def interactive_hist(
arr,
density=False,
bins="auto",
weights=None,
ax=None,
slider_formats=None,
force_ipywidgets=False,
play_buttons=False,
controls=None,
display_controls=True,
**kwargs,
):
"""
Control the contents of a histogram using widgets.
See https://github.com/ianhi/mpl-interactions/pull/73#issue-470638134 for a discussion
of the limitations of this function. These limitations will be improved once
https://github.com/matplotlib/matplotlib/pull/18275 has been merged.
parameters
----------
arr : arraylike or function
The array or the funciton that returns an array that is to be histogrammed
density : bool, optional
whether to plot as a probability density. Passed to np.histogram
bins : int or sequence of scalars or str, optional
bins argument to np.histogram
weights : array_like, optional
passed to np.histogram
ax : matplotlib axis, optional
The axis on which to plot. If none the current axis will be used.
slider_formats : None, string, or dict
If None a default value of decimal points will be used. Uses the new {} style formatting
force_ipywidgets : boolean
If True ipywidgets will always be used, even if not using the ipympl backend.
If False the function will try to detect if it is ok to use ipywidgets
If ipywidgets are not used the function will fall back on matplotlib widgets
play_buttons : bool or str or dict, optional
Whether to attach an ipywidgets.Play widget to any sliders that get created.
If a boolean it will apply to all kwargs, if a dictionary you choose which sliders you
want to attach play buttons too.
- None: no sliders
- True: sliders on the lft
- False: no sliders
- 'left': sliders on the left
- 'right': sliders on the right
controls : mpl_interactions.controller.Controls
An existing controls object if you want to tie multiple plot elements to the same set of
controls
display_controls : boolean
Whether the controls should display themselve on creation. Ignored if controls is specified.
returns
-------
controls
Examples
--------
With numpy arrays::
loc = np.linspace(-5, 5, 500)
scale = np.linspace(1, 10, 100)
def f(loc, scale):
return np.random.randn(1000)*scale + loc
interactive_hist(f, loc=loc, scale=scale)
with tuples::
def f(loc, scale):
return np.random.randn(1000)*scale + loc
interactive_hist(f, loc=(-5, 5, 500), scale=(1, 10, 100))
"""
ipympl = notebook_backend()
fig, ax = gogogo_figure(ipympl, ax=ax)
use_ipywidgets = ipympl or force_ipywidgets
slider_formats = create_slider_format_dict(slider_formats)
controls, params = gogogo_controls(kwargs, controls, display_controls,
slider_formats, play_buttons)
pc = PatchCollection([])
ax.add_collection(pc, autolim=True)
def update(params, indices, cache):
arr_ = callable_else_value(arr, params, cache)
new_x, new_y, new_patches = simple_hist(arr_,
density=density,
bins=bins,
weights=weights)
stretch(ax, new_x, new_y)
pc.set_paths(new_patches)
ax.autoscale_view()
controls.register_function(update, fig, params.keys())
new_x, new_y, new_patches = simple_hist(callable_else_value(arr, params),
density=density,
bins=bins,
weights=weights)
sca(ax)
pc.set_paths(new_patches)
ax.set_xlim(new_x)
ax.set_ylim(new_y)
return controls
def interactive_hist(
f,
density=False,
bins="auto",
weights=None,
figsize=None,
ax=None,
slider_format_string=None,
display=True,
force_ipywidgets=False,
play_buttons=False,
play_button_pos="right",
**kwargs,
):
"""
Control the contents of a histogram using widgets.
See https://github.com/ianhi/mpl-interactions/pull/73#issue-470638134 for a discussion
of the limitations of this function. These limitations will be improved once
https://github.com/matplotlib/matplotlib/pull/18275 has been merged.
parameters
----------
f : function
A function that will return a 1d array of which to take the histogram
density : bool, optional
whether to plot as a probability density. Passed to np.histogram
bins : int or sequence of scalars or str, optional
bins argument to np.histogram
weights : array_like, optional
passed to np.histogram
figsize : tuple or scalar
If tuple it will be used as the matplotlib figsize. If a number
then it will be used to scale the current rcParams figsize
ax : matplotlib axis, optional
If None a new figure and axis will be created
slider_format_string : None, string, or dict
If None a default value of decimal points will be used. For ipywidgets this uses the new f-string formatting
For matplotlib widgets you need to use `%` style formatting. A string will be used as the default
format for all values. A dictionary will allow assigning different formats to different sliders.
note: For matplotlib >= 3.3 a value of None for slider_format_string will use the matplotlib ScalarFormatter
object for matplotlib slider values.
display : boolean
If True then the output and controls will be automatically displayed
force_ipywidgets : boolean
If True ipywidgets will always be used, even if not using the ipympl backend.
If False the function will try to detect if it is ok to use ipywidgets
If ipywidgets are not used the function will fall back on matplotlib widgets
play_buttons : bool or dict, optional
Whether to attach an ipywidgets.Play widget to any sliders that get created.
If a boolean it will apply to all kwargs, if a dictionary you choose which sliders you
want to attach play buttons too.
play_button_pos : str, or dict, or list(str)
'left' or 'right'. Whether to position the play widget(s) to the left or right of the slider(s)
returns
-------
fig : matplotlib figure
ax : matplotlib axis
controls : list of widgets
Examples
--------
With numpy arrays::
loc = np.linspace(-5, 5, 500)
scale = np.linspace(1, 10, 100)
def f(loc, scale):
return np.random.randn(1000)*scale + loc
interactive_hist(f, loc=loc, scale=scale)
with tuples::
def f(loc, scale):
return np.random.randn(1000)*scale + loc
interactive_hist(f, loc=(-5, 5, 500), scale=(1, 10, 100))
"""
params = {}
funcs = np.atleast_1d(f)
# supporting more would require more thought
if len(funcs) != 1:
raise ValueError(
f"Currently only a single function is supported. You passed in {len(funcs)} functions"
)
ipympl = notebook_backend()
fig, ax = gogogo_figure(ipympl, figsize=figsize, ax=ax)
use_ipywidgets = ipympl or force_ipywidgets
pc = PatchCollection([])
ax.add_collection(pc, autolim=True)
slider_format_strings = create_slider_format_dict(slider_format_string,
use_ipywidgets)
# update plot
def update(change, key, label):
if label:
# continuous
params[key] = kwargs[key][change["new"]]
label.value = slider_format_strings[key].format(
kwargs[key][change["new"]])
else:
# categorical
params[key] = change["new"]
arr = funcs[0](**params)
new_x, new_y, new_patches = simple_hist(arr,
density=density,
bins=bins,
weights=weights)
stretch(ax, new_x, new_y)
pc.set_paths(new_patches)
ax.autoscale_view()
fig.canvas.draw_idle()
# this line implicitly fills the params dict
if use_ipywidgets:
(
sliders,
slabels,
controls,
play_buttons,
) = kwargs_to_ipywidgets(kwargs, params, update, slider_format_strings,
play_buttons, play_button_pos)
else:
controls = kwargs_to_mpl_widgets(kwargs, params, update,
slider_format_strings)
new_x, new_y, new_patches = simple_hist(funcs[0](**params),
density=density,
bins=bins,
weights=weights)
pc.set_paths(new_patches)
ax.set_xlim(new_x)
ax.set_ylim(new_y)
controls = gogogo_display(ipympl, use_ipywidgets, display, controls, fig)
return fig, ax, controls
class Strat(object):
def __init__(self, gui):
'''
initiation of the main strat object
'''
self.gui = gui
# self.gui.strat_ax = gui.strat_ax
self.fig = gui.fig
self.sm = gui.sm
self.config = gui.config
self.Bast = self.sm.Bast
self.avul_num = 0
self.color = False
self.avulCmap = plt.cm.Set1(range(9))
# self._paused = gui._paused
# create an active channel and corresponding PatchCollection
self.activeChannel = ActiveChannel(Bast=self.Bast,
age=0,
Ta=self.sm.Ta,
avul_num=0,
sm=self.sm)
self.activeChannelPatchCollection = PatchCollection([
Rectangle(self.activeChannel.state.ll, self.activeChannel.state.Bc,
self.activeChannel.state.H)
])
# create a channelbody and corresponding PatchCollection
self.channelBodyList = []
self.channelBodyPatchCollection = PatchCollection(self.channelBodyList)
# add PatchCollestions
self.gui.strat_ax.add_collection(self.channelBodyPatchCollection)
self.gui.strat_ax.add_collection(self.activeChannelPatchCollection)
# set fixed color attributes of PatchCollections
self.channelBodyPatchCollection.set_edgecolor('0')
self.activeChannelPatchCollection.set_facecolor('0.6')
self.activeChannelPatchCollection.set_edgecolor('0')
self.BastLine, = self.gui.strat_ax.plot(
[-self.sm.Bbmax * 1000 / 2, gui.sm.Bbmax * 1000 / 2],
[self.Bast, self.Bast],
'k--',
animated=False) # plot basin top
self.VE_val = plt.text(0.675,
0.025,
'VE = ' +
str(round(self.sm.Bb / self.sm.yView, 1)),
fontsize=12,
transform=self.gui.strat_ax.transAxes,
backgroundcolor='white')
def __call__(self, i):
'''
called every loop
'''
# find new slider vals
self.sm.get_all()
if not self.gui._paused:
# timestep the current channel objects
dz = self.sm.sig * self.sm.dt
for c in self.channelBodyList:
c.subside(dz)
if not self.activeChannel.avulsed:
# when an avulsion has not occurred:
self.activeChannel.timestep()
else:
# once an avulsion has occurred:
self.channelBodyList.append(ChannelBody(self.activeChannel))
self.avul_num += 1
self.color = True
# create a new Channel
self.activeChannel = ActiveChannel(Bast=self.Bast,
age=i,
Ta=self.sm.Ta,
avul_num=self.avul_num,
sm=self.sm)
# remove outdated channels
stratMin = self.Bast - self.sm.yViewmax
outdatedIdx = [
c.polygonYs.max() < stratMin for c in self.channelBodyList
]
self.channelBodyList = [
c for (c, i) in zip(self.channelBodyList, outdatedIdx)
if not i
]
# generate new patch lists for updating the PatchCollection objects
self.activeChannelPatches = [
Rectangle(s.ll, s.Bc, s.H)
for s in iter(self.activeChannel.stateList)
]
self.channelBodyPatchList = [
c.get_patch() for c in self.channelBodyList
]
# set paths of the PatchCollection Objects
self.channelBodyPatchCollection.set_paths(self.channelBodyPatchList)
self.activeChannelPatchCollection.set_paths(self.activeChannelPatches)
# self.qs = sedtrans.qsEH(D50, Cf,
# sedtrans.taubfun(self.channel.H, self.channel.S, cong, conrhof),
# conR, cong, conrhof) # sedment transport rate based on new geom
# update plot
if self.color:
if self.sm.colFlag == 'age':
age_array = np.array([c.age for c in self.channelBodyList])
if age_array.size > 0:
self.channelBodyPatchCollection.set_array(age_array)
self.channelBodyPatchCollection.set_clim(
vmin=age_array.min(), vmax=age_array.max())
self.channelBodyPatchCollection.set_cmap(plt.cm.viridis)
elif self.sm.colFlag == 'Qw':
self.channelBodyPatchCollection.set_array(
np.array([c.Qw for c in self.channelBodyList]))
self.channelBodyPatchCollection.set_clim(
vmin=self.config.Qwmin, vmax=self.config.Qwmax)
self.channelBodyPatchCollection.set_cmap(plt.cm.viridis)
elif self.sm.colFlag == 'avul':
self.channelBodyPatchCollection.set_array(
np.array([c.avul_num % 9 for c in self.channelBodyList]))
self.channelBodyPatchCollection.set_clim(vmin=0, vmax=9)
self.channelBodyPatchCollection.set_cmap(plt.cm.Set1)
elif self.sm.colFlag == 'sig':
sig_array = np.array([c.sig for c in self.channelBodyList])
self.channelBodyPatchCollection.set_array(sig_array)
self.channelBodyPatchCollection.set_clim(
vmin=self.config.sigmin / 1000,
vmax=self.config.sigmax / 1000)
self.channelBodyPatchCollection.set_cmap(plt.cm.viridis)
# yview and xview
ylims = utils.new_ylims(yView=self.sm.yView, Bast=self.Bast)
self.gui.strat_ax.set_ylim(ylims)
self.gui.strat_ax.set_xlim(-self.sm.Bb / 2, self.sm.Bb / 2)
# vertical exagg text
if i % 10 == 0:
self.axbbox = self.gui.strat_ax.get_window_extent().transformed(
self.fig.dpi_scale_trans.inverted())
width, height = self.axbbox.width, self.axbbox.height
self.VE_val.set_text(
'VE = ' +
str(round((self.sm.Bb / width) / (self.sm.yView / height), 1)))
return self.BastLine, self.VE_val, \
self.channelBodyPatchCollection, self.activeChannelPatchCollection
class SceneVisualizer:
"""Context for social nav vidualization"""
def __init__(self,
scene,
output=None,
limits=None,
writer="imagemagick",
cmap="viridis",
agent_colors=None,
**kwargs):
self.scene = scene
self.states, self.group_states = self.scene.get_states()
self.cmap = cmap
self.agent_colors = agent_colors
self.frames = self.scene.get_length()
self.output = output
self.writer = writer
self.limits = limits
self.fig, self.ax = plt.subplots(**kwargs)
self.ani = None
self.group_actors = None
self.group_collection = PatchCollection([])
self.group_collection.set(
animated=True,
alpha=0.2,
cmap=self.cmap,
facecolors="none",
edgecolors="purple",
linewidth=2,
clip_on=True,
)
self.human_actors = None
self.human_collection = PatchCollection([])
self.human_collection.set(animated=True,
alpha=0.6,
cmap=self.cmap,
clip_on=True)
def plot(self):
"""Main method for create plot"""
self.plot_obstacles()
self.plot_fires()
self.plot_exits()
groups = self.group_states[0] # static group for now
if not groups:
for ped in range(self.scene.peds.size()):
x = self.states[:, ped, 0]
y = self.states[:, ped, 1]
self.ax.plot(x, y, "-o", label=f"ped {ped}", markersize=2.5)
else:
colors = plt.cm.rainbow(np.linspace(0, 1, len(groups)))
for i, group in enumerate(groups):
for ped in group:
x = self.states[:, ped, 0]
y = self.states[:, ped, 1]
self.ax.plot(x,
y,
"-o",
label=f"ped {ped}",
markersize=2.5,
color=colors[i])
self.ax.legend()
return self.fig
def animate(self):
"""Main method to create animation"""
self.ani = mpl_animation.FuncAnimation(
self.fig,
init_func=self.animation_init,
func=self.animation_update,
frames=self.frames,
blit=True,
interval=200,
)
return self.ani
def __enter__(self):
logger.info("Start plotting.")
self.fig.set_tight_layout(True)
self.ax.grid(linestyle="dotted")
self.ax.set_aspect("equal")
self.ax.margins(2.0)
self.ax.set_axisbelow(True)
self.ax.set_xlabel("x [m]")
self.ax.set_ylabel("y [m]")
plt.rcParams["animation.html"] = "jshtml"
# x, y limit from states, only for animation
margin = 2.0
if self.limits is None:
xy_limits = np.array([minmax(state) for state in self.states
]) # (x_min, y_min, x_max, y_max)
xy_min = np.min(xy_limits[:, :2], axis=0) - margin
xy_max = np.max(xy_limits[:, 2:4], axis=0) + margin
self.ax.set(xlim=(xy_min[0], xy_max[0]),
ylim=(xy_min[1], xy_max[1]))
else:
limits = self.limits
self.ax.set(xlim=(limits[0] - margin, limits[1] + margin),
ylim=(limits[2] - margin, limits[3] + margin))
# # recompute the ax.dataLim
# self.ax.relim()
# # update ax.viewLim using the new dataLim
# self.ax.autoscale_view()
return self
def __exit__(self, exception_type, exception_value, traceback):
if exception_type:
logger.error(
f"Exception type: {exception_type}; Exception value: {exception_value}; Traceback: {traceback}"
)
logger.info("Plotting ends.")
if self.output:
if self.ani:
output = self.output + ".gif"
logger.info(f"Saving animation as {output}")
self.ani.save(output, writer=self.writer)
else:
output = self.output + ".png"
logger.info(f"Saving plot as {output}")
self.fig.savefig(output, dpi=300)
plt.close(self.fig)
def plot_human(self, step=-1):
"""Generate patches for human
:param step: index of state, default is the latest
:return: list of patches
"""
states, _ = self.scene.get_states()
current_state = states[step]
# radius = 0.2 + np.linalg.norm(current_state[:, 2:4], axis=-1) / 2.0 * 0.3
radius = [0.2] * current_state.shape[0]
if self.human_actors:
for i, human in enumerate(self.human_actors):
human.center = current_state[i, :2]
human.set_radius(0.2)
# human.set_radius(radius[i])
else:
self.human_actors = [
Circle(pos, radius=r)
for pos, r in zip(current_state[:, :2], radius)
]
self.human_collection.set_paths(self.human_actors)
if not self.agent_colors:
self.human_collection.set_array(np.arange(current_state.shape[0]))
else:
# set colors for each agent
assert len(self.human_actors) == len(
self.agent_colors
), "agent_colors must be the same length as the agents"
self.human_collection.set_facecolor(self.agent_colors)
def plot_groups(self, step=-1):
"""Generate patches for groups
:param step: index of state, default is the latest
:return: list of patches
"""
states, group_states = self.scene.get_states()
current_state = states[step]
current_groups = group_states[step]
if self.group_actors: # update patches, else create
points = [current_state[g, :2] for g in current_groups]
for i, p in enumerate(points):
self.group_actors[i].set_xy(p)
else:
self.group_actors = [
Polygon(current_state[g, :2]) for g in current_groups
]
self.group_collection.set_paths(self.group_actors)
def plot_obstacles(self):
for s in self.scene.get_obstacles():
self.ax.add_patch(
Rectangle((s[:, 0][0], s[:, 1][0]),
s[:, 0][-1] - s[:, 0][0],
s[:, 1][-1] - s[:, 1][0],
fill=True,
color="black"))
def plot_fires(self):
if self.scene.get_fires() is not None:
f = self.scene.get_fires()[0]
self.ax.add_patch(
Rectangle((f[:, 0][0], f[:, 1][0]),
f[:, 0][-1] - f[:, 0][0],
f[:, 1][-1] - f[:, 1][0],
fill=True,
color="red"))
if len(self.scene.get_fires()) > 1:
b = self.scene.get_fires()[1]
self.ax.add_patch(
Rectangle((b[:, 0][0], b[:, 1][0]),
b[:, 0][-1] - b[:, 0][0],
b[:, 1][-1] - b[:, 1][0],
fill=False,
color="red"))
def plot_exits(self):
if self.scene.get_exits() is not None:
for e in self.scene.get_exits():
# continue
# self.ax.add_patch(Circle((e[0],e[1]), 1, fill=True, color="green", alpha=0.1))
self.ax.add_patch(
Circle((e[0], e[1]),
e[2],
fill=True,
color="green",
alpha=0.1))
def plot_smoke(self, step=-1):
if self.scene.get_fires() is not None:
f = self.scene.get_fires()[0]
fcx = f[:, 0][0] + (f[:, 0][-1] - f[:, 0][0]) / 2
fcy = f[:, 1][0] + (f[:, 1][-1] - f[:, 1][0]) / 2
rad = self.scene.peds.get_smoke_radii()[step]
self.ax.add_patch(
Circle((fcx, fcy), rad, fill=True, color="black", alpha=0.005))
def animation_init(self):
self.plot_obstacles()
self.plot_fires()
self.plot_exits()
self.ax.add_collection(self.group_collection)
self.ax.add_collection(self.human_collection)
return (self.group_collection, self.human_collection)
def animation_update(self, i):
self.plot_groups(i)
self.plot_human(i)
self.plot_smoke(i)
return (self.group_collection, self.human_collection)
def plot_data(self):
fig, ax = plt.subplots()
escaped = [
i / self.scene.peds.get_nr_peds() * 100
for i in self.scene.peds.escaped
]
health = [i * 100 for i in self.scene.peds.av_health]
panic = [i * 100 for i in self.scene.peds.av_panic]
dead = [
i / self.scene.peds.get_nr_peds() * 100
for i in self.scene.peds.dead
]
timesteps = [t for t in range(len(escaped))]
ax.plot(timesteps, escaped, color="green", label="escaped")
ax.plot(timesteps, health, color="red", label="health")
ax.plot(timesteps, panic, color="purple", label="panic")
ax.plot(timesteps, dead, color="black", label="dead")
ax.set_xlabel("Timestep")
ax.set_ylabel("[%]")
ax.set_ylim([0, 100])
ax.set_title(
"Number of escaped and dead people and average health and panic.")
ax.legend()
ax.grid(linestyle="dotted")
fig.savefig(self.output + "_data.png")
logger.info("Created plot of data.")
class SceneVisualizer:
"""Context for social nav vidualization"""
def __init__(self,
scene,
output=None,
writer="imagemagick",
cmap="viridis",
**kwargs):
self.scene = scene
self.states, self.group_states = self.scene.get_states()
self.cmap = cmap
self.frames = self.scene.get_length()
self.output = output
self.writer = writer
self.fig, self.ax = plt.subplots(**kwargs)
self.ani = None
self.group_actors = None
self.group_collection = PatchCollection([])
self.group_collection.set(
animated=True,
alpha=0.2,
cmap=self.cmap,
facecolors="none",
edgecolors="purple",
linewidth=2,
clip_on=True,
)
self.human_actors = None
self.human_collection = PatchCollection([])
self.human_collection.set(animated=True,
alpha=0.6,
cmap=self.cmap,
clip_on=True)
def plot(self):
"""Main method for create plot"""
self.plot_obstacles()
groups = self.group_states[0] # static group for now
if not groups:
for ped in range(self.scene.peds.size()):
x = self.states[:, ped, 0]
y = self.states[:, ped, 1]
self.ax.plot(x, y, "-o", label=f"ped {ped}", markersize=2.5)
else:
colors = plt.cm.rainbow(np.linspace(0, 1, len(groups)))
for i, group in enumerate(groups):
for ped in group:
x = self.states[:, ped, 0]
y = self.states[:, ped, 1]
self.ax.plot(x,
y,
"-o",
label=f"ped {ped}",
markersize=2.5,
color=colors[i])
self.ax.legend()
return self.fig
def animate(self):
"""Main method to create animation"""
self.ani = mpl_animation.FuncAnimation(
self.fig,
init_func=self.animation_init,
func=self.animation_update,
frames=self.frames,
blit=True,
)
return self.ani
def __enter__(self):
logger.info("Start plotting.")
self.fig.set_tight_layout(True)
self.ax.grid(linestyle="dotted")
self.ax.set_aspect("equal")
self.ax.margins(2.0)
self.ax.set_axisbelow(True)
self.ax.set_xlabel("x [m]")
self.ax.set_ylabel("y [m]")
plt.rcParams["animation.html"] = "jshtml"
# x, y limit from states, only for animation
margin = 2.0
xy_limits = np.array([minmax(state) for state in self.states
]) # (x_min, y_min, x_max, y_max)
xy_min = np.min(xy_limits[:, :2], axis=0) - margin
xy_max = np.max(xy_limits[:, 2:4], axis=0) + margin
self.ax.set(xlim=(xy_min[0], xy_max[0]), ylim=(xy_min[1], xy_max[1]))
# # recompute the ax.dataLim
# self.ax.relim()
# # update ax.viewLim using the new dataLim
# self.ax.autoscale_view()
return self
def __exit__(self, exception_type, exception_value, traceback):
if exception_type:
logger.error(
f"Exception type: {exception_type}; Exception value: {exception_value}; Traceback: {traceback}"
)
logger.info("Plotting ends.")
if self.output:
if self.ani:
output = self.output + ".gif"
logger.info(f"Saving animation as {output}")
self.ani.save(output, writer=self.writer)
else:
output = self.output + ".png"
logger.info(f"Saving plot as {output}")
self.fig.savefig(output, dpi=300)
plt.close(self.fig)
def plot_human(self, step=-1):
"""Generate patches for human
:param step: index of state, default is the latest
:return: list of patches
"""
states, _ = self.scene.get_states()
current_state = states[step]
# radius = 0.2 + np.linalg.norm(current_state[:, 2:4], axis=-1) / 2.0 * 0.3
radius = [0.2] * current_state.shape[0]
if self.human_actors:
for i, human in enumerate(self.human_actors):
human.center = current_state[i, :2]
human.set_radius(0.2)
# human.set_radius(radius[i])
else:
self.human_actors = [
Circle(pos, radius=r)
for pos, r in zip(current_state[:, :2], radius)
]
self.human_collection.set_paths(self.human_actors)
self.human_collection.set_array(np.arange(current_state.shape[0]))
def plot_groups(self, step=-1):
"""Generate patches for groups
:param step: index of state, default is the latest
:return: list of patches
"""
states, group_states = self.scene.get_states()
current_state = states[step]
current_groups = group_states[step]
if self.group_actors: # update patches, else create
points = [current_state[g, :2] for g in current_groups]
for i, p in enumerate(points):
self.group_actors[i].set_xy(p)
else:
self.group_actors = [
Polygon(current_state[g, :2]) for g in current_groups
]
self.group_collection.set_paths(self.group_actors)
def plot_obstacles(self):
for s in self.scene.get_obstacles():
self.ax.plot(s[:, 0], s[:, 1], "-o", color="black", markersize=2.5)
def animation_init(self):
self.plot_obstacles()
self.ax.add_collection(self.group_collection)
self.ax.add_collection(self.human_collection)
return (self.group_collection, self.human_collection)
def animation_update(self, i):
self.plot_groups(i)
self.plot_human(i)
return (self.group_collection, self.human_collection)