zdelrosario/pybuck

Dimensional analysis in Python

Overview

This package supports dimensional analysis in Python. See the application guide for more examples of using pybuck. A quick tour follows below.

Install

Clone repo and run python setup.py install.

Example usage

Use compact syntax to record the physical dimensions of quantities.

from pybuck import *

df_dim = col_matrix(
    rho = dict(M=1, L=-3),
    U   = dict(L=1, T=-1),
    D   = dict(L=1),
    mu  = dict(M=1, L=-1, T=-1),
    eps = dict(L=1)
)
df_dim

  rowname  rho  U  D  mu  eps
0       T    0 -1  0  -1    0
1       M    1  0  0   1    0
2       L   -3  1  1  -1    1

Use the dimension matrix df_dim to check the physical dimensions of quantities.

df_weights = col_matrix(q = dict(rho=1, U=2))
df_res = inner(df_dim, df_weights)
transpose(df_res)

  rowname  L  M  T
0       q -1  1 -2

Use nondim to compute the canonical non-dimensionalizing factor [Theorem 8.1, 1].

df_flowrate = col_matrix(Q = dict(M=1, L=-3, T=-1))
df_nondim = nondim(df_flowrate, df_dim)
print(inner(df_dim, df_nondim))
df_nondim

  rowname         Q
0     rho  0.571429
1       U  0.571429
2       D -0.714286
3      mu  0.428571
4     eps -0.714286

See the demo for a quick look at package functionality.

References

[1] Z. del Rosario, M. Lee, and G. Iaccarino, "Lurking Variable Detection via Dimensional Analysis" (2019) SIAM/ASA Journal on Uncertainty Quantification