{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viewer Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is to view events from an HDF5 set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cait as ai\n",
    "%config InlineBackend.figure_formats = ['svg']  # we need this for a suitable resolution of the plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapt these parameters to your HDF5 set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "ExecuteTime": {
     "end_time": "2021-07-04T22:43:31.879406Z",
     "start_time": "2021-07-04T22:43:31.873352Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adapt these parameters to your measurement!\n"
     ]
    }
   ],
   "source": [
    "print('Adapt these parameters to your measurement!')\n",
    "\n",
    "PATH_DATA = ...  # the path to the data set\n",
    "FNAME = ...  # the file name, without extension ('.h5') and appendix, e.g. bck_001\n",
    "NMBR_CHANNELS = 2  # the number channels in the file\n",
    "RECORD_LENGTH = 16384  # the number of samples per record window (written in par file)\n",
    "SAMPLE_FREQUENCY = 25000  # the sample frequency of the measurement (written in par file)\n",
    "APPENDIX = False  # if the file has an automatically appended Cait appendix, e.g. '-P_0-L_1'\n",
    "CHANNELS = None  # if you set the appendix to true, you need to put here a list of the channel number inside the rdt file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Event Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create instance\n",
    "\n",
    "ei = ai.EventInterface(record_length=RECORD_LENGTH,\n",
    "                       sample_frequency=SAMPLE_FREQUENCY,\n",
    "                       nmbr_channels=NMBR_CHANNELS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ei.load_h5(path=PATH_DATA,\n",
    "               fname=FNAME,\n",
    "               channels=CHANNELS,\n",
    "               appendix=APPENDIX,\n",
    "               which_to_label=['events'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# includes\n",
    "\n",
    "# ei.load_labels_csv(path='', type='events')\n",
    "# ei.create_labels_csv(path='')\n",
    "ei.load_of()\n",
    "ei.load_sev_par(name_appendix='_down4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# start\n",
    "\n",
    "ei.start(start_from_idx=0, print_label_list=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dh = ai.DataHandler(nmbr_channels=NMBR_CHANNELS)\n",
    "\n",
    "dh.set_filepath(path_h5=PATH_DATA,\n",
    "                fname=FNAME,\n",
    "                channels=CHANNELS,\n",
    "                appendix=APPENDIX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dh.content()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Done."
   ]
  }
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