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

# # Simple Animations Using clear_output

# Sometimes you want to clear the output area in the middle of a calculation.  This can be useful for doing simple animations. In terminals, there is the carriage-return (`'\r'`) for overwriting a single line, but the notebook frontend can clear the whole output area, not just a single line.
# 
# To clear output in the Notebook you can use the `clear_output()` function.  If you are clearing the output every frame of an animation, calling `clear_output()` will create noticeable flickering.  You can use `clear_output(wait=True)` to add the *clear_output* call to a queue.  When data becomes available to replace the existing output, the *clear_output* will be called immediately before the new data is added.  This avoids the flickering by not rendering the cleared output to the screen.

# ## Simple example

# Here we show our progress iterating through a list:

# In[1]:


import sys
import time


# In[2]:


from IPython.display import display, clear_output
for i in range(10):
    time.sleep(0.25)
    clear_output(wait=True)
    print(i)
    sys.stdout.flush()


# ## AsyncResult.wait_interactive

# The AsyncResult object has a special `wait_interactive()` method, which prints its progress interactively,
# so you can watch as your parallel computation completes.
# 
# **This example assumes you have an IPython cluster running, which you can start from the [cluster panel](/#clusters)**

# In[3]:


from IPython import parallel
rc = parallel.Client()
view = rc.load_balanced_view()

amr = view.map_async(time.sleep, [0.5]*100)

amr.wait_interactive()


# ## Matplotlib example

# You can also use `clear_output()` to clear figures and plots.

# In[4]:


get_ipython().run_line_magic('matplotlib', 'inline')
import numpy as np
import matplotlib.pyplot as plt


# In[5]:


from scipy.special import jn
x = np.linspace(0,5)
f, ax = plt.subplots()
ax.set_title("Bessel functions")

for n in range(1,10):
    time.sleep(1)
    ax.plot(x, jn(x,n))
    clear_output(wait=True)
    display(f)

# close the figure at the end, so we don't get a duplicate
# of the last plot
plt.close()