Merge branch 'mnt/use-conda' into 'master'

Use conda to install additional packages See merge request !34

Merge branch 'mnt/use-conda' into 'master'
22c8cdef · Paul McCarthy · 59c060e4 · 5aa5ab09 · 22c8cdef · 22c8cdef
Commit 22c8cdef authored 1 year ago by Paul McCarthy
--- a/applications/data_visualisation/bokeh/bokeh.ipynb
+++ b/applications/data_visualisation/bokeh/bokeh.ipynb
@@ -14,7 +14,7 @@
    "\n",
    "## Install `bokeh`\n",
    "\n",
-    "`bokeh` is not installed in the `fslpython` environment so you will need to install it to run this notebook (e.g. `pip install bokeh`)\n",
+    "`bokeh` is not installed in the `fslpython` environment so you will need to install it to run this notebook (e.g. `$FSLDIR/bin/conda install bokeh`)\n",
    "\n",
    "Setup bokeh to work in this notebook:"
   ]

 %% Cell type:markdown id: tags:
 # `bokeh`
 [`bokeh`](https://docs.bokeh.org/en/latest/index.html) is a Python library for creating interactive visualizations for modern web browsers. `bokeh` allows you to create these interactive web-based plots without having to code in javascript.
 `bokeh` has excellent documentation: https://docs.bokeh.org/en/latest/index.html
 This notebook is not intended to instruct you how to use `bokeh`.  Instead it pulls together interesting examples from the `bokeh` documentation into a single notebook to give you a taster of what can be done with `bokeh`.
 ## Install `bokeh`
-`bokeh` is not installed in the `fslpython` environment so you will need to install it to run this notebook (e.g. `pip install bokeh`)
+`bokeh` is not installed in the `fslpython` environment so you will need to install it to run this notebook (e.g. `$FSLDIR/bin/conda install bokeh`)
 Setup bokeh to work in this notebook:
 %% Cell type:code id: tags:
 ``` python
 from bokeh.plotting import figure, output_file, show
 from bokeh.io import output_notebook
 output_notebook()
 ```
 %% Cell type:markdown id: tags:
 Fetch some sampledata for the examples:
 %% Cell type:code id: tags:
 ``` python
 from bokeh import sampledata
 sampledata.download()
 ```
 %% Cell type:markdown id: tags:
 ## Scatter Plots
 https://docs.bokeh.org/en/latest/docs/gallery/iris.html
 %% Cell type:code id: tags:
 ``` python
 from bokeh.sampledata.iris import flowers
 colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}
 colors = [colormap[x] for x in flowers['species']]
 p = figure(title = "Iris Morphology")
 p.xaxis.axis_label = 'Petal Length'
 p.yaxis.axis_label = 'Petal Width'
 p.circle(flowers["petal_length"], flowers["petal_width"],
         color=colors, fill_alpha=0.2, size=10)
 # output_file("iris.html", title="iris.py example")
 show(p)
 ```
 %% Cell type:markdown id: tags:
 ## Line Plots
 https://docs.bokeh.org/en/latest/docs/gallery/box_annotation.html
 %% Cell type:code id: tags:
 ``` python
 from bokeh.models import BoxAnnotation
 from bokeh.sampledata.glucose import data
 TOOLS = "pan,wheel_zoom,box_zoom,reset,save"
 data = data.loc['2010-10-04':'2010-10-04']
 p = figure(x_axis_type="datetime", tools=TOOLS, title="Glocose Readings, Oct 4th (Red = Outside Range)")
 p.background_fill_color = "#efefef"
 p.xgrid.grid_line_color=None
 p.xaxis.axis_label = 'Time'
 p.yaxis.axis_label = 'Value'
 p.line(data.index, data.glucose, line_color='grey')
 p.circle(data.index, data.glucose, color='grey', size=1)
 p.add_layout(BoxAnnotation(top=80, fill_alpha=0.1, fill_color='red', line_color='red'))
 p.add_layout(BoxAnnotation(bottom=180, fill_alpha=0.1, fill_color='red', line_color='red'))
 show(p)
 ```
 %% Cell type:markdown id: tags:
 # Bar Charts
 https://docs.bokeh.org/en/latest/docs/gallery/bar_stacked.html
 %% Cell type:code id: tags:
 ``` python
 from bokeh.palettes import Spectral5
 from bokeh.sampledata.autompg import autompg_clean as df
 from bokeh.transform import factor_cmap
 df.cyl = df.cyl.astype(str)
 df.yr = df.yr.astype(str)
 group = df.groupby(['cyl', 'mfr'])
 index_cmap = factor_cmap('cyl_mfr', palette=Spectral5, factors=sorted(df.cyl.unique()), end=1)
 p = figure(plot_width=800, plot_height=500, title="Mean MPG by # Cylinders and Manufacturer",
           x_range=group, toolbar_location=None, tooltips=[("MPG", "@mpg_mean"), ("Cyl, Mfr", "@cyl_mfr")])
 p.vbar(x='cyl_mfr', top='mpg_mean', width=1, source=group,
       line_color="white", fill_color=index_cmap, )
 p.y_range.start = 0
 p.x_range.range_padding = 0.05
 p.xgrid.grid_line_color = None
 p.xaxis.axis_label = "Manufacturer grouped by # Cylinders"
 p.xaxis.major_label_orientation = 1.2
 p.outline_line_color = None
 show(p)
 ```
 %% Cell type:markdown id: tags:
 # Distribution Plots
 https://docs.bokeh.org/en/latest/docs/gallery/histogram.html
 %% Cell type:code id: tags:
 ``` python
 import numpy as np
 import scipy.special
 from bokeh.layouts import gridplot
 def make_plot(title, hist, edges, x, pdf, cdf):
    p = figure(title=title, tools='', background_fill_color="#fafafa")
    p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
           fill_color="navy", line_color="white", alpha=0.5)
    p.line(x, pdf, line_color="#ff8888", line_width=4, alpha=0.7, legend_label="PDF")
    p.line(x, cdf, line_color="orange", line_width=2, alpha=0.7, legend_label="CDF")
    p.y_range.start = 0
    p.legend.location = "center_right"
    p.legend.background_fill_color = "#fefefe"
    p.xaxis.axis_label = 'x'
    p.yaxis.axis_label = 'Pr(x)'
    p.grid.grid_line_color="white"
    return p
 # Normal Distribution
 mu, sigma = 0, 0.5
 measured = np.random.normal(mu, sigma, 1000)
 hist, edges = np.histogram(measured, density=True, bins=50)
 x = np.linspace(-2, 2, 1000)
 pdf = 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2 / (2*sigma**2))
 cdf = (1+scipy.special.erf((x-mu)/np.sqrt(2*sigma**2)))/2
 p1 = make_plot("Normal Distribution (μ=0, σ=0.5)", hist, edges, x, pdf, cdf)
 # Log-Normal Distribution
 mu, sigma = 0, 0.5
 measured = np.random.lognormal(mu, sigma, 1000)
 hist, edges = np.histogram(measured, density=True, bins=50)
 x = np.linspace(0.0001, 8.0, 1000)
 pdf = 1/(x* sigma * np.sqrt(2*np.pi)) * np.exp(-(np.log(x)-mu)**2 / (2*sigma**2))
 cdf = (1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2
 p2 = make_plot("Log Normal Distribution (μ=0, σ=0.5)", hist, edges, x, pdf, cdf)
 # Gamma Distribution
 k, theta = 7.5, 1.0
 measured = np.random.gamma(k, theta, 1000)
 hist, edges = np.histogram(measured, density=True, bins=50)
 x = np.linspace(0.0001, 20.0, 1000)
 pdf = x**(k-1) * np.exp(-x/theta) / (theta**k * scipy.special.gamma(k))
 cdf = scipy.special.gammainc(k, x/theta)
 p3 = make_plot("Gamma Distribution (k=7.5, θ=1)", hist, edges, x, pdf, cdf)
 # Weibull Distribution
 lam, k = 1, 1.25
 measured = lam*(-np.log(np.random.uniform(0, 1, 1000)))**(1/k)
 hist, edges = np.histogram(measured, density=True, bins=50)
 x = np.linspace(0.0001, 8, 1000)
 pdf = (k/lam)*(x/lam)**(k-1) * np.exp(-(x/lam)**k)
 cdf = 1 - np.exp(-(x/lam)**k)
 p4 = make_plot("Weibull Distribution (λ=1, k=1.25)", hist, edges, x, pdf, cdf)
 show(gridplot([p1,p2,p3,p4], ncols=2, plot_width=400, plot_height=400, toolbar_location=None))
 ```
 %% Cell type:markdown id: tags:
 # Boxplot
 https://docs.bokeh.org/en/latest/docs/gallery/boxplot.html
 %% Cell type:code id: tags:
 ``` python
 import numpy as np
 import pandas as pd
 # generate some synthetic time series for six different categories
 cats = list("abcdef")
 yy = np.random.randn(2000)
 g = np.random.choice(cats, 2000)
 for i, l in enumerate(cats):
    yy[g == l] += i // 2
 df = pd.DataFrame(dict(score=yy, group=g))
 # find the quartiles and IQR for each category
 groups = df.groupby('group')
 q1 = groups.quantile(q=0.25)
 q2 = groups.quantile(q=0.5)
 q3 = groups.quantile(q=0.75)
 iqr = q3 - q1
 upper = q3 + 1.5*iqr
 lower = q1 - 1.5*iqr
 # find the outliers for each category
 def outliers(group):
    cat = group.name
    return group[(group.score > upper.loc[cat]['score']) | (group.score < lower.loc[cat]['score'])]['score']
 out = groups.apply(outliers).dropna()
 # prepare outlier data for plotting, we need coordinates for every outlier.
 if not out.empty:
    outx = list(out.index.get_level_values(0))
    outy = list(out.values)
 p = figure(tools="", background_fill_color="#efefef", x_range=cats, toolbar_location=None)
 # if no outliers, shrink lengths of stems to be no longer than the minimums or maximums
 qmin = groups.quantile(q=0.00)
 qmax = groups.quantile(q=1.00)
 upper.score = [min([x,y]) for (x,y) in zip(list(qmax.loc[:,'score']),upper.score)]
 lower.score = [max([x,y]) for (x,y) in zip(list(qmin.loc[:,'score']),lower.score)]
 # stems
 p.segment(cats, upper.score, cats, q3.score, line_color="black")
 p.segment(cats, lower.score, cats, q1.score, line_color="black")
 # boxes
 p.vbar(cats, 0.7, q2.score, q3.score, fill_color="#E08E79", line_color="black")
 p.vbar(cats, 0.7, q1.score, q2.score, fill_color="#3B8686", line_color="black")
 # whiskers (almost-0 height rects simpler than segments)
 p.rect(cats, lower.score, 0.2, 0.01, line_color="black")
 p.rect(cats, upper.score, 0.2, 0.01, line_color="black")
 # outliers
 if not out.empty:
    p.circle(outx, outy, size=6, color="#F38630", fill_alpha=0.6)
 p.xgrid.grid_line_color = None
 p.ygrid.grid_line_color = "white"
 p.grid.grid_line_width = 2
 p.xaxis.major_label_text_font_size="16px"
 show(p)
 ```
 %% Cell type:markdown id: tags:
 ## Connectivity Matrix
 https://docs.bokeh.org/en/latest/docs/gallery/les_mis.html
 %% Cell type:code id: tags:
 ``` python
 import numpy as np
 from bokeh.sampledata.les_mis import data
 nodes = data['nodes']
 names = [node['name'] for node in sorted(data['nodes'], key=lambda x: x['group'])]
 N = len(nodes)
 counts = np.zeros((N, N))
 for link in data['links']:
    counts[link['source'], link['target']] = link['value']
    counts[link['target'], link['source']] = link['value']
 colormap = ["#444444", "#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99",
            "#e31a1c", "#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a"]
 xname = []
 yname = []
 color = []
 alpha = []
 for i, node1 in enumerate(nodes):
    for j, node2 in enumerate(nodes):
        xname.append(node1['name'])
        yname.append(node2['name'])
        alpha.append(min(counts[i,j]/4.0, 0.9) + 0.1)
        if node1['group'] == node2['group']:
            color.append(colormap[node1['group']])
        else:
            color.append('lightgrey')
 data=dict(
    xname=xname,
    yname=yname,
    colors=color,
    alphas=alpha,
    count=counts.flatten(),
 )
 p = figure(title="Les Mis Occurrences",
           x_axis_location="above", tools="hover,save",
           x_range=list(reversed(names)), y_range=names,
           tooltips = [('names', '@yname, @xname'), ('count', '@count')])
 p.plot_width = 800
 p.plot_height = 800
 p.grid.grid_line_color = None
 p.axis.axis_line_color = None
 p.axis.major_tick_line_color = None
 p.axis.major_label_text_font_size = "7px"
 p.axis.major_label_standoff = 0
 p.xaxis.major_label_orientation = np.pi/3
 p.rect('xname', 'yname', 0.9, 0.9, source=data,
       color='colors', alpha='alphas', line_color=None,
       hover_line_color='black', hover_color='colors')
 show(p) # show the plot
 ```
 %% Cell type:markdown id: tags:
 ## Sliders
 https://docs.bokeh.org/en/latest/docs/gallery/slider.html
 %% Cell type:code id: tags:
 ``` python
 import numpy as np
 from bokeh.layouts import column, row
 from bokeh.models import CustomJS, Slider
 from bokeh.plotting import ColumnDataSource
 x = np.linspace(0, 10, 500)
 y = np.sin(x)
 source = ColumnDataSource(data=dict(x=x, y=y))
 plot = figure(y_range=(-10, 10), plot_width=400, plot_height=400)
 plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
 amp_slider = Slider(start=0.1, end=10, value=1, step=.1, title="Amplitude")
 freq_slider = Slider(start=0.1, end=10, value=1, step=.1, title="Frequency")
 phase_slider = Slider(start=0, end=6.4, value=0, step=.1, title="Phase")
 offset_slider = Slider(start=-5, end=5, value=0, step=.1, title="Offset")
 callback = CustomJS(args=dict(source=source, amp=amp_slider, freq=freq_slider, phase=phase_slider, offset=offset_slider),
                    code="""
    const data = source.data;
    const A = amp.value;
    const k = freq.value;
    const phi = phase.value;
    const B = offset.value;
    const x = data['x']
    const y = data['y']
    for (var i = 0; i < x.length; i++) {
        y[i] = B + A*Math.sin(k*x[i]+phi);
    }
    source.change.emit();
 """)
 amp_slider.js_on_change('value', callback)
 freq_slider.js_on_change('value', callback)
 phase_slider.js_on_change('value', callback)
 offset_slider.js_on_change('value', callback)
 layout = row(
    plot,
    column(amp_slider, freq_slider, phase_slider, offset_slider),
 )
 show(layout)
 ```
 %% Cell type:code id: tags:
 ``` python
 ```

--- a/applications/data_visualisation/nilearn.ipynb
+++ b/applications/data_visualisation/nilearn.ipynb
@@ -27,7 +27,7 @@
    "3. 2D maximum intensity projection\n",
    "4. Surfaces\n",
    "\n",
-    "`nilearn` is not installed in the `fslpython` environment so you will need to install it to run this notebook (e.g. `pip install nilearn`)"
+    "`nilearn` is not installed in the `fslpython` environment so you will need to install it to run this notebook (e.g. `$FSLDIR/bin/conda install nilearn`)"
   ]
  },
  {

 %% Cell type:markdown id: tags:
 # `nilearn`
 `nilearn` is a python package that provides **statistical** and **machine learning** tools for working with neuroimaging data.
 According to https://nilearn.github.io/:
 >
 > Nilearn enables approachable and versatile analyses of brain volumes. It provides **statistical** and **machine-learning** tools, with instructive documentation & open community.
 >
 > It supports general linear model (GLM) based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.
 However, `nilearn` also provides a very convenient set of visualisation routines for neuroimaging data.  This notebook will focus on these visualisation tools.
 `nilearn` has very good documentation, and the examples below borrow heavily from the visualisation documentation: https://nilearn.github.io/plotting/index.html
 ## This notebook
 1. Plotting an anatomical image
 2. Plotting a statistical map
 3. 2D maximum intensity projection
 4. Surfaces
-`nilearn` is not installed in the `fslpython` environment so you will need to install it to run this notebook (e.g. `pip install nilearn`)
+`nilearn` is not installed in the `fslpython` environment so you will need to install it to run this notebook (e.g. `$FSLDIR/bin/conda install nilearn`)
 %% Cell type:markdown id: tags:
 First we will import the necessary packages for this notebook:
 %% Cell type:code id: tags:
 ``` python
 import os
 from nilearn import plotting, datasets, surface
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import nibabel as nb
 %matplotlib inline
 # get path to FSL installation for the FSLDIR environment variable
 FSLDIR = os.environ['FSLDIR']
 ## figure styling
 mpl.rcParams['figure.dpi'] = 150
 ```
 %% Cell type:markdown id: tags:
 ## Plotting an anatomical image
 In this section we will use `nilearn` to plot an anatomical volume.  For these examples we will use the 1mm MNI152 T1w that is shipped with `FSL`.  In these examples you will see different plotting layouts, as well as different styling options.
 First we will use the `plot_anat` function (with default values) to plot the MNI152 T1w in an **ortho** view.
 > **NOTE:**
 > 1. Here we use [`plot_anat`](https://nilearn.github.io/modules/generated/nilearn.plotting.plot_anat.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to plot the orthographic images
 > 2.  Here we use python [f-strings](https://www.python.org/dev/peps/pep-0498/), formally known as literal string interpolation, which allow for easy formatting
 %% Cell type:code id: tags:
 ``` python
 plotting.plot_anat(
    f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'
 )
 ```
 %% Cell type:markdown id: tags:
 Here we adjust the brightness of the image using the `dim` argument, and add a title to the plot with the `title` argument.
 %% Cell type:code id: tags:
 ``` python
 plotting.plot_anat(
    f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz',
    dim=-0.5,
    title='MNI T1 1mm'
 )
 ```
 %% Cell type:markdown id: tags:
 Now we are going to use the `display_mode` argument to change to a **tiled** ortho view where the coronal and axial views are in a column, and the coronal and sagittal views are in a row.
 %% Cell type:code id: tags:
 ``` python
 plotting.plot_anat(
    f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz',
    dim=-0.5,
    title='MNI T1 1mm',
    display_mode='tiled'
 )
 ```
 %% Cell type:markdown id: tags:
 Now we are going to combine the `display_mode` and `cut_coords` arguments to create a row of 10 axial slices.
 Options for `display_mode` include:
 - `'x'` - sagittal
 - `'y'` - coronal
 - `'z'` - axial
 - `'ortho'` - three cuts are performed in orthogonal directions
 - `'tiled'` - three cuts are performed and arranged in a 2x2 grid
 In this instance, we give `cut_coords` a scalar integer that indicates the number of slices to show in the axial view.
 %% Cell type:code id: tags:
 ``` python
 plotting.plot_anat(
    f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz',
    dim=-0.5,
    title='MNI T1 1mm',
    display_mode='z',
    cut_coords=10
 )
 ```
 %% Cell type:markdown id: tags:
 In the example below, a `display` object is returned by `plot_anat`.  We can use this object to update/amend the plot.  Here we add an overlay of the *HarvardOxford* atlas that ships with `FSL` to the image.
 We also use the `display` object to save the plot as a `*.png` image.
 %% Cell type:code id: tags:
 ``` python
 # plot MNI152 T1w and return display object
 display = plotting.plot_anat(
    f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz',
    dim=-0.5,
    title='MNI T1 1mm'
 )
 # overlay the HarvardOxford atlas
 display.add_contours(
    f'{FSLDIR}/data/atlases/HarvardOxford/HarvardOxford-cort-maxprob-thr50-1mm.nii.gz',
    filled=True
 )
 # save plot to file
 display.savefig('myplot.png')
 ```
 %% Cell type:markdown id: tags:
 `nilearn` plotting is built upon `matplotlib`, so we can use constructs from `matplotlib` to help us create more complex figures.
 In this example we:
 1. create a 1x2 grid of subplots using `subplots` from `matplotlib`
 2. plot a single slice of the MNI152 T1w in the first subplot using `plot_anat` from `nilearn`
 3. plot a histogram of the intensities of the MNI152 T12 in the second subplot using `hist` from `matplotlib`
 4. style the histogram by setting the x/y labels
 > **NOTE:** Here we use `load` and `get_fdata` from the [`nibabel`](https://nipy.org/nibabel/) package to load the data from the MNI152 T1w nifti for the histogram.
 %% Cell type:code id: tags:
 ``` python
 # create matplotlib figure with 1x2 subplots
 fig, ax = plt.subplots(1, 2, figsize=(10, 5))
 # plot MNI T1w slice in first subplot
 mni_t1 = f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'
 display = plotting.plot_anat(
    mni_t1,
    dim=-0.5,
    axes=ax[0],
    display_mode='z',
    cut_coords=[15]
 )
 # plot histogram of MNI T1w intensity in second subplot
 mni_t1_data = nb.load(mni_t1).get_fdata().ravel()
 ax[1].hist(mni_t1_data, bins=25)
 ax[1].set_ylabel('count')
 ax[1].set_xlabel('intensity')
 ```
 %% Cell type:markdown id: tags:
 >
 > **Exercise:**
 >
 > Create a PNG figure that displays the same **Harvard-Oxford** parcellation, using a different colormap, and axial images in a 3x5 grid.
 >
 %% Cell type:code id: tags:
 ``` python
 # YOUR CODE HERE
 ```
 %% Cell type:markdown id: tags:
 ## Plotting a statistical map
 The examples in this section demonstrate how to plot a statistical map as an overlay on an anatomical image.  Both images must be in the same space.
 First we will download a motor task statistical map from NeuroVault.
 > **Note:** We use a method from [`nilearn`](https://nilearn.github.io/index.html) called [`fetch_neurovault_motor_task`](https://nilearn.github.io/modules/generated/nilearn.datasets.fetch_neurovault_motor_task.html) to download the statistical map.
 %% Cell type:code id: tags:
 ``` python
 motor_images = datasets.fetch_neurovault_motor_task()
 stat_img = motor_images.images[0]
 print(stat_img)
 ```
 %% Cell type:markdown id: tags:
 Now we can plot the statistical map as an overlay on the MNI152 T1w.  We theshold the statistical map using the `threshold` argument.
 > **NOTE:** Here we use [`plot_stat_map`](https://nilearn.github.io/modules/generated/nilearn.plotting.plot_stat_map.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to plot the orthographic images
 %% Cell type:code id: tags:
 ``` python
 plotting.plot_stat_map(
    stat_img,
    threshold=3,
    bg_img=f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz'
 )
 ```
 %% Cell type:markdown id: tags:
 Like with the `plot_anat` examples earlier, we can style the plot and change the layout and views.
 %% Cell type:code id: tags:
 ``` python
 plotting.plot_stat_map(
    stat_img,
    threshold=3,
    bg_img=f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz',
    display_mode='z',
    cut_coords=10,
    title='motor-task',
    dim=-0.5,
    vmax=10
 )
 ```
 %% Cell type:markdown id: tags:
 In this next example we first find the coordinate of the centre of the largest connected component in the statistical map, then we plot an ortho view that is centred on this coordinate.
 %% Cell type:code id: tags:
 ``` python
 # find the coordinate of the centre of the largest connected component in the statistical map
 coord = plotting.find_xyz_cut_coords(stat_img)
 print(f'Center of the largest activation connected component = {coord}')
 # plot an ortho view that is centred on this coordinate
 plotting.plot_stat_map(
    stat_img,
    threshold=3,
    bg_img=f'{FSLDIR}/data/standard/MNI152_T1_1mm.nii.gz',
    display_mode='ortho',
    cut_coords=coord,
    title='motor-task',
    dim=-0.5,
    vmax=10
 )
 ```
 %% Cell type:markdown id: tags:
 `nilearn` has some support for **interactive** viewing of volumetic images with the `view_img` function.  Try clicking on the plot and moving the cursor around!
 %% Cell type:code id: tags:
 ``` python
 view = plotting.view_img(stat_img, threshold=3)
 view  # view interactive plot inline
 ```
 %% Cell type:code id: tags:
 ``` python
 view = plotting.view_img(stat_img, threshold=3)
 view.open_in_browser() # open interactive plot in new web browser
 ```
 %% Cell type:markdown id: tags:
 ## 2D maximum intensity projection
 > Maximum intensity projection (MIP) is a method for 3D data that projects in the visualization plane the voxels with maximum intensity that fall in the way of parallel rays traced from the viewpoint to the plane of projection. https://en.wikipedia.org/wiki/Maximum_intensity_projection
 `nilearn` can plot a maximum intensity projection overlayed on a brain schematic referred to as the "glass brain".  In this example, the MIP of the motor task statistical map used in the previous examples is plotted on the glass brain.
 > **NOTE:** Here we use [`plot_glass_brain`](https://nilearn.github.io/modules/generated/nilearn.plotting.plot_glass_brain.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to plot the maximum intensity projection.
 %% Cell type:code id: tags:
 ``` python
 plotting.plot_glass_brain(
    stat_img,
    title='2D max-intensity projection',
    threshold=3,
 )
 ```
 %% Cell type:markdown id: tags:
 ## Surfaces
 `nilearn` has baked in functionality to project a volumetric image onto the surface and visualise it.
 Here we visualise the same volumetric motor task statistical map, from earlier examples, on the inflated surface.
 > **NOTE:** Here we use [`plot_img_on_surf`](https://nilearn.github.io/modules/generated/nilearn.plotting.plot_img_on_surf.html) from the [`nilearn`](https://nilearn.github.io/index.html) package to plot the volumetric statistical map on the surface.
 %% Cell type:code id: tags:
 ``` python
 plotting.plot_img_on_surf(
    stat_img,
    inflate=True,
    threshold=0.5,
    vmax=6
 );
 ```
 %% Cell type:markdown id: tags:
 `nilearn` also has some support for **interactive** viewing of volumetic images on the surface with the `view_img_on_surf` function. Try clicking on the plot and moving the cursor around!
 %% Cell type:code id: tags:
 ``` python
 view = plotting.view_img_on_surf(
    stat_img,
    threshold=0.5,
    surf_mesh='fsaverage',
    vmax=6
 )
 view # view interactive plot inline
 ```
 %% Cell type:code id: tags:
 ``` python
 view = plotting.view_img_on_surf(
    stat_img,
    threshold=0.5,
    surf_mesh='fsaverage',
    vmax=6
 )
 view.open_in_browser() # view interactive plot inline
 ```
 %% Cell type:markdown id: tags:
 >
 > **Exercise:**
 >
 > Visualise thee **Harvard-Oxford** atlas on a surface in the web browser
 %% Cell type:code id: tags:
 ``` python
 # YOUR CODE HERE
 ```
 %% Cell type:markdown id: tags:
 That's all folks....

--- a/applications/data_visualisation/plotly.ipynb
+++ b/applications/data_visualisation/plotly.ipynb
@@ -14,7 +14,7 @@
    "\n",
    "### Install `plotly.py`\n",
    "\n",
-    "`plotly` is not installed in the `fslpython` environment so you will need to install it to run this notebook. (e.g. with `pip install plotly`)\n",
+    "`plotly` is not installed in the `fslpython` environment so you will need to install it to run this notebook. (e.g. with `$FSLDIR/bin/conda install plotly`)\n",
    "\n",
    "### Line & Scatter Plots\n",
    "https://plotly.com/python/line-and-scatter\n",

 %% Cell type:markdown id: tags:
 ## PLOTLY
 [`plotly.py`](https://plotly.com/python/) is a python interface to the `plotly` javascript library which is an open-source graphing library which specialises in high-quality web-based interactive graphs. `plotly.py` allows you to create these interactive web-based plots without having to code in javascript.
 `plotly.py` has excellent documentation: https://plotly.com/python/
 This notebook is not intended to instruct you how to use `plotly.py`.  Instead it pulls together interesting examples from the `plotly.py` documentation into a single notebook to give you a taster of what can be done with `plotly.py`.
 ### Install `plotly.py`
-`plotly` is not installed in the `fslpython` environment so you will need to install it to run this notebook. (e.g. with `pip install plotly`)
+`plotly` is not installed in the `fslpython` environment so you will need to install it to run this notebook. (e.g. with `$FSLDIR/bin/conda install plotly`)
 ### Line & Scatter Plots
 https://plotly.com/python/line-and-scatter
 The graph_objects class gives you access to all the types of graphs that plotly has to offer.
 Below are some random data plotted as scatter plots. Run the cell below and try the interactive interface. You can zoom in and out, you can see data values as you hover on the plot, and you can toggle on and off the different scatter plots.
 %% Cell type:code id: tags:
 ``` python
 import plotly.graph_objects as go
 # Create random data with numpy
 import numpy as np
 np.random.seed(1)
 N = 100
 random_x = np.linspace(0, 1, N)
 random_y0 = np.random.randn(N) + 5
 random_y1 = np.random.randn(N)
 random_y2 = np.random.randn(N) - 5
 fig = go.Figure()
 # Add traces
 fig.add_trace(go.Scatter(x=random_x, y=random_y0,
                    mode='markers',
                    name='markers'))
 fig.add_trace(go.Scatter(x=random_x, y=random_y1,
                    mode='lines+markers',
                    name='lines+markers'))
 fig.add_trace(go.Scatter(x=random_x, y=random_y2,
                    mode='lines',
                    name='lines'))
 fig.show()
 ```
 %% Cell type:markdown id: tags:
 ## Bar Charts
 https://plotly.com/python/bar-charts/
 %% Cell type:code id: tags:
 ``` python
 import plotly.graph_objects as go
 months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
          'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
 fig = go.Figure()
 fig.add_trace(go.Bar(
    x=months,
    y=[20, 14, 25, 16, 18, 22, 19, 15, 12, 16, 14, 17],
    name='Primary Product',
    marker_color='indianred'
 ))
 fig.add_trace(go.Bar(
    x=months,
    y=[19, 14, 22, 14, 16, 19, 15, 14, 10, 12, 12, 16],
    name='Secondary Product',
    marker_color='lightsalmon'
 ))
 # Here we modify the tickangle of the xaxis, resulting in rotated labels.
 fig.update_layout(barmode='group', xaxis_tickangle=-45)
 fig.show()
 ```
 %% Cell type:markdown id: tags:
 ## Box Plots
 https://plotly.com/python/box-plots
 Here we use `plotly.express`. It is probably the easiest way to create plotly plots (it uses graphical_objects under the hood). Here we also use it to get access to some of the toy datasets.
 %% Cell type:code id: tags:
 ``` python
 import plotly.express as px
 df = px.data.tips()
 # have a peek inside this dataset
 print(df)
 ```
 %% Cell type:markdown id: tags:
 Now let's do a boxplot to see how tips change by day and by smoker status. Try to interact with the plot. It shows you useful quantities, such as min/max, as you hover.
 %% Cell type:code id: tags:
 ``` python
 fig = px.box(df, x="day", y="total_bill", color="smoker")
 fig.update_traces(quartilemethod="exclusive") # or "inclusive", or "linear" by default
 fig.show()
 ```
 %% Cell type:markdown id: tags:
 ## Distribution Plots
 https://plotly.com/python/distplot
 Here we use figure_factory, yet another collection of pre-specified nice plots.
 %% Cell type:code id: tags:
 ``` python
 import plotly.figure_factory as ff
 import numpy as np
 # Add histogram data
 x1 = np.random.randn(200) - 2
 x2 = np.random.randn(200)
 x3 = np.random.randn(200) + 2
 x4 = np.random.randn(200) + 4
 # Group data together
 hist_data = [x1, x2, x3, x4]
 group_labels = ['Group 1', 'Group 2', 'Group 3', 'Group 4']
 # Create distplot with custom bin_size
 fig = ff.create_distplot(hist_data, group_labels, bin_size=.2)
 fig.show()
 ```
 %% Cell type:markdown id: tags:
 ## Violin Plots
 https://plotly.com/python/violin/
 %% Cell type:code id: tags:
 ``` python
 import plotly.graph_objects as go
 import pandas as pd
 df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")
 fig = go.Figure()
 days = ['Thur', 'Fri', 'Sat', 'Sun']
 for day in days:
    fig.add_trace(go.Violin(x=df['day'][df['day'] == day],
                            y=df['total_bill'][df['day'] == day],
                            name=day,
                            box_visible=True,
                            meanline_visible=True))
 fig.show()
 ```
 %% Cell type:markdown id: tags:
 ## Dendrogram with Heatmap
 https://plotly.com/python/dendrogram/
 %% Cell type:code id: tags:
 ``` python
 import plotly.graph_objects as go
 import plotly.figure_factory as ff
 import numpy as np
 from scipy.spatial.distance import pdist, squareform
 # get data
 data = np.genfromtxt("http://files.figshare.com/2133304/ExpRawData_E_TABM_84_A_AFFY_44.tab",
                     names=True,usecols=tuple(range(1,30)),dtype=float, delimiter="\t")
 data_array = data.view((float, len(data.dtype.names)))
 data_array = data_array.transpose()
 labels = data.dtype.names
 # Initialize figure by creating upper dendrogram
 fig = ff.create_dendrogram(data_array, orientation='bottom', labels=labels)
 for i in range(len(fig['data'])):
    fig['data'][i]['yaxis'] = 'y2'
 # Create Side Dendrogram
 dendro_side = ff.create_dendrogram(data_array, orientation='right')
 for i in range(len(dendro_side['data'])):
    dendro_side['data'][i]['xaxis'] = 'x2'
 # Add Side Dendrogram Data to Figure
 for data in dendro_side['data']:
    fig.add_trace(data)
 # Create Heatmap
 dendro_leaves = dendro_side['layout']['yaxis']['ticktext']
 dendro_leaves = list(map(int, dendro_leaves))
 data_dist = pdist(data_array)
 heat_data = squareform(data_dist)
 heat_data = heat_data[dendro_leaves,:]
 heat_data = heat_data[:,dendro_leaves]
 heatmap = [
    go.Heatmap(
        x = dendro_leaves,
        y = dendro_leaves,
        z = heat_data,
        colorscale = 'Blues'
    )
 ]
 heatmap[0]['x'] = fig['layout']['xaxis']['tickvals']
 heatmap[0]['y'] = dendro_side['layout']['yaxis']['tickvals']
 # Add Heatmap Data to Figure
 for data in heatmap:
    fig.add_trace(data)
 # Edit Layout
 fig.update_layout({'width':800, 'height':800,
                         'showlegend':False, 'hovermode': 'closest',
                         })
 # Edit xaxis
 fig.update_layout(xaxis={'domain': [.15, 1],
                                  'mirror': False,
                                  'showgrid': False,
                                  'showline': False,
                                  'zeroline': False,
                                  'ticks':""})
 # Edit xaxis2
 fig.update_layout(xaxis2={'domain': [0, .15],
                                   'mirror': False,
                                   'showgrid': False,
                                   'showline': False,
                                   'zeroline': False,
                                   'showticklabels': False,
                                   'ticks':""})
 # Edit yaxis
 fig.update_layout(yaxis={'domain': [0, .85],
                                  'mirror': False,
                                  'showgrid': False,
                                  'showline': False,
                                  'zeroline': False,
                                  'showticklabels': False,
                                  'ticks': ""
                        })
 # Edit yaxis2
 fig.update_layout(yaxis2={'domain':[.825, .975],
                                   'mirror': False,
                                   'showgrid': False,
                                   'showline': False,
                                   'zeroline': False,
                                   'showticklabels': False,
                                   'ticks':""})
 # Plot!
 fig.show()
 ```
 %% Cell type:markdown id: tags:
 ## Network Graph
 https://plotly.com/python/network-graphs
 This one is very ugly. I don't know why anyone would use this...
 %% Cell type:code id: tags:
 ``` python
 import plotly.graph_objects as go
 import networkx as nx
 G = nx.random_geometric_graph(200, 0.125)
 edge_x = []
 edge_y = []
 for edge in G.edges():
    x0, y0 = G.nodes[edge[0]]['pos']
    x1, y1 = G.nodes[edge[1]]['pos']
    edge_x.append(x0)
    edge_x.append(x1)
    edge_x.append(None)
    edge_y.append(y0)
    edge_y.append(y1)
    edge_y.append(None)
 edge_trace = go.Scatter(
    x=edge_x, y=edge_y,
    line=dict(width=0.5, color='#888'),
    hoverinfo='none',
    mode='lines')
 node_x = []
 node_y = []
 for node in G.nodes():
    x, y = G.nodes[node]['pos']
    node_x.append(x)
    node_y.append(y)
 node_trace = go.Scatter(
    x=node_x, y=node_y,
    mode='markers',
    hoverinfo='text',
    marker=dict(
        showscale=True,
        # colorscale options
        #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |
        #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |
        #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |
        colorscale='YlGnBu',
        reversescale=True,
        color=[],
        size=10,
        colorbar=dict(
            thickness=15,
            title='Node Connections',
            xanchor='left',
            titleside='right'
        ),
        line_width=2))
 node_adjacencies = []
 node_text = []
 for node, adjacencies in enumerate(G.adjacency()):
    node_adjacencies.append(len(adjacencies[1]))
    node_text.append('# of connections: '+str(len(adjacencies[1])))
 node_trace.marker.color = node_adjacencies
 node_trace.text = node_text
 fig = go.Figure(data=[edge_trace, node_trace],
             layout=go.Layout(
                titlefont_size=16,
                showlegend=False,
                hovermode='closest',
                margin=dict(b=20,l=5,r=5,t=40),
                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
                )
 fig.show()
 ```
 %% Cell type:markdown id: tags:
 ## Custom Controls: Sliders
 https://plotly.com/python/sliders
 Sliders are a very nice way to interact with plots. Here is a basic example where we plot a sine function and change its frequency. Note how all the data for plotting, and all the traces are pre-generated. This is not ideal if you need to change more than one parameter (e.g. both phase and frequency).
 %% Cell type:code id: tags:
 ``` python
 import plotly.graph_objects as go
 import numpy as np
 # Create figure
 fig = go.Figure()
 # Add traces, one for each slider step
 for step in np.arange(0, 5, 0.1):
    fig.add_trace(
        go.Scatter(
            visible=False,
            line=dict(color="#00CED1", width=6),
            name="𝜈 = " + str(step),
            x=np.arange(0, 10, 0.01),
            y=np.sin(step * np.arange(0, 10, 0.01))))
 # Make 10th trace visible
 fig.data[10].visible = True
 # Create and add slider
 steps = []
 for i in range(len(fig.data)):
    step = dict(
        method="update",
        args=[{"visible": [False] * len(fig.data)},
              {"title": "Slider switched to step: " + str(i)}],  # layout attribute
    )
    step["args"][0]["visible"][i] = True  # Toggle i'th trace to "visible"
    steps.append(step)
 sliders = [dict(
    active=10,
    currentvalue={"prefix": "Frequency: "},
    pad={"t": 50},
    steps=steps
 )]
 fig.update_layout(
    sliders=sliders
 )
 fig.show()
 ```
 %% Cell type:markdown id: tags:
 ## Custom Controls: Dropdown Menus
 https://plotly.com/python/dropdowns/
 %% Cell type:code id: tags:
 ``` python
 import plotly.graph_objects as go
 import pandas as pd
 # load dataset
 df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/volcano.csv")
 # Create figure
 fig = go.Figure()
 # Add surface trace
 fig.add_trace(go.Heatmap(z=df.values.tolist(), colorscale="Viridis"))
 # Update plot sizing
 fig.update_layout(
    width=800,
    height=900,
    autosize=False,
    margin=dict(t=100, b=0, l=0, r=0),
 )
 # Update 3D scene options
 fig.update_scenes(
    aspectratio=dict(x=1, y=1, z=0.7),
    aspectmode="manual"
 )
 # Add dropdowns
 button_layer_1_height = 1.08
 fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(
                    args=["colorscale", "Viridis"],
                    label="Viridis",
                    method="restyle"
                ),
                dict(
                    args=["colorscale", "Cividis"],
                    label="Cividis",
                    method="restyle"
                ),
                dict(
                    args=["colorscale", "Blues"],
                    label="Blues",
                    method="restyle"
                ),
                dict(
                    args=["colorscale", "Greens"],
                    label="Greens",
                    method="restyle"
                ),
            ]),
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=button_layer_1_height,
            yanchor="top"
        ),
        dict(
            buttons=list([
                dict(
                    args=["reversescale", False],
                    label="False",
                    method="restyle"
                ),
                dict(
                    args=["reversescale", True],
                    label="True",
                    method="restyle"
                )
            ]),
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.37,
            xanchor="left",
            y=button_layer_1_height,
            yanchor="top"
        ),
        dict(
            buttons=list([
                dict(
                    args=[{"contours.showlines": False, "type": "contour"}],
                    label="Hide lines",
                    method="restyle"
                ),
                dict(
                    args=[{"contours.showlines": True, "type": "contour"}],
                    label="Show lines",
                    method="restyle"
                ),
            ]),
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.58,
            xanchor="left",
            y=button_layer_1_height,
            yanchor="top"
        ),
    ]
 )
 fig.update_layout(
    annotations=[
        dict(text="colorscale", x=0, xref="paper", y=1.06, yref="paper",
                             align="left", showarrow=False),
        dict(text="Reverse<br>Colorscale", x=0.25, xref="paper", y=1.07,
                             yref="paper", showarrow=False),
        dict(text="Lines", x=0.54, xref="paper", y=1.06, yref="paper",
                             showarrow=False)
    ])
 fig.show()
 ```
 %% Cell type:code id: tags:
 ``` python
 ```