From df4d82797868d03d10993e135a4b9e30c023ce59 Mon Sep 17 00:00:00 2001 From: Joel Ostblom Date: Fri, 22 Sep 2023 09:48:54 -0700 Subject: [PATCH] Add tutorial on numpy tooltip images --- doc/case_studies/exploring-weather.rst | 7 - doc/case_studies/index.rst | 10 ++ doc/case_studies/numpy-tooltip-images.rst | 195 ++++++++++++++++++++++ pyproject.toml | 1 + sphinxext/altairgallery.py | 2 +- 5 files changed, 207 insertions(+), 8 deletions(-) create mode 100644 doc/case_studies/index.rst create mode 100644 doc/case_studies/numpy-tooltip-images.rst diff --git a/doc/case_studies/exploring-weather.rst b/doc/case_studies/exploring-weather.rst index 4b16bef2a..b041ba212 100644 --- a/doc/case_studies/exploring-weather.rst +++ b/doc/case_studies/exploring-weather.rst @@ -266,10 +266,3 @@ If you want to further customize your charts, you can refer to Altair's :ref:`api`. .. _Pandas: http://pandas.pydata.org/ - - -.. toctree:: - :maxdepth: 1 - :hidden: - - self diff --git a/doc/case_studies/index.rst b/doc/case_studies/index.rst new file mode 100644 index 000000000..3c24761c6 --- /dev/null +++ b/doc/case_studies/index.rst @@ -0,0 +1,10 @@ +Tutorials +--------- + +These tutorials explore more advanced use cases than the gallery. + +.. toctree:: + :hidden: + + exploring-weather + numpy-tooltip-images diff --git a/doc/case_studies/numpy-tooltip-images.rst b/doc/case_studies/numpy-tooltip-images.rst new file mode 100644 index 000000000..cbacce5ce --- /dev/null +++ b/doc/case_studies/numpy-tooltip-images.rst @@ -0,0 +1,195 @@ +.. _numpy-tooltip-imgs: + +Displaying Numpy Images in Tooltips +----------------------------------- + +In this tutorial, +you’ll learn how to display images stored as Numpy arrays +in tooltips with any Altair chart. + +First, +we create some example image arrays with blobs of different sizes. +We measure the area of the blobs +in order to have a quantitative measurement to plot. + +.. altair-plot:: + :output: repr + + import numpy as np + import pandas as pd + from scipy import ndimage as ndi + + rng = np.random.default_rng([ord(c) for c in 'altair']) + n_rows = 200 + + def create_blobs(blob_shape, img_width=96, n_dim=2, sizes=[0.05, 0.1, 0.15]): + """Helper function to create blobs in the images""" + shape = tuple([img_width] * n_dim) + mask = np.zeros(shape) + points = (img_width * rng.random(n_dim)).astype(int) + mask[tuple(indices for indices in points)] = 1 + if blob_shape == 'circle': + im = ndi.gaussian_filter(mask, sigma=rng.choice(sizes) * img_width) + elif blob_shape == 'square': + im = ndi.uniform_filter(mask, size=rng.choice(sizes) * img_width, mode='constant') * rng.normal(4, size=(img_width, img_width)) + return im / im.max() + + df = pd.DataFrame({ + 'image1': [create_blobs('circle') for _ in range(n_rows)], + 'image2': [create_blobs('square', sizes=[0.3, 0.4, 0.5]) for _ in range(n_rows)], + 'group': rng.choice(['a', 'b', 'c'], size=n_rows) + }) + # Compute the area as the proportion of pixels above a threshold + df[['image1_area', 'image2_area']] = df[['image1', 'image2']].applymap(lambda x: (x > 0.4).mean()) + df + +Next, we define the function +that will convert the Numpy arrays to base64-encoded_ strings. +This is a necessary step +for the tooltip to recognize that the data +is in the form of an image and render it appropriately. + + +.. altair-plot:: + :output: repr + + from io import BytesIO + from PIL import Image, ImageDraw + import base64 + + + def create_tooltip_image(df_row): + """Concatenate, rescale, and convert images to base64 strings.""" + # Concatenate images to show together in the tooltip + # This can be skipped if only one image is to be displayed + img_gap = np.ones([df_row['image1'].shape[0], 10]) # 10 px white gap between imgs + img_arr = np.concatenate( + [ + df_row['image1'], + img_gap, + df_row['image2'] + ], + axis=1 + ) + + # Create a PIL image from the array. + # Multiplying by 255 and recasting as uint8 for the images to occupy the entire supported instensity space from 0-255 + img = Image.fromarray((255 * img_arr).astype('uint8')) + + # Optional: Burn in labels as pixels in the images. Can be helpful to keep track of which image is which + ImageDraw.Draw(img).text((3, 0), 'im1', fill=255) + ImageDraw.Draw(img).text((3 + df_row['image1'].shape[1] + img_gap.shape[1], 0), 'im2', fill=255) + + # Convert to base64 encoded image string that can be displayed in the tooltip + buffered = BytesIO() + img.save(buffered, format="PNG") + img_str = base64.b64encode(buffered.getvalue()).decode() + return f"data:image/png;base64,{img_str}" + + # The column with the base64 image string must be called "image" in order for it to trigger the image rendering in the tooltip + df['image'] = df[['image1', 'image2']].apply(create_tooltip_image, axis=1) + + # Dropping the image arrays since they are large an no longer needed + df_plot = df.drop(columns=['image1', 'image2']) + df_plot + +Now we are ready to create the charts that show the images as tooltips +when the dots are hovered with the mouse. +We can see that the large white blobs +correspond to the higher area measurements +as expected. + +.. altair-plot:: + import altair as alt + + # The random() function is used to jitter points in the x-direction + alt.Chart(df_plot, width=alt.Step(40)).mark_circle(xOffset=alt.expr('random() * 16 - 8')).encode( + x='group', + y=alt.Y(alt.repeat(), type='quantitative'), + tooltip=['image'], + color='group', + ).repeat( + ['image1_area', 'image2_area'] + ).resolve_scale( + y='shared' + ).properties( + title='Comparison of blob areas' + ) + +If we want to have even more fun and get a bit more sophisticated, +we could show one chart at a time +and update what is shown on the y-axis +as well as what is shown in the image tooltip +based on a dropdown selector. +We start by defining a tooltip that only contains a single image +instead of both the images concatenated together. + +.. altair-plot:: + :output: repr + + def create_tooltip_image(img_arr): + """Rescale and convert an image to a base64 string.""" + # print(img_arr) + # Create a PIL image from the array. + # Multiplying by 255 and recasting as uint8 for the images to occupy the entire supported instensity space from 0-255 + img = Image.fromarray((255 * img_arr).astype('uint8')) + + # Convert to base64 encoded image string that can be displayed in the tooltip + buffered = BytesIO() + img.save(buffered, format="PNG") + img_str = base64.b64encode(buffered.getvalue()).decode() + return f"data:image/png;base64,{img_str}" + + # The column with the base64 image string must be called "image" in order for it to trigger the image rendering in the tooltip + df[['image1_base64', 'image2_base64']] = df[['image1', 'image2']].applymap(create_tooltip_image) + # Dropping the image arrays since they are large an no longer needed + # Also drop the previous tooltip image for clarity + df_plot = df.drop(columns=['image1', 'image2', 'image']) + df_plot + +In our chart, +we need to use a transform to update +both the y-axis column as well as the tooltip column +dynamically based on the selection in the dropdown. +The comments in the code explain more in detail what each line +in this chart specification does. + +.. altair-plot:: + metric_dropdown = alt.binding_select( + options=['image1_area', 'image2_area'], + name='Image metric ' + ) + metric_param = alt.param( + value='image1_area', + bind=metric_dropdown + ) + alt.hconcat( + # This first chart is the axis title and is only needed because + # Vega-Lite does not yet support passing an expression directly to the axis title + alt.Chart().mark_text(angle=270, dx=-150, fontWeight='bold').encode( + alt.TextValue(alt.expr(f'{metric_param.name}')) + ), + alt.Chart(df_plot, width=alt.Step(40)).mark_circle(xOffset=alt.expr('random() * 16 - 8')).encode( + x='group', + y=alt.Y('image_area:Q').title(''), + tooltip=['image:N'], + color='group', + ).properties( + title='Area of blobs' + ).transform_calculate( + # This first line updates the image_area which is used for the y axis + # to correspond to the selected string in the dropdown + image_area=f'datum[{metric_param.name}]', + # Since altair needs the tooltip field to be called `image`, we need to dynamically + # change what's in the `image` field depending on the selection in the dropdown + # This is further complicated by the fact that the string in the dropdown is not + # an exact match for the column holding the image data so we need + # to replace part of the name to match to match the corresponding base 64 image field + image=f'datum[replace({metric_param.name}, "_area", "_base64")]', + ) + ).add_params( + metric_param + ) + + +.. _base64-encoded: https://en.wikipedia.org/wiki/Binary-to-text_encoding diff --git a/pyproject.toml b/pyproject.toml index 652ecbe16..4def443b6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,6 +86,7 @@ doc = [ "myst-parser", "sphinx_copybutton", "sphinx-design", + "scipy", ] [tool.hatch.version] diff --git a/sphinxext/altairgallery.py b/sphinxext/altairgallery.py index 94f4066aa..f76805c90 100644 --- a/sphinxext/altairgallery.py +++ b/sphinxext/altairgallery.py @@ -86,7 +86,7 @@ :hidden: Gallery - Tutorials <../case_studies/exploring-weather> + Tutorials <../case_studies/index> """ )