XNAT Python Client¶

A new XNAT client that exposes XNAT objects/functions as python objects/functions.

Warning

This is NOT pyxnat, but a new module which is not as mature but uses a different philisophy for the user interface. Pyxnat is located at: https://pythonhosted.org/pyxnat/

The XNAT Python client is open-source (licensed under the Apache 2.0 license) and hosted on bitbucket at https://bitbucket.org/bigr_erasmusmc/xnatpy

To get yourself a copy:

hg clone https://<yourusername>@bitbucket.org/bigr_erasmusmc/xnatpy

or if you have a ssh key pair:

hg clone ssh://hg@bitbucket.org/bigr_erasmusmc/xnatpy

The official documentation can be found at xnat.readthedocs.org

XNAT Client Documentation¶

Introduction¶

A new XNAT client that exposes XNAT objects/functions as python objects/functions.

Getting started¶

To install just use the setup.py normally:

python setup.py install

To get started, create a connection and start querying:

>>> import xnat
>>> session = xnat.connect('https://central.xnat.org', user="", password="")
>>> session.projects['Sample_DICOM'].subjects
>>> session.disconnect()

To see all options for creating connections see the xnat.connect().

The XNAT session is the main class for interacting with XNAT. It contains the main communication functions.

When using IPython most functionality can be figured out by looking at the available attributes/methods of the returned objects.

Credentials¶

To store credentials this module uses the .netrc file. 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).

Status¶

Currently we do not support the creation of Projects, Subjects, Experiments, etc via code. You can create resources and uploads files to them. Also it is possible to import data via the import service (upload a zip file). There is also support for working with the prearchive (reading, moving, deleting and archiving).

There is virtuall no documentation or testing, this is a known limitation.

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 the file is located in ~/.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:

echo "machine images.xnat.org
>     login admin
>     password admin" > ~/.netrc
chmod 600 ~/.netrc

This will create the netrc file with the correct contents and set the permission correct.

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.

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')
[INFO] Found an 1.7 version (1.7.0)
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/security
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/birn/birnprov
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/screening/screeningAssessment
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/catalog
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/pipeline/workflow
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/pipeline/build
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/pipeline/repository
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/validation/protocolValidation
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/assessments
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/xdat
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/xdat/instance
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/xdat/PlexiViewer
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/xdat/display
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/xnat
[INFO] Retrieving schema from http://images.xnat.org/xapi/schemas/project
>>> 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

Dowloading data¶

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 exerpiments 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. Experiments, scans and resources can also be downloaded in a zip bundle using the download_zip method.

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. For more information on importing data see import_

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, more or remove the session using simple methods. For more information see PrearchiveSession

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()

XNAT scripting tutorial¶

In the previous part of the tutorial you were introduced to the XNAT web interface. This is useful to inspect data and perform simple operations. However, when the size of a study increases this might become cumbersome. In that case, XNAT allows users to interface via a 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. In this tutorial we will use xnatpy: a Python package that is build on top of requests.

Create a connection¶

Start up ipython and create a connection, it will prompt you to enter the password for the user test:

>> ipython
Python 2.7.12+ (default, Sep  1 2016, 20:27:38)
Type "copyright", "credits" or "license" for more information.

IPython 2.4.1 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.

In [1]: import xnat

In [2]: session = xnat.connect('http://145.100.58.186/xnat', user='test')
Please enter the password for user 'test':
[INFO] Found an 1.6 version (1.6.4)
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/xnat/xnat.xsd
[INFO] Found additional schemas: ['http://145.100.58.186/xnat/schemas/pipeline/workflow.xsd', 'http://145.100.58.186/xnat/schemas/catalog/catalog.xsd', 'http://145.100.58.186/xnat/schemas/pipeline/repository.xsd', 'http://145.100.58.186/xnat/schemas/screening/screeningAssessment.xsd', 'http://145.100.58.186/xnat/schemas/project/project.xsd', 'http://145.100.58.186/xnat/schemas/validation/protocolValidation.xsd', 'http://145.100.58.186/xnat/schemas/assessments/assessments.xsd', 'http://145.100.58.186/xnat/schemas/birn/birnprov.xsd', 'http://145.100.58.186/xnat/schemas/security/security.xsd']
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/pipeline/workflow.xsd
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/catalog/catalog.xsd
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/pipeline/repository.xsd
[ERROR] Could not parse schema from http://145.100.58.186/xnat/schemas/pipeline/repository.xsd, not valid XML found
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/screening/screeningAssessment.xsd
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/project/project.xsd
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/validation/protocolValidation.xsd
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/assessments/assessments.xsd
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/birn/birnprov.xsd
[INFO] Retrieving schema from http://145.100.58.186/xnat/schemas/security/security.xsd

Once a connection is established it is possible to browse the projects. This can achieved by simply looking at the projects attribute of the session:

In [3]: session.projects
Out[3]: <XNATListing (brainimages, Brain Image Analysis): <ProjectData Brain Image Analysis (brainimages)>, (fastrtutorial, Fastr Tutorial): <ProjectData Fastr Tutorial (fastrtutorial)>>

We can select a project by simply indexing the project listing using other the id or name of the project:

In [4]: project = session.projects['brainimages']

In a similar fashion one can explore and selection subjects (and experiments) from the project:

In [5]: project.subjects
Out[5]: <XNATListing (demo_S00081, ANONYMIZ): <SubjectData ANONYMIZ (demo_S00081)>>

Importing data into XNAT¶

In the earlier part of the tutorial you uploaded data to XNAT and used the prearchive. This functionality is also exposed using xnatpy. It is both possible to upload data straight into the archive and to upload via the prearchive with more controlled archiving.

For the uploading we use an import service. This service automatically sorts the DICOM data in an zip archive into scans. Uploading the data we used earlier straight into the archive under the subject and experiment ANONYMIZ2 is one simple command:

## Import directly into archive:
In [6]: experiment = session.services.import_('/home/hachterberg/temp/ANONYMIZ.zip', project='brainimages', subject='ANONYMIZ2', experiment='ANONYMIZ2')

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:
In [7]: prearchive_session = session.services.import_('/home/hachterberg/temp/ANONYMIZ.zip', project='brainimages', destination='/prearchive')

In [8]: prearchive_session
Out[8]: <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:

In [9]: session.prearchive.sessions()
Out[9]: [<PrearchiveSession brainimages/20161107_114859342/ANONYMIZ>]

Once the data in the prearchive is located it can be archived as follows:

In [10]: prearchive_session = session.prearchive.sessions()[0]

In [11]: experiment = prearchive_session.archive(subject='ANONYMIZ3', experiment='ANONYMIZ3')

In [11]: experiment
Out[11]: <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.

Download data¶

It is possible to list the scans contained in an experiment and explore them further:

In [12]: experiment.scans
Out[12]: <XNATListing (1001-MR2, FLAIR): <MrScanData FLAIR (1001-MR2)>, (1001-MR3, T1): <MrScanData T1 (1001-MR3)>, (1001-MR1, PD): <MrScanData PD (1001-MR1)>>

In [13]: experiment.scans['T1']
Out[13]: <MrScanData T1 (1001-MR3)>

In some cases you might want to download an individual scan to inspect/process locally. This is using:

In [14]: experiment.scans['T1'].download('/home/hachterberg/temp/T1.zip')
Downloading http://145.100.58.186/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:

In [15]: subject = experiment.subject

In [16]: subject.download_dir('/home/hachterberg/temp/')
Downloading http://145.100.58.186/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:

In [17]: !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
...

Inspect DICOM tags¶

You can retrieve the dicom tags of a scan, in both the archive and the prearchive, using the dicom_dump method of a Scan or PrearchiveScan object.:

In [10]: experiment.scans['T1'].dicom_dump()

You can also filter on DICOM tags using the field argument:

In [11]: experiment.scans['T1'].dicom_dump(fields="PatientID")
In [12]: experiment.scans['T1'].dicom_dump(fields=["PatientID", "PatientName"])

Custom variables¶

The custom variables are 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'

Code reference¶

`xnat` Package¶

This package contains the entire client. The connect function is the only function actually in the package. All following classes are created based on the https://central.xnat.org/schema/xnat/xnat.xsd schema and the xnatcore and xnatbase modules, using the convert_xsd.

xnat.connect(server, user=None, password=None, verify=True, netrc_file=None, debug=False, extension_types=True, loglevel=None, logger=None, detect_redirect=True)¶

Connect to a server and generate the correct classed based on the servers xnat.xsd This function returns an object that can be used as a context operator. It will call disconnect automatically when the context is left. If it is used as a function, then the user should call .disconnect() to destroy the session and temporary code file.

Parameters:	server (str) – uri of the server to connect to (including http:// or https://) user (str) – username to use, leave empty to use netrc entry or anonymous login. password (str) – password to use with the username, leave empty when using netrc. If a username is given and no password, there will be a prompt on the console requesting the password. verify (bool) – verify the https certificates, if this is false the connection will be encrypted with ssl, but the certificates are not checked. This is potentially dangerous, but required for self-signed certificates. netrc_file (str) – alternative location to use for the netrc file (path pointing to a file following the netrc syntax) bool (debug) – Set debug information printing on and print extra debug information. This is meant for xnatpy developers and not for normal users. If you want to debug your code using xnatpy, just set the loglevel to DEBUG which will show you all requests being made, but spare you the xnatpy internals. loglevel (str) – Set the level of the logger to desired level logger (logging.Logger) – A logger to reuse instead of creating an own logger detect_redirect (bool) – Try to detect a redirect (via a 302 response) and short-cut for subsequent requests
Returns:	XNAT session object
Return type:	XNATSession

Preferred use:

>>> import xnat
>>> with xnat.connect('https://central.xnat.org') as session:
...    subjects = session.projects['Sample_DICOM'].subjects
...    print('Subjects in the SampleDICOM project: {}'.format(subjects))
Subjects in the SampleDICOM project: <XNATListing (CENTRAL_S01894, dcmtest1): <SubjectData CENTRAL_S01894>, (CENTRAL_S00461, PACE_HF_SUPINE): <SubjectData CENTRAL_S00461>>

Alternative use:

>>> import xnat
>>> session = xnat.connect('https://central.xnat.org')
>>> subjects = session.projects['Sample_DICOM'].subjects
>>> print('Subjects in the SampleDICOM project: {}'.format(subjects))
Subjects in the SampleDICOM project: <XNATListing (CENTRAL_S01894, dcmtest1): <SubjectData CENTRAL_S01894>, (CENTRAL_S00461, PACE_HF_SUPINE): <SubjectData CENTRAL_S00461>>
>>> session.disconnect()

`session` Module¶

class xnat.session.XNATSession(server, logger, interface=None, user=None, password=None, keepalive=840, debug=False, original_uri=None)¶

Bases: object

The main XNATSession session class. It keeps a connection to XNATSession alive and manages the main communication to XNATSession. To keep the connection alive there is a background thread that sends a heart-beat to avoid a time-out.

The main starting points for working with the XNATSession server are:

XNATSession.projects
XNATSession.subjects
XNATSession.experiments
XNATSession.prearchive
XNATSession.services
XNATSession.users

Note

Some methods create listing that are using the xnat.XNATListing class. They allow for indexing with both XNATSession ID and a secondary key (often the label). Also they support basic filtering and tabulation.

There are also methods for more low level communication. The main methods are XNATSession.get, XNATSession.post, XNATSession.put, and XNATSession.delete. The methods do not query URIs but instead query XNATSession REST paths as described in the XNATSession 1.6 REST API Directory.

For an even lower level interfaces, the XNATSession.interface gives access to the underlying requests interface. This interface has the user credentials and benefits from the keep alive of this class.

Note

XNATSession Objects have a client-side cache. This is for efficiency, but might cause problems if the server is being changed by a different client. It is possible to clear the current cache using XNATSession.clearcache. Turning off caching complete can be done by setting XNATSession.caching.

Warning

You should NOT try use this class directly, it should only be created by xnat.connect.

clearcache()¶: Clear the cache of the listings in the Session object

delete(path, headers=None, accepted_status=None, query=None, timeout=None)¶

Delete the content of a given REST directory.

Parameters:	path (str) – the path of the uri to retrieve (e.g. “/data/archive/projects”) the remained for the uri is constructed automatically format (str) – the format of the request, this will add the format= to the query string query (dict) – the values to be added to the query string in the uri accepted_status (list) – a list of the valid values for the return code, default [200] timeout (float or tuple) – timeout in seconds, float or (connection timeout, read timeout)
Returns:	the requests reponse
Return type:	requests.Response

download(uri, target, format=None, verbose=True, timeout=None)¶: Download uri to a target file

download_stream(uri, target_stream, format=None, verbose=False, chunk_size=524288, update_func=None, timeout=None)¶

Download the given uri to the given target_stream.

Parameters:

uri (str) – Path of the uri to retrieve.
target_stream (file) – A writable file-like object to save the stream to.
format (str) – Request format
verbose (bool) – If True, and an update_func is not specified, a progress bar is shown on stdout.
chunk_size (int) – Download this many bytes at a time
update_func (func) –
If provided, will be called every chunk_size bytes. Must accept three parameters:
- the number of bytes downloaded so far
- the total number of bytse to be downloaded (might be None),
- A boolean flag which is False during the download, and True when the download has completed (or failed)
timeout (float or tuple) – timeout in seconds, float or (connection timeout, read timeout)

download_zip(uri, target, verbose=True, timeout=None)¶: Download uri to a target zip file

experiments¶

Listing of all experiments on the XNAT server

Cached using the caching decorator

get(path, format=None, query=None, accepted_status=None, timeout=None)¶

Retrieve the content of a given REST directory.

Parameters:	path (str) – the path of the uri to retrieve (e.g. “/data/archive/projects”) the remained for the uri is constructed automatically format (str) – the format of the request, this will add the format= to the query string query (dict) – the values to be added to the query string in the uri accepted_status (list) – a list of the valid values for the return code, default [200] timeout (float or tuple) – timeout in seconds, float or (connection timeout, read timeout)
Returns:	the requests reponse
Return type:	requests.Response

get_json(uri, query=None, accepted_status=None)¶

Helper function that perform a GET, but sets the format to JSON and parses the result as JSON

Parameters:	uri (str) – the path of the uri to retrieve (e.g. “/data/archive/projects”) the remained for the uri is constructed automatically query (dict) – the values to be added to the query string in the uri

head(path, accepted_status=None, allow_redirects=False, timeout=None)¶

Retrieve the header for a http request of a given REST directory.

Parameters:	path (str) – the path of the uri to retrieve (e.g. “/data/archive/projects”) the remained for the uri is constructed automatically accepted_status (list) – a list of the valid values for the return code, default [200] allow_redirects (bool) – allow you request to be redirected timeout (float or tuple) – timeout in seconds, float or (connection timeout, read timeout)
Returns:	the requests reponse
Return type:	requests.Response

interface¶: The underlying requests interface used.

post(path, data=None, json=None, format=None, query=None, accepted_status=None, timeout=None)¶

Post data to a given REST directory.

Parameters:	path (str) – the path of the uri to retrieve (e.g. “/data/archive/projects”) the remained for the uri is constructed automatically data – Dictionary, bytes, or file-like object to send in the body of the `Request`. json – json data to send in the body of the `Request`. format (str) – the format of the request, this will add the format= to the query string query (dict) – the values to be added to the query string in the uri accepted_status (list) – a list of the valid values for the return code, default [200, 201] timeout (float or tuple) – timeout in seconds, float or (connection timeout, read timeout)
Returns:	the requests reponse
Return type:	requests.Response

prearchive¶: Representation of the prearchive on the XNAT server, see xnat.prearchive

projects¶

Listing of all projects on the XNAT server

Cached using the caching decorator

put(path, data=None, files=None, json=None, format=None, query=None, accepted_status=None, timeout=None)¶

Put the content of a given REST directory.

Parameters:	path (str) – the path of the uri to retrieve (e.g. “/data/archive/projects”) the remained for the uri is constructed automatically data – Dictionary, bytes, or file-like object to send in the body of the `Request`. json – json data to send in the body of the `Request`. files – Dictionary of `'name': file-like-objects` (or `{'name': file-tuple}`) for multipart encoding upload. `file-tuple` can be a 2-tuple `('filename', fileobj)`, 3-tuple `('filename', fileobj, 'content_type')` or a 4-tuple `('filename', fileobj, 'content_type', custom_headers)`, where `'content-type'` is a string defining the content type of the given file and `custom_headers` a dict-like object containing additional headers to add for the file. format (str) – the format of the request, this will add the format= to the query string query (dict) – the values to be added to the query string in the uri accepted_status (list) – a list of the valid values for the return code, default [200, 201] timeout (float or tuple) – timeout in seconds, float or (connection timeout, read timeout)
Returns:	the requests reponse
Return type:	requests.Response

scan_types¶: A list of scan types associated with this XNATSession instance

scanners¶: A list of scanners referenced in XNATSession

services¶: Collection of services, see xnat.services

subjects¶

Listing of all subjects on the XNAT server

Cached using the caching decorator

upload(uri, file_, retries=1, query=None, content_type=None, method=u'put', overwrite=False, timeout=None)¶

Upload data or a file to XNAT

Parameters:

uri (str) – uri to upload to
file – the file handle, path to a file or a string of data (which should not be the path to an existing file!)
retries (int) – amount of times xnatpy should retry in case of failure
query (dict) – extra query string content
content_type – the content type of the file, if not given it will default to application/octet-stream
method (str) – either put (default) or post
overwrite (bool) – indicate if previous data should be overwritten
timeout (float or tuple) – timeout in seconds, float or (connection timeout, read timeout)

Returns:

users¶: Representation of the users registered on the XNAT server

xnat_version¶

The version of the XNAT server

Cached using the caching decorator

xnat.session.default_update_func(total)¶

Set up a default update function to be used by the Session.download_stream method. This function configures a progressbar.ProgressBar object which displays progress as a file is downloaded.

Parameters:	total (int) – Total number of bytes to be downloaded (might be `None`)
Returns:	A function to be used as the `update_func` by the `Session.download_stream` method.

`inspect` Module¶

class xnat.inspect.Inspect(xnat_session)¶

Bases: object

datafields(datatype, pattern=u'*', prepend_type=True)¶

datatypes(pattern=u'*', fields_pattern=None)¶

xnat_session¶

`prearchive` Module¶

class xnat.prearchive.Prearchive(xnat_session)¶

Bases: object

sessions(project=None)¶

Get the session in the prearchive, optionally filtered by project. This function is not cached and returns the results of a query at each call.

Parameters:	project (str) – the project to filter on
Returns:	list of prearchive session found
Return type:	list

xnat_session¶

class xnat.prearchive.PrearchiveFile(uri, xnat_session, id_=None, datafields=None, parent=None, fieldname=None)¶

Bases: xnat.core.XNATBaseObject

data¶

download(path)¶

Download the file

Parameters:	path (str) – the path to download to
Returns:	the path of the downloaded file
Return type:	str

fulldata¶

name¶: The name of the file

size¶: The size of the file

xpath¶

class xnat.prearchive.PrearchiveScan(uri, xnat_session, id_=None, datafields=None, parent=None, fieldname=None)¶

Bases: xnat.core.XNATBaseObject

data¶

dicom_dump(fields=None)¶

Retrieve a dicom dump as a JSON data structure See the XAPI documentation for more detailed information: DICOM Dump Service

Parameters:	fields (list) – Fields to filter for DICOM tags. It can either a tag name or tag number in the format GGGGEEEE (G = Group number, E = Element number)
Returns:	JSON object (dict) representation of DICOM header
Return type:	dict

download(path)¶

Download the scan as a zip

Parameters:	path (str) – the path to download to
Returns:	the path of the downloaded file
Return type:	str

files¶: List of files contained in the scan

fulldata¶

series_description¶: The series description of the scan

xpath¶

class xnat.prearchive.PrearchiveSession(uri=None, xnat_session=None, id_=None, datafields=None, parent=None, fieldname=None, overwrites=None, **kwargs)¶

Bases: xnat.core.XNATBaseObject

archive(overwrite=None, quarantine=None, trigger_pipelines=None, project=None, subject=None, experiment=None)¶

Method to archive this prearchive session to the main archive

Parameters:	overwrite (str) – how the handle existing data (none, append, delete) quarantine (bool) – flag to indicate session should be quarantined trigger_pipelines (bool) – indicate that archiving should trigger pipelines project (str) – the project in the archive to assign the session to subject (str) – the subject in the archive to assign the session to experiment (str) – the experiment in the archive to assign the session content to
Returns:	the newly created experiment
Return type:	ExperimentData

autoarchive¶

data¶

delete(asynchronous=None)¶

Delete the session from the prearchive

Parameters:	asynchronous (bool) – flag to delete asynchronously
Returns:	requests response

download(path)¶

Method to download the zip of the prearchive session

Parameters:	path (str) – path to download to
Returns:	path of the downloaded zip file
Return type:	str

folder_name¶

fulldata¶

id¶: A unique ID for the session in the prearchive :return:

label¶

lastmod¶

move(new_project, asynchronous=None)¶

Move the session to a different project in the prearchive

Parameters:	new_project (str) – the id of the project to move to asynchronous (bool) – flag to move asynchronously
Returns:	requests response

name¶

prevent_anon¶

prevent_auto_commit¶

project¶

rebuild(asynchronous=None)¶

Rebuilt the session in the prearchive

Parameters:	asynchronous (bool) – flag to rebuild asynchronously
Returns:	requests response

scan_date¶

scan_time¶

scans¶: List of scans in the prearchive session

status¶

subject¶

tag¶

timestamp¶

uploaded¶: Datetime when the session was uploaded

xpath¶

`services` Module¶

class xnat.services.Services(xnat_session)¶

Bases: object

The class representing all service functions in XNAT found in the /data/services REST directory

dicom_dump(src, fields=None)¶

Retrieve a dicom dump as a JSON data structure See the XAPI documentation for more detailed information: DICOM Dump Service

Parameters:	fields (lst) – Fields to filter for DICOM tags. It can either a tag name or tag number in the format GGGGEEEE (G = Group number, E = Element number)
Returns:	JSON object (dict) representation of DICOM header
Return type:	dict

import_(path, overwrite=None, quarantine=False, destination=None, trigger_pipelines=None, project=None, subject=None, experiment=None, content_type=None)¶

Import a file into XNAT using the import service. See the XNAT wiki for a detailed explanation.

Parameters:

path (str) – local path of the file to upload and import
overwrite (str) – how the handle existing data (none, append, delete)
quarantine (bool) – flag to indicate session should be quarantined
trigger_pipelines (bool) – indicate that archiving should trigger pipelines
destination (str) – the destination to upload the scan to
project (str) – the project in the archive to assign the session to
subject (str) – the subject in the archive to assign the session to
experiment (str) – the experiment in the archive to assign the session content to
content_type (str) – overwite the content_type (by the mimetype will be guessed)

Returns:

issue_token(user=None)¶

Issue a login token, by default for the current logged in user. If username is given, for that user. To issue tokens for other users you must be an admin.

Parameters:	user (str) – User to issue token for, default is current user
Returns:	Token in a named tuple (alias, secret)

xnat_session¶

class xnat.services.TokenResult(alias, secret)¶

Bases: tuple

alias¶: Alias for field number 0

secret¶: Alias for field number 1

`users` Module¶

class xnat.users.User(data)¶

Bases: object

Representation of a user on the connected XNAT systen

data¶

email¶: The email of the user

first_name¶: The first name of the user

id¶: The id of the user

last_name¶: The last name of the user

login¶: The login name of the user

class xnat.users.Users(xnat_session)¶

Bases: _abcoll.Mapping

Listing of the users on the connected XNAT installation

data¶: Cached using the caching decorator

xnat_session¶

XNAT Python Client¶

XNAT Client Documentation¶

Introduction¶

Getting started¶

Credentials¶

Status¶

XNATpy Tutorial¶

XNAT REST API¶

Installation¶

Connecting to a server¶

Credentials¶

Self-closing sessions¶

Exploring your xnat server¶

Looping over data¶

Dowloading data¶

Importing data into XNAT¶

Prearchive¶

Example scripts¶

XNAT scripting tutorial¶

Create a connection¶

Importing data into XNAT¶

Download data¶

Inspect DICOM tags¶

Custom variables¶

Code reference¶

xnat Package¶

session Module¶

inspect Module¶

prearchive Module¶

services Module¶

users Module¶

Indices and tables¶

`xnat` Package¶

`session` Module¶

`inspect` Module¶

`prearchive` Module¶

`services` Module¶

`users` Module¶