{
"cells": [
{
"cell_type": "markdown",
"id": "emotional-civilian",
"metadata": {},
"source": [
"# Wilson plots generated from sparse datasets\n",
"\n",
"The input data comes from the sparsification of the zenodo dataset available at:\n",
"https://zenodo.org/record/3834335"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "wireless-psychiatry",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/mnt/data/jungfrau'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/mnt/data/jungfrau/sparse_512_3_0_2_fit_q2.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdirname\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mperf_counter()\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/mnt/data/jungfrau'"
]
}
],
"source": [
"filename=\"/mnt/data/jungfrau/sparse_512_3_0_2_fit_q2.h5\"\n",
"import os, time\n",
"os.chdir(os.path.dirname(filename))\n",
"start_time = time.perf_counter()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "stopped-illinois",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib nbagg\n",
"import json, pyFAI\n",
"import hdf5plugin\n",
"import h5py\n",
"import numpy\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"from matplotlib.pyplot import subplots"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "laughing-reservoir",
"metadata": {},
"outputs": [],
"source": [
"root = h5py.File(filename, \"r\")\n",
"de = root.attrs[\"default\"]\n",
"entry = root[de]\n",
"nx_data = entry[entry.attrs[\"default\"]]\n",
"dict(nx_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "medical-elder",
"metadata": {},
"outputs": [],
"source": [
"radius = nx_data[\"radius\"]\n",
"I_bg = nx_data[\"background_avg\"]\n",
"resolution = 10/numpy.sqrt(radius)\n",
"resolution[:4],resolution[-4:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "designing-vertical",
"metadata": {},
"outputs": [],
"source": [
"fig, ax = subplots()\n",
"ax.plot(radius, numpy.log(I_bg[0]))\n",
"ax.set_ylabel(\"$log(\\overline{I_{bg}})$\")\n",
"ax.set_xlabel(\"$d^*² (nm^{-2})$\")\n",
"ax.set_title(\"Wilson-like plot on frame #0\")\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fancy-orleans",
"metadata": {},
"outputs": [],
"source": [
"frame_ptr = nx_data[\"frame_ptr\"]\n",
"npix = frame_ptr[1:]-frame_ptr[:-1]\n",
"fig,ax = subplots()\n",
"ax.plot(npix)\n",
"ax.set_xlabel(\"Frame number\")\n",
"ax.set_ylabel(\"Number of recorded pixels\")\n",
"ax.set_title(\"Number of peaks per frame\")\n",
"frame = numpy.argmax(npix)\n",
"print(f\"Frame index with largest number of pixel recorded: {frame}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "weird-newport",
"metadata": {},
"outputs": [],
"source": [
"mask = nx_data[\"mask\"]\n",
"shape = mask.shape\n",
"size = numpy.prod(shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "weird-dominican",
"metadata": {},
"outputs": [],
"source": [
"nticks = 7\n",
"fig, ax = subplots()\n",
"ax.plot(radius, numpy.log(I_bg[frame]), label=\"Background\")\n",
"tick_value = numpy.linspace(radius[0], radius[-1], nticks)\n",
"ax.set_xticks(tick_value)\n",
"tick_label = [f\"{10/numpy.sqrt(i) if i>0 else numpy.NaN:6.4f}\" for i in tick_value]\n",
"ax.set_xticklabels(tick_label)\n",
"ax.set_xlabel(\"Resolution ($\\mathrm{\\AA}$)\")\n",
"ax.set_ylabel(\"$log(\\overline{I_{bg}})$\")\n",
"ax.set_title(f\"Wilson-like plot on frame #{frame}\")\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "specialized-pattern",
"metadata": {},
"outputs": [],
"source": [
"indexes = nx_data[\"index\"][frame_ptr[frame]:frame_ptr[frame+1]]\n",
"intensities= nx_data[\"intensity\"][frame_ptr[frame]:frame_ptr[frame+1]]\n",
"signal = numpy.zeros(size)\n",
"norm = numpy.zeros(size)\n",
"signal[indexes] = intensities\n",
"norm[indexes] = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "confused-cleaners",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"sparsify = nx_data.parent[\"sparsify\"]\n",
"config = json.loads(sparsify[\"configuration/data\"][()])\n",
"ai = pyFAI.load(config[\"geometry\"])\n",
"engine = ai.setup_sparse_integrator(shape=ai.detector.shape,\n",
" npt=resolution.size, \n",
" mask=numpy.logical_not(numpy.isfinite(mask)), \n",
" unit=\"d*2_nm^-2\",\n",
" split='no',\n",
" algo='CSR',\n",
" scale=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "popular-munich",
"metadata": {},
"outputs": [],
"source": [
"from scipy.sparse import csr as csr_module\n",
"csr = csr_module.csr_matrix(engine.lut, shape=(resolution.size, size))\n",
"csr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "severe-outreach",
"metadata": {},
"outputs": [],
"source": [
"nticks = 7\n",
"fig, ax = subplots()\n",
"ax.plot(radius, numpy.log(I_bg[frame]), label=\"Background\")\n",
"tick_value = numpy.linspace(radius[0], radius[-1], nticks)\n",
"ax.set_xticks(tick_value)\n",
"tick_label = [f\"{10/numpy.sqrt(i) if i>0 else numpy.NaN:6.4f}\" for i in tick_value]\n",
"ax.set_xticklabels(tick_label)\n",
"ax.set_xlabel(\"Resolution ($\\mathrm{\\AA}$)\")\n",
"#ax.plot(radius, numpy.log(csr.dot(signal)/csr.dot(norm)-I_bg[frame]), label=\"Peaks-Background\")\n",
"ax.plot(radius, numpy.log(csr.dot(signal)/csr.dot(norm)), label=\"Peaks\")\n",
"ax.set_ylabel(\"$log(\\overline{I})$\")\n",
"ax.legend()\n",
"ax.set_title(f\"Wilson-like plot for frame #{frame}\")\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecological-press",
"metadata": {},
"outputs": [],
"source": [
"def update_plot(frame):\n",
" ax.set_title(f\"Wilson-like plot for frame #{frame}\")\n",
" ax.lines[0].set_data(radius, numpy.log(I_bg[frame]))\n",
" signal = numpy.zeros(size)\n",
" norm = numpy.zeros(size)\n",
" indexes = nx_data[\"index\"][frame_ptr[frame]:frame_ptr[frame+1]]\n",
" intensities= nx_data[\"intensity\"][frame_ptr[frame]:frame_ptr[frame+1]]\n",
" signal[indexes] = intensities\n",
" norm[indexes] = 1\n",
" #ax.lines[0].set_data(radius, numpy.log(csr.dot(signal)/csr.dot(norm)-I_bg[frame]))\n",
" ax.lines[1].set_data(radius, numpy.log(csr.dot(signal)/csr.dot(norm)))\n",
" return frame\n",
"interact(update_plot, frame=widgets.IntSlider(min=0, max=len(npix)-1, step=1, value=1));"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "related-rider",
"metadata": {},
"outputs": [],
"source": [
"root.close()\n",
"print(f\"Total execution time: {time.perf_counter()-start_time:6.3f} s\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}