Filtered Trajectory

Single criteria filter

Based on your criteria, you can select only a fraction of the Trajectory with the where() condition. Then you will get a MaskedSpacecraftTrajectory object (or a MaskedInstrumentTrajectory for a InstrumentTrajectory) that contains only the selected portion of the trajectory. In the following example, the trajectory is split in 8 segments and only 548 points were kept out of the initial 1441.

sc_traj.where(sc_traj.alt > 500)

<MaskedSpacecraftTrajectory> Observer: JUICE | Target: GANYMEDE
 - First UTC start time: 2035-06-01T00:00:00.000
 - Last UTC stop time: 2035-06-02T00:00:00.000
 - Nb of pts: 514 (+927 masked)
 - Nb of segments: 9

Multi-criteria filter

Some shortcuts, like day (inc <= 90°) and night (inc > 90°) are available and you can chain the conditions with & (and) and | (or) logical operators:

mask_traj = sc_traj.where((sc_traj.alt > 500) & sc_traj.day)

mask_traj

<MaskedSpacecraftTrajectory> Observer: JUICE | Target: GANYMEDE
 - First UTC start time: 2035-06-01T01:58:00.000
 - Last UTC stop time: 2035-06-02T00:00:00.000
 - Nb of pts: 293 (+1,148 masked)
 - Nb of segments: 8

Warning

When you are using mutli-criteria filters, you need to encapsulate non-boolean conditions with parenthesis (...) between the logical operators, otherwise the & and | operators will be executed before the < or > operators. For example, this syntax is incorrect:

sc_traj.where(sc_traj.alt > 500 & sc_traj.day)

Masked trajectory representation

Here, we can represent the correlation between the altitude and the incidence on the masked trajectory:

fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot()

ax.plot(mask_traj.inc, mask_traj.alt, color='tab:green')

# Optional labels
ax.set_xlabel('Incidence')
ax.set_ylabel('Altitude')
ax.xaxis.set_major_formatter(deg_ticks)
ax.yaxis.set_major_formatter(km_ticks)

ax.set_title('Incidence vs. altitude above 500 km an on the day side of Ganymede');

Masked trajectory on a Map

When reported on a basemap, only the valid data will be represented:

Tip

You can customize your plots by changing the color map (cmap) and its color extend (vmin and vmax). If the data extends the defined color range, a pointing arrow will be displayed on the colorbar to show that some values were clipped. Here, we also added a custom the colorbar label.

fig = plt.figure(figsize=(12, 9))
ax = fig.add_subplot(projection=GANYMEDE)

ax.plot(
  mask_traj, 'inc', linewidth=3,
  cmap='hot_r', vmin=70, vmax=90,
  label='Incidence angle in the day side'
)

ax.set_title('Juice groundtrack higher than 500 km on the day side of Ganymede');

Segmented trajectories

When you have a MaskedTrajectory, most of the time, you will have multiple segment of valid trajectories. If you need to extract these segments to perform some statistical analysis, you can perform a loop over the MaskedTrajectory and retrieve a list of SegmentedTrajectory which have the same properties as their parent:

for seg_traj in mask_traj:
  print(
    f'Start: {seg_traj.start} | Stop: {seg_traj.stop} | '
    f'Incidence: {seg_traj.inc.min():.2f}° <-> {seg_traj.inc.max():.2f}°'
  )

Start: 2035-06-01T01:58:00.000 | Stop: 2035-06-01T02:35:00.000 | Incidence: 68.73° <-> 89.77°
Start: 2035-06-01T05:02:00.000 | Stop: 2035-06-01T05:38:00.000 | Incidence: 68.88° <-> 89.40°
Start: 2035-06-01T08:06:00.000 | Stop: 2035-06-01T08:42:00.000 | Incidence: 69.03° <-> 89.74°
Start: 2035-06-01T11:10:00.000 | Stop: 2035-06-01T11:45:00.000 | Incidence: 69.16° <-> 89.32°
Start: 2035-06-01T14:14:00.000 | Stop: 2035-06-01T14:49:00.000 | Incidence: 69.29° <-> 89.62°
Start: 2035-06-01T17:17:00.000 | Stop: 2035-06-01T17:53:00.000 | Incidence: 69.12° <-> 89.89°
Start: 2035-06-01T20:21:00.000 | Stop: 2035-06-01T20:56:00.000 | Incidence: 69.22° <-> 89.41°
Start: 2035-06-01T23:25:00.000 | Stop: 2035-06-02T00:00:00.000 | Incidence: 69.31° <-> 89.66°

You can also export each segment as temporal windows to use them in the Juice Segment Harmonization Tool or in MAPPS. More details can be found here.

ROI intersection

You can also filter a Trajectory by taking the points that are inside or outside a pre-defined region of interest (ROI). More details can be found here.