def make(self, key):
x, y, z, axial = (ChannelBrainLocation & key).fetch(
'channel_x', 'channel_y', 'channel_z', 'channel_axial',
order_by='channel_axial')
xyz_channels = np.c_[x, y, z]
key['region_boundaries'], key['region_label'], \
key['region_color'], key['region_id'] = \
EphysAlignment.get_histology_regions(
xyz_channels.astype('float')/1e6, axial.astype('float'))
self.insert1(key)
def _load_brain_regions(eid, probe_idx=0):
one = ONE()
ba = AllenAtlas()
probe = 'probe0%d' % probe_idx
ins = one.alyx.rest('insertions', 'list', session=eid, name=probe)[0]
xyz_chans = load_channels_from_insertion(ins,
depths=SITES_COORDINATES[:, 1],
one=one,
ba=ba)
region, region_label, region_color, _ = EphysAlignment.get_histology_regions(
xyz_chans, SITES_COORDINATES[:, 1], brain_atlas=ba)
return region, region_label, region_color
def make(self, key):
x, y, z = (ChannelBrainLocation & key).fetch('channel_ml',
'channel_ap',
'channel_dv')
coords = (ephys.ChannelGroup & key).fetch1('channel_local_coordinates')
xyz_channels = np.c_[x, y, z]
key['region_boundaries'], key['region_label'], \
key['region_color'], key['region_id'] = \
EphysAlignment.get_histology_regions(
xyz_channels.astype('float')/1e6, coords[:, 1])
self.insert1(key)
from ibllib.pipes.ephys_alignment import EphysAlignment
from oneibl.one import ONE
from brainbox.io.one import load_channel_locations
import numpy as np
import matplotlib.pyplot as plt
one = ONE(base_url="https://alyx.internationalbrainlab.org")
# Load data from 'ZM_2407' '2019-12-06'
eid = '03d3cffc-755a-43db-a839-65d8359a7b93'
probe = 'probe_00'
channels = load_channel_locations(eid, one=one, probe=probe)
# xyz coords of channels
xyz_channels = np.c_[channels[probe].x, channels[probe].y, channels[probe].z]
# depth along probe of channels
depths = channels[probe].axial_um
region, region_label, region_colour, region_id = EphysAlignment.get_histology_regions(xyz_channels, depths)
fig, ax1 = plt.subplots(figsize=(4,10))
for reg, col in zip(region, region_colour):
height = np.abs(reg[1]- reg[0])
bottom = reg[0]
color = col/255
ax1.bar(x=0.5, height=height, width=1, color=color, bottom=reg[0], edgecolor='w')
ax1.set_yticks(region_label[:, 0].astype(int))
ax1.set_yticklabels(region_label[:, 1])
ax1.hlines([0, 3840], *ax1.get_xlim(), linestyles='dashed', linewidth = 3, colors='k')
plt.show()
def plot_alignment(insertion, traj, ind=None):
depths = SITES_COORDINATES[:, 1]
xyz_picks = np.array(insertion['json']['xyz_picks']) / 1e6
alignments = traj['json'].copy()
k = list(alignments.keys())[-1] # if only I had a Walrus available !
alignments = {k: alignments[k]}
# Create a figure and arrange using gridspec
widths = [1, 2.5]
heights = [1] * len(alignments)
gs_kw = dict(width_ratios=widths, height_ratios=heights)
fig, axis = plt.subplots(len(alignments),
2,
constrained_layout=True,
gridspec_kw=gs_kw,
figsize=(8, 9))
# Iterate over all alignments for trajectory
# 1. Plot brain regions that channel pass through
# 2. Plot coronal slice along trajectory with location of channels shown as red points
# 3. Save results for each alignment into a dict - channels
channels = {}
for iK, key in enumerate(alignments):
# Location of reference lines used for alignmnet
feature = np.array(alignments[key][0])
track = np.array(alignments[key][1])
chn_coords = SITES_COORDINATES
# Instantiate EphysAlignment object
ephysalign = EphysAlignment(xyz_picks,
depths,
track_prev=track,
feature_prev=feature)
# Find xyz location of all channels
xyz_channels = ephysalign.get_channel_locations(feature, track)
# Find brain region that each channel is located in
brain_regions = ephysalign.get_brain_locations(xyz_channels)
# Add extra keys to store all useful information as one bunch object
brain_regions['xyz'] = xyz_channels
brain_regions['lateral'] = chn_coords[:, 0]
brain_regions['axial'] = chn_coords[:, 1]
# Store brain regions result in channels dict with same key as in alignment
channel_info = {key: brain_regions}
channels.update(channel_info)
# For plotting -> extract the boundaries of the brain regions, as well as CCF label and colour
region, region_label, region_colour, _ = ephysalign.get_histology_regions(
xyz_channels, depths)
# Make plot that shows the brain regions that channels pass through
ax_regions = fig.axes[iK * 2]
for reg, col in zip(region, region_colour):
height = np.abs(reg[1] - reg[0])
bottom = reg[0]
color = col / 255
ax_regions.bar(x=0.5,
height=height,
width=1,
color=color,
bottom=reg[0],
edgecolor='w')
ax_regions.set_yticks(region_label[:, 0].astype(int))
ax_regions.yaxis.set_tick_params(labelsize=8)
ax_regions.get_xaxis().set_visible(False)
ax_regions.set_yticklabels(region_label[:, 1])
ax_regions.spines['right'].set_visible(False)
ax_regions.spines['top'].set_visible(False)
ax_regions.spines['bottom'].set_visible(False)
ax_regions.hlines([0, 3840],
*ax_regions.get_xlim(),
linestyles='dashed',
linewidth=3,
colors='k')
# ax_regions.plot(np.ones(channel_depths_track.shape), channel_depths_track, '*r')
# Make plot that shows coronal slice that trajectory passes through with location of channels
# shown in red
ax_slice = fig.axes[iK * 2 + 1]
brain_atlas.plot_tilted_slice(xyz_channels, axis=1, ax=ax_slice)
ax_slice.plot(xyz_channels[:, 0] * 1e6, xyz_channels[:, 2] * 1e6, 'r*')
ax_slice.title.set_text(insertion['id'] + '\n' + str(key))
# Make sure the plot displays
plt.show()
def get_brain_regions(self, traj, ins=None, mapping='Allen'):
depths = SITES_COORDINATES[:, 1]
xyz_channels = self.get_channels(traj, ins=ins, depths=depths)
(region, region_label,
region_colour, _) = EphysAlignment.get_histology_regions(xyz_channels, depths,
brain_atlas=self.ba,
mapping=mapping)
return region, region_label, region_colour
#
# cvol[np.unravel_index(ba._lookup(all_channels), cvol.shape)] = 1
# from ibllib.atlas import AllenAtlas
# ba = AllenAtlas()
# import vedo
# import numpy as np
#
# actor = vedo.Volume(ba.image, c='bone', spacing = np.array([25]*3), mapper='smart', mode=0, alphaGradient=0.5)
# plt = vedo.Plotter()
# plt.add(actor)
# plt.show()
#from vedo import *
#from ibllib.atlas import AllenAtlas
#ba = AllenAtlas()
#import numpy as np
#import vtk
#la = ba.label
#la[la != 0] = 1
#vol2 = Volume(la).alpha([0, 0, 0.5])
#vol = Volume(ba.image).alpha([0, 0, 0.8]).c('bone').pickable(False)
##vol = Volume(ba.image).c('bone').pickable(False)
#
#plane = vtk.vtkPlane()
#clipping_planes = vtk.vtkPlaneCollection()
#clipping_planes.AddItem(plane)
#vol2.mapper().SetClippingPlanes(clipping_planes)
##
#plane.SetOrigin(vol.center() + np.array([0, -100, 0]))
#plane.SetNormal(0.5, 0.866, 0)
##
#sl = vol.slicePlane(origin=vol.center() + np.array([0, 100, 50]), normal=(0.5, 0.866, 0))
#s2 = vol.slicePlane(origin=vol.center(), normal=(0.5, 0, 0.866))
#s3 = vol.slicePlane(origin=vol.center() + np.array([0, -100, -50]), normal=(1, 0, 0)) # this is 30 degree in coronal
##s3 = vol.slicePlane(origin=vol.center(), normal=(0.5, 0, 0.866)) # this is 30 degree in coronal
#
#sl.cmap('Purples_r').lighting('off').addScalarBar(title='Slice', c='w')
#s2.cmap('Blues_r').lighting('off')
#s3.cmap('Greens_r').lighting('off')
#def func(evt):
# if not evt.actor:
# return
# pid = evt.actor.closestPoint(evt.picked3d, returnPointId=True)
# txt = f"Probing:\n{precision(evt.actor.picked3d, 3)}\nvalue = {pid}"
# sph = Sphere(evt.actor.points(pid), c='orange7').pickable(False)
# vig = sph.vignette(txt, s=7, offset=(-150,15), font=2).followCamera()
# plt.remove(plt.actors[-2:]).add([sph, vig]) #remove old 2 & add the new 2
#
#plt = show(vol, sl, s2, s3, __doc__, axes=9, bg='k', bg2='bb', interactive=False)
#plt.actors += [None, None] # 2 placeholders for [sphere, vignette]
#plt.addCallback('as my mouse moves please call', func)
#interactive()
#
#from vedo.utils import versor
#
#
#from vedo import *
#
#vol = Volume(dataurl+'embryo.slc').alpha([0,0,0.5]).c('k')
#
#slices = []
#for i in range(4):
# sl = vol.slicePlane(origin=[150,150,i*50+50], normal=(-1,0,1))
# slices.append(sl)
#
#amap = [0, 1, 1, 1, 1] # hide low value points giving them alpha 0
#mslices = merge(slices) # merge all slices into a single Mesh
#mslices.cmap('hot_r', alpha=amap).lighting('off').addScalarBar3D()
#
#show(vol, mslices, __doc__, axes=1)
# Find xyz location of all channels
xyz_channels = ephysalign.get_channel_locations(feature, track)
# Find brain region that each channel is located in
brain_regions = ephysalign.get_brain_locations(xyz_channels)
# Add extra keys to store all useful information as one bunch object
brain_regions['xyz'] = xyz_channels
brain_regions['lateral'] = chn_coords[:, 0]
brain_regions['axial'] = chn_coords[:, 1]
# Store brain regions result in channels dict with same key as in alignment
channel_info = {key: brain_regions}
channels.update(channel_info)
# For plotting -> extract the boundaries of the brain regions, as well as CCF label and colour
region, region_label, region_colour, _ = ephysalign.get_histology_regions(
xyz_channels, depths)
channel_depths_track = (ephysalign.feature2track(depths, feature, track) -
ephysalign.track_extent[0])
# Make plot that shows the brain regions that channels pass through
ax_regions = fig.axes[iK * 2]
for reg, col in zip(region, region_colour):
height = np.abs(reg[1] - reg[0])
bottom = reg[0]
color = col / 255
ax_regions.bar(x=0.5,
height=height,
width=1,
color=color,
bottom=reg[0],