various updates

notes.org

@@ -183,10 +183,10 @@ Viele Gruesse, Frank
 ** TODO lab xs kuerzen
 ** TODO shower scale anpassen
 ** TODO effekt shower und kperp
-** TODO y-axis a.u.!
-** TODO mean, var einzeichnen
-** TODO Variance of vegas weighted f!
-** TODO look at xs plot -> they seem different
+** DONE y-axis a.u.!
+** DONE mean, var einzeichnen
+** DONE Variance of vegas weighted f!
+** DONE look at xs plot -> they seem different
 ** TODO take new sample: still bias?
 
 * Observations

prog/python/qqgg/analytical_xs.org

@@ -756,6 +756,18 @@ We define an auxilliary method for convenience.
   import matplotlib.gridspec as gridspec
 
 
+  def test_compatibility(hist_1, hist_2, confidence=1):
+      mask = hist_1 > hist_2
+      comp_greater = hist_1[mask] - confidence * np.sqrt(hist_1[mask]) <= hist_2[
+          mask
+      ] + confidence * np.sqrt(hist_2[mask])
+      comp_lower = hist_1[~mask] + confidence * np.sqrt(hist_1[~mask]) >= hist_2[
+          ~mask
+      ] - confidence * np.sqrt(hist_2[~mask])
+
+      return (np.count_nonzero(comp_greater) + np.count_nonzero(comp_lower)) / len(hist_1)
+
+
   def draw_ratio_plot(histograms, normalize_to=1, **kwargs):
       fig, (ax_hist, ax_ratio) = set_up_plot(
           2, 1, sharex=True, gridspec_kw=dict(height_ratios=[3, 1], hspace=0), **kwargs
@@ -774,6 +786,8 @@ We define an auxilliary method for convenience.
               if "hist" in histogram
               else np.histogram(histogram["samples"], bins=edges)
           )
+          print(test_compatibility(heights, histograms[0]["hist"][0]))
+
           integral = hist_integral([heights, edges])
           errors = np.sqrt(heights) / integral
           heights = heights / integral
@@ -832,7 +846,7 @@ We define an auxilliary method for convenience.
       errorbars=True,
       hist_kwargs=dict(color="#1f77b4"),
       errorbar_kwargs=dict(),
-          autoau=True,
+      autoau=True,
       normalize_to=None,
   ):
       """Draws a histogram with optional errorbars using the step style.

prog/python/qqgg/monte_carlo.py

@@ -1061,15 +1061,14 @@ def sample_stratified_vector(
     total_accepted = 0
 
     cubes = [cube for cube in cubes if cube[2] > 0]  # filter out cubes with zero weight
+    # print([integrate(f, cube).result for cube, _, _ in cubes])
     weights = np.array([weight for _, _, weight in cubes])
     integral = weights.sum()
     weights = np.cumsum(weights / integral)
 
     maxima = np.array(
         [
-            find_upper_bound_vector(
-                f, cube[0], cube[1][1:], tolerance=overestimate_factor - 1
-            )
+            find_upper_bound_vector(f, cube[0], cube[1][1:], tolerance=0.01)
             for cube in cubes
         ]
     )

prog/python/qqgg/parton_density_function_stuff.org

@@ -27,6 +27,8 @@ from tangled import observables
 #+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
@@ -691,7 +693,7 @@ Lets plot how the pdf looks.
 
 Overestimating the upper bounds helps with bias.
 #+begin_src jupyter-python :exports both :results raw drawer
-  overestimate = 1.0
+  overestimate = 1.1
   tex_value(
       (overestimate - 1) * 100,
       unit=r"\percent",
@@ -701,12 +703,7 @@ Overestimating the upper bounds helps with bias.
 #+end_src
 
 #+RESULTS:
-:RESULTS:
-: \(\SI{0}{\percent}\)
-[[file:./.ob-jupyter/542b03d025920448ba653b470ec6492cbdd1e4a7.png]]
-[[file:./.ob-jupyter/d47db0dde9ae59979f271a7cba8dfc46be3f1dd3.png]]
-[[file:./.ob-jupyter/7fe9d3bd60427cf20af835649efbcbaafefbb3e0.png]]
-:END:
+: \(\SI{10}{\percent}\)
 
 Now we sample some events. Doing this in parallel helps. We let the os
 figure out the cpu mapping.
@@ -718,7 +715,7 @@ figure out the cpu mapping.
       cubes=xs_int_res.cubes,
       proc="auto",
       report_efficiency=True,
-      cache="cache/pdf/total_xs_10000_000_2_5_take11",
+      cache="cache/pdf/total_xs_10000_000_2_5_take12",
       status_path="/tmp/status1",
       overestimate_factor=overestimate,
   )
@@ -786,7 +783,11 @@ And now we compare all the observables with sherpa.
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/800bffd38987eae852b74eb2483698169c08c4de.png]]
+:RESULTS:
+: 1.0
+: 0.78
+[[file:./.ob-jupyter/1b1562da892b4caff29588dde88a04cc3256ae35.png]]
+:END:
 
 Hah! there we have it!
 
@@ -817,7 +818,11 @@ both equal.
 #+end_src
 
 #+RESULTS:
+:RESULTS:
+: 1.0
+: 0.86
 [[file:./.ob-jupyter/45cc8fbfa523c8faf704505d716ebc299a1a44fd.png]]
+:END:
 
 The invariant mass is not constant anymore.
 #+begin_src jupyter-python :exports both :results raw drawer
@@ -860,7 +865,11 @@ The cosθ distribution looks more like the paronic one.
 #+end_src
 
 #+RESULTS:
+:RESULTS:
+: 1.0
+: 0.82
 [[file:./.ob-jupyter/d834978baba81dba0e2f052f1965b62ae736c151.png]]
+:END:
 
 
 #+begin_src jupyter-python :exports both :results raw drawer
@@ -882,7 +891,11 @@ The cosθ distribution looks more like the paronic one.
 #+end_src
 
 #+RESULTS:
+:RESULTS:
+: 1.0
+: 0.9
 [[file:./.ob-jupyter/22100d74fc1e07aa9ac75e22dd38ce49eaad89d4.png]]
+:END:
 
 In this case the opening angles are the same because the CS frame is
 the same as the ordinary rest frame. The z-axis is the beam axis

prog/python/qqgg/results/pdf/overesimate.tex

@@ -1 +1 @@
-\(\SI{0}{\percent}\)
+\(\SI{10}{\percent}\)

prog/python/qqgg/tangled/plot_utils.py

@@ -90,6 +90,18 @@ def plot_stratified_rho(ax, points, increment_borders, *args, **kwargs):
 import matplotlib.gridspec as gridspec
 
 
+def test_compatibility(hist_1, hist_2, confidence=1):
+    mask = hist_1 > hist_2
+    comp_greater = hist_1[mask] - confidence * np.sqrt(hist_1[mask]) <= hist_2[
+        mask
+    ] + confidence * np.sqrt(hist_2[mask])
+    comp_lower = hist_1[~mask] + confidence * np.sqrt(hist_1[~mask]) >= hist_2[
+        ~mask
+    ] - confidence * np.sqrt(hist_2[~mask])
+
+    return (np.count_nonzero(comp_greater) + np.count_nonzero(comp_lower)) / len(hist_1)
+
+
 def draw_ratio_plot(histograms, normalize_to=1, **kwargs):
     fig, (ax_hist, ax_ratio) = set_up_plot(
         2, 1, sharex=True, gridspec_kw=dict(height_ratios=[3, 1], hspace=0), **kwargs
@@ -108,6 +120,8 @@ def draw_ratio_plot(histograms, normalize_to=1, **kwargs):
             if "hist" in histogram
             else np.histogram(histogram["samples"], bins=edges)
         )
+        print(test_compatibility(heights, histograms[0]["hist"][0]))
+
         integral = hist_integral([heights, edges])
         errors = np.sqrt(heights) / integral
         heights = heights / integral
@@ -166,7 +180,7 @@ def draw_histogram(
     errorbars=True,
     hist_kwargs=dict(color="#1f77b4"),
     errorbar_kwargs=dict(),
-        autoau=True,
+    autoau=True,
     normalize_to=None,
 ):
     """Draws a histogram with optional errorbars using the step style.

prog/runcards/pp_phaeno_299_port/Sherpa_Base.dat

@@ -23,7 +23,7 @@
 
 (selector){
   PseudoRapidity 22 -3 3;
-  PT 22 2 200000000;
+  PT 22 5 200000000;
 }(selector)
 
 (analysis){

prog/runcards/pp_phaeno_299_port/makefile

@@ -18,7 +18,7 @@ OUT_DIR = out
 RES_DIR = results
 
 # Event Count
-EVENT_COUNT = 10000000
+EVENT_COUNT = 1000
 
 # List of Runcards, see RUNCARD_FOLDER
 RUNCARDS =\

prog/runcards/pp_phaeno_299_port/qqgg_proton/MC_DIPHOTON_PROTON.plot

@@ -50,7 +50,7 @@ Title=Azimuthal angle between the photons
 XLabel=$\pi-\Delta\phi_{\gamma\gamma}$ [rad]
 YLabel=$\mathrm{d}\sigma/\mathrm{d}\Delta\phi_{\gamma\gamma}$ [pb rad$^{-1}$]
 LogY=0
-LogX=1
+LogX=0
 # END PLOT
 
 # BEGIN PLOT /MC_DIPHOTON_PROTON/o_angle
@@ -68,7 +68,7 @@ LogY=0
 # END PLOT
 
 # BEGIN PLOT /MC_DIPHOTON_PROTON/total_pT
-Title=total $\pT$
+Title=$\pT$ of the $\gamma\gamma$ system
 XLabel=$\pT$
 YLabel=$\mathrm{d}\sigma/\mathrm{d}\pT_{\gamma\gamma}$ [pb GeV$^{-1}$]
 LogY=1

prog/runcards/pp_phaeno_299_port/runcards/with_pT_and_fragmentation_and_mi/Run.dat

@@ -29,7 +29,7 @@ BEAM_REMNANTS 1;
 
 (selector){
   PseudoRapidity 22 -3 3;
-  PT 22 2 200000000;
+  PT 22 5 200000000;
 }(selector)
 
 (analysis){

sherpa

@@ -1 +1 @@
-Subproject commit e4cf1cd24ef048ed99a4447b51c3c76e1116cdf2
+Subproject commit 75b7ac47eb8c0f592660419dad5e87678d5db789