def plot_situation(tracks, n_tracks=6*3, n_dcs=50, tcz_volume=0.524e9/400,
min_distance=0, min_distance_std=200/10, zoom=1, t_detail=None, save=False,
context='notebook'):
"""Plot some T cell tracks, DC positions and T cell zone volume"""
sns.set(style='ticks', context=context)
_ = plt.figure(figsize=(8, 5.5))
gs = gridspec.GridSpec(2,3)
space_ax = plt.subplot(gs[:,:-1], projection='3d')
time_ax = plt.subplot(gs[0,-1])
reach_ax = plt.subplot(gs[1,-1])
plt.locator_params(nbins=6)
space_ax.set_title('{} T Cell Tracks & {} DCs'.format(n_tracks, n_dcs))
n_conditions = len(tracks['Condition'].unique())
palette = itertools.cycle(sns.color_palette())
if min_distance_std != 0:
moved_tracks = tracks.copy()
for id in tracks['Track_ID'].unique():
moved_tracks.loc[moved_tracks['Track_ID'] == id, ['X', 'Y', 'Z']] += \
np.random.randn(3)*min_distance_std
else:
moved_tracks = tracks
for i, (cond, cond_tracks) in enumerate(moved_tracks.groupby('Condition')):
choice = np.random.choice(cond_tracks['Track_ID'].unique(),
n_tracks/n_conditions)
chosen_tracks = cond_tracks[cond_tracks['Track_ID'].isin(choice)]
for _, track in chosen_tracks.groupby(track_identifiers(chosen_tracks)):
if t_detail:
track = track[track['Time'] <= t_detail*60]
if n_conditions > 1:
color = sns.color_palette(n_colors=i+1)[-1]
else:
color = next(palette)
space_ax.plot(track['X'].values, track['Y'].values, track['Z'].values,
color=color)
tcz_radius = (3*tcz_volume/(4*np.pi))**(1/3)
ratio = (min_distance/tcz_radius)**3
r = tcz_radius*(ratio + (1 - ratio)*np.random.rand(n_dcs))**(1/3)
theta = np.random.rand(n_dcs)*2*np.pi
phi = np.arccos(2*np.random.rand(n_dcs) - 1)
dcs = pd.DataFrame({
'X': r*np.sin(theta)*np.sin(phi),
'Y': r*np.cos(theta)*np.sin(phi),
'Z': r*np.cos(phi)})
space_ax.scatter(dcs['X'], dcs['Y'], dcs['Z'], c='y')
r = (3*tcz_volume/(4*np.pi))**(1/3)
for i in ['x', 'y', 'z']:
circle = Circle((0, 0), r, fill=False, linewidth=2)
space_ax.add_patch(circle)
art3d.pathpatch_2d_to_3d(circle, z=0, zdir=i)
time_ax.set_xlabel('Time within Lymph Node [h]')
time_ax.set_ylabel('Probab. Density')
reach_ax.set_xlabel(r'Maximal Reach [$\mu$m]')
reach_ax.set_ylabel('Probab. Density')
def residence_time(track): return track['Time'].diff().mean()/60*len(
track[np.linalg.norm(track[['X', 'Y', 'Z']], axis=1) < r])
for i, (cond, cond_tracks) in enumerate(moved_tracks.groupby('Condition')):
color = sns.color_palette(n_colors=i+1)[-1]
residence_times = [residence_time(track)
for _, track in cond_tracks.groupby('Track_ID')]
if not all(time == residence_times[0] for time in residence_times):
sns.distplot(residence_times, kde=False, norm_hist=True, ax=time_ax,
label=cond, color=color)
max_reaches = [max(np.linalg.norm(track[['X', 'Y', 'Z']], axis=1))
for _, track in cond_tracks.groupby('Track_ID')]
sns.distplot(max_reaches, kde=False, norm_hist=True, ax=reach_ax,
label=cond, color=color)
time_ax.set_yticks([])
time_ax.axvline(np.median(residence_times), c='0', ls=':')
sns.despine(ax=time_ax)
reach_ax.set_yticks([])
reach_ax.legend()
reach_ax.axvline(tcz_radius, c='0', ls=':')
sns.despine(ax=reach_ax)
equalize_axis3d(space_ax, zoom)
plt.tight_layout()
if save == True:
save = 'situation.png'
if save:
plt.savefig(save, dpi=300)
else:
plt.show()
def plot_tracks(raw_tracks, summary=None, draw_turns=True, n_tracks=25,
condition='Condition', context='notebook', save=False):
"""Plot tracks"""
tracks = raw_tracks.copy()
_uniquize_tracks(tracks)
if type(summary) == pd.core.frame.DataFrame:
skip_steps = int(next(word
for column in summary.columns
for word in column.split() if word.isdigit()))
if summary is not None and draw_turns:
alpha = 0.33
else:
alpha = 1
if condition not in tracks.columns:
tracks[condition] = 'Default'
n_conditions = len(tracks[condition].unique())
sns.set(style='ticks', context=context)
fig = plt.figure(figsize=(12,12))
if 'Z' in tracks.columns:
ax = fig.add_subplot(111, projection='3d')
else:
ax = fig.add_subplot(111, aspect='equal')
labels = []
for i, (cond, cond_tracks) in enumerate(tracks.groupby(condition)):
if summary is not None and draw_turns:
cond_summary = summary[summary[condition] == cond]
max_turn_column = next(column for column in summary.columns
if column.startswith('Max. Turn'))
if len(cond_tracks['Track_ID'].unique()) > n_tracks/n_conditions:
choice = cond_summary.sort_values(max_turn_column, ascending=False)\
['Track_ID'][:int(n_tracks/n_conditions)]
cond_tracks = cond_tracks[cond_tracks['Track_ID'].isin(choice)]
elif len(cond_tracks['Track_ID'].unique()) > n_tracks/n_conditions:
choice = np.random.choice(cond_tracks['Track_ID'].unique(),
n_tracks/n_conditions, replace=False)
cond_tracks = cond_tracks[cond_tracks['Track_ID'].isin(choice)]
color = sns.color_palette(n_colors=i+1)[-1]
for j, (_, track) in enumerate(cond_tracks.groupby(track_identifiers(cond_tracks))):
labels.append(cond)
track_id = track['Track_ID'].iloc[0]
if 'Z' in tracks.columns:
ax.plot(track['X'].values, track['Y'].values, track['Z'].values,
color=color, alpha=alpha, label=track_id, picker=5)
else:
ax.plot(track['X'].values, track['Y'].values,
color=color, alpha=alpha, label=track_id, picker=5)
if summary is not None and draw_turns:
turn_time = cond_summary[cond_summary['Track_ID'] == track_id]['Turn Time']
turn_loc = track.index.get_loc(
track[np.isclose(track['Time'], turn_time.values[0])].index.values[0])
turn_times = track['Time'][turn_loc - 1:turn_loc + skip_steps]
turn = track[track['Time'].isin(turn_times)]
if 'Z' in tracks.columns:
ax.plot(turn['X'].values, turn['Y'].values, turn['Z'].values,
color=color)
else:
ax.plot(turn['X'].values, turn['Y'].values, color=color)
def on_pick(event):
track_id = event.artist.get_label()
if summary is not None:
print(summary[summary['Track_ID'] == float(track_id)]
[['Track_ID', 'Condition', 'Mean Velocity', 'Track Duration']])
else:
print('Track_ID: ' + track_id)
fig.canvas.mpl_connect('pick_event', on_pick)
if 'Z' in tracks.columns:
equalize_axis3d(ax)
else:
sns.despine()
handles, _ = ax.get_legend_handles_labels()
unique_entries = OrderedDict(zip(labels, handles))
ax.legend(unique_entries.values(), unique_entries.keys())
plt.tight_layout()
if save:
conditions = [cond.replace('= ', '')
for cond in tracks[condition].unique()]
plt.savefig('Tracks' + '-'.join(conditions) + '.png', dpi=300)
else:
plt.show()
