How to Create Time Slices

In this example we show how to create a filled contourplot of transient data over a sampling line. For this purpose we use a component transport example from the ogs benchmark gallery (https://www.opengeosys.org/docs/benchmarks/hydro-component/elder/).

To see this benchmark results over all timesteps have a look at How to create Animations.

Let’s load the data which we want to investigate.

import numpy as np

import ogstools as ot
from ogstools import examples

mesh_series = examples.load_meshseries_CT_2D_XDMF(time_unit="a")
si = ot.variables.saturation

Now we setup two sampling lines.

pts_vert = np.linspace([25, 0, -75], [25, 0, 75], num=300)
pts_diag = np.linspace([25, 0, 75], [100, 0, 0], num=300)
fig = ot.plot.contourf(mesh_series.mesh(-1), si, vmin=0)
fig.axes[0].plot(pts_vert[:, 0], pts_vert[:, 2], "-k", linewidth=3)
fig.axes[0].plot(pts_diag[:, 0], pts_diag[:, 2], "-.k", linewidth=3)

[<matplotlib.lines.Line2D object at 0x7c0740bb5600>]

Here, we first show a regular line sample plot for the vertical sampling line for each timestep.

ms_vert = mesh_series.probe(pts_vert)
labels = [f"{tv:.1f} a" for tv in ms_vert.timevalues]
fig = ms_vert.plot_line(si, "z", labels=labels, colors="coolwarm")
fig.tight_layout()

As the above kind of plot is getting cluttered for lots of timesteps we provide a function to create a filled contour plot over the transient data. The function plot_time_slice() creates a heatmap over time and space.

fig = ms_vert.plot_time_slice("time", "z", si, vmin=0, vmax=100)

The stepping in this heatmap corresponds to the individual timesteps. To create a smoother image, we can resample the MeshSeries to more timesteps.

ms_vert_fine = ms_vert.resample_temporal(np.linspace(0, 4.2, 300))
fig = ms_vert_fine.plot_time_slice(
    "time", "z", si, vmin=0, vmax=100, time_logscale=True
)

/builds/yQxD4hQx/0/ogs/tools/ogstools/ogstools/meshlib/mesh_series.py:901: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.
  ax.pcolormesh(x_vals, y_vals, values, cmap=cmap, norm=norm)

You can also change the order of the arguments for spatial coordinate and time to flip the x- and y-axis.

ms_diag = mesh_series.probe(pts_diag)
ms_diag_fine = ms_diag.resample_temporal(np.linspace(0, 4.2, 300))
fig = ms_diag_fine.plot_time_slice("x", "time", si, vmin=0, vmax=100)
fig.axes[0].invert_yaxis()