final_analysis_v3.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":["AWEXaFm9iQdj","XKaObWXZTFTL","V714fjJ_bpWO","pdCiPurwecK7","5lcbJdsfblm8","l11JwoQ0DydS","vIL2fpnXTQW4","jsVx1ov8h5Is","yozDCpZo_0D2","96jbuaLqkTeI","05nfwPjvALO8"]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","source":["Preregistartion: https://osf.io/z9d4x\n","\n","Data storage: https://osf.io/ayk28/files/osfstorage\n","\n","Danila Valko"],"metadata":{"id":"8xe0zRPyS7NO"}},{"cell_type":"markdown","source":["### setup"],"metadata":{"id":"AWEXaFm9iQdj"}},{"cell_type":"code","metadata":{"id":"5_U-ib_dQXcz"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KdxmyCS2GgrX","executionInfo":{"status":"ok","timestamp":1675627887076,"user_tz":-60,"elapsed":11681,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"source":["%%capture\n","!pip install semopy\n","import math, random\n","import scipy\n","import semopy as sem\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","import numpy as np\n","from scipy.stats import wilcoxon, mannwhitneyu\n","from scipy.stats import pearsonr, spearmanr\n","from statsmodels.stats.multitest import multipletests\n","from scipy import stats\n","import statsmodels.api as sm\n","import statsmodels.formula.api as smf\n","import json\n","import seaborn as sns\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","%matplotlib inline"],"execution_count":3,"outputs":[]},{"cell_type":"code","source":["def bootstrap(data, n=10000, func=np.median, p=0.95, seed=13):\n","    random.seed(seed)\n","    np.random.seed(seed)\n","    sample_size = len(data)\n","    simulations = [func(np.random.choice(data, size=sample_size, replace=True)) for i in range(n)]\n","    simulations.sort()\n","    u_pval = (1+p)/2.\n","    l_pval = (1-u_pval)\n","    l_indx = int(np.floor(n*l_pval))\n","    u_indx = int(np.floor(n*u_pval))\n","    m = func(data)\n","    return (m, (simulations[u_indx] - simulations[l_indx]) / 2)\n","\n","def cohen_r(p, n):\n","    # https://www.researchgate.net/publication/51554230_Effect_Size_Estimates_Current_Use_Calculations_and_Interpretation\n","    # Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18. https://doi.org/10.1037/a0024338\n","    # the r proposed by Cohen (1988);Cohen’s guidelines for r are that \n","    # a large effect is .5, a medium effect is .3, and a small effect is .1 \n","    # (Coolican, 2009, p. 395).\n","    # Cohen’s r (Fritz et al., 2012; Cohen 1988) for Wilcoxon test is reported.\n","    return abs(scipy.stats.norm.ppf(p/2)) / math.sqrt(n) \n","\n","def cohen_d(f):\n","    # https://www.researchgate.net/publication/51554230_Effect_Size_Estimates_Current_Use_Calculations_and_Interpretation\n","    # https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00863/full\n","    # Cohen’s d derived from F statistic (Fritz et al., 2012)\n","    eta_sq = (f.fvalue * f.df_num) / ((f.fvalue * f.df_num) + f.df_denom)\n","    d = 2 * math.sqrt(eta_sq) / math.sqrt(1 - eta_sq)\n","    return d\n","\n","def spearmanCI(r, n):\n","  stderr = 1.0 / math.sqrt(n - 3)\n","  delta = 1.96 * stderr\n","  lower = math.tanh(math.atanh(r) - delta)\n","  upper = math.tanh(math.atanh(r) + delta)\n","  return lower, upper\n","\n","def set_axis_style(ax, labels):\n","    ax.xaxis.set_tick_params(direction='out')\n","    ax.xaxis.set_ticks_position('bottom')\n","    ax.set_xticks(np.arange(1, len(labels) + 1))\n","    ax.set_xticklabels(labels)\n","    ax.set_xlim(0.25, len(labels) + 0.75)\n","def make_xy(x, y):\n","  df = None\n","  for i in range(len(x)):\n","    d = pd.DataFrame(y[i])\n","    d['_group'] = x[i]\n","    df = pd.concat([df, d], ignore_index=True)\n","  return df.rename(columns={df.columns[0]:'y', '_group':'x'})"],"metadata":{"id":"yy5m8J0MfuXT","executionInfo":{"status":"ok","timestamp":1675627891952,"user_tz":-60,"elapsed":3,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TGbdEIfB8fWV"},"source":["![image.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"pMzJDzAoJBlF"},"source":["![image.png](data:image/png;base64,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Study1 (pilot)"],"metadata":{"id":"XKaObWXZTFTL"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EDJTUyTgzwwc","outputId":"d6961b32-c980-4fa7-ff6f-928d06152c23","executionInfo":{"status":"ok","timestamp":1675627906251,"user_tz":-60,"elapsed":566,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"source":["data = pd.read_csv('/content/drive/MyDrive/pilot_data_v1.0.csv', sep=';')\n","print(len(data), int(len(data) / 32))\n","rec, sav, sho, tra, pos, neg, neut = 1, 2, 3, 4, 5, 6, 7\n","eco = [rec, sav, sho, tra]"],"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["5856 183\n"]}]},{"cell_type":"code","source":["print(len(data.drop_duplicates(subset=['id'])[['age', 'gender', 'income', 'work', 'car']].dropna()))\n","data.drop_duplicates(subset=['id']).describe().T[17:23]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"id":"qHGv8KaY256n","outputId":"cbcde1fa-e65f-4fd1-81d8-611d84ecb995","executionInfo":{"status":"ok","timestamp":1675627908628,"user_tz":-60,"elapsed":4,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["166\n"]},{"output_type":"execute_result","data":{"text/plain":["        count       mean       std   min   25%   50%    75%   max\n","age     179.0  23.726257  7.260632  14.0  20.0  21.0  25.00  55.0\n","gender  180.0   0.283333  0.451874   0.0   0.0   0.0   1.00   1.0\n","income  172.0   1.988372  1.333282   1.0   1.0   1.0   2.25   6.0\n","work    181.0   0.469613  0.500460   0.0   0.0   0.0   1.00   1.0\n","car     183.0   0.568306  0.496671   0.0   0.0   1.0   1.00   1.0\n","edu     182.0   2.714286  0.754884   1.0   3.0   3.0   3.00   4.0"],"text/html":["\n","  <div id=\"df-ec3ea41f-5ade-42c3-86dc-66de939089a6\">\n","    <div class=\"colab-df-container\">\n","      <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>count</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>min</th>\n","      <th>25%</th>\n","      <th>50%</th>\n","      <th>75%</th>\n","      <th>max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>age</th>\n","      <td>179.0</td>\n","      <td>23.726257</td>\n","      <td>7.260632</td>\n","      <td>14.0</td>\n","      <td>20.0</td>\n","      <td>21.0</td>\n","      <td>25.00</td>\n","      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'edu']].dropna().drop_duplicates(subset=['id']).describe().T"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"nsKjRAfhX4T9","outputId":"618d2f61-dc67-4966-e6f9-5d1a757d7349","executionInfo":{"status":"ok","timestamp":1675627911096,"user_tz":-60,"elapsed":235,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        count       mean        std   min   25%   50%    75%    max\n","id      165.0  91.236364  52.722570   0.0  47.0  91.0  136.0  181.0\n","age     165.0  24.006061   7.343071  14.0  20.0  21.0   26.0   55.0\n","gender  165.0   0.290909   0.455564   0.0   0.0   0.0    1.0    1.0\n","income  165.0   1.987879   1.334294   1.0   1.0   1.0    2.0    6.0\n","work    165.0   0.490909   0.501439   0.0   0.0   0.0    1.0    1.0\n","car     165.0   0.551515   0.498853   0.0   0.0   1.0    1.0    1.0\n","edu     165.0   2.763636   0.714748   1.0   3.0   3.0    3.0    4.0"],"text/html":["\n","  <div id=\"df-d5b72fc6-c79d-499d-817f-dbf46b5e56a0\">\n","    <div class=\"colab-df-container\">\n","      <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>count</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>min</th>\n","      <th>25%</th>\n","      <th>50%</th>\n","      <th>75%</th>\n","      <th>max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>id</th>\n","      <td>165.0</td>\n","      <td>91.236364</td>\n","      <td>52.722570</td>\n","      <td>0.0</td>\n","      <td>47.0</td>\n","      <td>91.0</td>\n","      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<td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>car</th>\n","      <td>165.0</td>\n","      <td>0.551515</td>\n","      <td>0.498853</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>edu</th>\n","      <td>165.0</td>\n","      <td>2.763636</td>\n","      <td>0.714748</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>3.0</td>\n","      <td>3.0</td>\n","      <td>4.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5b72fc6-c79d-499d-817f-dbf46b5e56a0')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" 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dropout\n","n=114, inc/con mean diff 67.9 ms, eco-neg/eco-pos mean diff 166.5 ms\n"]}]},{"cell_type":"code","source":["# Normality tests\n","# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html\n","k, p = stats.normaltest(eco_pos.time)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(eco_neg.time)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(con.time)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(inc.time)\n","print(f\"p = {p:g} ~ {p:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lINrh3GRRyHV","executionInfo":{"status":"ok","timestamp":1675627919451,"user_tz":-60,"elapsed":219,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"1dd854df-d0af-4ea5-feb9-e620a94f6d79"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["p = 0.00231246 ~ 0.002\n","p = 7.87519e-05 ~ 0.000\n","p = 0.0890267 ~ 0.089\n","p = 1.85934e-05 ~ 0.000\n"]}]},{"cell_type":"code","source":["print(f'AFP, response time analysis with the paired Wilcoxon signed-rank test')\n","t1, p1 = wilcoxon(con.time, inc.time)\n","t2, p2 = wilcoxon(con.time, inc.time, alternative='less')\n","r1 = cohen_r(p1, len(con.time))\n","r2 = cohen_r(p2, len(con.time))\n","m1 = bootstrap(con.time)\n","m2 = bootstrap(inc.time)\n","print(f'''\n","congruent vs incongruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_pos.time, eco_neg.time)\n","t2, p2 = wilcoxon(eco_pos.time, eco_neg.time, alternative='less')\n","r1 = cohen_r(p1, len(eco_neg.time))\n","r2 = cohen_r(p2, len(eco_neg.time))\n","m1 = bootstrap(eco_pos.time)\n","m2 = bootstrap(eco_neg.time)\n","print(f'''\n","env.+positive vs env.+negative\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_pos.time, con.time)\n","t2, p2 = wilcoxon(eco_pos.time, con.time, alternative='less')\n","r1 = cohen_r(p1, len(con.time))\n","r2 = cohen_r(p2, len(con.time))\n","m1 = bootstrap(eco_pos.time)\n","m2 = bootstrap(con.time)\n","print(f'''\n","env.+positive vs congruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_pos.time, inc.time)\n","t2, p2 = wilcoxon(eco_pos.time, inc.time, alternative='less')\n","r1 = cohen_r(p1, len(inc.time))\n","r2 = cohen_r(p2, len(inc.time))\n","m1 = bootstrap(eco_pos.time)\n","m2 = bootstrap(inc.time)\n","print(f'''\n","env.+positive vs incongruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_neg.time, con.time)\n","t2, p2 = wilcoxon(eco_neg.time, con.time, alternative='greater')\n","r1 = cohen_r(p1, len(con.time))\n","r2 = cohen_r(p2, len(con.time))\n","m1 = bootstrap(eco_neg.time)\n","m2 = bootstrap(con.time)\n","print(f'''\n","env.+negative vs congruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-greater: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_neg.time, inc.time)\n","t2, p2 = wilcoxon(eco_neg.time, inc.time, alternative='less')\n","r1 = cohen_r(p1, len(inc.time))\n","r2 = cohen_r(p2, len(inc.time))\n","m1 = bootstrap(eco_neg.time)\n","m2 = bootstrap(inc.time)\n","print(f'''\n","env.+negative vs incongruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","print('Note. Null hypothesis H0: the median of the differences is located at zero.')\n","print('One-sided alternative (less) hypothesis H1: the median of the differences is located on the left μ < 0.')\n","print('One-sided alternative (greater) hypothesis H2: the median of the differences is located on the right μ > 0.')\n","print('† Bootstrapped 95% confidence intervals for the median from 10000 resamples are reported.')\n","print('‡ Cohen’s r (Fritz et al., 2012; Cohen, 1988) for Wilcoxon signed-rank test is reported.')\n","# a large effect is .5, a medium effect is .3, and a small effect is .1 (Coolican, 2009, p. 395)."],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I7BNt5LB4EW4","outputId":"a147890e-6736-4a88-b7d5-548dce6beb77","executionInfo":{"status":"ok","timestamp":1675627928982,"user_tz":-60,"elapsed":7859,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["AFP, response time analysis with the paired Wilcoxon signed-rank test\n","\n","congruent vs incongruent\n","Mdn1=1510.1, 95%CI†[1439.1–1581.2],\n","Mdn2=1581.6, 95%CI†[1527.2–1636.0],\n","two-sided: W=2397.0, p=0.013, r‡=0.23,\n","alternative-less: W=2397.0, p=0.006, r‡=0.26\n","\n","\n","env.+positive vs env.+negative\n","Mdn1=1491.5, 95%CI†[1444.4–1538.6],\n","Mdn2=1631.0, 95%CI†[1550.9–1711.1],\n","two-sided: W=1233.0, p=0.000, r‡=0.54,\n","alternative-less: W=1233.0, p=0.000, r‡=0.55\n","\n","\n","env.+positive vs congruent\n","Mdn1=1491.5, 95%CI†[1444.4–1538.6],\n","Mdn2=1510.1, 95%CI†[1439.1–1581.2],\n","two-sided: W=2909.5, p=0.298, r‡=0.10,\n","alternative-less: W=2909.5, p=0.149, r‡=0.14\n","\n","\n","env.+positive vs incongruent\n","Mdn1=1491.5, 95%CI†[1444.4–1538.6],\n","Mdn2=1581.6, 95%CI†[1527.2–1636.0],\n","two-sided: W=2085.0, p=0.001, r‡=0.32,\n","alternative-less: W=2085.0, p=0.000, r‡=0.33\n","\n","\n","env.+negative vs congruent\n","Mdn1=1631.0, 95%CI†[1550.9–1711.1],\n","Mdn2=1510.1, 95%CI†[1439.1–1581.2],\n","two-sided: W=1578.5, p=0.000, r‡=0.45,\n","alternative-greater: W=4976.5, p=0.000, r‡=0.46\n","\n","\n","env.+negative vs incongruent\n","Mdn1=1631.0, 95%CI†[1550.9–1711.1],\n","Mdn2=1581.6, 95%CI†[1527.2–1636.0],\n","two-sided: W=2268.0, p=0.004, r‡=0.27,\n","alternative-less: W=4287.0, p=0.998, r‡=0.00\n","\n","Note. Null hypothesis H0: the median of the differences is located at zero.\n","One-sided alternative (less) hypothesis H1: the median of the differences is located on the left μ < 0.\n","One-sided alternative (greater) hypothesis H2: the median of the differences is located on the right μ > 0.\n","† Bootstrapped 95% confidence intervals for the median from 10000 resamples are reported.\n","‡ Cohen’s r (Fritz et al., 2012; Cohen, 1988) for Wilcoxon signed-rank test is reported.\n"]}]},{"cell_type":"code","source":["d = [[eco_pos.time, eco_neg.time],\n","     [eco_pos.time, con.time],\n","#    [eco_neg.time, inc.time],\n","     [con.time, inc.time]]\n","w = wilcoxon(d[0][0], d[0][1], alternative='less',)\n","r = cohen_r(w[1], len(d[0][0]))\n","t = [f'env.+positive vs env.+negative\\nH1: μ<0, W={w[0]}, p<{w[1]:.3f}, r={r:.2f}']\n","w = wilcoxon(d[1][0], d[1][1], alternative='two-sided')\n","r = cohen_r(w[1], len(d[1][0]))\n","t += [f'env.+positive vs congruent\\nH1: μ≠0, W={w[0]}, p={w[1]:.3f}, r={r:.2f}']\n","w = wilcoxon(d[2][0], d[2][1], alternative='less')\n","r = cohen_r(w[1], len(d[2][0]))\n","t += [f'congruent vs incongruent\\nH1: μ<0, W={w[0]}, p={w[1]:.3f}, r={r:.2f}']\n","\n","l=[['env.+positive', 'env.+negative'],\n","   ['env.+positive', 'congruent'],\n","#  ['env.+negative', 'incongruent'],\n","   ['congruent', 'incongruent']]\n","\n","barWidth = 0.2\n","fig, axs = plt.subplots(1, len(d), figsize=(11.5, 4.5))\n","for i, ax in enumerate(axs.flat):\n","    m1 = bootstrap(d[i][0])\n","    m2 = bootstrap(d[i][1])\n","    ax.bar(0.0, m1[0], width = barWidth, color = 'lightgreen', edgecolor = 'gray', capsize=5, label=l[i][0], yerr=m1[1])\n","    ax.bar(0.5, m2[0], width = barWidth, color = 'silver', edgecolor = 'gray', capsize=5, label=l[i][1], yerr=m2[1])\n","    ax.plot((0.0, 0.5), (m1[0], m2[0]), 'k--', linewidth = 1.2)\n","    if i > 0:\n","      pass\n","    else:\n","      ax.set_ylabel('Response time (ms)')\n","    ax.set_xticklabels([])\n","    ax.legend()\n","    ax.set_xticks([])\n","    ax.set_title(t[i], fontsize=12, fontweight='bold')\n","    ax.tick_params(axis='y', labelsize=12)\n","    ax.set_ylim([0, 2400])\n","plt.tight_layout()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":333},"id":"e4XI92fxDz3B","outputId":"a7dbe899-6346-4e9f-dc4f-27cb1d045279","executionInfo":{"status":"ok","timestamp":1675627933849,"user_tz":-60,"elapsed":4871,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 828x324 with 3 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAzUAAAE8CAYAAAD5fJ8NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeXgURfrHP28OQAyEU0FBgq7AgtwQWPAIoMIq63pxrBfoKioriAfq6qq46rquF8gPbwRBVhBkPVgPPAAFXOVGARWFgAgiRAIknEnq90dVTzrDzGQSkkwG3s/z9DNTXUfX+e2q6upqMcagKIqiKIqiKIoSryTEOgKKoiiKoiiKoiiHgw5qFEVRFEVRFEWJa3RQoyiKoiiKoihKXKODGkVRFEVRFEVR4hod1CiKoiiKoiiKEtfooEZRFEVRFEVRlLhGBzVxiIjMFREjIoOLcWfckVYhEVMqFBHJcOWbGeu4KEcWqjGKosQjel88utFBTXwyAxgDrAYQkVGuEU8McjfGHbsqNnpKWROmk7kJW74vxyZWyhGMaswRhohMdGU4KtZxUY4eYjDxoffFEhJB3+OOpFhHIF5xncsJxhip6GsbY/4vSncjyjsuRwoiYoCrjTETYx2XaDHGfA9oGR+hqMYc+YhIsjHmYKzjoRxdHMn17ki5Lx7JZVSuGGPi9gBOAqYCPwHZwGzgNJ99JmCAu4BlQC7wLlAbEGC9s+/o87POnetUzLUH2+wLaTfXhfEPYKG77hwgzeemDfA+sB3YBrwDNPfZjwB+APY7N3M9e1/4g4FR7r//mOvceeY04AH3f6zvGve7c/8XTX4GpfFq5/cd37lB7tys4tIQIrwk4A5gjcuv1cAQn72XzhnAJCAH+B4429kXm75iytMAg8OVMzAfeMrly0/A5T431YF/uvjkAkuBC332qcA07Gz2SuBWF2a2z82/Xbj7gd3AJ0DroPL2H6OADPc/07mb58yX+MKd485d6synu/B2AJuxs1l1Y92WK+tRXJtANSbeNOZm4GtgD7AVuM/ZCTAE+Mrl5ffAQ0A1Z5/hrpsJ3A384o6RJWznXn6NcHVjncs74y9rYKI7N8p37hpgBVb71rp4JPnrCmF0yhee/5gY6/Z1NBxAI+AVYAOwD3uP6+zs6gMvARtdvfkf0CdEPXgO2373uLrVzufmdF+9nezalwFGh6gbz2LvL6MobNcTfWEF2rMz1wWed/V+N7AAOMPnfq5z/wjwqYvfAqBJUHj+IyMof1Jcnc4DjnPnqvvONQA6AJ+5PMrBtuEbw+R3BkXvi2m+a1/j8noH8FSQvyuBJS6dvwLP++wuAhY5uw3AOKBWtOEDVVze78Dq1RCfHy+cTGe+B1gF5Icpk1DldgHwpcufDcATQPXg/CCEdhFB3+PxiHkESh1xW+m/BwqA97Ad3QOusOoFVZI9WFHJcuYHnf2DzvyoM3d25lVRXH8wxXc4DmBF5itnXuTsG7rKbYBZ2Bu7AbZgO0O/ceZtWDH7N/bmlxEU/mCgD1YIDXYgMBq4KbgxAKe4/5uBBGe/yp1LjyY/g9JYAyui+ylslP914fUvLg0hwnvEuf8GeJHCjt+gEA3vI1+aNzr7iOmLojwNkQc1BisaXlnlADWdm9fcuSUu7ltdPnrl9Yqz3wRMwApPcGdnATAFK5be4GSNs7vJ+TXu+qNduWdQVLz/7MzTnPl4IB8r0FWB0ygcNL2G7fAa7ABKYt2mK9sRTZtANSaeNOZhX1lNBl4H/u3shjq7bOxA3yvX5519hi+tq4GZ7n8BcGoJ2rkXhldfniOKQQ1wvTNvdPFb6cz3R6NTwGUu3saV5Wjgsli3sSP9cHX+O5fv32LvDwuBP2KX/3/u7Ja6OnkQq9ndguqBAf7ja0+fOftaFLbzOcAHzr/h0EGNF4fngasoZlDj4jffmT8Fxrs6vZdDJz/ysPevDc482dmP9oX5sjP/JkQ+TXZubnTmfs78rjN78Zjh8vAz4KUweZ5B+EHNRuBVXx71cm6u86VjunPzibM7z9ntx7bZr535/RKE70347HD58LPPT/CgJg87MJ0eXCbOXKTcgN4U6uAr2L6EwT7l9+dHSO0igr7H4xHzCByGWHiVfpMrhNHYG6YBbgiqJN6I1KtY3izfqc683pkfc+a7wlyzj+9a3k3DM4/2uZvr7J5y5npYsTJAK+wTCQPM8flZ5s4NAX7r/q8AzgUaOTeJQeEPDlXJfWEGNwavsme4eBgKO87F5meI/JjixQMrrgewjbZacWkICkewHW2/8L3tzP8LSuPXzn1TX/rqFZe+ENes4y87ig4YRuMGQhTeELJcupKxomOATtiZNoMVsbHOr1c+U4FErBga4CwX5i0c2tk5ERiGfeIz1pe2E0KVuTuXQVHxrontLOUCxwJ/cfbPOftxXp760rnPnWsR6zZd2Y5o2gSqMfGoMRf5zie7X6/DP8iZ21LYrqtR2NbygAbOjdeBu5To27mXX9f4zqV5533nJlJ0UON1Zme4fHvVmX+ORqdChalHhWjIpS7PN+Nmzr16hx3oG1cvj3Xnn3LnvMG2V2b/deYezpzjzFc48w+4iSlgOaEHNbtwHWh3fhSRBzWdff689rrUnfuncz/Xmcc5s/d09etQYUbIp3Ocm7nOPN2ZBzrzF167wU7OJROinTu3GYQf1HhPyLyJw9ud2Ruo3OIvI/f7rrO735n9WtssyvA9nRvkzBf5/AQPav4elJ5gjS1SbhRO9Hj9l2coHLRUpxjtClcX4vWI53dq0tzvidglBX5+E2Re5n6z3W8KgDFmrYh8DvxORLpgb7oF2BtGKLqGuJbfHLyOc427znYR2Y59jNrIF/c1PrffAO2wj21fEJH7geHYmRdE5FusQH4dJm7R8ArQDRiAnR0FO1sKJctPf3iXufAEKzSvG2P2AWtKkIZ6uDLBimKkay83xhgRyfadS8EuPYmUvmBqcmg6z3EH2BvDlz67NS5diEiu859CYb4lYJ+oBMe9HvbRMxSW92q/IxE5FXuzSOFQ6mNviMVijNklIm8CfwL6YmezweYLvrh2cUdwXL+J5jpHEWnuVzUmeuJBY/7nnTSFa9a9uHn55bWFBKCxL5yfjTE/u//Z2OV0KUTRzoNYEMEO7CDJjxe/S4LOHy8ift0Ip1NKbGjqfr8yxuzxThpjDvpenP/RGJPr/nv1rklQOMH6cqz7PdH9fmtc7xRb/9qGiMsqY0x2iPMAiEi4OleDUupfCfgYu1zyDBH5DfbpyE7gTWd/K7az/hJWB3KA+7CDwJIQLp5eORWrDUFa2wS7FLS48L1yKk9t8PdfwObTyT5zOO06oojn3c8y3e8S7FIHMfaF2trYZQZ+8tyv4VC8G+5j2Ar6iTFmk4gki0gLdyQAGGNG+a5ztTsnvnPB/BZAROphb3pgZym9uLfwuW3ufjc4cXnYGFPPxelRZ39LmLzId7/Flefr2Jn5S7CdBH/nyotTNPnp8RG2w90LO/sLLj9LmIbt2KcLAG19107APg3xE6ksw6ZPRE5yZVkbwBiTGaLsrvadmxjmusHXznS/B4D6vvCqYGdjtjs7sLP2ULTcAc7Histy7Gz08T47L27RlrFXn4dh11qvNcZ8HhTXp4LSfooxZlYx4R6NZLpf1ZgjQ2Ny3P/AgF5EvIk9L25efnl5VQD86AsnnA5E08797Pf997QPEanp/p4W5N6L3x+D6sPJxpgcn7tw8YPoy1ApO9a739Yicox30tW7TGdsLCLV3f9AGw0KJ5y+/OR+/YOMcPVuf5DZq3fF1bkt2HfLvDpXnUMn8CLpX4H7DVvvjDGeTiRgV2tUxy6/2uecLDbGtMVqRQZ2cuOfvvYbFcaYcPH0yqlYbRCRuhRqbZFyihC+V04l1Qawqy+g+HK6OcR93T+5c1RoQzwn4F1sRewILBCR50TkXewNMNQsRTimYSvRGc7sdUBOxI6q11BYmUrKUBGZjF3rmoSdjV+Nbbw7gR4i8raIvA+0x76LMQM7M7hZRKYDd2KXpEDh6D8Y76b7exEZKyLBs3kAuFmad7Cz/82xj3o9vyXOTydEU7AC0xX4wRjjzTJEnQY3wzTOGWeLyIsi8hp2ffyoMGkuafomYctyULThRXnNbdiOXBXgC5dv07Fl8mdjTD72/RWA10TkZeDvQcFsdb/NsFtRzg5xKS8dN4vIaBEJV8c/xN6EumPb92Sf3QvYx+bDReQ/Lp/nYx+NK4eiGlPIkaAxTzvjFBGZ5DRmojvn6c8YERkPvOXM430dq7BE2c7D+d2GHYgCvCoir2OfqPnxdqOb7LZmniQiq7Hv7kSLVw5XiMgYEelRAr9K6XgXO5PfEFgmIs+LyDzsRNZi7LKqFOAzEZmEnYwy2KcS0TALW99PFZGPXDtvE6Vf76nCeSLyBHa5tJ8l2Hd+GgKLXHt9E9te+xA9Xr37P3fvOjaMO08Xg3US4B0R+Rg7MTQU+47obgo744fLGPf7mIi8LiKvYN/7g0JtuNtteTwXq7UfGmO+izJ8b2Lnaacvz5Ugbl45/Z+IvIh9H8uPF79HReQ1EXlZRBZjn35FS1T6Hg/E7aDGPa7tib2RnITtrDbHVp5vSxDODuxNGOxM3swyjOY/KXyBdh7Qz1g2Y9fGzsZ2Pjth10X2MMb8il3D+qWzuw44ASs4D4W5znTs8otjsTMokW5Wr/j+B0TjMPLTH56/A13SNPwN2zH5FbtOuKe77rQI1y4uPuGWnpU1f8aWdQF2/XI37M3gfWd/M7aMamPL+lF33puReR37EmY+cDZ204RgnsC+HNzShXdqCDde52qKZ8RXJsaYFS78T4EzgYHYpQWhrnfUoxpThCNBY+7HPsVZj12i1hP7EjfYTuRQ7Izqn7Bt+REOXXYTieLaeST+jJ3EOcNd+60g++eAa52bS7HLc7Zhl+NEi/eS+onYJXsdS+BXKQVuyVkvbL2tjq3zxwGb3YD9AuzA9Djsk/1lwAXGmPlRhp8N/AG73PJ32DrxtrOOWO+MMR9hB/p73bXHBdkXYDvQz2EnXQZjJ0b+i2+ZVhTciR2098G2kWNCOTLGrMYO9MC2UX8ezMW278uxA8JFwADfkrvDwhjzInbzhJXYtvUHbFvDGPNf7FLuVdi2l4rdbGFACS7xD2w+JmCfNPnvuQdCefAxDLsRTDvs0uIiExnGmHex5bfCxf1irIaMIXpKou+VGimjOqH4EJG5wFnE2XdPlPJBRGpgX+w0zvxXrMjNN8acEdGzooRANabyoe1ciQUikmqM2en+J2A73y2Aa40x42MaOQUAt7zwoPeejoj8Cbtb4yZjTOOInpUSEc8bBShKvNAL+JuIvIfd99/bDOHp8F4URYkztJ0rseAlEcnDLmM9Czug2Qy8EdNYKX6aAdNE5D/Yfve17rxqQxmjgxpFKX82YncsuQ37qHkF8IQxZnpMY6UoSlmi7VyJBUuxSycvxu44OA34W6SdzpQKJwu7tP4vzvwDdjnaCzGL0RGKLj9TFEVRFEVRFCWuiduNAhRFURRFURRFUUAHNYqiKIqiKIqixDkVOqgRkUwRMSJyoe9chjuX7Ts3QkRWiki+sxtVkfEMhYj8RUR+EJH9IvKtiET9vRMRaebSsV9Eqrlznd05IyLt3bnqInLQnTupBOEf4747ssUXZlqQm5dEZI2I5IhIloi8KyKn+ez7i8hqZ58rIqtEZKjP/rci8oWIZLt0bHDfOqhWTNx6iMgiEdnn4vcvKeEHs8qS0pRjcX5EpK2IzBGRvS5vX3Q7IXn21dze7784NwvEfl2+whCRBBEZJSKbXDqWi8h5xfiZ6KtP3pHjsx8cwt6ISPAHU0sTX9WK8tGKjiIyy9WDfa4d/0tEqvrctBSRd0Rku4jsFpE3RaRJUDj9nEbsd2V1R5D9+U4vckRkq4g8HYVWhKpL/xfJT3lSXBpDuL9MRD4TkW0iskdEvhaRPwe5iZgvIpImIm84rdgnIt+LyH0iEurDq+VGSeuwiJwtIvOdvhmxu/MFu4mok4cZX9WL2OnFKyLyk4vDdhF537uus68tIs+IyI8ujC9F5Cyffaj7jBGRiO9GlGd9Kg0l1Yto/Dg9eMvpxU6x39BpEOTmTBGZJ7bfliMii0WkWVmnr5h0lFQvbnb14FdfnC8IcnOMiDwmIhtF5ICIbBaRcNv1F2KMqbAD++VTA1zoO5fhzmX7zk3G7kvuuR9VwfFMAHr7zANdPH7BfqztV2fuXYIwf3F+TnfmEc5sgL+4cz2c+ccSxjcV+5Lq274w04LcGOy3U17E7gFvsHvHV3P2I7Efm3oW+0EvL5wezv50YIHzPxn7lVsDPBAhXk2wXxc/SOG3KAzwSLyUY3F+sN958cp2BvaDZQZ4zRfGc+7cV9hvdBRgv7FR7zDSllxC93e5OKzHfvdjH/YLw60i+Jno/LwMjHbHoz77wc5+ts9+NNC4DMouk/jQigf8ba00dSxEmOWpFYNd3XvLtckDLpwnnH0t7LdaDPbbOm+4/18DCc7N71wd3u3q0ibn5nqffZ6rYxOxHxg0wLPFxM3TJH9duqQiy9MXl4hpjNBefnT5OttXZn+INl9cXTbYnazGY78hYrBbd5c2LQle2UXpvjQ6ORT7wvoi53ZukH2xOnmY5ZVJfOhFvPUtBhNBL3x19t/YvoN3j9/gs3/XnVvq6vQe1waaOPvLKNrmFzr330eIV7nWp1KUa2n0ojgd9bbnNthvyMx1/z/3hXGGK5N8V0bPY78h1O0w62h568Vc7DfCJmL7pQbbT2zv7AXbHzXYTRVexH7Pb2qx8anggs8kCuHx2b1JCOGJ5MfnZqJzM9qZ2zmzieCnGfa7Apv87oDlzu8lzvxnQgh3MWn30nKnM0/Hfvn9V+Df7tzfnJtpzlyHoo09+EgPukYtCsUsLciuo+9/ms9dhzDxXensrwlj/7SznxQhzaOdm7HO/BtnzgFSosy3wc7PZ+6au7Afxbq8IsqxOD8U3kDeceYUbEckHzgZ+1E1T3SOc24mh6rXUbade7BCl+/OB98Q/Md9zk0SsN357+jOPejME6NoQ2lh7L2yGRxtOkqR3kqpFc7dbdiP3Z10OHUsQlrKXCuwbTDVd61RLpyVzny+M6+PkCYvfrc5cy9nznTmx5x5gjN7unQAaBAh3SXKpxD+vbLOxLaT7dgB2m0+N+nF5FOdaNIY5vrtgESfea7z83S0+YIdFBmgrzPPoJjJoxDx8K77KHbglIfV/D6R0l4WdZhCPZwb5nxInSxtmfvCz6QS6wVx2regGL0IEZcOzj4fSMYOPgrcuSbOTZF+QYgwFjv74VHUs1LXJ2KvF8Xp6IX+vMburJjpzmW4c59xmPdgYqAX+PqdLl0/OD+3BuXFGtzEe7RHrJYB/VlEMtz/RjGKAwAiUhP7ZdjB2C/BZ2NvJJOdfRLgLdNaHPTbrgSXWoD9Ou/p2MrTDTsjUdX9x9l5bsF+xTfSF62XY7+oXSzGmCU+YxX3m48VPwBEJB37xd5TgNbYCvWWz74OcB/2GwyXYPPq2QiX9R5BL3Zx+N4tBaiFFcvl0cTd0R3bOZ/trj1ZRL4yxqx0cSvzcozST3Aac0TkG2ffBtiJFfdMY8wvPrdXhLtuMTzg0rXamc/FfqU6FBuAvwONsWVWgJ0tC5WOSCxzebEKO1CaHWQ/RkSeddd71hhTki8ZF0el0Aq3TOX+MNYbRCTZ/a/UWmGM+T7ovKcFm9zvPvdbV0ROxna4T3Dn2mKf3BSp877fJiJSyxdGc9cuOztzMtAS+DlCPLuIyB5su/kIuMMYsyWC+1CchJ09fM/9Pi4i3xlj3nHXj5RPo7GdwYhpNCG2yzXGBOtZuLyNlC9PuOMxt5TqfGy7mhghzuEYif36+2vYr8t3JXLaR5Th/S6Y4nRy3WGE7adS6AUcGX2LKPQCABG5CVuHe7lTTxhjDor9GGg+9t7dUUSygFbOTdvgi4rIGUBHbPt/OUL8yrI+xUQvivPjs18CYIzJF5Fl2BUw7UTkC+zTHoALRWQMdtL3ZewkSEEx6Q6mwvTCGLM06FRwvfLqUQ6wQkROdOHebIxZESkRsRrU9D1M/18Cv8U2llIjIldhlwYlYh+RXgrMMsbs9zmr5+zBZjBArvtNFZFqxph9FI8nJt1EpCm2o7AAKzzni0hjbCUKuDXGZGIfw5UZIpICTHDGJ4M6DC2B4e5/AfA+9tGoR7AQzsMuewvH8e43x3cuFzuoaXCo84hsA850Qvkf7CzGlcDIcizHYv1ESCMujdWKsS8p/zDG3OcZjDGDsTfNSHhx3GPcNEiUcdgHfIi9QbTG3iDfEZGOxpivsXVkEfZ7HHWBC4DRIrLXGFNW++9XCq3Azqpluv9NsDOWz2FntnDhH08caYWIdAduwZbz39zpeS7M7tjZMz9eXQmu87lBbl4EbsDecHeGCSMUW9z1c7DlfgV2gqVbBD+hyMcum90uItuxs7pXYWd1JxLdAKG4NIbqpAQQkVux6f8eW08gunz5GLtMtS32I4pgOxqRBoLheNUYc5XPPModkSir+10wxelkWVEp9OJI7FuE0QuPS7Ef/gTbMfWus19EngTu4NCPgYYq9xHud7wxJieEvUdZ1qdY6UVxfopLY20K609H7PKsS7ETzzuwA66SECu9eBI7AbEQmOkLF6ATdmL9J+wSyv+KSHNjTO4hoThiNai5yBjzJtiX+YA5JfFsjNkDfFMG8agHHIOdCVuBfcy3P8jNdmylT8Q+4sxyvwA7SyDwS7Cj3zrYx3NgC9Gb4b0e+25MrouL/8lIOP5tjInqSY0Lrz5WYDthb7B3+u2NMRNFZBJ22dQ0rIBlAQ87+0wbjNTHzghdjW3w54S55FagOYX5he9/SW/SPxhjDrr/Xtl7M3HlVY7F+hGRrUHp8v//mcLOS1nkARTewAD7cjL2EXkofjXG/B1bDgDVRSTBzeBEE4cbfYMgRGQhtkP2B+w7FpONMZN89o9g3925hLL7qFhl0YpMCm9ME7DrgR/HdX6MMcbdEONCK8RuEjGdwiU7S1068kSkB9AfO8mxETgTu8xxm/O+FTu76aXNX7d/NsZki0gL4E9AQ+wM22PYAco2wnOiV99EpDm23H4nIg1L+LRmmzFmu/tfRCvc0+jLIvj9uzHmV4pJY6SL+57qrQN6GWN2ARhjNkbKFxFJxE4knYB9Yv42MAX7vkoutmNYEoK1og92SUlIjDEjKLv7XTDF6WRZUVn04ojqW4TTCw9jTIab5OuN7ZjOEJHfGGM2GGPuFJEPsU+L9jov/yRIC8RubvRHbJ6MLSbNZVmfYqUXxfkpLo3eknLBLtuaLiLfAf/CTTBGiHcoKlQvnN49j62zi7FLbvOctVc3VhtjLnRPhLKAE7EDuE/DpqIka9UO96Ds1r1Wx85inRrhWhOd38mm6Bo9E+SuNXakuNXZf4l9GtHA52aFs+vnzNc587wSpn++8/crtnFXwc7k76fw5apPfO7TvDiHOQYHhR/pnZomFL7E948QcasRZH7euX0ljP2fnL3/hcBTXLmkOPMY52acM5/qzKV5p2Yr7uV44D/u3GNlWY7Ym0ALdyRE6ecWZ/6vl0/YmawClx/HU/hOzfHOzRRKvk4+E99a2hD1PNSR6dx4gmCAzu7cw0Hle0ibAn4TdC3vBc47w9g/4uzfO9K0gsK15OGOpHjRCuwTzoOuTvwuxLWr+P7Xx95gDIWbhrzlzCOd+Rx8WoC9ySb5wvBeUs7B6Qi2U9+CwvfMTgCq+/w098X9+CjzzKsfB3GbcABPuXPTg/Qk3JEWTRrdOU8rqjpzAvCMc7eUoPeHissXbEfYi0dT5+YBZ36nBHVnbnCZR1OHfe5KrJM+v+HeqYmok0eaXrjzR0Tfggh6gR24+d8jq4qdyPPrhV9PErFPIw1B9z/ssksDzAiRvpNcudQuq/pE7PWiOB29yJm/xmpHInaSyZ+3a4Lqz0hnfrsy6wW2bnptcDZB/UHssk0DrHLmJF+9CvkeeMDv4YpJCRteJlEID3AtVji8AlzuzBeG8xNBePZTOLPqFcijIdwnYWcJ/oPthOb57C5z/rzdHXY48+9DCEStCHF61OfuM9/5hb7zD5YybydidyDxwpnhznmN1dvRaAOhXwj8DrvDxrPYGcJ85/4yZz8Bu9TmJeyaYK+CvRCufF2+7Me+ePaqrwweDecnRLoGO/t8l0/ei7MFQLvyLsco/NSgcMZkBrYzY3AvZDo3L1AoTlMp3PGkflAal0fRdjJKWT/udv4zgUkU7n52WoR2mI+d6XweO4tjsLN9zZ39XOyGEuOxs3N5zs0VR5pWYJ9ejnH5djlwdtAh8aAV2HewvBd33yP0i59zsUtFxgObndtZPvvuLowc7K49nrbc6OxTsEtQJmE1ydvB6/YQee69bD0Y22majn2KvMXZfxQULwOMCJM2r6zzsUu4JmM7LAa4oIT5FDGNzo1XDu2c+WHf9cf78vamEuSLN/G0wuXDLme+MyiNkeqzl0+DS9n2SqOTpzu3X7rzPzvzXc6+WJ08kvQiyH3c9i0oRi9cfm3G3teexd7jvPSk+uL2EfY+6L1UvhH3kr2vbWQ7u9Mj1OkR0danYD+VUC+K09EECgcts7FLcw3whS/Mq925H7F64Q1gL63keuFN7O4FxvnqldffTMIu3TXYwY83EF6Gb2IoZHzKQlDKQXgm+jLBf4wqhfB8i+2Af4Lt2GXju0GH8Vs/uCFg3zVZhxWl7/DtCgY0pbCjXSNCuBf40uLv2P/Ld/73pczbUPllKJxJCGc/2Nk/48rHm9n5AhjkC38I9kXxXGwjXIN9Cb1qMeXbC/tocT/2Rvc4vu2IsYMsg9vtJ0S6Bjv7T53fne46V0aRJyUtxzRfvtSKxo+zb48Vhr0u78YDNX32x2Ab7jZsp3ghvhkvCoVpcRRtJ6OU9SMRu+PZTy4dK/x5Tuh2OBr7OD4XewP5EN9WkdgOwpfYNrUbe2MZVJr4xYNWYF+gvCqKuFdarSDCzKPPzdPY2eWD2M7HPwnagQY7k7bapXEjdtmhN7Crim2v2dh2/xXw5zB57nWOTsN2+jY7P5kuHv6Oj3dTvylM2ryyzi3xSaAAACAASURBVMTOWG5z4d1RyjoYNo3O3su7dkFpCj7mliBfmrt82IrVih+w7TbR2XtPd7ZHiPdcDqOTEkUdTvOlzeukhKtXc33+IurkkaYXYfzGVd8iQrkaZ9/MlWmWi/9P2Hc7TvOFMQj7KYH9zt2/Cdr2H7iJCPdAQgxQiqtPVHK9iNJPU+zW+jnYe+wM4ISg697q0rAXO6gc5LOrrHoxl9D1aqLP32+wr0vkYgdMr2GXKEeOS2kTUdkPgm6a5XytP7prjYt1uuPpwL7olo8dfYf87gqFojo31vEtx3zwHnn3i3VcjsYjWq3w32wO41qqFaXLtwRsh2gTYWas/Z2UWMe3HPPhZpfGkbGOy9F6aN+i8h+qF4E0HnV6EbMvux9h9MLOqJX0Rc6jnR7YJUtXmcJNAI5GemE/KjU91hFRwmPcXeIwUa0oHe2wL0L3MaG3Rz1a6IVdCvpErCOiVAiqF6VD9cJy1OmFDmrKAGPMcAq3QlaixBgzE7sk46jGGNMm1nFQKgbVitJh7G5LZbq9fTxijLkg1nFQKg7Vi9KhemE5GvVCymbyUVEURVEURVEUJTYkxDoCiqIoiqIoiqIoh8NRs/ysXr16Ji0tLdbRUJQKYcmSJduNMfVjHY94RfVCOZpQvTg8VC+Uo4XKrhVHzaAmLS2NxYsXxzoailIhiMiGWMchnlG9UI4mVC8OD9UL5WihsmuFLj9TFEVRFEVRFCWu0UGNoiiKoiiKoihxjQ5qFEVRFEVRFEWJa46ad2qU6Dh48CCbNm1i3759sY6KEgXVqlWjUaNGJCcnxzoqiqL6UclRvVDiCdWT2BGvWqGDGqUImzZtokaNGqSlpSFy1H+7qlJjjCErK4tNmzbRtGnTWEdHUVQ/KjGqF0q8oXoSG+JZK3T5mVKEffv2UbduXRWQOEBEqFu3rs5iKZUG1Y/Ki+qFEm+onsSGeNYKHdQoh6ACEj9oWSmVDa2TlRctGyXe0DobG+I133VQoyiKoiiKoihKXKPv1CgReWL0E+TszCmz8FJSU7htxG1lFl5lYvHixUyaNImnn36auXPnUqVKFbp16wbAc889R/Xq1bnqqqtiHEtFqThUP6JH9UNRIqN6UjJGjx7NkCFDqF69eqyjUmHooEaJSM7OHGoNr1Vm4WU/nV1mYZWGiRMnkpmZyahRo8o87E6dOtGpUycA5s6dS0pKSqBTcsMNN5T59RSlsqP6ET2qH4oSmSNNT4LJz88nMTGxzMIbPXo0V1xxxVE1qNHlZ0ql5NVXXyU9PZ127dpx/fXXk5+fT0pKCvfccw9t27ala9eubN26lZ07d9KkSRMKCgoAyM3NpXHjxhw8eLBE10tLS+OOO+6gdevWpKen8/333wOQmZlJz549adOmDb169WLjxo0ATJ8+ndNOO422bdty5plnArYj0rdvXzIzM3nuued46qmnaNeuHZ999hmjRo3i8ccf55tvviE9PT1w3czMTFq3bg3AkiVLOOuss+jYsSO9e/dmy5Yth52PinI0ovqh+qEoZcWkSZNo06YNbdu25corrwzbrgcPHszw4cPp1q0bJ598MjNmzACgoKCAoUOH0qJFC8455xzOO++8gF1aWhp33nknHTp0YPr06WRkZLB48WIAtm/fTlpaGmAHPCNHjqRz5860adOG559/HrC6kZGRwaWXXkqLFi24/PLLMcbw9NNPs3nzZnr06EGPHj0qOMdihw5qlErHmjVrmDZtGgsWLGD58uUkJiYyZcoUcnNz6dq1KytWrODMM8/kxRdfJDU1lXbt2jFv3jwAZs2aRe/evUu1t3pqaipfffUVN910EyNGjABg2LBhDBo0iJUrV3L55ZczfPhwAP7+97/zwQcfsGLFCt5+++0i4aSlpXHDDTdwyy23sHz5cs4444yAXYsWLThw4ADr168HYNq0aQwYMICDBw8ybNgwZsyYwZIlS7jmmmu45557SpV/inI0o/qh+qEoZcWqVat46KGH+OSTT1ixYgVjxowJ264BtmzZwvz585k1axZ33XUXADNnziQzM5PVq1czefJkPv/88yLXqFu3LkuXLmXgwIFh4zF+/HhSU1NZtGgRixYt4sUXXwzowLJlyxg9ejSrV69m3bp1LFiwgOHDh3PCCScwZ84c5syZUw45UznR5WdKpePjjz9myZIldO7cGYC9e/dy3HHHUaVKFfr27QtAx44d+fDDDwEYMGAA06ZNo0ePHkydOpWhQ4cWCS8rK4tevXoB8Ouvv3LgwAHefPNNACZPnhyY6fzTn/4U+L3lllsA+Pzzz5k5cyYAV155JXfccQcA3bt3Z/DgwfTv35+LL764ROnr378/06ZN46677mLatGlMmzaNb7/9lq+//ppzzjkHsLMyDRs2LFG4iqKofoDqh6KUFZ988gn9+vWjXr16ANSpUydsuwa48MILSUhIoGXLlmzduhWA+fPn069fPxISEmjQoMEhT04GDBhQbDxmz57NypUrA094du7cydq1a6lSpQrp6ek0atQIgHbt2pGZmcnpp59++ImPQ3RQo1Q6jDEMGjSIRx55pMj5xx9/PLDNYGJiInl5eQBccMEF3H333fz6668sWbKEnj17FvFXt25dli9fDkReE+/fwrC47Qyfe+45vvjiC/773//SsWNHlixZEnX6BgwYQL9+/bj44osREU499VS++uorWrVqdcgMjqIoJUP1Q1GUWFG1atXAf2NMVH6OPfbYwP+kpKTAclj/d2KMMYwdO5bevXsX8Tt37twi1/Rr29GILj9TKh29evVixowZ/PLLL4CdHd2wYUNY9ykpKXTu3Jmbb76Zvn37lvpFu2nTpgV+f/e73wHQrVs3pk6dCsCUKVMCS0F++OEHunTpwt///nfq16/Pjz/+WCSsGjVqsHv37pDXOeWUU0hMTOTBBx8MzNA0b96cbdu2BTolBw8eZNWqVaVKh6Iczah+qH4oSlnRs2dPpk+fTlZWFmD1JFy7Dkf37t154403KCgoYOvWrcydOzes27S0tMAkh/dUBqB37948++yzgff9vvvuO3JzcyNeN5KOHKnokxolIimpKWW6Q0hKakqxblq2bMlDDz3EueeeS0FBAcnJyYwbNy6iH2/20hOLxYsX89xzz/HSSy9FHbcdO3bQpk0bqlatymuvvQbA2LFjufrqq3nssceoX78+EyZMAGDkyJGsXbsWYwy9evWibdu2gXX5AH/4wx+49NJLeeuttxg7dmzI+I4cOTKwJrZKlSrMmDGD4cOHs3PnTvLy8hgxYgStWrWKOv6KUtlQ/VD9UJSyIhZ60qpVK+655x7OOussEhMTad++fdh2HY5LLrmEjz/+mJYtW9K4cWM6dOhAampqSLe33347/fv354UXXuD8888PnL/22mvJzMykQ4cOGGOoX79+YBlsOIYMGUKfPn0C79YcDUi0j8finU6dOhlvRwklPGvWrOG3v/1trKNR4aSlpbF48eLAutl4IlSZicgSY0ynGEUp7lG9KB2qH5VfP1Qvyh7Vi/LhSNKTnJwcUlJSyMrKIj09nQULFtCgQYNYRysi8agV+qRGURRFURRFUcqJvn37kp2dzYEDB7j33nsr/YAmXtFBjaJgv/egKIpSGlQ/FEWJRKT3aJSyQzcKUBRFURRFURQlrtFBjaIoiqIoiqIocY0OahRFURRFURRFiWt0UKMoiqIoiqIoSlyjGwUoEXnyySfL9ONNNWrU4NZbby2z8OKJ0aNHM2TIEKpXrw7Aeeedx7///W9q1aoV45gpSvmg+lEygjVCUZRCYqEn3bp1Y+HChWV2zcrGm2++SbNmzWjZsmWso1Im6KBGicju3bvJyMgos/BivQPIxIkTyczMZNSoURV+7dGjR3PFFVcEOizvvvtuhcdBUSqSI00/gsnPzycxMbHMwgvWCEVRComFnlS2AU1eXh5JSWXXdX/zzTfp27fvETOo0eVnSqXk1VdfJT09nXbt2nH99deTn59PSkoK99xzD23btqVr165s3bqVnTt30qRJEwoKCgDIzc2lcePGHDx4sETXS0tL4/7776dDhw60bt2ab775JhDeNddcQ3p6Ou3bt+ett94CYM+ePfTv35+WLVty0UUX0aVLF7yPr91444106tSJVq1acf/99wPw9NNPs3nzZnr06EGPHj0C19y+fTt33XVXkS+ejxo1iscffxyAxx57jM6dO9OmTZtAWIqiRGbSpEm0adOGtm3bcuWVV5KZmUnPnj1p06YNvXr1YuPGjQAMHjyY4cOH061bN04++WRmzJgBQEFBAUOHDqVFixacc845nHfeeQG7tLQ07rzzTjp06MD06dPJyMgItP3t27eTlpYG2AHPyJEjA+33+eefB2xHKiMjg0svvZQWLVpw+eWXY4wJqRGKosSWlJQUIHy7BVi0aBHdunWjbdu2pKens3v3bvbt28fVV19N69atad++PXPmzAHsxOrFF19Mnz59OPXUU7njjjsC1xo/fjzNmjUjPT2d6667jptuugmwOnXDDTfQpUsX7rjjjiJ9BIDTTjstsK18qL6Tl47g/tPChQt5++23GTlyJO3ateOHH34o9/wsb3RQo1Q61qxZw7Rp01iwYAHLly8nMTGRKVOmkJubS9euXVmxYgVnnnkmL774IqmpqbRr14558+YBMGvWLHr37k1ycnKJr1uvXj2WLl3KjTfeGBCMhx9+mJ49e/Lll18yZ84cRo4cSW5uLs888wy1a9dm9erVPPjggyxZsiQQzsMPP8zixYtZuXIl8+bNY+XKlQwfPpwTTjiBOXPmBMTNY8CAAbz++usB8+uvv86AAQOYPXs2a9eu5csvv2T58uUsWbKETz/9tDRZqihHDatWreKhhx7ik08+YcWKFYwZM4Zhw4YxaNAgVq5cyeWXX87w4cMD7rds2cL8+fOZNWsWd911FwAzZ84kMzOT1atXM3nyZD7//PMi16hbty5Lly5l4MCBYeMxfvx4UlNTWbRoEYsWLeLFF19k/fr1ACxbtozRo0ezevVq1q1bx4IFCyJqhKIosSdUuz1w4AADBgxgzJgxrFixgo8++ohjjjmGcePGISJ89dVXvPbaawwaNIh9+/YBsHz5cqZNm8ZXX33FtGnT+PHHH9m8eTMPPvgg//vf/1iwYEFgYtVj06ZNLFy4kCeffDJs/ML1nYCQ/adu3bpxwQUX8Nhjj7F8+XJOOeWU8su8CkKXnymVjo8//pglS5bQuXNnAPbu3ctxxx1HlSpV6Nu3LwAdO3bkww8/BOygYNq0afTo0YOpU6cydOjQIuFlZWXRq1cvAH799VcOHDjAm2++CcDkyZNp3bo1ABdffHEg7JkzZwIwe/Zs3n777cAgZ9++fWzcuJH58+dz8803A3aWpE2bNoHrvf7667zwwgvk5eWxZcsWVq9eXcQ+mPbt2/PLL7+wefNmtm3bRu3atWncuDFjxoxh9uzZtG/fHoCcnBzWrl3LmWeeWdqsVZQjnk8++YR+/fpRr149AOrUqcPnn38eaNNXXnllkdnRCy+8kISEBFq2bMnWrVsBmD9/Pv369SMhIYEGDRoc8uRkwIABxcZj9uzZrFy5MvCEZ+fOnaxdu5YqVaqQnp5Oo0aNAGjXrh2ZmZmcfvrph594RVHKjVDtNjU1lYYNGwb6KzVr1gSshgwbNgyAFi1a0KRJE7777jsAevXqRWpqKgAtW7Zkw4YNbN++nbPOOos6deoA0K9fv4B7z1zcUtdwfScgbP/pSEMHNUqlwxjDoEGDeOSRR4qcf/zxxxERABITE8nLywPgggsu4O677+bXX39lyZIl9OzZs4i/unXrsnz5ciDyOzVVq1Y9JGxjDG+88QbNmzePKu7r16/n8ccfZ9GiRdSuXZvBgwcHZmci0a9fP2bMmMHPP/8c6DAZY/jrX//K9ddfH9W1FUUpOV67BwLLSYrj2GOPDfxPSkoKLH/1t3VjDGPHjqV3795F/M6dO7fINf16oyhK5aWs2m1pwgmnOVCoO+H6TgDJyckh+09HGrr8TKl09OrVixkzZvDLL78A9unKhg0bwrpPSUmhc+fO3HzzzfTt27dMX9zt3bs3Y8eODXR2li1bBkD37t0DS8ZWr17NV199BcCuXbs49thjSU1NZevWrbz33nuBsGrUqBF255YBAwYwdepUZsyYQb9+/QLXfvnll8nJyQHgp59+CuSJoiih6dmzJ9OnTycrKwuw+tGtWzemTp0KwJQpUzjjjDMihtG9e3feeOMNCgoK2Lp1a8QXitPS0gLLT72nMmDb77PPPht4v++7774jNzc34nUjaYSiKJWP5s2bs2XLFhYtWgTYzQzy8vI444wzAku/vvvuOzZu3BhxcrRz587MmzePHTt2kJeXxxtvvBHWbVpaGkuXLgVg6dKlgWWtJe07wZGnOfqkRolIjRo1ynTHoRo1ahTrpmXLljz00EOce+65FBQUkJycXORF+lAMGDCAfv36BeK6ePFinnvuOV566aXDiu+9997LiBEjaNOmDQUFBTRt2pRZs2YxdOhQBg0aRMuWLWnRogWtWrUiNTWVU089lfbt29OiRQsaN25M9+7dA2ENGTKEPn36BNbN+2nVqhW7d+/mxBNPpGHDhgCce+65rFmzht/97neAHby9+uqrgcfJilLZiYV+tGrVinvuuYezzjqLxMRE2rdvz9ixY7n66qt57LHHqF+/PhMmTIgYxiWXXMLHH39My5Ytady4MR06dAgsFwnm9ttvp3///rzwwgucf/75gfPXXnstmZmZdOjQAWMM9evXDyx7DUckjVCUo51Y6ElxVKlShWnTpjFs2DD27t3LMcccw0cffcTQoUO58cYbad26NUlJSUycOLHIE5pgTjzxRO6++27S09OpU6cOLVq0CKs5l1xyCZMmTaJVq1Z06dKFZs2aAeH7Tk2aNAl73YEDB3Ldddfx9NNPM2PGjLh/r0aifdwe73Tq1Ml4O9Qo4VmzZg2//e1vYx2NSk9+fj4HDx6kWrVq/PDDD5x99tl8++23VKlSpcLjEqrMRGSJMaZThUfmCEH1onQcSfqRk5NDSkoKWVlZpKens2DBAho0aBDraB02qhdlj+pF+XAk6Uk0eJqTl5fHRRddxDXXXMNFF10Us/jEo1bokxpFKQV79uyhR48eHDx4EGMMzzzzTEwGNIqilA99+/YlOzubAwcOcO+99x4RAxpFUSovo0aN4qOPPmLfvn2ce+65XHjhhbGOUtyhgxpFKQU1atRAZ+YU5cilsn3oU1GUIxv/t2eU0qEbBSiHcLQsSTwS0LJSKhtaJysvWjZKvKF1NjbEa77roEYpQrVq1cjKyorbCn00YYwhKyuLatWqxToqigKoflRmVC+UeEP1JDbEs1bo8jOlCI0aNWLTpk1s27Yt1lFRoqBatWqBj4EpSqxR/ajcqF4o8YTqSeyIV63QQY1ShOTkZJo2bRrraCiKEoeofiiKUlaoniglRZefKYqiKIqiKIoS15TboEZEqorIeBHZICK7RWS5iPzeZ99LRL4RkT0iMkdEmgT5fVlEdonIzyJya1DYYf0qihJ/qF4oihItqheKooSiPJ/UJAE/AmcBqcDfgNdFJE1E6gEzgXuBOsBiYJrP7yjgVKAJ0AO4Q0T6AEThV1GU+EP1QlGUaFG9UBTlEMrtnRpjTC5WPDxmich6oCNQF1hljJkOICKjgO0i0sIY8w0wCBhsjNkB7BCRF4HBwPvAxcX4VRQlzlC9UBQlWlQvFEUJRYW9UyMixwPNgFVAK2CFZ+cE6geglYjUBhr67d3/Vu5/WL8hrjlERBaLyGLdPUNR4gfVC0VRokX1QlEUqKBBjYgkA1OAV9xsRwqwM8jZTqCGsyPI3rOjGL9FMMa8YIzpZIzpVL9+/cNLhKIoFYLqhaIo0aJ6oSiKR7kPakQkAZgMHABucqdzgJpBTmsCu50dQfaeXXF+FUWJY1QvFEWJFtULRVH8lOugRkQEGA8cD1xijDnorFYBbX3ujgVOwa5l3QFs8du7/6uK81tOyVAUpQJQvVAUJVpULxRFCaa8n9Q8C/wW+IMxZq/v/H+A00TkEhGpBtwHrPS9iDcJ+JuI1BaRFsB1wMQo/SqKEp+oXiiKEi2qF4qiFKE8v1PTBLgeaAf8LCI57rjcGLMNuAR4GNgBdAEG+rzfj305bwMwD3jMGPM+QBR+FUWJM1QvFEWJFtULRVFCIcaYWMehQujUqZNZvHhxrKOhKBWCiCwxxnSKdTziFdUL5WhC9eLwUL1QjhYqu1ZU2JbOiqIoiqIoiqIo5YEOahRFURRFURRFiWt0UKMoiqIoiqIoSlyjgxpFURRFURRFUeIaHdQoiqIoiqIoihLX6KBGURRFURRFUZS4Rgc1iqIoiqIoiqLENTqoURRFURRFURQlrtFBjaIoiqIoiqIocY0OahRFURRFURRFiWt0UKMoiqIoiqIoSlyjgxpFURRFURRFUeIaHdQoiqIoiqIoihLX6KBGURRFURRFUZS4Rgc1iqIoiqIoiqLENTqoURRFURRFURQlrtFBjaIoiqIoiqIocY0OahRFURRFURRFiWt0UKMoiqIoiqIoSlyjgxpFURRFURRFUeIaHdQoiqIoiqIoihLX6KBGURRFURRFUZS4Rgc1iqIoiqIoiqLENTqoURRFURRFURQlrtFBjaIoiqIoiqIocY0OahTlMBg1ahQiUuwxatSoWEdVURRFURTliCUp1hFQlHhm1KhRRQYsGRkZAMydOzcm8VEURVEURTka0Sc1iqIoiqIoiqLENTqoURRFURRFURQlrtFBjaIoiqIoiqIocY0OahRFURRFURRFiWt0UKMoiqIoiqIoSlyjgxpFURRFURRFUeKaYgc1IpIgIu1F5HwR6Skix1VExBSlMlNQUMCmTZuYP38+u3fvBmDEiBGsWLGCvLy8GMdOURRFUZR4Rr+DV3LCfqdGRE4B7gTOBtYC24BqQDMR2QM8D7xijCmoiIgqSlnx5JNPBgYi4SgoKCAnJ4fs7Gyys7PZv38/nTt35pdffmHq1KlkZ2dTUFBAYmIigwcPpnHjxixfvhyAjRs38sADD5Rb/GvUqMGtt95abuEriqIoihJb9Dt4JSfSxzcfAp4FrjfGGL+Fe1pzGXAl8Er5RU9Ryp7du3cHxOHbb79l06ZN/Pzzz2zZsoVmzZpxwQUX8PLLLzNp0iSSkpI4/vjjadKkCRkZGeTk5FC7dm0aNGhAw4YNqVOnDgkJ9oFnRkYGN998c+B/eaGCpiiKoiiKUpSwgxpjzJ8i2P0CjC6XGClKGWCMISsri3r16rFjxw6ef/55MjMzyczMZPHixaSmptK+fXvGjBlDdnY2DRo0oEGDBtSsWROACy+8kL59+1K3bl0SExMD4aakpHDOOefEKlmKoiiKoihKCCI9qQFARPoB7xtjdovIvUB74CFjzNJyj52ihMEYw7Zt28jMzCQpKYkOHTrw7rvvMnbs2MDgJT8/n71795Kfn8/s2bNJS0ujW7duHHPMMTRu3BiAcePGISKHhF+nTp2KTpKiKIqiKIpSSood1AD3GmOmi8jpQC/gMeyytC7lGjPlqMYYw/bt2wMDlMzMTH7/+99z2mmncdlll/Hmm2+yd+9eEhISuOqqq5gwYQKpqal07dqVgQMHkpaWRtOmTUlISKBevXp88skngbAfeOAB6tWrBxByQKMoiqIoihILCgoK2LVrF9nZ2dSuXZvU1FTmzZvH7t27qVGjRqyjV6mJZlCT737PB14wxvxXRB4qxzgpRxGbN29m4cKFRQYv//nPf8jNzeW4444jISGBRo0akZaWRocOHTjttNO4/vrrGTJkCGlpaZx44okkJycD0L17d7p37x7jFCmKooRm1KhRUW0icv/99+uORkpYtB5VToI3IcrNzWXPnj3s3buXffv2BVaJ/Pjjj6xevZp9+/YFjiuvvJL9+/czZswY9u3bFwjjj3/8I+3bt+fNN98kMzOT1NRU3YgoAtEMan4SkeeBc4BHRaQq+n0bpRiMMezYsYPExERSU1OZPn06n376aWDg0qxZM9544w3mz5/P7bffTlpaWmDgcuDAAWrVqsW6deto1KhRYNDicdZZZ8UoVYqiKKVHdzNSygKtR+VPQUEBO3fuxBhDnTp1WLduHcuWLSM7O5sdO3aQm5vL/fffz7Zt2xg8eDA7duxg7dq1FBQUcPvtt9OtWzeGDx/OypUrSUxMJCUlhTPPPJMrr7ySL7/8knXr1lGvXj1q1KhBSkoKZ5xxBgkJCdSvX5+UlJQiR1JSkm5EFCXRDGr6A32Ax40x2SLSEBhZvtFS4oEdO3YEBinnnXceSUlJXHzxxaxfv57MzEx2797NU089xYgRI1i7di3Z2dm0a9eOCy+8kFatWgHQv39/+vfvHzL8pk2bVmRyFEVRFEU5Qti/fz9ZWVmBgcjevXs5++yzycrK4tlnnw2cz87O5u6776Zjx46cf/75LFiwgF27dmGM4YorrmDy5Ml88MEHPPLII9SuXZtatWpRp04djDFUr16dLl26UKtWLebMmUN6ejrNmzcH4MEHHyQ5OZljjjmmyFL39PR00tPTQ8a5U6dOFZI3RyrFDmqMMXtEZA7QWEQ6uNPbyzdaSmVg165drF+/PjBISUpK4qabbuLDDz+kX79+7Ny5E4ATTjiBzz//nJNOOonOnTvzhz/8IfDkxXsh/+67745lUhRFURRFiSPy8/PJzc2lZs2a7N69my+++KLIQGTgwIE0btyYW265hW+//TZw/qKLLuIf//gH//znPwNPtJKSkmjUqBHr16/nwIEDzJ8/PzBAad68OSkpKQDcfvvtjBgxgtq1a1O7du3A+7c33ngjN9544yFxPPbYY7nvvvsAO9HbrVu3gF1qamo555ASTDS7nz0IDAZ+ALzv1RigZ/lFS6kIjDGICLNmzWLdunWBwctFL+5FtQAAGH5JREFUF13EVVddxW233cZLL71Ew4YNSUtLo0sXuzdEu3btmD59OmlpaZx00klUrVo1EObf/va3WCUnJkyYMIFXXjn0U03Bj4cHDRrE1VdfXUGxUhRFUZTYYoxh7969gcHGjh07aNu2LTVq1GD8+PFs3rw5YNezZ0+uuOIKnnjiCcaOHcuOHTvYtWsXDRo0YMuWLWRmZnLJJZdQq1atwGDknHPOoXHjxjRq1Ig6deoE7Fq2bAnAsGHDuPbaa6lVqxbVq1cPPC1p2LAh77//fsg49+jRo8LyRyl7ol1+doox5kB5R+ZIojK8yLd79262bdvGySefzPr164tsd5yZmcm8efNo3bo19913H1WrViUtLY0WLVoEln3961//YuzYsVSrVq1IuPXr19dvtTiuvvpqHawoh01l0AtFUZRg8vLySEhIICEhgWXLlhVZztW6dWu6du3KlClTmDVrVuB87dq1ee+991i4cCGnn356IKzk5GQWLlxIp06deO+998jNzQ0MULxdvXr27EmTJk0CA5TatWsD0Lp168DqkGBuu+22kOf10wxHH9EMar4GagG/lDRwEbkJ+5SnNfCaMWawO58GrAdyfc4fNcY86OyrYreNvhTYA/zLGPOkL9xewDjgJOALYLAxZkNJ41eeVMSLfDk5OYEBSoMGDejUqRMvvPBC4EOTv/76K40aNeLHH38kPz+fzZs306xZM84991zS0tJo0qQJAEuXhv7kkCcmilLeHM1aAfrir6KUhKNdL0qCMYY9e/YEnojUrVuXhg0b8umnn7J8+fIiA5H77ruPL774giFDhgTO7969m1WrVtGyZUv69OnD/v37AwOOG264ga5du5KcnEzDhg1p2bIltWvXpmHDhgC0b9+eVatWBdxXq1Yt8LRkxowZIePbvn172rdvX2H5U5nRlSAlJ5pBzSPAMhH5GtjvnTTGXBCF383AQ0Bv4JgQ9rWMMXkhzo8CTgWaAA2AOSKy2hjzvojUA2YC1wLvAA8C04CuUcQnrjhw4ABr164t8nTlhhtu4JRTTqFjx46Bwchxxx3H8OHD6dSpE82aNePaa68NvNPiDVx+85vfMHXq1FgmR1EioVqhVDjGGAoKCsjLy+PgwYMkJydTtWrVQGcuLy+PvLw8kpOTadq0KdnZ2Xz77bcB93l5eXTr1o3q1asza9Ys9u7dG/DTunVr2rVrx6effsqyZcsCfmrUqMFf/vIXNm7cyKZNm2jUqFGssyEeUb3wkZOTw+bNmxk4cGBg8PLWW2/RoEEDUlNTi2wz/I9//IO//vWvzJkzhw8++KDIci6ARo0aMWzYsMC52rVrB1ZvbNmyhYSEQze/DbfhT/Xq1QNLwZSSoytBSk40g5pXgEeBr4CCkgRujJkJICKdgJIo9yDsDMkOYIeIvIidlXkfuBhYZYyZ7sIeBWwXkRbGmG9KEr9YU1BQQEJCAqtWrWLevHmBgcvOnTv54IMPWLduHaeddhr16tULfExyz549AIwbN46aNWvSpEkTjj322ECYGRkZ5brdn6KUB6oVlYuCggL27dsX6LgfPHiQunXrkpyczPr169m3b1+g816vXj0aN27MunXryMzMDLhPTEykT58+ZGdnM2vWrID7vLw8zj//fBo3bsyECRPYsmVL4Dpt2rShX79+fPLJJ8ycOTPgPj8/nwkTJpCdnc2f//znIoOKu+66i549ezJs2DA+//zzgN3pp5/O888/z/Tp0xkxYkTAfV5eHhs2bKBmzZokJRW9BY4ZM4bhw4czdOjQIpNAJ554Itdddx3fffcdr732WmA5TmJiItdddx1169Zl3LhxHDx4MGDXuXNnunTpwsKFC1mzZk3gfM2aNdm+fTtZWVn88ssv5OXl6XcnSki86cUTo58gZ2fO4QQRkc2bN7Nr1y42btxItWrVqFu3LmPGjKFatWoMHDiQKlWqUK1aNapVq8aBAwcC9a13795FwvHXw59++inw/5133iElNYXbRoRe5qUolYVoBjV7jDFPl9P1N4iIAT4ERhpjtotIbaAhsMLnbgVwofvfym9njMkVkR/c+SLCIyJDgCEAJ510UjklIXp+/PFHBgwYEBi8/P73v2fixIksWLCAl19+OfB0pWnTphhjOPXUU9m9e3dgVw4/XbtW+skjRSlLylUroHLpRUFBARs3buSee+4JdMYvvfRSunXrxjPPPMOiRYsCHfRmzZrxwAMPMH/+fB588MEiHf6ZM2fSoEEDOnfuTE5OTsDPbbfdxk033cQtt9zChAkTAu5PPfVUvv76az777LNDJkeWLFlChw4dOPPMM9m0aVPg/IgRI3jqqaeYNGkSTz31FElJSSQlJXH88cfTp08ftm/fzkMPPURSUhLJyckkJSXRrl07GjduzP/+97/AzopJSUmBnYb2799PTk4OycnJgS1RjTEkJSXRvHnzgPvk5GSOP/54AM4++2xatWoVOO/tvNilSxeeffbZItdPSUkhISGBL7/8MnDOizPYSaPRo0fz5JNPctZZZ5GUlETVqlXJyMhgyJAhIcss3GRSpEmmTz/9tFg3h8tRuoSxUulFzs4cag2vVfrUFEP1D6tTneoMeGfAIXa1KJvrZj+dXSbhKEp5Es2g5jMReQR4m6LLz0K/iBEd24HOwHKgLnYN6xTso2SvB+9/I2wnUMP9TwG2BYXntw9gjHkBeAGg0/+3d/fBmtZlHcC/F7sksLAhLzmZabIiDJhrM2v0bk6mriNpUY2CZlY49s40vdCIBGTWVDrGaA2Y76mVExRU6GATNmppa6PYJlqamxXRgrwsGIR09cd5DvNw9uw+9+7ynHPuPZ/PzDP73L/fc59znf3jmud7v/zubdt66fxS8zya8vnPfz533HFHvvCFL+TEE0/Mli1bHvJk2LPPPvvBz95666257LLLDur3OJrCYWZFekWytvrFrl27snv37lx11VUPHuG/7bbbcv3112fHjh255ZZbHhzfs2dPLr300uzevfvBm3qPPvroHHHEEQ8erV38gr+4z0033ZTXvO41Of/88/Pc5z73wS/8i2d9t23blp07dy77hX/nzp3ZsGHDg3OLl6MsvS9o0ROe8ITcdNPyB7qvuOKKZce3b9+e7du37zV+7LHH5tWvfvWy+zzvec9bdvyxj33sPr90PvWpT112fPEG42OOOeYhZ8JZ89ZsvwDmb0ioWbxja/rUwCEt6dzddyfZMdm8ZXLT381VdVySxW8Jm5PcO/V+8aLQuyfb06bnD9o8j6ZsvH5jTnrMScseSXk4OZrC4WSt9opkvv3iyOuPzKO/9tF5+bUv32vuGXnGsvscn+Nzak5ddu5pedpeY3dcfkfOOOOMZa9537Rp0z6vhd+8eel/KawNa7lfAPO39x1fS3T305d5PdzPqFk8ynHE5FrXm5NsnZrfmmTn5P3O6bmq2pRky9Q8cPjSK4Ch9AtYR/YZaqrqRVW1v/ktVfVt+5qffGZjVR2VZEOSDVV11GTsrKo6raqOqKoTk1ye5IbuXjwt/PYkF1XVI6vq9CTnJ3nrZO7qJE+qqnMmP/viJDe68RfGS68AhtIvgOXs70zNiVlYyvnNVfWTVfWDVfVDVXVZVX0gyW8muWXGz78oyf8kuTDJiybvL0pyShZWG9mThefg3JfkhVP7/UqSzybZleQDSX6ru9+bJN29O8k5SX4tye1JzkryguF/MrAG6RXAUPoFsJd93lPT3b9TVa/Pwr0z35rkyVloHJ9K8uLu/rdZP7y7L8nCuvDLefd+9rsvyY9MXsvNvz/J6bN+/2q67jeuy/t+8317jV9wwgUP2X7WLz4r2y/c+4ZYWE/Wc69I9As4EOu9XwDL2+9CAd39QBaWRLx+Zco5fGy/cLsvH8Ag+sX64AnhPBwcBIHlDVn9DAA4RJ4QzsPBQRBY3szVzwAAANYyoQYAABi1maGmqh5VVW+qqusm22dU1Y/OvzQAAIDZhpypeWuS9yV59GT7M0ku2OenAQAAVtCQUHNSd/9xkv9Lku7+cpIH5loVAADAQENCzT2TJ/N2klTVNyW5c/+7AAAArIwhSzr/XJJrkmypqg8lOTnJ98+1KgAAgIFmhpru/oeqelqS05JUkk939/1zrwwAAGCAmaGmqjYkeU6Sr5t8/plVle5+7ZxrAwAAmGnI5WfXJrk3ySczWSwAAABgrRgSah7T3U+eeyUAAAAHYcjqZ9dV1TPnXgkAAMBBGHKm5u+SXF1VRyS5PwuLBXR3b55rZQAAAAMMCTWvTfLNST7Z3T3negAAAA7IkMvPvpDkHwUaAABgLRpypuZzSW6oquuS3Lc4aElnAABgLRgSav518vqKyQsAAGDNmBlquvvSlSgEAADgYOwz1FTV67r7gqq6Nsle99N09/fMtTIAAIAB9nem5h2Tf397JQoBAAA4GPsMNd39scnbp3T370zPVdXPJvnAPAsDAAAYYsiSzi9ZZuyHH+Y6AAAADsr+7ql5YZJzkzy+qq6ZmjouyRfnXRgAAMAQ+7un5sNJbk5yUpLXTI3vSXLjPIsCAAAYan/31OxKsivJN69cOQAAAAdmyD01AAAAa5ZQAwAAjNqgUFNVR1fVafMuBgAA4EDNDDVVdXaSjyd572T7KUtWQwMAAFg1Q87UXJLkG5PckSTd/fEkj59jTQAAAIMNCTX3d/edS8Z6HsUAAAAcqP09p2bRzqo6N8mGqjo1yc9k4Rk2AAAAq27ImZqfTnJmkvuSvDvJXUkumGdRAAAAQ808U9PdX0ryiiSvqKoNSTZ1971zrwwAAGCAIaufvauqNlfVpiSfTPJPVfUL8y8NAABgtiGXn53R3XcleX6S67Kw8tmL51oVAADAQENCzZFVdWQWQs013X1/rH4GAACsEUNCzRVJPp9kU5K/qarHZWGxAAAAgFU3ZKGAy5NcPjW0q6qePr+SAAAAhpsZaqrqEUnOSfJ1Sz5/2ZxqAgAAGGzIwzf/LMmdST6WhWfVAAAArBlDQs1juvvZc68EAADgIAxZKODDVfX1c68EAADgIAwJNd+W5GNV9emqurGqPllVNw754VX1U1W1o6ruq6q3Lpn7rqq6qaq+VFV/PVlVbXHuEVX15qq6q6r+q6p+bui+wPjoFcBQ+gWwnCGhZnuSU5M8M8nZSZ47+XeI/0zyqiRvnh6sqpOSXJXklUlOSLIjyR9NfeSSye98XJKnJ/nFqnr2wH2B8dErgKH0C2AvM0NNd+9KcnwWgszZSY6fjM3U3Vd1958muW3J1Pcl2dnd7+nue7PQaLZW1emT+Zck+dXuvr27P5XkjUl+eOC+wMjoFcBQ+gWwnJmhpqp+Nsk7k3zV5PUHVfXTh/h7z0zyicWN7r4nyWeTnFlVj0zy1dPzk/dnztp3mdpfNjlFvWP37t2HWDKwClakVyT6BRwG9AtYx4ZcfvajSc7q7ou7++Ik35Tk/EP8vcdmYZnoaXcmOW4ylyXzi3Oz9n2I7r6yu7d197aTTz75EEsGVsGK9IpEv4DDgH4B69iQUFNJHpjafmAydijuTrJ5ydjmJHsmc1kyvzg3a1/g8KJXAEPpF7CODQk1b0nykaq6pKouTfJ3Sd50iL93Z5KtixtVtSnJlixcz3p7kpun5yfvd87a9xBrAtYevQIYSr+AdWzIQgGvTfLSJF9McmuSl3b364b88KraWFVHJdmQZENVHVVVG5NcneRJVXXOZP7iJDd2902TXd+e5KKqeuTkJr3zk7x1MjdrX2Bk9ApgKP0CWM6QMzWLasm/Q1yU5H+SXJjkRZP3F3X37iTnJPm1JLcnOSvJC6b2+5Us3KC3K8kHkvxWd783SQbsC4yPXgEMpV8Ae9k46wNVdXGSH0jyJ1kING+pqvd096tm7dvdl2RhWcTl5t6fZNmlErv7viQ/Mnkd0L7A+OgVwFD6BbCcmaEmyXlJtk7WbU9V/UaSj2fhwVcAAACrasjlZ/+Z5Kip7Uck+Y/5lAMAAHBghpypuTPJzqq6Pkkn+e4kH62qy5Oku39mjvUBAADs15BQc/XkteiG+ZQCAABw4GaGmu5+2+L7qnpkkq/t7hvnWhUAAMBAM++pqaobqmpzVZ2Q5B+SvLGqXjv/0gAAAGYbslDAV3b3XUm+L8nbu/usJM+Yb1kAAADDDAk1G6vqq5P8YJI/n3M9AAAAB2RIqLksyfuSfLa7/76qTknyz/MtCwAAYJghCwW8J8l7prY/l+SceRYFAAAw1JCFAp5YVX9VVf842X5yVV00/9IAAABmG3L52RuT/HKS+5NkspzzC+ZZFAAAwFBDQs0x3f3RJWNfnkcxAAAAB2pIqLm1qrYk6SSpqu9PcvNcqwIAABho5kIBSX4yyZVJTq+q/0jyr0nOm2tVAAAAAw1Z/exzSZ5RVZuycGbnS1m4p2bXnGsDAACYaZ+Xn1XV5qr65ap6fVV9dxbCzEuS/EsWHsQJAACw6vZ3puYdSW5P8rdJzk/yiiSV5Hu7++MrUBsAAMBM+ws1p3T31ydJVf1+FhYHeGx337silQEAAAywv9XP7l98090PJPl3gQYAAFhr9nemZmtV3TV5X0mOnmxXku7uzXOvDgAAYIZ9hpru3rCShQAAAByMIQ/fBAAAWLOEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNRWNdRU1Q1VdW9V3T15fXpq7tyq2lVV91TVn1bVCVNzJ1TV1ZO5XVV17ur8BcBK0CuAofQLWJ/Wwpman+ruYyev05Kkqs5MckWSFyd5VJIvJfndqX3ekOR/J3PnJfm9yT7A4UuvAIbSL2Cd2bjaBezDeUmu7e6/SZKqemWST1XVcUn+L8k5SZ7U3Xcn+WBVXZOFJnXhahUMrAq9AhhKv4DD2Fo4U/PrVXVrVX2oqr5zMnZmkk8sfqC7P5uFoydPnLy+3N2fmfoZn5js8xBV9bKq2lFVO3bv3j23PwBYEXPrFYl+AYcZ/QLWmdUONb+U5JQkX5PkyiTXVtWWJMcmuXPJZ+9Mctxk7q59zD1Ed1/Z3du6e9vJJ5/8cNcOrJy59opEv4DDiH4B69CqXn7W3R+Z2nxbVb0wyXOS3J1k85KPb06yJwuniPc1BxyG9ApgKP0C1qfVPlOzVCepJDuTbF0crKpTkjwiyWcmr41VderUflsn+wDrg14BDKVfwDqwaqGmqo6vqmdV1VFVtbGqzkvyHUnem+SdSc6uqm+vqk1JLktyVXfv6e57klyV5LKq2lRV35rkeUnesVp/CzA/egUwlH4B69dqXn52ZJJXJTk9yQNJbkry/MWb9Krq5VloQCcmeX+Sl07t+xNJ3pzkv5PcluTHu9vRFDg86RXAUPoFrFOrFmq6e3eSp+5n/l1J3rWPuS8mef6cSgPWEL0CGEq/gPVrrd1TAwAAcECEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNSEGgAAYNRGG2qq6oSqurqq7qmqXVV17mrXBKw9egUwlH4B47VxtQs4BG9I8r9JHpXkKUn+oqo+0d07V7csYI3RK4Ch9AsYqVGeqamqTUnOSfLK7r67uz+Y5JokL17dyoC1RK8AhtIvYNyqu1e7hgNWVd+Q5EPdfczU2M8neVp3nz019rIkL5tsnpbk0ytaKKyex3X3yatdxGob2ism4/oF65V+Ef0CBljTvWKsl58dm+SuJWN3JjlueqC7r0xy5UoVBaw5g3pFol8A+gWM2SgvP0tyd5LNS8Y2J9mzCrUAa5deAQylX8CIjTXUfCbJxqo6dWpsaxI38gHT9ApgKP0CRmyU99QkSVX9YZJO8mNZWKHkL5N8ixVKgGl6BTCUfgHjNdYzNUnyE0mOTvLfSd6d5Mc1HWAZegUwlH4BIzXaMzUAAADJuM/UAAAACDUAAMC4CTUAAMCoCTUAAMCoCTUAAMCoCTUAAMCoCTUAAMCoCTUAAMCo/T/OYPJxnWMqMwAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["def plot_ecdf(data, label=''):\n","    fig = plt.figure()\n","    ax = fig.add_subplot()\n","    sns.ecdfplot(data = data, legend = data)\n","    plt.xlabel(label)\n","    plt.ylabel(\"ECDF\")\n","    plt.show()\n","\n","_d = {'congruent': con.time,\n","      'incongruent': inc.time,\n","      'env.+positive': eco_pos.time,\n","      'env.+negative': eco_neg.time}\n","\n","plot_ecdf(_d, 'Response time (ms)')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"rz-gqGFcmWcM","outputId":"d42e241a-9234-4480-d2e4-408486ca7fab","executionInfo":{"status":"ok","timestamp":1675627933850,"user_tz":-60,"elapsed":7,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#### Contrast analysis"],"metadata":{"id":"pdCiPurwecK7"}},{"cell_type":"markdown","source":["For the AFP, participants will be quicker to respond when the primes are positive or associated with the environment and the target is also positive rather than negative. This can be reflected in contrast codes (-1,-1,2) X (-1, 1) vs. (-1,1,0) X (-1,1). Then, the authors could look at the simple effects by looking at the effect of the prime when the targets are positive (-1,-1, 2) X (1, 0), and when the targets are negative (-1, -1, 2) X (0, 1)."],"metadata":{"id":"1CpuUTBGfSE2"}},{"cell_type":"code","source":["# https://personal.utdallas.edu/~herve/abdi-contrasts2010-pretty.pdf\n","# https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLSResults.t_test.html\n","# https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLSResults.f_test.html\n","d = data.copy()\n","invalid = d[d.time > 4000].id.unique()\n","d = d[d.id.apply(lambda x: not x in invalid)]\n","print(f'{len(invalid)} - dropout')\n","d['p_env'] = d['test'].apply(lambda x: x in eco)\n","d['p_pos'] = d['test'] == pos\n","d['p_neg'] = d['test'] == neg\n","d['t_pos'] = d['ctrl'] == pos\n","d['t_neg'] = d['ctrl'] == neg\n","d['t_neut'] = d['ctrl'] == neut\n","d = d.groupby(by=['id', 'p_env', 'p_pos', 'p_neg', 't_pos', 't_neg', 't_neut'])\\\n","          .aggregate(np.mean).reset_index()\n","d = d[d['t_neut'] == False][['id', 'p_env', 'p_pos', 'p_neg', 't_pos', 't_neg', 'time']]\n","\n","f = 'time ~ p_env + p_pos + p_neg + t_pos + t_neg'\n","# use robust linear regression https://www.statsmodels.org/stable/rlm.html\n","m = smf.rlm(f, d, M=sm.robust.norms.HuberT()).fit()\n","f_test = m.f_test([[0, 1, 1, -2, 1, -1], \n","                   [0, 1, -1, 0, 1, -1]])\n","print(f_test)\n","print(f'Cohen’s d* = {cohen_d(f_test)}')\n","print('* Cohen’s d derived from F statistic (Fritz et al., 2012)')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iwhyyFc1kt5_","executionInfo":{"status":"ok","timestamp":1675627943013,"user_tz":-60,"elapsed":244,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"d6100497-77d1-4268-c58f-9b82993be8c2"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["69 - dropout\n","<F test: F=array([[4.33178689]]), p=0.01350865921162594, df_denom=680, df_num=2>\n","Cohen’s d* = 0.22574808670150187\n","* Cohen’s d derived from F statistic (Fritz et al., 2012)\n"]}]},{"cell_type":"code","source":["m.t_test([[0, 1, 1, -2, 1, -1],\n","          [0, 1, -1, 0, 1, -1],\n","          [0, 1, 1, -2, 1, 0],\n","          [0, 1, 1, -2, 0, 1]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JA0vHDZju7Lk","executionInfo":{"status":"ok","timestamp":1675627981681,"user_tz":-60,"elapsed":354,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"ad2b1e5d-8c15-4028-db00-a9d8768a0db6"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","                             Test for Constraints                             \n","==============================================================================\n","                 coef    std err          z      P>|z|      [0.025      0.975]\n","------------------------------------------------------------------------------\n","c0          -139.3363     59.270     -2.351      0.019    -255.503     -23.170\n","c1           -93.4854     39.960     -2.339      0.019    -171.805     -15.166\n","c2           353.6095     55.188      6.407      0.000     245.443     461.776\n","c3           486.9039     55.188      8.823      0.000     378.737     595.071\n","=============================================================================="]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["m.f_test([0, 1, 1, -2, 1, -1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3-k2RAgTGWzB","executionInfo":{"status":"ok","timestamp":1675627983701,"user_tz":-60,"elapsed":261,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"5a07c425-540e-4ca8-c0c4-621ef3fbc1d5"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","<F test: F=array([[5.52666604]]), p=0.01901337400107113, df_denom=680, df_num=1>"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["m.f_test([0, 1, -1, 0, 1, -1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SM-rqNB9urxL","executionInfo":{"status":"ok","timestamp":1675627985758,"user_tz":-60,"elapsed":235,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"2ff069a9-77c0-43ef-e3a9-16f184e5bf90"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","<F test: F=array([[5.47325194]]), p=0.019598162834424348, df_denom=680, df_num=1>"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["# https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html\n","# http://jpktd.blogspot.com/2013/04/multiple-testing-p-value-corrections-in.html\n","\n","# holm - step-down method that applies the Bonferroni correction (or Holm–Bonferroni method, see [Holm, 1979])\n","# hommel - controls the FWER and it is valid under independence and under positive dependence of the p-value, see [Hommel, 1988]\n","# fdr_tsbh - controls the FDR under independence and is robust to some form of positive dependence of the p-values, see [Benjamini & Hochberg, 1995]\n","# fdr_by - controls the FDR also for other forms of dependence including the case when the p-values are negatively correlated, see [Benjamini & Yekutiely, 2001]\n","print(multipletests([0.019, 0.019], alpha=0.05, method='holm', is_sorted=False, returnsorted=False)[:2])\n","print(multipletests([0.019, 0.019], alpha=0.05, method='hommel', is_sorted=False, returnsorted=False)[:2])\n","print(multipletests([0.019, 0.019], alpha=0.05, method='fdr_tsbh', is_sorted=False, returnsorted=False)[:2])\n","print(multipletests([0.019, 0.019], alpha=0.05, method='fdr_by', is_sorted=False, returnsorted=False)[:2])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RNDOMzjwef2o","executionInfo":{"status":"ok","timestamp":1675627988053,"user_tz":-60,"elapsed":246,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"2aaaa2f9-7992-45ab-ec12-0928e05c72ba"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["(array([ True,  True]), array([0.038, 0.038]))\n","(array([ True,  True]), array([0.019, 0.019]))\n","(array([ True,  True]), array([0.019, 0.019]))\n","(array([ True,  True]), array([0.0285, 0.0285]))\n"]}]},{"cell_type":"markdown","source":["### AMP"],"metadata":{"id":"5lcbJdsfblm8"}},{"cell_type":"code","source":["d = data.copy()\n","a = d[(d.test == neg) & (d.ctrl == neg) & (d.valence > 3)].id.unique()\n","b = d[(d.test == pos) & (d.ctrl == pos) & (d.valence < 3)].id.unique()\n","invalid = {*a, *b}\n","print(f'{len(invalid)} - dropout')\n","d = d[d.id.apply(lambda x: not x in invalid)]\n","pos_neut = d[(d.test == pos) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #positive-neutral\n","neg_neut = d[(d.test == neg) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #negative-neutral\n","eco_neut = d[d.test.apply(lambda x: x in eco) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #eco-neutral\n","rec_neut = d[(d.test == rec) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sav_neut = d[(d.test == sav) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sho_neut = d[(d.test == sho) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","tra_neut = d[(d.test == tra) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","print(f'n={len(pos_neut)}')\n","print(f'eco valence median: {eco_neut.valence.median()}')\n","print(f'pos vs neg mean diff {pos_neut.valence.mean()-neg_neut.valence.mean():.2f} pts')\n","print(f'eco vs neg mean diff {eco_neut.valence.mean()-neg_neut.valence.mean():.2f} pts') \n","print(f'eco vs pos mean diff {eco_neut.valence.mean()-pos_neut.valence.mean():.2f} pts')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4IStjopgbYgb","outputId":"992c10cb-9bf0-4d10-93a4-514d12ef934a","executionInfo":{"status":"ok","timestamp":1675628010136,"user_tz":-60,"elapsed":222,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["43 - dropout\n","n=140\n","eco valence median: 3.125\n","pos vs neg mean diff 0.38 pts\n","eco vs neg mean diff 0.29 pts\n","eco vs pos mean diff -0.09 pts\n"]}]},{"cell_type":"code","source":["# Normality tests\n","# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html\n","k, p = stats.normaltest(eco_neut.valence)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(pos_neut.valence)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(neg_neut.valence)\n","print(f\"p = {p:g} ~ {p:.3f}\")"],"metadata":{"id":"vfXgQ6mFLu0Z","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628013179,"user_tz":-60,"elapsed":212,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"9274a069-376c-4a75-ea0b-45a0470c7218"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["p = 0.922197 ~ 0.922\n","p = 0.440298 ~ 0.440\n","p = 0.149909 ~ 0.150\n"]}]},{"cell_type":"code","source":["cor_p = pearsonr(eco_neut.valence, eco_neut.time)\n","cor_s = spearmanr(eco_neut.valence, eco_neut.time)\n","ci = spearmanCI(cor_s[0], len(eco_neut.valence))\n","print(f'Correlation between valence and responce time (env.):')\n","print(f'Pearson r({len(eco_neut.valence)-2})={cor_p[0]:.2f} p={cor_p[1]:.3f}')\n","print(f'Spearman r({len(eco_neut.valence)-2})={cor_s[0]:.2f}, 95%CI [{ci[0]:.2f}–{ci[1]:.2f}], p={cor_s[1]:.3f}')\n","\n","cor_p = pearsonr(pos_neut.valence, pos_neut.time)\n","cor_s = spearmanr(pos_neut.valence, pos_neut.time)\n","ci = spearmanCI(cor_s[0], len(pos_neut.valence))\n","print(f'Correlation between valence and responce time (pos):')\n","print(f'Pearson r({len(pos_neut.valence)-2})={cor_p[0]:.2f} p={cor_p[1]:.3f}')\n","print(f'Spearman r({len(pos_neut.valence)-2})={cor_s[0]:.2f}, 95%CI [{ci[0]:.2f}–{ci[1]:.2f}], p={cor_s[1]:.3f}')\n","\n","cor_p = pearsonr(neg_neut.valence, neg_neut.time)\n","cor_s = spearmanr(neg_neut.valence, neg_neut.time)\n","ci = spearmanCI(cor_s[0], len(neg_neut.valence))\n","print(f'Correlation between valence and responce time (neg):')\n","print(f'Pearson r({len(neg_neut.valence)-2})={cor_p[0]:.2f} p={cor_p[1]:.3f}')\n","print(f'Spearman r({len(neg_neut.valence)-2})={cor_s[0]:.2f}, 95%CI [{ci[0]:.2f}–{ci[1]:.2f}], p={cor_s[1]:.3f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HaMK7yamHsiO","outputId":"a1d86c1d-6fa6-4ed6-d2a5-e168bc9740b8","executionInfo":{"status":"ok","timestamp":1675628015063,"user_tz":-60,"elapsed":240,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Correlation between valence and responce time (env.):\n","Pearson r(138)=0.08 p=0.337\n","Spearman r(138)=0.05, 95%CI [-0.12–0.21], p=0.555\n","Correlation between valence and responce time (pos):\n","Pearson r(138)=-0.05 p=0.582\n","Spearman r(138)=-0.05, 95%CI [-0.22–0.12], p=0.549\n","Correlation between valence and responce time (neg):\n","Pearson r(138)=0.15 p=0.081\n","Spearman r(138)=0.14, 95%CI [-0.03–0.30], p=0.104\n"]}]},{"cell_type":"code","source":["print(f'AMP, affective valence analysis with the paired Wilcoxon signed-rank test')\n","t1, p1 = wilcoxon(pos_neut.valence, neg_neut.valence)\n","t2, p2 = wilcoxon(pos_neut.valence, neg_neut.valence, alternative='greater')\n","r1 = cohen_r(p1, len(pos_neut.valence))\n","r2 = cohen_r(p2, len(pos_neut.valence))\n","m1 = bootstrap(pos_neut.valence)\n","m2 = bootstrap(neg_neut.valence)\n","print(f'''\n","positive vs negative\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-greater: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_neut.valence, pos_neut.valence)\n","t2, p2 = wilcoxon(eco_neut.valence, pos_neut.valence, alternative='less')\n","r1 = cohen_r(p1, len(eco_neut.valence))\n","r2 = cohen_r(p2, len(eco_neut.valence))\n","m1 = bootstrap(eco_neut.valence)\n","m2 = bootstrap(pos_neut.valence)\n","print(f'''\n","env. vs positive\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_neut.valence, neg_neut.valence)\n","t2, p2 = wilcoxon(eco_neut.valence, neg_neut.valence, alternative='greater')\n","r1 = cohen_r(p1, len(eco_neut.valence))\n","r2 = cohen_r(p2, len(eco_neut.valence))\n","m1 = bootstrap(eco_neut.valence)\n","m2 = bootstrap(neg_neut.valence)\n","print(f'''\n","env. vs negative\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-greater: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","print('Note. Null hypothesis H0: the median of the differences is located at zero, μ = 0.')\n","print('One-sided alternative (less) hypothesis H1: the median of the differences is located on the left, μ < 0.')\n","print('One-sided alternative (greater) hypothesis H2: the median of the differences is located on the right, μ > 0.')\n","print('† Bootstrapped 95% confidence intervals for the median from 10000 resamples are reported.')\n","print('‡ Cohen’s r (Fritz et al., 2012; Cohen, 1988) for Wilcoxon signed-rank test is reported.')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_H183gPnbZDy","outputId":"651351d7-1ac6-46c7-e67f-8e50a0072c5b","executionInfo":{"status":"ok","timestamp":1675628021655,"user_tz":-60,"elapsed":4473,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["AMP, affective valence analysis with the paired Wilcoxon signed-rank test\n","\n","positive vs negative\n","Mdn1=3.2, 95%CI†[3.0–3.5],\n","Mdn2=2.8, 95%CI†[2.6–2.9],\n","two-sided: W=2389.0, p=0.000, r‡=0.31,\n","alternative-greater: W=5361.0, p=0.000, r‡=0.33\n","\n","\n","env. vs positive\n","Mdn1=3.1, 95%CI†[3.0–3.2],\n","Mdn2=3.2, 95%CI†[3.0–3.5],\n","two-sided: W=3780.5, p=0.267, r‡=0.09,\n","alternative-less: W=3780.5, p=0.133, r‡=0.13\n","\n","\n","env. vs negative\n","Mdn1=3.1, 95%CI†[3.0–3.2],\n","Mdn2=2.8, 95%CI†[2.6–2.9],\n","two-sided: W=2788.0, p=0.000, r‡=0.31,\n","alternative-greater: W=5990.0, p=0.000, r‡=0.32\n","\n","Note. Null hypothesis H0: the median of the differences is located at zero, μ = 0.\n","One-sided alternative (less) hypothesis H1: the median of the differences is located on the left, μ < 0.\n","One-sided alternative (greater) hypothesis H2: the median of the differences is located on the right, μ > 0.\n","† Bootstrapped 95% confidence intervals for the median from 10000 resamples are reported.\n","‡ Cohen’s r (Fritz et al., 2012; Cohen, 1988) for Wilcoxon signed-rank test is reported.\n"]}]},{"cell_type":"code","source":["d = [[eco_neut.valence, pos_neut.valence],\n","     [eco_neut.valence, neg_neut.valence],\n","     [pos_neut.valence, neg_neut.valence]]\n","\n","w = wilcoxon(d[0][0], d[0][1], alternative='two-sided',)\n","r = cohen_r(w[1], len(d[0][0]))\n","t = [f'env. vs positive\\nH1: μ≠0, W={w[0]}, p={w[1]:.3f}, r={r:.2f}']\n","w = wilcoxon(d[1][0], d[1][1], alternative='greater')\n","r = cohen_r(w[1], len(d[1][0]))\n","t += [f'env. vs negative\\nH1: μ>0, W={w[0]}, p<{w[1]:.3f}, r={r:.2f}']\n","w = wilcoxon(d[2][0], d[2][1], alternative='greater')\n","r = cohen_r(w[1], len(d[2][0]))\n","t += [f'positive vs negative\\nH1: μ>0, W={w[0]}, p<{w[1]:.3f}, r={r:.2f}']\n","\n","l=[['env.', 'positive'],\n","   ['env.', 'negative'],\n","   ['positive', 'negative']]\n","\n","barWidth = 0.2\n","fig, axs = plt.subplots(1, len(d), figsize=(11.5, 4.5))\n","for i, ax in enumerate(axs.flat):\n","    m1 = bootstrap(d[i][0])\n","    m2 = bootstrap(d[i][1])\n","    ax.bar(0.0, m1[0], width = barWidth, color = 'lightgreen', edgecolor = 'gray', capsize=5, label=l[i][0], yerr=m1[1])\n","    ax.bar(0.5, m2[0], width = barWidth, color = 'silver', edgecolor = 'gray', capsize=5, label=l[i][1], yerr=m2[1])\n","    ax.plot((0.0, 0.5), (m1[0], m2[0]), 'k--', linewidth = 1.2)\n","    if i > 0:\n","      pass\n","    else:\n","      ax.set_ylabel('Affective valence (pts)')\n","    ax.set_xticklabels([])\n","    ax.legend()\n","    ax.set_xticks([])\n","    ax.set_title(t[i], fontsize=12, fontweight='bold')\n","    ax.tick_params(axis='y', labelsize=12)\n","    ax.set_ylim([0.9, 3.8])\n","plt.tight_layout()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":333},"id":"l1Q9x71zzoDK","outputId":"d137cff4-1048-4070-dbfb-d6365c121423","executionInfo":{"status":"ok","timestamp":1675628027259,"user_tz":-60,"elapsed":4893,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 828x324 with 3 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["def plot_ecdf(data, label=''):\n","    fig = plt.figure()\n","    ax = fig.add_subplot()\n","    sns.ecdfplot(data = data, legend = data)\n","    plt.xlabel(label)\n","    plt.ylabel(\"ECDF\")\n","    plt.show()\n","\n","_d = {'env.': eco_neut.valence,\n","      'positive': pos_neut.valence,\n","      'negative': neg_neut.valence}\n","\n","plot_ecdf(_d, 'Affective valence (pts)')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"fsLL5h1fsdAB","outputId":"1eb960fb-005b-4701-f801-38920e0994ee","executionInfo":{"status":"ok","timestamp":1675628027260,"user_tz":-60,"elapsed":9,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3xV9Znv8c8jRMIdQSwIKDjFxEAQ5erQHu+KlJrxyM1qFatVh7HltKOt1WprL6e2Oo6jongDayuC4FgYi219eWnPKCJBUTCIpYAIIgIRBIEq9Tl/rJXMbkiyN2StfVvf9+uVV9Ze65fferIg+9m/dXl+5u6IiEhyHZLrAEREJLeUCEREEk6JQEQk4ZQIREQSTolARCThlAhERBIutkRgZjPM7AMzW9HEdjOzO81stZm9YWYnxhWLiIg0Lc4RwcPA6Ga2nwP0D7+uAO6NMRYREWlCbInA3f8E1DbTpAp4xAMvA13MrGdc8YiISONa53DfvYB3U15vCNdtatjQzK4gGDXQvn37IeXl5VkJUESkJdZs+RiAY7q3b3FfezcvB6D0c5UH9fNLly7d6u7dG9uWy0SQMXe/H7gfYOjQoV5dXZ3jiESkWM1avJ75yzZG0teujdsobbuVyhP+2OK+vvH6Rtq1bkf5vx7c+5+ZvdPUtlwmgo1An5TXvcN1IiKRy/QNfvHa4Iz2iH5dW7zP0rZb8Q5LWtwPQLvW7ejatuUxNSaXiWABcLWZzQZGADvcfb/TQiIiUZi/bCM1mz6iomenZtuN6NeVqsG9+MqIo1q8z0t/dz8AM0fPbHFfbPpSy/toQmyJwMweA04BDjezDcAPgBIAd58OLATGAKuB3cClccUiIgJQ0bMTc648qdk2c9+ey8I19/PM71q+v/I1i6ja82k0b+LvL4ceB3d9IJ3YEoG7X5BmuwP/EsW+Pv30UzZs2MDevXuj6C5RSktL6d27NyUlJbkORSQvLFyzkFW1qyjrWtbivqr2fMoxez+OICqCJFA5Lpq+GiiIi8XpbNiwgY4dO9K3b1/MLNfhFAx3Z9u2bWzYsIF+/frlOhyRg5bJ+f9MTgsBnLx5Ld/dupnyvx7W8sA++RSOHAKX/rblfcWoKEpM7N27l27duikJHCAzo1u3bhpJScGrO//fnIqenaga3CttXyO3vstRu3dEE1iMn+KjVBQjAkBJ4CDpuEmxyOT8f6bWt+tMeZ5/io9SUYwIRETk4BXNiEBEkmvLni3U7qmtv12zJb6xbzftWreLIKrCoRGBiBS82j217N63O5K+4nxwK19pRBChX//619x555188sknjBgxgnvuuYfOnTszdepUnnrqKdq2bcv8+fMpLS1l0KBBrF27lkMOOYSPP/6Y8vJy1qxZo9s4RVJk+jTwnp0dKWu1nZmbPmj5Tj/5FLoe0fJ+CkjRJYKb/+tNat5r/u6BA1VxZCd+8OUBzbZZuXIlc+bM4cUXX6SkpIQpU6bw6KOP8vHHHzNy5Eh++tOf8p3vfIcHHniA73//+wwePJg//vGPnHrqqTz11FOcffbZSgIiDWT6NPCxrd7lfPvvaHZaIHf6RKnoEkGuPPvssyxdupRhw4YBsGfPHo444ggOPfRQxo4dC8CQIUN45plnAJg4cSJz5szh1FNPZfbs2UyZMiVnsYvks0zuBnrr3y4KFi79SxYiKj5FlwjSfXKPi7tzySWX8LOf/ezv1t922231t2i2atWKffv2AXDuuedy/fXXU1tby9KlSznttNOyHrNILkX5EJi0TNElglw5/fTTqaqq4lvf+hZHHHEEtbW17Ny5s8n2HTp0YNiwYUydOpWxY8fSqlWrLEYrknsPLlrG+i1/o0OHph/eatUGtpcsTns3UBLv9ImSEkFEKioq+MlPfsJZZ53FZ599RklJCdOmTWv2ZyZOnMj48eN54YUXAKiurmb69Ok8+OCDWYhYJLdq99RySOluTjih5WWak3inT5QsqP1WOBqbmGblypUcd9xxOYqo8On4SS4M/tnDACz73uSWdzYzrO6ZoKeBD5SZLXX3oY1t03MEIiIJp1NDIhKpTO/937Wrc7PXByR7NCIQkUhlUgkUoEOHHXzuc+uzEJGkoxGBiEQuk3v/o6gLJNFQIhCRjAXTOC5sts2q2pOB9G/0Uc0CJi2nU0MikrG6aRyjUNa1jDHHjImkL2kZjQjyyPTp02nXrh0XX3wxDz/8MGeddRZHHnkkAJdffjnf/va3qaioyHGUknRlXcuYOXpmk9snvrMIgJmjJ2cpImkpJYI8ctVVV9UvP/zwwwwcOLA+EeghMxGJixJBRNatW8fo0aMZMmQIr776KgMGDOCRRx5h0aJFXHPNNezbt49hw4Zx77330qZNG6677joWLFhA69atOeuss7jtttv44Q9/SIcOHejbty/V1dVceOGFtG3blkWLFnHOOedw2223UV1dzV/+8hduvfVWIEgY1dXV3H333Y2WwVbpConSe+/1Y/Pmo+o/9Tcm4/pA1TNh+bxoAnt/eVA1VA5K8SWCp68L/lNEqUclnHNL2marVq3ioYceYtSoUXzta1/j9ttv57777uPZZ5/l2GOP5eKLL+bee+/lq1/9Kk8++SRvvfUWZsb27dv/rp9x48Zx9913c9tttzF06N8/CHj++edz0kkn1SeCOXPmcMMNNzRZBvviiy+O7jhI4m3efBS7dnWGZqo5ZDpJPMvnRfcGnsDS0VEqvkSQQ3369GHUqFEAXHTRRfz4xz+mX79+HHvssQBccsklTJs2jauvvprS0lIuu+wyxo4dW1+mOhPdu3fnmGOO4eWXX6Z///689dZbjBo1imnTpjVaBlskah067GDOledF01mPSpWFyAPFlwgy+OQel7py03W6dOnCtm3b9mvXunVrXnnlFZ599lnmzZvH3XffzXPPPZfxfiZNmsTjjz9OeXk55513HmbWZBlsEZF0dPtohNavX8+iRcG501mzZjF06FDWrVvH6tWrAfjVr37FySefzK5du9ixYwdjxozh3//933n99df366tjx45NlrE+77zzmD9/Po899hiTJk0CgjLY8+bN44MPgqn6amtreeedd+L4NUWkyBTfiCCHysrKmDZtGl/72teoqKjgzjvvZOTIkYwfP77+YvFVV11FbW0tVVVV7N27F3fn9ttv36+vyZMnc9VVV9VfLE512GGHcdxxx1FTU8Pw4cOBpstgH3300Vn53UWkcKkMdUTWrVvH2LFjWbFiRU7jOBj5cPykMKh0dOFSGWoREWmSEkFE+vbtW5CjARERJQIRkYRTIhARSTglAhGRhFMiEBFJOD1HUCC2b9/OrFmzmDJlCgDvvfce3/zmN5k3L6KiXVK0MplMpq6YXDo7dnakc8fGH3SUwhXriMDMRpvZKjNbbWbXNbL9KDN73sxeM7M3zEyzVDRh+/bt3HPPPfWvjzzySCUByUgmk8nUF5NLo3PHnZw+IIPKolJY3D2WL6AV8BfgGOBQ4HWgokGb+4F/DpcrgHXp+h0yZIg3VFNTs9+6bFu7dq2Xl5f75Zdf7hUVFX7mmWf67t27ffXq1X722Wf7iSee6F/4whd85cqV7u6+evVqHzFihA8cONBvuOEGb9++vbu779y500877TQ/4YQTfODAgf6b3/zG3d0nTpzopaWlfvzxx/s111zja9eu9QEDBri7+4gRI3zFihX1sZx88sm+ZMkS37Vrl1966aU+bNgwHzx4cH1fDeXD8ZP4TH56sk9+enKzbSZMf8knTH8pSxGFZowJviQrgGpv4n01zlNDw4HV7r4GwMxmA1VATWoeAuo+XnQG3mvpTn/+ys95q/atlnbzd8q7lvPd4d9N2+7Pf/4zjz32GA888AATJkzgiSeeYObMmUyfPp3+/fuzePFipkyZwnPPPcfUqVOZOnUqF1xwAdOnT6/vo7S0lCeffJJOnTqxdetWRo4cybnnnsstt9zCihUrWLZsGRA8yVxn4sSJPP7449x8881s2rSJTZs2MXToUK6//npOO+00ZsyYwfbt2xk+fDhnnHEG7du3j/T4iEhhi/PUUC/g3ZTXG8J1qX4IXGRmG4CFwDca68jMrjCzajOr3rJlSxyxRqJfv34MHjwYgCFDhrBu3Tpeeuklxo8fz+DBg7nyyivZtGkTAIsWLWL8+PEAfOUrX6nvw925/vrrGTRoEGeccQYbN25k8+bNze53woQJ9aeJHn/8ccaNC+qy/+EPf+CWW25h8ODBnHLKKezdu5f169dH/nuLSGHL9cXiC4CH3f3fzOwk4FdmNtDdP0tt5O73E5xGYujQoc0WR8rkk3tc2rRpU7/cqlUrNm/eTJcuXeo/xWfi0UcfZcuWLSxdupSSkhL69u3L3r17m/2ZXr160a1bN9544w3mzJlTP8Jwd5544gnKysoO7hcSkUSIc0SwEeiT8rp3uC7VZcDjAO6+CCgFDo8xpqzq1KkT/fr1Y+7cuUDwxlxXcnrkyJE88cQTAMyePbv+Z3bs2MERRxxBSUkJzz//fH0p6ebKUkNweugXv/gFO3bsYNCgQQCcffbZ3HXXXXXXY3jttdei/yVFpODFmQiWAP3NrJ+ZHQpMAhY0aLMeOB3AzI4jSAT5e+7nIDz66KM89NBDHH/88QwYMID58+cDcMcdd3D77bczaNAgVq9eTefOwR0bF154IdXV1VRWVvLII49QXl4OQLdu3Rg1ahQDBw7k2muv3W8/48aNY/bs2UyYMKF+3Y033sinn37KoEGDGDBgADfeeGMWfmMRKTSxlqEObwe9g+AOohnu/lMz+xHB1esFZlYBPAB0ILhw/B13/0NzfeZrGeoDtXv3btq2bYuZMXv2bB577LH6JJFthXj8JHNnz/gRmzcfRVnXpk8R1k04P+fKk7IXmMpQZ1VzZahjvUbg7gsJLgKnrrspZbkGGBVnDPlq6dKlXH311bg7Xbp0YcaMGbkOSYpUpBPOS1HK9cXixPriF7/Y6BSVInGIdMJ5KTpFkwjcfb/J4yW9OE8NShGqngnLI3qi/f3l0KMymr6kRYqi6FxpaSnbtm3Tm9oBcne2bdtGaWlprkORQrF8XvAGHoUelVA5Lpq+pEWKYkTQu3dvNmzYQD4/bJavSktL6d27d67DkELSo1IXeItMUSSCkpIS+vXrl+swREQKUlEkApFikknZ6AOxe98w2rVuF1l/UnyK4hqBSDHJpGz0gWjXuh1d2zZz76gknkYEInmorGsZM0fPjKSvie8siqQfKV4aEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCSc7hoSKVCzFq9n/rKGcz3tr2bDVipab4KZP2n5TlUfqChpRCBSoOYv20jNpo/StqtovYkqXohmp6oPVJQ0IhApYBlNJlM3ElB9IGmCRgQiIgmnRCAiknBKBCIiCadEICKScEoEIiIJp0QgIpJwun1UJAJRTiazqnYVZV3LIulLJBMaEYhEIMrJZMq6ljHmmDGR9CWSCY0IRCIS5WQyItmkEYGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJJweKBPJMxnPRbzpIyp6dspCRFLsYh0RmNloM1tlZqvN7Lom2kwwsxoze9PMZsUZj0ghyHgu4p6dqBrcKwsRSbGLbURgZq2AacCZwAZgiZktcPealDb9ge8Bo9z9QzM7Iq54RApJRnMRi0QkzhHBcGC1u69x90+A2UBVgzZfB6a5+4cA7v5BjPGIiEgj4kwEvYB3U15vCNelOhY41sxeNLOXzWx0Yx2Z2RVmVm1m1Vu2bIkpXBGRZMr1XUOtgf7AKcAFwANm1qVhI3e/392HuvvQ7t27ZzlEEZHiFuddQxuBPimve4frUm0AFrv7p8BaM3ubIDEsiTEukZzJ5I4g3Q0k2RbniGAJ0N/M+pnZocAkYEGDNr8hGA1gZocTnCpaE2NMIjmVyR1BuhtIsi22EYG77zOzq4HfA62AGe7+ppn9CKh29wXhtrPMrAb4G3Ctu2+LKyaRfKA7giTfxPpAmbsvBBY2WHdTyrID3w6/REQkB/RksUi+qZ4Jy+dF19/7y6FHZXT9SdHJ9V1DItLQ8nnBm3dUelRC5bjo+pOioxGBSD7qUQmX/jbXUUhCaEQgIpJwSgQiIgnXbCIwM506EhEpcune6F8BTgQws7vc/RvxhySSHXPfnsvCNQvTN8zAqtpVlHUti6QvkWxLd2rIUpZHxRmISLYtXLOQVbWrIumrrGsZY44ZE0lfItmWbkTgWYlCJEfKupYxc/TMXIchklPpEkG5mb1BMDL4h3CZ8LW7+6BYoxMRkdilSwTHZSUKERHJmWYTgbu/AxDOEdA/XP22u++IOzAREcmOZhOBmbUB7gP+CVhLcEroaDN7ErgqnIJSREQKWLq7hr4PlAB93P0Edx8MHEWQQG6MOzgREYlfukRwHvB1d99ZtyJcnhJuExGRApcuEXzm7rsbrnT3XejWUhGRopD2OQIzO4y/f7CszmcxxCMiIlmWLhF0BpbSeCLQiEBEpAiku320b5biEClosxavZ/6yjWnb1Wz6iIqenbIQkUjm0lUfPdvM9pvayMzON7Mz4wtLpLDMX7aRmk0fpW1X0bMTVYN7ZSEikcylOzV0E8EzBA39Efgv4JnIIxIpUBU9OzHnypNyHYbIAUt311Abd9/ScKW7bwXaxxOSiIhkU7pE0KmxyWnMrARoG09IIiKSTekSwX8CD5hZ/ad/M+sATA+3iYhIgcukxMRm4B0zW2pmSwlqDm0Jt4mISIFLd/voPuA6M7sZ+Hy4erW774k9MhERyYp0t49+ByB84y939+V1ScDM/m8W4hMRkZilOzU0KWX5ew22jY44FhERyYF0zxFYE8uNvRZJrp3vw8dbYOZPWt7X+8uhR2XL+xHJULoRgTex3NhrkeT6eAt88nE0ffWohMr9HugXiU26EcHxZvYRwaf/tuEy4evSWCMTKTSHtodLf5vrKEQOWLq7hlplKxAREcmNdKeGRESkyCkRiIgknBKBiEjCpbtY3CJmNhr4D6AV8KC739JEu/OBecAwd6+OMyYpbHPfnsvCNQsj6WtV7SrKupZF0pdIIYttRGBmrYBpwDlABXCBmVU00q4jMBVYHFcsUjwWrlnIqtpVkfRV1rWMMceMiaQvkUIW54hgOEFdojUAZjYbqAJqGrT7MfBz4NoYY5EiUta1jJmjZ+Y6DJGiEec1gl7AuymvN4Tr6pnZiUAfd2/25mszu8LMqs2sesuW/ebJERGRFsjZxWIzOwS4HfjXdG3d/X53H+ruQ7t37x5/cCIiCRLnqaGNQJ+U173DdXU6AgOBF8wMoAewwMzO1QVjySezFq9n/rKNzbap+evhVLTZmqWIRKIV54hgCdDfzPqZ2aEElUwX1G109x3ufri793X3vsDLgJKA5J35yzZSs+mjZttUtNlKVac/ZykikWjFNiJw931mdjXwe4LbR2e4+5tm9iOg2t0XNN+DSP6o6NmJOVee1HSDKKqOiuRIrM8RuPtCYGGDdTc10faUOGMREZHG6cliEZGEUyIQEUk4JQIRkYRTIhARSTglAhGRhFMiEBFJOCUCEZGEi/U5AhHQHAIi+U4jAomd5hAQyW8aEUhWaA4BkfylRCDJVT0Tls9L3+79quB7c/WE3l8OPSqjiUsky3RqSJJr+bzgDTwKPSqhclw0fYlkmUYEkmw9KuHSZifIg/sWBd8vvSL+eERyQIlAEmvW9grmf9T/f97om1Cz6SMqenbKUlQi2adTQ5JY8z/qT81fD0/brqJnJ6oG90rbTqRQaUQgiVbRZitzrvzfuQ5DJKc0IhARSTglAhGRhFMiEBFJOCUCEZGEUyIQEUk43TUkjcrbiqEZloWof0agGTV7DqOi7YfRxCVSwDQikEblbcXQDMtCZPKMQEXbD6k6rmM0cYkUMI0IpEl5WzE0w7IQFaBnBEQyoBGBiEjCKRGIiCScEoGISMIpEYiIJJwSgYhIwikRiIgknBKBiEjCKRGIiCScEoGISMIpEYiIJFysicDMRpvZKjNbbWbXNbL922ZWY2ZvmNmzZnZ0nPGIiMj+Yqs1ZGatgGnAmcAGYImZLXD3mpRmrwFD3X23mf0z8AtgYlwxSeGrryp636Jm29Vs+oiKnp2yFJVIYYtzRDAcWO3ua9z9E2A2UJXawN2fd/fd4cuXgd4xxiNFIJOqogAVPTtRNbhXFiISKXxxVh/tBbyb8noDMKKZ9pcBTze2wcyuAK4AOOqoo6KKTwpURZutqioqEqG8uFhsZhcBQ4FbG9vu7ve7+1B3H9q9e/fsBiciUuTiHBFsBPqkvO4drvs7ZnYGcANwsrv/NcZ4RESkEXGOCJYA/c2sn5kdCkwCFqQ2MLMTgPuAc939gxhjERGRJsQ2InD3fWZ2NfB7oBUww93fNLMfAdXuvoDgVFAHYK6ZAax393PjiklyRPMMi+S1WKeqdPeFwMIG625KWT4jzv1LnqibZ7hHZbPN6u4Iqmiztck2mmdYJHqas1iyQ/MMi+StvLhrSEREckeJQEQk4ZQIREQSTtcIisjct+eycM3C9A0zsKp2FWVdyyLpS0Tym0YERWThmoWsql0VSV9lXcsYc8yYSPoSkfymEUGRKetaxszRM3MdhogUEI0IREQSTolARCThlAhERBJO1wikcRnWB8pIBuUlRCR3NCKQxtXVB4pCj0qoHBdNXyISOY0IpGmZ1AcSkYKnEYGISMJpRCCxm7V4PfOX7Tc53X5qNn1ERc9OWYhIRFIpEeRYEspCzF+2MaM3+Yqenaga3CtLUYlIHSWCHKsrCxHFG3g+l4Wo6NmJOVeelOswRKQRSgR5QGUhRCSXlAikRTI5/69z/yL5TXcNSYvUnf9vjs79i+Q3jQikxXT+X6SwaUQgIpJwSgQiIgmnRCAiknC6RlBMIqwYOmt9Z+ZzCty3qNl2uiNIpPBpRFBMIqwYOp9TqNnXM2073REkUvg0Iig2UVUMvW8RFaC7gUQSQIkgx07evJaRW9+FTV9qeWeaAEZEDoJODeXYyK3vctTuHdF0pglgROQgaESQB9a360x5FieAUVkIEUmlEUECqSyEiKTSiKCIHOgEMLoQLCKgRHBQopxM5hv7dtOudbu07TJ5k1+8thaAEf26NttOn/ZFJJUSwUGIcjKZdq3b0bVt82/ckNksXyP6daVqcC++MuKoFsclIsmRnEQQ4VO33619C4Dyvx7W8s4++RS6HpFRU53OEZE4xJoIzGw08B9AK+BBd7+lwfY2wCPAEGAbMNHd18URywev3EOHbWtZ365zi/vaneHpnEzMKp3I/G3/qFIOIpIzsSUCM2sFTAPOBDYAS8xsgbvXpDS7DPjQ3T9vZpOAnwMT44indk8tGw4t4a4B/yuS/sYcM4byY8e3uJ/59y0K3uQ7Nt9O5/VFJC5xjgiGA6vdfQ2Amc0GqoDURFAF/DBcngfcbWbm7h5HQO1at8vLuYF1ykdEcinORNALeDfl9QZgRFNt3H2fme0AugFbUxuZ2RXAFeHLXWa26iBjOpxrbGv6Zll3+ONXkZdxgeI6APkaF+RvbIrrwLQkrqOb2lAQF4vd/X7g/pb2Y2bV7j40gpAipbgOjOI6cPkam+I6MHHFFeeTxRuBPimve4frGm1jZq2BzgQXjUVEJEviTARLgP5m1s/MDgUmAQsatFkAXBIujwOei+v6gIiINC62U0PhOf+rgd8T3D46w93fNLMfAdXuvgB4CPiVma0GagmSRZxafHopJorrwCiuA5evsSmuAxNLXKYP4CIiyabqoyIiCadEICKScEWXCMxshpl9YGYrmthuZnanma02szfM7MQ8iesUM9thZsvCr5uyFFcfM3vezGrM7E0zm9pIm6wfswzjyvoxM7NSM3vFzF4P47q5kTZtzGxOeLwWm1nfPIlrspltSTlel8cdV8q+W5nZa2b2VCPbsn68Mowrl8drnZktD/db3cj2aP8m3b2ovoD/BZwIrGhi+xjgacCAkcDiPInrFOCpHByvnsCJ4XJH4G2gItfHLMO4sn7MwmPQIVwuARYDIxu0mQJMD5cnAXPyJK7JwN3Z/j8W7vvbwKzG/r1ycbwyjCuXx2sdcHgz2yP9myy6EYG7/4ngDqSmVAGPeOBloIuZ9cyDuHLC3Te5+6vh8k5gJcET36myfswyjCvrwmOwK3xZEn41vOOiCvhluDwPON3MLA/iygkz6w18CXiwiSZZP14ZxpXPIv2bLLpEkIHGSl/k/A0mdFI4tH/azAZke+fhkPwEgk+TqXJ6zJqJC3JwzMLTCcuAD4Bn3L3J4+Xu+4C60im5jgvg/PBUwjwz69PI9jjcAXwH+KyJ7Tk5XhnEBbk5XhAk8T+Y2VILSuw0FOnfZBITQb56FTja3Y8H7gJ+k82dm1kH4Ang/7h78xMaZ1GauHJyzNz9b+4+mOBp+eFmNjAb+00ng7j+C+jr7oOAZ/ifT+GxMbOxwAfuvjTufR2IDOPK+vFK8QV3PxE4B/gXM4umbHITkpgIMil9kXXu/lHd0N7dFwIlZnZ4NvZtZiUEb7aPuvt/NtIkJ8csXVy5PGbhPrcDzwOjG2zKaemUpuJy923u/tfw5YME84DEbRRwrpmtA2YDp5nZrxu0ycXxShtXjo5X3b43ht8/AJ4kqOacKtK/ySQmggXAxeFV95HADnfflOugzKxH3XlRMxtO8G8T+5tHuM+HgJXufnsTzbJ+zDKJKxfHzMy6m1mXcLktwXwbbzVolvXSKZnE1eAc8rkE111i5e7fc/fe7t6X4ELwc+5+UYNmWT9emcSVi+MV7re9mXWsWwbOAhrebRjp32RBVB89EGb2GMHdJIeb2QbgBwQXznD36cBCgivuq4HdwKV5Etc44J/NbB+wB5gU9x9DaBTwVWB5eH4Z4HrgqJTYcnHMMokrF8esJ/BLCyZeOgR43N2fstyWTsk0rm+a2bnAvjCuyVmIq1F5cLwyiStXx+tzwJPhZ5zWwCx3/52ZXQXx/E2qxISISMIl8dSQiIikUCIQEUk4JQIRkYRTIhARSTglAhGRhFMikKwzs38yMzez8pR13S2oPPmamX3RzMab2SY73+EAAASJSURBVEoze/4g+p9sZkemvH7QzCqiiv8A4njBzHIyAbqZ3ZHuadTw36HZ42JmY8NbKqWIKRFILlwA/Hf4vc7pwHJ3P8Hd/x9wGfB1dz/1IPqfDNQnAne/3N1rWhBvQTGzbgSVR/+Upuk/AekS5G+BL5tZu0iCk7ykRCBZFdYO+gLBG/2kcN1g4BdAlQX1138QtnnIzG4Ni6ndamZLwgJgV6b0910L6ra/bma3mNk4YCjwaNhX27pP5mZ2lZndmvKzk83s7nD5Igvq+S8zs/vCB7NS4x5tZnNTXp9iYQ17M7vXzKqtiXkAwjZnmdkiM3vVzOaGx6Gu7vzN4frldaMkM+tgZjPDdW+Y2fnN9dPA+cDvUva9zsx+Efb1ipl93sz+keBp2VvD3/kfzOybFsz/8IaZzYagqinwAjA23b+tFLCW1LDWl74O9Au4EHgoXH4JGBIuTyal9jvBm8/QcPkK4PvhchugGuhHUJDrJaBduK1rw59NfQ10B1anrH+aIOEcR1BgrCRcfw9wcYO4WwPrgfbh63uBixrst1W4r0EN9ns48KeUn/0ucFO4vA74Rrg8BXgwXP45cEfK/g9rrp8Gsf4S+HLK63XADeHyxYS194GHgXEp7d4D2oTLXRr8m92V6/87+orvSyMCybYLCIp8EX6/oJm2dc4iqKuyjKAUdTegP3AGMNPddwO4e7PzPbj7FmCNmY0MT5+UAy8SnJYaAiwJ93E6cEyDn91H8Cn7yxYURvsSMD/cPMHMXgVeAwaw/+mWkeG6F8P+LwGOTtleV1BvKdA3XD4DmJay/w8z6KdOT2BLg3WPpXw/qZGfAXiDYCR1EUFZhTofkHKqTYpP0dUakvxlZl2B04BKM3OCT9BuZtem+1GCT82/b9Df2QcRxmxgAkFBtifd3S0o6vJLd/9eBj97NUHdmWp332lm/YBrgGHu/qGZPQyUNhL/M+7eVNKrq3D5N5r/m0zXT509jcTgTSyn+hLBTHpfBm4ws8owAZaGfUqR0ohAsmkc8Ct3P9rd+7p7H2At8MU0P/d7guJyJQBmdqwFVRmfAS6tu5AZJhqAnQTTWzbmSYLZnVJHJs8C48zsiLp+zKyxT9p/JJhu9OspP9sJ+BjYYWafIzhd1dDLwCgz+3zYf3szOzbN7/wM8C91L8zssAPoZyXw+QbrJqZ8XxQu1x8nMzsE6OPuzxOccuoM1F1/OJb9q19KEVEikGy6gOCNONUTpD899CBQA7xqZiuA+4DW7v47gnK81eGpkmvC9g8D0+suFqd2FJ5iWUkwoc0r4boa4PsEM0K9QfAmvN+0f+7+N+Apgjf7p8J1rxOcEnqLYO7bFxv5uS0E10AeC/tfRHBaqjk/AQ4zsxVm9jpw6gH081uCSrepDgt/ZirwrXDdbOBaM3uN4FTbr81sefj73OnBvAYAp4Z9SpFS9VGRImRm/w2MdfftFky+MtTdtx5EP58jKIN8etQxSv7QiECkOP0r4dwNLXRU2JcUMY0IREQSTiMCEZGEUyIQEUk4JQIRkYRTIhARSTglAhGRhPv/7kT8gOdbBB8AAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["d = data.copy()\n","a = d[(d.test == neg) & (d.ctrl == neg) & (d.valence > 3)].id.unique()\n","b = d[(d.test == pos) & (d.ctrl == pos) & (d.valence < 3)].id.unique()\n","c = d[(d.q1 == 6) & (d.q2 == 5) & (d.q3 == 4) & (d.q4 == 3) & \\\n","      (d.q5 == 3) & (d.q6 == 4) & (d.q7 == 5) & (d.q8 == 6) & \\\n","      (d.q9 == 6) & (d.q10 == 5) & (d.q11 == 4) & (d.q12 == 3)].id.unique()\n","invalid = {*a, *b, *c}\n","print(f'{len([i for i in c if i not in {*a, *b}])} - dropout')\n","print(f'{len(invalid)} - total dropout')\n","d = d[d.id.apply(lambda x: not x in invalid)]#.dropna() #6 uncomplete\n","rec_v = d[(d.test == rec) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sav_v = d[(d.test == sav) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sho_v = d[(d.test == sho) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","tra_v = d[(d.test == tra) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","eco_v = d[d.test.apply(lambda x: x in eco) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #eco-neutral\n","print(f'n={len(eco_v)}')\n","\n","random.seed(13)\n","np.random.seed(13)\n","#https://arxiv.org/pdf/2106.01140.pdf\n","d = eco_v.copy()\n","d.time = np.log(d.time)\n","d.age = np.log(d.age)\n","formula = \"\"\"\n","B =~ q1 + q2 + q3 + q4\n","E =~ q5 + q6 + q7 + q8\n","A =~ q9 + q10 + q11 + q12\n","IA =~ valence\n","IA ~ B + E + A + gender + age + edu + car + income + work \n","\"\"\"\n","model = sem.Model(formula)\n","model.fit(d, obj=\"MLW\", solver=\"SLSQP\") #MLW ULS GLS FIML DWLS WLS\n","sem.bias_correction(model, n=1000, resample_mean=True)\n","print(sem.calc_stats(model).T)\n","sem.semplot(model, \"fig.pdf\", plot_covs=True, std_ests=True, show=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":923},"id":"PGGvBOakPqsC","outputId":"23216369-ce89-4f64-b8ac-c2a811d0c80e","executionInfo":{"status":"ok","timestamp":1675628125809,"user_tz":-60,"elapsed":96222,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["15 - dropout\n","58 - total dropout\n","n=125\n","                      Value\n","DoF            1.520000e+02\n","DoF Baseline   1.770000e+02\n","chi2           2.499517e+02\n","chi2 p-value   9.490730e-07\n","chi2 Baseline  9.952812e+02\n","CFI            8.802958e-01\n","GFI            7.488632e-01\n","AGFI           7.075578e-01\n","NFI            7.488632e-01\n","TLI            8.606076e-01\n","RMSEA          7.208973e-02\n","AIC            7.198505e+01\n","BIC            1.794610e+02\n","LogLik         2.007473e+00\n"]},{"output_type":"execute_result","data":{"text/plain":["<graphviz.dot.Digraph 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font-family=\"Times,serif\" font-size=\"12.00\">0.077</text>\n<text text-anchor=\"middle\" x=\"1195\" y=\"-146.7\" font-family=\"Times,serif\" font-size=\"12.00\">p&#45;val: 0.68</text>\n</g>\n</g>\n</svg>\n"},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["d = data.copy()\n","a = d[(d.test == neg) & (d.ctrl == neg) & (d.valence > 3)].id.unique()\n","b = d[(d.test == pos) & (d.ctrl == pos) & (d.valence < 3)].id.unique()\n","c = d[(d.q1 == 6) & (d.q2 == 5) & (d.q3 == 4) & (d.q4 == 3) & \\\n","      (d.q5 == 3) & (d.q6 == 4) & (d.q7 == 5) & (d.q8 == 6) & \\\n","      (d.q9 == 6) & (d.q10 == 5) & (d.q11 == 4) & (d.q12 == 3)].id.unique()\n","invalid = {*a, *b, *c}\n","print(f'{len([i for i in c if i not in {*a, *b}])} - dropout')\n","print(f'{len(invalid)} - total dropout')\n","d = d[d.id.apply(lambda x: not x in invalid)]#.dropna() #6 uncomplete\n","rec_v = d[(d.test == rec) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sav_v = d[(d.test == sav) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sho_v = d[(d.test == sho) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","tra_v = d[(d.test == tra) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","eco_v = d[d.test.apply(lambda x: x in eco) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #eco-neutral\n","print(f'n={len(eco_v)}')\n","\n","random.seed(13)\n","np.random.seed(13)\n","#https://arxiv.org/pdf/2106.01140.pdf\n","d = eco_v.copy()\n","d.time = np.log(d.time)\n","d.age = np.log(d.age)\n","formula = \"\"\"\n","B =~ q1 + q2 + q3 + q4\n","E =~ q5 + q6 + q7 + q8\n","A =~ q9 + q10 + q11 + q12\n","IA =~ valence\n","IA ~ B + E + A\n","\"\"\"\n","model = sem.Model(formula)\n","model.fit(d, obj=\"MLW\", solver=\"SLSQP\") #MLW ULS GLS FIML DWLS WLS\n","sem.bias_correction(model, n=1000, resample_mean=True)\n","print(sem.calc_stats(model).T)\n","sem.semplot(model, \"fig.pdf\", plot_covs=True, std_ests=True, show=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":903},"id":"XlLywosugwFF","outputId":"7ce48a29-3f15-440b-9b48-18108b1ee164","executionInfo":{"status":"ok","timestamp":1675628209070,"user_tz":-60,"elapsed":83271,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["15 - dropout\n","58 - total dropout\n","n=125\n","                      Value\n","DoF            5.900000e+01\n","DoF Baseline   7.800000e+01\n","chi2           1.599049e+02\n","chi2 p-value   3.029366e-11\n","chi2 Baseline  7.343521e+02\n","CFI            8.462640e-01\n","GFI            7.822503e-01\n","AGFI           7.121276e-01\n","NFI            7.822503e-01\n","TLI            7.967558e-01\n","RMSEA          1.174409e-01\n","AIC            6.142763e+01\n","BIC            1.519337e+02\n","LogLik         1.286185e+00\n"]},{"output_type":"execute_result","data":{"text/plain":["<graphviz.dot.Digraph 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evaluations of the target after exposure to positive or pro-environmental stimuli than after exposure to negative stimuli. This can be tested with contrast analyses. In concrete terms, this amounts to giving a weight to each condition, which here translates into the contrast codes (-1,-1,2) and (-1,1,0). The advantage here would be to present only one statistical test per study for each task, and thus gain statistical power. "],"metadata":{"id":"OUZhZ0-ODydZ"}},{"cell_type":"code","source":["# https://personal.utdallas.edu/~herve/abdi-contrasts2010-pretty.pdf\n","# https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLSResults.t_test.html\n","d = data.copy()\n","a = d[(d.test == neg) & (d.ctrl == neg) & (d.valence > 3)].id.unique()\n","b = d[(d.test == pos) & (d.ctrl == pos) & (d.valence < 3)].id.unique()\n","invalid = {*a, *b}\n","d = d[d.id.apply(lambda x: not x in invalid)]\n","print(f'{len(invalid)} - dropout')\n","d['p_env'] = d['test'].apply(lambda x: x in eco)\n","d['p_pos'] = d['test'] == pos\n","d['p_neg'] = d['test'] == neg\n","d['t_pos'] = d['ctrl'] == pos\n","d['t_neg'] = d['ctrl'] == neg\n","d['t_neut'] = d['ctrl'] == neut\n","d = d.groupby(by=['id', 'p_env', 'p_pos', 'p_neg', 't_pos', 't_neg', 't_neut'])\\\n","          .aggregate(np.mean).reset_index()\n","d = d[d['t_neut'] == True][['id', 'p_env', 'p_pos', 'p_neg', 'valence']]\n","\n","#m = smf.ols(f, d).fit()\n","f = 'valence ~ p_env + p_pos + p_neg'\n","# use robust linear regression https://www.statsmodels.org/stable/rlm.html\n","m = smf.rlm(f, d, M=sm.robust.norms.HuberT()).fit()\n","f_test = m.f_test([[0, 1, 1, -2], \n","                   [0, 1, -1, 0]])\n","print(f_test)\n","print(f'Cohen’s d* = {cohen_d(f_test)}')\n","print('* Cohen’s d derived from F statistic (Fritz et al., 2012)')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628209071,"user_tz":-60,"elapsed":26,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"6c24efac-8021-4998-85a2-e192823a85f3","id":"7uRhMUclDyda"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["43 - dropout\n","<F test: F=array([[10.68530324]]), p=2.9814675637404594e-05, df_denom=417, df_num=2>\n","Cohen’s d* = 0.45276244014519784\n","* Cohen’s d derived from F statistic (Fritz et al., 2012)\n"]}]},{"cell_type":"code","source":["m.t_test([[0, 1, 1, -2], \n","          [0, 1, -1, 0]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628209071,"user_tz":-60,"elapsed":21,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"9ae9feac-756b-4d32-b0f5-e44d9f24dafd","id":"AD7oMoxIDyda"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","                             Test for Constraints                             \n","==============================================================================\n","                 coef    std err          z      P>|z|      [0.025      0.975]\n","------------------------------------------------------------------------------\n","c0             0.7515      0.168      4.471      0.000       0.422       1.081\n","c1            -0.1142      0.097     -1.177      0.239      -0.304       0.076\n","=============================================================================="]},"metadata":{},"execution_count":27}]},{"cell_type":"code","source":["m.f_test([0, 1, 1, -2])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F4cJ8Iy4lAW9","executionInfo":{"status":"ok","timestamp":1675628209071,"user_tz":-60,"elapsed":17,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"32a30d2b-67f9-401b-b225-de28b49b95b6"},"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","<F test: F=array([[19.98642781]]), p=1.0065583694063421e-05, df_denom=417, df_num=1>"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["m.f_test([[0, 1, -1, 0]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e8eGrPGckYZc","executionInfo":{"status":"ok","timestamp":1675628209395,"user_tz":-60,"elapsed":4,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"22085d2e-4f20-48ca-b98e-c4eb19977ce9"},"execution_count":29,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","<F test: F=array([[1.38417867]]), p=0.24006181100941712, df_denom=417, df_num=1>"]},"metadata":{},"execution_count":29}]},{"cell_type":"markdown","source":["## Study2"],"metadata":{"id":"vIL2fpnXTQW4"}},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"outputId":"61135ff8-5b55-4f45-aa7f-00c89c25b026","id":"ZSUHE6bsTQW5","executionInfo":{"status":"ok","timestamp":1675628433498,"user_tz":-60,"elapsed":606,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"source":["data = pd.read_csv('/content/drive/MyDrive/final_data_v1.0.csv', sep=';')\n","print(len(data), int(len(data) / 32))\n","rec, sav, sho, tra, pos, neg, neut = 1, 2, 3, 4, 5, 6, 7\n","eco = [rec, sav, sho, tra]"],"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["6752 211\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"outputId":"5b6b0f29-2eda-4b8d-c5e5-e2a3d6a83ab0","id":"QKUoKuYKTQW6","executionInfo":{"status":"ok","timestamp":1675628435074,"user_tz":-60,"elapsed":233,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"source":["print(len(data.drop_duplicates(subset=['id'])[['age', 'gender', 'income', 'work', 'car']].dropna()))\n","data.drop_duplicates(subset=['id']).describe().T[17:23]"],"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["209\n"]},{"output_type":"execute_result","data":{"text/plain":["        count       mean       std   min   25%   50%   75%   max\n","age     209.0  24.516746  8.127645  12.0  19.0  22.0  26.0  61.0\n","gender  210.0   0.266667  0.443273   0.0   0.0   0.0   1.0   1.0\n","income  210.0   2.366667  1.418886   1.0   1.0   2.0   3.0   6.0\n","work    210.0   0.638095  0.481700   0.0   0.0   1.0   1.0   1.0\n","car     210.0   0.557143  0.497911   0.0   0.0   1.0   1.0   1.0\n","edu     210.0   2.542857  0.864267   1.0   2.0   3.0   3.0   4.0"],"text/html":["\n","  <div id=\"df-db55c744-9b51-47d1-af75-62db08bfe351\">\n","    <div class=\"colab-df-container\">\n","      <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>count</th>\n","      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'edu']].dropna().drop_duplicates(subset=['id']).describe().T"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"b2766ddb-4837-4b3f-ec23-9c06b4f0f4fc","id":"mmOR3OImTQW6","executionInfo":{"status":"ok","timestamp":1675628437102,"user_tz":-60,"elapsed":248,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        count        mean        std   min   25%    50%    75%    max\n","id      209.0  104.181818  60.766146   0.0  52.0  104.0  156.0  210.0\n","age     209.0   24.516746   8.127645  12.0  19.0   22.0   26.0   61.0\n","gender  209.0    0.267943   0.443951   0.0   0.0    0.0    1.0    1.0\n","income  209.0    2.373206   1.419117   1.0   1.0    2.0    3.0    6.0\n","work    209.0    0.636364   0.482201   0.0   0.0    1.0    1.0    1.0\n","car     209.0    0.559809   0.497602   0.0   0.0    1.0    1.0    1.0\n","edu     209.0    2.545455   0.865521   1.0   2.0    3.0    3.0    4.0"],"text/html":["\n","  <div id=\"df-297015aa-449d-4f7b-b29b-14f6c0b7dbdc\">\n","    <div class=\"colab-df-container\">\n","      <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>count</th>\n","      <th>mean</th>\n","      <th>std</th>\n","      <th>min</th>\n","      <th>25%</th>\n","      <th>50%</th>\n","      <th>75%</th>\n","      <th>max</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>id</th>\n","      <td>209.0</td>\n","      <td>104.181818</td>\n","      <td>60.766146</td>\n","      <td>0.0</td>\n","      <td>52.0</td>\n","      <td>104.0</td>\n","      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<td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>car</th>\n","      <td>209.0</td>\n","      <td>0.559809</td>\n","      <td>0.497602</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>edu</th>\n","      <td>209.0</td>\n","      <td>2.545455</td>\n","      <td>0.865521</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>3.0</td>\n","      <td>4.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-297015aa-449d-4f7b-b29b-14f6c0b7dbdc')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" 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dropout\n","n=140, inc/con mean diff 51.6 ms, eco-neg/eco-pos mean diff 180.0 ms\n"]}]},{"cell_type":"code","source":["# Normality tests\n","# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html\n","k, p = stats.normaltest(eco_pos.time)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(eco_neg.time)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(con.time)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(inc.time)\n","print(f\"p = {p:g} ~ {p:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KJfXX2ptZOiH","executionInfo":{"status":"ok","timestamp":1675628440444,"user_tz":-60,"elapsed":263,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"7d67e745-6747-4931-cec0-c74bec569857"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["p = 0.000482309 ~ 0.000\n","p = 0.000542425 ~ 0.001\n","p = 0.072706 ~ 0.073\n","p = 0.0392718 ~ 0.039\n"]}]},{"cell_type":"code","source":["print(f'AFP, response time analysis with the paired Wilcoxon signed-rank test')\n","t1, p1 = wilcoxon(con.time, inc.time)\n","t2, p2 = wilcoxon(con.time, inc.time, alternative='less')\n","r1 = cohen_r(p1, len(con.time))\n","r2 = cohen_r(p2, len(con.time))\n","m1 = bootstrap(con.time)\n","m2 = bootstrap(inc.time)\n","print(f'''\n","congruent vs incongruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_pos.time, eco_neg.time)\n","t2, p2 = wilcoxon(eco_pos.time, eco_neg.time, alternative='less')\n","r1 = cohen_r(p1, len(eco_pos.time))\n","r2 = cohen_r(p2, len(eco_pos.time))\n","m1 = bootstrap(eco_pos.time)\n","m2 = bootstrap(eco_neg.time)\n","print(f'''\n","env.+positive vs env.+negative\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_pos.time, con.time)\n","t2, p2 = wilcoxon(eco_pos.time, con.time, alternative='less')\n","r1 = cohen_r(p1, len(con.time))\n","r2 = cohen_r(p2, len(con.time))\n","m1 = bootstrap(eco_pos.time)\n","m2 = bootstrap(con.time)\n","print(f'''\n","env.+positive vs congruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_pos.time, inc.time)\n","t2, p2 = wilcoxon(eco_pos.time, inc.time, alternative='less')\n","r1 = cohen_r(p1, len(inc.time))\n","r2 = cohen_r(p2, len(inc.time))\n","m1 = bootstrap(eco_pos.time)\n","m2 = bootstrap(inc.time)\n","print(f'''\n","env.+positive vs incongruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_neg.time, con.time)\n","t2, p2 = wilcoxon(eco_neg.time, con.time, alternative='greater')\n","r1 = cohen_r(p1, len(con.time))\n","r2 = cohen_r(p2, len(con.time))\n","m1 = bootstrap(eco_neg.time)\n","m2 = bootstrap(con.time)\n","print(f'''\n","env.+negative vs congruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-greater: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_neg.time, inc.time)\n","t2, p2 = wilcoxon(eco_neg.time, inc.time, alternative='less')\n","r1 = cohen_r(p1, len(inc.time))\n","r2 = cohen_r(p2, len(inc.time))\n","m1 = bootstrap(eco_neg.time)\n","m2 = bootstrap(inc.time)\n","print(f'''\n","env.+negative vs incongruent\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","print('Note. Null hypothesis H0: the median of the differences is located at zero, μ = 0.')\n","print('One-sided alternative (less) hypothesis H1: the median of the differences is located on the left, μ < 0.')\n","print('One-sided alternative (greater) hypothesis H2: the median of the differences is located on the right, μ > 0.')\n","print('† Bootstrapped 95% confidence intervals for the median from 10000 resamples are reported.')\n","print('‡ Cohen’s r (Fritz et al., 2012; Cohen, 1988) for Wilcoxon signed-rank test is reported.')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WaxoRZ82iJI9","outputId":"36fc50bf-2770-45e5-da69-e9fbfca8c627","executionInfo":{"status":"ok","timestamp":1675628450681,"user_tz":-60,"elapsed":8811,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":["AFP, response time analysis with the paired Wilcoxon signed-rank test\n","\n","congruent vs incongruent\n","Mdn1=1541.8, 95%CI†[1472.2–1611.2],\n","Mdn2=1570.2, 95%CI†[1496.2–1644.3],\n","two-sided: W=3904.0, p=0.032, r‡=0.18,\n","alternative-less: W=3904.0, p=0.016, r‡=0.20\n","\n","\n","env.+positive vs env.+negative\n","Mdn1=1541.5, 95%CI†[1481.9–1601.1],\n","Mdn2=1668.0, 95%CI†[1586.9–1749.1],\n","two-sided: W=1850.0, p=0.000, r‡=0.54,\n","alternative-less: W=1850.0, p=0.000, r‡=0.55\n","\n","\n","env.+positive vs congruent\n","Mdn1=1541.5, 95%CI†[1481.9–1601.1],\n","Mdn2=1541.8, 95%CI†[1472.2–1611.2],\n","two-sided: W=3944.5, p=0.039, r‡=0.17,\n","alternative-less: W=3944.5, p=0.020, r‡=0.20\n","\n","\n","env.+positive vs incongruent\n","Mdn1=1541.5, 95%CI†[1481.9–1601.1],\n","Mdn2=1570.2, 95%CI†[1496.2–1644.3],\n","two-sided: W=2963.5, p=0.000, r‡=0.35,\n","alternative-less: W=2963.5, p=0.000, r‡=0.36\n","\n","\n","env.+negative vs congruent\n","Mdn1=1668.0, 95%CI†[1586.9–1749.1],\n","Mdn2=1541.8, 95%CI†[1472.2–1611.2],\n","two-sided: W=2528.5, p=0.000, r‡=0.42,\n","alternative-greater: W=7341.5, p=0.000, r‡=0.43\n","\n","\n","env.+negative vs incongruent\n","Mdn1=1668.0, 95%CI†[1586.9–1749.1],\n","Mdn2=1570.2, 95%CI†[1496.2–1644.3],\n","two-sided: W=3250.0, p=0.000, r‡=0.30,\n","alternative-less: W=6620.0, p=1.000, r‡=0.00\n","\n","Note. Null hypothesis H0: the median of the differences is located at zero, μ = 0.\n","One-sided alternative (less) hypothesis H1: the median of the differences is located on the left, μ < 0.\n","One-sided alternative (greater) hypothesis H2: the median of the differences is located on the right, μ > 0.\n","† Bootstrapped 95% confidence intervals for the median from 10000 resamples are reported.\n","‡ Cohen’s r (Fritz et al., 2012; Cohen, 1988) for Wilcoxon signed-rank test is reported.\n"]}]},{"cell_type":"code","source":["d = [[eco_pos.time, eco_neg.time],\n","     [eco_pos.time, con.time],\n","#    [eco_neg.time, inc.time],\n","     [con.time, inc.time]]\n","\n","w = wilcoxon(d[0][0], d[0][1], alternative='less',)\n","r = cohen_r(w[1], len(d[0][0]))\n","t = [f'env.+positive vs env.+negative\\nH1: μ<0, W={w[0]}, p<{w[1]:.3f}, r={r:.2f}']\n","w = wilcoxon(d[1][0], d[1][1], alternative='two-sided')\n","r = cohen_r(w[1], len(d[1][0]))\n","t += [f'env.+positive vs congruent\\nH1: μ≠0, W={w[0]}, p={w[1]:.3f}, r={r:.2f}']\n","w = wilcoxon(d[2][0], d[2][1], alternative='less')\n","r = cohen_r(w[1], len(d[2][0]))\n","t += [f'congruent vs incongruent\\nH1: μ<0, W={w[0]}, p={w[1]:.3f}, r={r:.2f}']\n","\n","l=[['env.+positive', 'env.+negative'],\n","   ['env.+positive', 'congruent'],\n","#  ['env.+negative', 'incongruent'],\n","   ['congruent', 'incongruent']]\n","\n","barWidth = 0.2\n","fig, axs = plt.subplots(1, len(d), figsize=(11.5, 4.5))\n","for i, ax in enumerate(axs.flat):\n","    m1 = bootstrap(d[i][0])\n","    m2 = bootstrap(d[i][1])\n","    ax.bar(0.0, m1[0], width = barWidth, color = 'lightgreen', edgecolor = 'gray', capsize=5, label=l[i][0], yerr=m1[1])\n","    ax.bar(0.5, m2[0], width = barWidth, color = 'silver', edgecolor = 'gray', capsize=5, label=l[i][1], yerr=m2[1])\n","    ax.plot((0.0, 0.5), (m1[0], m2[0]), 'k--', linewidth = 1.2)\n","    if i > 0:\n","      pass\n","    else:\n","      ax.set_ylabel('Response time (ms)')\n","    ax.set_xticklabels([])\n","    ax.legend()\n","    ax.set_xticks([])\n","    ax.set_title(t[i], fontsize=12, fontweight='bold')\n","    ax.tick_params(axis='y', labelsize=12)\n","    ax.set_ylim([0, 2400])\n","plt.tight_layout()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":333},"id":"HETpPW3X2s92","outputId":"0c1cc87d-34df-49ea-8da7-3cf0f29e0150","executionInfo":{"status":"ok","timestamp":1675628455103,"user_tz":-60,"elapsed":4426,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 828x324 with 3 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["def plot_ecdf(data, label=''):\n","    fig = plt.figure()\n","    ax = fig.add_subplot()\n","    sns.ecdfplot(data = data, legend = data)\n","    plt.xlabel(label)\n","    plt.ylabel(\"ECDF\")\n","    plt.show()\n","\n","_d = {'congruent': con.time,\n","      'incongruent': inc.time,\n","      'env.+positive': eco_pos.time,\n","      'env.+negative': eco_neg.time}\n","\n","plot_ecdf(_d, 'Response time (ms)')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"jKlQR2FwsVtN","outputId":"1ff5f5fa-2b00-4f7c-9c1d-949596b616a9","executionInfo":{"status":"ok","timestamp":1675628455617,"user_tz":-60,"elapsed":521,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#### Contrast analysis"],"metadata":{"id":"yozDCpZo_0D2"}},{"cell_type":"markdown","source":["For the AFP, participants will be quicker to respond when the primes are positive or associated with the environment and the target is also positive rather than negative. This can be reflected in contrast codes (-1,-1,2) X (-1, 1) vs. (-1,1,0) X (-1,1). Then, the authors could look at the simple effects by looking at the effect of the prime when the targets are positive (-1,-1, 2) X (1, 0), and when the targets are negative (-1, -1, 2) X (0, 1)."],"metadata":{"id":"qwxufqAJ_0D-"}},{"cell_type":"code","source":["# https://personal.utdallas.edu/~herve/abdi-contrasts2010-pretty.pdf\n","# https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLSResults.t_test.html\n","d = data.copy()\n","invalid = d[d.time > 4000].id.unique()\n","d = d[d.id.apply(lambda x: not x in invalid)]\n","print(f'{len(invalid)} - dropout')\n","d['p_env'] = d['test'].apply(lambda x: x in eco)\n","d['p_pos'] = d['test'] == pos\n","d['p_neg'] = d['test'] == neg\n","d['t_pos'] = d['ctrl'] == pos\n","d['t_neg'] = d['ctrl'] == neg\n","d['t_neut'] = d['ctrl'] == neut\n","d = d.groupby(by=['id', 'p_env', 'p_pos', 'p_neg', 't_pos', 't_neg', 't_neut'])\\\n","          .aggregate(np.mean).reset_index()\n","d = d[d['t_neut'] == False][['id', 'p_env', 'p_pos', 'p_neg', 't_pos', 't_neg', 'time']]\n","\n","f = 'time ~ p_env + p_pos + p_neg + t_pos + t_neg'\n","# use robust linear regression https://www.statsmodels.org/stable/rlm.html\n","m = smf.rlm(f, d, M=sm.robust.norms.HuberT()).fit()\n","f_test = m.f_test([[0, 1, 1, -2, 1, -1], \n","                   [0, 1, -1, 0, 1, -1]])\n","print(f_test)\n","print(f'Cohen’s d* = {cohen_d(f_test)}')\n","print('* Cohen’s d derived from F statistic (Fritz et al., 2012)')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628460875,"user_tz":-60,"elapsed":612,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"35b1fefd-6297-4185-80fb-c51a14bc5902","id":"T1K34XrE_0D-"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["71 - dropout\n","<F test: F=array([[4.98956417]]), p=0.007012791453211962, df_denom=836, df_num=2>\n","Cohen’s d* = 0.21851092783227735\n","* Cohen’s d derived from F statistic (Fritz et al., 2012)\n"]}]},{"cell_type":"code","source":["m.t_test([[0, 1, 1, -2, 1, -1], \n","          [0, 1, -1, 0, 1, -1]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1jMkELW4eTDI","executionInfo":{"status":"ok","timestamp":1675628478939,"user_tz":-60,"elapsed":218,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"3e188f5c-8668-47d1-d904-018c9530fcd2"},"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","                             Test for Constraints                             \n","==============================================================================\n","                 coef    std err          z      P>|z|      [0.025      0.975]\n","------------------------------------------------------------------------------\n","c0          -134.9980     53.520     -2.522      0.012    -239.895     -30.101\n","c1           -90.6247     36.083     -2.512      0.012    -161.347     -19.903\n","=============================================================================="]},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":["m.f_test([0, 1, 1, -2, 1, -1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628480754,"user_tz":-60,"elapsed":247,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"dc88ca9d-23a3-44f9-84e7-6aed46756d01","id":"WgaAWNDy_0D-"},"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","<F test: F=array([[6.36239893]]), p=0.011840994788163549, df_denom=836, df_num=1>"]},"metadata":{},"execution_count":41}]},{"cell_type":"code","source":["m.f_test([0, 1, -1, 0, 1, -1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675620081696,"user_tz":-60,"elapsed":3,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"62485690-d482-4729-95aa-0e8019b1eaea","id":"UHgJQDVl_0D-"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","<F test: F=array([[6.30785685]]), p=0.012207891615787348, df_denom=836, df_num=1>"]},"metadata":{},"execution_count":56}]},{"cell_type":"markdown","source":["### AMP"],"metadata":{"id":"96jbuaLqkTeI"}},{"cell_type":"code","source":["d = data.copy()\n","a = d[(d.test == neg) & (d.ctrl == neg) & (d.valence > 3)].id.unique()\n","b = d[(d.test == pos) & (d.ctrl == pos) & (d.valence < 3)].id.unique()\n","invalid = {*a, *b}\n","print(f'{len(invalid)} - dropout')\n","d = d[d.id.apply(lambda x: not x in invalid)]\n","pos_neut = d[(d.test == pos) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #positive-neutral\n","neg_neut = d[(d.test == neg) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #negative-neutral\n","eco_neut = d[d.test.apply(lambda x: x in eco) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #eco-neutral\n","rec_neut = d[(d.test == rec) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sav_neut = d[(d.test == sav) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sho_neut = d[(d.test == sho) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","tra_neut = d[(d.test == tra) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","print(f'n={len(pos_neut)}')\n","print(f'eco valence median: {eco_neut.valence.median()}')\n","print(f'pos vs neg mean diff {pos_neut.valence.mean()-neg_neut.valence.mean():.2f} pts')\n","print(f'eco vs neg mean diff {eco_neut.valence.mean()-neg_neut.valence.mean():.2f} pts') \n","print(f'eco vs pos mean diff {eco_neut.valence.mean()-pos_neut.valence.mean():.2f} pts')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NiNtuhXqkNTL","outputId":"f471e4d5-f8b1-4a6d-f6a6-31a005c37725","executionInfo":{"status":"ok","timestamp":1675628514981,"user_tz":-60,"elapsed":231,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":42,"outputs":[{"output_type":"stream","name":"stdout","text":["43 - dropout\n","n=168\n","eco valence median: 3.125\n","pos vs neg mean diff 0.39 pts\n","eco vs neg mean diff 0.33 pts\n","eco vs pos mean diff -0.06 pts\n"]}]},{"cell_type":"code","source":["# Normality tests\n","from scipy import stats\n","# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html\n","k, p = stats.normaltest(eco_neut.valence)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(pos_neut.valence)\n","print(f\"p = {p:g} ~ {p:.3f}\")\n","k, p = stats.normaltest(neg_neut.valence)\n","print(f\"p = {p:g} ~ {p:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GXRPXcU0ZjfJ","executionInfo":{"status":"ok","timestamp":1675628517611,"user_tz":-60,"elapsed":3,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"1d3960f1-cd98-4daa-e5b1-5b9ad393a769"},"execution_count":43,"outputs":[{"output_type":"stream","name":"stdout","text":["p = 0.104995 ~ 0.105\n","p = 0.572761 ~ 0.573\n","p = 0.430807 ~ 0.431\n"]}]},{"cell_type":"code","source":["cor_p = pearsonr(eco_neut.valence, eco_neut.time)\n","cor_s = spearmanr(eco_neut.valence, eco_neut.time)\n","ci = spearmanCI(cor_s[0], len(eco_neut.valence))\n","print(f'Correlation between valence and responce time (env.):')\n","print(f'Pearson r({len(eco_neut.valence)-2})={cor_p[0]:.2f} p={cor_p[1]:.3f}')\n","print(f'Spearman r({len(eco_neut.valence)-2})={cor_s[0]:.2f}, 95%CI [{ci[0]:.2f}–{ci[1]:.2f}], p={cor_s[1]:.3f}')\n","\n","cor_p = pearsonr(pos_neut.valence, pos_neut.time)\n","cor_s = spearmanr(pos_neut.valence, pos_neut.time)\n","ci = spearmanCI(cor_s[0], len(pos_neut.valence))\n","print(f'Correlation between valence and responce time (pos):')\n","print(f'Pearson r({len(pos_neut.valence)-2})={cor_p[0]:.2f} p={cor_p[1]:.3f}')\n","print(f'Spearman r({len(pos_neut.valence)-2})={cor_s[0]:.2f}, 95%CI [{ci[0]:.2f}–{ci[1]:.2f}], p={cor_s[1]:.3f}')\n","\n","cor_p = pearsonr(neg_neut.valence, neg_neut.time)\n","cor_s = spearmanr(neg_neut.valence, neg_neut.time)\n","ci = spearmanCI(cor_s[0], len(neg_neut.valence))\n","print(f'Correlation between valence and responce time (neg):')\n","print(f'Pearson r({len(neg_neut.valence)-2})={cor_p[0]:.2f} p={cor_p[1]:.3f}')\n","print(f'Spearman r({len(neg_neut.valence)-2})={cor_s[0]:.2f}, 95%CI [{ci[0]:.2f}–{ci[1]:.2f}], p={cor_s[1]:.3f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D5G2AJPGQQuG","outputId":"7cfede45-3c2d-4c26-ae55-5fb535bffb3d","executionInfo":{"status":"ok","timestamp":1675628519093,"user_tz":-60,"elapsed":3,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":44,"outputs":[{"output_type":"stream","name":"stdout","text":["Correlation between valence and responce time (env.):\n","Pearson r(166)=0.05 p=0.508\n","Spearman r(166)=0.03, 95%CI [-0.12–0.18], p=0.706\n","Correlation between valence and responce time (pos):\n","Pearson r(166)=-0.06 p=0.406\n","Spearman r(166)=-0.08, 95%CI [-0.23–0.07], p=0.304\n","Correlation between valence and responce time (neg):\n","Pearson r(166)=0.02 p=0.817\n","Spearman r(166)=0.11, 95%CI [-0.05–0.25], p=0.175\n"]}]},{"cell_type":"code","source":["print(f'AMP, affective valence analysis with the paired Wilcoxon signed-rank test')\n","t1, p1 = wilcoxon(pos_neut.valence, neg_neut.valence)\n","t2, p2 = wilcoxon(pos_neut.valence, neg_neut.valence, alternative='greater')\n","r1 = cohen_r(p1, len(pos_neut.valence))\n","r2 = cohen_r(p2, len(pos_neut.valence))\n","m1 = bootstrap(pos_neut.valence)\n","m2 = bootstrap(neg_neut.valence)\n","print(f'''\n","positive vs negative\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-greater: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_neut.valence, pos_neut.valence)\n","t2, p2 = wilcoxon(eco_neut.valence, pos_neut.valence, alternative='less')\n","r1 = cohen_r(p1, len(eco_neut.valence))\n","r2 = cohen_r(p2, len(eco_neut.valence))\n","m1 = bootstrap(eco_neut.valence)\n","m2 = bootstrap(pos_neut.valence)\n","print(f'''\n","env. vs positive\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-less: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","t1, p1 = wilcoxon(eco_neut.valence, neg_neut.valence)\n","t2, p2 = wilcoxon(eco_neut.valence, neg_neut.valence, alternative='greater')\n","r1 = cohen_r(p1, len(eco_neut.valence))\n","r2 = cohen_r(p2, len(eco_neut.valence))\n","m1 = bootstrap(eco_neut.valence)\n","m2 = bootstrap(neg_neut.valence)\n","print(f'''\n","env. vs negative\n","Mdn1={m1[0]:.1f}, 95%CI†[{m1[0] - m1[1]:.1f}–{m1[0] + m1[1]:.1f}],\n","Mdn2={m2[0]:.1f}, 95%CI†[{m2[0] - m2[1]:.1f}–{m2[0] + m2[1]:.1f}],\n","two-sided: W={t1}, p={p1:.3f}, r‡={r1:.2f},\n","alternative-greater: W={t2}, p={p2:.3f}, r‡={r2:.2f}\n","''')\n","print('Note. Null hypothesis H0: the median of the differences is located at zero, μ = 0.')\n","print('One-sided alternative (less) hypothesis H1: the median of the differences is located on the left, μ < 0.')\n","print('One-sided alternative (greater) hypothesis H2: the median of the differences is located on the right, μ > 0.')\n","print('† Bootstrapped 95% confidence intervals for the median from 10000 resamples are reported.')\n","print('‡ Cohen’s r (Fritz et al., 2012; Cohen, 1988) for Wilcoxon signed-rank test is reported.')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gpTSgsAwkNWn","outputId":"d26f38c5-1134-45a3-bbdf-facb32d7c0a4","executionInfo":{"status":"ok","timestamp":1675628527936,"user_tz":-60,"elapsed":6034,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":45,"outputs":[{"output_type":"stream","name":"stdout","text":["AMP, affective valence analysis with the paired Wilcoxon signed-rank test\n","\n","positive vs negative\n","Mdn1=3.2, 95%CI†[3.1–3.4],\n","Mdn2=2.8, 95%CI†[2.6–2.9],\n","two-sided: W=3971.5, p=0.000, r‡=0.31,\n","alternative-greater: W=8589.5, p=0.000, r‡=0.32\n","\n","\n","env. vs positive\n","Mdn1=3.1, 95%CI†[3.0–3.2],\n","Mdn2=3.2, 95%CI†[3.1–3.4],\n","two-sided: W=5250.0, p=0.155, r‡=0.11,\n","alternative-less: W=5250.0, p=0.077, r‡=0.14\n","\n","\n","env. vs negative\n","Mdn1=3.1, 95%CI†[3.0–3.2],\n","Mdn2=2.8, 95%CI†[2.6–2.9],\n","two-sided: W=3845.5, p=0.000, r‡=0.33,\n","alternative-greater: W=8715.5, p=0.000, r‡=0.34\n","\n","Note. Null hypothesis H0: the median of the differences is located at zero, μ = 0.\n","One-sided alternative (less) hypothesis H1: the median of the differences is located on the left, μ < 0.\n","One-sided alternative (greater) hypothesis H2: the median of the differences is located on the right, μ > 0.\n","† Bootstrapped 95% confidence intervals for the median from 10000 resamples are reported.\n","‡ Cohen’s r (Fritz et al., 2012; Cohen, 1988) for Wilcoxon signed-rank test is reported.\n"]}]},{"cell_type":"code","source":["d = [[eco_neut.valence, pos_neut.valence],\n","     [eco_neut.valence, neg_neut.valence],\n","     [pos_neut.valence, neg_neut.valence]]\n","\n","w = wilcoxon(d[0][0], d[0][1], alternative='two-sided',)\n","r = cohen_r(w[1], len(d[0][0]))\n","t = [f'env. vs positive\\nH1: μ≠0, W={w[0]}, p={w[1]:.3f}, r={r:.2f}']\n","w = wilcoxon(d[1][0], d[1][1], alternative='greater')\n","r = cohen_r(w[1], len(d[1][0]))\n","t += [f'env. vs negative\\nH1: μ>0, W={w[0]}, p<{w[1]:.3f}, r={r:.2f}']\n","w = wilcoxon(d[2][0], d[2][1], alternative='greater')\n","r = cohen_r(w[1], len(d[2][0]))\n","t += [f'positive vs negative\\nH1: μ>0, W={w[0]}, p<{w[1]:.3f}, r={r:.2f}']\n","\n","l=[['env.', 'positive'],\n","   ['env.', 'negative'],\n","   ['positive', 'negative']]\n","\n","barWidth = 0.2\n","fig, axs = plt.subplots(1, len(d), figsize=(11.5, 4.5))\n","for i, ax in enumerate(axs.flat):\n","    m1 = bootstrap(d[i][0])\n","    m2 = bootstrap(d[i][1])\n","    ax.bar(0.0, m1[0], width = barWidth, color = 'lightgreen', edgecolor = 'gray', capsize=5, label=l[i][0], yerr=m1[1])\n","    ax.bar(0.5, m2[0], width = barWidth, color = 'silver', edgecolor = 'gray', capsize=5, label=l[i][1], yerr=m2[1])\n","    ax.plot((0.0, 0.5), (m1[0], m2[0]), 'k--', linewidth = 1.2)\n","    if i > 0:\n","      pass\n","    else:\n","      ax.set_ylabel('Affective valence (pts)')\n","    ax.set_xticklabels([])\n","    ax.legend()\n","    ax.set_xticks([])\n","    ax.set_title(t[i], fontsize=12, fontweight='bold')\n","    ax.tick_params(axis='y', labelsize=12)\n","    ax.set_ylim([0.9, 3.8])\n","plt.tight_layout()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":333},"id":"pyPhixw_2KgW","outputId":"4f479b10-e034-4fa8-897a-5875bc7c3aaa","executionInfo":{"status":"ok","timestamp":1675628532600,"user_tz":-60,"elapsed":4669,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 828x324 with 3 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["cor_p = pearsonr(eco_neut.valence, eco_neut.time)\n","cor_s = spearmanr(eco_neut.valence, eco_neut.time)\n","ci = spearmanCI(cor_s[0], len(eco_neut.valence))\n","print(f'Correlation between valence and responce time:')\n","print(f'Pearson r={cor_p[0]:.2f} p={cor_p[1]:.3f}')\n","print(f'Spearman r={cor_s[0]:.2f}, 95%CI [{ci[0]:.2f}–{ci[1]:.2f}], p={cor_s[1]:.3f}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v5q8PW1QIYD8","outputId":"3a9b7479-ade9-44fa-8500-3551778897be","executionInfo":{"status":"ok","timestamp":1675628532600,"user_tz":-60,"elapsed":10,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["Correlation between valence and responce time:\n","Pearson r=0.05 p=0.508\n","Spearman r=0.03, 95%CI [-0.12–0.18], p=0.706\n"]}]},{"cell_type":"code","source":["def plot_ecdf(data, label=''):\n","    fig = plt.figure()\n","    ax = fig.add_subplot()\n","    sns.ecdfplot(data = data, legend = data)\n","    plt.xlabel(label)\n","    plt.ylabel(\"ECDF\")\n","    plt.show()\n","\n","_d = {'env.': eco_neut.valence,\n","      'positive': pos_neut.valence,\n","      'negative': neg_neut.valence}\n","\n","plot_ecdf(_d, 'Affective valence (pts)')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"issNxMQsuC7b","outputId":"073cd8a7-2cdd-479e-b2db-bd16c247a6de","executionInfo":{"status":"ok","timestamp":1675628532601,"user_tz":-60,"elapsed":8,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["d = data.copy()\n","a = d[(d.test == neg) & (d.ctrl == neg) & (d.valence > 3)].id.unique()\n","b = d[(d.test == pos) & (d.ctrl == pos) & (d.valence < 3)].id.unique()\n","c = d[(d.q1 == 6) & (d.q2 == 5) & (d.q3 == 4) & (d.q4 == 3) & \\\n","      (d.q5 == 3) & (d.q6 == 4) & (d.q7 == 5) & (d.q8 == 6) & \\\n","      (d.q9 == 6) & (d.q10 == 5) & (d.q11 == 4) & (d.q12 == 3)].id.unique()\n","invalid = {*a, *b, *c}\n","print(f'{len([i for i in c if i not in {*a, *b}])} - dropout')\n","print(f'{len(invalid)} - total dropout')\n","d = d[d.id.apply(lambda x: not x in invalid)]#.dropna() #6 uncomplete\n","rec_v = d[(d.test == rec) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sav_v = d[(d.test == sav) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sho_v = d[(d.test == sho) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","tra_v = d[(d.test == tra) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","eco_v = d[d.test.apply(lambda x: x in eco) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #eco-neutral\n","print(f'n={len(eco_v)}')\n","\n","random.seed(13)\n","np.random.seed(13)\n","#https://arxiv.org/pdf/2106.01140.pdf\n","d = eco_v.copy()\n","d.time = np.log(d.time)\n","d.age = np.log(d.age)\n","formula = \"\"\"\n","B =~ q1 + q2 + q3 + q4\n","E =~ q5 + q6 + q7 + q8\n","A =~ q9 + q10 + q11 + q12\n","IA =~ valence\n","IA ~ B + E + A + gender + age + edu + car + income + work \n","\"\"\"\n","model = sem.Model(formula)\n","model.fit(d, obj=\"MLW\", solver=\"SLSQP\") #MLW ULS GLS FIML DWLS WLS\n","sem.bias_correction(model, n=1000, resample_mean=True)\n","print(sem.calc_stats(model).T)\n","sem.semplot(model, \"fig.pdf\", plot_covs=True, std_ests=True, show=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":923},"id":"uaJ_Z7tdW1Sd","outputId":"318780bb-16d3-4009-9d8f-5b077d835010","executionInfo":{"status":"ok","timestamp":1675628667231,"user_tz":-60,"elapsed":134637,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["17 - dropout\n","60 - total dropout\n","n=151\n","                     Value\n","DoF             152.000000\n","DoF Baseline    177.000000\n","chi2            214.593304\n","chi2 p-value      0.000626\n","chi2 Baseline  1520.654984\n","CFI               0.953416\n","GFI               0.858881\n","AGFI              0.835671\n","NFI               0.858881\n","TLI               0.945754\n","RMSEA             0.052396\n","AIC              73.152658\n","BIC             187.809291\n","LogLik            1.423671\n"]},{"output_type":"execute_result","data":{"text/plain":["<graphviz.dot.Digraph at 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(d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","sho_v = d[(d.test == sho) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","tra_v = d[(d.test == tra) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean)\n","eco_v = d[d.test.apply(lambda x: x in eco) & (d.ctrl == neut)].groupby(by=['id']).aggregate(np.mean) #eco-neutral\n","print(f'n={len(eco_v)}')\n","\n","random.seed(13)\n","np.random.seed(13)\n","#https://arxiv.org/pdf/2106.01140.pdf\n","d = eco_v.copy()\n","d.time = np.log(d.time)\n","d.age = np.log(d.age)\n","formula = \"\"\"\n","B =~ q1 + q2 + q3 + q4\n","E =~ q5 + q6 + q7 + q8\n","A =~ q9 + q10 + q11 + q12\n","IA =~ valence\n","IA ~ B + E + A\n","\"\"\"\n","model = sem.Model(formula)\n","model.fit(d, obj=\"MLW\", solver=\"SLSQP\") #MLW ULS GLS FIML DWLS WLS\n","sem.bias_correction(model, n=1000, resample_mean=True)\n","print(sem.calc_stats(model).T)\n","sem.semplot(model, \"fig.pdf\", plot_covs=True, std_ests=True, show=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":921},"id":"GDGLaSyWiMu_","outputId":"9e9349f9-4598-435d-9aa7-f431da949f55","executionInfo":{"status":"ok","timestamp":1675628709043,"user_tz":-60,"elapsed":41821,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}}},"execution_count":50,"outputs":[{"output_type":"stream","name":"stdout","text":["17 - dropout\n","60 - total dropout\n","n=151\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:root:Fisher Information Matrix is not PD.Moore-Penrose inverse will be used instead of Cholesky decomposition. See 10.1109/TSP.2012.2208105.\n"]},{"output_type":"stream","name":"stdout","text":["                      Value\n","DoF            5.900000e+01\n","DoF Baseline   7.800000e+01\n","chi2           1.305158e+02\n","chi2 p-value   2.531516e-07\n","chi2 Baseline  1.250855e+03\n","CFI            9.390242e-01\n","GFI            8.956587e-01\n","AGFI           8.620573e-01\n","NFI            8.956587e-01\n","TLI            9.193879e-01\n","RMSEA          8.989374e-02\n","AIC            6.226930e+01\n","BIC            1.588223e+02\n","LogLik         8.653489e-01\n"]},{"output_type":"execute_result","data":{"text/plain":["<graphviz.dot.Digraph at 0x7f6f46842760>"],"image/svg+xml":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: G Pages: 1 -->\n<svg width=\"915pt\" height=\"427pt\"\n viewBox=\"0.00 0.00 915.00 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This can be tested with contrast analyses. In concrete terms, this amounts to giving a weight to each condition, which here translates into the contrast codes (-1,-1,2) and (-1,1,0). The advantage here would be to present only one statistical test per study for each task, and thus gain statistical power. "],"metadata":{"id":"FSxPop82ALO9"}},{"cell_type":"code","source":["# https://personal.utdallas.edu/~herve/abdi-contrasts2010-pretty.pdf\n","# https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLSResults.t_test.html\n","d = data.copy()\n","a = d[(d.test == neg) & (d.ctrl == neg) & (d.valence > 3)].id.unique()\n","b = d[(d.test == pos) & (d.ctrl == pos) & (d.valence < 3)].id.unique()\n","invalid = {*a, *b}\n","d = d[d.id.apply(lambda x: not x in invalid)]\n","print(f'{len(invalid)} - dropout')\n","d['p_env'] = d['test'].apply(lambda x: x in eco)\n","d['p_pos'] = d['test'] == pos\n","d['p_neg'] = d['test'] == neg\n","d['t_pos'] = d['ctrl'] == pos\n","d['t_neg'] = d['ctrl'] == neg\n","d['t_neut'] = d['ctrl'] == neut\n","d = d.groupby(by=['id', 'p_env', 'p_pos', 'p_neg', 't_pos', 't_neg', 't_neut'])\\\n","          .aggregate(np.mean).reset_index()\n","d = d[d['t_neut'] == True][['id', 'p_env', 'p_pos', 'p_neg', 'valence']]\n","\n","f = 'valence ~ p_env + p_pos + p_neg'\n","# use robust linear regression https://www.statsmodels.org/stable/rlm.html\n","m = smf.rlm(f, d, M=sm.robust.norms.HuberT()).fit()\n","f_test = m.f_test([[0, 1, 1, -2], \n","                   [0, 1, -1, 0]])\n","print(f_test)\n","print(f'Cohen’s d* = {cohen_d(f_test)}')\n","print('* Cohen’s d derived from F statistic (Fritz et al., 2012)')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628709391,"user_tz":-60,"elapsed":378,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"f0741af3-739a-4f60-eb1c-7f2843a1cf54","id":"lAdXZ327ALO9"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["43 - dropout\n","<F test: F=array([[11.54926135]]), p=1.2485616149248625e-05, df_denom=501, df_num=2>\n","Cohen’s d* = 0.4294407326305965\n","* Cohen’s d derived from F statistic (Fritz et al., 2012)\n"]}]},{"cell_type":"code","source":["m.t_test([[0, 1, 1, -2], \n","          [0, 1, -1, 0]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628709391,"user_tz":-60,"elapsed":11,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"5da1ec35-3d14-412a-8b38-33797df1cdf9","id":"Lc43bw5DALO9"},"execution_count":52,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","                             Test for Constraints                             \n","==============================================================================\n","                 coef    std err          z      P>|z|      [0.025      0.975]\n","------------------------------------------------------------------------------\n","c0             0.7393      0.156      4.745      0.000       0.434       1.045\n","c1            -0.0685      0.090     -0.761      0.447      -0.245       0.108\n","=============================================================================="]},"metadata":{},"execution_count":52}]},{"cell_type":"code","source":["m.f_test([0, 1, 1, -2])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628709391,"user_tz":-60,"elapsed":7,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"cb2b0e54-1ace-4f66-d8a2-934310984998","id":"Y2uwiQuXALO9"},"execution_count":53,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","<F test: F=array([[22.5191591]]), p=2.7182232080519303e-06, df_denom=501, df_num=1>"]},"metadata":{},"execution_count":53}]},{"cell_type":"code","source":["m.f_test([[0, -1, 1, 0]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1675628709393,"user_tz":-60,"elapsed":6,"user":{"displayName":"Ellariel","userId":"04871811629800677381"}},"outputId":"a45142e8-62b8-4fca-f061-f924db65bd9b","id":"ZaJafmVrALO9"},"execution_count":54,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<class 'statsmodels.stats.contrast.ContrastResults'>\n","<F test: F=array([[0.57936359]]), p=0.44691999047581976, df_denom=501, df_num=1>"]},"metadata":{},"execution_count":54}]}]}