Splitting part of source_detection.ipynb off into separate imaging.ipynb example notebook

doc/src/examples/example_structure.md

@@ -8,6 +8,14 @@ The following example showcases a simple telescope simulation using the main pip
 <example_interfe_simu.py>
 ```
 
+## Imaging
+
+The notebook [imaging.ipynb](https://github.com/i4Ds/Karabo-Pipeline/blob/main/karabo/examples/imaging.ipynb), shows how to use different dirty imaging and image cleaning algorithms.
+
+## Source detection
+
+In the example notebook [source_detection.ipynb](https://github.com/i4Ds/Karabo-Pipeline/blob/main/karabo/examples/source_detection.ipynb), we simulate data, perform dirty imaging, clean the image, and then run and evaluate a source detection algorithm.
+
 ## SRCNet
 
 Karabo is used in the SRCNet to generate simulated test data resembling SKAO data.
@@ -29,10 +37,6 @@ See the script [line_emission.py](https://github.com/i4Ds/Karabo-Pipeline/blob/m
 
 This simulation begins with a `SkyModel` instance, and with the definition of the desired `Observation` and `Telescope` details. Then, the `InterferometerSimulation` instance uses the requested backend (OSKAR and RASCIL are currently supported) to compute the corresponding visibilities, and the desired `DirtyImager` instance is used to convert the visibilities into dirty images. Optionally, we can include primary beam effects and correct for such effects in the final dirty images. Finally, we can mosaic different dirty images into one larger image using the `ImageMosaicker` class.
 
-## Source detection
-
-In the example notebook [source_detection.ipynb](https://github.com/i4Ds/Karabo-Pipeline/blob/main/karabo/examples/source_detection.ipynb), we simulate data, perform dirty imaging, clean the image, and then run and evaluate a source detection algorithm.
-
 ## Show telescope config
 
 ```python

doc/src/examples/examples.md

@@ -44,6 +44,14 @@ observation = Observation(
 simulation.run_simulation(telescope, sky, observation)
 ```
 
+## Imaging
+
+The notebook [imaging.ipynb](https://github.com/i4Ds/Karabo-Pipeline/blob/main/karabo/examples/imaging.ipynb), shows how to use different dirty imaging and image cleaning algorithms.
+
+## Source detection
+
+In the example notebook [source_detection.ipynb](https://github.com/i4Ds/Karabo-Pipeline/blob/main/karabo/examples/source_detection.ipynb), we simulate data, perform dirty imaging, clean the image, and then run and evaluate a source detection algorithm.
+
 ## SRCNet
 
 Karabo is used in the SRCNet to generate simulated test data resembling SKAO data.
@@ -65,10 +73,6 @@ See the script [line_emission.py](https://github.com/i4Ds/Karabo-Pipeline/blob/m
 
 This simulation begins with a `SkyModel` instance, and with the definition of the desired `Observation` and `Telescope` details. Then, the `InterferometerSimulation` instance uses the requested backend (OSKAR and RASCIL are currently supported) to compute the corresponding visibilities, and the desired `DirtyImager` instance is used to convert the visibilities into dirty images. Optionally, we can include primary beam effects and correct for such effects in the final dirty images. Finally, we can mosaic different dirty images into one larger image using the `ImageMosaicker` class.
 
-## Source detection
-
-In the example notebook [source_detection.ipynb](https://github.com/i4Ds/Karabo-Pipeline/blob/main/karabo/examples/source_detection.ipynb), we simulate data, perform dirty imaging, clean the image, and then run and evaluate a source detection algorithm.
-
 ## Show telescope config
 
 ```python