def create_array_plot(path, time_indices, source_expr, img_gen_func, times):
pv.set_plot_theme("document")
nind = len(time_indices)
size = 8
print("start array plot generation")
fig, axes = plt.subplots(nind, 3, figsize=(size*3.3, nind*size))
for j, idx in enumerate(time_indices):
for i,view in enumerate(views):
p, _ = img_gen_func(j, view=view)
img = p.screenshot(transparent_background=False, return_img=True,
window_size=None,
filename=path + f"_{view}_{times[idx]:.3f}.png")
p.clear()
#fig2,ax2 = plt.subplots()
#ax2.imshow(img)
#ax2.axis('off')
#fig2.tight_layout()
#fig2.savefig(path + f"{view}_{times[idx]:.4f}.pdf")
axes[j,i].imshow(img)
axes[j,i].set_title(f"t = {times[idx]:.3f} s",fontsize=26)
axes[j,i].axis('off')
fig.tight_layout()
fig.savefig(path + "_array_plot.pdf")
def visualize(points, predict):
pv.set_plot_theme("document")
plt = pv.Plotter(shape=(4, 2), window_size=(400, 800))
for i in range(4):
plt.subplot(i, 0)
plt.add_mesh(
points[i],
render_points_as_spheres=True,
point_size=8.0,
ambient=0.1,
diffuse=0.8,
specular=0.5,
specular_power=100.0,
)
plt.subplot(i, 1)
mesh = pv.PolyData(predict[i])
mesh["colors"] = np.arange(predict[i].shape[0])
plt.add_mesh(
mesh,
render_points_as_spheres=True,
point_size=8.0,
ambient=0.1,
diffuse=0.8,
specular=0.5,
specular_power=100.0,
)
plt.link_views()
plt.camera_position = [
(5.0, 0.0, 0.0),
(0.0, 0.0, 0.0),
(0.0, 1.0, 0.0),
]
plt.show("ShapeNet")
plt.close()
def render_surface(self, scalar_func):
obj = numpy_to_pyvista(self.v, self.f)
pv.set_plot_theme('document')
surf_plotter = pv.Plotter()
surf_plotter.link_views()
surf_plotter.add_mesh(obj, scalars=scalar_func, cmap='jet')
return surf_plotter
def create_movie(disloc_files, solutes_files, output_name):
pv.set_plot_theme("document")
plotter = pv.Plotter()
plotter.open_movie(output_name)
plotter.remove_scalar_bar()
display = True
plotter.view_isometric()
plotter.view_xz()
plotter.add_axes(interactive=True)
for disloc_f, solutes_f in tqdm(zip(disloc_files, solutes_files)):
# import pdb; pdb.set_trace()
plotter.remove_actor(['disloc', 'solute'])
dislocation = pv.read(disloc_f)
solutes = pv.read(solutes_f)
plotter.add_mesh(dislocation,
name = 'disloc',
show_scalar_bar=False,
render_lines_as_tubes=True,
line_width=10)
# plotter.add_mesh(solutes, show_scalar_bar=False,name = 'solute',)
plotter.add_mesh(solutes,
# name = 'solute',
show_scalar_bar=False,
render_points_as_spheres=True,
point_size=20,)
if display:
print('Orient the view, then press "q" to close window and produce movie')
plotter.show(auto_close=False)
display = False
plotter.write_frame()
plotter.close()
def test_themes(theme):
try:
pyvista.set_plot_theme(theme.name)
assert pyvista.global_theme == theme.value()
finally:
# always return to testing theme
pyvista.set_plot_theme('testing')
def plot_grid(self, filename='Data/Results/dome.vtu', lighting=False, property='PORO', show_edges=True, specular=0.0,
specular_power=0.0, show_scalar_bar=True, cmap='viridis'):
r"""
Plot the grid with PyVista.
Parameters
----------
filename : string, default is 'Data/Results/dome.vtu'
String holding the path to VTU file.
lighting : boolean, default is False
Enable or disable view direction lighting.
property : string, default is 'PORO'
String holding the name of property that will be plotted.
show_edges : boolean, default is True
Shows the edges of a mesh. Does not apply to a wireframe representation.
specular : float, default is 0.0
The specular lighting coefficient.
specular_power : float, default is 0.0
The specular power. Between 0.0 and 128.0.
show_scalar_bar : boolean, default is True
If False, a scalar bar will not be added to the scene.
cmap : string, default is 'viridis'
Name of the Matplotlib colormap to us when mapping the 'scalars'. See available Matplotlib colormaps.
Notes
-----
https://www.pyvista.org/
"""
import matplotlib.pyplot as plt
import pyvista as pv
# Check if file exists and can be open
misc.file_open_exception(filename)
# Check if grid is already defined
if self._grid_type == 'corner-point':
misc.check_corner_point_grid(self._cart_dims, self._coord, self._zcorn)
else:
misc.check_cartesian_grid(self._cart_dims, self._dx, self._dy, self._dz, self._tops)
# Set theme
pv.set_plot_theme("document")
# Color map
cmap = plt.cm.get_cmap(cmap, 5)
# Mesh to be plotted
mesh = pv.UnstructuredGrid(misc.get_path(filename))
# Remove ghost cells if they are present in the mesh
if 'ACTNUM' in mesh.array_names:
ghosts = np.argwhere(mesh["ACTNUM"] < 1)
mesh.remove_cells(ghosts)
# Plot the grid
mesh.plot(lighting=lighting, specular=specular, specular_power=specular_power, show_edges=show_edges,
scalars=property, show_scalar_bar=show_scalar_bar, cmap=cmap)
def test_set_theme():
theme = pyvista.themes.DarkTheme()
try:
pyvista.set_plot_theme(theme)
assert pyvista.global_theme == theme
finally:
# always return to testing theme
pyvista.set_plot_theme('testing')
def plot2d(**kargs):
cmd_args = get_cmd()
args = update_args(cmd_args, dict_args=kargs)
args = check_args(args)
pv.set_plot_theme("document")
for f in args.fName:
print('\n', '-' * 80, '\n', f)
print(f)
fpng = _plot_(f, args)
yield(fpng)
def displayer(numpy_mask):
data_matrix = numpy_mask
opacity = [0, 0, 0, 4, 8, 0, 0]
data = pv.wrap(data_matrix)
pv.set_plot_theme("night")
#print(type(data)) #pyvista.core.grid.UniformGrid'
#print(dir(data)) #x = pickle.dumps(data) #print(x)
#print(BytesIO(data))
return data
def plot_obj_vtk(obj_path, vtk_path):
pv.set_plot_theme("default")
obj = pv.read(obj_path)
vtk = pv.read(vtk_path)
plotter = pv.Plotter()
plotter.add_axes()
plotter.add_mesh(obj, label='My Mesh', show_scalar_bar=False)
plotter.add_mesh(vtk, label='Rays', cmap="jet", show_scalar_bar=False)
plotter.show_bounds()
plotter.show(cpos="xy", screenshot="obj_vtk.png")
def plot_mesh_montage(vb,
fb=None,
nb=None,
strategy='mesh',
labelb=None,
grid_on=False,
clr='lightcoral',
normal_clr='lightblue',
smooth_shade_on=False,
show_edges=False,
normal_scale=1):
"""
:param vb: tensor | list - [b x nv x 3] batch of meshes or list of length b with tensors [nvx3]
:param fb: tensor | list | None - (optional) [b x nf x 3]
batch of face indices OR a list of length b with tensors [nfx3]
OR a [nf x 3] in the case of a uniform face array for all meshes
:param nb: tensor | list | None - (optional) [b x nf|nv x 3] batch of normals. See above
:param labelb: list of titles for each mesh, or None
* For other arguments, see plot_mesh
* For windows keyboard options, see: https://docs.pyvista.org/plotting/plotting.html
"""
if isinstance(vb, (np.ndarray, np.generic)):
n_meshes = vb.shape[0]
else:
n_meshes = len(vb)
pv.set_plot_theme("document") # White background
n_rows = math.floor(math.sqrt(n_meshes))
n_cols = math.ceil(n_meshes / n_rows)
shape = (n_rows, n_cols)
p = pv.Plotter(shape=shape)
r, c = np.unravel_index(range(n_meshes), shape)
for i in range(n_meshes):
f = fb if fb is None or (hasattr(fb, 'shape')
and len(fb.shape) == 2) else fb[i]
# Uniform faces support. fb[i] is equiv to fb[i,:,:]
n = nb if nb is None else nb[i]
label = labelb if labelb is None else labelb[i]
p.subplot(r[i], c[i])
mesh_append(p,
v=vb[i],
f=f,
n=n,
strategy=strategy,
label=label,
grid_on=grid_on,
normal_scale=normal_scale,
clr=clr,
normal_clr=normal_clr,
smooth_shade_on=smooth_shade_on,
show_edges=show_edges)
p.link_views()
p.show()
def test_issue_566():
from pyvista import set_plot_theme
set_plot_theme('document')
geo_model = gp.create_model('Model1')
geo_model = gp.init_data(geo_model,
extent=[0, 791, 0, 200, -582, 0],
resolution=[100, 10, 100])
geo_model.set_default_surfaces()
geo_model.add_surface_points(X=223, Y=0.01, Z=-94, surface='surface1')
def plot_seeg_3dbrain(raw):
"""Convenience function for plotting 3d brain and the 3 leads."""
# camera position for ventral view
cpos = [
(192.1355480872916, -142.5172055994041, -261.47527261474386),
(24.065850569725583, -19.416839467317363, 15.642167878335641),
(0.20039110730958792, 0.9346693866417147, -0.29366058943283213),
]
pv.set_plot_theme("document")
plotter = pv.Plotter(off_screen=True, window_size=[350, 700])
cloud = helper.plot_3d_brain()
actor = plotter.add_mesh(cloud, opacity=1)
xyz_all = np.array([ich["loc"][:3] for ich in raw.info["chs"]])
picks_all = [range(8), range(8, 14), range(14, 19)]
colors = ["purple", "#41A859", "dodgerblue"]
# plot each lead in a different color
for i, picks in enumerate(picks_all):
ch_names = ["ecog%i" % i for i in picks]
lead1 = raw.copy().pick_channels(ch_names)
xyz = np.array([ich["loc"][:3] for ich in lead1.info["chs"]])
# slightly adjust coordinates to prevent electrodes from being invisible
xyz[:, 0] += 4
xyz[:, 2] -= 4
electrodes = pv.PolyData(xyz)
act_electrodes = plotter.add_mesh(
electrodes,
point_size=25,
color=colors[i],
render_points_as_spheres=True,
)
xyz_all[:, 0] += 4
xyz_all[:, 2] -= 4
for ii, pick in enumerate(picks1):
electrodes = pv.PolyData(xyz_all[pick])
act_electrodes = plotter.add_mesh(
electrodes,
point_size=40,
color=colors_sel[ii],
render_points_as_spheres=True,
)
plotter.show(cpos=cpos,
title=str(cpos),
screenshot="../figures/3dbrain_seeg.png")
plotter.screenshot("../figures/3dbrain_seeg.png",
transparent_background=True)
plotter.close()
def plot_heatmap(vtk_path):
pv.set_plot_theme("Paraview")
vtk = pv.read(vtk_path)
plotter = pv.Plotter()
plotter.add_axes()
plotter.show_bounds(ticks="outside")
plotter.add_mesh(vtk, show_scalar_bar=False)
plotter.add_scalar_bar(title="Incident flux, (W)",
width=0.5,
position_x=0.25,
position_y=0.9)
plotter.show(cpos="xz", screenshot="heatmap.png")
def disp_results(filename, items):
pv.set_plot_theme("document")
mesh = pv.PolyData(filename + '.STL')
plotter = pv.Plotter()
plotter.add_mesh(mesh, opacity=0.3, color='#FFFFFF')
shapetypes = [
'O ring', 'Through hole', 'Blind hole', 'Triangular passage',
'Rectangular passage', 'Circular through slot',
'Triangular through slot', 'Rectangular through slot',
'Rectangular blind slot', 'Triangular pocket', 'Rectangular pocket',
'Circular end pocket', 'Triangular blind step', 'Circular blind step',
'Rectangular blind step', 'Rectangular through step',
'2-sides through step', 'Slanted through step', 'Chamfer', 'Round',
'Vertical circular end blind slot',
'Horizontal circular end blind slot', '6-sides passage',
'6-sides pocket'
]
colors = [
'#000080', '#FF0000', '#FFFF00', '#00BFFF', '#DC143C', '#DAA520',
'#DDA0DD', '#708090', '#556B2F', '#483D8B', '#CD5C5C', '#21618C',
'#1C2833', '#4169E1', '#1E90FF', '#FFD700', '#FF4500', '#646464',
'#DC143C', '#98FB98', '#9370DB', '#8B4513', '#00FF00', '#008080'
]
flag = np.zeros(24)
for i in range(items.shape[0]):
if flag[int(items[i, 6])] == 0:
plotter.add_mesh(pv.Cube((0, 0, 0), 0, 0, 0,
(items[i, 0], items[i, 3], items[i, 1],
items[i, 4], items[i, 2], items[i, 5])),
opacity=1,
color=colors[int(items[i, 6])],
style='wireframe',
line_width=2,
label=shapetypes[int(items[i, 6])])
#print(shapetypes[int(items[i,6])])
flag[int(items[i, 6])] = 1
else:
plotter.add_mesh(pv.Cube((0, 0, 0), 0, 0, 0,
(items[i, 0], items[i, 3], items[i, 1],
items[i, 4], items[i, 2], items[i, 5])),
opacity=1,
color=colors[int(items[i, 6])],
style='wireframe',
line_width=2)
plotter.add_legend()
plotter.show()
def test_backwards_compatibility():
try:
color = (0.1, 0.4, 0.7)
pyvista.rcParams['color'] = color
assert pyvista.rcParams['color'] == color
# test nested values
init_value = pyvista.rcParams['axes']['show']
pyvista.rcParams['axes']['show'] = not init_value
assert pyvista.rcParams['axes']['show'] is not init_value
finally:
# always return to testing theme
pyvista.set_plot_theme('testing')
def plot_results(mapdl: MapdlCorba, theme="document"):
mapdl.post1()
pv.set_plot_theme(theme)
plotter = pv.Plotter(shape=(2, 2))
mapdl.result.plot_nodal_stress(0,
comp="x",
show_displacement=True,
cpos="xy",
plotter=plotter,
add_text=False,
title="Nodal stress X")
plotter.subplot(0, 1)
mapdl.result.plot_nodal_stress(0,
comp="y",
show_displacement=True,
cpos="xy",
plotter=plotter,
add_text=False,
title="Nodal stress y")
plotter.subplot(1, 0)
mapdl.result.plot_nodal_solution(0,
comp='norm',
show_displacement=True,
lighting=False,
background='w',
text_color='k',
show_edges=True,
cpos='xy',
add_text=False,
plotter=plotter,
title="Displacement norm")
plotter.subplot(1, 1)
mapdl.result.plot_nodal_solution(0,
comp='x',
show_displacement=True,
lighting=False,
background='w',
text_color='k',
show_edges=True,
cpos='xy',
add_text=False,
plotter=plotter,
title="Displacement X")
# mapdl.result.plot_nodal_displacement(0, show_displacement=True, cpos="xy")
plotter.link_views()
plotter.show(full_screen=True)
#plotter.show(screenshot=True)
#plotter.screenshot(f'multiplot_results')
return
def new_plot(self, ph_opacity = .1,el_opacity = 1, po_opacity = 1):
import pyvista as pv
pv.set_plot_theme("night")
plotter = pv.Plotter()
if self.photon_mesh:
plotter.add_mesh(self.photon_mesh, line_width = 0.001, color='white', opacity=ph_opacity)
if self.electron_mesh:
plotter.add_mesh(self.electron_mesh, line_width = 0.001, color='blue', opacity=el_opacity)
if self.positron_mesh:
plotter.add_mesh(self.positron_mesh,line_width = 0.001, color='red', opacity=po_opacity)
return plotter
def test_themes():
old_rcParms = dict(pyvista.rcParams) # must cache old rcParams
pyvista.set_plot_theme('paraview')
pyvista.set_plot_theme('document')
pyvista.set_plot_theme('night')
pyvista.set_plot_theme('default')
for key, value in old_rcParms.items():
pyvista.rcParams[key] = value
def plot_geometry(mapdl: MapdlCorba, theme="document"):
pv.set_plot_theme(theme)
plotter = pv.Plotter(shape=(2, 2))
mapdl.nplot(plotter=plotter, knum=False, color='')
plotter.subplot(0, 1)
mapdl.lplot(plotter=plotter,
color='') #color_lines=True) # color="#000000")
plotter.subplot(1, 0)
mapdl.aplot(plotter=plotter, color='')
plotter.subplot(1, 1)
mapdl.eplot(plotter=plotter, color='')
plotter.link_views()
plotter.show()
# plotter.show(screenshot=True)
# plotter.screenshot(f'multiplot_example')
return
def run(self):
# Init on consumer side:
pv.set_plot_theme("document")
while 1:
try:
if self.last_plotted_epoch != -1 and self.sd['poison']: # Plotted at least one time + Poison
print(f'Pipe poison detected. Displaying one last time, and then exiting plotting supervisor')
self.try_update_data(final=True)
self.plot_data()
break
except (BrokenPipeError, EOFError): # Missing parent
print(f'Producer missing. Exiting plotting supervisor')
break
self.try_update_data()
self.plot_data()
def plotCSD3d_pyvistaSlice(coords,
path=None,
filename_root='Solution.dat',
**kwargs): # add kwards or *args for pyvista plot
if path is None:
cwd = os.getcwd()
path = cwd
filename = path + filename_root
data_2_plot = np.genfromtxt(filename)
coord_x = data_2_plot[:, 0]
coord_y = data_2_plot[:, 1]
coord_z = data_2_plot[:, 2]
coord = data_2_plot[:, :-1]
opacity = [0, 0, 0.1, 0.3, 0.6, 0.9, 1]
grid = pv.UniformGrid()
spc = (max(coord_x) - min(coord_x)) / 10
xdim = int(round((max(coord_x) - min(coord_x)) / spc))
ydim = int(round((max(coord_y) - min(coord_y)) / spc))
zdim = int(round((max(coord_z) - min(coord_z)) / spc))
grid.dimensions = (xdim, ydim, zdim)
grid.dimensions = np.array(grid.dimensions) + 1
grid.origin = (min(coord_x), min(coord_y), min(coord_z)
) # The bottom left corner of the data set
grid.spacing = (spc, spc, spc) # These are the cell sizes along each axis
pv.set_plot_theme('document')
poly = pv.PolyData(coord)
print('interpolation spacing=' + str(spc))
interpolated = grid.interpolate(poly, radius=spc * 2)
cmap = plt.cm.get_cmap('jet', 10)
contours = interpolated.contour()
single_slice = interpolated.slice(normal=[0, 1, 0])
p = pv.Plotter(notebook=False)
p.add_mesh(interpolated.outline(), color="k")
p.add_mesh(single_slice, cmap=cmap)
p.view_xz()
p.add_axes()
p.show_axes()
p.show_grid()
p.show_bounds(font_size=16)
p.show(auto_close=True)
def multiblock_gif(mb, s):
# mb = mb.clip_box([0, 31.5, -2, 0.1, 0, 0], invert=False)
pv.set_plot_theme("document")
plotter = pv.Plotter(window_size=(1300, 600))
plotter.open_gif('mbgif.gif')
plotter.open_movie('mbgif.mp4', framerate=1)
# camera
xc = (mb.bounds[1] + mb.bounds[0]) / 2
yc = (mb.bounds[3] + mb.bounds[2]) * 2.5 / 4
cam = [[xc, yc, mb.length], [xc, yc, 0], [0, 1, 0]]
plotter.camera_position = cam
# color bar args
sba = dict(width=0.5, position_x=0.25, height=0.1, position_y=0.025)
# data
plotter.show(auto_close=False)
for vtk in mb:
vtk = prepare_cov(vtk, 'cov')
plotter.add_mesh(
vtk,
scalars=s,
opacity='cov',
show_edges=False,
edge_color='darkgrey',
show_scalar_bar=True,
scalar_bar_args=sba,
stitle='volumetric water content',
cmap='viridis',
clim=(0.2, 0.6),
)
plotter.show_bounds(
mesh=vtk,
grid='k',
location='outer',
ticks=True,
font_size=14,
font_family='times',
use_2d=True,
padding=0,
xlabel='m',
ylabel='m',
)
plotter.write_frame()
plotter.clear()
plotter.close()
def __init__(self,model=None):
"""Initilaization method for the MeshVisualization class
This method initializes a new instance of the MeshVisualization
class and visualizes the mesh of the passed GmshModel instance.
Within the method, basic visualization settings are defined, before the
visualization method is called.
Parameters:
model: GmshModel object instance
model for which the mesh has to be visualized
"""
# plausibility checks for input arguments
if model is None:
raise TypeError("No model instance to visualize passed. For the visualization of a model, that model has to be known. Check your input data.")
self.model=model
# set plotting theme
pv.set_plot_theme("paraview") # use Paraview style for plotting
# get necessary model information for the default configuration
physicalIDs=model.getIDsFromTags(model.gmshAPI.getPhysicalGroups(dim=model.dimension)) # get group IDs for physical groups of the highest dimension
modelBBox=model._getGmshModelBoundingBox() # get the overall bounding box of the Gmsh model
# define settings
self.defaultSettings={
"lowerThreshold": min(physicalIDs), # set lower threshold value to minimum value of active scalar field
"upperThreshold": max(physicalIDs), # set upper threshold value to minimum value of active scalar field
"boxBounds": modelBBox.T.reshape(6) # define bounds of extraction box to match bounds of the model (factor 1.25 will be applied automatically)
}
self.currentSettings=cp.deepcopy(self.defaultSettings) # copy default settings as initial version of current settings
# initialize arrays required for proper mesh visualization
self.plotterObj=pv.Plotter(title="GmshModel Mesh Visualization") # initialize plotterObj from pyvista and set title
self.mesh=None # initialize mesh
self.thresholdAlgorithm=None # initialize threshold algorithm
self.extractionBox=vtk.vtkBox() # initiliaze dummy extraction box to update information of boxWidget
self.extractionBoxBounds=pv.PolyData() # initialize dummy extraction box bounds to store information of boxWidget bounds
self.extractionAlgorithm=None # initialize extraction algorithm
self.activeWidgets=[] # initialize list of active widgets
# visualize the model mesh
self.visualizeMesh()
def visualize(points, labels, predict=None):
pv.set_plot_theme("document")
categories = dsets.ModelNet40.categories
plt = pv.Plotter(shape=(4, 4), window_size=(800, 800))
for row in range(4):
for col in range(4):
i = row * 4 + col
plt.subplot(row, col)
plt.add_mesh(
points[i],
render_points_as_spheres=True,
point_size=8.0,
ambient=0.1,
diffuse=0.8,
specular=0.5,
specular_power=100.0,
)
plt.add_text(
categories[labels[i]],
font_size=13,
position="upper_edge",
font="arial",
shadow=False,
)
if predict is not None:
if predict is not None:
color = "red" if predict[i] != labels[i] else "green"
plt.add_text(
categories[predict[i]],
font_size=13,
color=color,
position="lower_edge",
font="arial",
shadow=False,
)
plt.link_views()
plt.camera_position = [
(0.0, 0.0, -5.0),
(0.0, 0.0, 0.0),
(0.0, 1.0, 0.0),
]
plt.show("ModelNet40")
plt.close()
def plot_mesh(v,
f=None,
n=None,
strategy='mesh',
grid_on=False,
clr='lightcoral',
normal_clr='lightblue',
label=None,
smooth_shade_on=False,
show_edges=False,
cmap=None,
normal_scale=1):
"""
:param v: tensor - A numpy or torch [nv x 3] vertex tensor
:param f: tensor | None - (optional) A numpy or torch [nf x 3] vertex tensor OR None
:param n: tensor | None - (optional) A numpy or torch [nf x 3] or [nv x3] vertex or face normals. Must input f
when inputting a face-normal tensor
:param strategy: One of ['spheres','cloud','mesh']
:param grid_on: bool - Plots an xyz grid with the mesh. Default is False
:param clr: str or [R,G,B] float list or tensor - Plots mesh with color clr. clr = v is cool
:param normal_clr: str or [R,G,B] float list or tensor - Plots mesh normals with color normal_clr
:param label: str - (optional) - When inputted, displays a legend with the title label
:param smooth_shade_on: bool - Plot a smooth version of the facets - just like 3D-Viewer
:param show_edges: bool - Show edges in black. Only applicable for strategy == 'mesh'
For color list, see pyvista.plotting.colors
* For windows keyboard options, see: https://docs.pyvista.org/plotting/plotting.html
"""
pv.set_plot_theme("document") # White background
p = pv.Plotter()
mesh_append(p,
v=v,
f=f,
n=n,
grid_on=grid_on,
strategy=strategy,
clr=clr,
normal_clr=normal_clr,
label=label,
smooth_shade_on=smooth_shade_on,
show_edges=show_edges,
cmap=cmap,
normal_scale=normal_scale)
p.show()
def PlotMesh3D(self, NodeNumber=True, ElementNumber=True, Loads=True, BC=True, FontMag=1):
pyvista.set_plot_theme('document')
pyvista.global_theme.axes.box = False
pyvista.global_theme.axes.x_color = 'black'
pyvista.global_theme.axes.y_color = 'black'
pyvista.global_theme.axes.z_color = 'black'
pyvista.global_theme.font.color = 'black'
pyvista.global_theme.transparent_background = True
plotter = pyvista.Plotter(
lighting='three lights',
off_screen=False,
)
mesh = pyvista.PolyData(self.Nodes, np.vstack((np.ones(np.array(self.El).shape[0], int)*2, (np.array(self.El)-1).T)).T)
plotter.add_mesh(
mesh.copy(),
render_lines_as_tubes=False,
line_width=2-5,
style='wireframe',
cmap="turbo",
color="#1f77b4",
show_scalar_bar=False,
culling=True,
)
plotter.enable_parallel_projection()
plotter.add_mesh(
mesh.copy(),
render_points_as_spheres=True,
point_size=5,
style='points',
cmap="turbo",
color="#1f77b4",
show_scalar_bar=False,
culling=True,
)
plotter.show(
full_screen=False,
interactive=True,
)
def test_themes(theme):
pyvista.set_plot_theme(theme)
if theme != 'default':
assert pyvista.rcParams != pyvista.DEFAULT_THEME
pyvista.set_plot_theme('default')
assert pyvista.rcParams == pyvista.DEFAULT_THEME
# always return to testing theme
pyvista.set_plot_theme('testing')
names=['time', 'vtu'])
files = files.dropna()
time_list = files['time'].tolist()
list_vtu = files['vtu'].tolist()
list_vtu = [i.replace('.pvtu', '.0000.vtu') for i in list_vtu]
#Read VTK data
for i in range(0, len(list_vtu)):
#Read DF from VTK files
exec(f'df_{i} = pv.read(f\'{currentPath}/{list_vtu[i]}\')')
#Selecet a data to apply the slice
exec(f'df = df_{0}')
#Choose the white background for the figure
pv.set_plot_theme("document")
#Convert Cell data to point data, and then do it the opposite way
#to spread the cell data to the entire cell region
df = df.cell_data_to_point_data()
df = df.point_data_to_cell_data()
#Select a slice of the domain
single_slice = df.slice(normal=[0, 1, 0], origin=[0, 0, 0])
#Create a plotter
plotter = pv.Plotter(off_screen=None)
plotter.camera.position = (0, 2.5, 0.55)
plotter.camera.focal_point = (0, 0, 0.55)
plotter.camera.roll -= 90
from matplotlib.colors import rgb2hex
from PyPDF2 import PdfFileWriter, PdfFileReader
from scipy.spatial.transform import Rotation as R
from matplotlib.colors import LinearSegmentedColormap
sys.path.append(r"D:\git_pulls\Phenotypic_PointCloud_Analysis")
# script_dir = pathlib.Path(os.path.dirname(os.path.realpath(__file__)))
from Code.visual_utils import *
from Code.pycpd_registrations_3D import consolidate_vtk, consolidate_case
directory = pathlib.Path(r"D:\Desktop\Carla\paper_3_final\results")
os.chdir(directory)
# Set the theme, default, dark,
pv.set_plot_theme("default")
rename_dict = {"__": "_"}
# Define the input name and read in to memory for visualization
mesh_name = "consolidated_means.vtk"
input_mesh = pv.read(mesh_name)
# This expects variable names to follow a convention.
rename_dict = {"_std": "_standard_dev", "__": "_", "_coef": "_coef_var"}
input_mesh = scalar_name_cleanup(input_mesh=input_mesh,
replace_dict=rename_dict)
input_mesh.save(f"{mesh_name}")
max_norm_list = [
item for item in list(input_mesh.point_arrays) if "max_norm" in item