diff --git a/examples/1d_histograms/1d_comparison_asymmetry.py b/examples/1d_histograms/1d_comparison_asymmetry.py
index 2103ac79..6980b009 100644
--- a/examples/1d_histograms/1d_comparison_asymmetry.py
+++ b/examples/1d_histograms/1d_comparison_asymmetry.py
@@ -9,7 +9,6 @@
 # --8<-- [start:imports]
 import hist
 import numpy as np
-from hist import Hist
 
 import mplhep as mh
 
@@ -21,11 +20,9 @@
 x1 = np.r_[np.random.normal(0.4, 0.1, 5000), np.random.normal(0.7, 0.1, 5000)]
 x2 = np.r_[np.random.normal(0.4, 0.1, 1000), np.random.normal(0.7, 0.11, 7000)]
 
-# Create and fill histograms"
-h1 = Hist(hist.axis.Regular(50, 0, 1), storage=hist.storage.Weight())  # Long interface
-h2 = hist.new.Regular(50, 0, 1).Weight()  # Shorthand interface
-h1.fill(x1)
-h2.fill(x2)
+# Create and fill histograms
+h1 = hist.new.Regular(50, 0, 1).Weight().fill(x1)
+h2 = hist.new.Regular(50, 0, 1).Weight().fill(x2)
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
diff --git a/examples/1d_histograms/1d_comparison_difference.py b/examples/1d_histograms/1d_comparison_difference.py
index 89d78eb8..405208c8 100644
--- a/examples/1d_histograms/1d_comparison_difference.py
+++ b/examples/1d_histograms/1d_comparison_difference.py
@@ -9,7 +9,6 @@
 # --8<-- [start:imports]
 import hist
 import numpy as np
-from hist import Hist
 
 import mplhep as mh
 
@@ -21,17 +20,13 @@
 x1 = np.r_[np.random.normal(0.4, 0.1, 5000), np.random.normal(0.7, 0.1, 5000)]
 x2 = np.r_[np.random.normal(0.4, 0.1, 1000), np.random.normal(0.7, 0.11, 7000)]
 
-# Create and fill histograms"
-h1 = Hist(hist.axis.Regular(50, 0, 1), storage=hist.storage.Weight())  # Long interface
-h2 = hist.new.Regular(50, 0, 1).Weight()  # Shorthand interface
-h1.fill(x1)
-h2.fill(x2)
+# Create and fill histograms
+h1 = hist.new.Regular(50, 0, 1).Weight().fill(x1)
+h2 = hist.new.Regular(50, 0, 1).Weight().fill(x2)
 # --8<-- [end:setup]
 
 
 # --8<-- [start:plot_body]
-from mplhep import add_text
-
 fig, ax_main, ax_comparison = mh.comp.hists(
     h1,
     h2,
@@ -42,13 +37,13 @@
     comparison="difference",  # <--
 )
 
-add_text(
+mh.add_text(
     "Comparison of two hist with difference plot",
     ax=ax_main,
     fontsize="small",
     loc="over left",
 )
-add_text("Difference ax", ax=ax_comparison, loc="over right", fontsize="small")
+mh.add_text("Difference ax", ax=ax_comparison, loc="over right", fontsize="small")
 
 # --8<-- [end:plot_body]
 # --8<-- [end:full_code]
diff --git a/examples/1d_histograms/1d_comparison_efficiency.py b/examples/1d_histograms/1d_comparison_efficiency.py
index 7987332f..e0c83cf4 100644
--- a/examples/1d_histograms/1d_comparison_efficiency.py
+++ b/examples/1d_histograms/1d_comparison_efficiency.py
@@ -9,7 +9,6 @@
 # --8<-- [start:imports]
 import hist
 import numpy as np
-from hist import Hist
 
 import mplhep as mh
 
@@ -22,16 +21,11 @@
 x_sample = x_total[:7500]  # 75% subset
 
 # Create and fill histograms
-h_sample = Hist(
-    hist.axis.Regular(50, 0, 1), storage=hist.storage.Weight()
-)  # Long interface
-h_total = hist.new.Regular(50, 0, 1).Weight()  # Shorthand interface
-h_sample.fill(x_sample)
-h_total.fill(x_total)
+h_sample = hist.new.Regular(50, 0, 1).Weight().fill(x_sample)
+h_total = hist.new.Regular(50, 0, 1).Weight().fill(x_total)
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
 fig, ax_main, ax_comparison = mh.comp.hists(
     h_sample,
     h_total,
diff --git a/examples/1d_histograms/1d_comparison_only_efficiency.py b/examples/1d_histograms/1d_comparison_only_efficiency.py
index 4c694306..e4e9b0d2 100644
--- a/examples/1d_histograms/1d_comparison_only_efficiency.py
+++ b/examples/1d_histograms/1d_comparison_only_efficiency.py
@@ -10,7 +10,6 @@
 import hist
 import matplotlib.pyplot as plt
 import numpy as np
-from hist import Hist
 
 import mplhep as mh
 
@@ -23,16 +22,11 @@
 x_sample = x_total[:7500]  # 75% subset
 
 # Create and fill histograms
-h_sample = Hist(
-    hist.axis.Regular(50, 0, 1), storage=hist.storage.Weight()
-)  # Long interface
-h_total = hist.new.Regular(50, 0, 1).Weight()  # Shorthand interface
-h_sample.fill(x_sample)
-h_total.fill(x_total)
+h_sample = hist.new.Regular(50, 0, 1).Weight().fill(x_sample)
+h_total = hist.new.Regular(50, 0, 1).Weight().fill(x_total)
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-# Plot comparison using efficiency
 fig, ax = plt.subplots()
 mh.comp.comparison(h_sample, h_total, ax=ax, xlabel="Variable", comparison="efficiency")
 
diff --git a/examples/1d_histograms/1d_comparison_pull.py b/examples/1d_histograms/1d_comparison_pull.py
index e024a5ea..87921281 100644
--- a/examples/1d_histograms/1d_comparison_pull.py
+++ b/examples/1d_histograms/1d_comparison_pull.py
@@ -9,7 +9,6 @@
 # --8<-- [start:imports]
 import hist
 import numpy as np
-from hist import Hist
 
 import mplhep as mh
 
@@ -22,14 +21,11 @@
 x2 = np.r_[np.random.normal(0.4, 0.1, 1000), np.random.normal(0.7, 0.11, 7000)]
 
 # Create and fill histograms
-h1 = Hist(hist.axis.Regular(50, 0, 1), storage=hist.storage.Weight())  # Long interface
-h2 = hist.new.Regular(50, 0, 1).Weight()  # Shorthand interface
-h1.fill(x1)
-h2.fill(x2)
+h1 = hist.new.Regular(50, 0, 1).Weight().fill(x1)
+h2 = hist.new.Regular(50, 0, 1).Weight().fill(x2)
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
 fig, ax_main, ax_comparison = mh.comp.hists(
     h1,
     h2,
diff --git a/examples/1d_histograms/1d_comparison_ratio.py b/examples/1d_histograms/1d_comparison_ratio.py
index 63e34b6d..b0c35520 100644
--- a/examples/1d_histograms/1d_comparison_ratio.py
+++ b/examples/1d_histograms/1d_comparison_ratio.py
@@ -9,7 +9,6 @@
 # --8<-- [start:imports]
 import hist
 import numpy as np
-from hist import Hist
 
 import mplhep as mh
 
@@ -22,14 +21,11 @@
 x2 = np.r_[np.random.normal(0.4, 0.1, 1000), np.random.normal(0.7, 0.11, 7000)]
 
 # Create and fill histograms
-h1 = Hist(hist.axis.Regular(50, 0, 1), storage=hist.storage.Weight())  # Long interface
-h2 = hist.new.Regular(50, 0, 1).Weight()  # Shorthand interface
-h1.fill(x1)
-h2.fill(x2)
+h1 = hist.new.Regular(50, 0, 1).Weight().fill(x1)
+h2 = hist.new.Regular(50, 0, 1).Weight().fill(x2)
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
 fig, ax_main, ax_comparison = mh.comp.hists(
     h1,
     h2,
diff --git a/examples/1d_histograms/1d_comparison_relative_difference.py b/examples/1d_histograms/1d_comparison_relative_difference.py
index 3a88166e..ceb19f70 100644
--- a/examples/1d_histograms/1d_comparison_relative_difference.py
+++ b/examples/1d_histograms/1d_comparison_relative_difference.py
@@ -9,7 +9,6 @@
 # --8<-- [start:imports]
 import hist
 import numpy as np
-from hist import Hist
 
 import mplhep as mh
 
@@ -22,14 +21,11 @@
 x2 = np.r_[np.random.normal(0.4, 0.1, 1000), np.random.normal(0.7, 0.11, 7000)]
 
 # Create and fill histograms
-h1 = Hist(hist.axis.Regular(50, 0, 1), storage=hist.storage.Weight())  # Long interface
-h2 = hist.new.Regular(50, 0, 1).Weight()  # Shorthand interface
-h1.fill(x1)
-h2.fill(x2)
+h1 = hist.new.Regular(50, 0, 1).Weight().fill(x1)
+h2 = hist.new.Regular(50, 0, 1).Weight().fill(x2)
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
 fig, ax_main, ax_comparison = mh.comp.hists(
     h1,
     h2,
diff --git a/examples/1d_histograms/1d_comparison_split_ratio.py b/examples/1d_histograms/1d_comparison_split_ratio.py
index f57c5101..f51e715f 100644
--- a/examples/1d_histograms/1d_comparison_split_ratio.py
+++ b/examples/1d_histograms/1d_comparison_split_ratio.py
@@ -9,7 +9,6 @@
 # --8<-- [start:imports]
 import hist
 import numpy as np
-from hist import Hist
 
 import mplhep as mh
 
@@ -22,14 +21,11 @@
 x2 = np.r_[np.random.normal(0.4, 0.1, 1000), np.random.normal(0.7, 0.11, 7000)]
 
 # Create and fill histograms
-h1 = Hist(hist.axis.Regular(50, 0, 1), storage=hist.storage.Weight())  # Long interface
-h2 = hist.new.Regular(50, 0, 1).Weight()  # Shorthand interface
-h1.fill(x1)
-h2.fill(x2)
+h1 = hist.new.Regular(50, 0, 1).Weight().fill(x1)
+h2 = hist.new.Regular(50, 0, 1).Weight().fill(x2)
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
 fig, ax_main, ax_comparison = mh.comp.hists(
     h1,
     h2,
diff --git a/examples/model_ex/model_all_comparisons.py b/examples/model_ex/model_all_comparisons.py
index ef33d4e3..628f6f25 100644
--- a/examples/model_ex/model_all_comparisons.py
+++ b/examples/model_ex/model_all_comparisons.py
@@ -7,80 +7,58 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-from plothist_utils import get_dummy_data
-
-df = get_dummy_data()
-
+import hist
+import matplotlib.pyplot as plt
+import numpy as np
 import seaborn as sns
 
 import mplhep as mh
 
+np.random.seed(42)
 # --8<-- [end:imports]
 
 # --8<-- [start:setup]
-# Define the histograms
-
-key = "variable_1"
-x_range = (-9, 12)
-category = "category"
-
-# Define masks
-signal_mask = df[category] == 7
-data_mask = df[category] == 8
-
-background_categories = [0, 1, 2]
-background_categories_labels = [f"c{i}" for i in background_categories]
-background_categories_colors = sns.color_palette(
-    "cubehelix", len(background_categories)
+# Create data histogram with mixed components
+data_hist = hist.new.Regular(50, -8, 8).Weight()
+data_hist.fill(
+    np.concatenate(
+        [
+            np.random.normal(0, 2, 3000),
+            np.random.normal(-3, 0.8, 1500),
+            np.random.normal(-2, 1.5, 1200),
+            np.random.normal(0, 0.5, 300),
+        ]
+    )
 )
 
-background_masks = [df[category] == p for p in background_categories]
-
-# Make histograms
-import hist
-from hist import Hist
+# Create background component histograms
+background_hists = [
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
+]
 
-axis = hist.axis.Regular(50, x_range[0], x_range[1])
-
-data_hist = Hist(axis, storage=hist.storage.Weight())
-signal_hist = Hist(axis, storage=hist.storage.Weight())
-background_hists = []
-
-data_hist.fill(df[key][data_mask])
-signal_hist.fill(df[key][signal_mask])
-
-for mask in background_masks:
-    h_bkg = Hist(axis, storage=hist.storage.Weight())
-    h_bkg.fill(df[key][mask])
-    background_hists.append(h_bkg)
-
-# Optional: scale to data
-background_scaling_factor = data_hist.sum().value / sum(background_hists).sum().value
-background_hists = [background_scaling_factor * h for h in background_hists]
+# Scale backgrounds to match data
+scale = data_hist.sum().value / sum(background_hists).sum().value
+background_hists = [scale * h for h in background_hists]
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-import matplotlib.pyplot as plt
-
-from mplhep import comp
-
 fig, axes = plt.subplots(
-    nrows=6,
-    figsize=(6, 13),
-    gridspec_kw={"height_ratios": [3, 1, 1, 1, 1, 1]},
+    nrows=6, figsize=(6, 13), gridspec_kw={"height_ratios": [3, 1, 1, 1, 1, 1]}
 )
 fig.subplots_adjust(hspace=0.3)
+
+# Hide x-axis labels for all but the bottom plot
 for ax in axes[:-1]:
     ax.xaxis.set_ticklabels([])
     ax.set_xlabel(" ")
-background_sum = sum(background_hists)
 
-comp.data_model(
+mh.comp.data_model(
     data_hist=data_hist,
     stacked_components=background_hists,
-    stacked_labels=background_categories_labels,
-    stacked_colors=background_categories_colors,
+    stacked_labels=["c0", "c1", "c2"],
+    stacked_colors=sns.color_palette("cubehelix", 3),
     xlabel="",
     ylabel="Entries",
     comparison="ratio",
@@ -99,28 +77,27 @@
     r'  $\mathbf{→}$ comparison = "ratio"', ax=axes[1], loc="over left", fontsize=13
 )
 
-for k_comp, comparison in enumerate(
+# Add remaining comparison types
+for k, comp in enumerate(
     ["split_ratio", "pull", "relative_difference", "difference"], start=2
 ):
-    ax_comparison = axes[k_comp]
-
-    comp.comparison(
+    mh.comp.comparison(
         data_hist,
-        background_sum,
-        ax=ax_comparison,
-        comparison=comparison,
+        sum(background_hists),
+        ax=axes[k],
+        comparison=comp,
         xlabel="",
         h1_label="Data",
         h2_label="MC",
         h1_w2method="poisson",
     )
     mh.add_text(
-        rf'  $\mathbf{{→}}$ comparison = "{comparison}"',
-        ax=ax_comparison,
+        rf'  $\mathbf{{→}}$ comparison = "{comp}"',
+        ax=axes[k],
         fontsize=13,
         loc="over left",
     )
-    mh.set_fitting_ylabel_fontsize(ax_comparison)
+    mh.set_fitting_ylabel_fontsize(axes[k])
 
 axes[-1].set_xlabel("Observable")
 # --8<-- [end:plot_body]
diff --git a/examples/model_ex/model_all_comparisons_no_model_unc.py b/examples/model_ex/model_all_comparisons_no_model_unc.py
index d3eb16a2..01d49fdd 100644
--- a/examples/model_ex/model_all_comparisons_no_model_unc.py
+++ b/examples/model_ex/model_all_comparisons_no_model_unc.py
@@ -11,7 +11,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
-from hist import Hist
 
 import mplhep as mh
 
@@ -35,14 +34,13 @@
         np.random.normal(0, 2.5, 3500),  # Less background here
         np.random.normal(3, 1.2, 1800),  # Similar background
         np.random.normal(-1, 1.8, 1400),  # Similar background
-        np.random.normal(5, 0.8, 800),  # Clear signal peak
+        np.random.normal(0, 0.8, 500),  # Clear signal peak
         np.random.normal(-3, 0.5, 200),  # Some deficit here (under-predicted)
     ]
 )
 
 # Create histograms
-data_hist = Hist(hist.axis.Regular(50, -8, 8), storage=hist.storage.Weight())
-data_hist.fill(data_data)
+data_hist = hist.new.Regular(50, -8, 8).Weight().fill(data_data)
 
 background_hists = [
     hist.new.Regular(50, -8, 8).Weight().fill(bkg1_data),
diff --git a/examples/model_ex/model_examples_pull.py b/examples/model_ex/model_examples_pull.py
index c16aae43..32bdbf87 100644
--- a/examples/model_ex/model_examples_pull.py
+++ b/examples/model_ex/model_examples_pull.py
@@ -7,66 +7,48 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-from plothist_utils import get_dummy_data
-
-df = get_dummy_data()
-
+import hist
+import numpy as np
 import seaborn as sns
 
+import mplhep as mh
+
+np.random.seed(42)
 # --8<-- [end:imports]
 
 # --8<-- [start:setup]
-# Define the histograms
-
-key = "variable_1"
-x_range = (-9, 12)
-category = "category"
-
-# Define masks
-signal_mask = df[category] == 7
-data_mask = df[category] == 8
-
-background_categories = [0, 1, 2]
-background_categories_labels = [f"c{i}" for i in background_categories]
-background_categories_colors = sns.color_palette(
-    "cubehelix", len(background_categories)
+# Create data histogram
+data_hist = hist.new.Regular(50, -8, 8).Weight()
+data_hist.fill(
+    np.concatenate(
+        [
+            np.random.normal(0, 2, 2000),
+            np.random.normal(-3, 0.8, 1500),
+            np.random.normal(-2, 1.5, 1200),
+            np.random.normal(0, 0.5, 500),
+        ]
+    )
 )
 
-background_masks = [df[category] == p for p in background_categories]
+# Create background histograms
+background_hists = [
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
+]
 
-# Make histograms
-import hist
-from hist import Hist
-
-axis = hist.axis.Regular(50, x_range[0], x_range[1])
-
-data_hist = Hist(axis, storage=hist.storage.Weight())
-signal_hist = Hist(axis, storage=hist.storage.Weight())
-background_hists = []
-
-data_hist.fill(df[key][data_mask])
-signal_hist.fill(df[key][signal_mask])
-
-for mask in background_masks:
-    h_bkg = Hist(axis, storage=hist.storage.Weight())
-    h_bkg.fill(df[key][mask])
-    background_hists.append(h_bkg)
-
-# Optional: scale to data
-background_scaling_factor = data_hist.sum().value / sum(background_hists).sum().value
-background_hists = [background_scaling_factor * h for h in background_hists]
+# Scale backgrounds to match data
+scale = data_hist.sum().value / sum(background_hists).sum().value
+background_hists = [scale * h for h in background_hists]
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-from mplhep import comp
-
-fig, ax_main, ax_comparison = comp.data_model(
+fig, ax_main, ax_comparison = mh.comp.data_model(
     data_hist=data_hist,
     stacked_components=background_hists,
-    stacked_labels=background_categories_labels,
-    stacked_colors=background_categories_colors,
-    xlabel=rf"${key}\,\,[TeV/c^2]$",
+    stacked_labels=["c0", "c1", "c2"],
+    stacked_colors=sns.color_palette("cubehelix", 3),
+    xlabel=r"$m_{\ell\ell}\,\,[TeV/c^2]$",
     ylabel="Candidates per 0.42 $TeV/c^2$",
     comparison="pull",
 )
diff --git a/examples/model_ex/model_examples_pull_no_model_unc.py b/examples/model_ex/model_examples_pull_no_model_unc.py
index e40a8bcf..9ea651ba 100644
--- a/examples/model_ex/model_examples_pull_no_model_unc.py
+++ b/examples/model_ex/model_examples_pull_no_model_unc.py
@@ -7,66 +7,48 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-from plothist_utils import get_dummy_data
-
-df = get_dummy_data()
-
+import hist
+import numpy as np
 import seaborn as sns
 
+import mplhep as mh
+
+np.random.seed(42)
 # --8<-- [end:imports]
 
 # --8<-- [start:setup]
-# Define the histograms
-
-key = "variable_1"
-x_range = (-9, 12)
-category = "category"
-
-# Define masks
-signal_mask = df[category] == 7
-data_mask = df[category] == 8
-
-background_categories = [0, 1, 2]
-background_categories_labels = [f"c{i}" for i in background_categories]
-background_categories_colors = sns.color_palette(
-    "cubehelix", len(background_categories)
+# Create data histogram
+data_hist = hist.new.Regular(50, -8, 8).Weight()
+data_hist.fill(
+    np.concatenate(
+        [
+            np.random.normal(0, 2, 2000),
+            np.random.normal(-3, 0.8, 1500),
+            np.random.normal(-2, 1.5, 1200),
+            np.random.normal(0, 0.5, 500),
+        ]
+    )
 )
 
-background_masks = [df[category] == p for p in background_categories]
+# Create background histograms
+background_hists = [
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
+]
 
-# Make histograms
-import hist
-from hist import Hist
-
-axis = hist.axis.Regular(50, x_range[0], x_range[1])
-
-data_hist = Hist(axis, storage=hist.storage.Weight())
-signal_hist = Hist(axis, storage=hist.storage.Weight())
-background_hists = []
-
-data_hist.fill(df[key][data_mask])
-signal_hist.fill(df[key][signal_mask])
-
-for mask in background_masks:
-    h_bkg = Hist(axis, storage=hist.storage.Weight())
-    h_bkg.fill(df[key][mask])
-    background_hists.append(h_bkg)
-
-# Optional: scale to data
-background_scaling_factor = data_hist.sum().value / sum(background_hists).sum().value
-background_hists = [background_scaling_factor * h for h in background_hists]
+# Scale backgrounds to match data
+scale = data_hist.sum().value / sum(background_hists).sum().value
+background_hists = [scale * h for h in background_hists]
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-from mplhep import comp
-
-fig, ax_main, ax_comparison = comp.data_model(
+fig, ax_main, ax_comparison = mh.comp.data_model(
     data_hist=data_hist,
     stacked_components=background_hists,
-    stacked_labels=background_categories_labels,
-    stacked_colors=background_categories_colors,
-    xlabel=rf"${key}\,\,[eV/c^2]$",
+    stacked_labels=["c0", "c1", "c2"],
+    stacked_colors=sns.color_palette("cubehelix", 3),
+    xlabel=r"$m_{\ell\ell}\,\,[TeV/c^2]$",
     ylabel=r"Hits in the LMN per $4.2\times 10^{-1}\,\,eV/c^2$",
     comparison="pull",
     model_uncertainty=False,  # <--
diff --git a/examples/model_ex/model_examples_stacked.py b/examples/model_ex/model_examples_stacked.py
index fd669f89..f8f1cc30 100644
--- a/examples/model_ex/model_examples_stacked.py
+++ b/examples/model_ex/model_examples_stacked.py
@@ -7,81 +7,56 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-from plothist_utils import get_dummy_data
-
-df = get_dummy_data()
-
+import hist
+import numpy as np
 import seaborn as sns
 
+import mplhep as mh
+
+np.random.seed(42)
 # --8<-- [end:imports]
 
 # --8<-- [start:setup]
-# Define the histograms
-
-key = "variable_1"
-x_range = (-9, 12)
-category = "category"
-
-# Define masks
-signal_mask = df[category] == 7
-data_mask = df[category] == 8
-
-background_categories = [0, 1, 2]
-background_categories_labels = [f"c{i}" for i in background_categories]
-background_categories_colors = sns.color_palette(
-    "cubehelix", len(background_categories)
+# Create data histogram
+data_hist = hist.new.Regular(50, -8, 8).Weight()
+data_hist.fill(
+    np.concatenate(
+        [
+            np.random.normal(0, 2, 3000),
+            np.random.normal(-3, 0.8, 1500),
+            np.random.normal(-2, 1.5, 1200),
+            np.random.normal(0, 0.5, 500),
+        ]
+    )
 )
 
-background_masks = [df[category] == p for p in background_categories]
-
-# Make histograms
-import hist
-from hist import Hist
-
-axis = hist.axis.Regular(50, x_range[0], x_range[1])
-
-data_hist = Hist(axis, storage=hist.storage.Weight())
-signal_hist = Hist(axis, storage=hist.storage.Weight())
-background_hists = []
-
-data_hist.fill(df[key][data_mask])
-signal_hist.fill(df[key][signal_mask])
+# Create background histograms
+background_hists = [
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
+]
 
-for mask in background_masks:
-    h_bkg = Hist(axis, storage=hist.storage.Weight())
-    h_bkg.fill(df[key][mask])
-    background_hists.append(h_bkg)
+# Scale backgrounds to match data
+scale = data_hist.sum().value / sum(background_hists).sum().value
+background_hists = [scale * h for h in background_hists]
 
-# Optional: scale to data
-background_scaling_factor = data_hist.sum().value / sum(background_hists).sum().value
-background_hists = [background_scaling_factor * h for h in background_hists]
-
-signal_scaling_factor = data_hist.sum().value / signal_hist.sum().value
-signal_hist *= signal_scaling_factor
+# Create signal histogram (not part of the model)
+signal_hist = hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-1, 0.5, 400))
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-from mplhep import comp, histplot
-
-fig, ax_main, ax_comparison = comp.data_model(
+fig, ax_main, ax_comparison = mh.comp.data_model(
     data_hist=data_hist,
     stacked_components=background_hists,
-    stacked_labels=background_categories_labels,
-    stacked_colors=background_categories_colors,
-    xlabel=key,
+    stacked_labels=["c0", "c1", "c2"],
+    stacked_colors=sns.color_palette("cubehelix", 3),
+    xlabel="Observable",
     ylabel="Entries",
 )
 
 # Signal histogram not part of the model and therefore not included in the comparison
-histplot(
-    signal_hist,
-    ax=ax_main,
-    color="red",
-    label="Signal",
-    histtype="step",
-)
-
+mh.histplot(signal_hist, ax=ax_main, color="red", label="Signal", histtype="step")
 ax_main.legend()
 # --8<-- [end:plot_body]
 
diff --git a/examples/model_ex/model_examples_stacked_unstacked.py b/examples/model_ex/model_examples_stacked_unstacked.py
index 20807f1b..f051da34 100644
--- a/examples/model_ex/model_examples_stacked_unstacked.py
+++ b/examples/model_ex/model_examples_stacked_unstacked.py
@@ -7,69 +7,51 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-from plothist_utils import get_dummy_data
-
-df = get_dummy_data()
-
+import hist
+import numpy as np
 import seaborn as sns
 
+import mplhep as mh
+
+np.random.seed(42)
 # --8<-- [end:imports]
 
 # --8<-- [start:setup]
-# Define the histograms
-
-key = "variable_1"
-x_range = (-9, 12)
-category = "category"
-
-# Define masks
-signal_mask = df[category] == 7
-data_mask = df[category] == 8
-
-background_categories = [0, 1, 2]
-background_categories_labels = [f"c{i}" for i in background_categories]
-background_categories_colors = sns.color_palette(
-    "cubehelix", len(background_categories)
+# Create data histogram
+data_hist = hist.new.Regular(50, -8, 8).Weight()
+data_hist.fill(
+    np.concatenate(
+        [
+            np.random.normal(0, 2, 3000),
+            np.random.normal(-3, 0.8, 1500),
+            np.random.normal(-2, 1.5, 1200),
+            np.random.normal(0, 0.5, 500),
+        ]
+    )
 )
 
-background_masks = [df[category] == p for p in background_categories]
+# Create background histograms
+background_hists = [
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
+]
 
-# Make histograms
-import hist
-from hist import Hist
-
-axis = hist.axis.Regular(50, x_range[0], x_range[1])
-
-data_hist = Hist(axis, storage=hist.storage.Weight())
-signal_hist = Hist(axis, storage=hist.storage.Weight())
-background_hists = []
-
-data_hist.fill(df[key][data_mask])
-signal_hist.fill(df[key][signal_mask])
-
-for mask in background_masks:
-    h_bkg = Hist(axis, storage=hist.storage.Weight())
-    h_bkg.fill(df[key][mask])
-    background_hists.append(h_bkg)
-
-# Optional: scale to data
-background_scaling_factor = data_hist.sum().value / sum(background_hists).sum().value
-background_hists = [background_scaling_factor * h for h in background_hists]
+# Scale backgrounds to match data
+scale = data_hist.sum().value / sum(background_hists).sum().value
+background_hists = [scale * h for h in background_hists]
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-from mplhep import comp
-
-fig, ax_main, ax_comparison = comp.data_model(
+fig, ax_main, ax_comparison = mh.comp.data_model(
     data_hist=data_hist,
     stacked_components=background_hists[:2],
-    stacked_labels=background_categories_labels[:2],
-    stacked_colors=background_categories_colors[:2],
+    stacked_labels=["c0", "c1"],
+    stacked_colors=sns.color_palette("cubehelix", 3)[:2],
     unstacked_components=background_hists[2:],
-    unstacked_labels=background_categories_labels[2:],
-    unstacked_colors=background_categories_colors[2:],
-    xlabel=key,
+    unstacked_labels=["c2"],
+    unstacked_colors=[sns.color_palette("cubehelix", 3)[2]],
+    xlabel="Observable",
     ylabel="Entries",
     model_sum_kwargs={"show": True, "label": "Model", "color": "navy"},
     comparison_ylim=(0.5, 1.5),
diff --git a/examples/model_ex/model_examples_unstacked.py b/examples/model_ex/model_examples_unstacked.py
index 53bffca0..0be18341 100644
--- a/examples/model_ex/model_examples_unstacked.py
+++ b/examples/model_ex/model_examples_unstacked.py
@@ -7,66 +7,48 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-from plothist_utils import get_dummy_data
-
-df = get_dummy_data()
-
+import hist
+import numpy as np
 import seaborn as sns
 
+import mplhep as mh
+
+np.random.seed(42)
 # --8<-- [end:imports]
 
 # --8<-- [start:setup]
-# Define the histograms
-
-key = "variable_1"
-x_range = (-9, 12)
-category = "category"
-
-# Define masks
-signal_mask = df[category] == 7
-data_mask = df[category] == 8
-
-background_categories = [0, 1, 2]
-background_categories_labels = [f"c{i}" for i in background_categories]
-background_categories_colors = sns.color_palette(
-    "cubehelix", len(background_categories)
+# Create data histogram
+data_hist = hist.new.Regular(50, -8, 8).Weight()
+data_hist.fill(
+    np.concatenate(
+        [
+            np.random.normal(0, 2, 3000),
+            np.random.normal(-3, 0.8, 1500),
+            np.random.normal(-2, 1.5, 1200),
+            np.random.normal(0, 0.5, 500),
+        ]
+    )
 )
 
-background_masks = [df[category] == p for p in background_categories]
+# Create background histograms
+background_hists = [
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
+]
 
-# Make histograms
-import hist
-from hist import Hist
-
-axis = hist.axis.Regular(50, x_range[0], x_range[1])
-
-data_hist = Hist(axis, storage=hist.storage.Weight())
-signal_hist = Hist(axis, storage=hist.storage.Weight())
-background_hists = []
-
-data_hist.fill(df[key][data_mask])
-signal_hist.fill(df[key][signal_mask])
-
-for mask in background_masks:
-    h_bkg = Hist(axis, storage=hist.storage.Weight())
-    h_bkg.fill(df[key][mask])
-    background_hists.append(h_bkg)
-
-# Optional: scale to data
-background_scaling_factor = data_hist.sum().value / sum(background_hists).sum().value
-background_hists = [background_scaling_factor * h for h in background_hists]
+# Scale backgrounds to match data
+scale = data_hist.sum().value / sum(background_hists).sum().value
+background_hists = [scale * h for h in background_hists]
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-from mplhep import comp
-
-fig, ax_main, ax_comparison = comp.data_model(
+fig, ax_main, ax_comparison = mh.comp.data_model(
     data_hist=data_hist,
     unstacked_components=background_hists,
-    unstacked_labels=background_categories_labels,
-    unstacked_colors=background_categories_colors,
-    xlabel=key,
+    unstacked_labels=["c0", "c1", "c2"],
+    unstacked_colors=sns.color_palette("cubehelix", 3),
+    xlabel="Observable",
     ylabel="Entries",
     model_sum_kwargs={"label": "Sum(hists)", "color": "navy"},
     comparison_ylim=[0.5, 1.5],
diff --git a/examples/model_ex/model_with_stacked_and_unstacked_function_components.py b/examples/model_ex/model_with_stacked_and_unstacked_function_components.py
index 2a710acf..6aa7d712 100644
--- a/examples/model_ex/model_with_stacked_and_unstacked_function_components.py
+++ b/examples/model_ex/model_with_stacked_and_unstacked_function_components.py
@@ -7,22 +7,17 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-key = "variable_1"
-range = (-9, 12)
-
-background_categories = [0, 1, 2]
-background_categories_labels = [f"c{i}" for i in background_categories]
-
-
-# Define some random functions that will be used as model components with functions
 from scipy.stats import norm
 
+import mplhep as mh
+
 # --8<-- [end:imports]
 
 
 # --8<-- [start:setup]
+# Define model function components
 def f_signal(x):
-    return 1000 * norm.pdf(x, loc=0.5, scale=3)
+    return 600 * norm.pdf(x, loc=0.2, scale=3)
 
 
 def f_background1(x):
@@ -30,28 +25,25 @@ def f_background1(x):
 
 
 def f_background2(x):
-    return 3000 * norm.pdf(x, loc=-1.8, scale=1.8)
+    return 3000 * norm.pdf(x, loc=-3.2, scale=1.2)
 
 
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-from mplhep import add_text, model
-
-fig, ax = model(
+fig, ax = mh.model(
     stacked_components=[f_background1, f_background2],
-    stacked_labels=background_categories_labels[:2],
+    stacked_labels=["c0", "c1"],
     unstacked_components=[f_signal],
     unstacked_labels=["Signal"],
     unstacked_colors=["black"],
-    xlabel=key,
-    ylabel=f"f({key})",
+    xlabel="Observable",
+    ylabel="f(Observable)",
     model_sum_kwargs={"show": True, "label": "Model", "color": "navy"},
-    function_range=range,
+    function_range=(-9, 12),
 )
 
-add_text("Model made of functions", ax=ax, loc="over left", fontsize="small")
+mh.add_text("Model made of functions", ax=ax, loc="over left", fontsize="small")
 # --8<-- [end:plot_body]
 
 # --8<-- [end:full_code]
diff --git a/examples/model_ex/model_with_stacked_and_unstacked_histograms_components.py b/examples/model_ex/model_with_stacked_and_unstacked_histograms_components.py
index 33f0cee7..62ab53b2 100644
--- a/examples/model_ex/model_with_stacked_and_unstacked_histograms_components.py
+++ b/examples/model_ex/model_with_stacked_and_unstacked_histograms_components.py
@@ -7,82 +7,43 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-from plothist_utils import get_dummy_data
-
-df = get_dummy_data()
-
+import hist
+import numpy as np
 import seaborn as sns
 
+import mplhep as mh
+
+np.random.seed(42)
 # --8<-- [end:imports]
 
 # --8<-- [start:setup]
-# Define the histograms
-
-key = "variable_1"
-x_range = (-9, 12)
-category = "category"
-
-# Define masks
-signal_mask = df[category] == 7
-data_mask = df[category] == 8
-
-background_categories = [0, 1, 2]
-background_categories_labels = [f"c{i}" for i in background_categories]
-background_categories_colors = sns.color_palette(
-    "cubehelix", len(background_categories)
-)
-
-background_masks = [df[category] == p for p in background_categories]
-
-# Make histograms
-import hist
-from hist import Hist
-
-axis = hist.axis.Regular(50, x_range[0], x_range[1])
-
-data_hist = Hist(axis, storage=hist.storage.Weight())
-signal_hist = Hist(axis, storage=hist.storage.Weight())
-background_hists = []
-
-data_hist.fill(df[key][data_mask])
-signal_hist.fill(df[key][signal_mask])
-
-for mask in background_masks:
-    h_bkg = Hist(axis, storage=hist.storage.Weight())
-    h_bkg.fill(df[key][mask])
-    background_hists.append(h_bkg)
-
-# Optional: scale to data
-background_scaling_factor = data_hist.sum().value / sum(background_hists).sum().value
-background_hists = [background_scaling_factor * h for h in background_hists]
-
-signal_scaling_factor = data_hist.sum().value / signal_hist.sum().value
-signal_hist *= signal_scaling_factor
+# Create background histograms
+background_hists = [
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 2, 3500)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-3, 0.8, 1800)),
+    hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(-2, 1.5, 1400)),
+]
+
+# Create signal histogram
+signal_hist = hist.new.Regular(50, -8, 8).Weight().fill(np.random.normal(0, 0.5, 500))
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-from mplhep import add_text, model
-
-fig, ax = model(
+fig, ax = mh.model(
     stacked_components=background_hists,
-    stacked_labels=background_categories_labels,
-    stacked_colors=background_categories_colors,
+    stacked_labels=["c0", "c1", "c2"],
+    stacked_colors=sns.color_palette("cubehelix", 3),
     unstacked_components=[signal_hist],
     unstacked_labels=["Signal"],
     unstacked_colors=["black"],
     unstacked_kwargs_list=[{"linestyle": "dotted"}],
-    xlabel=key,
+    xlabel="Observable",
     ylabel="Entries",
     model_sum_kwargs={"show": True, "label": "Model", "color": "navy"},
     model_uncertainty_label="Stat. unc.",
 )
 
-add_text(
-    "Model made of histograms",
-    ax=ax,
-    loc="over left",
-)
+mh.add_text("Model made of histograms", ax=ax, loc="over left")
 # --8<-- [end:plot_body]
 
 # --8<-- [end:full_code]
diff --git a/examples/model_ex/ratio_data_vs_model_with_stacked_and_unstacked_function_components.py b/examples/model_ex/ratio_data_vs_model_with_stacked_and_unstacked_function_components.py
index 84abe4ac..900bb14e 100644
--- a/examples/model_ex/ratio_data_vs_model_with_stacked_and_unstacked_function_components.py
+++ b/examples/model_ex/ratio_data_vs_model_with_stacked_and_unstacked_function_components.py
@@ -7,60 +7,55 @@
 
 # --8<-- [start:full_code]
 # --8<-- [start:imports]
-from plothist_utils import get_dummy_data
-
-df = get_dummy_data()
-
-# Define the histograms
-
-key = "variable_1"
-range = (-9, 12)
-category = "category"
-
-# Define masks
-data_mask = df[category] == 8
-
-# Make histograms
 import hist
-from hist import Hist
-
-axis = hist.axis.Regular(50, range[0], range[1])
-data_hist = Hist(axis, storage=hist.storage.Weight())
-data_hist.fill(df[key][data_mask])
-
-# Define some random functions that will be used as model components with functions
+import numpy as np
 from scipy.stats import norm
 
+import mplhep as mh
+
+np.random.seed(42)
 # --8<-- [end:imports]
 
 
 # --8<-- [start:setup]
+# Create data histogram
+data_hist = hist.new.Regular(50, -8, 8).Weight()
+data_hist.fill(
+    np.concatenate(
+        [
+            np.random.normal(0, 2, 3500),
+            np.random.normal(-3, 1, 2000),
+            np.random.normal(5, 0.5, 200),
+        ]
+    )
+)
+_binwidth = data_hist.axes[0].widths[0]
+
+
+# Define model function components
 def f_signal(x):
-    return 1000 * norm.pdf(x, loc=0.5, scale=3)
+    return 200 * _binwidth * norm.pdf(x, loc=5, scale=0.5)
 
 
 def f_background1(x):
-    return 1000 * norm.pdf(x, loc=-1.5, scale=4)
+    return 3500 * _binwidth * norm.pdf(x, loc=0, scale=2)
 
 
 def f_background2(x):
-    return 3000 * norm.pdf(x, loc=-1.8, scale=1.8)
+    return 2000 * _binwidth * norm.pdf(x, loc=-3, scale=1)
 
 
 # --8<-- [end:setup]
 
 # --8<-- [start:plot_body]
-###
-from mplhep import comp
-
-fig, ax_main, ax_comparison = comp.data_model(
+fig, ax_main, ax_comparison = mh.comp.data_model(
     data_hist=data_hist,
     stacked_components=[f_background1, f_background2],
     stacked_labels=["c0", "c1"],
     unstacked_components=[f_signal],
     unstacked_labels=["Signal"],
     unstacked_colors=["#8EBA42"],
-    xlabel=key,
+    xlabel="Observable",
     ylabel="Entries",
     model_sum_kwargs={"show": True, "label": "Model", "color": "navy"},
     comparison="pull",
diff --git a/pyproject.toml b/pyproject.toml
index 254c5b86..9fdb37d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -51,11 +51,11 @@ docs = [
     "mkdocs-git-revision-date-localized-plugin",
     "mkdocs-git-committers-plugin-2",
     "mkdocstrings[python]>=0.20",
+    "griffe_inherited_docstrings",
 ]
 test = [
     "boost_histogram",
     "hist",
-    "plothist_utils",
     "pytest-mock",
     "pytest-mpl",
     "pytest-xdist",
diff --git a/tests/baseline/model_all_comparisons.png b/tests/baseline/model_all_comparisons.png
index 3caefd29..3f713508 100644
Binary files a/tests/baseline/model_all_comparisons.png and b/tests/baseline/model_all_comparisons.png differ
diff --git a/tests/baseline/model_all_comparisons_no_model_unc.png b/tests/baseline/model_all_comparisons_no_model_unc.png
index ad25f8a9..8dc982fa 100644
Binary files a/tests/baseline/model_all_comparisons_no_model_unc.png and b/tests/baseline/model_all_comparisons_no_model_unc.png differ
diff --git a/tests/baseline/model_examples_pull.png b/tests/baseline/model_examples_pull.png
index 659091ed..0e680c5c 100644
Binary files a/tests/baseline/model_examples_pull.png and b/tests/baseline/model_examples_pull.png differ
diff --git a/tests/baseline/model_examples_pull_no_model_unc.png b/tests/baseline/model_examples_pull_no_model_unc.png
index a92714b3..c1e6e2c5 100644
Binary files a/tests/baseline/model_examples_pull_no_model_unc.png and b/tests/baseline/model_examples_pull_no_model_unc.png differ
diff --git a/tests/baseline/model_examples_stacked.png b/tests/baseline/model_examples_stacked.png
index 5ab1440b..f1537d0b 100644
Binary files a/tests/baseline/model_examples_stacked.png and b/tests/baseline/model_examples_stacked.png differ
diff --git a/tests/baseline/model_examples_stacked_unstacked.png b/tests/baseline/model_examples_stacked_unstacked.png
index d70ee29f..1ded2500 100644
Binary files a/tests/baseline/model_examples_stacked_unstacked.png and b/tests/baseline/model_examples_stacked_unstacked.png differ
diff --git a/tests/baseline/model_examples_unstacked.png b/tests/baseline/model_examples_unstacked.png
index 32cacf39..fc17a4bb 100644
Binary files a/tests/baseline/model_examples_unstacked.png and b/tests/baseline/model_examples_unstacked.png differ
diff --git a/tests/baseline/model_with_stacked_and_unstacked_function_components.png b/tests/baseline/model_with_stacked_and_unstacked_function_components.png
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