ships.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "fn = \"C:/Users/User/Documents/Leeds/lsdiaga_1982_2021_sat_ts_5day.txt\"\n",
    "ln = 725997"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "AL012000\n",
      "AL022000\n",
      "AL032000\n"
     ]
    }
   ],
   "source": [
    "line_numbers = np.arange(725997,ln+(139*100)) ## I just picked an arbitrary range of line numbers because I didn't want to\n",
    "                                              ## do the whole dataset   \n",
    "\n",
    "\n",
    "storms = []\n",
    "storm_name = \"NULL\"\n",
    "\n",
    "\n",
    "with open(fn, 'r') as fp:\n",
    "    for i, line in enumerate(fp):\n",
    "        # read lines\n",
    "        if i in line_numbers:\n",
    "            if \"HEAD\" in line.strip():\n",
    "                head_nums.append(i) \n",
    "                if storm_name == line[39:55].strip():\n",
    "                    pass\n",
    "                else:\n",
    "                    print(storm_name)\n",
    "                    if storm_name == \"NULL\":\n",
    "                        pass\n",
    "                    else:\n",
    "                        data = np.transpose(np.stack([dates,shear_speed,shear_dir]))\n",
    "                        df = pd.DataFrame(data,\n",
    "                                            columns=[\"Date\",\"Shear speed (m/s)\",\"Shear direction (deg)\"],\n",
    "                                         ).to_csv(storm_name+\".csv\",index = False)\n",
    "                    \n",
    "                    storm_name = line[39:55].strip(); storms.append(storm_name)\n",
    "                    shear_speed, shear_dir, dates = [], [], []\n",
    "                \n",
    "                ## ADD THE DATE\n",
    "                date_string = line.strip().split()[1] + line.strip().split()[2]\n",
    "                time = datetime.strptime(date_string, \"%y%m%d%H\")\n",
    "                dates.append(time)\n",
    "            \n",
    "            ## VARIABLES YOU WANT TO EXTRACT\n",
    "            if \"SDDC\" in line.strip():\n",
    "                shear_speed.append((float(line.strip().split()[0]) / 10) / 1.944) # T + 0 is column 0\n",
    "            elif \"SHGC\" in line.strip():\n",
    "                shear_dir.append(float(line.strip().split()[0])) # T + 0 is column 0\n",
    "                \n",
    "        elif i > line_numbers[-1]: ## stop when you reach the last line number, you can remove this line to do whole dataset\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Each storm now has its own .csv file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AL012000', 'AL022000', 'AL032000']\n",
      "                  Date  Shear speed (m/s)  Shear direction (deg)\n",
      "0  2000-06-07 18:00:00           4.732510                  248.0\n",
      "1  2000-06-08 00:00:00           4.269547                  248.0\n",
      "2  2000-06-08 06:00:00           4.681070                  260.0\n",
      "3  2000-06-08 12:00:00           5.658436                  271.0\n",
      "                   Date  Shear speed (m/s)  Shear direction (deg)\n",
      "0   2000-06-23 00:00:00          13.837449                  482.0\n",
      "1   2000-06-23 06:00:00          14.248971                  478.0\n",
      "2   2000-06-23 12:00:00          14.557613                  457.0\n",
      "3   2000-06-23 18:00:00          14.506173                  260.0\n",
      "4   2000-06-24 00:00:00          14.506173                  230.0\n",
      "5   2000-06-24 06:00:00          14.969136                  231.0\n",
      "6   2000-06-24 12:00:00          15.020576                  177.0\n",
      "7   2000-06-24 18:00:00          14.763374                  189.0\n",
      "8   2000-06-25 00:00:00          15.483539                  102.0\n",
      "9   2000-06-25 06:00:00           3.292181                  150.0\n",
      "10  2000-06-25 12:00:00           3.703704                  189.0\n",
      "11  2000-06-25 18:00:00           4.938272                  179.0\n",
      "                   Date  Shear speed (m/s)  Shear direction (deg)\n",
      "0   2000-08-03 18:00:00          13.425926                  403.0\n",
      "1   2000-08-04 00:00:00          14.197531                  401.0\n",
      "2   2000-08-04 06:00:00          13.117284                  367.0\n",
      "3   2000-08-04 12:00:00          12.911523                  291.0\n",
      "4   2000-08-04 18:00:00          12.602881                  283.0\n",
      "..                  ...                ...                    ...\n",
      "74  2000-08-22 06:00:00           3.137860                  167.0\n",
      "75  2000-08-22 12:00:00           3.137860                  220.0\n",
      "76  2000-08-22 18:00:00           3.806584                  240.0\n",
      "77  2000-08-23 00:00:00           3.600823                  222.0\n",
      "78  2000-08-23 06:00:00           2.211934                  301.0\n",
      "\n",
      "[79 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(storms[:-1])\n",
    "\n",
    "for storm in storms[:-1]:\n",
    "    data = pd.read_csv(storm+\".csv\")\n",
    "    print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, (ax1,ax2) = plt.subplots(1,2)\n",
    "\n",
    "ax1.plot(data['Date'],data['Shear speed (m/s)'])\n",
    "ax1.set_title(\"Shear speed (m/s)\")\n",
    "\n",
    "ax2.plot(data['Date'],data['Shear direction (deg)'])\n",
    "ax2.set_title(\"Shear direction (deg)\")\n",
    "\n",
    "for ax in [ax1,ax2]:\n",
    "    ax.set_xticklabels(np.arange(0,4))"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}