Tutorial: Plot Rover Positions¶
This notebook focuses on the rover positions stored in the RF xarray.
The extractor already removed:
- invalid or missing rover samples
- duplicate consecutive rover positions within the configured tolerance
This version of the tutorial can merge multiple experiment IDs into one combined set of valid rover positions.
# Optional: uncomment when this Jupyter kernel misses the plotting dependencies.
# import sys
# !{sys.executable} -m pip install matplotlib numpy requests xarray pyyaml
from pathlib import Path
import importlib.util
import sys
import matplotlib.pyplot as plt
NOTEBOOK_DIR = Path.cwd().resolve()
for candidate_dir in (
NOTEBOOK_DIR,
NOTEBOOK_DIR / "tutorials",
NOTEBOOK_DIR / "processing" / "tutorials",
):
if (candidate_dir / "csi_plot_utils.py").exists():
NOTEBOOK_DIR = candidate_dir.resolve()
break
else:
raise ImportError(f"Could not locate csi_plot_utils.py from {Path.cwd().resolve()}")
UTILS_PATH = NOTEBOOK_DIR / "csi_plot_utils.py"
PROCESSING_DIR = NOTEBOOK_DIR.parent
PROJECT_ROOT = PROCESSING_DIR.parent
spec = importlib.util.spec_from_file_location("csi_plot_utils", UTILS_PATH)
if spec is None or spec.loader is None:
raise ImportError(f"Could not load utility module from {UTILS_PATH}")
csi = importlib.util.module_from_spec(spec)
sys.modules["csi_plot_utils"] = csi
spec.loader.exec_module(csi)
REQUESTED_EXPERIMENT_IDS = None # Example: ["EXP003", "EXP005"]. None means: use all experiment IDs in the opened dataset.
DATASET_PATH = None # Set this to a specific .nc file when needed.
if REQUESTED_EXPERIMENT_IDS is None:
ds, dataset_path = csi.open_dataset(dataset_path=DATASET_PATH)
EXPERIMENT_IDS = csi.available_experiment_ids(ds)
else:
EXPERIMENT_IDS = csi.normalize_experiment_ids(REQUESTED_EXPERIMENT_IDS)
ds, dataset_path = csi.open_dataset(experiment_id=EXPERIMENT_IDS, dataset_path=DATASET_PATH)
print(f"Loaded dataset: {dataset_path}")
print(f"Selected experiment IDs: {EXPERIMENT_IDS}")
csi.print_experiment_overview(ds, EXPERIMENT_IDS)
Loaded dataset: C:\Users\Calle\OneDrive\Documenten\GitHub\ELLIIIT-dataset-26\results\csi_EXP003__EXP005__EXP006__EXP007__EXP008__EXP009__EXP010__EXP011__EXP012.nc Selected experiment IDs: ['EXP003', 'EXP005', 'EXP006', 'EXP007', 'EXP008', 'EXP009', 'EXP010', 'EXP011', 'EXP012'] EXP003: cycles_with_csi=529, valid_positions=529, first_csi_cycle=1, last_csi_cycle=529 EXP005: cycles_with_csi=777, valid_positions=777, first_csi_cycle=1, last_csi_cycle=777 EXP006: cycles_with_csi=238, valid_positions=238, first_csi_cycle=1, last_csi_cycle=238 EXP007: cycles_with_csi=378, valid_positions=378, first_csi_cycle=1, last_csi_cycle=378 EXP008: cycles_with_csi=201, valid_positions=201, first_csi_cycle=1, last_csi_cycle=201 EXP009: cycles_with_csi=1355, valid_positions=1355, first_csi_cycle=1, last_csi_cycle=1356 EXP010: cycles_with_csi=458, valid_positions=458, first_csi_cycle=1, last_csi_cycle=458 EXP011: cycles_with_csi=291, valid_positions=291, first_csi_cycle=1, last_csi_cycle=291 EXP012: cycles_with_csi=784, valid_positions=784, first_csi_cycle=1, last_csi_cycle=784
Inspect The Merged Valid Position Rows¶
The helper below keeps only rows with valid rover coordinates and merges the selected experiment IDs into one measurement table.
positions = csi.positions_for_experiments(ds, EXPERIMENT_IDS)
print(f"Valid rover positions across the selected experiments: {positions.sizes['measurement_index']}")
positions.isel(measurement_index=slice(0, min(10, positions.sizes['measurement_index'])))
Valid rover positions across the selected experiments: 5011
<xarray.Dataset> Size: 720B
Dimensions: (measurement_index: 10)
Coordinates:
* measurement_index (measurement_index) int64 80B 0 1 2 3 4 5 6 7 8 9
Data variables:
experiment_id (measurement_index) <U6 240B 'EXP003' ... 'EXP003'
cycle_id (measurement_index) int64 80B 1 2 3 4 5 6 7 8 9 10
rover_x (measurement_index) float64 80B 1.918 2.541 ... 2.534
rover_y (measurement_index) float64 80B 2.865 2.254 ... 3.213
rover_z (measurement_index) float64 80B 0.7394 0.7401 ... 0.7428
csi_host_count (measurement_index) int64 80B 42 42 42 42 ... 42 42 42 42
Attributes:
experiment_ids: ['EXP003', 'EXP005', 'EXP006', 'EXP007', 'EXP008', 'EXP0...Plot The Full Trajectory As Merged Scatter Points¶
This plot ignores the experiment boundaries and shows all valid rover positions as one combined point cloud. The antenna positions are fetched in csi_plot_utils.py and overlaid as square markers.
csi.plot_position_cloud(ds, EXPERIMENT_IDS, show_antennas=True)
plt.show()
Plot The Per-Experiment Trajectories¶
Use this second plot when you still want to see the trajectory order for each experiment separately.
csi.plot_trajectory(ds, EXPERIMENT_IDS)
plt.show()
Resolve A Physical Point To An Experiment And Cycle ID¶
Replace the example coordinates below with any target position in meters. The helper searches across the merged experiment IDs and returns the nearest recorded measurement.
target_x = float(positions['rover_x'].values[-1])
target_y = float(positions['rover_y'].values[-1])
nearest = csi.find_nearest_position_cycle(
ds,
EXPERIMENT_IDS,
x=target_x,
y=target_y,
)
print("Nearest recorded measurement for the chosen point:")
nearest
Nearest recorded measurement for the chosen point:
{'experiment_id': 'EXP012',
'target_x': 5.2856826171875,
'target_y': 2.95383740234375,
'target_z': None,
'cycle_id': 784,
'rover_x': 5.2856826171875,
'rover_y': 2.95383740234375,
'rover_z': 0.75779052734375,
'distance_m': 0.0,
'csi_host_count': 41}