mirror of
https://github.com/vale981/bachelor_thesis
synced 2025-03-10 12:56:41 -04:00
135 lines
4.5 KiB
Python
135 lines
4.5 KiB
Python
def plot_increments(ax, increment_borders, label=None, *args, **kwargs):
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"""Plot the increment borders from a list. The first and last one
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:param ax: the axis on which to draw
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:param list increment_borders: the borders of the increments
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:param str label: the label to apply to one of the vertical lines
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"""
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ax.axvline(x=increment_borders[1], label=label, *args, **kwargs)
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for increment in increment_borders[1:-1]:
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ax.axvline(x=increment, *args, **kwargs)
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def plot_vegas_weighted_distribution(
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ax, points, dist, increment_borders, *args, **kwargs
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):
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"""Plot the distribution with VEGAS weights applied.
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:param ax: axis
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:param points: points
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:param dist: distribution
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:param increment_borders: increment borders
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"""
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num_increments = increment_borders.size
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weighted_dist = dist.copy()
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for left_border, right_border in zip(increment_borders[:-1], increment_borders[1:]):
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length = right_border - left_border
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mask = (left_border <= points) & (points <= right_border)
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weighted_dist[mask] = dist[mask] * num_increments * length
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ax.plot(points, weighted_dist, *args, **kwargs)
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def plot_stratified_rho(ax, points, increment_borders, *args, **kwargs):
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"""Plot the weighting distribution resulting from the increment
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borders.
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:param ax: axis
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:param points: points
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:param increment_borders: increment borders
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"""
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num_increments = increment_borders.size
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ρ = np.empty_like(points)
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for left_border, right_border in zip(increment_borders[:-1], increment_borders[1:]):
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length = right_border - left_border
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mask = (left_border <= points) & (points <= right_border)
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ρ[mask] = 1 / (num_increments * length)
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ax.plot(points, ρ, *args, **kwargs)
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"""
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Some shorthands for common plotting tasks related to the investigation
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of monte-carlo methods in one rimension.
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Author: Valentin Boettcher <hiro at protagon.space>
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"""
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import matplotlib.pyplot as plt
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def draw_histo(points, xlabel, bins=50, range=None, **kwargs):
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heights, edges = np.histogram(points, bins, range=range, **kwargs)
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centers = (edges[1:] + edges[:-1]) / 2
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deviations = np.sqrt(heights)
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integral = heights @ (edges[1:] - edges[:-1])
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heights = heights / integral
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deviations = deviations / integral
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fig, ax = set_up_plot()
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ax.errorbar(centers, heights, deviations, linestyle="none", color="orange")
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ax.step(edges, [heights[0], *heights], color="#1f77b4")
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ax.set_xlabel(xlabel)
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ax.set_ylabel("Count")
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ax.set_xlim(range if range is not None else [points.min(), points.max()])
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return fig, ax
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def draw_yoda_histo(h, xlabel):
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edges = np.append(h.xMins(), h.xMax())
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heights = np.append(h.yVals(), h.yVals()[-1])
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centers = (edges[1:] + edges[:-1]) / 2
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fig, ax = set_up_plot()
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ax.errorbar(h.xVals(), h.yVals(), h.yErrs(), linestyle="none", color="orange")
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ax.step(edges, heights, color="#1f77b4", where="post")
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ax.set_xlabel(xlabel)
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ax.set_ylabel("Count")
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ax.set_xlim([h.xMin(), h.xMax()])
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return fig, ax
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#+end_srctypes pytohn
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#+RESULTS:
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#+begin_src jupyter-python :exports both :results raw drawer
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yoda_file = yoda.read("../../runcards/qqgg/analysis/Analysis.yoda")
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sherpa_histos = {"pT": r"$p_T$ [GeV]", "eta": r"$\eta$", "cos_theta": r"$\cos\theta$"}
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for key, label in sherpa_histos.items():
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fig, ax = draw_yoda_histo(
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yoda_file["/MC_DIPHOTON_SIMPLE/" + key], r"Sherpa " + label
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)
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save_fig(fig, "histo_sherpa_" + key, "xs_sampling", size=(3, 3))
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def draw_yoda_histo(h, xlabel):
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edges = np.append(h.xMins(), h.xMax())
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heights = np.append(h.yVals(), h.yVals()[-1])
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centers = (edges[1:] + edges[:-1]) / 2
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fig, ax = set_up_plot()
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ax.errorbar(h.xVals(), h.yVals(), h.yErrs(), linestyle="none", color="orange")
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ax.step(edges, heights, color="#1f77b4", where="post")
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ax.set_xlabel(xlabel)
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ax.set_ylabel("Count")
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ax.set_xlim([h.xMin(), h.xMax()])
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return fig, ax
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#+end_srctypes pytohn
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#+RESULTS:
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#+begin_src jupyter-python :exports both :results raw drawer
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yoda_file = yoda.read("../../runcards/qqgg/analysis/Analysis.yoda")
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sherpa_histos = {"pT": r"$p_T$ [GeV]", "eta": r"$\eta$", "cos_theta": r"$\cos\theta$"}
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for key, label in sherpa_histos.items():
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fig, ax = draw_yoda_histo(
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yoda_file["/MC_DIPHOTON_SIMPLE/" + key], r"Sherpa " + label
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)
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save_fig(fig, "histo_sherpa_" + key, "xs_sampling", size=(3, 3))
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