[DOC] command line tools

docs/data.md

@@ -67,28 +67,31 @@ for team in teams:
     collection.rectify() # Rectified versions are created
 ```
 
+> [! TIP]
+> In the following examples, use `narps_results` or `python narps_open/data/results` indifferently to launch the command line tool.
+
 ```bash
 # From the command line
-$ python narps_open/data/results -h
-usage: results [-h] (-t TEAMS [TEAMS ...] | -a) [-r]
+narps_results -h
+    usage: results [-h] (-t TEAMS [TEAMS ...] | -a) [-r]
 
-Get Neurovault collection of results from NARPS teams.
+    Get Neurovault collection of results from NARPS teams.
 
-options:
-  -h, --help            show this help message and exit
-  -t TEAMS [TEAMS ...], --teams TEAMS [TEAMS ...]
-                        a list of team IDs
-  -a, --all             download results from all teams
-  -r, --rectify         rectify the results
+    options:
+      -h, --help            show this help message and exit
+      -t TEAMS [TEAMS ...], --teams TEAMS [TEAMS ...]
+                            a list of team IDs
+      -a, --all             download results from all teams
+      -r, --rectify         rectify the results
 
 # Either download all collections
-python narps_open/utils/results -a
+narps_results -a
 
 # Or select the ones you need
-python narps_open/utils/results -t 2T6S C88N L1A8
+narps_results -t 2T6S C88N L1A8
 
 # Download and rectify the collections
-python narps_open/utils/results -r -t 2T6S C88N L1A8
+narps_results -r -t 2T6S C88N L1A8
 ```
 
 The collections are also available [here](https://zenodo.org/record/3528329/) as one release on Zenodo that you can download.

docs/description.md

@@ -12,8 +12,11 @@ The class `TeamDescription` of module `narps_open.data.description` acts as a pa
 
 You can use the command-line tool as so. Option `-t` is for the team id, option `-d` allows to print only one of the sub parts of the description among : `general`, `exclusions`, `preprocessing`, `analysis`, `categorized_for_analysis`, `derived`, and `comments`. Options `--json` and `--md` allow to choose the export format you prefer between JSON and Markdown.
 
+> [! TIP]
+> In the following examples, use `narps_description` or `python narps_open/data/description` indifferently to launch the command line tool.
+
 ```bash
-python narps_open/data/description -h
+narps_description -h
 # usage: __init__.py [-h] -t TEAM [-d {general,exclusions,preprocessing,analysis,categorized_for_analysis,derived,comments}]
 #
 # Get description of a NARPS pipeline.
@@ -26,7 +29,7 @@ python narps_open/data/description -h
 #  --json                output team description as JSON
 #  --md                  output team description as Markdown
 
-python narps_open/data/description -t 2T6S --json
+narps_description -t 2T6S --json
 # {
 #     "general.teamID": "2T6S",
 #     "general.NV_collection_link": "https://neurovault.org/collections/4881/",
@@ -41,7 +44,7 @@ python narps_open/data/description -t 2T6S --json
 #     "preprocessing.preprocessing_order": "We used the provided preprocessed data by fMRIPprep 1.1.4 (Esteban, Markiewicz, et al. (2018); Esteban, Blair, et al. (2018); RRID:SCR_016216), which is based on Nipype 1.1.1 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502) and we additionally conducted a spatial smoothing using the provided preprocessed data set and SPM12. Here, we attach the preprocessing steps described in the provided data set. \nAnatomical data preprocessing\nThe T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) using N4BiasFieldCorrection (Tustison et al. 2010, ANTs 2.2.0), and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using antsBrainExtraction.sh (ANTs 2.2.0), using OASIS as target template. Brain surfaces we
 # ...
 
-python narps_open/data/description -t 2T6S -d general --json
+narps_description -t 2T6S -d general --json
 # {
 #    "teamID": "2T6S",
 #    "NV_collection_link": "https://neurovault.org/collections/4881/",
@@ -53,7 +56,7 @@ python narps_open/data/description -t 2T6S -d general --json
 #    "general_comments": "NA"
 # }
 
-python narps_open/data/description -t 2T6S --md
+narps_description -t 2T6S --md
 # # NARPS team description : 2T6S
 # ## General
 # * `teamID` : 2T6S

docs/running.md

@@ -2,10 +2,13 @@
 
 ## Using the runner application
 
-The `narps_open.runner` module allows to run pipelines from the command line :
+The `narps_open.runner` module allows to run pipelines from the command line.
+
+> [! TIP]
+> In the following examples, use `narps_open_runner` or `python narps_open/runner.py` indifferently to launch the command line tool.
 
 ```bash
-python narps_open/runner.py -h
+narps_open_runner -h
 	usage: runner.py [-h] -t TEAM (-r RANDOM | -s SUBJECTS [SUBJECTS ...]) [-g | -f]
 
 	Run the pipelines from NARPS.
@@ -19,13 +22,14 @@ python narps_open/runner.py -h
 	  -f, --first           run the first levels only (preprocessing + subjects + runs)
 	  -c, --check           check pipeline outputs (runner is not launched)
 
-python narps_open/runner.py -t 2T6S -s 001 006 020 100
-python narps_open/runner.py -t 2T6S -r 4
-python narps_open/runner.py -t 2T6S -r 4 -f
-python narps_open/runner.py -t 2T6S -r 4 -f -c # Check the output files without launching the runner
+narps_open_runner -t 2T6S -s 001 006 020 100
+narps_open_runner -t 2T6S -r 4
+narps_open_runner -t 2T6S -r 4 -f
+narps_open_runner -t 2T6S -r 4 -f -c # Check the output files without launching the runner
 ```
 
-In this usecase, the paths where to store the outputs and to the dataset are picked by the runner from the [configuration](docs/configuration.md).
+> [! NOTE]
+> In this usecase, the paths where to store the outputs and to the dataset are picked by the runner from the [configuration](docs/configuration.md).
 
 ## Using the `PipelineRunner` object
 

docs/status.md

@@ -46,8 +46,11 @@ report.markdown() # Returns a string containing the markdown
 
 You can also use the command-line tool as so.
 
+> [! TIP]
+> In the following examples, use `narps_open_status` or `python narps_open/utils/status.py` indifferently to launch the command line tool.
+
 ```bash
-python narps_open/utils/status -h
+narps_open_status -h
 # usage: status.py [-h] [--json | --md]
 # 
 # Get a work progress status report for pipelines.
@@ -57,7 +60,7 @@ python narps_open/utils/status -h
 #   --json      output the report as JSON
 #   --md        output the report as Markdown
 
-python narps_open/utils/status --json
+narps_open_status --json
 # {
 #     "08MQ": {
 #         "softwares": "FSL",
@@ -83,7 +86,7 @@ python narps_open/utils/status --json
 #     },
 # ...
 
-python narps_open/utils/status --md
+narps_open_status --md
 # ...
 # | team_id | status | main software | fmriprep used ? | related issues | related pull requests | excluded from NARPS analysis | reproducibility |
 # | --- |:---:| --- | --- | --- | --- | --- | --- |