feat: Add time-domain feature extraction functionality

This commit adds code to the `03_feature_extraction.ipynb` notebook to print time-domain features. The features include mean, max, peak, peak-to-peak, RMS, variance, standard deviation, power, crest factor, form factor, pulse indicator, margin, kurtosis, and skewness. The features are calculated using the `FeatureExtractor` class and displayed in a pandas DataFrame.

code/notebooks/03_feature_extraction.ipynb

@@ -186,6 +186,103 @@
     "plt.grid(True)\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Print Time-domain Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mean</th>\n",
+       "      <th>Max</th>\n",
+       "      <th>Peak (Pm)</th>\n",
+       "      <th>Peak-to-Peak (Pk)</th>\n",
+       "      <th>RMS</th>\n",
+       "      <th>Variance</th>\n",
+       "      <th>Standard Deviation</th>\n",
+       "      <th>Power</th>\n",
+       "      <th>Crest Factor</th>\n",
+       "      <th>Form Factor</th>\n",
+       "      <th>Pulse Indicator</th>\n",
+       "      <th>Margin</th>\n",
+       "      <th>Kurtosis</th>\n",
+       "      <th>Skewness</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.448061</td>\n",
+       "      <td>1.579213</td>\n",
+       "      <td>1.579213</td>\n",
+       "      <td>2.048687</td>\n",
+       "      <td>0.819284</td>\n",
+       "      <td>0.470467</td>\n",
+       "      <td>0.723008</td>\n",
+       "      <td>0.671226</td>\n",
+       "      <td>1.927553</td>\n",
+       "      <td>1.828509</td>\n",
+       "      <td>3.524548</td>\n",
+       "      <td>2.067638</td>\n",
+       "      <td>1.917716</td>\n",
+       "      <td>0.412307</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Mean       Max  Peak (Pm)  Peak-to-Peak (Pk)       RMS  Variance  \\\n",
+       "0  0.448061  1.579213   1.579213           2.048687  0.819284  0.470467   \n",
+       "\n",
+       "   Standard Deviation     Power  Crest Factor  Form Factor  Pulse Indicator  \\\n",
+       "0            0.723008  0.671226      1.927553     1.828509         3.524548   \n",
+       "\n",
+       "     Margin  Kurtosis  Skewness  \n",
+       "0  2.067638  1.917716  0.412307  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "# Extract features using our class\n",
+    "extracted = FeatureExtractor(mock_df['SampleData'])\n",
+    "\n",
+    "# Format with pandas DataFramw\n",
+    "features = pd.DataFrame(extracted.features, index=[0])\n",
+    "features\n"
+   ]
   }
  ],
  "metadata": {