3-MatplotLib_ReproNim2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MatplotLib\n",
    "- A python package for data visualization\n",
    "- https://matplotlib.org/stable/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.colors as normCol       # Allows us to normalize colors later\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets #This is for the dataset\n",
    "import numpy as np # This is for data manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib style\n",
    "Add some nilearn at the end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Packages:\n",
    "- normCol: Allows us to normalize colors to the dataset.\n",
    "- plt: This is the primary matplotlib tool.\n",
    "- datasets: The sklearn package contains many useful machine learning tool and also comes with some handy datasets. Here we import only the datasets just for some test data.\n",
    "- np: Numpy is heavily integrated in matplotlib and is a very handy data maniuplation tool."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Here we can see all the editable atributes of a matplotlib figure"
   ]
  },
  {
   "attachments": {
    "e986f18a-e091-4207-b364-edf0fda12282.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:e986f18a-e091-4207-b364-edf0fda12282.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this sample data to generate a simple scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [0,1,2,3,4,5]\n",
    "Y = [0,2,4,6,8,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter(X, Y)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Although relativly bare, extensive customization options exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['green', 'red', 'blue', 'orange', 'hotpink', 'purple']\n",
    "plt.scatter(X,Y, marker = 'd', color = colors, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalize = normCol.Normalize(vmin=0, vmax=5)\n",
    "plt.style.use('bmh')\n",
    "plt.scatter(X,Y, s=100, marker = 'd', c=[1,2,3,4,5,6], norm=normalize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From here we will be working primarily with the iris dataset.\n",
    "\n",
    "Here is a link if you want to learn more about the dataset:\n",
    "https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "print(iris[\"target_names\"])\n",
    "print(iris[\"feature_names\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The code above tells us that there are 3 different iris species and each iris had 4 measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Select_iris = iris.data\n",
    "Target_iris = iris.target\n",
    "classes = len(iris[\"target_names\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Much like other forms of programming, there are multiple paths to the same end.\n",
    "First lets try a more code intensive approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "\n",
    "for i in range(classes):\n",
    "    index = np.where(Target_iris == i)\n",
    "    plt.scatter(Select_iris[index,2], Select_iris[index, 3], label = iris.target_names[i])\n",
    "plt.legend()\n",
    "plt.xlabel(iris.feature_names[2])\n",
    "plt.ylabel(iris.feature_names[3])\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the above code is easily reproducable, it does limit our freedoms for custimization. Lets try a more liberating approach.\n",
    "\n",
    "To start we split the iris dataset into three different groups by their species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newarr = np.array_split(Target_iris, len(Target_iris))\n",
    "NetIris = np.append(newarr, Select_iris, axis=1)\n",
    "iris1 = NetIris[NetIris[:,0] == 0]\n",
    "iris2 = NetIris[NetIris[:,0] == 1]\n",
    "iris3 = NetIris[NetIris[:,0] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(iris1[:,3], iris1[:,4], label = iris.target_names[0], marker = 'd')\n",
    "ax1.scatter(iris2[:,3], iris2[:,4], label = iris.target_names[1], color = 'purple')\n",
    "ax1.scatter(iris3[:,3], iris3[:,4], label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(iris.feature_names[2])\n",
    "plt.ylabel(iris.feature_names[3])\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "plt.show\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## As we can see, although more code may be present it can be easier to follow and allows for additional custimization of options. Still we can improve this further.\n",
    "\n",
    "Prior to now we would have to change every reference to the iris variables. How does the code below increase reproducability?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newarr = np.array_split(Target_iris, len(Target_iris))\n",
    "NetIris = np.append(newarr, Select_iris, axis=1)\n",
    "iris1 = NetIris[NetIris[:,0] == 0]\n",
    "iris2 = NetIris[NetIris[:,0] == 1]\n",
    "iris3 = NetIris[NetIris[:,0] == 2]\n",
    "\n",
    "x = 3\n",
    "y = 4\n",
    "\n",
    "x1 = iris1[:,x]\n",
    "y1 = iris1[:,y]\n",
    "\n",
    "x2 = iris2[:,x]\n",
    "y2 = iris2[:,y]\n",
    "\n",
    "x3 = iris3[:,x]\n",
    "y3 = iris3[:,y]\n",
    "\n",
    "xName = iris.feature_names[x-1]\n",
    "yName = iris.feature_names[y-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, label = iris.target_names[0], marker = 'd')\n",
    "ax1.scatter(x2, y2, label = iris.target_names[1], color = 'purple')\n",
    "ax1.scatter(x3, y3, label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(xName)\n",
    "plt.ylabel(yName)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "plt.show\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, s = iris1[:,2]*50, alpha = 0.25, label = iris.target_names[0])\n",
    "ax1.scatter(x2, y2, s = iris2[:,2]*50, alpha = 0.5, label = iris.target_names[1])\n",
    "ax1.scatter(x3, y3, s = iris3[:,2]*50, alpha = 0.75, label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(xName)\n",
    "plt.ylabel(yName)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises (1):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.) Change the color of one cluster and the shape of another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, label = iris.target_names[0])\n",
    "ax1.scatter(x2, y2, label = iris.target_names[1])\n",
    "ax1.scatter(x3, y3, label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(xName)\n",
    "plt.ylabel(yName)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.) Remove the versacolor cluster from the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, label = iris.target_names[0])\n",
    "ax1.scatter(x2, y2, label = iris.target_names[1])\n",
    "ax1.scatter(x3, y3, label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(xName)\n",
    "plt.ylabel(yName)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.) Play around with parameters in the next cell. Change colors, size, opacity, and or variables. (You can change almost anyting in the graph so feel free to explore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, label = iris.target_names[0])\n",
    "ax1.scatter(x2, y2, label = iris.target_names[1])\n",
    "ax1.scatter(x3, y3, label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(xName)\n",
    "plt.ylabel(yName)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The matplotlib tools come with a wide variety of graphs to represent data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_yticklabels(iris.target_names)\n",
    "ax.set_xlabel(xName)\n",
    "ax.set_yticks(np.arange(1, len(iris.target_names) + 1))\n",
    "ax.violinplot([x1,x2,x3],positions =[3,2,1],vert=False,showmeans=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The functionality of matplotlib extends beyond only representing data and can intergrate a variety of analyses too.\n",
    "\n",
    "For example, we can easily disply the means of each meausre for all three types of iris."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Select_iris[:,2]\n",
    "xLabel = iris.feature_names[2]\n",
    "y = Select_iris[:,3]\n",
    "yLabel = iris.feature_names[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get averages\n",
    "averages = [Select_iris[Target_iris == i].mean(axis=0) for i in range (classes)]\n",
    "\n",
    "# Arange data\n",
    "x = np.arange(len(iris.feature_names))\n",
    "\n",
    "# Generate bar char\n",
    "fig = plt.figure(figsize = (10,10))\n",
    "ax = fig.add_subplot()\n",
    "bar1 = ax.bar(x - 0.25, averages[0], 0.25, label = iris.target_names[0])\n",
    "bar2 = ax.bar(x, averages[1], 0.25, label = iris.target_names[1])\n",
    "bar3 = ax.bar(x + 0.25, averages[2], 0., label = iris.target_names[2])\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(iris.feature_names)\n",
    "plt.legend()\n",
    "plt.title(\"Bar Chart Iris Averages\")\n",
    "plt.ylabel(\"Average\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## When combined with linear regression, we can add the line of best fit to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, label = iris.target_names[0])\n",
    "ax1.scatter(x2, y2, label = iris.target_names[1])\n",
    "ax1.scatter(x3, y3, label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(xLabel)\n",
    "plt.ylabel(yLabel)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "\n",
    "\n",
    "m1, b1 = np.polyfit(x1, y1, 1)\n",
    "m2, b2 = np.polyfit(x2, y2, 1)\n",
    "m3, b3 = np.polyfit(x3, y3, 1)\n",
    "plt.plot(iris1[:,3], b1+m1*iris1[:,3], color = 'red')\n",
    "plt.plot(iris2[:,3], b2+m2*iris2[:,3], color = 'red')\n",
    "plt.plot(iris3[:,3], b3+m3*iris3[:,3], color = 'red')\n",
    "\n",
    "\n",
    "plt.show\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further extend these lines by ploting between the minimum and maximum x values from the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, label = iris.target_names[0])\n",
    "ax1.scatter(x2, y2, label = iris.target_names[1])\n",
    "ax1.scatter(x3, y3, label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(xLabel)\n",
    "plt.ylabel(yLabel)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "\n",
    "\n",
    "poly1 = np.polyfit(x1, y1, 1)\n",
    "poly2 = np.polyfit(x2, y2, 1)\n",
    "poly3 = np.polyfit(x3, y3, 1)\n",
    "\n",
    "est1 = np.poly1d(poly1)\n",
    "est2 = np.poly1d(poly2)\n",
    "est3 = np.poly1d(poly3)\n",
    "\n",
    "irisCompare = np.concatenate((x1,x2,x3))\n",
    "xmin = np.min(irisCompare)\n",
    "xmax = np.max(irisCompare)\n",
    "print(xmin)\n",
    "print(xmax)\n",
    "plt.plot((xmin, xmax), (est1(xmin), est1(xmax)), 'blue')\n",
    "plt.plot((xmin, xmax), (est2(xmin), est2(xmax)), 'orange')\n",
    "plt.plot((xmin, xmax), (est3(xmin), est3(xmax)), 'green')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.show\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans1 = KMeans(n_clusters = 1).fit(iris1) \n",
    "kmeans2 = KMeans(n_clusters = 1).fit(iris2)\n",
    "kmeans3 = KMeans(n_clusters = 1).fit(iris3)\n",
    "print(kmeans1.cluster_centers_)\n",
    "print(kmeans2.cluster_centers_)\n",
    "print(kmeans3.cluster_centers_)\n",
    "cen_x = [kmeans1.cluster_centers_[0,3], kmeans2.cluster_centers_[0,3], kmeans3.cluster_centers_[0,3]]\n",
    "cen_y = [kmeans1.cluster_centers_[0,4], kmeans2.cluster_centers_[0,4], kmeans3.cluster_centers_[0,4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, s = iris1[:,2]*50, alpha = 0.2, label = iris.target_names[0])\n",
    "ax1.scatter(x2, y2, s = iris2[:,2]*50, alpha = 0.2, label = iris.target_names[1])\n",
    "ax1.scatter(x3, y3, s = iris3[:,2]*50, alpha = 0.2, label = iris.target_names[2])\n",
    "ax1.scatter(cen_x, cen_y, c='red', marker='^', s = 100)\n",
    "plt.legend()\n",
    "plt.xlabel(xLabel)\n",
    "plt.ylabel(yLabel)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x1, y1, s = iris1[:,2]*50, alpha = 0.2, label = iris.target_names[0])\n",
    "ax1.scatter(x2, y2, s = iris2[:,2]*50, alpha = 0.2, label = iris.target_names[1])\n",
    "ax1.scatter(x3, y3, s = iris3[:,2]*50, alpha = 0.2, label = iris.target_names[2])\n",
    "plt.legend()\n",
    "plt.xlabel(xLabel)\n",
    "plt.ylabel(yLabel)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "\n",
    "for i in range(len((x1))):\n",
    "    x = x1[i], cen_x[0]\n",
    "    y = y1[i], cen_y[0]\n",
    "    plt.plot(x,y, c= 'blue', alpha = .6)\n",
    "\n",
    "for i in range(len((x2))):\n",
    "    x = x2[i], cen_x[1]\n",
    "    y = y2[i], cen_y[1]\n",
    "    plt.plot(x,y, c='orange', alpha = .6)\n",
    "\n",
    "for i in range(len((x3))):\n",
    "    x = x3[i], cen_x[2]\n",
    "    y = y3[i], cen_y[2]\n",
    "    plt.plot(x,y, c = 'green', alpha = .6)    \n",
    "\n",
    "ax1.scatter(cen_x, cen_y, c='red', marker='^', s = 100)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "ax1.scatter(x3, y3, alpha = 0.6, label = iris.target_names[2], c = 'green')\n",
    "plt.legend()\n",
    "plt.xlabel(xLabel)\n",
    "plt.ylabel(yLabel)\n",
    "plt.grid(linestyle = \"--\", linewidth = 0.5)\n",
    "\n",
    "for i in range(len((x3))):\n",
    "    x = x3[i], cen_x[2]\n",
    "    y = y3[i], cen_y[2]\n",
    "    plt.plot(x,y, c = 'green', alpha = .2)    \n",
    "\n",
    "ax1.scatter(cen_x[2], cen_y[2], c='red', marker='^', s = 100)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises (2):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.) Generate 2 scatter plots (1 per cell) with different shaped points using sepal length and width (feel free to copy and paste prior code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.) Generate a regression line for the total iris sample (one line for all three species)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.) Find the centroid of another iris metric and generate a scatterplot with lines to the centroid for each species independently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nilearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what about brain data? The ability to generate reproducable and customizable plots is awesome, but how do we go about doing this with brain data? Well first of all most neuroscience data can be converted into as easily plotable form, but that does not mean that tools to work directly with neuroimaging data don't exist. Nilearn is an example of one such tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn import plotting  \n",
    "dmn_coords = [(0, -52, 18), (-46, -68, 32), (46, -68, 32), (1, 50, -5)] \n",
    "dmn_colors = ['red', 'cyan', 'magenta', 'orange']\n",
    "marker_size = [25, 35, 45, 55]\n",
    "dmn_tags = ['PCC', 'Parietal', 'Parietal', 'MFC']\n",
    "\n",
    "view = plotting.view_markers(dmn_coords, dmn_colors, marker_size, dmn_tags) \n",
    "view.open_in_browser() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## You may notice the structure is similar to matplotlib. Similarly the elements are customizable, feel free to change any item in the lists above to change the figure.\n",
    "More complex forms of graphing are also available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn import datasets\n",
    "\n",
    "dataset = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')\n",
    "atlas_filename = dataset.maps\n",
    "labels = dataset.labels\n",
    "\n",
    "print('Atlas ROIs are located in nifti image (4D) at: %s' %\n",
    "      atlas_filename)  # 4D data\n",
    "\n",
    "# One subject of brain development fmri data\n",
    "data = datasets.fetch_development_fmri(n_subjects=1)\n",
    "fmri_filenames = data.func[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.input_data import NiftiLabelsMasker\n",
    "masker = NiftiLabelsMasker(labels_img=atlas_filename, standardize=True,\n",
    "                           memory='nilearn_cache', verbose=5)\n",
    "\n",
    "# Here we go from nifti files to the signal time series in a numpy\n",
    "# array. Note how we give confounds to be regressed out during signal\n",
    "# extraction\n",
    "time_series = masker.fit_transform(fmri_filenames, confounds=data.confounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilearn.connectome import ConnectivityMeasure\n",
    "correlation_measure = ConnectivityMeasure(kind='correlation')\n",
    "correlation_matrix = correlation_measure.fit_transform([time_series])[0]\n",
    "\n",
    "# Plot the correlation matrix\n",
    "import numpy as np\n",
    "from nilearn import plotting\n",
    "# Make a large figure\n",
    "# Mask the main diagonal for visualization:\n",
    "np.fill_diagonal(correlation_matrix, 0)\n",
    "# The labels we have start with the background (0), hence we skip the\n",
    "# first label\n",
    "# matrices are ordered for block-like representation\n",
    "plotting.plot_matrix(correlation_matrix, figure=(10, 8), labels=labels[1:], tri = 'lower',\n",
    "                     vmax=0.8, vmin=-0.8, reorder=True)"
   ]
  }
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