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Merge pull request #476 from int-brain-lab/regions
Regions
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9 files changed

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‎brainbox/io/one.py‎

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Original file line numberDiff line numberDiff line change
@@ -999,7 +999,7 @@ def load_spike_sorting(self, **kwargs):
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spikes = alfio.load_object(self.files['spikes'], wildcards=self.one.wildcards)
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if 'brainLocationIds_ccf_2017' not in channels:
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_channels, self.histology = _load_channel_locations_traj(
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self.eid, probe=self.pname, one=self.one, brain_atlas=self.atlas, return_source=True)
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self.eid, probe=self.pname, one=self.one, brain_atlas=self.atlas, return_source=True, aligned=True)
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if _channels:
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channels = _channels[self.pname]
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else:

‎examples/atlas/atlas_swanson_flatmap.ipynb‎

Lines changed: 219 additions & 133 deletions
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@@ -1,133 +1,219 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Plotting brain region values on the Swanson flat map"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The Swanson flatmap is a 2D representation of the mouse brain to facilitate comparative analysis of brain data.\n",
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"We extended the mouse atlas presented by [Hahn et al.](https://onlinelibrary.wiley.com/doi/full/10.1002/cne.24966?casa_token=kRb4fuUae6wAAAAA%3AHoiNx1MNVgZNUXT-MZN_mU6LAjKBiz5OE5cFj2Aj-GUE9l-oBllFUaM11XwCtEbpJyxKrwaMRnXC7MjY)\n",
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"to interface programmatically with the Allen Atlas regions."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## The swanson flatmap"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from ibllib.atlas.flatmaps import plot_swanson\n",
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"from ibllib.atlas import BrainRegions\n",
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"br = BrainRegions()\n",
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"\n",
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"plot_swanson(br=br)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"## Plotting values on the swanson flatmap\n",
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"\n",
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"### Simple example with acronyms\n",
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"The flatmap is used when each of the region is assigned a scalar value, displayed on a color scale."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# prepare array of acronyms\n",
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"acronyms = ['ACAd1', 'ACAv1', 'AId1', 'AIp1', 'AIv1', 'AUDd1', 'AUDp1', 'AUDpo1', 'AUDv1',\n",
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" 'SSp-m1', 'SSp-n1', 'SSp-tr1', 'SSp-ul1', 'SSp-un1', 'SSs1',\n",
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" 'VISC1', 'VISa1', 'VISal1', 'VISam1', 'VISl1', 'VISli1', 'VISp1', 'VISp2/3', 'VISpl1', 'VISpm1',\n",
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" 'SSp-n2/3', 'SSp-tr2/3', 'SSp-ul2/3', 'SSp-un2/3', 'SSs2/3',\n",
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" 'VISC2/3', 'VISa2/3', 'VISal2/3', 'VISam2/3', 'VISl2/3', 'VISli2/3', 'VISp2/3', 'VISpl1', 'VISpl2/3']\n",
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"# assign a scalar to each acronym\n",
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"values = np.arange(len(acronyms))\n",
76-
"\n",
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"# and display on a single hemishphere, using a blue colormap\n",
78-
"plot_swanson(acronyms, values, cmap='Blues', br=br)"
79-
]
80-
},
81-
{
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"cell_type": "markdown",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"### Lateralized display\n",
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"A more advanced example is when each hemisphere is assigned a different value."
91-
]
92-
},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# In our atlas convention, differentiating between hemishperes is done using negative indices\n",
104-
"regions_rl = np.r_[br.acronym2id(acronyms), -br.acronym2id(acronyms)]\n",
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"# assign random values for the sake of this example\n",
106-
"values_rl = np.random.randn(regions_rl.size)\n",
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"# display with an explicit dual hemisphere setup\n",
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"plot_swanson(regions_rl, values_rl, hemisphere='both', cmap='magma', br=br)\n"
109-
]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
132-
"nbformat_minor": 1
133-
}
1+
{
2+
"cells": [
3+
{
4+
"cell_type": "markdown",
5+
"metadata": {},
6+
"source": [
7+
"# Plotting brain region values on the Swanson flat map"
8+
]
9+
},
10+
{
11+
"cell_type": "markdown",
12+
"metadata": {},
13+
"source": [
14+
"The Swanson flatmap is a 2D representation of the mouse brain to facilitate comparative analysis of brain data.\n",
15+
"We extended the mouse atlas presented by [Hahn et al.](https://onlinelibrary.wiley.com/doi/full/10.1002/cne.24966?casa_token=kRb4fuUae6wAAAAA%3AHoiNx1MNVgZNUXT-MZN_mU6LAjKBiz5OE5cFj2Aj-GUE9l-oBllFUaM11XwCtEbpJyxKrwaMRnXC7MjY)\n",
16+
"to interface programmatically with the Allen Atlas regions."
17+
]
18+
},
19+
{
20+
"cell_type": "markdown",
21+
"metadata": {},
22+
"source": [
23+
"## The swanson flatmap"
24+
]
25+
},
26+
{
27+
"cell_type": "code",
28+
"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
33+
},
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"outputs": [],
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"source": [
36+
"import numpy as np\n",
37+
"from ibllib.atlas.flatmaps import plot_swanson\n",
38+
"from ibllib.atlas import BrainRegions\n",
39+
"br = BrainRegions()\n",
40+
"\n",
41+
"plot_swanson(br=br, annotate=True)\n"
42+
]
43+
},
44+
{
45+
"cell_type": "markdown",
46+
"metadata": {
47+
"pycharm": {
48+
"name": "#%% md\n"
49+
}
50+
},
51+
"source": [
52+
"## Plotting values on the swanson flatmap\n",
53+
"\n",
54+
"### Simple example with acronyms\n",
55+
"The flatmap is used when each of the region is assigned a scalar value, displayed on a color scale."
56+
]
57+
},
58+
{
59+
"cell_type": "code",
60+
"execution_count": null,
61+
"metadata": {
62+
"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# prepare array of acronyms\n",
69+
"acronyms = ['ACAd1', 'ACAv1', 'AId1', 'AIp1', 'AIv1', 'AUDd1', 'AUDp1', 'AUDpo1', 'AUDv1',\n",
70+
" 'SSp-m1', 'SSp-n1', 'SSp-tr1', 'SSp-ul1', 'SSp-un1', 'SSs1',\n",
71+
" 'VISC1', 'VISa1', 'VISal1', 'VISam1', 'VISl1', 'VISli1', 'VISp1', 'VISp2/3', 'VISpl1', 'VISpm1',\n",
72+
" 'SSp-n2/3', 'SSp-tr2/3', 'SSp-ul2/3', 'SSp-un2/3', 'SSs2/3',\n",
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" 'VISC2/3', 'VISa2/3', 'VISal2/3', 'VISam2/3', 'VISl2/3', 'VISli2/3', 'VISp2/3', 'VISpl1', 'VISpl2/3']\n",
74+
"# assign a scalar to each acronym\n",
75+
"values = np.arange(len(acronyms))\n",
76+
"\n",
77+
"# and display on a single hemishphere, using a blue colormap\n",
78+
"plot_swanson(acronyms, values, cmap='Blues', br=br)"
79+
]
80+
},
81+
{
82+
"cell_type": "markdown",
83+
"metadata": {
84+
"pycharm": {
85+
"name": "#%% md\n"
86+
}
87+
},
88+
"source": [
89+
"### Lateralized display\n",
90+
"A more advanced example is when each hemisphere is assigned a different value."
91+
]
92+
},
93+
{
94+
"cell_type": "code",
95+
"execution_count": null,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# In our atlas convention, differentiating between hemishperes is done using negative indices\n",
104+
"regions_rl = np.r_[br.acronym2id(acronyms), -br.acronym2id(acronyms)]\n",
105+
"# assign random values for the sake of this example\n",
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"values_rl = np.random.randn(regions_rl.size)\n",
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"# display with an explicit dual hemisphere setup\n",
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"plot_swanson(regions_rl, values_rl, hemisphere='both', cmap='magma', br=br)"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"One can also mirror the hemishperes and orient the display in portrait mode."
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from ibllib.atlas.flatmaps import plot_swanson\n",
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"from ibllib.atlas import BrainRegions\n",
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"\n",
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"br = BrainRegions()\n",
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"\n",
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"acronyms = np.array(\n",
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" ['VPLpc', 'PO', 'LP', 'DG', 'CA1', 'PTLp', 'MRN', 'APN', 'POL',\n",
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" 'VISam', 'MY', 'PGRNl', 'IRN', 'PARN', 'SPVI', 'NTS', 'SPIV',\n",
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" 'NOD', 'IP', 'AON', 'ORBl', 'AId', 'MOs', 'GRN', 'P', 'CENT',\n",
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" 'CUL', 'COApm', 'PA', 'CA2', 'CA3', 'HY', 'ZI', 'MGv', 'LGd',\n",
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" 'LHA', 'SF', 'TRS', 'PVT', 'LSc', 'ACAv', 'ACAd', 'MDRNv', 'MDRNd',\n",
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" 'COPY', 'PRM', 'DCO', 'DN', 'SIM', 'MEA', 'SI', 'RT', 'MOp', 'PCG',\n",
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" 'ICd', 'CS', 'PAG', 'SCdg', 'SCiw', 'VCO', 'ANcr1', 'ENTm', 'ENTl',\n",
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" 'NOT', 'VPM', 'VAL', 'VPL', 'CP', 'SSp-ul', 'MV', 'VISl', 'LGv',\n",
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" 'SSp-bfd', 'ANcr2', 'DEC', 'LD', 'SSp-ll', 'V', 'SUT', 'PB', 'CUN',\n",
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" 'ICc', 'PAA', 'EPv', 'BLAa', 'CEAl', 'GPe', 'PPN', 'SCig', 'SCop',\n",
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" 'SCsg', 'RSPd', 'RSPagl', 'VISp', 'HPF', 'MGm', 'SGN', 'TTd', 'DP',\n",
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" 'ILA', 'PL', 'RSPv', 'SSp-n', 'ORBm', 'ORBvl', 'PRNc', 'ACB',\n",
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" 'SPFp', 'VM', 'SUV', 'OT', 'MA', 'BST', 'LSv', 'LSr', 'UVU',\n",
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" 'SSp-m', 'LA', 'CM', 'MD', 'SMT', 'PFL', 'MARN', 'PRE', 'POST',\n",
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" 'PRNr', 'SSp-tr', 'PIR', 'CTXsp', 'RN', 'PSV', 'SUB', 'LDT', 'PAR',\n",
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" 'SPVO', 'TR', 'VISpm', 'MS', 'COApl', 'BMAp', 'AMd', 'ICe', 'TEa',\n",
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" 'MOB', 'SNr', 'GU', 'VISC', 'SSs', 'AIp', 'NPC', 'BLAp', 'SPVC',\n",
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" 'PYR', 'AV', 'EPd', 'NLL', 'AIv', 'CLA', 'AAA', 'AUDv', 'TRN'],\n",
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" dtype='<U8')\n",
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"values = np.array([ 7.76948616, 25.51506047, 21.31094194, 23.11353701, 26.18071135,\n",
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" 16.42116195, 22.4522099 , 20.04564731, 9.98702368, 11.00518771,\n",
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" 11.23163309, 3.90841049, 11.44982496, 7.49984019, 10.59146742,\n",
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" 7.68845853, 10.38817938, 6.53187499, 14.22331705, 19.26731921,\n",
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" 14.6739601 , 10.37711987, 19.87087356, 12.56497513, 11.03204901,\n",
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" 12.85149192, 10.39367399, 5.26234078, 7.36780286, 7.77672633,\n",
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" 22.30843636, 9.63356153, 11.33369508, 7.70210975, 14.56984632,\n",
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" 7.95488849, 9.85956065, 10.40381726, 6.31529234, 7.82651245,\n",
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" 11.3339313 , 12.26268021, 8.67874273, 8.07579753, 10.14307203,\n",
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" 10.08081832, 7.88595354, 7.49586605, 12.6491355 , 7.92629876,\n",
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" 12.52110187, 14.27405322, 25.95808524, 6.52603939, 3.15160563,\n",
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" 11.60061018, 11.1043498 , 8.0733422 , 11.71522066, 4.62765218,\n",
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" 7.49833868, 18.78977643, 17.00685931, 6.3841865 , 21.0516987 ,\n",
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" 13.16635271, 13.32514284, 39.00407907, 10.17439742, 10.71338756,\n",
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" 12.98324876, 9.36698057, 18.72583288, 8.86341551, 8.59402471,\n",
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" 14.40309408, 11.2151223 , 8.54318159, 7.27041139, 7.54384726,\n",
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" 7.12004486, 8.61247715, 6.24836557, 7.61490273, 7.97743213,\n",
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" 5.90638179, 11.18067752, 9.60402511, 10.27972062, 4.88568098,\n",
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" 5.15238733, 9.48240265, 5.5200633 , 17.34425384, 20.51738915,\n",
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" 8.67575586, 10.13415575, 12.55792577, 11.28995505, 12.01846393,\n",
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" 16.44519718, 11.55540348, 12.6760064 , 14.59124425, 16.08650743,\n",
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" 5.49252396, 14.21853759, 9.80928243, 11.1998899 , 8.53843453,\n",
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" 8.95692822, 7.44622149, 9.41208445, 10.00368097, 18.36862111,\n",
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" 5.90905433, 18.73273459, 10.41462726, 10.38639344, 13.71164211,\n",
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" 8.1023596 , 7.57087137, 3.95315742, 12.24423806, 10.4316517 ,\n",
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" 10.75912468, 9.21246988, 21.71756051, 8.55320981, 10.69256597,\n",
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" 8.20796144, 24.13594074, 4.55095547, 12.43055174, 7.00374928,\n",
181+
" 4.72499044, 6.22081559, 6.50700078, 6.73499461, 12.77964412,\n",
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" 8.8475468 , 11.20443401, 6.59475644, 8.59815892, 7.16696761,\n",
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" 10.62813483, 7.77992602, 16.02889234, 9.21649532, 7.08618021,\n",
184+
" 5.56980282, 3.61976479, 6.86178595, 13.44050831, 11.9525432 ,\n",
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" 7.21974504, 6.28513041, 6.8381433 , 5.93095918, 8.12844537,\n",
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" 8.62486916])\n",
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"\n",
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"plot_swanson(acronyms=acronyms, values=values, orientation='portrait', cmap='Blues', hemisphere='mirror')"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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