Checks Datasets and SOS endpoints for standards compliance
Project description
# IOOS Compliance Checker
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)](http://wiki.esipfed.org/index.php/Attribute_Convention_for_Data_Discovery_%28ACDD%29) | Complete | - |
| IOOS Asset Concept | - | GetCapabilities, DescribeSensor |
| [CF (1.6)](http://cf-convention.github.io/1.6.html) | Partial (chs 2-5) | - |
### Concepts & Terminology
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.
For example, ...
See the Development section for more details on implementation.
### Usage (command line)
```
$ 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 Verbosity
```
```
$ 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 -v --test=acdd test-data/ru07-20130824T170228_rt0.nc
Running Compliance Checker on the dataset from: test-data/ru07-20130824T170228_rt0.nc
-------------------------------------------------------
The following tests failed:
----High priority tests failed-----
Name :Priority: Score
varattr :3: 69/120
----Medium priority tests failed-----
Name :Priority: Score
acknowledgement :2: 0/1
cdm_data_type :2: 0/1
time_coverage_duration :2: 0/1
```
### Installation
To install locally, set up a virtual environment (recommend using [virtualenv-burrito](https://github.com/brainsik/virtualenv-burrito)):
```
$ mkvirtualenv --no-site-packages compliance-checker
$ workon compliance-checker
```
Install dependencies (you may need C dependencies for netCDF-python), numpy must be installed on its own:
```
$ pip install numpy
$ pip install compliance-checker
```
### Usage (from Python code)
```python
from compliance_checker.runner import ComplianceCheckerCheckSuite
cs = ComplianceCheckerCheckSuite()
groups = cs.run(dataset, 'acdd')
scores = groups['acdd']
```
### Development
The compliance-checker is designed to be simple and hackable to edit existing compliance suites or introduce new ones. See the [Development](https://github.com/ioos/compliance-checker/wiki/Development) wiki page for more information.
### Roadmap
- Complete CF 1.6 checks
- Improve text output
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)](http://wiki.esipfed.org/index.php/Attribute_Convention_for_Data_Discovery_%28ACDD%29) | Complete | - |
| IOOS Asset Concept | - | GetCapabilities, DescribeSensor |
| [CF (1.6)](http://cf-convention.github.io/1.6.html) | Partial (chs 2-5) | - |
### Concepts & Terminology
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.
For example, ...
See the Development section for more details on implementation.
### Usage (command line)
```
$ 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 Verbosity
```
```
$ 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 -v --test=acdd test-data/ru07-20130824T170228_rt0.nc
Running Compliance Checker on the dataset from: test-data/ru07-20130824T170228_rt0.nc
-------------------------------------------------------
The following tests failed:
----High priority tests failed-----
Name :Priority: Score
varattr :3: 69/120
----Medium priority tests failed-----
Name :Priority: Score
acknowledgement :2: 0/1
cdm_data_type :2: 0/1
time_coverage_duration :2: 0/1
```
### Installation
To install locally, set up a virtual environment (recommend using [virtualenv-burrito](https://github.com/brainsik/virtualenv-burrito)):
```
$ mkvirtualenv --no-site-packages compliance-checker
$ workon compliance-checker
```
Install dependencies (you may need C dependencies for netCDF-python), numpy must be installed on its own:
```
$ pip install numpy
$ pip install compliance-checker
```
### Usage (from Python code)
```python
from compliance_checker.runner import ComplianceCheckerCheckSuite
cs = ComplianceCheckerCheckSuite()
groups = cs.run(dataset, 'acdd')
scores = groups['acdd']
```
### Development
The compliance-checker is designed to be simple and hackable to edit existing compliance suites or introduce new ones. See the [Development](https://github.com/ioos/compliance-checker/wiki/Development) wiki page for more information.
### Roadmap
- Complete CF 1.6 checks
- Improve text output
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