\n",
- " \n",
+ " oninput=\"anim2040b86f397e40cb93b48194e742a588.set_frame(parseInt(this.value));\">\n",
" \n",
- " \n",
"
\n",
"![](\"images/animaion_colorbar_oops.gif\")
\n",
+ ":::"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Creating the animation\n",
+ "Now it's time to create the animation using `FuncAnimation`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ani = animation.FuncAnimation(fig, animate, frames=len(t), interval=150, repeat_delay=1000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This function looks similar to `ArtistAnimation`, but with one main difference: instead of a list of artists to plot, `FuncAnimation` takes the update function we created as the second argument. Also, we've provided the `frames` kwarg, which is the number of times the update function will be called. We've set it to the number of time slices in the data to make sure the FuncAnimation object does not try to go outside the bounds of the data.\n",
"\n",
- "# create animation\n",
- "ani = animation.FuncAnimation(fig, animate, frames=31, interval=200)"
+ "And that's it! We've created a function animation in matplotlib. Let's take a look at it.\n"
]
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -103212,42 +103390,42 @@
"\n",
"\n",
"