diff --git a/jupyter-book/conditions/compositional.ipynb b/jupyter-book/conditions/compositional.ipynb
index 01fdc80..69b5a1b 100644
--- a/jupyter-book/conditions/compositional.ipynb
+++ b/jupyter-book/conditions/compositional.ipynb
@@ -804,7 +804,7 @@
     }
    ],
    "source": [
-    "pt.pl.coda.boxplots(\n",
+    "sccoda_model.plot_boxplots(\n",
     "    sccoda_data,\n",
     "    modality_key=\"coda\",\n",
     "    feature_name=\"condition\",\n",
@@ -855,7 +855,7 @@
     }
    ],
    "source": [
-    "pt.pl.coda.stacked_barplot(\n",
+    "sccoda_model.plot_stacked_barplot(\n",
     "    sccoda_data, modality_key=\"coda\", feature_name=\"condition\", figsize=(4, 2)\n",
     ")\n",
     "plt.show()"
@@ -1265,7 +1265,7 @@
     }
    ],
    "source": [
-    "pt.pl.coda.effects_barplot(sccoda_data, \"coda\", \"condition\")\n",
+    "sccoda_model.plot_effects_barplot(sccoda_data, \"coda\", \"condition\")\n",
     "plt.show()"
    ]
   },
@@ -1386,8 +1386,6 @@
     "adata.layers[\"logcounts\"] = sc.pp.log1p(adata.layers[\"counts\"]).copy()\n",
     "adata.X = adata.layers[\"logcounts\"].copy()\n",
     "sc.pp.neighbors(adata, n_neighbors=10, n_pcs=30, random_state=1234)\n",
-    "\n",
-    "# Calculate UMAP for visualization purposes\n",
     "sc.tl.umap(adata)"
    ]
   },
@@ -1414,7 +1412,10 @@
     },
     "pycharm": {
      "name": "#%%\n"
-    }
+    },
+    "tags": [
+     "hide-output"
+    ]
    },
    "outputs": [
     {
@@ -1895,7 +1896,7 @@
     }
    ],
    "source": [
-    "pt.pl.coda.draw_tree(tasccoda_data)"
+    "tasccoda_model.plot_draw_tree(tasccoda_data)"
    ]
   },
   {
@@ -2002,7 +2003,11 @@
    "cell_type": "code",
    "execution_count": 26,
    "id": "68cca84d",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -2839,7 +2844,7 @@
     }
    ],
    "source": [
-    "pt.pl.coda.draw_effects(\n",
+    "tasccoda_model.plot_draw_effects(\n",
     "    tasccoda_data,\n",
     "    modality_key=\"coda\",\n",
     "    tree=\"tree\",\n",
@@ -2868,7 +2873,7 @@
     }
    ],
    "source": [
-    "pt.pl.coda.draw_effects(\n",
+    "tasccoda_model.plot_draw_effects(\n",
     "    tasccoda_data,\n",
     "    modality_key=\"coda\",\n",
     "    tree=\"tree\",\n",
@@ -2897,7 +2902,7 @@
     }
    ],
    "source": [
-    "pt.pl.coda.draw_effects(\n",
+    "tasccoda_model.plot_draw_effects(\n",
     "    tasccoda_data,\n",
     "    modality_key=\"coda\",\n",
     "    tree=\"tree\",\n",
@@ -2948,7 +2953,9 @@
     }
    ],
    "source": [
-    "pt.pl.coda.effects_barplot(tasccoda_data, modality_key=\"coda\", covariates=\"condition\")"
+    "tasccoda_model.plot_effects_barplot(\n",
+    "    tasccoda_data, modality_key=\"coda\", covariates=\"condition\"\n",
+    ")"
    ]
   },
   {
@@ -2992,7 +2999,7 @@
    ],
    "source": [
     "kwargs = {\"ncols\": 3, \"wspace\": 0.25, \"vcenter\": 0, \"vmax\": 1.5, \"vmin\": -1.5}\n",
-    "pt.pl.coda.effects_umap(\n",
+    "tasccoda_model.plot_effects_umap(\n",
     "    tasccoda_data,\n",
     "    effect_name=[\n",
     "        \"effect_df_condition[T.Salmonella]\",\n",
@@ -4022,7 +4029,7 @@
    "source": [
     "milo.build_nhood_graph(mdata)\n",
     "with matplotlib.rc_context({\"figure.figsize\": [10, 10]}):\n",
-    "    pt.pl.milo.nhood_graph(mdata, alpha=0.1, min_size=5, plot_edges=False)\n",
+    "    milo.plot_nhood_graph(mdata, alpha=0.1, min_size=5, plot_edges=False)\n",
     "    sc.pl.umap(mdata[\"rna\"], color=\"cell_label\", legend_loc=\"on data\")"
    ]
   },
@@ -4087,7 +4094,7 @@
     }
    ],
    "source": [
-    "pt.pl.milo.da_beeswarm(mdata)\n",
+    "milo.plot_da_beeswarm(mdata)\n",
     "plt.show()"
    ]
   },
@@ -4207,7 +4214,7 @@
    ],
    "source": [
     "with matplotlib.rc_context({\"figure.figsize\": [10, 10]}):\n",
-    "    pt.pl.milo.nhood_graph(mdata, alpha=0.1, min_size=5, plot_edges=False)"
+    "    milo.plot_nhood_graph(mdata, alpha=0.1, min_size=5, plot_edges=False)"
    ]
   },
   {
@@ -4236,7 +4243,7 @@
     }
    ],
    "source": [
-    "pt.pl.milo.da_beeswarm(mdata)\n",
+    "milo.plot_da_beeswarm(mdata)\n",
     "plt.show()"
    ]
   },
@@ -4287,7 +4294,7 @@
     "]\n",
     "\n",
     "plt.title(\"Enterocyte\")\n",
-    "pt.pl.milo.nhood_counts_by_cond(\n",
+    "milo.plot_nhood_counts_by_cond(\n",
     "    mdata, test_var=\"Hpoly_timecourse\", subset_nhoods=entero_ixs\n",
     ")\n",
     "plt.show()\n",
@@ -4299,7 +4306,7 @@
     "    & (mdata[\"milo\"].var[\"nhood_annotation\"] == \"Tuft\")\n",
     "]\n",
     "plt.title(\"Tuft cells\")\n",
-    "pt.pl.milo.nhood_counts_by_cond(\n",
+    "milo.plot_nhood_counts_by_cond(\n",
     "    mdata, test_var=\"Hpoly_timecourse\", subset_nhoods=tuft_ixs\n",
     ")\n",
     "plt.show()"
@@ -4811,7 +4818,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.12.2"
   },
   "vscode": {
    "interpreter": {
diff --git a/jupyter-book/conditions/perturbation_modeling.ipynb b/jupyter-book/conditions/perturbation_modeling.ipynb
index 015ed6b..fc09829 100644
--- a/jupyter-book/conditions/perturbation_modeling.ipynb
+++ b/jupyter-book/conditions/perturbation_modeling.ipynb
@@ -2685,7 +2685,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.2"
   },
   "vscode": {
    "interpreter": {