biotite.structure.io.pdbx.PDBxFile¶

class biotite.structure.io.pdbx.PDBxFile[source]¶

Bases: File, MutableMapping

This class represents the legacy interface to CIF files.

The categories of the file can be accessed using the get_category()/set_category() methods. The content of each category is represented by a dictionary. The dictionary contains the entry (e.g. label_entity_id in atom_site) as key. The corresponding values are either strings in non-looped categories, or 1-D numpy arrays of string objects in case of looped categories.

A category can be changed or added using set_category(): If a string-valued dictionary is provided, a non-looped category will be created; if an array-valued dictionary is given, a looped category will be created. In case of arrays, it is important that all arrays have the same size.

Alternatively, The content of this file can also be read/write accessed using dictionary-like indexing: You can either provide a data block and a category or only a category, in which case the first data block is taken.

DEPRECATED: Use CIFFile instead.

Notes

This class is also able to detect and parse multiline entries in the file. However, when writing a category no multiline values are used. This could lead to long lines.

This class uses a lazy category dictionary creation: When reading the file only the line positions of all categories are checked. The time consuming task of dictionary creation is done when get_category() is called.

Examples

Read the file and get author names:

>>> import os.path
>>> file = PDBxFile.read(os.path.join(path_to_structures, "1l2y.cif"))
>>> author_dict = file.get_category("citation_author", block="1L2Y")
>>> print(author_dict["name"])
['Neidigh, J.W.' 'Fesinmeyer, R.M.' 'Andersen, N.H.']

Dictionary style indexing, no specification of data block:

>>> print(file["citation_author"]["name"])
['Neidigh, J.W.' 'Fesinmeyer, R.M.' 'Andersen, N.H.']

Get the structure from the file:

>>> arr = get_structure(file)
>>> print(type(arr).__name__)
AtomArrayStack
>>> arr = get_structure(file, model=1)
>>> print(type(arr).__name__)
AtomArray

Modify atom array and write it back into the file:

>>> arr_mod = rotate(arr, [1,2,3])
>>> set_structure(file, arr_mod)
>>> file.write(os.path.join(path_to_directory, "1l2y_mod.cif"))

clear() → None. Remove all items from D.¶

copy()¶

Create a deep copy of this object.

Returns

copy: A copy of this object.

get(k[, d]) → D[k] if k in D, else d. d defaults to None.¶

get_block_names()¶

Get the names of all data blocks in the file.

Returns

blockslist: List of data block names.

get_category(category, block=None, expect_looped=False)¶

Get the dictionary for a given category.

Parameters

categorystring: The name of the category. The leading underscore is omitted.
blockstring, optional: The name of the data block. Default is the first (and most times only) data block of the file.
expect_loopedbool, optional: If set to true, the returned dictionary will always contain arrays (only if the category exists): If the category is non-looped, each array will contain only one element.

Returns

category_dictdict of (str or ndarray, dtype=str) or None: A entry keyed dictionary. The corresponding values are strings or array of strings for non-looped and looped categories, respectively. Returns None, if the data block does not contain the given category.

items() → a set-like object providing a view on D's items¶

keys() → a set-like object providing a view on D's keys¶

pop(k[, d]) → v, remove specified key and return the corresponding value.¶: If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair¶: as a 2-tuple; but raise KeyError if D is empty.

classmethod read(file)¶

Read a PDBx/mmCIF file.

Parameters

filefile-like object or str: The file to be read. Alternatively a file path can be supplied.

Returns

file_objectPDBxFile: The parsed file.

set_category(category, category_dict, block=None)¶

Set the content of a category.

If the category is already existing, all lines corresponding to the category are replaced. Otherwise a new category is created and the lines are appended at the end of the data block.

Parameters

categorystring: The name of the category. The leading underscore is omitted.
category_dictdict: The category content. The dictionary must have strings (subcategories) as keys and strings or ndarray objects as values.
blockstring, optional: The name of the data block. Default is the first (and most times only) data block of the file. If the block is not contained in the file yet, a new block is appended at the end of the file.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D¶

update([E, ]**F) → None. Update D from mapping/iterable E and F.¶: If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values¶

write(file)¶

Write the contents of this File object into a file.

Parameters

file_namefile-like object or str: The file to be written to. Alternatively a file path can be supplied.