Weather-robability-Forecast-Per-Location.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import gurobipy as gp\n",
    "from gurobipy import GRB\n",
    "from gurobipy import Model, GRB\n",
    "import requests\n",
    "import random\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Luke Wolf\\AppData\\Local\\Temp\\ipykernel_88160\\3745321696.py:8: DtypeWarning: Columns (1,18,20,22,24,26,28,32,36) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  sc = pd.read_table(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\satcat.tsv', header=0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  STATE  CWA TIME_ZONE FE_AREA ZONE     NAME STATE_ZONE      LON      LAT  \\\n",
      "0    AL  BMX         C      ec  019  Calhoun      AL019 -85.8261  33.7714   \n",
      "1    AL  MOB         C      sc  057   Butler      AL057 -86.6803  31.7524   \n",
      "2    AL  BMX         C      se  046  Bullock      AL046 -85.7161  32.1005   \n",
      "3    AL  BMX         C      cc  017   Blount      AL017 -86.5674  33.9809   \n",
      "4    AL  BMX         C      cc  034     Bibb      AL034 -87.1264  32.9986   \n",
      "\n",
      "  SHORTNAME                                           geometry  \n",
      "0   Calhoun  POLYGON ((-85.53010 33.94181, -85.53200 33.897...  \n",
      "1    Butler  POLYGON ((-86.44800 31.96431, -86.44769 31.952...  \n",
      "2   Bullock  POLYGON ((-85.43349 32.23421, -85.43120 32.198...  \n",
      "3    Blount  POLYGON ((-86.45229 34.25951, -86.44420 34.257...  \n",
      "4      Bibb  POLYGON ((-87.02679 33.24601, -87.02599 33.231...  \n"
     ]
    }
   ],
   "source": [
    "#Data Import\n",
    "#Data (1-7) from GCAT (J. McDowell, planet4589.org/space/gcat), Data (8) from Our World in Data (https://ourworldindata.org/grapher/cost-space-launches-low-earth-orbit)\n",
    "#1 Launch Pad\n",
    "lp = pd.read_table(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\launch.tsv', header=0)\n",
    "#lp.head()\n",
    "\n",
    "#2 Satellite Catalog\n",
    "sc = pd.read_table(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\satcat.tsv', header=0)\n",
    "satcat = sc[sc['OpOrbit'].str.contains('Leo|LEO|LEO/I|LLEO/I', na=False)]\n",
    "#print(satcat.head())\n",
    "\n",
    "#3 Auxiliary Satellite Data\n",
    "ax = pd.read_table(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\auxcat.tsv', header=0)\n",
    "auxcat = ax[ax['OpOrbit'].str.contains('Leo|LEO|LEO/I|LLEO/I', na=False)]\n",
    "#print(auxcat.head())\n",
    "\n",
    "#4 Failed to orbit satellites\n",
    "ftorbit = pd.read_table(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\ftocat.tsv', header=0)\n",
    "#print(ftorbit)\n",
    "\n",
    "#5 Launch List\n",
    "launchlist = pd.read_table(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\launch.tsv', header=0)\n",
    "launchlist = launchlist.dropna()\n",
    "#launchlist.head()\n",
    " \n",
    "#6 Organizations DB \n",
    "#Astronomical polities (AP), countries/regions (CY), intergovernmental organizations (IGO), academic/non-profit organizations (A), businesses (B), and civilian/military government agencies (C/D).  \n",
    "orgs = pd.read_table(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\orgs.tsv', header=0)\n",
    "#print(orgs.head())\n",
    "\n",
    "#7 Sites\n",
    "sites = pd.read_table(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\sites.tsv', header=0)\n",
    "\n",
    "#8 Cost of LEO\n",
    "leocost = pd.read_csv(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\cost-space-launches-low-earth-orbit.csv', header=0)\n",
    "leocost['Year'] = pd.to_datetime(leocost['Year'], format='%Y')\n",
    "#leocost.head()\n",
    "\n",
    "#9 NWS Lat Lon Zone Data\n",
    "nwsdata = gpd.read_file(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\z_05mr24.shp', header=0)\n",
    "print(nwsdata.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    #Site Code UCode Type StateCode       TStart        TStop ShortName  \\\n",
      "0   AFMTC    -    CC   LS        US  1951 Jun 30  1955 Dec 16     AFMTC   \n",
      "1    BARK    -  BARK   LS        US            -            -   BARK  -   \n",
      "2     CCA    -   CCA   LS        US         1984  1998 Jan  1    CC (A)   \n",
      "3   CCAFS    -    CC   LS        US     1974 Mar         1992     CCAFS   \n",
      "4  CCAFS1    -    CC   LS        US  2000 Feb  4  2020 Dec  9     CCAFS   \n",
      "\n",
      "                                                Name                 Location  \\\n",
      "0                      Air Force Missile Test Center  Cape Canaveral, Florida   \n",
      "1                                      Barksdale AFB                Louisiana   \n",
      "2  Cape Canaveral Air Force Station, Eastern Test...  Cape Canaveral, Florida   \n",
      "3  Cape Canaveral Air Force Station, Eastern Test...  Cape Canaveral, Florida   \n",
      "4  Cape Canaveral Air Force Station, Eastern Test...  Cape Canaveral, Florida   \n",
      "\n",
      "       Longitude     Latitude  Error Parent ShortEName EName Group  \\\n",
      "0      -80.5400      28.4600    0.05   USAF          -     -    CC   \n",
      "1      -93.7300      32.5200    0.02   USAF          -     -     -   \n",
      "2      -80.5400      28.4600    0.05   USAF          -     -    CC   \n",
      "3      -80.5400      28.4600    0.05   USAF          -     -    CC   \n",
      "4      -80.5400      28.4600    0.05   USAF          -     -    CC   \n",
      "\n",
      "                                               UName  State  \n",
      "0                      Air Force Missile Test Center   True  \n",
      "1                                      Barksdale AFB   True  \n",
      "2  Cape Canaveral Air Force Station, Eastern Test...   True  \n",
      "3  Cape Canaveral Air Force Station, Eastern Test...   True  \n",
      "4  Cape Canaveral Air Force Station, Eastern Test...   True  \n",
      "13\n",
      "    #JCAT Satcat       Piece          Type                      Name  \\\n",
      "0  S00001    1.0  1957 ALP 1  R2            8K71PS No. M1-10 Stage 2   \n",
      "1  S00002    2.0  1957 ALP 2  P                              1-y ISZ   \n",
      "2  S00003    3.0  1957 BET 1  P A                            2-y ISZ   \n",
      "3  S00004    4.0    1958 ALP  P A                         Explorer I   \n",
      "4  S00006    6.0    1958 GAM  P A                       Explorer III   \n",
      "\n",
      "                 PLName        LDate  Parent             SDate Primary  ...  \\\n",
      "0  8K71A M1-10 (M1-1PS)  1957 Oct  4       -  1957 Oct  4 1933   Earth  ...   \n",
      "1                  PS-1  1957 Oct  4  S00001  1957 Oct  4 1933   Earth  ...   \n",
      "2                  PS-2  1957 Nov  3  A00002  1957 Nov  3 0235   Earth  ...   \n",
      "3            Explorer 1  1958 Feb  1  A00004  1958 Feb  1 0355   Earth  ...   \n",
      "4            Explorer 3  1958 Mar 26  A00005  1958 Mar 26 1745   Earth  ...   \n",
      "\n",
      "         ODate Perigee PF    Apogee AF    Inc IF OpOrbit OQUAL  \\\n",
      "0  1957 Oct  4   214.0          938      65.1     LLEO/I     -   \n",
      "1  1957 Oct  4   214.0          938      65.1     LLEO/I     -   \n",
      "2  1957 Nov  3   211.0         1659     65.33      LEO/I     -   \n",
      "3  1958 Feb  1   359.0         2542     33.18      LEO/I     -   \n",
      "4  1958 Mar 26   195.0         2810     33.38      LEO/I     -   \n",
      "\n",
      "            AltNames  \n",
      "0                  -  \n",
      "1            :RE,:RC  \n",
      "2            :RE,:RC  \n",
      "3  :UA,:UB,DEAL I:IA  \n",
      "4            :UA,:UB  \n",
      "\n",
      "[5 rows x 41 columns]\n",
      "0.0 260.0 780.4048915185373\n",
      "    #JCAT Satcat       Piece        Type_x        Name_x  \\\n",
      "0  S00004    4.0    1958 ALP  P A             Explorer I   \n",
      "1  S00006    6.0    1958 GAM  P A           Explorer III   \n",
      "2  S00009    9.0    1958 EPS  P A            Explorer IV   \n",
      "3  S00010   10.0    1958 ZET  P A                  SCORE   \n",
      "4  S00011   11.0  1959 ALP 1  P              Vanguard II   \n",
      "\n",
      "                  PLName        LDate Parent_x             SDate Primary  ...  \\\n",
      "0             Explorer 1  1958 Feb  1   A00004  1958 Feb  1 0355   Earth  ...   \n",
      "1             Explorer 3  1958 Mar 26   A00005  1958 Mar 26 1745   Earth  ...   \n",
      "2             Explorer 4  1958 Jul 26   A00009  1958 Jul 26 1507   Earth  ...   \n",
      "3                  SCORE  1958 Dec 18   A00015  1958 Dec 18 2306   Earth  ...   \n",
      "4  Cloud cover satellite  1959 Feb 17   S00012  1959 Feb 17 1605   Earth  ...   \n",
      "\n",
      "                          Name_y                 Location      Longitude  \\\n",
      "0  Air Force Missile Test Center  Cape Canaveral, Florida      -80.5400    \n",
      "1  Air Force Missile Test Center  Cape Canaveral, Florida      -80.5400    \n",
      "2  Air Force Missile Test Center  Cape Canaveral, Florida      -80.5400    \n",
      "3  Air Force Missile Test Center  Cape Canaveral, Florida      -80.5400    \n",
      "4  Air Force Missile Test Center  Cape Canaveral, Florida      -80.5400    \n",
      "\n",
      "      Latitude  Error Parent_y ShortEName EName Group  \\\n",
      "0     28.4600    0.05     USAF          -     -    CC   \n",
      "1     28.4600    0.05     USAF          -     -    CC   \n",
      "2     28.4600    0.05     USAF          -     -    CC   \n",
      "3     28.4600    0.05     USAF          -     -    CC   \n",
      "4     28.4600    0.05     USAF          -     -    CC   \n",
      "\n",
      "                           UName  \n",
      "0  Air Force Missile Test Center  \n",
      "1  Air Force Missile Test Center  \n",
      "2  Air Force Missile Test Center  \n",
      "3  Air Force Missile Test Center  \n",
      "4  Air Force Missile Test Center  \n",
      "\n",
      "[5 rows x 51 columns]\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'lat'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3791\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3790\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3791\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3792\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'lat'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 104\u001b[0m\n\u001b[0;32m    101\u001b[0m     dataframe[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdistance_to_closest_zone\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m min_distances\n\u001b[0;32m    102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m dataframe\n\u001b[1;32m--> 104\u001b[0m dataframe \u001b[38;5;241m=\u001b[39m \u001b[43mfind_closest_zone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnwsdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    105\u001b[0m weatherdf \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m    107\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, row \u001b[38;5;129;01min\u001b[39;00m filtered_sites\u001b[38;5;241m.\u001b[39miterrows():\n",
      "Cell \u001b[1;32mIn[11], line 95\u001b[0m, in \u001b[0;36mfind_closest_zone\u001b[1;34m(dataframe, nwsdata)\u001b[0m\n\u001b[0;32m     93\u001b[0m closest_zones \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, row \u001b[38;5;129;01min\u001b[39;00m dataframe\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[1;32m---> 95\u001b[0m     distances \u001b[38;5;241m=\u001b[39m \u001b[43mnwsdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mhaversine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlon\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mLAT\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mLON\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     min_distance_index \u001b[38;5;241m=\u001b[39m distances\u001b[38;5;241m.\u001b[39midxmin()\n\u001b[0;32m     97\u001b[0m     closest_zone \u001b[38;5;241m=\u001b[39m nwsdata\u001b[38;5;241m.\u001b[39miloc[min_distance_index][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mZONE\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\geopandas\\geodataframe.py:1568\u001b[0m, in \u001b[0;36mGeoDataFrame.apply\u001b[1;34m(self, func, axis, raw, result_type, args, **kwargs)\u001b[0m\n\u001b[0;32m   1566\u001b[0m \u001b[38;5;129m@doc\u001b[39m(pd\u001b[38;5;241m.\u001b[39mDataFrame)\n\u001b[0;32m   1567\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, raw\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, result_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, args\u001b[38;5;241m=\u001b[39m(), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m-> 1568\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1569\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mraw\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mraw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresult_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresult_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m   1570\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1571\u001b[0m     \u001b[38;5;66;03m# pandas <1.4 re-attach last geometry col if lost\u001b[39;00m\n\u001b[0;32m   1572\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   1573\u001b[0m         \u001b[38;5;129;01mnot\u001b[39;00m compat\u001b[38;5;241m.\u001b[39mPANDAS_GE_14\n\u001b[0;32m   1574\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, GeoDataFrame)\n\u001b[0;32m   1575\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m result\u001b[38;5;241m.\u001b[39m_geometry_column_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1576\u001b[0m     ):\n",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\pandas\\core\\frame.py:10034\u001b[0m, in \u001b[0;36mDataFrame.apply\u001b[1;34m(self, func, axis, raw, result_type, args, by_row, **kwargs)\u001b[0m\n\u001b[0;32m  10022\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapply\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frame_apply\n\u001b[0;32m  10024\u001b[0m op \u001b[38;5;241m=\u001b[39m frame_apply(\n\u001b[0;32m  10025\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m  10026\u001b[0m     func\u001b[38;5;241m=\u001b[39mfunc,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  10032\u001b[0m     kwargs\u001b[38;5;241m=\u001b[39mkwargs,\n\u001b[0;32m  10033\u001b[0m )\n\u001b[1;32m> 10034\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:837\u001b[0m, in \u001b[0;36mFrameApply.apply\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    834\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw:\n\u001b[0;32m    835\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_raw()\n\u001b[1;32m--> 837\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:965\u001b[0m, in \u001b[0;36mFrameApply.apply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    964\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply_standard\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 965\u001b[0m     results, res_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_series_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    967\u001b[0m     \u001b[38;5;66;03m# wrap results\u001b[39;00m\n\u001b[0;32m    968\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwrap_results(results, res_index)\n",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\pandas\\core\\apply.py:981\u001b[0m, in \u001b[0;36mFrameApply.apply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    978\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m option_context(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode.chained_assignment\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    979\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(series_gen):\n\u001b[0;32m    980\u001b[0m         \u001b[38;5;66;03m# ignore SettingWithCopy here in case the user mutates\u001b[39;00m\n\u001b[1;32m--> 981\u001b[0m         results[i] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    982\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results[i], ABCSeries):\n\u001b[0;32m    983\u001b[0m             \u001b[38;5;66;03m# If we have a view on v, we need to make a copy because\u001b[39;00m\n\u001b[0;32m    984\u001b[0m             \u001b[38;5;66;03m#  series_generator will swap out the underlying data\u001b[39;00m\n\u001b[0;32m    985\u001b[0m             results[i] \u001b[38;5;241m=\u001b[39m results[i]\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "Cell \u001b[1;32mIn[11], line 95\u001b[0m, in \u001b[0;36mfind_closest_zone.<locals>.<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m     93\u001b[0m closest_zones \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, row \u001b[38;5;129;01min\u001b[39;00m dataframe\u001b[38;5;241m.\u001b[39miterrows():\n\u001b[1;32m---> 95\u001b[0m     distances \u001b[38;5;241m=\u001b[39m nwsdata\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: haversine(\u001b[43mrow\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlat\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m, row[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlon\u001b[39m\u001b[38;5;124m'\u001b[39m], x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLAT\u001b[39m\u001b[38;5;124m'\u001b[39m], x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLON\u001b[39m\u001b[38;5;124m'\u001b[39m]), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     96\u001b[0m     min_distance_index \u001b[38;5;241m=\u001b[39m distances\u001b[38;5;241m.\u001b[39midxmin()\n\u001b[0;32m     97\u001b[0m     closest_zone \u001b[38;5;241m=\u001b[39m nwsdata\u001b[38;5;241m.\u001b[39miloc[min_distance_index][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mZONE\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\pandas\\core\\series.py:1040\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1037\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[0;32m   1039\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[1;32m-> 1040\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1042\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[0;32m   1043\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[0;32m   1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\pandas\\core\\series.py:1156\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1153\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[0;32m   1155\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[1;32m-> 1156\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[0;32m   1159\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n",
      "File \u001b[1;32mc:\\Users\\Luke Wolf\\.venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3798\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3793\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3794\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3795\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3796\u001b[0m     ):\n\u001b[0;32m   3797\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3798\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3800\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3801\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3802\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'lat'"
     ]
    }
   ],
   "source": [
    "#Data Cleaning and Manipulation\n",
    "#Cost Average from last 20 years Per Launch Class\n",
    "leoyear = leocost[leocost['Year'] >= '2000-01-01']\n",
    "leoavgcost = leoyear.groupby('launch_class')['cost_per_kg'].mean()\n",
    "leoavgcost = pd.DataFrame(leoavgcost).reset_index()\n",
    "#print(leoavgcost)\n",
    "#Sites only located in the Southeast US Region (Florida, Georgia, Alabama, Mississippi, Louisiana, Texas)\n",
    "locations = 'Florida|Georgia|Alabama|Mississippi|Louisiana|Texas|FL|GA|AL|MS|LA|TX|US|United States'\n",
    "filtered_sites = sites[sites['Location'].str.contains(locations, na=False)]\n",
    "filtered_sites = filtered_sites.reset_index(drop=True)\n",
    "#filtered_sites['State, Country'] = filtered_sites['Location'].str.split(',').str[1] + \", \" + filtered_sites['StateCode']\n",
    "filtered_sites['State'] = filtered_sites['Location'].str.contains(locations, na=False)\n",
    "'''\n",
    "def state_short(state):\n",
    "    state_str = str(state)\n",
    "    if re.search(r'Florida|FL', state_str):\n",
    "        return 'Fl'\n",
    "    elif re.search(r'Georgia|GA', state_str):\n",
    "        return 'Ga'\n",
    "    elif re.search(r'Alabama|AL', state_str):\n",
    "        return 'Al'\n",
    "    elif re.search(r'Mississippi|MS', state_str):\n",
    "        return 'Ms'\n",
    "    elif re.search(r'Louisiana|LA', state_str):\n",
    "        return 'La'\n",
    "    elif re.search(r'Texas|TX', state_str):\n",
    "        return 'Tx'\n",
    "    else:\n",
    "        return 'US'\n",
    "\n",
    "filtered_sites['State'] = filtered_sites['State'].apply(state_short)\n",
    "'''\n",
    "print(filtered_sites.head())\n",
    "print(len(filtered_sites))\n",
    "#filtered_sites.to_excel(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\filtered_sites.xlsx', index=False)\n",
    "\n",
    "#Combining Satellite Catalog (satcat, auxcat, ftorbit)\n",
    "df = pd.concat([satcat, auxcat, ftorbit], axis=0, ignore_index=True)\n",
    "print(df.head())\n",
    "df.to_excel(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\Satellite Launches.xlsx', index=False)\n",
    "#Cost Assumptions of Satellites (Auxcat, Satcat, Ftorbit) with Orgaization Type\n",
    "'''\n",
    "Cost value is arbitary since  there is no cost data for these satellites\n",
    "Additionally, the real cost does not matter since the OR solver will optimize the cost (no matter the value)\n",
    "'''\n",
    "df['TotMass'] = df['TotMass'].replace({'?', '-'}, 0)\n",
    "df['TotMass'] = df['TotMass'].astype(float)\n",
    "\n",
    "lq = df['TotMass'].quantile(0.25)\n",
    "uq = df['TotMass'].quantile(0.75)\n",
    "mean = np.mean(df['TotMass'])\n",
    "print(lq, uq, mean)\n",
    "\n",
    "def random_cost(row):\n",
    "    if row['TotMass'] < 260: \n",
    "        return leoavgcost['cost_per_kg'][1]\n",
    "    elif row['TotMass'] >= 260: #Upper Quartile of Total Mass (right skweded dis.)\n",
    "        return leoavgcost['cost_per_kg'][2]\n",
    "    else:\n",
    "        return leoavgcost['cost_per_kg'][0]\n",
    "\n",
    "df['Cost'] = df.apply(random_cost, axis=1)\n",
    "#Final Dataframe\n",
    "df = df.drop(columns=['PF', 'AF', 'IF', 'MassFlag', 'OQUAL', 'LFlag', 'DryFlag', 'DFlag', 'SpanFlag'])\n",
    "df['State'] = df['State'].str.contains(locations, na=False)\n",
    "dataframe = pd.merge(df, filtered_sites, on='State', how='right')\n",
    "print(dataframe.head())\n",
    "#make an excel file with the data\n",
    "#dataframe.to_excel(r'C:\\Users\\Luke Wolf\\Desktop\\Personal Projects\\OR Satellite Launch Schedule\\df.xlsx', index=False)\n",
    "\n",
    "#Creating Weather Probability for Southeast US Regaion from National Weather Service (NWS) API\n",
    "def haversine(lat1, lon1, lat2, lon2):\n",
    "    \"\"\"\n",
    "    Calculate the great-circle distance between two points \n",
    "    on the Earth surface.\n",
    "    :param lat1, lon1: Latitude and Longitude of point 1 (in decimal degrees)\n",
    "    :param lat2, lon2: Latitude and Longitude of point 2 (in decimal degrees)\n",
    "    :return: Distance in kilometers between the two points.\n",
    "    \"\"\"\n",
    "    # convert decimal degrees to radians \n",
    "    lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])\n",
    "\n",
    "    # haversine formula \n",
    "    dlat = lat2 - lat1 \n",
    "    dlon = lon2 - lon1 \n",
    "    a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
    "    c = 2 * np.arcsin(np.sqrt(a)) \n",
    "    r = 6371 # Radius of earth in kilometers\n",
    "    return c * r\n",
    "\n",
    "def find_closest_zone(dataframe, nwsdata):\n",
    "    min_distances = []\n",
    "    closest_zones = []\n",
    "    for index, row in dataframe.iterrows():\n",
    "        distances = nwsdata.apply(lambda x: haversine(row['lat'], row['lon'], x['LAT'], x['LON']), axis=1)\n",
    "        min_distance_index = distances.idxmin()\n",
    "        closest_zone = nwsdata.iloc[min_distance_index]['ZONE']\n",
    "        min_distances.append(distances[min_distance_index])\n",
    "        closest_zones.append(closest_zone)\n",
    "    dataframe['closest_zone'] = closest_zones\n",
    "    dataframe['distance_to_closest_zone'] = min_distances\n",
    "    return dataframe\n",
    "\n",
    "dataframe = find_closest_zone(dataframe, nwsdata)\n",
    "weatherdf = []\n",
    "\n",
    "for index, row in filtered_sites.iterrows():\n",
    "    zone = row['closest_zone']\n",
    "    url = 'https://api.weather.gov/zones/forecast/{zone}/observations'\n",
    "    print(f'{url} {row[\"Location\"]}')\n",
    "\n",
    "    response = requests.get(url)\n",
    "    if response.status_code == 200:\n",
    "        forecast_data = response.json()\n",
    "        \n",
    "        for period in forecast_data['properties']['periods']:\n",
    "            print(f\"{period['name']}: {period['detailedForecast']}\")\n",
    "    else:\n",
    "        print(f\"Failed to fetch forecast for {row['Location']}\")\n",
    "print(forecast_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Visualization Prior to Modeling\n",
    "'''\n",
    "plt.figure(figsize=(20, 20))\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_context('talk')\n",
    "sns.pairplot(df, hue='OpOrbit', palette='viridis')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optimization Modeling: Cost Minimization of LEO Satellite Launches\n",
    "model = Model(\"LEO Satellite Optimization\")\n",
    "#Decision Variables\n",
    "Xij = 1 if satellite i is launched from launch site j\n",
    "Xij = 0 otherwise\n",
    "\n",
    "#Objective Function\n",
    "#Minimize the cost of a satellite "
   ]
  }
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