How to create Animations

Section author: Florian Zill (Helmholtz Centre for Environmental Research GmbH - UFZ)

To demonstrate the creation of an animated plot we use a component transport example from the ogs benchmark gallery (https://www.opengeosys.org/docs/benchmarks/hydro-component/elder/).

import matplotlib.pyplot as plt
import numpy as np

import ogstools as ot
from ogstools import examples

mesh_series = examples.load_meshseries_CT_2D_XDMF()

To read your own data as a mesh series you can do:

from ogstools.meshlib import MeshSeries
mesh_series = MeshSeries("filepath/filename_pvd_or_xdmf")

You can choose which timesteps to render by passing either an int array corresponding to the indices, or a float array corresponding to the timevalues to render. If a requested timevalue is not part of the timeseries it will be interpolated. In this case every second frame will be interpolated.

timevalues = np.linspace(
    mesh_series.timevalues[0], mesh_series.timevalues[-1], num=25
)

Now, let’s animate the saturation solution. A timescale at the top indicates existing timesteps and the position of the current timevalue. Note that rendering many frames in conjunction with large meshes might take a really long time. We can pass a plot_func which can apply custom formatting and / or plotting. To modify the domain, we can use the transform method of MeshSeries.

def mesh_func(mesh: ot.Mesh) -> ot.Mesh:
    "Clip the left half of the mesh."
    return mesh.clip("-x", [0, 0, 0])


def plot_func(ax: plt.Axes, timevalue: float) -> None:
    "Add the time to the title."
    ax.set_title(f"{timevalue/(365.25*86400):.1f} yrs")

anim = mesh_series.transform(mesh_func).animate(
    ot.variables.saturation, timevalues, plot_func, vmin=0, vmax=100, dpi=50
)

Once Loop Reflect

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The animation can be saved (as mp4) like so:

ot.plot.utils.save_animation(anim, "Saturation", fps=5)