#!/usr/bin/env python
# coding: utf-8

# # Using Matplotlib Widgets
# 
# .. warning::
#     When using Matplotlib widgets you need to make sure you always keep a reference to the control sliders, otherwise the callbacks will be garbage collected and cease to work.
# 
# This page is dedicated to showing how to use the ``interactive_*`` functions---such as ``interactive_plot``---with Matplotlib widgets. For
# a more general discussion of how to use the ``ipyplot`` submodule see the [Usage-Guide](Usage-Guide.ipynb) page.
# 
# 
# ## Differences from ipywidgets sliders
# 
# **1.** mpl-sliders are different from ipywidgets sliders in that they will only take a min and and max with
# an optional step, while for ipywidgets sliders you need to specify all values (at least until version 8).
# A consequence of this is that the various ``interactive_*`` methods will only use the first two values of any tuples
# passed as a parameter (i.e. they will ignore the ``num`` argument to linspace).
# 
# **2.** Laying out Matplotlib widgets is signifcantly more difficult than laying out elements in a web browser. If you
# pass an existing Matplotlib widget a parameter then the plot will update as expected; however, the ``interactive_*``
# methods will *not* display it for you. Alternatively, when using ipywidgets widgets, if you give a widget as a parameter
# it will be included in the display of the controls created by the function.
# 
# ## Basic example
# 
# If you are not using the ``ipympl`` backend then ``mpl_interactions`` will automatically create a new figure to hold all the controls.

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# NBVAL_SKIP
# setting the backend in case this is run in a notebook
get_ipython().run_line_magic('matplotlib', 'qt')


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import matplotlib.pyplot as plt
import numpy as np

import mpl_interactions.ipyplot as iplt

x = np.linspace(0, np.pi, 100)
tau = np.linspace(1, 10, 100)
beta = np.linspace(0.001, 1)


def f(x, tau, beta):
    return np.sin(x * tau) * x ** beta


fig, ax = plt.subplots()
controls = iplt.plot(x, f, tau=tau, beta=beta, slider_formats={"beta": "{:.3e}"})
plt.show()


# ## Custom positioning of Matplotlib widgets
# 
# There does not seem to be a consistent and simple way to layout Matplotlib widgets in the same figure as the controlled plot.
# To address this, mpl_interactions will open a new figure to place all the controls. 
# 
# If you would like the sliders and the plot to live in the same figure, you will need to create and position your own slider widget.

# In[ ]:


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=0.25)
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=0.05, valmax=10)
controls = iplt.plot(x, f, freq=slider, ax=ax)
plt.show()


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