DASCore

A python library for distributed fiber optic sensing.

Code

Documentation [stable, development]

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	✅	✅	✅	❌
H5SIMPLE	1	✅	✅	✅	❌
NEUBREX	1	✅	✅	✅	❌
OPTODAS	8	✅	✅	✅	❌
PICKLE		❌	✅	✅	✅
PRODML	2.0	✅	✅	✅	❌
PRODML	2.1	✅	✅	✅	❌
RSF	1	❌	❌	❌	✅
SEGY	2	✅	✅	✅	❌
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
from dascore.utils.downloader import fetch

# get a path to an example file, replace with your path
file_path = fetch('terra15_das_1_trimmed.hdf5')

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

Working with a directory of DAS files

import dascore as dc
from dascore.utils.downloader import fetch

# get a path to a directory of das files, replace with your path
directory_path = fetch('terra15_das_1_trimmed.hdf5').parent

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.

Feedback and Support

Use the project discussions to ask a question.

Use the project issues to report an issue.