def show_mask(self, ax: axes.Axes, title="", fontfamily="times new roman", fontsize=15, scatter=20):
phi = np.linspace(0, 2 * np.pi, 360 * 4)
circle = ax.fill(self._aperture * np.cos(phi), self._aperture * np.sin(phi), color="lightblue", zorder=0)
elements = ax.scatter(x=self._locations[0], y=self._locations[1], s=scatter, c="gray", zorder=2)
if title == "":
title = "Total number: " + str(self.get_total_number())
# loop line
for radius in self._radius:
ax.plot(radius * np.cos(phi), radius * np.sin(phi), color="orange", zorder=1)
ax.set_aspect("equal", 'box')
ax.set_axis_off()
ax.grid(True)
ax.set_title(title, fontsize=fontsize, fontfamily=fontfamily)
return ax
def IFT_LoopMaskArray(aperture, loop_number: np.array, loop_radius: np.array, ax: axes.Axes, centred=False):
"""
Draw circular array on the circular aperture face
:param aperture: the radius of the array
:param loop_number: number of each loop
:param loop_radius: radius of each loop
:param ax: handle of subplot
:param centred: the centre whether has the element for True or False
:return:
"""
detal_phi = 2 * np.pi / loop_number
total_number = np.einsum("i->", loop_number)
if centred:
loc = np.zeros([2, total_number + 1], dtype=np.float64)
loop_index = 1
else:
loc = np.zeros([2, total_number], dtype=np.float64)
loop_index = 0
for zmc, radius in enumerate(loop_radius):
current_number = loop_number[zmc]
phi = np.arange(0, current_number) * detal_phi[zmc]
loc_x = radius * np.cos(phi)
loc_y = radius * np.sin(phi)
loc[0, loop_index:loop_index + current_number] = loc_x
loc[1, loop_index:loop_index + current_number] = loc_y
loop_index = loop_index + current_number
phi = np.linspace(0, 2 * np.pi, 360 * 4)
# draw
circle = ax.fill(aperture * np.cos(phi), aperture * np.sin(phi), color='lightblue', zorder=0)
elements = ax.scatter(x=loc[0], y=loc[1], s=10, c='gray', zorder=1)
ax.set_aspect('equal', 'box')
ax.set_axis_off()
ax.grid(True)
return ax
def plot_spatial_data_matplotlib(
data: geo.SpatialData,
axes: Axes = None,
color: Union[int, Tuple[int, int, int], Tuple[float, float, float]] = None,
label: str = None,
show_wireframe: bool = True,
) -> Axes:
"""Visualize a `weldx.geometry.SpatialData` instance.
Parameters
----------
data :
The data that should be visualized
axes :
The target `matplotlib.axes.Axes` object of the plot. If 'None' is passed, a
new figure will be created
color :
A 24 bit integer, a triplet of integers with a value range of 0-255
or a triplet of floats with a value range of 0.0-1.0 that represent an RGB
color
label :
Label of the plotted geometry
show_wireframe :
If `True`, the mesh is plotted as wireframe. Otherwise only the raster
points are visualized. Currently, the wireframe can't be visualized if a
`weldx.geometry.VariableProfile` is used.
Returns
-------
matplotlib.axes.Axes :
The `matplotlib.axes.Axes` instance that was used for the plot.
"""
if axes is None:
_, axes = new_3d_figure_and_axes()
if not isinstance(data, geo.SpatialData):
data = geo.SpatialData(data)
if color is None:
color = (0.0, 0.0, 0.0)
else:
color = color_to_rgb_normalized(color)
coordinates = data.coordinates.data
triangles = data.triangles
# if data is time dependent or has other extra dimensions, just take the first value
while coordinates.ndim > 2:
coordinates = coordinates[0]
axes.scatter(
coordinates[:, 0],
coordinates[:, 1],
coordinates[:, 2],
marker=".",
color=color,
label=label,
zorder=2,
)
if triangles is not None and show_wireframe:
for triangle in triangles:
triangle_data = coordinates[[*triangle, triangle[0]], :]
axes.plot(
triangle_data[:, 0],
triangle_data[:, 1],
triangle_data[:, 2],
color=color,
zorder=1,
)
return axes
def plot_spatial_data_matplotlib(
data: Union[np.ndarray, geo.SpatialData],
axes: Axes = None,
color: Union[int, tuple[int, int, int], tuple[float, float, float]] = None,
label: str = None,
limits: types_limits = None,
show_wireframe: bool = True,
) -> Axes:
"""Visualize a `weldx.geometry.SpatialData` instance.
Parameters
----------
data :
The data that should be visualized
axes :
The target `matplotlib.axes.Axes` object of the plot. If 'None' is passed, a
new figure will be created
color :
A 24 bit integer, a triplet of integers with a value range of 0-255
or a triplet of floats with a value range of 0.0-1.0 that represent an RGB
color
label :
Label of the plotted geometry
limits :
Each tuple marks lower and upper boundary of the x, y and z axis. If only a
single tuple is passed, the boundaries are used for all axis. If `None`
is provided, the axis are adjusted to be of equal length.
show_wireframe :
If `True`, the mesh is plotted as wireframe. Otherwise only the raster
points are visualized. Currently, the wireframe can't be visualized if a
`weldx.geometry.VariableProfile` is used.
Returns
-------
matplotlib.axes.Axes :
The `matplotlib.axes.Axes` instance that was used for the plot.
"""
if axes is None:
_, axes = new_3d_figure_and_axes()
if not isinstance(data, geo.SpatialData):
data = geo.SpatialData(data)
if color is None:
color = (0.0, 0.0, 0.0)
else:
color = color_to_rgb_normalized(color)
coordinates = data.coordinates.data
if isinstance(coordinates, Q_):
coordinates = coordinates.to(_DEFAULT_LEN_UNIT).m
coordinates = coordinates.reshape(-1, 3)
triangles = data.triangles
# if data is time dependent or has other extra dimensions, just take the first value
while coordinates.ndim > 2:
coordinates = coordinates[0]
axes.scatter(
coordinates[:, 0],
coordinates[:, 1],
coordinates[:, 2],
marker=".",
color=color,
label=label,
zorder=2,
)
if triangles is not None and show_wireframe:
for triangle in triangles:
triangle_data = coordinates[[*triangle, triangle[0]], :]
axes.plot(
triangle_data[:, 0],
triangle_data[:, 1],
triangle_data[:, 2],
color=color,
zorder=1,
)
_set_limits_matplotlib(axes, limits)
return axes