gsn_numpy_util 0.1.0

numpy_util.py

All of the symbols defined in numpy_util are imported into the gsn_numpy_util module, so these symbols are accessible via:

>>> import gsn_numpy_util as nu
>>> nu.y(2,1,pi/2, 3*pi/2)

Contents:

• real and complex spherical harmonics (y, ry)

• functions to remove inf and nan from arrays (all_good, good_data, clipOdd)

• fortran unformatted i/o (write_fortran, read_fortran, read_fortran_inplace, skip_fortran)

• Making coordinate grids (grid_nd, make_grid)

• Binning particle positions in N dimensions (image, flattenMap, unflattenMap, histo, histo2d, partition)

• Elaborations of Fourier transforms–sin transform, cosine transform, etc. (power_spectrum, sine_transform, cosine_transform, fst, ifst, fct, ifct transform_n, fstn, ifstn, fctn, ifctn, trig_freq, rdct, irdct, rdst, irdst, dct, idct, dst, idst rdstfreq, rdctfreq)

• Poisson solver using various FFT-based methods (poisson, poisson_fft, poisson_fst, poisson_fct, big_poisson, big_poisson_fft, big_poisson_fst, big_poisson_fct)

• An implementation of large, disk-based arrays (BigArray) along with transformations on those arrays (e.g. big_fftn)

• Properties of time-series information crossing a threshold (seq_transitions, seq_transitions_idx, seq_length, seq_length_above, seq_length_below)

• Averaging and rebinning arrays (rebin, ave, lave)

• vector calculus, (div, grad, curl, laplacian)

• bit and boolean arrays (boolmat, bitmat, boolarr, bitarr)

• Random deviates (randp, randlog)

• coordinate systems and transformations (cartesian, spherical, graham_schmidt)

• Weighted mean, standard deviation, geometric mean, etc (weighted_mean, weighted_std, geometric_mean, rms)

• Getting unique values and determining set membership with arrays. Note that numpy has a setmember1d function, but years ago it got confused with when there were duplicate elements in the array. (unique1d, setmember1d)

particles.py

Calculate properties of particle distributions.

Accessible via

>>> import gsn_numpy_util as nu
>>> nu.particles.ellipticity(rs, ms)

Contents:

• basic transformations: rotations, affine transforms, etc.

• basic vector operations: magnitude, inner product, etc.

• properties of particle distributions: center of mass, angular momentum

• shape of particle distributions calculated by diagonalizing moment of inertia tensor

• shape of particle distributions by minimizing dipole and quadrupole moments of distribution

• Calculation of higher order (octupole, etc) Fourier coefficients

• mass profile, density profile, velocity dispersion profile

• find center of particle dist by various algorithms

• implementations of friend-of-friends (transitive closure) algorithms

• find particle groups via bound-density-maximum algorithm from Anatoly Klypin

• Coordinate transformations spherical, cylindrical,

• binning particles into grids in N dimensions

graph.py

Simple implementation of graphs and functions to compute a few properties.

Accessible via

>>> import gsn_numpy_util as nu
>>> nu.graph.dfs(graph)

Contents:

• Graph class

• Equivalence class class

• Several implementations of transitive closure

• breadth first search, depth first search