powerlaw-general

Hi, First off, thanks for maintaining such an excellent package! I work outside this area, and having

Dear all, If the data are dependent, is it okay to use "compare distribution" method ?

Dear all, Power law fit doesn't provide options to manually indicate what should be bin size when

I saved the results from powerlaw.Fit() to a pickle file. When I try to read the results back, I get

When I call powerlaw.Fit, it outputs: xmin progress: X% and keeps updating the progress. This is nice

I'm just trying to fit a curve to a power-law dataset. I'm used to using scipy for curve

Hi, I try to plot pdf with my own bins. Line 1970 in powerlaw.py in function pdf: if 'bins'

Hello Jeff, I am facing the same issue as the previous user but in past, you solved the problem by

Hi Jeff, As Philipp, I was also looking for the p-value for the goodness-of-fit of power law. While I

Adding 3.5 as xmax does not help either. And multiplying the smallest value with the data also didn

Congratulations! On Mon, May 3, 2021 at 9:48 AM Ilya Belalov <ilya.b...@gmail.com> wrote:

Amazing, thank you so much, Jeff! I looked for hours to find an explanation and this is just perfect

I didn't parse what you did, but did you not threshold your data above xmin before fitting?

I've already posted a question on Stackoverflow. Does anyone mind to take a look and advise?

Hi Jeff (and any future readers), I found a workable solution that should have occurred to me earlier

Glad you got it sorted! Thanks for using powerlaw. On Fri, May 24, 2019 at 10:19 AM 'Jan Grießer

Not sure what you're asking, but powerlaw is not network analysis software. Thanks, Jeff On Wed,

"Can I trust those results?" Yes. https://github.com/jeffalstott/powerlaw/issues/25 "I

Thanks Jeff. I have been understanding the concept of the algorithm. I would like to express my

Hi Jeff, Brilliant, thanks. Sorry for my wording and thanks for trying to reply. I managed to

Great insight about xmin and AIC! The xmin issue is controversial. it is clear that you are assuming

Hi Jeff That's brilliant, thank you. I must have misread the docs. On Sunday, September 3, 2017

Updated to version 1.4.2. Check it out on PyPI or Github. Changes: - 'verbose' option added

fit.truncated_power_law.alpha fit.truncated_power_law.Lambda or fit.truncated_power_law.parameters or

Hi Alex, Have you tried taking samples of your data and seeing how the runtime scales as you increase

Hi again, Thank you. Possible reason is that while lognormal is found a better fit for our data, it

Great, thanks! Your answer is more than helpful. Indeed, when plotting the fit.Ds, sigma and sigma/

Thanks a lot! Now it is clear as well as your decision not to have it implemented. I did read the

(The answer was: "distribution_fit old code that is unadvertised and no one should be using and

Nope. They are totally different methods. Look at the other paper referenced for methods for fitting