BNMPy.model_compressor
======================

The model_compressor module provides tools for simplifying Boolean Networks before optimization.

.. automodule:: BNMPy.model_compressor
   :members:
   :undoc-members:
   :show-inheritance:

Overview
--------

Model compression simplifies networks while preserving relevant behavior. Currently supports Boolean Networks only.

Features
--------------------

The compressor can:

1. **Remove non-observable nodes**: Nodes without paths to measured species
2. **Remove non-controllable nodes**: Nodes not influenced by perturbed species
3. **Collapse linear paths**: Simplify cascades of intermediate nodes
4. **Visualize results**: Show which nodes and edges were removed

Functions
---------

compress_model
~~~~~~~~~~~~~~

.. autofunction:: BNMPy.model_compressor.compress_model

Main compression function with automatic processing.

ModelCompressor Class
~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: BNMPy.model_compressor.ModelCompressor
   :members:
   :undoc-members:
   :show-inheritance:

For advanced compression with step-by-step control.

Basic Usage
-----------

.. code-block:: python

   import BNMPy

   # Load network
   network = BNMPy.load_network_from_file("network.txt")

   # Define nodes
   measured_nodes = {'Output1', 'Output2', 'Biomarker'}
   perturbed_nodes = {'Drug1', 'Drug2', 'Input'}

   # Compress
   compressed_network, compression_info = BNMPy.compress_model(
       network,
       measured_nodes=measured_nodes,
       perturbed_nodes=perturbed_nodes,
       verbose=True
   )

   print(f"Original: {len(network.nodeDict)} nodes")
   print(f"Compressed: {compressed_network.N} nodes")


Extract nodes directly from experimental data:

.. code-block:: python

   import BNMPy

   # Load network
   network = BNMPy.load_network_from_file("network.txt")

   # Extract nodes from experiments
   measured_nodes, perturbed_nodes = BNMPy.extract_experiment_nodes("experiments.csv")

   # Compress using experimental information
   compressed_network, compression_info = BNMPy.compress_model(
       network,
       measured_nodes=measured_nodes,
       perturbed_nodes=perturbed_nodes
   )


Step-by-Step Compression
------------------------

For detailed control:

.. code-block:: python

   from BNMPy.model_compressor import ModelCompressor

   # Initialize compressor
   compressor = ModelCompressor(network, measured_nodes, perturbed_nodes)

   # Analyze network
   non_observable = compressor.find_non_observable_nodes()
   non_controllable = compressor.find_non_controllable_nodes()
   collapsible_paths = compressor.find_collapsible_paths()

   print(f"Non-observable nodes: {non_observable}")
   print(f"Non-controllable nodes: {non_controllable}")
   print(f"Collapsible paths: {collapsible_paths}")

   # Selective compression
   compression_info = compressor.compress(
       remove_non_observable=True,
       remove_non_controllable=True,
       collapse_linear_paths=True
   )

   # Get compressed network
   compressed_network = compressor.get_compressed_network()

   # Get summary
   summary = compressor.get_compression_summary(compression_info)
   print(summary)

Visualization
-------------

The module provides visualization capabilities:

.. code-block:: python

   import BNMPy

   # Compress network
   compressed_network, compression_info = BNMPy.compress_model(
       network,
       measured_nodes=measured_nodes,
       perturbed_nodes=perturbed_nodes
   )

   # Visualize compression results
   BNMPy.vis_compression(
       network,                    # Original network
       compressed_network,         # Compressed network
       compression_info,          # Compression information
       "compression_results.html" # Output file
   )

Visualization:

- **Node colors**: Source nodes (grey), sink nodes (orange), intermediate (blue)
- **Removed nodes**: Shown in light grey
- **Edge styling**: Different colors for inhibitory vs activating edges
- **Interactive**: Draggable nodes with physics simulation


See Also
--------

- :doc:`parameter_optimizer` - Main optimization interface
- :doc:`experiment_data` - Experimental data handling
- :doc:`vis` - Visualization functions