{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Required for autocnet imports\n",
    "import os\n",
    "os.environ['ISISROOT'] = '/usgs/cpkgs/anaconda3_linux/envs/isis3.9.0'\n",
    "\n",
    "# Autocnet Imports\n",
    "from autocnet.graph.network import NetworkCandidateGraph\n",
    "from autocnet.io.db.model import Overlay\n",
    "\n",
    "# Helpful Python Modules\n",
    "import matplotlib.pyplot as plt  #     plotting package\n",
    "import numpy as np               #     numerical computing package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ingest Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can either upload information from an isis control network to a database or use an existing database. Both methods require a configuration file (See from_cnet.ipynb for configuration file expectations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config_path = 'config.yml'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If creating a database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# lis_path = \"AS15_landingsite_apollolro.lis\"\n",
    "# cnet_path = \"AS15_landingsite_apollolro_jig1.net\"\n",
    "\n",
    "# ncg = NetworkCandidateGraph.from_filelist(lis_path, config_path, clear_db=True)\n",
    "# ncg.place_points_from_cnet(cnet_path) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If database exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ladoramkershner/miniconda3/envs/autocnet_local/lib/python3.7/site-packages/sqlalchemy/orm/relationships.py:1917: SAWarning: Setting backref / back_populates on relationship Points.overlay to refer to viewonly relationship Overlay.points will be deprecated in SQLAlchemy 1.4, and will be disallowed in a future release.  viewonly relationships should not be mutated (this warning may be suppressed after 10 occurrences)\n",
      "  (self, other),\n",
      "/home/ladoramkershner/miniconda3/envs/autocnet_local/lib/python3.7/site-packages/sqlalchemy/orm/relationships.py:1917: SAWarning: Setting backref / back_populates on relationship Overlay.points to refer to viewonly relationship Points.overlay will be deprecated in SQLAlchemy 1.4, and will be disallowed in a future release.  viewonly relationships should not be mutated (this warning may be suppressed after 10 occurrences)\n",
      "  (self, other),\n"
     ]
    }
   ],
   "source": [
    "ncg = NetworkCandidateGraph();\n",
    "ncg.config_from_file(config_path);\n",
    "ncg.from_database();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncg.plot(); # check ncg is populated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check For Empty Overlaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function Documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ncg.empty_overlays?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ncg.overlays?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Command Visualize Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Review the function documentation (noticing the argument defaults) and run the cell below. Alter the valid point filters and/or the size_threshold.\n",
    "\n",
    "By default, a valid point is defined as Points.ignore==False, which converts to filters={'ignore':False}. This network contains a mix of constrained (pointtype=3) and free points (pointtype=2), corresponding with ground and relative control points. Try passing a 'filters' kwarg to find overlaps that do not contain non-ignored, relative control points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlays_wo_pts = ncg.empty_overlays(filters={'ignore': False}, size_threshold=0.05)\n",
    "overlays = ncg.overlays(size_threshold=0.001)\n",
    "\n",
    "#################### Plotting ####################\n",
    "fig, axs = plt.subplots(2, 1, figsize=(15, 15))\n",
    "\n",
    "# plot connected overlaps\n",
    "for o in overlays:\n",
    "    x, y = o.geom.exterior.xy\n",
    "    axs[0].plot(x, y, c='black')\n",
    "\n",
    "# plot empty overlaps\n",
    "for o in overlays_wo_pts:\n",
    "    x, y = o.geom.exterior.xy\n",
    "    axs[0].fill(x, y, c='red', alpha=0.5)\n",
    "    axs[1].fill(x, y, alpha=0.5)\n",
    "\n",
    "# plot properties\n",
    "axs[0].set_xlabel('Longitude', size=16);\n",
    "axs[0].set_ylabel('Latitude', size=16);\n",
    "axs[1].set_xlabel('Longitude', size=16);\n",
    "axs[1].set_ylabel('Latitude', size=16);\n",
    "axs[1].set_xlim(axs[0].get_xlim());\n",
    "axs[1].set_ylim(axs[0].get_ylim());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Overlay Fully Connectivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above function only looks at the number of points residing in an overlap. If you would like to evaluate to effective connectivity of an overlap, use the following function.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function Documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ncg.overlay_connection?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(2, 3), (2, 4), (2, 5), (2, 6), (2, 8), (2, 11), (3, 4), (3, 5), (3, 6), (3, 8), (3, 11), (4, 5), (4, 6), (4, 8), (4, 11), (5, 6), (5, 8), (5, 11)]\n"
     ]
    }
   ],
   "source": [
    "oid = 5\n",
    "missing_edges = ncg.overlay_connection(oid)\n",
    "print(missing_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overlap 1 is empty\n",
      "[]\n",
      "\n",
      "Overlap 2 is empty\n",
      "[]\n",
      "\n",
      "Overlap 3 is empty\n",
      "[]\n",
      "\n",
      "Overlap 4 is empty\n",
      "[]\n",
      "\n",
      "Overlap 7 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 10), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 10), (4, 5), (4, 6), (4, 7), (4, 8), (4, 10), (5, 6), (5, 7), (5, 8), (5, 10)]\n",
      "\n",
      "Overlap 9 is empty\n",
      "[]\n",
      "\n",
      "Overlap 10 is empty\n",
      "[]\n",
      "\n",
      "Overlap 11 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 12 is empty\n",
      "[]\n",
      "\n",
      "Overlap 14 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 6), (2, 8), (2, 11), (3, 4), (3, 5), (3, 6), (3, 8), (3, 11), (4, 5), (4, 6), (4, 8), (4, 11), (5, 6), (5, 8), (5, 11)]\n",
      "\n",
      "Overlap 6 is empty\n",
      "[]\n",
      "\n",
      "Overlap 15 is empty\n",
      "[]\n",
      "\n",
      "[(2, 10), (3, 10), (4, 10), (5, 10)]\n",
      "\n",
      "Overlap 16 is empty\n",
      "[]\n",
      "\n",
      "Overlap 17 is empty\n",
      "[]\n",
      "\n",
      "Overlap 18 is empty\n",
      "[]\n",
      "\n",
      "Overlap 19 is empty\n",
      "[]\n",
      "\n",
      "Overlap 21 is empty\n",
      "[]\n",
      "\n",
      "Overlap 22 is empty\n",
      "[]\n",
      "\n",
      "Overlap 23 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 11), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 11), (4, 5), (4, 6), (4, 7), (4, 8), (4, 11), (5, 6), (5, 7), (5, 8), (5, 11)]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (4, 5), (4, 6), (4, 7), (4, 8), (5, 6), (5, 7), (5, 8), (7, 8)]\n",
      "\n",
      "Overlap 25 is empty\n",
      "[]\n",
      "\n",
      "Overlap 26 is empty\n",
      "[]\n",
      "\n",
      "[(2, 5)]\n",
      "\n",
      "[(2, 6), (2, 10), (3, 6), (3, 10), (4, 6), (4, 10), (5, 6), (5, 10)]\n",
      "\n",
      "Overlap 29 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 30 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 31 is empty\n",
      "[]\n",
      "\n",
      "Overlap 32 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 7), (2, 9), (3, 4), (3, 5), (3, 7), (3, 9), (4, 5), (4, 7), (4, 9), (5, 7), (5, 9)]\n",
      "\n",
      "Overlap 34 is empty\n",
      "[]\n",
      "\n",
      "Overlap 52 is empty\n",
      "[]\n",
      "\n",
      "Overlap 35 is empty\n",
      "[]\n",
      "\n",
      "Overlap 36 is empty\n",
      "[]\n",
      "\n",
      "Overlap 37 is empty\n",
      "[]\n",
      "\n",
      "Overlap 38 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 39 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 40 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 41 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 42 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 43 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 44 is empty\n",
      "[]\n",
      "\n",
      "Overlap 45 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 8), (3, 4), (3, 5), (3, 8), (4, 5), (4, 8), (5, 8)]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 7), (2, 8), (2, 9), (2, 11), (3, 4), (3, 5), (3, 7), (3, 8), (3, 9), (3, 11), (4, 5), (4, 7), (4, 8), (4, 9), (4, 11), (5, 7), (5, 8), (5, 9), (5, 11)]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 7), (2, 8), (2, 10), (2, 11), (3, 4), (3, 5), (3, 7), (3, 8), (3, 10), (3, 11), (4, 5), (4, 7), (4, 8), (4, 10), (4, 11), (5, 7), (5, 8), (5, 10), (5, 11), (7, 10), (8, 10), (10, 11)]\n",
      "\n",
      "Overlap 49 is empty\n",
      "[]\n",
      "\n",
      "Overlap 50 is empty\n",
      "[]\n",
      "\n",
      "Overlap 51 is empty\n",
      "[]\n",
      "\n",
      "Overlap 53 is empty\n",
      "[]\n",
      "\n",
      "Overlap 54 is empty\n",
      "[]\n",
      "\n",
      "Overlap 55 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 56 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 7), (2, 8), (2, 10), (3, 4), (3, 5), (3, 7), (3, 8), (3, 10), (4, 5), (4, 7), (4, 8), (4, 10), (5, 7), (5, 8), (5, 10)]\n",
      "\n",
      "Overlap 58 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 6), (2, 8), (3, 4), (3, 5), (3, 6), (3, 8), (4, 5), (4, 6), (4, 8), (5, 6), (5, 8)]\n",
      "\n",
      "Overlap 60 is empty\n",
      "[]\n",
      "\n",
      "Overlap 61 is fully connected\n",
      "[]\n",
      "\n",
      "Overlap 62 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 6), (3, 4), (3, 5), (3, 6), (4, 5), (4, 6), (5, 6)]\n",
      "\n",
      "Overlap 72 is empty\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 7), (2, 9), (3, 4), (3, 5), (3, 7), (3, 9), (4, 5), (4, 7), (4, 9), (5, 7), (5, 9)]\n",
      "\n",
      "Overlap 65 is empty\n",
      "[]\n",
      "\n",
      "Overlap 66 is empty\n",
      "[]\n",
      "\n",
      "Overlap 67 is empty\n",
      "[]\n",
      "\n",
      "Overlap 68 is empty\n",
      "[]\n",
      "\n",
      "Overlap 69 is fully connected\n",
      "[]\n",
      "\n",
      "[(2, 3), (2, 4), (2, 5), (2, 6), (2, 8), (2, 10), (2, 11), (3, 4), (3, 5), (3, 6), (3, 8), (3, 10), (3, 11), (4, 5), (4, 6), (4, 8), (4, 10), (4, 11), (5, 6), (5, 8), (5, 10), (5, 11)]\n",
      "\n",
      "Overlap 71 is empty\n",
      "[]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with ncg.session_scope() as session:\n",
    "    oids = session.query(Overlay.id).all()\n",
    "    for oid in oids:\n",
    "        oid = oid[0]\n",
    "        missing_edges = ncg.overlay_connection(oid)\n",
    "        print(missing_edges)\n",
    "        print()"
   ]
  }
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