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This module is intended to accelerate development of Distributed Acoustic Sensing (DAS) analytics and interpretation. Often DAS comes with huge data volumes (typically 5km fibre will produce >450 Mb/s) and these data volumes can be overwhelming, particularly outside of geophysical applications.
This module is intended to lower the bar for researchers interested in developing their own DAS data analytics systems and workflows. Reproducibility of results is required to avoid "doodle bugs" and so the distpy module is designed to make signal processing flows readily transferrable as JSON files so that researchers can have their findings tested and verified. This is not so rich as the Madagascar seismic project, which reasonably serves the geophysical community, but is aimed more at the community of flow assurance and well monitoring specialists who have less history of manipulating data at these volumes.
It is a lightweight layer designed to sit within existing Cloud, Edge, Windows and Linux systems and is released under a permissive MIT License to engage the widest possible community.
If you are new to distpy start by reading the Basic Principles, and then check out the discussion on Directed Graphs, the disection of CASE00.py or jump straight into one of the Tutorials.
distpy is installed from packages at pypi.org using pip install
On Windows
python -m pip install distpy
On Linux
pip install distpy
data abs angle add analytic_signal argmax approx_vlf bounded_select broaden butter clip conj convolve copy correlate count_peaks data_load deconvolve destripe diff downsample dip_filter down_wave extract fft from_gpu gather gaussian geometric_mean gradient harmonic_mean hard_threshold ifft keras kmeans kurtosis lin_transform macro median_filter mean multiply multiple_calcs peak_to_peak rms_from_fft real rescale roll running_mean sobel soft_threshold sum skewness sta_lta std_dev to_gpu unwrap up_wave velocity_map velocity_mask virtual_cmp wiener write_npy write_witsml