pydicom.dataset.Dataset¶

class pydicom.dataset.Dataset(*args: Dataset | MutableMapping[BaseTag, DataElement | RawDataElement], **kwargs: Any)[source]¶

A DICOM dataset as a mutable mapping of DICOM Data Elements.

Examples

Add an element to the Dataset (for elements in the DICOM dictionary):

>>> ds = Dataset()
>>> ds.PatientName = "CITIZEN^Joan"
>>> ds.add_new(0x00100020, 'LO', '12345')
>>> ds[0x0010, 0x0030] = DataElement(0x00100030, 'DA', '20010101')

Add a sequence element to the Dataset

>>> ds.BeamSequence = [Dataset(), Dataset(), Dataset()]
>>> ds.BeamSequence[0].Manufacturer = "Linac, co."
>>> ds.BeamSequence[1].Manufacturer = "Linac and Sons, co."
>>> ds.BeamSequence[2].Manufacturer = "Linac and Daughters, co."

Add private elements to the Dataset

>>> block = ds.private_block(0x0041, 'My Creator', create=True)
>>> block.add_new(0x01, 'LO', '12345')

Updating and retrieving element values:

>>> ds.PatientName = "CITIZEN^Joan"
>>> ds.PatientName
'CITIZEN^Joan'
>>> ds.PatientName = "CITIZEN^John"
>>> ds.PatientName
'CITIZEN^John'

Retrieving an element’s value from a Sequence:

>>> ds.BeamSequence[0].Manufacturer
'Linac, co.'
>>> ds.BeamSequence[1].Manufacturer
'Linac and Sons, co.'

Accessing the DataElement items:

>>> elem = ds['PatientName']
>>> elem
(0010, 0010) Patient's Name                      PN: 'CITIZEN^John'
>>> elem = ds[0x00100010]
>>> elem
(0010, 0010) Patient's Name                      PN: 'CITIZEN^John'
>>> elem = ds.data_element('PatientName')
>>> elem
(0010, 0010) Patient's Name                      PN: 'CITIZEN^John'

Accessing a private DataElement item:

>>> block = ds.private_block(0x0041, 'My Creator')
>>> elem = block[0x01]
>>> elem
(0041, 1001) Private tag data                    LO: '12345'
>>> elem.value
'12345'

Alternatively:

>>> ds.get_private_item(0x0041, 0x01, 'My Creator').value
'12345'

Deleting an element from the Dataset

>>> del ds.PatientID
>>> del ds.BeamSequence[1].Manufacturer
>>> del ds.BeamSequence[2]

Deleting a private element from the Dataset

>>> block = ds.private_block(0x0041, 'My Creator')
>>> if 0x01 in block:
...     del block[0x01]

Determining if an element is present in the Dataset

>>> 'PatientName' in ds
True
>>> 'PatientID' in ds
False
>>> (0x0010, 0x0030) in ds
True
>>> 'Manufacturer' in ds.BeamSequence[0]
True

Iterating through the top level of a Dataset only (excluding Sequences):

>>> for elem in ds:
...    print(elem)
(0010, 0010) Patient's Name                      PN: 'CITIZEN^John'

Iterating through the entire Dataset (including Sequences):

>>> for elem in ds.iterall():
...     print(elem)
(0010, 0010) Patient's Name                      PN: 'CITIZEN^John'

Recursively iterate through a Dataset (including Sequences):

>>> def recurse(ds):
...     for elem in ds:
...         if elem.VR == 'SQ':
...             [recurse(item) for item in elem.value]
...         else:
...             # Do something useful with each DataElement

Converting the Dataset to and from JSON:

>>> ds = Dataset()
>>> ds.PatientName = "Some^Name"
>>> jsonmodel = ds.to_json()
>>> ds2 = Dataset()
>>> ds2.from_json(jsonmodel)
(0010, 0010) Patient's Name                      PN: 'Some^Name'

default_element_format¶

The default formatting for string display.

Type:: str

default_sequence_element_format¶

The default formatting for string display of sequences.

Type:: str

indent_chars¶

For string display, the characters used to indent nested Sequences. Default is " ".

Type:: str

is_little_endian¶

Shall be set before writing with write_like_original=False. The Dataset (excluding the pixel data) will be written using the given endianness.

Type:: bool

is_implicit_VR¶

Shall be set before writing with write_like_original=False. The Dataset will be written using the transfer syntax with the given VR handling, e.g Little Endian Implicit VR if True, and Little Endian Explicit VR or Big Endian Explicit VR (depending on Dataset.is_little_endian) if False.

Type:: bool

__init__(*args: Dataset | MutableMapping[BaseTag, DataElement | RawDataElement], **kwargs: Any) → None[source]¶: Create a new Dataset instance.

Methods

`__init__`(args, *kwargs)	Create a new `Dataset` instance.
`add`(data_element)	Add an element to the `Dataset`.
`add_new`(tag, VR, value)	Create a new element and add it to the `Dataset`.
`clear`()	Delete all the elements from the `Dataset`.
`compress`(transfer_syntax_uid[, arr, ...])	Compress and update an uncompressed dataset in-place with the resulting encapsulated pixel data.
`convert_pixel_data`([handler_name])	Convert pixel data to a `numpy.ndarray` internally.
`copy`()	Return a shallow copy of the dataset.
`data_element`(name)	Return the element corresponding to the element keyword name.
`decode`()	Apply character set decoding to the elements in the `Dataset`.
`decompress`([handler_name])	Decompresses Pixel Data and modifies the `Dataset` in-place.
`dir`(*filters)	Return an alphabetical list of element keywords in the `Dataset`.
`elements`()	Yield the top-level elements of the `Dataset`.
`ensure_file_meta`()	Create an empty `Dataset.file_meta` if none exists.
`fix_meta_info`([enforce_standard])	Ensure the file meta info exists and has the correct values for transfer syntax and media storage UIDs.
`formatted_lines`([element_format, ...])	Iterate through the `Dataset` yielding formatted `str` for each element.
`from_json`(json_dataset[, bulk_data_uri_handler])	Return a `Dataset` from a DICOM JSON Model object.
`get`()	Simulate `dict.get()` to handle element tags and keywords.
`get_item`()	Return the raw data element if possible.
`get_private_item`(group, element_offset, ...)	Return the data element for the given private tag group.
`group_dataset`(group)	Return a `Dataset` containing only elements of a certain group.
`items`()	Return the `Dataset` items to simulate `dict.items()`.
`iterall`()	Iterate through the `Dataset`, yielding all the elements.
`keys`()	Return the `Dataset` keys to simulate `dict.keys()`.
`overlay_array`(group)	Return the Overlay Data in group as a `numpy.ndarray`.
`pop`(key, *args)	Emulate `dict.pop()` with support for tags and keywords.
`popitem`()	Emulate `dict.popitem()`.
`private_block`(group, private_creator[, create])	Return the block for the given tag group and private_creator.
`private_creators`(group)	Return a list of private creator names in the given group.
`remove_private_tags`()	Remove all private elements from the `Dataset`.
`save_as`(filename[, write_like_original])	Write the `Dataset` to filename.
`set_original_encoding`(is_implicit_vr, ...)	Set the values for the original transfer syntax and encoding.
`setdefault`(key[, default])	Emulate `dict.setdefault()` with support for tags and keywords.
`to_json`([bulk_data_threshold, ...])	Return a JSON representation of the `Dataset`.
`to_json_dict`([bulk_data_threshold, ...])	Return a dictionary representation of the `Dataset` conforming to the DICOM JSON Model as described in the DICOM Standard, Part 18, Annex F.
`top`()	Return a `str` representation of the top level elements.
`trait_names`()	Return a `list` of valid names for auto-completion code.
`update`(d)	Extend `dict.update()` to handle DICOM tags and keywords.
`values`()	Return the `Dataset` values to simulate `dict.values()`.
`walk`(callback[, recursive])	Iterate through the `Dataset's` elements and run callback on each.
`waveform_array`(index)	Return an `ndarray` for the multiplex group at index in the (5400,0100) Waveform Sequence.

Attributes

`default_element_format`
`default_sequence_element_format`
`indent_chars`
`is_original_encoding`	Return `True` if the encoding to be used for writing is set and is the same as that used to originally encode the `Dataset`.
`parent`	Return a weak reference to the parent Sequence's parent Dataset.
`parent_seq`	Return a weak reference to the parent `Sequence`.
`pixel_array`	Return the pixel data as a `numpy.ndarray`.

add(data_element: DataElement) → None[source]¶

Add an element to the Dataset.

Equivalent to ds[data_element.tag] = data_element

Parameters:: data_element (dataelem.DataElement) – The DataElement to add.

add_new(tag: int | str | Tuple[int, int] | BaseTag, VR: str, value: Any) → None[source]¶

Create a new element and add it to the Dataset.

Parameters:

tag – The DICOM (group, element) tag in any form accepted by Tag() such as [0x0010, 0x0010], (0x10, 0x10), 0x00100010, etc.
VR (str) – The 2 character DICOM value representation (see DICOM Standard, Part 5, Section 6.2).
value –
The value of the data element. One of the following:
- a single string or number
- a list or tuple with all strings or all numbers
- a multi-value string with backslash separator
- for a sequence element, an empty list or list of Dataset

clear() → None[source]¶: Delete all the elements from the Dataset.

compress(transfer_syntax_uid: str, arr: ndarray | None = None, encoding_plugin: str = '', decoding_plugin: str = '', encapsulate_ext: bool = False, **kwargs: Any) → None[source]¶

Compress and update an uncompressed dataset in-place with the resulting encapsulated pixel data.

New in version 2.2.

The dataset must already have the following Image Pixel module elements present with correct values that correspond to the resulting compressed pixel data:

(0028,0002) Samples per Pixel
(0028,0004) Photometric Interpretation
(0028,0008) Number of Frames (if more than 1 frame will be present)
(0028,0010) Rows
(0028,0011) Columns
(0028,0100) Bits Allocated
(0028,0101) Bits Stored
(0028,0103) Pixel Representation

This method will add the file meta dataset if none is present and add or modify the following elements:

(0002,0010) Transfer Syntax UID
(7FE0,0010) Pixel Data

If Samples per Pixel is greater than 1 then the following element will also be added:

(0028,0006) Planar Configuration

If the compressed pixel data is too large for encapsulation using a basic offset table then an extended offset table will also be used, in which case the following elements will also be added:

(7FE0,0001) Extended Offset Table
(7FE0,0002) Extended Offset Table Lengths

Supported Transfer Syntax UIDs

UID	Plugins	Encoding Guide
RLE Lossless - 1.2.840.10008.1.2.5	pydicom, pylibjpeg, gdcm	RLE Lossless

Examples

Compress the existing uncompressed Pixel Data in place:

>>> from pydicom.data import get_testdata_file
>>> from pydicom.uid import RLELossless
>>> ds = get_testdata_file("CT_small.dcm", read=True)
>>> ds.compress(RLELossless)
>>> ds.save_as("CT_small_rle.dcm")

Parameters:

transfer_syntax_uid (pydicom.uid.UID) – The UID of the transfer syntax to use when compressing the pixel data.
arr (numpy.ndarray, optional) – Compress the uncompressed pixel data in arr and use it to set the Pixel Data. If arr is not used then the existing Pixel Data in the dataset will be compressed instead. The shape, dtype and contents of the array should match the dataset.
encoding_plugin (str, optional) – Use the encoding_plugin to compress the pixel data. See the user guide for a list of plugins available for each UID and their dependencies. If not specified then all available plugins will be tried (default).
decoding_plugin (str, optional) – Placeholder for future functionality.
encapsulate_ext (bool, optional) – If True then force the addition of an extended offset table. If False (default) then an extended offset table will be added if needed for large amounts of compressed Pixel Data, otherwise just the basic offset table will be used.
**kwargs – Optional keyword parameters for the encoding plugin may also be present. See the encoding plugins options for more information.

convert_pixel_data(handler_name: str = '') → None[source]¶

Convert pixel data to a numpy.ndarray internally.

Parameters:

handler_name (str, optional) – The name of the pixel handler that shall be used to decode the data. Supported names are: 'gdcm', 'pillow', 'jpeg_ls', 'rle', 'numpy' and 'pylibjpeg'. If not used (the default), a matching handler is used from the handlers configured in pixel_data_handlers.

Returns:

Converted pixel data is stored internally in the dataset.

Return type:

None

Raises:

ValueError – If handler_name is not a valid handler name.
NotImplementedError – If the given handler or any handler, if none given, is unable to decompress pixel data with the current transfer syntax
RuntimeError – If the given handler, or the handler that has been selected if none given, is not available.

Notes

If the pixel data is in a compressed image format, the data is decompressed and any related data elements are changed accordingly.

copy() → Dataset[source]¶: Return a shallow copy of the dataset.

data_element(name: str) → DataElement | None[source]¶

Return the element corresponding to the element keyword name.

Parameters:: name (str) – A DICOM element keyword.
Returns:: For the given DICOM element keyword, return the corresponding DataElement if present, None otherwise.
Return type:: dataelem.DataElement or None

decode() → None[source]¶

Apply character set decoding to the elements in the Dataset.

See DICOM Standard, Part 5, Section 6.1.1.

decompress(handler_name: str = '') → None[source]¶

Decompresses Pixel Data and modifies the Dataset in-place.

New in version 1.4: The handler_name keyword argument was added

If not a compressed transfer syntax, then pixel data is converted to a numpy.ndarray internally, but not returned.

If compressed pixel data, then is decompressed using an image handler, and internal state is updated appropriately:

Dataset.file_meta.TransferSyntaxUID is updated to non-compressed form
is_undefined_length is False for the (7FE0,0010) Pixel Data element.

Changed in version 1.4: The handler_name keyword argument was added

Parameters:: handler_name (str, optional) – The name of the pixel handler that shall be used to decode the data. Supported names are: 'gdcm', 'pillow', 'jpeg_ls', 'rle', 'numpy' and 'pylibjpeg'. If not used (the default), a matching handler is used from the handlers configured in pixel_data_handlers.
Return type:: None
Raises:: NotImplementedError – If the pixel data was originally compressed but file is not Explicit VR Little Endian as required by the DICOM Standard.

dir(*filters: str) → List[str][source]¶

Return an alphabetical list of element keywords in the Dataset.

Intended mainly for use in interactive Python sessions. Only lists the element keywords in the current level of the Dataset (i.e. the contents of any sequence elements are ignored).

Parameters:: filters (str) – Zero or more string arguments to the function. Used for case-insensitive match to any part of the DICOM keyword.
Returns:: The matching element keywords in the dataset. If no filters are used then all element keywords are returned.
Return type:: list of str

elements() → Iterator[DataElement][source]¶

Yield the top-level elements of the Dataset.

New in version 1.1.

Examples

>>> ds = Dataset()
>>> for elem in ds.elements():
...     print(elem)

The elements are returned in the same way as in Dataset.__getitem__().

Yields:: dataelem.DataElement or dataelem.RawDataElement – The unconverted elements sorted by increasing tag order.

ensure_file_meta() → None[source]¶: Create an empty Dataset.file_meta if none exists.

New in version 1.2.

fix_meta_info(enforce_standard: bool = True) → None[source]¶

Ensure the file meta info exists and has the correct values for transfer syntax and media storage UIDs.

New in version 1.2.

Warning

The transfer syntax for is_implicit_VR = False and is_little_endian = True is ambiguous and will therefore not be set.

Parameters:: enforce_standard (bool, optional) – If True, a check for incorrect and missing elements is performed (see validate_file_meta()).

formatted_lines(element_format: str = '%(tag)s %(name)-35.35s %(VR)s: %(repval)s', sequence_element_format: str = '%(tag)s %(name)-35.35s %(VR)s: %(repval)s', indent_format: str | None = None) → Iterator[str][source]¶

Iterate through the Dataset yielding formatted str for each element.

Parameters:

element_format (str) – The string format to use for non-sequence elements. Formatting uses the attributes of DataElement. Default is "%(tag)s %(name)-35.35s %(VR)s: %(repval)s".
sequence_element_format (str) – The string format to use for sequence elements. Formatting uses the attributes of DataElement. Default is "%(tag)s %(name)-35.35s %(VR)s: %(repval)s"
indent_format (str or None) – Placeholder for future functionality.

Yields:

str – A string representation of an element.

Return a Dataset from a DICOM JSON Model object.

New in version 1.3.

See the DICOM Standard, Part 18, Annex F.

Parameters:

json_dataset (dict, str, bytes or bytearray) – dict, str, bytes or bytearray representing a DICOM Data Set formatted based on the DICOM JSON Model.
bulk_data_uri_handler (callable, optional) – Callable function that accepts either the tag, vr and “BulkDataURI” value or just the “BulkDataURI” value of the JSON representation of a data element and returns the actual value of that data element (retrieved via DICOMweb WADO-RS). If no bulk_data_uri_handler is specified (default) then the corresponding element will have an “empty” value such as "", b"" or None depending on the vr (i.e. the Value Multiplicity will be 0).

Return type:

Dataset

get(key: str, default: Any | None = None) → Any[source]¶

get(key: int | Tuple[int, int] | BaseTag, default: Any | None = None) → DataElement

Simulate dict.get() to handle element tags and keywords.

Parameters:

key (str or int or Tuple[int, int] or BaseTag) – The element keyword or tag or the class attribute name to get.
default (obj or None, optional) – If the element or class attribute is not present, return default (default None).

Returns:

value – If key is the keyword for an element in the Dataset then return the element’s value.
dataelem.DataElement – If key is a tag for a element in the Dataset then return the DataElement instance.
value – If key is a class attribute then return its value.

get_item(key: slice) → Dataset[source]¶

get_item(key: int | str | Tuple[int, int] | BaseTag) → DataElement

Return the raw data element if possible.

It will be raw if the user has never accessed the value, or set their own value. Note if the data element is a deferred-read element, then it is read and converted before being returned.

Parameters:: key – The DICOM (group, element) tag in any form accepted by Tag() such as [0x0010, 0x0010], (0x10, 0x10), 0x00100010, etc. May also be a slice made up of DICOM tags.
Returns:: The corresponding element.
Return type:: dataelem.DataElement

get_private_item(group: int, element_offset: int, private_creator: str) → DataElement[source]¶

Return the data element for the given private tag group.

New in version 1.3.

This is analogous to Dataset.__getitem__(), but only for private tags. This allows to find the private tag for the correct private creator without the need to add the tag to the private dictionary first.

Parameters:

group (int) – The private tag group where the item is located as a 32-bit int.
element_offset (int) – The lower 16 bits (e.g. 2 hex numbers) of the element tag.
private_creator (str) – The private creator for the tag. Must match the private creator for the tag to be returned.

Returns:

The corresponding element.

Return type:

dataelem.DataElement

Raises:

ValueError – If group is not part of a private tag or private_creator is empty.
KeyError – If the private creator tag is not found in the given group. If the private tag is not found.

group_dataset(group: int) → Dataset[source]¶

Return a Dataset containing only elements of a certain group.

Parameters:: group (int) – The group part of a DICOM (group, element) tag.
Returns:: A Dataset containing elements of the group specified.
Return type:: Dataset

property is_original_encoding: bool¶

Return True if the encoding to be used for writing is set and is the same as that used to originally encode the Dataset.

New in version 1.1.

This includes properties related to endianness, VR handling and the (0008,0005) Specific Character Set.

items() → AbstractSet[Tuple[BaseTag, DataElement | RawDataElement]][source]¶

Return the Dataset items to simulate dict.items().

Returns:: The top-level (BaseTag, DataElement) items for the Dataset.
Return type:: dict_items

iterall() → Iterator[DataElement][source]¶

Iterate through the Dataset, yielding all the elements.

Unlike iter(Dataset), this does recurse into sequences, and so yields all elements as if dataset were “flattened”.

Yields:: dataelem.DataElement

keys() → AbstractSet[BaseTag][source]¶

Return the Dataset keys to simulate dict.keys().

Returns:: The BaseTag of all the elements in the Dataset.
Return type:: dict_keys

overlay_array(group: int) → ndarray[source]¶

Return the Overlay Data in group as a numpy.ndarray.

New in version 1.4.

Parameters:: group (int) – The group number of the overlay data.
Returns:: The (group,3000) Overlay Data converted to a numpy.ndarray.
Return type:: numpy.ndarray

property parent: weakref.ReferenceType[Dataset] | None¶: Return a weak reference to the parent Sequence’s parent Dataset.

Deprecated since version 2.4.

property parent_seq: weakref.ReferenceType[Sequence] | None¶: Return a weak reference to the parent Sequence.

New in version 2.4: Returned value is a weak reference to the parent Sequence.

property pixel_array: ndarray¶

Return the pixel data as a numpy.ndarray.

Changed in version 1.4: Added support for Float Pixel Data and Double Float Pixel Data

Returns:: The (7FE0,0008) Float Pixel Data, (7FE0,0009) Double Float Pixel Data or (7FE0,0010) Pixel Data converted to a numpy.ndarray.
Return type:: numpy.ndarray

Emulate dict.pop() with support for tags and keywords.

Removes the element for key if it exists and returns it, otherwise returns a default value if given or raises KeyError.

Parameters:

key (int or str or 2-tuple) –
- If tuple - the group and element number of the DICOM tag
- If int - the combined group/element number
- If str - the DICOM keyword of the tag
*args (zero or one argument) – Defines the behavior if no tag exists for key: if given, it defines the return value, if not given, KeyError is raised

Returns:

The element for key if it exists, or the default value if given.

Return type:

RawDataElement or DataElement

Raises:

KeyError – If the key is not a valid tag or keyword. If the tag does not exist and no default is given.

popitem() → Tuple[BaseTag, DataElement | RawDataElement][source]¶

Emulate dict.popitem().

Return type:: tuple of (BaseTag, DataElement)

private_block(group: int, private_creator: str, create: bool = False) → PrivateBlock[source]¶

Return the block for the given tag group and private_creator.

New in version 1.3.

If create is True and the private_creator does not exist, the private creator tag is added.

Notes

We ignore the unrealistic case that no free block is available.

Parameters:

group (int) – The group of the private tag to be found as a 32-bit int. Must be an odd number (e.g. a private group).
private_creator (str) – The private creator string associated with the tag.
create (bool, optional) – If True and private_creator does not exist, a new private creator tag is added at the next free block. If False (the default) and private_creator does not exist, KeyError is raised instead.

Returns:

The existing or newly created private block.

Return type:

PrivateBlock

Raises:

ValueError – If group doesn’t belong to a private tag or private_creator is empty.
KeyError – If the private creator tag is not found in the given group and the create parameter is False.

private_creators(group: int) → List[str][source]¶

Return a list of private creator names in the given group.

New in version 1.3.

Examples

This can be used to check if a given private creator exists in the group of the dataset:

>>> ds = Dataset()
>>> if 'My Creator' in ds.private_creators(0x0041):
...     block = ds.private_block(0x0041, 'My Creator')

Parameters:: group (int) – The private group as a 32-bit int. Must be an odd number.
Returns:: All private creator names for private blocks in the group.
Return type:: list of str
Raises:: ValueError – If group is not a private group.

remove_private_tags() → None[source]¶: Remove all private elements from the Dataset.

save_as(filename: str | os.PathLike[AnyStr] | BinaryIO, write_like_original: bool = True) → None[source]¶

Write the Dataset to filename.

Wrapper for pydicom.filewriter.dcmwrite, passing this dataset to it. See documentation for that function for details.

See also

pydicom.filewriter.dcmwrite: Write a DICOM file from a FileDataset instance.

set_original_encoding(is_implicit_vr: bool | None, is_little_endian: bool | None, character_encoding: None | str | MutableSequence[str]) → None[source]¶

Set the values for the original transfer syntax and encoding.

New in version 1.2.

Can be used for a Dataset with raw data elements to enable optimized writing (e.g. without decoding the data elements).

setdefault(key: int | str | Tuple[int, int] | BaseTag, default: Any | None = None) → DataElement[source]¶

Emulate dict.setdefault() with support for tags and keywords.

Examples

>>> ds = Dataset()
>>> elem = ds.setdefault((0x0010, 0x0010), "Test")
>>> elem
(0010, 0010) Patient's Name                      PN: 'Test'
>>> elem.value
'Test'
>>> elem = ds.setdefault('PatientSex',
...     DataElement(0x00100040, 'CS', 'F'))
>>> elem.value
'F'

Parameters:

key (int, str or 2-tuple of int) –
- If tuple - the group and element number of the DICOM tag
- If int - the combined group/element number
- If str - the DICOM keyword of the tag
default (pydicom.dataelem.DataElement or object, optional) – The DataElement to use with key, or the value of the DataElement to use with key (default None).

Returns:

The DataElement for key.

Return type:

pydicom.dataelem.DataElement or object

Raises:

ValueError – If key is not convertible to a valid tag or a known element keyword.
KeyError – If reading_validation_mode is RAISE and key is an unknown non-private tag.

to_json(bulk_data_threshold: int = 1024, bulk_data_element_handler: Callable[[DataElement], str] | None = None, dump_handler: Callable[[Dict[str, Any]], str] | None = None, suppress_invalid_tags: bool = False) → str[source]¶

Return a JSON representation of the Dataset.

New in version 1.3.

See the DICOM Standard, Part 18, Annex F.

Parameters:

bulk_data_threshold (int, optional) – Threshold for the length of a base64-encoded binary data element above which the element should be considered bulk data and the value provided as a URI rather than included inline (default: 1024). Ignored if no bulk data handler is given.
bulk_data_element_handler (callable, optional) – Callable function that accepts a bulk data element and returns a JSON representation of the data element (dictionary including the “vr” key and either the “InlineBinary” or the “BulkDataURI” key).
dump_handler (callable, optional) –
Callable function that accepts a dict and returns the serialized (dumped) JSON string (by default uses json.dumps()).
suppress_invalid_tags (bool, optional) – Flag to specify if errors while serializing tags should be logged and the tag dropped or if the error should be bubbled up.

Returns:

Dataset serialized into a string based on the DICOM JSON Model.

Return type:

str

Examples

>>> def my_json_dumps(data):
...     return json.dumps(data, indent=4, sort_keys=True)
>>> ds.to_json(dump_handler=my_json_dumps)

to_json_dict(bulk_data_threshold: int = 1024, bulk_data_element_handler: Callable[[DataElement], str] | None = None, suppress_invalid_tags: bool = False) → Dict[str, Any][source]¶

Return a dictionary representation of the Dataset conforming to the DICOM JSON Model as described in the DICOM Standard, Part 18, Annex F.

New in version 1.4.

Parameters:

bulk_data_threshold (int, optional) – Threshold for the length of a base64-encoded binary data element above which the element should be considered bulk data and the value provided as a URI rather than included inline (default: 1024). Ignored if no bulk data handler is given.
bulk_data_element_handler (callable, optional) – Callable function that accepts a bulk data element and returns a JSON representation of the data element (dictionary including the “vr” key and either the “InlineBinary” or the “BulkDataURI” key).
suppress_invalid_tags (bool, optional) – Flag to specify if errors while serializing tags should be logged and the tag dropped or if the error should be bubbled up.

Returns:

Dataset representation based on the DICOM JSON Model.

Return type:

dict

top() → str[source]¶: Return a str representation of the top level elements.

trait_names() → List[str][source]¶

Return a list of valid names for auto-completion code.

Used in IPython, so that data element names can be found and offered for autocompletion on the IPython command line.

update(d: Dataset | MutableMapping[BaseTag, DataElement | RawDataElement]) → None[source]¶

Extend dict.update() to handle DICOM tags and keywords.

Parameters:: d (dict or Dataset) – The dict or Dataset to use when updating the current object.

values() → ValuesView[DataElement | RawDataElement][source]¶

Return the Dataset values to simulate dict.values().

Returns:: The DataElements that make up the values of the Dataset.
Return type:: dict_keys

walk(callback: Callable[[Dataset, DataElement], None], recursive: bool = True) → None[source]¶

Iterate through the Dataset's elements and run callback on each.

Visit all elements in the Dataset, possibly recursing into sequences and their items. The callback function is called for each DataElement (including elements with a VR of ‘SQ’). Can be used to perform an operation on certain types of elements.

For example, remove_private_tags() finds all elements with private tags and deletes them.

The elements will be returned in order of increasing tag number within their current Dataset.

Parameters:

callback –
A callable function that takes two arguments:
- a Dataset
- a DataElement belonging to that Dataset
recursive (bool, optional) – Flag to indicate whether to recurse into sequences (default True).

waveform_array(index: int) → ndarray[source]¶

Return an ndarray for the multiplex group at index in the (5400,0100) Waveform Sequence.

New in version 2.1.

Parameters:: index (int) – The index of the multiplex group to return the array for.
Returns:: The Waveform Data for the multiplex group as an ndarray with shape (samples, channels). If (003A,0210) Channel Sensitivity is present then the values will be in the units specified by the (003A,0211) Channel Sensitivity Units Sequence.
Return type:: numpy.ndarray