![\"\"](\"http:\/\/pulsars.info\/ignotur\/wp-content\/uploads\/2021\/06\/sinus_gif.gif\")
<\/figure><\/div>\n\n\n\nFirst we import all packages which we use:<\/p>\n\n\n\n
from<\/span> pygifsicle<\/span> import<\/span> optimize\nimport<\/span> os<\/span>\nimport<\/span> imageio<\/span>\nfrom<\/span> math<\/span> import<\/span> *<\/span>\nimport<\/span> numpy<\/span> as<\/span> np<\/span>\nimport<\/span> matplotlib<\/span> as<\/span> mpl<\/span>\nimport<\/span> matplotlib.pyplot<\/span> as<\/span> plt<\/span>\nplt.<\/span>rc('font'<\/span>, family=<\/span>'serif'<\/span>) ## setting for figures which I usually use in my publications<\/span>\nmpl.<\/span>rcParams.<\/span>update({'font.size'<\/span>: 12<\/span>})\nmpl.<\/span>rcParams.<\/span>update({'legend.labelspacing'<\/span>:0.25<\/span>, 'legend.fontsize'<\/span>: 12<\/span>})\nmpl.<\/span>rcParams.<\/span>update({'errorbar.capsize'<\/span>: 4<\/span>})\n<\/pre><\/div>\n\n\n\nI change standard parameters for font in Matplotlib, so the final product can be inserted in an article. I use package pygifsicle to make the final gif more compact. In this example the file is compressed from 19 MB to 4.9 MB with a single command. Next I write a function which generates data for individual frame. Instead of data generation it is possible to read individual files in this function.<\/p>\n\n\n\n
## Define a function which produces numerical input for our individual frame.<\/span>\n## It could be simply a fuction which reads individual files <\/span>\n\ndef<\/span> fun<\/span> (i, x): ## argument of the function is the frame number<\/span>\n\n return<\/span> np.<\/span>sin ((i\/<\/span>30.0<\/span>+<\/span>1<\/span>)*<\/span>x) ## we increase frequency in each frame <\/span>\n<\/pre><\/div>\n\n\n\nAt the next stage we plot our data and save them as individual png files.<\/p>\n\n\n\n
filenames =<\/span> []\n\nN =<\/span> 300<\/span> ## Total number of separate frames in animation<\/span>\n\nx =<\/span> np.<\/span>linspace (0<\/span>, 2<\/span>*<\/span>pi, 200<\/span>)\n\nfor<\/span> i in<\/span> range<\/span> (0<\/span>, N):\n\n y =<\/span> fun (i, x)\n\n plt.<\/span>plot (x,y) #, rasterized=True)<\/span>\n\n filename =<\/span> 'frame_'<\/span>+<\/span>str<\/span>(i)+<\/span>'.png'<\/span>\n\n filenames.<\/span>append (filename)\n\n plt.<\/span>xlim([0<\/span>, 2.0<\/span>*<\/span>pi]) ## This adds stability to the axis<\/span>\n plt.<\/span>ylim([-<\/span>1.1<\/span>, 1.3<\/span>])\n\n plt.<\/span>text (1.5<\/span>*<\/span>pi, 1.1<\/span>, r'$\\omega=$'<\/span>+<\/span>str<\/span>(round<\/span>(i\/<\/span>30.0<\/span>+<\/span>1<\/span>, 1<\/span>)))\n\n plt.<\/span>savefig (filename)\n #plt.show() ## this part could be used for debugging to see how individual frame looks like.<\/span>\n plt.<\/span>close() \n<\/pre><\/div>\n\n\n\nplt.close() is used here to clean information from current plot, so the next frame does not have data shown at the previous frame. If our data are three-dimensional we can use plt.imshow() instead of plt.plot(). <\/p>\n\n\n\n
At the final step we call function get_writer from package imageio. This function combines all our individual figures into a single .gif file. After this step we can remove individual .png files.<\/p>\n\n\n\n
gif_filename =<\/span> 'sinus_gif.gif'<\/span>\n\nwith<\/span> imageio.<\/span>get_writer(gif_filename, mode=<\/span>'I'<\/span>) as<\/span> writer:\n for<\/span> filename in<\/span> filenames: ## here we go through all prepared files<\/span>\n image =<\/span> imageio.<\/span>imread(filename) \n writer.<\/span>append_data(image)\n\n os.<\/span>remove(filename) ## now we remove individual frames<\/span>\n\noptimize(gif_filename) ## to optimise the file size<\/span>\n<\/pre><\/div>\n","protected":false},"excerpt":{"rendered":"Sometimes the outcomes of our models are very complicated and are worth showing as a short video or animation. The best format for a short…<\/p>\n