def do_differences(recalculate=False):
trace_cols = ["duration", "dist_err", "x_err", "y_err", "rms_x", "rms_y"]
trace_coeffs = [[(5, 50), 50]] + [[(0, 2), 0.5]]*5
trace_cols = ["path_length", "move_l", "move_r", "move_x", "move_b",
"move_f", "move_y"]
trace_coeffs = [[(0, 10), 10], [(0, 5), 3], [(0, 5), 3], [(0, 10), 6],
[(0, 5), 1.5], [(0, 5), 7], [(0, 10), 9]]
if recalculate:
traces = analyze_differences(analyses, trace_cols, trace_coeffs)
else:
traces = {col: load_best_result(col) for col in trace_cols}
for col, best_result in traces.items():
trace = best_result.trace
with figure(f"mean_std_{col}"):
ax = pm.plot_posterior(trace[100:],
varnames=[r"group1_mean", r"group2_mean",
r"group1_std", "group2_std"],
kde_plot=True, color="C0")
for a in (1, 3):
ax[a].lines[0].set_color("C1")
with figure(f"difference_{col}"):
pm.plot_posterior(trace[1000:],
varnames=["difference of means", "effect size"],
ref_val=0, kde_plot=True, color="C2")
def do_distributions():
with figure("distribution_onboard", figsize=fig_size(0.44, 1)):
plot_distribution(results, ExperimentType.Onboard, color="C0",
crosshair=True, drone_width=0.5)
with figure("distribution_spirit", figsize=fig_size(0.44, 1)):
plot_distribution(results, ExperimentType.Spirit, color="C1",
crosshair=True, drone_width=0.5)
def do_surveys():
with figure("tlx_results", figsize=fig_size(0.44, 1)):
sns.factorplot(x="experiment", y="tlx", data=tlx, kind="box")
sns.swarmplot(x="experiment", y=r"tlx",
data=tlx, palette=cmap_complement, split=True)
plt.ylim(0, plt.ylim()[1])
plt.ylabel("NASA-TLX weighted score")
with figure("tlx_components", figsize=fig_size(0.44, 1)):
components = ["mental", "physical", "temporal", "performance",
"effort", "frustration"]
molten = pd.melt(tlx, id_vars=["user", "experiment", "order"],
value_vars=components,
var_name="component", value_name="score")
g = sns.barplot(x=r"component", y="score", hue="experiment",
data=molten)
plt.gca().set_xticklabels(
["MD", "PD", "TD", "P", "E", "F"])
plt.xlabel("NASA-TLX component")
plt.ylabel("score")
with figure("survey_results", fig_size(0.44, 1)):
sns.factorplot(x="experiment", y="total", data=surveys, kind="box")
sns.swarmplot(x="experiment", y=r"total", data=surveys, palette=cmap_complement, split=True)
plt.ylim(0, plt.ylim()[1])
plt.ylabel("survey score")
with figure("survey_components", figsize=fig_size(0.9, 0.5)):
molten = pd.melt(surveys, id_vars=["user", "experiment", "order"],
value_vars=[r"orientation_understanding",
r"orientation_control",
r"position_understanding",
r"position_control",
r"spacial_understanding",
r"spacial_control"],
var_name="question", value_name="rating")
g = sns.barplot(x=r"rating", y=r"question", hue="experiment",
data=molten)
sns.stripplot(x="rating", y=r"question", data=molten, hue="experiment",
split=True, palette=cmap_complement, jitter=0.6, size=3)
plt.gca().set_yticklabels(
["angle aware", "angle control",
"position aware", "position control",
"rel. pos. aware", "rel. pos. control"])
handles, labels = g.get_legend_handles_labels()
plt.legend(handles[2:], labels[2:])
plt.xlabel("rating")
plt.title("Survey results")
def do_durations():
with figure("duration", figsize=fig_size(0.44, 1)):
sns.factorplot(x="experiment", y="duration", data=analyses, kind="box")
sns.swarmplot(x="experiment", y="duration", split=True, data=analyses,
palette=cmap_complement)
plt.ylim(0, plt.ylim()[1])
plt.ylabel("duration (s)")
with figure("duration_runs", figsize=fig_size(0.44, 1)):
sns.factorplot(x="order", y="duration", hue="experiment", data=analyses,
capsize=0.2)
plt.ylim(0, plt.ylim()[1])
plt.ylabel("duration (s)")
plt.xlabel("run")
def do_paths():
with figure("paths_overview", figsize=fig_size(0.75, 0.8)):
ax1 = plt.subplot("121")
plot_overview(results, ExperimentType.Onboard, color="C0", size_point=2,
drone_width=0.5)
ax2 = plt.subplot("122", sharex=ax1, sharey=ax1)
plot_overview(results, ExperimentType.Spirit, color="C1", size_point=2,
ylabel="", drone_width=0.5)
plt.setp(ax2.get_yticklabels(), visible=False)
with figure("paths_detailed", figsize=fig_size(0.75, 0.7)):
ax1 = plt.subplot("121")
plot_detailed(results, ExperimentType.Onboard, color="C0",
size_point=2, crosshair=True, drone_width=0.5)
ax1.legend_.remove()
ax2 = plt.subplot("122", sharex=ax1, sharey=ax1)
plot_detailed(results, ExperimentType.Spirit, color="C1", ylabel="",
size_point=2, crosshair=True, drone_width=0.5)
ax2.legend_.remove()
plt.setp(ax2.get_yticklabels(), visible=False)
def do_errors():
with figure("rms", figsize=fig_size(0.9, 0.4)):
molten = pd.melt(analyses,
id_vars=["user", "experiment", "order", "group"],
value_vars=["rms", "rms_x", "rms_y"])
g = sns.factorplot(x="experiment", y="value", col="variable",
data=molten, kind="box")
g.fig.axes[0].set_title("RMS Error")
g.fig.axes[1].set_title("RMS Error in $x$")
g.fig.axes[2].set_title("RMS Error in $y$")
g.fig.axes[0].set_ylabel("error (m)")
with figure("rms_runs", figsize=fig_size(0.9, 0.4)):
molten = pd.melt(analyses,
id_vars=["user","experiment", "order", "group"],
value_vars=["rms", "rms_x", "rms_y"]),
g = sns.factorplot(x="order", y="value", hue="experiment",
col="variable", data=molten, capsize=0.2)
g.fig.axes[0].set_title("RMS Error")
g.fig.axes[1].set_title("RMS Error in $x$")
g.fig.axes[2].set_title("RMS Error in $y$")
g.fig.axes[0].set_ylabel("error (m)")
g.fig.axes[0].set_xlabel("run")
g.fig.axes[1].set_xlabel("run")
g.fig.axes[2].set_xlabel("run")
with figure("distance", figsize=fig_size(0.9, 0.4)):
molten = pd.melt(analyses,
id_vars=["user", "experiment", "order", "group"],
value_vars=[r"dist_err", r"x_err", r"y_err"])
g = sns.factorplot(x="experiment", y="value", col="variable",
data=molten, kind="box")
g.fig.axes[0].set_title("Distance from target")
g.fig.axes[1].set_title("Distance from target in $x$")
g.fig.axes[2].set_title("Distance from target in $y$")
g.fig.axes[0].set_ylabel("distance (m)")
g.axes[0][0].axhline(0, color="black", linewidth=1, zorder=-1)
g.axes[0][1].axhline(0, color="black", linewidth=1, zorder=-1)
g.axes[0][2].axhline(0, color="black", linewidth=1, zorder=-1)
def do_drone_dos():
with figure("ardrone_dos", figsize=fig_size(0.45)):
distances = np.array([0, 2, 8, 18, 23, 29, 34, 40,
45, 51, 56, 62, 67, 72, 78, 80])
powers = np.array([90, 90, 86, 60, 50, 62, 35, 26,
24, 12, 20, 22, 26, 22, 12, 5])
fig, ax1 = plt.subplots()
ax1.step(distances, powers, lw=0.5)
ax1.set_xlabel("distance (m)")
ax1.set_ylabel(r"signal (\%)")
ax1.set_ylim(0, 100)
x_range = np.arange(80)
best_fit = 10 * np.log10(6 / (1e5 * x_range**2.7))
ax2 = ax1.twinx()
ax2.plot(x_range, best_fit, c="C1", lw=0.5)
ax2.set_ylim(-100, -50)
ax2.yaxis.set_tick_params(which="both", labelright=False, right=False)
plt.legend([ax.get_children()[0] for ax in (ax1, ax2)], ["data", "fit"])
def do_movement():
with figure("movement", figsize=fig_size(0.9, 0.4)):
molten = pd.melt(analyses,
id_vars=["user", "experiment", "order", "group"],
value_vars=["path_length", "move_x", "move_y"])
g = sns.factorplot(x="experiment", y="value", col="variable",
data=molten, kind="box")
g.fig.axes[0].set_title("Path length")
g.fig.axes[1].set_title("Movement in $x$")
g.fig.axes[2].set_title("Movement in $y$")
g.fig.axes[0].set_ylabel("distance (m)")
plt.ylim(0, plt.ylim()[1])
with figure("movement_x"):
molten = pd.melt(analyses,
id_vars=["user", "experiment", "order", "group"],
value_vars=["move_l", "move_r", "move_x"])
g = sns.factorplot(x="experiment", y="value", col="variable",
data=molten, kind="box")
g.fig.axes[0].set_title("Movement left")
g.fig.axes[1].set_title("Movement right")
g.fig.axes[2].set_title("Movement in $x$")
g.fig.axes[0].set_ylabel("distance (m)")
plt.ylim(0, plt.ylim()[1])
with figure("movement_y"):
molten = pd.melt(analyses,
id_vars=["user", "experiment", "order", "group"],
value_vars=["move_b", "move_f", "move_y"])
g = sns.factorplot(x="experiment", y="value", col="variable",
data=molten, kind="box")
g.fig.axes[0].set_title("Movement backwards")
g.fig.axes[1].set_title("Movement forwards")
g.fig.axes[2].set_title("Movement in $y$")
g.fig.axes[0].set_ylabel("distance (m)")
plt.ylim(0, plt.ylim()[1])
with figure("movement_back"):
sns.factorplot(x="experiment", y="move_b", data=analyses, kind="box")
sns.swarmplot(x="experiment", y="move_b", split=True, data=analyses,
palette=cmap_complement)
plt.ylabel("distance (m)")
plt.title("Movement backwards")
with figure("movement_runs", figsize=fig_size(0.9, 0.4)):
molten = pd.melt(analyses,
id_vars=["user", "experiment", "order", "group"],
value_vars=["path_length", "move_x", "move_y"])
g = sns.factorplot(x="order", y="value", col="variable",
data=molten, hue="experiment", capsize=0.2)
g.fig.axes[0].set_title("Path length")
g.fig.axes[1].set_title("Movement in $x$")
g.fig.axes[2].set_title("Movement in $y$")
g.fig.axes[0].set_ylabel("distance (m)")
g.fig.axes[0].set_xlabel("run")
g.fig.axes[1].set_xlabel("run")
g.fig.axes[2].set_xlabel("run")
plt.ylim(0, plt.ylim()[1])
with figure("movement_x_runs"):
molten = pd.melt(analyses,
id_vars=["user", "experiment", "order", "group"],
value_vars=["move_l", "move_r", "move_x"])
g = sns.factorplot(x="order", y="value", col="variable",
data=molten, hue="experiment")
g.fig.axes[0].set_title("Movement left")
g.fig.axes[1].set_title("Movement right")
g.fig.axes[2].set_title("Movement in $x$")
g.fig.axes[0].set_ylabel("distance (m)")
g.fig.axes[0].set_xlabel("run")
g.fig.axes[1].set_xlabel("run")
g.fig.axes[2].set_xlabel("run")
plt.ylim(0, plt.ylim()[1])
with figure("movement_y_runs"):
molten = pd.melt(analyses,
id_vars=["user", "experiment", "order", "group"],
value_vars=["move_b", "move_f", "move_y"])
g = sns.factorplot(x="order", y="value", col="variable",
data=molten, hue="experiment")
g.fig.axes[0].set_title("Movement backwards")
g.fig.axes[1].set_title("Movement forwards")
g.fig.axes[2].set_title("Movement in $y$")
g.fig.axes[0].set_ylabel("distance (m)")
g.fig.axes[0].set_xlabel("run")
g.fig.axes[1].set_xlabel("run")
g.fig.axes[2].set_xlabel("run")
plt.ylim(0, plt.ylim()[1])
