def test_histology_get_brain_regions(self):
# first part of the test is to check on an actual track file
for file_track in self.path_tracks.rglob("*_pts.csv"):
xyz = histology.load_track_csv(file_track)
channels, ins = histology.get_brain_regions(xyz=xyz, brain_atlas=brain_atlas)
# also check that it works from an insertion
channels, ins2 = histology.get_brain_regions(xyz=ins.xyz, brain_atlas=brain_atlas)
self.assertTrue(channels.acronym[-1] == 'VISpm1')
self.assertTrue(channels.acronym[0] == 'APN')
a = np.array([ins.x, ins.y, ins.z, ins.phi, ins.theta, ins.depth])
b = np.array([ins2.x, ins2.y, ins2.z, ins2.phi, ins2.theta, ins2.depth])
self.assertTrue(np.all(np.isclose(a, b)))
def compare_2_probes(dict1,dict2):
'''
Given two probes in the following format, it calculates the cerror between
the probes. Dict2 is compared to dict1
Dict format for Insertion function
{'x': 544.0,
'y': 1285.0,
'z': 0.0,
'phi': 0.0,
'theta': 5.0,
'depth': 4501.0}
INPUT:
dict_1,dict_2: 2 dictionaries with probe coordinates
OUTPUT:
cecler: Comulative ecludian error
r_miss: each channel that debiates from the intetion adds 1/divided by
total number of channels. 1 is all are different
r_out: number of unique regions in dict1 missed divided by total possible
'''
# mean eucledian error
dict_1_ins = atlas.Insertion.from_dict(dict1)
dict1_regions, _, dict1_xyz = histology.get_brain_regions(dict_1_ins.xyz)
dict_2_ins = atlas.Insertion.from_dict(dict2)
dict2_regions, _, dict2_xyz = histology.get_brain_regions(dict_2_ins.xyz)
cecler = 0
for i in range(len(dict1_xyz)):
cecler += distance.euclidean(dict1_xyz[i,:], dict2_xyz[i,:])
mean_cecler = (cecler/len(dict1_xyz))*(10**6)
#score areas missmatch
r_miss = 0
for i in range(len(dict1_regions['id'])):
r_miss += 1 - int(dict1_regions['id'][i] == dict2_regions['id'][i])
r_miss = r_miss/len(dict1_regions['id'])
#percentage of regions totally missed
r_out = (1 - len(np.intersect1d(np.unique(dict1_regions['id']),
np.unique(dict2_regions['id'])))/
len(np.unique(dict1_regions['id'])))
return mean_cecler, r_miss, r_out
environment installation guide https://github.com/int-brain-lab/iblenv
'''
# Author: Olivier Winter
import numpy as np
from ibllib.pipes import histology
import ibllib.atlas as atlas
# === Parameters section (edit) ===
track_file = "/Users/gaelle/Downloads/electrodetracks_lic3/2019-08-27_lic3_002_probe00_pts.csv"
FULL_BLOWN_GUI = True # set to False for simple matplotlib view
# === Code (do not edit) ===
ba = atlas.AllenAtlas(res_um=25)
xyz_picks = histology.load_track_csv(track_file)
bl, ins = histology.get_brain_regions(xyz_picks)
if FULL_BLOWN_GUI:
from iblapps.histology import atlas_mpl
mw, cax = atlas_mpl.viewatlas(ba, ap_um=np.mean(ins.xyz[:, 1]) * 1e6)
else:
cax = ba.plot_cslice(ap_coordinate=np.mean(ins.xyz[:, 1]))
cax.plot(ins.xyz[:, 0] * 1e6, ins.xyz[:, 2] * 1e6)
cax.plot(bl.xyz[:, 0] * 1e6, bl.xyz[:, 2] * 1e6, '*')
# cax.plot(ba.bc.xscale * 1e6, ba.top[ba.bc.y2i(np.mean(ins.xyz[:, 1])), :] * 1e6)
if FULL_BLOWN_GUI:
mw.mpl_widget.draw()