Mercurial > public > atmospheric_lidar / file revision

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Reading a *scanning* Licel binary files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Import plotting library\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Import the module for raymetrics scanning files\n",
    "from atmospheric_lidar import raymetrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time: 2021-02-12 14:55:44+00:00, Stop times: 2021-02-12 14:56:12+00:00\n",
      "Latitude: 49.0953, Longitude: 8.4298\n",
      "Zenith angle:  0.0\n",
      "Azimuth angle:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Read the file\n",
    "licel_file = raymetrics.ScanningFile('./sample_data/RM2121214.561377')\n",
    "\n",
    "# The licel file object has several properties describing the file\n",
    "print(\"Start time: {}, Stop times: {}\".format(licel_file.start_time, licel_file.stop_time))\n",
    "print(\"Latitude: {}, Longitude: {}\".format(licel_file.latitude, licel_file.longitude))\n",
    "print(\"Zenith angle: \", licel_file.zenith_angle)\n",
    "print(\"Azimuth angle: \", licel_file.azimuth_angle)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available channels:  odict_keys(['00355.p_an', '00355.p_ph', '00355.s_an', '00355.s_ph'])\n"
     ]
    }
   ],
   "source": [
    "# See available channel\n",
    "# Note that the channels have a semi-descriptive name.\n",
    "# This does not work always and you might need to set \"use_id_as_name=True\" when reading the file.\n",
    "print(\"Available channels: \", licel_file.channels.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel name:  00355.p_an\n",
      "Channel id:  BT0\n",
      "Wavelength:  355\n",
      "Number of shots:  601\n",
      "Is analog?:  True\n",
      "ADC bits:  12\n"
     ]
    }
   ],
   "source": [
    "# Get one channel of interest\n",
    "channel = licel_file.channels['00355.p_an']\n",
    "\n",
    "# The channel object has several properties describing the channel data. E.g.\n",
    "print(\"Channel name: \", channel.channel_name)\n",
    "print('Channel id: ', channel.id)\n",
    "print(\"Wavelength: \", channel.wavelength)\n",
    "print(\"Number of shots: \", channel.number_of_shots)\n",
    "print(\"Is analog?: \", channel.is_analog)\n",
    "print(\"ADC bits: \", channel.adcbits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.05932928, 3.05884169, 3.05900422, ..., 3.05876043, 3.05811031,\n",
       "       3.05530668])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data are stored in the .data property, already converted in physical quantities\n",
    "channel.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Range (m)')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(channel.z, channel.data)\n",
    "plt.ylabel('Signal (mV)')\n",
    "plt.xlabel('Range (m)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Has photodiode?: True\n",
      "Photodiode keys:  odict_keys(['00355.p_an'])\n"
     ]
    }
   ],
   "source": [
    "# Photodiodes are stored in the .photodiodes dictionary\n",
    "print(\"Has photodiode?:\", licel_file.has_photodiode )\n",
    "\n",
    "print(\"Photodiode keys: \", licel_file.photodiodes.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Photodiode id:  PD0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mV')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get one photodiode\n",
    "photodiode = licel_file.photodiodes['00355.p_an']\n",
    "\n",
    "# Again, several properties are available\n",
    "print('Photodiode id: ', photodiode.id)\n",
    "\n",
    "plt.plot(photodiode.data)\n",
    "plt.xlabel('Shot #')\n",
    "plt.ylabel('mV')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}