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gallantlab · Apr 26, 2024 · 6d121d1 · 6d121d1
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diff --git a/.buildinfo b/.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
diff --git a/_downloads/0171ca85d45eca4cef1870c2e499de10/plot_make_gif.ipynb b/_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,

diff --git a/_downloads/03727e1a4b42addea2598e45140498aa/static_with_custom_template.ipynb b/_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"
     }
   },
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diff --git a/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/_downloads/09288dd002fd14fba94fc085d758d914/plot_roi_voxel_index_volume.ipynb b/_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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diff --git a/_downloads/0f8be1e4c7dbdc2b5c70b92872c6b4bf/multiple_datasets.ipynb b/_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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diff --git a/_downloads/123c014fb1951d73e7b1f19570a43c66/plot_get_roi_vertices.ipynb b/_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,

diff --git a/_downloads/1bc319bfa2fe17d7880c31c36c39b670/plot_thickness_nanmean.ipynb b/_downloads/1bc319bfa2fe17d7880c31c36c39b670/plot_thickness_nanmean.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,

diff --git a/_downloads/2adc80653ddc362ff5e8f800f300132f/subject_to_mni.py b/_downloads/2adc80653ddc362ff5e8f800f300132f/subject_to_mni.py
@@ -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

diff --git a/_downloads/2b386e6cf21fd3a3a1c01882f5073cd7/mni_to_subject.py b/_downloads/2b386e6cf21fd3a3a1c01882f5073cd7/mni_to_subject.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

diff --git a/_downloads/2c1d4ccc800c14e88c119609426fa416/plot_geodesic_path.py b/_downloads/2c1d4ccc800c14e88c119609426fa416/plot_geodesic_path.py
@@ -24,6 +24,8 @@
 surfs = [cortex.polyutils.Surface(*d)
          for d in cortex.db.get_surf(subject, "fiducial")]
 numl = surfs[0].pts.shape[0]
+numr = surfs[1].pts.shape[0]
+num_vertices = numl + numr
 
 # Now we need to pick the start and end points of the line we will draw
 pt_a = 100
@@ -33,12 +35,12 @@
 path = surfs[0].geodesic_path(pt_a, pt_b)
 
 # In order to plot this on the cortical surface, we need an array that is the
-# same size as the number of vertices in the left hemisphere
-path_data = np.zeros(numl)
+# same size as the number of vertices
+path_data = np.zeros(num_vertices) * np.nan
 for v in path:
     path_data[v] = 1
 
 # And now plot these distances onto the cortical surface
-path_verts = cortex.Vertex(path_data, subject, cmap="Blues_r")
-cortex.quickshow(path_verts, with_colorbar=False)
+path_verts = cortex.Vertex(path_data, subject, cmap="Reds", vmin=0, vmax=1)
+cortex.quickshow(path_verts, with_colorbar=False, with_curvature=True)
 plt.show()
diff --git a/_downloads/344a9c20fa2e5b85144a3143c290594a/static.ipynb b/_downloads/344a9c20fa2e5b85144a3143c290594a/static.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,

diff --git a/_downloads/34bc427b5d07c6d8727ed358794c8cc9/plot_voxel_distance_from_surface.ipynb b/_downloads/34bc427b5d07c6d8727ed358794c8cc9/plot_voxel_distance_from_surface.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,

diff --git a/_downloads/387ca9e74b721456c024802f5fb1210c/plot_subsurfaces.ipynb b/_downloads/387ca9e74b721456c024802f5fb1210c/plot_subsurfaces.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -143,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.12"
+      "version": "3.9.19"
     }
   },
   "nbformat": 4,

diff --git a/_downloads/38bd34ef15da8508933ae951daa9dfc6/plot_dropout.ipynb b/_downloads/38bd34ef15da8508933ae951daa9dfc6/plot_dropout.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,

diff --git a/_downloads/3a293f5fef99cd1b3cbd8e7036b48449/plot_roi_voxel_mask.ipynb b/_downloads/3a293f5fef99cd1b3cbd8e7036b48449/plot_roi_voxel_mask.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Get ROI Voxel Mask\n\nGet proportion of each voxel that exists within a named ROI (this \nconstitutes a probability map for the ROI, with values ranging from\n0-1). Plot this probablistic roi mask onto a flatmap. \n\nIn order for this to work, the specified ROI must exist in the\noverlays.svg file in the pycortex filestore for this subject.\n"
+        "\n# Get ROI Voxel Mask\n\nGet proportion of each voxel that exists within a named ROI (this \nconstitutes a probability map for the ROI, with values ranging from\n0-1). Plot this probabilistic roi mask onto a flatmap. \n\nIn order for this to work, the specified ROI must exist in the\noverlays.svg file in the pycortex filestore for this subject.\n"
       ]
     },
     {
@@ -26,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import cortex\nimport matplotlib.pyplot as plt\n\nsubject = \"S1\"\nxfm = \"fullhead\"\nroi = \"EBA\"\n\n# Get the map of which voxels are inside of our ROI\nroi_masks = cortex.utils.get_roi_masks(subject, xfm, \n                                       roi_list=[roi],\n                                       gm_sampler='cortical-conservative', # Select only voxels mostly within cortex\n                                       split_lr=False, # No separate left/right ROIs\n                                       threshold=None, # Leave roi mask values as probabilites / fractions\n                                       return_dict=True\n                                       )\n\n# Plot the mask for one ROI onto a flatmap\nroi_data = cortex.Volume(roi_masks[roi], subject, xfm, \n                         vmin=0, # This is a probability mask, so only\n                         vmax=1, # so scale btw zero and one\n                         cmap=\"inferno\", # For pretty\n                         )\n\ncortex.quickflat.make_figure(roi_data,\n                             thick=1, # select a single depth (btw white matter & pia)\n                             sampler='nearest', # no interpolation\n                             with_curvature=True,\n                             with_colorbar=True,\n                             )\n\nplt.show()"
+        "import cortex\nimport matplotlib.pyplot as plt\n\nsubject = \"S1\"\nxfm = \"fullhead\"\nroi = \"EBA\"\n\n# Get the map of which voxels are inside of our ROI\nroi_masks = cortex.utils.get_roi_masks(subject, xfm, \n                                       roi_list=[roi],\n                                       gm_sampler='cortical-conservative', # Select only voxels mostly within cortex\n                                       split_lr=False, # No separate left/right ROIs\n                                       threshold=None, # Leave roi mask values as probabilities / fractions\n                                       return_dict=True\n                                       )\n\n# Plot the mask for one ROI onto a flatmap\nroi_data = cortex.Volume(roi_masks[roi], subject, xfm, \n                         vmin=0, # This is a probability mask, so only\n                         vmax=1, # so scale btw zero and one\n                         cmap=\"inferno\", # For pretty\n                         )\n\ncortex.quickflat.make_figure(roi_data,\n                             thick=1, # select a single depth (btw white matter & pia)\n                             sampler='nearest', # no interpolation\n                             with_curvature=True,\n                             with_colorbar=True,\n                             )\n\nplt.show()"
       ]
     }
   ],
@@ -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,

diff --git a/_downloads/3dca224e23400dbb33007465a7b359a6/plot_connected_vertices.ipynb b/_downloads/3dca224e23400dbb33007465a7b359a6/plot_connected_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"
     }
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   "nbformat": 4,