From a52df4abb26a62a0afcc8fd6570a141dd525bf82 Mon Sep 17 00:00:00 2001
From: Mark Jenkinson <mark@fmrib.ox.ac.uk>
Date: Wed, 14 Feb 2018 23:28:02 +0000
Subject: [PATCH] New practical on plotting
---
getting_started/06_plotting.ipynb | 228 ++++++++++++++++++++++++++++++
getting_started/06_plotting.md | 135 ++++++++++++++++++
2 files changed, 363 insertions(+)
create mode 100644 getting_started/06_plotting.ipynb
create mode 100644 getting_started/06_plotting.md
diff --git a/getting_started/06_plotting.ipynb b/getting_started/06_plotting.ipynb
new file mode 100644
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--- /dev/null
+++ b/getting_started/06_plotting.ipynb
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+{
+ "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."
+ ]
+ }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/getting_started/06_plotting.md b/getting_started/06_plotting.md
new file mode 100644
index 0000000..130b1ed
--- /dev/null
+++ b/getting_started/06_plotting.md
@@ -0,0 +1,135 @@
+# Plotting with python
+
+The main plotting module in python is `matplotlib`. There is a lot
+that can be done with it - see the [webpage](https://matplotlib.org/gallery/index.html)
+
+## Inside a notebook
+
+Inside a jupyter notebook you get access to this in a slightly
+different way, compared to other modules:
+
+```
+%matplotlib inline
+```
+
+This only needs to be done once in a notebook, like for standard imports.
+
+> There are also interactive versions - see the practical on Jupyter notebooks for more information about this.
+
+
+The library works very similarly to plotting in matlab. Let's start
+with some simple examples.
+
+### 2D plots
+
+```
+import matplotlib.pyplot as plt
+import numpy as np
+
+x = np.linspace(-np.pi, np.pi, 256)
+cosx, sinx = np.cos(x), np.sin(x)
+
+plt.plot(x, cosx)
+plt.plot(x, sinx, color='red', linewidth=4, linestyle='-.')
+plt.plot(x, sinx**2)
+plt.xlim(-np.pi, np.pi)
+plt.title('Our first plots')
+```
+
+
+### Histograms and bar charts
+
+For a simple histogram you can do this:
+```
+r = np.random.rand(1000)
+n,bins,_ = plt.hist((r-0.5)**2, bins=30)
+```
+where it also returns the number of elements in each bin, as `n`, and
+the bin centres, as `bins`. The `_` in the third part on the left
+hand side is a shorthand for just throwing away the corresponding part
+of the return structure.
+
+
+There is also a call for doing bar plots:
+```
+fig, ax = plt.subplots()
+samp1 = r[0:10]
+samp2 = r[10:20]
+bwidth = 0.3
+xcoord = np.arange(10)
+ax.bar(xcoord-bwidth, samp1, width=bwidth, color='red', label='Sample 1')
+ax.bar(xcoord, samp2, width=bwidth, color='blue', label='Sample 2')
+ax.legend(loc='upper left')
+```
+Note that the first line returns a handle to the axis, as `ax`, that
+we can use instead of `plt`
+
+### Scatter plots
+
+```
+fig, ax = plt.subplots()
+ssize = 100*abs(samp1-samp2) # just an arbitrary example
+ax.scatter(samp1, samp2, s=ssize, alpha=0.5)
+allsamps = np.hstack((samp1,samp2))
+ax.plot([min(allsamps),max(allsamps)],[min(allsamps),max(allsamps)], color='red', linestyle='--')
+plt.xlim(min(allsamps),max(allsamps))
+plt.ylim(min(allsamps),max(allsamps))
+```
+
+
+### Subplots
+
+```
+plt.subplot(2, 1, 1)
+plt.plot(x,cosx, '.-')
+plt.xlim(-np.pi, np.pi)
+plt.ylabel('Full sampling')
+plt.subplot(2, 1, 2)
+plt.plot(x[::30], cosx[::30], '.-')
+plt.xlim(-np.pi, np.pi)
+plt.ylabel('Subsampled')
+```
+
+### Displaying images
+
+```
+import nibabel as nib
+import os.path as op
+nim = nib.load(op.expandvars('${FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'), mmap=False)
+imdat = nim.get_data().astype(float)
+plt.imshow(imdat[:,:,70], cmap=plt.cm.gray)
+plt.colorbar()
+```
+
+
+### 3D plots
+
+```
+# Taken from https://matplotlib.org/gallery/mplot3d/wire3d.html#sphx-glr-gallery-mplot3d-wire3d-py
+
+from mpl_toolkits.mplot3d import axes3d
+
+fig = plt.figure()
+ax = fig.add_subplot(111, projection='3d')
+
+# Grab some test data.
+X, Y, Z = axes3d.get_test_data(0.05)
+
+# Plot a basic wireframe.
+ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
+```
+
+Surface renderings are many other plots are possible - see 3D examples on
+the [matplotlib webpage](https://matplotlib.org/gallery/index.html#mplot3d-examples-index)
+
+## Plotting from standalone scripts
+
+When running from a standalone script, the same `matplotlib` import is required,
+but the line `%matplotlib <backend>` should *not* be used.
+
+In a script it is necessary to also _finish_ with `plt.show()` as
+otherwise nothing is actually displayed. For example, the above
+examples would setup a plot but the actual graphic would only appear
+after the `plt.show()` command was executed. Furthermore, control is
+not returned to the script immediately as the plot is interactive by default.
+
--
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