Plotting
This example shows how one can pull a log file from the drone and use pandas and matplotlib to plot it.
We'll start by downloading a log file from the drone
from blueye.sdk import Drone
myDrone = Drone()
log = myDrone.logs[0]
Next we'll parse the log to a stream and create a pandas dataframe from the records. We'll also specify the column names to make it easier to work with the dataframe later.
import pandas as pd
import blueye.protocol as bp
columns = ["rt", "delta", "meta", "message"]
log_stream = log.parse_to_stream()
divelog = pd.DataFrame.from_records(log_stream, columns=columns)
We'll now filter out all entries that are not depth telemetry messages and messages that were logged before the start of the dive.
depth_log = divelog[divelog.meta == bp.DepthTel]
depth_log = depth_log[depth_log.rt > log.start_time]
We can now extract the depth value from the remaining entries
depth_log["depth"] = depth_log["message"].apply(lambda x: x.depth.value)
We'll prepare our axes for plotting
x = depth_log["rt"]
y = depth_log["depth"]
We'll start by downloading a log file from the drone
from blueye.sdk import Drone
myDrone = Drone()
myDrone.legacy_logs[0].download(output_name="log0.csv")
We can now read the csv-file into a pandas object for easy manipulation
import pandas as pd
divelog = pd.read_csv("log0.csv")
and then we'll convert the unix timestamp in rt_clock into a more readable format:
divelog["rt_clock"] = pandas.to_datetime(divelog["rt_clock"], unit="s")
We'll prepare our axes for plotting
x = divelog["rt_clock"]
y = divelog["depth"] / 1000 # Dividing by 1000 to get depth in meters
Next we will plot depth vs time with matplotlib:
import matplotlib.pyplot as plt
# Instantiate our figure and axes to plot on
figure, axes = plt.subplots()
# Plot the depth values against time
axes.plot(x, y, label="depth")
# Set title, labels, and legend
plt.title("Depth chart")
plt.xlabel("Time")
plt.ylabel("Depth [m]")
plt.legend()
# Save the figure
figure.savefig("depth_plot.svg")
This should yield us a plot that looks something like this:
See the matplotlib documentation for more ways to plot your data.