MNT: refactoring class to match review suggestions

Author: Lucas-Prates

.vscode/settings.json

@@ -288,6 +288,7 @@
         "statsmodels",
         "STFT",
         "subintervals",
+        "supremum",
         "suptitle",
         "supxlabel",
         "supylabel",

test_mrs.ipynb renamed to docs/notebooks/test_mrs.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -29,78 +29,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from rocketpy.simulation.multivariate_rejection_sampler import MultivariateRejectionSampler\n",
+    "from rocketpy.simulation.multivariate_rejection_sampler import (\n",
+    "    MultivariateRejectionSampler,\n",
+    ")\n",
     "from rocketpy import MonteCarlo\n",
     "from scipy.stats import norm\n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "montecarlo_filepath = \"docs/notebooks/monte_carlo_analysis/monte_carlo_analysis_outputs/monte_carlo_class_example\"\n",
-    "mrs_filepath = \"mrs\"\n",
+    "monte_carlo_filepath = (\n",
+    "    \"monte_carlo_analysis/monte_carlo_analysis_outputs/monte_carlo_class_example\"\n",
+    ")\n",
+    "mrs_filepath = \"monte_carlo_analysis/monte_carlo_analysis_outputs/mrs\"\n",
     "old_mass_pdf = norm(15.426, 0.5).pdf\n",
-    "new_mass_pdf = norm(15, 0.5) .pdf\n",
+    "new_mass_pdf = norm(15, 0.5).pdf\n",
     "distribution_dict = {\n",
     "    \"mass\": (old_mass_pdf, new_mass_pdf),\n",
     "}\n",
     "mrs = MultivariateRejectionSampler(\n",
-    "    montecarlo_filepath=montecarlo_filepath,\n",
+    "    monte_carlo_filepath=monte_carlo_filepath,\n",
     "    mrs_filepath=mrs_filepath,\n",
-    "    distribution_dict=distribution_dict,\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "107.0"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mrs.expected_sample_size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrs.sample()"
+    "mrs.sample(distribution_dict=distribution_dict)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The following input file was imported: mrs.inputs.txt\n",
-      "A total of 109 simulations results were loaded from the following output file: mrs.outputs.txt\n",
+      "The following input file was imported: monte_carlo_analysis/monte_carlo_analysis_outputs/mrs.inputs.txt\n",
+      "A total of 116 simulations results were loaded from the following output file: monte_carlo_analysis/monte_carlo_analysis_outputs/mrs.outputs.txt\n",
       "\n",
-      "The following error file was imported: mrs.errors.txt\n"
+      "The following error file was imported: monte_carlo_analysis/monte_carlo_analysis_outputs/mrs.errors.txt\n"
      ]
     }
    ],
@@ -110,17 +93,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "A total of 109 simulations results were loaded from the following output file: mrs.outputs.txt\n",
+      "A total of 116 simulations results were loaded from the following output file: monte_carlo_analysis/monte_carlo_analysis_outputs/mrs.outputs.txt\n",
       "\n",
-      "The following input file was imported: mrs.inputs.txt\n",
-      "The following error file was imported: mrs.errors.txt\n"
+      "The following input file was imported: monte_carlo_analysis/monte_carlo_analysis_outputs/mrs.inputs.txt\n",
+      "The following error file was imported: monte_carlo_analysis/monte_carlo_analysis_outputs/mrs.errors.txt\n"
      ]
     }
    ],
@@ -130,15 +113,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MRS mass mean after resample: 15.029610376989238\n",
-      "MRS mass std after resample: 0.5213162519453568\n"
+      "MRS mass mean after resample: 15.041934326472004\n",
+      "MRS mass std after resample: 0.48924085702427966\n"
      ]
     }
    ],
@@ -150,11 +133,38 @@
     "print(f\"MRS mass mean after resample: {np.mean(mrs_mass_list)}\")\n",
     "print(f\"MRS mass std after resample: {np.std(mrs_mass_list)}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "107.0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrs.expected_sample_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "testnotebook",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -168,7 +178,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,