backends.md

Matplotlib Backends

:::{note}

For discussion of what a Matplotlib backend is see: https://matplotlib.org/faq/usage_faq.html#what-is-a-backend

:::

All of the functions in this library will work with any interactive backend to Matplotlib. However, if you are working in a Jupyter Notebook then you should make sure to use the ipympl backend. If you are using a different backend (such as qt5agg), then the built-in Matplotlib widgets will be used instead of the ipywidgets widgets. It is trickier to achieve a good layout of Matplotlib widgets versus ipywidgets widgets, so if in a Notebook it is best to use the ipympl backend by including the Jupyter Magic:

%matplotlib ipympl

Further options

If you want to use a non-ipympl backend in a Jupyter Notebook but still want {doc}ipywidgets <ipywidgets:index> style sliders then you have a few options.

• With {func}.interactive_plot and {func}.interactive_hist you can pass force_ipywidgets=True:

  %matplotlib qt
  import numpy as np
  from mpl_interactions import interactive_plot
  
  x = np.linspace(0,np.pi,100)
  tau = np.linspace(1,10, 100)
  beta = np.linspace(.001,1)
  def f(x, tau, beta):
      return np.sin(x*tau)*x**beta
  fig, ax, sliders = interactive_plot(f, x=x, tau = tau, beta = beta, force_ipywidgets=True)

  This will result in sliders in the notebook, but the plot will be in a qt window:

• You can also explicitly set whether to use Matplotlib sliders or not with the use_ipywidgets argument in {func}.interactive_plot. If {obj}None then the function will try to infer whether to use ipywidgets or not. By setting the argument to {obj}False, you will be able to use Matplotlib sliders in the notebook.

  %matplotlib ipympl
  import matplotlib.pyplot as plt
  import numpy as np
  from matplotlib.widgets import Slider
  import mpl_interactions.ipyplot as iplt
  
  fig, ax = plt.subplots()
  plt.subplots_adjust(bottom=.25)
  x = np.linspace(0,2*np.pi,200)
  def f(x, freq):
      return np.sin(x*freq)
  axfreq = plt.axes([0.25, 0.1, 0.65, 0.03])
  slider = Slider(axfreq,label='freq', valmin=.05, valmax = 10)
  controls = iplt.plot(x,f, freq=slider, use_ipywidgets=False, ax=ax)