diff --git a/brainlit/BrainLine/analyze_results.py b/brainlit/BrainLine/analyze_results.py
index b4801e950..724e0be17 100644
--- a/brainlit/BrainLine/analyze_results.py
+++ b/brainlit/BrainLine/analyze_results.py
@@ -9,6 +9,7 @@
_setup_atlas_graph,
_get_atlas_level_nodes,
_get_corners,
+ _find_custom_label
)
import napari
import scipy.ndimage as ndi
@@ -31,6 +32,9 @@
from pathlib import Path
import random
+import vedo
+vedo.settings.default_backend= 'vtk'
+
class BrainDistribution:
"""Helps generate plots and visualizations for brain connection distributions across subtyeps.
@@ -63,23 +67,30 @@ def __init__(
data = json.load(f)
self.ontology_fixes = data
- def _slicetolabels(self, slice, fold_on: bool = False, atlas_level: int = 5):
+ def _slicetolabels(self, slice, fold_on: bool = False, atlas_level: int = 5, custom_regions: list = None):
+ replacements = self.ontology_fixes
region_graph = _setup_atlas_graph(self.ontology_file)
atlas_level_nodes = _get_atlas_level_nodes(atlas_level, region_graph)
newslice = np.copy(slice)
new_labels = {}
for label in tqdm(np.unique(slice), desc=f"Relabeling slice"):
- atlas_level_label = _find_atlas_level_label(
- label, atlas_level_nodes, atlas_level, region_graph
- )
- newslice[slice == label] = atlas_level_label
- if atlas_level_label not in new_labels.keys():
- if atlas_level_label in region_graph.nodes:
- name = region_graph.nodes[atlas_level_label]["name"]
+ try:
+ fixed_label = replacements[str(label)]["replacement"]
+ except KeyError:
+ fixed_label = label
+ if custom_regions == None:
+ new_label = _find_atlas_level_label(fixed_label, atlas_level_nodes, atlas_level, region_graph)
+ else:
+ new_label = _find_custom_label(fixed_label, custom_regions, region_graph)
+
+ newslice[slice == label] = new_label
+ if new_label not in new_labels.keys():
+ if new_label in region_graph.nodes:
+ name = region_graph.nodes[new_label]["name"]
else:
name = "??"
- new_labels[atlas_level_label] = name
+ new_labels[new_label] = name
labels = measure.label(newslice)
borders = 0 * labels
@@ -1050,7 +1061,7 @@ def _setup_regiongraph(self, regional_distribution_dir):
return region_graph, total_axon_vols
def napari_coronal_section(
- self, z: int, subtype_colors: dict, fold_on: bool = False
+ self, z: int, subtype_colors: dict, custom_regions: list = None, fold_on: bool = False
):
"""Generate napari viewer with allen parcellation and heat map of axon segmentations.
@@ -1068,7 +1079,7 @@ def napari_coronal_section(
slice = np.squeeze(np.array(vol_atlas[z, :, :]))
- newslice, borders, half_width = self._slicetolabels(slice, fold_on=fold_on)
+ newslice, borders, half_width = self._slicetolabels(slice, fold_on=fold_on, custom_regions=custom_regions)
if self.show_plots:
v = napari.Viewer()
@@ -1125,7 +1136,7 @@ def brainrender_axons(self, subtype_colors: dict, brain_region: str = "DR"):
brain2paths = self.brain2paths
brainrender.settings.WHOLE_SCREEN = False
- scene = Scene(atlas_name="allen_mouse_50um", title="Input Somas")
+ scene = Scene(atlas_name="allen_mouse_50um", title="Axon Projections", screenshots_folder="/home/user/misc_tommy/figures/")
scene.add_brain_region(brain_region, alpha=0.15)
for subtype in subtype_colors.keys():
@@ -1136,6 +1147,9 @@ def brainrender_axons(self, subtype_colors: dict, brain_region: str = "DR"):
vol = CloudVolume(
brain2paths[brain_id]["transformed_mask"], fill_missing=True
)
+ if vol.resolution[0] / 1000 != 10:
+ raise ValueError(f"Resolution at {brain2paths[brain_id]['transformed_mask']} is {vol.resolution}, not 10nm")
+
if im_total == None:
im_total = np.array(vol[:, :, :, :])
else:
@@ -1147,7 +1161,7 @@ def brainrender_axons(self, subtype_colors: dict, brain_region: str = "DR"):
# make a volume actor and add
actor = Volume(
im_total,
- voxel_size=20, # size of a voxel's edge in microns
+ voxel_size=10, # size of a voxel's edge in microns
as_surface=False, # if true a surface mesh is rendered instead of a volume
c=subtype_colors[
subtype
@@ -1161,6 +1175,7 @@ def brainrender_axons(self, subtype_colors: dict, brain_region: str = "DR"):
if self.show_plots:
scene.render()
+ scene.screenshot()
def region_barchart(
self, regions: list, composite_regions: dict = {}, normalize_region: int = -1
diff --git a/brainlit/BrainLine/util.py b/brainlit/BrainLine/util.py
index 5b3b5a61e..3325cd748 100644
--- a/brainlit/BrainLine/util.py
+++ b/brainlit/BrainLine/util.py
@@ -298,6 +298,21 @@ def _find_atlas_level_label(label, atlas_level_nodes, atlas_level, G):
raise ValueError(f"{counter} atlas level predecessors of {label}")
return atlas_level_label
+def _find_custom_label(label, custom_regions, G):
+ if label == 0 or label not in G.nodes or G.nodes[label]["st_level"] in custom_regions:
+ return label
+ else:
+ counter = 0
+ # find which region of atlas_level is parent
+ for custom_node in custom_regions:
+ if nx.has_path(G, custom_node, label):
+ custom_label = custom_node
+ counter += 1
+ if counter > 1:
+ raise ValueError(f"{counter} custom label predecessors of {label}")
+ elif counter == 0:
+ custom_label = -1
+ return custom_label
def _fold(image):
"""Take a 2D image and add the left half to a reflected version of the right half.
diff --git a/experiments/BrainLine/axon_analysis.ipynb b/experiments/BrainLine/axon_analysis.ipynb
index 2f6d17108..e9110a74d 100644
--- a/experiments/BrainLine/axon_analysis.ipynb
+++ b/experiments/BrainLine/axon_analysis.ipynb
@@ -10,7 +10,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -77,9 +77,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Path to brainlit: /home/user/brainlit\n"
+ ]
+ }
+ ],
"source": [
"brainlit_path = Path(os.path.abspath(\"\"))\n",
"brainlit_path = brainlit_path.parents[1]\n",
@@ -618,7 +626,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -645,12 +653,13 @@
"] # list of sample IDs to be shown\n",
"\n",
"\n",
+ "\n",
"colors = {\n",
" \"tph2 gad2\": \"green\",\n",
" \"tph2 vglut3\": \"blue\",\n",
" \"gad2 vgat\": \"red\",\n",
"} # colors for different genotypes\n",
- "fold_on = False\n",
+ "fold_on = True\n",
"ontology_file = (\n",
" brainlit_path / \"brainlit\" / \"BrainLine\" / \"data\" / \"ara_structure_ontology.json\"\n",
")\n",
@@ -660,4232 +669,119 @@
"\n",
"axon_data_dir = \"/home/user/brainlit/experiments/BrainLine/data/wholebrain-results/axon/\"\n",
"\n",
+ "\n",
+ "brain_ids = [\"8613\"]\n",
+ "colors = {\"tph2 vglut3\": \"blue\"}\n",
+ "\n",
+ "\n",
"ad = AxonDistribution(\n",
" brain_ids=brain_ids,\n",
" regional_distribution_dir=axon_data_dir,\n",
" data_file=data_file,\n",
" ontology_file=ontology_file,\n",
" fixes_file=ontology_fixes,\n",
- ")"
+ ")\n",
+ "\n",
+ "custom_regions = [688, 1097, 549, 354, 512, 477, 313]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ad.napari_coronal_section(z=100, subtype_colors=colors, fold_on=fold_on, custom_regions = custom_regions)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dir = \"/Users/thomasathey/Documents/mimlab/mouselight/ailey/matt-coronals/\"\n",
+ "z_size = 1320\n",
+ "\n",
+ "for z in tqdm(np.arange(50, z_size, 50)):\n",
+ " fname = dir + f\"{brain}_{z}_axon.tif\"\n",
+ " v = ad.napari_coronal_section(z=z, subtype_colors=colors, fold_on=False)\n",
+ " im = v.screenshot(canvas_only=False)\n",
+ " io.imsave(fname, im)"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 4,
"metadata": {},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Relabeling slice: 100%|██████████| 104/104 [00:00<00:00, 621.42it/s]\n",
- "Processing labels: 100%|██████████| 61/61 [00:00<00:00, 94.97it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "8613\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- ]
- },
- {
- "name": "stderr",
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- ]
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- "output_type": "stream",
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- ]
- },
- {
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- ]
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- "output_type": "stream",
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- },
{
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+ "Downloading transformed_mask from brain: 8613\n"
]
},
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- ]
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+ "data": {
+ "text/html": [
+ "
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+ "│ │\n",
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+ "│ - root (type: brain region) │\n",
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- "output_type": "stream",
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- "output_type": "stream",
- "text": [
- "MS12\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "MS33\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
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- ]
- },
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- "output_type": "stream",
- "text": [
- "MS9\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
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- ]
- }
- ],
- "source": [
- "z_size = 1320\n",
- "for z in tqdm(np.arange(200, 1200, 50)):\n",
- " ad.napari_coronal_section(z=z, subtype_colors=colors, fold_on=fold_on)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dir = \"/Users/thomasathey/Documents/mimlab/mouselight/ailey/matt-coronals/\"\n",
- "z_size = 1320\n",
- "\n",
- "for z in tqdm(np.arange(50, z_size, 50)):\n",
- " fname = dir + f\"{brain}_{z}_axon.tif\"\n",
- " v = ad.napari_coronal_section(z=z, subtype_colors=colors, fold_on=False)\n",
- " im = v.screenshot(canvas_only=False)\n",
- " io.imsave(fname, im)"
+ "ad.brainrender_axons(subtype_colors=colors)"
]
},
{
diff --git a/experiments/BrainLine/data/axon_data.json b/experiments/BrainLine/data/axon_data.json
index 509b67208..bb3488020 100644
--- a/experiments/BrainLine/data/axon_data.json
+++ b/experiments/BrainLine/data/axon_data.json
@@ -3,7 +3,7 @@
"brain2paths": {
"atlas": {
"url": "precomputed://https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_10um/annotation_10um_2017",
- "filepath": "/home/user/brainlit/experiments/BrainLine/data/ara_10um.tif"
+ "filepath": "/mnt/NAS/Neuroglancer Data/brainlit_archive/ara_10um.tif"
},
"test": {
"base_s3": "precomputed://file:///Users/thomasathey/Documents/mimlab/mouselight/brainlit_parent/brainlit/brainlit/BrainLine/data/example/",
diff --git a/experiments/BrainLine/data/soma_data.json b/experiments/BrainLine/data/soma_data.json
index 3142a079b..d75966aaa 100644
--- a/experiments/BrainLine/data/soma_data.json
+++ b/experiments/BrainLine/data/soma_data.json
@@ -3,7 +3,7 @@
"brain2paths": {
"atlas": {
"url": "precomputed://https://open-neurodata.s3.amazonaws.com/ara_2016/sagittal_10um/annotation_10um_2017",
- "filepath": "/home/user/brainlit/experiments/BrainLine/data/ara_10um.tif"
+ "filepath": "/mnt/NAS/Neuroglancer Data/brainlit_archive/ara_10um.tif"
},
"test": {
"base_local": "precomputed://file:///Users/thomasathey/Documents/mimlab/mouselight/brainlit_parent/brainlit/brainlit/BrainLine/data/example/",
@@ -466,6 +466,20 @@
"somas_atlas_url": "",
"detection_viz_links": ["https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=1piV5x28Zs2_Ag", "https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=ejR6GFGwpeye3g", " https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=XBsH4CV-swNkkg", "https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=zD7OgoFRFuokjw", "https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=pilLDKPkEjqVsA", "https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=OGINsganTU2IeA"]
+ },
+ "028L": {
+ "base_local": "precomputed://file:///mnt/data/Neuroglancer Data/2024_06_10/028L/",
+ "base_s3": "precomputed://s3://smartspim-precomputed-volumes/2024_06_10/028L/",
+ "val_info": {
+ "url": "https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=JYinS3kWcRREEw",
+ "somas_layer": "soma_val",
+ "nonsomas_layer": "nonsoma_val"
+ },
+ "subtype": "tph2 gad2",
+ "somas_atlas_path": "/home/user/brainlit/experiments/BrainLine/data/wholebrain-results/soma/atlas_somas_028L/",
+ "somas_atlas_url": "",
+ "detection_viz_links": ["https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=MENqxx5mL5nhew", "https://viz.neurodata.io/?json_url=https://json.neurodata.io/v1?NGStateID=xc7ugxmVgIFV6A"]
+
}
},
"brain2centers": {
diff --git a/experiments/BrainLine/mip-example.ipynb b/experiments/BrainLine/mip-example.ipynb
new file mode 100644
index 000000000..f5ac33347
--- /dev/null
+++ b/experiments/BrainLine/mip-example.ipynb
@@ -0,0 +1,339 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from cloudvolume import CloudVolume\n",
+ "import numpy as np\n",
+ "from skimage import io\n",
+ "import matplotlib.pyplot as plt\n",
+ "from pathlib import Path"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Inputs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path = \"/mnt/data/Neuroglancer_Data/2024_05_10/M40/Ch_561_transformed\"\n",
+ "outpath = \"/mnt/data/Neuroglancer_Data/2024_05_10/M40/MIPs/Ch_561\" #path where images will be saved\n",
+ "step = 10 # number of slices to be projected, and spacing between projections\n",
+ "projection_type = \"max\" # mean or max\n",
+ "projection_direction = \"coronal\" # axial coronal or sagittal"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Save images"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
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+ " io.imsave(Path(outpath) / fname, subvol)\n",
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+ "/tmp/ipykernel_68255/184815157.py:28: UserWarning: /mnt/data/Neuroglancer_Data/2024_05_10/M40/MIPs/Ch_561/coronal_z750_step10_projmax.tif is a low contrast image\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n",
+ "Downloading: 100%|██████████| 1800/1800 [00:01<00:00, 1186.63it/s]\n",
+ "/tmp/ipykernel_68255/184815157.py:28: UserWarning: /mnt/data/Neuroglancer_Data/2024_05_10/M40/MIPs/Ch_561/coronal_z760_step10_projmax.tif is a low contrast image\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n",
+ "Downloading: 100%|██████████| 900/900 [00:00<00:00, 1164.35it/s]\n",
+ "/tmp/ipykernel_68255/184815157.py:28: UserWarning: /mnt/data/Neuroglancer_Data/2024_05_10/M40/MIPs/Ch_561/coronal_z770_step10_projmax.tif is a low contrast image\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n",
+ "Downloading: 100%|██████████| 900/900 [00:00<00:00, 1179.70it/s]\n",
+ "/tmp/ipykernel_68255/184815157.py:28: UserWarning: /mnt/data/Neuroglancer_Data/2024_05_10/M40/MIPs/Ch_561/coronal_z780_step10_projmax.tif is a low contrast image\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n",
+ "Downloading: 100%|██████████| 900/900 [00:00<00:00, 1162.77it/s]\n",
+ "/tmp/ipykernel_68255/184815157.py:28: UserWarning: /mnt/data/Neuroglancer_Data/2024_05_10/M40/MIPs/Ch_561/coronal_z790_step10_projmax.tif is a low contrast image\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n",
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+ "Downloading: 100%|██████████| 900/900 [00:00<00:00, 1162.28it/s]\n",
+ "/tmp/ipykernel_68255/184815157.py:28: UserWarning: /mnt/data/Neuroglancer_Data/2024_05_10/M40/MIPs/Ch_561/coronal_z1230_step10_projmax.tif is a low contrast image\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n",
+ "Downloading: 100%|██████████| 1800/1800 [00:01<00:00, 1230.04it/s]\n",
+ "Downloading: 100%|██████████| 900/900 [00:00<00:00, 1160.29it/s]\n",
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+ "Downloading: 100%|██████████| 900/900 [00:00<00:00, 1170.43it/s]\n",
+ "Downloading: 100%|██████████| 900/900 [00:00<00:00, 1157.48it/s]\n",
+ "Downloading: 100%|██████████| 1800/1800 [00:01<00:00, 1306.37it/s]\n",
+ "/tmp/ipykernel_68255/184815157.py:28: UserWarning: /mnt/data/Neuroglancer_Data/2024_05_10/M40/MIPs/Ch_561/coronal_z1310_step10_projmax.tif is a low contrast image\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n"
+ ]
+ }
+ ],
+ "source": [
+ "vol = CloudVolume(\"precomputed://file://\" + path)\n",
+ "direction_to_axis = {\"axial\": 1, \"coronal\":0, \"sagittal\": 2}\n",
+ "axis = direction_to_axis[projection_direction]\n",
+ "\n",
+ "for z in range(0, vol.shape[axis], step):\n",
+ " z_end = np.amin([vol.shape[axis], z+step])\n",
+ "\n",
+ " if axis == 0:\n",
+ " subvol = vol[z:z_end,:,:]\n",
+ " elif axis == 1:\n",
+ " subvol = vol[:,z:z_end,:]\n",
+ " elif axis == 2:\n",
+ " subvol = vol[:,:,z:z_end]\n",
+ " \n",
+ " subvol = np.array(subvol) # need to cast cloudvolume cutout to numpy array\n",
+ " subvol = np.squeeze(subvol)\n",
+ "\n",
+ " if projection_type == \"mean\":\n",
+ " subvol = np.mean(subvol, axis=axis)\n",
+ " elif projection_type == \"max\":\n",
+ " subvol = np.max(subvol, axis=axis)\n",
+ " else:\n",
+ " raise ValueError(f\"projection_type must be max or mean, not {projection_type}\")\n",
+ " \n",
+ " subvol = subvol/np.max(subvol) * 255\n",
+ " subvol = subvol.astype(np.uint8) # image viewer needs image to be integer\n",
+ " fname = f\"{projection_direction}_z{z}_step{step}_proj{projection_type}.tif\"\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "IndentationError",
+ "evalue": "expected an indented block (3453626423.py, line 25)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;36m Cell \u001b[0;32mIn[14], line 25\u001b[0;36m\u001b[0m\n\u001b[0;31m else:\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Save Atlas\n",
+ "vol = CloudVolume(\"precomputed://file://\" + path)\n",
+ "direction_to_axis = {\"axial\": 1, \"coronal\":0, \"sagittal\": 2}\n",
+ "axis = direction_to_axis[projection_direction]\n",
+ "\n",
+ "for z in range(0, vol.shape[axis], step):\n",
+ " z_end = np.amin([vol.shape[axis], z+step])\n",
+ "\n",
+ " if axis == 0:\n",
+ " subvol = vol[z:z_end,:,:]\n",
+ " elif axis == 1:\n",
+ " subvol = vol[:,z:z_end,:]\n",
+ " elif axis == 2:\n",
+ " subvol = vol[:,:,z:z_end]\n",
+ " \n",
+ " subvol = np.array(subvol) # need to cast cloudvolume cutout to numpy array\n",
+ " subvol = np.squeeze(subvol)\n",
+ "\n",
+ " if projection_type == \"mean\":\n",
+ " subvol = np.mean(subvol, axis=axis)\n",
+ " elif projection_type == \"max\":\n",
+ " subvol = np.max(subvol, axis=axis)\n",
+ " elif projection_type == \"atlas\":\n",
+ " subvol = subvol(:,:,0)\n",
+ " else:\n",
+ " raise ValueError(f\"projection_type must be max or mean, not {projection_type}\")\n",
+ " \n",
+ " subvol = subvol/np.max(subvol) * 255\n",
+ " subvol = subvol.astype(np.uint8) # image viewer needs image to be integer\n",
+ " fname = f\"{projection_direction}_z{z}_step{step}_proj{projection_type}.tif\"\n",
+ " io.imsave(Path(outpath) / fname, subvol)\n",
+ " \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "brainlit",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/experiments/BrainLine/scripts/soma_detect_image.py b/experiments/BrainLine/scripts/soma_detect_image.py
index 702934120..7bcad43fb 100644
--- a/experiments/BrainLine/scripts/soma_detect_image.py
+++ b/experiments/BrainLine/scripts/soma_detect_image.py
@@ -9,13 +9,13 @@
# -dir_base
# data_dir and results_dir ARE CLEAR
# threshold IS CORRECT
-brain = "274L"
+brain = "028L"
antibody_layer = "Ch_647"
background_layer = "Ch_561"
endogenous_layer = "Ch_488"
-threshold = 0.7 # threshold to use for ilastik
+threshold = 0.8 # threshold to use for ilastik
brainline_exp_dir = Path(os.getcwd()) / Path(__file__).parents[1]
data_dir = (
brainline_exp_dir / "data" / "brainr_temp"
@@ -38,9 +38,9 @@
-1,
] # max coords or -1 if you want to process everything along that dimension
max_coords = [
- 6144,
- -1,
- -1,
+ 6100,
+ 9100,
+ 4096,
] # max coords or -1 if you want to process everything along that dimension
ncpu = 2 # 16 # number of cores to use for detection
chunk_size = [1024, 1024, 1024] # [256, 256, 300]
@@ -57,13 +57,13 @@
data_file=data_file,
results_dir=results_dir,
)
-alli.apply_ilastik_parallel(
- brain_id=brain,
- layer_names=layer_names,
- threshold=threshold,
- data_dir=data_dir,
- chunk_size=chunk_size,
- min_coords=min_coords,
- max_coords=max_coords,
-)
+# alli.apply_ilastik_parallel(
+# brain_id=brain,
+# layer_names=layer_names,
+# threshold=threshold,
+# data_dir=data_dir,
+# chunk_size=chunk_size,
+# min_coords=min_coords,
+# max_coords=max_coords,
+# )
alli.collect_soma_results(brain_id=brain)
diff --git a/experiments/BrainLine/soma_analysis.ipynb b/experiments/BrainLine/soma_analysis.ipynb
index 2e5b3207b..c4b711e92 100644
--- a/experiments/BrainLine/soma_analysis.ipynb
+++ b/experiments/BrainLine/soma_analysis.ipynb
@@ -10,7 +10,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -87,17 +87,9 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Path to brainlit: {brainlit_path}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"brainlit_path = Path(os.path.abspath(\"\"))\n",
"brainlit_path = brainlit_path.parents[1]\n",
@@ -108,7 +100,7 @@
" data = json.load(f)\n",
"brain2paths = data[\"brain2paths\"]\n",
"\n",
- "brain = \"274L\" # brain ID\n",
+ "brain = \"028L\" # brain ID\n",
"soma_data_dir = \"/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/\" # path to directory where training/validation data should be stored"
]
},
@@ -130,7 +122,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -152,7 +144,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -170,89 +162,9 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Ch_647 shape: (7417, 10221, 4348, 1) at [1830. 1830. 2000.]\n",
- "Ch_561 shape: (7417, 10221, 4348, 1) at [1830. 1830. 2000.]\n",
- "Ch_488 shape: (7417, 10221, 4348, 1) at [1830. 1830. 2000.]\n",
- "[10, 10] centers\n",
- "Creating directory: /home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
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- ]
- }
- ],
+ "outputs": [],
"source": [
"download_subvolumes(\n",
" soma_data_dir,\n",
@@ -281,7 +193,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -291,7 +203,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -335,7 +247,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -352,32 +264,9 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Gathering brains...: 0%| | 0/1 [00:00, ?it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Applying ilastik to ['/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/4448_7122_1687_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/2207_5418_2357_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/2294_4725_2357_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/2395_1820_1381_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/1290_4167_2361_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/579_4946_1689_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/3413_4951_2361_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/2593_1705_1383_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/1165_4621_2361_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/3027_2634_1683_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/4416_6830_1687_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/2129_3535_1058_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/2080_3566_1058_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/2393_5757_2355_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/3465_5596_3008_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/3870_5203_1058_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/5617_3139_2361_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/1283_4656_2361_pos.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/205_4834_1689_neg.h5', '/home/user/brainlit/experiments/BrainLine/data/validation-and-models/soma/brain274L/val/2944_3157_1684_pos.h5']\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "running ilastik...: 100%|██████████| 1/1 [01:23<00:00, 83.75s/it]\n",
- "Gathering brains...: 100%|██████████| 1/1 [01:23<00:00, 83.76s/it]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"applyilastik = ApplyIlastik(\n",
" ilastik_path=ilastik_path,\n",
@@ -400,7 +289,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -444,35 +333,9 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Processing Brains: 100%|██████████| 1/1 [00:03<00:00, 3.72s/it]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "If this performance is not adequate, improve model and try again\n",
- "Brain 274L Validation: 10+ 10-\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
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