XNATpy Tutorial#
XNAT REST API#
The XNAT REST API allows users to work with xnat via scripts. The REST API is
an interface that is language independent and is build on top of HTTP. Operations
are carried out by HTTP requests with one of the verbs GET
, PUT
,
POST
or DELETE
. The GET
request is generally used for retrieving
data, whereas the PUT
, POST
, and DELETE
are used for modifying data.
A simple GET
request can be send by simply putting the target url in a web
browser and looking at the result. For a sending more complex HTTP requests,
you can for example use curl
(a command-line tool for linux), postman
(an extension for the chrome browser), or the requests
package for Python
(on top of which this package as well as pyxnat is build)
To get an idea of how the XNAT REST API works it is helpful to visit the following URLs in your browser:
The first URL give you a table with an overview of all projects you can access on XNAT central. The second and third URL give the same information, but in different machine readable formats (XML and JSON respectively). This is extremely useful when creating scripts to automatically retrieve or store data from XNAT.
Installation#
The easiest way to install xnat is via to python package index via pip:
pip install xnat
However, if you do not have pip or want to install from source just use the setup.py normally:
python setup.py install
Connecting to a server#
To get started, create a connection:
>>> import xnat
>>> session = xnat.connect('https://central.xnat.org')
To see all options for creating connections see the xnat.connect()
.
The connection holds your login information, the server information and a
session. It will also send a heartbeat every 14 minutes to keep the connection
alive.
When working with a session it is always important to disconnect when done:
>>> session.disconnect()
Credentials#
It is possible to pass your credentials for the session when connecting. This would look like:
>>> session = xnat.connect('http://my.xnat.server', user='admin', password='secret')
This would work and log in fine, but your password might be visible in your source code, command history or just on your screen. If you only give a user, but not a password xnatpy will prompt you for your password. This is fine for interactive use, but for automated scripts this is useless.
To store credentials this xnatpy uses the .netrc file on Linux and the _netrc
file on Windows. On linux the file is located in ~/.netrc
and on Windows
the file is located in %USERPROFILE%\_netrc
(which typically resolves to
C:\Users\YourUsername\_netrc
). This file contains login information and
should be accessible ONLY by the user (if not, the module with throw an error
to let you know the file is unsafe). For example, on Linux use:
echo "machine images.xnat.org
> login USERNAME
> password PASSWORD" > ~/.netrc
chmod 600 ~/.netrc
This will create the netrc file with the correct contents and set the permission correct.
On Windows you can use the command prompt to do the same:
(echo machine images.xnat.org & echo login USERNAME & echo password PASSWORD) > %userprofile%/_netrc
Note
Replace ‘images.xnat.org’ with your XNAT server, ‘USERNAME’ with your username and ‘PASSWORD’ with your password
Self-closing sessions#
When in a script where there is a possibility for unforeseen errors it is safest to use a context operator in Python. This can be achieved by using the following:
>>> with xnat.connect('http://my.xnat.server') as session:
... print session.projects
As soon as the scope of the with exists (even if because of an exception thrown!) the session will be disconnected automatically.
Low level REST directives#
Though xnatpy is designed to offer a high level pythonic interface, it also easily exposes all default REST verbs using the following functions:
These methods take a (partial) uri and return a requests response. However they do make use of the session established by xnatpy, so user auth and default error checking are still in place, for example:
>>> connection.get('/data/projects')
# Note that 'https://xnat.example.com/data/projects' would also work but is not needed
# as the connection already knows the server connected to
<Response [200]>
These methods also accept arguments for query strings and data (for put
and post
). The details
can be found in the documentation of the separate methods.
There is also a useful helper method that gets and unpacks json data xnat.session.BaseXNATSession.get_json()
:
>>> connection.get_json('/data/project/PROJECT_ID')
{'items': [{'children': ..... }]}
Finally there are also methods for data upload and download:
These methods can help you implement arbitrary functionality without limitations.
Warning
A lot of functionality has higher level interfaces which are easier to use and it is recommended to use those instead.
Note
The requests session used by xnatpy can be accessed via connection.interface
.
This allows you to anything that requests can but bypasses all error checking of
xnatpy and is not recommended.
Exploring your xnat server#
When a session is established, it is fairly easy to explore the data on the XNAT server. The data structure of XNAT is mimicked as Python objects. The connection gives access to a listing of all projects, subjects, and experiments on the server.
>>> import xnat
>>> session = xnat.connect('http://images.xnat.org', user='admin', password='admin')
>>> session.projects
<XNATListing (sandbox, sandbox project): <ProjectData sandbox project (sandbox)>>
The XNATListing is a special type of mapping in which you can access elements by a primary key (usually the ID or Accession #) and a secondary key (e.g. the label for a subject or experiment). Selection can be performed the same as a Python dict:
>>> sandbox_project = session.projects["sandbox"]
>>> sandbox_project.subjects
<XNATListing (XNAT_S00001, test001): <SubjectData test001 (XNAT_S00001)>>
You can browse the following levels on the XNAT server: projects, subjects, experiments, scans, resources, files. Also under experiments you have assessors which again can contain resources and files. This all following the same structure as XNAT.
Warning
Loading all subjects/experiments on a server can take very long if there is a lot of data. Going down through the project level is more efficient.
Looping over data#
There are situations in which you want to perform an action for each subject or
experiment. To do this, you can think of an XNATListing
as a Python dict
and most things will work naturally. For example:
>>> sandbox_project.subjects.keys()
[u'XNAT_S00001']
>>> sandbox_project.subjects.values()
[<SubjectData test001 (XNAT_S00001)>]
>>> len(sandbox_project.subjects)
1
>>> for subject in sandbox_project.subjects.values():
... print(subject.label)
test001
Selecting an object based on its uri#
If you already have the uri for an object you can easily fetch the correct xnatpy object. For example:
>>> experiment_object = connection.create_object('/data/projects/$PROJECT_ID/experiments/$EXPERIMENT_ID')
>>> experiment_object
<MrSessionData EXPERIMENT_LABEL (EXPERIMENT_ID)>
This object is exactly the same as if it would be acquired from a listing, so you can reference the parameters, fields, etc.
This works for any valid url of which xnatpy can retrieve the data and figure out the xsitype, see
xnat.session.BaseXNATSession.create_object()
for details.
Note
xnatpy can also be called using urls that start with the uri connected to, e.g. if
given https://xnat.example.com
as argument when connecting, using the uri
https://xnat.example.com/data/projects/$PROJECT_ID/experiments/$EXPERIMENT_ID
would
also work.
Downloading data#
If you have the following in your XNAT:
>>> experiment.scans['T1']
<MrScanData T1 (1001-MR3)>
In some cases you might want to download an individual scan to inspect/process locally. This is using:
>>> experiment.scans['T1'].download('/home/hachterberg/temp/T1.zip')
Downloading http://127.0.0.1/xnat/data/experiments/demo_E00091/scans/1001-MR3/files?format=zip:
13035 kb
Saved as /home/hachterberg/temp/T1.zip...
As you can see, the scan is downloaded as a zip archive that contains all the DICOM files.
If you are interested in downloading all data of an entire subject, it is possible to use a helper function that downloads the data and extracts it in the target directory. This will create a data structure similar to that of XNAT on your local disk:
>>> subject = experiment.subject
>>> subject.download_dir('/home/hachterberg/temp/')
Downloading http://120.0.0.1/xnat/data/experiments/demo_E00091/scans/ALL/files?format=zip:
23736 kb
Downloaded image session to /home/hachterberg/temp/ANONYMIZ3
Downloaded subject to /home/hachterberg/temp/ANONYMIZ3
To see what is downloaded, we can use the linux command find from ipython:
$ find /home/hachterberg/temp/ANONYMIZ3
/home/hachterberg/temp/ANONYMIZ3
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3/scans
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3/scans/1001-MR2-FLAIR
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3/scans/1001-MR2-FLAIR/resources
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3/scans/1001-MR2-FLAIR/resources/DICOM
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3/scans/1001-MR2-FLAIR/resources/DICOM/files
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3/scans/1001-MR2-FLAIR/resources/DICOM/files/IM2.dcm
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3/scans/1001-MR2-FLAIR/resources/DICOM/files/IM32.dcm
/home/hachterberg/temp/ANONYMIZ3/ANONYMIZ3/scans/1001-MR2-FLAIR/resources/DICOM/files/IM11.dcm
...
The REST API allows for downloading of data from XNAT. The xnatpy package includes helper functions to make the downloading of data easier. For example, to download all experiments belonging to a subject:
>>> subject = sandbox_project.subjects['test001']
>>> subject.download_dir('./Downloads/test001')
This will download all the relevant experiments and unpack them in the target
folder. This is available for
projects
,
subjects
,
experiments
,
scans
, and
resources
.
Experiments, scans and resources can also be downloaded in a zip bundle
using the download
method for experiments
,
scans
, and
resources
.
Custom variables#
Custom variable are exposes primiary using the object.custom_variables
property.
This is a mapping that exposes the custom variable groups. Each group is a mapping
that gives access to the variables:
In [4]: subject.custom_variables
Out[4]: <CustomVariableMap groups: [default]>
In [5]: subject.custom_variables['default']
Out[5]: <CustomVariableGroup default {Notes (string): "some note", Diagnosis (string): None}>
In [6]: subject.custom_variables['default']['Notes']
Out[6]: "some note"
In [7]: subject.custom_variables['default']['Notes'] = "update note"
The good thing about this way of accessing custom variables this way is that they are casted to the right type and constraints are checked client side when trying to update them.
The custom variables are also exposed as a dict
-like object in xnatpy
. They are located in the
field
attribute under the objects that can have custom variables:
In [18]: experiment = project.subjects['ANONYMIZ'].experiments['ANONYMIZ']
In [19]: experiment.fields
Out[19]: <VariableMap {u'brain_volume': u'0'}>
In [20]: experiment.fields['brain_volume']
Out[20]: u'0'
In [21]: experiment.fields['brain_volume'] = 42.0
In [22]: experiment.fields
Out[22]: <VariableMap {u'brain_volume': u'42.0'}>
In [27]: experiment.fields['brain_volume']
Out[27]: u'42.0'
Note
Accessing custom variables via .fields
is low-level and bypasses
all typing and constraints set via the XNAT interface. Also non-defined
fields can be added and retrieved (those will not show in the interface).
Getting external urls of an object#
Sometimes you want to know the full external URL of a resource in XNAT, for this all XNAT objects have a function to retrieve this:
>>> experiment_01.external_uri()
'https://xnat.server.com/data/archive/projects/project/subjects/XNAT_S09618/experiments/XNAT_E36346'
You can change the query string or scheme used with extra arguments:
>>> experiment_01.external_uri(scheme='test', query={'hello': 'world'})
'test://xnat.server.com/data/archive/projects/project/subjects/XNAT_S09618/experiments/XNAT_E36346?hello=world'
Importing data into XNAT#
To add new data into XNAT it is possible to use the REST import service. It allows you to upload a zip file containing an experiment and XNAT will automatically try to store it in the correct place:
>>> session.services.import_('/path/to/archive.zip', project='sandbox', subject='test002')
Will upload the DICOM files in archive.zip and add them as scans under the subject test002
in project sandbox (the project ID needs to be sandbox, not the label). For more information
on importing data see import_
As it is dangerous to add data straight into the archive due to lack of reviewing, it is possible to also upload
the data to the prearchive first. This can be achieved by adding the destination
argument as follows:
# Import via prearchive:
>>> prearchive_session = session.services.import_('/home/hachterberg/temp/ANONYMIZ.zip', project='brainimages', destination='/prearchive')
>>> print(prearchive_session)
<PrearchiveSession brainimages/20161107_114859342/ANONYMIZ>
Once the data is uploaded (either via xnatpy
or other means) it is possible to query the prearchive and
process the scans in it. To get a list of sessions
waiting for archiving use the following:
>>> session.prearchive.sessions()
[<PrearchiveSession brainimages/20161107_114859342/ANONYMIZ>]
Once the data in the prearchive is located it can be archived as follows:
>>> prearchive_session = session.prearchive.sessions()[0]
>>> experiment = prearchive_session.archive(subject='ANONYMIZ3', experiment='ANONYMIZ3')
>>> print(experiment)
<MrSessionData ANONYMIZ3 (demo_E00092)>
Note
It is worth noting that it is possible to inspect the scan before archiving: one can look at the status, move it between projects, list the scans and files contained in the scans.
Prearchive#
When scans are send to the XNAT they often end up in the prearchive pending review before adding them to the main archive. It is possible to view the prearchive via xnatpy:
>>> session.prearchive.sessions()
[]
This gives a list of PrearchiveSessions
in the archive. It is possible to
archive
,
rebuild
,
move
, or
delete
the session using simple methods. For more information
see PrearchiveSession
Searching#
XNATpy allows using the XNAT search via the REST API. For this XNAT expects an XML document that specifies your query. The general information on search with the XNAT REST API is taken from XNAT wiki: How to Query the XNAT Search Engine with REST API
To make it simple to search, XNATpy offers its own search intertface. It is inspired by SQLAlchemy and allows using the object model to specify your query:
>>> SubjectData = connection.classes.SubjectData
>>> SubjectData.query().filter(SubjectData.project == 'sandbox').all()
[<SubjectData ANONYMIZ (BMIAXNAT03_S00525)>,
<SubjectData case001 (BMIAXNAT07_S00009)>,
<SubjectData SUBJECT001 (BMIAXNAT12_S00261)>,
<SubjectData TEST_001 (BMIAXNAT15_S00874)>,
<SubjectData Brain-0001 (BMIAXNAT34_S00001)>,
<SubjectData Brain-0002 (BMIAXNAT34_S00002)>,
<SubjectData TEST01 (BMIAXNAT_S17618)>]
In the example above, we use the subject data class to query. We get the generated class
from connection.classes
and give it a local name for convenience. Then we create a query
for this class, so the result of our query will be subjects. Subsequently we add a filter
where we constrain the results to match a certain project id. Finally we request all matching
objects.
Multiple constraints can be used by giving multiple arguments to filter:
>>> SubjectData.query().filter(SubjectData.project == 'sandbox', SubjectData.label.like('Brain*')).all()
[<SubjectData Brain-0001 (BMIAXNAT34_S00001)>,
<SubjectData Brain-0002 (BMIAXNAT34_S00002)>]
Also, filter can be called on the resulting query to stack filters:
>>> SubjectData.query().filter(SubjectData.project == 'sandbox').filter(SubjectData.label.like('Brain*')).all()
[<SubjectData Brain-0001 (BMIAXNAT34_S00001)>,
<SubjectData Brain-0002 (BMIAXNAT34_S00002)>]
Finally compound constraints can be created using the &
(for and) and |
(for or) operators:
>>> SubjectData.query().filter((SubjectData.project == 'sandbox') & (SubjectData.label.like('Brain*'))).all()
[<SubjectData Brain-0001 (BMIAXNAT34_S00001)>,
<SubjectData Brain-0002 (BMIAXNAT34_S00002)>]
The following operators can be used for creating constraints on properties:
Operator |
Description |
---|---|
|
Equals |
|
Smaller or equal |
|
Smaller |
|
Larger or equal |
|
Larger |
|
Like for fuzzy string matching |
The following compounding multiple contstrains operators are available:
Operator |
Description |
---|---|
|
AND operator |
|
OR operator |
Note
Do not forget to use the correct parenthesis as the & and | operators have a high
priority in Python, e.g. a == b & c == d
will fail, use (a == b) & (c == d)
The search query can be executed using the all()
method to find all matching objects.
There are other options available as well ways to create a table of results similar to the
original XNAT search. For example:
>>> query = SubjectData.query().filter((SubjectData.project == 'sandbox') & (SubjectData.label.like('Brain*')))
>>> query.all()
[<SubjectData Brain-0001 (BMIAXNAT34_S00001)>,
<SubjectData Brain-0002 (BMIAXNAT34_S00002)>]
>>> query.first()
<SubjectData Brain-0001 (BMIAXNAT34_S00001)>
>>> query.last()
<SubjectData Brain-0002 (BMIAXNAT34_S00002)>
>>> query.tabulate_csv()
'subject_label,subjectid,insert_user,insert_date,projects,project,gender,handedness,dob,educ,ses,quarantine_status\nBrain-0001,BMIAXNAT34_S00001,ibocharov,2022-11-15 22:26:38.676,",<sandbox>",sandbox,,,,,,active\nBrain-0002,BMIAXNAT34_S00002,ibocharov,2022-11-15 22:42:20.324,",<sandbox>",sandbox,,,,,,active\n'
>>> query.tabulate_dict()
[{'subject_label': 'Brain-0001',
'subjectid': 'BMIAXNAT34_S00001',
'insert_user': 'ibocharov',
'insert_date': '2022-11-15 22:26:38.676',
'projects': ',<sandbox>',
'project': 'sandbox',
'gender': '',
'handedness': '',
'dob': '',
'educ': '',
'ses': '',
'quarantine_status': 'active'},
{'subject_label': 'Brain-0002',
'subjectid': 'BMIAXNAT34_S00002',
'insert_user': 'ibocharov',
'insert_date': '2022-11-15 22:42:20.324',
'projects': ',<sandbox>',
'project': 'sandbox',
'gender': '',
'handedness': '',
'dob': '',
'educ': '',
'ses': '',
'quarantine_status': 'active'}]
# This requires pandas to be installed
>>> query.tabulate_pandas()
subject_label subjectid insert_user insert_date ... dob educ ses quarantine_status
0 Brain-0001 BMIAXNAT34_S00001 ibocharov 2022-11-15 22:26:38.676 ... NaN NaN NaN active
1 Brain-0002 BMIAXNAT34_S00002 ibocharov 2022-11-15 22:42:20.324 ... NaN NaN NaN active
[2 rows x 12 columns]
As you can see there are quite some ways to request the result from a query, for completeness see the following table:
Method |
Description |
---|---|
|
Get all objects |
|
Get first matching object |
|
Get last matching object |
|
Get one object, throws error if not exactly one object is matched |
|
Get one object or return None if no match is found. Throws error if not exactly zero or one objects are matched. |
|
Return a string containing a CSV tabulation of the data |
|
Return a list of dicts representing a tabulation of the data |
|
Return a string with the JSON response from the server |
|
Return a pandas DataFrame with the tabulation of the data |
Object creation#
It is possible to create object on the XNAT server (such as a new subject, experiment, etc). This is achieved by creating such an object in python and xnatpy will create a version of the server. For example you can create a subject:
>>> import xnat
>>> connection = xnat.connect('https://xnat.example.com')
>>> project = connection.projects['myproject']
>>> subject = connection.classes.SubjectData(parent=project, label='new_subject_label')
>>> subject
<SubjectData new_subject_label>
Note
the parent need to be the correct parent for the type, so an MRSessionData
would
need a SubjectData
to be the parent.
In the connection.classes
are all classes known the XNAT, also
MRSessionData
, CTSessionData
. To get a complete list you can do:
>>> dir(connection.classes)
Note
the valid parent for a project (ProjectData
) would be the connection object itself
Accessing XNAT files as local files (partial read)#
There is a helper added in xnatpy that allows you to open a remote file (FileData object) similarly as a local file. Note that it will read the file from the start and until it is done, seeking will download until the seek point.
For example:
>>> import xnat
>>> connection = xnat.connect('https://xnat.server.com')
>>> file_obj = connection.projects['project'].subjects['S'].experiments['EXP'].scans['T1'].resources['DICOM'].files[0]
<FileData 1.3.6.1...-18s1eb2.dcm (1.3.6.1...-18s1eb2.dcm)>
>>> with file_obj.open() as fin:
data = fin.read(3000)
>>> print(len(data))
3000
You can also use this to read the headers of a dicom file using pydicom:
>>> import pydicom
>>> with file_obj.open() as fin:
data = pydicom.dcmread(fin, stop_before_pixels=True)
This should read the header and stop downloading once the entire header is read.
Note
The file is read in chucks so there might be a bit too much data downloaded
Note
If you open the file and not close it, the memory buffer might not be cleaned properly
Accessing DICOM headers of scan#
Sometimes it is desired to read DICOM headers without downloading the entire scan. XNAT has a dicomdump service which can be used:
>>> connection.service.dicom_dump(scan_uri)
For more details see import_
. As
a helper we added a dicom_dump method to ScanData:
>>> scan.dicom_dump()
See ScanData.dicom_dump
for the details.
A limitation of the dicomdump of XNAT is that field values are truncated under
64 characters. If you want to access the entire dicom header, a convenience method
is added that reads the header via pydicom
:
>>> scan.read_dicom()
This reads only the header and not the pixel data and will only download part of the file. To read the pixel data use:
>>> scan.read_dicom(read_pixel_data=True)
For the details see ScanData.dicom_dump
Note
Only one file is loaded, so the pixel data will only contain a single slice unless it is a DICOM Enhanced file
Re-using XNAT jsession#
In same cases you might want multiple instance of xnatpy share a login session on the XNAT server. This can be achieved by supplying the jsession argument on connect. This will bypass all login logic and create a JSESSION cookie.
By default xnatpy actived closes a jsession on disconnect. If you want to be able to re-use the session after you disconnected xnatpy, you can set cli=True when creating the connection. However, if you do this, you have to actively destroy the jsession or it will time out after a set time (15 minutes by default).
For example:
# Create a connection and get the JSESSION
>>> connection = xnat.connect('htpps://xnat.example.com', user=...)
>>> connection.JSESSION
'24FA18BFA3DD4EB9C634AD79FE050339'
# Create a connection with a shared JSESSION
>>> connection2 = xnat.connect('https://xnat.example.com', jsession=connection.JSESSION, cli=True)
# If the jsession is still alive it should be the same (if not an error will be raised)
>>> connection2.JSESSION
'24FA18BFA3DD4EB9C634AD79FE050339'
# We can close connection2 safely without affecting connection because of
# the cli=True, however closing connection will destroy the JSESSION on
# server and make connection2 fail
>>> connection2.disconnect()
# This should still work
>>> connection.projects[...].subjects
...
>>> connection.disconnect
Example scripts#
There is a number of example scripts located in the examples
folder in the source code.
The following code is a small command-line tool that prints all files for a given scan in
the XNAT archive:
#!/usr/bin/env python
import xnat
import argparse
import re
def get_files(connection, project, subject, session, scan):
xnat_project = connection.projects[project]
xnat_subject = xnat_project.subjects[subject]
xnat_experiment = xnat_subject.experiments[session]
xnat_scan = xnat_experiment.scans[scan]
files = xnat_scan.files.values()
return files
def filter_files(xnat_files, regex):
filtered_files = []
regex = re.compile(regex)
for file in xnat_files:
found = regex.match(file.name)
if found:
filtered_files.append(file)
return filtered_files
def main():
parser = argparse.ArgumentParser(description='Prints all files from a certain scan.')
parser.add_argument('--xnathost', type=unicode, required=True, help='xnat host name')
parser.add_argument('--project', type=unicode, required=True, help='Project id')
parser.add_argument('--subject', type=unicode, required=True, help='subject')
parser.add_argument('--session', type=unicode, required=True, help='session')
parser.add_argument('--scan', type=unicode, required=True, help='scan')
parser.add_argument('--filter', type=unicode, required=False, default='.*', help='regex filter for file names')
args = parser.parse_args()
with xnat.connect(args.xnathost) as connection:
xnat_files = get_files(connection, args.project, args.subject, args.session, args.scan)
xnat_files = filter_files(xnat_files, args.filter)
for file in xnat_files:
print('{}'.format(file.name))
if __name__ == '__main__':
main()