Simplify the MDSA SNN creation.

[a-t-0]

• Create a dict with neuron prototyes, and their respective neuron and synapse types.
• Then simplify the MDSA SNN creation using that dict.
• Verify the MDSA algorithm still works.
• Remove the old_conversion if possible.

[a-t-0]

Write test to get mdsa graph:

def specify_mdsa_network_properties(

Then write dict code.
Then test mdsa_snn_graph objects are identical between def specify_mdsa_network_properties( and new method().

[a-t-0]

• Test the synaptic weight values are identical to old network
• Test the neuron properties are identical to old network

[a-t-0]

• [ ] Ensure the following command only performs a single run:

python -m src.snncompare -e mdsa_creation_only_size_3_4 -v -x

• Then run with vulture:
  https://pypi.org/project/vulture/
• Then remove unused code.

[a-t-0]

After that:

• Ensure visualisation prints the specific timestep/works again
• Then reduce snn runtime by tweaking snn initialisation, add monitor on when counters are done.
• Visualise runtime.
• Determine if you can export (faster) with .svg.
• Restore adaptation.
• Restore radiation.
• Create performance measurements.
• Apply strict pre-commit (with typing).

[a-t-0]

Manually evaulated unused functions (excluded test_ usage):

Unused:
adaptation_mech_2_networkx_and_snn
get_nr_of_spikes
verify_nx_neuron_properties_are_specified
assert_synaptic_edgeweight_type_is_correct
assert_no_duplicate_edges_exist
fill_dictionary
delete_files_in_folder
delete_file_if_exists
get_counter_neurons_from_dict
write_results_to_file
full_alipour
old_graph_to_new_graph_properties
verify_args
plot_unstructured_graph
add_recursive_edges_to_graph
json_dicts_of_graph_results_exist
output_files_stage_4
create_custom_plot_titles
stage_4_results_exist

Lava:
add_monitor_to_dict
simulate_snn_on_lava
remove_monitors_from_get_degree
create_neuron_monitors
store_spike_values_in_lava_neurons

Dupes:
store_input_synapses
store_output_synapses
create_redundant_node