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docs: extend user documentation for BEC Widgets

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(user.command_line_introduction)=
# Command Line Introduction
In order to use BEC Widgets as a plotting tool for BEC, it needs to be [installed](#user.installation) in the same python environment as the BECIpythonClient. If that is the case, it will automatically launch a GUI and assign the control of the GUI to the `gui` object in the client. The GUI backend will also be automatically connect to the BEC server, giving access to all information on the server and allowing the user to visualize the data in real-time.
## BECDockArea
The `gui` object represents the top level of hierarchy in BEC Widgets. It is a [`BECDockArea`](/api_reference/_autosummary/bec_widgets.cli.client.BECDockArea) based on the [pyqtgraph.dockarea](https://pyqtgraph.readthedocs.io/en/latest/api_reference/dockarea.html). The GUI can be further composed of multiple [`BECDock`](/api_reference/_autosummary/bec_widgets.cli.client.BECDock)s that can be detached and attached to the main area. These docks allow users to freely arrange and customize the widgets they add to the gui, giving them the necessary freedom to design the user interface they desire.
## Widgets
Widgets are the building blocks of the BEC Widgets framework. They are the visual components that allow users to interact with the data and control the behavior of the application. Each dock can contain multiple widgets, although we recommend using a direct mapping of a single widget to a dock. We currently support two type of widgets:
- [`BECFigure`](/api_reference/_autosummary/bec_widgets.cli.client.BECFigure): A widget that can be used to visualize data from BEC. It is automatically connected to the BEC server and can be used to plot data from BEC.
- [`SpiralProgressBar`](/api_reference/_autosummary/bec_widgets.cli.client.SpiralProgressBar): A custom widget that can be used to show progress in a spiral shape.
**BECFigure**
The [`BECFigure`](/api_reference/_autosummary/bec_widgets.cli.client.BECFigure) widget is one of the core widgets developed for BEC and designed to be flexible and can be used to visualize different types of data. It can be used to visualize [1D Waveform](user.widgets.waveform_1d), [2D Scatter Plot](user.widgets.scatter_2d), [Motor Position Map](user.widgets.motor_map) and 2D image data. As mentioned before, starting the BECIPythonClient will automatically launch a `gui` with a single dock area and a BECFigure widget attached to it. This widget is accessible via the `fig` object from the client directly. We also provide two methods [`plot()`](/api_reference/_autosummary/bec_widgets.cli.client.BECFigure.rst#bec_widgets.cli.client.BECFigure.plot), [`image()`](/api_reference/_autosummary/bec_widgets.cli.client.BECFigure.rst#bec_widgets.cli.client.BECFigure.image) and [`motor_map()`](/api_reference/_autosummary/bec_widgets.cli.client.BECFigure.rst#bec_widgets.cli.client.BECFigure.motor_map) as shortcuts to add a plot, image or motor map to the BECFigure.
**Waveform Plot**
The [`BECWaveForm`](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveform) is a widget that can be used to visualize 1D waveform data, i.e. to plot data of a monitor against a motor position. The method [`plot()`](/api_reference/_autosummary/bec_widgets.cli.client.BECFigure.rst#bec_widgets.cli.client.BECFigure.plot) of BECFigure adds a BECWaveForm widget to the figure, and returns the plot object.
```python
plt = fig.add_plot('samx', 'bpm4i')
```
Here, we assign the plot to the object `plt`. We can now use this object to further customize the plot, e.g. changing the title ([`set_title()`](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveform.rst#bec_widgets.cli.client.BECWaveform.set_title)), axis labels ([`set_x_label()`](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveform.rst#bec_widgets.cli.client.BECWaveform.set_x_label)) or limits ([`set_x_lim()`](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveform.rst#bec_widgets.cli.client.BECWaveform.set_x_lim)). We invite you to explore the API of the BECWaveForm in the [documentation](user.widgets.waveform_1d) or directly in the command line.
**Scatter Plot**
The [`BECWaveForm`](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveForm) widget can also be used to visualize 2D scatter plots. More details on setting up the scatter plot are available in the widget documentation of the [scatter plot](user.widgets.scatter_2d).
**Motor Map**
The [`BECMotorMap`](/api_reference/_autosummary/bec_widgets.cli.client.BECMotorMap) widget can be used to visualize the position of motors. It's focused on tracking and visualizing the position of motors, crucial for precise alignment and movement tracking during scans. More details on setting up the motor map are available in the widget documentation of the [motor map](user.widgets.motor_map).
**Image Plot**
The [`BECImageItem`](/api_reference/_autosummary/bec_widgets.cli.client.BECImageItem) widget can be used to visualize 2D image data for example a camera. More details on setting up the image plot are available in the widget documentation of the [image plot](user.widgets.image)
### Useful Commands
We recommend users to explore the API of the widgets by themselves since we assume that the user interface is supposed to be intuitive and self-explanatory. We appreciate feedback from user in order to constantly improve the experience and allow easy access to the gui, widgets and their functionality. We recommend checking the [API documentation](user.api_reference), but also by using BEC Widgets, exploring the available functions and check their dockstrings.
```python
gui.add_dock? # shows the dockstring of the add_dock method
```
In addition, we list below a few useful commands that can be used to interface with the widgets:
```python
gui.panels # returns a dictionary of all docks in the gui
gui.add_dock() # adds a new dock to the gui
dock = gui.panels['dock_2']
dock.add_widget_bec('BECFigure') # adds a new widget of BECFigure to the dock
dock.widget_list # returns a list of all widgets in the dock
figure = dock.widget_list[0] # assigns the created BECFigure to figure
plt = figure.add_plot('samx', 'bpm4i') # adds a BECWaveForm plot to the figure
plt.curves # returns a list of all curves in the plot
```
We note that commands can also be chained. For example, `gui.add_dock().add_widget_bec('BECFigure')` will add a new dock to the gui and add a new widget of `BECFigure` to the dock.
## Composing a larger GUI
The example given above introduces BEC Widgets with its different components, and provides an overview of how to interact with the widgets. Nevertheless, another power aspect of BEC Widgets lies in the ability to compose a larger GUI with multiple docks and widgets. This section aims to provide a tutorial like guide on how to compose a more complex GUI that (A) live-plots a 1D waveform, (B) plots data from a camera, and (C) tracks the positions of two motors.
Let's assume BEC was just started and the `gui` object is available in the client. A single dock is already attached together with a BEC Figure. Let's add the 1D waveform to this dock, change the color of the line to white and add the title *1D Waveform* to the plot.
```python
plt = fig.add_plot('samx', 'bpm4i')
plt.curves[0].set_color(color="white")
plt.set_title('1D Waveform')
```
Next, we add 2 new docks to the gui, one to plot the data of a camera and one to track the positions of two motors.
```ipython
cam_widget= gui.add_dock(name="cam_dock").add_widget_bec('BECFigure').image("eiger")
motor_widget = gui.add_dock(name="mot_dock").add_widget_bec('BECFigure').motor_map("samx", "samy")
```
Note, we chain commands here which is possible since the `add_dock` and `add_widget_bec` methods return the dock and the widget respectively. We can now further customize the widgets by changing the title, axis labels, etc.
```python
cam_widget.set_title("Camera Image Eiger")
cam_widget.set_vrange(vmin=0, vmax=100)
```
As a final step, we can now add also a SpiralProgressBar to a new dock, and perform a grid_scan with the motors *samx* and *samy*.
As you see in the example below, all docks are arranged below each other. This is the default behavior of the `add_dock` method. However, the docks can be freely arranged by drag and drop as desired by the user. We invite you to explore this by yourself following the example in the video, and build your custom GUI with BEC Widgets.
```python
prog_bar = gui.add_dock(name="prog_dock").add_widget_bec('SpiralProgressBar')
prog_bar.set_line_widths(15)
scans.grid_scan(dev.samy, -2, 2, 10, dev.samx, -5, 5, 10, exp_time=0.1, relative=False)
```
![gui_complex_gui](./gui_complex_gui.gif)

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(user.installation)=
# Installation
**Prerequisites**
Before installing BEC Widgets, please ensure the following requirements are met:
@ -11,25 +9,14 @@ Before installing BEC Widgets, please ensure the following requirements are met:
**Standard Installation**
Install BEC Widgets using the pip package manager. Open your terminal and execute:
To install BEC Widgets using the pip package manager, execute the following command in your terminal for getting the default PyQT6 version in your python environment:
```bash
pip install bec_widgets PyQt6
```
This command installs BEC Widgets along with its dependencies, including the default PyQt6.
**Selecting a PyQt Version**
BEC Widgets supports both PyQt5 and PyQt6. To install a specific version, use:
For PyQt6:
```bash
pip install bec_widgets[pyqt6]
```
For PyQt5:
In case you want to use PyQt5, you can install it by using the following command:
```bash
pip install bec_widgets[pyqt5]

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(user.widgets.bec_figure)=
# BECFigure
[`BECFigure`](/api_reference/_autosummary/bec_widgets.cli.client.BECFigure) is a widget that provides a graphical user interface for creating and managing plots. It is a versatile tool that allows users to create a wide range of plots, from simple 1D waveforms to complex 2D scatter plots. BECFigure is designed to be user-friendly and interactive, enabling users to customize plots and visualize data in real-time.
In the following, we describe 4 different type of widgets thaat are available in BECFigure.
(user.widgets.waveform_1d)=
## [1D Waveform Widget](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveform)
**Purpose:** This widget provides a straightforward visualization of 1D data. It is particularly useful for plotting positioner movements against detector readings, enabling users to observe correlations and patterns in a simple, linear format.
**Key Features:**
- Real-time plotting of positioner versus detector values.
- Interactive controls for zooming and panning through the data.
- Customizable visual elements such as line color and style.
**Example of Use:**
![Waveform 1D](./w1D.gif)
**Code example**
The following code snipped demonstrates how to create a 1D waveform plot using BEC Widgets within BEC. More details about BEC Widgets in BEC can be found in the getting started section within the [introduction to the command line.](user.command_line_introduction)
```python
# adds a new dock, a new BECFigure and a BECWaveForm to the dock
plt = gui.add_dock().add_widget_bec('BECFigure').plot('samx', 'bpm4i')
# add a second curve to the same plot
plt.add_curve_scan('samx', 'bpm3i')
plt.set_title("Gauss plots vs. samx")
plt.set_x_label("Motor X")
plt.set_y_label("Gauss Signal (A.U.")
```
Note, the return value of the simulated devices *bpm4i* and *bpm3i* may not be gaussian signals, but they can be easily configured with the code snippet below. For more details please check the documentation of the [simulation](https://bec.readthedocs.io/en/latest/developer/devices/bec_sim.html).
```python
# bpm4i uses GaussianModel and samx as a reference; default settings
dev.bpm4i.sim.select_sim_model("GaussianModel")
# bpm3i uses StepModel and samx as a reference; default settings
dev.bpm3i.sim.select_sim_model("StepModel")
```
(user.widgets.scatter_2d)=
## [2D Scatter Plot](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveform)
**Purpose:** The 2D scatter plot widget is designed for more complex data visualization. It employs a false color map to represent a third dimension (z-axis), making it an ideal tool for visualizing multidimensional data sets.
**Key Features:**
- 2D scatter plot with color-coded data points based on a third variable (two positioners for x/y vs. one detector for colormap).
- Interactive false color map for enhanced data interpretation.
- Tools for selecting and inspecting specific data points.
**Example of Use:**
![Waveform 1D](./scatter_2D.gif)
**Code example**
The following code snipped demonstrates how to create a 2D scatter plot using BEC Widgets within BEC.
```python
# adds a new dock, a new BECFigure and a BECWaveForm to the dock
plt = gui.add_dock().add_widget_bec('BECFigure').add_plot('samx', 'samy', 'bpm4i')
```
(user.widgets.motor_map)=
## [Motor Position Map](/api_reference/_autosummary/bec_widgets.cli.client.BECMotorMap)
**Purpose:** A specialized component derived from the Motor Alignment Tool. It's focused on tracking and visualizing the position of motors, crucial for precise alignment and movement tracking during scans.
**Key Features:**
- Real-time tracking of motor positions.
- Visual representation of motor trajectories, aiding in alignment tasks.
**Example of Use:**
![Waveform 1D](./motor.gif)
**Code example**
The following code snipped demonstrates how to create a motor map using BEC Widgets within BEC.
```python
# add a motor map to the gui
mot_map = gui.add_dock().add_widget_bec('BECFigure').motor_map('samx', 'samy')
# change the number of points displayed
```
(user.widgets.image_2d)=
## [Image Plot](/api_reference/_autosummary/bec_widgets.cli.client.BECImageItem)
**Purpose:** A versatile widget for visualizing 2D image data, such as camera images. It provides a detailed representation of image data, with an attached color and scale bar to dynamically adjust the image display.
**Key Features:**
- Live-plotting of 2D image data from cameras (*if data stream is available in BEC*).
- Color maps and scale bars for customizing image display.
**Example of Use:**
![Image 2D](./image_plot.gif)
**Code example**
The following code snipped demonstrates how to create a motor map using BEC Widgets within BEC.
```python
# add a camera view for the eiger camera to the gui
cam_widget = gui.add_dock().add_widget_bec('BECFigure').image('eiger')
# set the title of the camera view
cam_widget.set_title("Camera Image Eiger")
# change the color map range, e.g. from 0 to 100, per default it is autoscaling
# Note, the simulation has hot pixels on the detector
cam_widget.set_vrange(vmin=0, vmax=100)
```

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(user.widgets.spiral_progress_bar)=
# [Spiral Progress Bar](/api_reference/_autosummary/bec_widgets.cli.client.SpiralProgressBar)
**Purpose** The Spiral Progress Bar widget is a circular progress bar that can be used to visualize the progress of a task. The widget is designed to be used in applications where the progress of a task is represented as a percentage. The Spiral Progress Bar widget is a part of the BEC Widgets library and can be controlled directly using its API, or hooked up to the progress of a device readback or scan.
**Key Features:**
- circular progress bar to show updates on the progress of a task.
- hooks to update the progress bar from a device readback or scan.
- multiple progress rings to show different tasks in parallel.
**Example of Use:**
![SpiralProgressBar](./progress_bar.gif)
**Code example**
The following code snipped demonstrates how to create a 2D scatter plot using BEC Widgets within BEC.
```python
# adds a new dock with a spiral progress bar
progress = gui.add_dock().add_widget_bec("SpiralProgressBar")
# customize the size of the ring
progress.set_line_widfth(20)
```

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(user.widgets)=
# Widgets
## Visualization Widgets
BEC Widgets includes a variety of visualization widgets designed to cater to diverse data representation needs in beamline experiments. These widgets enhance the user experience by providing intuitive and interactive data visualizations.
### 1D Waveform Widget
```{toctree}
---
maxdepth: 2
hidden: false
---
**Purpose:** This widget provides a straightforward visualization of 1D data. It is particularly useful for plotting positioner movements against detector readings, enabling users to observe correlations and patterns in a simple, linear format.
bec_figure/
spiral_progress_bar/
```
**Key Features:**
- Real-time plotting of positioner versus detector values.
- Interactive controls for zooming and panning through the data.
- Customizable visual elements such as line color and style.
**Example of Use:**
![Waveform 1D](./w1D.gif)
### 2D Scatter Plot
**Purpose:** The 2D scatter plot widget is designed for more complex data visualization. It employs a false color map to represent a third dimension (z-axis), making it an ideal tool for visualizing multidimensional data sets.
**Key Features:**
- 2D scatter plot with color-coded data points based on a third variable (two positioners for x/y vs. one detector for colormap).
- Interactive false color map for enhanced data interpretation.
- Tools for selecting and inspecting specific data points.
**Example of Use:**
![Waveform 1D](./scatter_2D.gif)
### Motor Position Map
**Purpose:** A specialized component derived from the Motor Alignment Tool. It's focused on tracking and visualizing the position of motors, crucial for precise alignment and movement tracking during scans.
**Key Features:**
- Real-time tracking of motor positions.
- Visual representation of motor trajectories, aiding in alignment tasks.
- Ability to record and recall specific motor positions for repetitive tasks.
**Example of Use:**
![Waveform 1D](./motor.gif)