CIF Parser
##########

This section covers working with CIF files on a syntactic level.

Higher-level functions that understand semantics of:

* small molecule or inorganic CIF files,
* macromolecular PDBx/mmCIF,
* and monomer/ligand cif files as used for macromolecular restraints

are documented in section :ref:`molecular`.

.. _cif_intro:

What are STAR, CIF, DDL, mmCIF?
==================================

(in case someone comes here when looking for a serialization format)

STAR is a human-readable data serialization format (think XML or JSON)
that happens to be known and used only in molecular-structure sciences.

CIF (Crystallographic Information File) -- a file format used
in crystallography -- is a restricted derivative of STAR.
It is restricted in features (to make implementation easier),
but also imposes arbitrary limits -- for example on the line length.

DDL is a schema language for STAR/CIF.

All of them (STAR, CIF and DDL) have multiple versions.
We will be more specific in the following sections.

The STAR/CIF syntax is relatively simple, but may be confusing at first.
(Note that the initial version of STAR was published by Sydney Hall in 1991 --
before XML and long before JSON and YAML, not to mention TOML).

.. highlight:: default

Key-value pairs have the form::

    _year 2017  # means year = 2017

Blocks (sections) begin with the ``data_`` keyword
with a block name glued to it::

    data_tomato   # [tomato]
    _color red    # color = "red"

Importantly, unlike XML/JSON/YAML/TOML, STAR is designed
to concisely represent tabular data. This example from TOML docs::

    # TOML
    points = [ { x = 1, y = 2, z = 3 },
               { x = 7, y = 8, z = 9 },
               { x = 2, y = 4, z = 8 } ]

could be expressed as a so-called *loop* in STAR (keyword ``loop_``
followed by column names followed by values)::

    # STAR/CIF
    loop_
    _points.x _points.y _points.z
    1 2 3
    7 8 9
    2 4 8

Typically, long tables (loops) make most of the CIF content::

    1    N N   . LEU A 11  ? 0.5281 0.5618 0.5305 -0.0327 -0.0621 0.0104
    2    C CA  . LEU A 11  ? 0.5446 0.5722 0.5396 -0.0317 -0.0632 0.0080
    # many, many lines...
    5331 S SD  . MET C 238 ? 2.2952 2.3511 2.3275 -0.0895 0.0372  -0.0230
    5332 C CE  . MET C 238 ? 1.5699 1.6247 1.6108 -0.0907 0.0388  -0.0244

The dot and question mark in the example above are two null types.
In the CIF spec: ``?`` = *unknown* and ``.`` = *not applicable*.
In mmCIF files ``.`` is used for mandatory items, ``?`` for not mandatory.

The CIF syntax has a serious flaw resulting from historical trade-offs:
a string that can be interpreted as a number does not need to be quoted.
Therefore, the type of ``5332`` above is not certain:
the JSON equivalent can be either ``5332`` or ``"5332"``.

Note: "STAR File" is trademarked by IUCr, and it used to be patented_.

.. _patented: https://patents.google.com/patent/WO1991016682A1

The mmCIF format (by mmCIF we mean what is more formally called PDBx/mmCIF)
is the CIF syntax + a huge dictionary (ontology/schema) in DDL2.
The dictionary defines relations between columns in different tables,
which makes it resemble a relational database (it was designed at the
height of popularity of RDBMSs).

**Where are the specs?**

International Tables for Crystallography
`Vol. G (2006) <http://it.iucr.org/Ga/contents/>`_
describes all of the STAR, CIF 1.1, DDL1 and DDL2.
If you don't have access to it -- IUCr website has specs of
`CIF1.1 <http://www.iucr.org/resources/cif/spec/version1.1>`_
and `DDLs <http://www.iucr.org/resources/cif/ddl>`_.
As far as I can tell all versions of the STAR spec are behind paywalls.

Later versions of the formats (hardly used as of 2017)
are described in these articles:
`STAR <https://dx.doi.org/10.1021/ci300074v>`_ (2012),
`DDLm <http://pubs.acs.org/doi/abs/10.1021/ci300075z>`_ and
`dREL <http://pubs.acs.org/doi/abs/10.1021/ci300076w>`_ (2012),
and `CIF 2.0 <http://journals.iucr.org/j/issues/2016/01/00/aj5269/>`_ (2016).
Only the last one is freely available.

PDBx/mmCIF is documented at `mmcif.pdb.org <http://mmcif.pdb.org/>`_.

.. _what_is_parsed:

What is parsed?
===============

The parser supports CIF 1.1 spec and some extras.

Currently, it is available as:

* C++11 header-only library, and
* Python (2 and 3) extension module.

We use it to read:

* mmCIF files (both coordinates and structure factors)
* CIF files from Crystallography Open Database (COD)
* Chemical Component Dictionary from PDB
* DDL1 and DDL2 dictionaries from IUCr and PDB
* monomer library a.k.a. Refmac dictionary

The parser handles:

* all constructs of CIF 1.1 (including *save frames*),
* the ``global_`` and ``stop_`` keywords from STAR -- needed for Refmac
  monomer library and ``mmcif_nmr-star.dic``, respectively.

It could be extended to handle also the new features of CIF 2.0
or even the full STAR format, but we don't have a good reason to do this.
The same goes for DDLm/dREL.

The parser does not handle CIF 1.1 conventions, as they are not part
of the syntax:
line wrapping (``eol;\eol``),
Greek letters (``\m`` -> µ),
accented letters (``\'o`` -> ó),
special alphabetic characters (``\%A`` -> Å)
and other codes (``\\infty`` -> ∞).

CIF parsers in the small-molecules field need to deal with incorrect syntax.
The papers about `iotbx.cif <https://doi.org/10.1107/S0021889811041161>`_
and `COD::CIF::Parser <http://dx.doi.org/10.1107/S1600576715022396>`_
enumerate 12 and 10 common error categories, respectively.
Nowadays the problem is less severe, especially in the MX community
that embraced the CIF format later. So we've decided to add only
the following rules to relax the syntax:

* names and lines can have any length like in STAR
  (the CIF spec imposes the limit of 2048 characters, but some mmCIF files
  from PDB exceed it, e.g. 3j3q.cif),
* quoted strings may contain non-ascii characters (if nothing has changed
  one entry in the PDB has byte A0 corresponding to non-breaking space),
* a table (loop) can have no values if it is followed by a keyword or EOF
  (such files were written by old versions of Refmac and SHELXL,
  and were also present in the CCP4 monomer library),
* block name (*blockcode*) can be empty, i.e. the block can start
  with bare ``data_`` keyword (RELION writes such files),
* unquoted strings cannot start with keywords (STAR spec is ambiguous
  about this -- see
  `StarTools doc <http://www.globalphasing.com/startools/>`_ for details;
  this rule is actually about simplifying not relaxing),
* missing value in a key-value pair is optionally allowed
  if whitespace after the tag ends with a new-line character.
  More specifically, the parsing step allows for missing value in such case,
  but the next validation step (which can be skipped when using using
  low-level functions, such as ``parse_file()``) throws an error.


Getting started
===============

C++
---

CIF parser is implemented in header files,
so you do not need to compile Gemmi.
It has a single dependency: PEGTL (also header-only),
which is included under the ``include/gemmi/third_party`` directory.
All you need is to make sure that Gemmi headers are in your
project's include path, and compile your program as C++11 or later.

Let us start with a simple example.
This little program reads mmCIF file and shows weights of the chemical
components:

.. literalinclude:: code/cif_cc.cpp
   :language: cpp
   :lines: 1-11

To compile it on Unix system you need to fetch Gemmi source code
and run a compiler:

.. code-block:: none

    git clone https://github.com/project-gemmi/gemmi.git
    c++ -std=c++11 -Igemmi/include -O2 my_program.cpp

Python
------

Python module for Python 2.7 and 3.x
can be installed with pip, as described in the
:ref:`Installation <install_py>` section.
After installation ``pydoc gemmi.cif`` should list all classes and methods.

To start with a simple example, here is a program that says hello to each
element found in mmCIF:

.. literalinclude:: ../examples/hello.py
   :language: python
   :lines: 2-
   :emphasize-lines: 2,7-9

More complex examples are shown in the :ref:`cif_examples` section.

Internally, Python bindings use
`pybind11 <https://github.com/pybind/pybind11>`_.


DOM and SAX
===========

The terms DOM and SAX originate from XML parsing, but they became also
names of general parsing styles.
Gemmi can parse CIF files in two ways that correspond to DOM and SAX:

* The usual way is to parse a file or a string into a document (DOM)
  that can be easily accessed and manipulated.

* Alternatively, from C++ only, one can define own
  `PEGTL Actions <https://github.com/taocpp/PEGTL/blob/master/doc/Actions-and-States.md>`_
  for to the grammar rules from ``cif.hpp``.
  These actions will be triggered while reading a CIF file.

This documentation covers the DOM parsing only.
The hierarchy in the DOM reflects the structure of CIF 1.1:

* Document contains blocks.
* Block can contain name-value pairs, loops and frames.
* Frame can contain name-value pairs and loops.
* Loop (*m*\ ×\ *n* table) contains *n* column names and *m*\ ×\ *n* values.

Names are often called *tags*. The leading ``_`` is usually treated
as part of the tag, not just a syntactic feature. So we store tag string
with the underscore (``_my_tag``), and function that take tags as arguments
expect strings starting with ``_``.
The case of tags is preserved.

Values have types. CIF 1.1 defines four base types:

* char (string)
* uchar (ughh.. case-insensitive string)
* numb (number that cannot be recognized as number on the syntax level)
* null (one of two possible nulls: ``?`` and ``.``)

Additionally, DDL2 dictionaries specify subtypes.
For example, ``int`` and ``float`` are mmCIF subtypes of ``numb``.

Since in general it is not possible to infer type without
the corresponding dictionary, the DOM stores raw strings (including quotes).
They can be later converted to required type using the following helper
functions:

* ``as_string()`` -- gets unquoted string,
* ``as_number()`` -- gets floating-point number,
* ``as_int()`` -- gets integer,
* ``as_char()`` -- gets single character,
* ``is_null()`` -- check if the value is null (i.e. ``?`` or ``.``),

and we have also

* ``quote()`` -- the opposite of ``as_string()`` -- add quotes appropriate
  for the content of the string (usually, no quotes are necessary and no
  quotes are added).

C++
---

.. highlight:: cpp

All these helper functions are defined in ``gemmi/cifdoc.hpp``
except for ``as_number()`` which is in ``gemmi/numb.hpp``.
They take a string as an argument and work as expected, for example::

  double rfree = cif::as_number(raw_rfree_string); // NaN if it's '?' or '.'

Python
------

.. doctest::

  >>> from gemmi import cif
  >>> cif.as_number('123')
  123.0
  >>> cif.as_int('123')
  123
  >>> cif.quote('two words')
  "'two words'"
  >>> cif.as_string(_)
  'two words'
  >>> cif.as_number(_)
  nan


Reading a file
==============

We have a few reading functions that read a file (or a string, or a stream)
and return a document (DOM) -- an instance of class ``Document``.

C++
---

The reading functions are in the ``gemmi::cif`` namespace::

  #include <gemmi/cif.hpp>

  Document read_file(const std::string& filename)
  Document read_memory(const char* data, const size_t size, const char* name)
  Document read_cstream(std::FILE *f, size_t bufsize, const char* name)
  Document read_istream(std::istream &is, size_t bufsize, const char* name)

Parameter ``name`` is used only when reporting errors.
Parameter ``bufsize`` determines the buffer size and only affects performance.
Regardless of the buffer size, the last two options are slower
than ``read_file()`` -- they were not optimized for.

Additional header ``<gemmi/gz.hpp>`` is needed to transparently open
a gzipped file (by uncompressing it first into a memory buffer)
if the filename ends with ``.gz``::

    // in this and all the next examples: namespace cif = gemmi::cif;
    cif::Document doc = cif::read(gemmi::MaybeGzipped(path));

If you use it, you must also link the program with zlib. On Unix systems
it usually means adding ``-lz`` to the compiler invocation.

And if the ``path`` above is ``-``, the standard input is read.

If you use these functions in multiple compilation units, having
the CIF parser implemented in headers makes the compilation time longer.
To avoid it, include only ``<gemmi/read_cif.hpp>``
and use functions declared there instead. In one compilation unit
include this file after defining a macro that guards the implementation::

  #define GEMMI_READ_CIF_IMPLEMENTATION
  #include <gemmi/read_cif.cpp>

Python
------

.. testcode::

  from gemmi import cif

  # read and parse a CIF file
  doc = cif.read_file('components.cif')

  # the same, but if the filename ends with .gz it is uncompressed on the fly
  doc = cif.read('../tests/1pfe.cif.gz')

  # read content of a CIF file from string
  doc = cif.read_string('data_this _is valid _cif content')

Low-level functions
-------------------

The ``read_file()`` call is equivalent to the following sequence:

.. testcode::

  path = 'components.cif'
  doc = cif.Document()
  doc.source = path
  doc.parse_file(path)
  doc.check_for_missing_values()
  doc.check_for_duplicates()

The last two functions check for, respectively, missing values in tag-value
pairs and duplicated names.
It is possible to read erroneous CIF files by skipping these checks.

Analogically, function ``read_string()`` can be replaced by a similar
sequence with ``Document.parse_string()`` doing the main job:

.. doctest::

  >>> doc = cif.Document()
  >>> doc.parse_string('data_this _tag_has_no_value\n')
  >>> doc[0].find_value('_tag_has_no_value')
  ''
  >>> try: doc.check_for_missing_values()
  ... except RuntimeError: print('missing value')
  ...
  missing value


Writing a file
==============

One often wants to write a modified Document back to a file.
That file will contain blocks separated with blank lines.
It is also possible to write only a single block.

Reading and writing a file does not preserve whitespaces.
Instead, we have a few choices for "styling" of the output:

* ``Style::Simple`` writes out the DOM structure adding blank lines between
  mmCIF categories,
* ``Style::NoBlankLines`` does not add blank lines,
* ``Style::PreferPairs`` writes single-row loops as pairs,
* ``Style::Pdbx`` additionally puts ``#`` (empty comments) between categories,
  mimicking the peculiar formatting of PDBx/mmCIF files in the official
  wwPDB archive. It enables diff-ing original and modified files with
  option ``--ignore-space-change``.
* ``Style::Indent35`` writes values from pairs from 35th column,


C++
---

The functions writing ``cif::Document`` and ``cif::Block`` to C++ stream
is in a separate header ``gemmi/to_cif.hpp``::

  void write_cif_to_stream(std::ostream& os, const Document& doc, Style style)
  void write_cif_block_to_stream(std::ostream& os, const Block& block, Style style)

Python
------

In Python, the function that writes the document to a file is a method
of the ``Document`` class:

.. doctest::

  >>> doc.write_file('1pfe-modified.cif')

It can take the style as optional, second argument:

.. doctest::

  >>> doc.write_file('1pfe-modified.cif', cif.Style.PreferPairs)
  >>> doc.write_file('1pfe-styled.cif', cif.Style.Pdbx)

The ``Document`` class also has a method ``as_string()`` which returns
the text that would be written by ``write_file()``.

The ``Block`` class also has methods ``write_file()`` and ``as_string()``
with the same arguments.

Document
========

``Document`` is made of blocks with data. The blocks can be iterated over,
accessed by index or by name (each CIF block must have a unique name).

As it is common for cif files to contain only a single block,
gemmi has a method ``sole_block()`` that returns the first block
if the document has only one block; otherwise it throws an exception.

At last, is also has a member variable ``source`` that contains
the path of the file from which the document was read (if it was read
from a file).

C++
---

``Document`` has the two member variables::

  std::string source;  // filename or the name passed to read_memory()
  std::vector<Block> blocks;

Each ``Block`` corresponds to a data block.
To access a block with known name use::

  Block* Document::find_block(const std::string& name)

To access the only block in the file you may use::

  Block& Document::sole_block()

A new ``Document`` instance can be created with default constructor.
To modify a document you need to access directly its member variables.
With one exception: when adding new blocks you can use a function that
additionally checks if the new name is unique::

  Block& Document::add_new_block(const std::string& name, int pos=-1)


Python
------

``Document`` can be iterated, accessed by block index and by block name:

.. doctest::

  >>> doc = cif.read_file("components.cif")
  >>> len(doc)  #doctest: +SKIP
  25219
  >>> doc[0]
  <gemmi.cif.Block 000>
  >>> doc[-1]
  <gemmi.cif.Block ZZZ>
  >>> doc['MSE']
  <gemmi.cif.Block MSE>

It has two other ways of accessing a block:

.. doctest::

  >>> # The function block.find_block(name) is like block[name] ...
  >>> doc.find_block('MSE')
  <gemmi.cif.Block MSE>
  >>> # ... except when the block is not found:
  >>> doc.find_block('no such thing')  # -> None
  >>> # doc['no such thing'] # -> KeyError

  >>> # Get the only block; throws exception if the document has more blocks.
  >>> cif.read("../tests/1pfe.cif.gz").sole_block()
  <gemmi.cif.Block 1PFE>

Blocks can be inserted (by default -- appended after existing blocks)
using one of the two functions:

* ``Document.add_new_block(name, pos=-1)``
* ``Document.add_copied_block(block, pos=-1)``

As an example, here is how to start a new document:

.. doctest::

  >>> d = cif.Document()
  >>> block_one = d.add_new_block('block-one')
  >>> # populate block_one

To delete a block use ``Document.__delitem__`` (for example: ``del doc[1]``).

Document has also one property

.. doctest::

  >>> doc.source
  'components.cif'


Block
=====

Each block has a name and a list of items.
Each item is one of:

* name-value pair (Pair),
* table, a.k.a loop (Loop)
* or save frame (Block -- the same data structure as for block).

(Although keyword ``global_`` and empty block name (``data_``) are
not valid CIF, gemmi parses them as :ref:`exceptions <what_is_parsed>`
and stores them as blocks with names, respectively, empty and ``#``.)

C++
---

Each block contains::

    std::string name;
    std::vector<Item> items;

where ``Item`` is implemented as an unrestricted (C++11) union
that holds one of Pair, Loop or Block.

Python
------

Each block has a name:

.. doctest::

  >>> doc = cif.read("../tests/1pfe.cif.gz")
  >>> block = doc.sole_block()
  >>> block.name
  '1PFE'

and a list of items (class Item):

.. doctest::

  >>> for item in block:
  ...    if item.line_number > 1670:
  ...        if item.pair is not None:
  ...            print('pair', item.pair)
  ...        elif item.loop is not None:
  ...            print('loop', item.loop)
  ...        elif item.frame is not None:
  ...            print('frame', item.frame)
  ...
  pair ['_ndb_struct_conf_na.entry_id', '1PFE']
  pair ['_ndb_struct_conf_na.feature', "'double helix'"]
  loop <gemmi.cif.Loop 8 x 25>
  loop <gemmi.cif.Loop 5 x 43>
  loop <gemmi.cif.Loop 3 x 3>
  loop <gemmi.cif.Loop 83 x 10>


Frame
=====

(Very few people need it, skip this section.)

The *named save frames* (keyword ``save_``) from the STAR specification
are used in CIF files only as sub-sections of a block.
The only place where they are encountered are mmCIF dictionaries.

The save-frame is stored as ``Block`` and can be accessed with::

  Block* Block::find_frame(std::string name)

.. doctest::

  >>> frame = block.find_frame('my_frame')

Additionally, in C++ one may iterate over all Block's items,
check each item type and handle all the save frames::

  for (cif::Item& item : block.items)
    if (item.type == cif::ItemType::Frame)
      // doing something with item.frame which is a (nested) Block
      cif::Block& frame = item.frame;


Pairs and Loops
===============

The functions in this section can be considered low-level, because they are
specific to either name-value pairs or to loops.


.. warning::

    Assuming what is a pair and what is in loop is a common source of bugs
    when handling mmCIF files, so instead of these functions we recommend
    using abstractions introduced in the next sections. For example,
    when working with proteins one could assume that anisotropic ADP values
    are in a loop, but wwPDB has entries with anisotropic ADP
    for one atom only -- as name-value pairs.
    On the other hand, one could think that R-free is always given as
    name-value, but in entries from joint X-ray and neutron refinement
    it is in a loop.
    Be careful.

The next sections introduce function that work with both pairs and loops.

C++
---

Pair is simply defined as::

    using Pair = std::array<std::string, 2>;

A pair with a particular tag can be located using::

  const Pair* Block::find_pair(const std::string& tag) const

or, if you want just the value::

  const std::string* Block::find_value(const std::string& tag) const

Both functions return ``nullptr`` if the tag is not found (and they
do not search in CIF loops).

To add a pair to the block, or modify an existing one, use::

  void Block::set_pair(const std::string& tag, std::string value)

The value needs quoting, the passed argument needs to be already quoted
(you may pass ``cif::quote(value)``).

----

Loop is defined as::

  struct Loop {
    std::vector<std::string> tags;
    std::vector<std::string> values;
    // and a number of functions
  };

To get values corresponding to a tag in a loop (table) you may use::

  Column Block::find_loop(const std::string& tag)

``struct Column``, which is documented further on, has method ``get_loop()``
which gives access to ``struct Loop``.

A new loop can be added using function::

  Loop& Block::init_loop(const std::string& prefix, std::vector<std::string> tags)

Then it can be populated by either setting directly ``tags`` and ``values``,
or my using Loop's methods such as ``add_row()`` or ``set_all_values()``.

Python
------

Accessing name-value pairs:

.. doctest::

  >>> # (1) tag and value
  >>> block.find_pair('_cell.length_a')
  ['_cell.length_a', '39.374']
  >>> block.find_pair('_no_such_tag')  # return None

  >>> # (2) only value
  >>> block.find_value('_cell.length_b')
  '39.374'
  >>> block.find_value('_cell.no_such_tag')  # returns None

  >>> # (3) Item
  >>> item = block.find_pair_item('_cell.length_c')
  >>> item.pair
  ['_cell.length_c', '79.734']
  >>> item.line_number
  72
  >>> block.find_pair_item('_nothing')  # return None

To add a name-value pair, replacing current item if it exists,
use function ``set_pair``:

.. doctest::

  >>> block.set_pair('_year', '2030')

If the value needs quoting, it must be passed quoted:

.. doctest::

  >>> block.set_pair('_title', cif.quote('Goldilocks and the Three Bears'))

Now we can create a CIF file can from scratch:

.. doctest::

  >>> d = cif.Document()
  >>> d.add_new_block('oak')
  <gemmi.cif.Block oak>
  >>> _.set_pair('_nut', 'acorn')
  >>> print(d.as_string().strip())
  data_oak
  _nut acorn

----

To access values in loop:

.. doctest::

  >>> # (1) get a Column in Loop
  >>> block.find_loop('_atom_type.symbol')
  <gemmi.cif.Column _atom_type.symbol length 6>
  >>> list(_)
  ['C', 'CL', 'N', 'O', 'P', 'S']

  >>> # (2) get Item containing the Loop
  >>> block.find_loop_item('_atom_type.symbol')  # doctest: +ELLIPSIS
  <gemmi.cif.Item object at 0x...>
  >>> _.loop
  <gemmi.cif.Loop 6 x 1>

To add a row to an existing table (loop) use ``add_row``:

.. doctest::

  >>> loop = block.find_loop('_atom_type.symbol').get_loop()
  >>> loop.add_row(['Au'], pos=0)
  >>> loop.add_row(['Zr'])  # appends
  >>> list(block.find_loop('_atom_type.symbol'))
  ['Au', 'C', 'CL', 'N', 'O', 'P', 'S', 'Zr']

``add_row`` takes as an argument a list of strings, which should be quoted
if necessary. If you have a list Python values use ``quote_list`` first:

.. doctest::

  >>> cif.quote_list([None, False, 3, -2.5, 'word', 'two words'])
  ['?', '.', '3', '-2.5', 'word', "'two words'"]

``set_all_values`` sets all the data in a table. It takes as an argument
a list of lists of string. The lists of strings correspond to columns.

.. doctest::

  >>> loop = block.find_loop_item('_citation_author.citation_id').loop
  >>> loop.tags
  ['_citation_author.citation_id', '_citation_author.name', '_citation_author.ordinal']
  >>> loop.set_all_values([['primary']*2, [cif.quote('Alice A.'), cif.quote('Bob B.')], ['1', '2']])
  >>> for row in block.find_mmcif_category('_citation_author.'):
  ...   print(list(row))
  ['primary', "'Alice A.'", '1']
  ['primary', "'Bob B.'", '2']

To add a new loop (replacing old one if it exists) use ``init_loop``:

.. doctest::

  >>> loop = block.init_loop('_ocean_', ['id', 'name'])
  >>> # empty table is invalid in CIF, we need to add something
  >>> loop.add_row(['1', cif.quote('Atlantic Ocean')])

In the above example, if the block already has tags ``_ocean_id``
and/or ``_ocean_name`` and

* if they are in a table: the table will be cleared and re-used,
* if they are in name-value pairs: the pairs will be removed
  and a table will be created at the position of the first pair.

----

To reorder items, use ``block.move_item(old_pos, new_pos)``.
The current position of a tag can be obtained using ``block.get_index(tag)``.

.. doctest::

  >>> block.get_index('_entry.id')
  0
  >>> block.get_index('_ocean_id')
  385
  >>> block.move_item(0, -1)  # move first item to the end
  >>> block.get_index('_entry.id')
  385
  >>> block.get_index('_ocean_id')
  384
  >>> block.move_item(385, 0)  # let's move it back (385 == -1 here)

To copy an item:

.. doctest::

  >>> item = block.find_loop_item('_ocean_id')
  >>> new_block = cif.Block('new_block')
  >>> new_block.add_item(item)  # doctest: +ELLIPSIS
  <gemmi.cif.Item object at 0x...>


Column
======

``Column`` is a lightweight proxy class for working with both loop columns
and name-value pairs.

It was returned from ``find_loop`` above, but it is also returned from
a more general function ``find_values()``, which searches for a given
tag in both loops and pairs.

C++
---
The C++ signature of ``find_values`` is::

  Column Block::find_values(const std::string& tag)

``Column`` has a few member functions::

  // Number of rows in the loop. 0 means that the tag was not found.
  int length() const;

  // Returns pointer to the column name in the DOM.
  // The name is the same as argument to find_loop() or find_values().
  std::string* get_tag();

  // Returns underlying Item (which contains either Pair or Loop).
  Item* item();

  // Returns pointer to the DOM structure containing the whole table.
  Loop* get_loop() const;

  // Get raw value (no bounds checking).
  std::string& operator[](int n);

  // Get raw value (after bounds checking).
  std::string& at(int n);

  // Short-cut for cif::as_string(column.at(n)).
  std::string str(int n) const;

``Column`` also provides support for C++11 range-based ``for``::

  // mmCIF _chem_comp_atom is usually a table, but not always
  for (const std::string &s : block.find_values("_chem_comp_atom.type_symbol"))
    std::cout << s << std::endl;

If the column represents a name-value pair,
``Column::get_loop()`` return ``nullptr``
(and ``Column::get_length()`` returns 1).

Python
------

.. doctest::

  >>> block.find_values('_cell.length_a')  # name-value pair
  <gemmi.cif.Column _cell.length_a length 1>
  >>> block.find_values('_atom_site.Cartn_x')  # column in a loop
  <gemmi.cif.Column _atom_site.Cartn_x length 342>

Column's special method ``__bool__`` tells if the tag was found.
``__len__`` returns the number of corresponding values.
``__iter__``,  ``__getitem__`` and ``__setitem__``
get or set a raw string (i.e. string with quotes, if applicable).

As an example, let us shift all the atoms by 1A in the x direction:

.. doctest::

  >>> col_x = block.find_values('_atom_site.Cartn_x')
  >>> for n, x in enumerate(col_x):
  ...   col_x[n] = str(float(x) + 1)

To get the actual string content (without quotes) one may use
the method ``str``:

.. doctest::

  >>> column = block.find_values('_chem_comp.formula')
  >>> column[7]
  "'H2 O'"
  >>> column.str(7)  # short-cut for cif.as_string(column[7])
  'H2 O'

The tag is accessible (read/write) through the ``tag`` property:

.. doctest::

  >>> column.tag
  '_chem_comp.formula'

If the tag was found in a loop, method ``get_loop`` returns a reference
to this ``Loop`` in the DOM. Otherwise it returns ``None``.

.. doctest::

  >>> column.get_loop()
  <gemmi.cif.Loop 12 x 7>

Table
=====

Usually we want to access multiple columns at once,
so the library has another abstraction (``Table``)
that can be used with multiple tags.

``Table`` is returned by ``Block.find()``.
Like column, it is a lightweight, iterable view of the data,
but it is for querying multiple related tags at the same time.

The first form of ``find()`` takes a list of tags::

  Table Block::find(const std::vector<std::string>& tags)

.. doctest::

  >>> block.find(['_entity_poly_seq.entity_id', '_entity_poly_seq.num', '_entity_poly_seq.mon_id'])
  <gemmi.cif.Table 18 x 3>

Since tags in one loop tend to have a common prefix (category name),
the library provides also a second form that takes the common prefix
as the first argument::

  Table Block::find(const std::string& prefix, const std::vector<std::string>& tags)

  // These two calls are equivalent:

  block.find({"_entity_poly_seq.entity_id", "_entity_poly_seq.num", "_entity_poly_seq.mon_id"});
  block.find("_entity_poly_seq.", {"entity_id", "num", "mon_id"});

.. doctest::

  >>> block.find('_entity_poly_seq.', ['entity_id', 'num', 'mon_id'])
  <gemmi.cif.Table 18 x 3>


Note that ``find`` is not aware of dictionaries and categories,
therefore the category name should end with a separator
(dot for mmCIF files, as shown above).


In the example above, all the tags are required. If one of them is absent,
the returned ``Table`` is empty. Tags (all except the first one) can be marked
as *optional* by adding prefix ``?``::

  Table table = block.find({"_required_tag", "?_optional_tag"})

.. doctest::

  >>> table = block.find(['_required_tag', '?_optional_tag'])

In such case the returned table may contain either one or two columns.
Before accessing column corresponding to an optional tag one must check
if the column exists with ``Table::has_column()`` (or, alternatively,
with equivalent function ``Table::Row::has()`` which will be introduced
later)::

  bool Table::has_column(int n) const

.. doctest::

  >>> block.find('_entity_poly_seq.', ['entity_id', '?num', '?bleh'])
  <gemmi.cif.Table 18 x 3>
  >>> _.has_column(0), _.has_column(1), _.has_column(2)
  (True, True, False)

The ``Table`` has functions to check its shape::

  bool ok() const;  // true if the table is not empty
  size_t width() const;  // number of columns
  size_t length() const;  // number of rows

.. doctest::

  >>> table = block.find('_entity_poly_seq.', ['entity_id', 'num', 'mon_id'])
  >>> # instead of ok() in Python we use __bool__()
  >>> assert table, "table.__bool__() is expected to return True"
  >>> table.width()  # number of columns
  3
  >>> len(table)  # number of rows
  18

If Table's data is in Loop, the Loop class can be accessed using::

  Loop* get_loop();  // nullptr for tag-value pairs

.. doctest::

  >>> table.loop     # None for tag-value pairs
  <gemmi.cif.Loop 18 x 4>

If a prefix was specified when calling find, the prefix length is stored
and the prefix can be retrieved::

  size_t prefix_length;
  std::string get_prefix() const;

.. doctest::

  >>> table.prefix_length
  17
  >>> table.get_prefix()
  '_entity_poly_seq.'

Table also has function ``erase()`` that deletes all tags and data
associated with the table.
See an example in the :ref:`CCD section <ccd_example>` below.


Row-wise access
---------------

Most importantly, ``Table`` provides access to rows (``Table::Row``)
that in turn provide access to value strings::

  Row Table::operator[](int n)  // access Row
  Row Table::at(int n)          // the same but with bounds checking
  // and also begin(), end() and iterator that enable range-for.

  // Returns the first row that has the specified string in the first column.
  Row Table::find_row(const std::string& s)

  // Makes sure that the table has only one row and returns it.
  Row Table::one()

.. doctest::

  >>> table[0]
  <gemmi.cif.Table.Row: 1 1 DG>

  >>> # and of course it's iterable
  >>> for row in table: pass

  >>> # Returns the first row that has the specified string in the first column.
  >>> table.find_row('2')
  <gemmi.cif.Table.Row: 2 1 DSN>

as well as to the tags::

  Row tags();  // pseudo-row that contains tags

.. doctest::

  >>> table.tags
  <gemmi.cif.Table.Row: _entity_poly_seq.entity_id _entity_poly_seq.num _entity_poly_seq.mon_id>


``Table::Row`` has functions for accessing the values::

  // Get raw value.
  std::string& Table::Row::operator[](int n)  // no bounds checking
  std::string& Table::Row::at(int n)          // with bounds checking
  // and also begin(), end(), iterator, const_supports iterators.

.. doctest::

  >>> for row in table: print(row[-1], end=',')
  DG,DC,DG,DT,DA,DC,DG,DC,DSN,ALA,N2C,NCY,MVA,DSN,ALA,NCY,N2C,MVA,
  >>>
  >>> row = table[9]
  >>> for value in row: print(value, end=',')
  2,2,ALA,
  >>> row[2]
  'ALA'
  >>> row[-1]  # the same
  'ALA'
  >>> row.get(2) # the same, but returns None instead of IndexError
  'ALA'
  >>> row['mon_id']  # the same, but slightly slower than numeric index
  'ALA'
  >>> row['_entity_poly_seq.mon_id']  # the same
  'ALA'

and a few convenience functions, including::

  size_t Table::Row::size() const           // the width of the table
  std::string Table::Row::str(int n) const  // short-cut for cif::as_string(row.at(n))
  bool Table::Row::has(int n) const         // the same as Table::has_column(n)

.. doctest::

  >>> len(row)    # the same as table.width()
  3
  >>> row.str(2)  # if the value is in quotes, it gets un-quoted
  'ALA'
  >>> row.has(2)  # the same as table.has_column(2)
  True

and a property::

  int row_index

.. doctest::

  >>> row.row_index
  9

----

We can append a row to Table (function ``Table::append_row``):

.. doctest::

  >>> table.append_row(['3', '4', 'new'])
  >>> table[-1]
  <gemmi.cif.Table.Row: 3 4 new>
  >>> _.row_index
  18

move a row to a different position:

.. doctest::

  >>> table.move_row(old_pos=-1, new_pos=0)  # move the last row to the front
  >>> table[0]
  <gemmi.cif.Table.Row: 3 4 new>

and remove a row from Table (function ``Table::remove_row``):

.. doctest::

  >>> table.remove_row(18)  # or: del table[18]

To efficiently remove multiple consecutive rows use
C++ function ``Table::remove_row`` or in Python:

.. doctest::

  >>> del table[12:15]

As is usual with any containers (in both Python and C++)
if you want to remove items while iterating over them,
it's better to iterate backward.
Here is an example that removes atoms with zero occupancy:

.. doctest::

  >>> doc = cif.read('../tests/3dg1_final.cif')
  >>> atom_table = doc[0].find(['_atom_site.occupancy'])
  >>> for i in range(len(atom_table)-1, -1, -1):
  ...   if float(atom_table[i][0]) == 0:
  ...     del atom_table[i]
  ...
  >>> doc.write_file('out.cif')

Individual tags and values can also be modified.
As an example, let us swap two tag names
(these two tend to have identical values, so no one will notice):

.. literalinclude:: code/cif_cc.cpp
   :language: cpp
   :lines: 32-33

.. doctest::

  >>> tags = block.find('_atom_site.', ['label_atom_id', 'auth_atom_id']).tags
  >>> tags[0], tags[1] = tags[1], tags[0]

Column-wise access
------------------

``Table`` gives also access to columns, represented by the previously
introduced ``Column``::

    Column Table::column(int index)

    // alternatively, specify tag name
    Column Table::find_column(const std::string& tag)

.. doctest::

  >>> table.column(0)
  <gemmi.cif.Column _entity_poly_seq.entity_id length 15>
  >>> table.find_column('_entity_poly_seq.mon_id')
  <gemmi.cif.Column _entity_poly_seq.mon_id length 15>

If the table is created in a function that uses prefix,
the prefix can be omitted in ``find_column``::

  Table t = block.find("_entity_poly_seq.", {"entity_id", "num", "mon_id"});
  Column col = t.find_column(2);
  // is equivalent to
  Column col = t.find_column("_entity_poly_seq.mon_id");
  // is equivalent to
  Column col = t.find_column("mon_id");

.. doctest::

  >>> table.find_column('mon_id')
  <gemmi.cif.Column _entity_poly_seq.mon_id length 15>

C++ note:
both ``Column`` and ``Table::Row`` have functions ``begin()`` and ``end()``
in const and non-const variants, returning ``iterator`` and
``const_iterator`` types, respectively. These types satisfy requirements
of the BidirectionalIterator concept.
Conversely, the iterator over the rows of ``Table`` is a minimalistic
structure -- just enough get the range-for work.


mmCIF categories
----------------

mmCIF files group data into categories. All mmCIF tags have a dot
(e.g. ``_entry.id``) and the category name is the part before the dot.

C++
~~~

We have two functions to work with categories.
One returns a list of all categories in the block::

  std::vector<std::string> Block::get_mmcif_category_names() const

The other returns a ``Table`` with all tags (and values) belonging to
the specified category::

  Table Block::find_mmcif_category(std::string cat)


Python
~~~~~~

Python bindings have the same two functions:

.. doctest::

  >>> block.get_mmcif_category_names()[:3]
  ['_entry.', '_audit_conform.', '_database_2.']
  >>> block.find_mmcif_category('_entry.')
  <gemmi.cif.Table 1 x 1>
  >>> _.tags[0], _[0][0]
  ('_entry.id', '1PFE')
  >>> cat = block.find_mmcif_category('_database_2.')
  >>> cat
  <gemmi.cif.Table 4 x 2>
  >>> list(cat.tags)
  ['_database_2.database_id', '_database_2.database_code']
  >>> for row in cat:
  ...   print('%s: %s' % tuple(row))
  PDB: 1PFE
  NDB: DD0057
  RCSB: RCSB019291
  WWPDB: D_1000019291
  >>> cat[3][1]
  'D_1000019291'

Additionally, two Python-specific functions: ``get_mmcif_category``
and ``set_mmcif_category`` translate between an mmCIF category and
Python dictionary:

.. doctest::

  >>> block.get_mmcif_category('_entry.')
  {'id': ['1PFE']}
  >>> sorted(block.get_mmcif_category('_database_2.').keys())
  ['database_code', 'database_id']

``?`` and ``.`` are translated to ``None`` and ``False``.
To disable this translation and get "raw" strings, add argument ``raw=True``.

.. doctest::

  >>> default = block.get_mmcif_category('_software')
  >>> raw = block.get_mmcif_category('_software', raw=True)
  >>> for name in ['name', 'version', 'citation_id']:
  ...     print(default[name][0], raw[name][0])
  ...
  EPMR EPMR
  False .
  None ?

``set_mmcif_category()`` takes a category name and a dictionary (that
maps tags to lists) and creates or replaces the corresponding category:

.. doctest::

  >>> ocean_data = {'id': [1, 2, 3], 'name': ['Atlantic', 'Pacific', 'Indian']}
  >>> block.set_mmcif_category('_ocean', ocean_data)

It also takes the optional ``raw`` parameter (default: ``False``).
Specify ``raw=True`` if the values are already quoted, otherwise
they will be quoted twice.

.. doctest::

  >>> block.set_mmcif_category('_raw', {'a': ['?', '.', "'a b'"]}, raw=True)
  >>> list(block.find_values('_raw.a'))
  ['?', '.', "'a b'"]
  >>> block.set_mmcif_category('_nop', {'a': ['?', '.', "'a b'"]}, raw=False)
  >>> list(block.find_values('_nop.a'))
  ["'?'", "'.'", '"\'a b\'"']
  >>> block.set_mmcif_category('_ok', {'a': [None, False, 'a b']}, raw=False)
  >>> list(block.find_values('_ok.a'))
  ['?', '.', "'a b'"]


Finally, we have a variant of ``init_loop`` for working with mmCIF categories:

.. doctest::

  >>> loop = block.init_mmcif_loop('_ocean.', ['id', 'name'])
  >>> loop.add_row(['1', 'Atlantic'])

The subtle difference from generic ``init_loop`` is that if the block
has other name-value pairs in the same category (say, ``_ocean.depth 8.5``)
``init_loop`` leaves them untouched, but ``init_mmcif_loop`` removes them.
Additionally, like in other ``_mmcif_`` functions, the trailing dot
in the category name may be omitted (but the leading underscore is required).


JSON
====

``cif::Document`` can be stored in a JSON format, and it can be read from
JSON file. This is in general true about CIF files - their content can
be converted to JSON and back. In gemmi we have a number of options to
customize the translation. In particular, both mmCIF and CIF-JSON flavours
are supported.
More details about the flavours are given in the description of
:ref:`gemmi convert <json>`.

C++
---

Header ``gemmi/to_json.hpp`` provides code for serializing
``cif::Document`` as JSON.

Such JSON files can be read back into the ``cif::Document`` structure
using function from ``gemmi/json.hpp``.

Python
------

``Document.as_json()`` returns the document serialized in JSON string.
To output mmJSON add argument ``mmjson=True``.

mmJSON (possibly gzipped) can be read using function ``cif.read_mmjson()``.
In addition, the function ``cif.read()`` will also read mmJSON format
if the file name ends with ``.json`` or ``.json.gz``.


Design choices
==============

Parser
------

Parsing formal languages is a well-researched topic in computer science.
The first versions of lex and yacc - popular tools that generate lexical
analyzers and parsers - were written in 1970's. Today many tools exist
to translate grammar rules into C/C++ code.
On the other hand many compilers and high-profile tools use hand-coded
parsers - as it is more flexible.

Looking at other STAR and CIF parsers -- some use parser generators
(COD::CIF::Parser and starlib2 use yacc/bison, iotbx.cif uses Antlr3),
others are hand-coded.

I had experience with flex/bison and Boost.Spirit
(and I wanted to try also Lemon and re2c)
but I decided to use PEGTL for this task (and I'm very happy with this choice).
I was convinced by the
`TAOC++ JSON <https://github.com/taocpp/json>`_
parser that is based on PEGTL and has a good balance of simplicity
and performance.

`PEGTL <https://github.com/taocpp/PEGTL/>`_ is a C++ library (not a generator)
for creating PEG parsers.
PEG stands for Parsing Expression Grammar -- a simpler approach than
the traditional Context Free Grammar.

As a result, our parser depends on a third-party (header-only) library,
but the parser itself is pretty simple.

Data structures
---------------

The next thing is how we store the data read from file.
We decided to rely on the C++ standard library where we can.

Generally, storage of such data involves (in C++ terms) some containers
and a union/variant type for storing values of mixed types.

We use primarily ``std::vector`` as a container.

A custom structure ``Item`` with (unrestricted) union is used as
a variant-like class.

Strings are stored in ``std::string`` and it is fast enough.
Mainstream C++ standard libraries have short string optimization (SSO)
for up to 15 or 22 characters, which covers most of the values in mmCIF files.

Performance
===========

Gemmi has `the fastest <https://github.com/project-gemmi/mmcif-benchmark>`_
open-source CIF parser (at least in the hands of the author).
While further improvement would be possible (some JSON parsers are
`much faster <https://github.com/project-gemmi/benchmarking-json>`_
and parsing CIF and JSON is not that different),
it is not a priority.

Directory walking
=================

Many of the utilities and examples developed for this project
work with archives of CIF files such as wwPDB or COD.
To make it easier to iterate over all CIF files in a directory tree
we provide a class ``CifWalk``.

C++
---

.. code-block:: cpp

  #include <gemmi/dirwalk.hpp>

  // ...
  // throws std::runtime_error if top_dir doesn't exist
  for (const std::string& cif_file : gemmi::CifWalk(top_dir)) {
    cif::Document doc = cif::read(gemmi::MaybeGzipped(cif_file));
    // ...
  }

This header file contains also a more general ``DirWalk`` class,
and classes specific to macromolecular files (``PdbWalk``, ``MmCifWalk``,
``CoorFileWalk``). The file type of each file is guessed from
the file name.

Python
------

Since Python comes with the os.walk() function for iterating over files
and directories, this functionality is less important here.
Anyway, we provide bindings for CifWalk:

.. doctest::

  >>> import gemmi
  >>> list(gemmi.CifWalk('../tests/'))[:2]
  ['../tests/list/mon_lib_list.cif', '../tests/1011031.cif']

We also have Python bindings for ``CoorFileWalk`` that picks macromolecular
coordinate files.

----

When the user has no permission to read one of the traversed directories,
the functions above raise an error (std::runtime_error / RuntimeError).

All these directory walking functions are powered by the
`tinydir <https://github.com/cxong/tinydir>`_ library
(a single-header library copied into ``include/gemmi/third_party``).

.. _cif_examples:

Examples
========

The examples here use C++11 or Python 2.7/3.x.
Full working code code can be found in the examples__ directory.

If you have the Python ``gemmi`` module installed you should also have
the Python examples -- try ``python -m gemmi-examples`` if not sure
where they are.

The examples below can be run on one or more PDBx/mmCIF files.
The ones that perform PDB-wide analysis are meant to be run on a
`local copy <https://www.wwpdb.org/download/downloads>`_ of the mmCIF
archive (30GB+ gzipped, don't uncompress!).

__ https://github.com/project-gemmi/gemmi/tree/master/examples

mmCIF to XYZ
------------

To start with something simple, let us convert mmCIF
to the `XYZ format <https://en.wikipedia.org/wiki/XYZ_file_format>`_:

.. literalinclude:: code/cif_cc.cpp
   :language: cpp
   :lines: 15-27


.. _auth_label_example:

auth vs label
-------------

When you look at the list of atoms (``_atom_site.*``) in mmCIF files
some columns seem to be completely redundant. Are they?

.. raw:: html

    <table style="font-size:11px; border-spacing:5px 0; margin-top:60px; width:100%; border-collapse:separate;">
    <colgroup>
    <col span="3">
    <col style="background-color:#fc8">
    <col>
    <col style="background-color:#ffa">
    <col style="background-color:#aff">
    <col>
    <col style="background-color:#bd8">
    <col span="12">
    <col style="background-color:#bd8">
    <col style="background-color:#ffa">
    <col style="background-color:#aff">
    <col style="background-color:#fc8">
    <col>
    </colgroup>
    <tr style="font-size:10px;">
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">group_PDB</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">type_symbol</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">label_atom_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">label_alt_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">label_comp_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">label_asym_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">label_entity_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">label_seq_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">pdbx_PDB_ins_code</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">Cartn_x</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">Cartn_y</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">Cartn_z</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">occupancy</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">B_iso_or_equiv</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">Cartn_x_esd</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">Cartn_y_esd</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">Cartn_z_esd</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">occupancy_esd</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">B_iso_or_equiv_esd</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">pdbx_formal_charge</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">auth_seq_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">auth_comp_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">auth_asym_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">auth_atom_id</th>
    <th style="max-width:12px; transform-origin:left; transform:rotate(315deg);">pdbx_PDB_model_num</th>
    <tr>
    <td>ATOM</td><td>1</td><td>N</td><td>N</td><td>.</td><td>GLY</td><td>A</td>
    <td>1</td><td>1</td><td>?</td><td>-9.009</td><td>4.612</td><td>6.102</td>
    <td>1.00</td><td>16.77</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>1</td><td>GLY</td><td>A</td><td>N</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>2</td><td>C</td><td>CA</td><td>.</td><td>GLY</td><td>A</td>
    <td>1</td><td>1</td><td>?</td><td>-9.052</td><td>4.207</td><td>4.651</td>
    <td>1.00</td><td>16.57</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>1</td><td>GLY</td><td>A</td><td>CA</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>3</td><td>C</td><td>C</td><td>.</td><td>GLY</td><td>A</td>
    <td>1</td><td>1</td><td>?</td><td>-8.015</td><td>3.140</td><td>4.419</td>
    <td>1.00</td><td>16.16</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>1</td><td>GLY</td><td>A</td><td>C</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>4</td><td>O</td><td>O</td><td>.</td><td>GLY</td><td>A</td>
    <td>1</td><td>1</td><td>?</td><td>-7.523</td><td>2.521</td><td>5.381</td>
    <td>1.00</td><td>16.78</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>1</td><td>GLY</td><td>A</td><td>O</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>5</td><td>N</td><td>N</td><td>.</td><td>ASN</td><td>A</td>
    <td>1</td><td>2</td><td>?</td><td>-7.656</td><td>2.923</td><td>3.155</td>
    <td>1.00</td><td>15.02</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>2</td><td>ASN</td><td>A</td><td>N</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>6</td><td>C</td><td>CA</td><td>.</td><td>ASN</td><td>A</td>
    <td>1</td><td>2</td><td>?</td><td>-6.522</td><td>2.038</td><td>2.831</td>
    <td>1.00</td><td>14.10</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>2</td><td>ASN</td><td>A</td><td>CA</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>7</td><td>C</td><td>C</td><td>.</td><td>ASN</td><td>A</td>
    <td>1</td><td>2</td><td>?</td><td>-5.241</td><td>2.537</td><td>3.427</td>
    <td>1.00</td><td>13.13</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>2</td><td>ASN</td><td>A</td><td>C</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>8</td><td>O</td><td>O</td><td>.</td><td>ASN</td><td>A</td>
    <td>1</td><td>2</td><td>?</td><td>-4.978</td><td>3.742</td><td>3.426</td>
    <td>1.00</td><td>11.91</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>2</td><td>ASN</td><td>A</td><td>O</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>9</td><td>C</td><td>CB</td><td>.</td><td>ASN</td><td>A</td>
    <td>1</td><td>2</td><td>?</td><td>-6.346</td><td>1.881</td><td>1.341</td>
    <td>1.00</td><td>15.38</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>2</td><td>ASN</td><td>A</td><td>CB</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>10</td><td>C</td><td>CG</td><td>.</td><td>ASN</td><td>A</td>
    <td>1</td><td>2</td><td>?</td><td>-7.584</td><td>1.342</td><td>0.692</td>
    <td>1.00</td><td>14.08</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>2</td><td>ASN</td><td>A</td><td>CG</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>11</td><td>O</td><td>OD1</td><td>.</td><td>ASN</td><td>A</td>
    <td>1</td><td>2</td><td>?</td><td>-8.025</td><td>0.227</td><td>1.016</td>
    <td>1.00</td><td>17.46</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>2</td><td>ASN</td><td>A</td><td>OD1</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>12</td><td>N</td><td>ND2</td><td>.</td><td>ASN</td><td>A</td>
    <td>1</td><td>2</td><td>?</td><td>-8.204</td><td>2.155</td><td>-0.169</td>
    <td>1.00</td><td>11.72</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>2</td><td>ASN</td><td>A</td><td>ND2</td><td>1</td>
    </tr><tr>
    <td colspan="25">...</td>
    </tr><tr>
    <td>ATOM</td><td>58</td><td>O</td><td>OH</td><td>.</td><td>TYR</td><td>A</td>
    <td>1</td><td>7</td><td>?</td><td>3.766</td><td>0.589</td><td>10.291</td>
    <td>1.00</td><td>14.39</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>7</td><td>TYR</td><td>A</td><td>OH</td><td>1</td>
    </tr><tr>
    <td>ATOM</td><td>59</td><td>O</td><td>OXT</td><td>.</td><td>TYR</td><td>A</td>
    <td>1</td><td>7</td><td>?</td><td>11.358</td><td>2.999</td><td>7.612</td>
    <td>1.00</td><td>17.49</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>7</td><td>TYR</td><td>A</td><td>OXT</td><td>1</td>
    </tr><tr>
    <td>HETATM</td><td>60</td><td>O</td><td>O</td><td>.</td><td>HOH</td><td>B</td>
    <td>2</td><td>.</td><td>?</td><td>-6.471</td><td>5.227</td><td>7.124</td>
    <td>1.00</td><td>22.62</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>8</td><td>HOH</td><td>A</td><td>O</td><td>1</td>
    </tr><tr>
    <td>HETATM</td><td>61</td><td>O</td><td>O</td><td>.</td><td>HOH</td><td>B</td>
    <td>2</td><td>.</td><td>?</td><td>10.431</td><td>1.858</td><td>3.216</td>
    <td>1.00</td><td>19.71</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>9</td><td>HOH</td><td>A</td><td>O</td><td>1</td>
    </tr><tr>
    <td>HETATM</td><td>62</td><td>O</td><td>O</td><td>.</td><td>HOH</td><td>B</td>
    <td>2</td><td>.</td><td>?</td><td>-11.286</td><td>1.756</td><td>-1.468</td>
    <td>1.00</td><td>17.08</td><td>?</td><td>?</td><td>?</td><td>?</td><td>?</td>
    <td>?</td><td>10</td><td>HOH</td><td>A</td><td>O</td><td>1</td>
    </tr><tr>
    </tr>
    </table>

It is hard to manually find an example
where ``_atom_site.auth_atom_id`` differs from ``_atom_site.label_atom_id``, or
where ``_atom_site.auth_comp_id`` differs from ``_atom_site.label_comp_id``.
So here is a small C++ program:

.. literalinclude:: ../examples/auth_label.cpp

We compile it, run it, and come back after an hour:

.. code-block:: none

    $ g++-6 -O2 -Iinclude examples/auth_label.cpp -lstdc++fs -lz
    $ ./a.out pdb_copy/mmCIF
    3D3W: atom_id  O1 -> OD
    1TNI: atom_id  HN2 -> HN3
    1S01: comp_id  CA -> UNL
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    2KI7: atom_id  H -> H1
    4E1U: atom_id  H101 -> H103
    4WH8: atom_id  H3 -> H391
    4WH8: atom_id  H4 -> H401
    3V2I: atom_id  UNK -> UNL
    1AGG: atom_id  H3 -> H
    1AGG: atom_id  H3 -> H
    1AGG: atom_id  H3 -> H

So, as of April 2017, only a single author's residue name was changed,
and atom names were changed in 7 PDB entries.

Amino acid frequency
--------------------

.. highlight:: python

After seeing a `paper <https://doi.org/10.1093/nar/gkw978>`_
in which amino acid frequency was averaged over 5000+ proteomes
(Ala 8.76%, Cys 1.38%, Asp 5.49%, etc)
we may want to compare it with the frequency in the PDB database.
So we write a little script

.. literalinclude:: ../examples/aafreq.py

We can run this script on our copy of the PDB database
(mmCIF format) and get such an output:

.. code-block:: none

    200L ALA:17 LEU:15 LYS:13 ARG:13 THR:12 ASN:12 GLY:11 ILE:10 ASP:10 VAL:9 GLU:8 SER:6 TYR:6 GLN:5 PHE:5 MET:5 PRO:3 TRP:3 HIS:1
    ...
    4ZZZ LEU:40 LYS:33 SER:30 ASP:25 GLY:25 ILE:25 VAL:23 ALA:19 PRO:18 GLU:18 ASN:17 THR:16 TYR:16 GLN:14 ARG:11 PHE:10 HIS:9 MET:9 CYS:2 TRP:2
    TOTAL LEU:8.90% ALA:7.80% GLY:7.34% VAL:6.95% GLU:6.55% SER:6.29% LYS:6.18% ASP:5.55% THR:5.54% ILE:5.49% ARG:5.35% PRO:4.66% ASN:4.16% PHE:3.88% GLN:3.77% TYR:3.45% HIS:2.63% MET:2.14% CYS:1.38% TRP:1.35%

On my laptop it takes about an hour, using a single core.
Most of this hour is spent on tokenizing the CIF files and copying
the content into a DOM structure, what could be largely avoided given
that we use only sequences not atoms.
But it is not worth to optimize one-off scripts.
The same goes for using multiple processor cores.

Custom PDB search
-----------------

We may need to go through a local copy of the PDB archive to find entries
according to criteria that cannot be queried in RCSB/PDBe/PDBj web interfaces.
For example, if for some reason we need PDB entries with large number
of anisotropic B-factors, we may write a quick script:

.. literalinclude:: ../examples/simple_search.py

and then run it for an hour or so.

.. code-block:: none

    $ ./examples/simple_search.py pdb_copy/mmCIF
    5AEW 60155
    5AFI 98325
    3AIB 52592
    ...

Search PDB by elements
----------------------

.. image:: img/periodic-table-thumb.png
   :align: right
   :scale: 100
   :target: https://project-gemmi.github.io/periodic-table/

Let say we want to be able to search the PDB by specifying a set of elements
present in the model. First we write down elements present in each
PDB entry::

    block = cif.read(path).sole_block()
    elems = set(block.find_loop("_atom_site.type_symbol"))
    print(name + ' ' + ' '.join(elems))

This example ended up overdone a bit and it was put into a
`separate repository <https://github.com/project-gemmi/periodic-table>`_.

Here is a demo: `<https://project-gemmi.github.io/periodic-table/>`_


Solvent content vs resolution
-----------------------------

.. |Vm| replace:: *V*\ :sub:`M`
.. |Vs| replace:: *V*\ :sub:`S`
.. |dmin| replace:: *d*\ :sub:`min`

Let say that we would like to generate a plot of solvent content
as a function of |dmin|, similar to the plots
by C. X. Weichenberger and B. Rupp
in `Acta Cryst D <https://www.ncbi.nlm.nih.gov/pubmed/24914969>`_
and `CNN <http://www.phenix-online.org/newsletter/CCN_2015_01.pdf#page=14>`_),
but less smoothed and with duplicate entries included.

In macromolecular crystallography solvent content is conventionally
estimated as:

    |Vs| = 1 -- 1.230 / |Vm|

where |Vm| is Matthews coefficient defined as |Vm|\ =\ *V*\ /\ *m*
(volume of the asymmetric unit over the molecular weight of all
molecules in this volume).

Weichenberger and Rupp calculated |Vs| themselves, but we will simply
use the values present in mmCIF files.
So first we extract |Vm|, |Vs| and |dmin|,
as well as number of DNA/RNA chains (protein-only entries will have 0 here),
deposition date and group ID (which will be explained later).

.. literalinclude:: ../examples/matthews.py
   :pyobject: gather_data

After a few checks (see :file:`examples/matthews.py`) we may decide to use
only those PDB entries in which |Vm| and |Vs| are consistent.
To make things more interesting we plot separately depositions from before
and after 1/1/2015.

.. figure:: img/solvent-content-old.png
   :alt: A plot of solvent content vs resolution. PDB entries up to 2014.
   :align: center
   :scale: 100

   About 90,000 PDB entries (up to 2014)
   plotted as intentionally undersmoothed kernel density estimate.
   The code used to produce this plot is in :file:`examples/matthews.py`.

Ripples in the right subplot show that in many entries |Vs|
is reported as integer, so we should calculate it ourselves
(like Weichenberger & Rupp) for better precision.
Ripples in the top plot show that we should use a less arbitrary metric
of resolution than |dmin| (but it's not so easy).
Or we could just smooth them out by changing parameters of this plot.

.. figure:: img/solvent-content-recent.png
   :alt: A plot of solvent content vs resolution. PDB entries since 2015.
   :align: center
   :scale: 100

   About 17,000 PDB entries (2015 - April 2017)
   plotted as intentionally undersmoothed kernel density estimate.
   The code used to produce this plot is in :file:`examples/matthews.py`.

On the left side of the yellow egg you can see dark stripes
caused by *group depositions*, which were introduced by PDB in 2016.
They came from two European high-throughput beamlines and
serve as an illustration of how automated software can analyze hundreds
of similar samples (fragment screening) and submit them quickly to the PDB.

We can easily filter out group depositions -- either using the group IDs
extracted from mmCIF files (we do this for pdb-stats_)
or, like W&B, by excluding redundant entries based on the unit cell and |Vm|.

.. _pdb-stats: https://project-gemmi.github.io/pdb-stats/

Not all the dark spots are group depositions.
For example, the one at |Vs|\ ≈66.5%, |dmin| 2.5-3A is proteasome 20S
studied over years by Huber *et al*, with dozens PDB submissions.

Weights
-------

Let say we would like to verify consistency of molecular weights.
First, let us look at chem_comp tables:

.. code-block:: none

    loop_
    _chem_comp.id
    _chem_comp.type
    _chem_comp.mon_nstd_flag
    _chem_comp.name
    _chem_comp.pdbx_synonyms
    _chem_comp.formula
    _chem_comp.formula_weight
    ALA 'L-peptide linking' y ALANINE         ?                 'C3 H7 N O2'     89.093
    ARG 'L-peptide linking' y ARGININE        ?                 'C6 H15 N4 O2 1' 175.209
    ASN 'L-peptide linking' y ASPARAGINE      ?                 'C4 H8 N2 O3'    132.118
    ASP 'L-peptide linking' y 'ASPARTIC ACID' ?                 'C4 H7 N O4'     133.103
    CYS 'L-peptide linking' y CYSTEINE        ?                 'C3 H7 N O2 S'   121.158
    EDO non-polymer         . 1,2-ETHANEDIOL  'ETHYLENE GLYCOL' 'C2 H6 O2'       62.068
    ...

We expect that by using molecular weights of elements and a simple arithmetic
we can recalculate ``_chem_comp.formula_weight`` from ``_chem_comp.formula``.
The full code is in :file:`examples/weights.py``.
It includes a function that converts ``'C2 H6 O2'`` to ``{C:2, H:6, O:2}``.
Here we only show the few lines of code that sum the element weights
and compare the result:

.. literalinclude:: ../examples/weight.py
   :pyobject: check_chem_comp_formula_weight

This script prints differences above 0.1 u:

.. code-block:: none

    3A1R DOD  D2 O                20.028 -    18.015 = +2.013
    5AI2 D8U  D 1                  2.014 -         ? = +nan
    5AI2 DOD  D2 O                20.028 -    18.015 = +2.013
    4AR3 D3O  O 1                 15.999 -    22.042 = -6.043
    4AR4 D3O  O 1                 15.999 -    22.042 = -6.043
    4AR5 DOD  D2 O                20.028 -    18.015 = +2.013
    4AR6 DOD  D2 O                20.028 -    18.015 = +2.013
    4B1A K3G  MO12 O40 P 1      1822.350 -  1822.230 = +0.120
    4BVO E43  H2 O40 W12 6      2848.072 -  2848.192 = -0.120
    ...
    5FHW HFW  Hf O61 P2 W17     4341.681 -  4343.697 = -2.016
    ...

The differences are few and minor.
We see a few PDB entries with the weight of D\ :sub:`2`\ O
set to the weight H\ :sub:`2`\ O. The second line shows missing weight.
The differences +0.12 and -0.12 next to Mo12 and W12 probably come from
the 0.01u difference in the input masses of the elements.
In two entries D3O is missing D in the formula, and -2.016 in HFW
suggests two missing hydrogens.

Now let us try to re-calculate ``_entity.formula_weight`` from the chem_comp
weights and the sequence.
The PDB software calculates it as a sum of components in the chain,
minus the weight of N-1 waters.
In case of nucleic acids also PO\ :sub:`2` is subtracted
(why not PO\ :sub:`3`\ ? -- to be checked).
And in case of microheterogeneity only the main conformer is taken into
account. As the PDB software uses single precision for these computations,
we ignore differences below 0.003%, which we checked to be enough to
account for numerical errors.

.. literalinclude:: ../examples/weight.py
   :pyobject: check_entity_formula_weight

Running this script on a local copy of the PDB database prints 26 lines,
and the difference is always (except for 4PMN) the mass of PO\ :sub:`2`
showing that we have not fully reproduced the rule when to subtract this group.

.. code-block:: none

    ...
    4D67 entity_id: 44 1625855.77 - 1625917.62 =  -61.855
    1HZS entity_id:  1    1733.88 -    1796.85 =  -62.973
    2K4G entity_id:  1    2158.21 -    2221.18 =  -62.974
    3MBS entity_id:  1    2261.35 -    2324.33 =  -62.974
    1NR8 entity_id:  2    2883.07 -    2946.05 =  -62.974
    3OK2 entity_id:  1    3417.14 -    3354.17 =  +62.968
    ...

Disulfide bonds
---------------

If we were curious what residues take part in disulfide bonds we could
write a little script that inspects annotation in the _struct_conn category.
But to show something else, here we will use ``gemmi grep``, a little
utility that is documented in a :ref:`separate section <grep>`.

First we try how to extract interesting data from a single entry:

.. code-block:: console

    $ gemmi grep --delimiter=' ' _struct_conn.conn_type_id \
    > -a _struct_conn.ptnr1_label_comp_id -a _struct_conn.ptnr1_label_atom_id \
    > -a _struct_conn.ptnr2_label_comp_id -a _struct_conn.ptnr2_label_atom_id \
    > 5CBL
    5CBL disulf CYS SG BME S2
    5CBL covale ILE CD1 BME C2

Then we pipe the output through Unix shell utilities.
``| grep disulf`` limits the output to the disulfide bonds.
``| awk '{ print $3, $4 "\n" $5, $6 }'`` changes each line into two;
the first output line above becomes:

.. code-block:: none

    CYS SG
    BME S2

Then we run it on the whole PDB archive, sort, count and print the
statistics. The complete command is:

.. code-block:: console

    $ gemmi grep --delimiter=' ' _struct_conn.conn_type_id \
    > -a _struct_conn.ptnr1_label_comp_id -a _struct_conn.ptnr1_label_atom_id \
    > -a _struct_conn.ptnr2_label_comp_id -a _struct_conn.ptnr2_label_atom_id \
    > /hdd/mmCIF \
    > | grep disulf | awk '{ print $3, $4 "\n" $5, $6 }' \
    > | sort | uniq -c | sort -nr
       367980 CYS SG
        274 DCY SG
         28 BME S2
         23 SO4 S
         19 CY3 SG
         14 MET SD
         13 MTN S1
         12 V1A S3
         10 MPT SG
          8 NCY SG
          7 LE1 SG
          7 CSX SG
          6 CSO SG
          5 GSH SG2
          5 DTT S1
          4 SC2 SG
          4 MRG S24
          3 H2S S
          3 DTT S4
          3 1WD S7
          2 P8S S1
          2 MTE S2'
          2 MTE S1'
          2 EPE S
          2 DHL SG
          2 DCD S1
          2 CSD SG
          2 COM S1
          2 6LN S2
          2 6LN S1
          2 4K3 S4
          2 3C7 S01
          2 2ON S2
          1 SCN S
          1 RXR SD
          1 RXR S10
          1 MEE S
          1 G47 SG
          1 COA S1P
          1 6ML S1
          1 5O8 SBH

And what other bond types are annotated in ``_struct_conn``?

.. code-block:: console

    $ gemmi grep -O -b _struct_conn.conn_type_id /hdd/mmCIF/ | sort | uniq -c
     501851 covale
     184231 disulf
    4035115 hydrog
    1394732 metalc


mmJSON-like data
----------------

Gemmi has a built-in support for mmJSON and comes with
a :ref:`converter <json>` utility, but just as an exercise
let us convert mmJSON to mmCIF in Python:

.. literalinclude:: ../examples/from_json.py
   :lines: 6-

.. _ccd_example:

Chemical Component Dictionary
-----------------------------

For something a bit different, let us look at the data from
the :file:`components.cif` from `CCD <https://www.wwpdb.org/data/ccd>`_.
This file describes all the monomers (residues,
ligands, solvent molecules) from the PDB entries.

As an exercise, let us check heavy atoms in selenomethionine:

.. doctest::

    >>> from gemmi import cif
    >>> cif.read('components.cif')  # doctest: +ELLIPSIS
    <gemmi.cif.Document with ... blocks (000, 001, 002...)>
    >>> _.find_block('MSE')
    <gemmi.cif.Block MSE>
    >>> _.find('_chem_comp_atom.', ['atom_id', 'type_symbol'])
    <gemmi.cif.Table 20 x 2>
    >>> [':'.join(a) for a in _ if a[1] != 'H']
    ['N:N', 'CA:C', 'C:C', 'O:O', 'OXT:O', 'CB:C', 'CG:C', 'SE:SE', 'CE:C']

One may wonder what is the heaviest CCD component:

.. doctest::

    >>> ccd = cif.read('components.cif')
    >>> max(ccd, key=lambda b: float(b.find_value('_chem_comp.formula_weight')))
    <gemmi.cif.Block WO2>
    >>> _.find_value('_chem_comp.formula')
    '"O62 P2 W18"'

Or which one has the largest number of heavy atoms:

.. doctest::

    >>> el_tag = '_chem_comp_atom.type_symbol'
    >>> max(ccd, key=lambda b: sum(el != 'H' for el in b.find_values(el_tag)))
    <gemmi.cif.Block X12>
    >>> _.find_value('_chem_comp.formula')
    '"C96 H153 N31 O25"'

The :file:`components.cif` file is big, so we may want to split it into
multiple file. As an example, here we create a new document with only
one block, and we write it to a file:

.. doctest::

    >>> block = ccd['X12']
    >>> d = cif.Document()
    >>> d.add_copied_block(block)
    <gemmi.cif.Block X12>
    >>> d.write_file('X12.cif')

Alternatively, we could do:

.. doctest::

    >>> block.write_file('X12.cif')

In the next example we delete things we do not need.
Let us write only components on letter A to a new file.
Additionally, we remove the descriptor category:

.. doctest::

    >>> ccd = cif.read('components.cif')
    >>> len(ccd)  #doctest: +SKIP
    25219
    >>> to_be_deleted = [n for n, block in enumerate(ccd) if block.name[0] != 'A']
    >>> for index in reversed(to_be_deleted):
    ...   del ccd[index]
    ...
    >>> len(ccd)  #doctest: +SKIP
    991
    >>> for block in ccd:
    ...     block.find_mmcif_category('_pdbx_chem_comp_descriptor.').erase()
    ...
    >>> ccd.write_file('A.cif')

The examples here present low-level, generic handling of CCD as a CIF file.
If we would need to look into coordinates, it would be better to use
higher level representation of the chemical component, which is provided
by :ref:`gemmi::ChemComp <chemcomp>`.
