bachelor_thesis/prog/python/qqgg/tangled/plot_utils.py

def plot_increments(ax, increment_borders, label=None, *args, **kwargs):
    """Plot the increment borders from a list.  The first and last one

    :param ax: the axis on which to draw
    :param list increment_borders: the borders of the increments
    :param str label: the label to apply to one of the vertical lines
    """

    ax.axvline(x=increment_borders[1], label=label, *args, **kwargs)

    for increment in increment_borders[1:-1]:
        ax.axvline(x=increment, *args, **kwargs)


def plot_vegas_weighted_distribution(
    ax, points, dist, increment_borders, *args, **kwargs
):
    """Plot the distribution with VEGAS weights applied.

    :param ax: axis
    :param points: points
    :param dist: distribution
    :param increment_borders: increment borders
    """

    num_increments = increment_borders.size
    weighted_dist = dist.copy()

    for left_border, right_border in zip(increment_borders[:-1], increment_borders[1:]):
        length = right_border - left_border
        mask = (left_border <= points) & (points <= right_border)
        weighted_dist[mask] = dist[mask] * num_increments * length

    ax.plot(points, weighted_dist, *args, **kwargs)


def plot_stratified_rho(ax, points, increment_borders, *args, **kwargs):
    """Plot the weighting distribution resulting from the increment
    borders.

    :param ax: axis
    :param points: points
    :param increment_borders: increment borders

    """

    num_increments = increment_borders.size
    ρ = np.empty_like(points)
    for left_border, right_border in zip(increment_borders[:-1], increment_borders[1:]):
        length = right_border - left_border
        mask = (left_border <= points) & (points <= right_border)
        ρ[mask] = 1 / (num_increments * length)

    ax.plot(points, ρ, *args, **kwargs)

"""
Some shorthands for common plotting tasks related to the investigation
of monte-carlo methods in one rimension.

Author: Valentin Boettcher <hiro at protagon.space>
"""

import matplotlib.pyplot as plt


def draw_histo(points, xlabel, bins=50, range=None, **kwargs):
    heights, edges = np.histogram(points, bins, range=range, **kwargs)
    centers = (edges[1:] + edges[:-1]) / 2
    deviations = np.sqrt(heights)
    integral = heights @ (edges[1:] - edges[:-1])
    heights = heights / integral
    deviations = deviations / integral

    fig, ax = set_up_plot()
    ax.errorbar(centers, heights, deviations, linestyle="none", color="orange")
    ax.step(edges, [heights[0], *heights], color="#1f77b4")

    ax.set_xlabel(xlabel)
    ax.set_ylabel("Count")
    ax.set_xlim(range if range is not None else [points.min(), points.max()])
    return fig, ax

def draw_yoda_histo(h, xlabel):
      edges = np.append(h.xMins(), h.xMax())
      heights = np.append(h.yVals(), h.yVals()[-1])
      centers = (edges[1:] + edges[:-1]) / 2

      fig, ax = set_up_plot()
      ax.errorbar(h.xVals(), h.yVals(), h.yErrs(), linestyle="none", color="orange")
      ax.step(edges, heights, color="#1f77b4", where="post")

      ax.set_xlabel(xlabel)
      ax.set_ylabel("Count")
      ax.set_xlim([h.xMin(), h.xMax()])
      return fig, ax
#+end_srctypes pytohn

#+RESULTS:

#+begin_src jupyter-python :exports both :results raw drawer
  yoda_file = yoda.read("../../runcards/qqgg/analysis/Analysis.yoda")
  sherpa_histos = {"pT": r"$p_T$ [GeV]", "eta": r"$\eta$", "cos_theta": r"$\cos\theta$"}

  for key, label in sherpa_histos.items():
      fig, ax = draw_yoda_histo(
          yoda_file["/MC_DIPHOTON_SIMPLE/" + key], r"Sherpa " + label
      )
      save_fig(fig, "histo_sherpa_" + key, "xs_sampling", size=(3, 3))

def draw_yoda_histo(h, xlabel):
      edges = np.append(h.xMins(), h.xMax())
      heights = np.append(h.yVals(), h.yVals()[-1])
      centers = (edges[1:] + edges[:-1]) / 2

      fig, ax = set_up_plot()
      ax.errorbar(h.xVals(), h.yVals(), h.yErrs(), linestyle="none", color="orange")
      ax.step(edges, heights, color="#1f77b4", where="post")

      ax.set_xlabel(xlabel)
      ax.set_ylabel("Count")
      ax.set_xlim([h.xMin(), h.xMax()])
      return fig, ax
#+end_srctypes pytohn

#+RESULTS:

#+begin_src jupyter-python :exports both :results raw drawer
  yoda_file = yoda.read("../../runcards/qqgg/analysis/Analysis.yoda")
  sherpa_histos = {"pT": r"$p_T$ [GeV]", "eta": r"$\eta$", "cos_theta": r"$\cos\theta$"}

  for key, label in sherpa_histos.items():
      fig, ax = draw_yoda_histo(
          yoda_file["/MC_DIPHOTON_SIMPLE/" + key], r"Sherpa " + label
      )
      save_fig(fig, "histo_sherpa_" + key, "xs_sampling", size=(3, 3))