plotly · nicolaskruchten · Dec 3, 2020 · Sep 3, 2020 · Sep 3, 2020 · Sep 4, 2020
diff --git a/doc/python/imshow.md b/doc/python/imshow.md
@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.4.2
+      jupytext_version: 1.3.0
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.7
+    version: 3.7.3
   plotly:
     description: How to display image data in Python with Plotly.
     display_as: scientific
@@ -399,6 +399,95 @@ for compression_level in range(0, 9):
 fig.show()
 ```
 
+### Exploring 3-D images, timeseries and sequences of images with `facet_col`
+
+*Introduced in plotly 4.14*
+
+For three-dimensional image datasets, obtained for example by MRI or CT in medical imaging, one can explore the dataset by representing its different planes as facets. The `facet_col` argument specifies along which axis the image is sliced through to make the facets. With `facet_col_wrap`, one can set the maximum number of columns. For image datasets passed as xarrays, it is also possible to specify the axis by its name (label), thus passing a string to `facet_col`.
+
+It is recommended to use `binary_string=True` for facetted plots of images in order to keep a small figure size and a short rendering time.
+
+See the [tutorial on facet plots](/python/facet-plots/) for more information on creating and styling facet plots.
+
+```python
+import plotly.express as px
+from skimage import io
+from skimage.data import image_fetcher
+path = image_fetcher.fetch('data/cells.tif')
+data = io.imread(path)
+img = data[20:45:2]
+fig = px.imshow(img, facet_col=0, binary_string=True, facet_col_wrap=5)
+fig.show()
+```
+
+Facets can also be used to represent several images of equal shape, like in the example below where different values of the blurring parameter of a Gaussian filter are compared.
+
+```python
+import plotly.express as px
+import numpy as np
+from skimage import data, filters, img_as_float
+img = data.camera()
+sigmas = [1, 2, 4]
+img_sequence = [filters.gaussian(img, sigma=sigma) for sigma in sigmas]
+fig = px.imshow(np.array(img_sequence), facet_col=0, binary_string=True,
+                labels={'facet_col':'sigma'})
+# Set facet titles
+for i, sigma in enumerate(sigmas):
+    fig.layout.annotations[i]['text'] = 'sigma = %d' %sigma 
+fig.show()
+```
+
+```python
+print(fig)
+```
+
+### Exploring 3-D images and timeseries with `animation_frame`
+
+*Introduced in plotly 4.14*
+
+For three-dimensional image datasets, obtained for example by MRI or CT in medical imaging, one can explore the dataset by sliding through its different planes in an animation. The `animation_frame` argument of `px.imshow` sets the axis along which the 3-D image is sliced in the animation.
+
+```python
+import plotly.express as px
+from skimage import io
+from skimage.data import image_fetcher
+path = image_fetcher.fetch('data/cells.tif')
+data = io.imread(path)
+img = data[25:40]
+fig = px.imshow(img, animation_frame=0, binary_string=True)
+fig.show()
+```
+
+### Animations of xarray datasets
+
+*Introduced in plotly 4.14*
+
+For xarray datasets, one can pass either an axis number or an axis name to `animation_frame`. Axis names and coordinates are automatically used for the labels, ticks and animation controls of the figure.
+
+```python
+import plotly.express as px
+import xarray as xr
+# Load xarray from dataset included in the xarray tutorial
+ds = xr.tutorial.open_dataset('air_temperature').air[:20]
+fig = px.imshow(ds, animation_frame='time', zmin=220, zmax=300, color_continuous_scale='RdBu_r')
+fig.show()
+```
+
+### Combining animations and facets
+
+It is possible to view 4-dimensional datasets (for example, 3-D images evolving with time) using a combination of `animation_frame` and `facet_col`.
-It is possible to view 4-dimensional datasets (for example, 3-D images evolving with time) using a combination of `animation_frame` and `facet_col`.
+It is possible to view four-dimensional datasets (for example, 3-D images evolving with time) using a combination of `animation_frame` and `facet_col`.
-It is possible to view 4-dimensional datasets (for example, 3-D images evolving with time) using a combination of `animation_frame` and `facet_col`.
+It is possible to view four-dimensional datasets (for example, 3-D images evolving with time) using a combination of `animation_frame` and `facet_col`.
+
+```python
+import plotly.express as px
+from skimage import io
+from skimage.data import image_fetcher
+path = image_fetcher.fetch('data/cells.tif')
+data = io.imread(path)
+data = data.reshape((15, 4, 256, 256))[5:]
+fig = px.imshow(data, animation_frame=0, facet_col=1, binary_string=True)
+fig.show()
+```
+
 #### Reference
 
 See https://plotly.com/python/reference/image/ for more information and chart attribute options!
diff --git a/doc/requirements.txt b/doc/requirements.txt
@@ -28,4 +28,5 @@ pyarrow
 cufflinks==0.17.3
 kaleido
 umap-learn
+pooch
 wget
-Original file line number
+Diff line change
@@ Expand Up / @@ -28,4 +28,5 @@ pyarrow @@
     cufflinks==0.17.3
     kaleido
     umap-learn
+    pooch
     wget