TIPCommon.validation

The TIPCommon.validation module contains the ParameterValidator class, a critical tool for ensuring data integrity within integrations. It automates the process of checking input types, enforcing constraints (such as numerical ranges or email formats), and providing safe fallback values to prevent runtime crashes.

The ParameterValidator class

The ParameterValidator class provides a standardized interface for validating various parameter types. Each method accepts a parameter name and value, returning the validated value in its appropriate Python type. If validation fails and no default_value is provided, a ParameterValidationError is raised.

Usage example

To use the validator, initialize it with the siemplify SDK object:

from TIPCommon.validation import ParameterValidator

# Initialize the validator
validator = ParameterValidator(siemplify)

# Validate and cast strings to specific types
validated_float = validator.validate_float(param_name='Threshold', value='3.7')
validated_int = validator.validate_integer(param_name='Retry Count', value='5')

Numerical and limit validation

These methods ensure that numerical inputs fall within expected boundaries and are correctly cast to their appropriate Python types.

Function Parameters Description & Returns
validate_float()
  • param_name: (str)
  • value: (str | float)
 Returns: float

Validates that a value can be cast to a float. Useful for thresholds or probability scores.
validate_integer()
  • param_name: (str)
  • value: (str | int)
 Returns: int

Validates and casts a value to an integer.
validate_range()
  • value: (str | int)
  • min_limit: (int)
  • max_limit: (int)
 Returns: int

Ensures an integer falls within the inclusive range defined by min_limit and max_limit.
validate_percentage()
  • value: (str | int)
 Returns: int

Ensures the value is an integer between 0 and 100 inclusive.
validate_non_negative()
  • value: (str | int)
 Returns: int

Ensures the value is an integer ≥ 0.
validate_non_zero()
  • value: (str | int)
 Returns: int

Ensures the value is an integer and is not equal to 0.
validate_positive()
  • value: (str | int)
 Returns: int

Ensures the value is an integer > 0.
validate_lower_limit()
  • value: (str | int)
  • limit: (int)
 Returns: int

Ensures the value is ≥ the specified limit.
validate_upper_limit()
  • value: (str | int)
  • limit: (int)
 Returns: int

Ensures the value is ≤ the specified limit.

Data structure and string validation

These methods validate more complex input types, such as delimited strings, JSON objects, and specific formats like email addresses.

Function Parameters Description & Returns
validate_csv()
  • csv_string: (str)
  • delimiter: (str) Defaults to ', '.
  • possible_values: (list) Optional whitelist.
 Returns: list

Splits a string into a list using the delimiter. If possible_values is provided, it validates each item against that list.
validate_json()
  • json_string: (str)
  • **kwargs: Pass-through for json.loads().
 Returns: dict | list

Parses a string into a JSON object. Raises an error if the string is not valid JSON.
validate_email()
  • email: (str)
 Returns: str

Validates that the provided string follows a standard email format.
validate_ddl()
  • value: (str)
  • ddl_values: (list) Whitelist of valid values.
  • case_sensitive: (bool) Defaults to False.
 Returns: str

Validates that the input matches one of the values in a defined DDL.
validate_severity()
  • severity: (str | int)
  • possible_values: (list)
 Returns: int

Validates a platform severity input. Correctly maps between platform-standard severity labels (such as "High") and their equivalent integer ranks.

Global validation configuration

Every validation method in the ParameterValidator class supports the following optional keyword arguments to control behavior upon failure or for debugging:

  • default_value: If provided, this value is returned instead of raising a ParameterValidationError when validation fails.

  • print_value: (bool) When True, the invalid value that caused the failure is included in the log message. Defaults to True.

  • print_error: (bool) When True, the specific exception message is included in the platform logs. Defaults to False.