447 lines
18 KiB
Python
447 lines
18 KiB
Python
# target_simulator/gui/ppi_display.py
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"""
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A reusable Tkinter widget that displays a Plan Position Indicator (PPI)
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using Matplotlib, capable of showing both live targets and trajectory previews,
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and comparing simulated vs. real-time data.
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"""
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import tkinter as tk
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from tkinter import ttk
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import math
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import time
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import logging
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import numpy as np
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import collections
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from matplotlib.figure import Figure
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
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from typing import List, Dict, Union
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from target_simulator.core.models import Target, Waypoint, ManeuverType, NM_TO_FT
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# Module-level logger
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logger = logging.getLogger(__name__)
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class PPIDisplay(ttk.Frame):
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"""
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A custom widget for the PPI radar display.
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"""
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TRAIL_LENGTH = 100
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def __init__(
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self,
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master,
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max_range_nm: int = 100,
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scan_limit_deg: int = 60,
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trail_length: int = None,
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):
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super().__init__(master)
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self.max_range = max_range_nm
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self.scan_limit_deg = scan_limit_deg
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self.sim_target_artists, self.real_target_artists = [], []
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self.sim_trail_artists, self.real_trail_artists = [], []
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# Keep label artists separated so we can update simulated labels
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# without removing real labels when a simulated-only update happens.
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self.sim_label_artists, self.real_label_artists = [], []
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self.trail_length = trail_length or self.TRAIL_LENGTH
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self._trails = {
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"simulated": collections.defaultdict(
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lambda: collections.deque(maxlen=self.trail_length)
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),
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"real": collections.defaultdict(
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lambda: collections.deque(maxlen=self.trail_length)
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),
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}
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self.preview_artists = []
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self.show_sim_points_var = tk.BooleanVar(value=True)
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self.show_real_points_var = tk.BooleanVar(value=True)
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self.show_sim_trail_var = tk.BooleanVar(value=False)
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# Default: do not show real trails unless the user enables them.
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self.show_real_trail_var = tk.BooleanVar(value=False)
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self.canvas = None
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self._create_controls()
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self._create_plot()
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# Track timestamps of real update calls to compute display update rate
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self._real_update_timestamps = collections.deque(maxlen=10000)
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self._last_update_summary_time = time.monotonic()
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self._update_summary_interval_s = 1.0
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def _on_display_options_changed(self):
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# A full redraw is needed, but we don't have the last data sets.
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# The best approach is to clear everything. The next update cycle from
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# the simulation engine and/or the server communicator will repopulate
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# the display with the correct visibility settings.
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self.clear_all_targets()
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if self.canvas:
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self.canvas.draw()
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def _create_controls(self):
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top_frame = ttk.Frame(self)
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top_frame.pack(side=tk.TOP, fill=tk.X, padx=5, pady=5)
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self.controls_frame = ttk.Frame(top_frame)
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self.controls_frame.pack(side=tk.LEFT, fill=tk.X, expand=True)
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ttk.Label(self.controls_frame, text="Range (NM):").pack(side=tk.LEFT)
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all_steps = [10, 20, 40, 80, 100, 160, 240, 320]
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valid_steps = sorted([s for s in all_steps if s <= self.max_range])
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if not valid_steps or self.max_range not in valid_steps:
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valid_steps.append(self.max_range)
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valid_steps.sort()
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self.range_var = tk.IntVar(value=self.max_range)
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self.range_selector = ttk.Combobox(
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self.controls_frame,
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textvariable=self.range_var,
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values=valid_steps,
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state="readonly",
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width=5,
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)
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self.range_selector.pack(side=tk.LEFT, padx=5)
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options_frame = ttk.LabelFrame(top_frame, text="Display Options")
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options_frame.pack(side=tk.RIGHT, padx=(10, 0))
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cb_sim_points = ttk.Checkbutton(
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options_frame,
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text="Sim Points",
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variable=self.show_sim_points_var,
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command=self._on_display_options_changed,
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)
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cb_sim_points.grid(row=0, column=0, sticky="w", padx=5)
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cb_real_points = ttk.Checkbutton(
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options_frame,
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text="Real Points",
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variable=self.show_real_points_var,
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command=self._on_display_options_changed,
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)
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cb_real_points.grid(row=0, column=1, sticky="w", padx=5)
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cb_sim_trail = ttk.Checkbutton(
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options_frame,
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text="Sim Trail",
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variable=self.show_sim_trail_var,
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command=self._on_display_options_changed,
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)
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cb_sim_trail.grid(row=1, column=0, sticky="w", padx=5)
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cb_real_trail = ttk.Checkbutton(
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options_frame,
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text="Real Trail",
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variable=self.show_real_trail_var,
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command=self._on_display_options_changed,
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)
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cb_real_trail.grid(row=1, column=1, sticky="w", padx=5)
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legend_frame = ttk.Frame(top_frame)
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legend_frame.pack(side=tk.RIGHT, padx=(10, 5))
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sim_sw = tk.Canvas(legend_frame, width=16, height=12, highlightthickness=0)
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sim_sw.create_rectangle(0, 0, 16, 12, fill="green", outline="black")
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sim_sw.pack(side=tk.LEFT, padx=(0, 4))
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ttk.Label(legend_frame, text="Simulated").pack(side=tk.LEFT, padx=(0, 8))
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real_sw = tk.Canvas(legend_frame, width=16, height=12, highlightthickness=0)
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real_sw.create_rectangle(0, 0, 16, 12, fill="red", outline="black")
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real_sw.pack(side=tk.LEFT, padx=(2, 4))
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ttk.Label(legend_frame, text="Real").pack(side=tk.LEFT)
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def _create_plot(self):
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fig = Figure(figsize=(5, 5), dpi=100, facecolor="#3E3E3E")
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fig.subplots_adjust(left=0.05, right=0.95, top=0.9, bottom=0.05)
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self.ax = fig.add_subplot(111, projection="polar", facecolor="#2E2E2E")
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self.ax.set_theta_zero_location("N")
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self.ax.set_theta_direction(1)
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self.ax.set_rlabel_position(90)
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self.ax.set_ylim(0, self.range_var.get())
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angles_deg = np.arange(0, 360, 30)
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labels = [f"{(a - 360) if a > 180 else a}°" for a in angles_deg]
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self.ax.set_thetagrids(angles_deg, labels)
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self.ax.tick_params(axis="x", colors="white", labelsize=8)
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self.ax.tick_params(axis="y", colors="white", labelsize=8)
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self.ax.grid(color="white", linestyle="--", linewidth=0.5, alpha=0.5)
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self.ax.spines["polar"].set_color("white")
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self.ax.set_title("PPI Display", color="white")
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(self._path_plot,) = self.ax.plot([], [], "g--", linewidth=1.5)
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(self._start_plot,) = self.ax.plot([], [], "go", markersize=8)
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(self._waypoints_plot,) = self.ax.plot([], [], "y+", markersize=10, mew=2)
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self.preview_artists = [self._path_plot, self._start_plot, self._waypoints_plot]
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limit_rad = np.deg2rad(self.scan_limit_deg)
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(self._scan_line_1,) = self.ax.plot(
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[limit_rad, limit_rad], [0, self.max_range], "y--", linewidth=1
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)
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(self._scan_line_2,) = self.ax.plot(
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[-limit_rad, -limit_rad], [0, self.max_range], "y--", linewidth=1
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)
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self.canvas = FigureCanvasTkAgg(fig, master=self)
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self.canvas.draw()
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self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)
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self.range_selector.bind("<<ComboboxSelected>>", self._on_range_selected)
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self._update_scan_lines()
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def clear_all_targets(self):
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"""Clears all target artists from the display."""
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all_artists = (
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self.sim_target_artists
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+ self.real_target_artists
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+ self.sim_trail_artists
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+ self.real_trail_artists
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+ self.sim_label_artists
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+ self.real_label_artists
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)
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for artist in all_artists:
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artist.remove()
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self.sim_target_artists.clear()
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self.real_target_artists.clear()
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self.sim_trail_artists.clear()
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self.real_trail_artists.clear()
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self.sim_label_artists.clear()
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self.real_label_artists.clear()
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def update_simulated_targets(self, targets: List[Target]):
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"""Updates and redraws only the simulated targets."""
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self._update_target_category(targets, "simulated")
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if self.canvas:
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self.canvas.draw()
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def update_real_targets(self, targets: List[Target]):
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"""Updates and redraws only the real targets."""
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# Instrument update rate
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try:
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now = time.monotonic()
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self._real_update_timestamps.append(now)
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# Periodic (throttled) summary log of PPI update rate
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if (
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now - self._last_update_summary_time
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) >= self._update_summary_interval_s:
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# compute recent updates per second
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cutoff = now - self._update_summary_interval_s
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cnt = 0
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for ts in reversed(self._real_update_timestamps):
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if ts >= cutoff:
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cnt += 1
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else:
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break
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rate = (
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cnt / float(self._update_summary_interval_s)
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if self._update_summary_interval_s > 0
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else float(cnt)
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)
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# try:
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# logger.info("[PPIDisplay] update_real_targets: recent_rate=%.1f updates/s display_targets=%d", rate, len(targets))
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# except Exception:
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# pass
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self._last_update_summary_time = now
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except Exception:
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pass
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self._update_target_category(targets, "real")
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if self.canvas:
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self.canvas.draw()
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def get_real_update_rate(self, window_seconds: float = 1.0) -> float:
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"""
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Returns approximate PPI "real targets" update rate (updates/sec) over the
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last `window_seconds` seconds.
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"""
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try:
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now = time.monotonic()
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cutoff = now - float(window_seconds)
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count = 0
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for ts in reversed(self._real_update_timestamps):
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if ts >= cutoff:
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count += 1
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else:
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break
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return count / float(window_seconds) if window_seconds > 0 else float(count)
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except Exception:
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return 0.0
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def _update_target_category(self, new_data: List[Target], category: str):
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"""
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Generic helper to update targets for a specific category ('simulated' or 'real').
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"""
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if category == "simulated":
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target_artists = self.sim_target_artists
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trail_artists = self.sim_trail_artists
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label_artists = self.sim_label_artists
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trail_data = self._trails["simulated"]
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show_points = self.show_sim_points_var.get()
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show_trail = self.show_sim_trail_var.get()
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color = "green"
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trail_color = "limegreen"
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else: # "real"
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target_artists = self.real_target_artists
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trail_artists = self.real_trail_artists
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label_artists = self.real_label_artists
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trail_data = self._trails["real"]
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show_points = self.show_real_points_var.get()
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show_trail = self.show_real_trail_var.get()
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color = "red"
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trail_color = "tomato"
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# 1. Clear existing artists for this category
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for artist in target_artists + trail_artists + label_artists:
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artist.remove()
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target_artists.clear()
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trail_artists.clear()
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label_artists.clear()
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# 2. Update trail data
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if show_points or show_trail:
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for t in new_data:
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if t.active:
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pos = (np.deg2rad(-t.current_azimuth_deg), t.current_range_nm)
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trail_data[t.target_id].append(pos)
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# 3. Draw new visuals
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if show_points:
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self._draw_target_visuals(
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[t for t in new_data if t.active], color, target_artists, label_artists
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)
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if show_trail:
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self._draw_trails(trail_data, trail_color, trail_artists)
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def _draw_target_visuals(
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self,
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targets: List[Target],
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color: str,
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artist_list: List,
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label_artist_list: List,
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):
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vector_len_nm = self.range_var.get() / 20.0
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# Determine marker size based on the target type (color)
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marker_size = (
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6 if color == "red" else 8
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) # Simulated targets (green) are smaller
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for target in targets:
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# Plotting position (theta, r)
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r_nm = target.current_range_nm
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# MODIFICATION: Removed negation. The azimuth from the model is now used directly.
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theta_rad_plot = np.deg2rad(target.current_azimuth_deg)
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(dot,) = self.ax.plot(
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theta_rad_plot, r_nm, "o", markersize=marker_size, color=color
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)
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artist_list.append(dot)
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# --- Robust Vector Calculation ---
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az_rad_model = math.radians(target.current_azimuth_deg)
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x_start_nm = r_nm * math.sin(az_rad_model)
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y_start_nm = r_nm * math.cos(az_rad_model)
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# MODIFICATION: Heading should also be consistent.
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# A positive heading (e.g. 10 deg) means turning left (CCW), which matches
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# the standard polar plot direction.
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hdg_rad_plot = math.radians(target.current_heading_deg)
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dx_nm = vector_len_nm * math.sin(hdg_rad_plot)
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dy_nm = vector_len_nm * math.cos(hdg_rad_plot)
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x_end_nm = x_start_nm + dx_nm
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y_end_nm = y_start_nm + dy_nm
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r_end_nm = math.hypot(x_end_nm, y_end_nm)
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# MODIFICATION: Removed negation here as well for consistency.
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theta_end_rad_plot = math.atan2(x_end_nm, y_end_nm)
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(line,) = self.ax.plot(
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[theta_rad_plot, theta_end_rad_plot],
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[r_nm, r_end_nm],
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color=color,
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linewidth=1.2,
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)
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artist_list.append(line)
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txt = self.ax.text(
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theta_rad_plot,
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r_nm + (vector_len_nm * 0.5),
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str(target.target_id),
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color="white",
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fontsize=8,
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ha="center",
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va="bottom",
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)
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label_artist_list.append(txt)
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def _draw_trails(self, trail_data: Dict, color: str, artist_list: List):
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for trail in trail_data.values():
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if len(trail) > 1:
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thetas, rs = zip(*trail)
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(line,) = self.ax.plot(
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thetas, rs, color=color, linestyle="-", linewidth=0.8, alpha=0.7
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)
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artist_list.append(line)
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def clear_trails(self):
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self._trails["simulated"].clear()
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self._trails["real"].clear()
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self.clear_all_targets()
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if self.canvas:
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self.canvas.draw()
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def _update_scan_lines(self):
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max_r = self.ax.get_ylim()[1]
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limit_rad = np.deg2rad(self.scan_limit_deg)
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self._scan_line_1.set_data([limit_rad, limit_rad], [0, max_r])
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self._scan_line_2.set_data([-limit_rad, -limit_rad], [0, max_r])
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def _on_range_selected(self, event=None):
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self.ax.set_ylim(0, self.range_var.get())
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self._update_scan_lines()
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if self.canvas:
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self.canvas.draw()
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def clear_previews(self):
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for artist in self.preview_artists:
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artist.set_data([], [])
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if self.canvas:
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self.canvas.draw()
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def draw_trajectory_preview(self, waypoints: List[Waypoint], use_spline: bool):
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self.clear_previews()
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self.clear_trails()
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if not waypoints or waypoints[0].maneuver_type != ManeuverType.FLY_TO_POINT:
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return
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path, _ = Target.generate_path_from_waypoints(waypoints, use_spline)
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if not path:
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return
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path_thetas, path_rs = [], []
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for point in path:
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x_ft, y_ft = point[1], point[2]
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r_ft = math.sqrt(x_ft**2 + y_ft**2)
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# Use the same plotting convention used elsewhere: theta_plot = atan2(x, y).
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# This convention is established in the _draw_target_visuals helper,
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# which computes theta via -current_azimuth_deg.
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az_rad_plot = math.atan2(x_ft, y_ft)
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path_rs.append(r_ft / NM_TO_FT)
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path_thetas.append(az_rad_plot)
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self._path_plot.set_data(path_thetas, path_rs)
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wp_thetas, wp_rs = [], []
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for wp in waypoints:
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if wp.maneuver_type == ManeuverType.FLY_TO_POINT:
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r_nm = wp.target_range_nm or 0.0
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# The path uses theta_plot = atan2(x, y). Waypoint azimuths
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# provided in the waypoint are geometric azimuth degrees
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# (0 = North, positive CCW). Convert directly to radians so
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# plotted waypoint markers align with the generated path.
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az_rad_plot = np.deg2rad(wp.target_azimuth_deg or 0.0)
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wp_rs.append(r_nm)
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wp_thetas.append(az_rad_plot)
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self._waypoints_plot.set_data(wp_thetas, wp_rs)
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start_wp = waypoints[0]
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start_r = start_wp.target_range_nm or 0.0
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start_theta = np.deg2rad(start_wp.target_azimuth_deg or 0.0)
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self._start_plot.set_data([start_theta], [start_r])
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if self.canvas:
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self.canvas.draw()
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def reconfigure_radar(self, max_range_nm: int, scan_limit_deg: int):
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self.max_range, self.scan_limit_deg = max_range_nm, scan_limit_deg
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steps = [10, 20, 40, 80, 100, 160, 240, 320]
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valid_steps = sorted(
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[s for s in steps if s <= max_range_nm]
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+ ([max_range_nm] if max_range_nm not in steps else [])
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)
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self.range_selector["values"] = valid_steps
|
|
if self.range_var.get() not in valid_steps:
|
|
self.range_var.set(max_range_nm)
|
|
self._on_range_selected()
|
|
|
|
def set_connect_callback(self, cb):
|
|
self._connect_callback = cb
|
|
|
|
def update_connect_state(self, is_connected: bool):
|
|
# This method should only reflect state, not change UI elements.
|
|
# The parent window is responsible for enabling/disabling controls.
|
|
pass
|