Usage
=====

A general workflow using Ensembles GUI would look like this:

1. Execute EnsemblesGUI
-----------------------

    Once you have Ensembles GUI installed in your system, run `python main.py`

2. Load Your Data
-----------------

#. Navigate to the "Load file" tab.
#. Click "Open file" and select your data file.
#. Use the "File structure preview" panel to locate the variable you wish to load.
#. In the "Set the selected variable as" panel, select the variable name to assign it.
#. Preview the data in the "Input data preview" panel.
#. If needed, modify the loaded variable using the "Edit variable" panel.
#. Optionally, assign labels to the variable's elements.
#. Repeat this process until all necessary variables are loaded.

3. Run Analyses
---------------

#. Open any analysis tab, such as SVD, PCA, ICA, Xsembles2P, or SGC.
#. Ensure the "Input data" panel shows "Loaded" for the required data. If not, return to step 2.
#. Use the "Load default values" button to explore the analysis or adjust parameters as needed.
#. Click "Run analysis" to begin.
#. Monitor the "Console log" panel and the terminal for additional information.
#. Review the plots and adjust parameters as needed. Refer to the cited papers in each analysis tab for further understanding.
#. Repeat this process for each analysis.

4. Visualize Results
--------------------

#. Go to the "Ensembles visualizer" tab.
#. Click on the name of the analysis you wish to review. Only completed analyses will be clickable.
#. Explore the spatial distributions of neurons and the dFFo signal (if available).
#. Use the slider in the "General" tab to select ensembles for visualization.
#. Examine all visualizations, including the spatial distributions of recorded cells, their activations, and identified ensembles.

5. Compare Results
------------------

#. Open the "Ensemble compare" tab.
#. Use the "Similarities in members" or "Similarities in timecourses" tabs to identify similar results across algorithms.
#. Adjust the sliders on the left to select ensembles for each algorithm.
#. Optionally, filter by stimuli or behavior.
#. Explore the "Space map" and "Time profiles" tabs to compare selected ensembles.
#. Customize visualizations using the "Visualization options" panel.

6. Evaluate Algorithm Performance
---------------------------------

#. Go to the "Performance Comparison" tab.
#. If stimuli or behavior data are loaded, view performance comparisons. Missing data will be indicated in the plots.
#. Check the "Correlation between cells" tab to view correlations within ensembles.
#. Select the most suitable analysis or adjust parameters as needed.

7. Save Results
---------------

#. Open the "Save" tab.
#. Choose the data to save:
    - Minimal results used by Ensembles GUI: Three matrices for each algorithm—neuron membership in ensembles, ensemble activation, and the total number of ensembles.
    - Full results of every analysis: Includes additional data used by the original algorithms.
#. Select the desired export format.

Additional Tips
---------------

- Hover over any option or button to view tooltips with detailed explanations.

- Save plots using the "Save" icon below each figure.

- Interact with visualizations by moving or zooming with the cursor.

- Plots will display helpful messages if additional data or analyses are required.

- Check the "Console log" panel and Python console for additional information during analysis.