Here's what the plot would look like with the default figure size parameters: matplotlib plot with default figure size parameters Lastly, the plt.show() displays the plot. Using plt.plot(), we plotted list x on the x-axis and list y on the y-axis: plt.plot(x,y). We then created two lists - x and y - with values to be plotted. In the code above, we first imported matplotlib. Here's a code example: import matplotlib.pyplot as plt Let's get started! How to Change Plot Size in Matplotlib with plt.figsize()Īs stated in the previous section, the default parameters (in inches) for Matplotlib plots are 6.4 wide and 4.8 high. In this article, you'll learn how to change the plot size using the following: The use of reduces the density of scatter points.When creating plots using Matplotlib, you get a default figure size of 6.4 for the width and 4.8 for the height (in inches). Note the set_offsets must be passed an N×2 array. Note that the length of G in each dimension must be one shorter than x and y, and that a flattened array must be passed to set_array. We’ll be needing a three dimensional array G for some of these examples.Ĭax = ax.pcolormesh(x, y, G, Here are examples of steps four and five for the most common types of plots. Typically, the only differences involve getting the equivalent of the line object, and changing the animate function. Beyond a line plotĪnimating any other type of plot is as simple as the example above. However, depending on your computer and file type, some configuration may be required to ensure the export works correctly. Optional step: export the animationĮxporting the animation should be as simple asĪnim.save('filename.gif', writer='imagemagick') with IPython).īe sure to assign FuncAnimation to a variable (here it’s anim). The final two lines may or may not be necessary depending on whether you’re working interactively (e.g. The frames argument is needed only if the animation is to be exported. The interval argument is optional and sets the interval between frames in milliseconds. Step six: call FuncAnimation and showįig, animate, interval=100, frames=len(t)-1)įuncAnimation requires two arguments: the figure object ‘fig’ and animation function ‘animate’. See later for other things that can be changed. The only command in the function is to change the object’s y data. Here, we are only plotting a single line, so we simply want the first (i.e., zeroth) object in the list of lines.īe sure to use ax.plot(…), not plt.plot(…) Step five: create a function to update the line This is necessary because the plot command returns a list of line objects. This sets up a line object with the desired attributes, which in this case are that it’s coloured black and has a line weight of 2. Well, it’s not actually arbitrary, it’s set up to produce a perfectly looping gif. Step three: create some data to plotį is a 2D array comprising some arbitrary data to be animated. Setting the limits in advance stops any rescaling of the limits that may make the animation jumpy and unusable. The first line sets up the figure and its axis, and the second line fixes the axis limits. The first two lines will be familiar to anyone who has used Python for science, and the third line is obviously specific to animation. I’ve left the final line commented as it isn’t necessary and will not work if your matplotlib version is <1.5. This animation requires less than 20 lines of code Step one: import the necessary modulesįrom matplotlib.animation import FuncAnimation However, the first four steps will involve nothing new to anyone who has made a plot using Matplotlib.Įach step contains a few lines of code that you can copy and paste, but a script with all the code for all examples can be found here. This example walks through how to create the animation below in six steps. It’s futile to try and display these in a single plot. In many cases these datasets will have more than two dimensions for example, temperature or salinity in an ocean circulation model has four dimensions: x, y, z, t. See here for a follow up post with more elaborate animation examples.Įxploring datasets is a big part of what many scientists do these days. In many cases all I need is a quick-and-dirty script that works, rather than longer code that adheres to best practices. However, when learning I found the tutorials and examples online either daunting, overly sophisticated, or lacking explanation. Creating animations with Python’s Matplotlib is quick and easy once you know how to do it.
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