{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Look at focussing the GCT\n", "\n", "First import numpy, matplot lib, 3D plotting routines and ensure plots are produced inline." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Check the distance that need to move GCT to focus from infinity to about 1 km. Relevant formula is $\\frac{1}{f} = \\frac{1}{u} + \\frac{1}{v}$. Reorganising: $u = \\frac{vf}{v - f} $." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference between maximum and minimum image distances is 5.3022 mm\n", "Total distance that must be moved 11.3022 mm\n" ] } ], "source": [ "def imageDist(focalLength,objectDist):\n", " dist = objectDist*focalLength/(objectDist - focalLength)\n", " return dist\n", "maxDist = 10**12\n", "minDist = 1000\n", "focalLength = 2.3\n", "diff = imageDist(focalLength,minDist) - imageDist(focalLength,maxDist)\n", "lidFPdist = 0.006\n", "print(\"Difference between maximum and minimum image distances is {:.4f} mm\".format(1000*diff))\n", "print(\"Total distance that must be moved {:.4f} mm\".format(1000*(diff + lidFPdist)))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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zBuAlM7sAeBKoIAyYPj3W5p/AJWY2A5gIDIy+e0wOv0FasN694YILQpk2rWYC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\nBwyv8mohn8r5AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "longDist = 10**4\n", "nArray = 50\n", "objectArray = np.linspace(minDist,longDist,nArray)\n", "imageArray = np.zeros(nArray)\n", "imageArray = imageDist(focalLength,objectArray) - focalLength\n", "plt.plot(objectArray[:],imageArray[:],\"r\")\n", "plt.plot(objectArray[:],imageArray[:] + lidFPdist,\"b\")\n", "plt.xlim([0,longDist])\n", "plt.ylim([0,0.014])\n", "ax = plt.gca()\n", "ax.set_title(\"GCT focussing\", size = 12, weight = \"bold\")\n", "ax.set_xlabel(\"Object distance (m)\")\n", "ax.set_ylabel(\"Image distance (m)\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }