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circular flatmap examples
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‎examples/atlas/atlas_circular_pyramidal_flatmap.ipynb‎

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"cells": [
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{
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"cell_type": "markdown",
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"id": "3bf392ab",
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"id": "f199bec2",
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"metadata": {},
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"source": [
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"# Plotting brain region values on circular 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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"id": "72266ba4",
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"cell_type": "markdown",
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"id": "94c08e66",
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"metadata": {},
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"outputs": [],
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"source": [
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"This example walks through various ways to overlay brain region values on a circular flatmap"
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]
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},
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{
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"cell_type": "markdown",
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"id": "54faf5f5",
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"id": "17fd07ec",
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"metadata": {},
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"source": [
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"## The circular flatmap"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d5799f85",
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"id": "3ca88864",
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"metadata": {},
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"source": [
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"The circular flatmap is obtained by sampling the volume using concentric circles through the brain."
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "110cd0ce",
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"id": "1178246b",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d7ae44f1",
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"id": "490614c3",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "037bf3c3",
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"id": "135dd187",
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"metadata": {},
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"source": [
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"This results in a flatmap that can be displayed in the following way"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "965f1ab1",
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"id": "8b8c4223",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"fig, ax = plt.subplots(figsize=(20,4))\n",
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"fig, ax = plt.subplots(figsize=(18,4))\n",
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"flmap_cr.plot_flatmap(ax)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5c4317cf",
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"id": "ec15f88c",
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"metadata": {},
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"source": [
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"It is also possible to display this flatmap such that each circle is stacked on top of eachother. For this, the **pyramid** flatmap should be used"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "072b12d5",
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"id": "7461e3f8",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d47484a5",
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"id": "1f78b2ab",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "60589bee",
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"id": "e7af738a",
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"metadata": {},
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"source": [
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"## Data preparation"
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]
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},
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{
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"cell_type": "markdown",
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"id": "13a160d9",
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"id": "40fa09d0",
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"metadata": {},
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"source": [
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"In order to plot brain regions values on the flatmap an array of acronyms and an array of values corresponding to each acronym must be provided. A detailed overview of how to prepare your data can be found here [LINK TO PLOTTING SCALAR]"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "86f3d236",
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"id": "20a1db83",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"# prepare array of acronyms\n",
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"acronyms = np.array(['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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"acronyms = np.array(['VPM', 'PO', 'LP', 'CA1', 'DG-mo', 'VISa5', 'SSs5'])\n",
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"# assign data to each acronym\n",
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"values = np.arange(acronyms.size)"
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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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"id": "3f7aec63",
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"id": "e6ae51d0",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fa067a5f",
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"id": "3724b968",
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "markdown",
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"id": "14f4b521",
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"id": "74fe528a",
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"metadata": {},
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"source": [
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"## Examples"
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "58f4162e",
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"id": "dfa2d623",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ibllib.atlas.plots import plot_scalar_on_flatmap\n",
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"# Plot region values on the left hemisphere of circle flatmap overlaid on brain region boundaries using Allen mapping\n",
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"fig, ax = plt.subplots(figsize=(18,4))\n",
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"fig, ax = plot_scalar_on_flatmap(acronyms, values, hemisphere='left', mapping='Allen', flmap_atlas=flmap_cr, ax=ax)"
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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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"id": "cc78a1c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot region values on the both hemispheres of circle flatmap overlaid on the dwi Allen image using Beryl mapping\n",
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"fig, ax = plt.subplots(figsize=(18,4))\n",
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"fig, ax = plot_scalar_on_flatmap(acronyms_beryl, values_beryl, hemisphere='both', mapping='Beryl', background='image', \n",
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" cmap='Reds', flmap_atlas=flmap_cr, ax=ax)"
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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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"id": "37bf7bd8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot region values on the right hemisphere of pyramidal flatmap overlaid on the dwi Allen image using Allen mapping\n",
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"fig, ax = plt.subplots(figsize=(8,8))\n",
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"fig, ax = plot_scalar_on_flatmap(acronyms, values, hemisphere='right', mapping='Allen', background='image', \n",
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" cmap='Reds', flmap_atlas=flmap_py, ax=ax)"
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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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"id": "28f7f30c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ibllib.atlas"
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"# Plot two column region values on the both hemispheres of pyramidal flatmap overlaid on brain region boundaries \n",
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"# using Beryl mapping\n",
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"fig, ax = plt.subplots(figsize=(8,8))\n",
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"fig, ax = plot_scalar_on_flatmap(acronyms_beryl, values_beryl_lr, hemisphere='both', mapping='Beryl', \n",
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" background='boundary', cmap='Blues', flmap_atlas=flmap_py, ax=ax)"
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]
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}
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],

‎ibllib/atlas/plots.py‎

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@@ -39,7 +39,7 @@ def reorder_data(acronyms, values, brain_regions=None):
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:param brain_regions: BrainRegions object
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:return: ordered array of acronyms and values
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"""
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# TODO test
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br = brain_regions or BrainRegions()
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atlas_id = br.acronym2id(acronyms, hemisphere='right')
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all_ids = br.id[br.order][:br.n_lr + 1]
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vmax=clevels[1], ax=ax)
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ba.plot_flatmap(d_idx, volume='boundary', mapping=map, ax=ax, cmap=cmap_bound, vmin=0.01, vmax=0.8)
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if hemisphere == 'left':
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ax.set_xlim(0, np.ceil(ba.flatmap.shape[1] / 2))
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elif hemisphere == 'right':
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ax.set_xlim(np.ceil(ba.flatmap.shape[1] / 2), ba.flatmap.shape[1])
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# For circle flatmap we don't want to cut the axis
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if ba.name != 'circles':
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if hemisphere == 'left':
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ax.set_xlim(0, np.ceil(ba.flatmap.shape[1] / 2))
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elif hemisphere == 'right':
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ax.set_xlim(np.ceil(ba.flatmap.shape[1] / 2), ba.flatmap.shape[1])
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return fig, ax
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‎ibllib/atlas/regions.py‎

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@@ -59,6 +59,17 @@ def __init__(self):
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self.mappings = {k: mappings[k].to_numpy() for k in mappings}
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self.n_lr = int((len(self.id) - 1) / 2)
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def _compute_order(self):
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"""
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Compute the order of regions, per region order by left hemisphere and then right hemisphere
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:return:
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"""
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orders = np.zeros_like(self.id)
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# Left hemisphere first
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orders[1::2] = np.arange(self.n_lr) + self.n_lr + 1
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# Then right hemisphere
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orders[2::2] = np.arange(self.n_lr) + 1
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def _compute_mappings(self):
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"""
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Recomputes the mapping indices for all mappings

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