DASCore

A python library for distributed fiber optic sensing.

Code

Documentation [stable, development]

Citation

Chambers, D., Jin, G., Tourei, A., Issah, A. H. S., Lellouch, A., Martin, E., Zhu, D., Girard, A., Yuan, S., Cullison, T., Snyder, T., Kim, S., Danes, N., Pnithan, N., Boltz, M. S. & Mendoza, M. M. (2024). DASCore: a Python Library for Distributed Fiber Optic Sensing. Seismica, 3(2).

Version-specific documentation builds are attached to the release pages.

Highlights

DASCore is a Python library for distributed acoustic sensing (DAS). It provides:

1. IO support for various DAS data formats
2. Common processing routines
3. Basic visualizations

Note

DASCore is a foundational package of the DAS Data Analysis Ecosystem (DASDAE). It facilitates rapid development of other DASDAE packages that do more specialized analysis/visualization.

Supported file formats

name	version	scan	get_format	read	write
APSENSING	10	✅	✅	✅	❌
DASDAE	1	✅	✅	✅	✅
DASHDF5	1.0	✅	✅	✅	❌
FEBUS	2	✅	✅	✅	❌
FEBUS	1	✅	✅	✅	❌
GDR_DAS	1	✅	✅	✅	❌
H5SIMPLE	1	✅	✅	✅	❌
NEUBREXRFS	1	✅	✅	✅	❌
NEUBREXDAS	1	✅	✅	✅	❌
OPTODAS	8	✅	✅	✅	❌
PICKLE		❌	✅	✅	✅
PRODML	2.0	✅	✅	✅	❌
PRODML	2.1	✅	✅	✅	❌
RSF	1	❌	❌	❌	✅
SEGY	1.0	✅	✅	✅	✅
SEGY	2.0	✅	✅	✅	✅
SEGY	2.1	✅	✅	✅	✅
SENTEK	5	✅	✅	✅	❌
SILIXA_H5	1	✅	✅	✅	❌
TDMS	4713	✅	✅	✅	❌
TERRA15	4	✅	✅	✅	❌
TERRA15	5	✅	✅	✅	❌
TERRA15	6	✅	✅	✅	❌
WAV		❌	❌	❌	✅
XMLBINARY	1	✅	✅	✅	❌

Introductory usage

Read a file

import dascore as dc
# Import fetch to read DASCore example files 
from dascore.utils.downloader import fetch 

# Fetch a sample file path from DASCore
file_path = fetch('terra15_das_1_trimmed.hdf5')
# To read DAS data stored locally on your machine, simply replace the above line with:
# file_path = "/path/to/data/directory/data.EXT"

spool = dc.spool(file_path)
patch = spool[0]

Working with a directory of DAS files

import dascore as dc
# Import fetch to read DASCore example files 
from dascore.utils.downloader import fetch

# Fetch a sample file path from DASCore (just to get a usable path for the rest of the cell)
directory_path = fetch('terra15_das_1_trimmed.hdf5').parent
# To read a directory of DAS data stored locally on your machine, 
# simply replace the above line with:
# directory_path = "/path/to/data/directory/"

spool = (
    # Create a spool to interact with directory data
    dc.spool(directory_path)
    # Index the directory contents
    .update()
    # Sub-select a specific time range
    .select(time_min=('2020-01-01', ...))
    # Specify chunk of the output patches
    .chunk(time=60, overlap=10)
)

Get patches (array w/ metadata)

import dascore as dc

spool = dc.get_example_spool('diverse_das')

# Spools are like lists, you can get patches through iteration
for patch in spool:
    ...

# or through indexing
patch = spool[0]

Perform processing

import dascore as dc

patch = dc.get_example_patch('random_das')

out = (
    # Decimate along time axis (keep every 8th sample)
    patch.decimate(time=8)
    # Detrend along the distance dimension
    .detrend(dim='distance') 
    # Apply 10Hz low-pass filter along time dimension
    .pass_filter(time=(..., 10))
)

Visualize

import dascore as dc

patch = dc.get_example_patch('example_event_2')

patch.viz.waterfall(show=True, scale=0.2);

Installation

Use pip or conda to install DASCore:

pip install dascore

conda install dascore -c conda-forge

See this recipe for installation with Docker.

See the contributing docs for development installations.

Please note that there is currently a fix in progress for the dependency issue with segyio, which does not yet support Python 3.13, so that segyio will be an optional dependency. For the time being, please use Python 3.12 for the environment.

Feedback and Support

Use the project discussions to ask a question.

Use the project issues to report an issue.