.182524e-01 9.685624e+02\n", "5.566415e-01 7.158431e+02\n", "5.950306e-01 5.435185e+02\n", "6.334196e-01 4.240502e+02\n", "6.718087e-01 3.373719e+02\n", "7.101978e-01 2.713134e+02\n", "7.485868e-01 2.221988e+02\n", "7.869759e-01 1.840064e+02\n", "8.253650e-01 1.547038e+02\n", "8.637540e-01 1.308537e+02\n", "9.021431e-01 1.118037e+02\n", "9.405322e-01 9.608986e+01\n", "9.789212e-01 8.361961e+01\n", "1.017310e+00 7.308742e+01\n" ] } ], "source": [ "with open(\"integrated.dat\") as f:\n", " for i in range(50):\n", " print(f.readline().strip())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Azimuthal regrouping using pyFAI\n", "\n", "This option is similar to the integration but performs N integrations on various azimuthal angle (chi) sections of the space. It is also named \"caking\" in Fit2D.\n", "\n", "The azimuthal regrouping of an image over 500 radial bins in 360 angular steps (of 1 degree) can be performed like this:\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "res2 = ai.integrate2d_ng(img_array, \n", " 500, 360, \n", " unit=\"r_mm\", \n", " filename=\"integrated.edf\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"EDF_DataBlockID\": \"0.Image.Psd\",\n", " \"EDF_BinarySize\": \"720000\",\n", " \"EDF_HeaderSize\": \"1536\",\n", " \"ByteOrder\": \"LowByteFirst\",\n", " \"DataType\": \"FloatValue\",\n", " \"Dim_1\": \"500\",\n", " \"Dim_2\": \"360\",\n", " \"Image\": \"0\",\n", " \"HeaderID\": \"EH:000000:000000:000000\",\n", " \"Size\": \"720000\",\n", " \"Engine\": \"Detector Eiger 4M PixelSize= 7.500e-05, 7.500e-05 m Wavelength= 9.218156e-11m SampleDetDist= 1.625467e-01m PONI= 9.636511e-02, 8.600623e-02m rot1=0.002605 rot2= 0.000641 rot3= 0.000000 rad DirectBeamDist= 162.547mm Center: x=1141.104, y=1286.257 pix Tilt=0.154 deg tiltPlanRotation= 166.178 deg\",\n", " \"detector\": \"Eiger 4M\",\n", " \"pixel1\": \"7.5e-05\",\n", " \"pixel2\": \"7.5e-05\",\n", " \"max_shape\": \"(2167, 2070)\",\n", " \"dist\": \"0.16254673947902704\",\n", " \"poni1\": \"0.09636511239091199\",\n", " \"poni2\": \"0.08600622810318177\",\n", " \"rot1\": \"0.0026048269580961157\",\n", " \"rot2\": \"0.0006408875619633374\",\n", " \"rot3\": \"7.381054962294179e-11\",\n", " \"wavelength\": \"9.218156017338309e-11\",\n", " \"r_mm_min\": \"0.1289601535655313\",\n", " \"r_mm_max\": \"128.83119341196578\",\n", " \"chi_min\": \"-179.4870352534758\",\n", " \"chi_max\": \"179.50004446145505\",\n", " \"has_mask_applied\": \"from detector\",\n", " \"has_dark_correction\": \"False\",\n", " \"has_flat_correction\": \"False\",\n", " \"polarization_factor\": \"None\",\n", " \"normalization_factor\": \"1.0\"\n", "}\n", "cake: (360, 500) float32\n" ] } ], "source": [ "cake = fabio.open(\"integrated.edf\")\n", "print(cake.header)\n", "print(\"cake:\", type(cake.data), cake.data.shape, cake.data.dtype)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this, it is trivial to perform a loop and integrate many images. \n", "\n", "*Attention:* The AzimuthalIntegrator object (called `ai` here) is rather large and costly to initialize. The best practice is to create it once and to use it many times, like this:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import glob, os\n", "\n", "all_images = glob.glob(\"Eiger4M_*.edf\")\n", "ai = pyFAI.load(\"alpha-Al2O3.poni\")\n", "\n", "for one_image in all_images:\n", " fimg = fabio.open(one_image)\n", " dest = os.path.splitext(one_image)[0] + \".dat\"\n", " ai.integrate1d_ng(fimg.data, \n", " 1000, \n", " unit=\"2th_deg\", \n", " filename=dest)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using some advanced features of Jupyter Notebooks\n", "\n", "Jupyter notebooks offer some advanced visualization features, especially when used with *matplotlib* and *pyFAI*.\n", "Unfortunately, the example shown hereafter will not work properly in normal Python scipts.\n", "\n", "### Initialization of the notebook for matplotlib integration:\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from pyFAI.gui import jupyter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualization of different types of results previously calculated" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "jupyter.plot2d(res2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "This cookbook explains the basic usage of pyFAI as a Python library for azimuthal integration and simple visualization in the Jupyter notebook." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total execution time: 4.419 s\n" ] } ], "source": [ "print(f\"Total execution time: {time.perf_counter() - t0 :6.3f} s\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 4 }