notebooks/main_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Frequency  Angle of Attack  Chord Length  Free-stream Velocity  \\\n",
      "0        800              0.0        0.3048                  71.3   \n",
      "1       1000              0.0        0.3048                  71.3   \n",
      "2       1250              0.0        0.3048                  71.3   \n",
      "3       1600              0.0        0.3048                  71.3   \n",
      "4       2000              0.0        0.3048                  71.3   \n",
      "\n",
      "   Suction Side Displacement  Sound Pressure Level  \n",
      "0                   0.002663               126.201  \n",
      "1                   0.002663               125.201  \n",
      "2                   0.002663               125.951  \n",
      "3                   0.002663               127.591  \n",
      "4                   0.002663               127.461  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1503 entries, 0 to 1502\n",
      "Data columns (total 6 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Frequency                  1503 non-null   int64  \n",
      " 1   Angle of Attack            1503 non-null   float64\n",
      " 2   Chord Length               1503 non-null   float64\n",
      " 3   Free-stream Velocity       1503 non-null   float64\n",
      " 4   Suction Side Displacement  1503 non-null   float64\n",
      " 5   Sound Pressure Level       1503 non-null   float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 70.6 KB\n",
      "None\n",
      "          Frequency  Angle of Attack  Chord Length  Free-stream Velocity  \\\n",
      "count   1503.000000      1503.000000   1503.000000           1503.000000   \n",
      "mean    2886.380572         6.782302      0.136548             50.860745   \n",
      "std     3152.573137         5.918128      0.093541             15.572784   \n",
      "min      200.000000         0.000000      0.025400             31.700000   \n",
      "25%      800.000000         2.000000      0.050800             39.600000   \n",
      "50%     1600.000000         5.400000      0.101600             39.600000   \n",
      "75%     4000.000000         9.900000      0.228600             71.300000   \n",
      "max    20000.000000        22.200000      0.304800             71.300000   \n",
      "\n",
      "       Suction Side Displacement  Sound Pressure Level  \n",
      "count                1503.000000           1503.000000  \n",
      "mean                    0.011140            124.835943  \n",
      "std                     0.013150              6.898657  \n",
      "min                     0.000401            103.380000  \n",
      "25%                     0.002535            120.191000  \n",
      "50%                     0.004957            125.721000  \n",
      "75%                     0.015576            129.995500  \n",
      "max                     0.058411            140.987000  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Free-stream Velocity\n",
       "39.6    480\n",
       "71.3    465\n",
       "31.7    281\n",
       "55.5    277\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load dataset\n",
    "column_names = ['Frequency', 'Angle of Attack', 'Chord Length', 'Free-stream Velocity', 'Suction Side Displacement', 'Sound Pressure Level']\n",
    "data = pd.read_csv('../data/airfoil_self_noise.dat', sep='\\t', header=None, names=column_names)\n",
    "\n",
    "print(data.head())\n",
    "\n",
    "\n",
    "# Some histogram charting\n",
    "plt.figure(figsize=(6, 3))\n",
    "data['Angle of Attack'].plot(kind='hist')\n",
    "plt.title('Angle of Attack distribution')\n",
    "plt.xlabel('Angle of Attack')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()\n",
    "\n",
    "# Some Scatter plotting\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.scatter(data['Chord Length'], data['Angle of Attack'], marker='o', linestyle='-', color=\"red\", alpha=0.7)\n",
    "plt.title('AoA vs Chord Length')\n",
    "plt.xlabel('Chord length')\n",
    "plt.ylabel('Angle of Attack ')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(data.info())\n",
    "print(data.describe())\n",
    "\n",
    "data['Free-stream Velocity'].value_counts()"
   ]
  }
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