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    "# Stryke Force Jupyter Hub\n",
    "\n",
    "## Table of Contents\n",
    "These notebooks are read-only but can be copied into your directory.\n",
    "\n",
    "\n",
    "### Pathfinder\n",
    "\n",
    "- [Pathfinder 1.0 Visualization](pathfinder/v1/pathfinder.ipynb)\n",
    "- [Pathfinder 1.0 Swerve Path Visualization](pathfinder/v1/pathfinder_swerve.ipynb)\n",
    "- [2018 POWER UP Autons Pathfinder](pathfinder/2018_auton)\n",
    "\n",
    "### Robot Controls\n",
    "\n",
    "- [Joystick Transfer Functions](controls/joystick_transfer_functions.ipynb) - algorithms for joystick transfer functions used to transform joystick operator input to the system demand signal.\n",
    "\n",
    "### Sensors\n",
    "\n",
    "- [Calculate LIDAR slope and offset correction](sensors/lidar/lidar_correction.ipynb)\n",
    "- [2018 POWER UP](sensors/lidar/lidar.ipynb) - Plotting reported vs. actual of the intake LIDAR.\n",
    "- [2018 LIDAR Assisted Carpet Calibration](sensors/lidar/lidar_carpet.ipynb)\n",
    "\n",
    "### Swerve Drive\n",
    "\n",
    "- [2018 POWER UP](swerve/wheels/powerup_wheel_tests/2018%20Carpet%20Test.ipynb) - conducted during auton development for the 2018 season.\n",
    "- [2018 Magic Wheels](swerve/wheels/magic_wheel_tests/) - conducted as port of our development of _magic_ wheels. There is a [summary report](swerve/wheels/magic_wheel_tests/Wheel%20Test%202018%20Summary.ipynb) of the results.\n",
    "\n",
    "### Vision\n",
    "\n",
    "#### 2019 DEEP SPACE\n",
    "\n",
    "- [2019 DEEP SPACE](vision/2019%20DEEP%20SPACE) - vision notebooks for 2019 season.\n",
    "- [Kick-off image samples](vision/2019%20DEEP%20SPACE/kickoff) - selection of example robot camera images provided by FIRST.\n",
    "\n",
    "#### 2016 STRONGHOLD\n",
    "\n",
    "- [HSV Colors](vision/2016%20STRONGHOLD/HSV%20Colorspace.ipynb) - brief Hue, Saturation, Value colorspace explanation and color samples.\n",
    "- [Basic Image Arithmatic](vision/2016%20STRONGHOLD/Basic%20Image%20Arithmetic.ipynb) - since each pixel in an image is represented by numbers we can use basic arithmetic such as addition and subtraction. Python makes this especially easy.\n",
    "- [Masking the Target](vision/2016%20STRONGHOLD/Masking%20the%20Target.ipynb) - experiment with several ways to isolate, or mask, the target in a sample image.\n",
    "- [Hough Line Transform](vision/2016%20STRONGHOLD/Hough%20Line%20Transform.ipynb) - experiment locating a target with a technique to detect any shape, if you can represent that shape in mathematical form.\n",
    "- [2016 STRONGHOLD Computer Vision & Targeting](vision/2016%20STRONGHOLD/2016%20STRONGHOLD%20Computer%20Vision%20%26%20Targeting.ipynb) - outlines the techniques used for the Stryke Force 2016 computer vision and targetting system.\n"
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