analiza_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import surfboard.feature_extraction as surf\n",
    "import os\n",
    "from statsmodels.stats.anova import AnovaRM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " 'audio/12_pos.wav',\n",
       " 'audio/13_neg.wav']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = []\n",
    "for r,d,f in os.walk('audio'):\n",
    "    for file in f:\n",
    "        if 'wav' in file:\n",
    "            files.append('audio/' + file)\n",
    "        \n",
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "waves = surf.load_waveforms_from_paths(paths=files, sample_rate=48000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Extracting features...:  17%|█▋        | 12/69 [00:19<01:30,  1.59s/it]/home/t/miniconda3/envs/projbad/lib/python3.8/site-packages/surfboard/hnr.py:81: RuntimeWarning: invalid value encountered in true_divide\n",
      "  r_x = ffts_outputs[0] / ffts_outputs[1]\n",
      "Extracting features...:  72%|███████▏  | 50/69 [01:21<00:32,  1.70s/it]/home/t/miniconda3/envs/projbad/lib/python3.8/site-packages/surfboard/hnr.py:81: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  r_x = ffts_outputs[0] / ffts_outputs[1]\n",
      "/home/t/miniconda3/envs/projbad/lib/python3.8/site-packages/peakutils/peak.py:43: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  thres = thres * (np.max(y) - np.min(y)) + np.min(y)\n",
      "Extracting features...: 100%|██████████| 69/69 [01:52<00:00,  1.62s/it]\n"
     ]
    }
   ],
   "source": [
    "res = surf.extract_features(\n",
    "    waves, \n",
    "    components_list=['rms','spectral_centroid', 'f0_statistics', 'hnr'],\n",
    "    statistics_list=['mean', 'std', 'max', 'min']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_subject(filepath):\n",
    "    subject = filepath[6]\n",
    "    return subject\n",
    "    \n",
    "def get_condition(filepath):\n",
    "    # cut unitl the end\n",
    "    cond = filepath[8:]\n",
    "     #cut extension\n",
    "    cond = cond[:-4]\n",
    "    return cond\n",
    "    \n",
    "\n",
    "res['filename'] = files\n",
    "res['subject'] = res.filename.apply(get_subject)\n",
    "res['condition'] = res.filename.apply(get_condition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>2</td>\n",
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      "text/plain": [
       "    rms_mean   rms_std   rms_max   rms_min  spectral_centroid_mean  \\\n",
       "0   0.017931  0.019250  0.111144  0.000203             5218.405863   \n",
       "1   0.024871  0.032905  0.168227  0.000037             4086.316251   \n",
       "2   0.038053  0.038166  0.169753  0.000274             4187.967438   \n",
       "3   0.018055  0.025417  0.169625  0.000212             4355.275424   \n",
       "4   0.086125  0.077493  0.266395  0.000146             3963.296414   \n",
       "..       ...       ...       ...       ...                     ...   \n",
       "64  0.093767  0.101875  0.450404  0.000025             2413.748359   \n",
       "65  0.025872  0.035865  0.222988  0.000027             4426.952284   \n",
       "66  0.007521  0.009095  0.048909  0.000004             4882.187457   \n",
       "67  0.022345  0.031470  0.201110  0.000220             4342.488783   \n",
       "68  0.130385  0.141813  0.460333  0.000255             1726.121591   \n",
       "\n",
       "    spectral_centroid_std  spectral_centroid_max  spectral_centroid_min  \\\n",
       "0             3701.905250           12518.859817             856.716248   \n",
       "1             3594.997320           12407.049863             502.148145   \n",
       "2             3184.686156           10437.066596             647.503530   \n",
       "3             2692.859851           10093.267784             740.981182   \n",
       "4             3355.119113           11468.070704             490.182811   \n",
       "..                    ...                    ...                    ...   \n",
       "64            2092.859075            7593.803735             430.525238   \n",
       "65            3569.551386           11228.892341             513.289965   \n",
       "66            3634.501499           11562.187644             612.290145   \n",
       "67            2747.557552            9400.819519             559.169883   \n",
       "68            1380.268808            6324.393558             338.992448   \n",
       "\n",
       "       f0_mean     f0_std        hnr           filename subject condition  \n",
       "0   176.649887  22.951265  11.798376    audio/2_pos.wav       2       pos  \n",
       "1   201.147730  25.017070  11.505237   audio/14_neg.wav       1      _neg  \n",
       "2   227.516987  23.271600  14.403130   audio/19_neg.wav       1      _neg  \n",
       "3   182.849631  31.212020  10.530582  audio/18_neut.wav       1     _neut  \n",
       "4   190.461634  23.039585  13.209085    audio/7_neg.wav       7       neg  \n",
       "..         ...        ...        ...                ...     ...       ...  \n",
       "64  211.911909  20.094876  12.022375   audio/3_neut.wav       3      neut  \n",
       "65  204.992579  27.863604  11.687023   audio/14_pos.wav       1      _pos  \n",
       "66  192.226669  22.582565  10.004964   audio/8_neut.wav       8      neut  \n",
       "67  187.722206  32.186628  10.857767   audio/18_neg.wav       1      _neg  \n",
       "68  197.634442  20.336683  14.401794   audio/21_pos.wav       2      _pos  \n",
       "\n",
       "[69 rows x 14 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.to_csv('features_do_obrobki_centroid.csv',index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('features_do_obrobki_centroid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rms_mean                  0\n",
       "rms_std                   0\n",
       "rms_max                   0\n",
       "rms_min                   0\n",
       "spectral_centroid_mean    0\n",
       "spectral_centroid_std     0\n",
       "spectral_centroid_max     0\n",
       "spectral_centroid_min     0\n",
       "f0_mean                   0\n",
       "f0_std                    0\n",
       "hnr                       0\n",
       "filename                  0\n",
       "subject                   0\n",
       "condition                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### features_do_obrobki_centroid dziala"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pingouin as pg\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Source</th>\n",
       "      <th>ddof1</th>\n",
       "      <th>ddof2</th>\n",
       "      <th>F</th>\n",
       "      <th>p-unc</th>\n",
       "      <th>p-GG-corr</th>\n",
       "      <th>np2</th>\n",
       "      <th>eps</th>\n",
       "      <th>sphericity</th>\n",
       "      <th>W-spher</th>\n",
       "      <th>p-spher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>condition</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>1.993454</td>\n",
       "      <td>0.14834</td>\n",
       "      <td>0.152579</td>\n",
       "      <td>0.083083</td>\n",
       "      <td>0.919158</td>\n",
       "      <td>True</td>\n",
       "      <td>0.912048</td>\n",
       "      <td>0.380353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Source  ddof1  ddof2         F    p-unc  p-GG-corr       np2       eps  \\\n",
       "0  condition      2     44  1.993454  0.14834   0.152579  0.083083  0.919158   \n",
       "\n",
       "   sphericity   W-spher   p-spher  \n",
       "0        True  0.912048  0.380353  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pg.rm_anova(data=df, dv='spectral_centroid_mean', within='condition', subject='subject', correction=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7ff065763340>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "centroid_plot = sns.catplot(x='condition', y='spectral_centroid_mean', data=df, kind='box')\n",
    "centroid_plot.set(xlabel=\"Warunek eskperymentalny\", ylabel = \"Wartość średnia Spectral Centroid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroid_plot.savefig(\"centroid_boxplot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>rms_mean</th>\n",
       "      <th>rms_std</th>\n",
       "      <th>rms_max</th>\n",
       "      <th>rms_min</th>\n",
       "      <th>spectral_centroid_mean</th>\n",
       "      <th>spectral_centroid_std</th>\n",
       "      <th>spectral_centroid_max</th>\n",
       "      <th>spectral_centroid_min</th>\n",
       "      <th>f0_mean</th>\n",
       "      <th>f0_std</th>\n",
       "      <th>hnr</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>neg</th>\n",
       "      <td>0.061282</td>\n",
       "      <td>0.064787</td>\n",
       "      <td>0.289398</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>3484.646237</td>\n",
       "      <td>2669.702784</td>\n",
       "      <td>9998.740425</td>\n",
       "      <td>468.759345</td>\n",
       "      <td>197.444540</td>\n",
       "      <td>25.917267</td>\n",
       "      <td>11.674868</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neut</th>\n",
       "      <td>0.058890</td>\n",
       "      <td>0.063585</td>\n",
       "      <td>0.276021</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>3571.359373</td>\n",
       "      <td>2728.867775</td>\n",
       "      <td>10117.407109</td>\n",
       "      <td>485.328594</td>\n",
       "      <td>197.256972</td>\n",
       "      <td>26.005058</td>\n",
       "      <td>11.641003</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos</th>\n",
       "      <td>0.059002</td>\n",
       "      <td>0.063985</td>\n",
       "      <td>0.287508</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>3604.205855</td>\n",
       "      <td>2680.730190</td>\n",
       "      <td>10006.494860</td>\n",
       "      <td>461.473738</td>\n",
       "      <td>198.388218</td>\n",
       "      <td>26.454598</td>\n",
       "      <td>11.800141</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rms_mean   rms_std   rms_max   rms_min  spectral_centroid_mean  \\\n",
       "condition                                                                   \n",
       "neg        0.061282  0.064787  0.289398  0.000089             3484.646237   \n",
       "neut       0.058890  0.063585  0.276021  0.000075             3571.359373   \n",
       "pos        0.059002  0.063985  0.287508  0.000077             3604.205855   \n",
       "\n",
       "           spectral_centroid_std  spectral_centroid_max  \\\n",
       "condition                                                 \n",
       "neg                  2669.702784            9998.740425   \n",
       "neut                 2728.867775           10117.407109   \n",
       "pos                  2680.730190           10006.494860   \n",
       "\n",
       "           spectral_centroid_min     f0_mean     f0_std        hnr  subject  \n",
       "condition                                                                    \n",
       "neg                   468.759345  197.444540  25.917267  11.674868       12  \n",
       "neut                  485.328594  197.256972  26.005058  11.641003       12  \n",
       "pos                   461.473738  198.388218  26.454598  11.800141       12  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df['condition']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rms_mean</th>\n",
       "      <th>rms_std</th>\n",
       "      <th>rms_max</th>\n",
       "      <th>rms_min</th>\n",
       "      <th>spectral_centroid_mean</th>\n",
       "      <th>spectral_centroid_std</th>\n",
       "      <th>spectral_centroid_max</th>\n",
       "      <th>spectral_centroid_min</th>\n",
       "      <th>f0_mean</th>\n",
       "      <th>f0_std</th>\n",
       "      <th>hnr</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>neg</th>\n",
       "      <td>0.064600</td>\n",
       "      <td>0.077493</td>\n",
       "      <td>0.315176</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>3571.391556</td>\n",
       "      <td>2656.135554</td>\n",
       "      <td>10878.138980</td>\n",
       "      <td>483.851591</td>\n",
       "      <td>197.872192</td>\n",
       "      <td>25.017070</td>\n",
       "      <td>11.597605</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neut</th>\n",
       "      <td>0.066898</td>\n",
       "      <td>0.072928</td>\n",
       "      <td>0.316067</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>3227.419346</td>\n",
       "      <td>2586.191026</td>\n",
       "      <td>11085.081514</td>\n",
       "      <td>457.845835</td>\n",
       "      <td>198.413973</td>\n",
       "      <td>24.557532</td>\n",
       "      <td>11.682766</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pos</th>\n",
       "      <td>0.069010</td>\n",
       "      <td>0.078391</td>\n",
       "      <td>0.343143</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>3420.125622</td>\n",
       "      <td>2564.890477</td>\n",
       "      <td>10588.773632</td>\n",
       "      <td>441.168459</td>\n",
       "      <td>200.647060</td>\n",
       "      <td>25.221915</td>\n",
       "      <td>11.794855</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           rms_mean   rms_std   rms_max   rms_min  spectral_centroid_mean  \\\n",
       "condition                                                                   \n",
       "neg        0.064600  0.077493  0.315176  0.000041             3571.391556   \n",
       "neut       0.066898  0.072928  0.316067  0.000035             3227.419346   \n",
       "pos        0.069010  0.078391  0.343143  0.000042             3420.125622   \n",
       "\n",
       "           spectral_centroid_std  spectral_centroid_max  \\\n",
       "condition                                                 \n",
       "neg                  2656.135554           10878.138980   \n",
       "neut                 2586.191026           11085.081514   \n",
       "pos                  2564.890477           10588.773632   \n",
       "\n",
       "           spectral_centroid_min     f0_mean     f0_std        hnr  subject  \n",
       "condition                                                                    \n",
       "neg                   483.851591  197.872192  25.017070  11.597605       12  \n",
       "neut                  457.845835  198.413973  24.557532  11.682766       12  \n",
       "pos                   441.168459  200.647060  25.221915  11.794855       12  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df['condition']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "art_mean = df[['f0_mean', 'hnr','rms_mean', 'condition', 'spectral_centroid_mean']].groupby(df['condition']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| condition   |   f0_mean |     hnr |   rms_mean |   spectral_centroid_mean |\n",
      "|:------------|----------:|--------:|-----------:|-------------------------:|\n",
      "| neg         |   13.1644 | 1.48125 |  0.0347097 |                  1099.31 |\n",
      "| neut        |   13.2095 | 1.66784 |  0.0360501 |                  1167.7  |\n",
      "| pos         |   11.501  | 1.81293 |  0.0341061 |                  1203.43 |\n"
     ]
    }
   ],
   "source": [
    "print(art_mean.to_markdown())"
   ]
  }
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