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Allow for irregular bin widths in hist.plot.plot_pull_array #369

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Allow for irregular bin widths in hist.plot.plot_pull_array #369

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f9d5961
Allow for irregular pull widths
meliache Jan 27, 2022
62c477d
Take into account variable bin widths in plot_ratio_like
meliache Feb 15, 2022
ef4d45e
Add WIP changelog entry for irregular bins in ratio/pull array plots
meliache Feb 15, 2022
37b6d3d
Correct values with bin width when fitting curve to hist
meliache Feb 16, 2022
f697e9e
Fix typo in attribute name
meliache Feb 16, 2022
e764997
tests: regenerate images
henryiii Feb 16, 2022
471d727
fix: add mult
henryiii Feb 16, 2022
658e2ba
Refactor: Use mean instead of sum divided by length
meliache Feb 16, 2022
3f0e7a3
Merge branch 'main' into feature/allow-for-irregular-pull-widths
henryiii Feb 16, 2022
8a470df
Account for variable bin widths in _plot_fit_result
meliache Mar 11, 2022
18ba205
Merge remote-tracking branch 'origin/main' into feature/allow-for-irr…
meliache Mar 14, 2022
2d598a8
Test `ratio_plot` with variably-binned axes with a callable
meliache Mar 14, 2022
0ac80f9
style: pre-commit fixes
pre-commit-ci[bot] Mar 14, 2022
6172c7e
Fix the tests by using re-seeding the global bit-generator
meliache Mar 14, 2022
46c372c
Apply bin-width normalization also to errorbars
meliache Mar 14, 2022
f8bb460
Fix: Take into account variable bin widths in _plot_ratiolike
meliache Mar 15, 2022
2baa648
Reword changelog for bin width normalization in pull/ratio plots
meliache Mar 15, 2022
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5 changes: 5 additions & 0 deletions docs/changelog.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,10 @@
# Changelog

## WIP
* Take into account the irregular bin widths of `Variable` axes in
`plot_pull_array` and `plot_ratio_array` when plotting bars
[#369](https://github.com/scikit-hep/hist/pull/369)

## Version 2.5.2

* Remove more-itertools requirement
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19 changes: 13 additions & 6 deletions src/hist/plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -331,8 +331,15 @@ def _fit_callable_to_hist(

# Infer best fit model parameters and covariance matrix
xdata = histogram.axes[0].centers

# For axes with varying bin widths correct hist values with widths while
# maintaining normalization.
bin_widths = histogram.axes[0].widths
bin_width_fractions = bin_widths / np.sum(bin_widths) * len(bin_widths)
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h_values_width_corrected = histogram.values() / bin_width_fractions

popt, pcov = _curve_fit_wrapper(
model, xdata, histogram.values(), hist_uncert, likelihood=likelihood
model, xdata, h_values_width_corrected, hist_uncert, likelihood=likelihood
)
model_values = model(xdata, *popt)

Expand Down Expand Up @@ -425,7 +432,7 @@ def plot_ratio_array(
)
axis_artists = RatioErrorbarArtists(central_value_artist, errorbar_artists)
elif uncert_draw_type == "bar":
bar_width = (right_edge - left_edge) / len(ratio)
bar_widths = __hist.axes[0].widths

bar_top = ratio + ratio_uncert[1]
bar_bottom = ratio - ratio_uncert[0]
Expand All @@ -440,7 +447,7 @@ def plot_ratio_array(
bar_artists = ax.bar(
x_values,
height=bar_height,
width=bar_width,
width=bar_widths,
bottom=bar_bottom,
fill=False,
linewidth=0,
Expand Down Expand Up @@ -494,12 +501,12 @@ def plot_pull_array(
right_edge = __hist.axes.edges[-1][-1]

# Pull: plot the pulls using Matplotlib bar method
width = (right_edge - left_edge) / len(pulls)
bar_artists = ax.bar(x_values, pulls, width=width, **bar_kwargs)
bin_widths = __hist.axes[0].widths
bar_artists = ax.bar(x_values, pulls, width=bin_widths, **bar_kwargs)

pp_num = pp_kwargs.pop("num", 5)
patch_height = max(np.abs(pulls)) / pp_num
patch_width = width * len(pulls)
patch_width = right_edge - left_edge
patch_artists = []
for i in range(pp_num):
# gradient color patches
Expand Down