{"nbformat_minor": 0, "metadata": {"signature": "sha256:52a995479d719dddc73acc75c055e2c63c8a026ff9838a3fb6664aca448aafce", "name": ""}, "nbformat": 3, "worksheets": [{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Arithmetic on Squiggles"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's \"import\" so-called \"modules\" that add features to our programming language (\"Python\")."]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [], "prompt_number": 13, "input": ["import numpy as np\n", "import matplotlib.pyplot as pt"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Here are two 'squiggles' represented as a bunch of numbers:"]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [], "prompt_number": 14, "input": ["squiggle_1 = \"141.03 291.04 141.28 291.50 141.92 291.50 142.67 291.04 143.94 290.13 145.58 288.32 147.22 285.25 149.62 281.27 152.40 276.15 155.31 270.24 158.21 264.10 161.37 258.19 164.02 252.96 166.67 248.41 168.69 244.89 170.72 241.82 172.48 239.55 174.25 237.84 175.52 236.59 176.27 236.13 176.65 236.48 176.65 237.50 177.03 240.00 177.41 244.21 177.79 250.12 178.04 257.39 178.04 265.12 178.42 273.08 178.67 280.93 179.05 288.43 179.56 295.71 179.94 302.64 180.69 309.01 181.71 314.35 182.72 318.10 183.85 320.03 184.86 321.06 186.00 320.72 187.52 319.35 189.28 316.40 191.31 311.39 193.33 304.35 195.60 295.93 198.00 286.84 200.65 277.86 203.56 269.67 206.21 262.62 208.61 257.17 210.51 253.07 212.02 250.23 213.03 248.64 213.79 247.84 214.17 247.73 214.17 248.53 214.17 250.35 214.04 253.53 213.92 258.19 213.66 263.99 213.54 270.13 213.41 276.38 213.41 282.18 213.92 287.41 214.80 291.84 216.32 295.48 218.21 298.09 220.11 299.80 222.13 300.59 224.27 300.71 226.55 300.03 228.95 298.43 231.22 295.71 233.24 292.07 235.26 287.07 237.16 280.81 239.31 273.76 241.83 265.81 244.86 257.39 248.15 249.21 251.68 241.14 255.09 233.63 258.00 227.27 260.40 222.49 261.92 219.54 262.80 218.63 263.18 218.63 263.18 219.88 263.05 222.49 262.55 226.93 261.92 233.75 261.03 242.16 260.40 251.82 260.27 262.40 260.53 273.08 260.91 284.11 261.79 294.91 262.80 304.35 264.44 312.30 266.72 317.99 269.12 321.63 271.64 323.56 274.17 324.69 276.95 324.47 279.98 322.42 283.26 318.56 286.42 311.96 289.83 303.21 293.24 292.98 296.78 282.18 300.32 272.17 303.60 263.42 306.38 256.60 308.65 251.82 310.04 248.75 310.93 247.50 311.18 247.96 311.05 248.98 310.93 250.91 310.80 253.98 310.55 258.76 310.42 265.12 310.17 272.74 310.55 280.81 311.31 289.00 312.57 296.50 314.46 302.64 316.86 307.41 319.77 310.37 322.80 311.73 325.96 312.08 328.86 311.17 331.64 309.01 334.29 304.91 336.82 299.00 339.47 291.73 342.38 283.43 345.54 275.13 348.95 267.51 352.36 260.69 355.14 255.57 357.03 252.16 358.17 250.46 358.55 250.23 358.55 250.69 358.80 251.94 359.43 254.32 360.06 258.19 360.69 264.33 361.20 271.95 361.71 280.59 362.59 289.23 363.98 296.84 366.00 303.44 368.53 308.44 371.56 311.73 375.22 313.33 379.26 312.64 383.81 310.26 388.74 306.62 394.29 301.50 \"\n", "squiggle_2 = \"243.60 219.20 243.60 218.06 242.84 216.92 242.21 215.67 241.07 214.19 239.56 212.60 237.66 211.01 235.26 209.65 232.23 208.62 228.82 207.60 225.03 206.92 220.86 206.46 216.57 206.12 212.27 206.80 207.60 208.05 202.80 210.33 198.13 213.74 193.20 217.72 188.78 222.15 184.74 226.59 181.45 231.13 178.93 235.91 177.28 241.02 176.27 246.59 175.89 252.62 176.27 258.99 177.28 265.81 178.93 272.85 181.33 279.79 184.74 286.38 188.91 292.64 193.83 298.32 199.52 303.78 205.71 308.67 212.40 312.99 219.60 316.62 226.80 319.69 234.00 322.19 241.20 323.90 248.27 324.81 255.60 325.26 262.93 324.81 270.25 323.90 277.96 322.53 285.54 320.60 293.12 318.56 300.32 316.17 306.63 313.33 312.44 310.14 317.75 306.39 322.80 301.96 327.60 296.73 332.15 290.82 336.44 284.11 340.48 276.83 344.15 269.22 347.05 261.60 349.07 253.98 350.34 246.59 350.59 239.20 349.83 231.59 348.19 223.63 345.41 215.67 341.75 208.17 337.07 201.35 331.77 195.66 326.21 191.12 320.65 187.48 315.09 184.75 309.16 182.93 302.84 182.02 296.02 181.91 288.82 182.36 281.87 183.27 274.93 184.52 268.11 185.89 261.54 187.25 255.35 188.61 249.66 190.21 244.86 191.68 240.82 193.28 237.03 194.87 233.87 196.57 230.97 198.28 228.44 199.98 226.42 201.69 224.65 203.51 223.52 205.55 222.51 207.94 221.75 210.78 221.24 213.97 220.74 217.72 220.48 221.24 220.23 224.43 219.85 227.04 219.22 229.20 218.72 230.91 218.21 232.04 217.83 232.72 217.71 233.07 217.33 233.07 216.95 232.50 216.06 231.13 215.43 228.86 215.05 225.56 \""]}, {"cell_type": "markdown", "metadata": {}, "source": ["\"Parsing\" those turns them from strings into arrays of numbers:"]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [], "prompt_number": 17, "input": ["def parse_squiggle(s):\n", " numbers = [float(num) for num in s.split()]\n", " a = np.array(numbers)\n", " return a.reshape(-1, 2).T"]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [], "prompt_number": 18, "input": ["s1 = parse_squiggle(squiggle_1)\n", "s2 = parse_squiggle(squiggle_2)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's plot both squiggles."]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [{"metadata": {}, "text": ["[]"], "output_type": "pyout", "prompt_number": 19}, {"text": ["/usr/lib/python3/dist-packages/matplotlib/backends/backend_agg.py:517: DeprecationWarning: npy_PyFile_Dup is deprecated, use npy_PyFile_Dup2\n", " filename_or_obj, self.figure.dpi)\n"], "output_type": "stream", "stream": "stderr"}, {"metadata": {}, "text": [""], "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD/CAYAAAAUnaZMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"}], "prompt_number": 19, "input": ["pt.plot(s1[0], s1[1])"]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [{"metadata": {}, "text": ["[]"], "output_type": "pyout", "prompt_number": 20}, {"metadata": {}, "text": [""], "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD/CAYAAAAUnaZMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIcQOnAAAAABJRU5ErkJggg==\n"}], "prompt_number": 20, "input": ["pt.plot(s2[0], s2[1])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Will this work?"]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [{"evalue": "operands could not be broadcast together with shapes (2,159) (2,105) ", "ename": "ValueError", "output_type": "pyerr", "traceback": ["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#keep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0ms1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ms2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,159) (2,105) "]}], "prompt_number": 22, "input": ["s1 + s2"]}, {"cell_type": "markdown", "metadata": {}, "source": ["---------------\n", "\n", "So we'll need to do something."]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [], "prompt_number": 24, "input": ["from scipy.interpolate import interp1d\n", "\n", "_, ns1 = s1.shape\n", "_, ns2 = s2.shape\n", "\n", "s1x_interp = interp1d(np.linspace(0, 1, ns1), s1[0])\n", "s1y_interp = interp1d(np.linspace(0, 1, ns1), s1[1])\n", "\n", "s1_new = np.array([\n", " s1x_interp(np.linspace(0, 1, ns2)),\n", " s1y_interp(np.linspace(0, 1, ns2))])"]}, {"language": "python", "collapsed": false, "metadata": {}, "cell_type": "code", "outputs": [{"metadata": {}, "text": ["[]"], "output_type": "pyout", "prompt_number": 33}, {"metadata": {}, "text": [""], "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD/CAYAAAAUnaZMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n"}], "prompt_number": 33, "input": ["\n", "s3 = s1_new + s2\n", "pt.plot(s3[0], s3[1])"]}], "metadata": {}}]}