import cauldron as cd
import plotly.graph_objs as go
from cauldron import plotting
from tracksim.coupling import prss
from tracksim.coupling.plotting import scatter
cd.refresh(prss)
cd.display.markdown("""
## Persistent Residuals (PRSS)
""")
df = cd.shared.df
data = prss.compute_many(cd.shared.trials)
fitness = prss.to_fitness(data['prss_norm'])
df['persistence'] = [fitness[tid] for tid in df.id]
cd.display.plotly(
data=scatter.create(
data_frame=df,
value_column=[data['prss'][tid].value for tid in df.id],
uncertainty_column=[data['prss'][tid].uncertainty for tid in df.id],
x_column='order'),
layout=plotting.create_layout(
{'hovermode': 'closest'},
title='Coupling Length Persistent Residual Sum of Squares',
x_label='Trial Index (#)',
y_label='Persistent RSS'))
cd.display.plotly(
data=scatter.create(df, [data['prss_norm'][tid].value for tid in df.id],
import cauldron as cd
from tracksim.coupling import loader as tk_loader
cd.refresh(tk_loader)
data = tk_loader.load([
'CRO-500-2004-3-S-2_v0_full_dc50',
'CRO-500-2004-3-S-2_v0_full',
'CRO-500-2004-3-S-2_v0_full_dc75'
],
row_filter=tk_loader.redundant_filter
)
cd.shared.groups = data['groups']
cd.shared.trials = data['trials']
cd.shared.df = data['df']
cd.display.table(data['df'][[
'short_id', 'coupling_length', 'uncertainty',
'start_time', 'end_time'
]])
import cauldron as cd
import pandas as pd
from cauldron import plotting
from plotly import graph_objs as go
from tracksim import coupling
from tracksim.coupling.plotting import stem
cd.refresh(coupling)
cd.refresh(stem)
df = cd.shared.df # type: pd.DataFrame
cd.display.header('Solution Fitness')
traces = []
for gait_id in df.gait_id.unique():
df_slice = df[df.gait_id == gait_id]
traces.append(go.Scatter(
x=df_slice['persistence'],
y=df_slice['swing'],
mode='markers',
marker={
'size': 8,
'color': df_slice.iloc[0].color
},
name=gait_id,
text=df_slice.short_id
))
cd.display.plotly(
data=traces,
import cauldron as cd
import plotly.graph_objs as go
from cauldron import plotting
from tracksim.coupling import swing
from tracksim.coupling.plotting import scatter
cd.refresh(swing)
df = cd.shared.df
swing_data = swing.compute_many(cd.shared.trials)
fitness = swing.to_fitness(swing_data)
df['swing'] = [fitness[tid] for tid in df.id]
cd.display.plotly(
data=scatter.create(df, [swing_data[tid].value for tid in df.id],
[swing_data[tid].uncertainty for tid in df.id],
x_column='order'),
layout=plotting.create_layout(title='Normalized Swing by Trial',
x_label='Trial Index (#)',
y_label='Swing'))
cd.display.plotly(data=go.Bar(y=df.swing,
text=df.short_id,
marker=dict(color=df.color)),
layout=plotting.create_layout(
title='Swing Deviation',
x_label='Trial Index (#)',
y_label='Fractional Deviation'))
import cauldron as cd
import plotly.graph_objs as go
from cauldron import plotting
from tracksim.coupling import swing
from tracksim.coupling.plotting import scatter
cd.refresh(swing)
df = cd.shared.df
swing_data = swing.compute_many(cd.shared.trials)
fitness = swing.to_fitness(swing_data)
df['swing'] = [fitness[tid] for tid in df.id]
cd.display.plotly(
data=scatter.create(
df,
[swing_data[tid].value for tid in df.id],
[swing_data[tid].uncertainty for tid in df.id],
x_column='order'
),
layout=plotting.create_layout(
title='Normalized Swing by Trial',
x_label='Trial Index (#)',
y_label='Swing'
)
)
cd.display.plotly(
data=go.Bar(
y=df.swing,
text=df.short_id,
import cauldron as cd
from cauldron import plotting
from tracksim.coupling.plotting import band
cd.refresh(band)
df = cd.shared.df.sort_values(by='fitness', ascending=False).head()
cd.display.header('Highest Fitness Trials', 2)
cd.display.markdown(
"""
The following are the coupling length plots for the simulation trials
with the highest fitness values
"""
)
def plot(trial):
plot_data = band.coupling(trial)
cd.display.plotly(
data=plot_data['data'],
layout=plotting.create_layout(
plot_data['layout'],
title='{} Coupling Length'.format(trial['short_id']),
x_label='Cycle (#)',
y_label='Coupling Length (m)'
)
)
ids = df['id'].tolist()
import cauldron as cd
from cauldron import plotting
from tracksim.coupling.plotting import band
cd.refresh(band)
df = cd.shared.df.sort_values(by='fitness', ascending=False).head()
cd.display.header('Highest Fitness Trials', 2)
cd.display.markdown("""
The following are the coupling length plots for the simulation trials
with the highest fitness values
""")
def plot(trial):
plot_data = band.coupling(trial)
cd.display.plotly(data=plot_data['data'],
layout=plotting.create_layout(
plot_data['layout'],
title='{} Coupling Length'.format(trial['short_id']),
x_label='Cycle (#)',
y_label='Coupling Length (m)'))
ids = df['id'].tolist()
for t in cd.shared.trials:
if t['id'] in ids:
plot(t)
import cauldron as cd
import pandas as pd
from cauldron import plotting
from plotly import graph_objs as go
from tracksim import coupling
from tracksim.coupling.plotting import stem
cd.refresh(coupling)
cd.refresh(stem)
df = cd.shared.df # type: pd.DataFrame
cd.display.header('Solution Fitness')
traces = []
for gait_id in df.gait_id.unique():
df_slice = df[df.gait_id == gait_id]
traces.append(
go.Scatter(x=df_slice['persistence'],
y=df_slice['swing'],
mode='markers',
marker={
'size': 8,
'color': df_slice.iloc[0].color
},
name=gait_id,
text=df_slice.short_id))
cd.display.plotly(data=traces,
layout=plotting.create_layout({'hovermode': 'closest'},
title='Fitness Parameters',
import cauldron as cd
import pandas as pd
import plotly.graph_objs as go
from cauldron import plotting
from tracksim.coupling.plotting import scatter
cd.refresh(scatter)
df = cd.shared.df # type: pd.DataFrame
cd.display.markdown(
"""
## Median Coupling Lengths
The median coupling length values, and their uncertainties, for each
simulation are shown below ordered from smallest to largest.
"""
)
cd.display.plotly(
data=scatter.create(df, 'coupling_length', 'uncertainty', 'order'),
layout=plotting.create_layout(
title='Median Coupling Lengths by Trial',
x_label='Trial Index (#)',
y_label='Coupling Length (m)'
)
)
cd.display.markdown(
"""
The uncertainties in the simulation trials do not scale with coupling
import cauldron as cd
import plotly.graph_objs as go
from cauldron import plotting
from tracksim import coupling
cd.refresh(coupling)
df = cd.shared.df
dist_data = coupling.distribution(df, 1.23, 2.1, 0.13)
distribution_trace = go.Scatter(
x=dist_data['values']['x'],
y=dist_data['values']['y'],
name='Distribution',
mode='lines',
fill='tozeroy'
)
segment_trace = go.Scatter(
x=dist_data['segment']['x'],
y=dist_data['segment']['y'],
mode='lines',
name='Region',
line=go.Line(
color=plotting.get_color(1, 1.0),
width=4
)
)
gaussian_trace = go.Scatter(
x=dist_data['gauss']['x'],