mpl-interactions
0.17.12
  • Installation
    • User install
    • Setup for Jupyterlab 3+
    • Setup for JupyterLab <= 2
    • Development installation
  • Matplotlib Backends
    • Further options
  • Comparison to ipywidgets
    • Performance
    • Portability
    • Convenience
      • Differences in generated widgets
        • Tuple of floats
        • NumPy array or list
        • Single number
  • API
    • pyplot
      • mpl_interactions.interactive_plot
      • mpl_interactions.interactive_hist
      • mpl_interactions.interactive_scatter
      • mpl_interactions.interactive_imshow
      • mpl_interactions.interactive_axhline
      • mpl_interactions.interactive_axvline
    • generic
      • mpl_interactions.heatmap_slicer
      • mpl_interactions.zoom_factory
      • mpl_interactions.panhandler
      • mpl_interactions.image_segmenter
      • mpl_interactions.hyperslicer
    • utilities
      • mpl_interactions.ioff
      • mpl_interactions.figure
      • mpl_interactions.nearest_idx
      • mpl_interactions.indexer
    • widgets
      • mpl_interactions.widgets.scatter_selector
      • mpl_interactions.widgets.scatter_selector_value
      • mpl_interactions.widgets.scatter_selector_index
  • Gallery
    • Matplotlib Sliders without a separate Controls Figure
    • Heamap Slicer
  • Contributing
    • Code Improvements
      • Seeing your changes
      • Working with Git
    • Documentation
      • Embedding GIFs
      • Autobuild the documentation
    • Thank you to our current team!

Tutorials

  • Usage Guide
    • Basic Usage
    • Only Using some of the parameters + specifying axes
      • Combining Multiple types of Plots
    • How to Specify Parameters
      • Tuples
      • Tuples for RangeSliders
      • Categoricals
      • Existing widgets
      • Fixed Parameters
    • Styling
      • Axis scaling
      • Reference parameters values in the Title, xlabel, or ylabel
      • Embedding the controls into a larger layout
  • Using Matplotlib Widgets
    • Differences from ipywidgets sliders
    • Basic example
    • Custom positioning of Matplotlib widgets
  • Custom Callbacks and Accessing Parameter Values
    • Custom Callbacks
      • Calling the callback on registration
      • Accepting multiple parameters
      • A more complex callback
    • Accessing parameters
  • Saving Animations
    • Basic Usage
      • Which Generates this GIF
    • Embeding the animation in a noteook.
    • Matplotlib Sliders with valstep=None
      • Gives this GIF:
  • Using RangeSliders
    • How to automatically generate a RangeSlider
    • Using a RangeSlider for Scalar arguments - Thresholding an Image
    • Using a Matplotlib RangeSlider
      • But maptlotlib doesn’t have range sliders???!?!?
  • Using Sliders to control scalar arguments
    • Setting the scalar parameter using a tuple
    • Setting Scalar parameters with an indexed controls
    • Other names for the sliders
  • Tidbits
    • Attaching play buttons to sliders
      • Specifying Which Sliders Get Play buttons
        • Boolean: All get a button
        • String:
        • List: Choose by name
        • defaultdict: Specify by name and choose default

Specific Functions

  • Hyperslicer Tutorial
    • Names and Values for sliders
    • Other ways of specifying axes
    • Hyperslicer with Xarray
  • Plot
    • Troubleshooting
    • set parameters with tuples
    • Use sets for categorical values
      • Using widgets for as parameters
      • With multiple functions
      • Styling of plot
    • Slider precision
    • fixed y-scale
  • Scatter
    • Basic example
    • Using functions and broadcasting
    • Functions for both x and y
    • Using functions for other attributes
    • Modifying the colors of individual points
    • Putting it together - Wealth of Nations
      • Data preprocessing
      • Define functions to provide the data
      • Marvel at data
  • Imshow
    • Providing an axis
    • Preventing colormap autoscaling
    • vmin and vmax: thresholding an image
  • Hist
  • scatter_selector widget
    • PCA of Stock Data
      • Data loading/cleaning
      • Making the plot
      • Datacleaning
  • Image Segmentation
    • Single class
      • Erasing
    • Looking at the mask.
    • Multiple classes
      • Change the class by changing the current_class variable
      • Integration with ipywidgets
      • pre-seed a mask
    • Styling
      • imshow parameters
    • LassoSelector line
  • Zooming and Panning
    • Load a sample image
    • enable scroll to zoom
    • Scrolling and panning
  • Heatmap Slicer
    • Comparing heatmaps

Showcase

  • Visualizing the Lotka-Volterra Model
    • Define the function
    • Make the plots
  • Rössler attractor
    • Define function to plot
      • Projecting on axes
      • Caching
      • kwarg collisions
    • Coloring by time point
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© Copyright 2020, Ian Hunt-Isaak. Revision 74966bcc.

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