"""
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
:ref:`sphx_glr_auto_examples_howto_plot_plot_animation.py`.
"""

# %% [markdown]
# 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().scale(time=("s", "a"))
si = ot.variables.saturation

# %% [markdown]
# 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 = mesh_series.mesh(-1).plot_contourf(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)

# %% [markdown]
# Here, we first show a regular line sample plot for the vertical sampling line
# for each timestep.

# %%
ms_vert = ot.MeshSeries.extract_probe(mesh_series, pts_vert)
labels = [f"{tv:.1f} a" for tv in ms_vert.timevalues]
fig = ot.plot.line(ms_vert, si, "z", labels=labels, colors="coolwarm")
fig.tight_layout()

# %% [markdown]
# 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 :meth:`~ogstools.meshlib.mesh_series.MeshSeries.plot_time_slice`
# creates a heatmap over time and space.

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

# %% [markdown]
# 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 = ot.MeshSeries.resample(ms_vert, np.linspace(0, 4.2, 300))
fig = ms_vert_fine.plot_time_slice("time", "z", si, vmin=0, vmax=100)

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

# %%
ms_diag = ot.MeshSeries.extract_probe(mesh_series, pts_diag)
ms_diag_fine = ot.MeshSeries.resample(ms_diag, 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()