Thank you Sir Alan for telling me about the Asymptote software, it surely is better than matplotlib(it would have been best if the scripting language was python), I will see if I could use that in visualizing Random Walk and Noise Processes : )
No Sir @Peter, this is different from what I am trying to implement.(Thanks for letting me know about different libraries though.) On Sunday, 3 April 2022 at 11:45:18 UTC+5:30 [email protected] wrote: > As I said, I have never uded this stats module, hence no idea what is does. > There is a module lmfit, which I have played around with a very little bit. > It is about fitting noisy data to some 'curve'. > No idea if this is similar to what you want to do. > > On Sun 3. Apr 2022 at 04:16, Kuldeep Borkar <[email protected]> > wrote: > >> Thank you for the response Sir, >> >> 1. It's just the opposite, I am not even trying to beat matplotlib, I am >> just trying to develop what's already there in SymPy . >> In SymPy presently the plots are rendered using matplotlib as a backend. >> If Random Walk is being implemented in SymPy user would like to visualize >> it in the most easy and efficient way possible. >> It's like either use matplotlib or in SymPy just use something like >> RandomWalk('rw', animated=True, store='C:\\').visualize. So, what would be >> better? >> >> 2. Thank you for telling me about sdeint library. >> I think since, stochastic processes is already implemented in SymPy so we >> should make it a full-fledged stat's module. >> For the point that we can get the sample paths of integrated white noise >> for free so yes it might not be the novel idea to implement that in SymPy >> so for that we can add more functionality like, from the given sample's >> identify does that belong to a noise process or not(return True if it >> belongs and False if it does not), I don't think this is already >> implemented. >> >> This way we could extend the stat's module and don't just implement >> what's already implemented. >> >> On Friday, 1 April 2022 at 11:42:08 UTC+5:30 [email protected] wrote: >> >>> I have never used sympy statistics, hence my comments may be without any >>> use. >>> >>> 1. >>> With matplotlib there is an excellent visualisation library available. >>> Hard for me to see, how you can beat it. >>> >>> 2. >>> There is a library sdeint available, which numerically integrates Ito or >>> Stratchonovich stochastic differential equations. >>> Hence, it gives sample paths of integrated white noise for free, of >>> course. >>> (I think, it is a bit of an one man show, not really being developed >>> much, but the stochastic integration works) >>> >>> >>> >>> >>> >>> >>> Am Fr., 1. Apr. 2022 um 02:48 Uhr schrieb Kuldeep Borkar < >>> [email protected]>: >>> >>>> Hello SymPy Community, >>>> >>>> Few days ago, I was having a discussion regarding my GSOC Project Idea >>>> with this year's mentor for the Probability Section and on my idea >>>> regarding implementation of noise processes I came up with the suggestion >>>> of adding a method to Visualize the noise processes, also the Random Walk >>>> which I am planning to complete as a part of my GSOC this summer. >>>> >>>> *My suggestion to visualize was to add a method like .visualize( ) to >>>> Noise Processes and Random Walk and pass some flags whether the user wants >>>> it in animated sort of thing or not but this idea was * >>>> *Request For Comments(RFC) from the SymPy community since .visualize( ) >>>> might not be the best the to implement this thing.* >>>> >>>> *So any feedback/comments regarding the above would be helpful a lot* >>>> : ) >>>> >>>> On Monday, 7 March 2022 at 18:12:16 UTC+5:30 Kuldeep Borkar wrote: >>>> >>>>> I was planning to be a part of GSOC this summer with SymPy to learn >>>>> more and >>>>> implement something big. >>>>> >>>>> >>>>> *For this,I was planning to work on improving SymPy's stats module;* >>>>> I checked there are many good things already implemented in the stats >>>>> module. >>>>> And, I found issue(https://github.com/sympy/sympy/issues/17197) >>>>> about the Random Walk implementation which is in progress, but it seems >>>>> the >>>>> issue created is closed for now and I am looking forward to complete it. >>>>> >>>>> On the ideas page of SymPy for GSOC in the Probability section I found >>>>> things which can be implemented as a part of GSOC project. >>>>> I saw things there which interests me to work on: >>>>> >>>>> --> Reproducibility of Sampling Outputs of Stochastic Processes, >>>>> WienerProcess(I think the idea of ito calculus is covered here), >>>>> Completing Random Walk(I think it was a typo there it should be >>>>> Prototype not Protorype), etc. >>>>> >>>>> >>>>> >>>>> >>>>> *But my query is:I was planning to implement something different;A >>>>> better way to use probability of events like one mentioned >>>>> here(https://github.com/sympy/sympy/issues/20111 >>>>> <https://github.com/sympy/sympy/issues/20111>):* >>>>> --> >>>>> >>>>> *Working with events rather than random variables.--> Currently I >>>>> don't think there's is a way to define just an event andnot specifically >>>>> a >>>>> random variable like* >>>>> A, B = event('A, B') >>>>> P(A) = x >>>>> P(B & A) = y >>>>> P(B & !A)? >>>>> *Also, currently there is not a way to assign probability like a >>>>> certain event A* >>>>> >>>>> *has probability P(A) = x(Please enlighten me if I am making a mistake >>>>> somewhere ; ) )* >>>>> and noise processes too, like white noise (White noise refers to a >>>>> statistical model for signals and signal sources, rather than to any >>>>> specific signal), >>>>> *is it possible to work on some of the things from ideas page and some >>>>> from our own?* >>>>> >>>>> *[I am trying to understand the codebase now(Are there any tips which >>>>> could help me to understand things more efficiently? ) *: ( >>>>> *It may not be a valid question though, but thought I should try to >>>>> ask first.]* >>>>> >>>> >>>>> -- >>>> You received this message because you are subscribed to the Google >>>> Groups "sympy" group. >>>> To unsubscribe from this group and stop receiving emails from it, send >>>> an email to [email protected]. >>>> To view this discussion on the web visit >>>> https://groups.google.com/d/msgid/sympy/82761518-db7a-4f23-9e72-5549c5986fabn%40googlegroups.com >>>> >>>> <https://groups.google.com/d/msgid/sympy/82761518-db7a-4f23-9e72-5549c5986fabn%40googlegroups.com?utm_medium=email&utm_source=footer> >>>> . >>>> >>> -- >> You received this message because you are subscribed to the Google Groups >> "sympy" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> > To view this discussion on the web visit >> https://groups.google.com/d/msgid/sympy/2d94a0de-d6a1-4a40-9855-ca1cb8ee056dn%40googlegroups.com >> >> <https://groups.google.com/d/msgid/sympy/2d94a0de-d6a1-4a40-9855-ca1cb8ee056dn%40googlegroups.com?utm_medium=email&utm_source=footer> >> . >> > -- > Best regards, > > Peter Stahlecker > -- You received this message because you are subscribed to the Google Groups "sympy" group. 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