das2numpy 0.0.2

A simple and universal package for loading large amounts of distributed acoustic sensing (DAS) data.

Module for loading Distributed Acoustic Sensing (DAS) data. SILIXA / OPTASENSE

Python: If you want to get started quickly, have a look at the example.py.

Install

You can install via PIP.

python -m pip install das2numpy

If you want to run the source have a look at install_dependencies.sh.

Use as python module

API

Recommended: simplest interface

def load_array(t_start:datetime, t_end:datetime, channel_start:int, channel_end:int) -> NP.ndarray:

Loads data and returns it as a numpy array. 
Args:
    t_start (datetime): datetime object which defines the start of the data to load.
    t_end (datetime): datetime object which defines the end of the data to load.
    channel_start (int): The starting index of sensor in the data (inclusive).
    channel_end (int): The ending index of sensors in the data (exclusive).
Returns:
    A 2d-numpy-array containing the data.
    The first axis corresponds to the time, the second, to the channel

More detailed interface

def load_array(t_start:datetime, t_end:datetime, t_step:int, channel_start:int, channel_end:int, channel_step:int) -> NP.ndarray:

Returns nothing, the data can be accessed by accessing the data field of this instance.
Warning: using a different value then 1 for t_step or channel_step can result in a high cpu-usage.
        Consider using multithreaded=True in the constructor and a high amount of workers if needed.
Constraints: 
    t_start has to be less or equal t_end, 
    same for channel_start and channel_end.
    t_step and channel_step have to be greater then 0
Args:
    t_start (datetime): datetime object which defines the start of the data to load.
    t_end (datetime): datetime object which defines the end of the data to load.
    t_step (int): If you, for example only want to load the data of every fourth timestep use t_end=4
    channel_start (int): The starting index of sensor in the data (inclusive).
    channel_end (int): The ending index of sensors in the data (exclusive).
    channel_step (int): Like t_step, but for the sensor position.
Returns:
    A 2d-numpy-array containing the data.
    The first axis corresponds to the time, the second, to the channel

Lower level interfaces

There are also lower level interfaces in the module. For example, the above interfaces also exist with POSIX timestamps in milliseconds instead of datetime objects. These timestamps have exactly the same resolution as the time axis of the resulting array.

Use as command line interface

Example call (make sure that the current working directory is not inside idas2numpy):

python -m idas2numpy "SILIXA" ~/iDAS/work/2024-05-10-desy/ 2024-05-10T10:01:00 2024-05-10T10:02:00 10 0 1000 10 default

For more information:

python -m idas2numpy -h