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Searching for codes credited to 'Digman, Matthew C.'

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[ascl:2108.022] COSMIC: Compact Object Synthesis and Monte Carlo Investigation Code

COSMIC (Compact Object Synthesis and Monte Carlo Investigation Code) generates synthetic populations with an adaptive size based on how the shape of binary parameter distributions change as the number of simulated binaries increases. It implements stellar evolution using SSE (ascl:1303.015) and binary interactions using BSE (ascl:1303.014). COSMIC can also be used to simulate a single binary at a time, a list of multiple binaries, a grid of binaries, or a fixed population size as well as restart binaries at a mid point in their evolution. The code is included in CMC-COSMIC (ascl:2108.023).

[submitted] PyIMRPhenomD

This module implements IMRPhenomD in a pure Python code, compiled with the Numba just-in-time compiler. The structure of the code is closely related to the C code; the module provides nearly identical function interfaces in The module implements the analytic first and second derivatives necessary to compute t(f) and t'(f), rather than computing them numerically, as is done in the C code. Using the analytic derivatives increases the code complexity but is wall-time faster and produces more numerically accurate results. The improvement in numerical accuracy is particularly significant for t'(f). In testing, PyIMRPhenomD is considerably faster than the C implementation. For large frequency grids, the Python version's speed-up is typically approximately a factor of 5 compared to the C version.

[submitted] WDMWaveletTransforms

This module implements the fast forward and inverse WDM wavelet transforms in python from both the time and frequency domains. The frequency domain transforms are inherently faster and more accurate. The wavelet domain->frequency domain and frequency domain->wavelet domain transforms are nearly exact numerical inverses of each other for a variety of inputs tested, including gaussian random noise.