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Reading and writing of data on regular grids in Python

Project description

The gridDataFormats package provides classes to unify reading and writing n-dimensional datasets. One can read grid data from files, make them available as a Grid object, and allows one to write out the data again.

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.

Supported file formats

The package can be easily extended. The OpenDX format is widely understood by many molecular viewers and is sufficient for many applications that were encountered so far. Hence, at the moment only a small number of file formats is directly supported.

format extension read write remarks
OpenDX dx x x subset of OpenDX implemented
gOpenMol plt x    
pickle pickle x x standard Python pickle of the Grid class

Examples

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

from gridData import Grid

Loading data

From a OpenDX file:

g = Grid("density.dx")

From a gOpenMol PLT file:

g = Grid("density.plt")

From the output of numpy.histogramdd:

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

For other ways to load data, see the docs for gridData.Grid

Subtracting two densities

Assuming one has two densities that were generated on the same grid positions, stored in files A.dx and B.dx, one first reads the data into two Grid objects:

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

Subtract A from B:

C = B - A

and write out as a dx file:

C.export('C.dx')

The resulting file C.dx can be visualized with any OpenDX-capable viewer, or later read-in again.

Resampling

Load data:

A = Grid('A.dx')

Interpolate with a cubic spline to twice the sample density:

A2 = A.resample_factor(2)

Downsample to half of the bins in each dimension:

Ahalf = A.resample_factor(0.5)

Resample to the grid of another density, B:

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

or even simpler

A_on_B = A.resample(B)

Note

The cubic spline generates region with values that did not occur in the original data; in particular if the original data’s lowest value was 0 then the spline interpolation will probably produce some values <0 near regions where the density changed abruptly.

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