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horrible efficiency

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hiro98 2020-04-27 10:23:28 +02:00
parent eeae315954
commit be1ddd9390
3 changed files with 7 additions and 9 deletions
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prog/python/qqgg/parton_density_function_stuff.org
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@ -252,7 +252,7 @@ Plotting it, we can see that the variance is reduced.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f3574d07820> |
| <matplotlib.lines.Line2D | at | 0x7f3562675a90> |
[[file:./.ob-jupyter/5597ca6056db11908cfca64c2090d67e3b94cc9e.png]]
:END:
@ -267,7 +267,7 @@ Lets plot how the pdf looks.
#+RESULTS:
:RESULTS:
| <matplotlib.lines.Line2D | at | 0x7f3572b7b8b0> |
| <matplotlib.lines.Line2D | at | 0x7f35627bde50> |
[[file:./.ob-jupyter/b92f0c4b2c9f2195ae14444748fcdb7708d81c19.png]]
:END:
@ -276,7 +276,7 @@ Now we sample some events. Doing this in parallel helps. We let the os
figure out the cpu mapping.
#+begin_src jupyter-python :exports both :results raw drawer
intervals_η = [interval_η, [.1, 1], [.1, 1]]
intervals_η = [interval_η, [.05, 1], [.05, 1]]
result, eff = monte_carlo.sample_unweighted_array(
10000,
@ -291,7 +291,7 @@ figure out the cpu mapping.
#+end_src
#+RESULTS:
: 0.003007891162077376
: 0.0009801272637920972
@ -303,11 +303,9 @@ file.
Let's look at a histogramm of eta samples.
#+begin_src jupyter-python :exports both :results raw drawer
draw_histo_auto(result[:, 0], r"$\eta$", bins=100)
fig, ax = draw_histo_auto(result[:, 0], r"$\eta$", bins=100)
ax.set_yscale("log")
#+end_src
#+RESULTS:
:RESULTS:
| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7f35728b26a0> |
[[file:./.ob-jupyter/ec474fc3576110c487c7fb31403cbab0a063efa9.png]]
:END:
[[file:./.ob-jupyter/721ccfd2a691a94a1e437f282bf2d95f5de25f0c.png]]