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Initial: ContinuousTransponder wrapper for Kogger USBL

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High-level Python wrapper around the upstream cosma-tech/kogger_acousticAntenna
driver. Configures a Kogger acoustic antenna as a permanent slave transponder
in a single start() call: address filter, echo filter, optional TDMA sync slot,
permanent response window, and Python callbacks for each ping received.

No modification to the upstream driver — only composes existing public methods
in the right order. Snapshot of upstream driver included read-only under driver/
for reference.

Includes:
- transponder_continu.py (302 lines): the wrapper class + CLI
- examples/auv_slave.py (79 lines): usage example with logging
- README.md: design rationale, usage, multi-AUV TDMA, watchdog, hardware wiring
- driver/: snapshot of cosma-tech/kogger_acousticAntenna at commit 1b539f9
  ('Add index slot for multi pinger', 2025-03-11)

Built for Cosma context (USV master + N AUVs slaves) following the design
conversation in Discord #ping-pong-ping (2026-04-27). See poulpe/ping-pong-ping
on Gitea for the interactive demo of the protocol.
This commit is contained in:
Poulpe (Gouvernance Gitea)
2026-04-27 22:08:44 +00:00
commit 9a158f5c5f
53 changed files with 7894 additions and 0 deletions
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driver/print_log.py Executable file
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#! /usr/bin/env python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.animation import FuncAnimation
from matplotlib.widgets import Slider
import sys
# --- Thresholds for filtering ---
DIST_DIFF_THRESHOLD = 20000000000.0
AZIMUTH_DIFF_THRESHOLD = 20000000000.0
ELEV_DIFF_THRESHOLD = 20000000000.0
SNR_DIFF_THRESHOLD = 50000000000.0
def create_animated_plot(filepath):
"""
Creates an animated 3D plot of AUV movement with play/pause and speed control.
"""
# --- Data Loading and Processing ---
df = pd.read_csv(filepath, header=None, names=['Timestamp', 'Measurement', 'Value'])
df_pivot = df.pivot_table(index='Timestamp', columns='Measurement', values='Value', aggfunc='first').reset_index()
# --- Filtering based on thresholds ---
for col in ['Dist_diff', 'Azimuth_diff', 'Elev_diff', 'SNR_diff']:
if col in df_pivot.columns:
df_pivot[col] = pd.to_numeric(df_pivot[col])
df_pivot[col] = df_pivot[col].bfill()
if 'Dist_diff' in df_pivot.columns:
df_pivot = df_pivot[np.abs(df_pivot['Dist_diff']) <= DIST_DIFF_THRESHOLD]
if 'Azimuth_diff' in df_pivot.columns:
df_pivot = df_pivot[np.abs(df_pivot['Azimuth_diff']) <= AZIMUTH_DIFF_THRESHOLD]
if 'Elev_diff' in df_pivot.columns:
df_pivot = df_pivot[np.abs(df_pivot['Elev_diff']) <= ELEV_DIFF_THRESHOLD]
if 'SNR_diff' in df_pivot.columns:
df_pivot = df_pivot[np.abs(df_pivot['SNR_diff']) <= SNR_DIFF_THRESHOLD]
try:
df_pivot = df_pivot.dropna(subset=['Dist', 'Azimuth', 'Elev']).copy()
except:
df_pivot = df_pivot.dropna(subset=['Azimuth', 'Elev']).copy()
df_pivot = df_pivot.reset_index(drop=True)
try:
df_pivot['Dist'] = pd.to_numeric(df_pivot['Dist'])
except:
df_pivot['Dist'] = pd.to_numeric(10000)
pass
df_pivot['Azimuth'] = pd.to_numeric(df_pivot['Azimuth'])
df_pivot['Elev'] = pd.to_numeric(df_pivot['Elev'])
df_pivot['Azimuth_rad'] = np.deg2rad(df_pivot['Azimuth'])
df_pivot['Elev_rad'] = np.deg2rad(df_pivot['Elev'])
df_pivot['x'] = df_pivot['Dist'] * np.cos(df_pivot['Elev_rad']) * np.cos(df_pivot['Azimuth_rad'])
df_pivot['y'] = df_pivot['Dist'] * np.cos(df_pivot['Elev_rad']) * np.sin(df_pivot['Azimuth_rad'])
df_pivot['z'] = df_pivot['Dist'] * np.sin(df_pivot['Elev_rad'])
# --- Plot Setup ---
fig = plt.figure(figsize=(12, 10))
ax = fig.add_subplot(111, projection='3d')
plt.subplots_adjust(bottom=0.25) # Adjust subplot to make room for slider
# Set plot limits
ax.set_xlim(df_pivot['x'].min() - 1, df_pivot['x'].max() + 1)
ax.set_ylim(df_pivot['y'].min() - 1, df_pivot['y'].max() + 1)
ax.set_zlim(df_pivot['z'].min() - 1, df_pivot['z'].max() + 1)
# Initialize plot elements
ax.plot(df_pivot['x'], df_pivot['y'], df_pivot['z'], c='gray', linestyle='--', label='Full Trajectory')
auv_position, = ax.plot([], [], [], 'ro', markersize=10, label='AUV Position')
current_trajectory, = ax.plot([], [], [], c='b', label='Current Trajectory')
usv_position = ax.scatter(0, 0, 0, c='g', marker='^', s=100, label='USV')
ax.set_xlabel('X coordinate (meters)')
ax.set_ylabel('Y coordinate (meters)')
ax.set_zlabel('Z coordinate (meters)')
ax.legend()
# --- Animation ---
def update(frame):
auv_position.set_data([df_pivot['x'][frame]], [df_pivot['y'][frame]])
auv_position.set_3d_properties([df_pivot['z'][frame]])
current_trajectory.set_data(df_pivot['x'][:frame+1], df_pivot['y'][:frame+1])
current_trajectory.set_3d_properties(df_pivot['z'][:frame+1])
time_label = pd.to_datetime(df_pivot['Timestamp'][frame]).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]
ax.set_title(f'AUV Movement at: {time_label}')
return auv_position, current_trajectory
fig.ani = FuncAnimation(fig, update, frames=len(df_pivot), blit=False, interval=50)
# --- Slider for Interval Control ---
axcolor = 'lightgoldenrodyellow'
ax_interval = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)
s_interval = Slider(
ax=ax_interval,
label='Interval (ms)',
valmin=1,
valmax=1000,
valinit=50,
valstep=10
)
def update_interval(val):
if fig.ani.event_source:
fig.ani.event_source.stop()
new_interval = int(val)
fig.ani = FuncAnimation(fig, update, frames=len(df_pivot), blit=False, interval=new_interval)
fig.canvas.draw_idle()
s_interval.on_changed(update_interval)
fig.s_interval = s_interval
# --- Play/Pause Functionality ---
paused = False
def toggle_pause(event):
nonlocal paused
if event.key == ' ':
if paused:
fig.ani.resume()
else:
fig.ani.pause()
paused = not paused
fig.canvas.mpl_connect('key_press_event', toggle_pause)
plt.show()
if __name__ == '__main__':
if len(sys.argv) != 2:
print("Usage: python animated_auv_tracker_fixed.py <path_to_data_file>")
sys.exit(1)
filepath = sys.argv[1]
create_animated_plot(filepath)