#!/usr/bin/env python
# coding: utf-8

# ## Animating slices  of a human head  MRI scan, using Plotly FigureWidget

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import plotly.graph_objects as go
import numpy as np
from skimage import io


# Read the volume data:

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volume = (io.imread("https://s3.amazonaws.com/assets.datacamp.com/blog_assets/attention-mri.tif")).T
volume.shape


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n_slices = volume.shape[0]
r, c = volume[0].shape


# Set the height of the first slice to be visualized:

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height = (volume.shape[0]-1) / 10
grid = np.linspace(0, height, n_slices)
slice_step = grid[1] - grid[0]
slice_step


# The normalized slice values are color-mapped to the bone colorscale (the usual colorscale for medical images).

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pl_bone=[[0.0, 'rgb(0, 0, 0)'],
         [0.05, 'rgb(10, 10, 14)'],
         [0.1, 'rgb(21, 21, 30)'],
         [0.15, 'rgb(33, 33, 46)'],
         [0.2, 'rgb(44, 44, 62)'],
         [0.25, 'rgb(56, 55, 77)'],
         [0.3, 'rgb(66, 66, 92)'],
         [0.35, 'rgb(77, 77, 108)'],
         [0.4, 'rgb(89, 92, 121)'],
         [0.45, 'rgb(100, 107, 132)'],
         [0.5, 'rgb(112, 123, 143)'],
         [0.55, 'rgb(122, 137, 154)'],
         [0.6, 'rgb(133, 153, 165)'],
         [0.65, 'rgb(145, 169, 177)'],
         [0.7, 'rgb(156, 184, 188)'],
         [0.75, 'rgb(168, 199, 199)'],
         [0.8, 'rgb(185, 210, 210)'],
         [0.85, 'rgb(203, 221, 221)'],
         [0.9, 'rgb(220, 233, 233)'],
         [0.95, 'rgb(238, 244, 244)'],
         [1.0, 'rgb(255, 255, 255)']]


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initial_slice = dict(type='surface',
                     z=height*np.ones((r,c)),
                     surfacecolor=np.flipud(volume[-1]),
                     colorscale=pl_bone,
                     showscale=False
                     )


# Define the plot layout:

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layout3d = dict(title='Head Scanning', 
                font=dict(family='Balto'),
                width=650,
                height=650,
                scene=dict(zaxis=dict(range=[-0.1, 6.8], autorange=False)))


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fw = go.FigureWidget(data=[initial_slice], layout=layout3d)


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#fw


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import ipywidgets as iw
slider = iw.IntSlider(value=0, min=0, 
                      max=n_slices-1, step=1, 
                      description='slice #')
slider.layout = dict(margin='1px 80px 40px 5px', width='400px')


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def slice_changed(change):
    k=slider.value
    fw.data[0].update(z=(height-slice_step*k)*np.ones((r,c)),
                      surfacecolor=np.flipud(volume[-1-k]))
        

slider.observe(slice_changed, 'value')


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#help(iw.Play)


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play_button = iw.Play(value=0, min=0, max=n_slices-1,
                      step=1, interval=40)
play_button.layout = dict(margin='1px 10px 50px 100px')
iw.jslink((play_button, 'value'), (slider, 'value'))
iw.VBox([fw,  iw.HBox([play_button, slider])])


# Animation without slider (watch the above cell):

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for k in range(n_slices):
    with fw.batch_animate(duration=10, easing='cubic-in-out'):
        fw.data[0].update(z=(height-k*slice_step)*np.ones((r,c)),
                          surfacecolor=np.flipud(volume[-1-k]))
        


# It seems that the latter animation (without slider) is faster.

# For a much faster animation  see this gif file created from the fig generated in the notebook  `scanning-the-head-classic.ipynb`: