def plot_probe_trajectory_histology_sagittal(
x,
y,
subject_ID,
provenance='Planned',
project='ibl_neuropixel_brainwide_01',
remove_primary_axis=False):
"""Plot slice of Histology data along the insertion at [x,y] for subject ID.
The slice through the Histology data can be made along any of the
provenances of the probe at [x,y] for subject ID - Planned,
Micro-manipulator, Histology track, Ephys aligned histology track.
"""
from one.api import ONE
import ibllib.atlas as atlas
from ibllib.atlas import Insertion
import atlaselectrophysiology.load_histology as hist
import numpy as np
from scipy import ndimage
import sys
import matplotlib.pyplot as plt
# connect to ONE
one = ONE()
# get list of all trajectories at [x,y], for project
trajs = one.alyx.rest('trajectories', 'list', x=x, y=y, project=project)
# keeping subjs and labs for look-up later if needed..
subjs = [sess['session']['subject'] for sess in trajs]
labs = [sess['session']['lab'] for sess in trajs]
#aidx = subjs.index(atlas_ID)
sidx = subjs.index(subject_ID)
# Fetch trajectory metadata for traj:
traj = one.alyx.rest('trajectories',
'list',
session=trajs[sidx]['session']['id'],
probe=trajs[sidx]['probe_name'],
provenance=provenance)
if traj == []:
raise Exception("No trajectory found with provenance: " + provenance)
# get insertion object from ANY (the first) trajectory
ins = Insertion.from_dict(traj[0])
axis_labels = np.array(['ml (µm)', 'dv (µm)', 'ap (µm)'])
fig1, ax1 = plt.subplots() # new figure and axes objects - SAGITTAL
lab = labs[sidx] # this returns index in labs where subject_ID is in subjs
hist_paths = hist.download_histology_data(subject_ID, lab)
# create the brain atlases from the data
ba_gr = atlas.AllenAtlas(
hist_path=hist_paths[0]) # green histology channel autofl.
ba_rd = atlas.AllenAtlas(
hist_path=hist_paths[1]) # red histology channel cm-dii
# implementing tilted slice here to modify its cmap
# get tilted slice of the green and red channel brain atlases
# using the .image data as this contains the signal
gr_tslice, width, height, depth = ba_gr.tilted_slice(ins.xyz,
0,
volume=ba_gr.image)
rd_tslice, width, height, depth = ba_rd.tilted_slice(ins.xyz,
0,
volume=ba_rd.image)
# gaussian filtered image of the whole 3D GR stack
gr_g4 = ndimage.gaussian_filter(ba_gr.image, 4)
## SET CONTRAST based on min() and max() of hippocampus & thalamus pixels ONLY
# use the gaussian blurred image for pixel values, use ba_gr.label
width = width * 1e6
height = height * 1e6
depth = depth * 1e6
cmap = plt.get_cmap('bone')
# get the transfer function from y-axis to squeezed axis for second axe
ab = np.linalg.solve(np.c_[height, height * 0 + 1], depth)
height * ab[0] + ab[1]
# linearly scale the values in 2d numpy arrays to between 0-255 (8bit)
# Using MEDIAN FILTERED (radius 3) gr_tslice min and max to scale the image
# weirdly rd_in has very large min and max (problem with the original data acquisition?) so best to scale whole RGB with gr_in/1.5!
#gr_ts_m3 = ndimage.median_filter(gr_tslice, 3)
#gr_ts_m6 = ndimage.median_filter(gr_tslice, 6)
#gr_ts_m8 = ndimage.median_filter(gr_tslice, 8)
#rd_ts_m3 = ndimage.median_filter(rd_tslice, 3)
#gr_in = np.interp(gr_tslice, (gr_ts_m3.min(), gr_ts_m3.max()), (0, 255))
#gr_in = np.interp(gr_tslice, (gr_m4.min(), gr_m4.max()), (0, 255))
#gr_in = np.interp(gr_tslice, (gr_ts_m8.min(), gr_ts_m8.max()), (0, 255))
gr_in = np.interp(gr_tslice, (gr_g4.min(), gr_g4.max()), (0, 255))
rd_in = np.interp(rd_tslice, (rd_tslice.min(), rd_tslice.max() / 3),
(0, 255))
# original normalisation
gr_in = np.interp(gr_tslice, (gr_tslice.min(), gr_tslice.max()), (0, 255))
rd_in = np.interp(rd_tslice, (gr_tslice.min(), gr_tslice.max() / 1.5),
(0, 255))
# join together red, green, blue numpy arrays to form a RGB image ALONG A NEW DIMENSION
# NOTE need a blue component, have added a set of zeros as blue channel should be BLANK
# NOTE2: converted to unit8 bit, as pyplot imshow() method only reads this format
Z = np.stack([
rd_in.astype(dtype=np.uint8),
gr_in.astype(dtype=np.uint8),
np.zeros(np.shape(gr_tslice)).astype(dtype=np.uint8)
])
# transpose the columns to the FIRST one is LAST
# i.e the NEW DIMENSION [3] is the LAST DIMENSION
Zt = np.transpose(Z, axes=[1, 2, 0])
# can now add the RGB array to imshow()
ax1.imshow(Zt,
interpolation='none',
aspect='auto',
extent=np.r_[width, height],
cmap=cmap,
vmin=np.min(gr_in),
vmax=np.max(gr_in))
sec_ax = ax1.secondary_yaxis('right',
functions=(lambda x: x * ab[0] + ab[1],
lambda y: (y - ab[1]) / ab[0]))
ax1.set_xlabel(axis_labels[0], fontsize=8)
ax1.set_ylabel(axis_labels[1], fontsize=8)
sec_ax.set_ylabel(axis_labels[2], fontsize=8)
#xmn = np.min(xCoords) - 500
#xmz = np.max(xCoords) + 500
xmn = np.min(ins.xyz[:, 0]) * 1e6 - 1000
xmz = np.max(ins.xyz[:, 0]) * 1e6 + 1000
ax1.set_xlim(xmn, xmz)
# ensure the resized xlim is not stretched!
ax1.axes.set_aspect('equal')
ax1.tick_params(axis='x', labelrotation=90)
ax1.tick_params(axis='x', labelsize=8)
ax1.tick_params(axis='y', labelsize=8)
sec_ax.tick_params(axis='y', labelsize=8)
if remove_primary_axis:
#ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
plt.tight_layout() # tighten layout around xlabel & ylabel
return fig1
def plot_probe_trajectory_histology(x,
y,
subject_ID,
axc,
axs,
provenance='Planned',
project='ibl_neuropixel_brainwide_01',
gr_percentile_min=0.2,
rd_percentile_min=1,
rd_percentile_max=99.99,
font_size=8,
label_size=8):
"""Plot slices of Histology data along the insertion at [x,y] for subject ID.
Slices made in coronal and sagittal planes.
The slices through the Histology data can be made along any of the
provenances of the probe at [x,y] for subject ID - Planned,
Micro-manipulator, Histology track, Ephys aligned histology track.
axc : AxesSubplot, None
MUST pass an AxesSubplot object for plotting to! For coronal plot.
axs : AxesSubplot, None
MUST pass an AxesSubplot object for plotting to! For sagittal plot.
"""
from one.api import ONE
import ibllib.atlas as atlas
from ibllib.atlas import Insertion
import atlaselectrophysiology.load_histology as hist
import numpy as np
from scipy import ndimage
import sys
import matplotlib.pyplot as plt
# connect to ONE
one = ONE()
# get list of all trajectories at [x,y], for project
trajs = one.alyx.rest('trajectories', 'list', x=x, y=y, project=project)
# keeping subjs and labs for look-up later if needed..
subjs = [sess['session']['subject'] for sess in trajs]
labs = [sess['session']['lab'] for sess in trajs]
#aidx = subjs.index(atlas_ID)
sidx = subjs.index(subject_ID)
# Fetch trajectory metadata for traj:
traj = one.alyx.rest('trajectories',
'list',
session=trajs[sidx]['session']['id'],
probe=trajs[sidx]['probe_name'],
provenance=provenance)
if traj == []:
raise Exception("No trajectory found with provenance: " + provenance)
# get insertion object from ANY (the first) trajectory
ins = Insertion.from_dict(traj[0])
axis_labels = np.array(['ml (µm)', 'dv (µm)', 'ap (µm)'])
#fig1, ax1 = plt.subplots() # new figure and axes objects - CORONAL
#fig2, ax2 = plt.subplots() # new figure and axes objects - SAGITTAL
# set axes to local variables
ax1 = axc
ax2 = axs
lab = labs[sidx] # this returns index in labs where subject_ID is in subjs
hist_paths = hist.download_histology_data(subject_ID, lab)
# create the brain atlases from the data
ba_gr = atlas.AllenAtlas(
hist_path=hist_paths[0]) # green histology channel autofl.
ba_rd = atlas.AllenAtlas(
hist_path=hist_paths[1]) # red histology channel cm-dii
# CORONAL
# implementing tilted slice here to modify its cmap
# get tilted slice of the green and red channel brain atlases
# using the .image data as this contains the signal
gr_tslice, width, height, depth = ba_gr.tilted_slice(ins.xyz,
1,
volume=ba_gr.image)
rd_tslice, width, height, depth = ba_rd.tilted_slice(ins.xyz,
1,
volume=ba_rd.image)
gr_tslice_roi = gr_tslice[
120:240,
150:300] # isolate large slice over thalamus for max pixel value
rd_tslice_roi = rd_tslice[120:240, 150:300]
width = width * 1e6
height = height * 1e6
depth = depth * 1e6
cmap = plt.get_cmap('bone')
# get the transfer function from y-axis to squeezed axis for second axe
ab = np.linalg.solve(np.c_[height, height * 0 + 1], depth)
height * ab[0] + ab[1]
# linearly scale the values in 2d numpy arrays to between 0-255 (8bit)
# Using gr_tslice min and gr_tslice_roi max to scale autofl.
# using rd_tslice min and percentile (99.99 default) to scale CM-DiI
gr_in = np.interp(
gr_tslice,
(np.percentile(gr_tslice, gr_percentile_min), gr_tslice_roi.max()),
(0, 255))
rd_in = np.interp(rd_tslice, (np.percentile(rd_tslice, rd_percentile_min),
np.percentile(rd_tslice, rd_percentile_max)),
(0, 255))
# join together red, green, blue numpy arrays to form a RGB image ALONG A NEW DIMENSION
# NOTE need a blue component, have added a set of zeros as blue channel should be BLANK
# NOTE2: converted to unit8 bit, as pyplot imshow() method only reads this format
Z = np.stack([
rd_in.astype(dtype=np.uint8),
gr_in.astype(dtype=np.uint8),
np.zeros(np.shape(gr_tslice)).astype(dtype=np.uint8)
])
# transpose the columns to the FIRST one is LAST
# i.e the NEW DIMENSION [3] is the LAST DIMENSION
Zt = np.transpose(Z, axes=[1, 2, 0])
# can now add the RGB array to imshow()
ax1.imshow(Zt,
interpolation='none',
aspect='auto',
extent=np.r_[width, height],
cmap=cmap,
vmin=np.min(gr_in),
vmax=np.max(gr_in))
sec_ax = ax1.secondary_yaxis('right',
functions=(lambda x: x * ab[0] + ab[1],
lambda y: (y - ab[1]) / ab[0]))
ax1.set_xlabel(axis_labels[0], fontsize=font_size)
ax1.set_ylabel(axis_labels[1], fontsize=font_size)
sec_ax.set_ylabel(axis_labels[2], fontsize=font_size)
ax1.tick_params(axis='x', labelrotation=90)
ax1.tick_params(axis='x', labelsize=label_size)
ax1.tick_params(axis='y', labelsize=label_size)
sec_ax.tick_params(axis='y', labelsize=label_size)
# SAGITTAL
# implementing tilted slice here to modify its cmap
# get tilted slice of the green and red channel brain atlases
# using the .image data as this contains the signal
gr_tslice, width, height, depth = ba_gr.tilted_slice(ins.xyz,
0,
volume=ba_gr.image)
rd_tslice, width, height, depth = ba_rd.tilted_slice(ins.xyz,
0,
volume=ba_rd.image)
width = width * 1e6
height = height * 1e6
depth = depth * 1e6
cmap = plt.get_cmap('bone')
# get the transfer function from y-axis to squeezed axis for second axe
ab = np.linalg.solve(np.c_[height, height * 0 + 1], depth)
height * ab[0] + ab[1]
# linearly scale the values in 2d numpy arrays to between 0-255 (8bit)
# Using gr_tslice min and max to scale the image
# weirdly rd_in has very large min and max (problem with the original data acquisition?) so best to scale whole RGB with gr_in/1.5!
gr_in = np.interp(gr_tslice, (gr_tslice.min(), gr_tslice.max()), (0, 255))
rd_in = np.interp(rd_tslice, (gr_tslice.min(), gr_tslice.max() / 1.5),
(0, 255))
# join together red, green, blue numpy arrays to form a RGB image ALONG A NEW DIMENSION
# NOTE need a blue component, have added a set of zeros as blue channel should be BLANK
# NOTE2: converted to unit8 bit, as pyplot imshow() method only reads this format
Z = np.stack([
rd_in.astype(dtype=np.uint8),
gr_in.astype(dtype=np.uint8),
np.zeros(np.shape(gr_tslice)).astype(dtype=np.uint8)
])
# transpose the columns to the FIRST one is LAST
# i.e the NEW DIMENSION [3] is the LAST DIMENSION
Zt = np.transpose(Z, axes=[1, 2, 0])
# can now add the RGB array to ax2 via imshow()
ax2.imshow(Zt,
interpolation='none',
aspect='auto',
extent=np.r_[width, height],
cmap=cmap,
vmin=np.min(gr_in),
vmax=np.max(gr_in))
#start = ins.xyz[:, 1] * 1e6
#end = ins.xyz[:, 2] * 1e6
#xCoords = np.array([start[0], end[0]])
sec_ax = ax2.secondary_yaxis('right',
functions=(lambda x: x * ab[0] + ab[1],
lambda y: (y - ab[1]) / ab[0]))
ax2.set_xlabel(axis_labels[2], fontsize=font_size)
ax2.set_ylabel(axis_labels[1], fontsize=font_size)
sec_ax.set_ylabel(axis_labels[0], fontsize=font_size)
ax2.tick_params(axis='x', labelrotation=90)
ax2.tick_params(axis='x', labelsize=label_size)
ax2.tick_params(axis='y', labelsize=label_size)
sec_ax.tick_params(axis='y', labelsize=label_size)
plt.tight_layout() # tighten layout around xlabel & ylabel
# add a line of the Insertion object onto ax1 (cax - coronal)
# plotting PLANNED insertion
#ax1.plot(ins.xyz[:, 0] * 1e6, ins.xyz[:, 2] * 1e6, colour, linewidth=linewidth)
#ax2.plot(ins.xyz[:, 1] * 1e6, ins.xyz[:, 2] * 1e6, colour, linewidth=linewidth)
return {
'coronal-slice': ax1,
'sagittal-slice': ax2,
'x': x,
'y': y,
'provenance': provenance,
'subject_id': subject_ID
}
"""
one = one or ONE()
eid, probe = one.pid2eid(pid)
qt.create_app()
av = MainWindow._get_or_create(title=title, eid=eid, probe=probe, one=one,
spike_collection=spike_collection)
av.show()
return av
if __name__ == '__main__':
from argparse import ArgumentParser
import numpy as np
one = ONE()
app = QtWidgets.QApplication([])
parser = ArgumentParser()
parser.add_argument('-pid', '--pid', default=False, required=False,
help='Probe id')
parser.add_argument('-eid', '--eid', default=False, required=False,
help='Experiment id')
parser.add_argument('-name', '--probe_name', default=False, required=False,
help='Probe name')
parser.add_argument('-c', '--collection', default='', required=False,
help='spike sorting collection')
args = parser.parse_args()
if args.pid:
def setUpClass(cls):
cls.tmp_dir = tempfile.TemporaryDirectory()
cls.one = ONE(**TEST_DB)
import numpy as np
import copy
import random
import string
from one.api import ONE
from neuropixel import SITES_COORDINATES
from ibllib.tests import TEST_DB
from ibllib.atlas import AllenAtlas
from ibllib.pipes.misc import create_alyx_probe_insertions
from ibllib.qc.alignment_qc import AlignmentQC
from ibllib.pipes.histology import register_track
EPHYS_SESSION = 'b1c968ad-4874-468d-b2e4-5ffa9b9964e9'
one = ONE(**TEST_DB)
brain_atlas = AllenAtlas(25)
class TestTracingQc(unittest.TestCase):
probe01_id = None
probe00_id = None
@classmethod
def setUpClass(cls) -> None:
probe = [
''.join(random.choices(string.ascii_letters, k=5)),
''.join(random.choices(string.ascii_letters, k=5))
]
ins = create_alyx_probe_insertions(session_path=EPHYS_SESSION,
model='3B2',
