#IMPORTANT! This is only for development, this makes your code may make your project vulnerable.
Create Random Data For Models -- Without having to modify existing models.
- Multiple populations can be generated for each model
- Multiple samples can be generated for each model
- Can reference a single population for testing (without creating a sample)
- Can specify non-probabilistic distribution for boolean field on a population. You can do this by stating the percentage of true or false (field values are in UPPERCASE).
the percentage of the population size that should be generated
ensure that a choice is made a certain percentage of time. This means that if we generate n boolean choices with 50% true, that even though selection is random, the percentage of true in the population is 50%.
the spec option is meant to provide a means of specifying a value for part of the distribution or of getting a value, or set of values, for part of the distribution
#TODO ###Add filter 5109 s to ForeignKey Choice Distribution value ###Add Name to Choice Distribution ###In Discussion: Change choice distribution value to accept django code We could modify value to parse code into python code and validate it as a function or generator for providing values. This would make the code extra vulnerable, but this project should never be included in production.