def test_reshape():
a = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12],
])
b = cas.DM(a)
test_inputs = [
-1,
(4, 3),
(3, 4),
(12, 1),
(1, 12),
(-1),
(12, -1),
(-1, 12),
]
for i in test_inputs:
ra = np.reshape(a, i)
rb = np.reshape(b, i)
if len(ra.shape) == 1:
ra = ra.reshape(-1, 1)
assert np.all(ra == rb)
def draw(
self,
scalar_to_plot:
str = "potential", # "potential", "streamfunction", "xvel", "yvel", "velmag", "Cp"
x_points: np.ndarray = np.linspace(-10, 10, 400),
y_points: np.ndarray = np.linspace(-10, 10, 300),
percentiles_to_include=99.7,
show=True,
):
X, Y = np.meshgrid(x_points, y_points)
X_r = np.reshape(X, -1)
Y_r = np.reshape(Y, -1)
points = np.vstack((X_r, Y_r)).T
if scalar_to_plot == "potential":
scalar_to_plot_value = sum(
[object.get_potential_at(points) for object in self.objects])
elif scalar_to_plot == "streamfunction":
scalar_to_plot_value = sum([
object.get_streamfunction_at(points) for object in self.objects
])
elif scalar_to_plot == "xvel":
scalar_to_plot_value = sum(
[object.get_x_velocity_at(points) for object in self.objects])
elif scalar_to_plot == "yvel":
scalar_to_plot_value = sum(
[object.get_y_velocity_at(points) for object in self.objects])
elif scalar_to_plot == "velmag":
x_vels = sum(
[object.get_x_velocity_at(points) for object in self.objects])
y_vels = sum(
[object.get_y_velocity_at(points) for object in self.objects])
scalar_to_plot_value = np.sqrt(x_vels**2 + y_vels**2)
elif scalar_to_plot == "Cp":
x_vels = sum(
[object.get_x_velocity_at(points) for object in self.objects])
y_vels = sum(
[object.get_y_velocity_at(points) for object in self.objects])
V = np.sqrt(x_vels**2 + y_vels**2)
scalar_to_plot_value = 1 - V**2
else:
raise ValueError("Bad value of `scalar_to_plot`!")
min = np.nanpercentile(scalar_to_plot_value,
50 - percentiles_to_include / 2)
max = np.nanpercentile(scalar_to_plot_value,
50 + percentiles_to_include / 2)
contour(x_points,
y_points,
scalar_to_plot_value.reshape(X.shape),
levels=np.linspace(min, max, 80),
linelabels=False,
cmap=plt.get_cmap("rainbow"),
contour_kwargs={
"linestyles": 'solid',
"alpha": 0.4
})
plt.gca().set_aspect("equal", adjustable='box')
show_plot(f"Potential Flow: {scalar_to_plot}", "$x$", "$y$", show=show)
def convert_mesh_to_polydata_format(
points,
faces
):
"""
Pyvista uses a slightly different convention for the standard (points, faces) format as described above. They
give `faces` as a single 1D vector of roughly length (M*3), or (M*4) in the case of quadrilateral meshing.
Basically, the mesh displayer goes down the `faces` array, and when it sees a number N, it interprets that as the
number of vertices in the following face. Then, the next N entries are interpreted as integer references to the
vertices of the face.
This has the benefit of allowing for mixed tri/quad meshes.
Args:
points: `points` array of the original standard-format mesh
faces: `faces` array of the original standard-format mesh
Returns:
(points, faces), except that `faces` is now in a pyvista.PolyData compatible format.
"""
faces = [
[len(face), *face]
for face in faces
]
faces = np.array(faces)
faces = np.reshape(faces, -1)
return points, faces
def reshape(value):
try:
return np.reshape(value, shape_for_reshaping)
except ValueError:
if isinstance(value, int) or isinstance(value, float) or value.shape == tuple() or np.product(
value.shape) == 1:
return value * np.ones(shape_for_reshaping)
raise ValueError("Could not reshape value of one of the inputs!")
def __call__(self, x):
if isinstance(self.x_data_coordinates, dict):
def get_shape(value):
try:
return value.shape
except AttributeError:
return tuple()
shape = np.broadcast_shapes(
*[get_shape(v) for v in x.values()]
)
shape_for_reshaping = (int(np.product(shape)),)
def reshape(value):
try:
return np.reshape(value, shape_for_reshaping)
except ValueError:
if isinstance(value, int) or isinstance(value, float) or value.shape == tuple() or np.product(
value.shape) == 1:
return value * np.ones(shape_for_reshaping)
raise ValueError("Could not reshape value of one of the inputs!")
x = np.stack(tuple(
reshape(x[k])
for k, v in self.x_data_coordinates.items()
))
output = np.interpn(
points=self.x_data_coordinates_values,
values=self.y_data_structured,
xi=x,
method=self.method,
bounds_error=False, # Can't be set true if general MX-type inputs are to be expected.
fill_value=self.fill_value
)
try:
return np.reshape(output, shape)
except UnboundLocalError:
return output
def convert_mesh_to_polydata_format(points, faces):
faces = [[len(face), *face] for face in faces]
faces = np.array(faces)
faces = np.reshape(faces, -1)
return points, faces
def tall(array):
return np.reshape(array, (-1, 1))
def wide(array):
return np.reshape(array, (1, -1))
x_field=Xf,
y_field=Yf,
z_field=Zf,
x_left=left[0],
y_left=left[1],
z_left=left[2],
x_right=right[0],
y_right=right[1],
z_right=right[2],
gamma=1,
)
pos = np.stack((Xf, Yf, Zf)).T
dir = np.stack((Uf, Vf, Wf)).T
dir_norm = np.reshape(np.linalg.norm(dir, axis=1), (-1, 1))
dir = dir / dir_norm * dir_norm ** 0.2
import pyvista as pv
pv.set_plot_theme('dark')
plotter = pv.Plotter()
plotter.add_arrows(
cent=pos,
direction=dir,
mag=0.15
)
plotter.add_lines(
lines=np.array([
[Xf.max(), left[1], left[2]],
