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  1. Program Outline
  2.  
  3.  
  4.  
  5. I) Elements
  6.     A) Operation Outline
  7.         1) Program Initialization
  8.             a) Function Declarations
  9.                 1) avg
  10.                     def avg(*args)
  11.             b) Variable Declarations
  12.                 1) Declared Simplest
  13.                     a) testA    -1
  14.                     b) testB    -1
  15.                     c) testC    -1
  16.                     d) average  0
  17.                 2) Intuitive
  18.                     a) tests    [-1, -1, -1]
  19.                     b) average  0
  20.                 3) Optimal
  21.                     a) tests    []
  22.                     b) average  0
  23.         2) Main
  24.             a) Window Creation
  25.             b) User Input Loop  Loop user input request event until winclose state
  26.             c) WinClose State   Data set full or declared full
  27.             d) Data Validation  Data set checked for expected input type(s), converted to standard type (float)
  28.             f) Data Manipulation    Average of test scores calculated
  29.         3) Exit
  30.             a) Window Creation
  31.             b) Program Output   Average of test scores displayed
  32.     B) Data
  33.  
  34.         1) Requirements
  35.             a) Declared Three test scores entered as percentages (E.g. 34%) entered to unspecified precision.
  36.  
  37.  
  38.             b) Optimal  n test scores entered as any common representation of percentages (E.g. 34%, 34.00%, 0.34) of any precision
  39.  
  40.  
  41.         2) Acquisition Methods  **  Undeclared Prompt Type: Assumed prompt type Pop-up Window Input Box
  42.  
  43.     **
  44.             a) Simplest Repetitive windows acquiring requested data set individually
  45.  
  46.  
  47.                 1) Limitations
  48.                     A) Unintuitive  Inexperienced users will struggle with understanding the significance of each separate but indentical window.
  49.                             Solution is make each visually unique.
  50.                     B) Mistaken Data Entry. All users will occasionally enter mistaken data and be unable to edit.
  51.                                 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.
  52.                 2) Advantage
  53.                     A) Simplest to implement    Method requires smallest count of lines of code to implement applicable function.
  54.                     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).
  55.             b) Intuitive    Single window with required boxes directly specifying required data set.
  56.  
  57.  
  58.                 1) Limitations 
  59.                     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.
  60.                             Solution is bake in power-user functionality, such as field tabbing. Abscence does not limit full functionality of method.
  61.                     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)
  62.                             Solution is only to predict the required data set size and build accordingly.
  63.                 2) Advantage   
  64.                     A) Utility  Method directly bakes in functionality missing from other methods in the form of user-verifiable data.
  65.             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.
  66.  
  67.                 1) Limitations
  68.                     A) Unintuitive  Inexperienced users will struggle with understanding the significance of each separate but indentical window.
  69.                             Solution is make each visually unique.
  70.                     B) Learning Curve   Inexperienced users will struggle with learning and using power-user functionality (E.g. Delineated array entry)
  71.                                 Solution is include instruction information in window.
  72.                 2) Advantage
  73.                     A) Functionality    Method allows for most flexible entry method.
  74.         3) Validation   Validating the supplied data to ensure compliance with expect input type(s)
  75.             a) Numerical Statistics
  76.                 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.
  77.                 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%.
  78.                 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%.
  79.                                 Easiest to determine if value entered and value interpreted are identical.
  80.                 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.
  81.                                 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.
  82.             b) Type-Checking
  83.                 1) Expected Types
  84.                     A) Strings  (E.g. 34%)
  85.                     B) Numeric  (E.g. 0.34)
  86.             c) Data Error Solutions by Type
  87.                 1) Entered data not matching type-check requirements
  88.                     A) Re-request Data
  89.                     B) Discard mismatch
  90.             d) Type-Conversion
  91.                 1) Convert Strings (E.g. 34% -> 0.34) to Decimal based numeric representation by removing '%' and divide remaining numbers by 100.
  92.         4) Storage
  93.             a) Declared Simplest    Named variable names (E.g. testA, testB, testC)
  94.             b) Declared Preferred   Named array variable with initialized with null literals (-1) for all three values. Data acquisition saves sequentially to array.
  95.             c) Optimal      Named array variable with single initialized value that expands dynamically as data is added to the set.
  96.         5) Manipulation
  97.             a)  Take data set and output average.
  98.                 1) Declared Simplest   
  99.                         return testA + testB + testC / 3
  100.                 2) Optimal
  101.                         average = 0
  102.                         for element in array
  103.                             average += array[element]
  104.                         average /= len(array)
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