docs: update tutorial options and add peak-based uniformity example

Author: jgarciab

docs/index.ipynb

@@ -215,6 +215,34 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Uniformity Test (`peak_based`)\n",
+    "\n",
+    "With `definition=\"peak_based\"`, ReMoDe applies a Pearson Chi-square test for uniformity.\n",
+    "If the distribution is not significantly different from uniform (`p > 0.05`), it returns zero modes.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uniform_counts = np.array([10, 10, 10, 10, 10])\n",
+    "\n",
+    "uniform_result = ReMoDe(\n",
+    "    statistical_test=\"fisher\",\n",
+    "    definition=\"peak_based\",\n",
+    ").fit(uniform_counts)\n",
+    "\n",
+    "print(\"Number of modes:\", uniform_result[\"nr_of_modes\"])\n",
+    "print(\"Uniform distribution:\", uniform_result[\"uniform_distribution\"])\n",
+    "print(\"Chi-square p-value:\", uniform_result[\"uniformity_p_value\"])\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,

tutorial.ipynb

@@ -159,9 +159,11 @@
    "source": [
     "remode = ReMoDe(\n",
     "    alpha=0.05,  # alpha level desired (adjusted recursively for multiple testing)\n",
-    "    alpha_correction='descriptive_peaks',  # or 'max_modes', 'none', or custom function\n",
-    "    statistical_test=perform_fisher_test,  # or perform_binomial_test, or custom function\n",
-    ")\n"
+    "    alpha_correction=\"descriptive_peaks\",  # default; or \"max_modes\", \"none\", or custom function\n",
+    "    statistical_test=\"bootstrap\",  # default; or \"fisher\", \"binomial\", or custom function\n",
+    "    definition=\"shape_based\",  # default; set \"peak_based\" to exclude uniform distributions\n",
+    ")\n",
+    "\n"
    ]
   },
   {
@@ -173,9 +175,39 @@
    "source": [
     "remode = ReMoDe(\n",
     "    alpha=0.05,\n",
-    "    alpha_correction='max_modes',  # example of the alternative built-in correction\n",
-    "    statistical_test=perform_fisher_test,\n",
-    ")\n"
+    "    alpha_correction=\"max_modes\",  # alternative built-in correction\n",
+    "    statistical_test=\"fisher\",\n",
+    "    definition=\"peak_based\",\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Uniformity Test (`peak_based`)\n",
+    "\n",
+    "With `definition=\"peak_based\"`, ReMoDe applies a Pearson Chi-square test for uniformity.\n",
+    "If the distribution is not significantly different from uniform (`p > 0.05`), it returns zero modes.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uniform_counts = np.array([10, 10, 10, 10, 10])\n",
+    "\n",
+    "uniform_result = ReMoDe(\n",
+    "    statistical_test=\"fisher\",\n",
+    "    definition=\"peak_based\",\n",
+    ").fit(uniform_counts)\n",
+    "\n",
+    "print(\"Number of modes:\", uniform_result[\"nr_of_modes\"])\n",
+    "print(\"Uniform distribution:\", uniform_result[\"uniform_distribution\"])\n",
+    "print(\"Chi-square p-value:\", uniform_result[\"uniformity_p_value\"])\n"
    ]
   },
   {
@@ -244,4 +276,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}