{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Demo of running the Classic Clawpack Fortran code and producing an animation of results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're viewing this notebook online and wish to run it, you can download it [from here](https://github.com/clawpack/doc/blob/master/doc/classic_demo.ipynb)." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The environment variable CLAW should be set before starting the notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/rjl/git/clawpack\n" ] } ], "source": [ "import os\n", "CLAW = os.environ['CLAW'] \n", "print CLAW" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Move to the desired directory:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1\n" ] } ], "source": [ "os.chdir(CLAW + '/classic/examples/acoustics_1d_example1/')\n", "print os.getcwd()\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "['rm -f .output',\n", " 'python /Users/rjl/git/clawpack/clawutil/src/python/clawutil/runclaw.py xclaw _output \\\\',\n", " '\\tTrue False . False',\n", " 'Reading data file: claw.data ',\n", " ' first 5 lines are comments and will be skipped',\n", " ' running...',\n", " ' ',\n", " 'Reading data file: setprob.data ',\n", " ' first 5 lines are comments and will be skipped',\n", " 'CLAW1EZ: Frame 1 output files done at time t = 0.5000D-01',\n", " '',\n", " 'CLAW1EZ: Frame 2 output files done at time t = 0.1000D+00',\n", " '',\n", " 'CLAW1EZ: Frame 3 output files done at time t = 0.1500D+00',\n", " '',\n", " 'CLAW1EZ: Frame 4 output files done at time t = 0.2000D+00',\n", " '',\n", " 'CLAW1EZ: Frame 5 output files done at time t = 0.2500D+00',\n", " '',\n", " 'CLAW1EZ: Frame 6 output files done at time t = 0.3000D+00',\n", " '',\n", " 'CLAW1EZ: Frame 7 output files done at time t = 0.3500D+00',\n", " '',\n", " 'CLAW1EZ: Frame 8 output files done at time t = 0.4000D+00',\n", " '',\n", " 'CLAW1EZ: Frame 9 output files done at time t = 0.4500D+00',\n", " '',\n", " 'CLAW1EZ: Frame 10 output files done at time t = 0.5000D+00',\n", " '',\n", " 'CLAW1EZ: Frame 11 output files done at time t = 0.5500D+00',\n", " '',\n", " 'CLAW1EZ: Frame 12 output files done at time t = 0.6000D+00',\n", " '',\n", " 'CLAW1EZ: Frame 13 output files done at time t = 0.6500D+00',\n", " '',\n", " 'CLAW1EZ: Frame 14 output files done at time t = 0.7000D+00',\n", " '',\n", " 'CLAW1EZ: Frame 15 output files done at time t = 0.7500D+00',\n", " '',\n", " 'CLAW1EZ: Frame 16 output files done at time t = 0.8000D+00',\n", " '',\n", " '==> runclaw: Will take data from /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1',\n", " '==> runclaw: Will write output to /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " '==> runclaw: Removing all old fort files in /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " '',\n", " '==> runclaw: Finished executing',\n", " '',\n", " '==> runclaw: Done executing /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/xclaw via clawutil.runclaw.py',\n", " '==> runclaw: Output is in /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%system\n", "make output" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "['rm -f .plots',\n", " 'python /Users/rjl/git/clawpack/visclaw/src/python/visclaw/plotclaw.py _output _plots setplot.py ',\n", " 'Importing setplot.setplot from /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1.',\n", " 'Executed setplot successfully',\n", " 'Will plot 17 frames numbered: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]',\n", " 'Will make 1 figure(s) for each frame, numbered: [1]',\n", " '',\n", " '-----------------------------------',\n", " '',\n", " '',\n", " 'Creating html pages for figures...',\n", " '',\n", " \"Directory '/Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_plots' \",\n", " ' already exists, files may be overwritten ',\n", " 'Now making png files for all figures...',\n", " ' Reading Frame 0 at t = 0 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 0 at time t = 0.0',\n", " ' Reading Frame 1 at t = 0.05 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 1 at time t = 0.05',\n", " ' Reading Frame 2 at t = 0.1 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 2 at time t = 0.1',\n", " ' Reading Frame 3 at t = 0.15 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 3 at time t = 0.15',\n", " ' Reading Frame 4 at t = 0.2 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 4 at time t = 0.2',\n", " ' Reading Frame 5 at t = 0.25 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 5 at time t = 0.25',\n", " ' Reading Frame 6 at t = 0.3 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 6 at time t = 0.3',\n", " ' Reading Frame 7 at t = 0.35 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 7 at time t = 0.35',\n", " ' Reading Frame 8 at t = 0.4 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 8 at time t = 0.4',\n", " ' Reading Frame 9 at t = 0.45 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 9 at time t = 0.45',\n", " ' Reading Frame 10 at t = 0.5 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 10 at time t = 0.5',\n", " ' Reading Frame 11 at t = 0.55 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 11 at time t = 0.55',\n", " ' Reading Frame 12 at t = 0.6 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 12 at time t = 0.6',\n", " ' Reading Frame 13 at t = 0.65 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 13 at time t = 0.65',\n", " ' Reading Frame 14 at t = 0.7 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 14 at time t = 0.7',\n", " ' Reading Frame 15 at t = 0.75 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 15 at time t = 0.75',\n", " ' Reading Frame 16 at t = 0.8 from outdir = /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_output',\n", " 'Frame 16 at time t = 0.8',\n", " '',\n", " '-----------------------------------',\n", " '',\n", " 'Creating latex file...',\n", " \"Directory '/Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_plots' \",\n", " ' already exists, files may be overwritten ',\n", " '',\n", " 'Latex file created: ',\n", " ' /Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_plots/plots.tex',\n", " '',\n", " 'Use pdflatex to create pdf file',\n", " 'Created JSAnimation for figure 1',\n", " '',\n", " '--------------------------------------------------------',\n", " '',\n", " 'Point your browser to:',\n", " ' file:///Users/rjl/git/clawpack/classic/examples/acoustics_1d_example1/_plots/_PlotIndex.html',\n", " 'python(45105,0x7fff76101180) malloc: *** error for object 0x10413f410: pointer being freed was not allocated',\n", " '*** set a breakpoint in malloc_error_break to debug',\n", " 'make: *** [plots] Abort trap: 6']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%system\n", "make plots" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### To display a single frame:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "showing _plots/frame0002fig1.png\n" ] }, { "data": { "image/png": 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11FNPad68ebr++uv11VdfeaHkAAAAgH8K9HUB/NW2bdv0xhtvaN68eZoyZYok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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import Image\n", "frameno = 2\n", "fname = '_plots/frame00%sfig1.png' % str(frameno).zfill(2)\n", "print \"showing \",fname\n", "i = Image(fname)\n", "display(i)\n", " " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### One way to display a sequence of plot images as an animation:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import glob\n", "from matplotlib import image\n", "from clawpack.visclaw.JSAnimation import IPython_display\n", "from matplotlib import animation\n", "\n", "figno = 1\n", "fname = '_plots/*fig' + str(figno) + '.png'\n", "filenames=sorted(glob.glob(fname))\n", "\n", "fig = plt.figure()\n", "im = plt.imshow(image.imread(filenames[0]))\n", "def init():\n", " im.set_data(image.imread(filenames[0]))\n", " return im,\n", "\n", "def animate(i):\n", " image_i=image.imread(filenames[i])\n", " im.set_data(image_i)\n", " return im,\n", "\n", "animation.FuncAnimation(fig, animate, init_func=init,\n", " frames=len(filenames), interval=20, blit=True)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Another approach to animation:" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Based on the pyclaw example at https://www.wakari.io/usermgmt/nb/pyclaw/Figure_7_2\n", "\n", "This only works if the notebook is running, whereas the above animation should play from nbviewer." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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ZFPj1SdKWsMzDoBBK2ukmclMpj/YnXBQQjnZDIBXTUlM2M6ZltfoYu47xx0fj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\nECbm/3qAsbLrE/DKAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import time\n", "from IPython.display import clear_output\n", "\n", "def solution_plot(n,setplot=None,outdir='./_output'):\n", " from clawpack.visclaw import data, frametools\n", " plotdata = data.ClawPlotData()\n", " plotdata.outdir = outdir\n", " if setplot is None:\n", " from setplot import setplot\n", " plotdata.setplot = setplot\n", " plotdata = frametools.call_setplot(setplot,plotdata)\n", " frametools.plotframe(n,plotdata)\n", "\n", "for n in range(5):\n", " solution_plot(n)\n", " time.sleep(0.2)\n", " clear_output()\n", " f=plt.gcf()\n", " display(f) \n", " plt.clf()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": 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": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }