v0.12.4

Changes

• Respect the dictionary key-value order of categorical encoding maps in the legend. E.g., the following categorical color map legend will read C, then B, and finally A:

  scatter.legend(True)
  scatter.color(map=dict(C='red', B='blue', A='pink'))

• Update third-party JS libraries

v0.12.3

Changes

• Fix incorrect legend for categorical coloring

v0.12.2

Changes

• Update regl-scatterplot to v1.6.9

v0.12.1

Changes

• Fix the ordering of the legend's value labels for continuous encodings such that high to low values are order top to bottom (#70)

v0.12.0

Changes

• Add support for referencing points by the Pandas DataFrame's index via Scatter(data_use_index=True) or scatter.data(use_index=True). This is useful for synchronizing the selection or filtering of two Scatter instances that operate on different data frames with point correspondences. (#62

  import jscatter
  import numpy as np
  import pandas as pd
  
  df1 = pd.DataFrame(
      data=np.random.rand(16, 2),
      index=[chr(65 + x) for x in range(16)],
      columns=['x', 'y']
  )
  df2 = pd.DataFrame(
      data=np.random.rand(8, 2),
      index=[chr(76 - x) for x in range(8)],
      columns=['x', 'y']
  )
  
  s1 = jscatter.Scatter(data=df1, data_use_index=True, x='x', y='y', x_scale=[0, 2], y_scale=[0, 1])
  s2 = jscatter.Scatter(data=df2, data_use_index=True, x='x', y='y', x_scale=[-1, 1], y_scale=[0, 1])
  
  def on_selection_change(change):
      s2.selection(s1.selection())
  
  s1.widget.observe(on_selection_change, names='selection')
  
  jscatter.compose([s1, s2])

  shorter.mp4

• Avoid unregistering all observers when calling jscatter.compose() such that external observers remain registered

• Fix undefined this in codec preventing the scatter.selection() from working correctly (#66)

v0.11.0

Changes

• Add scatter.filter([0, 1, 2]) for filtering down points. Filtering down to a specific subset of points is much faster than than updating the underlying data (#61)
• Add scatter.data(df) to allow rendering new data points without having to re-initialize the scatter instance (#61)
• Add the ability to automatically zoom to filtered points via Scatter(zoom_on_filter=True) or scatter.zoom(on_filter=True)
• Add lasso on long press and make it the default. The behavior can be changed via Scatter(lasso_on_long_press=False) or scatter.lasso(on_long_press=False)
• Updated regl-scatterplot to v1.6

v0.10.0

Changes

• Add support for automatic zooming to selected points via scatter.zoom(on_selection=True)
• Fix view synchronization issue
• Add remove() to the JS widget to ensure that the scatterplot is destroyed in ipywidgets v8.

v0.9.0

Changes

• Add support for animated zooming to a set of points via scatter.zoom(pointIndices) (#49)
• Bump regl-scatterplot to v1.4.1
• Add support for VSCode and Colab (#37)
• Fix serving of numpy data for JS client. Use consistent serialization object between JS and Python.

v0.8.0

Changes

• Add support for end labeling of continuous data encoding via a new labeling argument of color(), opacity(), size(), connection_color(), connection_opacity(), and connection_size(). (#46)
• Fix the incorrect size legend when the size map is reversed (#47)

v0.7.3

Changes

• Fix broken build of v0.7.2
• Fix versions of ipywidgets and jupyterlab_widgets to avoid running into incompatibilities (#40)