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-             Design
-
-The application will be a single-window GUI built with PyQt5. It will be composed of three main components: a main
-window, a refactored plotter widget, and a background worker for data acquisition.
-
- 1 PicoStreamMainWindow (QMainWindow): This will be the application's central component, serving as the main entry point.
-    • Layout: It will feature a two-panel layout. The left panel will contain all user-configurable settings for the
-      acquisition (e.g., sample rate, voltage range, output file). The right panel will contain the embedded live plot.
-    • Control: It will have "Start" and "Stop" buttons to manage the acquisition lifecycle. It will manage the
-      application's state (e.g., idle, acquiring, error).
-    • Persistence: It will use QSettings to automatically save user-entered settings on exit and load them on startup.
-    • Lifecycle: It will be responsible for creating and managing the background worker thread and ensuring a graceful
-      shutdown.
- 2 HDF5LivePlotter (QWidget): The existing plotter will be refactored from a QMainWindow into a QWidget.
-    • Responsibility: Its sole responsibility will be to monitor the HDF5 file and display the live data. It will no
-      longer be a top-level window or control the application's lifecycle.
-    • Integration: An instance of this widget will be created and embedded directly into the right-hand panel of the
-      PicoStreamMainWindow.
- 3 StreamerWorker (QObject): This class will manage the acquisition task in a background thread to keep the GUI
-   responsive.
-    • Execution: It will be moved to a QThread. Its primary method will instantiate the Streamer class with parameters
-      from the GUI and call the blocking Streamer.run() method.
-    • Communication: It will use Qt signals to report its status (e.g., finished, error) back to the PicoStreamMainWindow
-      in a thread-safe manner. The main window will connect to these signals to update the UI, for example, by
-      re-enabling the "Start" button upon completion.
-
-                                               Phased Implementation Plan
-
-This plan breaks the work into five distinct, sequential phases.
-
-Phase 1: Project Restructuring and GUI Shell The goal is to set up the new file structure and a basic, non-functional GUI
-window.
-
- 1 Rename picostream/main.py to picostream/cli.py.
- 2 Create a new, empty picostream/main.py to serve as the GUI entry point.
- 3 In the new main.py, create a PicoStreamMainWindow class with a simple layout containing placeholders for the settings
-   panel and the plot.
- 4 Update the justfile with a new target to run the GUI application.
-
-Phase 2: Background Worker Implementation The goal is to run the data acquisition in a background thread, controlled by
-the GUI.
-
- 1 In picostream/main.py, create the StreamerWorker class inheriting from QObject.
- 2 Implement the QThread worker pattern in PicoStreamMainWindow to start the acquisition when a "Start" button is clicked
-   and to signal a stop using the existing shutdown_event.
- 3 Connect the worker's finished and error signals to GUI methods that update the UI state (e.g., re-enable buttons).
-
-Phase 3: GUI Controls and Settings Persistence The goal is to make the acquisition configurable through the GUI and to
-remember settings.
-
- 1 Populate the settings panel in PicoStreamMainWindow with input widgets for all acquisition parameters.
- 2 Pass the values from these widgets to the StreamerWorker when starting an acquisition.
- 3 Implement load_settings and save_settings methods using QSettings.
-
-Phase 4: Plotter Integration The goal is to embed the live plot directly into the main window.
-
- 1 In picostream/dfplot.py, refactor the HDF5LivePlotter class to inherit from QWidget instead of QMainWindow. Remove its
-   window-management logic.
- 2 In PicoStreamMainWindow, replace the plot placeholder with an instance of the refactored HDF5LivePlotter widget.
-
-Phase 5: Packaging The goal is to create a standalone, distributable executable.
-
- 1 Add a new build target to the justfile that uses PyInstaller to bundle the application.
- 2 Configure the build to handle dependencies, particularly creating a hook for Numba if necessary.
- 3 Test the final executable.