wdata 0.1.1

W-Data format for superfluid dynamics and the W-SLDA Toolkit.

W-data Format

This project contains tools for working with and manipulating the W-data format used for analyzing superfluid data.

This format was originally derived from the W-SLDA project led by Gabriel Wlazlowski as documented here:

http://git2.if.pw.edu.pl/gabrielw/cold-atoms/wikis/W-data-format

Here we augment this format slightly to facilitate working with Python.

Generalizations

The original format required a .wtxt file with lots of relevant information. Here we generalize the format to allow this information to be specified in the data files, which we allow to be in the NPY format.

Installation

pip install wdata

Basic Usage

The W-data format stores various arrays representing physical quantities such as the density (real), pairing field (complex), currents (3-component real vectors) etc. on a regular lattice of shape Nxyz = (Nx, Ny, Nz) at a bunch of Nt times.

The data is represented by two classes:

• Var: These are the data variables such as density, currents, etc. with additional metadata (ee the wdata.io.IVar interface for details):

  • Var.name: Name of variable as it will appear in VisIt for example.
  • Var.data: The actual data as a NumPy array.
  • Var.description: Description.
  • Var.filename: The file where the data is stored on disk.
  • Var.unit: Unit (mainly for use in VisIt... does not affect the data.)

• WData: This represents a complete dataset. Some relevant attributes are (see wdata.io.IWData for details):

  • WData.infofile: Location of the infofile (see below). This is where the metadata will be stored or loaded from.
  • WData.variables: List of Var variables.
  • WData.xyz: Abscissa (x, y, z) shaped so that they can be used with broadcasting. I.e. r = np.sqrt(x**2+y**2+z**2).
  • WData.t: Array of times.
  • WData.dim: Dimension of dataset. I.e. dim==1 for 1D simulations, dim==3 for 3D simulations.
  • WData.aliases: Dictionary of aliases. Convenience for providing alternative data access in VisIt.
  • WData.constants: Dictionary of constants such as kF, eF.

Minimal Example:

Here is a minimal set of data:

import numpy as np
np.random.seed(3)
from wdata.io import WData, Var

Nt = 10 
Nxyz = (4, 8, 16)
dxyz = (0.3, 0.2, 0.1)
dt = 0.1
Ntxyz = (Nt,) + Nxyz

density = np.random.random(Ntxyz)

data = WData(prefix='dataset', data_dir='_example_wdata',
             Nxyz=Nxyz, dxyz=dxyz,
             variables=[Var(density=density)],
             Nt=Nt)
data.save(force=True)

This will make a directory _example_wdata with infofile _example_wdata/dataset.wtxt:

$ tree _example_wdata
_example_wdata
|-- dataset.wtxt
`-- dataset_density.wdat

0 directories, 2 files
$ cat _example_wdata/dataset.wtxt
# Generated by wdata.io: [2020-12-18 06:41:29 UTC+0000 = 2020-12-17 22:41:29 PST-0800]

NX               4    # Lattice size in X direction
NY               8    #             ... Y ...
NZ              16    #             ... Z ...
DX             0.3    # Spacing in X direction
DY             0.2    #        ... Y ...
DZ             0.1    #        ... Z ...
prefix     dataset    # datafile prefix: <prefix>_<var>.<format>
datadim          3    # Block size: 1:NX, 2:NX*NY, 3:NX*NY*NZ
cycles          10    # Number Nt of frames/cycles per dataset
t0               0    # Time value of first frame
dt               1    # Time interval between frames

# variables
# tag       name    type    unit    format    # description
var      density    real    none      wdat    # density

The data can be loaded by specifying the infofile:

from wdata.io import WData
data = WData.load('_example_wdata/dataset.wtxt')

The data could be plotted using PyVista for example (the random data will not look so good...):

import numpy as np
import pyvista as pv
from wdata.io import WData

data = WData.load('_example_wdata/dataset.wtxt')
n = data.density[0]

grid = pv.StructuredGrid(*np.meshgrid(*data.xyz))
grid["vol"] = n.flatten(order="F")
contours = grid.contour(np.linspace(n.min(), n.max(), 5))

p = pv.Plotter()
p.add_mesh(contours, scalars=contours.points[:, 2])
p.show()

The recommended way to save data is to create variables for the data, times, and abscissa, then store this:

import numpy as np
from wdata.io import WData, Var

np.random.seed(3)

Nt = 10
Nxyz = (32, 32, 32)
dxyz = (10.0/32, 10.0/32, 10.0/32)
dt = 0.1

# Abscissa.  Not strictly needed, but if you have them, then use them
# instead.
t = np.arange(Nt)*dt
xyz = np.meshgrid(*[(np.arange(_N)-_N/2)*_dx
                    for _N, _dx in zip(Nxyz, dxyz)],
                  sparse=True, indexing='ij')

# Now make the WData object and save the data.
Ntxyz = (Nt,) + Nxyz
w = np.pi/t.max()
ws = [1.0 + 0.5*np.cos(w*t), 
      1.0 + 0.5*np.sin(w*t),
      1.0 + 0*t]
density = np.exp(-sum((_x[None,...].T*_w).T**2/2 for _x, _w in zip(xyz, ws)))
delta = np.random.random(Ntxyz) + np.random.random(Ntxyz)*1j - 0.5 - 0.5j
current = np.random.random((Nt, 3,) + Nxyz) - 0.5

variables = [
    Var(density=density),
    Var(delta=delta),
    Var(current=current)
]
    
data = WData(prefix='dataset2', 
             data_dir='_example_wdata/',
             xyz=xyz, t=t,
             variables=variables)
data.save()

Now load and plot the data:

import numpy as np
import pyvista as pv

from wdata.io import WData
data = WData.load(infofile='_example_wdata/dataset2.wtxt')

n = data.density[0]

grid = pv.StructuredGrid(*np.meshgrid(*data.xyz))
grid["vol"] = n.flatten(order="F")
contours = grid.contour(np.linspace(n.min(), n.max(), 5))

p = pv.Plotter()
p.add_mesh(contours, scalars=contours.points[:, 2])
p.show()