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    "# Basic Approach\n",
    "\n",
    "## Outline\n",
    "### Questions\n",
    "- What is the approach for finding events in **dupin**?\n",
    "- What is the basic definition for each step in the event detection scheme?\n",
    "\n",
    "### Objectives\n",
    "- Explain the six step model for event detection used by **dupin**.\n",
    "- Define the basic outline of each step.\n",
    "\n",
    "\n",
    "## Six Step Detection\n",
    "**dupin** is based on a 6 step method for detecting events in point\n",
    "cloud data.\n",
    "\n",
    "![alternative text](../../_static/scheme.png)\n",
    "\n",
    "1. Generate: Generate basic features from the point cloud. Such features can be per-point, aggregate, or global.\n",
    "2. Map: Transform the generated data into different distributions such as neighbor point averaging.\n",
    "3. Reduce: Take the non-scalar features and create scalar features from them (e.g. maximum, minimum, spread, mean, ...).\n",
    "4. Aggregate: Perform the first three steps across the time dimension of the point cloud data.\n",
    "5. Transform: Transform the signal using signal processing into a new feature signal (essentially the map step applied in the time direction). This step can involve feature selection or dimensionality reduction.\n",
    "6. Detect: Given the final feature signal detect events if any in the data.\n",
    "\n",
    "The first, fourth, and sixth steps are always necessary; however, most are useful in common use cases.\n",
    "This tutorial will go through all six step in the detection process."
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   "source": [
    "[Previous section](00-index.ipynb). [Next section](02-data-pipeline.ipynb)."
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