111 lines
4.6 KiB
Python
111 lines
4.6 KiB
Python
import math
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from kivy.properties import BooleanProperty, ListProperty, NumericProperty, Clock
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from katrain.gui.kivyutils import BackgroundMixin
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class ScoreGraph(BackgroundMixin):
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show_score = BooleanProperty(True)
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show_winrate = BooleanProperty(True)
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nodes = ListProperty([])
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score_points = ListProperty([])
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winrate_points = ListProperty([])
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score_dot_pos = ListProperty([0, 0])
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winrate_dot_pos = ListProperty([0, 0])
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highlighted_index = NumericProperty(None)
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highlight_size = NumericProperty(6)
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score_scale = NumericProperty(5)
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winrate_scale = NumericProperty(5)
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.bind(pos=self.update_graph, size=self.update_graph)
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def initialize_from_game(self, root):
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self.nodes = [root]
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node = root
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while node.children:
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node = node.favourite_child
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self.nodes.append(node)
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self.highlighted_index = 0
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def show_graphs(self, keys):
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self.show_score = keys["score"]
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self.show_winrate = keys["winrate"]
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def update_graph(self, *args):
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nodes = self.nodes
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if nodes:
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score_values = [n.score if n and n.score else math.nan for n in nodes]
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score_nn_values = [n.score for n in nodes if n and n.score]
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score_values_range = min(score_nn_values or [0]), max(score_nn_values or [0])
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winrate_values = [(n.winrate - 0.5) * 100 if n and n.winrate else math.nan for n in nodes]
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winrate_nn_values = [(n.winrate - 0.5) * 100 for n in nodes if n and n.winrate]
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winrate_values_range = min(winrate_nn_values or [0]), max(winrate_nn_values or [0])
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score_granularity = 5
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winrate_granularity = 10
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self.score_scale = (
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max(math.ceil(max(-score_values_range[0], score_values_range[1]) / score_granularity), 1)
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* score_granularity
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)
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self.winrate_scale = (
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max(math.ceil(max(-winrate_values_range[0], winrate_values_range[1]) / winrate_granularity), 1)
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* winrate_granularity
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)
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xscale = self.width / max(len(score_values) - 1, 15)
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available_height = self.height
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score_line_points = [
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[
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self.pos[0] + i * xscale,
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self.pos[1] + self.height / 2 + available_height / 2 * (val / self.score_scale),
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]
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for i, val in enumerate(score_values)
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]
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winrate_line_points = [
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[
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self.pos[0] + i * xscale,
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self.pos[1] + self.height / 2 + available_height / 2 * (val / self.winrate_scale),
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]
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for i, val in enumerate(winrate_values)
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]
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self.score_points = sum(score_line_points, [])
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self.winrate_points = sum(winrate_line_points, [])
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if self.highlighted_index is not None:
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self.highlighted_index = min(self.highlighted_index, len(score_values) - 1)
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score_dot_point = score_line_points[self.highlighted_index]
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winrate_dot_point = winrate_line_points[self.highlighted_index]
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if math.isnan(score_dot_point[1]):
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score_dot_point[1] = (
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self.pos[1]
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+ self.height / 2
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+ available_height / 2 * ((score_nn_values or [0])[-1] / self.score_scale)
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)
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self.score_dot_pos = [c - self.highlight_size / 2 for c in score_dot_point]
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if math.isnan(winrate_dot_point[1]):
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winrate_dot_point[1] = (
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self.pos[1]
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+ self.height / 2
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+ available_height / 2 * ((winrate_nn_values or [0])[-1] / self.winrate_scale)
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)
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self.winrate_dot_pos = [c - self.highlight_size / 2 for c in winrate_dot_point]
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def update_value(self, node):
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self.highlighted_index = index = node.depth
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self.nodes.extend([None] * max(0, index - (len(self.nodes) - 1)))
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self.nodes[index] = node
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if index + 1 < len(self.nodes) and (node is None or self.nodes[index + 1] not in node.children):
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self.nodes = self.nodes[: index + 1] # on branch switching, don't show history from other branch
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if index == len(self.nodes) - 1: # possibly just switched branch
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while node.children: # add children back
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node = node.children[0]
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self.nodes.append(node)
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Clock.schedule_once(self.update_graph,0)
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