Add unit tests for network scaling factors in test_scaling_network.py

tests/test_scaling_network.py

@@ -0,0 +1,82 @@
+import sys
+import os
+import numpy as np
+import pytest
+
+# Add the source directory to the system path
+sys.path.append('./src/')
+
+from data_preprocessing.cytoarchitecture import NeuronNumbers
+from data_preprocessing.connectivity import SynapseNumbers
+from network import Network, networkDictFromDump
+from default_net_params import params as net_params
+
+# Constants
+SCALING_FACTOR = 0.005
+
+@pytest.fixture
+def setup_network():
+    # Update network parameters with scaling factors
+    net_params.update({
+        'N_scaling': SCALING_FACTOR,
+        'K_scaling': SCALING_FACTOR,
+        'fullscale_rates': './simulated_data/base_theory_rates.pkl'
+    })
+
+    # Paths
+    base_path = os.getcwd()
+    outpath = net_params['outpath']
+
+    # Initialize NeuronNumbers and SynapseNumbers classes
+    neuron_numbers = NeuronNumbers(
+        surface_area=net_params['surface_area'],
+        **net_params['cytoarchitecture_params']
+    )
+    synapse_numbers = SynapseNumbers(
+        NN=neuron_numbers,
+        **net_params['predictive_connectomic_params']
+    )
+
+    # Get full-scale neuron and synapse numbers
+    fullscale_NN_SN = {
+        'NN': neuron_numbers.getNeuronNumbers(),
+        'SN': synapse_numbers.getSynapseNumbers()
+    }
+
+    # Create and dump the network
+    network = Network(neuron_numbers, synapse_numbers, net_params)
+    network.dump(outpath)
+
+    # Get network hash and load network dictionary
+    net_hash = network.getHash()
+    net_folder = os.path.join(outpath, net_hash)
+    net_dict = networkDictFromDump(net_folder)
+
+    return fullscale_NN_SN, net_dict
+
+def test_scaling_factors(setup_network, capsys):
+    fullscale_NN_SN, net_dict = setup_network
+
+    # Sort full-scale neuron and synapse numbers
+    full_scale_neurons = fullscale_NN_SN['NN'].sort_index()
+    full_scale_synapses = fullscale_NN_SN['SN'].sort_index(axis=0).sort_index(axis=1)
+
+    # Sort network dictionary neuron and synapse numbers
+    net_dict['neuron_numbers'] = net_dict['neuron_numbers'].sort_index()
+    net_dict['synapses_internal'] = net_dict['synapses_internal'].sort_index(axis=0).sort_index(axis=1)
+
+    # Validate the downscaling factors
+    expected_neurons = np.round(full_scale_neurons * SCALING_FACTOR).astype(int)
+    expected_synapses = np.round(full_scale_synapses * net_params['N_scaling'] * net_params['K_scaling']).astype(int)
+
+    assert np.all(net_dict['neuron_numbers'] == expected_neurons), "Neuron numbers do not match expected downscaled values."
+    assert np.all(net_dict['synapses_internal'] == expected_synapses), "Synapse numbers do not match expected downscaled values."
+
+    print("Success: Neuron and synapse numbers match the expected downscaled values.")
+
+    # Capture and print the output
+    captured = capsys.readouterr()
+    print(captured.out)
+
+if __name__ == "__main__":
+    pytest.main([__file__])