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tutorials/Binary Classifier.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Binary Classifier\n",
+    "Binary Classifiers are the most basic part of the framework. Each Binary Classifier represents a single classification function.\n",
+    "\n",
+    "<img src=\"../images/binary_classifier.png\" width=\"60%\" />\n",
+    "\n",
+    "## Structure\n",
+    "Binary Classifiers contain a single classifying function and have a unique name. The classifiying function must take in a list of Observation objects and return a probability.\n",
+    "\n",
+    "```\n",
+    "[Observation] -> Float\n",
+    "```\n",
+    "\n",
+    "The classifying function should process the given observations in order to get a probability value. The Binary Classifier class will be able to take the probability value and observation data and construct a Result object based on it.\n",
+    "\n",
+    "Additionally, each Binary Classifier should be given a unique name to make it identifiable in the results.\n",
+    "\n",
+    "## Examples\n",
+    "A Binary Classifier can be created by specifying a name and classifying function. This example uses a simple classification function which sums the light values of the given observations, takes the reminder of dividing the sum by 10, and divides that remainer by 10 to get a probability value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from lsstbroker import binary_classifier\n",
+    "\n",
+    "def classifying_function(obs):\n",
+    "    values = [o.light for o in obs]\n",
+    "    avg = sum(values)\n",
+    "    prob = (avg % 10) / 10\n",
+    "    return prob\n",
+    "\n",
+    "name = \"C-00001\"\n",
+    "bc = binary_classifier.BinaryClassifier(name, classifying_function)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once a Binary Classifier has been created, Observation objects can then be run against it to generate the results of the processing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('LSST-00001A', 0.7, 'C-00001', 0)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from lsstbroker import observation\n",
+    "\n",
+    "object_name = \"LSST-00001A\"\n",
+    "observation1 = observation.Observation(object_name, 0.5, 42, 0.005)\n",
+    "observation2 = observation.Observation(object_name, 0.7, 51, 0.005)\n",
+    "observations = [observation1, observation2]\n",
+    "\n",
+    "bc.run(observations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

tutorials/Introduction.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "The LSST Event Broker is a Python framework for developing astronomical data classification systems.\n",
+    "\n",
+    "## Tutorials\n",
+    "* [Binary Classifier](Binary Classifier.ipynb)\n",
+    "* Classifier\n",
+    "* Classifier Box\n",
+    "* Handler\n",
+    "* Example System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}