export_gxf_marks_to_csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "gxf_file_path = '../Documents/Colonsay.039'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "gxf_layer_name, gxf_layer_id = gxf_file_path.split('/')[-1].split('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gxf_layer = open(gxf_file_path).readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Colonsay'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gxf_layer_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'039'"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gxf_layer_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "internal_gxf_file_name = gxf_layer[1].split('\"')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gxf_layer_description = gxf_layer[4].split('\"')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "gxf_layer_limits = {'SW':list(filter(None,gxf_layer[9][:-1].split(' '))),\n",
    "                    'NW':list(filter(None,gxf_layer[10][:-1].split(' '))),\n",
    "                    'NE':list(filter(None,gxf_layer[11][:-1].split(' '))),\n",
    "                    'SE':list(filter(None,gxf_layer[12][:-1].split(' ')))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NE': ['-6.1205887', '56.0974781'],\n",
       " 'NW': ['-6.1313829', '56.0974781'],\n",
       " 'SE': ['-6.1205887', '56.0914611'],\n",
       " 'SW': ['-6.1313829', '56.0914611']}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gxf_layer_limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab the line indexes for the marks\n",
    "def get_mark_indexes(gxf_layer_lines):\n",
    "    mk_index = []\n",
    "    for index, gxf_line in enumerate(gxf_layer_lines):\n",
    "        if(gxf_line == '[ MK\\n'):\n",
    "            mk_index.append(index)\n",
    "    return mk_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_marks_df(gxf_layer_lines):\n",
    "    mk_index = get_mark_indexes(gxf_layer_lines)\n",
    "    mark_list = []\n",
    "    for mk in mk_index:\n",
    "        mark_dict = {}\n",
    "        for index in range(1,5):\n",
    "            mark_dict[gxf_layer[mk+index].split(' ')[0]] = gxf_layer[mk+index].split(' ')[1][:-1]\n",
    "        mark_dict['longitude'] = gxf_layer[mk+5].split(' ')[1]\n",
    "        mark_dict['latitude'] = gxf_layer[mk+5].split(' ')[2][:-1]\n",
    "        mark_list.append(mark_dict)\n",
    "    return pd.DataFrame(mark_list) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "gxf_marks_df = get_marks_df(gxf_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "gxf_marks_df.to_csv(gxf_layer_name + '_' + gxf_layer_id + '_marks.csv')"
   ]
  }
 ],
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