AttolightSEM_sur_to_hspy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Demo for LumiSpy package working with AttoLight SEM CL data\n",
    "\n",
    "This notebook shows:\n",
    "\n",
    "- Loading a `HYPCard.sur` file correctly with the lumispy load function\n",
    "- Plotting cathodoluminescence data in different ways\n",
    "- Dealing with metadata\n",
    "- Correcting for spectral defects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
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   "source": [
    "%matplotlib widget\n",
    "# Use '%matplotlib widget' in JupterLab and '%matplotlib notebook' in JupyterNotebook for interactive inline functionality (e.g. on binder)\n",
    "#For pop-up window plots on your local computer, use '%matplotlib tk' or '%matplotlib qt' instead\n",
    "import hyperspy.api as hs\n",
    "import lumispy as lum\n",
    "import os, glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading HYPCard files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `hs.load()` function with the `signal_type` class for AttoLight CL-SEM specific data (`CL_SEM`).\n",
    "State the relative path to the `HYPCard.sur` file.\n",
    "\n",
    "*You can also leave the path empty. A pop-up window will appear to select the `HYPCard.sur` file from the browser.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "root_folder = \"demo-files/load_from_AttoLightSEM/\"\n",
    "path = os.path.join(root_folder, 'HYPCard.sur')\n",
    "\n",
    "cl = hs.load(path, signal_type='CL_SEM',)\n",
    "cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cl.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Metadata\n",
    "\n",
    "The `.sur` file contains the general Hyperspy-specific metadata (`cl.metadata`).\n",
    "A more detailed metadata is also available via `cl.original_metadata`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
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   },
   "outputs": [],
   "source": [
    "cl.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "cl.original_metadata.Object_0_Channel_0.Parsed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data pre-processing\n",
    "\n",
    "Let's start some pre-processing methods:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Background removal\n",
    "\n",
    "In the AttoLight system, if a background is taken before mapping, the background is stored automatically in the signal folder.\n",
    "\n",
    "Otherwise, manually load a background file, using `np.loadtxt(path_to_file)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    bkg_path = os.path.join(root_folder, '*.txt')\n",
    "    bkg_path = glob.glob(bkg_path)[0]\n",
    "except IndexError:\n",
    "    print('Please, specify a `background.txt` file path below:')\n",
    "    bkg_path = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "cl_bkg = np.loadtxt(bkg_path)[1]\n",
    "print(cl_bkg.shape)\n",
    "\n",
    "# Run background subtraction\n",
    "cl = cl - cl_bkg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correction of acquisition defects\n",
    "\n",
    "Correct for the intrinsic shift caused by the misalignment of the grating with the spectrometer aperture centre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "calibration_factor = 131072\n",
    "grating = int(cl.original_metadata.Object_0_Channel_0.Parsed.SPECTROMETER.Grating__Groove_Density)\n",
    "\n",
    "if grating == 150:\n",
    "    correction_factor_grating = 2.73E-04 # 150 gr/mm grating\n",
    "elif grating == 600:\n",
    "    correction_factor_grating = 6.693659836087227e-05 # 600 gr/mm grating\n",
    "else:\n",
    "    raise ImportError('Grating correction not available')\n",
    "\n",
    "fov = cl.original_metadata.Object_0_Channel_0.Parsed.SITE_IMAGE.Field_of_view *1e6\n",
    "\n",
    "grating_calibrations = {\n",
    "    'cal_factor' : calibration_factor,\n",
    "    'corr_factor_grating' : correction_factor_grating,\n",
    "    'field_of_view_um' : fov,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cl.correct_grating_shift(*grating_calibrations.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The edges of the scan get higher intensities, so they can be cropped. \n",
    "If you set the `inplace` parameter to `True` the original CLSEMSpectrum object will be modified, if `False` a cropped copy of it will be created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cl = cl.crop_edges(crop_px=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the cosmic ray saturated pixels (pixels with sharp spikes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cl.remove_spikes(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "If not all spikes are removed, you can use the GUI to manually select pixels by calling `cl.remove_spikes(interactive=True, inplace=True)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting data\n",
    "\n",
    "Plot the corrected data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cl.plot()\n",
    "cl.mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Save the data as `.hspy` format\n",
    "\n",
    "Look out for other `lumispy-demos` notebooks to find examples on how to analyse and fit luminescence data.\n",
    "Many notebooks start directly with a `.hspy` file format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# cl.save('path_to_save.hspy')\n",
    "\n"
   ]
  }
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