Set interpolation to nearest for better visualization

doc/examples/reconst_ivim.py

@@ -22,20 +22,20 @@
  .. math::
     S(b)=S_0(fe^{-bD^*}+(1-f)e^{-bD})
 
-where $\mathbf{b}$ is the diffusion gradient weighing value (which is dependent on
-the measurement parameters), $\mathbf{S_{0}}$ is the signal in the absence
+where $\mathbf{b}$ is the diffusion gradient weighing value (which is dependent
+on the measurement parameters), $\mathbf{S_{0}}$ is the signal in the absence
 of diffusion gradient sensitization, $\mathbf{f}$ is the perfusion
 fraction, $\mathbf{D}$ is the diffusion coefficient and $\mathbf{D^*}$ is
 the pseudo-diffusion constant, due to vascular contributions.
 
-In the following example we show how to fit the IVIM model
-on a diffusion-weighted dataset and visualize the diffusion and pseudo diffuion
-coefficients. First, we import all relevant modules:
+In the following example we show how to fit the IVIM model on a
+diffusion-weighteddataset and visualize the diffusion and pseudo
+diffusion coefficients. First, we import all relevant modules:
 """
 
 import matplotlib.pyplot as plt
 
-from dipy.reconst.ivim import IvimModel, ivim_prediction
+from dipy.reconst.ivim import IvimModel
 from dipy.data.fetcher import read_ivim
 
 """
@@ -44,14 +44,15 @@
 Volumes corresponding to different directions were registered to each
 other, and averaged across directions. Thus, this dataset has 4 dimensions,
 with the length of the last dimension corresponding to the number
-of b-values.
+of b-values. In order to use this model the data should contain signals
+measured at 0 bvalue.
 """
 
 img, gtab = read_ivim()
 
 """
-The variable ``img`` contains a ``nibabel`` NIfTI image object(with the data)
-and gtab contains a GradientTable object(information about
+The variable ``img`` contains a ``nibabel`` NIfTI image object (with the data)
+and gtab contains a GradientTable object (information about
 the gradients e.g. b-values and b-vectors). We get the
 data from img using ``read_data``.
 """
@@ -60,16 +61,16 @@
 print('data.shape (%d, %d, %d, %d)' % data.shape)
 
 """
-The data has 54 slices, with 256-by-256 voxels in each slice.
-The fourth dimension corresponds to the b-values in the gtab.
-Let us visualize the data by taking a slice midway(z=27) at
-$\mathbf{b} = 0$.
+The data has 54 slices, with 256-by-256 voxels in each slice. The fourth
+dimension corresponds to the b-values in the gtab. Let us visualize the data
+by taking a slice midway(z=27) at $\mathbf{b} = 0$.
 """
 
 z = 27
 b = 20
 
-plt.imshow(data[:, :, z, b].T, origin='lower', cmap='gray', interpolation=None)
+plt.imshow(data[:, :, z, b].T, origin='lower', cmap='gray',
+           interpolation='nearest')
 plt.axhline(y=100)
 plt.axvline(x=170)
 plt.savefig("ivim_data_slice.png")
@@ -82,17 +83,19 @@
    Heat map of a slice of data
 
 The region around the intersection of the cross-hairs in the figure
-contains cerebral spinal fluid (CSF), so it so it should have a very high $\mathbf{f}$
-and $\mathbf{D^*}$, the area between the right and left is white matter so that
-should be lower, and the region on the right is gray matter and CSF. That should
-give us some contrast to see the values varying across the regions.
+contains cerebral spinal fluid (CSF), so it so it should have a very high
+$\mathbf{f}$ and $\mathbf{D^*}$, the area between the right and left is white
+matter so that should be lower, and the region on the right is gray matter
+and CSF. That should give us some contrast to see the values varying across
+the regions.
 """
 
 x1, x2 = 160, 180
 y1, y2 = 90, 110
 data_slice = data[x1:x2, y1:y2, z, :]
 
-plt.imshow(data[x1:x2, y1:y2, z, b].T, origin='lower', cmap="gray", interpolation=None)
+plt.imshow(data[x1:x2, y1:y2, z, b].T, origin='lower',
+           cmap="gray", interpolation='nearest')
 plt.savefig("CSF_slice.png")
 plt.close()
 
@@ -147,7 +150,7 @@
 This will give us an idea about the diffusion and perfusion in
 that section. Let(i, j) denote the coordinate of the voxel. We have
 already fixed the z component as 27 and hence we will get a slice
-which is 33 units avove.
+which is 27 units above.
 
 """
 
@@ -199,7 +202,8 @@
 def plot_map(raw_data, variable, limits, filename):
     lower, upper = limits
     plt.title('Map for {}'.format(variable))
-    plt.imshow(raw_data.T, origin='lower', clim=(lower, upper), cmap="gray", interpolation=None)
+    plt.imshow(raw_data.T, origin='lower', clim=(lower, upper),
+               cmap="gray", interpolation='nearest')
     plt.colorbar()
     plt.savefig(filename)
     plt.close()