Hello SymPy Community,

I have almost completed my GSOC Proposal but there are few things I want to 
ask regarding some of the tasks I mentioned in my proposal:

*1. Completing the Random Walks Prototype*
Here, after working on the Random Walks  user might want to visualize 
his/her Random Walk generated in SymPy only, rather than having difficulty 
using matplotlib or other software's, therefore I added a Task in my 
proposal "Visualizing the Random Walk".

Now, since SymPy currently uses matplotlib to render plots so we can also 
use it to render plot for Random Walk too and also I came up with the idea 
that since matplotlib can generate animated plots so we can show animation 
for Random Walks too like:

*RandomWalk.visualise(name="RW", dim=None, path = None, animated = True, 
save = False)*

where,
name is a string,
dim = dimensions,
path = path generated by our RandomWalk function which I will complete as a 
part of my GSOC first,
animated = Boolean (Whether the user wants animated plot or not)
save = Boolean (Save the plot image or not)

I think this functionality in SymPy will be easy and good for user(Any 
feedback regarding this would be helpful)

I have been searching for a while regarding what would be the best way to 
plot the graphs then I generated some plots using matplotlib ( I will 
attach files at the end )

*2. Identifying and Generating the Noise Processes*

The same thing as of Random Walk goes to the Noise Processes too(Regarding 
the visualization).
Visualization method for noise processes would be very similar to Random 
Walk.

Also, since I learned about some modules which can generate samples for 
Noise Processes so I added a new functionality of Identifying the noise 
process like:

*Noise.identify(X)*

where, 
X = Array which contains sample of a noise process.

And if X belongs to a noise process then it will return True and the name 
of the noise else False.
This way we would be able to extend Sympy's stats module without 
implementing something which is already implemented somewhere else.

*Example Output*: True, Gaussian White Noise

*Rest of the tasks included in my proposal are from the Sympy's GSOC Idea's 
Page(from Probability Section).*

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