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drive_path = '/Volumes/Brain2016 1'
import os
from allensdk.core.mouse_connectivity_cache import MouseConnectivityCache
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt

import get_connectivity as gc

import sys
sys.path.append('~/GIT/K-AND/daniel/')



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# When downloading 3D connectivity data volumes, what resolution do you want (in microns)?  
# Options are: 10, 25, 50, 100
resolution_um=25

# The manifest file is a simple JSON file that keeps track of all of
# the data that has already been downloaded onto the hard drives.
# If you supply a relative path, it is assumed to be relative to your
# current working directory.
manifest_file = os.path.join(drive_path, "MouseConnectivity","manifest.json")

mcc = MouseConnectivityCache(manifest_file=manifest_file, resolution=resolution_um)



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ontology = mcc.get_ontology()

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



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fid_VIS_SCm, fpd_VIS_SCm=get_connectivity('VIS','SCm')



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fid_SCm_PRNc, fpd_SCm_PRNc=get_connectivity('SCm','PRNc')



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fid_SCm_PRNr, fpd_SCm_PRNr=get_connectivity('SCm','PRNr')



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fid_PRNc_III, fpd_PRNc_III=get_connectivity('PRNc','III')



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fid_PRNc_VI, fpd_PRNc_VI=get_connectivity('PRNc','VI')



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fid_PRNr_VI, fpd_PRNr_VI=get_connectivity('PRNr','VI')



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fid_PRNr_III, fpd_PRNr_III=get_connectivity('PRNr','III')



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iu_c,iw_c=get_mean_injection_density(fid_SCm_PRNc,fpd_SCm_PRNc)



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iu,iw=get_mean_injection_density(fid_SCm_PRNr, fpd_SCm_PRNr)



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plot_max_voxels(iu,'SCm')



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plot_max_voxels(iw,'SCm')



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plot_max_voxels(iw_c,'SCm')



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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.



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



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



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



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



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