example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload\n",
    "import param_search as ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = '''\\\n",
    "#!/bin/bash\n",
    "#SBATCH --job-name={job_name}\n",
    "#SBATCH -o %J.stdout\n",
    "#SBATCH -e %J.stderr\n",
    "pwd\n",
    "product=`python3 -c \"print({term1}*{term2})\"`\n",
    "quotient=`python3 -c \"print({term1}/{term2})\"`\n",
    "\n",
    "echo \"product quotient\" > {job_name}.metrics\n",
    "echo \"$product $quotient\" >> {job_name}.metrics\n",
    "'''\n",
    "name = 'job_{term1}_{term2}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_space = ps.ParamSpace(term1=range(4), term2=range(4))\n",
    "len(param_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [00:01<00:00, 13.58it/s]\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",
       "        vertical-align: top;\n",
       "    }\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>term1</th>\n",
       "      <th>term2</th>\n",
       "      <th>job_name</th>\n",
       "      <th>stdout</th>\n",
       "      <th>stderr</th>\n",
       "      <th>work_dir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>job_0_0</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td>Traceback (most recent call last):\\n  File \"&lt;s...</td>\n",
       "      <td>./job_0_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>job_0_1</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_0_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>job_0_2</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_0_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>job_0_3</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_0_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>job_1_0</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td>Traceback (most recent call last):\\n  File \"&lt;s...</td>\n",
       "      <td>./job_1_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>job_1_1</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_1_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>job_1_2</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_1_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>job_1_3</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_1_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>job_2_0</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td>Traceback (most recent call last):\\n  File \"&lt;s...</td>\n",
       "      <td>./job_2_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>job_2_1</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_2_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>job_2_2</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_2_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>job_2_3</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_2_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>job_3_0</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td>Traceback (most recent call last):\\n  File \"&lt;s...</td>\n",
       "      <td>./job_3_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>job_3_1</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_3_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>job_3_2</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_3_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>job_3_3</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_3_3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    term1  term2 job_name                                             stdout  \\\n",
       "0       0      0  job_0_0  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "1       0      1  job_0_1  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "2       0      2  job_0_2  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "3       0      3  job_0_3  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "4       1      0  job_1_0  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "5       1      1  job_1_1  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "6       1      2  job_1_2  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "7       1      3  job_1_3  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "8       2      0  job_2_0  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "9       2      1  job_2_1  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "10      2      2  job_2_2  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "11      2      3  job_2_3  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "12      3      0  job_3_0  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "13      3      1  job_3_1  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "14      3      2  job_3_2  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "15      3      3  job_3_3  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "\n",
       "                                               stderr   work_dir  \n",
       "0   Traceback (most recent call last):\\n  File \"<s...  ./job_0_0  \n",
       "1                                                      ./job_0_1  \n",
       "2                                                      ./job_0_2  \n",
       "3                                                      ./job_0_3  \n",
       "4   Traceback (most recent call last):\\n  File \"<s...  ./job_1_0  \n",
       "5                                                      ./job_1_1  \n",
       "6                                                      ./job_1_2  \n",
       "7                                                      ./job_1_3  \n",
       "8   Traceback (most recent call last):\\n  File \"<s...  ./job_2_0  \n",
       "9                                                      ./job_2_1  \n",
       "10                                                     ./job_2_2  \n",
       "11                                                     ./job_2_3  \n",
       "12  Traceback (most recent call last):\\n  File \"<s...  ./job_3_0  \n",
       "13                                                     ./job_3_1  \n",
       "14                                                     ./job_3_2  \n",
       "15                                                     ./job_3_3  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jobs = ps.submit(template, name, param_space, use='local')\n",
    "jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"<string>\", line 1, in <module>\n",
      "ZeroDivisionError: division by zero\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(jobs.iloc[0].stderr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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",
       "        vertical-align: top;\n",
       "    }\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>term1</th>\n",
       "      <th>term2</th>\n",
       "      <th>job_name</th>\n",
       "      <th>stdout</th>\n",
       "      <th>stderr</th>\n",
       "      <th>work_dir</th>\n",
       "      <th>product</th>\n",
       "      <th>quotient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>job_0_0</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td>Traceback (most recent call last):\\n  File \"&lt;s...</td>\n",
       "      <td>./job_0_0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>job_0_1</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_0_1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>job_0_2</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_0_2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>job_0_3</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_0_3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>job_1_0</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td>Traceback (most recent call last):\\n  File \"&lt;s...</td>\n",
       "      <td>./job_1_0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>job_1_1</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_1_1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>job_1_2</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_1_2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>job_1_3</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_1_3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>job_2_0</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td>Traceback (most recent call last):\\n  File \"&lt;s...</td>\n",
       "      <td>./job_2_0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>job_2_1</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_2_1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>job_2_2</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_2_2</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>job_2_3</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_2_3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>job_3_0</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td>Traceback (most recent call last):\\n  File \"&lt;s...</td>\n",
       "      <td>./job_3_0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>job_3_1</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_3_1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>job_3_2</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_3_2</td>\n",
       "      <td>6</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>job_3_3</td>\n",
       "      <td>/ocean/projects/asc170022p/mtragoza/param_sear...</td>\n",
       "      <td></td>\n",
       "      <td>./job_3_3</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    term1  term2 job_name                                             stdout  \\\n",
       "0       0      0  job_0_0  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "1       0      1  job_0_1  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "2       0      2  job_0_2  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "3       0      3  job_0_3  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "4       1      0  job_1_0  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "5       1      1  job_1_1  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "6       1      2  job_1_2  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "7       1      3  job_1_3  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "8       2      0  job_2_0  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "9       2      1  job_2_1  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "10      2      2  job_2_2  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "11      2      3  job_2_3  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "12      3      0  job_3_0  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "13      3      1  job_3_1  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "14      3      2  job_3_2  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "15      3      3  job_3_3  /ocean/projects/asc170022p/mtragoza/param_sear...   \n",
       "\n",
       "                                               stderr   work_dir  product  \\\n",
       "0   Traceback (most recent call last):\\n  File \"<s...  ./job_0_0        0   \n",
       "1                                                      ./job_0_1        0   \n",
       "2                                                      ./job_0_2        0   \n",
       "3                                                      ./job_0_3        0   \n",
       "4   Traceback (most recent call last):\\n  File \"<s...  ./job_1_0        0   \n",
       "5                                                      ./job_1_1        1   \n",
       "6                                                      ./job_1_2        2   \n",
       "7                                                      ./job_1_3        3   \n",
       "8   Traceback (most recent call last):\\n  File \"<s...  ./job_2_0        0   \n",
       "9                                                      ./job_2_1        2   \n",
       "10                                                     ./job_2_2        4   \n",
       "11                                                     ./job_2_3        6   \n",
       "12  Traceback (most recent call last):\\n  File \"<s...  ./job_3_0        0   \n",
       "13                                                     ./job_3_1        3   \n",
       "14                                                     ./job_3_2        6   \n",
       "15                                                     ./job_3_3        9   \n",
       "\n",
       "    quotient  \n",
       "0        NaN  \n",
       "1   0.000000  \n",
       "2   0.000000  \n",
       "3   0.000000  \n",
       "4        NaN  \n",
       "5   1.000000  \n",
       "6   0.500000  \n",
       "7   0.333333  \n",
       "8        NaN  \n",
       "9   2.000000  \n",
       "10  1.000000  \n",
       "11  0.666667  \n",
       "12       NaN  \n",
       "13  3.000000  \n",
       "14  1.500000  \n",
       "15  1.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%autoreload\n",
    "metrics = ps.metrics(jobs)\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = ps.plot(metrics, x=['term1', 'term2'], y=['product', 'quotient'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_space_a = ps.ParamSpace(type='a', param=[1,2,3])\n",
    "param_space_b = ps.ParamSpace(type='b', param=[4,5,6])\n",
    "param_space_c = ps.ParamSpace(other_param=1.5)\n",
    "\n",
    "# addition iterates over the param spaces sequentially\n",
    "#   which requires that the keys be the same\n",
    "param_space_ab = param_space_a + param_space_b\n",
    "\n",
    "# scalar multiplication just repeats the samples\n",
    "#   which can be useful for balanced samples of two subspaces\n",
    "param_space_ab = 10 * param_space_ab\n",
    "\n",
    "# multiplying two spaces produces their Cartesian product\n",
    "#   which requires that the keys be different\n",
    "param_space_abc = param_space_ab * param_space_c\n",
    "\n",
    "len(param_space_abc) # 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ISSP2170",
   "language": "python",
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