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feat(charts): added xas-notebook with parameter injection using papermill
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‎notebooks/requirements.txt

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@@ -7,4 +7,4 @@ ipykernel==6.29.5
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hdfmap==0.9.0
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nexus2srs==1.1.0
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h5py==3.13.0
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hdf5plugin==5.1.0
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hdf5plugin==5.1.0

‎notebooks/xas_notebook.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ea95a75c-ab66-4cd5-bcc3-2daa8f7bc358",
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"metadata": {},
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"source": [
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"# XAS Notebook\n",
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"\n",
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"The XAS Notebook autoprocessor will run automatically on every scan performed that contains the [NXxas](https://manual.nexusformat.org/classes/applications/NXxas.html#nxxas) sub-entry.\n",
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"\n",
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"A simple analysis and plot of the data is provided.\n",
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"\n",
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"Note: [mmg_toolbox](https://github.com/DanPorter/mmg_toolbox) is in active development and likely to change in the future, therefore please don't rely to heavily on the functions at this point. You can see how everything works at the repository. If you have any comments, please do get in touch!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "42f203e1",
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"metadata": {
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"# Processor requirements\n",
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"inpath = \"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ef4acc26-df0a-43d2-9c90-3dd908def7a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys, os\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import hdfmap\n",
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"print(hdfmap.module_info())\n",
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"\n",
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"# Set environment variables\n",
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"os.environ[\"MPLCONFIGDIR\"] = \"/tmp/.config/matplotlib\"\n",
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"\n",
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"# Ensure the directories exist\n",
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"os.makedirs(os.environ[\"MPLCONFIGDIR\"], exist_ok=True)\n",
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"\n",
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"from mmg_toolbox.env_functions import get_scan_number, replace_scan_number\n",
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"from mmg_toolbox.spectra_scan import SpectraScan, find_pol_pairs, is_nxxas\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "591f3860-45f7-4545-9a8c-d58e27d9782e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load data from NeXus file using HdfMap\n",
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"\n",
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"nxs_map = hdfmap.create_nexus_map(inpath)\n",
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"\n",
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"with nxs_map.load_hdf() as nxs:\n",
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" def rd(expr, default=''):\n",
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" return nxs_map.format_hdf(nxs, expr, default=default)\n",
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"\n",
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" # currently accounts for i06-1 and i10-1 metadata\n",
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" metadata = {\n",
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" \"scan\": rd('{filename}'),\n",
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" \"cmd\": rd('{(cmd|user_command|scan_command)}'),\n",
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" \"title\": rd('{title}', os.path.basename(inpath)),\n",
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" \"endstation\": rd('{end_station}', 'unknown'),\n",
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" \"sample\": rd('{sample_name}', ''),\n",
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" \"energy\": rd('{np.mean((fastEnergy|pgm_energy|energye|energyh)):.2f} eV'),\n",
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" \"pol\": rd('{polarisation?(\"lh\")}'),\n",
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" \"height\": rd('{(em_y|hfm_y|scm_y?(0)):.2f}', 0),\n",
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" \"pitch\": rd('{(em_pitch|hfm_pitch|m7_pitch?(0)):.2f}', 0),\n",
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" \"temperature\": rd('{(T_sample|sample_temperature|lakeshore336_cryostat|lakeshore336_sample|itc3_device_sensor_temp?(300)):.2f} K'),\n",
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" \"field\": rd('{(field_z|sample_field|magnet_field|ips_demand_field?(0)):.2f} T'),\n",
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" }\n",
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"\n",
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" print(nxs_map.eval(nxs, '_(fastEnergy|pgm_energy|energye|energyh)'))\n",
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" print(nxs_map.eval(nxs, '_(C2|ca62sr|mcs16_data|mcse16_data|mcsh16_data)'))\n",
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" print(nxs_map.eval(nxs, '_(C1|ca61sr|mcs17_data|mcse17_data|mcsh17_data)'))\n",
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" print(nxs_map.eval(nxs, '_(C3|ca63sr|mcs18_data|mcse18_data|mcsh18_data|mcsd18_data)'))\n",
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"\n",
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" energy = nxs_map.eval(nxs, '(fastEnergy|pgm_energy|energye|energyh)')\n",
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" monitor = nxs_map.eval(nxs, '(C2|ca62sr|mcs16_data|mcse16_data|mcsh16_data)', default=1.0)\n",
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" tey = nxs_map.eval(nxs, '(C1|ca61sr|mcs17_data|mcse17_data|mcsh17_data)', default=np.ones(nxs_map.scannables_shape())) / monitor\n",
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" tfy = nxs_map.eval(nxs, '(C3|ca63sr|mcs18_data|mcse18_data|mcsh18_data|mcsd18_data)', default=np.ones(nxs_map.scannables_shape())) / monitor\n",
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"\n",
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"print('\\n'.join(f\"{n:12}: {d}\" for n, d in metadata.items()))\n",
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"\n",
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"title = \"{endstation} {sample} {scan}\\nE = {energy}, pol = {pol}, T = {temperature}, B = {field}\".format(**metadata)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d8849732",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load data from NeXus file from NXxas sub-entry\n",
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"\"\"\"\n",
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"import h5py \n",
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"with h5py.File(inpath, 'r') as nxs:\n",
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" # first entry\n",
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" entry = next(group for path, group in nxs.items() if group.attrs.get('NX_class', b'') == b'NXentry')\n",
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" nxxas = next(group for path, group in entry.items() if group.attrs.get('NX_class', b'') == b'NXsubentry' and group.get('definition').asstr()[()] == 'NXxas')\n",
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" print(f\"NXxas path: \\n{nxxas.name}\")\n",
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" print(f\" {'\\n '.join(nxxas.keys())}\")\n",
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"\n",
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" monitor = nxxas['monitor']['data'][()] if 'data' in nxxas['monitor'] else 1.0\n",
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" data = nxxas['data'] # or 'tey', 'tfy_ft'\n",
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" mode = data['mode'][()]\n",
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" energy = data['energy'][()]\n",
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" absorbed_beam = data['absorbed_beam'][()] / monitor\n",
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" tey = nxxas['tey']['absorbed_beam'][()] / monitor\n",
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" tfy = nxxas['tfy']['absorbed_beam'][()] / monitor\n",
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"\"\"\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "28b6a641-8f85-4c76-ae2a-ed2e6f588dce",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n",
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"fig.suptitle(title)\n",
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"\n",
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"ax[0].plot(energy, tey, '-', label=f\"{metadata['pol']} TEY\")\n",
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"ax[1].plot(energy, tfy, '-', label=f\"{metadata['pol']} TFY\")\n",
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"\n",
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"ax[0].set_xlabel('Energy [eV]')\n",
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"ax[0].set_ylabel('signal / monitor')\n",
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"ax[0].legend()\n",
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"\n",
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"ax[1].set_xlabel('Energy [eV]')\n",
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"ax[1].set_ylabel('signal / monitor')\n",
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"ax[1].legend()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6a75d026-7605-4745-ab45-c17bb254bb8c",
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"metadata": {},
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"source": [
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"# Search for previous NXxas scans\n",
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"If the previous scan was also a spectra scan, plot this and take the difference"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8f052bf9-4e7d-4fb6-8d26-d744268a0844",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Build list of scans\n",
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"scan_number = get_scan_number(inpath)\n",
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"scans = []\n",
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"while len(scans) < 9:\n",
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" filename = replace_scan_number(inpath, scan_number)\n",
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" scan_number -= 1\n",
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" if is_nxxas(filename):\n",
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" scans.append(SpectraScan(filename)) # Load scan data\n",
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" else:\n",
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" break\n",
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"\n",
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"for scan in scans:\n",
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" print(scan, '\\n')\n",
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"\n",
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"# Get polarisations\n",
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"pols = {scan.pol for scan in scans}\n",
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"print(f\"Polarisations: {pols}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3942b136-9d23-4065-b35a-2d39a4c50887",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot raw spectra\n",
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"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[12, 6], dpi=100)\n",
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"for scan in scans:\n",
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" scan.tey.plot(ax1)\n",
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" scan.tfy.plot(ax2)\n",
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"\n",
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"ax1.set_xlabel('E [eV]')\n",
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"ax1.set_ylabel('TEY / monitor')\n",
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"ax2.set_xlabel('E [eV]')\n",
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"ax2.set_ylabel('TFY / monitor')\n",
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"ax1.legend()\n",
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"ax2.legend()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "380001c6-950d-468f-92e0-aec5099f910d",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7a13e382-a30e-4b19-86c0-34e1604c7112",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Subtract background and normalise\n",
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"# background options: flat, norm, linear, curve, exp\n",
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"# normalisation option: .norm_to_jump(), .norm_to_peak()\n",
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"rem_bkg = [s.remove_background('flat').norm_to_jump() for s in scans]\n",
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"\n",
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"# Plot background subtracted, normalised spectra\n",
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"for scan in rem_bkg:\n",
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" # print(scan, '\\n')\n",
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" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[12, 6], dpi=100)\n",
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" scan.tey.plot(ax1)\n",
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" scan.tey.plot_bkg(ax1)\n",
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" scan.tfy.plot(ax2)\n",
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" scan.tfy.plot_bkg(ax2)\n",
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"\n",
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" ax1.set_xlabel('E [eV]')\n",
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" ax1.set_ylabel('TEY / monitor')\n",
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" ax2.set_xlabel('E [eV]')\n",
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" ax2.set_ylabel('TFY / monitor')\n",
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" ax1.legend()\n",
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" ax2.legend()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5d66604e-7dd5-4675-912f-88ccd5d5ee3e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Find polarisation pairs within the scans\n",
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"if len(scans) > 1 and len(pols) > 1:\n",
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" pairs = find_pol_pairs(*rem_bkg)\n",
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" \n",
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" for pair in pairs:\n",
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" print(pair)\n",
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" \n",
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" # pair.create_figure()\n",
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" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[12, 6], dpi=100)\n",
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" fig.suptitle(pair.description)\n",
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" \n",
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" pair.spectra1.tey.plot_parents(ax1) # background subtracted spectra\n",
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" pair.spectra2.tey.plot_parents(ax1)\n",
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" pair.tey.plot(ax1) # XMCD/XMLD\n",
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" \n",
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" pair.spectra1.tfy.plot_parents(ax2)\n",
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" pair.spectra2.tfy.plot_parents(ax2)\n",
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" pair.tfy.plot(ax2)\n",
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" \n",
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" ax1.set_xlabel('E [eV]')\n",
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" ax1.set_ylabel('TEY / monitor')\n",
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" ax2.set_xlabel('E [eV]')\n",
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" ax2.set_ylabel('TFY / monitor')\n",
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" ax1.legend()\n",
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" ax2.legend()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8869991d-3b3a-4e36-ba22-afd75ad296c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Average each polarisation of all scans\n",
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"if len(scans) > 1 and len(pols) > 1:\n",
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" pol1 = [pair.spectra1 for pair in pairs]\n",
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" pol2 = [pair.spectra2 for pair in pairs]\n",
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" av_pol1 = sum(pol1[1:], pol1[0])\n",
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" av_pol2 = sum(pol2[1:], pol2[0])\n",
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" diff = av_pol1 - av_pol2\n",
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" print(diff)\n",
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" \n",
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" # diff.create_figure()\n",
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" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[12, 6], dpi=100)\n",
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" fig.suptitle(diff.description)\n",
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" \n",
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" diff.spectra1.tey.plot_parents(ax1)\n",
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" diff.spectra2.tey.plot_parents(ax1)\n",
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" diff.tey.plot(ax1)\n",
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" \n",
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" diff.spectra1.tfy.plot_parents(ax2)\n",
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" diff.spectra2.tfy.plot_parents(ax2)\n",
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" diff.tfy.plot(ax2)\n",
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" \n",
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" ax1.set_xlabel('E [eV]')\n",
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" ax1.set_ylabel('TEY / monitor')\n",
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" ax2.set_xlabel('E [eV]')\n",
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" ax2.set_ylabel('TFY / monitor')\n",
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" ax1.legend()\n",
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" ax2.legend()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7e39fae3-b0ed-4aaf-be8c-a872d250bf5c",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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