def plot_x_y_time_series(scenario):
nrows = ncols = 5
if scenario == 3 or scenario == 4:
nrows = 11
ncols = 2
fig_x, ax_x = create_subplots_for_traces(scenario,
nrows=nrows,
ncols=ncols,
polar=False)
fig_y, ax_y = create_subplots_for_traces(scenario,
nrows=nrows,
ncols=ncols,
polar=False)
formatter = mt.FuncFormatter(timeTicks)
for idx, fn in enumerate(filter_list_by_scenario(tracking_files,
scenario)):
df = load_df_from_fn(fn)
df = prepare_angles_for_interp(df)
df = df.interpolate(limit_direction="both")
id, _ = get_id_and_scenario_from_fn(fn)
if scenario in [3, 4]:
row = (id - 1) % nrows
col = (id - 1) // nrows
else:
row = (id - 1) // nrows
col = (id - 1) % nrows
formatter = mt.FuncFormatter(timeTicks)
ax_x[row, col].xaxis.set_major_formatter(formatter)
ax_y[row, col].xaxis.set_major_formatter(formatter)
ax_x[row, col].plot(df["timestamp"], df["x"])
ax_y[row, col].plot(df["timestamp"], df["y"])
ax_x[row, col].set_title(f"{id}", fontsize=subplot_title_font_size)
ax_y[row, col].set_title(f"{id}", fontsize=subplot_title_font_size)
ax_x[row, col].set_ylim([-2, 2])
ax_y[row, col].set_ylim([-2, 2])
if scenario == 4:
for ax, vline in itertools.product([ax_x, ax_y],
segment_map[scenario]):
ax[row, col].axvline(vline[1][0] * 10**9,
color='green',
alpha=.2)
ax[row, col].axvline(vline[1][1] * 10**9,
color='red',
alpha=.2)
handle_fig(fig_x, f"trace_{scenario}_x.png")
handle_fig(fig_y, f"trace_{scenario}_y.png")
def plot_polar_example():
# Just take the first file as an example
rotation_file = filter_list_by_scenario(
filter_list_by_id(tracking_files, 1), 1)[0]
df = load_df_from_fn(rotation_file)
df = prepare_angles_for_interp(df)
df = df.interpolate(limit_direction="both")
fig = plt.figure()
plt.polar(df["yaw"] / 360 * 2 * np.pi, np.arange(0, 1, 1 / len(df)))
handle_fig(fig, f"polar_example.png")
def plot_angle_processing_example():
rotation_file = filter_list_by_scenario(
filter_list_by_id(tracking_files, 1), 1)[0]
df = load_df_from_fn(rotation_file)
fig, ax = plt.subplots(2, sharex=True)
ax[0].plot(df["timestamp"], df["yaw"].interpolate(limit_direction="both"))
df_prepared = prepare_angles_for_interp(df)
df_prepared = df_prepared.interpolate(limit_direction="both")
ax[1].plot(df_prepared["timestamp"],
df["yaw"].interpolate(limit_direction="both"))
formatter = mt.FuncFormatter(timeTicks)
ax[0].xaxis.set_major_formatter(formatter)
ax[1].xaxis.set_major_formatter(formatter)
ax[0].set_title(f"Vor Vorverarbeitung", fontsize=subplot_title_font_size)
ax[1].set_title(f"Nach Vorverarbeitung", fontsize=subplot_title_font_size)
handle_fig(fig, f"angle_preprocessing.png")
def plot_xy_data(scenario):
nrows = ncols = 5
fig_speed, ax_speed = create_subplots_for_traces(
scenario,
polar=False) #, title=f"Gier Positionen für Szenario {scenario}")
fig_speed_std, ax_speed_std = create_subplots_for_traces(
scenario,
polar=False) #, title=f"Gier Positionen für Szenario {scenario}")
total_df = pd.DataFrame()
results = {}
for idx, fn in enumerate(filter_list_by_scenario(tracking_files,
scenario)):
df = load_df_from_fn(fn)
df = prepare_angles_for_interp(df)
df = df.interpolate(limit_direction="both")
id, _ = get_id_and_scenario_from_fn(fn)
row = (id - 1) // nrows
col = (id - 1) % ncols
dist = df.diff().fillna(0)
dist["yaw_speed"] = dist["yaw"].rolling(
5).mean() / dist["timestamp"].dt.total_seconds()
dist["yaw_acc"] = dist["yaw_speed"].rolling(
5).mean() / dist["timestamp"].dt.total_seconds()
dist["xy_speed"] = np.sqrt((dist["x"].rolling(5).mean())**2 +
(dist["y"].rolling(5).mean()**2)
) / dist["timestamp"].dt.total_seconds()
ax_speed[row, col].plot(df["timestamp"], dist["xy_speed"])
formatter = mt.FuncFormatter(timeTicks)
plt.setp(ax_speed, xticks=np.arange(0, 154 * 10**9, 40 * 10**9))
speed_mean = dist["xy_speed"].mean()
speed_max = dist["xy_speed"].max()
v_mean_txt = r"$v_{mean}$"
v_max_txt = r"$v_{max}$"
ax_speed[row, col].set_title(
f"{id}, {v_mean_txt}={speed_mean:.1f}m/s, {v_max_txt}={speed_max:.1f}m/s",
fontsize=speed_subplot_title)
# ax_speed[row,col].set_ylim([0, 0.4])
ax_speed[row, col].xaxis.set_major_formatter(formatter)
result = []
for i in range(0, len(df), 75):
segment = dist.iloc[i:i + 150]["xy_speed"].dropna()
# result.append(stattools.adfuller(segment)[1])
result.append(segment.std())
ax_speed_std[row, col].plot(df["timestamp"][::75], result)
ax_speed_std[row, col].axhline(y=0.05, color="red")
for ax, vline in itertools.product([ax_speed], time_map[scenario]):
ax[row, col].axvline(x=vline * 10**9, color="grey", alpha=.5)
ax_speed_std[row, col].axvline(x=vline * 10**9,
color="grey",
alpha=.5)
# plt.setp(ax_speed_std, xticks=np.arange(0, 154*10**9, 40*10**9))
ax_speed_std[row, col].set_title(f"{id}",
fontsize=subplot_title_font_size)
# ax_speed[row,col].set_ylim([0, 0.4])
ax_speed_std[row, col].xaxis.set_major_formatter(formatter)
axes = [ax_speed, ax_speed_std]
if scenario == 4:
for ax, vline in itertools.product(axes, segment_map[scenario]):
ax[row, col].axvline(vline[1][0] * 10**9,
color='green',
alpha=.2)
ax[row, col].axvline(vline[1][1] * 10**9,
color='red',
alpha=.2)
results[id] = result
segment_length = 13 * 2
std_threshold = 0.05
einschwingzeiten = defaultdict(list)
for id, stds in results.items():
for segment_idx, segment_start in enumerate(
range(0, segment_length * 12, segment_length)):
segment = stds[segment_start:segment_length * (segment_idx + 1)]
has_moved = False
found_below_threshold = False
for idx, item in enumerate(segment):
if item > std_threshold:
has_moved = True
if has_moved and item < std_threshold:
einschwingzeiten[id].append(idx)
found_below_threshold = True
break
if not has_moved:
einschwingzeiten[id].append(0)
elif not found_below_threshold:
einschwingzeiten[id].append(12)
einschwingzeiten_sec = {
id: [t / 2 for t in ts]
for id, ts in einschwingzeiten.items()
}
fig_speed.text(0.5,
0.00,
'Zeit in Minuten und Sekunden',
ha='center',
va='bottom',
fontsize=axis_label_font_size)
fig_speed.text(0.00,
0.5,
"Geschwindigkeit in m/s",
ha="left",
va="center",
rotation="vertical",
fontsize=axis_label_font_size)
fig_speed_std.text(0.5,
0.00,
'Zeit in Minuten und Sekunden',
ha='center',
va='bottom',
fontsize=axis_label_font_size)
fig_speed_std.text(0.00,
0.5,
"Geschwindigkeit in m/s",
ha="left",
va="center",
rotation="vertical",
fontsize=axis_label_font_size)
handle_fig(fig_speed, f"xy_{scenario}_speed.png")
handle_fig(fig_speed_std, f"xy_{scenario}_speed_std.png")
fig_scatter = plt.figure()
for key, val in einschwingzeiten_sec.items():
if int(metadata[metadata["ID"] == key]["ErfBinSyn"]):
plt.scatter(np.arange(1,
len(val) + 1),
val,
color="blue",
alpha=0.2)
else:
plt.scatter(np.arange(1,
len(val) + 1),
val,
color="red",
alpha=0.2)
plt.xticks(np.arange(1, 13, 1))
plt.xlabel("Segment", fontsize=axis_label_font_size)
plt.ylabel("Einschwingzeit in Sekunden", fontsize=axis_label_font_size)
handle_fig(fig_scatter, f"einschwingzeiten_{scenario}_xy.png")
fig_verteilung = plt.figure()
plt.xticks(np.arange(1, 13, 1))
plt.xlabel("Segment", fontsize=axis_label_font_size)
plt.ylabel("Einschwingzeit in Sekunden", fontsize=axis_label_font_size)
means = []
errs = []
channel_loc_data = defaultdict(list)
if scenario not in [1, 2, 4]:
return
for segment_idx in range(0, len(segment_map[scenario])):
data = []
for key, val in einschwingzeiten_sec.items():
try:
data.append(val[segment_idx])
except IndexError:
pass
err = np.std(data)
mean = np.mean(data)
errs.append(err)
means.append(mean)
if scenario in [1, 2]:
channel_loc_data[segment_map[scenario][segment_idx]].extend(data)
elif scenario in [4]:
channel_loc_data[segment_map[scenario][segment_idx][0]].extend(
data)
plt.violinplot(data, [segment_idx + 1], showmeans=True)
handle_fig(fig_verteilung,
f"einschwingzeiten_{scenario}_xy_verteilung.png")
fig_positionen = plt.figure()
plt.xticks(np.arange(1, 7, 1))
plt.xlabel("Quellenposition", fontsize=axis_label_font_size)
plt.ylabel("Einschwingzeit in Sekunden", fontsize=axis_label_font_size)
for channel_loc, data in channel_loc_data.items():
err = np.std(data)
mean = np.mean(data)
plt.violinplot(data, [channel_loc], showmeans=True)
handle_fig(fig_positionen,
f"einschwingzeiten_{scenario}_xy_positionen.png")
def plot_einschwingzeiten_yaw(scenario):
results = {}
for idx, fn in enumerate(filter_list_by_scenario(tracking_files,
scenario)):
df = load_df_from_fn(fn)
df = prepare_angles_for_interp(df)
df = df.interpolate(limit_direction="both")
id, _ = get_id_and_scenario_from_fn(fn)
dist = df.diff().fillna(0)
dist["yaw_speed"] = dist["yaw"].rolling(
5).mean() / dist["timestamp"].dt.total_seconds()
dist["yaw_acc"] = dist["yaw_speed"].rolling(
5).mean() / dist["timestamp"].dt.total_seconds()
df.set_index("timestamp", inplace=True)
"""ADF"""
result = []
for i in range(0, len(df), 75):
segment = dist.iloc[i:i + 150]["yaw_speed"].dropna()
# result.append(stattools.adfuller(segment)[1])
result.append(segment.std())
results[id] = result
segment_length = 13 * 2
std_threshold = 5
einschwingzeiten = defaultdict(list)
for id, stds in results.items():
for segment_idx, segment_start in enumerate(
range(0, segment_length * 12, segment_length)):
segment = stds[segment_start:segment_length * (segment_idx + 1)]
has_moved = False
found_below_threshold = False
for idx, item in enumerate(segment):
if item > std_threshold:
has_moved = True
if has_moved and item < std_threshold:
einschwingzeiten[id].append(idx)
found_below_threshold = True
break
if not has_moved:
einschwingzeiten[id].append(0)
elif not found_below_threshold:
einschwingzeiten[id].append(12)
einschwingzeiten_sec = {
id: [t / 2 for t in ts]
for id, ts in einschwingzeiten.items()
}
fig_scatter = plt.figure()
for key, val in einschwingzeiten_sec.items():
if int(metadata[metadata["ID"] == key]["ErfBinSyn"]):
plt.scatter(np.arange(1,
len(val) + 1),
val,
color="blue",
alpha=0.2)
else:
plt.scatter(np.arange(1,
len(val) + 1),
val,
color="red",
alpha=0.2)
plt.xticks(np.arange(1, 13, 1))
plt.xlabel("Segment", fontsize=axis_label_font_size)
plt.ylabel("Einschwingzeit in Sekunden", fontsize=axis_label_font_size)
handle_fig(fig_scatter, f"einschwingzeiten_{scenario}_yaw.png")
fig_verteilung = plt.figure()
plt.xticks(np.arange(1, 13, 1))
plt.xlabel("Segment", fontsize=axis_label_font_size)
plt.ylabel("Einschwingzeit in Sekunden", fontsize=axis_label_font_size)
means = []
errs = []
channel_loc_data = defaultdict(list)
if scenario not in [1, 2, 4]:
return
for segment_idx in range(0, len(segment_map[scenario])):
data = []
for key, val in einschwingzeiten_sec.items():
try:
data.append(val[segment_idx])
except IndexError:
pass
err = np.std(data)
mean = np.mean(data)
errs.append(err)
means.append(mean)
if scenario in [1, 2]:
channel_loc_data[segment_map[scenario][segment_idx]].extend(data)
elif scenario in [4]:
channel_loc_data[segment_map[scenario][segment_idx][0]].extend(
data)
plt.violinplot(data, [segment_idx + 1], showmeans=True)
handle_fig(fig_verteilung,
f"einschwingzeiten_{scenario}_yaw_verteilung.png")
fig_positionen = plt.figure()
plt.xticks(np.arange(1, 7, 1))
plt.xlabel("Quellenposition", fontsize=axis_label_font_size)
plt.ylabel("Einschwingzeit in Sekunden", fontsize=axis_label_font_size)
for channel_loc, data in channel_loc_data.items():
err = np.std(data)
mean = np.mean(data)
plt.violinplot(data, [channel_loc], showmeans=True)
handle_fig(fig_positionen,
f"einschwingzeiten_{scenario}_yaw_positionen.png")
def plot_yaw_data(scenario):
nrows = ncols = 5
if scenario == 3:
nrows = 11
ncols = 2
fig_yaw, ax_yaw = create_subplots_for_traces(
scenario, nrows=nrows, ncols=ncols,
polar=False) #, title=f"Gier Positionen für Szenario {scenario}")
fig_speed, ax_speed = create_subplots_for_traces(
scenario, nrows=nrows, ncols=ncols, polar=False
) #, title=f"Gier Geschwindigkeiten für Szenario {scenario}")
fig_adf, ax_adf = create_subplots_for_traces(
scenario, nrows=nrows, ncols=ncols,
polar=False) #, title=f"ADF Ergebnisse")
for idx, fn in enumerate(filter_list_by_scenario(tracking_files,
scenario)):
df = load_df_from_fn(fn)
df = prepare_angles_for_interp(df)
df = df.interpolate(limit_direction="both")
id, _ = get_id_and_scenario_from_fn(fn)
if not scenario == 3:
row = (id - 1) // nrows
col = (id - 1) % nrows
else:
row = (id - 1) // ncols
col = (id - 1) % ncols
dist = df.diff().fillna(0)
dist["yaw_speed"] = dist["yaw"].rolling(
5).mean() / dist["timestamp"].dt.total_seconds()
dist["xy_speed"] = np.sqrt((dist["x"].rolling(5).mean())**2 +
(dist["y"].rolling(5).mean()**2)
) / dist["timestamp"].dt.total_seconds()
plot_color = plot_colors[int(
metadata[metadata["ID"] == id]["ErfBinSyn"])]
# Plot this exact plot with modulo 360, because other plots will will be
# displayed too small otherwise
if scenario == 5 and int(id) == 6:
df["yaw"] %= 360
ax_yaw[row, col].plot(df["timestamp"], df["yaw"], color=plot_color)
#ax_yaw[row,col].text(0.8,0.8, f"mean={df['yaw'].mean()}\nmax={df['yaw'].max()}\nsmoothness={stattools.acf(df['yaw'])[1]}", horizontalalignment='right', verticalalignment='top',transform=ax_yaw[row,col].transAxes)
ax_speed[row, col].plot(df["timestamp"],
dist["yaw_speed"],
color=plot_color)
axes = [ax_yaw, ax_speed, ax_adf]
# df.set_index("timestamp", inplace=True)
"""ADF"""
result = []
for i in range(0, len(df), 75):
segment = dist.iloc[i:i + 150]["yaw_speed"].dropna()
result.append(segment.std())
ax_adf[row, col].plot(
np.linspace(0, df["timestamp"].iloc[-1].total_seconds() * 10**9,
len(result)), result)
# Std Threshold for einschwingzeit
threshold = 5
ax_adf[row, col].axhline(y=threshold, color="red")
formatter = mt.FuncFormatter(timeTicks)
for ax in axes:
plt.setp(ax, xticks=np.arange(0, 154 * 10**9, 40 * 10**9))
ax[row, col].set_title(f"{id}", fontsize=subplot_title_font_size)
ax[row, col].xaxis.set_major_formatter(formatter)
if scenario == 4:
ax_adf[row, col].set_ylim([0, 10])
speed_mean = np.abs(dist["yaw_speed"]).mean()
speed_max = np.abs(dist["yaw_speed"]).max()
omega_mean_txt = r"$\omega_{mean}$"
omega_max_txt = r"$\omega_{max}$"
ax_speed[row, col].set_title(
f"{id}, {omega_mean_txt}={speed_mean:.1f}°/s, {omega_max_txt}={speed_max:.1f}°/s",
fontsize=speed_subplot_title)
#
# Add lines from time or segment maps for for segmentation
if scenario in [1, 2]:
for ax, vline in itertools.product(axes, time_map[scenario]):
ax[row, col].axvline(x=vline * 10**9, color="grey", alpha=.5)
if scenario == 3:
for ax, vline in itertools.product(axes,
segment_map[scenario][1::2]):
ax[row, col].axvspan(vline[0] * 10**9,
vline[1] * 10**9,
facecolor='grey',
edgecolor='none',
alpha=.2)
if scenario == 4:
for ax, vline in itertools.product(axes, segment_map[scenario]):
ax[row, col].axvline(vline[1][0] * 10**9,
color='green',
alpha=.2)
ax[row, col].axvline(vline[1][1] * 10**9,
color='red',
alpha=.2)
fig_yaw.text(0.5,
0.00,
'Zeit in Minuten und Sekunden',
ha='center',
va='bottom',
fontsize=axis_label_font_size)
fig_speed.text(0.5,
0.00,
'Zeit in Minuten und Sekunden',
ha='center',
va='bottom',
fontsize=axis_label_font_size)
fig_yaw.text(0.00,
0.5,
"Gierwinkel in °",
ha="left",
va="center",
rotation="vertical",
fontsize=axis_label_font_size)
fig_speed.text(0.00,
0.5,
'Gierwinkelgeschwindigkeit in °/sek',
ha='left',
va='center',
rotation='vertical',
fontsize=axis_label_font_size)
fig_adf.text(0.5,
0.00,
'Zeit in Minuten und Sekunden',
ha='center',
va='bottom',
fontsize=axis_label_font_size)
fig_adf.text(0.00,
0.5,
"Standardabweichung Gierwinkel in °",
ha="left",
va="center",
rotation="vertical",
fontsize=axis_label_font_size)
handle_fig(fig_yaw, f"yaw_{scenario}_position.png")
handle_fig(fig_speed, f"yaw_{scenario}_speed.png")
handle_fig(fig_adf, f"yaw_{scenario}_speed_std.png")
def plot_time_series_with_error_bars(scenario, feature):
scenario_files = filter_list_by_scenario(tracking_files, scenario)
fig = plt.figure()
datapoints = []
shortest_series = 999999999
# Length of time series varies between persons, so find shortest and only plot
# until length of shortest
for fn in scenario_files:
df = load_df_from_fn(fn)
id, _ = get_id_and_scenario_from_fn(fn)
ax = plt.gca()
formatter = mt.FuncFormatter(timeTicks)
ax.xaxis.set_major_formatter(formatter)
df["yaw"][df["yaw"] < 0] += 360
df = prepare_angles_for_interp(df)
df = df.interpolate(limit_direction="both")
datapoints.append(list(df[feature]))
shortest_series = min(shortest_series, len(df))
data = np.zeros((len(scenario_files), shortest_series))
for idx, datapoint in enumerate(datapoints):
data[idx, :] = datapoint[:shortest_series]
mean = np.mean(data, axis=0)
std = np.std(data, axis=0)
plt.plot(df["timestamp"].iloc[:shortest_series], mean)
plt.fill_between(pd.to_numeric(df["timestamp"].iloc[:shortest_series]),
mean - std,
mean + std,
alpha=0.3)
if scenario in [1, 2]:
for vline in time_map[scenario]:
plt.gca().axvline(x=vline * 10**9, color="grey", alpha=.5)
if scenario == 3:
for vline in segment_map[scenario][1::2]:
plt.gca().axvspan(vline[0] * 10**9,
vline[1] * 10**9,
facecolor='grey',
edgecolor='none',
alpha=.2)
plt.xlabel("Zeit in Minuten und Sekunden", fontsize=axis_label_font_size)
if feature in ["yaw", "pitch", "roll"]:
plt.ylabel("Winkel in °", fontsize=axis_label_font_size)
else:
plt.ylabel("Position in Meter", fontsize=axis_label_font_size)
#plt.xlim([int(df["timestamp"].iloc[0].microseconds*1000), int(df["timestamp"].iloc[shortest_series].microseconds*1000)])
handle_fig(fig, f"timeseries_{scenario}_{feature}.png")