Commit 3d03be82 authored by Romulo Pereira Goncalves's avatar Romulo Pereira Goncalves
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Add a section about the demo and information about the limitations on each situation.

parent 3f11a16e
......@@ -80,6 +80,10 @@ HaSa::iplot HaSa::write_Out_Samples HaSa::.__C__Habi
The message `The following object is masked from 'package:graphics':` means that there is a package called `graphics` which has a function with the same name. A detail explanation can be found in [here](https://stackoverflow.com/questions/39137110/what-does-the-following-object-is-masked-from-packagexxx-mean).
#### Demo
The user can use the file `demo/HabitatSampler.r` to explore HabitatSampler. However, it is important to keep in mind that plotting is not possible. Hence, the user when calling the function `multi_Class_Sampling` needs to set the argument `plot_on_browser=FALSE`. On each iteration, when the user defines the threshold, the interactive plot to determine the correct threshold needs to be open in a browser, i.e., open `demo/Data/Results/leaflet.html` in a browser.
### Jupyter-lab
To start a `jupyter-lab` the user needs to set the variable `jupyter_lab` to `TRUE` in the `start_container.sh` script. Once set, the user only needs to re-run the script. The user will see the following output:
......@@ -111,4 +115,6 @@ Starting HaSa container, please add the input files to /home/romulo/gitlab/Habit
The user should then open one of the provided URLs in a web-browser. For more information on how to use jupyter_lab, please refer to its [manual](https://jupyterlab.readthedocs.io/en/stable/).
The `jupyter-lab` will open directly into the `demo/` directory of HaSa repository. The user should load the notebook `HabitatSamplerDocker.ipynb` to run the demo. The docker version only supports single thread processing due to memory constraints, i.e., user should use the default value `parallel_mode = FALSE`.
#### Demo
The `jupyter-lab` will open directly into the `demo/` directory of HaSa repository. The user should load the notebook `HabitatSamplerDocker.ipynb` to run the demo. The docker version only supports single thread processing due to memory constraints. Hence, the user should call `multi_Class_Sampling` function with the default value `parallel_mode = FALSE`.
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