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    "# OPTaaS Cyclical Parameters\n",
    "\n",
    "### <span style=\"color:red\">Note:</span> To run this notebook, you need an API Key. You can get one <a href=\"mailto:info@mindfoundry.ai\">here</a>.\n",
    "\n",
    "A new flag on `FloatParameter` now allows you to specify that the parameter is **cyclical** (aka *circular* or *periodic*). OPTaaS will select values from a period starting from the `minimum` (inclusive) and ending at the `maximum` (exclusive). Values near the minimum and maximum will be considered to be close, as if they were on a circle.\n",
    "\n",
    "**Note:** If you use any Cyclical parameters in your task, all your parameters must be Floats, Constants or Groups (other types are not currently supported), and none of them can be `optional`.\n",
    "\n",
    "As a simple example, let's optimize `cos(x)` for x in the range `[0, 2π)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect to OPTaaS using your API Key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mindfoundry.optaas.client.client import OPTaaSClient\n",
    "\n",
    "client = OPTaaSClient('https://optaas.mindfoundry.ai', '<Your OPTaaS API Key>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define your task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import cos, pi\n",
    "\n",
    "from mindfoundry.optaas.client.parameter import FloatParameter\n",
    "\n",
    "def scoring_function(x):\n",
    "    return cos(x)\n",
    "\n",
    "x = FloatParameter(\"x\", minimum=0, maximum=2 * pi, cyclical=True)\n",
    "\n",
    "task = client.create_task(\n",
    "    title='Cyclical Example',\n",
    "    parameters=[x],\n",
    "    initial_configurations=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run your Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running task \"Cyclical Example\" for 10 iterations\n",
      "(no score threshold set)\n",
      "\n",
      "Iteration: 0    Score: -1.0\n",
      "Configuration: {'x': 3.141592653589793}\n",
      "\n",
      "Iteration: 1    Score: -0.5181244988793857\n",
      "Configuration: {'x': 2.115453031608477}\n",
      "\n",
      "Iteration: 2    Score: -0.41413695380966903\n",
      "Configuration: {'x': 1.997790746187629}\n",
      "\n",
      "Iteration: 3    Score: 0.4477296432117673\n",
      "Configuration: {'x': 1.1065716756360366}\n",
      "\n",
      "Iteration: 4    Score: 0.9964383191318358\n",
      "Configuration: {'x': 6.198760226305232}\n",
      "\n",
      "Iteration: 5    Score: 0.5181244988794008\n",
      "Configuration: {'x': 5.257045685198288}\n",
      "\n",
      "Iteration: 6    Score: 0.9962804147395031\n",
      "Configuration: {'x': 0.08627738331880817}\n",
      "\n",
      "Iteration: 7    Score: 0.9971137826607362\n",
      "Configuration: {'x': 0.07599482592806261}\n",
      "\n",
      "Iteration: 8    Score: 0.4141369532186448\n",
      "Configuration: {'x': 5.139383399128098}\n",
      "\n",
      "Iteration: 9    Score: -0.4491755260657931\n",
      "Configuration: {'x': 4.246546660377198}\n",
      "\n",
      "Task Completed\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{ 'configuration': {'type': 'exploitation', 'values': {'x': 0.07599482592806261}},\n",
       "  'score': 0.9971137826607362,\n",
       "  'user_defined_data': None}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task.run(scoring_function, max_iterations=10)"
   ]
  }
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