The IOOS Compliance Checker is a Python tool to check local/remote datasets against a variety of compliance standards. It is primarily a command-line tool (tested on OSX/Linux) and can also be used as a library import.
It currently supports the following sources and standards:
| Standard | .nc/OPeNDAP | SOS |
|---|---|---|
| ACDD (1.1) | Complete | - |
| IOOS Asset Concept | - | GetCapabilities, DescribeSensor |
| CF (1.6) | Complete | - |
Each compliance standard is executed by a Check Suite, which functions similar to a Python standard Unit Test. A Check Suite runs one or more checks against a dataset, returning a list of Results which are then aggregated into a summary.
Each Result has a (# passed / # total) score, a weight (HIGH/MEDIUM/LOW), a computer-readable name, an optional list of human-readable messages, and optionally a list of child Results.
A single score is then calculated by aggregating on the names, then multiplying the score by the weight and summing them together.
The computer-readable name field controls how Results are aggregated together - in order to prevent the overall score for a Check Suite varying on the number of variables, it is possible to group Results together via the name property. Grouped results will only add up to a single top-level entry.
See the Development wiki page for more details on implementation.
The compliance-checker can work against local files (.nc files, .xml files of SOS GetCapabilities/DescribeSensor requests) or against remote URLs (OPeNDAP data URLs, SOS GetCapabilities/DescribeSensor URLs).
WARNING The CF/ACDD checks will access data, so if using a remote OPenDAP URL, please be sure the size is reasonable!
$ compliance-checker --help
usage: compliance-checker [-h] [--test {acdd,cf,ioos} [{acdd,cf,ioos} ...]]
[--criteria [{lenient,normal,strict}]] [--verbose]
dataset_location
positional arguments:
dataset_location Defines the location of the dataset to be checked.
optional arguments:
-h, --help show this help message and exit
--test {acdd,cf,ioos} [{acdd,cf,ioos} ...], -t {acdd,cf,ioos} [{acdd,cf,ioos} ...], --test= {acdd,cf,ioos} [{acdd,cf,ioos} ...], -t= {acdd,cf,ioos} [{acdd,cf,ioos} ...]
Select the Checks you want to perform. Either all
(default), cf, ioos, or acdd.
--criteria [{lenient,normal,strict}], -c [{lenient,normal,strict}]
Define the criteria for the checks. Either Strict,
Normal, or Lenient. Defaults to Normal.
--verbose, -v Increase output. May be specified up to three times.
$ compliance-checker --test=acdd test-data/ru07-20130824T170228_rt0.nc
Running Compliance Checker on the dataset from: test-data/ru07-20130824T170228_rt0.nc
-------------------------------------------------------
The dataset scored 95 out of 149 required points
during the acdd check
This test has passed under normal critera
-------------------------------------------------------
$ compliance-checker --test=cf sss20140107.v2.0cap.nc
Running Compliance Checker on the dataset from: sss20140107.v2.0cap.nc
--------------------------------------------------------------------------------
The dataset scored 12 out of 14 points
during the cf check
--------------------------------------------------------------------------------
Scoring Breakdown:
High Priority
--------------------------------------------------------------------------------
Name :Priority: Score
Variable names :3: 3/3
conventions :3: 0/1
data_types :3: 3/3
dimension_names :3: 3/3
units :3: 0/1
Medium Priority
--------------------------------------------------------------------------------
Name :Priority: Score
all_features_are_same_type :2: 0/0
contiguous_ragged_array :2: 0/0
coordinate_type :2: 2/2
coordinates_and_metadata :2: 0/0
feature_type :2: 0/0
incomplete_multidim_array :2: 0/0
indexed_ragged_array :2: 0/0
missing_data :2: 0/0
orthogonal_multidim_array :2: 0/0
var :2: 1/1
--------------------------------------------------------------------------------
Reasoning for the failed tests given below:
Name Priority: Score:Reasoning
--------------------------------------------------------------------------------
conventions :3: 0/ 1 : Conventions field is not
present
units :3: 0/ 1 :
sss_cap :3: 0/ 1 :
known :3: 0/ 1 : unknown units type (PSU)
To install locally, set up a virtual environment (recommend using virtualenv-burrito):
$ mkvirtualenv --no-site-packages compliance-checker
$ workon compliance-checker
The Python dependencies require several underlying system packages that most package managers should have. See the Installation wiki page for more information.
Install dependencies, numpy must be installed on its own:
$ pip install numpy
$ pip install compliance-checker
Installing on Ubuntu? Check out the Ubuntu Install Guide
from compliance_checker.runner import ComplianceCheckerCheckSuite
cs = ComplianceCheckerCheckSuite()
dataset = cs.load_dataset("/path/or/url/to/your/dataset")
groups = cs.run(dataset, 'acdd')
scores = groups['acdd']The compliance-checker is designed to be simple and hackable to edit existing compliance suites or introduce new ones. See the Development wiki page for more information.
- Improved text output (#12)
- UGRID compliance (#33)
- Dave Foster dfoster@asascience.com
- Dan Maher dmaher@asascience.com
- Luke Campbell lcampbell@asascience.com
And many more testers!
Portions of the CF checker are based on Michael Decker's work, http://repositories.iek.fz-juelich.de/hg/CFchecker/