Additional comments

Author: mphemming

aodntools/ncwriter/schema.py

@@ -6,16 +6,21 @@
 from jsonschema import validators, Draft4Validator, FormatChecker, ValidationError
 from pkg_resources import resource_filename
 
+# helper function that will later be used to tell the schema validator how to validate objects of type "array"
 def is_array(checker, instance):
     return isinstance(instance, (list, np.ndarray))
 
 # Extend the default type checker by redefining "array"
+# whenever a schema expects a value of type "array", it will now use the is_array function to check if the value is acceptable.
 custom_type_checker = Draft4Validator.TYPE_CHECKER.redefine("array", is_array)
 
 # Create a custom validator that uses the new type checker.
+# any validation performed with CustomValidator will use the custom array checker 
 CustomValidator = validators.extend(Draft4Validator, type_checker=custom_type_checker)
 format_checker = FormatChecker()
 
+# Define a custom format checker
+# called when a JSON schema specifies that a value should have the format "datatype"
 @format_checker.checks('datatype')
 def is_python_datatype(value):
     """Return whether the given value is a valid data type specification for a NetCDF variable"""
@@ -25,14 +30,19 @@ def is_python_datatype(value):
         return issubclass(value, np.number)
     return False
 
+# Load JSON schema file
 TEMPLATE_SCHEMA_JSON = resource_filename(__name__, 'template_schema.json')
 with open(TEMPLATE_SCHEMA_JSON) as f:
     TEMPLATE_SCHEMA = json.load(f)
+
+# Use the custom validator to check it is valid according to Draft 4 rules
 CustomValidator.check_schema(TEMPLATE_SCHEMA)
 
-# Use the custom validator
+# ready-to-use validator that applies both custom type and format checks
 template_validator = CustomValidator(TEMPLATE_SCHEMA, format_checker=format_checker)
 
+
+# Validation checks
 def validate_template(t):
     template_validator.validate(t)