{ "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", " 'integration_with_scripts.ipynb',\n", " 'integration_with_python.ipynb',\n", " 'integration_with_the_gui.rst',\n", " '.ipynb_checkpoints',\n", " 'calib-gui',\n", " 'pyFAI-integrate.png',\n", " 'Eiger4M_Al2O3_13.45keV.edf',\n", " 'index.rst',\n", " 'calib-cli']" ] }, "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: 0.21.0-dev0\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": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = jupyter.display(img.data, label=img.filename)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = jupyter.plot1d(res)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = 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: 4.574 s\n" ] } ], "source": [ "print(f\"Total execution time: {time.perf_counter() - t0 :6.3f} s\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9 (local venv)", "language": "python", "name": "py39-venv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.2" } }, "nbformat": 4, "nbformat_minor": 4 }