Quickstart
==========

The most basic usage case is reading a single DICOM image (*.dcm* file) as
an :class:`~dicom_parser.image.Image` instance.

.. code:: python

    from dicom_parser import Image

    # Create a DICOM Image object
    image = Image('/path/to/dicom/file.dcm')


---------------------------------------------------------------


Coversion to Python's native types
----------------------------------

`dicom_parser` provides *dict*-like access to the parsed values of the
`header <https://dcm4che.atlassian.net/wiki/spaces/d2/pages/1835038/A+Very+Basic+DICOM+Introduction>`_'s
data-elements. The raw values as read by `pydicom <https://pydicom.github.io/>`_
remain accessible through the *raw* attribute.

Examples
........

Decimal String (DS) to *float* using the :class:`~dicom_parser.header.Header`
class's :meth:`~dicom_parser.header.Header.get` method:

.. code:: python

    raw_value = image.header.raw['ImagingFrequency'].value
    raw_value
    >> "123.25993"
    type(raw_value)
    >> str

    parsed_value = image.header.get('ImagingFrequency')
    parsed_value
    >> 123.25993
    type(parsed_value)
    >> float

Age String (AS) to *float*:

.. code:: python

    raw_value = image.header.raw['PatientAge'].value
    raw_value
    >> "027Y"
    type(raw_value)
    >> str

    parsed_value = image.header.get('PatientAge')
    parsed_value
    >> 27.0
    type(parsed_value)
    >> float


Date String (DA) to `datetime.date <https://docs.python.org/3/library/datetime.html#available-types>`_
using the :class:`~dicom_parser.header.Header` class's indexing operator/subscript notation:

.. code:: python

    raw_value = image.header.raw['PatientBirthDate'].value
    raw_value
    >> "19901214"
    type(raw_value)
    >> str

    parsed_value = image.header['PatientBirthDate']
    parsed_value
    >> datetime.date(1990, 12, 14)
    type(parsed_value)
    >> datetime.date


Et cetera.

.. note::

    The *dict*-like functionality also includes safe getting:

    .. code:: python

        image.header.get('MissingKey')
        >> None

        image.header.get('MissingKey', 'DefaultValue')
        >> 'DefaultValue'

    As well as raising a KeyError for missing keys with the indexing operator:

    .. code::

        image.header['MissingKey']
        >> ...
        >> KeyError: "The keyword: 'MissingKey' does not exist in the header!"


---------------------------------------------------------------


Read DICOM series directory as a :class:`~dicom_parser.series.Series`
---------------------------------------------------------------------

Another useful class this package offers is the
:class:`~dicom_parser.series.Series` class:

.. code:: python

    from dicom_parser import Series

    series = Series('/some/dicom/series/')


The :class:`~dicom_parser.series.Series` instance allows us to easily
query the underlying images' headers using its
:meth:`~dicom_parser.series.Series.get` method:

.. code:: python

    # Single value
    series.get('EchoTime')
    >> 3.04

    # Multiple values
    series.get('InstanceNumber')
    >> [1, 2, 3]

    # No value
    series.get('MissingKey')
    >> None

    # Default value
    series.get('MissingKey', 'default_value')
    >> 'default_value'

Similarly to the :class:`~dicom_parser.image.Image` class, we can also use
the indexing operator:

.. code:: python

    # Single value
    series['RepetitionTime']
    >> 7.6

    # Multiple values
    series['SOPInstanceUID']
    >> ["1.123.1241.123124124.12.1",
        "1.123.1241.123124124.12.2",
        "1.123.1241.123124124.12.3"]

    # No value
    series['MissingKey']
    ...
    KeyError: "The keyword: 'MissingKey' does not exist in the header!"

Another useful feature of the indexing operator is for querying an
:class:`~dicom_parser.image.Image` instance based on its index in the series:

.. code:: python

    series[6]
    >> dicom_parser.image.Image
    series[6].header['InstanceNumber]
    >> 7   # InstanceNumber is 1-indexed

The `data` property returns a stacked volume of the images' data:

.. code:: python

    type(series.data)
    >>> numpy.ndarray
    series.data.shape
    >> (224, 224, 208)



Siemens 4D data
...............

Reading Siemens 4D data
`encoded as mosaics <https://nipy.org/nibabel/dicom/dicom_mosaic.html>`_
is also supported:

.. code:: python

    fmri_series = Series('/path/to/dicom/fmri/')
    fmri_series.data.shape
    >> (96, 96, 64, 200)
