Program Outline



I) Elements
	A) Operation Outline
		1) Program Initialization
			a) Function Declarations
				1) avg
					def avg(*args)
			b) Variable Declarations
				1) Declared Simplest
					a) testA	-1
					b) testB	-1
					c) testC	-1
					d) average	0
				2) Intuitive
					a) tests	[-1, -1, -1]
					b) average	0
				3) Optimal
					a) tests	[]
					b) average	0
		2) Main
			a) Window Creation
			b) User Input Loop	Loop user input request event until winclose state
			c) WinClose State	Data set full or declared full
			d) Data Validation	Data set checked for expected input type(s), converted to standard type (float)
			f) Data Manipulation	Average of test scores calculated
		3) Exit
			a) Window Creation
			b) Program Output	Average of test scores displayed
	B) Data

		1) Requirements
			a) Declared	Three test scores entered as percentages (E.g. 34%) entered to unspecified precision.


			b) Optimal	n test scores entered as any common representation of percentages (E.g. 34%, 34.00%, 0.34) of any precision


		2) Acquisition Methods	**	Undeclared Prompt Type: Assumed prompt type Pop-up Window Input Box

	**
			a) Simplest	Repetitive windows acquiring requested data set individually


				1) Limitations
					A) Unintuitive	Inexperienced users will struggle with understanding the significance of each separate but indentical window.
							Solution is make each visually unique.
					B) Mistaken Data Entry.	All users will occasionally enter mistaken data and be unable to edit.
								Solution is final checks window with options to edit. Defeats purpose of this method when compared to the intuitive method as this solution is baked in by default.
				2) Advantage
					A) Simplest to implement	Method requires smallest count of lines of code to implement applicable function.
					B) Limitless Data Sets		For optimal data requirements, method allows data sets larger or smaller than predicted when cancelled or data deviates from expected input type(s).
			b) Intuitive	Single window with required boxes directly specifying required data set.


				1) Limitations
					A) Speed	Method directly sacrifices speed of operation for experienced users to accomodate more inexperienced users. Has smaller learning curve, but less ability for power-users.
							Solution is bake in power-user functionality, such as field tabbing. Abscence does not limit full functionality of method.
					B) Unflexible	Method does not allow for inputted data set's entry count to be larger than the count of available input boxes (E.g. 3 input boxes limits data set to 3)
							Solution is only to predict the required data set size and build accordingly.
				2) Advantage
					A) Utility	Method directly bakes in functionality missing from other methods in the form of user-verifiable data.
			c) Optimal	Smart window that accepts data set inputted as delineated array or individual data points, disappearing only when required data set acquired, or specified to stop acquisition.

				1) Limitations
					A) Unintuitive	Inexperienced users will struggle with understanding the significance of each separate but indentical window.
							Solution is make each visually unique.
					B) Learning Curve	Inexperienced users will struggle with learning and using power-user functionality (E.g. Delineated array entry)
								Solution is include instruction information in window.
				2) Advantage
					A) Functionality	Method allows for most flexible entry method.
		3) Validation	Validating the supplied data to ensure compliance with expect input type(s)
			a) Numerical Statistics
				1) Real Domain:		[0, ∞]		Test scores can be higher than 100.00% in specific circumstances. This domain is unreasonable due to the limited precision of large numbers represented in binary exponential notation.
				2) Ideal Domain:	[0, 2]		Test scores can be higher than 100.00% in specific circumstances. This domain is ideal due to the implicit inclusion of test scores > 100%.
				3) Preferred Domain:	[0, 1]		Vast majority of test scores will fall inside this range. This domain is most reasonable. Does not allow for test scores of larger than 100.00%.
								Easiest to determine if value entered and value interpreted are identical.
				4) Literal Domain:	[-1, 1]		-1 value represents null or unentered value in this context and thus is a way to identify missing data points in declared data sets.
								In strict languages without 'undeclared' or 'null' values, this is the itialized value for variables. Values -1 < x < 0 are all considered to be null.
			b) Type-Checking
				1) Expected Types
					A) Strings	(E.g. 34%)
					B) Numeric	(E.g. 0.34)
			c) Data Error Solutions by Type
				1) Entered data not matching type-check requirements
					A) Re-request Data
					B) Discard mismatch
			d) Type-Conversion
				1) Convert Strings (E.g. 34% -> 0.34) to Decimal based numeric representation by removing '%' and divide remaining numbers by 100.
		4) Storage
			a) Declared Simplest	Named variable names (E.g. testA, testB, testC)
			b) Declared Preferred	Named array variable with initialized with null literals (-1) for all three values. Data acquisition saves sequentially to array.
			c) Optimal		Named array variable with single initialized value that expands dynamically as data is added to the set.
		5) Manipulation
			a)	Take data set and output average.
				1) Declared Simplest
						return testA + testB + testC / 3
				2) Optimal
						average = 0
						for element in array
							average += array[element]
						average /= len(array)