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   "source": [
    "# MuonDataLib Tutorial 5: Peak Property Filtering and Discrimination\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4706a89-6c64-4125-948b-00dd82de4e14",
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   "source": [
    "In the previous tutorials we used the sample logs and times to filter the event data. These filters removed events corresponding to a whole frame. It is also possible to discriminate/filter on individual events. These filters operate on the properties of the peaks that define the events, for example the peak amplitude.  \n",
    "\n",
    "First lets create a histogram for some unfiltered data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17a8a944-357c-4b1d-99ae-712c4c99cb34",
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   "outputs": [],
   "source": [
    "from MuonDataLib.data.loader.load_events import load_events\n",
    "from MuonDataLib.plot.basic import Figure\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "file_name = 'HIFI00195790.nxs'\n",
    "input_file = os.path.join('..', '..', '..', '..', 'test', 'data_files', file_name)\n",
    "data = load_events(input_file, 64)\n",
    "\n",
    "no_filter_hist, bins = data.histogram()\n",
    "fig = Figure(y_label='Counts')\n",
    "fig.plot_from_histogram(bins, no_filter_hist, [0])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e7a894-447e-4839-81e8-65113dd5deed",
   "metadata": {},
   "source": [
    "## Inspecting a Peak Property\n",
    "\n",
    "At present the only available peak property is \"Amplitudes\", which relates to the height of the peak in the trace from the detector. Being able to descriminate/filter the events so that they all have amplitudes above some value is important for instrument calibration. To help the user decide on the value a method has been added to create a histogram of the peak property. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1ba64a6-bade-4df0-b329-9611059569ed",
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   "outputs": [],
   "source": [
    "hist, bins = data.get_peak_property_histogram('Amplitudes')\n",
    "fig = Figure(y_label='Counts', x_label='Amplitude')\n",
    "fig.plot_peak_property_histogram(hist, bins, 'unfiltered, ')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61f30dd-140f-4f7f-9471-f86dcaca8af6",
   "metadata": {},
   "source": [
    "Notice that the plot was generated with the `plot_peak_property_histogram` method. The above plot shows a rapid decrease in the number of events as the size of the amplitude increases. Lets remove the steepest part of the curve by using the `keep_data_peak_property_above` method to filter out the data below 2600."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad24827c-78ca-4eca-9eae-eca7a04933cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.keep_data_peak_property_above(\"Amplitudes\", 2600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ed1f0a6-b127-46f6-9879-3efa56754d36",
   "metadata": {},
   "source": [
    "Next lets look at the impact it has on the histograms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e1cc928-47c1-4b36-848b-f6249b7f08de",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_amp_filter, bins = data.histogram()\n",
    "fig = Figure(y_label='Counts')\n",
    "fig.plot_from_histogram(bins, no_filter_hist, [0], 'unfiltered, ')\n",
    "fig.plot_from_histogram(bins, hist_amp_filter, [0], 'filtered (amplitude), ')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb52e27e-c50f-4342-86fd-2709a69a150b",
   "metadata": {},
   "source": [
    "We can see that the filters have removed some data as expected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc61b639-09c5-435e-aab3-5c9beff539d4",
   "metadata": {},
   "source": [
    "## Removing the Filter\n",
    "\n",
    "The filter value can be changed by using the 'keep_data_peak_property` method with the new threshold value. Whereas, the `delete_data_peak_property_above` method will remove the filter, in reality this sets the threshold to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c39ecc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.delete_data_peak_property_above(\"Amplitudes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a60951df",
   "metadata": {},
   "source": [
    "Fianlly lets check that the histograms have been updated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bf38376-20c2-43e9-b315-b6b9893c20fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_del_filter, bins = data.histogram()\n",
    "fig = Figure(y_label='Counts')\n",
    "fig.plot_from_histogram(bins, no_filter_hist, [0], 'unfiltered, ')\n",
    "fig.plot_from_histogram(bins, hist_del_filter, [0], 'filter removed, ')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a4f9d6c-b737-41aa-b23b-bd9aaece1e40",
   "metadata": {},
   "source": [
    "As expected this data is identical to the unfiltered data. "
   ]
  }
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