ENH add ngrok to Colab setup to tunnel brain viewer out of Colab

TomDLT · TomDLT · commit 9f8cdc2d527b · 2021-09-10T17:53:52.000-07:00
diff --git a/doc/Makefile b/doc/Makefile
@@ -37,7 +37,7 @@ NBDIR = ../tutorials/notebooks/movies_3T
 
 merge-notebooks:
 	python merge_notebooks.py \
-		$(NBDIR)/00_load_colab.ipynb \
+		$(NBDIR)/00_setup_colab.ipynb \
 		$(NBDIR)/01_plot_explainable_variance.ipynb \
 		$(NBDIR)/02_plot_wordnet_model.ipynb \
 		$(NBDIR)/03_plot_hemodynamic_response.ipynb \
diff --git a/tutorials/movies_3T/00_setup_colab.py b/tutorials/movies_3T/00_setup_colab.py
@@ -28,9 +28,22 @@
 
 # ![ -f "vim-5-for-ccn.tar.gz" ] || gdown --id 1b0I0Ytj06m6GCmfxfNrZuyF97fDo3NZb
 # ![ -d "vim-5" ] || tar xzf vim-5-for-ccn.tar.gz
-# ![ -d "pycortex" ] || git clone https://github.com/gallantlab/pycortex
+# ![ -d "pycortex" ] || git clone --quiet https://github.com/gallantlab/pycortex
+# !apt-get install -qq inkscape > /dev/null
 # !pip install -q voxelwise_tutorials
+# ![ -f "ngrok-stable-linux-amd64.zip" ] || wget -q https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
+# ![ -f "ngrok" ] || unzip ngrok-stable-linux-amd64.zip
 
+###############################################################################
+# For the record, here is what each command does:
+#
+# - Download the dataset archive
+# - Extract the dataset archive
+# - Clone Pycortex to fix some filestore issues with Colab
+# - Install Inkscape, to use more features from Pycortex
+# - Install the tutorial helper package, and all the required dependencies
+# - Download ngrok to create a tunnel for pycortex 3D brain viewer
+# - Extract the ngrok archive
 
 ###############################################################################
 # Now run the following cell to set up the environment variables for the
diff --git a/tutorials/movies_3T/01_plot_explainable_variance.py b/tutorials/movies_3T/01_plot_explainable_variance.py
@@ -15,14 +15,14 @@
 are the same for each repetition of the stimulus. Thus, encoding models will
 predict only the repeatable stimulus-dependent signal.
 
-The stimulus-dependent signal can be estimated by taking the mean of
+The stimulus-dependent signal can be estimated by taking the mean of 
 brain responses over repeats of the same stimulus or experiment. The variance
 of the estimated stimulus-dependent signal, which we call the explainable
 variance, is proportional to the maximum prediction accuracy that can be
-obtained by a voxelwise encoding model in the test set.
+obtained by a voxelwise encoding model in the test set. 
 
 Mathematically, let :math:`y_i, i = 1 \\dots N` be the measured signal in
-a voxel for each of the :math:`N` repetitions of the same stimulus and
+a voxel for each of the :math:`N` repetitions of the same stimulus and 
 :math:`\\bar{y} = \\frac{1}{N}\\sum_{i=1}^Ny_i` the average brain response
 across repetitions. For each repeat, we define the residual timeseries
 between brain response and average brain response as :math:`r_i = y_i - \\bar{y}`.
@@ -114,7 +114,7 @@
 plt.show()
 
 ###############################################################################
-# We see that many voxels have low explainable variance. This is
+# We see that many voxels have low explainable variance. This is 
 # expected, since many voxels are not driven by a visual stimulus, and their
 # response changes over repeats of the same stimulus.
 # We also see that some voxels have high explainable variance (around 0.7). The
@@ -150,8 +150,8 @@
 plt.show()
 
 ###############################################################################
-# This figure is a flattened map of the cortical surface. A number of regions
-# of interest (ROIs) have been labeled to ease interpretation. If you have
+# This figure is a flattened map of the cortical surface. A number of regions of
+# interest (ROIs) have been labeled to ease interpretation. If you have
 # never seen such a flatmap, we recommend taking a look at a `pycortex brain
 # viewer <https://www.gallantlab.org/brainviewer/Deniz2019>`_, which displays
 # the brain in 3D. In this viewer, press "I" to inflate the brain, "F" to
@@ -198,6 +198,8 @@
     cortex.db = cortex.database.db
     cortex.utils.db = cortex.database.db
     cortex.dataset.braindata.db = cortex.database.db
+    cortex.quickflat.utils.db = cortex.database.db
+    cortex.quickflat.composite.db = cortex.database.db
 
 ###############################################################################
 # Then, we load the "fsaverage" mapper. The mapper is a matrix of shape
@@ -215,12 +217,33 @@
 # projected data. This object can be used either in a ``pycortex`` interactive
 # 3D viewer, or in a ``matplotlib`` figure showing only the flatmap.
 
-vertex = cortex.Vertex(ev_projected, surface, vmin=0, vmax=0.7, cmap='inferno')
+vertex = cortex.Vertex(ev_projected, surface, vmin=0, vmax=0.7, cmap='viridis')
 
 ###############################################################################
-# To start an interactive 3D viewer in the browser, use the following function:
-if False:
-    cortex.webshow(vertex, open_browser=True)
+# To start an interactive 3D viewer in the browser, use the ``webshow``
+# function.
+
+if True:
+    cortex.webshow(vertex, open_browser=False, port=8050)
+
+###############################################################################
+# If you are running the notebook on Colab, you need to tunnel the pycortex
+# application out of Colab. To do so, use the following cell to start a tunnel
+# with ``ngrok`` and to get an address where the pycortex viewer will be made
+# accessible.
+
+if in_colab:
+    from IPython import get_ipython
+    get_ipython().system_raw('./ngrok http 8050 &')
+
+    command = """
+        curl -s http://localhost:4040/api/tunnels | python3 -c \
+        "import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])"
+        """
+    result = get_ipython().getoutput(command, split=True)
+    print("Use the following address to connect to the brain viewer:\n"
+          f"{result}\n"
+          "and not the one proposed by pycortex ('Open viewer: ...')\n")
 
 ###############################################################################
 # Alternatively, to plot a flatmap in a ``matplotlib`` figure, use the
@@ -231,9 +254,10 @@
 
 from cortex.testing_utils import has_installed
 
-if has_installed("inkscape"):
-    fig = cortex.quickshow(vertex, colorbar_location='right')
-    plt.show()
+
+fig = cortex.quickshow(vertex, colorbar_location='right',
+                       with_rois=has_installed("inkscape"))
+plt.show()
 
 
 ###############################################################################
diff --git a/tutorials/notebooks/movies_3T/00_setup_colab.ipynb b/tutorials/notebooks/movies_3T/00_setup_colab.ipynb
@@ -51,7 +51,14 @@
       },
       "outputs": [],
       "source": [
-        "# ![ -f \"vim-5-for-ccn.tar.gz\" ] || gdown --id 1b0I0Ytj06m6GCmfxfNrZuyF97fDo3NZb\n# ![ -d \"vim-5\" ] || tar xzf vim-5-for-ccn.tar.gz\n# ![ -d \"pycortex\" ] || git clone https://github.com/gallantlab/pycortex\n# !pip install -q voxelwise_tutorials"
+        "# ![ -f \"vim-5-for-ccn.tar.gz\" ] || gdown --id 1b0I0Ytj06m6GCmfxfNrZuyF97fDo3NZb\n# ![ -d \"vim-5\" ] || tar xzf vim-5-for-ccn.tar.gz\n# ![ -d \"pycortex\" ] || git clone --quiet https://github.com/gallantlab/pycortex\n# !apt-get install -qq inkscape > /dev/null\n# !pip install -q voxelwise_tutorials\n# ![ -f \"ngrok-stable-linux-amd64.zip\" ] || wget -q https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n# ![ -f \"ngrok\" ] || unzip ngrok-stable-linux-amd64.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For the record, here is what each command does:\n\n- Download the dataset archive\n- Extract the dataset archive\n- Clone Pycortex to fix some filestore issues with Colab\n- Install Inkscape, to use more features from Pycortex\n- Install the tutorial helper package, and all the required dependencies\n- Download ngrok to create a tunnel for pycortex 3D brain viewer\n- Extract the ngrok archive\n\n"
       ]
     },
     {
diff --git a/tutorials/notebooks/movies_3T/01_plot_explainable_variance.ipynb b/tutorials/notebooks/movies_3T/01_plot_explainable_variance.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Compute the explainable variance\n\nBefore fitting any voxelwise model to fMRI responses, it is good practice to\nquantify the amount of signal in the test set that can be predicted by an\nencoding model. This quantity is called the *explainable variance*.\n\nThe measured signal can be decomposed into a sum of two components: the\nstimulus-dependent signal and noise. If we present the same stimulus multiple\ntimes and we record brain activity for each repetition, the stimulus-dependent\nsignal will be the same across repetitions while the noise will vary across\nrepetitions. In voxelwise modeling, the features used to model brain activity\nare the same for each repetition of the stimulus. Thus, encoding models will\npredict only the repeatable stimulus-dependent signal.\n\nThe stimulus-dependent signal can be estimated by taking the mean of\nbrain responses over repeats of the same stimulus or experiment. The variance\nof the estimated stimulus-dependent signal, which we call the explainable\nvariance, is proportional to the maximum prediction accuracy that can be\nobtained by a voxelwise encoding model in the test set.\n\nMathematically, let $y_i, i = 1 \\dots N$ be the measured signal in\na voxel for each of the $N$ repetitions of the same stimulus and\n$\\bar{y} = \\frac{1}{N}\\sum_{i=1}^Ny_i$ the average brain response\nacross repetitions. For each repeat, we define the residual timeseries\nbetween brain response and average brain response as $r_i = y_i - \\bar{y}$.\nThe explainable variance (EV) is estimated as\n\n\\begin{align}\\text{EV} = \\frac{1}{N}\\sum_{i=1}^N\\text{Var}(y_i) - \\frac{N}{N-1}\\sum_{i=1}^N\\text{Var}(r_i)\\end{align}\n\n\nIn the literature, the explainable\nvariance is also known as the *signal power*. For more information, see these\nreferences [1]_ [2]_ [3]_.\n"
+        "\n# Compute the explainable variance\n\nBefore fitting any voxelwise model to fMRI responses, it is good practice to\nquantify the amount of signal in the test set that can be predicted by an\nencoding model. This quantity is called the *explainable variance*.\n\nThe measured signal can be decomposed into a sum of two components: the\nstimulus-dependent signal and noise. If we present the same stimulus multiple\ntimes and we record brain activity for each repetition, the stimulus-dependent\nsignal will be the same across repetitions while the noise will vary across\nrepetitions. In voxelwise modeling, the features used to model brain activity\nare the same for each repetition of the stimulus. Thus, encoding models will\npredict only the repeatable stimulus-dependent signal.\n\nThe stimulus-dependent signal can be estimated by taking the mean of \nbrain responses over repeats of the same stimulus or experiment. The variance\nof the estimated stimulus-dependent signal, which we call the explainable\nvariance, is proportional to the maximum prediction accuracy that can be\nobtained by a voxelwise encoding model in the test set. \n\nMathematically, let $y_i, i = 1 \\dots N$ be the measured signal in\na voxel for each of the $N$ repetitions of the same stimulus and \n$\\bar{y} = \\frac{1}{N}\\sum_{i=1}^Ny_i$ the average brain response\nacross repetitions. For each repeat, we define the residual timeseries\nbetween brain response and average brain response as $r_i = y_i - \\bar{y}$.\nThe explainable variance (EV) is estimated as\n\n\\begin{align}\\text{EV} = \\frac{1}{N}\\sum_{i=1}^N\\text{Var}(y_i) - \\frac{N}{N-1}\\sum_{i=1}^N\\text{Var}(r_i)\\end{align}\n\n\nIn the literature, the explainable\nvariance is also known as the *signal power*. For more information, see these\nreferences [1]_ [2]_ [3]_.\n"
       ]
     },
     {
@@ -170,7 +170,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We see that many voxels have low explainable variance. This is\nexpected, since many voxels are not driven by a visual stimulus, and their\nresponse changes over repeats of the same stimulus.\nWe also see that some voxels have high explainable variance (around 0.7). The\nresponses in these voxels are highly consistent across repetitions of the\nsame stimulus. Thus, they are good targets for encoding models.\n\n"
+        "We see that many voxels have low explainable variance. This is \nexpected, since many voxels are not driven by a visual stimulus, and their\nresponse changes over repeats of the same stimulus.\nWe also see that some voxels have high explainable variance (around 0.7). The\nresponses in these voxels are highly consistent across repetitions of the\nsame stimulus. Thus, they are good targets for encoding models.\n\n"
       ]
     },
     {
@@ -195,7 +195,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "This figure is a flattened map of the cortical surface. A number of regions\nof interest (ROIs) have been labeled to ease interpretation. If you have\nnever seen such a flatmap, we recommend taking a look at a `pycortex brain\nviewer <https://www.gallantlab.org/brainviewer/Deniz2019>`_, which displays\nthe brain in 3D. In this viewer, press \"I\" to inflate the brain, \"F\" to\nflatten the surface, and \"R\" to reset the view (or use the ``surface/unfold``\ncursor on the right menu). Press \"H\" for a list of all keyboard shortcuts.\nThis viewer should help you understand the correspondance between the flatten\nand the folded cortical surface of the brain.\n\n"
+        "This figure is a flattened map of the cortical surface. A number of regions of\ninterest (ROIs) have been labeled to ease interpretation. If you have\nnever seen such a flatmap, we recommend taking a look at a `pycortex brain\nviewer <https://www.gallantlab.org/brainviewer/Deniz2019>`_, which displays\nthe brain in 3D. In this viewer, press \"I\" to inflate the brain, \"F\" to\nflatten the surface, and \"R\" to reset the view (or use the ``surface/unfold``\ncursor on the right menu). Press \"H\" for a list of all keyboard shortcuts.\nThis viewer should help you understand the correspondance between the flatten\nand the folded cortical surface of the brain.\n\n"
       ]
     },
     {
@@ -238,7 +238,7 @@
       },
       "outputs": [],
       "source": [
-        "try:\n    import google.colab  # noqa\n    in_colab = True\nexcept ImportError:\n    in_colab = False\nprint(in_colab)\n\nif in_colab:\n    filestore = cortex.options.config['basic']['filestore']\n    cortex.database.db = cortex.database.Database(filestore)\n    cortex.db = cortex.database.db\n    cortex.utils.db = cortex.database.db\n    cortex.dataset.braindata.db = cortex.database.db"
+        "try:\n    import google.colab  # noqa\n    in_colab = True\nexcept ImportError:\n    in_colab = False\nprint(in_colab)\n\nif in_colab:\n    filestore = cortex.options.config['basic']['filestore']\n    cortex.database.db = cortex.database.Database(filestore)\n    cortex.db = cortex.database.db\n    cortex.utils.db = cortex.database.db\n    cortex.dataset.braindata.db = cortex.database.db\n    cortex.quickflat.utils.db = cortex.database.db\n    cortex.quickflat.composite.db = cortex.database.db"
       ]
     },
     {
@@ -274,14 +274,14 @@
       },
       "outputs": [],
       "source": [
-        "vertex = cortex.Vertex(ev_projected, surface, vmin=0, vmax=0.7, cmap='inferno')"
+        "vertex = cortex.Vertex(ev_projected, surface, vmin=0, vmax=0.7, cmap='viridis')"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "To start an interactive 3D viewer in the browser, use the following function:\n\n"
+        "To start an interactive 3D viewer in the browser, use the ``webshow``\nfunction.\n\n"
       ]
     },
     {
@@ -292,7 +292,25 @@
       },
       "outputs": [],
       "source": [
-        "if False:\n    cortex.webshow(vertex, open_browser=True)"
+        "if True:\n    cortex.webshow(vertex, open_browser=False, port=8050)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you are running the notebook on Colab, you need to tunnel the pycortex\napplication out of Colab. To do so, use the following cell to start a tunnel\nwith ``ngrok`` and to get an address where the pycortex viewer will be made\naccessible.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "if in_colab:\n    from IPython import get_ipython\n    get_ipython().system_raw('./ngrok http 8050 &')\n\n    command = \"\"\"\n        curl -s http://localhost:4040/api/tunnels | python3 -c \\\n        \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n        \"\"\"\n    result = get_ipython().getoutput(command, split=True)\n    print(\"Use the following address to connect to the brain viewer:\\n\"\n          f\"{result}\\n\"\n          \"and not the one proposed by pycortex ('Open viewer: ...')\\n\")"
       ]
     },
     {
@@ -310,7 +328,7 @@
       },
       "outputs": [],
       "source": [
-        "from cortex.testing_utils import has_installed\n\nif has_installed(\"inkscape\"):\n    fig = cortex.quickshow(vertex, colorbar_location='right')\n    plt.show()"
+        "from cortex.testing_utils import has_installed\n\n\nfig = cortex.quickshow(vertex, colorbar_location='right',\n                       with_rois=has_installed(\"inkscape\"))\nplt.show()"
       ]
     },
     {
diff --git a/tutorials/notebooks/movies_3T/merged_for_colab.ipynb b/tutorials/notebooks/movies_3T/merged_for_colab.ipynb

Original file line number	Diff line number	Diff line change
`@@ -15,7 +15,7 @@`
`15`	`15`	`"cell_type": "markdown",`
`16`	`16`	`"metadata": {},`
`17`	`17`	`"source": [`
`18`		- "\n# Compute the explainable variance\n\nBefore fitting any voxelwise model to fMRI responses, it is good practice to\nquantify the amount of signal in the test set that can be predicted by an\nencoding model. This quantity is called the explainable variance.\n\nThe measured signal can be decomposed into a sum of two components: the\nstimulus-dependent signal and noise. If we present the same stimulus multiple\ntimes and we record brain activity for each repetition, the stimulus-dependent\nsignal will be the same across repetitions while the noise will vary across\nrepetitions. In voxelwise modeling, the features used to model brain activity\nare the same for each repetition of the stimulus. Thus, encoding models will\npredict only the repeatable stimulus-dependent signal.\n\nThe stimulus-dependent signal can be estimated by taking the mean of\nbrain responses over repeats of the same stimulus or experiment. The variance\nof the estimated stimulus-dependent signal, which we call the explainable\nvariance, is proportional to the maximum prediction accuracy that can be\nobtained by a voxelwise encoding model in the test set.\n\nMathematically, let $y_i, i = 1 \\dots N$ be the measured signal in\na voxel for each of the $N$ repetitions of the same stimulus and\n$\\bar{y} = \\frac{1}{N}\\sum_{i=1}^Ny_i$ the average brain response\nacross repetitions. For each repeat, we define the residual timeseries\nbetween brain response and average brain response as $r_i = y_i - \\bar{y}$.\nThe explainable variance (EV) is estimated as\n\n\\begin{align}\\text{EV} = \\frac{1}{N}\\sum_{i=1}^N\\text{Var}(y_i) - \\frac{N}{N-1}\\sum_{i=1}^N\\text{Var}(r_i)\\end{align}\n\n\nIn the literature, the explainable\nvariance is also known as the signal power. For more information, see these\nreferences [1]_ [2]_ [3]_.\n"
	`18`	+ "\n# Compute the explainable variance\n\nBefore fitting any voxelwise model to fMRI responses, it is good practice to\nquantify the amount of signal in the test set that can be predicted by an\nencoding model. This quantity is called the explainable variance.\n\nThe measured signal can be decomposed into a sum of two components: the\nstimulus-dependent signal and noise. If we present the same stimulus multiple\ntimes and we record brain activity for each repetition, the stimulus-dependent\nsignal will be the same across repetitions while the noise will vary across\nrepetitions. In voxelwise modeling, the features used to model brain activity\nare the same for each repetition of the stimulus. Thus, encoding models will\npredict only the repeatable stimulus-dependent signal.\n\nThe stimulus-dependent signal can be estimated by taking the mean of \nbrain responses over repeats of the same stimulus or experiment. The variance\nof the estimated stimulus-dependent signal, which we call the explainable\nvariance, is proportional to the maximum prediction accuracy that can be\nobtained by a voxelwise encoding model in the test set. \n\nMathematically, let $y_i, i = 1 \\dots N$ be the measured signal in\na voxel for each of the $N$ repetitions of the same stimulus and \n$\\bar{y} = \\frac{1}{N}\\sum_{i=1}^Ny_i$ the average brain response\nacross repetitions. For each repeat, we define the residual timeseries\nbetween brain response and average brain response as $r_i = y_i - \\bar{y}$.\nThe explainable variance (EV) is estimated as\n\n\\begin{align}\\text{EV} = \\frac{1}{N}\\sum_{i=1}^N\\text{Var}(y_i) - \\frac{N}{N-1}\\sum_{i=1}^N\\text{Var}(r_i)\\end{align}\n\n\nIn the literature, the explainable\nvariance is also known as the signal power. For more information, see these\nreferences [1]_ [2]_ [3]_.\n"
`19`	`19`	`]`
`20`	`20`	`},`
`21`	`21`	`{`
`@@ -170,7 +170,7 @@`
`170`	`170`	`"cell_type": "markdown",`
`171`	`171`	`"metadata": {},`
`172`	`172`	`"source": [`
`173`		`- "We see that many voxels have low explainable variance. This is\nexpected, since many voxels are not driven by a visual stimulus, and their\nresponse changes over repeats of the same stimulus.\nWe also see that some voxels have high explainable variance (around 0.7). The\nresponses in these voxels are highly consistent across repetitions of the\nsame stimulus. Thus, they are good targets for encoding models.\n\n"`
	`173`	`+ "We see that many voxels have low explainable variance. This is \nexpected, since many voxels are not driven by a visual stimulus, and their\nresponse changes over repeats of the same stimulus.\nWe also see that some voxels have high explainable variance (around 0.7). The\nresponses in these voxels are highly consistent across repetitions of the\nsame stimulus. Thus, they are good targets for encoding models.\n\n"`
`174`	`174`	`]`
`175`	`175`	`},`
`176`	`176`	`{`
`@@ -195,7 +195,7 @@`
`195`	`195`	`"cell_type": "markdown",`
`196`	`196`	`"metadata": {},`
`197`	`197`	`"source": [`
`198`		- "This figure is a flattened map of the cortical surface. A number of regions\nof interest (ROIs) have been labeled to ease interpretation. If you have\nnever seen such a flatmap, we recommend taking a look at a `pycortex brain\nviewer <https://www.gallantlab.org/brainviewer/Deniz2019>`_, which displays\nthe brain in 3D. In this viewer, press \"I\" to inflate the brain, \"F\" to\nflatten the surface, and \"R\" to reset the view (or use the ``surface/unfold``\ncursor on the right menu). Press \"H\" for a list of all keyboard shortcuts.\nThis viewer should help you understand the correspondance between the flatten\nand the folded cortical surface of the brain.\n\n"
	`198`	+ "This figure is a flattened map of the cortical surface. A number of regions of\ninterest (ROIs) have been labeled to ease interpretation. If you have\nnever seen such a flatmap, we recommend taking a look at a `pycortex brain\nviewer <https://www.gallantlab.org/brainviewer/Deniz2019>`_, which displays\nthe brain in 3D. In this viewer, press \"I\" to inflate the brain, \"F\" to\nflatten the surface, and \"R\" to reset the view (or use the ``surface/unfold``\ncursor on the right menu). Press \"H\" for a list of all keyboard shortcuts.\nThis viewer should help you understand the correspondance between the flatten\nand the folded cortical surface of the brain.\n\n"
`199`	`199`	`]`
`200`	`200`	`},`
`201`	`201`	`{`
`@@ -238,7 +238,7 @@`
`238`	`238`	`},`
`239`	`239`	`"outputs": [],`
`240`	`240`	`"source": [`
`241`		`- "try:\n import google.colab # noqa\n in_colab = True\nexcept ImportError:\n in_colab = False\nprint(in_colab)\n\nif in_colab:\n filestore = cortex.options.config['basic']['filestore']\n cortex.database.db = cortex.database.Database(filestore)\n cortex.db = cortex.database.db\n cortex.utils.db = cortex.database.db\n cortex.dataset.braindata.db = cortex.database.db"`
	`241`	+ "try:\n import google.colab # noqa\n in_colab = True\nexcept ImportError:\n in_colab = False\nprint(in_colab)\n\nif in_colab:\n filestore = cortex.options.config['basic']['filestore']\n cortex.database.db = cortex.database.Database(filestore)\n cortex.db = cortex.database.db\n cortex.utils.db = cortex.database.db\n cortex.dataset.braindata.db = cortex.database.db\n cortex.quickflat.utils.db = cortex.database.db\n cortex.quickflat.composite.db = cortex.database.db"
`242`	`242`	`]`
`243`	`243`	`},`
`244`	`244`	`{`
`@@ -274,14 +274,14 @@`
`274`	`274`	`},`
`275`	`275`	`"outputs": [],`
`276`	`276`	`"source": [`
`277`		`- "vertex = cortex.Vertex(ev_projected, surface, vmin=0, vmax=0.7, cmap='inferno')"`
	`277`	`+ "vertex = cortex.Vertex(ev_projected, surface, vmin=0, vmax=0.7, cmap='viridis')"`
`278`	`278`	`]`
`279`	`279`	`},`
`280`	`280`	`{`
`281`	`281`	`"cell_type": "markdown",`
`282`	`282`	`"metadata": {},`
`283`	`283`	`"source": [`
`284`		`- "To start an interactive 3D viewer in the browser, use the following function:\n\n"`
	`284`	+ "To start an interactive 3D viewer in the browser, use the ``webshow``\nfunction.\n\n"
`285`	`285`	`]`
`286`	`286`	`},`
`287`	`287`	`{`
`@@ -292,7 +292,25 @@`
`292`	`292`	`},`
`293`	`293`	`"outputs": [],`
`294`	`294`	`"source": [`
`295`		`- "if False:\n cortex.webshow(vertex, open_browser=True)"`
	`295`	`+ "if True:\n cortex.webshow(vertex, open_browser=False, port=8050)"`
	`296`	`+ ]`
	`297`	`+ },`
	`298`	`+ {`
	`299`	`+ "cell_type": "markdown",`
	`300`	`+ "metadata": {},`
	`301`	`+ "source": [`
	`302`	+ "If you are running the notebook on Colab, you need to tunnel the pycortex\napplication out of Colab. To do so, use the following cell to start a tunnel\nwith ``ngrok`` and to get an address where the pycortex viewer will be made\naccessible.\n\n"
	`303`	`+ ]`
	`304`	`+ },`
	`305`	`+ {`
	`306`	`+ "cell_type": "code",`
	`307`	`+ "execution_count": null,`
	`308`	`+ "metadata": {`
	`309`	`+ "collapsed": false`
	`310`	`+ },`
	`311`	`+ "outputs": [],`
	`312`	`+ "source": [`
	`313`	+ "if in_colab:\n from IPython import get_ipython\n get_ipython().system_raw('./ngrok http 8050 &')\n\n command = \"\"\"\n curl -s http://localhost:4040/api/tunnels \| python3 -c \\\n \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n \"\"\"\n result = get_ipython().getoutput(command, split=True)\n print(\"Use the following address to connect to the brain viewer:\\n\"\n f\"{result}\\n\"\n \"and not the one proposed by pycortex ('Open viewer: ...')\\n\")"
`296`	`314`	`]`
`297`	`315`	`},`
`298`	`316`	`{`
`@@ -310,7 +328,7 @@`
`310`	`328`	`},`
`311`	`329`	`"outputs": [],`
`312`	`330`	`"source": [`
`313`		`- "from cortex.testing_utils import has_installed\n\nif has_installed(\"inkscape\"):\n fig = cortex.quickshow(vertex, colorbar_location='right')\n plt.show()"`
	`331`	`+ "from cortex.testing_utils import has_installed\n\n\nfig = cortex.quickshow(vertex, colorbar_location='right',\n with_rois=has_installed(\"inkscape\"))\nplt.show()"`
`314`	`332`	`]`
`315`	`333`	`},`
`316`	`334`	`{`