NC_explorer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebe85abe",
   "metadata": {},
   "source": [
    "## Exploring NC files\n",
    "Updated to use functions and read-files\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "760de5b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---Packages---\n",
    "import cartopy.crs as ccrs\n",
    "#import hvplot.xarray\n",
    "import xarray as xr\n",
    "import cartopy.feature as cfeature\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import shapely.geometry as sgeom\n",
    "\n",
    "import functions\n",
    "import read_nav\n",
    "\n",
    "# removes warnings. Using np.datetime64() gives an outdated Deprecation warning.\n",
    "# to see warnings once use warnings.filterwarnings(action='once')\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a61b861",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Global formatting/settings/variables\n",
    "\n",
    "# formatting for only showing time on the x-axis for plots per flight\n",
    "# Use by setting axs[1,0].xaxis.set_major_formatter(formatter) for each axis where only time should show\n",
    "formatter = mdates.DateFormatter('%H:%M')\n",
    "\n",
    "# colors for flights (to use all the same colors!\n",
    "c_flights = {'IS22-01':'wheat',\n",
    "              'IS22-02':'tab:green',\n",
    "              'IS22-03':'tab:red',\n",
    "              'IS22-04':'tab:purple',\n",
    "              'IS22-05':'tab:blue',\n",
    "              'IS22-06':'tab:brown',\n",
    "              'IS22-07':'tab:gray',\n",
    "              'IS22-08':'tab:olive',\n",
    "              'IS22-09':'tab:cyan',\n",
    "              'IS22-10':'tab:pink',\n",
    "              'IS22-11':'tab:orange'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "317c4055",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---Data---\n",
    "# Based on scripts in files read_*.py\n",
    "\n",
    "# Navigational data\n",
    "# -- nav_df: data variables\n",
    "# -- nav_stats_dict: statistics for each flight\n",
    "nav_df, nav_stats_dict = read_nav.read_nav()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73dc77ac-4a7d-4dc4-b927-c6bcf84650ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee5a1713",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---Statistical overview of the flights----\n",
    "# collect information for displaying for all flights:\n",
    "# Directly from attributes\n",
    "# - flight id, takeoff, landing\n",
    "# Calculated:\n",
    "# - time in air (takeoff, landing)\n",
    "# - max/min temp (TAS1/TAS2) (in cloud or just in total?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8bea6514",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- Map initialization ---\n",
    "#-- Map initialization based on flight info --\n",
    "\n",
    "# Find the max and min lat and lon in the dataset\n",
    "inc = 1\n",
    "lat_max = nav_df['Latitude (degree)'].max() + inc\n",
    "lat_min = nav_df['Latitude (degree)'].min() - inc\n",
    "lon_max = nav_df['Longitude (degree)'].max() + inc\n",
    "lon_min = nav_df['Longitude (degree)'].min() - inc\n",
    "\n",
    "# coordinates of Andøya\n",
    "#lat_and = 69.3073\n",
    "#lon_and = 16.1312\n",
    "\n",
    "# coordinates of Kiruna\n",
    "lat_kir = 67.8256\n",
    "lon_kir = 20.3351\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45b4c236",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---- Plot coordinates -----\n",
    "# Plotting lat and lon on map\n",
    "def plot_map(nav_df, flight = \"\", file_str = \"\"):\n",
    "    # Function to create a map showing the flight paths. Uses set colors and area to plot in. \n",
    "    # INPUT arguments:\n",
    "    #   nav_df: dataframe that contains at least the two columns: 'Longitude (degree)' and 'Latitude (degree)'\n",
    "    # OPTIONAL INPUT arguments:\n",
    "    #   flight: name of single flight to plot. If no flight is added, all flights are plotted.\n",
    "    #   file_str: name of file to print plot to. If no file string is added, the plot is not saved.\n",
    "    # Additional information:\n",
    "    #   Uses global variable: c_flights\n",
    "\n",
    "    #-- Map initialization based on flight info --\n",
    "\n",
    "    # Find the max and min lat and lon in the dataset\n",
    "    inc = 1\n",
    "    lat_max = nav_df['Latitude (degree)'].max() + inc\n",
    "    lat_min = nav_df['Latitude (degree)'].min() - inc\n",
    "    lon_max = nav_df['Longitude (degree)'].max() + inc\n",
    "    lon_min = nav_df['Longitude (degree)'].min() - inc\n",
    "    \n",
    "    # coordinates of Andøya\n",
    "    #lat_and = 69.3073\n",
    "    #lon_and = 16.1312\n",
    "    \n",
    "    # coordinates of Kiruna\n",
    "    lat_kir = 67.8256\n",
    "    lon_kir = 20.3351\n",
    "\n",
    "    # --- Set up figure\n",
    "    fig = plt.figure(figsize=(10, 8))\n",
    "    ax = fig.add_subplot(1, 1, 1, projection=ccrs.NorthPolarStereo())\n",
    "    \n",
    "    ax.add_feature(cfeature.COASTLINE)\n",
    "    ax.add_feature(cfeature.BORDERS, linewidth=2)\n",
    "    data_projection = ccrs.PlateCarree()\n",
    "    \n",
    "    if flight == \"\":\n",
    "        # plot all the flights from ISLAS if \"flight\" is empty\n",
    "        for flight in nav_df['flightid'].unique().sort_values():\n",
    "            sel_df = nav_df[nav_df['flightid']==flight] # filtrate on flight\n",
    "            date = sel_df.index[0].date() # get the date of the flight\n",
    "            ax.plot(sel_df['Longitude (degree)'], sel_df['Latitude (degree)'],\n",
    "                label = f'{flight} ({date})', c =c_flights[flight], transform = data_projection)\n",
    "    else:\n",
    "        # Plot only the given flight if \"flight\" contains a flightid\n",
    "        try:\n",
    "            sel_df = nav_df[nav_df['flightid']==flight] # filtrate on flight\n",
    "            date = sel_df.index[0].date() # get the date of the flight\n",
    "            ax.plot(sel_df['Longitude (degree)'], sel_df['Latitude (degree)'],\n",
    "                    label = f'{flight} ({date})', c =c_flights[flight], transform = data_projection)\n",
    "        except ValueError:\n",
    "            print(f'Flightid: {flight} is not a valid flightid. Needs to be in the format \"IS22-XX\" where X is a number between 02 and 11')\n",
    "     \n",
    "    \n",
    "    #Plot Andøya on map\n",
    "    #ax.plot(lon_and, lat_and, marker='o', color='tab:red', transform=data_projection)\n",
    "    #ax.annotate('Andøya', (lon_and, lat_and))\n",
    "    \n",
    "    #Plot Kiruna on map\n",
    "    ax.plot(lon_kir, lat_kir, marker='o', color='tab:red', transform=data_projection)\n",
    "    #Add text \"Kiruna\" at the plotted point\n",
    "    offset_lon = 0.7  # adjust the horizontal offset\n",
    "    offset_lat = -0.7  # adjust the vertical offset\n",
    "    ax.text(lon_kir + offset_lon, lat_kir + offset_lat, \"Kiruna\", transform=data_projection, ha='right', va='bottom')\n",
    "    \n",
    "    \n",
    "    # --- Drawing a dashed box\n",
    "    #lons = [16, 22, 22, 16, 16]\n",
    "    #lats = [74, 74, 76, 76, 74]\n",
    "    #ax.plot(lons, lats, linestyle='--', color='black', transform=ccrs.PlateCarree())\n",
    "    # ---\n",
    "    \n",
    "    ax.set_extent([lon_min, lon_max, lat_min, lat_max])\n",
    "    plt.legend(loc='best')\n",
    "    \n",
    "    # save figure if filestring is given\n",
    "    if file_str !=\"\":\n",
    "        plt.savefig(file_str) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e48190ee-1afe-469d-a902-1ba35d685b16",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_map(nav_df, flight='IS22-03')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21dda8b0-be33-4701-a65d-f660979f0762",
   "metadata": {},
   "source": [
    "## Preprocessing NAV data\n",
    "Creating TAS file to use for correction of the CIP data in SODA2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ae05b14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --- creating TAS file for SODA2 processing ---\n",
    "\n",
    "# loop over all the datastructures in ds_list (ds5-ds15)\n",
    "for ds in ds_list:\n",
    "    # Create TAS files using TAS1:TAS from Scientific Static/Pitot system\n",
    "    # Alternative is TAS2:TAS from Avionic Static/Pitot system (ADC)\n",
    "    TAS = ds.TAS1\n",
    "    flight_id = ds.attrs['flight_id'] # extract flightid for use in filename\n",
    "    \n",
    "    # converts the TAS xarray to dataframe with time, LATITUDE, LONGITUDE, ALTITUDE and TAS1 as columns\n",
    "    # this conversion is done to simplify the creating of the csv file\n",
    "    tas_df = TAS.to_dataframe()\n",
    "    tas_df = tas_df.drop(['LATITUDE','LONGITUDE','ALTITUDE'], axis =1) #Need only TAS1\n",
    "\n",
    "    # add time as a separate column to simplify time manipulation\n",
    "    tas_df['time'] = tas_df.index\n",
    "\n",
    "    # recalculate time from datetime to seconds since midnight (func)\n",
    "    tas_df['time'] = tas_df['time'].apply(sec_since_midnigth)\n",
    "\n",
    "    # rearrange columns to get correct structure for use in SODA2\n",
    "    tas_df = tas_df.reindex(columns=['time','TAS1'])\n",
    "    # set TAS as integer\n",
    "    tas_df['TAS1']=tas_df['TAS1'].astype(int)\n",
    "\n",
    "    # create filename, format: TAS_fightid.csv  (TAS_as220005.csv)\n",
    "    # filepath to results folder\n",
    "    path_out = 'Results_2022-islas/' + flight_id + '/'\n",
    "    filename = path_out +'TAS_' + flight_id + '.csv'\n",
    "\n",
    "    # print tdf to csv-file to be used in SODA2, \n",
    "    # change headers to what SODA2 reads,\n",
    "    # do not add index as correct timeformat is already in column time\n",
    "    # make sure that it is encoded as ascii\n",
    "    tas_df.to_csv(filename, header=['time','tas'], encoding='ascii', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6f6bd1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
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