{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting with python\n",
    "\n",
    "The main plotting module in python is `matplotlib`.  There is a lot\n",
    "that can be done with it - see the [webpage](https://matplotlib.org/gallery/index.html)\n",
    "\n",
    "## Inside a notebook\n",
    "\n",
    "Inside a jupyter notebook you get access to this in a slightly\n",
    "different way, compared to other modules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This only needs to be done once in a notebook, like for standard imports.\n",
    "\n",
    "> There are also interactive versions - see the practical on Jupyter notebooks for more information about this.\n",
    "\n",
    "\n",
    "The library works very similarly to plotting in matlab.  Let's start\n",
    "with some simple examples.\n",
    "\n",
    "### 2D plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(-np.pi, np.pi, 256)\n",
    "cosx, sinx = np.cos(x), np.sin(x)\n",
    "\n",
    "plt.plot(x, cosx)\n",
    "plt.plot(x, sinx, color='red', linewidth=4, linestyle='-.')\n",
    "plt.plot(x, sinx**2)\n",
    "plt.xlim(-np.pi, np.pi)\n",
    "plt.title('Our first plots')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms and bar charts\n",
    "\n",
    "For a simple histogram you can do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.random.rand(1000)\n",
    "n,bins,_ = plt.hist((r-0.5)**2, bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where it also returns the number of elements in each bin, as `n`, and\n",
    "the bin centres, as `bins`.  The `_` in the third part on the left\n",
    "hand side is a shorthand for just throwing away the corresponding part\n",
    "of the return structure.\n",
    "\n",
    "\n",
    "There is also a call for doing bar plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "samp1 = r[0:10]\n",
    "samp2 = r[10:20]\n",
    "bwidth = 0.3\n",
    "xcoord = np.arange(10)\n",
    "ax.bar(xcoord-bwidth, samp1, width=bwidth, color='red', label='Sample 1')\n",
    "ax.bar(xcoord, samp2, width=bwidth, color='blue', label='Sample 2')\n",
    "ax.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the first line returns a handle to the axis, as `ax`, that\n",
    "we can use instead of `plt`\n",
    "\n",
    "### Scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ssize = 100*abs(samp1-samp2)  # just an arbitrary example\n",
    "ax.scatter(samp1, samp2, s=ssize, alpha=0.5)\n",
    "allsamps = np.hstack((samp1,samp2))\n",
    "ax.plot([min(allsamps),max(allsamps)],[min(allsamps),max(allsamps)], color='red', linestyle='--')\n",
    "plt.xlim(min(allsamps),max(allsamps))\n",
    "plt.ylim(min(allsamps),max(allsamps))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(x,cosx, '.-')\n",
    "plt.xlim(-np.pi, np.pi)\n",
    "plt.ylabel('Full sampling')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x[::30], cosx[::30], '.-')\n",
    "plt.xlim(-np.pi, np.pi)\n",
    "plt.ylabel('Subsampled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nibabel as nib\n",
    "import os.path as op\n",
    "nim = nib.load(op.expandvars('${FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'), mmap=False)\n",
    "imdat = nim.get_data().astype(float)\n",
    "plt.imshow(imdat[:,:,70], cmap=plt.cm.gray)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taken from https://matplotlib.org/gallery/mplot3d/wire3d.html#sphx-glr-gallery-mplot3d-wire3d-py\n",
    "\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Grab some test data.\n",
    "X, Y, Z = axes3d.get_test_data(0.05)\n",
    "\n",
    "# Plot a basic wireframe.\n",
    "ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surface renderings are many other plots are possible - see 3D examples on\n",
    "the [matplotlib webpage](https://matplotlib.org/gallery/index.html#mplot3d-examples-index)\n",
    "\n",
    "## Plotting from standalone scripts\n",
    "\n",
    "When running from a standalone script, the same `matplotlib` import is required,\n",
    "but the line `%matplotlib <backend>` should *not* be used.\n",
    "\n",
    "In a script it is necessary to also _finish_ with `plt.show()` as\n",
    "otherwise nothing is actually displayed.  For example, the above\n",
    "examples would setup a plot but the actual graphic would only appear\n",
    "after the `plt.show()` command was executed.  Furthermore, control is\n",
    "not returned to the script immediately as the plot is interactive by default."
   ]
  }
 ],
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}