fid_VIS_SCm, fpd_VIS_SCm=gc.get_connectivity('VIS','SCm',ontology,mcc)

fid_SCm_PRNc, fpd_SCm_PRNc=gc.get_connectivity('SCm','PRNc',ontology,mcc)

fid_SCm_PRNr, fpd_SCm_PRNr=gc.get_connectivity('SCm','PRNr',ontology,mcc)

fid_PRNc_III, fpd_PRNc_III=gc.get_connectivity('PRNc','III',ontology,mcc)

fid_PRNc_VI, fpd_PRNc_VI=gc.get_connectivity('PRNc','VI',ontology,mcc)

fid_PRNr_VI, fpd_PRNr_VI=gc.get_connectivity('PRNr','VI',ontology,mcc)

fid_PRNr_III, fpd_PRNr_III=gc.get_connectivity('PRNr','III',ontology,mcc)

iu_c,iw_c=get_mean_injection_density(fid_SCm_PRNc,fpd_SCm_PRNc)

iu,iw=get_mean_injection_density(fid_SCm_PRNr, fpd_SCm_PRNr)

plot_max_voxels(iu,'SCm')

plot_max_voxels(iw,'SCm')

plot_max_voxels(iw_c,'SCm')

"""
Explanation: Abbreviations for structures of interest in the oculomotor circuit

superior colliculus, motor-related: 'SCm'
pontine reticular formation, caudal part: 'PRNc'
pontine reticular formation: 'PRNr'
oculomotor nucleus: 'III'
abducens nucleus: 'VI'

get connectivity for your structures of interest
End of explanation
"""


# # plot histogram for VIS_SCm_npv data
# vs_npv = VIS_SCm_npv.values()
# _,_,_=plt.hist(vs_npv, 25, facecolor='green', alpha=0.75)

# spc_npv = SCm_PRNc_npv.values()
# _,_,_=plt.hist(spc_npv, 25, facecolor='green', alpha=0.75)

# spr_npv = SCm_PRNr_npv.values()
# _,_,_=plt.hist(spr_npv, 25, facecolor='green', alpha=0.75)

# plt.scatter(spr_npv,spc_npv,alpha=.5,s=80)

"""
Explanation: IDEAS

visualize injection locations in a particular area, color coded by NPV strength. See if there is spatial clustering.
figure out a way to infer the "path of least resistance" from one area to another, across multiple nodes.
End of explanation
"""


