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Logging

Creating a log file

If you run the following command with LOGGING = true in line 19

 include("examples/depower.jl")

A log file with the name sim_log.arrow will be created in the output folder. It uses an .xz compressed arrow format which is fast to load and small. This actually the same as the "Feather V2" file format.

Add logging to your own scripts

The following functions are needed to add logging to your script:

LOG_FILE = "output/sim_log"                 # name of the log file without file ending 
logger = Logger(se().segments + 5, steps)   # create a logger when using the 4 point model
# logger = Logger(se().segments + 1, steps) # create a logger when using the 1 point model

# each time step
state = SysState(kps4)
log!(logger, state)

# save the log file
save_log(logger, basename(LOG_FILE); path=dirname(LOG_FILE))

If you do not pass a path name to save_log the data_path is being used. You can check the data_path using the function get_data_path().

Playing a log file in the 3D viewer

Open the file examples/play_log.jl and change the following lines according to your needs:

TIME_LAPSE_RATIO = 5             # 1 = realtime, 2..10 faster
LOG_FILE      = "output/sim_log" # without extension!
PARTICLES = 7 + 4                # 7 for tether and KCU, 4 for the kite

Now you can play the log file with the following command:

include("examples/play_log.jl")

Converting log files

Converting into .csv format

The function export_log(log_file) can be used to export a log file to .csv format.

Example:

include("examples/convert_log.jl")

This script loads the log file with the name sim_log.arrow and exports it to .csv format under the name sim_log.csv.

Reading .arrow files in Python

It is very easy to read .arrow files and convert them to Pandas DataFrames which is a kind of standard data format for tabular data in Python.

Example:

import pandas as pd
import pyarrow as pa

print("Reading arrow file...")
mmap = pa.memory_map('../output/sim_log.arrow')

with mmap as source:
    array = pa.ipc.open_file(source).read_all()

table = array.to_pandas()
print(table)

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