UVW - Universal VTK Writer

 

UVW is a small utility library to write VTK files from data contained in Numpy arrays. It handles fully-fledged ndarrays defined over {1, 2, 3}-d domains, with arbitrary number of components. There are no constraints on the particular order of components, although copy of data can be avoided if the array is Fortran contiguous, as VTK files are written in Fortran order. UVW supports multi-process writing of VTK files, so that it can be used in an MPI environment.

Getting Started

Here is how to install and use uvw.

Prerequisites

• Python 3. It may work with python 2, but it hasn't been tested.
• Numpy. This code has been tested with Numpy version 1.14.3.
• mpi4py only if you wish to use the parallel classes of UVW (i.e. the submodule uvw.parallel)

Installing

This library can be installed with pip:

pip install --user uvw

If you want to activate parallel capabilities, run:

pip install --user uvw[mpi]

which will automatically pull mpi4py as a dependency.

Writing Numpy arrays

As a first example, let us write a multi-component numpy array into a rectilinear grid:

python
import numpy as np
from uvw import RectilinearGrid, DataArray

# Creating coordinates
x = np.linspace(-0.5, 0.5, 10)
y = np.linspace(-0.5, 0.5, 20)
z = np.linspace(-0.9, 0.9, 30)

# Creating the file (with possible data compression)
grid = RectilinearGrid('grid.vtr', (x, y, z), compression=True)

# A centered ball
x, y, z = np.meshgrid(x, y, z, indexing='ij')
r = np.sqrt(x**2 + y**2 + z**2)
ball = r < 0.3

# Some multi-component multi-dimensional data
data = np.zeros([10, 20, 30, 3, 3])
data[ball, ...] = np.array([[0, 1, 0],
                            [1, 0, 0],
                            [0, 1, 1]])

# Some cell data
cell_data = np.zeros([9, 19, 29])
cell_data[0::2, 0::2, 0::2] = 1

# Adding the point data (see help(DataArray) for more info)
grid.addPointData(DataArray(data, range(3), 'ball'))
# Adding the cell data
grid.addCellData(DataArray(cell_data, range(3), 'checkers'))
grid.write()

UVW also supports writing data on 2D and 1D physical domains, for example:

python
import sys
import numpy as np
from uvw import RectilinearGrid, DataArray

# Creating coordinates
x = np.linspace(-0.5, 0.5, 10)
y = np.linspace(-0.5, 0.5, 20)

# A centered disk
xx, yy = np.meshgrid(x, y, indexing='ij')
r = np.sqrt(xx**2 + yy**2)
R = 0.3
disk = r < R

data = np.zeros([10, 20])
data[disk] = np.sqrt(1-(r[disk]/R)**2)

# File object can be used as a context manager
# and you can write to stdout!
with RectilinearGrid(sys.stdout, (x, y)) as grid:
  grid.addPointData(DataArray(data, range(2), 'data'))

Writing in parallel with mpi4py

The classes contained in the uvw.parallel submodule support multi-process writing using mpi4py. Here is a code example:

python
import numpy as np

from mpi4py import MPI

from uvw.parallel import PRectilinearGrid
from uvw import DataArray

comm = MPI.COMM_WORLD
rank = comm.Get_rank()

N = 20

# Domain bounds per rank
bounds = [
    {'x': (-2, 0), 'y': (-2, 0)},
    {'x': (-2, 0), 'y': (0,  2)},
    {'x': (0,  2), 'y': (-2, 2)},
]

# Domain sizes per rank
sizes = [
    {'x': N, 'y': N},
    {'x': N, 'y': N},
    {'x': N, 'y': 2*N-1},  # account for overlap
]

# Size offsets per rank
offsets = [
    [0, 0],
    [0, N],
    [N, 0],
]

x = np.linspace(*bounds[rank]['x'], sizes[rank]['x'])
y = np.linspace(*bounds[rank]['y'], sizes[rank]['y'])

xx, yy = np.meshgrid(x, y, indexing='ij', sparse=True)
r = np.sqrt(xx**2 + yy**2)
data = np.exp(-r**2)

# Indicating rank info with a cell array
proc = np.ones((x.size-1, y.size-1)) * rank

with PRectilinearGrid('pgrid.pvtr', (x, y), offsets[rank]) as rect:
    rect.addPointData(DataArray(data, range(2), 'gaussian'))
    rect.addCellData(DataArray(proc, range(2), 'proc'))

As you can see, using PRectilinearGrid feels just like using RectilinearGrid, except that you need to supply the position of the local grid in the global grid numbering (the offsets[rank] in the above example). Note that RecilinearGrid VTK files need an overlap in point data, hence why the global grid size ends up being (2*N-1, 2*N-1). If you forget that overlap, Paraview (or another VTK-based software) may complain that some parts in the global grid (aka "extents" in VTK) are missing data.

List of features

Here is a list of what is available in UVW:

VTK file formats

• Image data (.vti)
• Rectilinear grid (.vtr)
• Structured grid (.vts)
• Parallel Rectilinear grid (.pvtr)

Data representation

• ASCII
• Base64 (raw and compressed: the compression argument of file constructors can be True, False, or an integer in [-1, 9] for compression levels)

Planned developments

Here is a list of future developments:

• Image data
• Unstructured grid
• Structured grid
• Parallel writing (mpi4py-enabled PRectilinearGrid *is now available!*)
• Benchmarking + performance comparison with pyevtk

Developing

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Git repository

First clone the git repository:

git clone https://github.com/prs513rosewood/uvw.git

Then you can use pip in development mode (possibly in virtualenv):

pip install --user -e .[mpi,tests]

Running the tests

The tests can be run using pytest:

pytest
# or for tests with mpi
mpiexec -n 2 pytest --with-mpi

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

• @PurpleBooth's README-Template