Source code for gridData.mrc

# gridDataFormats --- python modules to read and write gridded data
# Copyright (c) 2009-2021 Oliver Beckstein <[email protected]>
# Released under the GNU Lesser General Public License, version 3 or later.

""":mod:`mrc` --- the MRC/CCP4 volumetric data format

.. versionadded:: 0.7.0

Reading of MRC/CCP4 volumetric files (`MRC2014 file format`_) using
the mrcfile_ library [Burnley2017]_.

.. _mrcfile:
.. _`MRC2014 file format`:


.. [Burnley2017] Burnley T, Palmer C and Winn M (2017) Recent
                 developments in the CCP-EM software suite. *Acta
                 Cryst.* D73:469-477. doi: `10.1107/S2059798317007859`_

.. _`10.1107/S2059798317007859`:


import numpy as np
import mrcfile

[docs]class MRC(object): """Represent a MRC/CCP4 file. Load `MRC/CCP4 2014 <MRC2014 file format>`_ 3D volumetric data with the mrcfile_ library. Parameters ---------- filename : str (optional) input file (or stream), can be compressed Raises ------ ValueError If the unit cell is not orthorhombic or if the data are not volumetric. Attributes ---------- header : numpy.recarray Header data from the MRC file as a numpy record array. array : numpy.ndarray Data as a 3-dimensional array where axis 0 corresponds to X, axis 1 to Y, and axis 2 to Z. This order is always enforced, regardless of the order in the mrc file. delta : numpy.ndarray Diagonal matrix with the voxel size in X, Y, and Z direction (taken from the :attr:`mrcfile.mrcfile.voxel_size` attribute) origin : numpy.ndarray numpy array with coordinates of the coordinate system origin (computed from :attr:`header.origin`, the offsets :attr:`header.origin.nxstart`, :attr:`header.origin.nystart`, :attr:`header.origin.nzstart` and the spacing :attr:`delta`) rank : int The integer 3, denoting that only 3D maps are read. Notes ----- * Only volumetric (3D) densities are read. * Only orthorhombic unitcells supported (other raise :exc:`ValueError`) * Only reading is currently supported. .. versionadded:: 0.7.0 """ def __init__(self, filename=None): self.filename = filename if filename is not None:
[docs] def read(self, filename): """Populate the instance from the MRC/CCP4 file *filename*.""" if filename is not None: self.filename = filename with as mrc: if not mrc.is_volume(): #pragma: no cover raise ValueError( "MRC file {} is not a volumetric density.".format(filename)) self.header = h = mrc.header.copy() # check for being orthorhombic if not np.allclose([h.cellb.alpha, h.cellb.beta, h.cellb.gamma], [90, 90, 90]): raise ValueError("Only orthorhombic unitcells are currently " "supported, not " "alpha={0}, beta={1}, gamma={2}".format( h.cellb.alpha, h.cellb.beta, h.cellb.gamma)) #[z, y, x] indexed: convert to x,y,z as used in GridDataFormats # together with the axes orientation information in mapc/mapr/maps. # mapc, mapr, maps = 1, 2, 3 for Fortran-ordering and 3, 2, 1 for C-ordering. # Other combinations are possible. We reorder the data for the general case # by sorting mapc, mapr, maps in ascending order, i.e., to obtain x,y,z. # mrcfile provides the data in zyx shape (without regard to map*) so we first # transpose it to xyz and then reorient with axes_c_order. # # All other "xyz" quantitities are also reordered. axes_order = np.hstack([h.mapc, h.mapr, h.maps]) axes_c_order = np.argsort(axes_order) transpose_order = np.argsort(axes_order[::-1]) self.array = np.transpose(, axes=transpose_order) = np.diag(np.array([mrc.voxel_size.x, mrc.voxel_size.y, mrc.voxel_size.z])) # the grid is shifted to the MRC origin by offset # (assume orthorhombic) offsets = np.hstack([h.nxstart, h.nystart, h.nzstart])[axes_c_order] * np.diag( # GridData origin is centre of cell at x=col=0, y=row=0 z=seg=0 self.origin = np.hstack([h.origin.x, h.origin.y, h.origin.z]) + offsets self.rank = 3
@property def shape(self): """Shape of the :attr:`array`""" return self.array.shape @property def edges(self): """Edges of the grid cells, origin at centre of 0,0,0 grid cell. Only works for regular, orthonormal grids. """ # TODO: Add triclinic cell support. return [[d, d] * np.arange(self.shape[d] + 1) + self.origin[d] - 0.5 *[d, d] for d in range(self.rank)]
[docs] def histogramdd(self): """Return array data as (edges,grid), i.e. a numpy nD histogram.""" return (self.array, self.edges)