GridDataFormats Reading and writing of data on regular grids in Python
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Python library library formats grid data grids

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Reading and writing of data on regular grids in Python The gridData Python package provides a class to handle data on a regular grid --- basically NumPy n-dimensional arrays. It supports reading from and writing to some common formats (such as OpenDX).The GridDataFormats package reads grid data from files, makes them available as a :class:`Grid` object, and allows one to write out the data again.A :class:`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.:class:`Grid` objects have some convenient properties:* The data is represented as a :class:`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 :mod:`numpy` documentation for details) and that only grids defined on the same cell edges can be combined.* A :class:`Grid` object can also be created from within python code e.g. from the output of the :func:`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 :class:`gridData.Grid`, so we just load this right away:: from gridData import GridLoading data:From a OpenDX file:: g = Grid("density.dx")From a gOpenMol PLT file:: g = Grid("density.plt")From the output of :func:`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 :class:`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 :class:`Grid` objects: A = Grid('A.dx') B = Grid('B.dx')Subtract A from B:: C = B - Aand 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 Requirements: · Python

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