diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d7b7357a..fda2756c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,7 +3,7 @@ ci:
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.3.0
+  rev: v4.0.1
   hooks:
   - id: check-added-large-files
   - id: check-case-conflict
@@ -23,7 +23,7 @@ repos:
     args: ["--include-version-classifiers", "--max-py-version=3.11"]
 
 - repo: https://github.com/PyCQA/isort
-  rev: 5.10.1
+  rev: 5.12.0
   hooks:
   - id: isort
     args: ["-a", "from __future__ import annotations"]
diff --git a/src/mplhep/plot.py b/src/mplhep/plot.py
index 60695ed5..3b48ec57 100644
--- a/src/mplhep/plot.py
+++ b/src/mplhep/plot.py
@@ -7,10 +7,11 @@
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
+from matplotlib import patches
 from matplotlib.offsetbox import AnchoredText
 from matplotlib.transforms import Bbox
 from mpl_toolkits.axes_grid1 import axes_size, make_axes_locatable
-import scipy.stats
+
 from .utils import (
     Plottable,
     get_histogram_axes_title,
@@ -131,7 +132,7 @@ def histplot(
             Attempts to draw x-axis ticks coinciding with bin boundaries if feasible.
         ax : matplotlib.axes.Axes, optional
             Axes object (if None, last one is fetched or one is created)
-        flow :  str, optional {None, "show", "sum"}
+        flow :  str, optional {None, "show", "sum", "hint"}
             Whether plot the under/overflow bin. If "show", add additional under/overflow bin. If "sum", add the under/overflow bin content to first/last bin.
         **kwargs :
             Keyword arguments passed to underlying matplotlib functions -
@@ -153,6 +154,10 @@ def histplot(
     _err_message = f"Select 'histtype' from: {_allowed_histtype}"
     assert histtype in _allowed_histtype, _err_message
 
+    # hist overflow check
+    if flow is not None and "Hist" not in str(type(H)):
+        raise TypeError(f"{flow} option only works for Hist object")
+
     # Convert 1/0 etc to real bools
     stack = bool(stack)
     density = bool(density)
@@ -181,9 +186,9 @@ def histplot(
         plottables = []
         final_bins = np.array(
             [
-                final_bins[0] - (final_bins[1] - final_bins[0]) * 5,
+                final_bins[0] - (final_bins[1] - final_bins[0]) * 2,
                 *final_bins,
-                final_bins[-1] + (final_bins[1] - final_bins[0]) * 5,
+                final_bins[-1] + (final_bins[1] - final_bins[0]) * 2,
             ]
         )
         for h in hists:
@@ -429,6 +434,7 @@ def iterable_not_string(arg):
 
     # Add sticky edges for autoscale
     _artist.sticky_edges.y.append(0)
+
     if xtick_labels is None:
         if binticks:
             _slice = int(round(float(len(final_bins)) / len(ax.get_xticks()))) + 1
@@ -449,25 +455,9 @@ def iterable_not_string(arg):
             underflow = underflow + h.view(flow=True)["value"][0]
             overflow = overflow + h.view(flow=True)["value"][-1]
         if underflow > 0.0:
-            ax.text(
-                0.85,
-                0.65,
-                "underflow",
-                horizontalalignment="center",
-                verticalalignment="center",
-                transform=ax.transAxes,
-            )
-            print("underflow bin:", underflow)
+            ax.axvspan(final_bins[0], final_bins[1], facecolor="red", alpha=0.5)
         if overflow > 0.0:
-            ax.text(
-                0.85,
-                0.5,
-                "overflow",
-                horizontalalignment="center",
-                verticalalignment="center",
-                transform=ax.transAxes,
-            )
-            print("overflow bin:", overflow)
+            ax.axvspan(final_bins[-2], final_bins[-1], facecolor="red", alpha=0.5)
 
     return return_artists
 
@@ -526,8 +516,8 @@ def hist2dplot(
         Colorbar maximum.
     ax : matplotlib.axes.Axes, optional
         Axes object (if None, last one is fetched or one is created)
-    flow :  str, optional {None, "show", "sum"}
-            Whether plot the under/overflow bin. If "show", add additional under/overflow bin. If "sum", add the under/overflow bin content to first/last bin.
+    flow :  str, optional {None, "show", "sum","hint"}
+            Whether plot the under/overflow bin. If "show", add additional under/overflow bin. If "sum", add the under/overflow bin content to first/last bin. "hint" would highlight the bins with under/overflow contents
     **kwargs :
         Keyword arguments passed to underlying matplotlib function - pcolormesh.
 
@@ -544,29 +534,32 @@ def hist2dplot(
         if not isinstance(ax, plt.Axes):
             raise ValueError("ax must be a matplotlib Axes object")
 
+    # hist overflow check
+    if flow is not None and "Hist" not in str(type(H)):
+        raise TypeError(f"{flow} option only works for Hist object")
+
     hist = hist_object_handler(H, xbins, ybins)
 
     # TODO: use Histogram everywhere
     H = hist.values()
     xbins, xtick_labels = get_plottable_protocol_bins(hist.axes[0])
     ybins, ytick_labels = get_plottable_protocol_bins(hist.axes[1])
-
     # Show under/overflow bins
-    # "show": Add additional bin with 5 times bin width
+    # "show": Add additional bin with 2 times bin width
     if flow == "show":
         H = hist.view(flow=True)["value"]
         xbins = np.array(
             [
-                xbins[0] - (xbins[1] - xbins[0]) * 5,
+                xbins[0] - (xbins[1] - xbins[0]) * 2,
                 *xbins,
-                xbins[-1] + (xbins[1] - xbins[0]) * 5,
+                xbins[-1] + (xbins[1] - xbins[0]) * 2,
             ]
         )
         ybins = np.array(
             [
-                ybins[0] - (ybins[1] - ybins[0]) * 5,
+                ybins[0] - (ybins[1] - ybins[0]) * 2,
                 *ybins,
-                ybins[-1] + (ybins[1] - ybins[0]) * 5,
+                ybins[-1] + (ybins[1] - ybins[0]) * 2,
             ]
         )
     if flow == "sum":
@@ -632,23 +625,46 @@ def hist2dplot(
     plt.sca(ax)
     if flow == "hint":
         if hist.view(flow=True)["value"][0, 0] > 0.0:
-            print(
-                "underflow for x & underflow for y:",
-                hist.view(flow=True)["value"][0, 0],
+            ret1 = patches.Rectangle(
+                (xbins[0], ybins[0]),
+                width=xbins[1] - xbins[0],
+                height=ybins[1] - ybins[0],
+                fc="none",
+                ec="r",
+                lw=2,
             )
+            ax.add_patch(ret1)
+
         if hist.view(flow=True)["value"][-1, 0] > 0.0:
-            print(
-                "overflow for x & underflow for y:",
-                hist.view(flow=True)["value"][-1, 0],
+            ret2 = patches.Rectangle(
+                (xbins[-2], ybins[0]),
+                width=xbins[1] - xbins[0],
+                height=ybins[1] - ybins[0],
+                fc="none",
+                ec="r",
+                lw=2,
             )
+            ax.add_patch(ret2)
         if hist.view(flow=True)["value"][0, -1] > 0.0:
-            print(
-                "underflow for x & overflow for y:", hist.view(flow=True)["value"][0, 0]
+            ret3 = patches.Rectangle(
+                (xbins[0], ybins[-2]),
+                width=xbins[1] - xbins[0],
+                height=ybins[1] - ybins[0],
+                fc="none",
+                ec="r",
+                lw=2,
             )
+            ax.add_patch(ret3)
         if hist.view(flow=True)["value"][-1, -1] > 0.0:
-            print(
-                "overflow for x & overflow for y:", hist.view(flow=True)["value"][-1, 0]
+            ret4 = patches.Rectangle(
+                (xbins[-2], ybins[-2]),
+                width=xbins[1] - xbins[0],
+                height=ybins[1] - ybins[0],
+                fc="none",
+                ec="r",
+                lw=2,
             )
+            ax.add_patch(ret4)
     _labels: np.ndarray | None = None
     if isinstance(labels, bool):
         _labels = H if labels else None
@@ -683,320 +699,8 @@ def hist2dplot(
     return ColormeshArtists(pc, cb_obj, text_artists)
 
 
-### copy functions coffea.hist.plotratio https://github.com/CoffeaTeam/coffea/blob/master/coffea/hist/plot.py to boost-hist
-################
-## ratio plot ##
-################
-_coverage1sd = scipy.stats.norm.cdf(1) - scipy.stats.norm.cdf(-1)
-
-
-def plotratio(
-    num,
-    denom,
-    ax=None,
-    clear=True,
-    flow=None,
-    xerr=False,
-    error_opts={},
-    denom_fill_opts={},
-    guide_opts={},
-    unc="num",
-    label=None,
-    ext_denom_error=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
-        flow : str, optional {None, "show", "sum"}
-            Whether plot the under/overflow bin. If "show", add additional under/overflow bin. If "sum", add the under/overflow bin content to first/last bin.
-        xerr: bool, optional
-            If true, then error bars are drawn for x-axis to indicate the size of the bin.
-        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``)
-        ext_denom_error: list of np.array[error_up,error_down], optional
-            External MC errors not stored in the original histogram
-    Returns
-    -------
-        ax : matplotlib.axes.Axes
-            A matplotlib `Axes <https://matplotlib.org/3.1.1/api/axes_api.html>`_ object
-    """
-    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 compatible(num, denom):
-        raise ValueError(
-            "numerator and denominator histograms have incompatible axis definitions"
-        )
-    if len(num.axes) > 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]
-
-    ax.set_xlabel(axis.label)
-    ax.set_ylabel(num.label)
-    edges = axis.edges
-    if flow == "show":
-        edges = np.array(
-            [
-                edges[0] - (edges[1] - edges[0]) * 5,
-                *edges,
-                edges[-1] + (edges[1] - edges[0]) * 5,
-            ]
-        )
-    centers = (edges[1:] + edges[:-1]) / 2
-    ranges = (edges[1:] - edges[:-1]) / 2 if xerr else None
-    sumw_num, sumw2_num = num.values(), num.variances()
-    sumw_denom, sumw2_denom = denom.values(), denom.variances()
-
-    if flow == "show":
-        sumw_num, sumw2_num = (
-            num.view(flow=True)["value"],
-            num.view(flow=True)["variance"],
-        )
-        sumw_denom, sumw2_denom = (
-            denom.view(flow=True)["value"],
-            denom.view(flow=True)["variance"],
-        )
-    elif flow == "sum":
-        sumw_num[0], sumw2_num[0] = (
-            sumw_num[0] + num.view(flow=True)["value"][0],
-            sumw2_num[0] + num.view(flow=True)["value"][0],
-        )
-        sumw_num[-1], sumw2_num[-1] = (
-            sumw_num[-1] + num.view(flow=True)["value"][-1],
-            sumw2_num[-1] + num.view(flow=True)["value"][-1],
-        )
-        sumw_denom[0], sumw2_denom[0] = (
-            sumw_denom[0] + denom.view(flow=True)["value"][0],
-            sumw2_denom[0] + denom.view(flow=True)["value"][0],
-        )
-        sumw_denom[-1], sumw2_denom[-1] = (
-            sumw_denom[-1] + denom.view(flow=True)["value"][-1],
-            sumw2_denom[-1] + denom.view(flow=True)["value"][-1],
-        )
-    else:
-        sumw_num, sumw2_num = num.values(), num.variances()
-        sumw_denom, sumw2_denom = denom.values(), denom.variances()
-    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 == "efficiency":
-        rsumw_err = np.abs(
-            normal_interval(sumw_num, sumw_denom, sumw2_num, sumw2_denom)
-        )
-    else:
-        raise ValueError("Unrecognized uncertainty option: %r" % unc)
-
-    ## if additional uncertainties
-    if ext_denom_error is not None:
-        if denom_fill_opts is {}:
-            print("suggest to use different style for additional error")
-        if np.shape(rsumw_err) != np.shape(ext_denom_error / sumw_denom):
-            raise ValueError("Imcompatible error length")
-        rsumw_err = np.sqrt(rsumw_err**2 + (ext_denom_error / sumw_denom) ** 2)
-
-    if error_opts is not None:
-        opts = {
-            "label": label,
-            "linestyle": "none",
-            "lw": 1,
-            "marker": "o",
-            "color": "k",
-        }
-        opts.update(error_opts)
-        emarker = opts.pop("emarker", "")
-        errbar = ax.errorbar(x=centers, y=rsumw, xerr=ranges, 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 = {
-            "hatch": "////",
-            "facecolor": "none",
-            "lw": 0,
-            "color": "k",
-            "alpha": 0.4,
-        }
-        if ext_denom_error is not None:
-            denom_unc[0] = (
-                unity[0]
-                - np.sqrt(
-                    (denom_unc - unity) ** 2 + (ext_denom_error / sumw_denom) ** 2
-                )[0]
-            )
-            denom_unc[1] = (
-                unity[1]
-                + np.sqrt(
-                    (denom_unc - unity) ** 2 + (ext_denom_error / sumw_denom) ** 2
-                )[1]
-            )
-            opts = denom_fill_opts
-        ax.stairs(denom_unc[0], edges=edges, baseline=denom_unc[1], **opts)
-    if guide_opts is not None:
-        opts = {"linestyle": "--", "color": (0, 0, 0, 0.5), "linewidth": 1}
-        opts.update(guide_opts)
-        if clear is not False:
-            ax.axhline(1.0, **opts)
-
-    if clear:
-        ax.autoscale(axis="x", tight=True)
-        ax.set_ylim(0, None)
-
-    return ax
-
-
 #############################################
 # Utils
-def compatible(self, other):
-    """Checks if this histogram is compatible with another, i.e. they have identical binning"""
-    if len(self.axes) != len(other.axes):
-        return False
-    if set(self.axes.name) != set(other.axes.name):
-        return False
-    # if not all(d1 == d2 for d1, d2 in zip(self.dense_axes(), other.dense_axes())):
-    #     return False
-    return True
-
-
-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) / 2.0
-    hi = scale * scipy.stats.chi2.ppf((1 + coverage) / 2, 2 * (counts + 1)) / 2.0
-    interval = np.array([lo, hi])
-    interval[interval == np.nan] = 0.0  # chi2.ppf produces nan for counts=0
-    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 : np.ndarray
-        Numerator, or number of (weighted) successes, vectorized
-    tw : np.ndarray
-        Denominator or number of (weighted) trials, vectorized
-    pw2 : np.ndarray
-        Numerator sum of weights squared, vectorized
-    tw2 : np.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 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] = 0.0
-    interval[1, num == denom] = 1.0
-    return interval
-
-
 def overlap(ax, bbox, get_vertices=False):
     """
     Find overlap of bbox for drawn elements an axes.
diff --git a/tests/baseline/test_hist2dplot.png b/tests/baseline/test_hist2dplot.png
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diff --git a/tests/baseline/test_hist2dplot_cbar_False.png b/tests/baseline/test_hist2dplot_cbar_False.png
index 975bce0e..4c513b7a 100644
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diff --git a/tests/baseline/test_hist2dplot_cbar_True.png b/tests/baseline/test_hist2dplot_cbar_True.png
index c9e407b5..eda3b263 100644
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diff --git a/tests/baseline/test_hist2dplot_cbar_subplots.png b/tests/baseline/test_hist2dplot_cbar_subplots.png
index 234afefe..34246667 100644
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diff --git a/tests/baseline/test_hist2dplot_custom_labels.png b/tests/baseline/test_hist2dplot_custom_labels.png
index 8baf0ab1..7891e558 100644
Binary files a/tests/baseline/test_hist2dplot_custom_labels.png and b/tests/baseline/test_hist2dplot_custom_labels.png differ
diff --git a/tests/baseline/test_hist2dplot_flow.png b/tests/baseline/test_hist2dplot_flow.png
new file mode 100644
index 00000000..d6ea0895
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diff --git a/tests/test_basic.py b/tests/test_basic.py
index 1b9423a0..edd98232 100644
--- a/tests/test_basic.py
+++ b/tests/test_basic.py
@@ -2,6 +2,7 @@
 
 import os
 
+import hist
 import matplotlib.pyplot as plt
 import numpy as np
 import pytest
@@ -110,6 +111,27 @@ def test_histplot_density():
     return fig
 
 
+@pytest.mark.mpl_image_compare(style="default")
+def test_histplot_flow():
+    np.random.seed(0)
+    h = hist.Hist(hist.axis.Regular(20, 5, 15, name="x"), hist.storage.Weight())
+    h.fill(np.random.normal(10, 3, 400))
+    fig, axs = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(10, 10))
+    axs = axs.flatten()
+
+    hep.histplot(h, ax=axs[0], flow="hint")
+    hep.histplot(h, ax=axs[1], flow="None")
+    hep.histplot(h, ax=axs[2], flow="show")
+    hep.histplot(h, ax=axs[3], flow="sum")
+
+    axs[0].set_title("Default", fontsize=18)
+    axs[1].set_title("None", fontsize=18)
+    axs[2].set_title("Show", fontsize=18)
+    axs[3].set_title("Sum", fontsize=18)
+    fig.subplots_adjust(hspace=0.1, wspace=0.1)
+    return fig
+
+
 @pytest.mark.mpl_image_compare(style="default", remove_text=True)
 def test_histplot_multiple():
     np.random.seed(0)
@@ -175,6 +197,32 @@ def test_hist2dplot():
     return fig
 
 
+@pytest.mark.mpl_image_compare(style="default")
+def test_hist2dplot_flow():
+    np.random.seed(0)
+    h = hist.Hist(
+        hist.axis.Regular(20, 5, 15, name="x"),
+        hist.axis.Regular(20, -5, 5, name="y"),
+        hist.storage.Weight(),
+    )
+    h.fill(np.random.normal(10, 3, 400), np.random.normal(0, 4, 400))
+    fig, axs = plt.subplots(2, 2, figsize=(10, 10))
+    axs = axs.flatten()
+
+    hep.hist2dplot(h, ax=axs[0], flow="hint")
+    hep.hist2dplot(h, ax=axs[1], flow="None")
+    hep.hist2dplot(h, ax=axs[2], flow="show")
+    hep.hist2dplot(h, ax=axs[3], flow="sum")
+
+    axs[0].set_title("Default", fontsize=18)
+    axs[1].set_title("None", fontsize=18)
+    axs[2].set_title("Show", fontsize=18)
+    axs[3].set_title("Sum", fontsize=18)
+    fig.subplots_adjust(hspace=0.1, wspace=0.1)
+
+    return fig
+
+
 @pytest.mark.mpl_image_compare(style="default", remove_text=True)
 def test_hist2dplot_inputs_nobin():
     np.random.seed(0)