#!/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 {func}`.interactive_plot`---with Matplotlib widgets. For a more general discussion of how to use the `ipyplot` submodule, see {doc}`usage`.
# 
# 
# ## 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 {func}`numpy.linspace`).
# 
# **2.** Laying out Matplotlib widgets is significantly 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.

# In[ ]:


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


# In[ ]:


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()


# ![](../_static/images/mpl-sliders.gif)

# ## 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()


# ![](../_static/images/same-fig-mpl-slider.png)