{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1bfd1581", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dask: 2.11.0\n", "/home/lleguy/notebooks/ToolBox/src/toolbox_scs/__init__.py\n" ] } ], "source": [ "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['figure.constrained_layout.use'] = True\n", "\n", "import dask\n", "print(f'dask: {dask.__version__}')\n", "\n", "import toolbox_scs as tb\n", "print(tb.__file__)\n", "import toolbox_scs.routines.boz as boz" ] }, { "cell_type": "markdown", "id": "e94a5c90", "metadata": {}, "source": [ "# Improved method\n", "\n", "The delay is scan and saved in the DAQ while recording a single run." ] }, { "cell_type": "code", "execution_count": 2, "id": "975a36d1", "metadata": {}, "outputs": [], "source": [ "proposal = 900257\n", "dNB = 67\n", "rNB = 68" ] }, { "cell_type": "code", "execution_count": 3, "id": "23144fcb", "metadata": {}, "outputs": [], "source": [ "arr_dark, tid_dark = boz.load_dssc_module(proposal, dNB, drop_intra_darks=False)" ] }, { "cell_type": "code", "execution_count": 4, "id": "6a7a4169", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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" ], "text/plain": [ "dask.array" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr_dark" ] }, { "cell_type": "code", "execution_count": 5, "id": "6d233216", "metadata": {}, "outputs": [], "source": [ "dark = arr_dark.mean(axis=0).compute()" ] }, { "cell_type": "code", "execution_count": 6, "id": "43934f12", "metadata": {}, "outputs": [], "source": [ "arr, tid = boz.load_dssc_module(proposal, rNB, drop_intra_darks=False)\n", "arr = arr - dark" ] }, { "cell_type": "code", "execution_count": 7, "id": "300ce9c6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Bunch pattern table not found in run. Skipping!\n" ] } ], "source": [ "fields = [\"DSSC_delay\"]\n", "\n", "run, data = tb.load(proposal, rNB, fields)" ] }, { "cell_type": "code", "execution_count": 8, "id": "555a7a99", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "Dimensions: (trainId: 3987)\n", "Coordinates:\n", " * trainId (trainId) uint64 1356431136 1356431137 ... 1356435121 1356435122\n", "Data variables:\n", " DSSC_delay (trainId) int32 4756934 4756934 4756934 ... 4756963 4756963\n", "Attributes:\n", " runFolder: /gpfs/exfel/exp/SCS/202230/p900257/raw/r0068" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 9, "id": "133621fc", "metadata": {}, "outputs": [], "source": [ "intensity = (arr[:,::2,:,:] - arr[:,1::2,:,:]).mean(axis=(1,2,3)).compute()" ] }, { "cell_type": "code", "execution_count": 10, "id": "aa37ecee", "metadata": {}, "outputs": [], "source": [ "import xarray as xr" ] }, { "cell_type": "code", "execution_count": 11, "id": "b2ed51a8", "metadata": {}, "outputs": [], "source": [ "xrintensity = xr.DataArray(intensity, coords={'trainId':tid}, dims=['trainId']).to_dataset(name='intensity')" ] }, { "cell_type": "code", "execution_count": 12, "id": "40ecf913", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "\n", "Dimensions: (trainId: 3987)\n", "Coordinates:\n", " * trainId (trainId) uint64 1356431136 1356431137 ... 1356435121 1356435122\n", "Data variables:\n", " intensity (trainId) float64 -0.05101 -0.05173 -0.04971 ... 0.1101 0.09043" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xrintensity" ] }, { "cell_type": "code", "execution_count": 13, "id": "b12050ba", "metadata": {}, "outputs": [], "source": [ "r = xr.merge([data, xrintensity], join='inner', combine_attrs='no_conflicts')" ] }, { "cell_type": "code", "execution_count": 14, "id": "6c222b5e", "metadata": {}, "outputs": [], "source": [ "rbin = r.groupby('DSSC_delay').mean()" ] }, { "cell_type": "code", "execution_count": 15, "id": "be0a0b31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Intensity')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "offset = 4756949\n", "plt.figure()\n", "plt.plot(rbin['DSSC_delay'] - offset, rbin['intensity'], marker='o')\n", "plt.title(f'{proposal} r:{rNB} d:{dNB}')\n", "plt.xlabel(f'DSSC fine delay - {offset} (delay unit)')\n", "plt.ylabel('Intensity')" ] }, { "cell_type": "markdown", "id": "d1e47f42", "metadata": {}, "source": [ "# Legacy\n", "\n", "One run is recorded for one fine delay in sequence, the delay itself is not saved in the data" ] }, { "cell_type": "code", "execution_count": 16, "id": "69c17cda", "metadata": {}, "outputs": [], "source": [ "runs = np.arange(775, 814+1)\n", "delay = np.arange(4756930, 4756969+1)" ] }, { "cell_type": "code", "execution_count": 17, "id": "b436e44e", "metadata": {}, "outputs": [], "source": [ "proposal = 2937" ] }, { "cell_type": "markdown", "id": "fdec2470", "metadata": {}, "source": [ "use first run as dark" ] }, { "cell_type": "code", "execution_count": 18, "id": "6d5673f2", "metadata": {}, "outputs": [], "source": [ "arr_dark, tid_dark = boz.load_dssc_module(proposal, runs[0])\n", "dark = boz.average_module(arr_dark).compute()" ] }, { "cell_type": "code", "execution_count": 19, "id": "542d140c", "metadata": {}, "outputs": [], "source": [ "intensity = np.zeros((len(runs)))\n", "for k,r in enumerate(runs):\n", " arr, tid = boz.load_dssc_module(proposal, r)\n", " data = boz.average_module(arr, dark=dark).compute()\n", " sensor = data[:,:,:256]\n", " intensity[k] = sensor.mean(axis=(0,1,2))" ] }, { "cell_type": "code", "execution_count": 20, "id": "eab988af", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, '')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.plot(delay-delay[0], intensity)\n", "plt.xlabel(f'delay - {delay[0]}')\n", "plt.ylabel('')" ] }, { "cell_type": "code", "execution_count": null, "id": "369b3289", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "xfel (Python 3.7)", "language": "python", "name": "xfel" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 5 }