docs: small fixups

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]

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]

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,

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")
 

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,

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,

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,

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,

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]

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),