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https://github.com/vale981/bachelor_thesis
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fix caching
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parent
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73 changed files with 7645 additions and 10051 deletions
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@ -265,7 +265,7 @@ Intergrate σ with the mc method.
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#+end_src
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#+RESULTS:
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: IntegrationResult(result=0.05378648054059906, sigma=0.000937752421054302, N=2191)
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: IntegrationResult(result=0.053667312175396706, sigma=0.0009169990002543172, N=2290)
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We gonna export that as tex.
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#+begin_src jupyter-python :exports both :results raw drawer
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@ -275,7 +275,7 @@ We gonna export that as tex.
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#+end_src
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#+RESULTS:
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: \(N = 2191\)
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: \(N = 2290\)
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*** Integration over η
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Plot the intgrand of the pseudo rap.
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@ -301,7 +301,7 @@ Plot the intgrand of the pseudo rap.
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#+end_src
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#+RESULTS:
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: IntegrationResult(result=0.05358650745863841, sigma=0.0009625499313527285, N=131)
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: IntegrationResult(result=0.054691324175410276, sigma=0.0008576665267260218, N=155)
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As we see, the result is a little better if we use pseudo rapidity,
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because the differential cross section does not difverge anymore. But
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@ -316,7 +316,7 @@ And yet again export that as tex.
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#+end_src
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#+RESULTS:
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: \(N = 131\)
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: \(N = 155\)
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*** Using =VEGAS=
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Now we use =VEGAS= on the θ parametrisation and see what happens.
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@ -334,9 +334,9 @@ Now we use =VEGAS= on the θ parametrisation and see what happens.
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#+end_src
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#+RESULTS:
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: VegasIntegrationResult(result=0.054023061813239806, sigma=0.00013466995320612007, N=2805, increment_borders=array([0.16380276, 0.23700021, 0.34462362, 0.50810449, 0.76851565,
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: 1.22948173, 1.91477323, 2.37356001, 2.63313669, 2.79599811,
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: 2.90457286, 2.9777899 ]), vegas_iterations=6067)
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: VegasIntegrationResult(result=0.05388506711723066, sigma=0.00013404230661800357, N=2805, increment_borders=array([0.16380276, 0.23698033, 0.34597286, 0.50870877, 0.76853445,
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: 1.22569342, 1.91171522, 2.37349146, 2.6337053 , 2.79689719,
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: 2.90485333, 2.9777899 ]), vegas_iterations=8909)
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This is pretty good, although the variance reduction may be achieved
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partially by accumulating the results from all runns. Here this gives
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@ -367,9 +367,9 @@ This depends, of course, on the iteration count.
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#+end_src
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#+RESULTS:
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: VegasIntegrationResult(result=0.05379154293247947, sigma=1.201764609981918e-05, N=2805, increment_borders=array([0.16380276, 0.23693318, 0.34469593, 0.5085223 , 0.76755511,
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: 1.22588716, 1.91279852, 2.3717465 , 2.63277683, 2.79612676,
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: 2.90435134, 2.9777899 ]), vegas_iterations=15586)
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: VegasIntegrationResult(result=0.053793465122929734, sigma=1.2155519624452432e-05, N=2805, increment_borders=array([0.16380276, 0.2368871 , 0.34486266, 0.50853544, 0.76920153,
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: 1.22798025, 1.91348845, 2.37314199, 2.63231014, 2.79612106,
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: 2.90431041, 2.9777899 ]), vegas_iterations=14887)
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Let's define some little helpers.
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#+begin_src jupyter-python :exports both :tangle tangled/plot_utils.py
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@ -474,7 +474,7 @@ And now we plot the integrand with the incremens.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/2a36b9e4f2adc947114302c4523d5e60474f5170.png]]
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[[file:./.ob-jupyter/ad0aa4f2f769c5aafa350ce6fdce6d2e14a62c3e.png]]
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*** Testing the Statistics
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Let's battle test the statistics.
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#+begin_src jupyter-python :exports both :results raw drawer
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@ -491,7 +491,7 @@ Let's battle test the statistics.
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#+end_src
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#+RESULTS:
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: 0.695
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: 0.683
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So we see: the standard deviation is sound.
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@ -512,7 +512,7 @@ Doing the same thing with =VEGAS= works as well.
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#+end_src
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#+RESULTS:
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: 0.704
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: 0.685
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** Sampling and Analysis
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Define the sample number.
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@ -551,7 +551,10 @@ Now we monte-carlo sample our distribution. We observe that the efficiency his v
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#+end_src
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#+RESULTS:
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: 0.02744127583441648
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:RESULTS:
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: sample_unweighted_array
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: 0.027399137427054687
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:END:
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Let's save that.
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#+begin_src jupyter-python :exports both :results raw drawer
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@ -625,7 +628,10 @@ at least a little bit better. The numeric inversion is horribly inefficent.
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#+end_src
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#+RESULTS:
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: 0.07903728511692736
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:RESULTS:
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: sample_unweighted_array
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: 0.07903687969629128
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:END:
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<<cosθ-bare-eff>>
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#+begin_src jupyter-python :exports both :results raw drawer
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@ -682,7 +688,7 @@ We define an auxilliary method for convenience.
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)
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set_up_axis(ax_ratio, pimp_top=False)
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ax_ratio.set_ylabel('ratio')
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ax_ratio.set_ylabel("ratio")
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draw_histogram(
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ax_ratio,
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[heights / reference, edges],
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@ -711,7 +717,7 @@ We define an auxilliary method for convenience.
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histogram,
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errorbars=True,
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hist_kwargs=dict(color="#1f77b4"),
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errorbar_kwargs=dict(color="orange"),
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errorbar_kwargs=dict(),
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normalize_to=None,
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):
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"""Draws a histogram with optional errorbars using the step style.
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@ -741,14 +747,10 @@ We define an auxilliary method for convenience.
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hist_plot = ax.step(edges, [heights[0], *heights], **hist_kwargs)
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if errorbars is not False:
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ax.errorbar(
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centers,
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heights,
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deviations,
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linestyle="none",
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color=hist_plot[0].get_color(),
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,**errorbar_kwargs
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)
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if "color" not in errorbar_kwargs:
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errorbar_kwargs["color"] = hist_plot[0].get_color()
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ax.errorbar(centers, heights, deviations, linestyle="none", **errorbar_kwargs)
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ax.set_xlim(*[edges[0], edges[-1]])
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@ -786,7 +788,7 @@ The histogram for cosθ.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/00141b21f62165685b960a3dd7cc1313a2e4e0d5.png]]
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[[file:./.ob-jupyter/dde553030cdb96c1f0a0b223abf9bdd4602119af.png]]
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*** Observables
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Now we define some utilities to draw real 4-momentum samples.
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@ -813,6 +815,7 @@ Now we define some utilities to draw real 4-momentum samples.
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cosθ_sample = monte_carlo.sample_unweighted_array(
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sample_num, lambda x: diff_xs_cosθ(x, charge, esp), interval_cosθ, **kwargs
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)
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print(cosθ_sample)
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φ_sample = np.random.uniform(0, 1, sample_num)
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def make_momentum(esp, cosθ, φ):
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@ -868,8 +871,7 @@ Lets try it out.
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interval_cosθ,
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charge,
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esp,
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cache="cache/bare_cos_theta",
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proc=3,
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proc='auto',
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momentum_cache="cache/momenta_bare_cos_theta",
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)
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momentum_sample
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@ -877,14 +879,14 @@ Lets try it out.
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#+RESULTS:
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:RESULTS:
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: Trying cache
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: array([[100. , 54.98191622, 34.53483728, -76.05480855],
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: [100. , 87.95719691, 30.53342542, -36.48618154],
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: [100. , 29.02952127, 15.22393108, -94.47496397],
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: sample_momenta
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: array([[100. , 16.04646249, 12.03145593, 97.96813313],
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: [100. , 51.41640893, 11.55602144, 84.98712409],
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: [100. , 40.75310071, 39.90715071, 82.13771426],
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: ...,
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: [100. , 37.62519445, 37.66247038, -84.65153907],
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: [100. , 75.22515369, 0.79738586, 65.88277793],
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: [100. , 26.58309333, 4.84203005, -96.28028819]])
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: [100. , 20.83112183, 7.06626328, 97.55066523],
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: [100. , 33.23340199, 1.86567636, -94.29772131],
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: [100. , 32.9373831 , 47.67830405, -81.49790254]])
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:END:
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@ -898,7 +900,7 @@ Now let's make a histogram of the η distribution.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/7752e9a1c6e940c7b7b80cfc71a2f0cfe4cd534a.png]]
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[[file:./.ob-jupyter/e2b510b9e200304cea662510e2bb1448cddf5055.png]]
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And the same for the p_t (transverse momentum) distribution.
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@ -911,7 +913,7 @@ And the same for the p_t (transverse momentum) distribution.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/07eb7195522d9aad2b943f9ac737bbd14ed868a7.png]]
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[[file:./.ob-jupyter/fba21aa6168c255a5523d865bace1ed6cfd2cab6.png]]
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That looks somewhat fishy, but it isn't.
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#+begin_src jupyter-python :exports both :results raw drawer
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@ -949,7 +951,10 @@ An again we see that the efficiency is way, way! better...
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#+end_src
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#+RESULTS:
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:RESULTS:
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: sample_unweighted_array
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: \(\mathfrak{e} = 41\%\)
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:END:
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<<η-eff>>
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Let's draw a histogram to compare with the previous results.
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@ -973,7 +978,7 @@ Let's draw a histogram to compare with the previous results.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/172aa63c901d42651e1bd2e4d834702052881b03.png]]
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[[file:./.ob-jupyter/931d0c4522606eb420d0fb674a918a79e6244ce2.png]]
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Looks good to me :).
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@ -993,8 +998,8 @@ distribution. We throw away the integral, but keep the increments.
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#+end_src
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#+RESULTS:
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: array([-0.9866143 , -0.96970744, -0.93106302, -0.838359 , -0.60074914,
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: 0.00116799, 0.60518774, 0.8409529 , 0.93115044, 0.96958873,
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: array([-0.9866143 , -0.96977389, -0.93146639, -0.83915761, -0.6036579 ,
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: 0.00130069, 0.60172656, 0.8380989 , 0.9306508 , 0.96957082,
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: 0.9866143 ])
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Visualizing the increment borders gives us the information we want.
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@ -1012,8 +1017,8 @@ Visualizing the increment borders gives us the information we want.
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#+RESULTS:
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:RESULTS:
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: <matplotlib.legend.Legend at 0x7f7cef8f7910>
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[[file:./.ob-jupyter/135df39477282a6a84cb6775e6e33ba30027d901.png]]
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: <matplotlib.legend.Legend at 0x7f4d0ae3c8b0>
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[[file:./.ob-jupyter/8028f6a7830defe735841a3c729c14a4758a137f.png]]
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:END:
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We can now plot the reweighted distribution to observe the variance
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@ -1037,7 +1042,7 @@ reduction visually.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/bb992ad6136cabc8c9ba3f54cf04564bbb9481d4.png]]
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[[file:./.ob-jupyter/ab7b49de13782a6d80067e9f09d22c113d7b46a2.png]]
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I am batman! Let's plot the weighting distribution.
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@ -1052,7 +1057,7 @@ I am batman! Let's plot the weighting distribution.
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/15a1ba02afddb7520a5bcf78ef34e651a05d0d11.png]]
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[[file:./.ob-jupyter/d89926699b29891fa3a2b9602e5ebc79dd504575.png]]
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Now, draw a sample and look at the efficiency.
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@ -1069,7 +1074,10 @@ Now, draw a sample and look at the efficiency.
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#+end_src
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#+RESULTS:
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: 0.5954571428571428
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:RESULTS:
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: sample_unweighted_array
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: 0.5875845864661654
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:END:
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#+begin_src jupyter-python :exports both :results raw drawer
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tex_value(
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@ -1081,7 +1089,7 @@ Now, draw a sample and look at the efficiency.
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#+end_src
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#+RESULTS:
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: \(\mathfrak{e} = 60\%\)
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: \(\mathfrak{e} = 59\%\)
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If we compare that to [[cosθ-bare-eff]], we can see the improvement :P.
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It is even better the [[η-eff]]. The histogram looks just the same.
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@ -1092,7 +1100,7 @@ save_fig(fig, 'histo_cos_theta_strat', 'xs', size=(4,3))
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#+end_src
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#+RESULTS:
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[[file:./.ob-jupyter/bc48dc495d9bc8b96549de58d04489942f8c89dd.png]]
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[[file:./.ob-jupyter/1d90af7d456726ad3780a203acc8938f1894f6b1.png]]
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*** Some Histograms with Rivet
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**** Init
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@ -1101,7 +1109,6 @@ save_fig(fig, 'histo_cos_theta_strat', 'xs', size=(4,3))
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#+end_src
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#+RESULTS:
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: Welcome to JupyROOT 6.20/04
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**** Plot the Histos
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#+RESULTS:
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@ -1161,7 +1168,7 @@ save_fig(fig, 'histo_cos_theta_strat', 'xs', size=(4,3))
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#+RESULTS:
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:RESULTS:
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[[file:./.ob-jupyter/8526f4d62551118e729d80f06851ae67d31b28b4.png]]
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[[file:./.ob-jupyter/b37f5272c36f1b68cca42d9f8c83d27008023fef.png]]
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[[file:./.ob-jupyter/3faf2ae6a32cb4a1b25fb82aa59e1c983e6f6f25.png]]
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[[file:./.ob-jupyter/64647b90c47e531721f081591f3eebeb45ccab0f.png]]
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[[file:./.ob-jupyter/aacce79de91dc428a51ceb7a54af6297a48534cf.png]]
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[[file:./.ob-jupyter/9db0733def0d742d5894c4793b1c95eb7931c3cf.png]]
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:END:
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Load diff
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@ -3956,8 +3956,8 @@
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\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
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\pgfsetstrokecolor{currentstroke}%
|
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|
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|
|
@ -3969,8 +3969,8 @@
|
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|
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|
|
@ -3982,8 +3982,8 @@
|
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|
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|
|
@ -3995,8 +3995,8 @@
|
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|
|
@ -4008,8 +4008,8 @@
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|
|
@ -4021,8 +4021,8 @@
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|
|
@ -4034,8 +4034,8 @@
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|
|
@ -4047,8 +4047,8 @@
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|
@ -4060,8 +4060,8 @@
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@ -4073,8 +4073,8 @@
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||||
\pgfpathlineto{\pgfqpoint{2.263511in}{0.818306in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.307872in}{0.833882in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.348537in}{0.851403in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.385504in}{0.870741in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.418775in}{0.891666in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.440956in}{0.907914in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.442804in}{0.748606in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.483468in}{0.762903in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.516739in}{0.777704in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.546313in}{0.794103in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.572190in}{0.811867in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.596219in}{0.832251in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.616551in}{0.853463in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.635035in}{0.877004in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.651670in}{0.902840in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.660912in}{0.919647in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.662761in}{0.753483in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.681244in}{0.769805in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.697880in}{0.788972in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.712667in}{0.811326in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.725605in}{0.837051in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.736696in}{0.865982in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.747786in}{0.904639in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.749634in}{0.756993in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.758876in}{0.776555in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.768118in}{0.804022in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.775512in}{0.835520in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.782905in}{0.882047in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.784754in}{0.897367in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.786602in}{0.762697in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.792147in}{0.793310in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.797692in}{0.844855in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.801389in}{0.904573in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.801389in}{0.904573in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4122,8 +4121,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{0.970682in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{0.970682in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{0.970620in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{0.970620in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4135,8 +4134,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{0.970682in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{0.970682in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{0.970620in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{0.970620in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4148,8 +4147,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{1.006845in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{1.006845in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{1.006468in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{1.006468in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4161,8 +4160,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{1.093597in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{1.093597in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{1.092850in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{1.092850in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4174,8 +4173,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{1.315950in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{1.315950in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{1.313228in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{1.313228in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4187,8 +4186,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{1.879218in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{1.879218in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{1.879342in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{1.879342in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4200,8 +4199,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{2.444454in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.444454in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{2.441215in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.441215in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4213,8 +4212,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{2.665080in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.665080in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{2.662410in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.662410in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4226,8 +4225,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{2.749486in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.749486in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{2.749019in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.749019in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
@ -4239,8 +4238,8 @@
|
|||
\definecolor{currentstroke}{rgb}{0.501961,0.501961,0.501961}%
|
||||
\pgfsetstrokecolor{currentstroke}%
|
||||
\pgfsetdash{{3.700000pt}{1.600000pt}}{0.000000pt}%
|
||||
\pgfpathmoveto{\pgfqpoint{2.785457in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.785457in}{2.101389in}}%
|
||||
\pgfpathmoveto{\pgfqpoint{2.785440in}{0.594444in}}%
|
||||
\pgfpathlineto{\pgfqpoint{2.785440in}{2.101389in}}%
|
||||
\pgfusepath{stroke}%
|
||||
\end{pgfscope}%
|
||||
\begin{pgfscope}%
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ from multiprocessing import Pool, cpu_count
|
|||
import functools
|
||||
from scipy.optimize import minimize_scalar, root, shgo
|
||||
from dataclasses import dataclass
|
||||
import utility
|
||||
|
||||
|
||||
def _process_interval(interval):
|
||||
|
|
@ -547,6 +548,7 @@ class SamplingWorker:
|
|||
_FUN = None
|
||||
|
||||
|
||||
@utility.numpy_cache("cache")
|
||||
def sample_unweighted_array(
|
||||
num,
|
||||
f,
|
||||
|
|
@ -554,22 +556,12 @@ def sample_unweighted_array(
|
|||
increment_borders=None,
|
||||
report_efficiency=False,
|
||||
proc=None,
|
||||
cache=None,
|
||||
**kwargs
|
||||
):
|
||||
"""Sample `num` elements from a distribution. The rest of the
|
||||
arguments is analogous to `sample_unweighted`.
|
||||
"""
|
||||
global _FUN
|
||||
if cache is not None and os.path.isfile(cache + ".npy"):
|
||||
result = np.load(cache + ".npy")
|
||||
eff = None
|
||||
eff_path = cache + ".eff.npy"
|
||||
if report_efficiency:
|
||||
if os.path.isfile(eff_path):
|
||||
eff = np.load(eff_path)[0]
|
||||
|
||||
return (result, eff) if report_efficiency else result
|
||||
|
||||
sample_arr = None
|
||||
eff = None
|
||||
|
|
@ -643,9 +635,4 @@ def sample_unweighted_array(
|
|||
|
||||
eff = next(samples)[1] if report_efficiency else None
|
||||
|
||||
if cache is not None:
|
||||
np.save(cache + ".npy", sample_arr)
|
||||
if report_efficiency:
|
||||
np.save(cache + ".eff.npy", [eff])
|
||||
|
||||
return (sample_arr, eff) if report_efficiency else sample_arr
|
||||
|
|
|
|||
|
|
@ -22,6 +22,8 @@
|
|||
#+END_SRC
|
||||
|
||||
#+RESULTS:
|
||||
: The autoreload extension is already loaded. To reload it, use:
|
||||
: %reload_ext autoreload
|
||||
|
||||
** Global Config
|
||||
#+begin_src jupyter-python :exports both :results raw drawer
|
||||
|
|
@ -30,6 +32,7 @@ e_proton = 100 # GeV
|
|||
interval_η = [-η, η]
|
||||
interval = η_to_θ([-η, η])
|
||||
interval_cosθ = np.cos(interval)
|
||||
num_samples = 10_000
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
|
|
@ -236,9 +239,14 @@ calculate the 4-momentum kinematics twice. Maybe that can be done
|
|||
nicer.
|
||||
|
||||
#+begin_src jupyter-python :exports both :results raw drawer :tangle tangled/pdf.py
|
||||
def sample_momenta(num_samples, dist, interval, e_hadron, upper_bound=None):
|
||||
def sample_momenta(num_samples, dist, interval, e_hadron, upper_bound=None, **kwargs):
|
||||
res, eff = monte_carlo.sample_unweighted_array(
|
||||
num_samples, dist, interval, upper_bound=upper_bound, report_efficiency=True
|
||||
num_samples,
|
||||
dist,
|
||||
interval,
|
||||
upper_bound=upper_bound,
|
||||
report_efficiency=True,
|
||||
,**kwargs
|
||||
)
|
||||
cosθ, x_1, x_2 = res.T
|
||||
return momenta(e_hadron, x_1[None, :], x_2[None, :], cosθ[None, :]), eff
|
||||
|
|
@ -266,8 +274,8 @@ Let's plot it for some random values 😃.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
| <matplotlib.lines.Line2D | at | 0x7fb11a322c40> |
|
||||
[[file:./.ob-jupyter/a5954d2e2b47ff630695004830c3de94c2e34723.png]]
|
||||
| <matplotlib.lines.Line2D | at | 0x7f1f47902c10> |
|
||||
[[file:./.ob-jupyter/f12b49e327ad8ec823397b3bbe910e0d021f4dcd.png]]
|
||||
:END:
|
||||
|
||||
Having set both x to the same value, we get a symmetric distribution as expected.
|
||||
|
|
@ -283,39 +291,24 @@ But first we have to find an upper bound, which is expensive!
|
|||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: 2786.6683559915655
|
||||
: 2171.468698483163
|
||||
|
||||
Beware!, this is darn slow, becaus the efficiency is soooo low.
|
||||
#+begin_src jupyter-python :exports both :results raw drawer
|
||||
sample_momenta(100, dist, intervals, e_proton, upper_bound=upper_bound)[1]
|
||||
|
||||
sample_momenta(
|
||||
100,
|
||||
dist,
|
||||
intervals,
|
||||
e_proton,
|
||||
upper_bound=upper_bound,
|
||||
proc="auto",
|
||||
cache="cache/pdf/samp_costh_test",
|
||||
)[1]
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
# [goto error]
|
||||
#+begin_example
|
||||
|
||||
TypeErrorTraceback (most recent call last)
|
||||
<ipython-input-10-cc6814bf2dff> in <module>
|
||||
----> 1 sample_momenta(100, dist, intervals, e_proton, upper_bound=upper_bound)[1]
|
||||
|
||||
<ipython-input-6-abb92c78af23> in sample_momenta(num_samples, dist, interval, e_hadron, upper_bound)
|
||||
1 def sample_momenta(num_samples, dist, interval, e_hadron, upper_bound=None):
|
||||
----> 2 res, eff = monte_carlo.sample_unweighted_array(
|
||||
3 num_samples, dist, interval, upper_bound=upper_bound, report_efficiency=True
|
||||
4 )
|
||||
5 cosθ, x_1, x_2 = res.T
|
||||
|
||||
~/Documents/Projects/UNI/Bachelor/prog/python/qqgg/monte_carlo.py in sample_unweighted_array(num, f, interval, increment_borders, report_efficiency, *args, **kwargs)
|
||||
572 )
|
||||
573 else:
|
||||
--> 574 raise TypeError("Neiter interval nor increment_borders specified!")
|
||||
575
|
||||
576 for i, sample in zip(range(num), samples):
|
||||
|
||||
TypeError: Neiter interval nor increment_borders specified!
|
||||
#+end_example
|
||||
:END:
|
||||
: 0.00041597900424616594
|
||||
|
||||
** Switching Horses: Sampling η
|
||||
We set up a new distribution.
|
||||
|
|
@ -339,8 +332,8 @@ Plotting it, we can see that the variance is reduced.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
| <matplotlib.lines.Line2D | at | 0x7fb1154e17c0> |
|
||||
[[file:./.ob-jupyter/b5e9e8b157f5596913671e301fefee82daf805a9.png]]
|
||||
| <matplotlib.lines.Line2D | at | 0x7f1f44296940> |
|
||||
[[file:./.ob-jupyter/257473d72b7c4ffd6650dd4a736a2feaacac28cb.png]]
|
||||
:END:
|
||||
|
||||
Lets plot how the pdf looks.
|
||||
|
|
@ -354,7 +347,7 @@ Lets plot how the pdf looks.
|
|||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
| <matplotlib.lines.Line2D | at | 0x7fb111c4a5b0> |
|
||||
| <matplotlib.lines.Line2D | at | 0x7f1f4574b0d0> |
|
||||
[[file:./.ob-jupyter/db6aa636b2795408e0e7b762c7e43ed22136feaf.png]]
|
||||
:END:
|
||||
|
||||
|
|
@ -363,84 +356,27 @@ 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, [.01, 1], [.01, 1]]
|
||||
|
||||
result, eff = monte_carlo.sample_unweighted_array(
|
||||
10_000_000,
|
||||
num_samples,
|
||||
dist_η,
|
||||
interval=intervals_η,
|
||||
proc="auto",
|
||||
report_efficiency=True,
|
||||
cache="cache/huge",
|
||||
cache="cache/pdf/huge",
|
||||
)
|
||||
result
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
:RESULTS:
|
||||
# [goto error]
|
||||
#+begin_example
|
||||
|
||||
TypeErrorTraceback (most recent call last)
|
||||
<ipython-input-266-1e94dc16f128> in <module>
|
||||
----> 1 result, eff = monte_carlo.sample_unweighted_array(
|
||||
2 10_000_000,
|
||||
3 dist_η,
|
||||
4 interval=intervals_η,
|
||||
5 proc="auto",
|
||||
|
||||
~/Documents/Projects/UNI/Bachelor/prog/python/qqgg/monte_carlo.py in sample_unweighted_array(num, f, interval, increment_borders, report_efficiency, proc, cache, **kwargs)
|
||||
585 _FUN = f # there is no other way :(
|
||||
586
|
||||
--> 587 workers = [
|
||||
588 SamplingWorker.remote(
|
||||
589 num_samples=num,
|
||||
|
||||
~/Documents/Projects/UNI/Bachelor/prog/python/qqgg/monte_carlo.py in <listcomp>(.0)
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586
|
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587 workers = [
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--> 588 SamplingWorker.remote(
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589 num_samples=num,
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590 interval=interval,
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/usr/lib/python3.8/site-packages/ray/actor.py in remote(self, *args, **kwargs)
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377 A handle to the newly created actor.
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378 """
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--> 379 return self._remote(args=args, kwargs=kwargs)
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380
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381 def options(self, **options):
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/usr/lib/python3.8/site-packages/ray/actor.py in _remote(self, args, kwargs, num_cpus, num_gpus, memory, object_store_memory, resources, is_direct_call, max_concurrency, name, detached)
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527 # So, here pass actor_creation_function_descriptor to make
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528 # sure export actor class correct.
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--> 529 worker.function_actor_manager.export_actor_class(
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530 meta.modified_class,
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531 meta.actor_creation_function_descriptor,
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/usr/lib/python3.8/site-packages/ray/function_manager.py in export_actor_class(self, Class, actor_creation_function_descriptor, actor_method_names)
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357 "class_name": actor_creation_function_descriptor.class_name,
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358 "module": actor_creation_function_descriptor.module_name,
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--> 359 "class": pickle.dumps(Class),
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360 "job_id": job_id.binary(),
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361 "collision_identifier": self.compute_collision_identifier(Class),
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/usr/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle_fast.py in dumps(obj, protocol, buffer_callback)
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70 cp = CloudPickler(file, protocol=protocol,
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71 buffer_callback=buffer_callback)
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---> 72 cp.dump(obj)
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73 return file.getvalue()
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74
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/usr/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle_fast.py in dump(self, obj)
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615 def dump(self, obj):
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616 try:
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--> 617 return Pickler.dump(self, obj)
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618 except RuntimeError as e:
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619 if "recursion" in e.args[0]:
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/usr/lib/python3.8/site-packages/lhapdf.cpython-38-x86_64-linux-gnu.so in lhapdf.PDF.__reduce_cython__()
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TypeError: self._ptr cannot be converted to a Python object for pickling
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#+end_example
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:END:
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: array([[ 2.00392989, 0.03589851, 0.13781655],
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: [-0.77083958, 0.01088047, 0.1545276 ],
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: [-1.36224887, 0.02099844, 0.05346773],
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: ...,
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: [-0.22217984, 0.01724194, 0.06289104],
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: [-1.26972433, 0.23321426, 0.01352318],
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: [ 1.76699256, 0.01239091, 0.10577669]])
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|
|
@ -452,11 +388,11 @@ file.
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|
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Let's look at a histogramm of eta samples.
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#+begin_src jupyter-python :exports both :results raw drawer
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draw_histo(result[:, 0], "asht", bins=100)
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draw_histo_auto(result[:, 0], "asht", bins=100)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7fb080632490> |
|
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[[file:./.ob-jupyter/29ac0eecec5e1fb3efacb59e786c1a3231d26d54.png]]
|
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| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7f1f3dd9f340> |
|
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[[file:./.ob-jupyter/3658a99592960354e2b5997443ea0a9de8426d9a.png]]
|
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:END:
|
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|
|
|
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|
|
@ -1 +1 @@
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\(\mathfrak{e} = 60\%\)
|
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\(\mathfrak{e} = 59\%\)
|
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|
|
@ -1 +1 @@
|
|||
\(\sigma = \SI{0.0538\pm 0.0009}{\pico\barn}\)
|
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\(\sigma = \SI{0.0537\pm 0.0009}{\pico\barn}\)
|
||||
|
|
@ -1 +1 @@
|
|||
\(N = 2191\)
|
||||
\(N = 2290\)
|
||||
|
|
@ -1 +1 @@
|
|||
\(\sigma = \SI{0.0536\pm 0.0010}{\pico\barn}\)
|
||||
\(\sigma = \SI{0.0547\pm 0.0009}{\pico\barn}\)
|
||||
|
|
@ -1 +1 @@
|
|||
\(N = 131\)
|
||||
\(N = 155\)
|
||||
|
|
@ -1 +1 @@
|
|||
\(\sigma = \SI{0.05402\pm 0.00013}{\pico\barn}\)
|
||||
\(\sigma = \SI{0.05389\pm 0.00013}{\pico\barn}\)
|
||||
|
|
@ -183,9 +183,14 @@ def get_xs_distribution_with_pdf(xs, q, e_hadron, quarks=None, pdf=None):
|
|||
|
||||
return distribution, (pdf.xMin, pdf.xMax)
|
||||
|
||||
def sample_momenta(num_samples, dist, interval, e_hadron, upper_bound=None):
|
||||
def sample_momenta(num_samples, dist, interval, e_hadron, upper_bound=None, **kwargs):
|
||||
res, eff = monte_carlo.sample_unweighted_array(
|
||||
num_samples, dist, interval, upper_bound=upper_bound, report_efficiency=True
|
||||
num_samples,
|
||||
dist,
|
||||
interval,
|
||||
upper_bound=upper_bound,
|
||||
report_efficiency=True,
|
||||
**kwargs
|
||||
)
|
||||
cosθ, x_1, x_2 = res.T
|
||||
return momenta(e_hadron, x_1[None, :], x_2[None, :], cosθ[None, :]), eff
|
||||
|
|
|
|||
|
|
@ -102,7 +102,7 @@ def draw_ratio_plot(histograms, normalize_to=1, **kwargs):
|
|||
)
|
||||
|
||||
set_up_axis(ax_ratio, pimp_top=False)
|
||||
ax_ratio.set_ylabel('ratio')
|
||||
ax_ratio.set_ylabel("ratio")
|
||||
draw_histogram(
|
||||
ax_ratio,
|
||||
[heights / reference, edges],
|
||||
|
|
@ -131,7 +131,7 @@ def draw_histogram(
|
|||
histogram,
|
||||
errorbars=True,
|
||||
hist_kwargs=dict(color="#1f77b4"),
|
||||
errorbar_kwargs=dict(color="orange"),
|
||||
errorbar_kwargs=dict(),
|
||||
normalize_to=None,
|
||||
):
|
||||
"""Draws a histogram with optional errorbars using the step style.
|
||||
|
|
@ -161,14 +161,10 @@ def draw_histogram(
|
|||
hist_plot = ax.step(edges, [heights[0], *heights], **hist_kwargs)
|
||||
|
||||
if errorbars is not False:
|
||||
ax.errorbar(
|
||||
centers,
|
||||
heights,
|
||||
deviations,
|
||||
linestyle="none",
|
||||
color=hist_plot[0].get_color(),
|
||||
**errorbar_kwargs
|
||||
)
|
||||
if "color" not in errorbar_kwargs:
|
||||
errorbar_kwargs["color"] = hist_plot[0].get_color()
|
||||
|
||||
ax.errorbar(centers, heights, deviations, linestyle="none", **errorbar_kwargs)
|
||||
|
||||
ax.set_xlim(*[edges[0], edges[-1]])
|
||||
|
||||
|
|
|
|||
|
|
@ -140,6 +140,7 @@ def sample_momenta(sample_num, interval, charge, esp, seed=None, **kwargs):
|
|||
cosθ_sample = monte_carlo.sample_unweighted_array(
|
||||
sample_num, lambda x: diff_xs_cosθ(x, charge, esp), interval_cosθ, **kwargs
|
||||
)
|
||||
print(cosθ_sample)
|
||||
φ_sample = np.random.uniform(0, 1, sample_num)
|
||||
|
||||
def make_momentum(esp, cosθ, φ):
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ import matplotlib.ticker as ticker
|
|||
import functools
|
||||
import numpy as np
|
||||
import os
|
||||
import errno
|
||||
|
||||
###############################################################################
|
||||
# Utility #
|
||||
|
|
@ -31,15 +32,34 @@ def numpy_cache(cache_arg_name):
|
|||
@functools.wraps(f)
|
||||
def caching_wrapper(*args, **kwargs):
|
||||
if cache_arg_name in kwargs:
|
||||
if not kwargs[cache_arg_name]:
|
||||
del kwargs[cache_arg_name]
|
||||
return f(*args, **kwargs)
|
||||
|
||||
path = kwargs[cache_arg_name] + ".npy"
|
||||
print("Trying cache")
|
||||
|
||||
if os.path.isfile(path):
|
||||
return np.load(path)
|
||||
name, result = np.load(path, allow_pickle=True)
|
||||
print(f.__name__)
|
||||
if f.__name__ == name[0]:
|
||||
return result
|
||||
|
||||
raise RuntimeError(
|
||||
f"Try to read to cache from function '{name[0]}'."
|
||||
)
|
||||
|
||||
del kwargs[cache_arg_name]
|
||||
|
||||
result = f(*args, **kwargs)
|
||||
np.save(path, result)
|
||||
|
||||
if not os.path.exists(os.path.dirname(path)):
|
||||
try:
|
||||
os.makedirs(os.path.dirname(path))
|
||||
except OSError as exc: # Guard against race condition
|
||||
if exc.errno != errno.EEXIST:
|
||||
raise
|
||||
|
||||
np.save(path, ([f.__name__], result))
|
||||
|
||||
return result
|
||||
|
||||
|
|
|
|||
BIN
test.npy
BIN
test.npy
Binary file not shown.
Loading…
Add table
Reference in a new issue