ipynb update only

72a00c06 · Mark Jenkinson · f0c87987 · 72a00c06
Commit 72a00c06 authored 7 years ago by Mark Jenkinson
--- a/getting_started/06_plotting.ipynb
+++ b/getting_started/06_plotting.ipynb
@@ -123,8 +123,10 @@
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots()\n",
-    "ssize = 100*abs(samp1-samp2)  # just an arbitrary example\n",
+    "# setup some sizes for each point (arbitrarily example here)\n",
+    "ssize = 100*abs(samp1-samp2) + 10 \n",
    "ax.scatter(samp1, samp2, s=ssize, alpha=0.5)\n",
+    "# now add the y=x line\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",

 %% Cell type:markdown id: tags:

 # 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:

 %% Cell type:code id: tags:

 ``` 
 %matplotlib inline
 ```

 %% Cell type:markdown id: tags:

 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

 %% Cell type:code id: tags:

 ``` 
 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')
 ```

 %% Cell type:markdown id: tags:

 ### Histograms and bar charts

 For a simple histogram you can do this:

 %% Cell type:code id: tags:

 ``` 
 r = np.random.rand(1000)
 n,bins,_ = plt.hist((r-0.5)**2, bins=30)
 ```

 %% Cell type:markdown id: tags:

 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:

 %% Cell type:code id: tags:

 ``` 
 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')
 ```

 %% Cell type:markdown id: tags:

 Note that the first line returns a handle to the axis, as `ax`, that
 we can use instead of `plt`

 ### Scatter plots

 %% Cell type:code id: tags:

 ``` 
 fig, ax = plt.subplots()
-ssize = 100*abs(samp1-samp2)  # just an arbitrary example
+# setup some sizes for each point (arbitrarily example here)
+ssize = 100*abs(samp1-samp2) + 10
 ax.scatter(samp1, samp2, s=ssize, alpha=0.5)
+# now add the y=x line
 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))
 ```

 %% Cell type:markdown id: tags:

 ### Subplots

 %% Cell type:code id: tags:

 ``` 
 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')
 ```

 %% Cell type:markdown id: tags:

 ### Displaying images

 %% Cell type:code id: tags:

 ``` 
 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()
 ```

 %% Cell type:markdown id: tags:

 ### 3D plots

 %% Cell type:code id: tags:

 ``` 
 # 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)
 ```

 %% Cell type:markdown id: tags:

 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.