drautb · February 5, 2019 02:18
diff --git a/sweetpea-window-examples.py b/sweetpea-window-examples.py
 # SweetPea Window Examples

 # Derived Levels are defined by three things:
 # - A Derivation Function
 # - An Argument List
 # - A Window
 #
 # Windows in turn are defined by a width and a stride. WithinTrial and
 # Transition are just shorthand for Windows with widths of 1 and 2
 # respectively, and both with a stride of 1.
 #
 # Here's an example of a Window with width 1 and stride 1:
 con_factor = Factor("congruent?", [
    DerivedLevel("con", WithinTrial(lambda color, text: color == text, [color, text])),
    DerivedLevel("inc", WithinTrial(lambda color, text: color != text, [color, text]))
 ])

 # And here's an example of a Window with width 2 and stride 1:
 repeated_color_factor = Factor("color repeats?", [
    DerivedLevel("yes", Transition(lambda colors: colors[0] == colors[1], [color])),
    DerivedLevel("no",  Transition(lambda colors: colors[0] != colors[1], [color]))
 ])

 # Notice how the derivation function for the repeated color factor receives
 # a _list_ of color values. (colors[0], colors[1], etc)
 #
 # In contrast, notice how the congruent factor receives just a color and text
 # value directly.
 #
 # For all Windows with a width of 1, the derivation function receives the value
 # for each factor directly, rather than a list, because there is only one value
 # when the width is 1.
 #
 # On the other hand, when the width is greater than 1, the derivation function
 # receives a _list_ of values for each factor, where the number of values for
 # each factor corresponds to the width of the Window.

 # Here's a slightly more complicated example:
 congruent_bookend = Factor("congruent bookend?", [
    DerivedLevel("yes", Window(lambda color, text: color == text, [color, text], 1, 3)),
    DerivedLevel("no",  Window(lambda color, text: color != text, [color, text], 1, 3))
 ])

 # This Window has a width of 1, and a stride of 3. Because the width is 1, the
 # function receives factor values directly, not lists. This factor takes the
 # 'yes' level whenever every third trial (starting with the first) is congruent.

 # Consider another example, where we want to identify whether or not the first
 # and third trials in a sliding window have the same level. We can define such
 # a factor like this:
 first_third_match = Factor("first and third match?", [
    DerivedLevel("yes", Window(lambda colors: colors[0] == colors[2], [color], 3, 1)),
    DerivedLevel("no",  Window(lambda colors: colors[0] != colors[2], [color], 3, 1))
 ])

 # This window has a width of 3, and a stride of 1. Notice again how, because
 # the width is greater than 1, the derivation function receives a _list_ of 
 # values for each factor. The nth value in the list corresponds to the factor's
 # value in the nth trial of the Window.
	# SweetPea Window Examples

	# Derived Levels are defined by three things:
	# - A Derivation Function
	# - An Argument List
	# - A Window
	#
	# Windows in turn are defined by a width and a stride. WithinTrial and
	# Transition are just shorthand for Windows with widths of 1 and 2
	# respectively, and both with a stride of 1.
	#
	# Here's an example of a Window with width 1 and stride 1:
	con_factor = Factor("congruent?", [
	DerivedLevel("con", WithinTrial(lambda color, text: color == text, [color, text])),
	DerivedLevel("inc", WithinTrial(lambda color, text: color != text, [color, text]))
	])

	# And here's an example of a Window with width 2 and stride 1:
	repeated_color_factor = Factor("color repeats?", [
	DerivedLevel("yes", Transition(lambda colors: colors[0] == colors[1], [color])),
	DerivedLevel("no", Transition(lambda colors: colors[0] != colors[1], [color]))
	])

	# Notice how the derivation function for the repeated color factor receives
	# a _list_ of color values. (colors[0], colors[1], etc)
	#
	# In contrast, notice how the congruent factor receives just a color and text
	# value directly.
	#
	# For all Windows with a width of 1, the derivation function receives the value
	# for each factor directly, rather than a list, because there is only one value
	# when the width is 1.
	#
	# On the other hand, when the width is greater than 1, the derivation function
	# receives a _list_ of values for each factor, where the number of values for
	# each factor corresponds to the width of the Window.

	# Here's a slightly more complicated example:
	congruent_bookend = Factor("congruent bookend?", [
	DerivedLevel("yes", Window(lambda color, text: color == text, [color, text], 1, 3)),
	DerivedLevel("no", Window(lambda color, text: color != text, [color, text], 1, 3))
	])

	# This Window has a width of 1, and a stride of 3. Because the width is 1, the
	# function receives factor values directly, not lists. This factor takes the
	# 'yes' level whenever every third trial (starting with the first) is congruent.

	# Consider another example, where we want to identify whether or not the first
	# and third trials in a sliding window have the same level. We can define such
	# a factor like this:
	first_third_match = Factor("first and third match?", [
	DerivedLevel("yes", Window(lambda colors: colors[0] == colors[2], [color], 3, 1)),
	DerivedLevel("no", Window(lambda colors: colors[0] != colors[2], [color], 3, 1))
	])

	# This window has a width of 3, and a stride of 1. Notice again how, because
	# the width is greater than 1, the derivation function receives a _list_ of
	# values for each factor. The nth value in the list corresponds to the factor's
	# value in the nth trial of the Window.