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Random netlogo3/14/2023 ![]() The error primitive will properly generate stack traces.We removed some methods to overwrite special JavaScript values using built-in primitives.Fast-running models with large plots could sometimes stop updating and appear blank.When you get a runtime error message you can now easily copy it with the provided button.Many more primitives and agent built-in variables have proper runtime error checks and will now generate meaningful error messages when an unexpected condition is encountered.This should provide a small performance improvement for models that heavily use them ( lput and fput are examples). Primitives that take any kind of value (string, number, boolean, list, agent, agentset, etc) no longer check their argument types at runtime.The Table extension now contains to-json and from-json primitives for converting to and from JSON strings.The Functional Programming extension is now supported on NetLogo Web.July 6th 2022 - v2.10.0 The major feature in this release is the ability to create and edit plot widgets in NetLogo Web! Check out the Authoring mode documentation for more information on creating and editing widgets. The random-normal reporter is no longer broken as it was in v2.10.0.The random-exponential reporter is now properly checked for runtime errors.The major feature in this release is the ability to jump to the exact primitive in the code tab that causes a runtime error. Saving a model to an nlogo file with a plot with blank axes names correctly sets the values to NIL so the file can be loaded without errors.Using a variable argument primitive like word concisely with reduce would give incorrect results.When you display an anonymous procedure as a string, it will include the names of let variables.The range primitive will work when using the three-argument version.The initial random-number generator seeds are now taken from the Crypto Web API, which should eliminate the chance multiple users can wind up with the same seed when they load a page.This is a small release to fix a few different bugs. It also includes some changes to NetTango Web, including syntax highlighting in block code tips. Deleting a plot could cause unrecoverable errors.The Commands and Code position setting can now be determined by a URL query parameter when linking to NetLogo Web models.To get image data in base64 format into a NetLogo Web model for use by the Bitmap extension with bitmap:from-base64, see the Fetch extension. The Bitmap extension is now supported.Notice how we get much larger and much smaller bugs after running the model but the average bug size ( mean of turtles) barely changes.This is a small release with bugfixes and features. A histogram at the bottom shows the change in the distribution of bug sizes over time. We achieve this natural size difference between the adults and the children by using the random-normal primitive. A baby butterfly inherits its parent's color but can have a slightly different size. At every 50 ticks, each butterfly produces a new baby. In the model example below, we are a population of colorful butterflies. For example, if we wanted to create a model of bug evolution in which each bug had a different sized antenna, but with a normal distribution, we would write the following code: create-bugs 100 [ ![]() Normal distributions are found more often in nature than uniform distributions and can add accuracy to your model. Its syntax is as follows: random-normal mean stdev. So in random 4, 0, 1, 2, and 3 are all equally likely to be generated, and after many repetitions of random 4, there will be an equal number of 0’s, 1’s, 2’s, and 3’s.īut random-normal will take a mean value and a standard-deviation value to produce a normal distribution, which looks like a bell curve with many values in the middle, and fewer values on the lower and upper tails. Random will produce a uniform distribution, which means every number has an equal chance of being generated, and there will be roughly the same amount of every number. However, random and random-normal differ in the distribution that they generate. Random-normal is similar to the primitive random, in that it randomly generates and reports a number. ![]()
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