A Python library to manipulate the Neuroimaging Data Model.
Contents
- 1 PyNIDM: Neuroimaging Data Model in Python
- 2 PyNIDM: REST API and Command Line Usage
$ pip install pynidmThis software is open source and community developed. As such, we encourage anyone and everyone interested in semantic web and neuroimaging to contribute. To begin contributing code to the repository, please fork the main repo into your user space and use the pull request GitHub feature to submit code for review. Please provide a reasonably detailed description of what was changed and why in the pull request.
To establish development environment, we recommend to install the clone of this repository in development mode with development tools installed via
$ pip install -e .[devel]We also recommend using
pre-commit for ensuring
that your contributions would conform our conventions for code quality
etc. You can enable pre-commit by running once in your clone
$ pre-commit installwhich would then ensure that all commits would be subject to black code reformatting etc.
If you encounter a bug, you can directly report it in the issues section. Please describe how to reproduce the issue and include as much information as possible that can be helpful for fixing it. If you would like to suggest a fix, please open a new pull request or include your suggested fix in the issue.
We would love to hear your thoughts on our Python toolbox. Feedback, questions, or feature requests can also be submitted as issues. Note, we are a small band of researchers who mostly volunteer our time to this project. We will respond as quickly as possible.
This program will convert a BIDS MRI dataset to a NIDM-Experiment RDF document. It will parse phenotype information and simply store variables/values and link to the associated json data dictionary file. To use this tool please set your INTERLEX_API_KEY environment variable to your unique API key. To get an Interlex API key you visit SciCrunch, register for an account, then click on "MyAccount" and "API Keys" to add a new API key for your account.
$ bidsmri2nidm -d [ROOT BIDS DIRECT] -bidsignore
usage: bidsmri2nidm [-h] -d DIRECTORY [-jsonld] [-bidsignore] [-no_concepts]
[-json_map JSON_MAP] [-log LOGFILE] [-o OUTPUTFILE]
This program will represent a BIDS MRI dataset as a NIDM RDF document and provide user with opportunity to annotate
the dataset (i.e. create sidecar files) and associate selected variables with broader concepts to make datasets more
FAIR.
Note, you must obtain an API key to Interlex by signing up for an account at scicrunch.org then going to My Account
and API Keys. Then set the environment variable INTERLEX_API_KEY with your key.
optional arguments:
-h, --help show this help message and exit
-d DIRECTORY Full path to BIDS dataset directory
-jsonld, --jsonld If flag set, output is json-ld not TURTLE
-bidsignore, --bidsignore
If flag set, tool will add NIDM-related files to .bidsignore file
-no_concepts, --no_concepts
If flag set, tool will no do concept mapping
-log LOGFILE, --log LOGFILE
Full path to directory to save log file. Log file name is bidsmri2nidm_[basename(args.directory)].log
-o OUTPUTFILE Outputs turtle file called nidm.ttl in BIDS directory by default..or whatever path/filename is set here
map variables to terms arguments:
-json_map JSON_MAP, --json_map JSON_MAP
Optional full path to user-suppled JSON file containing data element definitions.This program will load in a CSV file and iterate over the header variable names performing an elastic search of https://scicrunch.org/nidm-terms for NIDM-ReproNim tagged terms that fuzzy match the variable names. The user will then interactively pick a term to associate with the variable name. The resulting annotated CSV data will then be written to a NIDM data file. To use this tool please set your INTERLEX_API_KEY environment variable to your unique API key. To get an Interlex API key you visit SciCrunch, register for an account, then click on "MyAccount" and "API Keys" to add a new API key for your account.
usage: csv2nidm [-h] -csv CSV_FILE [-json_map JSON_MAP | -redcap REDCAP]
[-nidm NIDM_FILE] [-no_concepts] [-log LOGFILE] -out
OUTPUT_FILE
This program will load in a CSV file and iterate over the header variable
names performing an elastic search of https://scicrunch.org/ for NIDM-ReproNim
tagged terms that fuzzy match the variable names. The user will then
interactively pick a term to associate with the variable name. The resulting
annotated CSV data will then be written to a NIDM data file. Note, you must
obtain an API key to Interlex by signing up for an account at scicrunch.org
then going to My Account and API Keys. Then set the environment variable
INTERLEX_API_KEY with your key. The tool supports import of RedCap data
dictionaries and will convert relevant information into a json-formatted
annotation file used to annotate the data elements in the resulting NIDM file.
optional arguments:
-h, --help show this help message and exit
-csv CSV_FILE Full path to CSV file to convert
-json_map JSON_MAP Full path to user-suppled JSON file containing
variable-term mappings.
-redcap REDCAP Full path to a user-supplied RedCap formatted data
dictionary for csv file.
-nidm NIDM_FILE Optional full path of NIDM file to add CSV->NIDM
converted graph to
-no_concepts If this flag is set then no concept associations will
beasked of the user. This is useful if you already
have a -json_map specified without concepts and want
tosimply run this program to get a NIDM file with user
interaction to associate concepts.
-log LOGFILE, --log LOGFILE
full path to directory to save log file. Log file name
is csv2nidm_[arg.csv_file].log
-out OUTPUT_FILE Full path with filename to save NIDM fileThis function will convert NIDM files to various RDF-supported formats and name then / put them in the same place as the input file.
Usage: pynidm convert [OPTIONS]
Options:
-nl, --nidm_file_list TEXT A comma separated list of NIDM files with
full path [required]
-t, --type [turtle|jsonld|xml-rdf|n3|trig]
If parameter set then NIDM file will be
exported as JSONLD [required]
--help Show this message and exit.This function will concatenate NIDM files. Warning, no merging will be done so you may end up with multiple prov:agents with the same subject id if you're concatenating NIDM files from multiple visits of the same study. If you want to merge NIDM files on subject ID see pynidm merge
Usage: pynidm concat [OPTIONS]
Options:
-nl, --nidm_file_list TEXT A comma separated list of NIDM files with full
path [required]
-o, --out_file TEXT File to write concatenated NIDM files
[required]
--help Show this message and exit.This command will produce a visualization(pdf) of the supplied NIDM files named the same as the input files and stored in the same directories.
Usage: pynidm visualize [OPTIONS]
Options:
-nl, --nidm_file_list TEXT A comma separated list of NIDM files with full
path [required]
--help Show this message and exit.This function will merge NIDM files. See command line parameters for supported merge operations.
Usage: pynidm merge [OPTIONS]
Options:
-nl, --nidm_file_list TEXT A comma separated list of NIDM files with full
path [required]
-s, --s If parameter set then files will be merged by
ndar:src_subjec_id of prov:agents
-o, --out_file TEXT File to write concatenated NIDM files
[required]
--help Show this message and exit.This function provides query support for NIDM graphs.
Usage: pynidm query [OPTIONS]
Options:
-nl, --nidm_file_list TEXT A comma separated list of NIDM files with
full path [required]
-nc, --cde_file_list TEXT A comma separated list of NIDM CDE files
with full path. Can also be set in the
CDE_DIR environment variable
-q, --query_file FILENAME Text file containing a SPARQL query to
execute
-p, --get_participants Parameter, if set, query will return
participant IDs and prov:agent entity IDs
-i, --get_instruments Parameter, if set, query will return list of
onli:assessment-instrument:
-iv, --get_instrument_vars Parameter, if set, query will return list of
onli:assessment-instrument: variables
-de, --get_dataelements Parameter, if set, will return all
DataElements in NIDM file
-debv, --get_dataelements_brainvols
Parameter, if set, will return all brain
volume DataElements in NIDM file along with
details
-bv, --get_brainvols Parameter, if set, will return all brain
volume data elements and values along with
participant IDs in NIDM file
-o, --output_file TEXT Optional output file (CSV) to store results
of query
-u, --uri TEXT A REST API URI query
-j / -no_j Return result of a uri query as JSON
-v, --verbosity TEXT Verbosity level 0-5, 0 is default
--help Show this message and exit.Details on the REST API URI format and usage can be found on the REST API usage page.
This function provides linear regression support for NIDM graphs.
Usage: pynidm linear-regression [OPTIONS]
Options:
-nl, --nidm_file_list TEXT A comma-separated list of NIDM files with
full path [required]
-r, --regularization TEXT Parameter, if set, will return the results of
the linear regression with L1 or L2 regularization
depending on the type specified, and the weight
with the maximum likelihood solution. This will
prevent overfitting. (Ex: -r L1)
-model, --ml TEXT An equation representing the linear
regression. The dependent variable comes
first, followed by "=" or "~", followed by
the independent variables separated by "+"
(Ex: -model "fs_003343 = age*sex + sex +
age + group + age*group + bmi") [required]
-contstant, --ctr TEXT Parameter, if set, will return differences in
variable relationships by group. One or
multiple parameters can be used (multiple
parameters should be separated by a comma-
separated list) (Ex: -contrast group,age)
-o, --output_file TEXT Optional output file (TXT) to store results
of query
--help Show this message and exit.To use the linear regression algorithm successfully, structure, syntax, and querying is important. Here is how to maximize the usefulness of the tool:
First, use pynidm query to discover the variables to use. PyNIDM allows for the use of either data elements (PIQ_tca9ck), specific URLs (http://uri.interlex.org/ilx_0100400), or source variables (DX_GROUP).
An example of a potential query is:
pynidm query -nl /simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl,/simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_b/nidm.ttl -u /projects?fields=fs_000008,DX_GROUP,PIQ_tca9ck,http://uri.interlex.org/ilx_0100400
You can also do:
pynidm query -nl /simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl,/Users/Ashu/Downloads/simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_b/nidm.ttl -gf fs_000008,DX_GROUP,PIQ_tca9ck,http://uri.interlex.org/ilx_0100400
The query looks in the two files specified in the -nl parameter for the variables specified. In this case, we use fs_000008 and DX_GROUP (source variables), a URL (http://uri.interlex.org/ilx_0100400), and a data element (PIQ_tca9ck). The output of the file is slightly different depending on whether you use -gf or -u. With -gf, it will return the variables from both files separately, while -u combines them.
Now that we have selected the variables, we can perform a linear regression. In this example, we will look at the effect of DX_GROUP, age at scan, and PIQ on supratentorial brain volume.
The command to use for this particular data is:
pynidm linear-regression -nl /simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl,/simple2_NIDM_examples/datasets.datalad.org/abide/RawDataBIDS/CMU_b/nidm.ttl -model "fs_000008 = DX_GROUP + PIQ_tca9ck + http://uri.interlex.org/ilx_0100400" -contrast "DX_GROUP" -r L1
-nl specifies the file(s) to pull data from, while -model is the model to perform a linear regression model on. In this case, the variables are fs_000008 (the dependent variable, supratentorial brain volume), DX_GROUP (diagnostic group), PIQ_tca9ck (PIQ), and http://uri.interlex.org/ilx_0100400 (age at scan). The -contrast parameter says to contrast the data using DX_GROUP, and then do a L1 regularization to prevent overfitting.
Details on the REST API URI format and usage can be found below.
There are two main ways to interact with NIDM data using the PyNIDM REST API. First, the pynidm query command line utility will accept queries formatted as REST API URIs. Second, the rest-server.py script can be used to run a HTTP server to accept and process requests. This script can either be run directly or using a docker container defined in the docker directory of the project.
Example usage:
$ pynidm query -nl "cmu_a.ttl,cmu_b.ttl" -u /projects
dc1bf9be-10a3-11ea-8779-003ee1ce9545
ebe112da-10a3-11ea-af83-003ee1ce9545To use the REST API query syntax on the command line, follow the PyNIDM installation instructions.
The simplest way to deploy a HTTP REST API server would be with the provided docker container. You can find instructions for that process in the README.md file in the docker directory of the Github repository.
You can find details on the REST API at the SwaggerHub API Documentation. The OpenAPI specification file is part of the Github repository in 'docs/REST_API_definition.openapi.yaml'
Here is a list of the current operations. See the SwaggerHub page for more details and return formats.
- /projects
- /projects/{project_id}
- /projects/{project_id}/subjects
- /projects/{project_id}/subjects?filter=[filter expression]
- /projects/{project_id}/subjects/{subject_id}
- /projects/{project_id}/subjects/{subject_id}/instruments/{instrument_id}
- /projects/{project_id}/subjects/{subject_id}/derivatives/{derivative_id}
- /statistics/projects/{project_id}
You can append the following query parameters to many of the operations:
- filter - field
/projectsGet a list of all project IDs available.
Supported query parameters: none
/projects/{project_id}See some details for a project. This will include the list of subject IDs and data elements used in the project
Supported query parameters: filter
/projects/{project_id}/subjectsGet the list of subjects in a project
Supported query parameters: filter
/projects/{project_id}/subjects/{subject_id}Get the details for a particular subject. This will include the results of any instrumnts or derivatives associated with the subject, as well as a list of the related activities.
Supported query parameters: none
/projects/{project_id}/subjects/{subject_id}/instruments/{instrument_id}Get the values for a particular instrument
Supported query parameters: none
/projects/{project_id}/subjects/{subject_id}/derivatives/{derivative_id}Get the values for a particular derivative
Supported query parameters: none
/statistics/projects/{project_id}See project statistics. You can also use this operation to get statsitcs on a particular instrument or derivative entry by use a field query option.
Supported query parameters: filter, field
/statistics/projects/{project_id}/subjects/{subject_id}See some details for a project. This will include the list of subject IDs and data elements used in the project
Supported query parameters: none
filterThe filter query parameter is used when you want to receive data only on subjects that match some criteria. The format for the filter value should be of the form:
identifier op value [ and identifier op value and ... ]
Identifiers should be formatted as "instrument.ID" or "derivatives.ID" You can use any value for the instrument ID that is shown for an instrument or in the data_elements section of the project details. For the derivative ID, you can use the last component of a derivative field URI (ex. for the URI http://purl.org/nidash/fsl#fsl_000007, the ID would be "fsl_000007") or the exact label shown when viewing derivative data (ex. "Left-Caudate (mm^3)").
The
opcan be one of "eq", "gt", "lt".- Example filters:
?filter=instruments.AGE_AT_SCAN gt 30?filter=instrument.AGE_AT_SCAN eq 21 and derivative.fsl_000007 lt 3500
fieldsThe fields query parameter is used to specify what fields should be detailed in a statistics operation. For each field specified the result will show minimum, maximum, average, median, and standard deviation for the values of that field across all subjects matching the operation and filter. Multiple fields can be specified by separating each field with a comma.
Fields should be formatted in the same way as identifiers are specified in the filter parameter.
- Example field query:
http://localhost:5000/statistics/projects/abc123?field=instruments.AGE_AT_SCAN,derivatives.fsl_000020
By default the HTTP REST API server will return JSON formatted objects or arrays. When using the pynidm query command line utility the default return format is text (when possible) or you can use the -j option to have the output formatted as JSON.
curl http://localhost:5000/projectsExample response:
[
"dc1bf9be-10a3-11ea-8779-003ee1ce9545"
]curl http://localhost:5000/projects/dc1bf9be-10a3-11ea-8779-003ee1ce9545Example response:
{
"http://www.w3.org/1999/02/22-rdf-syntax-ns#type": "http://purl.org/nidash/nidm#Project",
"dctypes:title": "ABIDE CMU_a Site",
"http://www.w3.org/ns/prov#Location": "/datasets.datalad.org/abide/RawDataBIDS/CMU_a",
"sio:Identifier": "1.0.1",
"nidm:NIDM_0000171": 14,
"age_max": 33.0,
"age_min": 21.0,
"ndar:gender": [
"1",
"2"
],
"obo:handedness": [
"R",
"L",
"Ambi"
]
}pynidm query -nl "cmu_a.nidm.ttl" -u http://localhost:5000/projects/dc1bf9be-10a3-11ea-8779-003ee1ce9545/subjectsExample response:
deef8eb2-10a3-11ea-8779-003ee1ce9545 df533e6c-10a3-11ea-8779-003ee1ce9545 ddbfb454-10a3-11ea-8779-003ee1ce9545 df21cada-10a3-11ea-8779-003ee1ce9545 dcfa35b2-10a3-11ea-8779-003ee1ce9545 de89ce4c-10a3-11ea-8779-003ee1ce9545 dd2ce75a-10a3-11ea-8779-003ee1ce9545 ddf21020-10a3-11ea-8779-003ee1ce9545 debc0f74-10a3-11ea-8779-003ee1ce9545 de245134-10a3-11ea-8779-003ee1ce9545 dd5f2f30-10a3-11ea-8779-003ee1ce9545 dd8d4faa-10a3-11ea-8779-003ee1ce9545 df87cbaa-10a3-11ea-8779-003ee1ce9545 de55285e-10a3-11ea-8779-003ee1ce9545
2.4.1.4 Use the command line to get statistics on a project for the AGE_AT_SCAN and a FSL data element
pynidm query -nl ttl/cmu_a.nidm.ttl -u /statistics/projects/dc1bf9be-10a3-11ea-8779-003ee1ce9545?fields=instruments.AGE_AT_SCAN,derivatives.fsl_000001Example response:
------------------------------------------------- ---------------------------------------------
"http://www.w3.org/1999/02/22-rdf-syntax-ns#type" http://www.w3.org/ns/prov#Activity
"title" ABIDE CMU_a Site
"Identifier" 1.0.1
"prov:Location" /datasets.datalad.org/abide/RawDataBIDS/CMU_a
"NIDM_0000171" 14
"age_max" 33.0
"age_min" 21.0
gender
--------
1
2
handedness
------------
R
L
Ambi
subjects
------------------------------------
de89ce4c-10a3-11ea-8779-003ee1ce9545
deef8eb2-10a3-11ea-8779-003ee1ce9545
dd8d4faa-10a3-11ea-8779-003ee1ce9545
ddbfb454-10a3-11ea-8779-003ee1ce9545
de245134-10a3-11ea-8779-003ee1ce9545
debc0f74-10a3-11ea-8779-003ee1ce9545
dd5f2f30-10a3-11ea-8779-003ee1ce9545
ddf21020-10a3-11ea-8779-003ee1ce9545
dcfa35b2-10a3-11ea-8779-003ee1ce9545
df21cada-10a3-11ea-8779-003ee1ce9545
df533e6c-10a3-11ea-8779-003ee1ce9545
de55285e-10a3-11ea-8779-003ee1ce9545
df87cbaa-10a3-11ea-8779-003ee1ce9545
dd2ce75a-10a3-11ea-8779-003ee1ce9545
----------- ------------------ --------
AGE_AT_SCAN max 33
AGE_AT_SCAN min 21
AGE_AT_SCAN median 26
AGE_AT_SCAN mean 26.2857
AGE_AT_SCAN standard_deviation 4.14778
----------- ------------------ --------
---------- ------------------ -----------
fsl_000001 max 1.14899e+07
fsl_000001 min 5.5193e+06
fsl_000001 median 7.66115e+06
fsl_000001 mean 8.97177e+06
fsl_000001 standard_deviation 2.22465e+06
---------- ------------------ -----------
Use -j for a JSON-formatted response
pynidm query -j -nl "cmu_a.nidm.ttl" -u http://localhost:5000/projects/dc1bf9be-10a3-11ea-8779-003ee1ce9545/subjects/df21cada-10a3-11ea-8779-003ee1ce9545Example response:
{
"uuid": "df21cada-10a3-11ea-8779-003ee1ce9545",
"id": "0050665",
"activity": [
"e28dc764-10a3-11ea-a7d3-003ee1ce9545",
"df28e95a-10a3-11ea-8779-003ee1ce9545",
"df21c76a-10a3-11ea-8779-003ee1ce9545"
],
"instruments": {
"e28dd218-10a3-11ea-a7d3-003ee1ce9545": {
"SRS_VERSION": "nan",
"ADOS_MODULE": "nan",
"WISC_IV_VCI": "nan",
"WISC_IV_PSI": "nan",
"ADOS_GOTHAM_SOCAFFECT": "nan",
"VINELAND_PLAY_V_SCALED": "nan",
"null": "http://www.w3.org/ns/prov#Entity",
"VINELAND_EXPRESSIVE_V_SCALED": "nan",
"SCQ_TOTAL": "nan",
"SRS_MOTIVATION": "nan",
"PIQ": "104.0",
"FIQ": "109.0",
"WISC_IV_PRI": "nan",
"FILE_ID": "CMU_a_0050665",
"VIQ": "111.0",
"WISC_IV_VOCAB_SCALED": "nan",
"VINELAND_DAILYLVNG_STANDARD": "nan",
"WISC_IV_SIM_SCALED": "nan",
"WISC_IV_DIGIT_SPAN_SCALED": "nan",
"AGE_AT_SCAN": "33.0"
}
},
"derivatives": {
"b9fe0398-16cc-11ea-8729-003ee1ce9545": {
"URI": "http://iri.nidash.org/b9fe0398-16cc-11ea-8729-003ee1ce9545",
"values": {
"http://purl.org/nidash/fsl#fsl_000005": {
"datumType": "ilx_0102597",
"label": "Left-Amygdala (voxels)",
"value": "1573",
"units": "voxel"
},
"http://purl.org/nidash/fsl#fsl_000004": {
"datumType": "ilx_0738276",
"label": "Left-Accumbens-area (mm^3)",
"value": "466.0",
"units": "mm^3"
},
"http://purl.org/nidash/fsl#fsl_000003": {
"datumType": "ilx_0102597",
"label": "Left-Accumbens-area (voxels)",
"value": "466",
"units": "voxel"
}
},
"StatCollectionType": "FSLStatsCollection"
}
}- NIDM-Terms <https://github.com/NIDM-Terms/terms>
- NIDM-Terms Scicrunch Interface <https://scicrunch.org/nidm-terms>
- Freesurfer stats -> NIDM <https://github.com/repronim/segstats_jsonld>
- FSL structural segmentation -> NIDM <https://github.com/ReproNim/fsl_seg_to_nidm>
- ANTS structural segmentation -> NIDM <https://github.com/ReproNim/ants_seg_to_nidm>
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