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294 lines
11 KiB
294 lines
11 KiB
import uuid
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import threading
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import numpy as np
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from collections import deque
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import dearpygui.dearpygui as dpg
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from abc import ABC, abstractmethod
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class ViewPanel(ABC):
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"""Abstract base class for all view panels that can be displayed in a plot container"""
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def __init__(self, panel_id: str = None):
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self.panel_id = panel_id or str(uuid.uuid4())
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self.title = "Untitled Panel"
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@abstractmethod
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def clear(self):
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pass
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@abstractmethod
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def create_ui(self, parent_tag: str):
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pass
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@abstractmethod
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def destroy_ui(self):
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pass
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@abstractmethod
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def get_panel_type(self) -> str:
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pass
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@abstractmethod
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def update(self):
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pass
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@abstractmethod
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def to_dict(self) -> dict:
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pass
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@classmethod
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@abstractmethod
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def load_from_dict(cls, data: dict, data_manager, playback_manager, worker_manager):
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pass
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class TimeSeriesPanel(ViewPanel):
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def __init__(self, data_manager, playback_manager, worker_manager, panel_id: str | None = None):
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super().__init__(panel_id)
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self.data_manager = data_manager
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self.playback_manager = playback_manager
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self.worker_manager = worker_manager
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self.title = "Time Series Plot"
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self.plot_tag = f"plot_{self.panel_id}"
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self.x_axis_tag = f"{self.plot_tag}_x_axis"
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self.y_axis_tag = f"{self.plot_tag}_y_axis"
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self.timeline_indicator_tag = f"{self.plot_tag}_timeline"
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self._ui_created = False
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self._series_data: dict[str, tuple[np.ndarray, np.ndarray]] = {}
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self._last_plot_duration = 0
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self._update_lock = threading.RLock()
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self._results_deque: deque[tuple[str, list, list]] = deque()
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self._new_data = False
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self._last_x_limits = (0.0, 0.0)
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self._queued_x_sync: tuple | None = None
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self._queued_reallow_x_zoom = False
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self._total_segments = self.playback_manager.num_segments
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def to_dict(self) -> dict:
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return {
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"type": "timeseries",
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"title": self.title,
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"series_paths": list(self._series_data.keys())
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}
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@classmethod
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def load_from_dict(cls, data: dict, data_manager, playback_manager, worker_manager):
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panel = cls(data_manager, playback_manager, worker_manager)
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panel.title = data.get("title", "Time Series Plot")
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panel._series_data = {path: (np.array([]), np.array([])) for path in data.get("series_paths", [])}
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return panel
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def create_ui(self, parent_tag: str):
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self.data_manager.add_observer(self.on_data_loaded)
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self.playback_manager.add_x_axis_observer(self._on_x_axis_sync)
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with dpg.plot(height=-1, width=-1, tag=self.plot_tag, parent=parent_tag, drop_callback=self._on_series_drop, payload_type="TIMESERIES_PAYLOAD"):
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dpg.add_plot_legend()
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dpg.add_plot_axis(dpg.mvXAxis, no_label=True, tag=self.x_axis_tag)
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dpg.add_plot_axis(dpg.mvYAxis, no_label=True, tag=self.y_axis_tag)
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timeline_series_tag = dpg.add_inf_line_series(x=[0], label="Timeline", parent=self.y_axis_tag, tag=self.timeline_indicator_tag)
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dpg.bind_item_theme(timeline_series_tag, "timeline_theme")
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self._new_data = True
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self._queued_x_sync = self.playback_manager.x_axis_bounds
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self._ui_created = True
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def update(self):
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with self._update_lock:
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if not self._ui_created:
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return
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if self._queued_x_sync:
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min_time, max_time = self._queued_x_sync
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self._queued_x_sync = None
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dpg.set_axis_limits(self.x_axis_tag, min_time, max_time)
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self._last_x_limits = (min_time, max_time)
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self._fit_y_axis(min_time, max_time)
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self._queued_reallow_x_zoom = True # must wait a frame before allowing user changes so that axis limits take effect
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return
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if self._queued_reallow_x_zoom:
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self._queued_reallow_x_zoom = False
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if tuple(dpg.get_axis_limits(self.x_axis_tag)) == self._last_x_limits:
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dpg.set_axis_limits_auto(self.x_axis_tag)
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else:
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self._queued_x_sync = self._last_x_limits # retry, likely too early
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return
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if self._new_data: # handle new data in main thread
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self._new_data = False
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if self._total_segments > 0:
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dpg.set_axis_limits_constraints(self.x_axis_tag, -10, self._total_segments * 60 + 10)
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self._fit_y_axis(*dpg.get_axis_limits(self.x_axis_tag))
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for series_path in list(self._series_data.keys()):
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self.add_series(series_path, update=True)
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current_limits = dpg.get_axis_limits(self.x_axis_tag)
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# downsample if plot zoom changed significantly
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plot_duration = current_limits[1] - current_limits[0]
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if plot_duration > self._last_plot_duration * 2 or plot_duration < self._last_plot_duration * 0.5:
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self._downsample_all_series(plot_duration)
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# sync x-axis if changed by user
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if self._last_x_limits != current_limits:
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self.playback_manager.set_x_axis_bounds(current_limits[0], current_limits[1], source_panel=self)
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self._last_x_limits = current_limits
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self._fit_y_axis(current_limits[0], current_limits[1])
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while self._results_deque: # handle downsampled results in main thread
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results = self._results_deque.popleft()
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for series_path, downsampled_time, downsampled_values in results:
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series_tag = f"series_{self.panel_id}_{series_path}"
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if dpg.does_item_exist(series_tag):
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dpg.set_value(series_tag, (downsampled_time, downsampled_values.astype(float)))
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# update timeline
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current_time_s = self.playback_manager.current_time_s
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dpg.set_value(self.timeline_indicator_tag, [[current_time_s], [0]])
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# update timeseries legend label
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for series_path, (time_array, value_array) in self._series_data.items():
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position = np.searchsorted(time_array, current_time_s, side='right') - 1
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if position >= 0 and (current_time_s - time_array[position]) <= 1.0:
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value = value_array[position]
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formatted_value = f"{value:.5f}" if np.issubdtype(type(value), np.floating) else str(value)
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series_tag = f"series_{self.panel_id}_{series_path}"
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if dpg.does_item_exist(series_tag):
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dpg.configure_item(series_tag, label=f"{series_path}: {formatted_value}")
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def _on_x_axis_sync(self, min_time: float, max_time: float, source_panel):
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with self._update_lock:
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if source_panel != self:
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self._queued_x_sync = (min_time, max_time)
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def _fit_y_axis(self, x_min: float, x_max: float):
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if not self._series_data:
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dpg.set_axis_limits(self.y_axis_tag, -1, 1)
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return
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global_min = float('inf')
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global_max = float('-inf')
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found_data = False
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for time_array, value_array in self._series_data.values():
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if len(time_array) == 0:
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continue
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start_idx, end_idx = np.searchsorted(time_array, [x_min, x_max])
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end_idx = min(end_idx, len(time_array) - 1)
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if start_idx <= end_idx:
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y_slice = value_array[start_idx:end_idx + 1]
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series_min, series_max = np.min(y_slice), np.max(y_slice)
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global_min = min(global_min, series_min)
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global_max = max(global_max, series_max)
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found_data = True
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if not found_data:
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dpg.set_axis_limits(self.y_axis_tag, -1, 1)
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return
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if global_min == global_max:
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padding = max(abs(global_min) * 0.1, 1.0)
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y_min, y_max = global_min - padding, global_max + padding
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else:
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range_size = global_max - global_min
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padding = range_size * 0.1
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y_min, y_max = global_min - padding, global_max + padding
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dpg.set_axis_limits(self.y_axis_tag, y_min, y_max)
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def _downsample_all_series(self, plot_duration):
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plot_width = dpg.get_item_rect_size(self.plot_tag)[0]
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if plot_width <= 0 or plot_duration <= 0:
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return
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self._last_plot_duration = plot_duration
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target_points_per_second = plot_width / plot_duration
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work_items = []
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for series_path, (time_array, value_array) in self._series_data.items():
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if len(time_array) == 0:
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continue
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series_duration = time_array[-1] - time_array[0] if len(time_array) > 1 else 1
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points_per_second = len(time_array) / series_duration
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if points_per_second > target_points_per_second * 2:
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target_points = max(int(target_points_per_second * series_duration), plot_width)
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work_items.append((series_path, time_array, value_array, target_points))
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elif dpg.does_item_exist(f"series_{self.panel_id}_{series_path}"):
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dpg.set_value(f"series_{self.panel_id}_{series_path}", (time_array, value_array.astype(float)))
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if work_items:
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self.worker_manager.submit_task(
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TimeSeriesPanel._downsample_worker, work_items, callback=lambda results: self._results_deque.append(results), task_id=f"downsample_{self.panel_id}"
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)
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def add_series(self, series_path: str, update: bool = False):
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with self._update_lock:
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if update or series_path not in self._series_data:
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self._series_data[series_path] = self.data_manager.get_timeseries(series_path)
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time_array, value_array = self._series_data[series_path]
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series_tag = f"series_{self.panel_id}_{series_path}"
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if dpg.does_item_exist(series_tag):
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dpg.set_value(series_tag, (time_array, value_array.astype(float)))
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else:
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line_series_tag = dpg.add_line_series(x=time_array, y=value_array.astype(float), label=series_path, parent=self.y_axis_tag, tag=series_tag)
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dpg.bind_item_theme(line_series_tag, "line_theme")
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self._fit_y_axis(*dpg.get_axis_limits(self.x_axis_tag))
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plot_duration = dpg.get_axis_limits(self.x_axis_tag)[1] - dpg.get_axis_limits(self.x_axis_tag)[0]
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self._downsample_all_series(plot_duration)
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def destroy_ui(self):
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with self._update_lock:
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self.data_manager.remove_observer(self.on_data_loaded)
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self.playback_manager.remove_x_axis_observer(self._on_x_axis_sync)
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if dpg.does_item_exist(self.plot_tag):
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dpg.delete_item(self.plot_tag)
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self._ui_created = False
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def get_panel_type(self) -> str:
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return "timeseries"
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def clear(self):
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with self._update_lock:
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for series_path in list(self._series_data.keys()):
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self.remove_series(series_path)
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def remove_series(self, series_path: str):
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with self._update_lock:
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if series_path in self._series_data:
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if dpg.does_item_exist(f"series_{self.panel_id}_{series_path}"):
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dpg.delete_item(f"series_{self.panel_id}_{series_path}")
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del self._series_data[series_path]
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def on_data_loaded(self, data: dict):
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with self._update_lock:
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self._new_data = True
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if data.get('metadata_loaded'):
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self._total_segments = data.get('total_segments', 0)
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limits = (-10, self._total_segments * 60 + 10)
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self._queued_x_sync = limits
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def _on_series_drop(self, sender, app_data, user_data):
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self.add_series(app_data)
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@staticmethod
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def _downsample_worker(series_path, time_array, value_array, target_points):
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if len(time_array) <= target_points:
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return series_path, time_array, value_array
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step = len(time_array) / target_points
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indices = []
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for i in range(target_points):
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start_idx = int(i * step)
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end_idx = int((i + 1) * step)
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if start_idx == end_idx:
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indices.append(start_idx)
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else:
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bucket_values = value_array[start_idx:end_idx]
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min_idx = start_idx + np.argmin(bucket_values)
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max_idx = start_idx + np.argmax(bucket_values)
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if min_idx != max_idx:
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indices.extend([min(min_idx, max_idx), max(min_idx, max_idx)])
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else:
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indices.append(min_idx)
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indices = sorted(set(indices))
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return series_path, time_array[indices], value_array[indices]
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