Basic use¶

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

from gridData import Grid


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 = np.histogramdd(r, bins = (5, 8, 4))
g = Grid(H, edges=edges)


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

Writing out data¶

Some formats support writing data (see Supported file formats for more details), using the gridData.core.Grid.export() method:

g.export("density.dx")


The format can also be specified explicitly:

g.export("density.pkl", file_format="pickle")


Some of the exporters (such as for OpenDX, see Reading and writing OpenDX files) may take additional, format-specific keywords, which are documented separately.

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¶

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.