# 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: https://mrcfile.readthedocs.io/
.. _`MRC2014 file format`: http://www.ccpem.ac.uk/mrc_format/mrc2014.php
References
----------
.. [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`: https://doi.org/10.1107/S2059798317007859
Classes
-------
.. autoclass:: MRC
:members:
"""
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
assume_volumetric : bool (optional)
If ``False`` (default), check the file header to determine whether
the data in `grid` is a 3D volume. If ``True``, assume `grid` is volumetric.
.. versionadded:: 1.1.0
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`)
* Reading and writing are supported.
.. versionadded:: 0.7.0
"""
def __init__(self, filename=None, assume_volumetric=False):
self.filename = filename
if filename is not None:
self.read(filename, assume_volumetric=assume_volumetric)
[docs]
@classmethod
def from_grid(cls, grid, **kwargs):
"""Create MRC object from a Grid.
If the Grid was originally created from an mrcfile (and thus has
the ``Grid._mrc_header`` attribute), the MRC header will be copied
into the returned MRC instance.
Parameters
----------
grid : Grid
Grid object to convert
**kwargs
Additional keyword arguments (currently unused)
Returns
-------
MRC
MRC wrapper object
.. versionadded:: 1.2.0
"""
mrc_obj = cls()
mrc_obj.array = grid.grid
mrc_obj.delta = np.diag(grid.delta)
mrc_obj.origin = grid.origin
mrc_obj.rank = 3
if hasattr(grid, "_mrc_header"):
mrc_obj.header = grid._mrc_header
return mrc_obj
@property
def native(self):
"""Return a native :class:`~mrcfile.mrcinterpreter.MrcInterpreter` object.
The object is :class:`~mrcfile.mrcobject.MrcObject` that is connected
to a stream. It is implemented as a managed property and regenerated
whenever :attr:`native` is used. In order to re-use the object, assign
it to a variable::
mrc = gdfMRC.native
In order to write to a file, open the file and assign it to the
:class:`~mrcfile.mrcinterpreter.MrcInterpreter._iostream` attribute and
call the :meth:`~mrcfile.mrcinterpreter.MrcInterpreter.flush` method::
mrc = gdfMRC.native # important: assign to variable mrc!
with open("new.mrc", "wb") as f:
mrc._iostream = f
mrc.flush()
Returns
-------
mrcfile.mrcinterpreter.MrcInterpreter
Native mrcfile object
SeeAlso
-------
write
.. versionadded:: 1.2.0
"""
# "write only", i.e., do not initialize with a stream
mrc = mrcfile.mrcinterpreter.MrcInterpreter()
mrc._create_default_attributes()
# Preserve header if it exists, otherwise use defaults
if hasattr(self, "header"):
# File was read - preserve original ordering
h = self.header
axes_order = np.hstack([h.mapc, h.mapr, h.maps])
mapc, mapr, maps = int(h.mapc), int(h.mapr), int(h.maps)
else:
# New file - use standard ordering
axes_order = np.array([1, 2, 3])
mapc, mapr, maps = 1, 2, 3
h = None
# Reverse the transformation done in read()
transpose_order = np.argsort(axes_order[::-1])
inverse_transpose_order = np.argsort(transpose_order)
# Transform our xyz array back to the file's native ordering
# Ensure proper data type (float32 is standard for mode 2)
data = np.transpose(self.array,
axes=inverse_transpose_order).astype(np.float32)
mrc.set_data(data)
# Set voxel size from delta (diagonal elements)
voxel_size = np.diag(self.delta).astype(np.float32)
mrc.voxel_size = tuple(voxel_size)
# Set map ordering
mrc.header.mapc = mapc
mrc.header.mapr = mapr
mrc.header.maps = maps
# Handle nstart and origin
if h is not None:
# Preserve original header values
nxstart = int(h.nxstart)
nystart = int(h.nystart)
nzstart = int(h.nzstart)
header_origin_xyz = np.array(
[h.origin.x, h.origin.y, h.origin.z], dtype=np.float32
)
mrc.header.mx = int(h.mx)
mrc.header.my = int(h.my)
mrc.header.mz = int(h.mz)
# Preserve cell dimensions
if hasattr(h, "cella"):
mrc.header.cella.x = float(h.cella.x)
mrc.header.cella.y = float(h.cella.y)
mrc.header.cella.z = float(h.cella.z)
if hasattr(h, "cellb"):
mrc.header.cellb.alpha = float(h.cellb.alpha)
mrc.header.cellb.beta = float(h.cellb.beta)
mrc.header.cellb.gamma = float(h.cellb.gamma)
# Copy space group if available
if hasattr(h, "ispg"):
mrc.header.ispg = int(h.ispg)
else:
# For new files, calculate nstart from origin
if np.any(voxel_size <= 0):
raise ValueError(f"Voxel size must be positive, got {voxel_size}")
# Set header.origin = 0 and encode everything in nstart
header_origin_xyz = np.zeros(3, dtype=np.float32)
nxstart = int(np.round(self.origin[0] / voxel_size[0]))
nystart = int(np.round(self.origin[1] / voxel_size[1]))
nzstart = int(np.round(self.origin[2] / voxel_size[2]))
# Set the start positions
mrc.header.nxstart = nxstart
mrc.header.nystart = nystart
mrc.header.nzstart = nzstart
# Set explicit origin
mrc.header.origin.x = float(header_origin_xyz[0])
mrc.header.origin.y = float(header_origin_xyz[1])
mrc.header.origin.z = float(header_origin_xyz[2])
# Update statistics only
mrc.update_header_stats()
return mrc
[docs]
def read(self, filename, assume_volumetric=False):
"""Populate the instance from the MRC/CCP4 file *filename*."""
if filename is not None:
self.filename = filename
with mrcfile.open(filename) as mrc:
if assume_volumetric:
# non 3D volumes should always fail, regardless of assume_volumetric value
is_volume = mrc.data is not None and len(mrc.data.shape) == 3
else:
is_volume = mrc.is_volume()
if not is_volume:
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
)
)
# mrc.data[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(mrc.data, axes=transpose_order)
self.delta = 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(self.delta)
# 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 [
self.delta[d, d] * np.arange(self.shape[d] + 1)
+ self.origin[d]
- 0.5 * self.delta[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)
[docs]
def write(self, filename):
"""Write grid data to MRC/CCP4 file format.
Parameters
----------
filename : str
Output filename for the MRC file
Notes
-----
The data array should be in xyz order (axis 0=X, axis 1=Y, axis 2=Z).
If the MRC object was created by reading an existing file, the original
header information (including mapc, mapr, maps ordering) is preserved.
Otherwise, standard ordering (mapc=1, mapr=2, maps=3) is used.
Currently, only *uncompressed* files are supported for writing. (For
reading, gzip and bzip2-compressed files are supported.)
.. versionadded:: 1.1.0
"""
if filename is not None:
self.filename = filename
mrc = self.native
# TODO: make compression work
with open(filename, "wb") as f:
mrc._iostream = f
mrc.flush()