# Extracting connectivity
Contact sites are the basis for synaptic classification. Therefore, contact sites need to be
combined with the synapse ``SegmentationObjects`` to conn ``SegmentationObjects`` and then further
 classified as synaptic or not-synaptic using an Random Forest Classifier (RFC).
The code is in `syconn.extraction.cs_processing_steps`, `syconn.proc.sd_proc` and `syconn.proc.ssd_proc`.

The exection script is located at ``SyConn/scrips/syns/syn_gen.py``.
## Prerequisites
* SegmentationDataset of type ``syn_ssv``, [see contact site extraction](contact_site_extraction.md)
* [Synapse type](synapse_type.md) predictions
* Labeled cellular compartments (see [neuron analysis](neuron_analysis.md))

## Classifying synapse objects
*TODO: refactor this part*

The previously generated [syn_ssv SegmentationObjects](contact_site_extraction.md) are in the following used to aggregate synaptic properties.

Other objects such as vesicle clouds and mitochondria are mapped by proximity.
Mapping these objects helps to improve the features used for classifying the conn objects.

    cps.map_objects_to_synssv(...)

In principle, one could imagine that the overlap between a synapse object and a contact site object is already a sufficient identification of a synapse between two neurites. In practice, we found that a further classification can improve the performance,
because it can incorporate other relevant features, such as vesicles clouds in proximity.

    cps.create_syn_rfc(sd_syn_ssv, path_to_gt_kzip)

creates the ground truth for the RFC and also trains and stores the classifier. Then, the ``SegmentationObjects`` of type ``syn_ssv`` can be classified with

    cps.classify_conn_objects(working_dir)


## Cellular compartment information
Each ``syn_ssv`` ``SegmentationObject`` will be assigned information about the "axoness" of both cellular partners forming the synapse. To collect this information from the ``ssv`` partners do

    cps.collect_axoness_from_ssv_partners(wd)

The axoness prediction key used here can currently only be changed in the code directly (see ``cps._collect_axoness_from_ssv_partners_thread``).


## Writing the connectivity information to the SuperSegmentationDataset
For convenience and efficiency, the connectivity information created in the last step can be written to the ``SuperSegmentationDataset``.

    ssd_proc.map_synaptic_conn_objects(ssd)

This enables direct look-ups on the level of ssv's, without having to go back to the sv objects, which would add delays.

## Exporting the connectivity matrix
The connectivity matrix can be exported in various formats, such as a networkx graph, or a csv file.

    cps.export_matrix(wd)