Deploying to gh-pages from @ b92e14d 🚀

Author: mvdoc

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 13b17c5c373045c1725623acad9b96b1
+config: 0e5232a893a37e47ccfdc6e2362e71ce
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/0171ca85d45eca4cef1870c2e499de10/plot_make_gif.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -125,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.12"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

_downloads/03727e1a4b42addea2598e45140498aa/static_with_custom_template.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.12"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.12"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

_downloads/09288dd002fd14fba94fc085d758d914/plot_roi_voxel_index_volume.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.12"
+      "version": "3.9.19"
     }
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   "nbformat": 4,

_downloads/0f8be1e4c7dbdc2b5c70b92872c6b4bf/multiple_datasets.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.12"
+      "version": "3.9.19"
     }
   },
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_downloads/123c014fb1951d73e7b1f19570a43c66/plot_get_roi_vertices.ipynb

@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.12"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

_downloads/1bc319bfa2fe17d7880c31c36c39b670/plot_thickness_nanmean.ipynb

@@ -32,7 +32,7 @@
 mni_data = mni.transform_to_mni(data, s1_to_mni)
 # mni_data is a nibabel Nifti1Image
 
-mni_data_vol = mni_data.get_data() # the actual array, shape=(182,218,182)
+mni_data_vol = mni_data.get_fdata() # the actual array, shape=(182,218,182)
 
 # That was the manual method. pycortex can also cache these transforms for you
 # if you get them using the pycortex database

_downloads/2adc80653ddc362ff5e8f800f300132f/subject_to_mni.py

@@ -37,7 +37,7 @@
                                             data.data, s1_to_mni)
 # subject_data is a nibabel Nifti1Image
 
-subject_data_vol = mni_data.get_data() # the actual array, shape=(100,100,31)
+subject_data_vol = mni_data.get_fdata() # the actual array, shape=(100,100,31)
 
 # That was the manual method. pycortex can also cache these transforms for you
 # if you get them using the pycortex database