{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dcb1dc42-3c5c-40fb-aefb-e7e93d551d5d",
   "metadata": {},
   "source": [
    "# Design space exploration plugin\n",
    "\n",
    "## Basics\n",
    "\n",
    "The `Design` plugin (`tidy3d.plugins.design`) is a user-friendly tool that simplifies the process of defining and optimizing experiments within the `Tidy3D` environment. This plugin helps you efficiently explore different design possibilities by setting up a structured approach to test and refine your ideas.\n",
    "\n",
    "To get started, define the key `Parameters` of your design problem and choose a `Method` that will suggest possible solutions. The ``Method`` can sample the design space systematically or randomly using `MethodGrid` or `MethodMonteCarlo`, or can optimize for a given problem through iterative search and evaluation using techniques like Bayesian optimization (`MethodBayOpt`), genetic algorithm (`MethodGenAlg`) or particle swarm optimization (`MethodParticleSwarm`). These choices are combined into a `DesignSpace` object, which serves to manage the search process.\n",
    "\n",
    "Next, provide a function that describes the relationship between your input variables (the dimensions of your design) and the desired outputs. This function can take any inputs and return any outputs that you want to investigate.\n",
    "\n",
    "The results of the search are automatically stored in a `Result` object. This object contains all the details of the search and can be quickly converted into a `pandas.DataFrame` for further analysis, post-processing, or visualization. In this `DataFrame`, each column represents an input or output from your function, and each row represents a single data point from your experiment.\n",
    "\n",
    "Here’s a diagram to details the process step-by-step.\n",
    "\n",
    "<img src=\"img/design.png\" alt=\"sweep\" width=\"800\"/>\n",
    "\n",
    "Another example of the `Design` plugin can be seen about halfway down our [Parameter Scan](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan) notebook. A simple practical case study of the `Design` plugin is available in the [All-Dielectric Structural Color ](https://www.flexcompute.com/tidy3d/examples/notebooks/AllDielectricStructuralColor/) notebook, or more complex case studies in the following optimization notebooks:\n",
    "\n",
    "1. [Bayesian Optimization of Y-Junction](https://www.flexcompute.com/tidy3d/examples/notebooks/BayesianOptimizationYJunction/)\n",
    "\n",
    "2. [Particle Swarm Optimizer PBS](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
    "\n",
    "3. [Particle Swarm Optimizer Bullseye Cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/)\n",
    "\n",
    "4. [Genetic Algorithm Reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/)\n",
    "\n",
    "\n",
    "The plugin is imported from `tidy3d.plugins.design` so it's convenient to import this namespace first along with the other packages we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7d44f419-15d4-41a7-b699-5c7351f3a952",
   "metadata": {},
   "outputs": [],
   "source": [
    "import typing\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.plugins.design as tdd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5b1c4a-8860-4f83-a7d9-a9f2b2815f03",
   "metadata": {},
   "source": [
    "## Quickstart\n",
    "\n",
    "While the `Design` plugin is built for `Tidy3D` and has special features for handling FDTD simulations, it can also be run with any general design problem. Here is a quick, complete example before diving into the use of the plugin with `Tidy3D` simulations.\n",
    "\n",
    "Here we want to sample a design space with two dimensions `x` and `y` with Monte Carlo. Our function simply returns a Gaussian centered at `(0,0)` and we'll evaluate the result for Gaussians with 3 different variances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41b9d573-6386-4f17-bae5-82d45dc0e56b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1000x300 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define your design space (parameters (x,y) within certain ranges)\n",
    "param_x = tdd.ParameterFloat(name=\"x\", span=(-15, 15))\n",
    "param_y = tdd.ParameterFloat(name=\"y\", span=(-15, 15))\n",
    "\n",
    "# Define your sampling method, Monte Carlo method with 10,000 points\n",
    "method = tdd.MethodMonteCarlo(num_points=10000)\n",
    "\n",
    "# Put everything together in a `DesignSpace` container\n",
    "design_space = tdd.DesignSpace(method=method, parameters=[param_x, param_y])\n",
    "\n",
    "# Define your fitness function / figure of merit. Here we compute a gaussian as a function of x and y with different values for the width.\n",
    "sigmas = {f\"sigma={s}\": s for s in [5, 10, 20]}\n",
    "\n",
    "\n",
    "def f(x: float, y: float) -> typing.Dict[str, float]:\n",
    "    \"\"\"Gaussian distribution as a function of x and y.\"\"\"\n",
    "    r2 = x**2 + y**2\n",
    "\n",
    "    def gaussian(sigma: float) -> float:\n",
    "        return np.exp(-r2 / sigma**2)\n",
    "\n",
    "    # return a dictionary, where the key is used to label the output names\n",
    "    return {key: gaussian(sigma) for key, sigma in sigmas.items()}\n",
    "\n",
    "\n",
    "# Call .run on the DesignSpace with our function and convert the results to a pandas.DataFrame\n",
    "df = design_space.run(f).to_dataframe()\n",
    "\n",
    "# Plot the results using pandas.DataFrame builtins\n",
    "f, axes = plt.subplots(1, 3, figsize=(10, 3), tight_layout=True)\n",
    "for ax, C in zip(axes, sigmas.keys()):\n",
    "    _ = df.plot.hexbin(x=\"x\", y=\"y\", gridsize=20, C=C, cmap=\"magma\", ax=ax)\n",
    "    ax.set_title(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29c56a39-ca58-4870-a65d-5e9842586c24",
   "metadata": {
    "tags": []
   },
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>sigma=5</th>\n",
       "      <th>sigma=10</th>\n",
       "      <th>sigma=20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-11.364949</td>\n",
       "      <td>10.073654</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.099619</td>\n",
       "      <td>0.561804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.944132</td>\n",
       "      <td>-14.283928</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.111262</td>\n",
       "      <td>0.577546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.842140</td>\n",
       "      <td>-4.418313</td>\n",
       "      <td>0.179297</td>\n",
       "      <td>0.650719</td>\n",
       "      <td>0.898149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.070862</td>\n",
       "      <td>9.099734</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>0.128261</td>\n",
       "      <td>0.598444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.197381</td>\n",
       "      <td>-10.461134</td>\n",
       "      <td>0.008343</td>\n",
       "      <td>0.302224</td>\n",
       "      <td>0.741451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y   sigma=5  sigma=10  sigma=20\n",
       "0 -11.364949  10.073654  0.000098  0.099619  0.561804\n",
       "1   3.944132 -14.283928  0.000153  0.111262  0.577546\n",
       "2   4.842140  -4.418313  0.179297  0.650719  0.898149\n",
       "3  11.070862   9.099734  0.000271  0.128261  0.598444\n",
       "4   3.197381 -10.461134  0.008343  0.302224  0.741451"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the first 5 outputs\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defd38d3-cbb0-4088-862f-7b5cd21004c8",
   "metadata": {},
   "source": [
    "##  Design Space Exploration in Tidy3D\n",
    "\n",
    "We can repeat the same process to perform a parameter sweep of a photonic device. The only difference is that for our function evaluation each point will involve a `Tidy3D` `Simulation`.\n",
    "\n",
    "Let's analyze the transmission of a 1D multilayer slab. Our system will have `num` layers with refractive index alternating between two values, each with thickness `t`. We write a function to compute the transmitted flux through this system at frequency `freq0` as a function of `num` and `t`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86300207-941f-4ccb-9ccb-f815cee5e7e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda0 = 0.63\n",
    "freq0 = td.C_0 / lambda0\n",
    "\n",
    "mnt_name = \"flux\"\n",
    "buffer = 1.5 * lambda0\n",
    "\n",
    "refractive_indices = [1.5, 1.2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43d38085-225e-4a39-b27e-bf264f9b52fd",
   "metadata": {},
   "source": [
    "We split our simulation process into pre- and post-processing functions so that the `Design` plugin can handle creating one large `Batch`, to be run in parallel on the `Tidy3D` cloud. The pre-processing function returns a `tidy3d.Simulation` as a function of the input parameters and the post-processing function returns the transmitted flux as a function of the `tidy3d.SimulationData` associated with the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fcf092e4-9d87-476b-a0a4-fe137bbf92c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pre(num: int, t: float) -> td.Simulation:\n",
    "    \"\"\"Pre-processing function, which creates a tidy3d simulation given the function inputs.\"\"\"\n",
    "    layers = []\n",
    "    z = 0\n",
    "    for i in range(num):\n",
    "        n = refractive_indices[i % len(refractive_indices)]\n",
    "        thickness = t * lambda0 / n\n",
    "        medium = td.Medium(permittivity=n**2)\n",
    "        geometry = td.Box(center=(0, 0, z + thickness / 2), size=(td.inf, td.inf, thickness))\n",
    "        layers.append(td.Structure(geometry=geometry, medium=medium))\n",
    "        z += thickness\n",
    "    Lz = z + 2 * buffer\n",
    "    sim = td.Simulation(\n",
    "        size=(0, 0, Lz),\n",
    "        center=(0, 0, z / 2),\n",
    "        structures=layers,\n",
    "        sources=[\n",
    "            td.PlaneWave(\n",
    "                size=(td.inf, td.inf, 0),\n",
    "                center=(0, 0, -buffer * 0.75),\n",
    "                source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
    "                direction=\"+\",\n",
    "            )\n",
    "        ],\n",
    "        monitors=[\n",
    "            td.FluxMonitor(\n",
    "                size=(td.inf, td.inf, 0),\n",
    "                center=(0, 0, z + buffer * 0.75),\n",
    "                freqs=[freq0],\n",
    "                name=mnt_name,\n",
    "            )\n",
    "        ],\n",
    "        boundary_spec=td.BoundarySpec.pml(x=False, y=False, z=True),\n",
    "        run_time=100 / freq0,\n",
    "    )\n",
    "    return sim\n",
    "\n",
    "\n",
    "def post(data: td.SimulationData) -> dict:\n",
    "    \"\"\"Post-processing function, which processes the tidy3d simulation data to return the function output.\"\"\"\n",
    "    flux = np.sum(data[\"flux\"].flux.values)\n",
    "    return {\"flux\": flux}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8588384b-d0bf-4978-b613-f4bd441a1fa8",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "When using `MethodMonteCarlo` or `MethodGrid` we can choose to have our post-processing function return a dictionary `{\"flux\": flux}` instead of just a `float`. If a dictionary is returned, the `Design` plugin will use the keys to label the dataset outputs. \n",
    "\n",
    "For the optimization tools (`MethodBayOpt`, `MethodGenAlg`, `MethodParticleSwarm`) we must return a float that the optimization can use to evaluate the solution and progress. However, we can return values named as auxiliary data using a notation like `[float, dict{\"aux1\": x}]`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80425b7e-5839-42df-bac1-f9c92c255e78",
   "metadata": {},
   "source": [
    "> Note: if we were to write the full transmission function using pre and post it would look like the function below. However, the `Design` plugin will handle running the simulation for us, as we'll see later.\n",
    "\n",
    "```python\n",
    "def transmission(num: int, t: float) -> float:\n",
    "    \"\"\"Transmission as a function of number of layers and their thicknesses.\n",
    "         Note: not used, just a demonstration of how the pre and post functions are related.\n",
    "    \"\"\"\n",
    "    sim = pre(num=num, t=t)\n",
    "    data = web.run(sim, task_name=f\"num={num}_t={t}\")\n",
    "    return post(data=data)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4376395-c51a-462b-9b29-32a575229387",
   "metadata": {},
   "source": [
    "Let's visualize the simulation for some example parameters to make sure it looks ok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e0cc1d2-0a15-4343-97ed-05f2686498c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = pre(num=5, t=0.4)\n",
    "ax = sim.plot_eps(x=0, hlim=(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aaa56f7-7def-471b-bdc8-9a4d485d01ef",
   "metadata": {},
   "source": [
    "\n",
    "##  Parameters\n",
    "\n",
    "We could query these functions directly to perform our own parameter scan, but the advantage of the `Design` plugin is that it lets us simply **define** our design problem as a specification and then takes manages the computation for us.\n",
    "\n",
    "The first step is to define the design \"parameters\" (or dimensions), which also serve as inputs to our `pre` function defined earlier.\n",
    "\n",
    "In this case, we have a parameter `num`, which is a non-negative integer and a parameter `t`, which is a positive float.\n",
    "\n",
    "We can construct a named `tdd.Parameter` for each of these and define some spans as well. The `name` fields should correspond to the argument names defined in the `pre` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c94ab9c-3b96-4431-8b35-d257b911d2e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_num = tdd.ParameterInt(name=\"num\", span=(1, 5))\n",
    "param_t = tdd.ParameterFloat(name=\"t\", span=(0.1, 0.5), num_points=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee49e47-6fbd-461b-88c1-d7856a50f4e4",
   "metadata": {},
   "source": [
    "The float parameter takes an optional `num_points` option, which tells the design tool how to discretize the continuous dimension only when doing a grid sweep with `MethodGrid`: for Monte Carlo sweeps, it is not used.\n",
    "\n",
    "It is also possible to define parameters that are simply sets of quantities that we might want to select using `allowed_values`, such as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "646f08e9-b6c8-4a33-8d8d-919e972ea4c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_str = tdd.ParameterAny(name=\"some_string\", allowed_values=(\"these\", \"are\", \"values\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c88ca13-1305-47d9-92f2-45638403618b",
   "metadata": {},
   "source": [
    "but we will ignore this case as it's not needed here and the internal logic is similar to that for integer parameters.\n",
    "\n",
    "> Note: to do more complex sets of integer parameters (like skipping every 'n' integers). We recommend simply using `ParameterAny` and just passing your integers to `allowed_values`.\n",
    "\n",
    "By defining our design parameters like this, we can more concretely define what types and allowed values can be passed to each argument in our function for the parameter sweep.\n",
    "\n",
    "##  Method\n",
    "\n",
    "Now that we've defined our parameters, we also need to define the procedure used to sample the parameter space we've defined. As mentioned above, one approach is to independently sample points within the parameter spans, for example using a Monte Carlo method, another is to perform a grid search to uniformly scan between the bounds of each dimension. There are also more complex methods, such as [Bayesian optimization]() and [genetic algorithms](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/), which can be seen in these other notebooks.\n",
    "\n",
    "In the `Design` plugin, we define the specifications for the parameter sweeps using `Method` objects.\n",
    "\n",
    "Here's one example, which defines a grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "efce81e5-b648-4788-91bc-57beec8a7dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "method = tdd.MethodGrid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c963e4c-e82b-4a5f-84a6-228fd353fbed",
   "metadata": {},
   "source": [
    "For this example, let's instead do a Monte Carlo sampling with a set number of points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6563ba39-7280-4c1b-aefa-86ce7c26c67d",
   "metadata": {},
   "outputs": [],
   "source": [
    "method = tdd.MethodMonteCarlo(num_points=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fd0b9f-2dc5-4697-8631-c993e20ecdb4",
   "metadata": {},
   "source": [
    "## Design Space\n",
    "\n",
    "With the design parameters and our method defined, we can combine everything into a `DesignSpace`, which is mainly a container that provides some higher level methods for interacting with these objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46e97289-edec-4ede-b109-e2bbf96e2264",
   "metadata": {},
   "outputs": [],
   "source": [
    "design_space = tdd.DesignSpace(\n",
    "    parameters=[param_num, param_t],\n",
    "    method=method,\n",
    "    path_dir=\"./data\",\n",
    "    folder_name=\"Design_Tutorial\",\n",
    "    task_name=\"Design_notebook\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273ea6e5-b1e3-4795-b961-366b192e28b6",
   "metadata": {},
   "source": [
    "We can provide a `path_dir` to specify the local directory location where files should be stored, and the `folder_name` which is the location on `Tidy3D` cloud where the `Simulations` will be kept.\n",
    "\n",
    "The `task_name` argument assigns the root of task name for `Tidy3D` cloud. This is combined with the index of the `Simulation` output by the `Pre` function (often 0) and a counter for the simulations run by the `DesignSpace` in this format: \n",
    "\n",
    "`{task_name}_{sim_index}_{counter}` \n",
    "\n",
    "If the `Pre` function outputs `Simulation` objects as a dictionary then the keys from the dictionary replace the `sim_index` to be: \n",
    "\n",
    "`{task_name}_{dict_key}_{counter}`\n",
    "\n",
    "### Working with Pre and Post Functions\n",
    "\n",
    "Now we need to pass our `Pre` and `Post` functions to the `DesignSpace` object to get our results.\n",
    "\n",
    "To start the parameter scan the `DesignSpace` uses the method `.run()`, which accepts our function(s) defined earlier. If a single function is supplied to the `DesignSpace` it will be called exactly as the user specified. If pre and post functions are supplied to the `DesignSpace` then the `Simulation` objects will be efficiently assembled into `Batch` objects to be run in parallel on the `Tidy3D` cloud. The return from the `Pre` function can be in many different forms, with the table below outlining some example `Pre` return formats and the associated format that the `Post` function should be able to receive.\n",
    "\n",
    "<center>\n",
    "\n",
    "| fn_pre return                             | fn_post call                                      |\n",
    "|-------------------------------------------|---------------------------------------------------|\n",
    "| 1.0                                       | fn_post(1.0)                                      |\n",
    "| [1,2,3]                                   | fn_post(1,2,3)                                    |\n",
    "| {'a': 2, 'b': 'hi'}                       | fn_post(a=2, b='hi')                              |\n",
    "| Simulation                                | fn_post(SimulationData)                           |\n",
    "| Batch                                     | fn_post(BatchData)                                |\n",
    "| [Simulation, Simulation]                  | fn_post(SimulationData, SimulationData)           |\n",
    "| [Simulation, 1.0]                         | fn_post(SimulationData, 1.0)                      |\n",
    "| [Simulation, Batch]                       | fn_post(SimulationData, BatchData)                |\n",
    "| {'a': Simulation, 'b': Batch, 'c': 2.0}   | fn_post(a=SimulationData, b=BatchData, c=2.0)     |\n",
    "\n",
    "</center>\n",
    "\n",
    "As you can see, it is possible to output `Batch` objects for the `DesignSpace` to compute. These batches are run sequentially, so it is often faster for the `Pre` function to output a container of `Simulation` objects which are then combined by the `DesignSpace` into a `Batch` for parallel computation. A `Batch` can be used if the user has specific needs when running on the `Tidy3D` cloud.\n",
    "\n",
    "## Results\n",
    "\n",
    "The `DesignSpace.run()` function returns a `Result` object, which is basically a dataset containing the function inputs, outputs, source code, and any task ID information corresponding to each data point.\n",
    "\n",
    "> Note that task ID information can only be gathered when using pre and post functions, as in a single function the `DesignSpace` can't know which tasks were involved in each data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d3b23d1-32ae-4460-9944-14919a990ba4",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:00:09 CEST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span> Simulations                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:00:09 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m40\u001b[0m Simulations                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = design_space.run(pre, post)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29792d63",
   "metadata": {},
   "source": [
    "We can pass `verbose=False` to the function if we don't want to see the outputs for large design searches. The \"name already exists\" error can be ignored here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6243946b-f8ce-4379-82a4-418c783f01ce",
   "metadata": {},
   "source": [
    "\n",
    "`Results` contains three main related data structures.\n",
    "\n",
    "* `dims`, which correspond to the `kwargs` of the pre-processing function, `('num', 't')` here.\n",
    "\n",
    "* `coords`, which is a tuple containing the values passed in for each of the dims. `coords[i]` is a tuple of `n`, and `t` values for the `i`th function call.\n",
    "\n",
    "* `values`, which is a tuple containing the outputs of the postprocessing functions. In this case `values[i]` stores the transmission of the `i`th function call.\n",
    "\n",
    "The `Results` can be converted to a `pandas.DataFrame` where each row is a separate data point and each column is either an input or output for a function. It also contains various methods for plotting and managing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82f5590d-fe19-4c57-ba19-aa80d2c2a60d",
   "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>num</th>\n",
       "      <th>t</th>\n",
       "      <th>flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.401888</td>\n",
       "      <td>0.942711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.249284</td>\n",
       "      <td>0.954082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.313907</td>\n",
       "      <td>0.896590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.356313</td>\n",
       "      <td>0.922831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.273844</td>\n",
       "      <td>0.841397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num         t      flux\n",
       "0    3  0.401888  0.942711\n",
       "1    2  0.249284  0.954082\n",
       "2    3  0.313907  0.896590\n",
       "3    2  0.356313  0.922831\n",
       "4    4  0.273844  0.841397"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first 5 data points\n",
    "df = results.to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3a3292-c4cc-489b-8157-5c70dc14922d",
   "metadata": {},
   "source": [
    "The `pandas` [documentation](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented) provides excellent explanations of the various postprocessing and visualization functions that can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "22546073-b479-4b72-bf4e-5c0cb5e14afa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.5))\n",
    "\n",
    "# plot a hexagonal binning of the results\n",
    "im = df.plot.hexbin(x=\"t\", y=\"num\", gridsize=10, C=\"flux\", cmap=\"magma\", ax=ax1)\n",
    "ax1.set_title(\"SCS (hexagonal binning)\")\n",
    "\n",
    "# scatterplot the raw results with flux on the y axis\n",
    "im = df.plot.scatter(x=\"t\", y=\"flux\", s=50, c=\"num\", cmap=\"magma\", ax=ax2)\n",
    "ax2.set_title(\"SCS (data scatter plot)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3b3165-d101-4983-8c98-606e1e63196a",
   "metadata": {},
   "source": [
    "> These functions just call `pandas.DataFrame` plotting under the hood, so it is worth referring to the [docs page on DataFrame visuazation](https://pandas.pydata.org/docs/user_guide/visualization.html) for more tips on plotting design problem results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c65fa2-c730-4cca-a749-a12fc9871518",
   "metadata": {},
   "source": [
    "### Modifying Results\n",
    "\n",
    "After the design problems is run, one might want to modify the results through adding or removing elements or combining different results. Here we will show how to perform some of these advanced features in the `Design` plugin.\n",
    "\n",
    "#### Combining Results\n",
    "\n",
    "We can combine the results of two separate design problems assuming they were created with the same functions.\n",
    "\n",
    "This can be done either with `result.combine(other_result)` or with a shorthand of `result + other_result`.\n",
    "\n",
    "In this example, let's say we want to explore more the region where the transmission seems to be lowest, for `t` between 0.2 and 0.3. We create a new parameter with the desired span for `t` and use it to create an updated copy of the previous design, still using the same method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "75f3ae9e-ebfe-476d-ad6b-d0f3df8cfafa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">20:04:10 CEST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span> Simulations                                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m20:04:10 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m40\u001b[0m Simulations                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_t2 = tdd.ParameterFloat(name=\"t\", span=(0.2, 0.3), num_points=5)\n",
    "design_space2 = design_space.updated_copy(parameters=[param_num, param_t2])\n",
    "results2 = design_space2.run(pre, post)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e004c0df-0968-4b33-b3ff-67d9e0c2cba2",
   "metadata": {},
   "source": [
    "Now we can inspect the new results and the combined data as we did before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1da98b32-0c37-4235-9244-d6630ed733e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x350 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_combined = results + results2\n",
    "\n",
    "df2 = results2.to_dataframe()\n",
    "df_combined = results_combined.to_dataframe()\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.5))\n",
    "\n",
    "im = df2.plot.scatter(x=\"t\", y=\"flux\", s=50, c=\"num\", cmap=\"magma\", ax=ax1)\n",
    "ax1.set_title(\"Second run\")\n",
    "\n",
    "im = df_combined.plot.scatter(x=\"t\", y=\"flux\", s=50, c=\"num\", cmap=\"magma\", ax=ax2)\n",
    "ax2.set_title(\"Combined results\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89843783-b956-452d-81aa-9957d9f2f2d6",
   "metadata": {},
   "source": [
    "#### Adding and removing results\n",
    "\n",
    "We can also add and remove individual entries from the `Results` with the `add` and `delete` methods.\n",
    "\n",
    "Let's add a new data point to our results and then delete it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20de0ff1-1bcc-4d48-a40d-83f52d3c1a08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the function inputs and outputs\n",
    "fn_args = {\"t\": 1.2, \"num\": 5}\n",
    "value = 1.9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a261ee2-481c-4c6d-9b3c-2042669473fd",
   "metadata": {},
   "source": [
    "To add a data element, we pass in the `fn_args` (inputs) and the `value` (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "85939227-dee3-491d-89cf-37768dd3dde9",
   "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>num</th>\n",
       "      <th>t</th>\n",
       "      <th>flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2</td>\n",
       "      <td>0.100711</td>\n",
       "      <td>0.916616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>0.336889</td>\n",
       "      <td>0.937396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>0.470362</td>\n",
       "      <td>0.988174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>0.499794</td>\n",
       "      <td>0.993845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    num         t      flux\n",
       "36    2  0.100711  0.916616\n",
       "37    4  0.336889  0.937396\n",
       "38    3  0.470362  0.988174\n",
       "39    4  0.499794  0.993845\n",
       "40    5  1.200000  1.900000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add it to the results and view the last 5 entries (data is present)\n",
    "results = results.add(fn_args=fn_args, value=value)\n",
    "results.to_dataframe().tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa5c2f7-cd40-435e-b652-15843c0ad8ce",
   "metadata": {},
   "source": [
    "We can select a specific data point by passing the function inputs as keyword arguments to the `Results.sel` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d09ce82-4498-4758-96ff-a07c363a07ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9\n"
     ]
    }
   ],
   "source": [
    "print(results.sel(**fn_args))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843fdca4-425a-4011-bd18-6dd914d3bc4a",
   "metadata": {},
   "source": [
    "Similarly, we can remove a data point by passing the `fn_args` (inputs) to the `Results.delete` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2d3b12e-18a6-4d90-afcb-5ad59e1ce308",
   "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>num</th>\n",
       "      <th>t</th>\n",
       "      <th>flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>0.383293</td>\n",
       "      <td>0.968793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2</td>\n",
       "      <td>0.100711</td>\n",
       "      <td>0.916616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4</td>\n",
       "      <td>0.336889</td>\n",
       "      <td>0.937396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>0.470362</td>\n",
       "      <td>0.988174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4</td>\n",
       "      <td>0.499794</td>\n",
       "      <td>0.993845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    num         t      flux\n",
       "35    3  0.383293  0.968793\n",
       "36    2  0.100711  0.916616\n",
       "37    4  0.336889  0.937396\n",
       "38    3  0.470362  0.988174\n",
       "39    4  0.499794  0.993845"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove this data from the results and view the last 5 entries (data is gone)\n",
    "results = results.delete(fn_args=fn_args)\n",
    "results.to_dataframe().tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37fe237f-5d8b-49e6-bd70-cfecf8059e05",
   "metadata": {},
   "source": [
    "#### Leveraging DataFrame functions\n",
    "\n",
    "Since we have the ability to convert the `Results` to and from `pandas.DataFrame` objects, we are able to perform various manipulations (including the ones above) on the data using `pandas`. For example, we can create a new `DataFrame` object that multiplies all of the original `flux` values by 1/2 and convert this back to a  `Result` if desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "45c2027f-d912-432a-bb91-904c078fe0e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>num</th>\n",
       "      <th>t</th>\n",
       "      <th>flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.249284</td>\n",
       "      <td>0.954082</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.313907</td>\n",
       "      <td>0.896590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.356313</td>\n",
       "      <td>0.922831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.273844</td>\n",
       "      <td>0.841397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num         t      flux\n",
       "0    3  0.401888  0.942711\n",
       "1    2  0.249284  0.954082\n",
       "2    3  0.313907  0.896590\n",
       "3    2  0.356313  0.922831\n",
       "4    4  0.273844  0.841397"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = results.to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95b601c7-043b-4566-aeca-e40d6c70e92e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
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       "      <th>num</th>\n",
       "      <th>t</th>\n",
       "      <th>flux</th>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.313907</td>\n",
       "      <td>0.448295</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.356313</td>\n",
       "      <td>0.461415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.273844</td>\n",
       "      <td>0.420698</td>\n",
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      ],
      "text/plain": [
       "   num         t      flux\n",
       "0    3  0.401888  0.471356\n",
       "1    2  0.249284  0.477041\n",
       "2    3  0.313907  0.448295\n",
       "3    2  0.356313  0.461415\n",
       "4    4  0.273844  0.420698"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a dataframe where everything is squared\n",
    "df_half_flux = df.copy()\n",
    "df_half_flux[\"flux\"] = df_half_flux[\"flux\"].map(lambda x: x * 0.5)\n",
    "df_half_flux.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21a735a-f561-4153-bcab-a332c01dd948",
   "metadata": {},
   "source": [
    "We generally recommend you make use of the many features provided by `pandas.DataFrame` to do further postprocessing and analysis of your sweep results, but to convert a `DataFrame` back to a `Result`, one can simply pass it to `Results.from_dataframe` along with the dimensions of the data (if the `DataFrame` has been modified)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b926506c-d54e-4b06-83dd-d1dd047b11e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load it into a Result, note that we need to pass dims because this is an entirely new DataFrame without metadata\n",
    "results_squared = tdd.Result.from_dataframe(df_half_flux, dims=(\"num\", \"t\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d044d6",
   "metadata": {},
   "source": [
    "## Accessing Batched Data\n",
    "\n",
    "It is possible to load the `SimulationData` objects from the `.hdf5` files that will have been downloaded locally during the `DesignSpace.run` operation. When using `Pre` and `Post` functions the `Results` object stores the `task_names` and `task_paths` to make it easy to identify and load the correct file for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7483cc3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['./data/fdve-cab46a19-d923-46f6-a195-9a41083ba29f.hdf5', './data/fdve-64522d1c-51c8-4bd9-afd5-ccd4b17d5e5a.hdf5', './data/fdve-046599c1-b965-4197-830a-2cb931ddf13e.hdf5']\n"
     ]
    }
   ],
   "source": [
    "# See the first three file paths\n",
    "print(results.task_paths[:3])\n",
    "\n",
    "# Load the SimulationData back into Python\n",
    "loaded_sim_data = td.SimulationData.from_file(results.task_paths[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2d1a678",
   "metadata": {},
   "source": [
    "The `task_names` and `task_paths` and stored in the same structure as the return from the `Pre` function to aid with accessing the appropriate `SimulationData` for a particular output value. For example, if the `Pre` function returns a list like `[Simulation, Simulation, Simulation]`, then the `task_paths` will be stored as a list of corresponding paths: `[path, path, path]`. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fe2d3f",
   "metadata": {},
   "source": [
    "## Working with Optimizers\n",
    "\n",
    "Using a grid search or Monte Carlo is a good way to sample a large design space. However, the `Design` plugin can go further and enable efficient optimization of a design problem using a range of different optimization techniques. `Method` objects such as Bayesian optimization, genetic algorithms, and particle swarm optimization can all be used in the same way as the above examples. These methods use a `float` value output by the post function to evaluate the effectiveness of a solution, and then propose new solutions that should prove better. More detailed examples can be found in the notebooks associated with each technique.\n",
    "\n",
    "1. [Bayesian Optimization of Y-Junction](https://www.flexcompute.com/tidy3d/examples/notebooks/BayesianOptimizationYJunction/)\n",
    "\n",
    "2. [Particle Swarm Optimizer PBS](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
    "\n",
    "3. [Particle Swarm Optimizer Bullseye Cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/)\n",
    "\n",
    "4. [Genetic Algorithm Reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/)\n",
    "\n",
    "\n",
    "There are a few additional points to consider when using optimizers:\n",
    "* Optimizers are more complicated to use than samplers and have many different options to consider in the `Method` object. It is worth reading the `Tidy3D` docs and relevant package documentation for each optimizer before use.\n",
    "* Optimizers require the output of `fn_post` to be either a `float` or `[float, list/tuple/dict]`. The additional information in the container will be handled as auxiliary data and stored in the `Results`. It can be added to the `pd.DataFrame` with `results.to_dataframe(include_aux=True)`.\n",
    "* Optimizers work best when the design problem is split into pre and post functions as the `DesignSpace` can create efficient batches of `Simulation` objects, instead of running the entire optimization process sequentially, which can take a long time."
   ]
  }
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