Validator Validator
RU English
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This mission was inspired by Raymond Hettinger's talk at the 4th annual PyBay conference.
This is the second mission related to his talk. The first mission can be seen by clicking here.

Data validation is a check process that confirms that type and value of the input (in our case) data matches the specification. In the mission, you need to create LogLine class which uses input data passing as an argument for its instances initialization. The class should have the next attributes: id, name, status and error. In order to retrieve the attribute values the get() method should be implemented. When initializing a class instance, it is required to validate the data, which must meet the following requirements:

  • input data type - string;
  • input string contains "id", "name" and "status" (in the certain order), separated by whitespaces, not less, not more;
  • id value should allow its conversion to an int, 0 <= id <= 1000;
  • len(name) should be not less than 2, not more than 20, any characters, except whitespace;
  • status should one of ["active", "idle", "shutdown"].

If the data type and value are successfully validated during a class instance initialization, its attributes id, name и status should have respective values. The value of the error attr should be "passed data is valid".

Example of the class instance initiated with the valid data:

log = LogLine("123 search active")
log.get("error") == "passed data is valid"
log.get("id") == 123
log.get("name") == "search"
log.get("status") == "active"
    

If data type or value do not pass validation during a class instance initialization, its attributes id, name и status values should be None. The value of the error attribute may be:

  • the input data is not an instance of a string - "wrong input type";
  • in the passed string more or less than 3 space-separated elements - "wrong input value";
  • the first element of the passed string (id) cannot be converted to an integer - "wrong id type";
  • 0 > id or id > 1000 - "wrong id value";
  • length... is less than 2 or more than 20 - ;
  • isn't one of ["active", "idle", "shutdown"] - ;
The order in which the possible values of the error attribute are listed corresponds to the order in which the data should be validated. It is not necessary to record all validation errors - if any error occurs, validation can be interrupted and the passed data can be considered invalid. If you want to track all the errors in the passed data when solving a task for some reason, the log.get("error") method should return a message for the first error found.
log1 = LogLine("123 k passive")
log1.get("error") == "wrong name value"
log1.get("id") == None
log1.get("name") == None
log1.get("status") == None

log2 = LogLine(["1001 search active"])
log2.get("error") == "wrong input type"

log3 = LogLine("1001 search passive")
log3.get("error") == "wrong id value"

log4 = LogLine("900 search passive")
log4.get("error") == "wrong status value"

log5 = LogLine("900 search active something")
log5.get("error") == "wrong input value"
    

Although the task of validating data does not necessarily require the using of classes, in this mission their usage is encouraged for educational purposes. Also, we can compare our data validation approach with how Raymond does it in this part of the , where he tells about the proper classes writing strategy (with inheritance) on the example of the Validator class.

arbitrary of any type.

  • the second parameter is name, any characters (except whitespaces), 2 <= len(name) <= 20;
  • the third parameter is status, any of ["active", "idle", "shutdown"].

a list of an instance attributes , , and in the exact order.

validate(LogLine("123 k passive")) == [None, None, None, "wrong name value"]                        #example #1
validate(LogLine("123 search active")) == [123, "search", "active", "passed data is valid"]         #example #2
validate(LogLine("1001 search active")) == [None, None, None, "wrong id value"]                     #example #3
    
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