def plot_coordinate_systems(
cs_data: Tuple[str, Dict],
axes: Axes = None,
title: str = None,
limits: types_limits = None,
time_index: int = None,
legend_pos: str = "lower left",
) -> Axes:
"""Plot multiple coordinate systems.
Parameters
----------
cs_data :
A tuple containing the coordinate system that should be plotted and a dictionary
with the key word arguments that should be passed to its plot function.
axes :
The target axes object that should be drawn to. If `None` is provided, a new
one will be created.
title :
The title of the plot
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.
time_index :
Index of a specific time step that should be plotted if the corresponding
coordinate system is time dependent
legend_pos :
A string that specifies the position of the legend. See the matplotlib
documentation for further details
Returns
-------
matplotlib.axes.Axes :
The axes object that was used as canvas for the plot
"""
if axes is None:
_, axes = new_3d_figure_and_axes()
for lcs, kwargs in cs_data:
if "time_index" not in kwargs:
kwargs["time_index"] = time_index
lcs.plot(axes, **kwargs)
_set_limits_matplotlib(axes, limits)
if title is not None:
axes.set_title(title)
axes.legend(loc=legend_pos)
return axes
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 show_rdm_panel(
rdm: rsatoolbox.rdm.RDMs,
ax: Axes = None,
cmap: Union[str, matplotlib.colors.Colormap] = None,
nanmask: npt.ArrayLike = None,
rdm_descriptor: str = None,
gridlines: npt.ArrayLike = None,
vmin: float = None,
vmax: float = None,
) -> matplotlib.image.AxesImage:
"""show_rdm_panel. Add RDM heatmap to the axis ax.
Args:
rdm (rsatoolbox.rdm.RDMs): RDMs object to be plotted (n_rdm must be 1).
ax (matplotlib.axes._axes.Axes): Matplotlib axis handle. plt.gca() by default.
cmap (Union[str, matplotlib.colors.Colormap]): colormap to be used (by
plt.imshow internally). By default we use rdm_colormap.
nanmask (npt.ArrayLike): boolean mask defining RDM elements to suppress
(by default, the diagonals).
rdm_descriptor (str): Key for rdm_descriptor to use as panel title, or
str for direct labeling.
gridlines (npt.ArrayLike): Set to add gridlines at these positions.
vmin (float): Minimum intensity for colorbar mapping. matplotlib imshow
argument.
vmax (float): Maximum intensity for colorbar mapping. matplotlib imshow
argument.
Returns:
matplotlib.image.AxesImage: Matplotlib handle.
"""
if rdm.n_rdm > 1:
raise ValueError(
"expected single rdm - use show_rdm for multi-panel figures")
if ax is None:
ax = plt.gca()
if cmap is None:
cmap = rdm_colormap()
if nanmask is None:
nanmask = np.eye(rdm.n_cond, dtype=bool)
if not np.any(gridlines):
gridlines = []
rdmat = rdm.get_matrices()[0, :, :]
if np.any(nanmask):
rdmat[nanmask] = np.nan
image = ax.imshow(rdmat, cmap=cmap, vmin=vmin, vmax=vmax)
ax.set_xlim(-0.5, rdm.n_cond - 0.5)
ax.set_ylim(rdm.n_cond - 0.5, -0.5)
ax.xaxis.set_ticks(gridlines)
ax.yaxis.set_ticks(gridlines)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.xaxis.set_ticks(np.arange(rdm.n_cond), minor=True)
ax.yaxis.set_ticks(np.arange(rdm.n_cond), minor=True)
# hide minor ticks by default
ax.xaxis.set_tick_params(length=0, which="minor")
ax.yaxis.set_tick_params(length=0, which="minor")
if rdm_descriptor in rdm.rdm_descriptors:
ax.set_title(rdm.rdm_descriptors[rdm_descriptor][0])
else:
ax.set_title(rdm_descriptor)
return image
def plot_coordinate_system_manager_matplotlib(
csm: CoordinateSystemManager,
axes: Axes = None,
reference_system: str = None,
coordinate_systems: List[str] = None,
data_sets: List[str] = None,
colors: Dict[str, int] = None,
time: types_timeindex = None,
time_ref: pd.Timestamp = None,
title: str = None,
limits: types_limits = None,
scale_vectors: Union[float, List, np.ndarray] = None,
set_axes_equal: bool = False,
show_origins: bool = True,
show_trace: bool = True,
show_vectors: bool = True,
show_wireframe: bool = True,
) -> Axes:
"""Plot the coordinate systems of a `weldx.transformations.CoordinateSystemManager`.
Parameters
----------
csm :
The coordinate system manager instance that should be plotted.
axes :
The target axes object that should be drawn to. If `None` is provided, a new
one will be created.
reference_system :
The name of the reference system for the plotted coordinate systems
coordinate_systems :
Names of the coordinate systems that should be drawn. If `None` is provided,
all systems are plotted.
data_sets :
Names of the data sets that should be drawn. If `None` is provided, all data
is plotted.
colors :
A mapping between a coordinate system name or a data set name and a color.
The colors must be provided as 24 bit integer values that are divided into
three 8 bit sections for the rgb values. For example `0xFF0000` for pure
red.
Each coordinate system or data set that does not have a mapping in this
dictionary will get a default color assigned to it.
time :
The time steps that should be plotted
time_ref :
A reference timestamp that can be provided if the ``time`` parameter is a
`pandas.TimedeltaIndex`
title :
The title of the plot
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.
scale_vectors :
A scaling factor or array to adjust the length of the coordinate system vectors
set_axes_equal :
(matplotlib only) If `True`, all axes are adjusted to cover an equally large
range of value. That doesn't mean, that the limits are identical
show_origins :
If `True`, the origins of the coordinate system are visualized in the color
assigned to the coordinate system.
show_trace :
If `True`, the trace of time dependent coordinate systems is plotted.
show_vectors :
If `True`, the coordinate cross of time dependent coordinate systems is plotted.
show_wireframe :
If `True`, the mesh is visualized as wireframe. Otherwise, it is not shown.
Returns
-------
matplotlib.axes.Axes :
The axes object that was used as canvas for the plot.
"""
if time is not None:
return plot_coordinate_system_manager_matplotlib(
csm.interp_time(time=time, time_ref=time_ref),
axes=axes,
reference_system=reference_system,
coordinate_systems=coordinate_systems,
title=title,
show_origins=show_origins,
show_trace=show_trace,
show_vectors=show_vectors,
)
if axes is None:
_, axes = new_3d_figure_and_axes()
axes.set_xlabel("x")
axes.set_ylabel("y")
axes.set_zlabel("z")
if reference_system is None:
reference_system = csm.root_system_name
if coordinate_systems is None:
coordinate_systems = csm.coordinate_system_names
if data_sets is None:
data_sets = csm.data_names
if title is not None:
axes.set_title(title)
# plot coordinate systems
color_gen = color_generator_function()
for lcs_name in coordinate_systems:
color = color_int_to_rgb_normalized(
get_color(lcs_name, colors, color_gen))
lcs = csm.get_cs(lcs_name, reference_system)
lcs.plot(
axes=axes,
color=color,
label=lcs_name,
scale_vectors=scale_vectors,
show_origin=show_origins,
show_trace=show_trace,
show_vectors=show_vectors,
)
# plot data
for data_name in data_sets:
color = color_int_to_rgb_normalized(
get_color(data_name, colors, color_gen))
data = csm.get_data(data_name, reference_system)
plot_spatial_data_matplotlib(
data=data,
axes=axes,
color=color,
label=data_name,
show_wireframe=show_wireframe,
)
_set_limits_matplotlib(axes, limits, set_axes_equal)
axes.legend()
return axes