def _show_graph(self, data_frame):
#create the plot panels for the Agent_Tasks
panels = self._create_task_panels(data_frame)
top_left_x_range = panels[list(panels.keys())[0]]['panel1'].x_range
#Altitude over ground
pAltitude = figure(plot_width=800,
plot_height=300,
x_range=top_left_x_range)
alti_legend = []
# Setting the second y axis range name and range
pAltitude.extra_y_ranges = {"speed": Range1d(50, 120)}
# Adding the second axis to the plot.
pAltitude.add_layout(
LinearAxis(y_range_name="speed", axis_label="IAS, TAS [Knots]"),
'right')
altitudeLine = pAltitude.line(data_frame.index * self.step_time,
data_frame['position_h_sl_ft'],
line_width=2,
color=Viridis4[2])
alti_legend.append(("Altitude [ftsl]", [altitudeLine]))
kiasLine = pAltitude.line(data_frame.index * self.step_time,
data_frame['velocities_vc_kts'],
line_width=2,
y_range_name="speed",
color=Viridis4[1])
alti_legend.append(("Indicated Airspeed [KIAS]", [kiasLine]))
tasLine = pAltitude.line(data_frame.index * self.step_time,
data_frame['velocities_vtrue_kts'],
line_width=2,
y_range_name="speed",
color=Viridis4[0])
alti_legend.append(("True Airspeed [KAS]", [tasLine]))
pAltitude.extra_y_ranges.renderers = [
kiasLine, tasLine
] #this does not quite work: https://stackoverflow.com/questions/48631530/bokeh-twin-axes-with-datarange1d-not-well-scaling
pAltitude.y_range.renderers = [altitudeLine]
lg_alti = Legend(items=alti_legend,
location=(0, 10),
glyph_width=25,
label_width=190)
lg_alti.click_policy = "hide"
pAltitude.add_layout(lg_alti, 'right')
tAlti = Title()
tAlti.text = 'Altitude and Speed [IAS, TAS] over Timesteps'
pAltitude.title = tAlti
pAltitude.xaxis.axis_label = 'timestep [s]'
pAltitude.yaxis[0].axis_label = 'Altitude [ftsl]'
pAltitude.legend.location = "center_right"
pSideslip = figure(plot_width=800,
plot_height=300,
x_range=top_left_x_range)
slip_legend = []
slip_skid_line = pSideslip.line(data_frame.index * self.step_time,
data_frame['aero_beta_deg'],
line_width=2,
color=Viridis4[2])
slip_legend.append(("Sideslip", [slip_skid_line]))
pSideslip.y_range.renderers = [slip_skid_line]
lg_slip = Legend(items=slip_legend,
location=(0, 10),
glyph_width=25,
label_width=190)
lg_slip.click_policy = "hide"
pSideslip.add_layout(lg_slip, 'right')
tSlip = Title()
tSlip.text = 'Sideslip'
pSideslip.title = tSlip
pSideslip.xaxis.axis_label = 'timestep [s]'
pSideslip.yaxis[0].axis_label = 'Sideslip [deg]'
pSideslip.legend.location = "center_right"
#activate the zooming on all plots
#this is not nice, but this not either: https://stackoverflow.com/questions/49282688/how-do-i-set-default-active-tools-for-a-bokeh-gridplot
pAltitude.toolbar.active_scroll = pAltitude.toolbar.tools[
1] #this selects the WheelZoomTool instance
pSideslip.toolbar.active_scroll = pSideslip.toolbar.tools[
1] #this selects the WheelZoomTool instance
reset_output()
# if self.env.meta_dict['model_type'] == 'trained':
# discriminator = self.env.meta_dict['model_base_name']+"_%+.2f" % (data_frame['reward'].sum())
# self.env.meta_dict['model_discriminator'] = discriminator
# else:
# discriminator = self.env.meta_dict['model_discriminator']
ts = time.time()
overshoot_frames_per_task = self._analyze_overshoot(data_frame)
overshoot_divs = [
Div(text=ovs_fr.round(3).to_html(), width=600)
for ovs_fr in overshoot_frames_per_task
]
print("Overshoot analysis done in %.2f sec" % (time.time() - ts))
ts = time.time()
settlement_times_per_task = self._analyze_settle_times(data_frame)
settlement_divs = [
Div(text=settle_fr.round(3).to_html(), width=600)
for settle_fr in settlement_times_per_task
]
print("Settlement analysis done in %.2f sec" % (time.time() - ts))
panel_grid = []
panel_grid.append([
Div(text='<h3>' + t.name + '</h3>', id='div_' + t.name)
for t in self.env.task_list
])
# to switch on and off the statistics panels, this is unfortuntely the best, I could achive
# https://stackoverflow.com/a/52416676/2682209
cols = []
checkbox = CheckboxGroup(
labels=["show stats"], active=[],
width=100) #checkbox is added to header_col later on
for i, t in enumerate(self.env.task_list):
# overshoot_stat = overshoot_divs[i]
c = column(Div()) #empty for the beginning
cols.append(c)
callback = CustomJS(args=dict(overshoot_divs=overshoot_divs,
settlement_divs=settlement_divs,
cols=cols,
checkbox=checkbox),
code="""
for (var j = 0; j < cols.length; j++) {
console.log('col', j)
const children = []
for (const i of checkbox.active) {
console.log('active', i)
children.push(overshoot_divs[j])
children.push(settlement_divs[j])
}
console.log('children', children)
cols[j].children = children
}
""")
checkbox.js_on_change('active', callback)
# show_stats_btn = [Div(text="""
# <button onclick="display_event(%s)">Try it</button>
# """ %t.name for t in self.env.task_list]
# for t, b in zip(self.env.task_list, show_stats_btn):
# b.tags = ['id', 'btn_'+t.name]
# panel_grid.append(show_stats_btn)
# [b.js_on_event(events.ButtonClick, display_event(b, t.name)) for t, b in zip(self.env.task_list, show_stats_btn)]
# panel_grid.append(chkbxs)
panel_grid.append(cols)
panel_grid_t = [[
panels[name]['panel1'], panels[name]['panel2'],
panels[name]['panel3']
] for name in self.task_names]
[panel_grid.append(fig) for fig in list(zip(*panel_grid_t))]
# add the additional plots
panel_grid.append([pAltitude, pSideslip])
panel_grid_plot = gridplot(panel_grid,
toolbar_location='right',
sizing_mode='stretch_width')
#for string formatting look here: https://pyformat.info/
titleString = ''
if 'experiment_name' in self.env.meta_dict:
titleString += "{}: ".format(self.env.meta_dict['experiment_name'])
titleString += "Run Date: {}; ".format(
datetime.datetime.now().strftime("%c"))
if 'train_step' in self.env.meta_dict:
titleString += "Training Step: {}; ".format(
self.env.meta_dict['train_step'])
if 'episode_number' in self.env.meta_dict:
titleString += "Episode: {}; ".format(
self.env.meta_dict['episode_number'])
if 'csv_line_nr' in self.env.meta_dict:
titleString += "Env in CSV line: {}; ".format(
self.env.meta_dict['csv_line_nr'])
# titleString += "Total Reward: {:.2f}; ".format(data_frame['reward'].sum())
# titleString += "Model Discriminator: {};".format(self.env.meta_dict['model_discriminator'])
header_col = column(
Div(text="<h1>" + self.env.unwrapped.spec.id +
(" - " + self.env.meta_dict['env_info']) if 'env_info' in
self.meta_dict else "" + "</h1>"),
row(Div(text="<h2>" + titleString + "</h2>", width=1200),
checkbox))
webpage = gridplot([[header_col], [panel_grid_plot]],
toolbar_location=None,
sizing_mode='stretch_width')
base_filename = 'Run_' + '_'.join([tsk.name for tsk in self.task_list])
html_output_name = os.path.join(self.save_path, 'plots',
base_filename + '_latest.html')
os.makedirs(os.path.dirname(html_output_name), exist_ok=True)
if self.showNextPlotFlag:
output_file(
html_output_name, mode='inline'
) #mode='absolute') #use mode='absolute' to make it work offline with the js and css installed in the bokeh package locally
if self.firstRun:
show(webpage) #opens up a new browser window
self.firstRun = False
else:
save(
webpage
) #just updates the HTML; Manual F5 in browser required :-(, (There must be a way to push...)
if self.exportNextPlotFlag and self.save_path:
#build the filename including the individual rewards
task_rewards = [
self.reward_variables[i].get_legal_name()
for i in range(len(self.task_list))
]
task_names_with_rewards = [
t.name + '_' + f'{data_frame[task_rewards[i]].sum():.2f}'
for i, t in enumerate(self.task_list)
]
name_with_rewards = 'Run_' + '_'.join(
task_names_with_rewards) + 'time_{}'.format(
datetime.datetime.now().strftime("%H-%M-%S"))
base_filename = os.path.join(
self.save_path, 'plots', name_with_rewards
) # 'glideAngle_Elevator_Reward_{:.2f}_time_{}'.format(data_frame['reward'].sum(), datetime.datetime.now().strftime("%H-%M-%S")))
if self.showNextPlotFlag:
#we keep the html as well for easy exploration
shutil.copyfile(html_output_name, base_filename + '.html')
def export(webpage):
png_filename = base_filename + '.png'
webpage.width = 1800 #set the width of the page instead of passing a width parameter to the export; https://stackoverflow.com/a/61563173/2682209
export_png(
webpage, filename=png_filename
) #TODO: the width parameter is ignored in bokeh/io/export.py get_layout_html() as webpage isn't a Plot
export(
gridplot([[header_col], [panel_grid_plot]],
toolbar_location=None))
self.showNextPlotFlag = False #only show the plot once and then reset
self.exportNextPlotFlag = False
print("Output Plot generated: " + titleString)
def _create_task_panels(self, data_frame):
panels = {}
top_left_x_range = None
for i, t in enumerate(self.env.task_list):
panels[t.name] = {}
# Panel 1: Setpoint and command panel
pCtrl = figure(plot_width=800, plot_height=300)
ctrl_legend = []
# Setting the second y axis range name and range
pCtrl.extra_y_ranges = {
t.name + '_cmd': Range1d(start=-1, end=1)
} # this should query the action space
# Adding the second axis to the plot.
pCtrl.add_layout(
LinearAxis(y_range_name=t.name + '_cmd',
axis_label=t.name + '_cmd [norm.]'), 'right')
try:
name = self.panel_contents[
t.name]['panel1']['action_prop'].get_legal_name(
) #maybe there are more entries in the future
action_Line = pCtrl.line(data_frame.index * self.step_time,
data_frame[name],
line_width=1,
y_range_name=t.name + '_cmd',
color=Viridis4[1])
ctrl_legend.append((t.name + ' Cmd.', [action_Line]))
except KeyError:
#we have no action prop, only setpoints to be evaluated
pass
try:
name = self.panel_contents[
t.name]['panel1']['current_value_prop'].get_legal_name()
current_value_line = pCtrl.line(data_frame.index *
self.step_time,
data_frame[name],
line_width=2,
color=Viridis4[0])
ctrl_legend.append((name, [current_value_line]))
name = self.panel_contents[
t.name]['panel1']['setpoint_value_prop'].get_legal_name()
setpoint_value_line = pCtrl.line(data_frame.index *
self.step_time,
data_frame[name],
line_width=2,
color=Viridis4[3])
ctrl_legend.append((name, [setpoint_value_line]))
except KeyError:
#there is no setpoint to be displayed, only actuations
pass
ctrl_lg = Legend(items=ctrl_legend,
location=(0, 10),
glyph_width=25,
label_width=190)
ctrl_lg.click_policy = "hide"
pCtrl.add_layout(ctrl_lg, 'right')
pCtrl.toolbar.active_scroll = pCtrl.toolbar.tools[
1] #this selects the WheelZoomTool instance
# Add the title...
tCtrl = Title()
tCtrl.text = 'Controlled Value over Time'
pCtrl.title = tCtrl
pCtrl.xaxis.axis_label = 'timestep [s]'
pCtrl.yaxis[0].axis_label = 'Controlled Value'
if not top_left_x_range:
top_left_x_range = pCtrl.x_range
else:
pCtrl.x_range = top_left_x_range
panels[t.name].update({'panel1': pCtrl})
#Panel 2: Rewards and reward components
pRwd = figure(plot_width=1057,
plot_height=300,
x_range=top_left_x_range)
rwd_cmp_lines = []
reward_legend = []
cmp_names = self.panel_contents[
t.name]['panel2']['reward_component_names']
for idx, rwd_component in enumerate(cmp_names):
rwd_cmp_lines.append(
pRwd.line(data_frame.index * self.step_time,
data_frame[rwd_component],
line_width=2,
color=Viridis7[idx % 6]))
reward_legend.append((rwd_component, [rwd_cmp_lines[-1]]))
name = self.panel_contents[
t.name]['panel2']['reward_prop'].get_legal_name()
reward_line = pRwd.line(data_frame.index * self.step_time,
data_frame[name],
line_width=2,
color=Viridis7[6])
reward_legend.append((name, [reward_line]))
reward_lg = Legend(items=reward_legend,
location=(48, 10),
glyph_width=25,
label_width=190)
reward_lg.click_policy = "hide"
pRwd.add_layout(reward_lg, 'right')
pRwd.toolbar.active_scroll = pRwd.toolbar.tools[
1] #this selects the WheelZoomTool instance
#Add the title
tReward = Title()
tReward.text = f'{t.name}: last Reward over {data_frame[name].size-1} Timesteps (∑ = {data_frame[name].sum():.2f})'
pRwd.title = tReward
pRwd.xaxis.axis_label = 'timestep [s]'
pRwd.yaxis[0].axis_label = 'actual Reward [norm.]'
panels[t.name].update({'panel2': pRwd})
#Panel 3: Presented Observations
pState = figure(plot_width=1057,
plot_height=300,
x_range=top_left_x_range)
# Setting the second y axis range name and range
norm_state_extents = 10
pState.extra_y_ranges = {
"normalized_data":
Range1d(start=-norm_state_extents, end=norm_state_extents)
}
# Adding the second axis to the plot.
pState.add_layout(
LinearAxis(y_range_name="normalized_data",
axis_label="normalized data"), 'right')
state_lines = []
state_legend = []
normalized_state_lines = []
obs_props_names = [
o_prp.get_legal_name()
for o_prp in self.panel_contents[t.name]['panel3']['obs_props']
]
for idx, state_name in enumerate(obs_props_names):
if (data_frame[state_name].max() <= norm_state_extents and
data_frame[state_name].min() >= -norm_state_extents):
normalized_state_lines.append(
pState.line(data_frame.index * self.step_time,
data_frame[state_name],
line_width=2,
y_range_name="normalized_data",
color=Inferno7[idx % 7],
visible=False))
state_legend.append(
("norm_" + state_name, [normalized_state_lines[-1]]))
else:
state_lines.append(
pState.line(data_frame.index * self.step_time,
data_frame[state_name],
line_width=2,
color=Inferno7[idx % 7],
visible=False))
state_legend.append((state_name, [state_lines[-1]]))
pState.y_range.renderers = state_lines
pState.extra_y_ranges.renderers = normalized_state_lines #this does not quite work: https://stackoverflow.com/questions/48631530/bokeh-twin-axes-with-datarange1d-not-well-scaling
lg_state = Legend(items=state_legend,
location=(0, 10),
glyph_width=25,
label_width=190)
lg_state.click_policy = "hide"
pState.add_layout(lg_state, 'right')
pState.toolbar.active_scroll = pState.toolbar.tools[
1] #this selects the WheelZoomTool instance
#Add the title
tState = Title()
tState.text = 'Actual Observation presented to Agent'
pState.title = tState
pState.xaxis.axis_label = 'timestep [s]'
pState.yaxis[0].axis_label = 'state data'
panels[t.name].update({'panel3': pState})
return panels