{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "6095c75f",
            "metadata": {},
            "source": [
                "# Getting Started\n"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "8e01056f",
            "metadata": {},
            "source": [
                "\n",
                "## Installation\n",
                "\n"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "332674b2",
            "metadata": {},
            "source": [
                "\n",
                "Install the `wristpy` package from PyPI via:\n",
                "\n",
                "```sh\n",
                "pip install wristpy\n",
                "```"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "ba0514a9",
            "metadata": {},
            "source": [
                "\n",
                "\n",
                "## Introduction \n",
                "The main processing pipeline of the wristpy module can be described as follows:\n",
                "\n",
                "- **Data loading**: sensor data is loaded using [`actfast`](https://github.com/childmindresearch/actfast), and a `WatchData` object is created to store all sensor data.\n",
                "- **Data calibration**: A post-manufacturer calibration step can be applied, to ensure that the acceleration sensor is measuring 1*g* force during periods of no motion. There are three possible options: `None`, `gradient`, `ggir`.\n",
                "- ***Data imputation*** In the special case when dealing with the Actigraph `idle_sleep_mode == enabled`, the gaps in acceleration are filled in after calibration, to avoid biasing the calibration phase.\n",
                "- **Metrics Calculation**: Calculates various activity metrics on the calibrated data, namely ENMO (Euclidean norm, minus one), MAD (mean amplitude deviation)<sup><a href=\"#ref1\">1</a></sup>, Actigraph activity counts<sup><a href=\"#ref2\">2</a></sup>, MIMS (monitor-independent movement summary) unit<sup><a href=\"#ref3\">3</a></sup>, and angle-Z (angle of acceleration relative to the *x-y* axis).\n",
                "- **Non-wear detection**: We find periods of non-wear based on the acceleration data. Specifically, the standard deviation of the acceleration values in a given time window, along each axis, is used as a threshold to decide `wear` or `not wear`. Additionally, we can use the temperature sensor, when available, to augment the acceleration data. This is used in the CTA (combined temperature and acceleration) algorithm<sup><a href=\"#ref4\">4</a></sup>, and in the `skdh` DETACH algorithm<sup><a href=\"#ref5\">5</a></sup>. Furthermore, ensemble classification of non-wear periods is possible by providing a list (of any length) of non-wear algorithm options.\n",
                "- **Sleep Detection**: Using the HDCZ<sup><a href=\"#ref6\">6</a></sup> and HSPT<sup><a href=\"#ref7\">7</a></sup> algorithms to analyze changes in arm angle we are able to find periods of sleep. We find the sleep onset–wakeup times for all sleep windows detected. Any sleep periods that overlap with detected non-wear times are removed, and any remaining sleep periods shorter than 15 minutes (default value) are removed. Additionally, the SIB (sustained inactivity bouts) and the SPT (sleep period time) windows are provided as part of the output to aid in sleep metric post-processing.\n",
                "- **Physical activity levels**: Using the chosen physical activity metric (aggregated into time bins, 5-second default) we compute activity levels into the following categories: [`inactive`, `light`, `moderate`, `vigorous`]. The threshold values can be defined by the user, while the default values are chosen based on the specific activity metric and the values found in the literature <sup><a href=\"#ref8\">8</a></sup><sup><a href=\"#ref9\">9</a></sup><sup><a href=\"#ref10\">10</a></sup>.\n",
                "- **Data output**: The output results can be saved in `.csv` or `.parquet` data formats, with the run-time configuration parameters saved in a `.json` dictionary.\n",
                "\n",
                "## Supported formats & devices\n",
                "\n",
                "The package currently supports the following formats:\n",
                "\n",
                "| Format | Manufacturer | Device | Implementation status |\n",
                "| --- | --- | --- | --- |\n",
                "| GT3X | Actigraph | wGT3X-BT | ✅ |\n",
                "| BIN | GENEActiv | GENEActiv | ✅ |\n",
                "\n",
                "## Command Line Tutorial \n",
                "\n",
                "Run a single file:\n",
                "\n",
                "```bash\n",
                "   wristpy /input/file/path.gt3x -o /save/path/file_name.csv -c gradient\n",
                "```\n",
                "Run an entire directory:\n",
                "```bash\n",
                "   wristpy /path/to/files/input_dir -o /path/to/files/output_dir -c gradient -O .csv\n",
                "```\n",
                "\n",
                "You can also choose one or more activity metric:\n",
                "```bash\n",
                "   wristpy /input/file/path.gt3x -o /save/path/file_name.csv -c gradient -a enmo -a mad\n",
                "```\n",
                "Specify the epoch length in seconds (will default to 5 seconds):\n",
                "```bash\n",
                "   wristpy /input/file/path.gt3x -o /save/path/file_name.csv -c gradient -a enmo -a mad -e 3\n",
                "```\n",
                "\n",
                "To specificy your own activity thresholds  enter them in the same order as the corresponding metric:\n",
                "```bash\n",
                "   wristpy /input/file/path.gt3x -o /save/path/file_name.csv -c gradient -a enmo -a mad -t \"1 2 3\" -t \"1.5 2.5 3.5\"\n",
                "```\n",
                "\n",
                "See all available command-line arguments:\n",
                "```bash\n",
                "   wristpy --help\n",
                "```"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "8a5edeed",
            "metadata": {},
            "source": [
                "## Python Tutorial \n",
                "\n",
                "Wristpy is a Python library designed for processing and analyzing wrist-worn accelerometer data.\n",
                "This tutorial will guide you through the basic steps of using `wristpy` to analyze your accelerometer data using python. Specifically,\n",
                "we will cover the following topics through a few examples:\n",
                "   - running the default processor, analyzing the output data, and visualizing the results.\n",
                "   - loading data and plotting the raw signals.\n",
                "   - how to calibrate the data, computing ENMO and angle-z from the calibrated data and then plotting those metrics.\n",
                "   - how to obtain non-wear windows and visualize them.\n",
                "   - how to obtain sleep windows and visualize them.\n",
                "\n",
                "\n",
                "\n",
                "\n",
                "\n",
                "### Example 1: Running the default processor\n",
                "\n",
                "#### Running files and directories\n",
                "\n",
                "\n",
                "The `orchestrator` module of wristpy contains the default processor that will run the entire wristpy processing pipeline. This can be called as simply as:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "id": "6c7f1b9e",
            "metadata": {},
            "outputs": [],
            "source": [
                "import pathlib\n",
                "\n",
                "from wristpy.core import orchestrator\n",
                "\n",
                "input_directory = pathlib.Path('tutorial_data')\n",
                "output_directory = pathlib.Path.cwd().parent / 'build'\n",
                "\n",
                "results = orchestrator.run(\n",
                "   input = input_directory / 'three_nights.bin',  \n",
                "   output = output_directory / 'three_nights_single_file.csv', \n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "c0a397a3",
            "metadata": {},
            "source": [
                "This runs the processing pipeline with all the default arguments, creates an output `.csv` file, a `.json` file with the pipeline configuration parameters, and will create a `results` object that contains the various output metrics (namely; the specified physical activity metric, angle-z, physical activity classification values, non-wear status, and sleep status).\n",
                "\n",
                "\n",
                "The orchestrator can also process entire directories. The call to the orchestrator remains largely the same but now output is expected to be a directory and the desired filetype for the saved files can be specified through the output_filetype arguement(default value is \".csv\"):"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "id": "e67419e1",
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "2025-09-18 17:33:30,057 - wristpy - ERROR - orchestrator.py:248 - _run_directory - Did not run file: tutorial_data/three_nights.bin, Error: Error reading file: No such file or directory (os error 2). File type is unsupported.\n"
                    ]
                }
            ],
            "source": [
                "from wristpy.core import orchestrator\n",
                "\n",
                "results = orchestrator.run(\n",
                "    input = input_directory,\n",
                "    output = output_directory,\n",
                "    output_filetype = \".csv\"\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "bfcb4604",
            "metadata": {},
            "source": [
                "If users would prefer to process specific files instead of entire directories we recommend looping through a list of file names. The following code snipet will save results objects into a dictionary, and the output files into the desired directory:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "id": "3d5d9267",
            "metadata": {},
            "outputs": [],
            "source": [
                "\n",
                "file_names = [pathlib.Path(\"three_nights.bin\")] # Add more file names to list as needed\n",
                "results_dict = {}\n",
                "\n",
                "for file in file_names:\n",
                "    input_path = input_directory / file\n",
                "    output_path = output_directory / file.with_suffix('.csv')\n",
                "    result = orchestrator.run(\n",
                "        input = input_path,\n",
                "        output = output_path)\n",
                "    results_dict[file.stem] = result"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "98831ff8",
            "metadata": {},
            "source": [
                "#### Physical Activity Metrics & Results\n",
                "\n",
                "Wristpy is capable of calculating the following physical activity metrics from actigraphy data:\n",
                "1. [Euclidean Norm Minus-One (ENMO)](https://childmindresearch.github.io/wristpy/getting_started.html#enmo-euclidean-norm-minus-one)\n",
                "2. [Activity Counts (ag_count)](https://childmindresearch.github.io/wristpy/getting_started.html#actigraph-activity-counts-ag-counts)\n",
                "3. [Mean Amplitude Deviation (MAD)](https://childmindresearch.github.io/wristpy/getting_started.html#mad-mean-amplitude-deviation)\n",
                "4. [Monitor Independent Movement Summary Units (MIMS)](https://childmindresearch.github.io/wristpy/getting_started.html#mims-monitor-independent-summary-units)\n",
                "\n",
                "The default metric is ENMO, but you can pass any combination of supported metrics as a list to the orchestrator:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "id": "0a87085e",
            "metadata": {},
            "outputs": [],
            "source": [
                "\n",
                "results = orchestrator.run(\n",
                "   input = input_directory / 'three_nights.bin',\n",
                "   output = output_directory / 'three_nights_multiple_metrics.csv',\n",
                "   activity_metric = ['enmo', 'mad',]\n",
                ")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "b5bad25f",
            "metadata": {},
            "source": [
                "The resulting [OrchestratorResults](https://childmindresearch.github.io/wristpy/api/wristpy.io.writers.writers.html#wristpy.io.writers.writers.OrchestratorResults) object will contain the outputs for each metric in the order they were provided. The same applies to the physical activity levels associated with each metric."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "id": "735bfc81",
            "metadata": {},
            "outputs": [],
            "source": [
                "enmo = results.physical_activity_metric[0] \n",
                "mad = results.physical_activity_metric[1] \n",
                "\n",
                "enmo_levels = results.physical_activity_levels[0] \n",
                "mad_levels = results.physical_activity_levels[1] "
            ]
        },
        {
            "cell_type": "markdown",
            "id": "540fa698",
            "metadata": {},
            "source": [
                "We can visualize some of the outputs within the `results` object, directly, with the following scripts:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "id": "7bf11cdf",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[<matplotlib.lines.Line2D at 0x2aef5b1d0>]"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "#Plot the default physical activity metrics (ENMO) across the entire data set:\n",
                "\n",
                "from matplotlib import pyplot as plt\n",
                "\n",
                "plt.plot(results.physical_activity_metric[0].time, results.physical_activity_metric[0].measurements) "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "id": "a6528466",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Plot the sleep windows with normalized angle-z data:\n",
                "\n",
                "from matplotlib import pyplot as plt\n",
                "\n",
                "plt.plot(results.anglez.time, results.anglez.measurements/90) \n",
                "plt.plot(results.sleep_status.time, results.sleep_status.measurements) \n",
                "plt.legend(['Angle Z', 'Sleep Windows'])\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "id": "13f3ff42",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[<matplotlib.lines.Line2D at 0x2af754850>]"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "#We can also view and process these outputs from the saved `.csv` output file:\n",
                "\n",
                "import matplotlib.pyplot as plt\n",
                "import polars as pl\n",
                "\n",
                "output_results = pl.read_csv(output_directory/ 'three_nights.csv', try_parse_dates=True)\n",
                "\n",
                "activity_mapping = {\n",
                "    \"inactive\": 0,\n",
                "    \"light\": 1,\n",
                "    \"moderate\": 2,\n",
                "    \"vigorous\": 3\n",
                "}\n",
                "\n",
                "phys_activity = output_results['enmo physical activity levels'].replace(activity_mapping).cast(int)\n",
                "\n",
                "plt.plot(output_results['time'], phys_activity)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "id": "4f662530",
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Light activity percent: 12.394840157038699\n",
                        "Moderate activity percent: 1.1030099083940923\n",
                        "Vigorous activity percent: 0.031158471988533682\n",
                        "Inactivity percent: 86.47099146257868\n"
                    ]
                }
            ],
            "source": [
                "# It is also possible to do some analysis on these output variables, for example, if we want to find the percent of time spent inactive, or in light, moderate, or vigorous physical activity:\n",
                "\n",
                "inactivity_count = sum(phys_activity == 0)\n",
                "light_activity_count = sum(phys_activity == 1)\n",
                "moderate_activity_count = sum(phys_activity == 2)\n",
                "vigorous_activity_count = sum(phys_activity == 3)\n",
                "total_activity_count = len(output_results['enmo physical activity levels'])\n",
                "\n",
                "print(f'Light activity percent: {light_activity_count*100/total_activity_count}')\n",
                "print(f'Moderate activity percent: {moderate_activity_count*100/total_activity_count}')\n",
                "print(f'Vigorous activity percent: {vigorous_activity_count*100/total_activity_count}')\n",
                "print(f'Inactivity percent: {inactivity_count*100/total_activity_count}')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "f3b4fbd1",
            "metadata": {},
            "source": [
                "> #### Configuring a custom pipeline\n",
                ">\n",
                "> A custom processing pipeline can be easily created by modifying the input arguments to the `orchestrator.run` call.\n",
                ">> Complete documentation on these parameters can be found [here](https://childmindresearch.github.io/wristpy/wristpy/core/orchestrator.html#run).\n",
                ">> Example:\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "id": "4e8e1c4b",
            "metadata": {},
            "outputs": [],
            "source": [
                "results = orchestrator.run(input = input_directory / \"three_nights.bin\", output = output_directory / \"custom_pipeline.csv\", output_filetype = \".parquet\", calibrator=\"gradient\", activity_metric=[\"ag_count\"], nonwear_algorithm=[\"detach\"], epoch_length=10, thresholds=[(0.05, 0.1, 0.3)])"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "0c3e7197",
            "metadata": {},
            "source": [
                "### Example 2: Loading data and plotting the raw signals\n",
                "\n",
                "\n",
                "In this example we will go over the built-in functions to directly read the raw accelerometer and light data, and how to quickly visualize this information.\n",
                "\n",
                "The built-in `readers` module can be used to load all the sensor and metadata from one of the support wristwatches (`.gt3x` or `.bin`), the reader will automatically select the appropriate loading methodology."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "id": "cb97a841",
            "metadata": {},
            "outputs": [],
            "source": [
                "from wristpy.io.readers import readers\n",
                "\n",
                "watch_data = readers.read_watch_data(input_directory / \"three_nights.bin\")"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "9d2101aa",
            "metadata": {},
            "source": [
                "We can then visualize the raw accelerometer and light sensor values very easily as follows:\n",
                "\n",
                "Plot the raw acceleration along the *x*-axis:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "id": "614cfd11",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[<matplotlib.lines.Line2D at 0x2afb8bb90>]"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.plot(watch_data.acceleration.time, watch_data.acceleration.measurements[:,0])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "id": "4157e71a",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[<matplotlib.lines.Line2D at 0x2afc2ad90>]"
                        ]
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "#Plot the light data:\n",
                "\n",
                "plt.plot(watch_data.lux.time, watch_data.lux.measurements) "
            ]
        },
        {
            "cell_type": "markdown",
            "id": "9dfefa97",
            "metadata": {},
            "source": [
                "### Example 3:  Plot the epoch-level measurements\n",
                "\n",
                "In this example we will expand on the skills learned in `Example 2`: we will load the sensor data, calibrate, and then calculate the ENMO and angle-z data in 5s windows (epoch-level data)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "id": "dd7128f8",
            "metadata": {},
            "outputs": [],
            "source": [
                "from wristpy.core import computations\n",
                "from wristpy.io.readers import readers\n",
                "from wristpy.processing import calibration, metrics\n",
                "\n",
                "watch_data = readers.read_watch_data(input_directory / \"three_nights.bin\")\n",
                "\n",
                "#Calibration phase\n",
                "calibrator_object = calibration.ConstrainedMinimizationCalibration()\n",
                "calibrated_data = calibrator_object.run_calibration(watch_data.acceleration)\n",
                "\n",
                "#Compute the desired metrics\n",
                "enmo = metrics.euclidean_norm_minus_one(calibrated_data)\n",
                "anglez = metrics.angle_relative_to_horizontal(calibrated_data)\n",
                "\n",
                "#Obtain the epoch-level data, default is 5s windows\n",
                "enmo_epoch1 = computations.moving_mean(enmo)\n",
                "anglez_epoch1 = computations.moving_mean(anglez)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "2f24cf85",
            "metadata": {},
            "source": [
                "We can then visualize the `epoch1` measurements as:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "id": "5767fb2b",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "\n",
                "fig, ax1 = plt.subplots()\n",
                "\n",
                "\n",
                "ax1.plot(enmo_epoch1.time, enmo_epoch1.measurements, color='blue')\n",
                "ax1.set_ylabel('ENMO', color='blue')\n",
                "\n",
                "ax2 = ax1.twinx()\n",
                "ax2.plot(anglez_epoch1.time, anglez_epoch1.measurements, color='red')\n",
                "ax2.set_ylabel('Anglez', color='red')\n",
                "\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "8ef53d4c",
            "metadata": {},
            "source": [
                "### Example 4: Visualize the detected non-wear times\n",
                "\n",
                "In this example we will build on `Example 3` by also solving for the non-wear periods, as follows:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "id": "2e519326",
            "metadata": {},
            "outputs": [],
            "source": [
                "from wristpy.io.readers import readers\n",
                "from wristpy.processing import calibration, metrics\n",
                "\n",
                "watch_data = readers.read_watch_data(input_directory / \"three_nights.bin\")\n",
                "calibrator_object = calibration.ConstrainedMinimizationCalibration()\n",
                "calibrated_data = calibrator_object.run_calibration(watch_data.acceleration)\n",
                "\n",
                "#Find non-wear periods, using the DETACH algorithm\n",
                "non_wear_array = metrics.detect_nonwear(calibrated_data)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "3bc050dd",
            "metadata": {},
            "source": [
                "We can then visualize the non-wear periods, in comparison to movement (ENMO at the epoch-level):"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "id": "e4c52a99",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.legend.Legend at 0x2afbe5f50>"
                        ]
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "from wristpy.core import computations\n",
                "\n",
                "enmo = metrics.euclidean_norm_minus_one(calibrated_data)\n",
                "enmo_epoch1 = computations.moving_mean(enmo)\n",
                "\n",
                "\n",
                "plt.plot(enmo_epoch1.time, enmo_epoch1.measurements)\n",
                "plt.plot(non_wear_array.time, non_wear_array.measurements)\n",
                "\n",
                "plt.legend(['ENMO Epoch1', 'Non-wear'])"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "09b1727f",
            "metadata": {},
            "source": [
                "### Example 5: Compute and plot the sleep windows\n",
                "\n",
                "\n",
                "We can visualize the sleep periods in comparison to other metrics; in this example, we compare the sleep windows to the angle-z data and the non-wear periods. In the default pipeline any sleep periods that overlap with non-wear periods are filtered out.\n",
                "This plot shows the sleep periods visualized by a blue trace, non-wear periods are visualized with a green trace, and the angle-z data with the semi-transparent red trace. These are all accessible directly from the results object created with the custom pipeline:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "id": "68095128",
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 640x480 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "import matplotlib.pyplot as plt\n",
                "\n",
                "fig, ax1 = plt.subplots()\n",
                "\n",
                "ax1.plot(results.sleep_status.time, results.sleep_status.measurements, color='blue', label='Sleep Periods')\n",
                "plt.plot(results.nonwear_status.time, results.nonwear_status.measurements, color='green')\n",
                "ax2 = ax1.twinx()\n",
                "ax2.plot(results.anglez.time, results.anglez.measurements, color='red', alpha=0.5)\n",
                "ax2.set_ylabel('Anglez Epoch1', color='red')\n",
                "\n",
                "ax1.set_ylabel('Sleep Period/Non-wear')\n",
                "ax1.set_ylim(0, 1.5)\n",
                "\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "6dc817d4",
            "metadata": {},
            "source": [
                "\n",
                "## Docker Tutorial \n",
                "\n",
                "1. **Install Docker**: Ensure you have Docker installed on your system. [Get Docker](https://docs.docker.com/get-docker/)\n",
                "\n",
                "2. **Pull the Docker image**:\n",
                "   ```bash\n",
                "   docker pull cmidair/wristpy:main\n",
                "   ```\n",
                "\n",
                "3. **Run the Docker image** with your data:\n",
                "   ```bash\n",
                "   docker run -it --rm \\\n",
                "     -v \"/local/path/to/data:/data\" \\\n",
                "     -v \"/local/path/to/output:/output\" \\\n",
                "     cmidair/wristpy:main\n",
                "   ```\n",
                "   Replace `/local/path/to/data` with the path to your input data directory and `/local/path/to/output` with where you want results saved.\n",
                "\n",
                "   To run a single file, we simply need to modify the mounting structure for the docker call slightly:\n",
                "    ```bash\n",
                "    docker run -it --rm \\\n",
                "     -v \"/local/path/to/data/file.bin:/data/file.bin\" \\\n",
                "     -v \"/local/path/to/output:/output\" \\\n",
                "     cmidair/wristpy:main\n",
                "   ```\n",
                "\n",
                "4. **Customizing the Pipeline**:\n",
                "\n",
                "The Docker image supports multiple input variables to customize processing. You can set these by simply chaining these inputs as you would for the CLI input:\n",
                "\n",
                "  ```bash\n",
                "  docker run -it --rm \\\n",
                "    -v \"/local/path/to/data/file.bin:/data/file.bin\" \\\n",
                "    -v \"/local/path/to/output:/output\" \\\n",
                "    cmidair/wristpy:main /data --output /output --epoch-length 5 --nonwear-algorithm ggir --nonwear-algorithm detach --thresholds 0.1 0.2 0.4\n",
                "  ```\n"
            ]
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                "\n",
                "## Physical Activity Metrics Explained\n",
                "\n",
                "### *ENMO— Euclidean Norm Minus One*  \n",
                "ENMO quantifies movement intensity from calibrated 3-axis acceleration by taking the vector magnitude and subtracting 1 g (gravity). Values below zero are set to 0 (they mostly reflect noise or tiny calibration errors). \n",
                "\n",
                "**References:**  \n",
                "- van Hees, V. T., Gorzelniak, L., Dean León, E. C., Eder, M., Pias, M., Taherian, S., Ekelund, U., Renström, F., Franks, P. W., Horsch, A., & Brage, S. (2013). Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. *PLoS ONE, 8*(4), e61691. https://doi.org/10.1371/journal.pone.0061691  \n",
                "- Hildebrand, M., van Hees, V. T., Hansen, B. H., & Ekelund, U. (2014). Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. *Medicine & Science in Sports & Exercise, 46*(9), 1816–1824. https://doi.org/10.1249/MSS.0000000000000289  \n",
                "\n",
                "\n",
                "### *MAD — Mean Amplitude Deviation*\n",
                "MAD summarizes how much the acceleration magnitude fluctuates within an epoch. Specifically it’s the mean of the absolute deviations from the epoch’s mean acceleration magnitude. It correlates well with energy expenditure and is orientation-independent.\n",
                "\n",
                "**Reference:**  \n",
                "- Vähä-Ypyä, H., Vasankari, T., Husu, P., Suni, J., & Sievänen, H. (2015). A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. *Clinical Physiology and Functional Imaging, 35*(1), 64–70. https://doi.org/10.1111/cpf.12127  \n",
                "\n",
                "\n",
                "### *MIMS — Monitor-Independent Summary Units*\n",
                "MIMS is a standardized, device-agnostic summary unit. The pipeline interpolates to a fixed frequency (100 Hz), extrapolates values outside device range, band-pass filters, integrates area-under-the-curve per axis over each epoch, then combines axes (sum or vector magnitude) with small-value truncation. It’s designed to be comparable across devices and studies. This metric is computationally intensive and will run significantly slower than the other algorithms.\n",
                "\n",
                "**Reference:**  \n",
                "- John, D., Tang, Q., Albinali, F., & Intille, S. (2019). An open-source monitor-independent movement summary for accelerometer data processing. *Journal for the Measurement of Physical Behaviour, 2*(4), 268–281. https://doi.org/10.1123/jmpb.2019-0011  \n",
                "\n",
                "\n",
                "### *ActiGraph Activity Counts (ag_counts)*\n",
                "This reproduces the open ActiGraph counts method: resample to 30 Hz, apply the published IIR band-pass filter, scale to device units, threshold, downsample to 10 Hz, and sum within each epoch. The three axes are finally combined  to yield epoch-level “counts.”\n",
                "\n",
                "**Reference:**  \n",
                "- Neishabouri, A., Wilson, K. E., Williams, D. K., Keadle, S. K., Sampson, J., John, D., & Staudenmayer, J. (2022). Quantification of acceleration as activity counts in ActiGraph wearable. *Scientific Reports, 12*(1), 11169. https://doi.org/10.1038/s41598-022-16003-x  \n",
                "\n",
                "\n"
            ]
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                "## A Special Note on Idle Sleep Mode. \n",
                "\n",
                "The `idle_sleep_mode` for Actigraph watches will lead to uneven sampling rates during periods of no motion (read about this [here](https://actigraphcorp.my.site.com/support/s/article/Idle-Sleep-Mode-Explained)). Consequently, this causes issues when implementing wristpy's non-wear and sleep detection. As of this moment, we fill in the missing acceleration data with the assumption that the watch is perfectly idle in the face-up position (Acceleration vector = [0, 0, -1]). The data is filled in at the same sampling rate as the raw acceleration data. In the special circumstance when acceleration samples are not evenly spaced, the data is resampled to the highest effective sampling rate to ensure linearly sampled data."
            ]
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                "## References\n",
                "\n",
                "<span id=\"ref1\">[1]</span> Vähä-Ypyä H, Vasankari T, Husu P, Suni J, Sievänen H. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. *Clin Physiol Funct Imaging*. 2015;35(1):64-70. doi: <https://doi.org/10.1111/cpf.12127>. PMID: 24393233.\n",
                "\n",
                "<span id=\"ref2\">[2]</span> Neishabouri A, Wilson KE, Williams DK, et al. Quantification of acceleration as activity counts in ActiGraph wearable. *Sci Rep*. 2022;12(1). doi: <https://doi.org/10.1038/s41598-022-16003-x>.\n",
                "\n",
                "<span id=\"ref3\">[3]</span> John D, Tang Q, Albinali F, Intille S. An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing. *Journal for the Measurement of Physical Behaviour*. 2019;2(4):268–281.\n",
                "\n",
                "<span id=\"ref4\">[4]</span> Zhou S, Hill RA, Morgan K, et al. Classification of accelerometer wear and non-wear events in seconds for monitoring free-living physical activity. *BMJ Open*. 2015;5:e007447. doi: <https://doi.org/10.1136/bmjopen-2014-007447>.\n",
                "\n",
                "<span id=\"ref5\">[5]</span> Vert A, et al. Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature. *BMC Med Res Methodol*. 2022;22(1):147. doi: <https://doi.org/10.1186/s12874-022-01633-6>.\n",
                "\n",
                "<span id=\"ref6\">[6]</span> van Hees VT, Sabia S, Jones SE, et al. Estimating sleep parameters using an accelerometer without sleep diary. *Sci Rep*. 2018;8:12975. doi: <https://doi.org/10.1038/s41598-018-31266-z>.\n",
                "\n",
                "<span id=\"ref7\">[7]</span> van Hees VT, et al. A Novel, Open Access Method to Assess Sleep Duration Using a Wrist-Worn Accelerometer. *PLoS One*. 2015;10:e0142533. doi: <https://doi.org/10.1371/journal.pone.0142533>.\n",
                "\n",
                "<span id=\"ref8\">[8]</span> Hildebrand M, et al. Age group comparability of raw accelerometer output from wrist- and hip-worn monitors. *Med Sci Sports Exerc*. 2014;46(9):1816–1824. doi: <https://doi.org/10.1249/mss.0000000000000289>.\n",
                "\n",
                "<span id=\"ref9\">[9]</span> Treuth MS, Schmitz K, Catellier DJ, et al. Defining accelerometer thresholds for activity intensities in adolescent girls. *Med Sci Sports Exerc*. 2004;36(7):1259–1266. PMID: 15235335; PMCID: PMC2423321.\n",
                "\n",
                "<span id=\"ref10\">[10]</span> Aittasalo M, Vähä-Ypyä H, Vasankari T, et al. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand. *BMC Sports Sci Med Rehabil*. 2015;7:18. doi: <https://doi.org/10.1186/s13102-015-0010-0>.\n"
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