Sam
848356a76e
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481 lines
21 KiB
Python
481 lines
21 KiB
Python
# ui/hud.py
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"""Handles HUD elements and text overlays."""
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import pygame
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from config.constants import *
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from world.base.brain import CellBrain, FlexibleNeuralNetwork
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from world.objects import DefaultCell
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import math
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class HUD:
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def __init__(self):
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self.font = pygame.font.Font("freesansbold.ttf", FONT_SIZE)
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self.legend_font = pygame.font.Font("freesansbold.ttf", LEGEND_FONT_SIZE)
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def render_mouse_position(self, screen, camera):
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"""Render mouse position in top left."""
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mouse_x, mouse_y = camera.get_real_coordinates(*pygame.mouse.get_pos())
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mouse_text = self.font.render(f"Mouse: ({mouse_x:.2f}, {mouse_y:.2f})", True, WHITE)
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text_rect = mouse_text.get_rect()
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text_rect.topleft = (HUD_MARGIN, HUD_MARGIN)
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screen.blit(mouse_text, text_rect)
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def render_fps(self, screen, clock):
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"""Render FPS in top right."""
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fps_text = self.font.render(f"FPS: {int(clock.get_fps())}", True, WHITE)
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fps_rect = fps_text.get_rect()
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fps_rect.topright = (SCREEN_WIDTH - HUD_MARGIN, HUD_MARGIN)
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screen.blit(fps_text, fps_rect)
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def render_tps(self, screen, actual_tps):
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"""Render TPS in bottom right."""
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tps_text = self.font.render(f"TPS: {actual_tps}", True, WHITE)
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tps_rect = tps_text.get_rect()
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tps_rect.bottomright = (SCREEN_WIDTH - HUD_MARGIN, SCREEN_HEIGHT - HUD_MARGIN)
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screen.blit(tps_text, tps_rect)
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def render_tick_count(self, screen, total_ticks):
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"""Render total tick count in bottom left."""
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tick_text = self.font.render(f"Ticks: {total_ticks}", True, WHITE)
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tick_rect = tick_text.get_rect()
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tick_rect.bottomleft = (HUD_MARGIN, SCREEN_HEIGHT - HUD_MARGIN)
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screen.blit(tick_text, tick_rect)
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def render_pause_indicator(self, screen, is_paused):
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"""Render pause indicator when paused."""
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if is_paused:
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pause_text = self.font.render("Press 'Space' to unpause", True, WHITE)
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pause_rect = pause_text.get_rect()
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pause_rect.center = (SCREEN_WIDTH // 2, 20)
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screen.blit(pause_text, pause_rect)
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def render_selected_objects_info(self, screen, selected_objects):
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"""Render information about selected objects."""
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if len(selected_objects) < 1:
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return
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max_width = SCREEN_WIDTH - 20
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i = 0
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for obj in selected_objects:
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text = f"Object: {str(obj)}"
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words = text.split()
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line = ""
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line_offset = 0
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for word in words:
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test_line = f"{line} {word}".strip()
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test_width, _ = self.font.size(test_line)
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if test_width > max_width and line:
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obj_text = self.font.render(line, True, WHITE)
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obj_rect = obj_text.get_rect()
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obj_rect.topleft = (HUD_MARGIN, 30 + i * LINE_HEIGHT + line_offset)
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screen.blit(obj_text, obj_rect)
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line = word
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line_offset += LINE_HEIGHT
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else:
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line = test_line
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if line:
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obj_text = self.font.render(line, True, WHITE)
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obj_rect = obj_text.get_rect()
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obj_rect.topleft = (HUD_MARGIN, 30 + i * LINE_HEIGHT + line_offset)
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screen.blit(obj_text, obj_rect)
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i += 1
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def render_legend(self, screen, showing_legend):
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"""Render the controls legend."""
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if not showing_legend:
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legend_text = self.legend_font.render("Press 'L' to show controls", True, WHITE)
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legend_rect = legend_text.get_rect()
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legend_rect.center = (SCREEN_WIDTH // 2, SCREEN_HEIGHT - 20)
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screen.blit(legend_text, legend_rect)
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return
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# Split into two columns
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mid = (len(KEYMAP_LEGEND) + 1) // 2
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left_col = KEYMAP_LEGEND[:mid]
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right_col = KEYMAP_LEGEND[mid:]
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legend_font_height = self.legend_font.get_height()
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column_gap = 40 # Space between columns
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# Calculate max width for each column
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left_width = max(self.legend_font.size(f"{k}: {v}")[0] for k, v in left_col)
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right_width = max(self.legend_font.size(f"{k}: {v}")[0] for k, v in right_col)
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legend_width = left_width + right_width + column_gap
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legend_height = max(len(left_col), len(right_col)) * legend_font_height + 10
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legend_x = (SCREEN_WIDTH - legend_width) // 2
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legend_y = SCREEN_HEIGHT - legend_height - 10
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# Draw left column
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for i, (key, desc) in enumerate(left_col):
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text = self.legend_font.render(f"{key}: {desc}", True, WHITE)
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text_rect = text.get_rect()
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text_rect.left = legend_x
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text_rect.top = legend_y + 5 + i * legend_font_height
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screen.blit(text, text_rect)
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# Draw right column
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for i, (key, desc) in enumerate(right_col):
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text = self.legend_font.render(f"{key}: {desc}", True, WHITE)
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text_rect = text.get_rect()
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text_rect.left = legend_x + left_width + column_gap
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text_rect.top = legend_y + 5 + i * legend_font_height
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screen.blit(text, text_rect)
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def render_neural_network_visualization(self, screen, cell: DefaultCell) -> None:
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"""Render neural network visualization. This is fixed to the screen size and is not dependent on zoom or camera position."""
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# Visualization layout constants
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VIZ_WIDTH = 280 # Width of the neural network visualization area
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VIZ_HEIGHT = 300 # Height of the neural network visualization area
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VIZ_RIGHT_MARGIN = VIZ_WIDTH + 50 # Distance from right edge of screen to visualization
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# Background styling constants
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BACKGROUND_PADDING = 30 # Padding around the visualization background
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BACKGROUND_BORDER_WIDTH = 2 # Width of the background border
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BACKGROUND_COLOR = (30, 30, 30) # Dark gray background color
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# Title positioning constants
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TITLE_TOP_MARGIN = 20 # Distance above visualization for title
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# Neuron appearance constants
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NEURON_RADIUS = 8 # Radius of neuron circles
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NEURON_BORDER_WIDTH = 2 # Width of neuron circle borders
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# Layer spacing constants
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LAYER_VERTICAL_MARGIN = 30 # Top and bottom margin within visualization for neurons
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# Connection appearance constants
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MAX_CONNECTION_THICKNESS = 4 # Maximum thickness for connection lines
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MIN_CONNECTION_THICKNESS = 1 # Minimum thickness for connection lines
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# Neuron activation colors
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NEURON_BASE_INTENSITY = 100 # Base color intensity for neurons
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NEURON_ACTIVATION_INTENSITY = 155 # Additional intensity based on activation
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# Text positioning constants
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ACTIVATION_TEXT_OFFSET = 15 # Distance below neuron for activation value text
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ACTIVATION_DISPLAY_THRESHOLD = 0.01 # Minimum activation value to display as text
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ACTIVATION_TEXT_PRECISION = 2 # Decimal places for activation values
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# Layer label positioning constants
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LAYER_LABEL_BOTTOM_MARGIN = 15 # Distance below visualization for layer labels
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# Info text positioning constants
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INFO_TEXT_TOP_MARGIN = 40 # Distance below visualization for info text
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INFO_TEXT_LINE_SPACING = 15 # Vertical spacing between info text lines
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# Activation value clamping
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ACTIVATION_CLAMP_MIN = -1 # Minimum activation value for visualization
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ACTIVATION_CLAMP_MAX = 1 # Maximum activation value for visualization
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# --- Tooltip constants ---
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TOOLTIP_X_OFFSET = 12
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TOOLTIP_Y_OFFSET = 8
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TOOLTIP_PADDING_X = 5
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TOOLTIP_PADDING_Y = 3
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TOOLTIP_BG_COLOR = (40, 40, 40)
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TOOLTIP_BORDER_COLOR = WHITE
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TOOLTIP_BORDER_WIDTH = 1
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TOOLTIP_MARGIN = 10
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TOOLTIP_LINE_SPACING = 0 # No extra spacing between lines
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if not hasattr(cell, 'behavioral_model'):
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return
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cell_brain: CellBrain = cell.behavioral_model
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if not hasattr(cell_brain, 'neural_network'):
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return
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network: FlexibleNeuralNetwork = cell_brain.neural_network
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# Calculate visualization position
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viz_x = SCREEN_WIDTH - VIZ_RIGHT_MARGIN # Right side of screen
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viz_y = (SCREEN_HEIGHT // 2) - (VIZ_HEIGHT // 2) # Centered vertically
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layer_spacing = VIZ_WIDTH // max(1, len(network.layers) - 1) if len(network.layers) > 1 else VIZ_WIDTH
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# Draw background
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background_rect = pygame.Rect(viz_x - BACKGROUND_PADDING, viz_y - BACKGROUND_PADDING,
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VIZ_WIDTH + 2 * BACKGROUND_PADDING, VIZ_HEIGHT + 2 * BACKGROUND_PADDING)
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pygame.draw.rect(screen, BACKGROUND_COLOR, background_rect)
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pygame.draw.rect(screen, WHITE, background_rect, BACKGROUND_BORDER_WIDTH)
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# Title
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title_text = self.font.render("Neural Network", True, WHITE)
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title_rect = title_text.get_rect()
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title_rect.centerx = viz_x + VIZ_WIDTH // 2
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title_rect.top = viz_y - TITLE_TOP_MARGIN
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screen.blit(title_text, title_rect)
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# Get current activations by running a forward pass with current inputs
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input_values = [cell_brain.inputs[key] for key in cell_brain.input_keys]
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# Store activations for each layer
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activations = [input_values] # Input layer
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# Calculate activations for each layer
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for layer_idx in range(1, len(network.layers)):
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layer_activations = []
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for neuron in network.layers[layer_idx]:
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if neuron['type'] == 'input':
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continue
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# Calculate weighted sum
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weighted_sum = neuron.get('bias', 0)
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for source_layer, source_neuron, weight in neuron.get('connections', []):
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if source_layer < len(activations) and source_neuron < len(activations[source_layer]):
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weighted_sum += activations[source_layer][source_neuron] * weight
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# Apply activation function
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activation = max(ACTIVATION_CLAMP_MIN, min(ACTIVATION_CLAMP_MAX, weighted_sum))
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layer_activations.append(activation)
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activations.append(layer_activations)
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# Calculate neuron positions
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neuron_positions = {}
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for layer_idx, layer in enumerate(network.layers):
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layer_neurons = [n for n in layer if n['type'] != 'input' or layer_idx == 0]
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layer_size = len(layer_neurons)
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if layer_size == 0:
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continue
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# X position based on layer
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if len(network.layers) == 1:
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x = viz_x + VIZ_WIDTH // 2
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else:
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x = viz_x + (layer_idx * layer_spacing)
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# Y positions distributed vertically
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if layer_size == 1:
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y_positions = [viz_y + VIZ_HEIGHT // 2]
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else:
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y_start = viz_y + LAYER_VERTICAL_MARGIN
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y_end = viz_y + VIZ_HEIGHT - LAYER_VERTICAL_MARGIN
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y_positions = [y_start + i * (y_end - y_start) / (layer_size - 1) for i in range(layer_size)]
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for neuron_idx, neuron in enumerate(layer_neurons):
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if neuron_idx < len(y_positions):
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neuron_positions[(layer_idx, neuron_idx)] = (int(x), int(y_positions[neuron_idx]))
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# Draw connections first (so they appear behind neurons)
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for layer_idx in range(1, len(network.layers)):
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for neuron_idx, neuron in enumerate(network.layers[layer_idx]):
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if neuron['type'] == 'input':
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continue
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target_pos = neuron_positions.get((layer_idx, neuron_idx))
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if not target_pos:
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continue
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for source_layer, source_neuron, weight in neuron.get('connections', []):
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source_pos = neuron_positions.get((source_layer, source_neuron))
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if not source_pos:
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continue
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# Get activation value of the source neuron
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if source_layer < len(activations) and source_neuron < len(activations[source_layer]):
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activation = activations[source_layer][source_neuron]
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else:
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activation = 0.0
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# Clamp activation to [-1, 1]
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activation = max(ACTIVATION_CLAMP_MIN, min(ACTIVATION_CLAMP_MAX, activation))
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# Color: interpolate from red (-1) to yellow (0) to green (+1)
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if activation <= 0:
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# Red to yellow
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r = 255
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g = int(255 * (activation + 1))
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b = 0
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else:
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# Yellow to green
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r = int(255 * (1 - activation))
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g = 255
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b = 0
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color = (r, g, b)
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# Thickness: proportional to abs(weight)
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thickness = max(MIN_CONNECTION_THICKNESS, int(abs(weight) * MAX_CONNECTION_THICKNESS))
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pygame.draw.line(screen, color, source_pos, target_pos, thickness)
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# Draw neurons
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for layer_idx, layer in enumerate(network.layers):
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layer_activations = activations[layer_idx] if layer_idx < len(activations) else []
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for neuron_idx, neuron in enumerate(layer):
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if neuron['type'] == 'input' and layer_idx != 0:
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continue
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pos = neuron_positions.get((layer_idx, neuron_idx))
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if not pos:
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continue
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# Get activation value
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activation = 0
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if neuron_idx < len(layer_activations):
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activation = layer_activations[neuron_idx]
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# Color based on activation: brightness represents magnitude
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activation_normalized = max(ACTIVATION_CLAMP_MIN, min(ACTIVATION_CLAMP_MAX, activation))
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activation_intensity = int(abs(activation_normalized) * NEURON_ACTIVATION_INTENSITY)
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if activation_normalized >= 0:
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# Positive activation: blue tint
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color = (NEURON_BASE_INTENSITY, NEURON_BASE_INTENSITY, NEURON_BASE_INTENSITY + activation_intensity)
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else:
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# Negative activation: red tint
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color = (NEURON_BASE_INTENSITY + activation_intensity, NEURON_BASE_INTENSITY, NEURON_BASE_INTENSITY)
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# Draw neuron
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pygame.draw.circle(screen, color, pos, NEURON_RADIUS)
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pygame.draw.circle(screen, WHITE, pos, NEURON_RADIUS, NEURON_BORDER_WIDTH)
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# Draw activation value as text
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if abs(activation) > ACTIVATION_DISPLAY_THRESHOLD:
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activation_text = self.legend_font.render(f"{activation:.{ACTIVATION_TEXT_PRECISION}f}", True,
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WHITE)
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text_rect = activation_text.get_rect()
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text_rect.center = (pos[0], pos[1] + NEURON_RADIUS + ACTIVATION_TEXT_OFFSET)
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screen.blit(activation_text, text_rect)
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# Draw layer labels
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num_layers = len(network.layers)
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for layer_idx in range(num_layers):
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if layer_idx == 0:
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label = "Input"
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elif layer_idx == num_layers - 1:
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label = "Output"
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else:
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label = f"Hidden {layer_idx}" if num_layers > 3 else "Hidden"
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# Find average x position for this layer
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x_positions = [pos[0] for (l_idx, n_idx), pos in neuron_positions.items() if l_idx == layer_idx]
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if x_positions:
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avg_x = sum(x_positions) // len(x_positions)
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label_text = self.legend_font.render(label, True, WHITE)
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label_rect = label_text.get_rect()
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label_rect.centerx = avg_x
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label_rect.bottom = viz_y + VIZ_HEIGHT + LAYER_LABEL_BOTTOM_MARGIN
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screen.blit(label_text, label_rect)
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# Draw network info
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info = network.get_structure_info()
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info_lines = [
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f"Layers: {info['total_layers']}",
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f"Neurons: {info['total_neurons']}",
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f"Connections: {info['total_connections']}",
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f"Network Cost: {info['network_cost']}",
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]
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for i, line in enumerate(info_lines):
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info_text = self.legend_font.render(line, True, WHITE)
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info_rect = info_text.get_rect()
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info_rect.left = viz_x
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info_rect.top = viz_y + VIZ_HEIGHT + INFO_TEXT_TOP_MARGIN + i * INFO_TEXT_LINE_SPACING
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screen.blit(info_text, info_rect)
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# --- Tooltip logic for neuron hover ---
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mouse_x, mouse_y = pygame.mouse.get_pos()
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tooltip_text = None
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for (layer_idx, neuron_idx), pos in neuron_positions.items():
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dx = mouse_x - pos[0]
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dy = mouse_y - pos[1]
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dist = math.hypot(dx, dy)
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if dist <= NEURON_RADIUS + 3:
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neuron = network.layers[layer_idx][neuron_idx]
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label = None
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value_str = None
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# Show input/output name if applicable
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if neuron['type'] == 'input' and layer_idx == 0:
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if neuron_idx < len(cell_brain.input_keys):
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key = cell_brain.input_keys[neuron_idx]
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label = f"Input: {key}"
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# Show normalized input value
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raw_value = cell_brain.inputs.get(key, 0.0)
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normalized_value = cell_brain._normalize_input(key, raw_value)
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value_str = f"Value: {normalized_value:.2f}"
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elif neuron['type'] == 'output':
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if neuron_idx < len(cell_brain.output_keys):
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key = cell_brain.output_keys[neuron_idx]
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label = f"Output: {key}"
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# Show output value (already actual, not normalized)
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value = cell_brain.outputs.get(key, 0.0)
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value_str = f"Value: {value:.2f}"
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else:
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# For hidden neurons, show activation value
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if layer_idx < len(activations) and neuron_idx < len(activations[layer_idx]):
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value = activations[layer_idx][neuron_idx]
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value_str = f"Value: {value:.2f}"
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# Show bias if present
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bias = neuron.get('bias', None)
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bias_str = f"Bias: {bias:.2f}" if bias is not None else None
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# Compose tooltip text
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tooltip_lines = []
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if label:
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tooltip_lines.append(label)
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if value_str:
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tooltip_lines.append(value_str)
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if bias_str:
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tooltip_lines.append(bias_str)
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tooltip_text = "\n".join(tooltip_lines) if tooltip_lines else None
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break
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if tooltip_text:
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lines = tooltip_text.split('\n')
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tooltip_surfs = [self.legend_font.render(line, True, WHITE) for line in lines]
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width = max(surf.get_width() for surf in tooltip_surfs) + TOOLTIP_MARGIN
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height = sum(surf.get_height() for surf in tooltip_surfs) + TOOLTIP_MARGIN
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# Default position: right and below cursor
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tooltip_x = mouse_x + TOOLTIP_X_OFFSET
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tooltip_y = mouse_y + TOOLTIP_Y_OFFSET
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|
|
|
# Adjust if off right edge
|
|
if tooltip_x + width > SCREEN_WIDTH:
|
|
tooltip_x = mouse_x - width - TOOLTIP_X_OFFSET
|
|
# Adjust if off bottom edge
|
|
if tooltip_y + height > SCREEN_HEIGHT:
|
|
tooltip_y = mouse_y - height - TOOLTIP_Y_OFFSET
|
|
|
|
tooltip_rect = pygame.Rect(tooltip_x, tooltip_y, width, height)
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|
pygame.draw.rect(screen, TOOLTIP_BG_COLOR, tooltip_rect)
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|
pygame.draw.rect(screen, TOOLTIP_BORDER_COLOR, tooltip_rect, TOOLTIP_BORDER_WIDTH)
|
|
y = tooltip_rect.top + TOOLTIP_PADDING_Y
|
|
for surf in tooltip_surfs:
|
|
screen.blit(surf, (tooltip_rect.left + TOOLTIP_PADDING_X, y))
|
|
y += surf.get_height() + TOOLTIP_LINE_SPACING
|
|
|
|
def render_sprint_debug(self, screen, actual_tps, total_ticks):
|
|
"""Render sprint debug info: header, TPS, and tick count."""
|
|
header = self.font.render("Sprinting...", True, (255, 200, 0))
|
|
tps_text = self.font.render(f"TPS: {actual_tps}", True, (255, 255, 255))
|
|
ticks_text = self.font.render(f"Ticks: {total_ticks}", True, (255, 255, 255))
|
|
|
|
y = SCREEN_HEIGHT // 2 - 40
|
|
header_rect = header.get_rect(center=(SCREEN_WIDTH // 2, y))
|
|
tps_rect = tps_text.get_rect(center=(SCREEN_WIDTH // 2, y + 40))
|
|
ticks_rect = ticks_text.get_rect(center=(SCREEN_WIDTH // 2, y + 80))
|
|
|
|
screen.blit(header, header_rect)
|
|
screen.blit(tps_text, tps_rect)
|
|
screen.blit(ticks_text, ticks_rect)
|