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encinass1u
School_project
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1cbc1375
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1cbc1375
authored
3 years ago
by
encinass1u
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stl_atributes.py
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1cbc1375
#Pyvista information from stl file
import
pyvista
as
pv
# operating system import
import
os
from
scipy
import
misc
from
mpl_toolkits
import
mplot3d
from
matplotlib
import
pyplot
as
plt
import
pyvista
test_list
=
[
"
SMAT
"
,
\
]
# file path
file_path
=
os
.
getcwd
()
+
os
.
sep
+
"
data
"
# file path
result_path
=
os
.
getcwd
()
+
os
.
sep
+
"
results
"
if
os
.
path
.
isdir
(
result_path
)
==
False
:
os
.
mkdir
(
result_path
)
name
=
'
Surface
'
# post processing loop
for
i
,
m
in
enumerate
(
test_list
)
:
# TRA filename
filename
=
m
+
"
.stl
"
tata
=
pv
.
read
(
file_path
+
os
.
sep
+
filename
)
#Printing and saving stl parameters
cells
=
tata
.
n_cells
points
=
tata
.
n_points
bounds
=
tata
.
bounds
arrays
=
tata
.
n_arrays
print
(
tata
.
n_cells
)
print
(
tata
.
n_points
)
print
(
tata
.
bounds
)
print
(
tata
.
n_arrays
)
print
(
tata
.
area
)
#show surface with metallic attributes for a clearer view and roughness signaling
tata
.
plot
(
cpos
=
'
xy
'
,
cmap
=
'
plasma
'
,
pbr
=
True
,
metallic
=
1.0
,
roughness
=
0.3
,
zoom
=
0.7
,
text
=
'
SMATed surface
'
,
return_cpos
=
False
,
hidden_line_removal
=
True
,
anti_aliasing
=
True
)
#Filter to obtain curvature values of the surface for further treatment into values of 0 and 1.
tata_filtered
=
tata
.
plot_curvature
(
curv_type
=
'
gaussian
'
,
smooth_shading
=
True
,
clim
=
[
0
,
1
])
Gaus
=
tata
.
get_array
(
'
Gaussian Curvature
'
)
print
(
Gaus
)
#Locate the arrays inside the PolyData, this provides the arrays containing values of scalars, normals
#faces and points.
print
(
tata
.
array_names
)
#printing the array founded and saving it as a tuple inside a callable variable
choc
=
tata
.
get_array
(
'
Normals
'
)
tata
.
save
(
'
tata.vtk
'
)
#Data range, min,max given
tata_r
=
tata
.
get_data_range
()
#Edges showing in red
tata
.
plot_boundaries
(
line_width
=
5
)
choc
=
choc
.
plot_curvature
(
curv_type
=
'
gaussian
'
,
smooth_shading
=
True
,
clim
=
[
0
,
1
])
#Make a plane figure with edges planed, erased the edges due to the high aglomeration
#In this model we can see how the values change along the plane
projected
=
tata
.
project_points_to_plane
()
projected
.
plot
(
show_edges
=
False
,
line_width
=
0.3
)
#Code to show the mesh that composes the figure
mesh
=
tata
mesh
.
point_data
.
clear
()
centers
=
mesh
.
cell_centers
()
pl
=
pyvista
.
Plotter
()
actor
=
pl
.
add_mesh
(
mesh
,
show_edges
=
False
)
actor
=
pl
.
add_points
(
centers
,
render_points_as_spheres
=
True
,
color
=
'
red
'
,
point_size
=
10
)
pl
.
show
()
#Code to show the elevation in the Z plane
tata_elv
=
tata
.
elevation
()
print
(
tata_elv
)
tata_elv
.
plot
(
smooth_shading
=
True
)
# Calculate de distance of entitites from a plane in the middle, doesnt stays in the middle the plane
plane
=
pv
.
Plane
()
_
=
tata
.
compute_implicit_distance
(
plane
,
inplace
=
True
)
dist
=
tata
[
'
implicit_distance
'
]
type
(
dist
)
pl
=
pv
.
Plotter
()
_
=
pl
.
add_mesh
(
tata
,
scalars
=
'
implicit_distance
'
,
cmap
=
'
bwr
'
)
_
=
pl
.
add_mesh
(
plane
,
color
=
'
w
'
,
style
=
'
wireframe
'
)
pl
.
show
()
merged
=
tata
.
merge
(
plane
)
merged
.
plot
(
style
=
'
wireframe
'
,
color
=
'
tan
'
)
ow
=
tata
.
overwrite
(
plane
)
merged
.
plot
(
style
=
'
wireframe
'
,
color
=
'
tan
'
)
#Interpolation of our data in a mesh
pdata
=
pyvista
.
PolyData
(
tata
)
plane
=
pyvista
.
Plane
()
plane
.
clear_data
()
plane
=
plane
.
interpolate
(
pdata
,
sharpness
=
3
)
pl
=
pyvista
.
Plotter
()
_
=
pl
.
add_mesh
(
pdata
,
render_points_as_spheres
=
True
,
point_size
=
50
)
_
=
pl
.
add_mesh
(
plane
,
style
=
'
wireframe
'
,
line_width
=
5
)
pl
.
show
()
# Normal ploting
tata
.
plot_normals
(
mag
=
0.1
,
faces
=
False
,
show_edges
=
False
)
\ No newline at end of file
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