"""
Visualizing 2D model data
=========================

To demonstrate the creation of filled contour plots we load a 2D THM meshseries
example. In the ``plot.setup`` we can provide a dictionary to map names
to material ids. Other plot configurations are also available, see:
:py:obj:`ogstools.plot.plot_setup.PlotSetup`. Some of these options are also
available as keyword arguments in the function call. Please see
:py:obj:`ogstools.plot.contourplots.contourf` for more information.
"""

# %%
import ogstools as ot
from ogstools import examples

ot.plot.setup.material_names = {i + 1: f"Layer {i+1}" for i in range(26)}
ms = examples.load_meshseries_THM_2D_PVD().scale(spatial="km")
mesh = ms.mesh(1)

# %% [markdown]
# To read your own data as a mesh series you can do:
#
# ..  code-block:: python
#
#   mesh_series = ot.MeshSeries("filepath/filename_pvd_or_xdmf", "km")
#

# %% [markdown]
# Plotting Cell Data
# ==================
# First, let's plot the material ids, which is part of the mesh's cell_data.
# Per default in the setup, this will automatically show the element edges.

# %%
fig = ot.plot.contourf(mesh, ot.variables.material_id)

# %% [markdown]
# Plotting Point Data
# ===================
# Now, let's plot the temperature field (point_data) at the first timestep.
# The default temperature variable from the ``variables`` reads the temperature
# data as Kelvin and converts them to degrees Celsius. This also shows how to
# only plot a specific part of the model by creating a clip with
# `pyvista.clip_box` beforehand.

# %%
part = mesh.clip_box(bounds=[2, 5, -1.1, -0.7, 6, 7], invert=False)
fig = ot.plot.contourf(part, ot.variables.temperature, show_max=True)

# %% [markdown]
# We can also plot components of vector variables:

# %%
fig = ot.plot.contourf(
    mesh, ot.variables.displacement[0], show_min=True, show_max=True
)

# %%
fig = ot.plot.contourf(mesh, ot.variables.displacement[1], show_max=True)

# %% [markdown]
# To have a continuous colormap instead of discrete colors per level pass a
# the equally named argument. In this case this helps to increase the level
# of detail of the negative displacements due to the bilinear colormap.

# %%
fig = ot.plot.contourf(mesh, ot.variables.displacement[1], continuous_cmap=True)

# %% [markdown]
# Plotting with deactivated subdomains
# ====================================
# This example has hydraulically deactivated subdomains, which will mask the
# related variables.

# %%
fig = ot.plot.contourf(mesh, ot.variables.pressure.get_mask(), fontsize=40)

# %%
fig = ot.plot.contourf(mesh, ot.variables.pressure)

# %% [markdown]
# Plotting vector data
# ====================
# Let's plot the fluid velocity field. As this is vectorial data, this will
# automatically add streamlines to indicate the vector directions.

# %%
fig = ot.plot.contourf(mesh, ot.variables.velocity, show_region_bounds=False)

# %% [markdown]
# Let's plot it again, this time log-scaled.

# %%
fig = ot.plot.contourf(mesh, ot.variables.velocity, log_scaled=True, vmin=-8)