figure tabularasa

latex/hirostyle.sty

@@ -25,6 +25,9 @@ labelformat=brace, position=top]{subcaption}
 \usepackage{pgfplots}
 \usepackage{ifdraft}
 
+%% use the current pgfplots
+\pgfplotsset{compat=1.16}
+
 %% minted
 \usemintedstyle{colorful}
 \newmintedfile{yaml}{linenos,mathescape=true}

latex/makefile

@@ -8,3 +8,4 @@ thesis: document.tex
 
 clean:
 	rm -rf $(OUTDIR)/*
+	mkdir -p $(OUTDIR)/tikz

latex/tex/monte-carlo/theory.tex

@@ -46,7 +46,7 @@ expected value \(\EX{X_i}=\mathbb{E}\) and variance
   \EX{\overline{X}} = \frac{1}{N}\sum_i\EX{X_i} = \mathbb{E} \label{eq:evalue-mean}\\
   \sigma^2_{\overline{X}} = \sum_i\frac{\sigma_i^2}{N^2} =
                             \frac{\sigma^2}{N}  \label{eq:variance-mean}
-\end{algin}
+\end{align}
 
 Evidently \(\frac{\sigma^2}{N}\xrightarrow{N\rightarrow\infty} 0\)
 thus the~\eqref{eq:approxexp} really converges to \(I\). For finite

latex/tex/qqgammagamma/calculation.tex

@@ -96,7 +96,7 @@ in the beams).
 \begin{equation}
   \label{eq:averagedm}
   \langle\abs{\mathcal{M}}^2\rangle = \frac{1}{4}\sum_{s_1 s_2}\sum_{\lambda_1
-    \lambda_2} \abs{\mathcal{M}}^2=\overbrace{\frac{1}{3}\frac{1}{4}\frac{(gQ)^4}{(2p)^4}}^\mathfrak{F}\sum_{\lambda_1
+    \lambda_2} \abs{\mathcal{M}}^2=\overbrace{\frac{1}{3}\frac{1}{4}\frac{\qty(gQ)^4}{\qty(2p)^4}}^\mathfrak{F}\sum_{\lambda_1
     \lambda_2}\tr[\qty(\frac{\Gamma_1}{s'^2}+\frac{\Gamma_2}{c'^2})
   \ps_2\qty(\frac{\bar{\Gamma}_1}{s'^2}+\frac{\bar{\Gamma}_2}{c'^2})\ps_1]
 \end{equation}

prog/python/qqgg/analytical_xs.org

@@ -271,7 +271,7 @@ Intergrate σ with the mc method.
 #+end_src
 
 #+RESULTS:
-| 0.05441643331124812 | 0.000850414167068247 |
+| 0.05442051377646951 | 0.0008434404330817717 |
 
 We gonna export that as tex.
 #+begin_src jupyter-python :exports both :results raw drawer
@@ -279,7 +279,7 @@ tex_value(xs_pb_mc, unit=r'\pico\barn', prefix=r'\sigma = ', err=xs_pb_mc_err, s
 #+end_src
 
 #+RESULTS:
-: \(\sigma = \SI{0.0544\pm 0.0009}{\pico\barn}\)
+: \(\sigma = \SI{0.0544\pm 0.0008}{\pico\barn}\)
 
 *** Integration over η
 Plot the intgrand of the pseudo rap.
@@ -302,7 +302,7 @@ save_fig(fig, 'xs_integrand_η', 'xs', size=[4, 4])
 #+end_src
 
 #+RESULTS:
-| 0.05387227556308623 | 0.00015784230303183058 |
+| 0.05380040768597333 | 0.00015708385929804225 |
 
 As we see, the result is a little better if we use pseudo rapidity,
 because the differential cross section does not difverge anymore.  But
@@ -316,7 +316,7 @@ And yet again export that as tex.
 #+end_src
 
 #+RESULTS:
-: \(\sigma = \SI{0.05387\pm 0.00016}{\pico\barn}\)
+: \(\sigma = \SI{0.05380\pm 0.00016}{\pico\barn}\)
 
 *** Using =VEGAS=
 Now we use =VEGAS= on the θ parametrisation and see what happens.
@@ -329,7 +329,7 @@ Now we use =VEGAS= on the θ parametrisation and see what happens.
 #+end_src
 
 #+RESULTS:
-| 0.05382003923613133 | 5.515086040159631e-05 |
+| 0.05379845560620143 | 4.388947283680102e-05 |
 
 This is pretty good, although the variance reduction may be achieved
 partially by accumulating the results from all runns. The uncertainty
@@ -342,7 +342,7 @@ And export that as tex.
 #+end_src
 
 #+RESULTS:
-: \(\sigma = \SI{0.05382\pm 0.00006}{\pico\barn}\)
+: \(\sigma = \SI{0.05380\pm 0.00004}{\pico\barn}\)
 
 Surprisingly, without acumulation, the result ain't much different.
 This depends, of course, on the iteration count.
@@ -353,7 +353,7 @@ This depends, of course, on the iteration count.
 #+end_src
 
 #+RESULTS:
-| 0.05378075568964776 | 7.452808684393069e-05 |
+| 0.05382687634979217 | 7.545090077243005e-05 |
 
 *** Testing the Statistics
 Let's battle test the statistics.
@@ -370,7 +370,7 @@ Let's battle test the statistics.
 #+end_src
 
 #+RESULTS:
-: 0.694
+: 0.681
 
 So we see: the standard deviation is sound.
 
@@ -390,7 +390,7 @@ Doing the same thing with =VEGAS= works as well.
 #+end_src
 
 #+RESULTS:
-: 0.672
+: 0.704
 
 ** Sampling and Analysis
 Define the sample number.
@@ -422,7 +422,7 @@ Now we monte-carlo sample our distribution. We observe that the efficiency his v
 #+end_src
 
 #+RESULTS:
-: 0.027772146766673424
+: 0.026353131968376242
 
 Our distribution has a lot of variance, as can be seen by plotting it.
 #+begin_src jupyter-python :exports both :results raw drawer
@@ -474,7 +474,7 @@ at least a little bit better. The numeric inversion is horribly inefficent.
 #+end_src
 
 #+RESULTS:
-: 0.07994628015406446
+: 0.07893269815528076
 <<cosθ-bare-eff>>
 
 Nice! And now draw some histograms.
@@ -513,166 +513,7 @@ save_fig(fig, 'histo_cos_theta', 'xs', size=(4,3))
 #+end_src
 
 #+RESULTS:
-:RESULTS:
-# [goto error]
-#+begin_example
-
-  BrokenPipeErrorTraceback (most recent call last)
-  <ipython-input-188-c43713624618> in <module>
-        1 fig, _ = draw_histo(cosθ_sample, r'$\cos\theta$')
-  ----> 2 save_fig(fig, 'histo_cos_theta', 'xs', size=(4,3))
-
-  ~/Documents/Projects/UNI/Bachelor/prog/python/utility.py in save_fig(fig, title, folder, size)
-      101
-      102     fig.savefig(f'./figs/{folder}/{title}.pdf')
-  --> 103     fig.savefig(f'./figs/{folder}/{title}.pgf')
-      104
-      105
-
-  /usr/lib/python3.8/site-packages/matplotlib/figure.py in savefig(self, fname, transparent, **kwargs)
-     2201             self.patch.set_visible(frameon)
-     2202
-  -> 2203         self.canvas.print_figure(fname, **kwargs)
-     2204
-     2205         if frameon:
-
-  /usr/lib/python3.8/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)
-     2096
-     2097             try:
-  -> 2098                 result = print_method(
-     2099                     filename,
-     2100                     dpi=dpi,
-
-  /usr/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py in print_pgf(self, fname_or_fh, *args, **kwargs)
-      888             if not cbook.file_requires_unicode(file):
-      889                 file = codecs.getwriter("utf-8")(file)
-  --> 890             self._print_pgf_to_fh(file, *args, **kwargs)
-      891
-      892     def _print_pdf_to_fh(self, fh, *args, **kwargs):
-
-  /usr/lib/python3.8/site-packages/matplotlib/cbook/deprecation.py in wrapper(*args, **kwargs)
-      356                 f"%(removal)s.  If any parameter follows {name!r}, they "
-      357                 f"should be pass as keyword, not positionally.")
-  --> 358         return func(*args, **kwargs)
-      359
-      360     return wrapper
-
-  /usr/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py in _print_pgf_to_fh(self, fh, dryrun, bbox_inches_restore, *args, **kwargs)
-      870                                      RendererPgf(self.figure, fh),
-      871                                      bbox_inches_restore=bbox_inches_restore)
-  --> 872         self.figure.draw(renderer)
-      873
-      874         # end the pgfpicture environment
-
-  /usr/lib/python3.8/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
-       36                 renderer.start_filter()
-       37
-  ---> 38             return draw(artist, renderer, *args, **kwargs)
-       39         finally:
-       40             if artist.get_agg_filter() is not None:
-
-  /usr/lib/python3.8/site-packages/matplotlib/figure.py in draw(self, renderer)
-     1733
-     1734             self.patch.draw(renderer)
-  -> 1735             mimage._draw_list_compositing_images(
-     1736                 renderer, self, artists, self.suppressComposite)
-     1737
-
-  /usr/lib/python3.8/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
-      135     if not_composite or not has_images:
-      136         for a in artists:
-  --> 137             a.draw(renderer)
-      138     else:
-      139         # Composite any adjacent images together
-
-  /usr/lib/python3.8/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
-       36                 renderer.start_filter()
-       37
-  ---> 38             return draw(artist, renderer, *args, **kwargs)
-       39         finally:
-       40             if artist.get_agg_filter() is not None:
-
-  /usr/lib/python3.8/site-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe)
-     2628             renderer.stop_rasterizing()
-     2629
-  -> 2630         mimage._draw_list_compositing_images(renderer, self, artists)
-     2631
-     2632         renderer.close_group('axes')
-
-  /usr/lib/python3.8/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
-      135     if not_composite or not has_images:
-      136         for a in artists:
-  --> 137             a.draw(renderer)
-      138     else:
-      139         # Composite any adjacent images together
-
-  /usr/lib/python3.8/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
-       36                 renderer.start_filter()
-       37
-  ---> 38             return draw(artist, renderer, *args, **kwargs)
-       39         finally:
-       40             if artist.get_agg_filter() is not None:
-
-  /usr/lib/python3.8/site-packages/matplotlib/axis.py in draw(self, renderer, *args, **kwargs)
-     1226
-     1227         ticks_to_draw = self._update_ticks()
-  -> 1228         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,
-     1229                                                                 renderer)
-     1230
-
-  /usr/lib/python3.8/site-packages/matplotlib/axis.py in _get_tick_bboxes(self, ticks, renderer)
-     1171     def _get_tick_bboxes(self, ticks, renderer):
-     1172         """Return lists of bboxes for ticks' label1's and label2's."""
-  -> 1173         return ([tick.label1.get_window_extent(renderer)
-     1174                  for tick in ticks if tick.label1.get_visible()],
-     1175                 [tick.label2.get_window_extent(renderer)
-
-  /usr/lib/python3.8/site-packages/matplotlib/axis.py in <listcomp>(.0)
-     1171     def _get_tick_bboxes(self, ticks, renderer):
-     1172         """Return lists of bboxes for ticks' label1's and label2's."""
-  -> 1173         return ([tick.label1.get_window_extent(renderer)
-     1174                  for tick in ticks if tick.label1.get_visible()],
-     1175                 [tick.label2.get_window_extent(renderer)
-
-  /usr/lib/python3.8/site-packages/matplotlib/text.py in get_window_extent(self, renderer, dpi)
-      903             raise RuntimeError('Cannot get window extent w/o renderer')
-      904
-  --> 905         bbox, info, descent = self._get_layout(self._renderer)
-      906         x, y = self.get_unitless_position()
-      907         x, y = self.get_transform().transform((x, y))
-
-  /usr/lib/python3.8/site-packages/matplotlib/text.py in _get_layout(self, renderer)
-      297             clean_line, ismath = self._preprocess_math(line)
-      298             if clean_line:
-  --> 299                 w, h, d = renderer.get_text_width_height_descent(
-      300                     clean_line, self._fontproperties, ismath=ismath)
-      301             else:
-
-  /usr/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py in get_text_width_height_descent(self, s, prop, ismath)
-      755
-      756         # get text metrics in units of latex pt, convert to display units
-  --> 757         w, h, d = (LatexManager._get_cached_or_new()
-      758                    .get_width_height_descent(s, prop))
-      759         # TODO: this should be latex_pt_to_in instead of mpl_pt_to_in
-
-  /usr/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py in get_width_height_descent(self, text, prop)
-      356
-      357         # send textbox to LaTeX and wait for prompt
-  --> 358         self._stdin_writeln(textbox)
-      359         try:
-      360             self._expect_prompt()
-
-  /usr/lib/python3.8/site-packages/matplotlib/backends/backend_pgf.py in _stdin_writeln(self, s)
-      257         self.latex_stdin_utf8.write(s)
-      258         self.latex_stdin_utf8.write("\n")
-  --> 259         self.latex_stdin_utf8.flush()
-      260
-      261     def _expect(self, s):
-
-  BrokenPipeError: [Errno 32] Broken pipe
-#+end_example
-[[file:./.ob-jupyter/2109dc7fa61ccb899191ba1a68895df01b749783.png]]
-:END:
+[[file:./.ob-jupyter/83492aefe020fdbf846e8d1cfcd26a605ed9217e.png]]
 
 *** Observables
 Now we define some utilities to draw real 4-momentum samples.
@@ -751,13 +592,13 @@ Lets try it out.
 #+end_src
 
 #+RESULTS:
-: array([[100.        ,  38.24641519,  22.76579296,  89.5484807 ],
-:        [100.        ,  48.14652483,  29.97226738, -82.36246314],
-:        [100.        ,  65.02515029,  25.82470275, -71.44798498],
+: array([[100.        ,  85.92614934,  24.50107541, -44.90427778],
+:        [100.        ,  36.88533839,  23.09524222, -90.03378032],
+:        [100.        ,  92.13915742,  33.63915073,  19.4623536 ],
 :        ...,
-:        [100.        ,  77.44294062,  27.84365663,  56.80952151],
-:        [100.        ,  51.47029015,  16.47983968,  84.13812522],
-:        [100.        ,  40.1313622 ,  23.07278301, -88.64039966]])
+:        [100.        ,  54.741625  ,  63.70705878, -54.2656904 ],
+:        [100.        ,  13.85807314,  17.1158491 , -97.54486926],
+:        [100.        ,  25.11845931,  16.28246943, -95.41459108]])
 
 Now let's make a histogram of the η distribution.
 #+begin_src jupyter-python :exports both :results raw drawer
@@ -767,8 +608,8 @@ Now let's make a histogram of the η distribution.
 
 #+RESULTS:
 :RESULTS:
-| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7f9f4c6f0700> |
-[[file:./.ob-jupyter/095e1870e99249bdc8fc6132b3fb473a09d41a08.png]]
+| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7f9f4ca17760> |
+[[file:./.ob-jupyter/cadc1b1460534226187087ea3ca5d084fa5fa2d7.png]]
 :END:
 
 
@@ -780,8 +621,8 @@ And the same for the p_t (transverse momentum) distribution.
 
 #+RESULTS:
 :RESULTS:
-| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7f9f4c266f40> |
-[[file:./.ob-jupyter/47d65a874109d682e1f2ca862c1560d63060867c.png]]
+| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7f9f4f3b7d30> |
+[[file:./.ob-jupyter/4ffcb7836b3b067252cdeb95d9a6392550b0d262.png]]
 :END:
 
 That looks somewhat fishy, but it isn't.
@@ -810,7 +651,7 @@ An again we see that the efficiency is way, way! better...
 #+end_src
 
 #+RESULTS:
-: 0.4063
+: 0.40798
 <<η-eff>>
 
 Let's draw a histogram to compare with the previous results.
@@ -820,8 +661,8 @@ Let's draw a histogram to compare with the previous results.
 
 #+RESULTS:
 :RESULTS:
-| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7f9f4bdf4310> |
-[[file:./.ob-jupyter/87c196391955136cfeb479ee23972bf971d855d3.png]]
+| <Figure | size | 432x288 | with | 1 | Axes> | <matplotlib.axes._subplots.AxesSubplot | at | 0x7f9f4bef7ca0> |
+[[file:./.ob-jupyter/14643279f5f2f9c909fce6d2349de0dfb32288f2.png]]
 :END:
 
 
@@ -879,8 +720,8 @@ distribution. We throw away the integral, but keep the increments.
 #+end_src
 
 #+RESULTS:
-: array([-0.9866143 , -0.87001384, -0.7269742 , -0.51294698, -0.26484439,
-:         0.00164873,  0.26764024,  0.5151555 ,  0.72867548,  0.87054888,
+: array([-0.9866143 , -0.95760273, -0.90040994, -0.77763674, -0.51247914,
+:        -0.00487403,  0.50718268,  0.77579038,  0.89960795,  0.9570795 ,
 :         0.9866143 ])
 
 Visualizing the increment borders gives us the information we want.
@@ -898,8 +739,8 @@ Visualizing the increment borders gives us the information we want.
 
 #+RESULTS:
 :RESULTS:
-: <matplotlib.legend.Legend at 0x7f9f4bdd9940>
-[[file:./.ob-jupyter/1979e2f4e4298c8863a47e15c2ccd833b29302bc.png]]
+: <matplotlib.legend.Legend at 0x7f9f4d3ce280>
+[[file:./.ob-jupyter/702ff465cf640f3fcc89e257b525ed6d9532c159.png]]
 :END:
 
 We can now plot the reweighted distribution to observe the variance
@@ -919,8 +760,8 @@ reduction visually.
 
 #+RESULTS:
 :RESULTS:
-: <matplotlib.legend.Legend at 0x7f9f4bb7ebb0>
-[[file:./.ob-jupyter/a506325aead3d6cf25f8e7cf5498c271cdc9ed7f.png]]
+: <matplotlib.legend.Legend at 0x7f9f4ae0c490>
+[[file:./.ob-jupyter/07e51a1bc82b177c00559879d2ee5d1dc43384da.png]]
 :END:
 
 
@@ -936,7 +777,7 @@ Now, draw a sample and look at the efficiency.
 #+end_src
 
 #+RESULTS:
-: 0.0913
+: 0.363
 
 If we compare that to [[cosθ-bare-eff]], we can see the improvement :P.
 It is even better the [[η-eff]].  The histogram looks just the same.
@@ -947,4 +788,4 @@ save_fig(fig, 'histo_cos_theta_strat', 'xs', size=(4,3))
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/a227f2678a85eae05938eadc8a2be8c9a7fd6835.png]]
+[[file:./.ob-jupyter/06d680621fedb2b84b0f55586a7e2152ba47a27f.png]]

prog/python/qqgg/figs/xs/histo_cos_theta.tex

@@ -1 +0,0 @@
-\input{figs/xs/histo_cos_theta.pgf}

prog/python/qqgg/figs/xs/xs_integrand.tex

@@ -1 +0,0 @@
-\input{figs/xs/xs_integrand.pgf}

prog/python/qqgg/results/xs_mc.tex

@@ -1 +1 @@
-\(\sigma = \SI{0.0544\pm 0.0009}{\pico\barn}\)
+\(\sigma = \SI{0.0544\pm 0.0008}{\pico\barn}\)

prog/python/qqgg/results/xs_mc_eta.tex

@@ -1 +1 @@
-\(\sigma = \SI{0.05387\pm 0.00016}{\pico\barn}\)
+\(\sigma = \SI{0.05380\pm 0.00016}{\pico\barn}\)

prog/python/qqgg/results/xs_mc_θ_vegas.tex

@@ -1 +1 @@
-\(\sigma = \SI{0.05382\pm 0.00006}{\pico\barn}\)
+\(\sigma = \SI{0.05380\pm 0.00004}{\pico\barn}\)