def exp5_plot():
datadir = Path("data", "exp5")
def get_data(name, label, style=dict()):
data = SN()
data.alg = fym.logging.load(Path(datadir, name))
data.style = dict(label=label, **style)
return data
def error_plot(data, estkey, optkey, style=dict()):
style = dict(data.style, **style)
plt.plot(
data.alg["i"],
np.sqrt(
np.square(data.alg[estkey] - data.alg[optkey]).sum(axis=(1, 2))),
**style
)
plt.yscale("log")
kleinman_style = dict(c="k", ls="--", marker="o", markersize=2)
sql_style = dict(c="b", ls="-", marker="o", markersize=2)
data_stable = [
get_data(name, label, style) for name, label, style in
(["kleinman-stable.h5", "Kleinman (stable)", kleinman_style],
["sql-stable.h5", "SQL (stable)", sql_style])]
data_unstable = [
get_data(name, label, style) for name, label, style in
(["kleinman-unstable.h5", "Kleinman (unstable)", kleinman_style],
["sql-unstable.h5", "SQL (unstable)", sql_style])]
subsize = (4.05, 0.946)
width = 4.94
top = 0.2
bottom = 0.671765
left = 0.5487688
hspace = 0.2716
# Figure 1 (stable)
figsize, pos = plot.posing(2, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
[error_plot(d, "P", "Popt") for d in data_stable]
plt.ylabel(r"${P}$ error")
plt.legend()
plot.subplot(pos, 1, sharex=ax)
[error_plot(d, "K", "Kopt") for d in data_stable]
plt.ylabel(r"${K}$ error")
plt.legend()
plt.xlabel("Iteration")
# Figure 2 (unstable)
figsize, pos = plot.posing(2, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
[error_plot(d, "P", "Popt") for d in data_unstable]
plt.ylabel(r"${P}$ error")
plt.legend()
plot.subplot(pos, 1, sharex=ax)
[error_plot(d, "K", "Kopt") for d in data_unstable]
plt.ylabel(r"${K}$ error")
plt.legend()
plt.xlabel("Iteration")
# Save
imgdir = Path("img", datadir.relative_to("data"))
imgdir.mkdir(exist_ok=True)
plt.figure(1)
plt.savefig(Path(imgdir, "figure_1.pdf"), bbox_inches="tight")
plt.figure(2)
plt.savefig(Path(imgdir, "figure_2.pdf"), bbox_inches="tight")
plt.show()
def exp1_plot():
def get_data(datadir):
data = SN()
env, info = fym.logging.load(list(datadir.glob("*env.h5"))[0],
with_info=True)
data.env = env
data.info = info
agentlist = list(datadir.glob("*agent.h5"))
if agentlist != []:
data.agent = fym.logging.load(agentlist[0])
data.style = dict(label=info["cfg"].label)
return data
plt.rc("font", family="Times New Roman")
plt.rc("text", usetex=True)
plt.rc("lines", linewidth=1)
plt.rc("axes", grid=True)
plt.rc("grid", linestyle="--", alpha=0.8)
datadir = Path("data", "exp1")
mrac = get_data(Path(datadir, "data00"))
hmrac = get_data(Path(datadir, "data01"))
data = [mrac, hmrac]
basestyle = dict(c="k", lw=0.7)
cmdstyle = dict(basestyle, c="r", ls="--", label="Command")
mrac.style.update(basestyle, c="g", ls="-")
hmrac.style.update(basestyle, c="k", ls="-")
# Figure common setup
t_range = (0, 50)
# All in inches
subsize = (4.05, 0.946)
width = 4.94
top = 0.2
bottom = 0.671765
left = 0.5487688
hspace = 0.2716
# =================
# States and inputs
# =================
figsize, pos = plot.posing(3, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plt.subplot(311, position=pos[0])
lines = []
lines += plt.plot(mrac.env["t"], mrac.env["c"][:, 0], **cmdstyle)
lines += [plot.states_and_input(d, "x", 0)[0] for d in data]
plt.ylabel(r"$x_1$")
plt.ylim(-2, 2)
plt.figlegend(
lines,
[line.get_label() for line in lines],
bbox_to_anchor=(0.99, 0.78)
)
plt.subplot(312, sharex=ax, position=pos[1])
[plot.states_and_input(d, "x", 1) for d in data]
plt.ylabel(r"$x_2$")
plt.ylim(-2, 2)
plt.subplot(313, sharex=ax, position=pos[2])
[plot.states_and_input(d, "u", 0) for d in data]
plt.ylabel(r'$u$')
plt.xlabel("Time, sec")
plt.xlim(t_range)
plt.ylim(-80, 80)
for ax in plt.gcf().get_axes():
ax.label_outer()
# ====================
# Parameter estimation
# ====================
figsize, pos = plot.posing(2, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plt.subplot(211, position=pos[0])
# [plot.parameters(d, (0, 1, 8, 9, 10)) for d in data]
plot.parameters(mrac)
plt.ylabel(r"$W$")
# plt.ylim(0, 0.6)
plt.legend(loc='best')
plt.subplot(212, sharex=ax, position=pos[1])
# [plot.parameters(d, (2, 3, 4, 5, 6, 7)) for d in data]
plot.parameters(hmrac)
plt.ylabel(r"$W$")
plt.legend(loc='best')
plt.xlabel("Time, sec")
plt.xlim(t_range)
# plt.ylim(0, 85)
# ===============================================
# Tracking and parameter estimation errors (norm)
# ===============================================
figsize, pos = plot.posing(2, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plt.subplot(211, position=pos[0])
[plot.tracking_error(d) for d in data]
plt.ylabel(r"$||e||$")
plt.ylim(0, 0.2)
plt.legend(loc='best')
plt.subplot(212, sharex=ax, position=pos[1])
[plot.estimation_error(d) for d in data]
plt.ylabel(r"$||\tilde{W}||$")
plt.xlabel("Time, sec")
plt.xlim(t_range)
plt.ylim(0, 85)
# =================
# Performance index
# =================
figsize, pos = plot.posing(2, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plt.subplot(211, position=pos[0])
[plot.performance_index(d) for d in data]
plt.ylabel(r"J")
plt.xlim(t_range)
plt.legend(loc="best")
plt.subplot(211, sharex=ax, position=pos[1])
[plot.HJB_error(d) for d in data]
plt.ylabel(r"$\epsilon_{\mathrm{HJB}}$")
plt.xlabel("Time, sec")
# Saving
# basedir = Path("img")
# basedir.mkdir(exist_ok=True)
# plt.figure(1)
# plt.savefig(Path(basedir, "figure_1.pdf"), bbox_inches="tight")
# plt.figure(2)
# plt.savefig(Path(basedir, "figure_2.pdf"), bbox_inches="tight")
plt.show()
def exp4_plot():
datadir = Path("data", "exp4")
zlearner = plot.get_data(Path(datadir, "data00"))
qlearner = plot.get_data(Path(datadir, "data01"))
zlearner_na = plot.get_data(Path(datadir, "data02"))
qlearner_na = plot.get_data(Path(datadir, "data03"))
data = [zlearner, qlearner]
data_na = [zlearner_na, qlearner_na]
basestyle = dict(c="k", lw=0.7)
refstyle = dict(basestyle, c="r", ls="--")
zstyle = dict(basestyle, c="y", ls="-")
qstyle = dict(basestyle, c="b", ls="-.")
zlearner.style.update(zstyle)
qlearner.style.update(qstyle)
zlearner_na.style.update(zstyle)
qlearner_na.style.update(qstyle)
# Figure common setup
cfg = linear.cfg
linear.load_config()
t_range = (0, cfg.final_time)
# All in inches
subsize = (4.05, 0.946)
width = 4.94
top = 0.2
bottom = 0.671765
left = 0.5487688
hspace = 0.2716
# =================
# States and inputs
# =================
figsize, pos = plot.posing(5, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
[plot.vector_by_index(d, "x", 0)[0] for d in data]
plt.ylabel(r"$x_1$")
# plt.ylim(-2, 2)
plt.legend()
plot.subplot(pos, 1, sharex=ax)
[plot.vector_by_index(d, "x", 1) for d in data]
plt.ylabel(r"$x_2$")
# plt.ylim(-2, 2)
plot.subplot(pos, 2, sharex=ax)
[plot.vector_by_index(d, "u", 0) for d in data]
plt.ylabel(r'$u$')
# plt.ylim(-80, 80)
# ====================
# Parameter estimation
# ====================
ax = plot.subplot(pos, 3)
plot.all(qlearner, "K", style=dict(refstyle, label="True"))
for d in data:
plot.all(
d, "K", is_agent=True,
style=dict(marker="o", markersize=2)
)
plt.ylabel(r"$\hat{K}$")
plt.legend()
# plt.ylim(-70, 30)
plot.subplot(pos, 4, sharex=ax)
plot.all(qlearner, "P", style=dict(qlearner.style, c="r", ls="--"))
for d in data:
plot.all(
d, "P", is_agent=True,
style=dict(marker="o", markersize=2)
)
plt.ylabel(r"$\hat{P}$")
# plt.ylim(-70, 30)
plt.xlabel("Time, sec")
plt.xlim(t_range)
for ax in plt.gcf().get_axes():
ax.label_outer()
# ==================================
# States and inputs (Non-Admissible)
# ==================================
figsize, pos = plot.posing(5, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
[plot.vector_by_index(d, "x", 0)[0] for d in data_na]
plt.ylabel(r"$x_1$")
# plt.ylim(-2, 2)
plt.legend()
plot.subplot(pos, 1, sharex=ax)
[plot.vector_by_index(d, "x", 1) for d in data_na]
plt.ylabel(r"$x_2$")
# plt.ylim(-2, 2)
plot.subplot(pos, 2, sharex=ax)
[plot.vector_by_index(d, "u", 0) for d in data_na]
plt.ylabel(r'$u$')
# plt.ylim(-80, 80)
# =====================================
# Parameter estimation (Non-Admissible)
# =====================================
ax = plot.subplot(pos, 3)
plot.all(qlearner_na, "K", style=dict(refstyle, label="True"))
for d in data_na:
plot.all(
d, "K", is_agent=True,
style=dict(marker="o", markersize=2)
)
plt.ylabel(r"$\hat{K}$")
plt.legend()
# plt.ylim(-70, 30)
plot.subplot(pos, 4, sharex=ax)
plot.all(qlearner_na, "P", style=refstyle)
for d in data_na:
plot.all(
d, "P", is_agent=True,
style=dict(marker="o", markersize=2)
)
plt.ylabel(r"$\hat{P}$")
# plt.ylim(-70, 30)
plt.xlabel("Time, sec")
plt.xlim(t_range)
for ax in plt.gcf().get_axes():
ax.label_outer()
imgdir = Path("img", datadir.relative_to("data"))
imgdir.mkdir(exist_ok=True)
plt.figure(1)
plt.savefig(Path(imgdir, "figure_1.pdf"), bbox_inches="tight")
plt.figure(2)
plt.savefig(Path(imgdir, "figure_2.pdf"), bbox_inches="tight")
plt.show()
def exp3_plot():
def get_data(datadir):
data = SN()
env, info = fym.logging.load(list(datadir.glob("*env.h5"))[0],
with_info=True)
data.env = env
data.info = info
agentlist = list(datadir.glob("*agent.h5"))
if agentlist != []:
data.agent = fym.logging.load(agentlist[0])
data.style = dict(label=info["cfg"].label)
return data
plt.rc("font", family="Times New Roman")
plt.rc("text", usetex=True)
plt.rc("lines", linewidth=1)
plt.rc("axes", grid=True)
plt.rc("grid", linestyle="--", alpha=0.8)
datadir = Path("data", "exp3")
mrac = get_data(Path(datadir, "data00"))
vlmrac = get_data(Path(datadir, "data01"))
data = [mrac, vlmrac]
basestyle = dict(c="k", lw=0.7)
cmdstyle = dict(basestyle, c="r", ls="--", label="Command")
mrac.style.update(basestyle, c="g", ls="-")
vlmrac.style.update(basestyle, c="b", ls="-")
# Figure common setup
cfg = wingrock.cfg
wingrock.load_config()
t_range = (0, cfg.final_time)
# All in inches
subsize = (4.05, 0.946)
width = 4.94
top = 0.2
bottom = 0.671765
left = 0.5487688
hspace = 0.2716
# =================
# States and inputs
# =================
figsize, pos = plot.posing(3, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plt.subplot(311, position=pos[0])
lines = []
lines += plt.plot(mrac.env["t"], mrac.env["c"][:, 0], **cmdstyle)
lines += [plot.states_and_input(d, "x", 0)[0] for d in data]
plt.ylabel(r"$x_1$")
plt.ylim(-2, 2)
plt.figlegend(
lines,
[line.get_label() for line in lines],
bbox_to_anchor=(0.99, 0.78)
)
plt.subplot(312, sharex=ax, position=pos[1])
[plot.states_and_input(d, "x", 1) for d in data]
plt.ylabel(r"$x_2$")
plt.ylim(-2, 2)
plt.subplot(313, sharex=ax, position=pos[2])
[plot.states_and_input(d, "u", 0) for d in data]
plt.ylabel(r'$u$')
plt.ylim(-80, 80)
plt.xlabel("Time, sec")
plt.xlim(t_range)
for ax in plt.gcf().get_axes():
ax.label_outer()
# ====================
# Parameter estimation
# ====================
figsize, pos = plot.posing(2, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
plot.all(mrac, "Wcirc", style=cmdstyle)
plot.all(mrac, "W", style=dict(mrac.style, c="k"))
plt.ylim(-70, 30)
plot.subplot(pos, 1, sharex=ax)
plot.all(mrac, "Wcirc", style=cmdstyle)
plot.all(vlmrac, "W", style=dict(mrac.style, c="k"))
plot.all(vlmrac, "F", style=dict(mrac.style, c="b"))
plot.all(vlmrac, "What", is_agent=True, style=dict(mrac.style, c="g"))
plt.ylim(-70, 30)
plt.xlabel("Time, sec")
plt.xlim(t_range)
plt.show()
def exp2_plot():
def get_data(datadir):
data = SN()
env, info = fym.logging.load(list(datadir.glob("*env.h5"))[0],
with_info=True)
data.env = env
data.info = info
agentlist = list(datadir.glob("*agent.h5"))
if agentlist != []:
data.agent = fym.logging.load(agentlist[0])
data.style = dict(label=info["cfg"].label)
return data
cfg = multirotor.cfg
multirotor.load_config()
plt.rc("font", family="Times New Roman")
plt.rc("text", usetex=True)
plt.rc("lines", linewidth=1)
plt.rc("axes", grid=True)
plt.rc("grid", linestyle="--", alpha=0.8)
datadir = Path("data", "exp2")
mrac = get_data(Path(datadir, "data00"))
hmrac = get_data(Path(datadir, "data01"))
data = [mrac, hmrac]
basestyle = dict(c="k", lw=0.7)
cmdstyle = dict(basestyle, c="r", ls="--", label="Command")
refstyle = dict(basestyle, c="k", ls="-", label="Ref. Model")
mrac.style.update(basestyle, c="g", ls="-")
hmrac.style.update(basestyle, c="b", ls="-")
# Figure common setup
t_range = (0, cfg.final_time)
# t_range = (0, 15)
# All in inches
subsize = (4.05, 0.946)
width = 4.94
top = 0.2
bottom = 0.671765
left = 0.5487688
hspace = 0.2716
r2d = np.rad2deg(1)
# =================
# States and inputs
# =================
figsize, pos = plot.posing(3, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plt.subplot(311, position=pos[0])
lines = []
# lines += plt.plot(mrac.env["t"], mrac.env["c"][:, 0], **cmdstyle)
lines += plot.vector_by_index(mrac, "c", 0, mult=r2d, style=cmdstyle)
lines += plot.vector_by_index(mrac, "xr", 0, mult=r2d, style=refstyle)
lines += [plot.vector_by_index(d, "x", 0, r2d)[0] for d in data]
plt.ylabel(r"$p$ [deg/s]")
# plt.ylim(-40, 40)
plt.figlegend(
lines,
[line.get_label() for line in lines],
bbox_to_anchor=(0.99, 0.78)
)
plt.subplot(312, sharex=ax, position=pos[1])
plot.vector_by_index(mrac, "c", 1, mult=r2d, style=cmdstyle)
plot.vector_by_index(mrac, "xr", 1, mult=r2d, style=refstyle)
[plot.vector_by_index(d, "x", 1, r2d) for d in data]
plt.ylabel(r"$q$ [deg/s]")
# plt.ylim(-40, 40)
plt.subplot(313, sharex=ax, position=pos[2])
plot.vector_by_index(mrac, "c", 2, mult=r2d, style=cmdstyle)
plot.vector_by_index(mrac, "xr", 2, mult=r2d, style=refstyle)
[plot.vector_by_index(d, "x", 2, r2d) for d in data]
plt.ylabel(r"$r$ [deg/s]")
# plt.ylim(-40, 40)
# plt.subplot(414, sharex=ax, position=pos[3])
# [plot.all(d, "u") for d in data]
# plt.ylabel(r'$u$')
# plt.ylim(1.07, 1.47)
plt.xlabel("Time, sec")
plt.xlim(t_range)
for ax in plt.gcf().get_axes():
ax.label_outer()
# =======================================
# Tracking error and parameter estimation
# =======================================
figsize, pos = plot.posing(3, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plt.subplot(311, position=pos[0])
[plot.tracking_error(d) for d in data]
plt.ylabel(r"$||e||$")
# plt.ylim(0, 0.2)
plt.legend(loc='best')
plt.subplot(312, sharex=ax, position=pos[1])
[plot.all(d, "W") for d in data]
plt.ylabel(r"$W$")
# plt.ylim(0, 85)
# plt.subplot(313, sharex=ax, position=pos[2])
# plot.all(hmrac, "What")
# plt.ylabel(r"$\hat{W}$")
# plt.ylim(0, 85)
plt.subplot(313, sharex=ax, position=pos[2])
[plot.all(d, "u") for d in data]
plt.ylabel(r'$u$')
# plt.ylim(1.07, 1.47)
plt.xlabel("Time, sec")
plt.xlim(t_range)
plt.show()
def comp(sqldir, klmdir):
# Data 001 ~ Data 002
sql = plot.get_data(Path(datadir, sqldir))
klm = plot.get_data(Path(datadir, klmdir))
data = [sql, klm]
# data_na = []
basestyle = dict(c="k", lw=0.7)
refstyle = dict(basestyle, c="r", ls="--")
klm_style = dict(basestyle, c="y", ls="-")
sql_style = dict(basestyle, c="b", ls="-.")
klm.style.update(klm_style)
sql.style.update(sql_style)
# zlearner_na.style.update(klm_style)
# qlearner_na.style.update(sql_style)
# Figure common setup
t_range = (0, sql.info["cfg"].env_kwargs.max_t)
# All in inches
subsize = (4.05, 0.946)
width = 4.94
top = 0.2
bottom = 0.671765
left = 0.5487688
hspace = 0.2716
# =================
# States and inputs
# =================
figsize, pos = plot.posing(3, subsize, width, top, bottom, left,
hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
[plot.vector_by_index(d, "x", 0)[0] for d in data]
plt.ylabel(r"$x_1$")
# plt.ylim(-2, 2)
plt.legend()
plot.subplot(pos, 1, sharex=ax)
[plot.vector_by_index(d, "x", 1) for d in data]
plt.ylabel(r"$x_2$")
# plt.ylim(-2, 2)
plot.subplot(pos, 2, sharex=ax)
[plot.vector_by_index(d, "x", 2) for d in data]
plt.ylabel(r'$x_3$')
# plt.ylim(-80, 80)
plt.xlabel("Time, sec")
plt.xlim(t_range)
for ax in plt.gcf().get_axes():
ax.label_outer()
# ====================
# Parameter estimation
# ====================
figsize, pos = plot.posing(3, subsize, width, top, bottom, left,
hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
[plot.vector_by_index(d, "u", 0) for d in data]
plt.ylabel(r'$\delta_t$')
plot.subplot(pos, 1, sharex=ax)
plot.all(sql, "K", style=dict(refstyle, label="True"))
for d in data:
plot.all(d,
"K",
is_agent=True,
style=dict(marker="o", markersize=2))
plt.ylabel(r"$\hat{K}$")
plt.legend()
# plt.ylim(-70, 30)
plot.subplot(pos, 2, sharex=ax)
plot.all(sql, "P", style=dict(sql.style, c="r", ls="--"))
for d in data:
plot.all(d,
"P",
is_agent=True,
style=dict(marker="o", markersize=2))
plt.ylabel(r"$\hat{P}$")
# plt.ylim(-70, 30)
plt.xlabel("Time, sec")
plt.xlim(t_range)
for ax in plt.gcf().get_axes():
ax.label_outer()
def exp6_plot():
def get_data(name, style=dict(), with_info=False):
path = Path(datadir, name)
style = datastyle | style
dataset = SN()
if with_info:
data, info = fym.logging.load(path, with_info=with_info)
dataset.info = info
dataset.style = style | dict(label=info["cfg"].label)
else:
data = fym.logging.load(path)
dataset.style = style
dataset.data = data
return dataset
# ------ Exp Setup ------ #
expdir = Path("data", "exp6")
basestyle = dict(c="k", lw=0.7)
refstyle = basestyle | dict(c="r", ls="--")
sql_style = basestyle | dict(c="b", ls="-.")
klm_style = basestyle | dict(c="g", ls="-.")
test_style = basestyle | dict(c="k", ls="--")
# ------ Data 001 ------ #
datadir = Path(expdir, "data-001")
datastyle = sql_style
sql_env = get_data("env.h5", with_info=True)
sql_agent = get_data("sql-agent.h5", style=sql_env.style)
sql_test = get_data("test-learnt-env.h5", style=sql_env.style)
datastyle = test_style
lqr_test = get_data("test-lqr-env.h5", style=dict(label="LQR"))
# ------ Data 002 ------ #
datadir = Path(expdir, "data-002")
datastyle = klm_style
klm_env = get_data("env.h5", with_info=True)
klm_agent = get_data("klm-agent.h5", style=klm_env.style)
klm_test = get_data("test-learnt-env.h5", style=klm_env.style)
data_train = [sql_env, klm_env]
data_agent = [sql_agent, klm_agent]
data_test = [sql_test, klm_test, lqr_test]
# Figure common setup
t_range = (0, sql_env.info["cfg"].env_kwargs.max_t)
# All in inches
subsize = (4.05, 0.946)
width, top, bottom, left = (4.94, 0.2, 0.671765, 0.5487688)
hspace = 0.2716
# ============================
# States and inputs (Training)
# ============================
figsize, pos = plot.posing(6, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
[plot.vector_by_index(d, "x", 0)[0] for d in data_train]
plt.ylabel(r"$V_T$, m/s")
# plt.ylim(19, 23)
plt.legend()
plot.subplot(pos, 1, sharex=ax)
[plot.vector_by_index(d, "x", 1, mult=np.rad2deg(1)) for d in data_train]
plt.ylabel(r"$\alpha$, deg")
# plt.ylim(-5, 8)
plot.subplot(pos, 2, sharex=ax)
[plot.vector_by_index(d, "x", 2, mult=np.rad2deg(1)) for d in data_train]
plt.ylabel(r"$q$, deg/s")
# plt.ylim(-50, 50)
plot.subplot(pos, 3, sharex=ax)
[plot.vector_by_index(d, "x", 3, mult=np.rad2deg(1)) for d in data_train]
plt.ylabel(r"$\gamma$, deg")
# plt.ylim(-5, 23)
plot.subplot(pos, 4, sharex=ax)
[plot.vector_by_index(d, "u", 0) for d in data_train]
plt.ylabel(r"$\delta_t$")
# plt.ylim(0, 0.2)
plot.subplot(pos, 5, sharex=ax)
[plot.vector_by_index(d, "u", 1, mult=np.rad2deg(1)) for d in data_train]
plt.ylabel(r'$\delta_e$, deg')
# plt.ylim(-15, 5)
plt.xlabel("Time, sec")
plt.xlim(t_range)
for ax in plt.gcf().get_axes():
ax.label_outer()
# ===============================
# Parameter estimation (Training)
# ===============================
figsize, pos = plot.posing(2, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
plot.subplot(pos, 0, sharex=ax)
plot.all(sql_env, "K", style=dict(refstyle, label="True"))
for d in data_agent:
plot.all(d, "K", is_agent=True, style=dict(marker="o", markersize=2))
plt.ylabel(r"$\hat{K}$")
plt.legend()
plt.ylim(-15, 7)
plot.subplot(pos, 1, sharex=ax)
plot.all(sql_env, "P", style=dict(sql_env.style, c="r", ls="--"))
for d in data_agent:
plot.all(d, "P", is_agent=True, style=dict(marker="o", markersize=2))
plt.ylabel(r"$\hat{P}$")
plt.ylim(-12, 28)
plt.xlabel("Time, sec")
plt.xlim(t_range)
for ax in plt.gcf().get_axes():
ax.label_outer()
# ========================
# States and inputs (Test)
# ========================
figsize, pos = plot.posing(6, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
ax = plot.subplot(pos, 0)
[plot.vector_by_index(d, "x", 0)[0] for d in data_test]
plt.ylabel(r"$V_T$, m/s")
plt.ylim(19, 23)
plt.legend()
plot.subplot(pos, 1, sharex=ax)
[plot.vector_by_index(d, "x", 1, mult=np.rad2deg(1)) for d in data_test]
plt.ylabel(r"$\alpha$, deg")
plt.ylim(-5, 8)
plot.subplot(pos, 2, sharex=ax)
[plot.vector_by_index(d, "x", 2, mult=np.rad2deg(1)) for d in data_test]
plt.ylabel(r"$q$, deg/s")
plt.ylim(-50, 50)
plot.subplot(pos, 3, sharex=ax)
[plot.vector_by_index(d, "x", 3, mult=np.rad2deg(1)) for d in data_test]
plt.ylabel(r"$\gamma$, deg")
plt.ylim(-5, 23)
plot.subplot(pos, 4, sharex=ax)
[plot.vector_by_index(d, "u", 0) for d in data_test]
plt.ylabel(r"$\delta_t$")
plt.ylim(0, 0.2)
plot.subplot(pos, 5, sharex=ax)
[plot.vector_by_index(d, "u", 1, mult=np.rad2deg(1)) for d in data_test]
plt.ylabel(r'$\delta_e$, deg')
plt.ylim(-15, 5)
plt.xlabel("Time, sec")
plt.xlim(0, 5)
for ax in plt.gcf().get_axes():
ax.label_outer()
# =================
# Performance Index
# =================
figsize, pos = plot.posing(1, subsize, width, top, bottom, left, hspace)
plt.figure(figsize=figsize)
plot.subplot(pos, 0)
[plot.vector_by_index(d, "PI", 0)[0] for d in data_test]
plt.ylabel(r"Performance Index")
plt.ylim(-1, 20)
plt.legend()
plt.xlabel("Time, sec")
plt.xlim(0, 5)
for ax in plt.gcf().get_axes():
ax.label_outer()
# # ==================================
# # States and inputs (Non-Admissible)
# # ==================================
# figsize, pos = plot.posing(5, subsize, width, top, bottom, left, hspace)
# plt.figure(figsize=figsize)
# ax = plot.subplot(pos, 0)
# [plot.vector_by_index(d, "x", 0)[0] for d in data_na]
# plt.ylabel(r"$x_1$")
# # plt.ylim(-2, 2)
# plt.legend()
# plot.subplot(pos, 1, sharex=ax)
# [plot.vector_by_index(d, "x", 1) for d in data_na]
# plt.ylabel(r"$x_2$")
# # plt.ylim(-2, 2)
# plot.subplot(pos, 2, sharex=ax)
# [plot.vector_by_index(d, "u", 0) for d in data_na]
# plt.ylabel(r'$u$')
# # plt.ylim(-80, 80)
# # =====================================
# # Parameter estimation (Non-Admissible)
# # =====================================
# ax = plot.subplot(pos, 3)
# plot.all(qlearner_na, "K", style=dict(refstyle, label="True"))
# for d in data_na:
# plot.all(
# d, "K", is_agent=True,
# style=dict(marker="o", markersize=2)
# )
# plt.ylabel(r"$\hat{K}$")
# plt.legend()
# # plt.ylim(-70, 30)
# plot.subplot(pos, 4, sharex=ax)
# plot.all(qlearner_na, "P", style=refstyle)
# for d in data_na:
# plot.all(
# d, "P", is_agent=True,
# style=dict(marker="o", markersize=2)
# )
# plt.ylabel(r"$\hat{P}$")
# # plt.ylim(-70, 30)
# plt.xlabel("Time, sec")
# plt.xlim(t_range)
# for ax in plt.gcf().get_axes():
# ax.label_outer()
imgdir = Path("img", datadir.relative_to("data"))
imgdir.mkdir(exist_ok=True)
plt.figure(1)
plt.savefig(Path(imgdir, "figure_1.pdf"), bbox_inches="tight")
plt.figure(2)
plt.savefig(Path(imgdir, "figure_2.pdf"), bbox_inches="tight")
plt.figure(3)
plt.savefig(Path(imgdir, "figure_3.pdf"), bbox_inches="tight")
plt.show()
