Jupyter notebook for a Bayesian inversion of critical wedge parameters

bayes_wedge_inversion.ipynb

Sorry, again...

=========
t_start = time.time()

priors = calc_row_likelihood(priors)
t_end = time.time()

print('done with {} calcs in {:.1f} seconds'.format(n_iters, (t_end - t_start)))

done with 100000 calcs in 0.1 seconds

---

C:\Users\vamaris\AppData\Local\Continuum\anaconda3\lib\site-packages\scipy\stats\stats.py:308: RuntimeWarning: divide by zero encountered in log
log_a = np.log(np.array(a, dtype=dtype))

priors['rel_likelihood'] = priors['likelihood'] / priors['likelihood'].max()

---

IndexError Traceback (most recent call last)
in
----> 1 priors['rel_likelihood'] = priors['likelihood'] / priors['likelihood'].max()

IndexError: invalid index to scalar variable.

priors.head()

---

AttributeError Traceback (most recent call last)
in
----> 1 priors.head()

AttributeError: 'numpy.float64' object has no attribute 'head'

---

As you see Im here.

I see the problem now.

You need to run something like

priors['likelihood'] = priors.apply(calc_row_likelihood, axis=1)

instead of

priors = calc_row_likelihood(priors)

If it doesn't work exactly, read the error messages and try to understand them to find the problem.

Ok. Perfect.

Thank you. It work correctly.

Great, I am glad I could help. Good luck with your research!

Thank you. El vie., 17 ene. 2020 a las 9:50, Richard Styron (<[email protected]>) escribió:

…

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