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BUG: Fix introduction episode non-solution notebook #166

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Jul 19, 2021
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BUG: Fix introduction episode non-solution notebook #166

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167 changes: 80 additions & 87 deletions _episodes/introduction.md
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,13 @@ gradient directions and corresponding amplitudes.

## Dataset

For the rest of this lesson, we will make use of a subset of a publicly
available dataset, ds000221, originally hosted at [openneuro.org](https://openneuro.org/datasets/ds000221/versions/1.0.0).
The dataset is structured according to the Brain Imaging Data Structure
([BIDS](https://bids-specification.readthedocs.io/en/etable/)). Please check
the [the BIDS-dMRI Setup page](https://carpentries-incubator.github.io/SDC-BIDS-dMRI/setup.html)
to download the dataset.

Below is a tree diagram showing the folder structure of a single MR subject and
session within ds000221. This was obtained by using the bash command
<code>tree<code>.
Expand Down Expand Up @@ -109,7 +116,7 @@ tree '../data/ds000221'
  │    │ ├── sub-010002_ses-01_acq-SEfmapDWI_dir-AP_epi.nii.gz
   │    │ ├── sub-010002_ses-01_acq-SEfmapDWI_dir-PA_epi.json
  │    │ └── sub-010002_ses-01_acq-SEfmapDWI_dir-PA_epi.nii.gz
│ └── fmap
│ └── func
   │    │ ├── sub-010002_ses-01_task-rest_acq-AP_run-01_bold.json
  │    │ └── sub-010002_ses-01_task-rest_acq-AP_run-01_bold.nii.gz
└── ses-02/
Expand All @@ -122,18 +129,23 @@ tree '../data/ds000221'
querying, summarizing and manipulating the BIDS folder structure. We will make
use of <code>pybids</code> to query the necessary files.

Lets first pull the metadata from its associated JSON file using the
Let's first pull the metadata from its associated JSON file using the
<code>get_metadata()</code> function for the first run.

~~~
import bids
from bids.layout import BIDSLayout

?BIDSLayout

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bids.config.set_option('extension_initial_dot', True)

layout = BIDSLayout("../data/ds000221", validate=False)
~~~
{: .language-python}

Now that we have a layout object, we can work with a BIDS dataset! Lets extract
the metadata. from the dataset.
Now that we have a layout object, we can work with a BIDS dataset! Let's
extract the metadata from the dataset.

~~~
dwi = layout.get(subject='010006', suffix='dwi', extension='nii.gz', return_type='file')[0]
Expand Down Expand Up @@ -171,16 +183,6 @@ Python) package for processing and analysing diffusion MRI.
- Implementations of many state-of-the art algorithms
- High performance. Many algorithms implemented in [cython](http://cython.org/)

### Installing <code>dipy</code>

The easiest way to install <code>Dipy</code> is to use <code>pip</code>!
Additionally, <code>Dipy</code> makes use of the FURY library for
visualization. We will also install this using <code>pip</code>!

We can install it by entering the following in a terminal
<code>pip install dipy</code>. We will do so using Jupyter Magic in the
following cell!

### Defining a measurement: <code>GradientTable</code>

<code>Dipy</code> has a built-in function that allows us to read in
Expand All @@ -195,7 +197,7 @@ bval = layout.get_bval(dwi)
~~~
{: .language-python}

Now that we have the necessary diffusion files, lets explore the data!
Now that we have the necessary diffusion files, let's explore the data!

~~~
import numpy as np
Expand Down Expand Up @@ -243,6 +245,8 @@ bvec_txt = np.genfromtxt(bvec)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(bvec_txt[0], bvec_txt[1], bvec_txt[2])

plt.show()
~~~
{: .language-python}

Expand Down Expand Up @@ -278,66 +282,66 @@ gtab.bvecs[~gtab.b0s_mask]
{: .language-python}

~~~
array([[ 0.0480948 , -0.518981 , 0.853432 ],
[ 0.980937 , -0.0827268 , -0.175836 ],
[-0.24275 , -0.355443 , -0.902625 ],
[-0.292642 , -0.878897 , 0.376696 ],
[ 0.085518 , -0.362038 , -0.928232 ],
[ 0.470646 , -0.695075 , 0.543473 ],
[-0.865701 , -0.485398 , 0.122274 ],
[ 0.226775 , -0.23832 , 0.944339 ],
[ 0.334443 , -0.89435 , -0.29713 ],
[ 0.727534 , 0.0823542 , 0.681111 ],
[-0.625823 , 0.126479 , 0.769642 ],
[ 0.353667 , -0.886595 , 0.298111 ],
[-0.853084 , -0.472587 , -0.221153 ],
[ 0.516586 , -0.856143 , -0.0125875 ],
[-0.766369 , -0.458916 , 0.449527 ],
[-0.125754 , -0.637058 , -0.760489 ],
[-0.149251 , -0.743085 , 0.652341 ],
[ 0.937341 , -0.348404 , -0.00268246],
[-0.124163 , -0.219376 , 0.967708 ],
[-0.36458 , -0.906103 , -0.214613 ],
[ 0.579767 , -0.204866 , 0.788606 ],
[ 0.445586 , -0.692181 , -0.567749 ],
[-0.905294 , -0.174158 , -0.387442 ],
[-0.282606 , -0.482101 , 0.829284 ],
[-0.731609 , -0.431568 , -0.527729 ],
[ 0.676757 , -0.438047 , -0.591705 ],
[ 0.171374 , -0.758177 , 0.629125 ],
[-0.59121 , -0.711294 , -0.380173 ],
[-0.46017 , -0.155292 , 0.874144 ],
[ 0.845645 , -0.162694 , 0.508345 ],
[-0.130717 , -0.98886 , 0.0712005 ],
[ 0.975624 , -0.0906642 , 0.199845 ],
[-0.288147 , 0.100936 , 0.952252 ],
[ 0.655193 , -0.70285 , 0.276989 ],
[-0.442479 , -0.607006 , -0.660118 ],
[-0.471845 , -0.674107 , 0.56828 ],
[ 0.638596 , -0.702848 , -0.313369 ],
[-0.642432 , -0.712214 , 0.2829 ],
[ 0.850936 , -0.431779 , 0.299125 ],
[-0.240808 , -0.830023 , -0.503063 ],
[-0.578162 , -0.401657 , 0.710211 ],
[ 0.100487 , -0.838741 , -0.535178 ],
[-0.924592 , -0.196278 , 0.326504 ],
[-0.0210952 , -0.967749 , -0.251032 ],
[-0.764669 , -0.142405 , 0.628492 ],
[ 0.197294 , -0.980191 , 0.0173175 ],
[ 0.405727 , 0.0420623 , 0.913026 ],
[ 0.859032 , -0.144763 , -0.491028 ],
[ 0.380277 , -0.486027 , 0.786872 ],
[-0.6891 , -0.721525 , -0.0674049 ],
[ 0.430722 , -0.388461 , -0.814602 ],
[ 0.0366712 , -0.92944 , 0.367147 ],
[-0.540564 , -0.318621 , -0.778634 ],
[ 0.775224 , -0.631369 , -0.0200052 ],
[ 0.0646129 , 0.0600214 , 0.996104 ],
[-0.978577 , -0.203636 , -0.0303294 ],
[ 0.199971 , -0.618334 , -0.760049 ],
[ 0.678143 , -0.446978 , 0.583381 ],
[-0.448761 , -0.888954 , 0.0915084 ],
[ 0.849148 , -0.426713 , -0.311228 ]])
array([[-2.51881e-02, -3.72268e-01, 9.27783e-01],
[ 9.91276e-01, -1.05773e-01, -7.86433e-02],
[-1.71007e-01, -5.00324e-01, -8.48783e-01],
[-3.28334e-01, -8.07475e-01, 4.90083e-01],
[ 1.59023e-01, -5.08209e-01, -8.46425e-01],
[ 4.19677e-01, -5.94275e-01, 6.86082e-01],
[-8.76364e-01, -4.64096e-01, 1.28844e-01],
[ 1.47409e-01, -8.01322e-02, 9.85824e-01],
[ 3.50020e-01, -9.29191e-01, -1.18704e-01],
[ 6.70475e-01, 1.96486e-01, 7.15441e-01],
[-6.85569e-01, 2.47048e-01, 6.84808e-01],
[ 3.21619e-01, -8.24329e-01, 4.65879e-01],
[-8.35634e-01, -5.07463e-01, -2.10233e-01],
[ 5.08740e-01, -8.43979e-01, 1.69950e-01],
[-8.03836e-01, -3.83790e-01, 4.54481e-01],
[-6.82578e-02, -7.53445e-01, -6.53959e-01],
[-2.07898e-01, -6.27330e-01, 7.50490e-01],
[ 9.31645e-01, -3.38939e-01, 1.30988e-01],
[-2.04382e-01, -5.95385e-02, 9.77079e-01],
[-3.52674e-01, -9.31125e-01, -9.28787e-02],
[ 5.11906e-01, -7.06485e-02, 8.56132e-01],
[ 4.84626e-01, -7.73448e-01, -4.08554e-01],
[-8.71976e-01, -2.40158e-01, -4.26593e-01],
[-3.53191e-01, -3.41688e-01, 8.70922e-01],
[-6.89136e-01, -5.16115e-01, -5.08642e-01],
[ 7.19336e-01, -5.25068e-01, -4.54817e-01],
[ 1.14176e-01, -6.44483e-01, 7.56046e-01],
[-5.63224e-01, -7.67654e-01, -3.05754e-01],
[-5.31237e-01, -1.29342e-02, 8.47125e-01],
[ 7.99914e-01, -7.30043e-02, 5.95658e-01],
[-1.43792e-01, -9.64620e-01, 2.20979e-01],
[ 9.55196e-01, -5.23107e-02, 2.91314e-01],
[-3.64423e-01, 2.53394e-01, 8.96096e-01],
[ 6.24566e-01, -6.44762e-01, 4.40680e-01],
[-3.91818e-01, -7.09411e-01, -5.85845e-01],
[-5.21993e-01, -5.74810e-01, 6.30172e-01],
[ 6.56573e-01, -7.41002e-01, -1.40812e-01],
[-6.68597e-01, -6.60616e-01, 3.41414e-01],
[ 8.20224e-01, -3.72360e-01, 4.34259e-01],
[-2.05263e-01, -9.02465e-01, -3.78714e-01],
[-6.37020e-01, -2.83529e-01, 7.16810e-01],
[ 1.37944e-01, -9.14231e-01, -3.80990e-01],
[-9.49691e-01, -1.45434e-01, 2.77373e-01],
[-7.31922e-03, -9.95911e-01, -9.00386e-02],
[-8.14263e-01, -4.20783e-02, 5.78969e-01],
[ 1.87418e-01, -9.63210e-01, 1.92618e-01],
[ 3.30434e-01, 1.92714e-01, 9.23945e-01],
[ 8.95093e-01, -2.18266e-01, -3.88805e-01],
[ 3.11358e-01, -3.49170e-01, 8.83819e-01],
[-6.86317e-01, -7.27289e-01, -4.54356e-03],
[ 4.92805e-01, -5.14280e-01, -7.01897e-01],
[-8.03482e-04, -8.56796e-01, 5.15655e-01],
[-4.77664e-01, -4.45734e-01, -7.57072e-01],
[ 7.68954e-01, -6.22151e-01, 1.47095e-01],
[-1.55099e-02, 2.22329e-01, 9.74848e-01],
[-9.74410e-01, -2.11297e-01, -7.66740e-02],
[ 2.56251e-01, -7.33793e-01, -6.29193e-01],
[ 6.24656e-01, -3.42071e-01, 7.01992e-01],
[-4.61411e-01, -8.64670e-01, 1.98612e-01],
[ 8.68547e-01, -4.66754e-01, -1.66634e-01]])
~~~
{: .output}

Expand Down Expand Up @@ -373,18 +377,7 @@ axonal trajectories via tractography.
> > ## Solution
> >
> > ~~~
> > dwi_data = layout.get(suffix=dwi', extension='nii.gz', return_type='file')
> > ~~~
> > {: .language-python}
> {: .solution}
>
> Get the metadata for the diffusion associated with subject 010016.
>
> > ## Solution
> >
> > ~~~
> > dwi = layout.get(subject='010016', suffix='dwi', extension='nii.gz', return_type='file')[0]
> > layout.get_metadata(dwi)
> > dwi_data = layout.get(suffix='dwi', extension='nii.gz', return_type='file')
> > ~~~
> > {: .language-python}
> {: .solution}
Expand Down
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