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- import numpy as np
- import matplotlib.pyplot as plt
- morphologies =["polar", "circular"]
- image_folder="../reports/images/"
- data_folder ="../../data/"
- connectivity_dict={}
- for c in connectivity:
- connectivity_dict[c]={}
- for morphology in morphologies:
- connectivity_dict[c][morphology] = np.load("{:s}{:s}_connectivity_{:s}.npy".format(data_folder, c, morphology))
- connectivity_dict[c]["distances"] = np.load("{:s}{:s}_connectivity_{:s}.npy".format(data_folder, c,
- "distance_bins"))
- # Plot connectivity matrix with neuron index sorted by their (x,y) coords?
- fig=plt.figure()
- ax = fig.add_subplot(111)
- colors = ["blue", "orange"]
- linestyle = ["-", "--" ]
- for c, morphology in zip(connectivity, morphologies):
- distance_bins = connectivity_dict[c]["distances"]
- for morphology, color in zip(morphologies, colors):
- number_of_connections = connectivity_dict[c][morphology]
- ax.plot(distance_bins[:-1], number_of_connections, label="{:s} {:s}".format(c, morphology), color=color, ls=ls)
- ax.grid()
- ax.legend()
- ax.set_xlabel("Distance (um)")
- ax.set_ylabel("Average number of connections")
- fig.savefig(image_folder+"distance_dependent_connectivity.png", dpi=300)
- # Effective interaction
- interaction_first_order = -1.0
- interaction_second_order = -interaction_first_order/10.0
- fig=plt.figure()
- ax = fig.add_subplot(111)
- interaction_weights = [interaction_first_order, interaction_second_order]
- for morphology, color in zip(morphologies, colors):
- distance_bins = connectivity_dict[connectivity[1]]["distances"]
- effective_interaction = np.zeros((distance_bins[:-1].shape))
- first_order_interaction = interaction_weights[0]*connectivity_dict[connectivity[0]][morphology]
- second_order_interaction = interaction_weights[1]*connectivity_dict[connectivity[1]][morphology]
- effective_interaction[:first_order_interaction.shape[0]] = first_order_interaction
- effective_interaction = effective_interaction+second_order_interaction
- ax.plot(distance_bins[1:], effective_interaction, label="weight_fo {:.1f} weight_so {:.1f} {:s}".format(
- interaction_first_order, interaction_second_order, morphology),
- color=color, ls=ls)
- ax.grid()
- ax.legend()
- ax.set_xlabel("Distance (um)")
- ax.set_ylabel("Effective interaction (a.u.)")
- fig.savefig(image_folder+"effective_interaction.png", dpi=300)
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