r"""
Nodal Responses Visualization
=============================
"""

# %%
import openseespy.opensees as ops

import opstool as opst
import opstool.vis.pyvista as opsvis

# %%
# Here, we use a built-in example from ``opstool``, which is an example of a deck arch bridge model primarily composed of frame elements and shell elements.

# %%
opst.load_ops_examples("ArchBridge2")
# or your model code here

# %%
# We use the :func:`opstool.vis.pyvista.set_plot_props` function to predefine some common visualization properties, which will affect all subsequent visualizations of models, eigenvalues, and responses.

# %%
# Model Geometry
# ---------------
opsvis.set_plot_props(point_size=0, line_width=3)

fig = opsvis.plot_model(show_outline=True)
fig.show()

# %%
# Gravity Analysis
# ----------------
# Apply the gravity load according to the mass in the model:
ops.timeSeries("Linear", 1)
ops.pattern("Plain", 1, 1)
_ = opst.pre.gen_grav_load(factor=-9810)

# %%
# Analysis Parameters:
ops.system("BandGeneral")
# Create the constraint handler, the transformation method
ops.constraints("Transformation")
# Create the DOF numberer, the reverse Cuthill-McKee algorithm
ops.numberer("RCM")
# Create the convergence test, the norm of the residual with a tolerance of
# 1e-12 and a max number of iterations of 10
ops.test("NormDispIncr", 1.0e-12, 10, 3)
# Create the solution algorithm, a Newton-Raphson algorithm
ops.algorithm("Newton")
# Create the integration scheme, the LoadControl scheme using steps of 0.1
ops.integrator("LoadControl", 0.1)
# Create the analysis object
ops.analysis("Static")

# %%
# Analysis and Saving Results

ODB = opst.post.CreateODB(odb_tag=1)
for i in range(10):
    ops.analyze(1)
    ODB.fetch_response_step()
ODB.save_response()

# %%
# Nodal Responses Visualization
# -----------------------------
# via Slides
# ~~~~~~~~~~~
opsvis.set_plot_props(scalar_bar_kargs={"title_font_size": 12, "label_font_size": 12})
# sphinx_gallery_thumbnail_number = 2
fig = opsvis.plot_nodal_responses(
    odb_tag=1,
    slides=True,
    resp_type="disp",
    resp_dof=["UX", "UY", "UZ"],
    unit_symbol="mm",
    show_outline=True,
    defo_scale="auto",
)
fig.show()

# %%
# Change the unit dispaly
# ~~~~~~~~~~~~~~~~~~~~~~~~
opsvis.set_plot_colors(cmap="Spectral_r")
fig = opsvis.plot_nodal_responses(
    odb_tag=1,
    slides=True,
    step=9,
    resp_type="disp",
    resp_dof=["UX", "UY", "UZ"],
    unit_symbol="m",
    unit_factor=1e-3,
    defo_scale=100,  # you can adjust the deformation scale factor here
)
fig.show()

# %%
# Animation
# ~~~~~~~~~~~~~
fig = opsvis.plot_nodal_responses_animation(
    odb_tag=1,
    framerate=2,
    defo_scale=100,
    savefig="images/NodalRespAnimation.gif",
    resp_type="disp",
    resp_dof=["UX", "UY", "UZ"],
    unit_symbol="m",
    unit_factor=1e-3,
)
fig.close()


# %%
# Interacting with Pyvista
# -----------------------------
# Since version 1.0.18, opstool provides a function ``get_nodal_responses_dataset`` that returns a pyvista
# `UnstructuredGrid <https://docs.pyvista.org/api/core/_autosummary/pyvista.unstructuredgrid#pyvista.UnstructuredGrid>`_
# so that you can take advantage of all the functionality on it.


grid = opsvis.get_nodal_responses_dataset(
    odb_tag=1,
    step="absMax",
    resp_type="disp",
    resp_dof=["UX", "UY", "UZ"],
    defo_scale=100,
)

# %%
# The name of the scalar data to be activated will be the passed in ``resp_type``:
print(grid)
print(grid.active_scalars_name)

# %%
# You can call the plot method directly:
grid.plot()

# %%
# You can also use the filters provided by pyvista:
# `DataSetFilters <https://docs.pyvista.org/api/core/_autosummary/pyvista.datasetfilters#pyvista.DataSetFilters>`_
#
# For example, using some common filters:
# `Using Common Filters <https://docs.pyvista.org/examples/01-filter/using_filters>`_
#
# Apply a threshold over a data range
import pyvista as pv

threshed = grid.threshold([80, 120])

p = pv.Plotter()
p.add_mesh(threshed)
p.show()

# %%
# More details can be found in the `PyVista Examples <https://docs.pyvista.org/examples/>`_.