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bbrc-validator-0.5.9
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مشاهده بیشترتوضیحات
Systematic sanity checks on imaging datasets within an XNAT environment
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | bbrc-validator-0.5.9 |
| نام | bbrc-validator |
| نسخه کتابخانه | 0.5.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Greg Operto, Jordi Huguet |
| ایمیل نویسنده | goperto@barcelonabeta.org |
| آدرس صفحه اصلی | https://gitlab.com/bbrc/xnat/bbrc-validator |
| آدرس اینترنتی | https://pypi.org/project/bbrc-validator/ |
| مجوز | - |
bbrc-validator
==============
[
](https://gitlab.com/bbrc/xnat/bbrc-validator/commits/master)
[
](https://gitlab.com/bbrc/xnat/bbrc-validator/commits/master)
[
](https://pypi.org/project/bbrc-validator)
[
](https://pypi.org/project/bbrc-validator)
<p align="center">
<a href="#main-concepts">Main Concepts</a> •
<a href="#commands">Commands</a> •
<a href="#examples">Examples</a> •
<a href="#install">Install</a> •
<a href="#contributing">Contributing</a>
</p>
**bbrc-validator** is a Python-based software package that performs automatic quality
assessment of neuroimaging datasets and their processing derivatives, through
collections of "checkpoints".
**bbrc-validator** is built on two core concepts: _Tests_ and _Validators_.
- A **Test** checks a specific trait from a given resource (either an imaging
session or a single scan). It asks a specific question whose answer can be
either `True` or `False` (eg. _"Does this MRI scan have a conversion to NIfTI
available?"_). As such, _Tests_ may be seen as [unit tests
](https://en.wikipedia.org/wiki/Unit_testing). A _Test_ class is defined by two
attributes (`passing` and `failing`) that refer to two "real-life" cases (one
expected to pass the _Test_ and another expected to fail it). In addition, these
attributes are systematically used by the [CI
](https://en.wikipedia.org/wiki/Continuous_integration) testing.
- A **Validator** is a collection of **Test** objects that may be executed against
any [XNAT](https://www.xnat.org/) imaging resource (by referring to their experiment
identifiers). Running a _Validator_ on a given experiment takes its associated
set of tests, runs them sequentially and collects their results in a JSON object.
A human-readable report can be generated (as a PDF document) with the results
of the whole procedure.
Main Concepts
-------------
- __Test__:
```python
class MyTest():
""" Test functionality description """
passing = 'PASSING_CASE_ID'
failing = 'FAILING_CASE_ID'
def run(): # executes the Test logic and returns some Results
return Results(has_passed=test_outcome, data=some_data)
def report(): # provides a human-readable version of Results data
```
- __Validator__:
```python
class MyValidator():
def __init__():
self.tests = [MyTest, ...]
def run(): # runs all Tests sequentially
def dump(): # compiles all Test results in a single JSON object
def report(): # generates a human-readable PDF report based on the results
```
- __Result__: Represents the outcome from the execution of a _Test_. It includes
a boolean attribute `has_passed` (representing the outcome of _Test_
execution) and some additional `data` object (optionally used for
storing contextual information from the execution).
Commands
--------
### `run_validator.py`
Executes the specified _Validator_ against a given image resource (a.k.a XNAT
_experiment_) and generates (a) a JSON object with the results of all the Tests
and (b) a human-readable PDF report.
```
usage: run_validator.py [-h] --config CONFIG --experiment EXPERIMENT
[--validator VALIDATOR] --output OUTPUT [--verbose]
Run a validator against an experiment
optional arguments:
-h, --help show this help message and exit
--config CONFIG, -c CONFIG XNAT configuration file
--experiment EXPERIMENT, -e EXPERIMENT XNAT experiment unique identifier
--validator VALIDATOR, -v VALIDATOR Validator name (default:ArchivingValidator)
--output OUTPUT, -o OUTPUT PDF file to store the report
--verbose, -V Display verbosal information (optional)
```
### `validation_scores.py`
Given a specific type of _Validator_, collects all results available in an XNAT
instance and compiles them in a CSV file.
```
usage: validation_scores.py [-h] --config CONFIG --version VERSION
[--validator VALIDATOR] --output OUTPUT
[--project PROJECT] [--verbose]
Compile validation scores
optional arguments:
-h, --help show this help message and exit
--config CONFIG XNAT configuration file
--version VERSION, -v VERSION Filter specific version
--validator VALIDATOR Validator name (default:ArchivingValidator)
--output OUTPUT, -o OUTPUT CSV output file
--verbose, -V Display verbosal information (optional)
```
Enables the creation of tables such as the following example obtained from
`ArchivingValidator` (table trimmed to fit the dimensions of the page).
_Tests_ included:
1. HasUncompressedPixelData
2. HasCorrectSequences
3. HasBvecBval
4. IsClassicDICOM
5. HasDuplicatedSequences
6. HasNifti
7. HasPhilipsPrivateTags
8. IsStudyDescriptionCorrect
<table border="1" cellpadding="0" cellspacing="0" dir="ltr">
<tbody>
<tr>
<td>Tests</td>
<td><pre>#1</pre></td>
<td><pre>#2</pre></td>
<td><pre>#3</pre></td>
<td><pre>#4</pre></td>
<td><pre>#5</pre></td>
<td><pre>#6</pre></td>
<td><pre>#7</pre></td>
<td><pre>#8</pre></td>
</tr>
<tr>
<td>Sums</td>
<td>11</td>
<td>11</td>
<td>0</td>
<td>11</td>
<td>11</td>
<td>6</td>
<td>0</td>
<td>11</td>
</tr>
<tr>
<td>BBRCDEV_E00211</td>
<td> </td>
<td> </td>
<td> </td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00210</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00213</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00212</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00196</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00214</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00217</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00216</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00219</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00218</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>BBRCDEV_E00198</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
Examples
--------
#### Create a Validator and review the list of its Tests
1. Set a [pyxnat connection
](https://pyxnat.github.io/pyxnat/tutorial.html#setting-up-a-connection)
to the XNAT instance hosting the data requiring validation.
2. Create an instance of `SPM12SegmentValidator`, a _Validator_ for segmentations
produced using [SPM12 Segment](https://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=45).
3. Print out a list of included tests.
```python
import pyxnat
intf = pyxnat.Interface(config='.xnat.cfg')
from bbrc.validation import SPM12SegmentValidator
spmv = SPM12SegmentValidator(lut={}, xnat_instance=intf)
print('{} tests (`{}`):'.format(spmv.__class__.__name__, spmv.version))
spmv.tests
```
SPM12SegmentValidator tests (`d6ca22c1`):
[<bbrc.validation.processing.spm.HasCorrectNumberOfItems at 0x273dee24e88>,
<bbrc.validation.processing.spm.HasCorrectItems at 0x273dee247c8>,
<bbrc.validation.processing.spm.HasCorrectSPMVersion at 0x273dda8f4c8>,
<bbrc.validation.processing.spm.HasCorrectMatlabVersion at 0x273dee28848>,
<bbrc.validation.processing.spm.HasCorrectOSVersion at 0x273dee287c8>,
<bbrc.validation.processing.spm.SPM12SegmentSnapshot at 0x273dee249c8>,
<bbrc.validation.processing.spm.HasNormalSPM12Volumes at 0x273dee28788>,
<bbrc.validation.processing.spm.SPM12SegmentExecutionTime at 0x273dee28bc8>]
#### Run `SPM12SegmentValidator` against an MRI session,
```python
spmv.run('XNAT_E00001')
```
2021-02-04 12:12:54,635 - root - INFO - Running <bbrc.validation.processing.spm.HasCorrectNumberOfItems object at 0x00000273DEE24E88>
2021-02-04 12:12:54,964 - root - ERROR - XNAT_E00001 has 15 items (different from 16)
2021-02-04 12:12:55,572 - root - INFO - Running <bbrc.validation.processing.spm.HasCorrectItems object at 0x00000273DEE247C8>
2021-02-04 12:12:56,120 - root - INFO - Running <bbrc.validation.processing.spm.HasCorrectSPMVersion object at 0x00000273DDA8F4C8>
2021-02-04 12:12:56,592 - root - INFO - Running <bbrc.validation.processing.spm.HasCorrectMatlabVersion object at 0x00000273DEE28848>
2021-02-04 12:12:56,782 - root - INFO - Running <bbrc.validation.processing.spm.HasCorrectOSVersion object at 0x00000273DEE287C8>
2021-02-04 12:12:57,001 - root - INFO - Running <bbrc.validation.processing.spm.SPM12SegmentSnapshot object at 0x00000273DEE249C8>
2021-02-04 12:13:04,997 - root - INFO - * Creating snapshots...
2021-02-04 12:13:46,472 - root - INFO - Saved in /tmp/tmp3j664u27.png
2021-02-04 12:13:46,515 - root - INFO - Running <bbrc.validation.processing.spm.HasNormalSPM12Volumes object at 0x00000273DEE28788>
2021-02-04 12:13:50,552 - root - INFO - Running <bbrc.validation.processing.spm.SPM12SegmentExecutionTime object at 0x00000273DEE28BC8>
#### Collect results from `SPM12SegmentValidator` execution,
```python
import json
result = spmv.dump()
json.loads(result)
```
{'experiment_id': 'XNAT_E00001',
'version': 'd6ca22c1',
'generated': '2021-02-04, 12:13',
'HasCorrectItems': {'has_passed': False,
'data': ["Missing SPM12_SEGMENT items: ['pyscript_setorigin.m']."]},
'HasCorrectSPMVersion': {'has_passed': True, 'data': []},
'HasCorrectMatlabVersion': {'has_passed': True, 'data': []},
'HasCorrectOSVersion': {'has_passed': True, 'data': []},
'SPM12SegmentSnapshot': {'has_passed': True,
'data': ['/tmp/tmp3j664u27.png']},
'HasNormalSPM12Volumes': {'has_passed': True,
'data': ['Volumes: 773592.1702940931 524339.7480925963']},
'SPM12SegmentExecutionTime': {'has_passed': True, 'data': ['0:07:15']}}
#### Generate a human-readable PDF report from the results,
```python
import tempfile
_,fp = tempfile.mkstemp(suffix='.pdf')
spmv.report(fp)
print('Report created: {}'.format(fp))
```
Loading pages (1/6)
Counting pages (2/6)
Resolving links (4/6)
Loading headers and footers (5/6)
Printing pages (6/6)
Done
Report created: '/home/jhuguet/notebooks/bbrc-validator/tmpcexwvwj5.pdf'
Install
-------
**bbrc-validator** can be installed via `pip`,
```bash
pip install bbrc-validator
```
`bbrc-validator` requires [wkhtmltopdf](http://wkhtmltopdf.org/) for PDF
generation. A static build release (with QT patches) is recommended, see
available releases
[here](https://wkhtmltopdf.org/downloads.html) by OS/distribution.
On Ubuntu 18.04:
```bash
wget https://github.com/wkhtmltopdf/packaging/releases/download/0.12.6-1/wkhtmltox_0.12.6-1.bionic_amd64.deb
dpkg -i wkhtmltox_0.12.6-1.bionic_amd64.deb
apt --fix-broken -y install
```
On CentOS 7:
```bash
wget https://github.com/wkhtmltopdf/packaging/releases/download/0.12.6-1/wkhtmltox-0.12.6-1.centos7.x86_64.rpm
yum -y localinstall wkhtmltox-0.12.6-1.centos7.x86_64.rpm
```
Contributing
------------
**bbrc-validator** is still under active development. The currently included _Tests_
and _Validators_ have been tailored to the particular needs and context of the
Barcelonaβeta Brain Research Center and as such might differ with the needs from
other organizations.
However, the software was designed with an aim towards genericity, modularity and
reusability. Since all Tests are based upon the same template (eg. each of them
being linked to XNAT data resources as test cases), this makes them virtually
shareable across groups and makes **bbrc-validator** open to public contributions.
Contact us for details on how to contribute or open an issue to start a discussion.
[
](https://barcelonabeta.org/)
نیازمندی
| مقدار | نام |
|---|---|
| >=3.0 | Markdown |
| >=1.4.2 | bbrc-pyxnat |
| >=4.5 | coverage |
| >=0.7 | dateparser |
| >=4.3 | lxml |
| - | matplotlib |
| >=2.3 | nibabel |
| >=0.5 | nilearn |
| ==0.4.post0 | nisnap |
| >=1.16 | numpy |
| >=0.24 | pandas |
| >=0.6 | pdfkit |
| >=2.0 | pydicom |
| >=4.0 | pytest-cov |
| >=7.1 | pytest |
| >=2019.1 | pytz |
| >=2.21 | requests |
| >=0.14 | scikit-image |
| >=0.2 | scikit-learn |
| >=1.2 | scipy |
| >=4.50 | tqdm |
| >=1.2 | xlrd |
| >=2 | xlutils |
| >=1.3 | xlwt |
نحوه نصب
نصب پکیج whl bbrc-validator-0.5.9:
pip install bbrc-validator-0.5.9.whl
نصب پکیج tar.gz bbrc-validator-0.5.9:
pip install bbrc-validator-0.5.9.tar.gz