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plots.jl
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plots.jl
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#= Spacecraft rendezvous plots.
Sequential convex programming algorithms for trajectory optimization.
Copyright (C) 2021 Autonomous Controls Laboratory (University of Washington)
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses/>. =#
using PyPlot
using Colors
# ..:: Globals ::..
const fig_opts = Dict(
"font.family" => "serif",
"text.latex.preamble" => string(
"\\usepackage{newtxtext}",
"\\usepackage{newtxmath}",
"\\usepackage{siunitx}",
),
)
const gray = rgb2pyplot(weighted_color_mean(0.1, colorant"black", colorant"white"))
const red = rgb2pyplot(weighted_color_mean(0.2, parse(RGB, Red), colorant"white"))
const time_mark_clr = rgb2pyplot(weighted_color_mean(0.5, colorant"black", colorant"white"))
# ..:: Methods ::..
"""
plot_trajectory_2d(mdl, sol[; attitude])
Plot the final converged trajectory, projected onto 2D planes.
# Arguments
- `mdl`: the problem description object.
- `sol`: the problem solution.
# Keywords
- `attitude`: (optional) whether to also plot the attitude time history.
"""
function plot_trajectory_2d(
mdl::RendezvousProblem,
sol::SCPSolution;
attitude::Bool = false,
)::Nothing
# Parameters
algo = sol.algo
veh = mdl.vehicle
traj = mdl.traj
axis_inf_scale = 1e4
pad_x = 0.1
pad_y = 0.1
pad_label_abs = 10
equal_ar = true # Equal aspect ratio
input_scale = 0.2
# Plotting data
ct_res = 1000
dt_res = length(sol.td)
ct_τ = RealVector(LinRange(0, 1, ct_res))
dt_pos = sol.xd[veh.id_r, :]
ct_pos = hcat([sample(sol.xc, τ)[veh.id_r] for τ in ct_τ]...)
ct_vel = hcat([sample(sol.xc, τ)[veh.id_v] for τ in ct_τ]...)
ct_speed = squeeze(mapslices(norm, ct_vel, dims = (1)))
n_rcs = length(veh.id_rcs)
N = size(sol.ud, 2)
dt_thrust = zeros(3, N)
dir_rcs = [veh.csm.f_rcs[veh.csm.rcs_select[i]] for i = 1:n_rcs]
for k = 1:size(dt_thrust, 2)
q = Quaternion(sol.xd[veh.id_q, k])
dir_rcs_iner = [rotate(dir_rcs[i], q) for i = 1:n_rcs]
dt_thrust[:, k] = sum(sol.ud[veh.id_rcs[i], k] * dir_rcs_iner[i] for i = 1:n_rcs)
end
# Colormap
v_cmap =
generate_colormap("inferno"; minval = minimum(ct_speed), maxval = maximum(ct_speed))
data = [
Dict(:prj => [1, 3], :x_name => "x", :y_name => "z"),
Dict(:prj => [1, 2], :x_name => "x", :y_name => "y"),
Dict(:prj => [2, 3], :x_name => "y", :y_name => "z"),
]
num_plts = length(data)
if !attitude
fig = create_figure((6, 10), options = fig_opts)
gspec = fig.add_gridspec(
ncols = 1,
nrows = num_plts + 1,
height_ratios = [0.05, fill(1, num_plts)...],
)
else
fig_opts_copy = convert(Dict{String,Any}, copy(fig_opts))
fig_opts_copy["axes.labelsize"] = 16
fig_opts_copy["xtick.labelsize"] = 14
fig_opts_copy["ytick.labelsize"] = 14
fig = create_figure((10, 10), options = fig_opts_copy)
gspec = fig.add_gridspec(
ncols = 3,
nrows = num_plts + 1,
height_ratios = [0.05, fill(1, num_plts)...],
width_ratios = [0.64, 0.06, 0.3],
)
end
axes = []
for i_plt = 1:num_plts
# Data
prj = data[i_plt][:prj]
ax_x_name = data[i_plt][:x_name]
ax_y_name = data[i_plt][:y_name]
ax = setup_axis!(
get(gspec, (i_plt, 0));
xlabel = @sprintf("LVLH \$%s\$ position [m]", ax_x_name),
ylabel = @sprintf("LVLH \$%s\$ position [m]", ax_y_name),
)
push!(axes, ax)
# Trajectory projection and bounding box
ct_pos_2d = ct_pos[prj, :]
dt_pos_2d = dt_pos[prj, :]
dt_thrust_2d = dt_thrust[prj, :]
bbox = Dict(
:x => Dict(:min => minimum(ct_pos_2d[1, :]), :max => maximum(ct_pos_2d[1, :])),
:y => Dict(:min => minimum(ct_pos_2d[2, :]), :max => maximum(ct_pos_2d[2, :])),
)
padding = Dict(
:x => pad_x * (bbox[:x][:max] - bbox[:x][:min]),
:y => pad_y * (bbox[:y][:max] - bbox[:y][:min]),
)
# Plot axes "guidelines"
origin = [0; 0]#mdl.traj.rf[prj]
xtip = [1; 0]
ytip = [0; 1]
ax.plot(
[origin[1], origin[1] + xtip[1] * axis_inf_scale],
[origin[2], origin[2] + xtip[2] * axis_inf_scale],
color = DarkBlue,
linewidth = 0.5,
solid_capstyle = "round",
)
ax.plot(
[origin[1], origin[1] + ytip[1] * axis_inf_scale],
[origin[2], origin[2] + ytip[2] * axis_inf_scale],
color = DarkBlue,
linewidth = 0.5,
solid_capstyle = "round",
)
# Plot the continuous-time trajectory
line_segs = Vector{RealMatrix}(undef, 0)
line_clrs = Vector{NTuple{4,RealValue}}(undef, 0)
overlap = 3
for k = 1:ct_res-overlap
push!(line_segs, ct_pos_2d[:, k:k+overlap]')
push!(line_clrs, v_cmap.to_rgba(ct_speed[k]))
end
trajectory = PyPlot.matplotlib.collections.LineCollection(
line_segs,
zorder = 10,
colors = line_clrs,
linewidths = 3,
)
ax.add_collection(trajectory)
# Plot the discrete-time trajectory
ax.scatter(
dt_pos_2d[1, :],
dt_pos_2d[2, :],
marker = "o",
c = DarkBlue,
s = 5,
edgecolors = "white",
linewidths = 0.2,
zorder = 20,
)
# Axis limits
pad_value = max(padding[:x], padding[:y])
xmin = bbox[:x][:min] - pad_value
xmax = bbox[:x][:max] + pad_value
ymin = bbox[:y][:min] - pad_value
ymax = bbox[:y][:max] + pad_value
# Detect zero-range axes
if (xmax - xmin) <= 1e-5
xmin, xmax = -1, 1
end
if (ymax - ymin) <= 1e-5
ymin, ymax = -1, 1
end
if !equal_ar
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
else
# First, try to leave ymax unconstrained
# Check to make sure that all y values contained in the resulting
# box
set_axis_equal(ax, (xmin, xmax, ymin, missing))
y_rng = collect(ax.get_ylim())
if (any(dt_pos_2d[2, :] .> y_rng[2]) || any(dt_pos_2d[2, :] .< y_rng[1]))
# The data does not fit, leave xmax unconstrained instead
set_axis_equal(ax, (xmin, missing, ymin, ymax))
end
end
x_rng = collect(ax.get_xlim())
y_rng = collect(ax.get_ylim())
# Thrust directions
ref_sz = min(x_rng[2] - x_rng[1], y_rng[2] - y_rng[1])
max_sz = maximum(mapslices(norm, dt_thrust_2d, dims = 1))
scale_factor = ref_sz / max_sz * input_scale
thrust_segs = Vector{RealMatrix}(undef, 0)
for k = 1:dt_res
thrust_whisker_base = dt_pos_2d[:, k]
thrust_whisker_tip = thrust_whisker_base + scale_factor * dt_thrust_2d[:, k]
push!(thrust_segs, hcat(thrust_whisker_base, thrust_whisker_tip)')
end
thrusts = PyPlot.matplotlib.collections.LineCollection(
thrust_segs,
zorder = 15,
colors = Red,
linewidths = 1.5,
)
thrusts.set_capstyle("round")
ax.add_collection(thrusts)
# Label the LVLH axes
ax.annotate(
@sprintf("\$\\hat %s_{\\mathcal L}\$", ax_x_name),
xy = (x_rng[2], origin[2]),
xytext = (-pad_label_abs, 0),
textcoords = "offset points",
ha = "right",
va = "center",
bbox = Dict(:pad => 2, :fc => "white", :ec => "none"),
)
ax.annotate(
@sprintf("\$\\hat %s_{\\mathcal L}\$", ax_y_name),
xy = (origin[1], y_rng[2]),
xytext = (0, pad_label_abs),
textcoords = "offset points",
ha = "center",
va = "center",
bbox = Dict(:pad => 2, :fc => "white", :ec => "none"),
)
# Plume cone
if 1 in prj
θ_sweep_sph = LinRange(0, 2 * pi, 100)
sph_perim = hcat(map(θ -> traj.r_plume * [cos(θ); sin(θ)], θ_sweep_sph)...)
sph_x = sph_perim[1, :]
sph_y = sph_perim[2, :]
ax.plot(
sph_x,
sph_y,
color = Red,
linewidth = 0.6,
solid_capstyle = "round",
solid_joinstyle = "round",
zorder = 4,
)
end
# Approach cone
if 1 in prj
# Side-view
θ_sweep = LinRange(-traj.θ_appch, traj.θ_appch, 100)
cone_perim = hcat(map(θ -> traj.r_appch * [cos(θ); sin(θ)], θ_sweep)...)
cone_x = [0; cone_perim[1, :]; 0]
cone_y = [0; cone_perim[2, :]; 0]
# Draw the approach sphere
θ_sweep_sph = LinRange(traj.θ_appch, 2 * pi - traj.θ_appch, 100)
sph_perim = hcat(map(θ -> traj.r_appch * [cos(θ); sin(θ)], θ_sweep_sph)...)
sph_x = sph_perim[1, :]
sph_y = sph_perim[2, :]
ax.plot(
sph_x,
sph_y,
color = DarkBlue,
linewidth = 0.6,
solid_capstyle = "round",
solid_joinstyle = "round",
zorder = 4,
)
else
# Front-on view
θ_sweep = LinRange(0, 2 * pi, 100)
r_intersect = traj.r_appch * sin(traj.θ_appch)
cone_perim = hcat(map(θ -> r_intersect * [cos(θ); sin(θ)], θ_sweep)...)
cone_x = cone_perim[1, :]
cone_y = cone_perim[2, :]
end
ax.plot(
cone_x,
cone_y,
color = Green,
linestyle = "--",
linewidth = 1.5,
solid_capstyle = "round",
solid_joinstyle = "round",
dash_capstyle = "round",
dash_joinstyle = "round",
dashes = (3, 3),
zorder = 5,
)
ax.invert_yaxis()
end
fig.align_ylabels(axes)
# Colorbar
cbar_ax = fig.add_subplot(get(gspec, (0, 0)))
fig.colorbar(
v_cmap,
aspect = 40,
label = "Velocity \$\\|v\\|_2\$ [m/s]",
orientation = "horizontal",
cax = cbar_ax,
)
cbar_ax.xaxis.set_label_position("top")
cbar_ax.xaxis.set_ticks_position("top")
ax_pos = cbar_ax.get_position()
ax_pos = [ax_pos.x0, ax_pos.y0 - 0.05, ax_pos.width, ax_pos.height]
cbar_ax.set_position(ax_pos)
cbar_ax.xaxis.labelpad = 10
# >> Timeseries plots <<
if attitude
marker_darken_factor = 0.3
outline_w = 1.5
y1_clr = Blue
y2_clr = Red
darker_y1_clr = darken_color(y1_clr, marker_darken_factor)
darker_y2_clr = darken_color(y2_clr, marker_darken_factor)
# Plotting data
dt_τ = sol.td
tf = sol.p[veh.id_t]
ct_t = ct_τ * tf
dt_t = dt_τ * tf
ct_r = hcat([sample(sol.xc, τ)[veh.id_r] for τ in ct_τ]...)
ct_q = hcat([sample(sol.xc, τ)[veh.id_q] for τ in ct_τ]...)
ct_rpy = mapslices(q -> collect(rpy(Quaternion(q))), ct_q, dims = 1)
ct_ω = hcat([sample(sol.xc, τ)[veh.id_ω] for τ in ct_τ]...)
dt_q = sol.xd[veh.id_q, :]
dt_rpy = mapslices(q -> collect(rpy(Quaternion(q))), dt_q, dims = 1)
dt_ω = sol.xd[veh.id_ω, :]
ct_rpy = rad2deg.(ct_rpy)
dt_rpy = rad2deg.(dt_rpy)
ct_ω = rad2deg.(ct_ω)
dt_ω = rad2deg.(dt_ω)
# Find approach and plume sphere entry times
k_appch = findfirst(k -> norm(ct_r[:, k]) <= traj.r_appch, 1:ct_res)
k_plume = findfirst(k -> norm(ct_r[:, k]) <= traj.r_plume, 1:ct_res)
t_appch = ct_t[k_appch]
t_plume = ct_t[k_plume]
data = [
Dict(
:y1_dt => dt_rpy[3, :],
:y1_ct => ct_rpy[3, :],
:y2_dt => dt_ω[1, :],
:y2_ct => ct_ω[1, :],
:y1_label => "Roll angle [\$^\\circ\$]",
:y2_label => "Body \$x\$ angular rate [\$^\\circ\$/s]",
),
Dict(
:y1_dt => dt_rpy[2, :],
:y1_ct => ct_rpy[2, :],
:y2_dt => dt_ω[2, :],
:y2_ct => ct_ω[2, :],
:y1_label => "Pitch angle [\$^\\circ\$]",
:y2_label => "Body \$y\$ angular rate [\$^\\circ\$/s]",
),
Dict(
:y1_dt => dt_rpy[1, :],
:y1_ct => ct_rpy[1, :],
:y2_dt => dt_ω[3, :],
:y2_ct => ct_ω[3, :],
:y1_label => "Yaw angle [\$^\\circ\$]",
:y2_label => "Body \$z\$ angular rate [\$^\\circ\$/s]",
),
]
ax_list = []
ax2_list = []
for i = 1:3
# Data
y1_data_dt = data[i][:y1_dt]
y1_data_ct = data[i][:y1_ct]
y2_data_dt = data[i][:y2_dt]
y2_data_ct = data[i][:y2_ct]
ax = setup_axis!(get(gspec, (i, 2)), tight = "both", xlabel = "Time [s]")
ax2 = ax.twinx()
push!(ax_list, ax)
push!(ax2_list, ax2)
ax.set_ylabel(data[i][:y1_label], color = y1_clr)
ax.tick_params(axis = "y", colors = y1_clr)
ax.spines."left".set_edgecolor(y1_clr)
ax2.set_ylabel(data[i][:y2_label], color = y2_clr)
ax2.tick_params(axis = "y", colors = y2_clr)
ax2.spines."right".set_edgecolor(y2_clr)
ax2.spines."top".set_visible(false)
ax2.spines."bottom".set_visible(false)
ax2.spines."left".set_visible(false)
ax2.set_axisbelow(true)
# Continuous-time trajectory
ax.plot(
ct_t,
y1_data_ct,
color = y1_clr,
linewidth = 2,
solid_joinstyle = "round",
solid_capstyle = "round",
clip_on = false,
zorder = 10,
)
ax2.plot(
ct_t,
y2_data_ct,
color = y2_clr,
linewidth = 2,
solid_joinstyle = "round",
solid_capstyle = "round",
clip_on = false,
zorder = 10,
)
ax2.plot(
ct_t,
y2_data_ct,
color = "white",
linewidth = 2 + outline_w,
solid_joinstyle = "round",
solid_capstyle = "round",
clip_on = false,
zorder = 9,
)
# Discrete-time trajectory
ax.plot(
dt_t,
y1_data_dt,
linestyle = "none",
marker = "o",
markersize = 3.5,
markerfacecolor = darker_y1_clr,
markeredgecolor = "white",
markeredgewidth = 0.2,
clip_on = false,
zorder = 20,
)
ax2.plot(
dt_t,
y2_data_dt,
linestyle = "none",
marker = "o",
markersize = 3.5,
markerfacecolor = darker_y2_clr,
markeredgecolor = "white",
markeredgewidth = 0.2,
clip_on = false,
zorder = 20,
)
ax2.plot(
dt_t,
y2_data_dt,
linestyle = "none",
marker = "o",
markersize = 3.5 + outline_w,
markerfacecolor = "white",
markeredgewidth = 0,
clip_on = false,
zorder = 9,
)
# Plot reflines for the approach and plume sphere entry times
label_text = Dict(t_appch => "Approach", t_plume => "Plume")
ax_ylim = ax.get_ylim()
for t in [t_appch, t_plume]
ax.axvline(
x = t,
color = time_mark_clr,
linestyle = "--",
linewidth = 0.7,
dash_joinstyle = "round",
dash_capstyle = "round",
dashes = (3, 3),
zorder = 5,
)
ax.annotate(
label_text[t],
color = time_mark_clr,
xy = (t, ax_ylim[2]),
xytext = (0, -10),#0.1*(ax_ylim[2]-ax_ylim[1])),
textcoords = "offset points",
ha = "center",
va = "top",
rotation = 90,
zorder = 5,
bbox = Dict(:pad => 1, :fc => "white", :ec => "none"),
)
end
# Make an x tick for the final time
ax_xticks = ax.get_xticks()
ax_xlim = ax.get_xlim()
if ax_xticks[end] != tf
push!(ax_xticks, tf)
end
ax.set_xticks(ax_xticks)
ax.set_xlim(ax_xlim)
end
fig.align_ylabels(ax_list[1:3])
fig.align_ylabels(ax2_list[1:3])
end
if attitude
save_figure(
"rendezvous_3d_trajectory_2d_with_attitude.pdf",
algo,
tight_layout = false,
)
else
save_figure("rendezvous_3d_trajectory_2d.pdf", algo, tight_layout = false)
end
return nothing
end
"""
plot_state_timeseries(mdl, sol)
Plot the state component evolution as a function of time.
# Arguments
- `mdl`: the problem description object.
- `sol`: the problem solution.
"""
function plot_state_timeseries(mdl::RendezvousProblem, sol::SCPSolution)::Nothing
# Parameters
algo = sol.algo
veh = mdl.vehicle
traj = mdl.traj
marker_darken_factor = 0.3
outline_w = 1.5
y1_clr = Blue
y2_clr = Red
darker_y1_clr = darken_color(y1_clr, marker_darken_factor)
darker_y2_clr = darken_color(y2_clr, marker_darken_factor)
# >> Plotting data <<
ct_res = 1000
dt_τ = sol.td
ct_τ = RealVector(LinRange(0, 1, ct_res))
tf = sol.p[veh.id_t]
ct_t = ct_τ * tf
dt_t = dt_τ * tf
ct_r = hcat([sample(sol.xc, τ)[veh.id_r] for τ in ct_τ]...)
ct_v = hcat([sample(sol.xc, τ)[veh.id_v] for τ in ct_τ]...)
ct_q = hcat([sample(sol.xc, τ)[veh.id_q] for τ in ct_τ]...)
ct_rpy = mapslices(q -> collect(rpy(Quaternion(q))), ct_q, dims = 1)
ct_ω = hcat([sample(sol.xc, τ)[veh.id_ω] for τ in ct_τ]...)
dt_r = sol.xd[veh.id_r, :]
dt_v = sol.xd[veh.id_v, :]
dt_q = sol.xd[veh.id_q, :]
dt_rpy = mapslices(q -> collect(rpy(Quaternion(q))), dt_q, dims = 1)
dt_ω = sol.xd[veh.id_ω, :]
ct_rpy = rad2deg.(ct_rpy)
dt_rpy = rad2deg.(dt_rpy)
ct_ω = rad2deg.(ct_ω)
dt_ω = rad2deg.(dt_ω)
# Find approach and plume sphere entry times
k_appch = findfirst(k -> norm(ct_r[:, k]) <= traj.r_appch, 1:ct_res)
k_plume = findfirst(k -> norm(ct_r[:, k]) <= traj.r_plume, 1:ct_res)
t_appch = ct_t[k_appch]
t_plume = ct_t[k_plume]
data = [
Dict(
:y1_dt => dt_r[1, :],
:y1_ct => ct_r[1, :],
:y2_dt => dt_v[1, :],
:y2_ct => ct_v[1, :],
:y1_label => "LVLH \$x\$ position [m]",
:y2_label => "LVLH \$x\$ velocity [m/s]",
),
Dict(
:y1_dt => dt_r[2, :],
:y1_ct => ct_r[2, :],
:y2_dt => dt_v[2, :],
:y2_ct => ct_v[2, :],
:y1_label => "LVLH \$y\$ position [m]",
:y2_label => "LVLH \$y\$ velocity [m/s]",
),
Dict(
:y1_dt => dt_r[3, :],
:y1_ct => ct_r[3, :],
:y2_dt => dt_v[3, :],
:y2_ct => ct_v[3, :],
:y1_label => "LVLH \$z\$ position [m]",
:y2_label => "LVLH \$z\$ velocity [m/s]",
:x_label => "Time [s]",
),
Dict(
:y1_dt => dt_rpy[3, :],
:y1_ct => ct_rpy[3, :],
:y2_dt => dt_ω[1, :],
:y2_ct => ct_ω[1, :],
:y1_label => "Roll angle [\$^\\circ\$]",
:y2_label => "Body \$x\$ angular rate [\$^\\circ\$/s]",
),
Dict(
:y1_dt => dt_rpy[2, :],
:y1_ct => ct_rpy[2, :],
:y2_dt => dt_ω[2, :],
:y2_ct => ct_ω[2, :],
:y1_label => "Pitch angle [\$^\\circ\$]",
:y2_label => "Body \$y\$ angular rate [\$^\\circ\$/s]",
),
Dict(
:y1_dt => dt_rpy[1, :],
:y1_ct => ct_rpy[1, :],
:y2_dt => dt_ω[3, :],
:y2_ct => ct_ω[3, :],
:y1_label => "Yaw angle [\$^\\circ\$]",
:y2_label => "Body \$z\$ angular rate [\$^\\circ\$/s]",
:x_label => "Time [s]",
),
]
# >> Initialize figure <<
fig = create_figure((10, 10), options = fig_opts)
gspec = fig.add_gridspec(ncols = 2, nrows = 3)
id_splot = [1 2; 3 4; 5 6]
ax_list = []
ax2_list = []
for i = 1:length(data)
# Data
y1_data_dt = data[i][:y1_dt]
y1_data_ct = data[i][:y1_ct]
y2_data_dt = data[i][:y2_dt]
y2_data_ct = data[i][:y2_ct]
j = id_splot[i]
ax = setup_axis!(get(gspec, (j - 1)), tight = "both")
ax2 = ax.twinx()
push!(ax_list, ax)
push!(ax2_list, ax2)
if :x_label in keys(data[i])
ax.set_xlabel(data[i][:x_label])
else
ax.tick_params(
axis = "x",
which = "both",
bottom = false,
top = false,
labelbottom = false,
)
end
ax.set_ylabel(data[i][:y1_label], color = y1_clr)
ax.tick_params(axis = "y", colors = y1_clr)
ax.spines."left".set_edgecolor(y1_clr)
ax2.set_ylabel(data[i][:y2_label], color = y2_clr)
ax2.tick_params(axis = "y", colors = y2_clr)
ax2.spines."right".set_edgecolor(y2_clr)
ax2.spines."top".set_visible(false)
ax2.spines."bottom".set_visible(false)
ax2.spines."left".set_visible(false)
ax2.set_axisbelow(true)
# Continuous-time trajectory
ax.plot(
ct_t,
y1_data_ct,
color = y1_clr,
linewidth = 2,
solid_joinstyle = "round",
solid_capstyle = "round",
clip_on = false,
zorder = 10,
)
ax2.plot(
ct_t,
y2_data_ct,
color = y2_clr,
linewidth = 2,
solid_joinstyle = "round",
solid_capstyle = "round",
clip_on = false,
zorder = 10,
)
ax2.plot(
ct_t,
y2_data_ct,
color = "white",
linewidth = 2 + outline_w,
solid_joinstyle = "round",
solid_capstyle = "round",
clip_on = false,
zorder = 9,
)
# Discrete-time trajectory
ax.plot(
dt_t,
y1_data_dt,
linestyle = "none",
marker = "o",
markersize = 3.5,
markerfacecolor = darker_y1_clr,
markeredgecolor = "white",
markeredgewidth = 0.2,
clip_on = false,
zorder = 20,
)
ax2.plot(
dt_t,
y2_data_dt,
linestyle = "none",
marker = "o",
markersize = 3.5,
markerfacecolor = darker_y2_clr,
markeredgecolor = "white",
markeredgewidth = 0.2,
clip_on = false,
zorder = 20,
)
ax2.plot(
dt_t,
y2_data_dt,
linestyle = "none",
marker = "o",
markersize = 3.5 + outline_w,
markerfacecolor = "white",
markeredgewidth = 0,
clip_on = false,
zorder = 9,
)
# Plot reflines for the approach and plume sphere entry times
label_text = Dict(t_appch => "Approach", t_plume => "Plume")
ax_ylim = ax.get_ylim()
for t in [t_appch, t_plume]
ax.axvline(
x = t,
color = time_mark_clr,
linestyle = "--",
linewidth = 0.7,
dash_joinstyle = "round",
dash_capstyle = "round",
dashes = (3, 3),
zorder = 5,
)
ax.annotate(
label_text[t],
color = time_mark_clr,
xy = (t, ax_ylim[2]),
xytext = (0, -10),#0.1*(ax_ylim[2]-ax_ylim[1])),
textcoords = "offset points",
ha = "center",
va = "top",
rotation = 90,
zorder = 5,
bbox = Dict(:pad => 1, :fc => "white", :ec => "none"),
)
end
# Make an x tick for the final time
ax_xticks = ax.get_xticks()
ax_xlim = ax.get_xlim()
if ax_xticks[end] != tf
push!(ax_xticks, tf)
end
ax.set_xticks(ax_xticks)
ax.set_xlim(ax_xlim)
# Make a y-tick for the largest time for yaw
if i == 3
ax_yticks = ax.get_yticks()
ax_ylim = ax.get_ylim()
max_val = maximum(y1_data_ct)
if ax_yticks[end] != max_val
push!(ax_yticks, max_val)
end
ax.set_yticks(ax_yticks)
ax.set_ylim(ax_ylim)
end
end
fig.align_ylabels(ax_list[1:3])
fig.align_ylabels(ax2_list[1:3])
fig.align_ylabels(ax_list[4:6])
fig.align_ylabels(ax2_list[4:6])
save_figure("rendezvous_3d_timeseries.pdf", algo)
return nothing
end
"""
plot_control(mdl, sol[; quad])
Plot the control inputs versus time.
# Arguments
- `mdl`: the problem description object.
- `sol`: the problem solution.
- `history`: SCP iteration data history.
# Keywords
- `quad`: (optional): which quad in particular to plot the inputs for.
"""
function plot_inputs(
mdl::RendezvousProblem,
sol::SCPSolution,
history::SCPHistory;
quad::Optional{String} = nothing,
)::Nothing
# Parameters
interm_sol = [spbm.sol for spbm in history.subproblems]
num_iter = length(interm_sol)
algo = sol.algo
veh = mdl.vehicle
traj = mdl.traj
spread = 0.4
stem_colors = [Red, Green, Blue, DarkBlue]
marker_darken_factor = 0.3
padx = 0.05
polar_resol = 1000
axis_label_size = 13
# Plotting data
dt_τ = sol.td
tf = sol.p[veh.id_t]
dt_t = dt_τ * tf
dt_res = length(dt_τ)
Δt = tf / (dt_res - 1)
t_spread = Δt * spread / 2
# Find approach and plume sphere entry times
ct_res = 1000
ct_τ = RealVector(LinRange(0, 1, ct_res))
ct_t = ct_τ * tf
ct_pos = hcat([sample(sol.xc, τ)[veh.id_r] for τ in ct_τ]...)
k_appch = findfirst(k -> norm(ct_pos[:, k]) <= traj.r_appch, 1:ct_res)
k_plume = findfirst(k -> norm(ct_pos[:, k]) <= traj.r_plume, 1:ct_res)
t_appch = ct_t[k_appch]
t_plume = ct_t[k_plume]
# Get RCS controls solution history
f_quad = Dict[]
f_quad_ref = Dict[]
hom_val = Real[]
for i = 1:num_iter
f = interm_sol[i].ud[veh.id_rcs, :]
f_ref = interm_sol[i].ud[veh.id_rcs_ref, :]
push!(f_quad, Dict())
push!(f_quad_ref, Dict())
push!(hom_val, interm_sol[i].bay[:hom])
for quad in (:A, :B, :C, :D)
_f_quad = RealVector[]
_f_quad_ref = RealVector[]
for thruster in (:pf, :pa, :rf, :ra)
push!(_f_quad, f[veh.csm.rcs_select[quad, thruster], :])
push!(_f_quad_ref, f_ref[veh.csm.rcs_select[quad, thruster], :])
end
f_quad[i][quad] = hcat(_f_quad...)'
f_quad_ref[i][quad] = hcat(_f_quad_ref...)'
end
end
dirs = [
"\$+\\hat x_{\\mathcal B}\$",
"\$-\\hat x_{\\mathcal B}\$",
"\$+\\hat y_{\\mathcal B}\$ (A)",
"\$-\\hat y_{\\mathcal B}\$ (A)",
]
if !isnothing(quad)
if quad == "A"
dirs[3] = "\$+\\hat y_{\\mathcal B}\$"
dirs[4] = "\$-\\hat y_{\\mathcal B}\$"
elseif quad == "B"
dirs[3] = "\$+\\hat z_{\\mathcal B}\$"
dirs[4] = "\$-\\hat z_{\\mathcal B}\$"
elseif quad == "C"
dirs[3] = "\$-\\hat y_{\\mathcal B}\$"
dirs[4] = "\$+\\hat y_{\\mathcal B}\$"
elseif quad == "D"
dirs[3] = "\$-\\hat z_{\\mathcal B}\$"
dirs[4] = "\$+\\hat z_{\\mathcal B}\$"
end
end
thruster_label = (i) -> @sprintf("Thruster %s", dirs[i])
data = [
Dict(
:u => (i) -> f_quad[i][:A],
:u_ref => (i) -> f_quad_ref[i][:A],
:ylabel =>
"Quad A impulse, \$\\Delta t_{ik}F_{\\mathrm{rcs}}\$" *
" [\\si{\\newton\\second}]",
:legend => thruster_label,
),
Dict(
:u => (i) -> f_quad[i][:B],
:u_ref => (i) -> f_quad_ref[i][:B],
:ylabel =>
"Quad B impulse, \$\\Delta t_{ik}F_{\\mathrm{rcs}}\$" *
" [\\si{\\newton\\second}]",
:legend => thruster_label,
),
Dict(
:u => (i) -> f_quad[i][:C],
:u_ref => (i) -> f_quad_ref[i][:C],
:ylabel =>
"Quad C impulse, \$\\Delta t_{ik}F_{\\mathrm{rcs}}\$" *
" [\\si{\\newton\\second}]",
:legend => thruster_label,
),
Dict(
:u => (i) -> f_quad[i][:D],
:u_ref => (i) -> f_quad_ref[i][:D],
:ylabel =>
"Quad D impulse, \$\\Delta t_{ik}F_{\\mathrm{rcs}}\$" *
" [\\si{\\newton\\second}]",
:legend => thruster_label,
),
]
if !isnothing(quad)
data_map = Dict("A" => 1, "B" => 2, "C" => 3, "D" => 4)
data = [data[data_map[quad]]]