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docs/source/examples/glm.ipynb

@@ -2,15 +2,15 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c8149a94",
+   "id": "0",
    "metadata": {},
    "source": [
     "# General Linear Model (GLM)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b54b132a",
+   "id": "1",
    "metadata": {},
    "source": [
     "In this tutorial, which is adapted from the Nilearn docs, we will go through a simple workflow of the first level general linear modeling with a BIDS dataset from openneuro. This analysis is only performed on **one** subject.\n",
@@ -21,7 +21,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9f514ffe",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8313a041",
+   "id": "3",
    "metadata": {},
    "source": [
     "## Preparation\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "72d1dfdd",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +83,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5716cb50",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1878928b",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6cafd6a1",
+   "id": "7",
    "metadata": {},
    "source": [
     "## Create tasks\n",
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "823780ab",
+   "id": "8",
    "metadata": {},
    "source": [
     "### fetch openneuro BIDS dataset\n",
@@ -142,7 +142,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e2ab134c",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b4899de",
+   "id": "10",
    "metadata": {},
    "source": [
     "### obtain FirstLevelModel objects automatically and fit arguments\n",
@@ -181,7 +181,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3c2710dc",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5af99cb",
+   "id": "12",
    "metadata": {},
    "source": [
     "### Get design matrix\n",
@@ -233,7 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2bdfcfd9",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1cb37d0",
+   "id": "14",
    "metadata": {},
    "source": [
     "### Fit the first level model\n",
@@ -278,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65cec504",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05576ba4",
+   "id": "16",
    "metadata": {},
    "source": [
     "### Get cluster table\n",
@@ -310,7 +310,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d4a86a6f",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +327,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1e8effd",
+   "id": "18",
    "metadata": {},
    "source": [
     "### Make plots\n",
@@ -344,7 +344,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0f78107",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12a99b96",
+   "id": "20",
    "metadata": {},
    "source": [
     "## Make a workflow from tasks\n",
@@ -434,7 +434,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6e79e9b1",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,7 +493,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "657690ea",
+   "id": "22",
    "metadata": {},
    "source": [
     "## The overaching workflow\n",
@@ -506,7 +506,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d055c5d0",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b2e9a46",
+   "id": "24",
    "metadata": {},
    "source": [
     "## Run Workflow Run"
@@ -579,7 +579,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9a90088e",
+   "id": "25",
    "metadata": {
     "tags": [
      "hide-output"
@@ -598,23 +598,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f540cdd4",
+   "id": "26",
    "metadata": {},
    "source": [
     "## Visualization"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e8def869",
+   "id": "27",
    "metadata": {},
    "source": [
     "If you arrive here without any errors, yay, you just made your first pydra workflow for a first-level GLM!"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9b0585e3",
+   "id": "28",
    "metadata": {},
    "source": [
     "## Examine folder structure\n",
@@ -625,7 +625,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "75c1cfc9",
+   "id": "29",
    "metadata": {
     "tags": [
      "hide-output"
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56aeee0c",
+   "id": "30",
    "metadata": {},
    "source": [
     "### Plot figures"
@@ -647,7 +647,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1f657571",
+   "id": "31",
    "metadata": {
     "tags": [
      "hide-input"
@@ -674,15 +674,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "081bf13a",
+   "id": "32",
    "metadata": {},
    "source": [
     "## Exercise"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a3d55272",
+   "id": "33",
    "metadata": {},
    "source": [
     "What if we need to run the first-level GLM on multiple subject? We will need the `splitter`.\n",