AC-7.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psycopg2\n",
    "import psycopg2.extras\n",
    "import altair\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = psycopg2.connect('host=127.0.0.1 dbname=postgres user=postgres password=atreehasmanyleafs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.rollback()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "info of the latest run\n",
      "---------------  -------------------------\n",
      "run_id           20190128_095609_+0000\n",
      "time start       2019-01-28 09:56:09+00:00\n",
      "time_stop        2019-01-28 09:56:25+00:00\n",
      "num generations  6\n",
      "num_species      99\n",
      "---------------  -------------------------\n"
     ]
    }
   ],
   "source": [
    "print('info of the latest run')\n",
    "with conn.cursor() as cur:\n",
    "    cur.execute('''\n",
    "      select run_id, time_started, time_stopped\n",
    "      from run\n",
    "      order by time_started desc''')\n",
    "    runs = cur.fetchall()\n",
    "\n",
    "    run_id = runs[0][0]\n",
    "\n",
    "    cur.execute('''\n",
    "      select count(distinct species_sn)\n",
    "      from population\n",
    "      where run_id = %s\n",
    "      ''', (run_id,))\n",
    "    [(num_species,)] = cur.fetchall()\n",
    "    \n",
    "    cur.execute('''\n",
    "      select count(distinct generation_sn)\n",
    "      from population\n",
    "      where run_id = %s\n",
    "      ''', (run_id,))\n",
    "    [(num_generations,)] = cur.fetchall()\n",
    "    \n",
    "print(tabulate.tabulate([\n",
    "    (\"run_id\", run_id),\n",
    "    (\"time start\", runs[0][1]),\n",
    "    (\"time_stop\", runs[0][2]),\n",
    "    (\"num generations\", num_generations),\n",
    "    (\"num_species\", num_species)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top original fitnesses of run 20190128_095609_+0000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>generation_sn</th>\n",
       "      <th>species_sn</th>\n",
       "      <th>genome_id</th>\n",
       "      <th>original_fitness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6a41549b-9cb3-4e78-9169-565c41fa5694</td>\n",
       "      <td>6.166850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>563cb992-9cbf-4f04-bb43-bb618cc2f503</td>\n",
       "      <td>6.150438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>d60cc5e8-12ba-4c32-bd9c-90fbc5d943c0</td>\n",
       "      <td>6.113151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>dc403c10-5374-446f-90ea-64801e15e05c</td>\n",
       "      <td>6.110964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>dcdfd8f8-5cb0-4bdc-a6dd-abde7d27923c</td>\n",
       "      <td>6.055729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0e5604b7-8abd-4403-9f41-0a94a38787c0</td>\n",
       "      <td>5.851717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>a1e50597-b77f-423b-9969-a614c30091cd</td>\n",
       "      <td>5.826341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>04679d5a-ba93-434d-a662-23536d60fee3</td>\n",
       "      <td>5.778124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>662654c2-9020-4f22-8a1d-e72eb2fa4648</td>\n",
       "      <td>5.763522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>e331b486-dedf-41a7-b0f8-0623950a0c47</td>\n",
       "      <td>5.754827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99c027f0-813d-4717-b7f4-851ab5a33060</td>\n",
       "      <td>5.753190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>641f122a-9d66-4dd3-aa16-4a715a31038c</td>\n",
       "      <td>5.720361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>b0b6face-798e-48e9-8f68-8a25feef0941</td>\n",
       "      <td>5.714757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>e0775deb-562f-4e1f-b4d8-519f7c7139cc</td>\n",
       "      <td>5.713795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>06d536f5-3414-4171-8d33-617d86c4ec57</td>\n",
       "      <td>5.693247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10021066-0c38-47f8-a0ed-5863736ab2c1</td>\n",
       "      <td>5.688318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5aebaffc-d091-4700-8af8-d70ac1c81d88</td>\n",
       "      <td>5.459376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3159e21f-e045-4e6d-8bec-839d754c924d</td>\n",
       "      <td>5.421993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>f3b7260c-3e56-42e1-920e-887987a8711e</td>\n",
       "      <td>5.415599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4a07f825-11d4-4847-b50e-e86246eb9c4a</td>\n",
       "      <td>5.401208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    generation_sn  species_sn                             genome_id  \\\n",
       "0               0           0  6a41549b-9cb3-4e78-9169-565c41fa5694   \n",
       "1               0           0  563cb992-9cbf-4f04-bb43-bb618cc2f503   \n",
       "2               0           0  d60cc5e8-12ba-4c32-bd9c-90fbc5d943c0   \n",
       "3               0           0  dc403c10-5374-446f-90ea-64801e15e05c   \n",
       "4               0           0  dcdfd8f8-5cb0-4bdc-a6dd-abde7d27923c   \n",
       "5               0           0  0e5604b7-8abd-4403-9f41-0a94a38787c0   \n",
       "6               0           0  a1e50597-b77f-423b-9969-a614c30091cd   \n",
       "7               0           0  04679d5a-ba93-434d-a662-23536d60fee3   \n",
       "8               1          79  662654c2-9020-4f22-8a1d-e72eb2fa4648   \n",
       "9               1          47  e331b486-dedf-41a7-b0f8-0623950a0c47   \n",
       "10              0           0  99c027f0-813d-4717-b7f4-851ab5a33060   \n",
       "11              0           0  641f122a-9d66-4dd3-aa16-4a715a31038c   \n",
       "12              0           0  b0b6face-798e-48e9-8f68-8a25feef0941   \n",
       "13              0           0  e0775deb-562f-4e1f-b4d8-519f7c7139cc   \n",
       "14              0           0  06d536f5-3414-4171-8d33-617d86c4ec57   \n",
       "15              0           0  10021066-0c38-47f8-a0ed-5863736ab2c1   \n",
       "16              0           0  5aebaffc-d091-4700-8af8-d70ac1c81d88   \n",
       "17              0           0  3159e21f-e045-4e6d-8bec-839d754c924d   \n",
       "18              0           0  f3b7260c-3e56-42e1-920e-887987a8711e   \n",
       "19              0           0  4a07f825-11d4-4847-b50e-e86246eb9c4a   \n",
       "\n",
       "    original_fitness  \n",
       "0           6.166850  \n",
       "1           6.150438  \n",
       "2           6.113151  \n",
       "3           6.110964  \n",
       "4           6.055729  \n",
       "5           5.851717  \n",
       "6           5.826341  \n",
       "7           5.778124  \n",
       "8           5.763522  \n",
       "9           5.754827  \n",
       "10          5.753190  \n",
       "11          5.720361  \n",
       "12          5.714757  \n",
       "13          5.713795  \n",
       "14          5.693247  \n",
       "15          5.688318  \n",
       "16          5.459376  \n",
       "17          5.421993  \n",
       "18          5.415599  \n",
       "19          5.401208  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'top original fitnesses of run {run_id}')\n",
    "sql = '''\n",
    "    select generation_sn, species_sn, genome_id, original_fitness\n",
    "    from population\n",
    "    where run_id = %s\n",
    "    order by original_fitness desc\n",
    "    limit 20'''\n",
    "genomes = pd.read_sql(sql, con=conn, params=(run_id,))\n",
    "genomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.7/site-packages/altair/utils/core.py:284: FutureWarning: A future version of pandas will default to `skipna=True`. To silence this warning, pass `skipna=True|False` explicitly.\n",
      "  attrs['type'] = infer_vegalite_type(data[attrs['field']])\n"
     ]
    },
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-033a4b0043750b3dfd0d665a90cb5e6f": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 71
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 71
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 71
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 71
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 71
         }
        ],
        "data-05a89da4bb70d8270d7bf7ef264719e0": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 48
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 48
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 48
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 48
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 48
         }
        ],
        "data-06f275cc8a4275a33f4e70169feec3b4": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 15
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 15
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 15
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 15
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 15
         }
        ],
        "data-0b82055c612442f6904467b5843dbddb": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 17
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 17
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 17
         },
         {
          "avg_original_fitness": 4.4477709,
          "generation_sn": 1,
          "species_sn": 17
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 17
         }
        ],
        "data-15cc862bb389de835f5c5a4a602e1c70": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 73
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 73
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 73
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 73
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 73
         }
        ],
        "data-186de8343e6a1ecbf85b119f5e753215": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 57
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 57
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 57
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 57
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 57
         }
        ],
        "data-1b2cf379fc7f00961d1898a123a7f6e2": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 60
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 60
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 60
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 60
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 60
         }
        ],
        "data-1e1212c408ce391e2547997f16c47dab": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 94
         }
        ],
        "data-1e78ef5134285de82929be9b452b2b80": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 13
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 13
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 13
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 13
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 13
         }
        ],
        "data-25c23c4d9f26177a32b54e464f1656b2": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 92
         }
        ],
        "data-262929c7cb987704093f175088b38ad8": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 70
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 70
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 70
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 70
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 70
         }
        ],
        "data-27b496fb66e7a4b32a004ea2b68a6b9b": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 59
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 59
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 59
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 59
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 59
         }
        ],
        "data-280f30269a6ab1d67053ee601f867df9": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 43
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 43
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 43
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 43
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 1,
          "species_sn": 43
         }
        ],
        "data-2932112a5cfdc2644de9616fbefcfea4": [
         {
          "avg_original_fitness": 4,
          "generation_sn": 4,
          "species_sn": 3
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 5,
          "species_sn": 3
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 2,
          "species_sn": 3
         },
         {
          "avg_original_fitness": 4,
          "generation_sn": 3,
          "species_sn": 3
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = '''\n",
    "  select generation_sn, species_sn, avg(original_fitness) as avg_original_fitness\n",
    "  from population\n",
    "  where run_id = %s\n",
    "  group by (generation_sn, species_sn)\n",
    "  '''\n",
    "\n",
    "avg_ofes = pd.read_sql(sql=sql, con=conn, params=(run_id,))\n",
    "avg_ofes.head()\n",
    "\n",
    "charts = []\n",
    "for i in range(num_species):\n",
    "    highlight = altair.selection(\n",
    "        type='single',\n",
    "        on='mouseover',\n",
    "        nearest=True)\n",
    "    chart = altair.Chart(\n",
    "        avg_ofes.query('species_sn == @i'),\n",
    "        title='avg OF of species over generation'\n",
    "    ).mark_line().encode(\n",
    "        x='generation_sn:N',\n",
    "        y='avg_original_fitness:Q',\n",
    "        color=altair.Color('species_sn:N', legend=None),\n",
    "        tooltip='species_sn',\n",
    "        opacity=altair.condition(highlight, altair.value(1), altair.value(0))\n",
    "    ).add_selection(highlight)\n",
    "    charts.append(chart)\n",
    "\n",
    "altair.layer(*charts).properties(width=600).interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "view": {
         "height": 300,
         "width": 400
        }
       },
       "datasets": {
        "data-0190df08925076732c60fbc9e2583c1b": [
         {
          "generation_sn": 4,
          "num_genomes": 3,
          "species_sn": 56
         },
         {
          "generation_sn": 5,
          "num_genomes": 3,
          "species_sn": 56
         },
         {
          "generation_sn": 3,
          "num_genomes": 3,
          "species_sn": 56
         },
         {
          "generation_sn": 1,
          "num_genomes": 1,
          "species_sn": 56
         },
         {
          "generation_sn": 2,
          "num_genomes": 2,
          "species_sn": 56
         }
        ],
        "data-067c07393caf95babba617c7f79be7a5": [
         {
          "generation_sn": 5,
          "num_genomes": 1,
          "species_sn": 91
         }
        ],
        "data-080a5130a1bd9779f692858ccc10cec5": [
         {
          "generation_sn": 5,
          "num_genomes": 3,
          "species_sn": 78
         },
         {
          "generation_sn": 1,
          "num_genomes": 1,
          "species_sn": 78
         },
         {
          "generation_sn": 4,
          "num_genomes": 3,
          "species_sn": 78
         },
         {
          "generation_sn": 2,
          "num_genomes": 2,
          "species_sn": 78
         },
         {
          "generation_sn": 3,
          "num_genomes": 3,
          "species_sn": 78
         }
        ],
        "data-0879620caad0742342d69ed9ef87d032": [
         {
          "generation_sn": 1,
          "num_genomes": 1,
          "species_sn": 48
         },
         {
          "generation_sn": 2,
          "num_genomes": 2,
          "species_sn": 48
         },
         {
          "generation_sn": 5,
          "num_genomes": 3,
          "species_sn": 48
         },
         {
          "generation_sn": 3,
          "num_genomes": 3,
          "species_sn": 48
         },
         {
          "generation_sn": 4,
          "num_genomes": 3,
          "species_sn": 48
         }
        ],
        "data-08d8087888b35e84669c41743a78b894": [
         {
          "generation_sn": 2,
          "num_genomes": 2,
          "species_sn": 84
         },
         {
          "generation_sn": 3,
          "num_genomes": 3,
          "species_sn": 84
         },
         {
          "generation_sn": 4,
          "num_genomes": 3,
          "species_sn": 84
         },
         {
          "generation_sn": 5,
          "num_genomes": 3,
          "species_sn": 84
         },
         {
          "generation_sn": 1,
          "num_genomes": 1,
          "species_sn": 84
         }
        ],
        "data-0c804d5b1c847bce32c7f238ac694a7f": [
         {
          "generation_sn": 3,
          "num_genomes": 3,
          "species_sn": 13
         },
         {
          "generation_sn": 4,
          "num_genomes": 3,
          "species_sn": 13
         },
         {
          "generation_sn": 5,
          "num_genomes": 1,
          "species_sn": 13
         },
         {
          "generation_sn": 2,
          "num_genomes": 2,
          "species_sn": 13
         },
         {
          "generation_sn": 1,
          "num_genomes": 1,
          "species_sn": 13
         }
        ],
        "data-0daa6f04009e0b57ff8ae4ce99cf2b63": [
         {
          "generation_sn": 2,
          "num_genomes": 2,
          "species_sn": 83
         },
         {
          "generation_sn": 4,
          "num_genomes": 3,
          "species_sn": 83
         },
         {
          "generation_sn": 5,
          "num_genomes": 3,
          "species_sn": 83
         },
         {
          "generation_sn": 3,
          "num_genomes": 3,
          "species_sn": 83
         },
         {
          "generation_sn": 1,
          "num_genomes": 1,
          "species_sn": 83
         }
        ],
        "data-0fc1177e8f172670d1f351d974b1c346": [
         {
          "generation_sn": 3,
          "num_genomes": 3,
          "species_sn": 69
         },
         {
          "generation_sn": 4,
          "num_genomes": 3,
          "species_sn": 69
         },
         {
          "generation_sn": 5,
          "num_genomes": 3,
          "species_sn": 69
         },
         {
          "generation_sn": 2,
          "num_genomes": 2,
          "species_sn": 69
         },
         {
          "generation_sn": 1,
          "num_genomes": 1,
          "species_sn": 69
         }
        ],
        "data-12b18ad6666a8d25aacc20f45374c219": [
         {
          "generation_sn": 5,
          "num_genomes": 3,
          "species_sn": 22
         },
         {
          "generation_sn": 1,
          "num_genomes": 2,
          "species_sn": 22
         },
         {
          "generation_sn": 4,
          "num_genomes": 3,
          "species_sn": 22
         },
         {
          "generation_sn": 3,
          "num_genomes": 3,
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = '''\n",
    "  select generation_sn, species_sn, count(1) as num_genomes\n",
    "  from population\n",
    "  where run_id = %s\n",
    "  group by (generation_sn, species_sn)\n",
    "  '''\n",
    "\n",
    "genomes = pd.read_sql(sql=sql, con=conn, params=(run_id,))\n",
    "\n",
    "charts = []\n",
    "for i in range(num_species):\n",
    "    highlight = altair.selection(\n",
    "        type='single',\n",
    "        on='mouseover',\n",
    "        nearest=True)\n",
    "    chart = altair.Chart(\n",
    "        genomes.query('species_sn == @i'),\n",
    "        title='population of species over generation'\n",
    "    ).mark_line().encode(\n",
    "        x='generation_sn:N',\n",
    "        y='num_genomes:Q',\n",
    "        color=altair.Color('species_sn:N', legend=None),\n",
    "        tooltip='species_sn',\n",
    "        opacity=altair.condition(highlight, altair.value(1), altair.value(0))\n",
    "    ).add_selection(highlight)\n",
    "    charts.append(chart)\n",
    "\n",
    "altair.layer(*charts).properties(width=600).interactive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}