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147 lines
6.5 KiB
Markdown
147 lines
6.5 KiB
Markdown
(user.widgets.bec_figure)=
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# BECFigure
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[`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.
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In the following, we describe 4 different type of widgets thaat are available in BECFigure.
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**Schema of the BECFigure**
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## [1D Waveform Widget](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveform)
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**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.
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**Key Features:**
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- Real-time plotting of positioner versus detector values.
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- Interactive controls for zooming and panning through the data.
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- Customizable visual elements such as line color and style.
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**Example of Use:**
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**Code example 1 - adding curves**
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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)
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```python
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# adds a new dock, a new BECFigure and a BECWaveForm to the dock
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plt = gui.add_dock().add_widget('BECFigure').plot(x_name='samx', y_name='bpm4i')
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# add a second curve to the same plot
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plt.plot(x_name='samx', y_name='bpm3i')
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plt.set_title("Gauss plots vs. samx")
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plt.set_x_label("Motor X")
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plt.set_y_label("Gauss Signal (A.U.")
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```
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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).
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```python
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# bpm4i uses GaussianModel and samx as a reference; default settings
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dev.bpm4i.sim.select_sim_model("GaussianModel")
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# bpm3i uses StepModel and samx as a reference; default settings
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dev.bpm3i.sim.select_sim_model("StepModel")
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```
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**Code example 2 - Adding Data Processing Pipeline Curve with LMFit Models**
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Together with the scan curve, one can also add a second curve that fits the signal using a specified model
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from [LMFit](https://lmfit.github.io/lmfit-py/builtin_models.html). The following code snippet demonstrates how to
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create a 1D waveform curve with an attached DAP process, or how to add a DAP process to an existing curve using the BEC
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CLI. Please note that for this example, both devices were set as Gaussian signals.
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```python
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# Add a new dock, a new BECFigure, and a BECWaveForm to the dock with a GaussianModel DAP
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plt = gui.add_dock().add_widget('BECFigure').plot(x_name='samx', y_name='bpm4i', dap="GaussianModel")
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# Add a second curve to the same plot without DAP
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plt.plot(x_name='samx', y_name='bpm3a')
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# Add DAP to the second curve
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plt.add_dap(x_name='samx', y_name='bpm3a', dap="GaussianModel")
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```
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To get the parameters of the fit, one has to retrieve the curve objects and call the dap_params property.
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```python
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# Get the curve object by name from the legend
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dap_bpm4i = plt.get_curve("bpm4i-bpm4i-GaussianModel")
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dap_bpm3a = plt.get_curve("bpm3a-bpm3a-GaussianModel")
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# Get the parameters of the fit
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print(dap_bpm4i.dap_params)
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# Output
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{'amplitude': 197.399639720862,
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'center': 5.013486095404885,
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'sigma': 0.9820868875739888}
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print(dap_bpm3a.dap_params)
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# Output
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{'amplitude': 698.3072786185278,
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'center': 0.9702840866173836,
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'sigma': 1.97139754785518}
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```
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(user.widgets.scatter_2d)=
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## [2D Scatter Plot](/api_reference/_autosummary/bec_widgets.cli.client.BECWaveform)
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**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.
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**Key Features:**
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- 2D scatter plot with color-coded data points based on a third variable (two positioners for x/y vs. one detector for colormap).
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- Interactive false color map for enhanced data interpretation.
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- Tools for selecting and inspecting specific data points.
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**Example of Use:**
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**Code example**
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The following code snipped demonstrates how to create a 2D scatter plot using BEC Widgets within BEC.
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```python
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# adds a new dock, a new BECFigure and a BECWaveForm to the dock
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plt = gui.add_dock().add_widget('BECFigure').add_plot(x_name='samx', y_name='samy', z_name='bpm4i')
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```
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(user.widgets.motor_map)=
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## [Motor Position Map](/api_reference/_autosummary/bec_widgets.cli.client.BECMotorMap)
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**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.
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**Key Features:**
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- Real-time tracking of motor positions.
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- Visual representation of motor trajectories, aiding in alignment tasks.
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**Example of Use:**
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**Code example**
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The following code snipped demonstrates how to create a motor map using BEC Widgets within BEC.
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```python
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# add a motor map to the gui
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mot_map = gui.add_dock().add_widget('BECFigure').motor_map('samx', 'samy')
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# change the number of points displayed
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```
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(user.widgets.image_2d)=
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## [Image Plot](/api_reference/_autosummary/bec_widgets.cli.client.BECImageItem)
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**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.
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**Key Features:**
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- Live-plotting of 2D image data from cameras (*if data stream is available in BEC*).
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- Color maps and scale bars for customizing image display.
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**Example of Use:**
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**Code example**
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The following code snipped demonstrates how to create a motor map using BEC Widgets within BEC.
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```python
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# add a camera view for the eiger camera to the gui
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cam_widget = gui.add_dock().add_widget('BECFigure').image('eiger')
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# set the title of the camera view
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cam_widget.set_title("Camera Image Eiger")
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# change the color map range, e.g. from 0 to 100, per default it is autoscaling
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# Note, the simulation has hot pixels on the detector
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cam_widget.set_vrange(vmin=0, vmax=100)
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```
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