Added example notebook

example/RIVET Python API Tour.ipynb

@@ -0,0 +1,375 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RIVET Python API (pyrivet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from pyrivet import rivet\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with some warmups. We can run RIVET on an existing data file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['circle_data_240pts_codensity.txt',\n",
+       " 'circle_data_60pts_codensity.txt.H0_x0_y0.rivet',\n",
+       " 'circle_data_600pts_codensity.txt',\n",
+       " 'circle_data_60pts_codensity.txt',\n",
+       " 'circle_data_60pts.png',\n",
+       " 'circle_data_240pts.png',\n",
+       " 'points1.txt',\n",
+       " 'points2.txt',\n",
+       " 'points3.txt',\n",
+       " 'circle_data_60pts_codensity.txt.H1_x20_y20.rivet',\n",
+       " 'points6.txt',\n",
+       " 'points4.txt',\n",
+       " 'points5.txt']"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The RIVET distribution comes with some test data files\n",
+    "import os\n",
+    "\n",
+    "rivet_location = '../../rivet' #Might be in a different location for you\n",
+    "os.listdir(rivet_location + '/data/Test_Point_Clouds')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'../../rivet/data/Test_Point_Clouds/circle_data_60pts_codensity.txt.H1_x20_y20.rivet'"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "computed_file_path = rivet.compute_file(\n",
+    "    rivet_location + '/data/Test_Point_Clouds/circle_data_60pts_codensity.txt',\n",
+    "    homology=1,\n",
+    "    x=20,\n",
+    "    y=20)\n",
+    "computed_file_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output when used this way is a path to a file generated by RIVET. We can load the contents of that computed file into memory and run other RIVET commands on it, like computing barcodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[((45.0, 0.0), Barcode([Bar(6.98695, 7.68564, 1), Bar(5.65685, 6.28825, 1)])),\n",
+       " ((45.0, 1.0), Barcode([Bar(5.65685, 5.68564, 1)]))]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open(computed_file_path, 'rb') as f:\n",
+    "    computed_data = f.read()\n",
+    "\n",
+    "codes = rivet.barcodes(computed_data, [\n",
+    "    (45, 0), # 45 degrees, offset 0\n",
+    "    (45, 1), # 45 degrees, offset 1\n",
+    "])\n",
+    "codes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at one barcode in the list. The format for each bar is Bar(birth, death, multiplicity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Barcode([Bar(6.98695, 7.68564, 1), Bar(5.65685, 6.28825, 1)])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "barcode1 = codes[0][1]\n",
+    "barcode1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A barcode can also be easily converted to a Numpy array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6.98695, 7.68564, 1.     ],\n",
+       "       [5.65685, 6.28825, 1.     ]])"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "barcode1.to_array()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also load data directly from memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiBetti(dimensions=Dimensions([Fraction(0, 1)], [Fraction(0, 1), Fraction(1838477, 1000000), Fraction(2012461, 1000000), Fraction(512957, 250000), Fraction(1159741, 500000), Fraction(3041381, 1000000), Fraction(3125699, 1000000), Fraction(388973, 100000)]), graded_rank=[[5.]\n",
+       " [4.]\n",
+       " [3.]\n",
+       " [2.]\n",
+       " [2.]\n",
+       " [1.]\n",
+       " [1.]\n",
+       " [1.]], xi_0=[(0, 0, 5)], xi_1=[(0, 1, 1), (0, 2, 1), (0, 3, 1), (0, 5, 1)], xi_2=[])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Equivalent to the 'points1.txt' sample data file in the RIVET distribution\n",
+    "points = rivet.PointCloud(\n",
+    "    rivet.points(\n",
+    "        (0.3, 1.5),\n",
+    "        (4.2, 3.8),\n",
+    "        (5.6, 2.3),\n",
+    "        (2.9, 5.1),\n",
+    "        (3.3, 2.0)\n",
+    "    ),\n",
+    "    second_param_name='birth',\n",
+    "    max_dist=4.1\n",
+    ")\n",
+    "\n",
+    "# Now let's take the Betti information\n",
+    "betti = rivet.betti(points)\n",
+    "betti"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's look at two persistence modules for molecules.\n",
+    "\n",
+    "First, let's see how to compute L2 distance on Hilbert functions of the persistence modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distance with Hilbert functions: 3442399.7063080464\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's look at H0, binned into 10x10.\n",
+    "aspirin_h0_betti = rivet.betti_file('aspirin-ZINC000000000053.sdf.txt', homology=0, x=10, y=10)\n",
+    "tylenol_h0_betti = rivet.betti_file('tylenol-ZINC000013550868.sdf.txt', homology=0, x=10, y=10)\n",
+    "\n",
+    "# We can compute distance between these using Hilbert functions\n",
+    "from pyrivet import hilbert_distance\n",
+    "print(\"Distance with Hilbert functions:\", \n",
+    "      hilbert_distance.distance(\n",
+    "          aspirin_h0_betti, \n",
+    "          tylenol_h0_betti\n",
+    "      )\n",
+    "     )\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now let's look at the matching distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Distance with matching distance: 1.2909725839903918e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can also compute the matching distance, if we have the full precomputed arrangement\n",
+    "aspirin_h0 = open(rivet.compute_file('aspirin-ZINC000000000053.sdf.txt', homology=0, x=10, y=10), 'rb').read()\n",
+    "tylenol_h0 = open(rivet.compute_file('tylenol-ZINC000013550868.sdf.txt', homology=0, x=10, y=10), 'rb').read()\n",
+    "\n",
+    "from pyrivet import matching_distance\n",
+    "print(\"Distance with matching distance:\",\n",
+    "      matching_distance.matching_distance(aspirin_h0, tylenol_h0, grid_size=10, normalize=True)\n",
+    "     )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While the RIVET GUI does many things that would be tricky to duplicate in a notebook, we can at least visualize the Hilbert\n",
+    "functions!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x10d768208>"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AmM05O3ufnQ30tkrwHk9y2VkfH0zyyDzjAADAtFYJ3juTXFFVl1fVniTXJ/nivGMBAMA0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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "fig = plt.figure(figsize=(12,8))\n",
+    "plots = fig.subplots(1, 2, sharex=True, sharey=True)\n",
+    "\n",
+    "plots[0].imshow(aspirin_h0_betti.graded_rank, origin='lower', aspect='auto', cmap='coolwarm')\n",
+    "plots[1].imshow(tylenol_h0_betti.graded_rank, origin='lower', aspect='auto', cmap='coolwarm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}