GridDataFormats 0.2.4

The Grid class

A Grid consists of a rectangular, regular, N-dimensional array of data. It contains

1. The position of the array cell edges.

2. The array data itself.

This is equivalent to knowing

1. The origin of the coordinate system (i.e. which data cell corresponds to (0,0,…,0)

2. The spacing of the grid in each dimension.

3. The data on a grid.

Grid objects have some convenient properties:

• The data is represented as a numpy.array and thus shares all the advantages coming with this sophisticated and powerful library.

• They can be manipulated arithmetically, e.g. one can simply add or subtract two of them and get another one, or multiply by a constant. Note that all operations are defined point-wise (see the NumPy documentation for details) and that only grids defined on the same cell edges can be combined.

• A Grid object can also be created from within python code e.g. from the output of the numpy.histogramdd function.

• The representation of the data is abstracted from the format that the files are saved in. This makes it straightforward to add additional readers for new formats.

• The data can be written out again in formats that are understood by other programs such as VMD or PyMOL.

Examples

In most cases, only one class is important, the gridData.Grid, so we just load this right away:

from gridData import Grid

g = Grid("density.dx")

g = Grid("density.plt")

import numpy
r = numpy.random.randn(100,3)
H, edges = numpy.histogramdd(r, bins = (5, 8, 4))
g = Grid(H, edges=edges)

A = Grid('A.dx')
B = Grid('B.dx')

C = B - A

C.export('C.dx')

A = Grid('A.dx')

A2 = A.resample_factor(2)

Ahalf = A.resample_factor(0.5)

B = Grid('B.dx')
A_on_B = A.resample(B.edges)

A_on_B = A.resample(B)