clarify where optimization results are stored (#525)

arviz-devs · Aug 17, 2024 · 49e3c82 · 49e3c82
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diff --git a/docs/examples/param_space_1d_examples.ipynb b/docs/examples/param_space_1d_examples.ipynb
@@ -569,44 +569,6 @@
     "dist.alpha, dist.beta"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ed0c9037",
-   "metadata": {},
-   "source": [
-    "`maxent` (and other optimization functions we will see later) returns and [OptimizeResult](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult) that users can inspect if they need more information of the result of the optimization routine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6cf499b5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       " message: Optimization terminated successfully\n",
-       " success: True\n",
-       "  status: 0\n",
-       "     fun: -1.4244960108798204\n",
-       "       x: [-1.330e-08  1.006e+00]\n",
-       "     nit: 4\n",
-       "     jac: [ 0.000e+00 -9.945e-01]\n",
-       "    nfev: 12\n",
-       "    njev: 4"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_, opt = pz.maxent(pz.Normal(), -1, 1, 0.68, plot=False)\n",
-    "opt"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f8500fef",

diff --git a/preliz/unidimensional/maxent.py b/preliz/unidimensional/maxent.py
@@ -43,6 +43,11 @@ def maxent(
     dict: dict with the parameters of the distribution
     axes: matplotlib axes (only if `plot=True`)
 
+    Notes
+    -----
+    After calling this function the attribute `opt` of the distribution will be updated with the
+    OptimizeResult object from the optimization step.
+
     See Also
     --------
     quartile : Find the distribution with the specified quartiles.

diff --git a/preliz/unidimensional/quartile.py b/preliz/unidimensional/quartile.py
@@ -44,6 +44,11 @@ def quartile(
     dict: dict with the parameters of the distribution
     axes: matplotlib axes (only if `plot=True`)
 
+    Notes
+    -----
+    After calling this function the attribute `opt` of the distribution will be updated with the
+    OptimizeResult object from the optimization step.
+
     See Also
     --------
     maxent : Find the maximum entropy distribution with a given mass inside a user defined interval.