już ostateczna zależne.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mlt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.stats.api as sms\n",
    "import statsmodels.stats.power as smp\n",
    "import scipy.stats\n",
    "from scipy.stats import t, norm, ttest_ind, ttest_rel, ttest_1samp\n",
    "import decimal\n",
    "import random\n",
    "#decimal.getcontext().prec = 4\n",
    "import itertools\n",
    "from numba import njit\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('C:/Users/Adam/Desktop/dep1_dozera', converters={'pu_mały':eval, 'pu_duży': eval, 'pu_nie_wiadomo': eval,'2mały': eval,'2duży': eval,'2nie wiadomo': eval, '3mały': eval,'3duży': eval, '3nie wiadomo': eval})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>delta</th>\n",
       "      <th>pop_mean</th>\n",
       "      <th>n</th>\n",
       "      <th>sd</th>\n",
       "      <th>x_w_próbie</th>\n",
       "      <th>pu_mały</th>\n",
       "      <th>pu_duży</th>\n",
       "      <th>pu_nie_wiadomo</th>\n",
       "      <th>2mały</th>\n",
       "      <th>2duży</th>\n",
       "      <th>2nie wiadomo</th>\n",
       "      <th>3mały</th>\n",
       "      <th>3duży</th>\n",
       "      <th>3nie wiadomo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>-2.0</td>\n",
       "      <td>18</td>\n",
       "      <td>1.5</td>\n",
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      ],
      "text/plain": [
       "       Unnamed: 0  delta  pop_mean   n   sd  x_w_próbie       pu_mały  \\\n",
       "0               0      4      -2.0  15  1.5   -2.090539  [0.01, 0.99]   \n",
       "1               1      4      -2.0  16  1.5   -1.613619  [0.01, 0.99]   \n",
       "2               2      4      -2.0  17  1.5   -1.819341  [0.01, 0.99]   \n",
       "3               3      4      -2.0  18  1.5   -1.955216  [0.01, 0.99]   \n",
       "4               4      4      -2.0  19  1.5   -2.512287  [0.01, 0.99]   \n",
       "...           ...    ...       ...  ..  ...         ...           ...   \n",
       "39396       39396      4       2.0  41  4.5    2.544195  [0.01, 0.99]   \n",
       "39397       39397      4       2.0  42  4.5    3.744325  [0.37, 0.63]   \n",
       "39398       39398      4       2.0  43  4.5    3.132034  [0.11, 0.89]   \n",
       "39399       39399      4       2.0  44  4.5    2.410342  [0.03, 0.97]   \n",
       "39400       39400      4       2.0  45  4.5    1.001660  [0.01, 0.99]   \n",
       "\n",
       "            pu_duży pu_nie_wiadomo         2mały   2duży  2nie wiadomo  \\\n",
       "0            [0, 0]         [0, 0]  [0.01, 0.99]  [0, 0]        [0, 0]   \n",
       "1            [0, 0]         [0, 0]  [0.01, 0.99]  [0, 0]        [0, 0]   \n",
       "2            [0, 0]         [0, 0]  [0.01, 0.99]  [0, 0]        [0, 0]   \n",
       "3            [0, 0]         [0, 0]  [0.01, 0.99]  [0, 0]        [0, 0]   \n",
       "4            [0, 0]         [0, 0]  [0.01, 0.99]  [0, 0]        [0, 0]   \n",
       "...             ...            ...           ...     ...           ...   \n",
       "39396        [0, 0]         [0, 0]        [0, 0]  [0, 0]  [0.01, 0.99]   \n",
       "39397  [0.01, 0.36]   [0.64, 0.99]        [0, 0]  [0, 0]  [0.01, 0.99]   \n",
       "39398   [0.01, 0.1]    [0.9, 0.99]        [0, 0]  [0, 0]  [0.01, 0.99]   \n",
       "39399  [0.01, 0.02]   [0.98, 0.99]        [0, 0]  [0, 0]  [0.01, 0.99]   \n",
       "39400        [0, 0]         [0, 0]  [0.91, 0.99]  [0, 0]   [0.01, 0.9]   \n",
       "\n",
       "              3mały   3duży  3nie wiadomo  \n",
       "0      [0.01, 0.99]  [0, 0]        [0, 0]  \n",
       "1      [0.01, 0.99]  [0, 0]        [0, 0]  \n",
       "2      [0.01, 0.99]  [0, 0]        [0, 0]  \n",
       "3      [0.01, 0.99]  [0, 0]        [0, 0]  \n",
       "4      [0.01, 0.99]  [0, 0]        [0, 0]  \n",
       "...             ...     ...           ...  \n",
       "39396  [0.01, 0.98]  [0, 0]  [0.99, 0.99]  \n",
       "39397  [0.01, 0.26]  [0, 0]  [0.27, 0.99]  \n",
       "39398  [0.01, 0.79]  [0, 0]   [0.8, 0.99]  \n",
       "39399  [0.01, 0.94]  [0, 0]  [0.95, 0.99]  \n",
       "39400  [0.01, 0.99]  [0, 0]        [0, 0]  \n",
       "\n",
       "[39401 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zamień(pop_mean,delta):\n",
    "    #if pop_mean>=delta:\n",
    "    #    m_kolor = 'white'\n",
    "    #    d_kolor = 'red'\n",
    "    #else:\n",
    "    #    m_kolor = 'blue'\n",
    "    #    d_kolor = 'white'\n",
    "    #return m_kolor, d_kolor\n",
    "    return np.where(pop_mean>=delta,1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#zamień(data.loc(0)[0]['pu_mały'],data.loc(0)[0]['pu_duży'],data.loc(0)[0]['pu_nie_wiadomo'],data.loc(0)[0]['pop_mean'],data.loc(0)[0]['delta'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wykres_ogólny(n,sd,data):\n",
    "    \n",
    "    #ax3.axvline(mis[i],ymin=d3[0],ymax=d3[1], color = 'white')\n",
    "    \n",
    "    fig, (ax1,ax2,ax3) = plt.subplots(3,sharex=True)\n",
    "    #mlt.rcParams['figure.figsize'] = [2,2]\n",
    "    #mlt.rcParams.update(mlt.rcParamsDefault)\n",
    "    #plt.figure(figsize=(8,6))\n",
    "    ax1.set_title('przedziały ufności')\n",
    "    ax2.set_title('podejście 2')\n",
    "    ax3.set_title('podejście 3')\n",
    "    sub = data[(data['n']==n) & (data['sd'] == sd)]\n",
    "    ax1.set_xlim(-3,3)\n",
    "    a = 0\n",
    "    for i in sub['Unnamed: 0']:\n",
    "        if sub['pop_mean'][i]>=sub['delta'][i]:\n",
    "            m_kolor = 'red'\n",
    "            d_kolor = 'green'\n",
    "        else:\n",
    "            m_kolor = 'blue'\n",
    "            d_kolor = 'red'\n",
    "        if a == 0:\n",
    "            a+=1\n",
    "            ax1.axvline(sub['pop_mean'][i],ymin=sub['pu_duży'][i][0],ymax=sub['pu_duży'][i][1], color = 'green',label = 'efekt duży')\n",
    "            ax1.axvline(sub['pop_mean'][i],ymin=sub['pu_mały'][i][0],ymax=sub['pu_mały'][i][1], color = 'blue',label = 'efekt mały')\n",
    "            ax1.axvline(sub['pop_mean'][i],ymin=sub['pu_nie_wiadomo'][i][0],ymax=sub['pu_nie_wiadomo'][i][1], color = 'black',label = 'nie wiadomo')\n",
    "        else:\n",
    "            ax1.axvline(sub['pop_mean'][i],ymin=sub['pu_duży'][i][0],ymax=sub['pu_duży'][i][1], color = d_kolor)\n",
    "            ax1.axvline(sub['pop_mean'][i],ymin=sub['pu_mały'][i][0],ymax=sub['pu_mały'][i][1], color = m_kolor)\n",
    "            ax1.axvline(sub['pop_mean'][i],ymin=sub['pu_nie_wiadomo'][i][0],ymax=sub['pu_nie_wiadomo'][i][1], color = 'black')\n",
    "        \n",
    "        ax2.axvline(sub['pop_mean'][i],ymin=sub['2duży'][i][0],ymax=sub['2duży'][i][1], color = d_kolor)\n",
    "        ax2.axvline(sub['pop_mean'][i],ymin=sub['2mały'][i][0],ymax=sub['2mały'][i][1], color = m_kolor)\n",
    "        ax2.axvline(sub['pop_mean'][i],ymin=sub['2nie wiadomo'][i][0],ymax=sub['2nie wiadomo'][i][1], color = 'black')\n",
    "        \n",
    "        ax3.axvline(sub['pop_mean'][i],ymin=sub['3duży'][i][0],ymax=sub['3duży'][i][1], color = d_kolor)\n",
    "        ax3.axvline(sub['pop_mean'][i],ymin=sub['3mały'][i][0],ymax=sub['3mały'][i][1], color = m_kolor)\n",
    "        ax3.axvline(sub['pop_mean'][i],ymin=sub['3nie wiadomo'][i][0],ymax=sub['3nie wiadomo'][i][1], color = 'black')\n",
    "    fig.legend(frameon=False, loc = 'center', bbox_to_anchor = (0.5,1))\n",
    "    #ax1.axvline(sub['pop_mean'][],ymin=sub['pu_duży'][-1][0],ymax=sub['pu_duży'][-1][1], color = 'green',label = 'efekt duży')\n",
    "    #ax1.axvline(sub['pop_mean'][-1],ymin=sub['pu_mały'][-1][0],ymax=sub['pu_mały'][-1][1], color = 'blue',label = 'efekt mały')\n",
    "    #ax1.axvline(sub['pop_mean'][-1],ymin=sub['pu_nie_wiadomo'][i][0],ymax=sub['pu_nie_wiadomo'][-1][1], color = 'black',label = 'nie wiadomo')\n",
    "    #ax2.axvline(sub['pop_mean'][-1],ymin=sub['2duży'][-1][0],ymax=sub['2duży'][-1][1], color = 'green',label = 'efekt duży')\n",
    "    #ax2.axvline(sub['pop_mean'][-1],ymin=sub['2mały'][-1][0],ymax=sub['2mały'][-1][1], color = 'blue',label = 'efekt mały')\n",
    "    #ax2.axvline(sub['pop_mean'][-1],ymin=sub['2nie wiadomo'][i][0],ymax=sub['2nie wiadomo'][-1][1], color = 'black',label = 'nie wiadomo')\n",
    "    #ax3.axvline(sub['pop_mean'][-1],ymin=sub['3duży'][-1][0],ymax=sub['3duży'][-1][1], color = 'green',label = 'efekt duży')\n",
    "    #ax3.axvline(sub['pop_mean'][-1],ymin=sub['3mały'][-1][0],ymax=sub['3mały'][-1][1], color = 'blue',label = 'efekt mały')\n",
    "    #ax3.axvline(sub['pop_mean'][-1],ymin=sub['3nie wiadomo'][-1][0],ymax=sub['3nie wiadomo'][-1][1], color = 'black',label = 'nie wiadomo')\n",
    "#plt.figure(figsize=(8,6))\n",
    "    ax2.set_ylabel('poziom ufności')\n",
    "    ax3.set_xlabel('poziom ufności dla poszczególnych odpowiedzi w zależności od średniej populacyjnej, delta=4, kolor czerwony oznacza błędną odpowiedź')\n",
    "    mlt.rcParams.update(mlt.rcParamsDefault)\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wykres_ogólny(30,3,data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tabele(data,n1,n2,sd1,sd2,mean1,mean2):\n",
    "    sub = data.loc[(data['n']>=n1) & (data['n']<=n2) & (data['sd'] >= sd1) & (data['sd'] <= sd2) & (data['pop_mean'] >= mean1) & (data['pop_mean'] <= mean2),:]\n",
    "    \n",
    "    # jak często, gdy różnica była duża, podejścia na to wskazywały?\n",
    "    # jak często, gdy różnica była mała, podejścia na to wskazywały?\n",
    "    # jak często, gdy różnica była duża, podejścia wskazywały na wynik mały?\n",
    "    # jak często, gdy różnica była mała, podejścia wskazywały na wynik duży?\n",
    "    # jak często było nie wiadomo?\n",
    "    # to wszystko trzeba \"przepuścić\" przez filtr poszczególnych wartości alfa - to znaczy - jako wynik wcześniejszych rzeczy będę miał przedziały z ufnością, dla\n",
    "    # których dane podejście wskazywało na taką odpowiedź - teraz przypatrzmy sie, czy są jakieś alfy, które lepiej sobie radzą w jakimś podejściu\n",
    "    \n",
    "    \n",
    "    sub.loc[:,'różnica'] = (sub.loc[:,'pop_mean'] - sub.loc[:,'delta'])\n",
    "    \n",
    "    pu = {'duże': [],\n",
    "         'małe': [],\n",
    "         'nie wiadomo': [],\n",
    "         'duże_błędne': [],\n",
    "         'małe_błędne': []\n",
    "         }\n",
    "\n",
    "    pod2 = {'duże': [],\n",
    "            'małe': [],\n",
    "            'nie wiadomo': [],\n",
    "            'duże_błędne': [],\n",
    "            'małe_błędne': []\n",
    "           }\n",
    "\n",
    "    pod3 = {'duże': [],\n",
    "            'małe': [],\n",
    "            'nie wiadomo': [],\n",
    "            'duże_błędne': [],\n",
    "            'małe_błędne': []\n",
    "           }\n",
    "\n",
    "    pu['duże'] = np.where(sub['różnica'] > 0,sub['pu_duży'],-1) #wszystkie przedziały dla podejścia, gdy wynik w rzeczywistości był ważny - -1 oznacza, że dla tego rzędu nie był\n",
    "    pu['małe'] = np.where(sub['różnica'] < 0,sub['pu_mały'],-1) #analogicznie tylko z małymi\n",
    "    pu['duże_błędne'] = np.where(sub['różnica'] < 0, sub['pu_duży'],-1) # wszystkie przedziały dla podejścia, gdy wynik nie był ważny w rzeczywistyości - -1 oznacza wynik ważny dla rzędu\n",
    "    pu['małe_błędne'] = np.where(sub['różnica'] > 0, sub['pu_mały'],-1) # jak wyżej\n",
    "    pu['nie wiadomo'] = sub['pu_nie_wiadomo']\n",
    "    \n",
    "    pod2['duże'] = np.where(sub['różnica'] > 0,sub['2duży'],-1)\n",
    "    pod2['małe'] = np.where(sub['różnica'] < 0,sub['2mały'],-1)\n",
    "    pod2['nie wiadomo'] = ['2nie wiadomo']\n",
    "    pod2['duże_błędne'] = np.where(sub['różnica'] < 0, sub['2duży'],-1)\n",
    "    pod2['małe_błędne'] = np.where(sub['różnica'] > 0, sub['2mały'],-1)\n",
    "    \n",
    "    \n",
    "    pod3['duże'] = np.where(sub['różnica'] > 0,sub['3duży'],-1)\n",
    "    pod3['małe'] = np.where(sub['różnica'] < 0,sub['3mały'],-1)\n",
    "    pod3['duże_błędne'] = np.where(sub['różnica'] < 0, sub['3duży'],-1)\n",
    "    pod3['małe_błędne'] = np.where(sub['różnica'] > 0, sub['3mały'],-1)\n",
    "    pod3['nie wiadomo'] = sub['3nie wiadomo']\n",
    "    \n",
    "    return pu,pod2,pod3,sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#-------------------------------------------------------- efekty duże poprawne--------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:845: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "D:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:966: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'przedziały ')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pu, pod2,pod3,sub = tabele(data,30,45,3,3,2,6)\n",
    "por = sub['pop_mean']\n",
    "pu_wielkości_duże = [i for i in zip(sub['pu_duży'],sub['różnica'],por)]\n",
    "fig, (ax1,ax2,ax3) = plt.subplots(3,sharex=True)\n",
    "for i in pu_wielkości_duże:\n",
    "    if i[1] < 0:\n",
    "        ax1.plot([i[2],i[2]],[i[0][0],i[0][1]],color = 'red')\n",
    "    else:\n",
    "        ax1.plot([i[2],i[2]],[i[0][0],i[0][1]],color = 'green')\n",
    "        \n",
    "pod2_wielkości_duże = [i for i in zip(sub['2duży'],sub['różnica'],por)]\n",
    "for i in pod2_wielkości_duże:\n",
    "    if i[1] < 0:\n",
    "        ax2.plot([i[2],i[2]],[i[0][0],i[0][1]],color = 'red')\n",
    "    else:\n",
    "        ax2.plot([i[2],i[2]],[i[0][0],i[0][1]],color = 'green')\n",
    "\n",
    "pod3_wielkości_duże = [i for i in zip(sub['3duży'],sub['różnica'],por)]\n",
    "for i in pod3_wielkości_duże:\n",
    "    if i[1] < 0:\n",
    "        ax3.plot([i[2],i[2]],[i[0][0],i[0][1]],color = 'red')\n",
    "    else:\n",
    "        ax3.plot([i[2],i[2]],[i[0][0],i[0][1]],color = 'green')\n",
    "ax2.set_ylabel('poziom ufności')\n",
    "ax3.set_xlabel('przedziały ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#----------------------------------------------- poprawność odpowiedzi ----------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "procent poprawnie wskazanych efektów dużych\n",
      "przedziały ufności:  brak efektów\n",
      "podejście 2:  brak efektów\n",
      "podejście 3:  brak efektów\n",
      "\n",
      "\n",
      "procent poprawnie wskazanych efektów małych\n",
      "przedziały ufności:  1.0\n",
      "podejście 2:  0.8536585365853658\n",
      "podejście 3:  1.0\n",
      "\n",
      "\n",
      "procent błędnie wskazanych efektów dużych\n",
      "przedziały ufności:  0.04065040650406504\n",
      "podejście 2:  0.0\n",
      "podejście 3:  0.0\n",
      "\n",
      "\n",
      "procent błędnie wskazanych efektów małych\n",
      "przedziały ufności:  brak efektów\n",
      "podejście 2:  brak efektów\n",
      "podejście 3:  brak efektów\n"
     ]
    }
   ],
   "source": [
    "pu, pod2,pod3,sub = tabele(data,40,45,4.5,4.5,-2,2)\n",
    "# test wskazał na efekt duży i taki był w rzeczywistości\n",
    "\n",
    "#przedziały ufności\n",
    "pu_duże = [i for i in pu['duże'] if i != -1]\n",
    "pu_duże_test = [i for i in pu_duże if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pu_procent_dużych_poprawnych = len(pu_duże_test)/len(pu_duże) if len(pu_duże) != 0 else 'brak efektów'\n",
    "\n",
    "#podejście 2\n",
    "pod2_duże = [i for i in pod2['duże'] if i != -1]\n",
    "pod2_duże_test = [i for i in pod2_duże if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pod2_procent_dużych_poprawnych = len(pod2_duże_test)/len(pod2_duże) if len(pod2_duże) != 0 else 'brak efektów'\n",
    "\n",
    "#podejście 3\n",
    "pod3_duże = [i for i in pod3['duże'] if i != -1]\n",
    "pod3_duże_test = [i for i in pod3_duże if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pod3_procent_dużych_poprawnych = len(pod3_duże_test)/len(pod3_duże) if len(pod3_duże) != 0 else 'brak efektów'\n",
    "\n",
    "print('procent poprawnie wskazanych efektów dużych')\n",
    "print('przedziały ufności: ',pu_procent_dużych_poprawnych)\n",
    "print('podejście 2: ',pod2_procent_dużych_poprawnych)\n",
    "print('podejście 3: ',pod3_procent_dużych_poprawnych)\n",
    "\n",
    "#test wskazał na efekt mały i taki był w rzeczywistości\n",
    "#przedziały ufności\n",
    "pu_małe = [i for i in pu['małe'] if i != -1]\n",
    "pu_małe_test = [i for i in pu_małe if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pu_procent_małych_poprawnych = len(pu_małe_test)/len(pu_małe) if len(pu_małe) != 0 else 'brak efektów'\n",
    "\n",
    "#podejście 2\n",
    "pod2_małe = [i for i in pod2['małe'] if i != -1]\n",
    "pod2_małe_test = [i for i in pod2_małe if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pod2_procent_małych_poprawnych = len(pod2_małe_test)/len(pod2_małe) if len(pod2_małe) != 0 else 'brak efektów'\n",
    "\n",
    "#podejście 3\n",
    "pod3_małe = [i for i in pod3['małe'] if i != -1]\n",
    "pod3_małe_test = [i for i in pod3_małe if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pod3_procent_małych_poprawnych = len(pod3_małe_test)/len(pod3_małe) if len(pod3_małe) != 0 else 'brak efektów'\n",
    "\n",
    "print('\\n')\n",
    "print('procent poprawnie wskazanych efektów małych')\n",
    "print('przedziały ufności: ',pu_procent_małych_poprawnych)\n",
    "print('podejście 2: ',pod2_procent_małych_poprawnych)\n",
    "print('podejście 3: ',pod3_procent_małych_poprawnych)\n",
    "\n",
    "# test wskazał na efekt duży, w rzeczywistości był mały\n",
    "#przedziały ufności\n",
    "pu_duże_źle = [i for i in pu['duże_błędne'] if i != -1]\n",
    "pu_duże_test_źle = [i for i in pu_duże_źle if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pu_procent_dużych_źle = len(pu_duże_test_źle)/len(pu_duże_źle) if len(pu_duże_źle) != 0 else 'brak efektów'\n",
    "\n",
    "#podejście 2\n",
    "pod2_duże_źle = [i for i in pod2['duże_błędne'] if i != -1]\n",
    "pod2_duże_test_źle = [i for i in pod2_duże_źle if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pod2_procent_dużych_źle = len(pod2_duże_test_źle)/len(pod2_duże_źle) if len(pod2_duże_źle) != 0 else 'brak efektów'\n",
    "\n",
    "#podejście 3\n",
    "pod3_duże_źle = [i for i in pod3['duże_błędne'] if i != -1]\n",
    "pod3_duże_test_źle = [i for i in pod3_duże_źle if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pod3_procent_dużych_źle = len(pod3_duże_test_źle)/len(pod3_duże_źle) if len(pod3_duże_źle) != 0 else 'brak efektów'\n",
    "\n",
    "print('\\n')\n",
    "print('procent błędnie wskazanych efektów dużych')\n",
    "print('przedziały ufności: ',pu_procent_dużych_źle)\n",
    "print('podejście 2: ',pod2_procent_dużych_źle)\n",
    "print('podejście 3: ',pod3_procent_dużych_źle)\n",
    "\n",
    "# test wskazał na efekt mały, w rzeczywistości był duży\n",
    "#przedziały ufności\n",
    "pu_małe_zle = [i for i in pu['małe_błędne'] if i != -1]\n",
    "pu_małe_test_zle = [i for i in pu_małe_zle if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pu_procent_małych_zle = len(pu_małe_test_zle)/len(pu_małe_zle) if len(pu_małe_zle) != 0 else 'brak efektów'\n",
    "\n",
    "#podejście 2\n",
    "pod2_małe_zle = [i for i in pod2['małe_błędne'] if i != -1]\n",
    "pod2_małe_test_zle = [i for i in pod2_małe_zle if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pod2_procent_małych_zle = len(pod2_małe_test_zle)/len(pod2_małe_zle) if len(pod2_małe_zle) != 0 else 'brak efektów'\n",
    "\n",
    "#podejście 3\n",
    "pod3_małe_zle = [i for i in pod3['małe_błędne'] if i != -1]\n",
    "pod3_małe_test_zle = [i for i in pod3_małe_zle if (i[0] != i[1] and float(i[0] != 0.00))]\n",
    "pod3_procent_małych_zle = len(pod3_małe_test_zle)/len(pod3_małe_zle) if len(pod3_małe_zle) != 0 else 'brak efektów'\n",
    "\n",
    "print('\\n')\n",
    "print('procent błędnie wskazanych efektów małych')\n",
    "print('przedziały ufności: ',pu_procent_małych_zle)\n",
    "print('podejście 2: ',pod2_procent_małych_zle)\n",
    "print('podejście 3: ',pod3_procent_małych_zle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18583a10b50>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lst = np.random.exponential(4,size=100)\n",
    "hist,edges = np.histogram(lst,bins=\"auto\",density=True)\n",
    "x = edges[:-1]+np.diff(edges)/2.\n",
    "plt.scatter(x,hist)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}