{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "8ad4167a-e4e7-498d-909a-c04da9f177ed",
"metadata": {
"tags": []
},
"source": [
"# Binning of temperature-dependent ARPES data using time-stamped external temperature data\n",
"In this example, we pull some temperature-dependent ARPES data from Zenodo, which was recorded as a continuous temperature ramp. We then add the respective temperature information from the respective timestamp/temperature values to the dataframe, and bin the data as function of temperature\n",
"For performance reasons, best store the data on a locally attached storage (no network drive). This can also be achieved transparently using the included MirrorUtil class."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb045e17-fa89-4c11-9d51-7f06e80d96d5",
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import glob\n",
"\n",
"import sed\n",
"from sed.dataset import dataset\n",
"\n",
"%matplotlib widget"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "42a6afaa-17dd-4637-ba75-a28c4ead1adf",
"metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "34f46d54",
"metadata": {},
"outputs": [],
"source": [
"dataset.get(\"TaS2\") # Put in Path to a storage of at least 20 GByte free space.\n",
"data_path = dataset.dir\n",
"scandir, caldir = dataset.subdirs # scandir contains the data, caldir contains the calibration files\n",
"\n",
"# correct timestamps if not correct timezone set\n",
"tzoffset = os.path.getmtime(scandir + '/Scan0121_1.h5') - 1594998158.0\n",
"if tzoffset:\n",
" for file in glob.glob(scandir +'/*.h5'):\n",
" os.utime(file, (os.path.getmtime(file)-tzoffset, os.path.getmtime(file)-tzoffset))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1f82054",
"metadata": {},
"outputs": [],
"source": [
"# create sed processor using the config file with time-stamps:\n",
"sp = sed.SedProcessor(folder=scandir, user_config=\"../sed/config/mpes_example_config.yaml\", time_stamps=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85ac3c83",
"metadata": {},
"outputs": [],
"source": [
"# Apply jittering to X, Y, t, ADC columns.\n",
"sp.add_jitter()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76bf8aad",
"metadata": {},
"outputs": [],
"source": [
"sp.bin_and_load_momentum_calibration(df_partitions=10, plane=33, width=3, apply=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f99c6b3f",
"metadata": {},
"outputs": [],
"source": [
"features = np.array([[337., 242.], [289., 327.], [187., 344.], [137., 258.], [189., 161.], [289., 158.], [236.0, 250.0]])\n",
"sp.define_features(features=features, rotation_symmetry=6, include_center=True, apply=True)\n",
"sp.generate_splinewarp(include_center=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62abfa41",
"metadata": {},
"outputs": [],
"source": [
"# Adjust pose alignment, using stored distortion correction\n",
"sp.pose_adjustment(xtrans=15, ytrans=8, angle=-5, apply=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "845f002d",
"metadata": {},
"outputs": [],
"source": [
"# Apply stored momentum correction\n",
"sp.apply_momentum_correction()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9ae5066",
"metadata": {},
"outputs": [],
"source": [
"# Apply stored config momentum calibration\n",
"sp.apply_momentum_calibration()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb1e2bee",
"metadata": {},
"outputs": [],
"source": [
"# Apply stored config energy correction\n",
"sp.apply_energy_correction()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de946733",
"metadata": {},
"outputs": [],
"source": [
"# Load energy calibration EDCs\n",
"scans = np.arange(127,136)\n",
"voltages = np.arange(22,13,-1)\n",
"files = [caldir + r'/Scan' + str(num).zfill(4) + '_1.h5' for num in scans]\n",
"sp.load_bias_series(data_files=files, normalize=True, biases=voltages, ranges=[(64000, 76000)])\n",
"rg = (65500, 66000)\n",
"sp.find_bias_peaks(ranges=rg, ref_id=5, infer_others=True, apply=True)\n",
"sp.calibrate_energy_axis(ref_energy=-0.5, ref_id=4, energy_scale=\"kinetic\", method=\"lmfit\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c470ffd9",
"metadata": {},
"outputs": [],
"source": [
"# Apply stored config energy calibration\n",
"sp.append_energy_axis()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0943d349",
"metadata": {},
"outputs": [],
"source": [
"# add time-stamped temperature data\n",
"# either, directly retrieve data from EPICS archiver instance (within FHI network),\n",
"#sp.add_time_stamped_data(dest_column=\"T_B\", archiver_channel=\"trARPES:Carving:TEMP-B\")\n",
"# or use externally provided timestamp/data pairs\n",
"import h5py\n",
"with h5py.File(f\"{data_path}/temperature_data.h5\", \"r\") as file:\n",
" data = file[\"temperatures\"][()]\n",
" time_stamps = file[\"timestamps\"][()]\n",
"sp.add_time_stamped_data(dest_column=\"sample_temperature\", time_stamps=time_stamps, data=data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c330da64",
"metadata": {},
"outputs": [],
"source": [
"# inspect calibrated event histogram\n",
"axes = ['kx', 'ky', 'energy', 'sample_temperature']\n",
"ranges = [[-3, 3], [-3, 3], [-6, 2], [10, 300]]\n",
"sp.view_event_histogram(dfpid=80, axes=axes, ranges=ranges)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6902fd56-1456-4da6-83a4-0f3f6b831eb6",
"metadata": {},
"source": [
"## Define the binning ranges and compute calibrated data volume"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7601cd7-cd51-40a9-8fc7-8b7d32ff15d0",
"metadata": {},
"outputs": [],
"source": [
"axes = ['kx', 'ky', 'energy', 'sample_temperature']\n",
"bins = [100, 100, 300, 100]\n",
"ranges = [[-2, 2], [-2, 2], [-6, 2], [20, 270]]\n",
"res = sp.compute(bins=bins, axes=axes, ranges=ranges, normalize_to_acquisition_time=\"sample_temperature\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "523794dc",
"metadata": {},
"source": [
"## Some visualization:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99d7d136-b677-4c16-bc8f-31ba8216579c",
"metadata": {},
"outputs": [],
"source": [
"fig, axs = plt.subplots(4, 1, figsize=(4, 12), constrained_layout=True)\n",
"res.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])\n",
"res.loc[{'kx':slice(-.2, .2)}].sum(axis=(0,3)).T.plot(ax=axs[1])\n",
"res.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])\n",
"res.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-2, 0.2)}].sum(axis=(0,1)).plot(ax=axs[3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "596a3217",
"metadata": {},
"outputs": [],
"source": [
"# Inspect effect of histogram normalization\n",
"fig, ax = plt.subplots(1,1)\n",
"(sp._normalization_histogram/sp._normalization_histogram.sum()).plot(ax=ax)\n",
"(sp._binned.sum(axis=(0,1,2))/sp._binned.sum(axis=(0,1,2,3))).plot(ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05488944",
"metadata": {},
"outputs": [],
"source": [
"# Remaining fluctuations are an effect of the varying count rate throughout the scan\n",
"plt.figure()\n",
"rate, secs = sp.loader.get_count_rate()\n",
"plt.plot(secs, rate)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4697a5b4",
"metadata": {},
"outputs": [],
"source": [
"# Normalize for intensity around the Gamma point\n",
"res_norm = res.copy()\n",
"res_norm = res_norm/res_norm.loc[{'kx':slice(-.3, .3), 'ky':slice(-.3, .3)}].sum(axis=(0,1,2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "124b3095",
"metadata": {},
"outputs": [],
"source": [
"fig, axs = plt.subplots(4, 1, figsize=(4, 12), constrained_layout=True)\n",
"res_norm.loc[{'energy':slice(-.1, 0)}].sum(axis=(2,3)).T.plot(ax=axs[0])\n",
"res_norm.loc[{'kx':slice(-.2, .2)}].sum(axis=(0,3)).T.plot(ax=axs[1])\n",
"res_norm.loc[{'ky':slice(-.2, .2)}].sum(axis=(1,3)).T.plot(ax=axs[2])\n",
"res_norm.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-2, 0.5)}].sum(axis=(0,1)).plot(ax=axs[3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7a07437",
"metadata": {},
"outputs": [],
"source": [
"# Lower Hubbard band intensity versus temperature\n",
"plt.figure()\n",
"res_norm.loc[{'kx':slice(-.2, .2), 'ky':slice(-.2, .2), 'energy':slice(-.6, 0.1)}].sum(axis=(0,1,2)).plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "15309e42",
"metadata": {},
"outputs": [],
"source": []
}
],
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