101 lines
2.9 KiB
GDScript
101 lines
2.9 KiB
GDScript
class_name FishData
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extends Resource
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const CatchDifficultyProfileType = preload(
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"res://fishing/catch_difficulty_profile.gd"
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)
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const FishAvailabilityType = preload("res://fish/fish_availability.gd")
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enum Rarity {
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COMMON,
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UNCOMMON,
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RARE,
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EPIC,
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LEGENDARY,
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}
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@export var id: StringName
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@export var display_name: String
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@export var rarity: Rarity = Rarity.COMMON
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@export_range(0.0, 1000.0, 0.01) var base_catch_weight: float = 1.0
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@export var catch_profile: CatchDifficultyProfileType
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@export var availability: FishAvailabilityType
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@export_range(0.01, 1000.0, 0.01) var weight_min_lb: float = 0.5
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@export_range(0.01, 1000.0, 0.01) var weight_max_lb: float = 1.0
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@export_range(0.01, 20.0, 0.01) var display_scale_min: float = 0.8
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@export_range(0.01, 20.0, 0.01) var display_scale_max: float = 1.2
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@export_range(0.01, 10.0, 0.01) var display_scale_curve: float = 1.0
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@export_range(0, 1000000, 1) var sell_value_min: int = 0
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@export_range(0, 1000000, 1) var sell_value_max: int = 1
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@export_range(0.01, 10.0, 0.01) var sell_value_curve: float = 1.0
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@export var display_texture: Texture2D
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func is_selectable() -> bool:
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return (
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not id.is_empty()
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and base_catch_weight > 0.0
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and catch_profile != null
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and weight_min_lb > 0.0
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and weight_max_lb >= weight_min_lb
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and display_scale_min > 0.0
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and display_scale_max >= display_scale_min
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and display_scale_curve > 0.0
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and sell_value_min >= 0
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and sell_value_max >= sell_value_min
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and sell_value_curve > 0.0
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)
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func get_minimum_weight() -> float:
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return maxf(weight_min_lb, 0.01)
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func get_maximum_weight() -> float:
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return maxf(weight_max_lb, get_minimum_weight())
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func get_display_scale_for_weight(weight_lb: float) -> float:
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var minimum_weight: float = get_minimum_weight()
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var maximum_weight: float = get_maximum_weight()
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var normalized_weight: float = 0.0
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if maximum_weight > minimum_weight:
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normalized_weight = inverse_lerp(
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minimum_weight,
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maximum_weight,
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weight_lb
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)
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normalized_weight = pow(
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clampf(normalized_weight, 0.0, 1.0),
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maxf(display_scale_curve, 0.01)
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)
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var minimum_scale: float = maxf(display_scale_min, 0.01)
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var maximum_scale: float = maxf(display_scale_max, minimum_scale)
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return lerpf(minimum_scale, maximum_scale, normalized_weight)
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func get_sale_value_for_weight(weight_lb: float) -> int:
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var minimum_value: int = maxi(sell_value_min, 0)
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var maximum_value: int = maxi(sell_value_max, minimum_value)
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var normalized_weight: float = 0.0
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var minimum_weight: float = get_minimum_weight()
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var maximum_weight: float = get_maximum_weight()
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if maximum_weight > minimum_weight:
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normalized_weight = inverse_lerp(
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minimum_weight,
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maximum_weight,
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weight_lb
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)
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normalized_weight = pow(
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clampf(normalized_weight, 0.0, 1.0),
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maxf(sell_value_curve, 0.01)
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)
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return clampi(
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roundi(lerpf(minimum_value, maximum_value, normalized_weight)),
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minimum_value,
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maximum_value
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)
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func get_rarity_name() -> String:
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return Rarity.keys()[rarity].capitalize()
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