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Update tutorial notebooks
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docs/notebook/01-intro.ipynb

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"# The problem of head-motion in dMRI"
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"A recurring problem for any MRI acquisition is that image reconstruction and modeling are extremely sensitive to very small changes in the position of the imaged object.\n",
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"remove-input"
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"## Dimensions of the head-motion problem\n",

docs/notebook/02-data.ipynb

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"cells": [
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"# Introduction to dMRI data"
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"Diffusion imaging probes the random, microscopic movement of water molecules by using MRI sequences that are sensitive to the geometry and environmental organization surrounding these protons.\n",
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"*Sourced from Dr. A. Rokem, DIPY Workshop 2021*\n",
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"This code implements several *attributes* as well as a *behavior* - the `__len__` *method*.\n",
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"The output of this `print()` statement is telling us that this (simulated) dataset has 64 diffusion-weighted samples.\n",
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"In this case, the dataset is reporting to have 102 diffusion-weighted samples.\n",
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"Perhaps, the most interesting aspect of our DWI data structure is that it allows indexed access.\n",
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"## Visualizing the data\n",
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"When calling `plot_dwi()`, we will be required a 3D data object, and optionally, an `affine` orientation matrix and a `gradient` or b-vector.\n",
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"Diffusion that exhibits directionality in the same direction as the gradient results in a loss of signal.\n",
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"We get a $4\\times102$ -- three spatial coordinates ($b_x$, $b_y$, $b_z$) of the unit-norm \"*b-vector*\", plus the gradient sensitization magnitude (the \"*b-value*\"), with a total of 102 different orientations for the case at hand.\n",
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"Later, we'll refer to this array as the gradient table.\n",
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"We've projected all of the gradient directions onto the surface of a sphere, with each unique gradient strength colour-coded.\n",

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