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plot.py
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# -*- coding: utf-8 -*-
from __future__ import division
from ..util import numpy as np
import scipy.stats
import copy
import warnings
import numbers
from .hist_tools import SparseAxis, DenseAxis, overflow_behavior, Interval, StringBin
# Plotting is always terrible
# Let's try our best to follow matplotlib idioms
# https://matplotlib.org/tutorials/introductory/usage.html#coding-styles
_coverage1sd = scipy.stats.norm.cdf(1) - scipy.stats.norm.cdf(-1)
def poisson_interval(sumw, sumw2, coverage=_coverage1sd):
"""Frequentist coverage interval for Poisson-distributed observations
Parameters
----------
sumw : numpy.ndarray
Sum of weights vector
sumw2 : numpy.ndarray
Sum weights squared vector
coverage : float, optional
Central coverage interval, defaults to 68%
Calculates the so-called 'Garwood' interval,
c.f. https://www.ine.pt/revstat/pdf/rs120203.pdf or
http://ms.mcmaster.ca/peter/s743/poissonalpha.html
For weighted data, this approximates the observed count by ``sumw**2/sumw2``, which
effectively scales the unweighted poisson interval by the average weight.
This may not be the optimal solution: see https://arxiv.org/pdf/1309.1287.pdf for a proper treatment.
When a bin is zero, the scale of the nearest nonzero bin is substituted to scale the nominal upper bound.
If all bins zero, a warning is generated and interval is set to ``sumw``.
"""
scale = np.empty_like(sumw)
scale[sumw != 0] = sumw2[sumw != 0] / sumw[sumw != 0]
if np.sum(sumw == 0) > 0:
missing = np.where(sumw == 0)
available = np.nonzero(sumw)
if len(available[0]) == 0:
warnings.warn("All sumw are zero! Cannot compute meaningful error bars", RuntimeWarning)
return np.vstack([sumw, sumw])
nearest = sum([np.subtract.outer(d, d0)**2 for d, d0 in zip(available, missing)]).argmin(axis=0)
argnearest = tuple(dim[nearest] for dim in available)
scale[missing] = scale[argnearest]
counts = sumw / scale
lo = scale * scipy.stats.chi2.ppf((1 - coverage) / 2, 2 * counts) / 2.
hi = scale * scipy.stats.chi2.ppf((1 + coverage) / 2, 2 * (counts + 1)) / 2.
interval = np.array([lo, hi])
interval[interval == np.nan] = 0. # chi2.ppf produces nan for counts=0
return interval
def clopper_pearson_interval(num, denom, coverage=_coverage1sd):
"""Compute Clopper-Pearson coverage interval for a binomial distribution
Parameters
----------
num : numpy.ndarray
Numerator, or number of successes, vectorized
denom : numpy.ndarray
Denominator or number of trials, vectorized
coverage : float, optional
Central coverage interval, defaults to 68%
c.f. http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval
"""
if np.any(num > denom):
raise ValueError("Found numerator larger than denominator while calculating binomial uncertainty")
lo = scipy.stats.beta.ppf((1 - coverage) / 2, num, denom - num + 1)
hi = scipy.stats.beta.ppf((1 + coverage) / 2, num + 1, denom - num)
interval = np.array([lo, hi])
interval[:, num == 0.] = 0.
interval[1, num == denom] = 1.
return interval
def normal_interval(pw, tw, pw2, tw2, coverage=_coverage1sd):
"""Compute errors based on the expansion of pass/(pass + fail), possibly weighted
Parameters
----------
pw : numpy.ndarray
Numerator, or number of (weighted) successes, vectorized
tw : numpy.ndarray
Denominator or number of (weighted) trials, vectorized
pw2 : numpy.ndarray
Numerator sum of weights squared, vectorized
tw2 : numpy.ndarray
Denominator sum of weights squared, vectorized
coverage : float, optional
Central coverage interval, defaults to 68%
c.f. https://root.cern.ch/doc/master/TEfficiency_8cxx_source.html#l02515
"""
eff = pw / tw
variance = (pw2 * (1 - 2 * eff) + tw2 * eff**2) / (tw**2)
sigma = np.sqrt(variance)
prob = 0.5 * (1 - coverage)
delta = np.zeros_like(sigma)
delta[sigma != 0] = scipy.stats.norm.ppf(prob, scale=sigma[sigma != 0])
lo = eff - np.minimum(eff + delta, np.ones_like(eff))
hi = np.maximum(eff - delta, np.zeros_like(eff)) - eff
return np.array([lo, hi])
def plot1d(hist, ax=None, clear=True, overlay=None, stack=False, overflow='none', line_opts=None,
fill_opts=None, error_opts=None, legend_opts={}, overlay_overflow='none',
density=False, binwnorm=None, order=None):
"""Create a 1D plot from a 1D or 2D `Hist` object
Parameters
----------
hist : Hist
Histogram with maximum of two dimensions
ax : matplotlib.axes.Axes, optional
Axes object (if None, one is created)
clear : bool, optional
Whether to clear Axes before drawing (if passed); if False, this function will skip drawing the legend
overlay : str, optional
In the case that ``hist`` is 2D, specify the axis of hist to overlay (remaining axis will be x axis)
stack : bool, optional
Whether to stack or overlay non-axis dimension (if it exists)
order : list, optional
How to order when stacking. Take a list of identifiers.
overflow : str, optional
If overflow behavior is not 'none', extra bins will be drawn on either end of the nominal
axis range, to represent the contents of the overflow bins. See `Hist.sum` documentation
for a description of the options.
line_opts : dict, optional
A dictionary of options to pass to the matplotlib
`ax.step <https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.step.html>`_ call
internal to this function. Leave blank for defaults.
fill_opts : dict, optional
A dictionary of options to pass to the matplotlib
`ax.fill_between <https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.fill_between.html>`_ call
internal to this function. Leave blank for defaults.
error_opts : dict, optional
A dictionary of options to pass to the matplotlib
`ax.errorbar <https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.errorbar.html>`_ call
internal to this function. Leave blank for defaults. Some special options are interpreted by
this function and not passed to matplotlib: 'emarker' (default: '') specifies the marker type
to place at cap of the errorbar.
legend_opts : dict, optional
A dictionary of options to pass to the matplotlib
`ax.legend <https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html>`_ call
internal to this fuction. Leave blank for defaults.
overlay_overflow : str, optional
If overflow behavior is not 'none', extra bins in the overlay axis will be overlayed or stacked,
to represent the contents of the overflow bins. See `Hist.sum` documentation for a description of the options.
density : bool, optional
If true, convert sum weights to probability density (i.e. integrates to 1 over domain of axis)
(Note: this option conflicts with ``binwnorm``)
binwnorm : float, optional
If true, convert sum weights to bin-width-normalized, with unit equal to supplied value (usually you want to specify 1.)
Returns
-------
ax : matplotlib.axes.Axes
A matplotlib `Axes <https://matplotlib.org/3.1.1/api/axes_api.html>`_ object
"""
import mplhep as hep
import matplotlib.pyplot as plt
if ax is None:
ax = plt.gca()
else:
if not isinstance(ax, plt.Axes):
raise ValueError("ax must be a matplotlib Axes object")
if clear:
ax.clear()
if hist.dim() > 2:
raise ValueError("plot1d() can only support up to two dimensions (one for axis, one to stack or overlay)")
if overlay is None and hist.sparse_dim() == 1 and hist.dense_dim() == 1:
overlay = hist.sparse_axes()[0].name
elif overlay is None and hist.dim() > 1:
raise ValueError("plot1d() can only support one dimension without an overlay axis chosen")
if density and binwnorm is not None:
raise ValueError("Cannot use density and binwnorm at the same time!")
if binwnorm is not None:
if not isinstance(binwnorm, numbers.Number):
raise ValueError("Bin width normalization not a number, but a %r" % binwnorm.__class__)
if line_opts is None and fill_opts is None and error_opts is None:
if stack:
fill_opts = {}
else:
line_opts = {}
error_opts = {}
axis = hist.axes()[0]
if overlay is not None:
overlay = hist.axis(overlay)
if axis == overlay:
axis = hist.axes()[1]
if isinstance(axis, SparseAxis):
raise NotImplementedError("Plot a sparse axis (e.g. bar chart)")
elif isinstance(axis, DenseAxis):
ax.set_xlabel(axis.label)
ax.set_ylabel(hist.label)
edges = axis.edges(overflow=overflow)
if order is None:
identifiers = hist.identifiers(overlay, overflow=overlay_overflow) if overlay is not None else [None]
else:
identifiers = order
plot_info = {
'identifier': identifiers,
'label': list(map(str, identifiers)),
'sumw': [],
'sumw2': []
}
for i, identifier in enumerate(identifiers):
if identifier is None:
sumw, sumw2 = hist.values(sumw2=True, overflow=overflow)[()]
elif isinstance(overlay, SparseAxis):
sumw, sumw2 = hist.integrate(overlay, identifier).values(sumw2=True, overflow=overflow)[()]
else:
sumw, sumw2 = hist.values(sumw2=True, overflow='allnan')[()]
the_slice = (i if overflow_behavior(overlay_overflow).start is None else i + 1, overflow_behavior(overflow))
if hist._idense(overlay) == 1:
the_slice = (the_slice[1], the_slice[0])
sumw = sumw[the_slice]
sumw2 = sumw2[the_slice]
plot_info['sumw'].append(sumw)
plot_info['sumw2'].append(sumw2)
def w2err(sumw, sumw2):
err = []
for a, b in zip(sumw, sumw2):
err.append(np.abs(poisson_interval(a, b) - a))
return err
kwargs = None
if line_opts is not None and error_opts is None:
_error = None
else:
_error = w2err(plot_info['sumw'], plot_info['sumw2'])
if fill_opts is not None:
histtype = 'fill'
kwargs = fill_opts
elif error_opts is not None and line_opts is None:
histtype = 'errorbar'
kwargs = error_opts
else:
histtype = 'step'
kwargs = line_opts
if kwargs is None:
kwargs = {}
hep.histplot(plot_info['sumw'], edges, label=plot_info['label'],
yerr=_error, histtype=histtype, ax=ax,
density=density, binwnorm=binwnorm, stack=stack,
**kwargs)
if stack and error_opts is not None:
stack_sumw = np.sum(plot_info['sumw'], axis=0)
stack_sumw2 = np.sum(plot_info['sumw2'], axis=0)
err = poisson_interval(stack_sumw, stack_sumw2)
if binwnorm is not None:
err *= binwnorm / np.diff(edges)[None, :]
opts = {'step': 'post', 'label': 'Sum unc.', 'hatch': '///',
'facecolor': 'none', 'edgecolor': (0, 0, 0, .5), 'linewidth': 0}
opts.update(error_opts)
ax.fill_between(x=edges, y1=np.r_[err[0, :], err[0, -1]],
y2=np.r_[err[1, :], err[1, -1]], **opts)
if legend_opts is not None:
_label = overlay.label if overlay is not None else ""
ax.legend(title=_label, **legend_opts)
else:
ax.legend(title=_label)
ax.autoscale(axis='x', tight=True)
ax.set_ylim(0, None)
return ax
def plotratio(num, denom, ax=None, clear=True, overflow='none', error_opts=None, denom_fill_opts=None, guide_opts=None, unc='clopper-pearson', label=None):
"""Create a ratio plot, dividing two compatible histograms
Parameters
----------
num : Hist
Numerator, a single-axis histogram
denom : Hist
Denominator, a single-axis histogram
ax : matplotlib.axes.Axes, optional
Axes object (if None, one is created)
clear : bool, optional
Whether to clear Axes before drawing (if passed); if False, this function will skip drawing the legend
overflow : str, optional
If overflow behavior is not 'none', extra bins will be drawn on either end of the nominal
axis range, to represent the contents of the overflow bins. See `Hist.sum` documentation
for a description of the options.
error_opts : dict, optional
A dictionary of options to pass to the matplotlib
`ax.errorbar <https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.errorbar.html>`_ call
internal to this function. Leave blank for defaults. Some special options are interpreted by
this function and not passed to matplotlib: 'emarker' (default: '') specifies the marker type
to place at cap of the errorbar.
denom_fill_opts : dict, optional
A dictionary of options to pass to the matplotlib
`ax.fill_between <https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.fill_between.html>`_ call
internal to this function, filling the denominator uncertainty band. Leave blank for defaults.
guide_opts : dict, optional
A dictionary of options to pass to the matplotlib
`ax.axhline <https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.axhline.html>`_ call
internal to this function, to plot a horizontal guide line at ratio of 1. Leave blank for defaults.
unc : str, optional
Uncertainty calculation option: 'clopper-pearson' interval for efficiencies; 'poisson-ratio' interval
for ratio of poisson distributions; 'num' poisson interval of numerator scaled by denominator value
(common for data/mc, for better or worse).
label : str, optional
Associate a label to this entry (note: y axis label set by ``num.label``)
Returns
-------
ax : matplotlib.axes.Axes
A matplotlib `Axes <https://matplotlib.org/3.1.1/api/axes_api.html>`_ object
"""
import mplhep as hep
import matplotlib.pyplot as plt
if ax is None:
fig, ax = plt.subplots(1, 1)
else:
if not isinstance(ax, plt.Axes):
raise ValueError("ax must be a matplotlib Axes object")
if clear:
ax.clear()
if not num.compatible(denom):
raise ValueError("numerator and denominator histograms have incompatible axis definitions")
if num.dim() > 1:
raise ValueError("plotratio() can only support one-dimensional histograms")
if error_opts is None and denom_fill_opts is None and guide_opts is None:
error_opts = {}
denom_fill_opts = {}
axis = num.axes()[0]
if isinstance(axis, SparseAxis):
raise NotImplementedError("Ratio for sparse axes (labeled axis with errorbars)")
elif isinstance(axis, DenseAxis):
ax.set_xlabel(axis.label)
ax.set_ylabel(num.label)
edges = axis.edges(overflow=overflow)
centers = axis.centers(overflow=overflow)
sumw_num, sumw2_num = num.values(sumw2=True, overflow=overflow)[()]
sumw_denom, sumw2_denom = denom.values(sumw2=True, overflow=overflow)[()]
rsumw = sumw_num / sumw_denom
if unc == 'clopper-pearson':
rsumw_err = np.abs(clopper_pearson_interval(sumw_num, sumw_denom) - rsumw)
elif unc == 'poisson-ratio':
# poisson ratio n/m is equivalent to binomial n/(n+m)
rsumw_err = np.abs(clopper_pearson_interval(sumw_num, sumw_num + sumw_denom) - rsumw)
elif unc == 'num':
rsumw_err = np.abs(poisson_interval(rsumw, sumw2_num / sumw_denom**2) - rsumw)
elif unc == "normal":
rsumw_err = np.abs(normal_interval(sumw_num, sumw_denom, sumw2_num, sumw2_denom))
else:
raise ValueError("Unrecognized uncertainty option: %r" % unc)
if error_opts is not None:
opts = {'label': label, 'linestyle': 'none'}
opts.update(error_opts)
emarker = opts.pop('emarker', '')
errbar = ax.errorbar(x=centers, y=rsumw, yerr=rsumw_err, **opts)
plt.setp(errbar[1], 'marker', emarker)
if denom_fill_opts is not None:
unity = np.ones_like(sumw_denom)
denom_unc = poisson_interval(unity, sumw2_denom / sumw_denom**2)
opts = {'step': 'post', 'facecolor': (0, 0, 0, 0.3), 'linewidth': 0}
opts.update(denom_fill_opts)
ax.fill_between(edges, np.r_[denom_unc[0], denom_unc[0, -1]], np.r_[denom_unc[1], denom_unc[1, -1]], **opts)
if guide_opts is not None:
opts = {'linestyle': '--', 'color': (0, 0, 0, 0.5), 'linewidth': 1}
opts.update(guide_opts)
ax.axhline(1., **opts)
if clear:
ax.autoscale(axis='x', tight=True)
ax.set_ylim(0, None)
return ax
def plot2d(hist, xaxis, ax=None, clear=True, xoverflow='none', yoverflow='none', patch_opts=None, text_opts=None, density=False, binwnorm=None):
"""Create a 2D plot from a 2D `Hist` object
Parameters
----------
hist : Hist
Histogram with two dimensions
xaxis : str or Axis
Which of the two dimensions to use as an x axis
ax : matplotlib.axes.Axes, optional
Axes object (if None, one is created)
clear : bool, optional
Whether to clear Axes before drawing (if passed); if False, this function will skip drawing the legend
xoverflow : str, optional
If overflow behavior is not 'none', extra bins will be drawn on either end of the nominal x
axis range, to represent the contents of the overflow bins. See `Hist.sum` documentation
for a description of the options.
yoverflow : str, optional
Similar to ``xoverflow``
patch_opts : dict, optional
Options passed to the matplotlib `pcolormesh <https://matplotlib.org/api/_as_gen/matplotlib.pyplot.pcolormesh.html>`_
call internal to this function, to plot a rectangular grid of patches colored according to the bin values.
Leave empty for defaults.
text_opts : dict, optional
Options passed to the matplotlib `text <https://matplotlib.org/api/text_api.html#matplotlib.text.Text>`_
call internal to this function, to place a text label at each bin center with the bin value. Special
options interpreted by this function and not passed to matplotlib: 'format': printf-style float format
, default '%.2g'.
density : bool, optional
If true, convert sum weights to probability density (i.e. integrates to 1 over domain of axis)
(Note: this option conflicts with ``binwnorm``)
binwnorm : float, optional
If true, convert sum weights to bin-width-normalized, with unit equal to supplied value (usually you want to specify 1.)
Returns
-------
ax : matplotlib.axes.Axes
A matplotlib `Axes <https://matplotlib.org/3.1.1/api/axes_api.html>`_ object
"""
import mplhep as hep
import matplotlib.pyplot as plt
if ax is None:
fig, ax = plt.subplots(1, 1)
else:
if not isinstance(ax, plt.Axes):
raise ValueError("ax must be a matplotlib Axes object")
if clear:
ax.clear()
fig = ax.figure
if hist.dim() != 2:
raise ValueError("plot2d() can only support exactly two dimensions")
if density and binwnorm is not None:
raise ValueError("Cannot use density and binwnorm at the same time!")
if binwnorm is not None:
if not isinstance(binwnorm, numbers.Number):
raise ValueError("Bin width normalization not a number, but a %r" % binwnorm.__class__)
if patch_opts is None and text_opts is None:
patch_opts = {}
xaxis = hist.axis(xaxis)
yaxis = hist.axes()[1]
transpose = False
if yaxis == xaxis:
yaxis = hist.axes()[0]
transpose = True
if isinstance(xaxis, SparseAxis) or isinstance(yaxis, SparseAxis):
raise NotImplementedError("Plot a sparse axis (e.g. bar chart or labeled bins)")
else:
xedges = xaxis.edges(overflow=xoverflow)
yedges = yaxis.edges(overflow=yoverflow)
sumw, sumw2 = hist.values(sumw2=True, overflow='allnan')[()]
if transpose:
sumw = sumw.T
sumw2 = sumw2.T
# no support for different overflow behavior per axis, do it ourselves
sumw = sumw[overflow_behavior(xoverflow), overflow_behavior(yoverflow)]
sumw2 = sumw2[overflow_behavior(xoverflow), overflow_behavior(yoverflow)]
if (density or binwnorm is not None) and np.sum(sumw) > 0:
overallnorm = np.sum(sumw) * binwnorm if binwnorm is not None else 1.
areas = np.multiply.outer(np.diff(xedges), np.diff(yedges))
binnorms = overallnorm / (areas * np.sum(sumw))
sumw = sumw * binnorms
sumw2 = sumw2 * binnorms**2
if patch_opts is not None:
opts = {'cmap': 'viridis'}
opts.update(patch_opts)
pc = ax.pcolormesh(xedges, yedges, sumw.T, **opts)
ax.add_collection(pc)
if clear:
fig.colorbar(pc, ax=ax, label=hist.label)
if text_opts is not None:
for ix, xcenter in enumerate(xaxis.centers()):
for iy, ycenter in enumerate(yaxis.centers()):
opts = {
'horizontalalignment': 'center',
'verticalalignment': 'center',
}
if patch_opts is not None:
opts['color'] = 'black' if pc.norm(sumw[ix, iy]) > 0.5 else 'lightgrey'
opts.update(text_opts)
txtformat = opts.pop('format', r'%.2g')
ax.text(xcenter, ycenter, txtformat % sumw[ix, iy], **opts)
if clear:
ax.set_xlabel(xaxis.label)
ax.set_ylabel(yaxis.label)
ax.set_xlim(xedges[0], xedges[-1])
ax.set_ylim(yedges[0], yedges[-1])
return ax
def plotgrid(h, figure=None, row=None, col=None, overlay=None, row_overflow='none', col_overflow='none', **plot_opts):
"""Create a grid of plots, enumerating identifiers on up to 3 axes
Parameters
----------
h : Hist
A histogram with up to 3 axes
figure : matplotlib.figure.Figure, optional
If omitted, a new figure is created. Otherwise, the axes will be redrawn on this existing figure.
row : str
Name of row axis
col : str
Name of column axis
overlay : str
name of overlay axis
row_overflow : str, optional
If overflow behavior is not 'none', extra bins will be drawn on either end of the nominal x
axis range, to represent the contents of the overflow bins. See `Hist.sum` documentation
for a description of the options.
col_overflow : str, optional
Similar to ``row_overflow``
``**plot_opts`` : kwargs
The remaining axis of the histogram, after removing any of ``row,col,overlay`` specified,
will be the plot axis, with ``plot_opts`` passed to the `plot1d` call.
Returns
-------
axes : numpy.ndarray
An array of matplotlib `Axes <https://matplotlib.org/3.1.1/api/axes_api.html>`_ objects
"""
import mplhep as hep
import matplotlib.pyplot as plt
haxes = set(ax.name for ax in h.axes())
nrow, ncol = 1, 1
if row:
row_identifiers = h.identifiers(row, overflow=row_overflow)
nrow = len(row_identifiers)
haxes.remove(row)
if col:
col_identifiers = h.identifiers(col, overflow=col_overflow)
ncol = len(col_identifiers)
haxes.remove(col)
if overlay:
haxes.remove(overlay)
if len(haxes) > 1:
raise ValueError("More than one dimension left: %s" % (",".join(ax for ax in haxes),))
elif len(haxes) == 0:
raise ValueError("Not enough dimensions available in %r" % h)
figsize = plt.rcParams['figure.figsize']
figsize = figsize[0] * max(ncol, 1), figsize[1] * max(nrow, 1)
if figure is None:
fig, axes = plt.subplots(nrow, ncol, figsize=figsize, squeeze=False, sharex=True, sharey=True)
else:
fig = figure
shape = (0, 0)
lastax = fig.get_children()[-1]
if isinstance(lastax, plt.Axes):
shape = lastax.rowNum + 1, lastax.colNum + 1
if shape[0] == nrow and shape[1] == ncol:
axes = np.array(fig.axes).reshape(shape)
else:
fig.clear()
# fig.set_size_inches(figsize)
axes = fig.subplots(nrow, ncol, squeeze=False, sharex=True, sharey=True)
for icol in range(ncol):
hcol = h
coltitle = None
if col:
vcol = col_identifiers[icol]
hcol = h.integrate(col, vcol)
coltitle = str(vcol)
if isinstance(vcol, Interval) and vcol.label is None:
coltitle = "%s ∈ %s" % (h.axis(col).label, coltitle)
for irow in range(nrow):
ax = axes[irow, icol]
hplot = hcol
rowtitle = None
if row:
vrow = row_identifiers[irow]
hplot = hcol.integrate(row, vrow)
rowtitle = str(vrow)
if isinstance(vrow, Interval) and vrow.label is None:
rowtitle = "%s ∈ %s" % (h.axis(row).label, rowtitle)
plot1d(hplot, ax=ax, overlay=overlay, **plot_opts)
if row is not None and col is not None:
ax.set_title("%s, %s" % (rowtitle, coltitle))
elif row is not None:
ax.set_title(rowtitle)
elif col is not None:
ax.set_title(coltitle)
for ax in axes.flatten():
ax.autoscale(axis='y')
ax.set_ylim(0, None)
return axes