{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Tutorial 6b: Monte Carlo Tolerancing Analysis\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "tags": [
                    "nbsphinx-toctree"
                ]
            },
            "source": [
                "### September 2024"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "In this tutorial, we will continue to explore the tolerancing capabilities of Optiland and will introduce Monte Carlo analysis. Monte Carlo analysis involves simulating a large number of random variations of an optical system, which helps to understand the statistical distribution of potential performance outcomes for a system. It is particularly valuable for understanding the impact of manufacturing defects and environmental conditions.\n",
                "\n",
                "The Monte Carlo analysis in Optiland is fundamentally similar to the sensitivity analysis, which was introduced in tutorial 8a. We require the following components to perform a Monte Carlo analysis:\n",
                "\n",
                "1. Optic - the optical system to be analyzed.\n",
                "2. Operands - the metrics which are assessed e.g., wavefront error.\n",
                "3. Perturbations - the variations applied to the optic or a surface of an optic e.g., surface tilt.\n",
                "4. Compensators - a parameter of the optical system that can be adjusted to counteract the effects of a perturbation.\n",
                "\n",
                "In this example, we will perform a Monte Carlo analysis on a Cooke triplet to understand how common variations, such as surface decenter and tilt, can impact the optical performance."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:30:57.416437Z",
                    "iopub.status.busy": "2026-03-24T11:30:57.414009Z",
                    "iopub.status.idle": "2026-03-24T11:31:07.552694Z",
                    "shell.execute_reply": "2026-03-24T11:31:07.551023Z"
                }
            },
            "outputs": [],
            "source": [
                "from optiland.samples.objectives import CookeTriplet\n",
                "from optiland.tolerancing.core import Tolerancing\n",
                "from optiland.tolerancing.monte_carlo import MonteCarlo\n",
                "from optiland.tolerancing.perturbation import DistributionSampler"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "1. Defining the tolerancing object\n",
                "\n",
                "The first step is to define our optic and pass it to a Tolerancing object."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:31:07.560152Z",
                    "iopub.status.busy": "2026-03-24T11:31:07.558759Z",
                    "iopub.status.idle": "2026-03-24T11:31:07.657521Z",
                    "shell.execute_reply": "2026-03-24T11:31:07.656610Z"
                }
            },
            "outputs": [],
            "source": [
                "optic = CookeTriplet()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:31:07.661084Z",
                    "iopub.status.busy": "2026-03-24T11:31:07.660686Z",
                    "iopub.status.idle": "2026-03-24T11:31:07.665077Z",
                    "shell.execute_reply": "2026-03-24T11:31:07.664005Z"
                }
            },
            "outputs": [],
            "source": [
                "tolerancing = Tolerancing(optic)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "2. Adding perturbations\n",
                "\n",
                "The Monte Carlo anlaysis requires that we apply random perturbations to optical properties of our system. We will apply both random tilt and random decenter to every surface of the triplet, so 6 surfaces in total."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We will apply perturbations to every surface and in both the x and y axes.\n",
                "\n",
                "Properties for tilt perturbation:\n",
                "\n",
                "- Normal distribution, mean = 0, standard deviation = 0.01 radians\n",
                "\n",
                "Properties for decenter perturbation:\n",
                "\n",
                "- Normal distribution, mean = 0, standard deviation = 0.1 mm"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:31:07.669236Z",
                    "iopub.status.busy": "2026-03-24T11:31:07.668862Z",
                    "iopub.status.idle": "2026-03-24T11:31:07.678401Z",
                    "shell.execute_reply": "2026-03-24T11:31:07.676820Z"
                }
            },
            "outputs": [],
            "source": [
                "# loop through all surfaces and add perturbations\n",
                "for k in range(1, 7):\n",
                "    # X-tilt\n",
                "    sampler = DistributionSampler(\"normal\", loc=0, scale=0.01)\n",
                "    tolerancing.add_perturbation(\"tilt\", sampler, surface_number=k, axis=\"x\")\n",
                "\n",
                "    # Y-tilt\n",
                "    sampler = DistributionSampler(\"normal\", loc=0, scale=0.01)\n",
                "    tolerancing.add_perturbation(\"tilt\", sampler, surface_number=k, axis=\"y\")\n",
                "\n",
                "    # X-decenter\n",
                "    sampler = DistributionSampler(\"normal\", loc=0, scale=0.1)\n",
                "    tolerancing.add_perturbation(\"decenter\", sampler, surface_number=k, axis=\"x\")\n",
                "\n",
                "    # Y-decenter\n",
                "    sampler = DistributionSampler(\"normal\", loc=0, scale=0.1)\n",
                "    tolerancing.add_perturbation(\"decenter\", sampler, surface_number=k, axis=\"y\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "3. Adding operands\n",
                "\n",
                "\n",
                "We wish to monitor the impact of perturbations on our triplet. We choose to monitor the following metrics:\n",
                "\n",
                "- RMS spot size for (Hx, Hy) = (0, 1) field\n",
                "- mean OPD difference for (Hx, Hy) = (0, 1) field\n",
                "- real y-intercept on image plane for (Hx, Hy) = (0, 1) field\n",
                "\n",
                "The syntax used here follows that used in the optimization module when variables are defined. In general, we pass the following arguments to the \"add_operand\" method to create a new operand:\n",
                "\n",
                "- operand type - see optiland.optimization.operand for complete list of options.\n",
                "- input_data - a dictionary containing the optic instance at a minimum, and generally other parameters related to the operand, such as wavelength.\n",
                "- target (optional) - if an operand has a target, we may specify it here. This is only used when we apply compensation, or optimize the system to counteract perturbations.\n",
                "- weight (optional) - if an operand is more important than others, it may be given a larger weight during compensation.\n",
                "\n",
                "We define the 3 operands as follows:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:31:07.684324Z",
                    "iopub.status.busy": "2026-03-24T11:31:07.683827Z",
                    "iopub.status.idle": "2026-03-24T11:31:07.741024Z",
                    "shell.execute_reply": "2026-03-24T11:31:07.739913Z"
                }
            },
            "outputs": [],
            "source": [
                "input_data = {\n",
                "    \"optic\": optic,\n",
                "    \"surface_number\": -1,\n",
                "    \"Hx\": 0,\n",
                "    \"Hy\": 1,\n",
                "    \"wavelength\": 0.55,\n",
                "    \"num_rays\": 5,\n",
                "}\n",
                "tolerancing.add_operand(\"rms_spot_size\", input_data, target=0)\n",
                "\n",
                "input_data = {\"optic\": optic, \"Hx\": 0, \"Hy\": 1, \"wavelength\": 0.55, \"num_rays\": 5}\n",
                "tolerancing.add_operand(\"OPD_difference\", input_data)\n",
                "\n",
                "input_data = {\n",
                "    \"optic\": optic,\n",
                "    \"surface_number\": -1,\n",
                "    \"Hx\": 0,\n",
                "    \"Hy\": 1,\n",
                "    \"Px\": 0,\n",
                "    \"Py\": 0,\n",
                "    \"wavelength\": 0.55,\n",
                "}\n",
                "tolerancing.add_operand(\"real_y_intercept\", input_data)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "4. Run Monte Carlo analysis\n",
                "\n",
                "\n",
                "We are now ready to run our Monte Carlo analysis. We first define our Monte Carlo analysis:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:31:07.744665Z",
                    "iopub.status.busy": "2026-03-24T11:31:07.744361Z",
                    "iopub.status.idle": "2026-03-24T11:31:07.749137Z",
                    "shell.execute_reply": "2026-03-24T11:31:07.748160Z"
                }
            },
            "outputs": [],
            "source": [
                "monte_carlo = MonteCarlo(tolerancing)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We can then run our Monte Carlo analysis. We choose to run 1000 iterations."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:31:07.752811Z",
                    "iopub.status.busy": "2026-03-24T11:31:07.752247Z",
                    "iopub.status.idle": "2026-03-24T11:32:34.531856Z",
                    "shell.execute_reply": "2026-03-24T11:32:34.530355Z"
                }
            },
            "outputs": [],
            "source": [
                "monte_carlo.run(num_iterations=1000)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "5. View and analyze results\n",
                "\n",
                "\n",
                "There are several ways to view the output data of a Monte Carlo analysis:\n",
                "\n",
                "- Plot the distributions of the performance metrics\n",
                "- Plot the cumulative distribution function (CDF) of the metrics\n",
                "- Plot a heatmap showing the correlations between the perturbations and the metrics"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:32:34.538668Z",
                    "iopub.status.busy": "2026-03-24T11:32:34.537871Z",
                    "iopub.status.idle": "2026-03-24T11:32:35.783199Z",
                    "shell.execute_reply": "2026-03-24T11:32:35.781303Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(<Figure size 1200x400 with 3 Axes>,\n",
                            " array([<Axes: xlabel='0: rms spot size', ylabel='Count'>,\n",
                            "        <Axes: xlabel='1: OPD difference', ylabel='Count'>,\n",
                            "        <Axes: xlabel='2: real y intercept', ylabel='Count'>], dtype=object))"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
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AAIDtSEoBAAAAAADAdiSlAAAAAAAAYDuSUgAAAAAAABC7/X+xBcEsI4E/awAAAABJRU5ErkJggg==",
                        "text/plain": [
                            "<Figure size 1200x400 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "monte_carlo.view_histogram(kde=False)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:32:35.793386Z",
                    "iopub.status.busy": "2026-03-24T11:32:35.792536Z",
                    "iopub.status.idle": "2026-03-24T11:32:36.504756Z",
                    "shell.execute_reply": "2026-03-24T11:32:36.503407Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(<Figure size 1200x400 with 3 Axes>,\n",
                            " array([<Axes: title={'center': '0: rms spot size'}, xlabel='0: rms spot size'>,\n",
                            "        <Axes: title={'center': '1: OPD difference'}, xlabel='1: OPD difference'>,\n",
                            "        <Axes: title={'center': '2: real y intercept'}, xlabel='2: real y intercept'>],\n",
                            "       dtype=object))"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1200x400 with 3 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "monte_carlo.view_cdf()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:32:36.509464Z",
                    "iopub.status.busy": "2026-03-24T11:32:36.509024Z",
                    "iopub.status.idle": "2026-03-24T11:32:37.406203Z",
                    "shell.execute_reply": "2026-03-24T11:32:37.401804Z"
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(<Figure size 1000x1000 with 2 Axes>, <Axes: >)"
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1000x1000 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "monte_carlo.view_heatmap(vmin=-0.2, vmax=0.2, figsize=(10, 10))"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "The heatmap gives an indication of the correlation between the metrics and the various pertrurbations applied. The strongest correlations exist between the real y-intercept and several of the surface tilts and decenters."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "As with the sensitivity analysis, we can also retrieve the Monte Carlo results for further analysis:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:32:37.413940Z",
                    "iopub.status.busy": "2026-03-24T11:32:37.413381Z",
                    "iopub.status.idle": "2026-03-24T11:32:37.419557Z",
                    "shell.execute_reply": "2026-03-24T11:32:37.417611Z"
                }
            },
            "outputs": [],
            "source": [
                "df = monte_carlo.get_results()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {
                "execution": {
                    "iopub.execute_input": "2026-03-24T11:32:37.423662Z",
                    "iopub.status.busy": "2026-03-24T11:32:37.422984Z",
                    "iopub.status.idle": "2026-03-24T11:32:37.449197Z",
                    "shell.execute_reply": "2026-03-24T11:32:37.448590Z"
                }
            },
            "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>Tilt X, Surface 1</th>\n",
                            "      <th>Tilt Y, Surface 1</th>\n",
                            "      <th>Decenter X, Surface 1</th>\n",
                            "      <th>Decenter Y, Surface 1</th>\n",
                            "      <th>Tilt X, Surface 2</th>\n",
                            "      <th>Tilt Y, Surface 2</th>\n",
                            "      <th>Decenter X, Surface 2</th>\n",
                            "      <th>Decenter Y, Surface 2</th>\n",
                            "      <th>Tilt X, Surface 3</th>\n",
                            "      <th>Tilt Y, Surface 3</th>\n",
                            "      <th>...</th>\n",
                            "      <th>Tilt Y, Surface 5</th>\n",
                            "      <th>Decenter X, Surface 5</th>\n",
                            "      <th>Decenter Y, Surface 5</th>\n",
                            "      <th>Tilt X, Surface 6</th>\n",
                            "      <th>Tilt Y, Surface 6</th>\n",
                            "      <th>Decenter X, Surface 6</th>\n",
                            "      <th>Decenter Y, Surface 6</th>\n",
                            "      <th>0: rms spot size</th>\n",
                            "      <th>1: OPD difference</th>\n",
                            "      <th>2: real y intercept</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>0.009207</td>\n",
                            "      <td>-0.011025</td>\n",
                            "      <td>-0.002190</td>\n",
                            "      <td>-0.016223</td>\n",
                            "      <td>0.007428</td>\n",
                            "      <td>0.013438</td>\n",
                            "      <td>0.014421</td>\n",
                            "      <td>0.060301</td>\n",
                            "      <td>0.005314</td>\n",
                            "      <td>0.010278</td>\n",
                            "      <td>...</td>\n",
                            "      <td>-0.006306</td>\n",
                            "      <td>-0.046377</td>\n",
                            "      <td>-0.157831</td>\n",
                            "      <td>0.021742</td>\n",
                            "      <td>0.000021</td>\n",
                            "      <td>-0.018989</td>\n",
                            "      <td>0.106433</td>\n",
                            "      <td>0.141910</td>\n",
                            "      <td>0.692562</td>\n",
                            "      <td>18.645609</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>0.008905</td>\n",
                            "      <td>-0.005739</td>\n",
                            "      <td>0.089045</td>\n",
                            "      <td>0.025248</td>\n",
                            "      <td>0.019523</td>\n",
                            "      <td>-0.011740</td>\n",
                            "      <td>0.000440</td>\n",
                            "      <td>0.014264</td>\n",
                            "      <td>0.003392</td>\n",
                            "      <td>0.006186</td>\n",
                            "      <td>...</td>\n",
                            "      <td>0.000579</td>\n",
                            "      <td>0.013588</td>\n",
                            "      <td>0.058864</td>\n",
                            "      <td>-0.003734</td>\n",
                            "      <td>0.011204</td>\n",
                            "      <td>0.150248</td>\n",
                            "      <td>-0.018275</td>\n",
                            "      <td>0.102292</td>\n",
                            "      <td>0.566152</td>\n",
                            "      <td>18.339462</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>0.008308</td>\n",
                            "      <td>-0.002635</td>\n",
                            "      <td>-0.188853</td>\n",
                            "      <td>0.001050</td>\n",
                            "      <td>-0.003705</td>\n",
                            "      <td>-0.011522</td>\n",
                            "      <td>-0.183701</td>\n",
                            "      <td>0.049986</td>\n",
                            "      <td>-0.004468</td>\n",
                            "      <td>-0.000680</td>\n",
                            "      <td>...</td>\n",
                            "      <td>-0.001322</td>\n",
                            "      <td>0.075018</td>\n",
                            "      <td>0.020952</td>\n",
                            "      <td>-0.000958</td>\n",
                            "      <td>-0.012948</td>\n",
                            "      <td>-0.030341</td>\n",
                            "      <td>0.141852</td>\n",
                            "      <td>0.120303</td>\n",
                            "      <td>0.515046</td>\n",
                            "      <td>18.346219</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>-0.002685</td>\n",
                            "      <td>0.011072</td>\n",
                            "      <td>0.109082</td>\n",
                            "      <td>-0.088453</td>\n",
                            "      <td>-0.001039</td>\n",
                            "      <td>0.004737</td>\n",
                            "      <td>0.244410</td>\n",
                            "      <td>0.041029</td>\n",
                            "      <td>0.009125</td>\n",
                            "      <td>0.004283</td>\n",
                            "      <td>...</td>\n",
                            "      <td>-0.001073</td>\n",
                            "      <td>-0.090255</td>\n",
                            "      <td>-0.125393</td>\n",
                            "      <td>0.013556</td>\n",
                            "      <td>-0.002803</td>\n",
                            "      <td>0.052818</td>\n",
                            "      <td>-0.149199</td>\n",
                            "      <td>0.039382</td>\n",
                            "      <td>0.189649</td>\n",
                            "      <td>18.189990</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>-0.000320</td>\n",
                            "      <td>-0.004726</td>\n",
                            "      <td>0.098729</td>\n",
                            "      <td>0.059416</td>\n",
                            "      <td>0.000899</td>\n",
                            "      <td>0.007604</td>\n",
                            "      <td>0.070222</td>\n",
                            "      <td>-0.027400</td>\n",
                            "      <td>-0.022558</td>\n",
                            "      <td>-0.003621</td>\n",
                            "      <td>...</td>\n",
                            "      <td>0.006263</td>\n",
                            "      <td>0.038823</td>\n",
                            "      <td>-0.244502</td>\n",
                            "      <td>0.011454</td>\n",
                            "      <td>-0.005382</td>\n",
                            "      <td>-0.002485</td>\n",
                            "      <td>-0.075603</td>\n",
                            "      <td>0.118383</td>\n",
                            "      <td>0.744495</td>\n",
                            "      <td>18.791629</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>5 rows \u00d7 27 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   Tilt X, Surface 1  Tilt Y, Surface 1  Decenter X, Surface 1  \\\n",
                            "0           0.009207          -0.011025              -0.002190   \n",
                            "1           0.008905          -0.005739               0.089045   \n",
                            "2           0.008308          -0.002635              -0.188853   \n",
                            "3          -0.002685           0.011072               0.109082   \n",
                            "4          -0.000320          -0.004726               0.098729   \n",
                            "\n",
                            "   Decenter Y, Surface 1  Tilt X, Surface 2  Tilt Y, Surface 2  \\\n",
                            "0              -0.016223           0.007428           0.013438   \n",
                            "1               0.025248           0.019523          -0.011740   \n",
                            "2               0.001050          -0.003705          -0.011522   \n",
                            "3              -0.088453          -0.001039           0.004737   \n",
                            "4               0.059416           0.000899           0.007604   \n",
                            "\n",
                            "   Decenter X, Surface 2  Decenter Y, Surface 2  Tilt X, Surface 3  \\\n",
                            "0               0.014421               0.060301           0.005314   \n",
                            "1               0.000440               0.014264           0.003392   \n",
                            "2              -0.183701               0.049986          -0.004468   \n",
                            "3               0.244410               0.041029           0.009125   \n",
                            "4               0.070222              -0.027400          -0.022558   \n",
                            "\n",
                            "   Tilt Y, Surface 3  ...  Tilt Y, Surface 5  Decenter X, Surface 5  \\\n",
                            "0           0.010278  ...          -0.006306              -0.046377   \n",
                            "1           0.006186  ...           0.000579               0.013588   \n",
                            "2          -0.000680  ...          -0.001322               0.075018   \n",
                            "3           0.004283  ...          -0.001073              -0.090255   \n",
                            "4          -0.003621  ...           0.006263               0.038823   \n",
                            "\n",
                            "   Decenter Y, Surface 5  Tilt X, Surface 6  Tilt Y, Surface 6  \\\n",
                            "0              -0.157831           0.021742           0.000021   \n",
                            "1               0.058864          -0.003734           0.011204   \n",
                            "2               0.020952          -0.000958          -0.012948   \n",
                            "3              -0.125393           0.013556          -0.002803   \n",
                            "4              -0.244502           0.011454          -0.005382   \n",
                            "\n",
                            "   Decenter X, Surface 6  Decenter Y, Surface 6  0: rms spot size  \\\n",
                            "0              -0.018989               0.106433          0.141910   \n",
                            "1               0.150248              -0.018275          0.102292   \n",
                            "2              -0.030341               0.141852          0.120303   \n",
                            "3               0.052818              -0.149199          0.039382   \n",
                            "4              -0.002485              -0.075603          0.118383   \n",
                            "\n",
                            "   1: OPD difference  2: real y intercept  \n",
                            "0           0.692562            18.645609  \n",
                            "1           0.566152            18.339462  \n",
                            "2           0.515046            18.346219  \n",
                            "3           0.189649            18.189990  \n",
                            "4           0.744495            18.791629  \n",
                            "\n",
                            "[5 rows x 27 columns]"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df.head()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Conclusions\n",
                "\n",
                "- This tutorial demonstrated Monte Carlo analyses in Optiland.\n",
                "- Monte Carlo analysis is a statistical technique to explore possible system performance variations due to manufacturing tolerances or environmental conditions.\n",
                "- Several plotting functions are available via the Monte Carlo analysis object, including plotting of distributions, CDFs, and heatmaps."
            ]
        }
    ],
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