--- a
+++ b/notebooks/manual_model_test.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "import numpy as np\n",
+    "import re\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data =  pd.read_csv('../assets/single_extracted_landmarks_inclass_front.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def landmarks_to_arr(stringarr):\n",
+    "    arr = stringarr.replace(\"[\", \"\").replace(\"]\", \"\")\n",
+    "    # removing the last bc of persoinal mistake kkkk\n",
+    "    return [float(x) for x in arr.split()[:-1]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['landmarks'] = data['landmarks'].apply(landmarks_to_arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preprocessing of the landmarks\n",
+    "### Consists on:\n",
+    "- Grouping the sequences:\n",
+    "   * Shapes must be (9, 10, 51)\n",
+    "   * numpy arrays\n",
+    "- Isolate each set within the sequence and normalize them.\n",
+    "   * Normalize with MinMaxScaler() no params."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(9, 10, 50)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sequences_list = []\n",
+    "SEQUENCE_LENGTH = 10\n",
+    "\n",
+    "# Grouping by sequences\n",
+    "grouped_data = data.groupby(['video', 'group'])\n",
+    "for i, group in grouped_data:\n",
+    "    landmarks = group['landmarks'].tolist()\n",
+    "    if len(landmarks) == 10:\n",
+    "        sequences_list.append(landmarks)\n",
+    "\n",
+    "sequences = np.array(sequences_list)\n",
+    "print(sequences.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "normalized_sequences = np.zeros_like(sequences)\n",
+    "# Normalizing\n",
+    "for i in range(sequences.shape[0]):\n",
+    "    for j in range(sequences.shape[1]):\n",
+    "        # Flatten the landmarks for each set within the sequence\n",
+    "        landmarks_flattened = np.reshape(sequences[i, j], (-1, 1))\n",
+    "        # Normalize the landmarks\n",
+    "        landmarks_normalized = scaler.fit_transform(landmarks_flattened)\n",
+    "        # Reshape the normalized landmarks back to the original shape\n",
+    "        normalized_landmarks = np.reshape(landmarks_normalized, sequences[i, j].shape)\n",
+    "        # Update the normalized landmarks in the sequences array\n",
+    "        normalized_sequences[i, j] = normalized_landmarks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9, 10, 50)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array(sequences).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 10, 50)\n",
+      "(1, 10, 50)\n",
+      "(1, 10, 50)\n",
+      "(1, 10, 50)\n",
+      "(1, 10, 50)\n",
+      "(1, 10, 50)\n",
+      "(1, 10, 50)\n",
+      "(1, 10, 50)\n",
+      "(1, 10, 50)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for seq in sequences:\n",
+    "    reshaped = seq[np.newaxis, :, :]\n",
+    "    print(reshaped.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}