Without loss of generality, why not simplify the function to (1/a)*exp(-b*t).

Then everything should work.

Hi Rody,

If you are serious about wanting to post, then I am happy to talk with you.

Best,

-paul

I would take the sigma values you obtain for each fit parameter and plot two extra curves:

One where you add or subtract (as needed) the sigma values to their related best fit values to get the largest value to plot, and

Another where you do the opposite. For example, if you fit y = a*x – b/x) to some data and determine best fit values for a and b, then

I’d plot the best fit curve along with two others: (1) (a+da)*x – (b-db)/x and (2) (a-da)*x – (b+db)/x

In general, this can get complicated, so another method would be to use transparency and plot all possible combination of fit parameters as error margin curves.

I would like to adapt your code for my data. I have measured data, I fit my curve with fit_curve in Python. I have the best fitting curve at the end of my code. And I calculate sigma that is the standard deviation. But I don’t know if in order to have the +1sigma curve I have to add this sigma to the measured curve or to the best fitting curve. Can you help with this ?

Best

]]>If you use the following magic in your Jupyter notebook, you will be able to see the animation inline.

%matplotlib notebook

I am not completely sure how to render the graphics outside the notebook, but there must be a way to save it as an mp4 or something. Perhaps something like this:

ani.save(‘basic_animation.mp4’, fps=30, extra_args=[‘-vcodec’, ‘libx264’])

Judging by the dates of your posts and the number of comments on each post, I gather that it’s not really taking off…

I want to help build this repository, and have it gain momentum. It’s what’s needed. How do I sign up?

]]>Tom,

Thanks for the info. I will have to try this option.

Best,

-paul

http://numba.pydata.org/numba-doc/0.31.0/user/jit.html says that there is a compilation time associated with numba, which explains the long code execution for the first run. This can be mitigated by using the cache=True option (or the ahead of time compilation options detailed in http://numba.pydata.org/numba-doc/0.31.0/user/pycc.html).

Hope that helps. ]]>

Yes, the line, piece befuddles me too. It’s called a generator, and my next step is to figure out how to understand this notation.

-p