Provided by: python3-mrcfile_1.5.4-2_all 
NAME
mrcfile - mrcfile Documentation
Get started by reading the Overview of mrcfile.py and the Usage Guide.
You can also look at the Source documentation.
OVERVIEW OF MRCFILE.PY
mrcfile is a Python implementation of the MRC2014 file format, which is used in structural biology to
store image and volume data.
It allows MRC files to be created and opened easily using a very simple API, which exposes the file's
header and data as numpy arrays. The code runs in Python 2 and 3 and is fully unit-tested.
This library aims to allow users and developers to read and write standard-compliant MRC files in Python
as easily as possible, and with no dependencies on any compiled libraries except numpy. You can use it
interactively to inspect files, correct headers and so on, or in scripts and larger software packages to
provide basic MRC file I/O functions.
Key Features
• Clean, simple API for access to MRC files
• Easy to install and use
• Validation of files according to the MRC2014 format
• Seamless support for gzip and bzip2 files
• Memory-mapped file option for fast random access to very large files
• Asynchronous opening option for background loading of multiple files
• Runs in Python 2 & 3, on Linux, Mac OS X and Windows
Installation
The mrcfile library is available from the Python package index:
pip install mrcfile
Or from conda-forge:
conda install --channel conda-forge mrcfile
It is also included in the ccpem-python environment in the CCP-EM software suite.
The source code (including the full test suite) can be found on GitHub.
Basic usage
The easiest way to open a file is with the mrcfile.open and mrcfile.new functions. These return an ‐
MrcFile object which represents an MRC file on disk.
To open an MRC file and read a slice of data:
>>> import mrcfile
>>> with mrcfile.open('tests/test_data/EMD-3197.map') as mrc:
... mrc.data[10,10]
...
array([ 2.58179283, 3.1406002 , 3.64495397, 3.63812137, 3.61837363,
4.0115056 , 3.66981959, 2.07317996, 0.1251585 , -0.87975615,
0.12517013, 2.07319379, 3.66982722, 4.0115037 , 3.61837196,
3.6381247 , 3.64495087, 3.14059472, 2.58178973, 1.92690361], dtype=float32)
To create a new file with a 2D data array, and change some values:
>>> array = np.zeros((5, 5), dtype=np.int8)
>>> with mrcfile.new('tmp.mrc') as mrc:
... mrc.set_data(array)
... mrc.data[1:4,1:4] = 10
... mrc.data
...
array([[ 0, 0, 0, 0, 0],
[ 0, 10, 10, 10, 0],
[ 0, 10, 10, 10, 0],
[ 0, 10, 10, 10, 0],
[ 0, 0, 0, 0, 0]], dtype=int8)
The data will be saved to disk when the file is closed, either automatically at the end of the with block
(like a normal Python file object) or manually by calling close(). You can also call flush() to write any
changes to disk and keep the file open.
To validate an MRC file:
>>> mrcfile.validate('tests/test_data/EMD-3197.map')
File does not declare MRC format version 20140 or 20141: nversion = 0
False
>>> mrcfile.validate('tmp.mrc')
True
Documentation
Full documentation is available on Read the Docs.
Citing mrcfile
If you find mrcfile useful in your work, please cite:
Burnley T, Palmer C & Winn M (2017) Recent developments in the CCP-EM software suite. Acta Cryst.
D73:469--477. doi: 10.1107/S2059798317007859
Contributing
Please use the GitHub issue tracker for bug reports and feature requests, or email CCP-EM.
Code contributions are also welcome, please submit pull requests to the GitHub repository.
To run the test suite, go to the top-level project directory (which contains the mrcfile and tests
packages) and run python -m unittest tests. (Or, if you have tox installed, run tox.)
Licence
The project is released under the BSD licence.
USAGE GUIDE
This is a detailed guide to using the mrcfile Python library. For a brief introduction, see the overview.
Opening MRC files
Normal file access
MRC files can be opened using the mrcfile.new() or mrcfile.open() functions. These return an instance of
the MrcFile class, which represents an MRC file on disk and makes the file's header, extended header and
data available for read and write access as numpy arrays. :
>>> # First, create a simple dataset
>>> import numpy as np
>>> example_data = np.arange(12, dtype=np.int8).reshape(3, 4)
>>> # Make a new MRC file and write the data to it:
>>> import mrcfile
>>> with mrcfile.new('tmp.mrc') as mrc:
... mrc.set_data(example_data)
...
>>> # The file is now saved on disk. Open it again and check the data:
>>> with mrcfile.open('tmp.mrc') as mrc:
... mrc.data
...
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]], dtype=int8)
Simple data access
Alternatively, for even quicker access to MRC data but with minimal control of the file header, you can
use the read() and write() functions. These do not return MrcFile objects but instead work directly with
numpy arrays:
>>> # First, create a simple dataset
>>> import numpy as np
>>> example_data_2 = np.arange(6, dtype=np.int8).reshape(3, 2)
>>> # Write the data to a new MRC file:
>>> mrcfile.write('tmp2.mrc', example_data_2)
>>> # Read it back:
>>> mrcfile.read('tmp2.mrc')
array([[0, 1],
[2, 3],
[4, 5]], dtype=int8)
Handling compressed files
All of the functions shown above can also handle gzip- or bzip2-compressed files very easily:
>>> # Make a new gzipped MRC file:
>>> with mrcfile.new('tmp.mrc.gz', compression='gzip') as mrc:
... mrc.set_data(example_data * 2)
...
>>> # Open it again with the normal open function:
>>> with mrcfile.open('tmp.mrc.gz') as mrc:
... mrc.data
...
array([[ 0, 2, 4, 6],
[ 8, 10, 12, 14],
[16, 18, 20, 22]], dtype=int8)
>>> # Same again for bzip2:
>>> with mrcfile.new('tmp.mrc.bz2', compression='bzip2') as mrc:
... mrc.set_data(example_data * 3)
...
>>> # Open it again with the normal read function:
>>> mrcfile.read('tmp.mrc.bz2')
array([[ 0, 3, 6, 9],
[12, 15, 18, 21],
[24, 27, 30, 33]], dtype=int8)
>>> # The write function applies compression automatically based on the file name
>>> mrcfile.write('tmp2.mrc.gz', example_data * 4)
>>> # The new file is opened as a GzipMrcFile object:
>>> with mrcfile.open('tmp2.mrc.gz') as mrc:
... print(mrc)
...
GzipMrcFile('tmp2.mrc.gz', mode='r')
Closing files and writing to disk
MrcFile objects should be closed when they are finished with, to ensure any changes are flushed to disk
and the underlying file object is closed:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # do things...
>>> mrc.close()
As we saw in the examples above, MrcFile objects support Python's with statement, which will ensure the
file is closed properly after use (like a normal Python file object). It's generally a good idea to use
with if possible, but sometimes when running Python interactively (as in some of these examples), it's
more convenient to open a file and keep using it without having to work in an indented block. If you do
this, remember to close the file at the end!
There's also a flush() method that writes the MRC data to disk but leaves the file open:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # do things...
>>> mrc.flush() # make sure changes are written to disk
>>> # continue using the file...
>>> mrc.close() # close the file when finished
MrcFile subclasses
For most purposes, the top-level functions in mrcfile should be all you need to open MRC files, but it is
also possible to directly instantiate MrcFile and its subclasses, GzipMrcFile, Bzip2MrcFile and
MrcMemmap:
>>> with mrcfile.mrcfile.MrcFile('tmp.mrc') as mrc:
... mrc
...
MrcFile('tmp.mrc', mode='r')
>>> with mrcfile.gzipmrcfile.GzipMrcFile('tmp.mrc.gz') as mrc:
... mrc
...
GzipMrcFile('tmp.mrc.gz', mode='r')
>>> with mrcfile.bzip2mrcfile.Bzip2MrcFile('tmp.mrc.bz2') as mrc:
... mrc
...
Bzip2MrcFile('tmp.mrc.bz2', mode='r')
>>> with mrcfile.mrcmemmap.MrcMemmap('tmp.mrc') as mrc:
... mrc
...
MrcMemmap('tmp.mrc', mode='r')
File modes
MrcFile objects can be opened in three modes: r, r+ and w+. These correspond to the standard Python file
modes, so r opens a file in read-only mode:
>>> # The default mode is 'r', for read-only access:
>>> mrc = mrcfile.open('tmp.mrc')
>>> mrc
MrcFile('tmp.mrc', mode='r')
>>> mrc.set_data(example_data)
Traceback (most recent call last):
...
ValueError: MRC object is read-only
>>> mrc.close()
r+ opens it for reading and writing:
>>> # Using mode 'r+' allows read and write access:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> mrc
MrcFile('tmp.mrc', mode='r+')
>>> mrc.set_data(example_data)
>>> mrc.data
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]], dtype=int8)
>>> mrc.close()
and w+ opens a new, empty file (also for both reading and writing):
>>> # Mode 'w+' creates a new empty file:
>>> mrc = mrcfile.open('empty.mrc', mode='w+')
>>> mrc
MrcFile('empty.mrc', mode='w+')
>>> mrc.data
array([], dtype=int8)
>>> mrc.close()
The new() function is effectively shorthand for open(name, mode='w+'):
>>> # Make a new file
>>> mrc = mrcfile.new('empty.mrc')
Traceback (most recent call last):
...
ValueError: File 'empty.mrc' already exists; set overwrite=True to overwrite it
>>> # Ooops, we've already got a file with that name!
>>> # If we're sure we want to overwrite it, we can try again:
>>> mrc = mrcfile.new('empty.mrc', overwrite=True)
>>> mrc
MrcFile('empty.mrc', mode='w+')
>>> mrc.close()
Permissive read mode
Normally, if an MRC file is badly invalid, an exception is raised when the file is opened. This can be a
problem if we want to, say, open a file and fix a header problem. To deal with this situation, open() and
mmap() provide an optional permissive argument. If this is set to True, problems with the file will cause
warnings to be issued (using Python's warnings module) instead of raising exceptions, and the file will
continue to be interpreted as far as possible.
Let's see an example. First we'll deliberately make an invalid file:
>>> # Make a new file and deliberately make a mistake in the header
>>> with mrcfile.new('invalid.mrc') as mrc:
... mrc.header.map = b'map ' # standard requires b'MAP '
...
Now when we try to open the file, an exception is raised:
>>> # Opening an invalid file raises an exception:
>>> mrc = mrcfile.open('invalid.mrc')
Traceback (most recent call last):
...
ValueError: Map ID string not found - not an MRC file, or file is corrupt
If we use permissive mode, we can open the file, and we'll see a warning about the problem (except that
here, we have to catch the warning and print the message manually, because warnings don't play nicely
with doctests!):
>>> # Opening in permissive mode succeeds, with a warning:
>>> with warnings.catch_warnings(record=True) as w:
... mrc = mrcfile.open('invalid.mrc', permissive=True)
... print(w[0].message)
...
Map ID string not found - not an MRC file, or file is corrupt
Now let's fix the file:
>>> # Fix the invalid file by correcting the header
>>> with mrcfile.open('invalid.mrc', mode='r+', permissive=True) as mrc:
... mrc.header.map = mrcfile.constants.MAP_ID
...
And now we should be able to open the file again normally:
>>> # Now we don't need permissive mode to open the file any more:
>>> mrc = mrcfile.open('invalid.mrc')
>>> mrc.close()
The problems that can cause an exception when opening an MRC file are:
1. The header's map field is not set correctly to confirm the file type. If the file is otherwise
correct, permissive mode should be able to read the file normally.
2. The machine stamp is invalid and so the file's byte order cannot be determined. In this case,
permissive mode assumes that the byte order is little-endian and continues trying to read the file. If
the file is actually big-endian, the mode and data size checks will also fail because these values
depend on the endianness and will be nonsensical.
3. The mode number is not recognised. Currently accepted modes are 0, 1, 2, 4, 6 and 12.
4. The data block is not large enough for the specified data type and dimensions.
In the last two cases, the data block will not be read and the data attribute will be set to None.
Fixing invalid files can be quite complicated! This usage guide might be expanded in future to explain
how to analyse and fix problems, or the library itself might be improved to fix certain problems
automatically. For now, if you have trouble with an invalid file, inspecting the code in this library
might help you to work out how to approach the problem (start with MrcInterpreter._read_header()), or you
could try asking on the CCP-EM mailing list for advice.
A note on axis ordering
mrcfile follows the Python / C-style convention for axis ordering. This means that the first index is the
slowest axis (typically Z for volume data or Y for images) and the last index is the fastest axis
(typically X), and the numpy arrays are C-contiguous:
>>> data = mrcfile.read('tmp.mrc')
>>> data
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]], dtype=int8)
>>> data[1, 0] # x = 0, y = 1
4
>>> data[2, 3] # x = 3, y = 2
11
>>> data.flags.c_contiguous
True
>>> data.flags.f_contiguous
False
Note that this axis order is the opposite of the FORTRAN-style convention that is used by some other
software in structural biology. This can cause confusing errors!
Using MrcFile objects
Accessing the header and data
The header and data arrays can be accessed using the header, extended_header and data attributes:
>>> mrc = mrcfile.open('tmp.mrc')
>>> mrc.header
rec.array((4, 3, 1, ...),
dtype=[('nx', ...)])
>>> mrc.extended_header
array([],
dtype='|V1')
>>> mrc.data
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]], dtype=int8)
>>> mrc.close()
These attributes are read-only and cannot be assigned to directly, but (unless the file mode is r) the
arrays can be modified in-place:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # A new data array cannot be assigned directly to the data attribute
>>> mrc.data = np.ones_like(example_data)
Traceback (most recent call last):
...
AttributeError: can't set attribute
>>> # But the data can be modified by assigning to a slice or index
>>> mrc.data[0, 0] = 10
>>> mrc.data
array([[10, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]], dtype=int8)
>>> # All of the data values can be replaced this way, as long as the data
>>> # size, shape and type are not changed
>>> mrc.data[:] = np.ones_like(example_data)
>>> mrc.data
array([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]], dtype=int8)
>>> mrc.close()
To replace the data or extended header completely, call the set_data() and set_extended_header() methods:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> data_3d = np.linspace(-1000, 1000, 20, dtype=np.int16).reshape(2, 2, 5)
>>> mrc.set_data(data_3d)
>>> mrc.data
array([[[-1000, -895, -790, -685, -579],
[ -474, -369, -264, -158, -53]],
[[ 52, 157, 263, 368, 473],
[ 578, 684, 789, 894, 1000]]], dtype=int16)
>>> # Setting a new data array updates the header dimensions to match
>>> mrc.header.nx
array(5, dtype=int32)
>>> mrc.header.ny
array(2, dtype=int32)
>>> mrc.header.nz
array(2, dtype=int32)
>>> # We can also set the extended header in the same way
>>> string_array = np.fromstring(b'The extended header can hold any kind of numpy array', dtype='S52')
>>> mrc.set_extended_header(string_array)
>>> mrc.extended_header
array([b'The extended header can hold any kind of numpy array'],
dtype='|S52')
>>> # Setting the extended header updates the header's nsymbt field to match
>>> mrc.header.nsymbt
array(52, dtype=int32)
>>> mrc.close()
Note that setting an extended header does not automatically set or change the header's exttyp field. You
should set this yourself to identify the type of extended header you are using.
For a quick overview of the contents of a file's header, call print_header():
>>> with mrcfile.open('tmp.mrc') as mrc:
... mrc.print_header()
...
nx : 5
ny : 2
nz : 2
mode : 1
nxstart ...
Indexing the extended header
The extended_header attribute will return an array of the bytes in the extended header. However, some
extended headers are structured and consist of a sequence of metadata blocks, where each block
corresponds to a single image, or slice, in the data array. The attribute indexed_extended_header is
intended for more convenient access to the indexed sequence of metadata blocks, for known extended header
types. It will return an array with the appropriate numpy dtype set (or None in the case of failure) for
an indexable extended header array, even if the extended header itself contains trailing padding bytes.
Currently two extended header types (exttyp) are recognised as indexable: 'FEI1' and 'FEI2'. Other types
may be added in future.
Voxel size
The voxel (or pixel) size in the file can be accessed using the voxel_size attribute, which returns a
numpy record array with three fields, x, y and z, for the voxel size in each dimension:
>>> with mrcfile.open('tmp.mrc') as mrc:
... mrc.voxel_size
...
rec.array((0., 0., 0.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
In a new file, the voxel size is zero by default. To set the voxel size, you can assign to the voxel_size
attribute, using a single number (for an isotropic voxel size), a 3-tuple or a single-item record array
with x, y and z fields (which must be in that order):
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # Set a new isotropic voxel size:
>>> mrc.voxel_size = 1.0
>>> mrc.voxel_size
rec.array((1., 1., 1.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> # Set an anisotropic voxel size using a tuple:
>>> mrc.voxel_size = (1.0, 2.0, 3.0)
>>> mrc.voxel_size
rec.array((1., 2., 3.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> # And set a different anisotropic voxel size using a record array:
>>> mrc.voxel_size = np.rec.array(( 4., 5., 6.), dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> mrc.voxel_size
rec.array((4., 5., 6.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> mrc.close()
The sizes are not stored directly in the MRC header, but are calculated when required from the header's
cell and grid size fields. The voxel size can therefore be changed by altering the cell size:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # Check the current voxel size in X:
>>> mrc.voxel_size.x
array(4., dtype=float32)
>>> # And check the current cell dimensions:
>>> mrc.header.cella
rec.array((20., 10., 6.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> # Now change the cell's X length:
>>> mrc.header.cella.x = 10
>>> # And we see the voxel size has also changed:
>>> mrc.voxel_size.x
array(2., dtype=float32)
>>> mrc.close()
Equivalently, the cell size will be changed if a new voxel size is given:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # Check the current cell dimensions:
>>> mrc.header.cella
rec.array((10., 10., 6.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> # Set a new voxel size:
>>> mrc.voxel_size = 1.0
>>> # And our cell size has been updated:
>>> mrc.header.cella
rec.array((5., 2., 1.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> mrc.close()
Because the voxel size array is calculated on demand, assigning back to it wouldn't work so it's flagged
as read-only:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # This doesn't work
>>> mrc.voxel_size.x = 2.0
Traceback (most recent call last):
...
ValueError: assignment destination is read-only
>>> # But you can do this
>>> vsize = mrc.voxel_size.copy()
>>> vsize.x = 2.0
>>> mrc.voxel_size = vsize
>>> mrc.voxel_size
rec.array((2., 1., 1.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> mrc.close()
Note that the calculated voxel size will change if the grid size is changed by replacing the data array:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # Check the current voxel size:
>>> mrc.voxel_size
rec.array((2., 1., 1.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> # And the current data dimensions:
>>> mrc.data.shape
(2, 2, 5)
>>> # Replace the data with an array with a different shape:
>>> mrc.set_data(example_data)
>>> mrc.data.shape
(3, 4)
>>> # ...and the voxel size has changed:
>>> mrc.voxel_size
rec.array((2.5, 0.6666667, 1.),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> mrc.close()
Keeping the header and data in sync
When a new data array is given (using set_data() or the data argument to mrcfile.new()), the header is
automatically updated to ensure the file is is valid:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # Check the current data shape and header dimensions match
>>> mrc.data.shape
(3, 4)
>>> mrc.header.nx
array(4, dtype=int32)
>>> mrc.header.nx == mrc.data.shape[-1] # X axis is always the last in shape
True
>>> # Let's also check the maximum value recorded in the header
>>> mrc.header.dmax
array(11., dtype=float32)
>>> mrc.header.dmax == mrc.data.max()
True
>>> # Now set a data array with a different shape, and check the header again
>>> mrc.set_data(data_3d)
>>> mrc.data.shape
(2, 2, 5)
>>> mrc.header.nx
array(5, dtype=int32)
>>> mrc.header.nx == mrc.data.shape[-1]
True
>>> # The data statistics are updated as well
>>> mrc.header.dmax
array(1000., dtype=float32)
>>> mrc.header.dmax == mrc.data.max()
True
>>> mrc.close()
If the data array is modified in place, for example by editing values or changing the shape or dtype
attributes, the header will no longer be correct:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> mrc.data.shape
(2, 2, 5)
>>> # Change the data shape in-place and check the header
>>> mrc.data.shape = (5, 4)
>>> mrc.header.nx == mrc.data.shape[-1]
False
>>> # We'll also change some values and check the data statistics
>>> mrc.data[2:] = 0
>>> mrc.data.max()
0
>>> mrc.header.dmax == mrc.data.max()
False
>>> mrc.close()
Note that the header is deliberately not updated automatically except when set_data() is called, so if
you need to override any of the automatic header values you can do.
To keep the header in sync with the data, three methods can be used to update the header:
• update_header_from_data(): This updates the header's dimension fields, mode, space group and machine
stamp to be consistent with the data array. Because it only inspects the data array's attributes, this
method is fast even for very large arrays.
• update_header_stats(): This updates the data statistics fields in the header (dmin, dmax, dmean and
rms). This method can be slow with large data arrays because it has to access the full contents of the
array.
• reset_header_stats(): If the data values have changed and the statistics fields are invalid, but the
data array is very large and you do not want to wait for update_header_stats() to run, you can call
this method to reset the header's statistics fields to indicate that the values are undetermined.
The file we just saved had an invalid header, but of course, that's what's used by mrcfile to work out
how to read the file from disk! When we open the file again, our change to the shape has disappeared:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> mrc.data.shape
(2, 2, 5)
>>> # Let's change the shape again, as we did before
>>> mrc.data.shape = (5, 4)
>>> mrc.header.nx == mrc.data.shape[-1]
False
>>> # Now let's update the dimensions:
>>> mrc.update_header_from_data()
>>> mrc.header.nx
array(4, dtype=int32)
>>> mrc.header.nx == mrc.data.shape[-1]
True
>>> # The data statistics are still incorrect:
>>> mrc.header.dmax
array(1000., dtype=float32)
>>> mrc.header.dmax == mrc.data.max()
False
>>> # So let's update those as well:
>>> mrc.update_header_stats()
>>> mrc.header.dmax
array(0., dtype=float32)
>>> mrc.header.dmax == mrc.data.max()
True
>>> mrc.close()
In general, if you're changing the shape, type or endianness of the data, it's easiest to use set_data()
and the header will be kept up to date for you. If you start changing values in the data, remember that
the statistics in the header will be out of date until you call update_header_stats() or
reset_header_stats().
Data dimensionality
MRC files can be used to store several types of data: single images, image stacks, volumes and volume
stacks. These are distinguished by the dimensionality of the data array and the space group number (the
header's ispg field):
┌──────────────┬────────────┬──────────────────────────────┐
│ Data type │ Dimensions │ Space group │
├──────────────┼────────────┼──────────────────────────────┤
│ Single image │ 2 │ 0 │
├──────────────┼────────────┼──────────────────────────────┤
│ Image stack │ 3 │ 0 │
├──────────────┼────────────┼──────────────────────────────┤
│ Volume │ 3 │ 1--230 (1 for normal EM │
│ │ │ data) │
├──────────────┼────────────┼──────────────────────────────┤
│ Volume stack │ 4 │ 401--630 (401 for normal EM │
│ │ │ data) │
└──────────────┴────────────┴──────────────────────────────┘
MrcFile objects have methods to allow easy identification of the data type: is_single_image(),
is_image_stack(), is_volume() and is_volume_stack().
>>> mrc = mrcfile.open('tmp.mrc')
>>> # The file currently contains two-dimensional data
>>> mrc.data.shape
(5, 4)
>>> len(mrc.data.shape)
2
>>> # This is intepreted as a single image
>>> mrc.is_single_image()
True
>>> mrc.is_image_stack()
False
>>> mrc.is_volume()
False
>>> mrc.is_volume_stack()
False
>>> mrc.close()
If a file already contains image or image stack data, new three-dimensional data is treated as an image
stack; otherwise, 3D data is treated as a volume by default:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # New 3D data in an existing image file is treated as an image stack:
>>> mrc.set_data(data_3d)
>>> len(mrc.data.shape)
3
>>> mrc.is_volume()
False
>>> mrc.is_image_stack()
True
>>> int(mrc.header.ispg)
0
>>> mrc.close()
>>> # But normally, 3D data is treated as a volume:
>>> mrc = mrcfile.new('tmp.mrc', overwrite=True)
>>> mrc.set_data(data_3d)
>>> mrc.is_volume()
True
>>> mrc.is_image_stack()
False
>>> int(mrc.header.ispg)
1
>>> mrc.close()
Call set_image_stack() and set_volume() to change the interpretation of 3D data. (Note: as well as
changing ispg, these methods also change mz to be 1 for image stacks and equal to nz for volumes.)
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # Change the file to represent an image stack:
>>> mrc.set_image_stack()
>>> mrc.is_volume()
False
>>> mrc.is_image_stack()
True
>>> int(mrc.header.ispg)
0
>>> # And now change it back to representing a volume:
>>> mrc.set_volume()
>>> mrc.is_volume()
True
>>> mrc.is_image_stack()
False
>>> int(mrc.header.ispg)
1
>>> mrc.close()
Note that the MRC format allows the data axes to be swapped using the header's mapc, mapr and maps
fields. This library does not attempt to swap the axes and simply assigns the columns to X, rows to Y and
sections to Z. (The data array is indexed in C style, so data values can be accessed using
mrc.data[z][y][x].) In general, EM data is written using the default axes, but crystallographic data
files might use swapped axes in certain space groups -- if this might matter to you, you should check the
mapc, mapr and maps fields after opening the file and consider transposing the data array if necessary.
Data types
Various numpy data types can be used for MRC data arrays. The conversions to MRC mode numbers are:
┌───────────┬───────────────────────────────────────┐
│ Data type │ MRC mode │
├───────────┼───────────────────────────────────────┤
│ float16 │ 12 (see note below) │
├───────────┼───────────────────────────────────────┤
│ float32 │ 2 │
├───────────┼───────────────────────────────────────┤
│ int8 │ 0 │
├───────────┼───────────────────────────────────────┤
│ int16 │ 1 │
├───────────┼───────────────────────────────────────┤
│ uint8 │ 6 (note that data will be widened to │
│ │ 16 bits in the file) │
├───────────┼───────────────────────────────────────┤
│ uint16 │ 6 │
├───────────┼───────────────────────────────────────┤
│ complex64 │ 4 │
└───────────┴───────────────────────────────────────┘
(Mode 3 and the proposed 4-bit mode 101 are not supported since there are no corresponding numpy dtypes.)
Note that mode 12 is a proposed extension to the MRC2014 format and is not yet widely supported by other
software. If you need to write float16 data to MRC files in a compatible way, you should cast to float32
first and use mode 2.
No other data types are accepted, including integer types of more than 16 bits, or float types of more
than 32 bits. Many numpy array creation routines use int64 or float64 dtypes by default, which means you
will need to give a dtype argument to ensure the array can be used in an MRC file:
>>> mrc = mrcfile.open('tmp.mrc', mode='r+')
>>> # This does not work
>>> mrc.set_data(np.zeros((4, 5)))
Traceback (most recent call last):
...
ValueError: dtype 'float64' cannot be converted to an MRC file mode
>>> # But this does
>>> mrc.set_data(np.zeros((4, 5), dtype=np.int16))
>>> mrc.data
array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]], dtype=int16)
>>> mrc.close()
Warning: be careful if you have an existing numpy array in float64, int64 or int32 data types. If they
try to convert them into one of the narrower types supported by mrcfile and they contain values outside
the range of the target type, the values will silently overflow. For floating point formats this can lead
to inf values, and with integers it can lead to entirely meaningless values. A full discussion of this
issue is outside the scope of this guide; see the numpy documentation for more information.
Dealing with large files
mrcfile provides two ways of improving performance when handling large files: memory mapping and
asynchronous (background) opening. Memory mapping treats the file's data on disk as if it is already in
memory, and only actually loads the data in small chunks when it is needed. Asynchronous opening uses a
separate thread to open the file, allowing the main thread to carry on with other work while the file is
loaded from disk in parallel.
Which technique is better depends on what you intend to do with the file and the characteristics of your
computer, and it's usually worth testing both approaches and seeing what works best for your particular
task. In general, memory mapping gives better performance when dealing with a single file, particularly
if the file is very large. If you need to process several files, asynchronous opening can be faster
because you can work on one file while loading the next one.
Memory-mapped files
With very large files, it might be helpful to use the mrcfile.mmap() function to open the file, which
will open the data as a memory-mapped numpy array. The contents of the array are only read from disk as
needed, so this allows large files to be opened very quickly. Parts of the data can then be read and
written by slicing the array:
>>> # Let's make a new file to work with (only small for this example!)
>>> mrcfile.write('maybe_large.mrc', example_data)
>>> # Open the file in memory-mapped mode
>>> mrc = mrcfile.mmap('maybe_large.mrc', mode='r+')
>>> # Now read part of the data by slicing
>>> mrc.data[1:3]
memmap([[ 4, 5, 6, 7],
[ 8, 9, 10, 11]], dtype=int8)
>>> # Set some values by assigning to a slice
>>> mrc.data[1:3,1:3] = 0
>>> # Read the entire array - with large files this might take a while!
>>> mrc.data[:]
memmap([[ 0, 1, 2, 3],
[ 4, 0, 0, 7],
[ 8, 0, 0, 11]], dtype=int8)
>>> mrc.close()
To create new large, empty files quickly, use the mrcfile.new_mmap() function. This creates an empty file
with a given shape and data mode. An optional fill value can be provided but filling a very large mmap
array can take a long time, so it's best to use this only when needed. If you plan to fill the array with
other data anyway, it's better to leave the fill value as None. A typical use case would be to create a
new file and then fill it slice by slice:
>>> # Make a new, empty memory-mapped MRC file
>>> mrc = mrcfile.new_mmap('mmap.mrc', shape=(3, 3, 4), mrc_mode=0)
>>> # Fill each slice with a different value
>>> for val in range(len(mrc.data)):
... mrc.data[val] = val
...
>>> mrc.data[:]
memmap([[[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]],
[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]],
[[2, 2, 2, 2],
[2, 2, 2, 2],
[2, 2, 2, 2]]], dtype=int8)
Asynchronous opening
When processing several files in a row, asynchronous (background) opening can improve performance by
allowing you to open multiple files in parallel. The mrcfile.open_async() function starts a background
thread to open a file, and returns a FutureMrcFile object which you can call later to get the file after
it's been opened:
>>> # Open the first example file
>>> mrc1 = mrcfile.open('maybe_large.mrc')
>>> # Start opening the second example file before we process the first
>>> future_mrc2 = mrcfile.open_async('tmp.mrc.gz')
>>> # Now we'll do some calculations with the first file
>>> mrc1.data.sum()
36
>>> # Get the second file from its "Future" container ('result()' will wait
>>> # until the file is ready)
>>> mrc2 = future_mrc2.result()
>>> # Before we process the second file, we'll start the third one opening
>>> future_mrc3 = mrcfile.open_async('tmp.mrc.bz2')
>>> mrc2.data.max()
22
>>> # Finally, we'll get the third file and process it
>>> mrc3 = future_mrc3.result()
>>> mrc3.data
array([[ 0, 3, 6, 9],
[12, 15, 18, 21],
[24, 27, 30, 33]], dtype=int8)
As we saw in that example, calling result() will give us the MrcFile from the file opening operation. If
the file hasn't been fully opened yet, result() will simply wait until it's ready. To avoid waiting, call
done() to check if it's finished.
Validating MRC files
MRC files can be validated with mrcfile.validate(), which prints an explanation of what is happening and
also returns True if the file is valid or False otherwise:
>>> mrcfile.validate('tmp.mrc')
Checking if tmp.mrc is a valid MRC2014 file...
File appears to be valid.
True
This works equally well for gzip- or bzip2-compressed files:
>>> mrcfile.validate('tmp.mrc.gz')
Checking if tmp.mrc.gz is a valid MRC2014 file...
File appears to be valid.
True
>>> mrcfile.validate('tmp.mrc.bz2')
Checking if tmp.mrc.bz2 is a valid MRC2014 file...
File appears to be valid.
True
Errors will cause messages to be printed to the console, and validate() will return False:
>>> # Let's make a file which is valid except for the header's mz value
>>> with mrcfile.new('tmp.mrc', overwrite=True) as mrc:
... mrc.set_data(example_data)
... mrc.header.mz = -1
...
>>> # Now it should fail validation and print a helpful message
>>> mrcfile.validate('tmp.mrc')
Checking if tmp.mrc is a valid MRC2014 file...
Header field 'mz' is negative
False
(More serious errors might also cause warnings to be printed to sys.stderr.)
Normally, messages are printed to sys.stdout (as normal for Python print() calls). validate() has an
optional print_file argument which allows any text stream to be used for the output instead:
>>> # Create a text stream to capture the output
>>> import io
>>> output = io.StringIO()
>>> # Now validate the file...
>>> mrcfile.validate('tmp.mrc', print_file=output)
False
>>> # ...and check the output separately
>>> print(output.getvalue().strip())
Checking if tmp.mrc is a valid MRC2014 file...
Header field 'mz' is negative
Behind the scenes, mrcfile.validate() opens the file in permissive mode using mrcfile.open() and then
calls MrcFile.validate(). If you already have an MrcFile open, you can call its validate() method
directly to check the file -- but note that the file size test might be inaccurate unless you call
flush() first. To ensure the file is completely valid, it's best to flush or close the file and then
validate it from scratch using mrcfile.validate().
If you find that a file created with this library is invalid, and you haven't altered anything in the
header in a way that might cause problems, please file a bug report on the issue tracker!
Command line usage
Some mrcfile functionality is available directly from the command line, via scripts that are installed
along with the package, or in some cases by running modules with python -m.
(If you've downloaded the source code instead of installing via pip, run pip install <path-to-mrcfile> or
python setup.py install to make the command line scripts available.)
Validation
MRC files can be validated with the mrcfile-validate script:
$ mrcfile-validate tests/test_data/EMD-3197.map
Checking if tests/test_data/EMD-3197.map is a valid MRC2014 file...
File does not declare MRC format version 20140 or 20141: nversion = 0
$ # Exit status is 1 if file is invalid
$ echo $?
1
This script wraps the mrcfile.validator module, which can also be called directly:
$ python -m mrcfile.validator valid_file.mrc
Checking if valid_file.mrc is a valid MRC2014 file...
File appears to be valid.
$ echo $?
0
Multiple file names can be passed to either form of the command, and because these commands call
mrcfile.validate() behind the scenes, gzip- and bzip2-compressed files can be validated as well:
$ mrcfile-validate valid_file_1.mrc valid_file_2.mrc.gz valid_file_3.mrc.bz2
Checking if valid_file_1.mrc is a valid MRC2014 file...
File appears to be valid.
Checking if valid_file_2.mrc is a valid MRC2014 file...
File appears to be valid.
Checking if valid_file_3.mrc is a valid MRC2014 file...
File appears to be valid.
Examining MRC headers
MRC file headers can be printed to the console with the mrcfile-header script:
$ mrcfile-header tests/test_data/EMD-3197.map
MRC header for tests/test_data/EMD-3197.map:
nx : 20
ny : 20
nz : 20
mode : 2
nxstart : -2
nystart : 0
nzstart : 0
mx : 20
my : 20
mz : 20
cella : (228.0, 228.0, 228.0)
cellb : (90.0, 90.0, 90.0)
...
...
Like mrcfile-validate, this also works for multiple files. If you want to access the same functionality
from within Python, call print_header() on an open MrcFile object, or
mrcfile.command_line.print_headers() with a list of file names.
API overview
Class hierarchy
The following classes are provided by the mrcfile.py library:
• MrcObject: Represents a generic MRC-like data object in memory, and provides header, extended header
and data arrays and methods for operating on them.
• MrcInterpreter: Subclass of MrcObject that can read and/or write its MRC data from arbitrary byte I/O
streams (including Python file objects).
• MrcFile: Subclass of MrcInterpreter that opens a file from disk to use as its I/O stream. This is the
normal class used for most interactions with MRC files.
• GzipMrcFile: Reads and writes MRC data using compressed gzip files.
• Bzip2MrcFile: Reads and writes MRC data using compressed bzip2 files.
• MrcMemmap: Uses a memory-mapped data array, for fast random access to very large data files. MrcMemmap
overrides various parts of the MrcFile implementation to ensure that the memory-mapped data array is
opened, closed and moved correctly when the data or extended header array sizes are changed.
MrcFile attributes and methods
Attributes:
• header
• extended_header
• indexed_extended_header
• data
• voxel_size
Methods:
• set_extended_header()
• set_data()
• is_single_image()
• is_image_stack()
• is_volume()
• is_volume_stack()
• set_image_stack()
• set_volume()
• update_header_from_data()
• update_header_stats()
• reset_header_stats()
• print_header()
• validate()
• flush()
• close()
SOURCE DOCUMENTATION
mrcfile -- Main package
mrcfile
A pure Python implementation of the MRC2014 file format.
For a full introduction and documentation, see http://mrcfile.readthedocs.io/
Functions
• new(): Create a new MRC file.
• open(): Open an MRC file.
• open_async(): Open an MRC file asynchronously.
• mmap(): Open a memory-mapped MRC file (fast for large files).
• new_mmap(): Create a new empty memory-mapped MRC file (fast for large files).
• validate(): Validate an MRC file
Basic usage
Examples assume that this package has been imported as mrcfile and numpy has been imported as np.
To open an MRC file and read a slice of data:
>>> with mrcfile.open('tests/test_data/EMD-3197.map') as mrc:
... mrc.data[10,10]
...
array([ 2.58179283, 3.1406002 , 3.64495397, 3.63812137, 3.61837363,
4.0115056 , 3.66981959, 2.07317996, 0.1251585 , -0.87975615,
0.12517013, 2.07319379, 3.66982722, 4.0115037 , 3.61837196,
3.6381247 , 3.64495087, 3.14059472, 2.58178973, 1.92690361], dtype=float32)
To create a new file with a 2D data array, and change some values:
>>> with mrcfile.new('tmp.mrc') as mrc:
... mrc.set_data(np.zeros((5, 5), dtype=np.int8))
... mrc.data[1:4,1:4] = 10
... mrc.data
...
array([[ 0, 0, 0, 0, 0],
[ 0, 10, 10, 10, 0],
[ 0, 10, 10, 10, 0],
[ 0, 10, 10, 10, 0],
[ 0, 0, 0, 0, 0]], dtype=int8)
Background
The MRC2014 format was described in the Journal of Structural Biology: ‐
http://dx.doi.org/10.1016/j.jsb.2015.04.002
The format specification is available on the CCP-EM website: ‐
http://www.ccpem.ac.uk/mrc_format/mrc2014.php
Members
mrcfile.new(name, data=None, compression=None, overwrite=False)
Create a new MRC file.
Parameters
• name -- The file name to use, as a string or Path.
• data -- Data to put in the file, as a numpy array. The default is None, to create an
empty file.
• compression -- The compression format to use. Acceptable values are: None (the default;
for no compression), 'gzip' or 'bzip2'. It's good practice to name compressed files with
an appropriate extension (for example, .mrc.gz for gzip) but this is not enforced.
• overwrite -- Flag to force overwriting of an existing file. If False and a file of the
same name already exists, the file is not overwritten and an exception is raised.
Returns
An MrcFile object (or a subclass of it if compression is specified).
Raises
• ValueError -- If the file already exists and overwrite is
False.
• ValueError -- If the compression format is not recognised.
Warns RuntimeWarning -- If the data array contains Inf or NaN values.
mrcfile.open(name, mode='r', permissive=False, header_only=False)
Open an MRC file.
This function opens both normal and compressed MRC files. Supported compression formats are: gzip,
bzip2.
It is possible to use this function to create new MRC files (using mode w+) but the new() function
is more flexible.
This function offers a permissive read mode for attempting to open corrupt or invalid files. In
permissive mode, warnings are issued instead of exceptions if problems with the file are
encountered. See MrcInterpreter or the usage guide for more information.
Parameters
• name -- The file name to open, as a string or Path.
• mode -- The file mode to use. This should be one of the following: r for read-only, r+
for read and write, or w+ for a new empty file. The default is r.
• permissive -- Read the file in permissive mode. The default is False.
• header_only -- Only read the header (and extended header) from the file. The default is
False.
Returns
An MrcFile object (or a GzipMrcFile object if the file is gzipped).
Raises
• ValueError -- If the mode is not one of r, r+ or w+.
• ValueError -- If the file is not a valid MRC file and
permissive is False.
• ValueError -- If the mode is w+ and the file already exists.
(Call new() with overwrite=True to deliberately overwrite
an existing file.)
• OSError -- If the mode is r or r+ and the file does not
exist.
Warns
• RuntimeWarning -- If the file appears to be a valid MRC file but the data block is longer
than expected from the dimensions in the header.
• RuntimeWarning -- If the file is not a valid MRC file and permissive is True.
• RuntimeWarning -- If the header's exttyp field is set to a known value but the extended
header's size is not a multiple of the number of bytes in the corresponding dtype.
mrcfile.read(name)
Read an MRC file's data into a numpy array.
This is a convenience function to read the data from an MRC file when there is no need for the
file's header information. To read the headers as well, or if you need access to an MrcFile object
representing the file, use mrcfile.open() instead.
Parameters
name -- The file name to read, as a string or Path.
Returns
A numpy array containing the data from the file.
mrcfile.write(name, data=None, overwrite=False, voxel_size=None)
Write a new MRC file.
This is a convenience function to allow data to be quickly written to a file (with optional
compression) using just a single function call. However, there is no control over the file's
metadata except for optionally setting the voxel size. For more control, or if you need access to
an MrcFile object representing the new file, use mrcfile.new() instead.
Parameters
• name -- The file name to use, as a string or Path. If the name ends with .gz or .bz2, the
file will be compressed using gzip or bzip2 respectively.
• data -- Data to put in the file, as a numpy array. The default is None, to create an
empty file.
• overwrite -- Flag to force overwriting of an existing file. If False and a file of the
same name already exists, the file is not overwritten and an exception is raised.
• voxel_size -- float | 3-tuple The voxel size to be written in the file header.
Raises ValueError -- If the file already exists and overwrite is
False.
Warns RuntimeWarning -- If the data array contains Inf or NaN values.
mrcfile.open_async(name, mode='r', permissive=False)
Open an MRC file asynchronously in a separate thread.
This allows a file to be opened in the background while the main thread continues with other work.
This can be a good way to improve performance if the main thread is busy with intensive
computation, but will be less effective if the main thread is itself busy with disk I/O.
Multiple files can be opened in the background simultaneously. However, this implementation is
relatively crude; each call to this function will start a new thread and immediately use it to
start opening a file. If you try to open many large files at the same time, performance will
decrease as all of the threads attempt to access the disk at once. You'll also risk running out of
memory to store the data from all the files.
This function returns a FutureMrcFile object, which deliberately mimics the API of the Future
object from Python 3's concurrent.futures module. (Future versions of this library might return
genuine Future objects instead.)
To get the real MrcFile object from a FutureMrcFile, call result(). This will block until the file
has been read and the MrcFile object is ready. To check if the MrcFile is ready without blocking,
call running() or done().
Parameters
• name -- The file name to open, as a string or Path.
• mode -- The file mode (one of r, r+ or w+).
• permissive -- Read the file in permissive mode. The default is False.
Returns
A FutureMrcFile object.
mrcfile.mmap(name, mode='r', permissive=False)
Open a memory-mapped MRC file.
This allows much faster opening of large files, because the data is only accessed on disk when a
slice is read or written from the data array. See the MrcMemmap class documentation for more
information.
Because the memory-mapped data array accesses the disk directly, compressed files cannot be opened
with this function. In all other ways, mmap() behaves in exactly the same way as open(). The
MrcMemmap object returned by this function can be used in exactly the same way as a normal MrcFile
object.
Parameters
• name -- The file name to open, as a string or Path.
• mode -- The file mode (one of r, r+ or w+).
• permissive -- Read the file in permissive mode. The default is False.
Returns
An MrcMemmap object.
mrcfile.new_mmap(name, shape, mrc_mode=0, fill=None, overwrite=False, extended_header=None, exttyp=None)
Create a new, empty memory-mapped MRC file.
This function is useful for creating very large files. The initial contents of the data array can
be set with the fill parameter if needed, but be aware that filling a large array can take a long
time.
If fill is not set, the new data array's contents are unspecified and system-dependent. (Some
systems fill a new empty mmap with zeros, others fill it with the bytes from the disk at the
newly-mapped location.) If you are definitely going to fill the entire array with new data anyway
you can safely leave fill as None, otherwise it is advised to use a sensible fill value (or ensure
you are on a system that fills new mmaps with a reasonable default value).
Parameters
• name -- The file name to use, as a string or Path.
• shape -- The shape of the data array to open, as a 2-, 3- or 4-tuple of ints. For
example, (nz, ny, nx) for a new 3D volume, or (ny, nx) for a new 2D image.
• mrc_mode --
The MRC mode to use for the new file. One of 0, 1, 2, 4 or 6, which correspond to numpy
dtypes as follows:
• mode 0 -> int8
• mode 1 -> int16
• mode 2 -> float32
• mode 4 -> complex64
• mode 6 -> uint16
The default is 0.
• fill -- An optional value to use to fill the new data array. If None, the data array will
not be filled and its contents are unspecified. Numpy's usual rules for rounding or
rejecting values apply, according to the dtype of the array.
• overwrite -- Flag to force overwriting of an existing file. If False and a file of the
same name already exists, the file is not overwritten and an exception is raised.
• extended_header -- The extended header object
• exttyp -- The extended header type
Returns
A new MrcMemmap object.
Raises
• ValueError -- If the MRC mode is invalid.
• ValueError -- If the file already exists and overwrite is
False.
• ValueError -- If the extended header or any of the dimensions of
the shape have a size greater than 2,147,483,647 (and
therefore the size cannot be stored in the header).
mrcfile.validate(name, print_file=None)
Validate an MRC file.
This function first opens the file by calling open() (with permissive=True), then calls
validate(), which runs a series of tests to check whether the file complies with the MRC2014
format specification.
If the file is completely valid, this function returns True, otherwise it returns False. Messages
explaining the validation result will be printed to sys.stdout by default, but if a text stream is
given (using the print_file argument) output will be printed to that instead.
Badly invalid files will also cause warning messages to be issued, which will be written to
sys.stderr by default. See the documentation of the warnings module for information on how to
suppress or capture warning output.
Because the file is opened by calling open(), gzip- and bzip2-compressed MRC files can be
validated easily using this function.
After the file has been opened, it is checked for problems. The tests are:
1. MRC format ID string: The map field in the header should contain "MAP ".
2. Machine stamp: The machine stamp should contain one of 0x44 0x44 0x00 0x00, 0x44 0x41 0x00
0x00 or 0x11 0x11 0x00 0x00.
3. MRC mode: the mode field should be one of the supported mode numbers: 0, 1, 2, 4, 6 or 12.
(Note that MRC modes 3 and 101 are also valid according to the MRC 2014 specification but are
not supported by mrcfile.)
4. Map and cell dimensions: The header fields nx, ny, nz, mx, my, mz, cella.x, cella.y and
cella.z must all be positive numbers.
5. Axis mapping: Header fields mapc, mapr and maps must contain the values 1, 2, and 3 (in any
order).
6. Volume stack dimensions: If the spacegroup is in the range 401--630, representing a volume
stack, the nz field should be exactly divisible by mz to represent the number of volumes in
the stack.
7. Header labels: The nlabl field should be set to indicate the number of labels in use, and the
labels in use should appear first in the label array.
8. MRC format version: The nversion field should be 20140 or 20141 for compliance with the
MRC2014 standard.
9. Extended header type: If an extended header is present, the exttyp field should be set to
indicate the type of extended header.
10. Data statistics: The statistics in the header should be correct for the actual data in the
file, or marked as undetermined.
11. File size: The size of the file on disk should match the expected size calculated from the MRC
header.
Parameters
• name -- The file name to open and validate.
• print_file -- The output text stream to use for printing messages about the validation.
This is passed directly to the file argument of Python's print() function. The default is
None, which means output will be printed to sys.stdout.
Returns
True if the file is valid, or False if the file does not meet the MRC format specification
in any way.
Raises OSError -- If the file does not exist or cannot be opened.
Warns RuntimeWarning -- If the file is seriously invalid because it has no map ID string, an
incorrect machine stamp, an unknown mode number, or is not the same size as expected from
the header.
Submodules
mrcfile.bzip2mrcfile module
bzip2mrcfile
Module which exports the Bzip2MrcFile class.
Classes:
Bzip2MrcFile: An object which represents a bzip2-compressed MRC file.
class mrcfile.bzip2mrcfile.Bzip2MrcFile(name, mode='r', overwrite=False, permissive=False,
header_only=False, **kwargs)
Bases: MrcFile
MrcFile subclass for handling bzip2-compressed files.
Usage is the same as for MrcFile.
_open_file(name)
Override _open_file() to open a bzip2 file.
_read(header_only=False)
Override _read() to ensure bzip2 file is in read mode.
_ensure_readable_bzip2_stream()
Make sure _iostream is a bzip2 stream that can be read.
_get_file_size()
Override _get_file_size() to ensure stream is readable first.
_read_bytearray_from_stream(number_of_bytes)
Override because BZ2File in Python 2 does not support readinto().
flush()
Override flush() since BZ2File objects need special handling.
mrcfile.command_line module
command_line
Module for functions used as command line entry points.
The names of the corresponding command line scripts can be found in the entry_points section of setup.py.
mrcfile.command_line.print_headers(names=None, print_file=None)
Print the MRC header contents from a list of files.
This function opens files in permissive mode to allow headers of invalid files to be examined.
Parameters
• names -- A list of file names. If not given or None, the names are taken from the command
line arguments.
• print_file -- The output text stream to use for printing the headers. This is passed
directly to the print_file argument of print_header(). The default is None, which means
output will be printed to sys.stdout.
mrcfile.constants module
constants
Constants used by the mrcfile.py library.
mrcfile.dtypes module
dtypes
numpy dtypes used by the mrcfile.py library.
The dtypes are defined in a separate module because they do not interact nicely with the from __future__
import unicode_literals feature used in the rest of the package.
mrcfile.dtypes.get_ext_header_dtype(exttyp, byte_order='=')
Get a dtype for an extended header.
Parameters
• exttyp -- One of b'FEI1' or b'FEI2', which are currently the only supported extended
header types.
• byte_order -- One of =, < or >.
Returns
A numpy dtype object for the extended header, or None
Raises ValueError -- If byte_order is not one of =, < or >.
mrcfile.future_mrcfile module
future_mrcfile
Module which exports the FutureMrcFile class.
Classes:
FutureMrcFile: An object which represents an MRC file being
opened asynchronously.
class mrcfile.future_mrcfile.FutureMrcFile(open_function, args=(), kwargs={})
Bases: object
Object representing an MRC file being opened asynchronously.
This API deliberately mimics a Future object from the concurrent.futures module in Python 3.2+
(which we do not use directly because this code still needs to run in Python 2.7).
__init__(open_function, args=(), kwargs={})
Initialise a new FutureMrcFile object.
This constructor starts a new thread which will invoke the callable given in open_function
with the given arguments.
Parameters
• open_function -- The callable to use to open the MRC file. (This will normally be
mrcfile.open(), but could also be MrcFile or any of its subclasses.)
• args -- A tuple of positional arguments to use when open_function is called.
(Normally a 1-tuple containing the name of the file to open.)
• kwargs -- A dictionary of keyword arguments to use when open_function is called.
_run(*args, **kwargs)
Call the open function and store the result in the holder list.
(For internal use only.)
cancel()
Return False.
(See concurrent.futures.Future.cancel() for more details. This implementation does not
allow jobs to be cancelled.)
cancelled()
Return False.
(See concurrent.futures.Future.cancelled() for more details. This implementation does not
allow jobs to be cancelled.)
running()
Return True if the MrcFile is currently being opened.
(See concurrent.futures.Future.running() for more details.)
done() Return True if the file opening has finished.
(See concurrent.futures.Future.done() for more details.)
result(timeout=None)
Return the MrcFile that has been opened.
(See concurrent.futures.Future.result() for more details.)
Parameters
timeout -- Time to wait (in seconds) for the file opening to finish. If timeout is
not specified or is None, there is no limit to the wait time.
Returns
An MrcFile object (or one of its subclasses).
Raises
• RuntimeError -- If the operation has not finished within the
time limit set by timeout. (Note that the type of this
exception will change in future if this class is replaced by
concurrent.futures.Future.)
• Exception -- Any exception raised by the
MrcFile opening operation will be
re-raised here.
exception(timeout=None)
Return the exception raised by the file opening operation.
(See concurrent.futures.Future.exception() for more details.)
Parameters
timeout -- Time to wait (in seconds) for the operation to finish. If timeout is not
specified or is None, there is no limit to the wait time.
Returns
An Exception, if one was raised by the file opening operation, or None if no
exception was raised.
Raises RuntimeError -- If the operation has not finished within the
time limit set by timeout. (Note that the type of this
exception will change in future if this class is replaced by
concurrent.futures.Future.)
_get_result(timeout)
Return the result or exception from the file opening operation.
(For internal use only.)
add_done_callback(fn)
Not implemented.
(See concurrent.futures.Future.add_done_callback() for more details.)
mrcfile.gzipmrcfile module
gzipmrcfile
Module which exports the GzipMrcFile class.
Classes:
GzipMrcFile: An object which represents a gzipped MRC file.
class mrcfile.gzipmrcfile.GzipMrcFile(name, mode='r', overwrite=False, permissive=False,
header_only=False, **kwargs)
Bases: MrcFile
MrcFile subclass for handling gzipped files.
Usage is the same as for MrcFile.
_open_file(name)
Override _open_file() to open both normal and gzip files.
_close_file()
Override _close_file() to close both normal and gzip files.
_read(header_only=False)
Override _read() to ensure gzip file is in read mode.
_ensure_readable_gzip_stream()
Make sure _iostream is a gzip stream that can be read.
_get_file_size()
Override _get_file_size() to avoid seeking from end.
flush()
Override flush() since GzipFile objects need special handling.
mrcfile.load_functions module
load_functions
Module for top-level functions that open MRC files and form the main API of the package.
mrcfile.load_functions.new(name, data=None, compression=None, overwrite=False)
Create a new MRC file.
Parameters
• name -- The file name to use, as a string or Path.
• data -- Data to put in the file, as a numpy array. The default is None, to create an
empty file.
• compression -- The compression format to use. Acceptable values are: None (the default;
for no compression), 'gzip' or 'bzip2'. It's good practice to name compressed files with
an appropriate extension (for example, .mrc.gz for gzip) but this is not enforced.
• overwrite -- Flag to force overwriting of an existing file. If False and a file of the
same name already exists, the file is not overwritten and an exception is raised.
Returns
An MrcFile object (or a subclass of it if compression is specified).
Raises
• ValueError -- If the file already exists and overwrite is
False.
• ValueError -- If the compression format is not recognised.
Warns RuntimeWarning -- If the data array contains Inf or NaN values.
mrcfile.load_functions.open(name, mode='r', permissive=False, header_only=False)
Open an MRC file.
This function opens both normal and compressed MRC files. Supported compression formats are: gzip,
bzip2.
It is possible to use this function to create new MRC files (using mode w+) but the new() function
is more flexible.
This function offers a permissive read mode for attempting to open corrupt or invalid files. In
permissive mode, warnings are issued instead of exceptions if problems with the file are
encountered. See MrcInterpreter or the usage guide for more information.
Parameters
• name -- The file name to open, as a string or Path.
• mode -- The file mode to use. This should be one of the following: r for read-only, r+
for read and write, or w+ for a new empty file. The default is r.
• permissive -- Read the file in permissive mode. The default is False.
• header_only -- Only read the header (and extended header) from the file. The default is
False.
Returns
An MrcFile object (or a GzipMrcFile object if the file is gzipped).
Raises
• ValueError -- If the mode is not one of r, r+ or w+.
• ValueError -- If the file is not a valid MRC file and
permissive is False.
• ValueError -- If the mode is w+ and the file already exists.
(Call new() with overwrite=True to deliberately overwrite
an existing file.)
• OSError -- If the mode is r or r+ and the file does not
exist.
Warns
• RuntimeWarning -- If the file appears to be a valid MRC file but the data block is longer
than expected from the dimensions in the header.
• RuntimeWarning -- If the file is not a valid MRC file and permissive is True.
• RuntimeWarning -- If the header's exttyp field is set to a known value but the extended
header's size is not a multiple of the number of bytes in the corresponding dtype.
mrcfile.load_functions.read(name)
Read an MRC file's data into a numpy array.
This is a convenience function to read the data from an MRC file when there is no need for the
file's header information. To read the headers as well, or if you need access to an MrcFile object
representing the file, use mrcfile.open() instead.
Parameters
name -- The file name to read, as a string or Path.
Returns
A numpy array containing the data from the file.
mrcfile.load_functions.write(name, data=None, overwrite=False, voxel_size=None)
Write a new MRC file.
This is a convenience function to allow data to be quickly written to a file (with optional
compression) using just a single function call. However, there is no control over the file's
metadata except for optionally setting the voxel size. For more control, or if you need access to
an MrcFile object representing the new file, use mrcfile.new() instead.
Parameters
• name -- The file name to use, as a string or Path. If the name ends with .gz or .bz2, the
file will be compressed using gzip or bzip2 respectively.
• data -- Data to put in the file, as a numpy array. The default is None, to create an
empty file.
• overwrite -- Flag to force overwriting of an existing file. If False and a file of the
same name already exists, the file is not overwritten and an exception is raised.
• voxel_size -- float | 3-tuple The voxel size to be written in the file header.
Raises ValueError -- If the file already exists and overwrite is
False.
Warns RuntimeWarning -- If the data array contains Inf or NaN values.
mrcfile.load_functions.open_async(name, mode='r', permissive=False)
Open an MRC file asynchronously in a separate thread.
This allows a file to be opened in the background while the main thread continues with other work.
This can be a good way to improve performance if the main thread is busy with intensive
computation, but will be less effective if the main thread is itself busy with disk I/O.
Multiple files can be opened in the background simultaneously. However, this implementation is
relatively crude; each call to this function will start a new thread and immediately use it to
start opening a file. If you try to open many large files at the same time, performance will
decrease as all of the threads attempt to access the disk at once. You'll also risk running out of
memory to store the data from all the files.
This function returns a FutureMrcFile object, which deliberately mimics the API of the Future
object from Python 3's concurrent.futures module. (Future versions of this library might return
genuine Future objects instead.)
To get the real MrcFile object from a FutureMrcFile, call result(). This will block until the file
has been read and the MrcFile object is ready. To check if the MrcFile is ready without blocking,
call running() or done().
Parameters
• name -- The file name to open, as a string or Path.
• mode -- The file mode (one of r, r+ or w+).
• permissive -- Read the file in permissive mode. The default is False.
Returns
A FutureMrcFile object.
mrcfile.load_functions.mmap(name, mode='r', permissive=False)
Open a memory-mapped MRC file.
This allows much faster opening of large files, because the data is only accessed on disk when a
slice is read or written from the data array. See the MrcMemmap class documentation for more
information.
Because the memory-mapped data array accesses the disk directly, compressed files cannot be opened
with this function. In all other ways, mmap() behaves in exactly the same way as open(). The
MrcMemmap object returned by this function can be used in exactly the same way as a normal MrcFile
object.
Parameters
• name -- The file name to open, as a string or Path.
• mode -- The file mode (one of r, r+ or w+).
• permissive -- Read the file in permissive mode. The default is False.
Returns
An MrcMemmap object.
mrcfile.load_functions.new_mmap(name, shape, mrc_mode=0, fill=None, overwrite=False,
extended_header=None, exttyp=None)
Create a new, empty memory-mapped MRC file.
This function is useful for creating very large files. The initial contents of the data array can
be set with the fill parameter if needed, but be aware that filling a large array can take a long
time.
If fill is not set, the new data array's contents are unspecified and system-dependent. (Some
systems fill a new empty mmap with zeros, others fill it with the bytes from the disk at the
newly-mapped location.) If you are definitely going to fill the entire array with new data anyway
you can safely leave fill as None, otherwise it is advised to use a sensible fill value (or ensure
you are on a system that fills new mmaps with a reasonable default value).
Parameters
• name -- The file name to use, as a string or Path.
• shape -- The shape of the data array to open, as a 2-, 3- or 4-tuple of ints. For
example, (nz, ny, nx) for a new 3D volume, or (ny, nx) for a new 2D image.
• mrc_mode --
The MRC mode to use for the new file. One of 0, 1, 2, 4 or 6, which correspond to numpy
dtypes as follows:
• mode 0 -> int8
• mode 1 -> int16
• mode 2 -> float32
• mode 4 -> complex64
• mode 6 -> uint16
The default is 0.
• fill -- An optional value to use to fill the new data array. If None, the data array will
not be filled and its contents are unspecified. Numpy's usual rules for rounding or
rejecting values apply, according to the dtype of the array.
• overwrite -- Flag to force overwriting of an existing file. If False and a file of the
same name already exists, the file is not overwritten and an exception is raised.
• extended_header -- The extended header object
• exttyp -- The extended header type
Returns
A new MrcMemmap object.
Raises
• ValueError -- If the MRC mode is invalid.
• ValueError -- If the file already exists and overwrite is
False.
• ValueError -- If the extended header or any of the dimensions of
the shape have a size greater than 2,147,483,647 (and
therefore the size cannot be stored in the header).
mrcfile.mrcfile module
mrcfile
Module which exports the MrcFile class.
Classes:
MrcFile: An object which represents an MRC file.
class mrcfile.mrcfile.MrcFile(name, mode='r', overwrite=False, permissive=False, header_only=False,
**kwargs)
Bases: MrcInterpreter
An object which represents an MRC file.
The header and data are handled as numpy arrays - see MrcObject for details.
MrcFile supports a permissive read mode for attempting to open corrupt or invalid files. See
mrcfile.mrcinterpreter.MrcInterpreter or the usage guide for more information.
Usage: To create a new MrcFile object, pass a file name and optional mode. To ensure the file is
written to disk and closed correctly, it's best to use the with statement:
>>> with MrcFile('tmp.mrc', 'w+') as mrc:
... mrc.set_data(np.zeros((10, 10), dtype=np.int8))
In mode r or r+, the named file is opened from disk and read. In mode w+ a new empty file
is created and will be written to disk at the end of the with block (or when flush() or
close() is called).
__init__(name, mode='r', overwrite=False, permissive=False, header_only=False, **kwargs)
Initialise a new MrcFile object.
The given file name is opened in the given mode. For mode r or r+ the header, extended
header and data are read from the file. For mode w+ a new file is created with a default
header and empty extended header and data arrays.
Parameters
• name -- The file name to open, as a string or pathlib Path.
• mode -- The file mode to use. This should be one of the following: r for
read-only, r+ for read and write, or w+ for a new empty file. The default is r.
• overwrite -- Flag to force overwriting of an existing file if the mode is w+. If
False and a file of the same name already exists, the file is not overwritten and
an exception is raised. The default is False.
• permissive -- Read the file in permissive mode. (See
mrcfile.mrcinterpreter.MrcInterpreter for details.) The default is False.
• header_only -- Only read the header (and extended header) from the file. The
default is False.
Raises
• ValueError -- If the mode is not one of r, r+ or
w+.
• ValueError -- If the file is not a valid MRC file and
permissive is False.
• ValueError -- If the mode is w+, the file already exists
and overwrite is False.
• OSError -- If the mode is r or r+ and the file does
not exist.
Warns
• RuntimeWarning -- If the file appears to be a valid MRC file but the data block is
longer than expected from the dimensions in the header.
• RuntimeWarning -- If the file is not a valid MRC file and permissive is True.
• RuntimeWarning -- If the header's exttyp field is set to a known value but the
extended header's size is not a multiple of the number of bytes in the
corresponding dtype.
_open_file(name)
Open a file object to use as the I/O stream.
_read(header_only=False)
Override _read() to move back to start of file first.
_read_data()
Override _read_data() to check file size matches data block size.
_get_file_size()
Return the size of the underlying file object, in bytes.
close()
Flush any changes to disk and close the file.
This override calls MrcInterpreter.close() to ensure the stream is flushed and closed, then
closes the file object.
_close_file()
Close the file object.
validate(print_file=None)
Validate this MRC file.
The tests are:
1. MRC format ID string: The map field in the header should contain "MAP ".
2. Machine stamp: The machine stamp should contain one of 0x44 0x44 0x00 0x00, 0x44 0x41
0x00 0x00 or 0x11 0x11 0x00 0x00.
3. MRC mode: the mode field should be one of the supported mode numbers: 0, 1, 2, 4, 6 or
12. (Note that MRC modes 3 and 101 are also valid according to the MRC 2014
specification but are not supported by mrcfile.)
4. Map and cell dimensions: The header fields nx, ny, nz, mx, my, mz, cella.x, cella.y and
cella.z must all be positive numbers.
5. Axis mapping: Header fields mapc, mapr and maps must contain the values 1, 2, and 3 (in
any order).
6. Volume stack dimensions: If the spacegroup is in the range 401--630, representing a
volume stack, the nz field should be exactly divisible by mz to represent the number of
volumes in the stack.
7. Header labels: The nlabl field should be set to indicate the number of labels in use,
and the labels in use should appear first in the label array.
8. MRC format version: The nversion field should be 20140 or 20141 for compliance with the
MRC2014 standard.
9. Extended header type: If an extended header is present, the exttyp field should be set
to indicate the type of extended header.
10. Data statistics: The statistics in the header should be correct for the actual data in
the file, or marked as undetermined.
11. File size: The size of the file on disk should match the expected size calculated from
the MRC header.
Parameters
print_file -- The output text stream to use for printing messages about the
validation. This is passed directly to the file argument of Python's print()
function. The default is None, which means output will be printed to sys.stdout.
Returns
True if the file is valid, or False if the file does not meet the MRC format
specification in any way.
mrcfile.mrcinterpreter module
mrcinterpreter
Module which exports the MrcInterpreter class.
Classes:
MrcInterpreter: An object which can interpret an I/O stream as MRC data.
class mrcfile.mrcinterpreter.MrcInterpreter(iostream=None, permissive=False, header_only=False, **kwargs)
Bases: MrcObject
An object which interprets an I/O stream as MRC / CCP4 map data.
The header and data are handled as numpy arrays - see MrcObject for details.
MrcInterpreter can be used directly, but it is mostly intended as a superclass to provide common
stream-handling functionality. This can be used by subclasses which will handle opening and
closing the stream.
This class implements the __enter__() and __exit__() special methods which allow it to be used by
the Python context manager in a with block. This ensures that close() is called after the object
is finished with.
When reading the I/O stream, a ValueError is raised if the data is invalid in one of the following
ways:
1. The header's map field is not set correctly to confirm the file type.
2. The machine stamp is invalid and so the data's byte order cannot be determined.
3. The mode number is not recognised. Currently accepted modes are 0, 1, 2, 4 and 6.
4. The file is not large enough for the specified extended header size.
5. The data block is not large enough for the specified data type and dimensions.
MrcInterpreter offers a permissive read mode for handling problematic files. If permissive is set
to True and any of the validity checks fails, a warning is issued instead of an exception, and
file interpretation continues. If the mode number is invalid or the data block is too small, the
data attribute will be set to None. In this case, it might be possible to inspect and correct the
header, and then call _read() again to read the data correctly. See the usage guide for more
details.
Methods:
• flush()
• close()
Methods relevant to subclasses:
• _read()
• _read_data()
• _read_bytearray_from_stream()
__init__(iostream=None, permissive=False, header_only=False, **kwargs)
Initialise a new MrcInterpreter object.
This initialiser reads the stream if it is given. In general, subclasses should call
__init__() without giving an iostream argument, then set the _iostream attribute themselves
and call _read() when ready.
To use the MrcInterpreter class directly, pass a stream when creating the object (or for a
write-only stream, create an MrcInterpreter with no stream, call
_create_default_attributes() and set the _iostream attribute directly).
Parameters
• iostream -- The I/O stream to use to read and write MRC data. The default is None.
• permissive -- Read the stream in permissive mode. The default is False.
• header_only -- Only read the header (and extended header) from the file. The
default is False.
Raises ValueError -- If iostream is given, the data it contains
cannot be interpreted as a valid MRC file and permissive
is False.
Warns RuntimeWarning -- If iostream is given, the data it contains cannot be interpreted
as a valid MRC file and permissive is True.
_read(header_only=False)
Read the header, extended header and data from the I/O stream.
Before calling this method, the stream should be open and positioned at the start of the
header. This method will advance the stream to the end of the data block (or the end of the
extended header if header_only is True.
Parameters
header_only -- Only read the header and extended header from the stream. The default
is False.
Raises ValueError -- If the data in the stream cannot be interpreted
as a valid MRC file and permissive is False.
Warns RuntimeWarning -- If the data in the stream cannot be interpreted as a valid MRC
file and permissive is True.
_read_header()
Read the MRC header from the I/O stream.
The header will be read from the current stream position, and the stream will be advanced
by 1024 bytes.
Raises ValueError -- If the data in the stream cannot be interpreted
as a valid MRC file and permissive is False.
Warns RuntimeWarning -- If the data in the stream cannot be interpreted as a valid MRC
file and permissive is True.
_read_extended_header()
Read the extended header from the stream.
If there is no extended header, a zero-length array is assigned to the extended_header
attribute.
The dtype is set as void ('V1').
Raises ValueError -- If the stream is not long enough to contain the
extended header indicated by the header and permissive
is False.
Warns RuntimeWarning -- If the stream is not long enough to contain the extended header
indicated by the header and permissive is True.
_read_data(max_bytes=0)
Read the data array from the stream.
This method uses information from the header to set the data array's shape and dtype.
Parameters
max_bytes -- Read at most this many bytes from the stream. If zero or negative, the
full size of the data block as defined in the header will be read, even if this is
very large.
Raises ValueError -- If the stream is not long enough to contain the
data indicated by the header and permissive is
False.
Warns RuntimeWarning -- If the stream is not long enough to contain the data indicated by
the header and permissive is True.
_read_bytearray_from_stream(number_of_bytes)
Read a bytearray from the stream.
This default implementation relies on the stream implementing the readinto() method to
avoid copying the new array while creating the mutable bytearray. Subclasses should
override this if their stream does not support readinto().
Returns
A 2-tuple of the bytearray and the number of bytes that were read from the stream.
close()
Flush to the stream and clear the header and data attributes.
flush()
Flush the header and data arrays to the I/O stream.
This implementation seeks to the start of the stream, writes the header, extended header
and data arrays, and then truncates the stream.
Subclasses should override this implementation for streams which do not support seek() or
truncate().
mrcfile.mrcmemmap module
mrcmemmap
Module which exports the MrcMemmap class.
Classes:
MrcMemmap: An MrcFile subclass that uses a memory-mapped data array.
class mrcfile.mrcmemmap.MrcMemmap(name, mode='r', overwrite=False, permissive=False, header_only=False,
**kwargs)
Bases: MrcFile
MrcFile subclass that uses a numpy memmap array for the data.
Using a memmap means that the disk access is done lazily: the data array will only be read or
written in small chunks when required. To access the contents of the array, use the array slice
operator.
Usage is the same as for MrcFile.
Note that memmap arrays use a fairly small chunk size and so performance could be poor on file
systems that are optimised for infrequent large I/O operations.
If required, it is possible to create a very large empty file by creating a new MrcMemmap and then
calling _open_memmap() to create the memmap array, which can then be filled slice-by-slice. Be
aware that the contents of a new, empty memmap array depend on your platform: the data values
could be garbage or zeros.
set_extended_header(extended_header)
Replace the file's extended header.
Note that the file's entire data block must be moved if the extended header size changes.
Setting a new extended header can therefore be very time consuming with large files, if the
new extended header occupies a different number of bytes than the previous one.
flush()
Flush the header and data arrays to the file buffer.
_read_data()
Read the data block from the file.
This method first calculates the parameters needed to read the data (block start position,
endian-ness, file mode, array shape) and then opens the data as a numpy memmap array.
_open_memmap(dtype, shape)
Open a new memmap array pointing at the file's data block.
_close_data()
Delete the existing memmap array, if it exists.
The array is flagged as read-only before deletion, so if a reference to it has been kept
elsewhere, changes to it should no longer be able to change the file contents.
_set_new_data(data)
Override of _set_new_data() to handle opening a new memmap and copying data into it.
mrcfile.mrcobject module
mrcobject
Module which exports the MrcObject class.
Classes:
MrcObject: An object representing image or volume data in the MRC format.
class mrcfile.mrcobject.MrcObject(**kwargs)
Bases: object
An object representing image or volume data in the MRC format.
The header, extended header and data are stored as numpy arrays and exposed as read-only
attributes. To replace the data or extended header, call set_data() or set_extended_header(). The
header cannot be replaced but can be modified in place.
Voxel size is exposed as a writeable attribute, but is calculated on-the-fly from the header's
cella and mx/my/mz fields.
Three-dimensional data can represent either a stack of 2D images, or a 3D volume. This is
indicated by the header's ispg (space group) field, which is set to 0 for image data and >= 1 for
volume data. The is_single_image(), is_image_stack(), is_volume() and is_volume_stack() methods
can be used to identify the type of information stored in the data array. For 3D data, the
set_image_stack() and set_volume() methods can be used to switch between image stack and volume
interpretations of the data.
If the data contents have been changed, you can use the update_header_from_data() and
update_header_stats() methods to make the header consistent with the data. These methods are
called automatically if the data array is replaced by calling set_data().
update_header_from_data() is fast, even with very large data arrays, because it only examines the
shape and type of the data array. update_header_stats() calculates statistics from all items in
the data array and so can be slow for very large arrays. If necessary, the reset_header_stats()
method can be called to set the header fields to indicate that the statistics are undetermined.
Attributes:
• header
• extended_header
• indexed_extended_header
• data
• voxel_size
• nstart
Methods:
• set_extended_header()
• set_data()
• is_single_image()
• is_image_stack()
• is_volume()
• is_volume_stack()
• set_image_stack()
• set_volume()
• update_header_from_data()
• update_header_stats()
• reset_header_stats()
• print_header()
• get_labels()
• add_label()
Attributes and methods relevant to subclasses:
• _read_only
• _check_writeable()
• _create_default_attributes()
• _close_data()
• _set_new_data()
__init__(**kwargs)
Initialise a new MrcObject.
This initialiser deliberately avoids creating any arrays and simply sets the header,
extended header and data attributes to None. This allows subclasses to call __init__() at
the start of their initialisers and then set the attributes themselves, probably by reading
from a file, or by calling _create_default_attributes() for a new empty object.
Note that this behaviour might change in future: this initialiser could take optional
arguments to allow the header and data to be provided by the caller, or might create the
standard empty defaults rather than setting the attributes to None.
_check_writeable()
Check that this MRC object is writeable.
Raises ValueError -- If this object is read-only.
_create_default_attributes()
Set valid default values for the header and data attributes.
_create_default_header()
Create a default MRC file header.
The header is initialised with standard file type and version information, default values
for some essential fields, and zeros elsewhere. The first text label is also set to
indicate the file was created by this module.
property header
Get the header as a numpy record array.
property extended_header
Get the extended header as a numpy array.
The dtype will be void (raw data, dtype V'). If the actual data type of the extended header
is known, the dtype of the array can be changed to match. For supported types (e.g. 'FEI1'
and 'FEI2'), the indexed part of the extended header (excluding any zero padding) can be
accessed using indexed_extended_header().
The extended header may be modified in place. To replace it completely, call
set_extended_header().
property indexed_extended_header
Get the indexed part of the extended header as a numpy array with the appropriate dtype
set.
Currently only 'FEI1' and 'FEI2' extended headers are supported. Modifications to the
indexed extended header will not change the extended header data recorded in this
MrcObject. If the extended header type is unrecognised or extended header data is not of
sufficient length a warning will be produced and the indexed extended header will be None.
set_extended_header(extended_header)
Replace the extended header.
If you set the extended header you should also set the header.exttyp field to indicate the
type of extended header.
Raises ValueError -- If the new extended header has more than
2,147,483,647 bytes (and therefore its size cannot be stored
in the header).
property data
Get the data as a numpy array.
set_data(data)
Replace the data array.
This replaces the current data with the given array (or a copy of it), and updates the
header to match the new data dimensions. The data statistics (min, max, mean and rms)
stored in the header will also be updated.
Raises ValueError -- if the new data has a dimension larger than
2,147,483,647 (and therefore its size cannot be stored in the
header).
Warns RuntimeWarning -- If the data array contains Inf or NaN values.
_close_data()
Close the data array.
_set_new_data(data)
Replace the data array with a new one.
The new data array is not checked - it must already be valid for use in an MRC file.
property voxel_size
Get or set the voxel size in angstroms.
The voxel size is returned as a structured NumPy record array with three fields (x, y and
z). For example:
>>> mrc.voxel_size
rec.array((0.44825, 0.3925, 0.45874998),
dtype=[('x', '<f4'), ('y', '<f4'), ('z', '<f4')])
>>> mrc.voxel_size.x
array(0.44825, dtype=float32)
Note that changing the voxel_size array in-place will not change the voxel size in the file
-- to prevent this being overlooked accidentally, the writeable flag is set to False on the
voxel_size array.
To set the voxel size, assign a new value to the voxel_size attribute. You may give a
single number, a 3-tuple (x, y ,z) or a modified version of the voxel_size array. The
following examples are all equivalent:
>>> mrc.voxel_size = 1.0
>>> mrc.voxel_size = (1.0, 1.0, 1.0)
>>> vox_sizes = mrc.voxel_size
>>> vox_sizes.flags.writeable = True
>>> vox_sizes.x = 1.0
>>> vox_sizes.y = 1.0
>>> vox_sizes.z = 1.0
>>> mrc.voxel_size = vox_sizes
_set_voxel_size(x_size, y_size, z_size)
Set the voxel size.
Parameters
• x_size -- The voxel size in the X direction, in angstroms
• y_size -- The voxel size in the Y direction, in angstroms
• z_size -- The voxel size in the Z direction, in angstroms
property nstart
Get or set the grid start locations.
This provides a convenient way to get and set the values of the header's nxstart, nystart
and nzstart fields. Note that these fields are integers and are measured in voxels, not
angstroms. The start locations are returned as a structured NumPy record array with three
fields (x, y and z). For example:
>>> mrc.header.nxstart
array(0, dtype=int32)
>>> mrc.header.nystart
array(-21, dtype=int32)
>>> mrc.header.nzstart
array(-12, dtype=int32)
>>> mrc.nstart
rec.array((0, -21, -12),
dtype=[('x', '<i4'), ('y', '<i4'), ('z', '<i4')])
>>> mrc.nstart.y
array(-21, dtype=int32)
Note that changing the nstart array in-place will not change the values in the file -- to
prevent this being overlooked accidentally, the writeable flag is set to False on the
nstart array.
To set the start locations, assign a new value to the nstart attribute. You may give a
single number, a 3-tuple (x, y ,z) or a modified version of the nstart array. The following
examples are all equivalent:
>>> mrc.nstart = -150
>>> mrc.nstart = (-150, -150, -150)
>>> starts = mrc.nstart
>>> starts.flags.writeable = True
>>> starts.x = -150
>>> starts.y = -150
>>> starts.z = -150
>>> mrc.nstart = starts
_set_nstart(nxstart, nystart, nzstart)
Set the grid start locations.
Parameters
• nxstart -- The location of the first column in the unit cell
• nystart -- The location of the first row in the unit cell
• nzstart -- The location of the first section in the unit cell
is_single_image()
Identify whether the file represents a single image.
Returns
True if the data array is two-dimensional.
is_image_stack()
Identify whether the file represents a stack of images.
Returns
True if the data array is three-dimensional and the space group is zero.
is_volume()
Identify whether the file represents a volume.
Returns
True if the data array is three-dimensional and the space group is not zero.
is_volume_stack()
Identify whether the file represents a stack of volumes.
Returns
True if the data array is four-dimensional.
set_image_stack()
Change three-dimensional data to represent an image stack.
This method changes the space group number (header.ispg) to zero.
Raises ValueError -- If the data array is not three-dimensional.
set_volume()
Change three-dimensional data to represent a volume.
If the space group was previously zero (representing an image stack), this method sets it
to one. Otherwise the space group is not changed.
Raises ValueError -- If the data array is not three-dimensional.
update_header_from_data()
Update the header from the data array.
This function updates the header byte order and machine stamp to match the byte order of
the data. It also updates the file mode, space group and the dimension fields nx, ny, nz,
mx, my and mz.
If the data is 2D, the space group is set to 0 (image stack). For 3D data the space group
is not changed, and for 4D data the space group is set to 401 (simple P1 volume stack)
unless it is already in the volume stack range (401--630).
This means that new 3D data will be treated as an image stack if the previous data was a
single image or image stack, or as a volume if the previous data was a volume or volume
stack.
Note that this function does not update the data statistics fields in the header (dmin,
dmax, dmean and rms). Use the update_header_stats() function to update the statistics.
(This is for performance reasons -- updating the statistics can take a long time for large
data sets, but updating the other header information is always fast because only the type
and shape of the data array need to be inspected.)
update_header_stats()
Update the header's dmin, dmax, dmean and rms fields from the data.
Note that this can take some time with large files, particularly with files larger than the
currently available memory.
Warns RuntimeWarning -- If the data array contains Inf or NaN values.
reset_header_stats()
Set the header statistics to indicate that the values are unknown.
print_header(print_file=None)
Print the contents of all header fields.
Parameters
print_file -- The output text stream to use for printing the header. This is passed
directly to the file argument of Python's print() function. The default is None,
which means output will be printed to sys.stdout.
get_labels()
Get the labels from the MRC header.
Up to ten labels are stored in the header as arrays of 80 bytes. This method returns the
labels as Python strings, filtered to remove non-printable characters. To access the raw
bytes (including any non-printable characters) use the header.label attribute (and note
that header.nlabl stores the number of labels currently set).
Returns
The labels, as a list of strings. The list will contain between 0 and 10 items, each
containing up to 80 characters.
add_label(label)
Add a label to the MRC header.
The new label will be stored after any labels already in the header. If all ten labels are
already in use, an exception will be raised.
Future versions of this method might add checks to ensure that labels containing valid text
are not overwritten even if the nlabl value is incorrect.
Parameters
label -- The label value to store, as a string containing only printable ASCII
characters.
Raises
• ValueError -- If the label is longer than 80 bytes or contains
non-printable or non-ASCII characters.
• IndexError -- If the file already contains 10 labels and so an
additional label cannot be stored.
validate(print_file=None)
Validate this MrcObject.
This method runs a series of tests to check whether this object complies strictly with the
MRC2014 format specification:
1. MRC format ID string: The header's map field must contain "MAP ".
2. Machine stamp: The machine stamp should contain one of 0x44 0x44 0x00 0x00, 0x44 0x41
0x00 0x00 or 0x11 0x11 0x00 0x00.
3. MRC mode: the mode field should be one of the supported mode numbers: 0, 1, 2, 4, 6 or
12. (Note that MRC modes 3 and 101 are also valid according to the MRC 2014
specification but are not supported by mrcfile.)
4. Map and cell dimensions: The header fields nx, ny, nz, mx, my, mz, cella.x, cella.y and
cella.z must all be positive numbers.
5. Axis mapping: Header fields mapc, mapr and maps must contain the values 1, 2, and 3 (in
any order).
6. Volume stack dimensions: If the spacegroup is in the range 401--630, representing a
volume stack, the nz field should be exactly divisible by mz to represent the number of
volumes in the stack.
7. Header labels: The nlabl field should be set to indicate the number of labels in use,
and the labels in use should appear first in the label array (that is, there should be
no blank labels between text-filled ones).
8. MRC format version: The nversion field should be 20140 or 20141 for compliance with the
MRC2014 standard.
9. Extended header type: If an extended header is present, the exttyp field should be set
to indicate the type of extended header.
10. Data statistics: The statistics in the header should be correct for the actual data, or
marked as undetermined.
Parameters
print_file -- The output text stream to use for printing messages about the
validation. This is passed directly to the file argument of Python's print()
function. The default is None, which means output will be printed to sys.stdout.
Returns
True if this MrcObject is valid, or False if it does not meet the MRC format
specification in any way.
mrcfile.utils module
utils
Utility functions used by the other modules in the mrcfile package.
Functions
• data_dtype_from_header(): Work out the data dtype from an MRC header.
• data_shape_from_header(): Work out the data array shape from an MRC header
• mode_from_dtype(): Convert a numpy dtype to an MRC mode number.
• dtype_from_mode(): Convert an MRC mode number to a numpy dtype.
• pretty_machine_stamp(): Get a nicely-formatted string from a machine stamp.
• machine_stamp_from_byte_order(): Get a machine stamp from a byte order indicator.
• byte_orders_equal(): Compare two byte order indicators for equal endianness.
• normalise_byte_order(): Convert a byte order indicator to < or >.
• spacegroup_is_volume_stack(): Identify if a space group number represents a volume stack.
mrcfile.utils.data_dtype_from_header(header)
Return the data dtype indicated by the given header.
This function calls dtype_from_mode() to get the basic dtype, and then makes sure that the byte
order of the new dtype matches the byte order of the header's mode field.
Parameters
header -- An MRC header as a numpy record array.
Returns
The numpy dtype object for the data array corresponding to the given header.
Raises ValueError -- If there is no corresponding dtype for the given
mode.
mrcfile.utils.data_shape_from_header(header)
Return the data shape indicated by the given header.
Parameters
header -- An MRC header as a numpy record array.
Returns
The shape tuple for the data array corresponding to the given header.
mrcfile.utils.mode_from_dtype(dtype)
Return the MRC mode number corresponding to the given numpy dtype.
The conversion is as follows:
• float16 -> mode 12
• float32 -> mode 2
• int8 -> mode 0
• int16 -> mode 1
• uint8 -> mode 6 (data will be widened to 16 bits in the file)
• uint16 -> mode 6
• complex64 -> mode 4
Note that there is no numpy dtype which corresponds to MRC mode 3.
Parameters
dtype -- A numpy dtype object.
Returns
The MRC mode number.
Raises ValueError -- If there is no corresponding MRC mode for the given
dtype.
mrcfile.utils.dtype_from_mode(mode)
Return the numpy dtype corresponding to the given MRC mode number.
The mode parameter may be given as a Python scalar, numpy scalar or single-item numpy array.
The conversion is as follows:
• mode 0 -> int8
• mode 1 -> int16
• mode 2 -> float32
• mode 4 -> complex64
• mode 6 -> uint16
• mode 12 -> float16
Note that modes 3 and 101 are not supported as there is no matching numpy dtype.
Parameters
mode -- The MRC mode number. This may be given as any type which can be converted to an
int, for example a Python scalar (int or float), a numpy scalar or a single-item numpy
array.
Returns
The numpy dtype object corresponding to the given mode.
Raises ValueError -- If there is no corresponding dtype for the given
mode, or if mode is an array and does not contain exactly one
item.
mrcfile.utils.pretty_machine_stamp(machst)
Return a human-readable hex string for a machine stamp.
mrcfile.utils.byte_order_from_machine_stamp(machst)
Return the byte order corresponding to the given machine stamp.
Parameters
machst -- The machine stamp, as a bytearray or a numpy array of bytes.
Returns
< if the machine stamp represents little-endian data, or > if it represents big-endian.
Raises ValueError -- If the machine stamp is invalid.
mrcfile.utils.machine_stamp_from_byte_order(byte_order='=')
Return the machine stamp corresponding to the given byte order indicator.
Parameters
byte_order -- The byte order indicator: one of =, < or >, as defined and used by numpy
dtype objects.
Returns
The machine stamp which corresponds to the given byte order, as a bytearray. This will be
either (0x44, 0x44, 0, 0) for little-endian or (0x11, 0x11, 0, 0) for big-endian. If the
given byte order indicator is =, the native byte order is used.
Raises ValueError -- If the byte order indicator is unrecognised.
mrcfile.utils.byte_orders_equal(a, b)
Work out if the byte order indicators represent the same endianness.
Parameters
• a -- The first byte order indicator: one of =, < or >, as defined and used by numpy dtype
objects.
• b -- The second byte order indicator.
Returns
True if the byte order indicators represent the same endianness.
Raises ValueError -- If the byte order indicator is not recognised.
mrcfile.utils.normalise_byte_order(byte_order)
Convert a numpy byte order indicator to one of < or >.
Parameters
byte_order -- One of =, < or >.
Returns
< if the byte order indicator represents little-endian data, or > if it represents
big-endian. Therefore on a little-endian machine, = will be converted to <, but on a
big-endian machine it will be converted to >.
Raises ValueError -- If byte_order is not one of =, < or
>.
mrcfile.utils.spacegroup_is_volume_stack(ispg)
Identify if the given space group number represents a volume stack.
Parameters
ispg -- The space group number, as an integer, numpy scalar or single- element numpy array.
Returns
True if the space group number is in the range 401--630.
mrcfile.utils.is_printable_ascii(string_)
Check if a string is entirely composed of printable ASCII characters.
mrcfile.utils.printable_string_from_bytes(bytes_)
Convert bytes into a printable ASCII string by removing non-printable characters.
mrcfile.utils.bytes_from_string(string_)
Convert a string to bytes.
Even though this is a one-liner, the details are tricky to get right so things work properly in
both Python 2 and 3. It's broken out as a separate function so it can be thoroughly tested.
Raises UnicodeError -- If the input contains non-ASCII characters.
mrcfile.validator module
validator
Module for top-level functions that validate MRC files.
This module is runnable to allow files to be validated easily from the command line.
mrcfile.validator.main(args=None)
Validate a list of MRC files given as command arguments.
The return value is used as the process exit code when this function is called by running this
module or from the corresponding console_scripts entry point.
Returns
0 if all command arguments are names of valid MRC files. 1 if no file names are given or
any of the files is not a valid MRC file.
mrcfile.validator.validate_all(names, print_file=None)
Validate a list of MRC files.
This function calls validate() for each file name in the given list.
Parameters
• names -- A sequence of file names to open and validate.
• print_file -- The output text stream to use for printing messages about the validation.
This is passed directly to the print_file argument of the validate() function. The
default is None, which means output will be printed to sys.stdout.
Returns
True if all of the files are valid, or False if any of the files do not meet the MRC format
specification in any way.
Raises OSError -- If one of the files does not exist or cannot be opened.
Warns RuntimeWarning -- If one of the files is seriously invalid because it has no map ID string,
an incorrect machine stamp, an unknown mode number, or is not the same size as expected
from the header.
mrcfile.validator.validate(name, print_file=None)
Validate an MRC file.
This function first opens the file by calling open() (with permissive=True), then calls
validate(), which runs a series of tests to check whether the file complies with the MRC2014
format specification.
If the file is completely valid, this function returns True, otherwise it returns False. Messages
explaining the validation result will be printed to sys.stdout by default, but if a text stream is
given (using the print_file argument) output will be printed to that instead.
Badly invalid files will also cause warning messages to be issued, which will be written to
sys.stderr by default. See the documentation of the warnings module for information on how to
suppress or capture warning output.
Because the file is opened by calling open(), gzip- and bzip2-compressed MRC files can be
validated easily using this function.
After the file has been opened, it is checked for problems. The tests are:
1. MRC format ID string: The map field in the header should contain "MAP ".
2. Machine stamp: The machine stamp should contain one of 0x44 0x44 0x00 0x00, 0x44 0x41 0x00
0x00 or 0x11 0x11 0x00 0x00.
3. MRC mode: the mode field should be one of the supported mode numbers: 0, 1, 2, 4, 6 or 12.
(Note that MRC modes 3 and 101 are also valid according to the MRC 2014 specification but are
not supported by mrcfile.)
4. Map and cell dimensions: The header fields nx, ny, nz, mx, my, mz, cella.x, cella.y and
cella.z must all be positive numbers.
5. Axis mapping: Header fields mapc, mapr and maps must contain the values 1, 2, and 3 (in any
order).
6. Volume stack dimensions: If the spacegroup is in the range 401--630, representing a volume
stack, the nz field should be exactly divisible by mz to represent the number of volumes in
the stack.
7. Header labels: The nlabl field should be set to indicate the number of labels in use, and the
labels in use should appear first in the label array.
8. MRC format version: The nversion field should be 20140 or 20141 for compliance with the
MRC2014 standard.
9. Extended header type: If an extended header is present, the exttyp field should be set to
indicate the type of extended header.
10. Data statistics: The statistics in the header should be correct for the actual data in the
file, or marked as undetermined.
11. File size: The size of the file on disk should match the expected size calculated from the MRC
header.
Parameters
• name -- The file name to open and validate.
• print_file -- The output text stream to use for printing messages about the validation.
This is passed directly to the file argument of Python's print() function. The default is
None, which means output will be printed to sys.stdout.
Returns
True if the file is valid, or False if the file does not meet the MRC format specification
in any way.
Raises OSError -- If the file does not exist or cannot be opened.
Warns RuntimeWarning -- If the file is seriously invalid because it has no map ID string, an
incorrect machine stamp, an unknown mode number, or is not the same size as expected from
the header.
AUTHOR
Colin Palmer
COPYRIGHT
2017, Science and Technology Facilities Council
1.5.4 Feb 07, 2025 MRCFILE(1)