Design.ipynb

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    "# 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."
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import typing\n",
    "\n",
    "import tidy3d as td\n",
    "import tidy3d.plugins.design as tdd"
   ]
  },
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   "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."
   ]
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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",
    "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",
    "    gaussian = lambda sigma: np.exp(-r2 / sigma ** 2)\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",
    "# 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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>-9.287730</td>\n",
       "      <td>-3.498281</td>\n",
       "      <td>1.944830e-02</td>\n",
       "      <td>0.373440</td>\n",
       "      <td>0.781727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.280786</td>\n",
       "      <td>-1.283115</td>\n",
       "      <td>1.123373e-01</td>\n",
       "      <td>0.578937</td>\n",
       "      <td>0.872284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-14.875910</td>\n",
       "      <td>-11.875533</td>\n",
       "      <td>5.079723e-07</td>\n",
       "      <td>0.026697</td>\n",
       "      <td>0.404217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.986373</td>\n",
       "      <td>-4.965107</td>\n",
       "      <td>5.294678e-02</td>\n",
       "      <td>0.479689</td>\n",
       "      <td>0.832223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.749807</td>\n",
       "      <td>6.478849</td>\n",
       "      <td>7.566453e-02</td>\n",
       "      <td>0.524473</td>\n",
       "      <td>0.851002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x          y       sigma=5  sigma=10  sigma=20\n",
       "0  -9.287730  -3.498281  1.944830e-02  0.373440  0.781727\n",
       "1   7.280786  -1.283115  1.123373e-01  0.578937  0.872284\n",
       "2 -14.875910 -11.875533  5.079723e-07  0.026697  0.404217\n",
       "3   6.986373  -4.965107  5.294678e-02  0.479689  0.832223\n",
       "4   4.749807   6.478849  7.566453e-02  0.524473  0.851002"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "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": 6,
   "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=[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",
    "        monitors=[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",
    "        boundary_spec=td.BoundarySpec.pml(x=False, y=False, z=True),\n",
    "        run_time=100 / freq0,\n",
    "    )\n",
    "    return sim\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": 7,
   "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": 8,
   "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": 9,
   "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": 10,
   "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": 11,
   "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": 21,
   "id": "46e97289-edec-4ede-b109-e2bbf96e2264",
   "metadata": {},
   "outputs": [],
   "source": [
    "design_space = tdd.DesignSpace(parameters=[param_num, param_t], method=method, path_dir=\"./data\", folder_name=\"Design_Tutorial\", task_name=\"Design_notebook\")"
   ]
  },
  {
   "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 asigns 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."
   ]
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   ],
   "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",
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    "\n",
    "`Results` contains three main related datastructures.\n",
    "\n",
    "* `dims`, which correpsond 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": 14,
   "id": "82f5590d-fe19-4c57-ba19-aa80d2c2a60d",
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    {
     "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>0.231423</td>\n",
       "      <td>0.712001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.327886</td>\n",
       "      <td>0.952495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.306500</td>\n",
       "      <td>0.862998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.366405</td>\n",
       "      <td>0.998939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.128428</td>\n",
       "      <td>0.913065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num         t      flux\n",
       "0    3  0.231423  0.712001\n",
       "1    3  0.327886  0.952495\n",
       "2    3  0.306500  0.862998\n",
       "3    4  0.366405  0.998939\n",
       "4    1  0.128428  0.913065"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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": 16,
   "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\">08:34:22 BST </span>Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span> Simulations                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m08:34:22 BST\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": 17,
   "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": 25,
   "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": 26,
   "id": "85939227-dee3-491d-89cf-37768dd3dde9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>num</th>\n",
       "      <th>t</th>\n",
       "      <th>flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>0.477944</td>\n",
       "      <td>0.995562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2</td>\n",
       "      <td>0.383136</td>\n",
       "      <td>0.929275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>0.267950</td>\n",
       "      <td>0.950784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>0.277211</td>\n",
       "      <td>0.773291</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    3  0.477944  0.995562\n",
       "37    2  0.383136  0.929275\n",
       "38    2  0.267950  0.950784\n",
       "39    3  0.277211  0.773291\n",
       "40    5  1.200000  1.900000"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "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": 28,
   "id": "d2d3b12e-18a6-4d90-afcb-5ad59e1ce308",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>0.362959</td>\n",
       "      <td>0.930869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>0.477944</td>\n",
       "      <td>0.995562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2</td>\n",
       "      <td>0.383136</td>\n",
       "      <td>0.929275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2</td>\n",
       "      <td>0.267950</td>\n",
       "      <td>0.950784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>0.277211</td>\n",
       "      <td>0.773291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    num         t      flux\n",
       "35    1  0.362959  0.930869\n",
       "36    3  0.477944  0.995562\n",
       "37    2  0.383136  0.929275\n",
       "38    2  0.267950  0.950784\n",
       "39    3  0.277211  0.773291"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "id": "45c2027f-d912-432a-bb91-904c078fe0e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>4</td>\n",
       "      <td>0.151689</td>\n",
       "      <td>0.946493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.427012</td>\n",
       "      <td>0.937147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.244466</td>\n",
       "      <td>0.954050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.326142</td>\n",
       "      <td>0.923577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.484898</td>\n",
       "      <td>0.999998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num         t      flux\n",
       "0    4  0.151689  0.946493\n",
       "1    4  0.427012  0.937147\n",
       "2    2  0.244466  0.954050\n",
       "3    2  0.326142  0.923577\n",
       "4    1  0.484898  0.999998"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = results.to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "95b601c7-043b-4566-aeca-e40d6c70e92e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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",
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       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0.151689</td>\n",
       "      <td>0.473247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.427012</td>\n",
       "      <td>0.468574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.244466</td>\n",
       "      <td>0.477025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.326142</td>\n",
       "      <td>0.461788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.499999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num         t      flux\n",
       "0    4  0.151689  0.473247\n",
       "1    4  0.427012  0.468574\n",
       "2    2  0.244466  0.477025\n",
       "3    2  0.326142  0.461788\n",
       "4    1  0.484898  0.499999"
      ]
     },
     "execution_count": 30,
     "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": 25,
   "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-ba077ef4-b5e0-47ad-bb00-7d991abb089e.hdf5', './data/fdve-e9887d59-1261-433a-a878-f6498c1c3ac4.hdf5', './data/fdve-358eb0f9-f1b9-4362-83a5-16489b5dd7e8.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 are good ways 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 partical 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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