figure1_allAlg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import multivariate_normal\n",
    "from scipy.stats import entropy\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import string\n",
    "import pickle\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import sys, os\n",
    "\n",
    "\n",
    "sns.set(color_codes=True, style=\"white\", context=\"talk\", font_scale=1.5)\n",
    "PALETTE = sns.color_palette(\"Set1\")\n",
    "sns.set_palette(PALETTE[1:5] + PALETTE[6:], n_colors=9)\n",
    "\n",
    "def truncate(n, dicimals=2):\n",
    "    return round(n, dicimals)\n",
    "\n",
    "# helper function to compare two numbers\n",
    "def compare(A, B):\n",
    "    if A > B:\n",
    "        return \">\"\n",
    "    elif A < B:\n",
    "        return \"<\"\n",
    "    else:\n",
    "        return \"=\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For generating Variable C\n",
    "def generate_gauss_mix(\n",
    "    N=5000,\n",
    "    prior=(0.5, 0.5),\n",
    "    p=1,\n",
    "    mu_class0=0,\n",
    "    mu_class0_0=0,\n",
    "    mu_class0_1=5,\n",
    "    mu_class1=1,\n",
    "    mu_class1_0=0,\n",
    "    mu_class1_1=5,\n",
    "    sig_class0=1,\n",
    "    sig_class0_0=1,\n",
    "    sig_class0_1=1,\n",
    "    sig_class1=1,\n",
    "    sig_class1_0=1,\n",
    "    sig_class1_1=1,\n",
    "    split_class0=None,\n",
    "    split_class1=None,\n",
    "):\n",
    "    class0_mix = True if split_class0 is not None else False\n",
    "    class1_mix = True if split_class1 is not None else False\n",
    "    \n",
    "    prior_0, prior_1 = prior\n",
    "    p_class0, p_class1 = prior\n",
    "    n0 = int(N * prior_0)  # number of samples from class 0\n",
    "    n1 = N - n0  # total number of samples from class 1\n",
    "\n",
    "    if class1_mix:\n",
    "        p_class0, p_class1_0, p_class1_1 = (\n",
    "            prior_0,\n",
    "            prior_1 * split_class1[0],\n",
    "            prior_1 * split_class1[1],\n",
    "        )\n",
    "        n0 = int(N * p_class0)  # number of samples from class 0\n",
    "        n1 = N - n0  # total number of samples from class 1\n",
    "        n1_0 = int(N * p_class1_0)  # number of samples from class 1_0\n",
    "        n1_1 = n1 - n1_0  # number of samples from class 1_1\n",
    "        mu_class1_0 = np.array([mu_class1_0] * p)\n",
    "        mu_class1_1 = np.array([mu_class1_1] * p)\n",
    "        sig_class0 = np.identity(p) * sig_class0\n",
    "        sig_class1_0 = np.identity(p) * sig_class1_0\n",
    "        sig_class1_1 = np.identity(p) * sig_class1_1\n",
    "        x_1_0 = np.random.multivariate_normal(mu_class1_0, sig_class1_0, size=n1_0)\n",
    "        x_1_1 = np.random.multivariate_normal(mu_class1_1, sig_class1_1, size=n1_1)\n",
    "        x_1 = np.vstack((x_1_0, x_1_1))\n",
    "    else:\n",
    "        # p_class0, p_class1 = prior\n",
    "        mu_class1 = np.array([mu_class1] * p)\n",
    "        sig_class0 = np.identity(p) * sig_class0\n",
    "        sig_class1 = np.identity(p) * sig_class1\n",
    "        x_1 = np.random.multivariate_normal(mu_class1, sig_class1, size=n1)\n",
    "\n",
    "    if class0_mix:\n",
    "        p_class0_0, p_class0_1 = split_class0\n",
    "        n0 = int(N * p_class0)\n",
    "        n0_0 = int(n0 * p_class0_0)\n",
    "        n0_1 = n0 - n0_0\n",
    "        mu_class0_0 = np.array([mu_class0_0] * p)\n",
    "        mu_class0_1 = np.array([mu_class0_1] * p)\n",
    "        sig_class0_0 = np.identity(p) * sig_class0_0\n",
    "        sig_class0_1 = np.identity(p) * sig_class0_1\n",
    "        x_0_0 = np.random.multivariate_normal(mu_class0_0, sig_class0_0, size=n0_0)\n",
    "        x_0_1 = np.random.multivariate_normal(mu_class0_1, sig_class0_1, size=n0_1)\n",
    "        x_0 = np.vstack((x_0_0, x_0_1))\n",
    "    else:\n",
    "        mu_class0 = np.array([mu_class0] * p)\n",
    "        x_0 = np.random.multivariate_normal(mu_class0, sig_class0, size=n0)\n",
    "\n",
    "    x = np.vstack((x_0, x_1))\n",
    "    y = np.array([0] * n0 + [1] * n1).reshape(-1, 1)\n",
    "\n",
    "    # Create the probability density functions (PDFs) for the two Gaussian distributions\n",
    "    if class0_mix:\n",
    "        pdf_class0_0 = multivariate_normal(mu_class1_0, sig_class1_0)\n",
    "        pdf_class0_1 = multivariate_normal(mu_class1_1, sig_class1_1)\n",
    "        p_x_given_class0_0 = pdf_class0_0.pdf(x)\n",
    "        p_x_given_class0_1 = pdf_class0_1.pdf(x)\n",
    "        p_x_given_class0 = (\n",
    "            split_class1[0] * p_x_given_class0_0 + split_class0[1] * p_x_given_class0_1\n",
    "        )\n",
    "    else:\n",
    "        pdf_class0 = multivariate_normal(mu_class0, sig_class0)\n",
    "        p_x_given_class0 = pdf_class0.pdf(x)\n",
    "\n",
    "    if class1_mix:\n",
    "        pdf_class1_0 = multivariate_normal(mu_class1_0, sig_class1_0)\n",
    "        pdf_class1_1 = multivariate_normal(mu_class1_1, sig_class1_1)\n",
    "        p_x_given_class1_0 = pdf_class1_0.pdf(x)\n",
    "        p_x_given_class1_1 = pdf_class1_1.pdf(x)\n",
    "        p_x_given_class1 = (\n",
    "            split_class1[0] * p_x_given_class1_0 + split_class1[1] * p_x_given_class1_1\n",
    "        )\n",
    "    else:\n",
    "        pdf_class1 = multivariate_normal(mu_class1, sig_class1)\n",
    "        p_x_given_class1 = pdf_class1.pdf(x)\n",
    "\n",
    "    p_x = p_x_given_class0 * p_class0 + p_x_given_class1 * p_class1\n",
    "\n",
    "    pos_class0 = p_x_given_class0 * p_class0 / p_x\n",
    "    pos_class1 = p_x_given_class1 * (1 - p_class0) / p_x\n",
    "\n",
    "    posterior = np.hstack((pos_class0.reshape(-1, 1), pos_class1.reshape(-1, 1)))\n",
    "    stats_conen = np.mean(entropy(posterior, base=np.exp(1), axis=1))\n",
    "\n",
    "    # if class0_mix:\n",
    "    #     prior_y_0 = np.array([p_class0_0, p_class0_1])\n",
    "    # else:\n",
    "    #     prior_y_0 = np.array(p_class0)\n",
    "\n",
    "    # if class1_mix:\n",
    "    #     prior_y_1 = np.array([p_class1_0, p_class1_1])\n",
    "    # else:\n",
    "    #     prior_y_1 = np.array(p_class1)\n",
    "        \n",
    "    entropy_y = entropy(prior, base=np.exp(1))\n",
    "    correlation = np.corrcoef(x_0.T, x_1.T)\n",
    "    MI = entropy_y - stats_conen\n",
    "    auc = roc_auc_score(y, posterior[:, 1])\n",
    "    pauc_90 = roc_auc_score(y, posterior[:, 1], max_fpr=0.1)\n",
    "    pauc_98 = roc_auc_score(y, posterior[:, 1], max_fpr=0.02)\n",
    "    if MI == 0.0:\n",
    "        # replace the posterior with random numbers ~ U(0,1) to calculate the ROC curve\n",
    "        posterior_ = np.random.uniform(0, 1, size=(N, 2))\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(\n",
    "            y, posterior_[:, 1], pos_label=1, drop_intermediate=False\n",
    "        )\n",
    "    else: \n",
    "        fpr, tpr, thresholds = metrics.roc_curve(\n",
    "        y, posterior[:, 1], pos_label=1, drop_intermediate=False\n",
    "    )\n",
    "    tpr_s = np.max(tpr[fpr <= 0.02])\n",
    "    y_pred = np.argmax(posterior, axis=1)\n",
    "    accuracy = metrics.accuracy_score(y, y_pred)\n",
    "    tn, fp, fn, tp = metrics.confusion_matrix(y, y_pred).ravel()\n",
    "    sensitivity = tp / (tp + fn)\n",
    "    specificity = tn / (tn + fp)\n",
    "    f1 = metrics.f1_score(y, y_pred)\n",
    "\n",
    "    statistics = {\n",
    "        # 'Correlation': correlation[0,1],\n",
    "        \"ACC\": accuracy,\n",
    "        \"AUC\": auc,\n",
    "        # \"F1\": f1,\n",
    "        \"MI\": MI,\n",
    "        # \"pAUC_90\": pauc_90,\n",
    "        # \"pAUC_98\": pauc_98,\n",
    "        \"S@98\": tpr_s,\n",
    "        # 'Sensitivity': sensitivity,\n",
    "        # 'Specificity': specificity,\n",
    "        # 'TN': tn,\n",
    "        # 'FP': fp,\n",
    "        # 'FN': fn,\n",
    "        # 'TP': tp\n",
    "        # \"tpr\": tpr,\n",
    "        # \"fpr\": fpr,\n",
    "    }\n",
    "    x_min, x_max = np.min(x), np.max(x)\n",
    "    # print(x_min, x_max)\n",
    "    xs = np.linspace(x_min - 1, x_max + 1, 1000)\n",
    "    pdf = pd.DataFrame()\n",
    "    pdf[\"x\"] = xs\n",
    "    if class0_mix:\n",
    "        pdf[\"pdf_class0\"] =  split_class0[0] * pdf_class0_0.pdf(xs) + split_class0[1] * pdf_class0_1.pdf(xs)\n",
    "    else:\n",
    "        pdf[\"pdf_class0\"] = pdf_class0.pdf(xs)\n",
    "    if class1_mix:\n",
    "        pdf[\"pdf_class1\"] =  split_class1[0] * pdf_class1_0.pdf(xs) + split_class1[1] * pdf_class1_1.pdf(xs)\n",
    "    else:\n",
    "        pdf[\"pdf_class1\"] = pdf_class1.pdf(xs)\n",
    "\n",
    "    return x, y, posterior[:, 1], statistics, pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 50000\n",
    "x_A, y_A, posterior_A, statistics_A, pdf_A = generate_gauss_mix(\n",
    "    N=N,\n",
    "    prior=(0.5, 0.5),\n",
    "    p=1,\n",
    "    mu_class0=0,\n",
    "    mu_class1=1,\n",
    "    sig_class0=1,\n",
    "    sig_class1=1,\n",
    "    split_class1=None,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the data\n",
    "x_B, y_B, posterior_B, statistics_B, pdf_B = generate_gauss_mix(\n",
    "    N=N,\n",
    "    prior=(0.5, 0.5),\n",
    "    p=1,\n",
    "    mu_class0=0,\n",
    "    mu_class1_0=0,\n",
    "    mu_class1_1=5,\n",
    "    sig_class0=1,\n",
    "    sig_class1_0=1,\n",
    "    sig_class1_1=1,\n",
    "    split_class1=(0.75, 0.25),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the data\n",
    "x_C, y_C, posterior_C, statistics_C, pdf_C = generate_gauss_mix(\n",
    "    N=N,\n",
    "    prior=(0.5, 0.5),\n",
    "    p=1,\n",
    "    mu_class0_0=0,\n",
    "    mu_class0_1=5,\n",
    "    mu_class1_0=0,\n",
    "    mu_class1_1=5,\n",
    "    sig_class0_0=1,\n",
    "    sig_class0_1=1,\n",
    "    sig_class1_0=1,\n",
    "    sig_class1_1=1,\n",
    "    split_class0=(0.75, 0.25),\n",
    "    split_class1=(0.75, 0.25),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_trunk_classification(\n",
    "    n_samples,\n",
    "    n_dim=4096,\n",
    "    n_informative=1,\n",
    "    simulation: str = \"trunk\",\n",
    "    mu_0: float = 0,\n",
    "    mu_1: float = 1,\n",
    "    rho: int = 0,\n",
    "    band_type: str = \"ma\",\n",
    "    return_params: bool = False,\n",
    "    mix: float = 0.5,\n",
    "    seed=None,\n",
    "):\n",
    "    if n_dim < n_informative:\n",
    "        raise ValueError(\n",
    "            f\"Number of informative dimensions {n_informative} must be less than number \"\n",
    "            f\"of dimensions, {n_dim}\"\n",
    "        )\n",
    "    rng = np.random.default_rng(seed=seed)\n",
    "    rng1 = np.random.default_rng(seed=seed)\n",
    "    mu_0 = np.array([mu_0 / np.sqrt(i) for i in range(1, n_informative + 1)])\n",
    "    mu_1 = np.array([mu_1 / np.sqrt(i) for i in range(1, n_informative + 1)])\n",
    "    if rho != 0:\n",
    "        if band_type == \"ma\":\n",
    "            cov = _moving_avg_cov(n_informative, rho)\n",
    "        elif band_type == \"ar\":\n",
    "            cov = _autoregressive_cov(n_informative, rho)\n",
    "        else:\n",
    "            raise ValueError(f'Band type {band_type} must be one of \"ma\", or \"ar\".')\n",
    "    else:\n",
    "        cov = np.identity(n_informative)\n",
    "    if mix < 0 or mix > 1:\n",
    "        raise ValueError(\"Mix must be between 0 and 1.\")\n",
    "    # speed up computations for large multivariate normal matrix with SVD approximation\n",
    "    if n_informative > 1000:\n",
    "        method = \"cholesky\"\n",
    "    else:\n",
    "        method = \"svd\"\n",
    "    if simulation == \"trunk\":\n",
    "        X = np.vstack(\n",
    "            (\n",
    "                rng.multivariate_normal(mu_0, cov, n_samples // 2, method=method),\n",
    "                rng1.multivariate_normal(mu_1, cov, n_samples // 2, method=method),\n",
    "            )\n",
    "        )\n",
    "    elif simulation == \"trunk_overlap\":\n",
    "        mixture_idx = rng.choice(\n",
    "            2, n_samples // 2, replace=True, shuffle=True, p=[mix, 1 - mix]\n",
    "        )\n",
    "        norm_params = [[mu_0, cov], [mu_1, cov]]\n",
    "        X_mixture = np.fromiter(\n",
    "            (\n",
    "                rng.multivariate_normal(*(norm_params[i]), size=1, method=method)\n",
    "                for i in mixture_idx\n",
    "            ),\n",
    "            dtype=np.dtype((float, n_informative)),\n",
    "        )\n",
    "        X_mixture_2 = np.fromiter(\n",
    "            (\n",
    "                rng1.multivariate_normal(*(norm_params[i]), size=1, method=method)\n",
    "                for i in mixture_idx\n",
    "            ),\n",
    "            dtype=np.dtype((float, n_informative)),\n",
    "        )\n",
    "        X = np.vstack(\n",
    "            (\n",
    "                X_mixture.reshape(n_samples // 2, n_informative),\n",
    "                X_mixture_2.reshape(n_samples // 2, n_informative),\n",
    "            )\n",
    "        )\n",
    "    elif simulation == \"trunk_mix\":\n",
    "        mixture_idx = rng.choice(\n",
    "            2, n_samples // 2, replace=True, shuffle=True, p=[mix, 1 - mix]\n",
    "        )\n",
    "        norm_params = [[mu_0, cov], [mu_1, cov]]\n",
    "        X_mixture = np.fromiter(\n",
    "            (\n",
    "                rng1.multivariate_normal(*(norm_params[i]), size=1, method=method)\n",
    "                for i in mixture_idx\n",
    "            ),\n",
    "            dtype=np.dtype((float, n_informative)),\n",
    "        )\n",
    "        X = np.vstack(\n",
    "            (\n",
    "                rng.multivariate_normal(\n",
    "                    np.zeros(n_informative), cov, n_samples // 2, method=method\n",
    "                ),\n",
    "                X_mixture.reshape(n_samples // 2, n_informative),\n",
    "            )\n",
    "        )\n",
    "    else:\n",
    "        raise ValueError(f\"Simulation must be: trunk, trunk_overlap, trunk_mix\")\n",
    "    if n_dim > n_informative:\n",
    "        X = np.hstack(\n",
    "            (X, rng.normal(loc=0, scale=1, size=(X.shape[0], n_dim - n_informative)))\n",
    "        )\n",
    "    y = np.concatenate((np.zeros(n_samples // 2), np.ones(n_samples // 2)))\n",
    "    if return_params:\n",
    "        returns = [X, y]\n",
    "        if simulation == \"trunk\":\n",
    "            returns += [[mu_0, mu_1], [cov, cov]]\n",
    "        elif simulation == \"trunk-overlap\":\n",
    "            returns += [[np.zeros(n_informative), np.zeros(n_informative)], [cov, cov]]\n",
    "        elif simulation == \"trunk-mix\":\n",
    "            returns += [*list(zip(*norm_params)), X_mixture]\n",
    "        return returns\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifier_ROC(model_name = 'svm',samplesize = 4096, sim = 'trunk',dim = 4096,reps = 1):\n",
    "    POS = np.array([], dtype=np.int64).reshape(0,3)\n",
    "    for cv_i in range(5):\n",
    "        if model_name =='drf':\n",
    "            pos_cv = np.genfromtxt(os.path.join(data_dir_drf, f\"{sim}_pos_samp{samplesize}_rep{reps}_cv{cv_i}.csv\"),delimiter=\",\")\n",
    "        else:\n",
    "            pos_cv = np.genfromtxt(os.path.join(data_dir_samp, f\"{sim}_{model_name}_pos_samp{samplesize}_rep{reps}_cv{cv_i}.csv\"),delimiter=\",\")\n",
    "        POS = np.vstack([POS,pos_cv])\n",
    "    fpr, tpr, thresholds = roc_curve(POS[:,0], POS[:,-1], pos_label=1, drop_intermediate=False,)\n",
    "    return fpr, tpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir_samp = '/Users/yuxin/Desktop/桌面 - SUKI’s MacBook Pro/CANCER-Paper/Simulaion_Compare/results/vs_samplesize/calibrated_classifier'\n",
    "data_dir_drf = '/Users/yuxin/Desktop/桌面 - SUKI’s MacBook Pro/CANCER-Paper/Simulaion_Compare/results/vs_samplesize/calibrated_classifier/drf'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODELNAME = ['might','lr','knn','svm','drf']\n",
    "MODELNAME_CAP = {'lr':'LR',\n",
    "                 'knn':'KNN',\n",
    "                 'svm':'SVM',\n",
    "                 'drf':'DRF',\n",
    "                 'might':'MIGHT'}\n",
    "base_fpr = np.linspace(0, 1, 1001)\n",
    "REPS = 10\n",
    "# fpr_s = []\n",
    "# tpr_s = []\n",
    "# for modelname in MODELNAME:\n",
    "#     TPR = np.zeros((REPS,len(base_fpr)))\n",
    "#     for rep in range(REPS):\n",
    "#         fpr_ori,tpr_ori = classifier_ROC(model_name = modelname,samplesize = 256, sim = 'trunk',dim = 4096,reps = rep)\n",
    "#         tpr = np.interp(base_fpr, fpr_ori, tpr_ori)\n",
    "#         print(tpr_ori,tpr)\n",
    "#         TPR[rep,:] = tpr\n",
    "#     tpr_s = np.mean(TPR, axis=0)\n",
    "#     plt.plot(base_fpr,tpr_s,label = MODELNAME_CAP[modelname])\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIRECTORY = \"/Users/yuxin/Downloads/fig1_results/\"\n",
    "SAMPLE = 256\n",
    "\n",
    "with open(DIRECTORY + \"fpr_linear_\" + str(SAMPLE) + \".pkl\", \"rb\") as f:\n",
    "    fpr_linear_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"tpr_linear_\" + str(SAMPLE) + \".pkl\", \"rb\") as f:\n",
    "    tpr_linear_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"fpr_nonlinear_\" + str(SAMPLE) + \".pkl\", \"rb\") as f:\n",
    "    fpr_nonlinear_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"tpr_nonlinear_\" + str(SAMPLE) + \".pkl\", \"rb\") as f:\n",
    "    tpr_nonlinear_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"fpr_null_\" + str(SAMPLE) + \".pkl\", \"rb\") as f:\n",
    "    fpr_null_s = pickle.load(f)\n",
    "\n",
    "with open(DIRECTORY + \"tpr_null_\" + str(SAMPLE) + \".pkl\", \"rb\") as f:\n",
    "    tpr_null_s = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_fpr = np.linspace(0, 1, 1001)\n",
    "N_ITR = len(tpr_null_s)\n",
    "\n",
    "linear_might = []\n",
    "nonlinear_might = []\n",
    "null_might = []\n",
    "for j in range(1):\n",
    "    tpr_s = []\n",
    "    for i in range(N_ITR):\n",
    "        tpr = np.interp(base_fpr, fpr_linear_s[i][j], tpr_linear_s[i][j])\n",
    "        tpr_s.append(tpr)\n",
    "        \n",
    "    tpr_s = np.mean(tpr_s, axis=0)\n",
    "    linear_might.append(tpr_s)\n",
    "    \n",
    "    tpr_s = []\n",
    "    for i in range(N_ITR):\n",
    "        tpr = np.interp(base_fpr, fpr_nonlinear_s[i][j], tpr_nonlinear_s[i][j])\n",
    "        tpr_s.append(tpr)\n",
    "        \n",
    "    tpr_s = np.mean(tpr_s, axis=0)\n",
    "    nonlinear_might.append(tpr_s)\n",
    "    \n",
    "    tpr_s = []\n",
    "    for i in range(N_ITR):\n",
    "        tpr = np.interp(base_fpr, fpr_null_s[i][j], tpr_null_s[i][j])\n",
    "        tpr_s.append(tpr)\n",
    "        \n",
    "    tpr_s = np.mean(tpr_s, axis=0)\n",
    "    null_might.append(tpr_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1001,)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_might[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 0.03125   0.0390625 0.0390625 0.0390625 0.046875  0.0546875 0.0546875\n",
      " 0.0546875 0.0625    0.0703125 0.078125  0.0859375 0.0859375 0.09375\n",
      " 0.09375   0.1015625 0.109375  0.1171875 0.1171875 0.125     0.1328125\n",
      " 0.1328125 0.1328125 0.140625  0.140625  0.1484375 0.1484375 0.15625\n",
      " 0.1640625 0.171875  0.1796875 0.1796875 0.1796875 0.1796875 0.1796875\n",
      " 0.1796875 0.1796875 0.1796875 0.1875    0.1953125 0.1953125 0.203125\n",
      " 0.2109375 0.21875   0.2265625 0.2265625 0.2265625 0.2265625 0.234375\n",
      " 0.2421875 0.2421875 0.25      0.2578125 0.2578125 0.265625  0.2734375\n",
      " 0.28125   0.2890625 0.2890625 0.2890625 0.296875  0.3046875 0.3046875\n",
      " 0.3125    0.3203125 0.328125  0.3359375 0.34375   0.34375   0.3515625\n",
      " 0.359375  0.3671875 0.3671875 0.375     0.375     0.3828125 0.3828125\n",
      " 0.390625  0.3984375 0.3984375 0.3984375 0.40625   0.4140625 0.421875\n",
      " 0.4296875 0.4375    0.4375    0.4375    0.4375    0.4375    0.4453125\n",
      " 0.4453125 0.453125  0.4609375 0.46875   0.4765625 0.4765625 0.4765625\n",
      " 0.4765625 0.484375  0.4921875 0.5       0.5       0.5078125 0.5078125\n",
      " 0.5078125 0.515625  0.5234375 0.53125   0.5390625 0.5390625 0.546875\n",
      " 0.5546875 0.5625    0.5625    0.5625    0.5703125 0.5703125 0.5703125\n",
      " 0.578125  0.5859375 0.5859375 0.5859375 0.59375   0.59375   0.6015625\n",
      " 0.6015625 0.609375  0.6171875 0.6171875 0.625     0.625     0.625\n",
      " 0.625     0.6328125 0.640625  0.640625  0.640625  0.6484375 0.6484375\n",
      " 0.6484375 0.6484375 0.65625   0.6640625 0.671875  0.671875  0.671875\n",
      " 0.671875  0.6796875 0.6796875 0.6875    0.6953125 0.703125  0.703125\n",
      " 0.7109375 0.7109375 0.7109375 0.7109375 0.71875   0.7265625 0.7265625\n",
      " 0.734375  0.734375  0.734375  0.734375  0.7421875 0.7421875 0.75\n",
      " 0.75      0.7578125 0.7578125 0.765625  0.7734375 0.78125   0.78125\n",
      " 0.78125   0.7890625 0.7890625 0.796875  0.796875  0.796875  0.796875\n",
      " 0.796875  0.8046875 0.8046875 0.8046875 0.8046875 0.8125    0.8125\n",
      " 0.8203125 0.828125  0.828125  0.828125  0.8359375 0.8359375 0.8359375\n",
      " 0.8359375 0.84375   0.84375   0.8515625 0.8515625 0.859375  0.859375\n",
      " 0.8671875 0.875     0.8828125 0.8828125 0.8828125 0.890625  0.8984375\n",
      " 0.8984375 0.8984375 0.8984375 0.8984375 0.8984375 0.8984375 0.90625\n",
      " 0.90625   0.9140625 0.921875  0.9296875 0.9375    0.9453125 0.9453125\n",
      " 0.9453125 0.9453125 0.9453125 0.9453125 0.9453125 0.9453125 0.953125\n",
      " 0.953125  0.953125  0.953125  0.953125  0.9609375 0.9609375 0.96875\n",
      " 0.9765625 0.9765625 0.9765625 0.9765625 0.9765625 0.9765625 0.984375\n",
      " 0.984375  0.9921875 0.9921875 0.9921875 1.       ] [0.0078125 0.0078125 0.0078125 ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.0078125 0.0078125 0.015625  0.015625  0.0234375 0.0234375\n",
      " 0.0234375 0.03125   0.0390625 0.0390625 0.0390625 0.046875  0.0546875\n",
      " 0.0625    0.0703125 0.078125  0.0859375 0.0859375 0.0859375 0.09375\n",
      " 0.1015625 0.109375  0.1171875 0.1171875 0.1171875 0.125     0.1328125\n",
      " 0.1328125 0.1328125 0.1328125 0.1328125 0.140625  0.140625  0.1484375\n",
      " 0.15625   0.1640625 0.1640625 0.171875  0.1796875 0.1875    0.1953125\n",
      " 0.1953125 0.1953125 0.203125  0.203125  0.2109375 0.21875   0.21875\n",
      " 0.21875   0.21875   0.2265625 0.2265625 0.2265625 0.234375  0.2421875\n",
      " 0.2421875 0.2421875 0.25      0.2578125 0.2578125 0.2578125 0.2578125\n",
      " 0.2578125 0.265625  0.2734375 0.2734375 0.28125   0.28125   0.2890625\n",
      " 0.296875  0.3046875 0.3046875 0.3046875 0.3125    0.3203125 0.3203125\n",
      " 0.328125  0.328125  0.328125  0.3359375 0.3359375 0.3359375 0.34375\n",
      " 0.3515625 0.359375  0.359375  0.3671875 0.375     0.375     0.375\n",
      " 0.3828125 0.390625  0.390625  0.3984375 0.40625   0.40625   0.40625\n",
      " 0.40625   0.40625   0.4140625 0.421875  0.421875  0.4296875 0.4296875\n",
      " 0.4375    0.4453125 0.453125  0.4609375 0.4609375 0.46875   0.4765625\n",
      " 0.4765625 0.484375  0.4921875 0.4921875 0.4921875 0.5       0.5078125\n",
      " 0.515625  0.515625  0.5234375 0.5234375 0.53125   0.5390625 0.546875\n",
      " 0.5546875 0.5625    0.5625    0.5625    0.5625    0.5625    0.5703125\n",
      " 0.578125  0.578125  0.578125  0.578125  0.5859375 0.59375   0.59375\n",
      " 0.59375   0.59375   0.59375   0.6015625 0.609375  0.6171875 0.6171875\n",
      " 0.6171875 0.625     0.6328125 0.640625  0.640625  0.6484375 0.6484375\n",
      " 0.6484375 0.6484375 0.65625   0.65625   0.6640625 0.6640625 0.671875\n",
      " 0.671875  0.671875  0.6796875 0.6796875 0.6796875 0.6796875 0.6875\n",
      " 0.6875    0.6875    0.6953125 0.6953125 0.703125  0.7109375 0.7109375\n",
      " 0.71875   0.7265625 0.7265625 0.734375  0.734375  0.7421875 0.7421875\n",
      " 0.7421875 0.7421875 0.7421875 0.75      0.7578125 0.7578125 0.765625\n",
      " 0.765625  0.765625  0.765625  0.765625  0.765625  0.765625  0.7734375\n",
      " 0.78125   0.7890625 0.7890625 0.7890625 0.796875  0.796875  0.796875\n",
      " 0.8046875 0.8046875 0.8125    0.8125    0.8125    0.8203125 0.828125\n",
      " 0.8359375 0.8359375 0.8359375 0.84375   0.84375   0.84375   0.84375\n",
      " 0.8515625 0.8515625 0.8515625 0.8515625 0.859375  0.859375  0.859375\n",
      " 0.8671875 0.8671875 0.875     0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.890625  0.8984375 0.8984375 0.8984375 0.90625   0.9140625 0.921875\n",
      " 0.921875  0.9296875 0.9296875 0.9375    0.9453125 0.953125  0.9609375\n",
      " 0.9609375 0.9609375 0.96875   0.96875   0.9765625 0.984375  0.984375\n",
      " 0.984375  0.9921875 0.9921875 0.9921875 1.       ] [0.0078125 0.0078125 0.0078125 ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625 0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0703125 0.078125  0.0859375 0.0859375\n",
      " 0.0859375 0.09375   0.1015625 0.109375  0.1171875 0.125     0.1328125\n",
      " 0.140625  0.1484375 0.1484375 0.1484375 0.1484375 0.15625   0.15625\n",
      " 0.1640625 0.171875  0.171875  0.1796875 0.1875    0.1953125 0.203125\n",
      " 0.2109375 0.2109375 0.21875   0.2265625 0.2265625 0.234375  0.2421875\n",
      " 0.25      0.2578125 0.2578125 0.2578125 0.2578125 0.265625  0.265625\n",
      " 0.265625  0.2734375 0.28125   0.2890625 0.296875  0.296875  0.296875\n",
      " 0.296875  0.296875  0.3046875 0.3125    0.3203125 0.328125  0.3359375\n",
      " 0.3359375 0.3359375 0.34375   0.34375   0.34375   0.34375   0.3515625\n",
      " 0.359375  0.3671875 0.3671875 0.3671875 0.375     0.3828125 0.390625\n",
      " 0.3984375 0.40625   0.4140625 0.4140625 0.4140625 0.421875  0.421875\n",
      " 0.4296875 0.4296875 0.4296875 0.4296875 0.4375    0.4375    0.4375\n",
      " 0.4375    0.4453125 0.453125  0.4609375 0.4609375 0.46875   0.4765625\n",
      " 0.4765625 0.484375  0.484375  0.4921875 0.4921875 0.4921875 0.5\n",
      " 0.5       0.5       0.5       0.5       0.5       0.5078125 0.515625\n",
      " 0.515625  0.515625  0.515625  0.5234375 0.5234375 0.5234375 0.53125\n",
      " 0.5390625 0.546875  0.5546875 0.5625    0.5703125 0.5703125 0.5703125\n",
      " 0.5703125 0.578125  0.5859375 0.5859375 0.59375   0.59375   0.59375\n",
      " 0.59375   0.59375   0.59375   0.6015625 0.609375  0.609375  0.609375\n",
      " 0.609375  0.6171875 0.6171875 0.6171875 0.625     0.625     0.625\n",
      " 0.6328125 0.640625  0.640625  0.6484375 0.65625   0.6640625 0.6640625\n",
      " 0.6640625 0.6640625 0.671875  0.6796875 0.6875    0.6875    0.6875\n",
      " 0.6875    0.6953125 0.703125  0.7109375 0.7109375 0.71875   0.7265625\n",
      " 0.734375  0.734375  0.734375  0.7421875 0.7421875 0.75      0.7578125\n",
      " 0.765625  0.765625  0.765625  0.7734375 0.7734375 0.7734375 0.7734375\n",
      " 0.7734375 0.7734375 0.7734375 0.78125   0.7890625 0.7890625 0.7890625\n",
      " 0.7890625 0.796875  0.8046875 0.8046875 0.8125    0.8203125 0.8203125\n",
      " 0.828125  0.828125  0.8359375 0.84375   0.84375   0.84375   0.84375\n",
      " 0.84375   0.84375   0.84375   0.84375   0.8515625 0.8515625 0.8515625\n",
      " 0.859375  0.859375  0.8671875 0.8671875 0.875     0.8828125 0.8828125\n",
      " 0.8828125 0.8828125 0.890625  0.8984375 0.8984375 0.8984375 0.90625\n",
      " 0.90625   0.90625   0.9140625 0.921875  0.921875  0.9296875 0.9296875\n",
      " 0.9296875 0.9296875 0.9296875 0.9296875 0.9296875 0.9296875 0.9296875\n",
      " 0.9296875 0.9296875 0.9375    0.9453125 0.953125  0.9609375 0.9609375\n",
      " 0.96875   0.96875   0.96875   0.9765625 0.9765625 0.984375  0.984375\n",
      " 0.984375  0.984375  0.9921875 1.        1.       ] [0.0390625 0.0390625 0.0390625 ... 1.        1.        1.       ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0703125 0.078125  0.0859375 0.09375\n",
      " 0.1015625 0.1015625 0.1015625 0.109375  0.1171875 0.125     0.125\n",
      " 0.125     0.125     0.125     0.1328125 0.140625  0.1484375 0.1484375\n",
      " 0.1484375 0.15625   0.1640625 0.1640625 0.1640625 0.171875  0.1796875\n",
      " 0.1875    0.1953125 0.203125  0.2109375 0.2109375 0.21875   0.21875\n",
      " 0.2265625 0.234375  0.2421875 0.2421875 0.25      0.25      0.2578125\n",
      " 0.265625  0.265625  0.265625  0.2734375 0.28125   0.28125   0.28125\n",
      " 0.2890625 0.2890625 0.296875  0.3046875 0.3046875 0.3125    0.3203125\n",
      " 0.3203125 0.3203125 0.3203125 0.328125  0.3359375 0.3359375 0.3359375\n",
      " 0.34375   0.34375   0.3515625 0.359375  0.359375  0.3671875 0.375\n",
      " 0.375     0.3828125 0.390625  0.3984375 0.40625   0.40625   0.40625\n",
      " 0.4140625 0.4140625 0.421875  0.421875  0.421875  0.4296875 0.4296875\n",
      " 0.4296875 0.4296875 0.4296875 0.4375    0.4453125 0.453125  0.4609375\n",
      " 0.46875   0.4765625 0.484375  0.484375  0.4921875 0.5       0.5078125\n",
      " 0.515625  0.515625  0.5234375 0.53125   0.5390625 0.5390625 0.5390625\n",
      " 0.546875  0.5546875 0.5625    0.5625    0.5625    0.5625    0.5625\n",
      " 0.5703125 0.578125  0.578125  0.578125  0.578125  0.578125  0.578125\n",
      " 0.5859375 0.5859375 0.59375   0.59375   0.6015625 0.6015625 0.609375\n",
      " 0.609375  0.609375  0.6171875 0.625     0.6328125 0.6328125 0.6328125\n",
      " 0.6328125 0.6328125 0.640625  0.6484375 0.65625   0.6640625 0.6640625\n",
      " 0.6640625 0.6640625 0.6640625 0.6640625 0.671875  0.6796875 0.6875\n",
      " 0.6875    0.6953125 0.703125  0.703125  0.7109375 0.7109375 0.7109375\n",
      " 0.71875   0.7265625 0.734375  0.7421875 0.75      0.75      0.75\n",
      " 0.7578125 0.7578125 0.7578125 0.765625  0.765625  0.7734375 0.78125\n",
      " 0.7890625 0.796875  0.8046875 0.8125    0.8125    0.8125    0.8125\n",
      " 0.8203125 0.828125  0.828125  0.828125  0.828125  0.828125  0.828125\n",
      " 0.8359375 0.84375   0.84375   0.84375   0.8515625 0.859375  0.8671875\n",
      " 0.8671875 0.8671875 0.8671875 0.8671875 0.875     0.875     0.875\n",
      " 0.875     0.875     0.875     0.875     0.875     0.875     0.8828125\n",
      " 0.8828125 0.890625  0.890625  0.8984375 0.8984375 0.8984375 0.8984375\n",
      " 0.90625   0.9140625 0.9140625 0.921875  0.921875  0.9296875 0.9296875\n",
      " 0.9296875 0.9296875 0.9296875 0.9296875 0.9296875 0.9375    0.9375\n",
      " 0.9453125 0.9453125 0.953125  0.9609375 0.96875   0.96875   0.96875\n",
      " 0.9765625 0.984375  0.984375  0.984375  0.984375  0.9921875 0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 1.        1.        1.        1.\n",
      " 1.        1.        1.        1.        1.       ] [0.0234375 0.0234375 0.0234375 ... 1.        1.        1.       ]\n",
      "[0.        0.        0.0078125 0.0078125 0.0078125 0.0078125 0.0078125\n",
      " 0.0078125 0.0078125 0.0078125 0.015625  0.015625  0.0234375 0.03125\n",
      " 0.03125   0.03125   0.0390625 0.046875  0.0546875 0.0625    0.0703125\n",
      " 0.078125  0.078125  0.0859375 0.09375   0.1015625 0.109375  0.1171875\n",
      " 0.1171875 0.125     0.1328125 0.1328125 0.140625  0.140625  0.140625\n",
      " 0.140625  0.1484375 0.15625   0.15625   0.1640625 0.171875  0.1796875\n",
      " 0.1796875 0.1875    0.1875    0.1953125 0.203125  0.203125  0.203125\n",
      " 0.2109375 0.21875   0.21875   0.21875   0.21875   0.21875   0.21875\n",
      " 0.2265625 0.234375  0.234375  0.2421875 0.2421875 0.2421875 0.2421875\n",
      " 0.2421875 0.2421875 0.25      0.2578125 0.2578125 0.265625  0.2734375\n",
      " 0.2734375 0.28125   0.28125   0.28125   0.28125   0.28125   0.2890625\n",
      " 0.2890625 0.2890625 0.296875  0.296875  0.3046875 0.3046875 0.3046875\n",
      " 0.3046875 0.3046875 0.3046875 0.3046875 0.3046875 0.3046875 0.3125\n",
      " 0.3125    0.3203125 0.3203125 0.3203125 0.328125  0.3359375 0.34375\n",
      " 0.3515625 0.359375  0.3671875 0.375     0.3828125 0.3828125 0.390625\n",
      " 0.390625  0.390625  0.390625  0.3984375 0.3984375 0.3984375 0.40625\n",
      " 0.4140625 0.4140625 0.421875  0.421875  0.4296875 0.4375    0.4375\n",
      " 0.4453125 0.4453125 0.4453125 0.4453125 0.4453125 0.4453125 0.453125\n",
      " 0.453125  0.4609375 0.46875   0.46875   0.46875   0.4765625 0.4765625\n",
      " 0.484375  0.484375  0.484375  0.4921875 0.5       0.5078125 0.5078125\n",
      " 0.5078125 0.515625  0.5234375 0.5234375 0.5234375 0.53125   0.5390625\n",
      " 0.546875  0.546875  0.5546875 0.5625    0.5625    0.5703125 0.578125\n",
      " 0.578125  0.5859375 0.59375   0.59375   0.6015625 0.609375  0.6171875\n",
      " 0.625     0.625     0.625     0.6328125 0.6328125 0.640625  0.640625\n",
      " 0.6484375 0.65625   0.65625   0.65625   0.65625   0.65625   0.6640625\n",
      " 0.671875  0.671875  0.6796875 0.6796875 0.6796875 0.6875    0.6953125\n",
      " 0.6953125 0.6953125 0.6953125 0.6953125 0.6953125 0.6953125 0.6953125\n",
      " 0.703125  0.7109375 0.7109375 0.71875   0.71875   0.71875   0.71875\n",
      " 0.71875   0.7265625 0.734375  0.7421875 0.7421875 0.7421875 0.7421875\n",
      " 0.75      0.75      0.75      0.75      0.75      0.75      0.7578125\n",
      " 0.765625  0.7734375 0.78125   0.78125   0.7890625 0.7890625 0.7890625\n",
      " 0.796875  0.796875  0.8046875 0.8125    0.8203125 0.828125  0.8359375\n",
      " 0.8359375 0.84375   0.8515625 0.859375  0.859375  0.859375  0.8671875\n",
      " 0.875     0.8828125 0.890625  0.8984375 0.90625   0.90625   0.9140625\n",
      " 0.921875  0.921875  0.921875  0.9296875 0.9375    0.9453125 0.9453125\n",
      " 0.9453125 0.953125  0.9609375 0.9609375 0.96875   0.96875   0.9765625\n",
      " 0.9765625 0.984375  0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.03125   0.03125   0.03125\n",
      " 0.03125   0.0390625 0.046875  0.0546875 0.0625    0.0703125 0.078125\n",
      " 0.0859375 0.0859375 0.0859375 0.0859375 0.0859375 0.09375   0.1015625\n",
      " 0.109375  0.1171875 0.1171875 0.125     0.1328125 0.140625  0.1484375\n",
      " 0.1484375 0.15625   0.1640625 0.1640625 0.171875  0.1796875 0.1796875\n",
      " 0.1796875 0.1796875 0.1796875 0.1875    0.1953125 0.203125  0.2109375\n",
      " 0.2109375 0.21875   0.2265625 0.234375  0.2421875 0.2421875 0.2421875\n",
      " 0.25      0.25      0.2578125 0.2578125 0.2578125 0.2578125 0.2578125\n",
      " 0.2578125 0.265625  0.265625  0.265625  0.265625  0.2734375 0.28125\n",
      " 0.2890625 0.296875  0.296875  0.3046875 0.3046875 0.3046875 0.3125\n",
      " 0.3125    0.3125    0.3125    0.3125    0.3203125 0.328125  0.3359375\n",
      " 0.34375   0.3515625 0.359375  0.359375  0.359375  0.359375  0.3671875\n",
      " 0.375     0.3828125 0.390625  0.3984375 0.3984375 0.3984375 0.3984375\n",
      " 0.3984375 0.40625   0.40625   0.4140625 0.4140625 0.421875  0.4296875\n",
      " 0.4296875 0.4375    0.4453125 0.453125  0.4609375 0.4609375 0.46875\n",
      " 0.46875   0.46875   0.46875   0.4765625 0.4765625 0.484375  0.484375\n",
      " 0.4921875 0.5       0.5       0.5       0.5       0.5078125 0.5078125\n",
      " 0.515625  0.515625  0.5234375 0.53125   0.5390625 0.546875  0.546875\n",
      " 0.546875  0.5546875 0.5625    0.5703125 0.578125  0.578125  0.578125\n",
      " 0.578125  0.5859375 0.5859375 0.5859375 0.59375   0.59375   0.6015625\n",
      " 0.6015625 0.609375  0.6171875 0.625     0.625     0.625     0.625\n",
      " 0.625     0.6328125 0.6328125 0.640625  0.6484375 0.65625   0.65625\n",
      " 0.6640625 0.671875  0.671875  0.6796875 0.6796875 0.6875    0.6875\n",
      " 0.6875    0.6953125 0.703125  0.7109375 0.7109375 0.71875   0.71875\n",
      " 0.71875   0.71875   0.71875   0.71875   0.7265625 0.734375  0.734375\n",
      " 0.734375  0.7421875 0.7421875 0.7421875 0.7421875 0.7421875 0.75\n",
      " 0.75      0.7578125 0.765625  0.765625  0.765625  0.765625  0.765625\n",
      " 0.765625  0.765625  0.7734375 0.78125   0.78125   0.7890625 0.796875\n",
      " 0.8046875 0.8125    0.8125    0.8125    0.8125    0.8125    0.8125\n",
      " 0.8125    0.8125    0.8203125 0.8203125 0.828125  0.8359375 0.8359375\n",
      " 0.84375   0.8515625 0.859375  0.859375  0.8671875 0.875     0.8828125\n",
      " 0.8828125 0.890625  0.890625  0.8984375 0.8984375 0.8984375 0.90625\n",
      " 0.9140625 0.9140625 0.921875  0.9296875 0.9375    0.9375    0.9375\n",
      " 0.9453125 0.953125  0.9609375 0.96875   0.96875   0.96875   0.96875\n",
      " 0.96875   0.9765625 0.9765625 0.9765625 0.9765625 0.9765625 0.9765625\n",
      " 0.984375  0.984375  0.984375  0.9921875 1.        1.        1.\n",
      " 1.        1.        1.        1.        1.       ] [0.03125 0.03125 0.03125 ... 1.      1.      1.     ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.0234375 0.03125   0.03125\n",
      " 0.03125   0.0390625 0.046875  0.046875  0.0546875 0.0625    0.0625\n",
      " 0.0703125 0.078125  0.078125  0.0859375 0.0859375 0.09375   0.1015625\n",
      " 0.1015625 0.1015625 0.1015625 0.109375  0.1171875 0.1171875 0.1171875\n",
      " 0.125     0.125     0.125     0.125     0.125     0.125     0.1328125\n",
      " 0.1328125 0.140625  0.140625  0.1484375 0.15625   0.1640625 0.171875\n",
      " 0.1796875 0.1875    0.1875    0.1875    0.1875    0.1953125 0.203125\n",
      " 0.2109375 0.21875   0.21875   0.21875   0.2265625 0.2265625 0.234375\n",
      " 0.234375  0.2421875 0.25      0.25      0.25      0.25      0.2578125\n",
      " 0.2578125 0.265625  0.2734375 0.28125   0.2890625 0.2890625 0.2890625\n",
      " 0.2890625 0.296875  0.296875  0.3046875 0.3125    0.3203125 0.328125\n",
      " 0.328125  0.328125  0.3359375 0.34375   0.34375   0.3515625 0.3515625\n",
      " 0.3515625 0.359375  0.3671875 0.3671875 0.375     0.3828125 0.390625\n",
      " 0.3984375 0.3984375 0.40625   0.4140625 0.4140625 0.421875  0.4296875\n",
      " 0.4375    0.4453125 0.4453125 0.453125  0.453125  0.4609375 0.4609375\n",
      " 0.4609375 0.46875   0.4765625 0.484375  0.4921875 0.5       0.5078125\n",
      " 0.515625  0.515625  0.5234375 0.53125   0.5390625 0.546875  0.5546875\n",
      " 0.5546875 0.5625    0.5703125 0.578125  0.5859375 0.5859375 0.59375\n",
      " 0.59375   0.6015625 0.609375  0.6171875 0.6171875 0.6171875 0.6171875\n",
      " 0.6171875 0.6171875 0.625     0.625     0.625     0.6328125 0.640625\n",
      " 0.640625  0.6484375 0.65625   0.6640625 0.6640625 0.6640625 0.671875\n",
      " 0.671875  0.671875  0.6796875 0.6796875 0.6875    0.6875    0.6875\n",
      " 0.6953125 0.6953125 0.6953125 0.6953125 0.6953125 0.6953125 0.6953125\n",
      " 0.703125  0.7109375 0.71875   0.7265625 0.7265625 0.734375  0.734375\n",
      " 0.734375  0.7421875 0.7421875 0.75      0.7578125 0.7578125 0.765625\n",
      " 0.7734375 0.78125   0.78125   0.7890625 0.796875  0.8046875 0.8046875\n",
      " 0.8046875 0.8046875 0.8046875 0.8046875 0.8125    0.8125    0.8125\n",
      " 0.8125    0.8125    0.8203125 0.828125  0.828125  0.8359375 0.8359375\n",
      " 0.8359375 0.8359375 0.8359375 0.8359375 0.8359375 0.84375   0.84375\n",
      " 0.8515625 0.859375  0.859375  0.859375  0.859375  0.8671875 0.8671875\n",
      " 0.8671875 0.8671875 0.8671875 0.875     0.875     0.8828125 0.8828125\n",
      " 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.890625  0.8984375 0.8984375 0.90625   0.90625   0.9140625 0.921875\n",
      " 0.9296875 0.9296875 0.9375    0.9375    0.9375    0.9453125 0.953125\n",
      " 0.9609375 0.96875   0.9765625 0.9765625 0.9765625 0.9765625 0.9765625\n",
      " 0.984375  0.9921875 0.9921875 0.9921875 1.        1.        1.\n",
      " 1.        1.        1.        1.        1.       ] [0.0234375 0.0234375 0.0234375 ... 1.        1.        1.       ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625 0.0390625\n",
      " 0.0390625 0.0390625 0.046875  0.046875  0.046875  0.0546875 0.0546875\n",
      " 0.0625    0.0703125 0.078125  0.0859375 0.09375   0.1015625 0.109375\n",
      " 0.1171875 0.125     0.1328125 0.140625  0.1484375 0.15625   0.1640625\n",
      " 0.171875  0.171875  0.171875  0.1796875 0.1875    0.1953125 0.203125\n",
      " 0.2109375 0.2109375 0.21875   0.2265625 0.234375  0.2421875 0.2421875\n",
      " 0.2421875 0.2421875 0.25      0.2578125 0.2578125 0.2578125 0.2578125\n",
      " 0.2578125 0.265625  0.2734375 0.28125   0.28125   0.28125   0.2890625\n",
      " 0.2890625 0.296875  0.3046875 0.3125    0.3203125 0.3203125 0.3203125\n",
      " 0.3203125 0.328125  0.3359375 0.34375   0.34375   0.3515625 0.359375\n",
      " 0.359375  0.3671875 0.375     0.375     0.3828125 0.3828125 0.3828125\n",
      " 0.390625  0.390625  0.390625  0.390625  0.390625  0.3984375 0.3984375\n",
      " 0.3984375 0.3984375 0.3984375 0.40625   0.4140625 0.4140625 0.4140625\n",
      " 0.4140625 0.421875  0.4296875 0.4375    0.4375    0.4375    0.4375\n",
      " 0.4375    0.4375    0.4453125 0.453125  0.4609375 0.46875   0.46875\n",
      " 0.4765625 0.484375  0.4921875 0.5       0.5       0.5       0.5\n",
      " 0.5       0.5078125 0.5078125 0.5078125 0.5078125 0.515625  0.5234375\n",
      " 0.5234375 0.53125   0.5390625 0.546875  0.5546875 0.5625    0.5703125\n",
      " 0.5703125 0.578125  0.5859375 0.59375   0.59375   0.59375   0.59375\n",
      " 0.6015625 0.6015625 0.6015625 0.609375  0.609375  0.609375  0.6171875\n",
      " 0.625     0.6328125 0.640625  0.640625  0.6484375 0.65625   0.65625\n",
      " 0.6640625 0.671875  0.671875  0.6796875 0.6796875 0.6875    0.6875\n",
      " 0.6953125 0.6953125 0.6953125 0.703125  0.703125  0.7109375 0.71875\n",
      " 0.7265625 0.7265625 0.7265625 0.734375  0.734375  0.734375  0.7421875\n",
      " 0.7421875 0.75      0.7578125 0.765625  0.765625  0.765625  0.765625\n",
      " 0.7734375 0.78125   0.78125   0.7890625 0.7890625 0.7890625 0.7890625\n",
      " 0.7890625 0.7890625 0.7890625 0.796875  0.796875  0.796875  0.796875\n",
      " 0.796875  0.8046875 0.8125    0.8125    0.8125    0.8203125 0.828125\n",
      " 0.828125  0.828125  0.828125  0.828125  0.828125  0.8359375 0.8359375\n",
      " 0.8359375 0.8359375 0.84375   0.8515625 0.8515625 0.859375  0.859375\n",
      " 0.859375  0.859375  0.859375  0.8671875 0.875     0.8828125 0.8828125\n",
      " 0.890625  0.890625  0.890625  0.8984375 0.8984375 0.8984375 0.90625\n",
      " 0.9140625 0.921875  0.921875  0.921875  0.9296875 0.9296875 0.9375\n",
      " 0.9375    0.9453125 0.953125  0.953125  0.953125  0.9609375 0.9609375\n",
      " 0.96875   0.96875   0.96875   0.96875   0.96875   0.9765625 0.984375\n",
      " 0.984375  0.984375  0.984375  0.984375  0.984375  0.9921875 0.9921875\n",
      " 0.9921875 0.9921875 1.        1.        1.       ] [0.0390625 0.0390625 0.0390625 ... 1.        1.        1.       ]\n",
      "[0.        0.0078125 0.0078125 0.015625  0.015625  0.0234375 0.03125\n",
      " 0.0390625 0.0390625 0.046875  0.046875  0.0546875 0.0546875 0.0546875\n",
      " 0.0625    0.0625    0.0625    0.0703125 0.078125  0.0859375 0.0859375\n",
      " 0.09375   0.1015625 0.1015625 0.1015625 0.109375  0.1171875 0.125\n",
      " 0.125     0.1328125 0.140625  0.1484375 0.15625   0.1640625 0.1640625\n",
      " 0.1640625 0.1640625 0.171875  0.1796875 0.1875    0.1953125 0.203125\n",
      " 0.203125  0.2109375 0.21875   0.2265625 0.234375  0.234375  0.234375\n",
      " 0.2421875 0.25      0.25      0.25      0.2578125 0.265625  0.2734375\n",
      " 0.2734375 0.2734375 0.28125   0.2890625 0.2890625 0.2890625 0.296875\n",
      " 0.296875  0.296875  0.296875  0.3046875 0.3125    0.3125    0.3125\n",
      " 0.3203125 0.328125  0.328125  0.328125  0.328125  0.328125  0.328125\n",
      " 0.3359375 0.34375   0.34375   0.34375   0.3515625 0.359375  0.359375\n",
      " 0.3671875 0.3671875 0.375     0.375     0.3828125 0.3828125 0.390625\n",
      " 0.3984375 0.40625   0.40625   0.40625   0.40625   0.4140625 0.4140625\n",
      " 0.421875  0.421875  0.4296875 0.4296875 0.4296875 0.4296875 0.4296875\n",
      " 0.4375    0.4375    0.4453125 0.4453125 0.4453125 0.4453125 0.453125\n",
      " 0.453125  0.4609375 0.46875   0.46875   0.46875   0.4765625 0.4765625\n",
      " 0.4765625 0.4765625 0.484375  0.484375  0.484375  0.4921875 0.5\n",
      " 0.5       0.5       0.5       0.5078125 0.5078125 0.515625  0.5234375\n",
      " 0.53125   0.5390625 0.546875  0.546875  0.546875  0.5546875 0.5546875\n",
      " 0.5625    0.5625    0.5703125 0.578125  0.578125  0.578125  0.578125\n",
      " 0.578125  0.5859375 0.5859375 0.5859375 0.59375   0.6015625 0.6015625\n",
      " 0.6015625 0.609375  0.6171875 0.625     0.6328125 0.640625  0.6484375\n",
      " 0.6484375 0.6484375 0.65625   0.65625   0.6640625 0.671875  0.6796875\n",
      " 0.6875    0.6953125 0.703125  0.703125  0.7109375 0.71875   0.71875\n",
      " 0.7265625 0.7265625 0.7265625 0.7265625 0.7265625 0.734375  0.7421875\n",
      " 0.75      0.75      0.7578125 0.7578125 0.7578125 0.765625  0.765625\n",
      " 0.765625  0.7734375 0.78125   0.78125   0.7890625 0.7890625 0.796875\n",
      " 0.796875  0.8046875 0.8046875 0.8125    0.8203125 0.828125  0.828125\n",
      " 0.828125  0.8359375 0.84375   0.84375   0.84375   0.84375   0.8515625\n",
      " 0.8515625 0.8515625 0.8515625 0.8515625 0.859375  0.8671875 0.875\n",
      " 0.8828125 0.890625  0.8984375 0.8984375 0.8984375 0.90625   0.90625\n",
      " 0.9140625 0.921875  0.921875  0.9296875 0.9375    0.9453125 0.953125\n",
      " 0.9609375 0.96875   0.96875   0.9765625 0.984375  0.984375  0.984375\n",
      " 0.984375  0.984375  0.984375  0.984375  0.984375  0.9921875 0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875\n",
      " 0.9921875 1.        1.        1.        1.       ] [0.0078125 0.0078125 0.0078125 ... 1.        1.        1.       ]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0703125 0.078125  0.078125  0.078125\n",
      " 0.0859375 0.09375   0.1015625 0.109375  0.109375  0.1171875 0.125\n",
      " 0.1328125 0.1328125 0.140625  0.1484375 0.1484375 0.15625   0.1640625\n",
      " 0.1640625 0.1640625 0.171875  0.1796875 0.1796875 0.1875    0.1875\n",
      " 0.1953125 0.1953125 0.203125  0.203125  0.2109375 0.2109375 0.21875\n",
      " 0.2265625 0.234375  0.234375  0.2421875 0.2421875 0.2421875 0.2421875\n",
      " 0.2421875 0.2421875 0.25      0.2578125 0.265625  0.2734375 0.28125\n",
      " 0.2890625 0.2890625 0.2890625 0.296875  0.296875  0.296875  0.3046875\n",
      " 0.3046875 0.3125    0.3125    0.3125    0.3203125 0.328125  0.328125\n",
      " 0.3359375 0.34375   0.3515625 0.359375  0.359375  0.3671875 0.375\n",
      " 0.3828125 0.390625  0.3984375 0.40625   0.4140625 0.4140625 0.4140625\n",
      " 0.421875  0.421875  0.4296875 0.4375    0.4453125 0.453125  0.4609375\n",
      " 0.4609375 0.46875   0.46875   0.4765625 0.4765625 0.484375  0.4921875\n",
      " 0.4921875 0.4921875 0.4921875 0.4921875 0.5       0.5078125 0.5078125\n",
      " 0.515625  0.5234375 0.53125   0.5390625 0.5390625 0.5390625 0.5390625\n",
      " 0.5390625 0.5390625 0.5390625 0.5390625 0.5390625 0.546875  0.5546875\n",
      " 0.5546875 0.5546875 0.5546875 0.5546875 0.5546875 0.5625    0.5625\n",
      " 0.5703125 0.578125  0.578125  0.578125  0.578125  0.578125  0.5859375\n",
      " 0.59375   0.59375   0.59375   0.59375   0.6015625 0.609375  0.609375\n",
      " 0.6171875 0.625     0.6328125 0.6328125 0.640625  0.640625  0.6484375\n",
      " 0.6484375 0.6484375 0.65625   0.65625   0.65625   0.65625   0.65625\n",
      " 0.65625   0.65625   0.65625   0.65625   0.6640625 0.671875  0.6796875\n",
      " 0.6875    0.6875    0.6953125 0.6953125 0.6953125 0.703125  0.7109375\n",
      " 0.7109375 0.71875   0.71875   0.71875   0.7265625 0.734375  0.7421875\n",
      " 0.7421875 0.7421875 0.7421875 0.75      0.75      0.75      0.7578125\n",
      " 0.7578125 0.7578125 0.7578125 0.7578125 0.765625  0.765625  0.7734375\n",
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      " 0.84375   0.84375   0.84375   0.8515625 0.859375  0.859375  0.859375\n",
      " 0.8671875 0.875     0.875     0.8828125 0.890625  0.890625  0.8984375\n",
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      " 0.9375    0.9375    0.9453125 0.953125  0.953125  0.9609375 0.9609375\n",
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      " 0.984375  0.984375  0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
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      " 0.9140625 0.9296875 0.9765625 0.9921875 1.       ] [0.    0.001 0.002 ... 0.998 0.999 1.   ]\n",
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      " 0.9921875 1.        1.       ] [0.00000000e+00 6.66666667e-04 1.33333333e-03 ... 1.00000000e+00\n",
      " 1.00000000e+00 1.00000000e+00]\n",
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      " 0.9921875 0.9921875 1.       ] [0.015625  0.015625  0.015625  ... 0.9921875 0.9921875 1.       ]\n",
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      " 0.6796875 0.6796875 0.6875    0.6875    0.6875    0.6875    0.6875\n",
      " 0.6875    0.6875    0.6875    0.6953125 0.703125  0.703125  0.703125\n",
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      " 0.859375  0.8671875 0.875     0.875     0.8828125 0.8828125 0.8828125\n",
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      " 0.8671875 0.875     0.8828125 0.890625  0.8984375 0.90625   0.90625\n",
      " 0.9140625 0.921875  0.921875  0.9296875 0.9296875 0.9296875 0.9296875\n",
      " 0.9375    0.9375    0.9453125 0.953125  0.953125  0.9609375 0.9609375\n",
      " 0.9609375 0.9609375 0.96875   0.9765625 0.984375  0.9921875 0.9921875\n",
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      " 0.296875  0.296875  0.3046875 0.3125    0.3203125 0.3203125 0.3203125\n",
      " 0.3203125 0.328125  0.328125  0.3359375 0.34375   0.3515625 0.3515625\n",
      " 0.3515625 0.3515625 0.3515625 0.3515625 0.359375  0.3671875 0.375\n",
      " 0.375     0.375     0.3828125 0.3828125 0.3828125 0.3828125 0.390625\n",
      " 0.3984375 0.40625   0.40625   0.4140625 0.421875  0.4296875 0.4296875\n",
      " 0.4375    0.4453125 0.453125  0.453125  0.4609375 0.4609375 0.4609375\n",
      " 0.46875   0.4765625 0.4765625 0.4765625 0.484375  0.4921875 0.5\n",
      " 0.5       0.5       0.5078125 0.515625  0.5234375 0.53125   0.53125\n",
      " 0.53125   0.53125   0.53125   0.53125   0.5390625 0.5390625 0.5390625\n",
      " 0.546875  0.546875  0.5546875 0.5625    0.5703125 0.5703125 0.5703125\n",
      " 0.5703125 0.5703125 0.5703125 0.578125  0.578125  0.5859375 0.5859375\n",
      " 0.5859375 0.59375   0.6015625 0.609375  0.6171875 0.625     0.6328125\n",
      " 0.6328125 0.6328125 0.640625  0.6484375 0.6484375 0.6484375 0.6484375\n",
      " 0.6484375 0.65625   0.65625   0.65625   0.6640625 0.671875  0.671875\n",
      " 0.671875  0.671875  0.6796875 0.6875    0.6875    0.6875    0.6953125\n",
      " 0.703125  0.703125  0.703125  0.703125  0.703125  0.703125  0.7109375\n",
      " 0.71875   0.71875   0.7265625 0.7265625 0.734375  0.734375  0.7421875\n",
      " 0.75      0.7578125 0.7578125 0.765625  0.7734375 0.78125   0.78125\n",
      " 0.78125   0.78125   0.78125   0.7890625 0.796875  0.8046875 0.8125\n",
      " 0.8125    0.8125    0.8203125 0.8203125 0.828125  0.8359375 0.8359375\n",
      " 0.8359375 0.84375   0.8515625 0.8515625 0.859375  0.8671875 0.8671875\n",
      " 0.875     0.875     0.8828125 0.890625  0.890625  0.890625  0.890625\n",
      " 0.8984375 0.90625   0.9140625 0.921875  0.9296875 0.9296875 0.9296875\n",
      " 0.9375    0.9375    0.9453125 0.953125  0.953125  0.953125  0.9609375\n",
      " 0.96875   0.9765625 0.984375  0.9921875 1.       ] [0.       0.       0.       ... 0.953125 0.953125 1.      ]\n",
      "[0.        0.        0.0078125 0.015625  0.015625  0.015625  0.0234375\n",
      " 0.0234375 0.0234375 0.03125   0.03125   0.03125   0.0390625 0.046875\n",
      " 0.0546875 0.0625    0.0703125 0.0703125 0.0703125 0.0703125 0.0703125\n",
      " 0.078125  0.0859375 0.09375   0.1015625 0.1015625 0.1015625 0.1015625\n",
      " 0.109375  0.1171875 0.125     0.1328125 0.1328125 0.1328125 0.1328125\n",
      " 0.1328125 0.1328125 0.140625  0.1484375 0.1484375 0.1484375 0.15625\n",
      " 0.1640625 0.1640625 0.171875  0.1796875 0.1875    0.1875    0.1875\n",
      " 0.1953125 0.203125  0.203125  0.203125  0.203125  0.2109375 0.21875\n",
      " 0.2265625 0.2265625 0.234375  0.2421875 0.2421875 0.25      0.25\n",
      " 0.25      0.2578125 0.265625  0.265625  0.2734375 0.28125   0.2890625\n",
      " 0.296875  0.3046875 0.3046875 0.3046875 0.3125    0.3125    0.3125\n",
      " 0.3203125 0.328125  0.328125  0.3359375 0.34375   0.34375   0.3515625\n",
      " 0.3515625 0.3515625 0.3515625 0.3515625 0.359375  0.359375  0.3671875\n",
      " 0.375     0.375     0.375     0.375     0.375     0.3828125 0.390625\n",
      " 0.390625  0.3984375 0.40625   0.40625   0.4140625 0.421875  0.421875\n",
      " 0.421875  0.421875  0.4296875 0.4296875 0.4296875 0.4296875 0.4296875\n",
      " 0.4296875 0.4375    0.4453125 0.453125  0.453125  0.453125  0.4609375\n",
      " 0.46875   0.46875   0.4765625 0.484375  0.4921875 0.5       0.5\n",
      " 0.5078125 0.5078125 0.515625  0.5234375 0.5234375 0.5234375 0.53125\n",
      " 0.5390625 0.546875  0.546875  0.546875  0.5546875 0.5546875 0.5625\n",
      " 0.5703125 0.5703125 0.5703125 0.5703125 0.5703125 0.578125  0.5859375\n",
      " 0.5859375 0.5859375 0.5859375 0.59375   0.6015625 0.6015625 0.6015625\n",
      " 0.6015625 0.6015625 0.6015625 0.6015625 0.6015625 0.6015625 0.609375\n",
      " 0.609375  0.609375  0.6171875 0.6171875 0.6171875 0.6171875 0.6171875\n",
      " 0.6171875 0.6171875 0.6171875 0.625     0.6328125 0.640625  0.6484375\n",
      " 0.65625   0.6640625 0.671875  0.6796875 0.6796875 0.6875    0.6953125\n",
      " 0.6953125 0.6953125 0.6953125 0.6953125 0.6953125 0.703125  0.703125\n",
      " 0.7109375 0.7109375 0.71875   0.7265625 0.734375  0.7421875 0.75\n",
      " 0.75      0.7578125 0.7578125 0.765625  0.7734375 0.78125   0.7890625\n",
      " 0.796875  0.8046875 0.8046875 0.8046875 0.8046875 0.8125    0.8203125\n",
      " 0.8203125 0.8203125 0.828125  0.8359375 0.84375   0.84375   0.84375\n",
      " 0.8515625 0.859375  0.8671875 0.8671875 0.8671875 0.8671875 0.875\n",
      " 0.875     0.8828125 0.890625  0.890625  0.8984375 0.90625   0.90625\n",
      " 0.9140625 0.921875  0.921875  0.921875  0.921875  0.921875  0.9296875\n",
      " 0.9375    0.9453125 0.9453125 0.953125  0.953125  0.953125  0.9609375\n",
      " 0.96875   0.9765625 0.984375  0.984375  0.9921875 0.9921875 0.9921875\n",
      " 1.        1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.0078125 0.0078125 0.0078125 0.015625  0.015625  0.015625\n",
      " 0.015625  0.0234375 0.03125   0.03125   0.03125   0.0390625 0.0390625\n",
      " 0.046875  0.046875  0.0546875 0.0546875 0.0546875 0.0625    0.0703125\n",
      " 0.078125  0.078125  0.0859375 0.0859375 0.09375   0.1015625 0.1015625\n",
      " 0.1015625 0.109375  0.109375  0.109375  0.1171875 0.125     0.1328125\n",
      " 0.140625  0.1484375 0.15625   0.15625   0.1640625 0.1640625 0.171875\n",
      " 0.171875  0.1796875 0.1796875 0.1875    0.1953125 0.1953125 0.203125\n",
      " 0.203125  0.2109375 0.2109375 0.21875   0.2265625 0.234375  0.234375\n",
      " 0.234375  0.2421875 0.25      0.2578125 0.265625  0.2734375 0.28125\n",
      " 0.2890625 0.2890625 0.296875  0.296875  0.296875  0.3046875 0.3046875\n",
      " 0.3046875 0.3125    0.3125    0.3203125 0.328125  0.3359375 0.34375\n",
      " 0.3515625 0.3515625 0.3515625 0.359375  0.359375  0.3671875 0.3671875\n",
      " 0.375     0.3828125 0.3828125 0.3828125 0.3828125 0.390625  0.3984375\n",
      " 0.40625   0.40625   0.40625   0.4140625 0.421875  0.421875  0.421875\n",
      " 0.421875  0.4296875 0.4296875 0.4296875 0.4375    0.4453125 0.4453125\n",
      " 0.4453125 0.453125  0.4609375 0.46875   0.4765625 0.4765625 0.4765625\n",
      " 0.4765625 0.484375  0.484375  0.4921875 0.4921875 0.4921875 0.4921875\n",
      " 0.4921875 0.5       0.5078125 0.515625  0.5234375 0.5234375 0.5234375\n",
      " 0.53125   0.53125   0.5390625 0.5390625 0.546875  0.5546875 0.5625\n",
      " 0.5703125 0.5703125 0.5703125 0.578125  0.578125  0.578125  0.5859375\n",
      " 0.59375   0.59375   0.59375   0.59375   0.6015625 0.6015625 0.609375\n",
      " 0.6171875 0.6171875 0.625     0.625     0.625     0.6328125 0.6328125\n",
      " 0.6328125 0.640625  0.6484375 0.6484375 0.6484375 0.65625   0.6640625\n",
      " 0.6640625 0.6640625 0.6640625 0.671875  0.671875  0.6796875 0.6875\n",
      " 0.6875    0.6875    0.6875    0.6953125 0.703125  0.703125  0.7109375\n",
      " 0.7109375 0.7109375 0.7109375 0.7109375 0.71875   0.71875   0.7265625\n",
      " 0.734375  0.734375  0.734375  0.7421875 0.75      0.7578125 0.7578125\n",
      " 0.765625  0.765625  0.7734375 0.7734375 0.78125   0.7890625 0.7890625\n",
      " 0.7890625 0.7890625 0.7890625 0.796875  0.8046875 0.8046875 0.8125\n",
      " 0.8125    0.8125    0.8125    0.8125    0.8203125 0.8203125 0.828125\n",
      " 0.8359375 0.84375   0.84375   0.8515625 0.859375  0.859375  0.859375\n",
      " 0.859375  0.859375  0.8671875 0.8671875 0.8671875 0.8671875 0.8671875\n",
      " 0.8671875 0.875     0.8828125 0.8828125 0.8828125 0.8828125 0.890625\n",
      " 0.8984375 0.8984375 0.90625   0.90625   0.9140625 0.921875  0.9296875\n",
      " 0.9296875 0.9296875 0.9296875 0.9375    0.9453125 0.953125  0.9609375\n",
      " 0.96875   0.96875   0.9765625 0.984375  0.984375  0.984375  0.9921875\n",
      " 0.9921875 0.9921875 1.        1.        1.       ] [0.0078125 0.0078125 0.0078125 ... 1.        1.        1.       ]\n",
      "[0.        0.0078125 0.015625  0.015625  0.0234375 0.0234375 0.03125\n",
      " 0.03125   0.0390625 0.0390625 0.0390625 0.0390625 0.0390625 0.0390625\n",
      " 0.046875  0.046875  0.0546875 0.0546875 0.0546875 0.0546875 0.0546875\n",
      " 0.0625    0.0625    0.0625    0.0703125 0.0703125 0.0703125 0.078125\n",
      " 0.078125  0.0859375 0.0859375 0.09375   0.1015625 0.109375  0.1171875\n",
      " 0.125     0.1328125 0.1328125 0.1328125 0.140625  0.1484375 0.1484375\n",
      " 0.15625   0.1640625 0.171875  0.1796875 0.1796875 0.1796875 0.1796875\n",
      " 0.1875    0.1953125 0.1953125 0.1953125 0.203125  0.2109375 0.21875\n",
      " 0.21875   0.2265625 0.2265625 0.2265625 0.2265625 0.2265625 0.234375\n",
      " 0.234375  0.234375  0.2421875 0.25      0.2578125 0.2578125 0.265625\n",
      " 0.265625  0.265625  0.2734375 0.28125   0.2890625 0.296875  0.296875\n",
      " 0.296875  0.296875  0.296875  0.3046875 0.3125    0.3125    0.3125\n",
      " 0.3125    0.3125    0.3125    0.3203125 0.3203125 0.3203125 0.3203125\n",
      " 0.3203125 0.328125  0.328125  0.3359375 0.3359375 0.34375   0.34375\n",
      " 0.3515625 0.3515625 0.3515625 0.359375  0.3671875 0.3671875 0.375\n",
      " 0.375     0.375     0.3828125 0.390625  0.390625  0.390625  0.3984375\n",
      " 0.40625   0.4140625 0.421875  0.421875  0.421875  0.4296875 0.4375\n",
      " 0.4375    0.4453125 0.453125  0.453125  0.4609375 0.4609375 0.4609375\n",
      " 0.46875   0.46875   0.46875   0.46875   0.4765625 0.484375  0.4921875\n",
      " 0.4921875 0.4921875 0.5       0.5       0.5078125 0.5078125 0.5078125\n",
      " 0.515625  0.515625  0.515625  0.5234375 0.53125   0.53125   0.5390625\n",
      " 0.546875  0.546875  0.5546875 0.5546875 0.5625    0.5703125 0.578125\n",
      " 0.5859375 0.59375   0.59375   0.59375   0.59375   0.59375   0.59375\n",
      " 0.6015625 0.609375  0.6171875 0.625     0.625     0.625     0.625\n",
      " 0.6328125 0.640625  0.6484375 0.65625   0.6640625 0.6640625 0.671875\n",
      " 0.6796875 0.6796875 0.6875    0.6953125 0.6953125 0.703125  0.7109375\n",
      " 0.71875   0.7265625 0.7265625 0.734375  0.7421875 0.75      0.7578125\n",
      " 0.7578125 0.7578125 0.765625  0.7734375 0.7734375 0.78125   0.78125\n",
      " 0.78125   0.78125   0.7890625 0.796875  0.8046875 0.8125    0.8125\n",
      " 0.8203125 0.8203125 0.828125  0.8359375 0.8359375 0.84375   0.84375\n",
      " 0.8515625 0.8515625 0.8515625 0.8515625 0.859375  0.859375  0.859375\n",
      " 0.8671875 0.875     0.875     0.875     0.875     0.875     0.8828125\n",
      " 0.8828125 0.8828125 0.8828125 0.890625  0.890625  0.890625  0.890625\n",
      " 0.8984375 0.8984375 0.90625   0.9140625 0.9140625 0.9140625 0.9140625\n",
      " 0.9140625 0.9140625 0.921875  0.9296875 0.9375    0.9375    0.9375\n",
      " 0.9453125 0.9453125 0.953125  0.9609375 0.9609375 0.9609375 0.96875\n",
      " 0.9765625 0.9765625 0.984375  0.9921875 1.       ] [0.015625  0.015625  0.015625  ... 0.9765625 0.9765625 1.       ]\n",
      "[0.        0.        0.0078125 0.0078125 0.0078125 0.015625  0.015625\n",
      " 0.0234375 0.0234375 0.03125   0.0390625 0.046875  0.046875  0.046875\n",
      " 0.0546875 0.0546875 0.0546875 0.0625    0.0703125 0.0703125 0.0703125\n",
      " 0.0703125 0.078125  0.0859375 0.09375   0.1015625 0.109375  0.1171875\n",
      " 0.125     0.125     0.125     0.1328125 0.1328125 0.140625  0.140625\n",
      " 0.140625  0.140625  0.1484375 0.15625   0.1640625 0.1640625 0.171875\n",
      " 0.1796875 0.1875    0.1875    0.1953125 0.1953125 0.1953125 0.1953125\n",
      " 0.1953125 0.1953125 0.203125  0.2109375 0.2109375 0.21875   0.21875\n",
      " 0.21875   0.2265625 0.2265625 0.234375  0.2421875 0.2421875 0.2421875\n",
      " 0.2421875 0.25      0.2578125 0.2578125 0.265625  0.2734375 0.28125\n",
      " 0.28125   0.28125   0.28125   0.2890625 0.296875  0.3046875 0.3046875\n",
      " 0.3046875 0.3046875 0.3046875 0.3046875 0.3125    0.3125    0.3125\n",
      " 0.3203125 0.328125  0.3359375 0.34375   0.34375   0.3515625 0.359375\n",
      " 0.359375  0.359375  0.3671875 0.375     0.375     0.375     0.3828125\n",
      " 0.390625  0.390625  0.390625  0.3984375 0.3984375 0.40625   0.4140625\n",
      " 0.4140625 0.421875  0.421875  0.4296875 0.4296875 0.4296875 0.4296875\n",
      " 0.4375    0.4375    0.4375    0.4453125 0.453125  0.4609375 0.46875\n",
      " 0.46875   0.4765625 0.4765625 0.4765625 0.4765625 0.4765625 0.484375\n",
      " 0.484375  0.4921875 0.4921875 0.4921875 0.4921875 0.4921875 0.5\n",
      " 0.5078125 0.5078125 0.515625  0.5234375 0.5234375 0.5234375 0.53125\n",
      " 0.53125   0.53125   0.5390625 0.546875  0.5546875 0.5625    0.5703125\n",
      " 0.578125  0.578125  0.578125  0.578125  0.5859375 0.5859375 0.59375\n",
      " 0.59375   0.59375   0.59375   0.59375   0.6015625 0.609375  0.6171875\n",
      " 0.625     0.6328125 0.640625  0.6484375 0.6484375 0.65625   0.6640625\n",
      " 0.671875  0.6796875 0.6796875 0.6796875 0.6875    0.6875    0.6953125\n",
      " 0.6953125 0.703125  0.7109375 0.71875   0.71875   0.71875   0.7265625\n",
      " 0.7265625 0.734375  0.734375  0.7421875 0.75      0.75      0.7578125\n",
      " 0.765625  0.7734375 0.7734375 0.78125   0.7890625 0.7890625 0.7890625\n",
      " 0.796875  0.8046875 0.8046875 0.8125    0.8125    0.8203125 0.8203125\n",
      " 0.828125  0.8359375 0.8359375 0.8359375 0.8359375 0.8359375 0.8359375\n",
      " 0.8359375 0.8359375 0.8359375 0.8359375 0.8359375 0.8359375 0.8359375\n",
      " 0.84375   0.84375   0.8515625 0.8515625 0.859375  0.859375  0.859375\n",
      " 0.8671875 0.875     0.875     0.875     0.8828125 0.890625  0.8984375\n",
      " 0.90625   0.90625   0.9140625 0.9140625 0.921875  0.921875  0.921875\n",
      " 0.921875  0.9296875 0.9375    0.9375    0.9375    0.9453125 0.953125\n",
      " 0.9609375 0.9609375 0.9609375 0.96875   0.96875   0.9765625 0.984375\n",
      " 0.984375  0.9921875 0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.0078125 0.015625  0.015625  0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0625    0.0703125 0.0703125 0.078125\n",
      " 0.078125  0.0859375 0.0859375 0.09375   0.1015625 0.109375  0.109375\n",
      " 0.109375  0.1171875 0.125     0.1328125 0.140625  0.1484375 0.1484375\n",
      " 0.1484375 0.1484375 0.1484375 0.15625   0.1640625 0.171875  0.1796875\n",
      " 0.1875    0.1875    0.1953125 0.203125  0.2109375 0.21875   0.21875\n",
      " 0.2265625 0.2265625 0.234375  0.234375  0.2421875 0.25      0.25\n",
      " 0.25      0.2578125 0.2578125 0.265625  0.2734375 0.2734375 0.28125\n",
      " 0.28125   0.28125   0.28125   0.28125   0.28125   0.2890625 0.2890625\n",
      " 0.296875  0.296875  0.3046875 0.3125    0.3125    0.3125    0.3125\n",
      " 0.3203125 0.3203125 0.328125  0.328125  0.3359375 0.3359375 0.3359375\n",
      " 0.3359375 0.3359375 0.3359375 0.3359375 0.3359375 0.3359375 0.3359375\n",
      " 0.3359375 0.3359375 0.3359375 0.3359375 0.3359375 0.3359375 0.3359375\n",
      " 0.3359375 0.34375   0.34375   0.3515625 0.359375  0.3671875 0.3671875\n",
      " 0.3671875 0.375     0.3828125 0.3828125 0.3828125 0.3828125 0.3828125\n",
      " 0.390625  0.390625  0.390625  0.3984375 0.3984375 0.3984375 0.40625\n",
      " 0.4140625 0.421875  0.4296875 0.4375    0.4375    0.4453125 0.453125\n",
      " 0.4609375 0.46875   0.4765625 0.484375  0.4921875 0.5       0.5\n",
      " 0.5       0.5078125 0.515625  0.5234375 0.5234375 0.5234375 0.53125\n",
      " 0.5390625 0.546875  0.5546875 0.5546875 0.5546875 0.5546875 0.5625\n",
      " 0.5625    0.5625    0.5703125 0.5703125 0.578125  0.578125  0.578125\n",
      " 0.578125  0.578125  0.5859375 0.5859375 0.59375   0.6015625 0.6015625\n",
      " 0.609375  0.609375  0.609375  0.609375  0.609375  0.6171875 0.6171875\n",
      " 0.6171875 0.625     0.625     0.6328125 0.6328125 0.640625  0.640625\n",
      " 0.6484375 0.6484375 0.6484375 0.65625   0.65625   0.65625   0.65625\n",
      " 0.65625   0.65625   0.6640625 0.671875  0.671875  0.6796875 0.6875\n",
      " 0.6953125 0.703125  0.703125  0.7109375 0.7109375 0.71875   0.71875\n",
      " 0.7265625 0.7265625 0.7265625 0.7265625 0.734375  0.734375  0.734375\n",
      " 0.7421875 0.75      0.75      0.7578125 0.765625  0.765625  0.765625\n",
      " 0.765625  0.7734375 0.78125   0.78125   0.78125   0.78125   0.7890625\n",
      " 0.7890625 0.796875  0.8046875 0.8125    0.8203125 0.8203125 0.8203125\n",
      " 0.8203125 0.828125  0.8359375 0.8359375 0.84375   0.8515625 0.859375\n",
      " 0.859375  0.8671875 0.8671875 0.875     0.875     0.8828125 0.890625\n",
      " 0.8984375 0.8984375 0.90625   0.9140625 0.921875  0.921875  0.921875\n",
      " 0.921875  0.9296875 0.9296875 0.9375    0.9375    0.9453125 0.9453125\n",
      " 0.953125  0.953125  0.9609375 0.96875   0.9765625 0.9765625 0.984375\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 1.       ] [0.015625  0.015625  0.015625  ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.        0.        0.        0.        0.0078125\n",
      " 0.015625  0.0234375 0.03125   0.03125   0.0390625 0.0390625 0.046875\n",
      " 0.046875  0.046875  0.0546875 0.0625    0.0625    0.0703125 0.0703125\n",
      " 0.0703125 0.0703125 0.078125  0.078125  0.078125  0.078125  0.078125\n",
      " 0.0859375 0.09375   0.09375   0.1015625 0.109375  0.1171875 0.125\n",
      " 0.125     0.125     0.1328125 0.140625  0.140625  0.1484375 0.15625\n",
      " 0.15625   0.1640625 0.171875  0.171875  0.1796875 0.1875    0.1875\n",
      " 0.1875    0.1953125 0.1953125 0.203125  0.2109375 0.2109375 0.2109375\n",
      " 0.2109375 0.21875   0.21875   0.2265625 0.234375  0.2421875 0.25\n",
      " 0.25      0.2578125 0.265625  0.2734375 0.28125   0.2890625 0.2890625\n",
      " 0.2890625 0.2890625 0.296875  0.296875  0.296875  0.3046875 0.3125\n",
      " 0.3203125 0.328125  0.328125  0.3359375 0.3359375 0.34375   0.34375\n",
      " 0.34375   0.3515625 0.3515625 0.3515625 0.3515625 0.359375  0.359375\n",
      " 0.359375  0.3671875 0.3671875 0.375     0.3828125 0.3828125 0.3828125\n",
      " 0.3828125 0.3828125 0.3828125 0.390625  0.390625  0.3984375 0.3984375\n",
      " 0.40625   0.4140625 0.4140625 0.421875  0.421875  0.421875  0.4296875\n",
      " 0.4375    0.4453125 0.4453125 0.4453125 0.4453125 0.453125  0.453125\n",
      " 0.4609375 0.4609375 0.4609375 0.4609375 0.4609375 0.46875   0.46875\n",
      " 0.46875   0.4765625 0.4765625 0.484375  0.484375  0.4921875 0.5\n",
      " 0.5       0.5078125 0.5078125 0.5078125 0.5078125 0.5078125 0.5078125\n",
      " 0.515625  0.5234375 0.5234375 0.5234375 0.5234375 0.5234375 0.53125\n",
      " 0.5390625 0.5390625 0.5390625 0.546875  0.5546875 0.5625    0.5625\n",
      " 0.5703125 0.578125  0.5859375 0.5859375 0.59375   0.59375   0.59375\n",
      " 0.6015625 0.609375  0.609375  0.6171875 0.625     0.625     0.625\n",
      " 0.625     0.6328125 0.640625  0.640625  0.6484375 0.6484375 0.65625\n",
      " 0.6640625 0.671875  0.671875  0.671875  0.671875  0.6796875 0.6796875\n",
      " 0.6796875 0.6875    0.6875    0.6953125 0.703125  0.7109375 0.71875\n",
      " 0.7265625 0.7265625 0.7265625 0.734375  0.734375  0.734375  0.734375\n",
      " 0.7421875 0.75      0.7578125 0.765625  0.765625  0.7734375 0.78125\n",
      " 0.7890625 0.796875  0.8046875 0.8046875 0.8046875 0.8125    0.8203125\n",
      " 0.828125  0.8359375 0.8359375 0.84375   0.84375   0.84375   0.8515625\n",
      " 0.8515625 0.859375  0.859375  0.859375  0.8671875 0.875     0.875\n",
      " 0.875     0.8828125 0.8828125 0.890625  0.8984375 0.8984375 0.8984375\n",
      " 0.8984375 0.8984375 0.90625   0.90625   0.9140625 0.921875  0.9296875\n",
      " 0.9375    0.9375    0.9375    0.9375    0.9453125 0.9453125 0.953125\n",
      " 0.953125  0.953125  0.9609375 0.9609375 0.96875   0.9765625 0.9765625\n",
      " 0.984375  0.9921875 0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.        0.        0.0078125 0.015625  0.0234375\n",
      " 0.03125   0.03125   0.0390625 0.046875  0.0546875 0.0546875 0.0546875\n",
      " 0.0546875 0.0546875 0.0625    0.0625    0.0703125 0.078125  0.0859375\n",
      " 0.09375   0.1015625 0.1015625 0.1015625 0.109375  0.109375  0.1171875\n",
      " 0.125     0.125     0.125     0.1328125 0.140625  0.140625  0.140625\n",
      " 0.140625  0.140625  0.140625  0.140625  0.1484375 0.1484375 0.1484375\n",
      " 0.15625   0.15625   0.1640625 0.171875  0.1796875 0.1796875 0.1796875\n",
      " 0.1875    0.1953125 0.203125  0.2109375 0.21875   0.21875   0.2265625\n",
      " 0.234375  0.234375  0.2421875 0.25      0.25      0.25      0.2578125\n",
      " 0.2578125 0.2578125 0.265625  0.2734375 0.28125   0.2890625 0.296875\n",
      " 0.3046875 0.3125    0.3203125 0.3203125 0.3203125 0.3203125 0.3203125\n",
      " 0.328125  0.328125  0.328125  0.3359375 0.3359375 0.3359375 0.3359375\n",
      " 0.3359375 0.34375   0.34375   0.34375   0.3515625 0.359375  0.3671875\n",
      " 0.3671875 0.375     0.375     0.375     0.375     0.3828125 0.3828125\n",
      " 0.390625  0.3984375 0.40625   0.4140625 0.4140625 0.421875  0.421875\n",
      " 0.421875  0.421875  0.421875  0.421875  0.421875  0.4296875 0.4375\n",
      " 0.4453125 0.453125  0.453125  0.4609375 0.4609375 0.46875   0.4765625\n",
      " 0.484375  0.484375  0.4921875 0.5       0.5       0.5078125 0.515625\n",
      " 0.515625  0.5234375 0.53125   0.53125   0.53125   0.53125   0.5390625\n",
      " 0.546875  0.546875  0.5546875 0.5546875 0.5546875 0.5625    0.5703125\n",
      " 0.578125  0.5859375 0.5859375 0.59375   0.59375   0.6015625 0.6015625\n",
      " 0.609375  0.609375  0.6171875 0.625     0.625     0.6328125 0.6328125\n",
      " 0.6328125 0.6328125 0.6328125 0.6328125 0.640625  0.640625  0.6484375\n",
      " 0.65625   0.65625   0.65625   0.65625   0.6640625 0.671875  0.671875\n",
      " 0.671875  0.6796875 0.6796875 0.6796875 0.6875    0.6953125 0.6953125\n",
      " 0.703125  0.7109375 0.7109375 0.71875   0.7265625 0.734375  0.734375\n",
      " 0.7421875 0.75      0.75      0.75      0.7578125 0.765625  0.765625\n",
      " 0.7734375 0.78125   0.7890625 0.796875  0.796875  0.8046875 0.8125\n",
      " 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125\n",
      " 0.828125  0.828125  0.8359375 0.84375   0.84375   0.84375   0.84375\n",
      " 0.84375   0.84375   0.84375   0.84375   0.8515625 0.8515625 0.8515625\n",
      " 0.859375  0.859375  0.8671875 0.8671875 0.875     0.875     0.875\n",
      " 0.8828125 0.8828125 0.890625  0.890625  0.890625  0.890625  0.8984375\n",
      " 0.8984375 0.90625   0.9140625 0.921875  0.9296875 0.9296875 0.9375\n",
      " 0.9375    0.9375    0.9453125 0.953125  0.9609375 0.9609375 0.96875\n",
      " 0.9765625 0.9765625 0.9765625 0.984375  0.984375  0.9921875 0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.0078125 0.015625  0.015625  0.015625  0.0234375\n",
      " 0.03125   0.0390625 0.0390625 0.046875  0.0546875 0.0625    0.0703125\n",
      " 0.078125  0.0859375 0.09375   0.09375   0.1015625 0.1015625 0.1015625\n",
      " 0.1015625 0.109375  0.1171875 0.1171875 0.1171875 0.125     0.125\n",
      " 0.1328125 0.140625  0.1484375 0.15625   0.1640625 0.1640625 0.171875\n",
      " 0.1796875 0.1875    0.1953125 0.203125  0.2109375 0.2109375 0.21875\n",
      " 0.2265625 0.234375  0.2421875 0.25      0.2578125 0.265625  0.2734375\n",
      " 0.2734375 0.2734375 0.28125   0.2890625 0.296875  0.3046875 0.3046875\n",
      " 0.3125    0.3203125 0.328125  0.3359375 0.3359375 0.3359375 0.34375\n",
      " 0.34375   0.3515625 0.359375  0.3671875 0.375     0.375     0.375\n",
      " 0.3828125 0.390625  0.390625  0.3984375 0.40625   0.4140625 0.421875\n",
      " 0.421875  0.421875  0.4296875 0.4375    0.4375    0.4375    0.4453125\n",
      " 0.453125  0.4609375 0.46875   0.4765625 0.484375  0.484375  0.4921875\n",
      " 0.4921875 0.4921875 0.4921875 0.5       0.5078125 0.515625  0.515625\n",
      " 0.5234375 0.53125   0.5390625 0.546875  0.5546875 0.5625    0.5703125\n",
      " 0.5703125 0.578125  0.5859375 0.59375   0.59375   0.59375   0.6015625\n",
      " 0.609375  0.609375  0.6171875 0.6171875 0.625     0.625     0.625\n",
      " 0.625     0.625     0.6328125 0.6328125 0.6328125 0.640625  0.6484375\n",
      " 0.6484375 0.65625   0.65625   0.6640625 0.6640625 0.671875  0.6796875\n",
      " 0.6875    0.6953125 0.703125  0.7109375 0.7109375 0.71875   0.71875\n",
      " 0.71875   0.71875   0.71875   0.71875   0.71875   0.71875   0.7265625\n",
      " 0.7265625 0.7265625 0.7265625 0.734375  0.734375  0.734375  0.734375\n",
      " 0.734375  0.734375  0.734375  0.7421875 0.75      0.7578125 0.7578125\n",
      " 0.7578125 0.765625  0.7734375 0.78125   0.78125   0.7890625 0.796875\n",
      " 0.8046875 0.8046875 0.8125    0.8203125 0.8203125 0.828125  0.8359375\n",
      " 0.8359375 0.8359375 0.8359375 0.8359375 0.84375   0.8515625 0.8515625\n",
      " 0.8515625 0.8515625 0.8515625 0.859375  0.859375  0.859375  0.859375\n",
      " 0.859375  0.859375  0.859375  0.859375  0.859375  0.859375  0.859375\n",
      " 0.859375  0.859375  0.8671875 0.8671875 0.875     0.8828125 0.8828125\n",
      " 0.8828125 0.8828125 0.8828125 0.8828125 0.890625  0.890625  0.890625\n",
      " 0.8984375 0.90625   0.90625   0.9140625 0.9140625 0.9140625 0.921875\n",
      " 0.921875  0.9296875 0.9296875 0.9296875 0.9296875 0.9375    0.9453125\n",
      " 0.953125  0.9609375 0.9609375 0.9609375 0.96875   0.9765625 0.9765625\n",
      " 0.9765625 0.9765625 0.9765625 0.9765625 0.984375  0.984375  0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875\n",
      " 0.9921875 0.9921875 1.        1.        1.        1.        1.\n",
      " 1.        1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.0234375 0.0234375\n",
      " 0.03125   0.0390625 0.046875  0.0546875 0.0625    0.0703125 0.078125\n",
      " 0.0859375 0.0859375 0.09375   0.1015625 0.109375  0.1171875 0.125\n",
      " 0.1328125 0.140625  0.1484375 0.15625   0.1640625 0.171875  0.1796875\n",
      " 0.1875    0.1953125 0.203125  0.2109375 0.21875   0.2265625 0.234375\n",
      " 0.2421875 0.25      0.2578125 0.265625  0.2734375 0.28125   0.2890625\n",
      " 0.296875  0.3046875 0.3125    0.3203125 0.3203125 0.328125  0.328125\n",
      " 0.328125  0.3359375 0.34375   0.3515625 0.359375  0.3671875 0.3671875\n",
      " 0.375     0.375     0.3828125 0.390625  0.3984375 0.40625   0.4140625\n",
      " 0.421875  0.4296875 0.4375    0.4453125 0.453125  0.4609375 0.46875\n",
      " 0.4765625 0.484375  0.484375  0.4921875 0.5       0.5078125 0.515625\n",
      " 0.5234375 0.53125   0.5390625 0.546875  0.546875  0.546875  0.546875\n",
      " 0.5546875 0.5625    0.5625    0.5625    0.5625    0.5625    0.5625\n",
      " 0.5625    0.5625    0.5625    0.5703125 0.578125  0.578125  0.5859375\n",
      " 0.5859375 0.59375   0.6015625 0.609375  0.609375  0.609375  0.609375\n",
      " 0.6171875 0.625     0.625     0.6328125 0.640625  0.6484375 0.65625\n",
      " 0.6640625 0.6640625 0.6640625 0.671875  0.671875  0.671875  0.671875\n",
      " 0.671875  0.6796875 0.6875    0.6953125 0.6953125 0.703125  0.703125\n",
      " 0.703125  0.703125  0.7109375 0.7109375 0.7109375 0.7109375 0.71875\n",
      " 0.71875   0.7265625 0.7265625 0.734375  0.734375  0.734375  0.734375\n",
      " 0.734375  0.734375  0.734375  0.7421875 0.75      0.7578125 0.7578125\n",
      " 0.7578125 0.7578125 0.7578125 0.7578125 0.765625  0.7734375 0.7734375\n",
      " 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375\n",
      " 0.7734375 0.7734375 0.7734375 0.7734375 0.78125   0.78125   0.78125\n",
      " 0.78125   0.7890625 0.7890625 0.7890625 0.7890625 0.7890625 0.7890625\n",
      " 0.796875  0.796875  0.796875  0.796875  0.796875  0.796875  0.796875\n",
      " 0.8046875 0.8046875 0.8046875 0.8046875 0.8046875 0.8125    0.8125\n",
      " 0.8125    0.8203125 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125\n",
      " 0.828125  0.8359375 0.84375   0.84375   0.84375   0.84375   0.84375\n",
      " 0.84375   0.8515625 0.859375  0.859375  0.859375  0.859375  0.8671875\n",
      " 0.8671875 0.8671875 0.8671875 0.875     0.8828125 0.8828125 0.890625\n",
      " 0.8984375 0.8984375 0.8984375 0.8984375 0.8984375 0.8984375 0.90625\n",
      " 0.9140625 0.9140625 0.9140625 0.921875  0.921875  0.921875  0.921875\n",
      " 0.921875  0.921875  0.9296875 0.9296875 0.9375    0.9453125 0.9453125\n",
      " 0.953125  0.9609375 0.96875   0.96875   0.96875   0.96875   0.9765625\n",
      " 0.9765625 0.9765625 0.9765625 0.9765625 0.984375  0.984375  0.984375\n",
      " 0.984375  0.984375  0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.0546875 0.0546875 0.0625    0.0625    0.0703125 0.0703125\n",
      " 0.078125  0.0859375 0.0859375 0.09375   0.1015625 0.109375  0.109375\n",
      " 0.109375  0.109375  0.1171875 0.1171875 0.125     0.125     0.1328125\n",
      " 0.140625  0.140625  0.1484375 0.15625   0.1640625 0.171875  0.171875\n",
      " 0.1796875 0.1875    0.1953125 0.1953125 0.203125  0.2109375 0.21875\n",
      " 0.2265625 0.2265625 0.2265625 0.234375  0.2421875 0.25      0.2578125\n",
      " 0.265625  0.2734375 0.2734375 0.28125   0.28125   0.2890625 0.2890625\n",
      " 0.2890625 0.296875  0.3046875 0.3125    0.3203125 0.328125  0.3359375\n",
      " 0.34375   0.3515625 0.3515625 0.3515625 0.359375  0.3671875 0.3671875\n",
      " 0.375     0.3828125 0.390625  0.390625  0.3984375 0.40625   0.4140625\n",
      " 0.421875  0.4296875 0.4375    0.4453125 0.453125  0.4609375 0.4609375\n",
      " 0.46875   0.46875   0.4765625 0.484375  0.4921875 0.5       0.5078125\n",
      " 0.515625  0.5234375 0.53125   0.53125   0.53125   0.53125   0.5390625\n",
      " 0.546875  0.546875  0.546875  0.5546875 0.5625    0.5703125 0.578125\n",
      " 0.5859375 0.59375   0.59375   0.59375   0.6015625 0.609375  0.6171875\n",
      " 0.6171875 0.6171875 0.625     0.625     0.6328125 0.640625  0.6484375\n",
      " 0.65625   0.65625   0.65625   0.6640625 0.6640625 0.6640625 0.671875\n",
      " 0.671875  0.6796875 0.6796875 0.6875    0.6875    0.6875    0.6875\n",
      " 0.6875    0.6875    0.6875    0.6875    0.6953125 0.6953125 0.6953125\n",
      " 0.703125  0.7109375 0.7109375 0.7109375 0.7109375 0.71875   0.7265625\n",
      " 0.734375  0.734375  0.734375  0.7421875 0.7421875 0.7421875 0.75\n",
      " 0.7578125 0.765625  0.7734375 0.7734375 0.7734375 0.78125   0.78125\n",
      " 0.78125   0.7890625 0.796875  0.796875  0.796875  0.8046875 0.8125\n",
      " 0.8203125 0.8203125 0.828125  0.828125  0.828125  0.828125  0.828125\n",
      " 0.828125  0.828125  0.8359375 0.8359375 0.8359375 0.84375   0.84375\n",
      " 0.84375   0.8515625 0.859375  0.8671875 0.875     0.875     0.8828125\n",
      " 0.8828125 0.8828125 0.890625  0.8984375 0.90625   0.90625   0.9140625\n",
      " 0.9140625 0.921875  0.921875  0.9296875 0.9296875 0.9296875 0.9296875\n",
      " 0.9375    0.9375    0.9453125 0.953125  0.953125  0.953125  0.953125\n",
      " 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375 0.96875\n",
      " 0.96875   0.96875   0.9765625 0.9765625 0.984375  0.984375  0.984375\n",
      " 0.984375  0.984375  0.984375  0.984375  0.984375  0.984375  0.984375\n",
      " 0.984375  0.984375  0.984375  0.984375  0.984375  0.9921875 0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 1.\n",
      " 1.        1.        1.        1.        1.        1.        1.\n",
      " 1.        1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.03125   0.03125\n",
      " 0.0390625 0.046875  0.046875  0.046875  0.0546875 0.0625    0.0703125\n",
      " 0.078125  0.0859375 0.09375   0.1015625 0.1015625 0.1015625 0.109375\n",
      " 0.1171875 0.125     0.1328125 0.1328125 0.140625  0.1484375 0.1484375\n",
      " 0.15625   0.1640625 0.171875  0.1796875 0.1875    0.1953125 0.203125\n",
      " 0.2109375 0.21875   0.21875   0.21875   0.2265625 0.234375  0.234375\n",
      " 0.2421875 0.25      0.2578125 0.265625  0.2734375 0.28125   0.2890625\n",
      " 0.296875  0.296875  0.296875  0.3046875 0.3046875 0.3046875 0.3046875\n",
      " 0.3125    0.3203125 0.328125  0.3359375 0.3359375 0.3359375 0.34375\n",
      " 0.3515625 0.3515625 0.3515625 0.359375  0.359375  0.359375  0.359375\n",
      " 0.3671875 0.375     0.3828125 0.390625  0.3984375 0.40625   0.40625\n",
      " 0.4140625 0.421875  0.421875  0.4296875 0.4375    0.4453125 0.4453125\n",
      " 0.4453125 0.453125  0.4609375 0.4609375 0.4609375 0.46875   0.4765625\n",
      " 0.4765625 0.484375  0.4921875 0.5       0.5       0.5       0.5078125\n",
      " 0.515625  0.5234375 0.5234375 0.5234375 0.53125   0.53125   0.53125\n",
      " 0.53125   0.53125   0.5390625 0.546875  0.5546875 0.5625    0.5625\n",
      " 0.5703125 0.5703125 0.578125  0.5859375 0.59375   0.6015625 0.609375\n",
      " 0.6171875 0.625     0.625     0.6328125 0.6328125 0.640625  0.6484375\n",
      " 0.65625   0.65625   0.65625   0.6640625 0.6640625 0.671875  0.6796875\n",
      " 0.6796875 0.6796875 0.6796875 0.6796875 0.6796875 0.6875    0.6953125\n",
      " 0.6953125 0.703125  0.703125  0.7109375 0.7109375 0.7109375 0.71875\n",
      " 0.71875   0.71875   0.71875   0.71875   0.7265625 0.734375  0.734375\n",
      " 0.734375  0.734375  0.734375  0.734375  0.7421875 0.7421875 0.7421875\n",
      " 0.7421875 0.7421875 0.7421875 0.7421875 0.75      0.75      0.7578125\n",
      " 0.7578125 0.765625  0.7734375 0.7734375 0.78125   0.78125   0.78125\n",
      " 0.78125   0.78125   0.78125   0.7890625 0.796875  0.8046875 0.8046875\n",
      " 0.8046875 0.8046875 0.8125    0.8125    0.8203125 0.8203125 0.8203125\n",
      " 0.8203125 0.8203125 0.828125  0.8359375 0.8359375 0.84375   0.84375\n",
      " 0.84375   0.8515625 0.8515625 0.859375  0.859375  0.859375  0.859375\n",
      " 0.8671875 0.8671875 0.8671875 0.875     0.875     0.875     0.875\n",
      " 0.875     0.875     0.875     0.875     0.875     0.875     0.875\n",
      " 0.875     0.8828125 0.8828125 0.8828125 0.8828125 0.890625  0.8984375\n",
      " 0.8984375 0.90625   0.9140625 0.9140625 0.9140625 0.9140625 0.9140625\n",
      " 0.9140625 0.9140625 0.9140625 0.9140625 0.9140625 0.921875  0.9296875\n",
      " 0.9296875 0.9375    0.9375    0.9453125 0.953125  0.953125  0.9609375\n",
      " 0.9609375 0.96875   0.9765625 0.984375  0.984375  0.984375  0.984375\n",
      " 0.9921875 0.9921875 1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.0078125 0.0078125 0.015625  0.015625  0.0234375 0.03125\n",
      " 0.0390625 0.046875  0.046875  0.0546875 0.0625    0.0703125 0.0703125\n",
      " 0.0703125 0.078125  0.078125  0.078125  0.0859375 0.0859375 0.09375\n",
      " 0.09375   0.1015625 0.109375  0.1171875 0.125     0.1328125 0.140625\n",
      " 0.1484375 0.15625   0.1640625 0.171875  0.1796875 0.1796875 0.1875\n",
      " 0.1875    0.1953125 0.203125  0.2109375 0.21875   0.21875   0.21875\n",
      " 0.2265625 0.2265625 0.234375  0.234375  0.2421875 0.25      0.2578125\n",
      " 0.265625  0.2734375 0.28125   0.2890625 0.296875  0.3046875 0.3125\n",
      " 0.3203125 0.3203125 0.3203125 0.328125  0.3359375 0.3359375 0.34375\n",
      " 0.3515625 0.359375  0.3671875 0.375     0.375     0.3828125 0.390625\n",
      " 0.3984375 0.40625   0.40625   0.4140625 0.421875  0.4296875 0.4375\n",
      " 0.4375    0.4375    0.4375    0.4375    0.4375    0.4375    0.4453125\n",
      " 0.453125  0.4609375 0.4609375 0.4609375 0.46875   0.4765625 0.484375\n",
      " 0.4921875 0.5       0.5078125 0.5078125 0.515625  0.5234375 0.53125\n",
      " 0.5390625 0.546875  0.5546875 0.5625    0.5625    0.5703125 0.5703125\n",
      " 0.578125  0.5859375 0.5859375 0.5859375 0.59375   0.6015625 0.6015625\n",
      " 0.609375  0.6171875 0.625     0.6328125 0.640625  0.640625  0.6484375\n",
      " 0.6484375 0.65625   0.65625   0.65625   0.6640625 0.6640625 0.671875\n",
      " 0.6796875 0.6875    0.6875    0.6875    0.6953125 0.703125  0.703125\n",
      " 0.7109375 0.7109375 0.7109375 0.71875   0.71875   0.7265625 0.7265625\n",
      " 0.734375  0.7421875 0.75      0.75      0.75      0.75      0.75\n",
      " 0.75      0.75      0.75      0.75      0.75      0.7578125 0.765625\n",
      " 0.7734375 0.7734375 0.7734375 0.78125   0.78125   0.78125   0.78125\n",
      " 0.7890625 0.7890625 0.7890625 0.7890625 0.7890625 0.7890625 0.796875\n",
      " 0.8046875 0.8125    0.8125    0.8125    0.8125    0.8125    0.8125\n",
      " 0.8125    0.8203125 0.828125  0.8359375 0.8359375 0.8359375 0.84375\n",
      " 0.8515625 0.8515625 0.859375  0.859375  0.859375  0.859375  0.8671875\n",
      " 0.8671875 0.8671875 0.8671875 0.8671875 0.875     0.875     0.875\n",
      " 0.875     0.8828125 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.8828125 0.8828125 0.8828125 0.890625  0.890625  0.890625  0.890625\n",
      " 0.8984375 0.8984375 0.8984375 0.8984375 0.90625   0.90625   0.9140625\n",
      " 0.9140625 0.9140625 0.9140625 0.9140625 0.921875  0.921875  0.921875\n",
      " 0.921875  0.921875  0.921875  0.921875  0.921875  0.9296875 0.9296875\n",
      " 0.9375    0.9375    0.9375    0.9375    0.9375    0.9375    0.9375\n",
      " 0.9453125 0.953125  0.953125  0.953125  0.953125  0.953125  0.953125\n",
      " 0.953125  0.9609375 0.9609375 0.96875   0.9765625 0.9765625 0.984375\n",
      " 0.984375  0.9921875 1.        1.        1.       ] [0.0078125 0.0078125 0.0078125 ... 1.        1.        1.       ]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0625    0.0703125 0.0703125 0.0703125\n",
      " 0.078125  0.0859375 0.0859375 0.09375   0.1015625 0.109375  0.109375\n",
      " 0.1171875 0.1171875 0.125     0.1328125 0.1328125 0.140625  0.1484375\n",
      " 0.1484375 0.1484375 0.15625   0.1640625 0.171875  0.171875  0.1796875\n",
      " 0.1796875 0.1875    0.1953125 0.1953125 0.203125  0.2109375 0.21875\n",
      " 0.2265625 0.234375  0.2421875 0.25      0.25      0.2578125 0.265625\n",
      " 0.2734375 0.28125   0.2890625 0.296875  0.296875  0.3046875 0.3125\n",
      " 0.3125    0.3203125 0.3203125 0.3203125 0.328125  0.3359375 0.3359375\n",
      " 0.3359375 0.3359375 0.3359375 0.34375   0.3515625 0.359375  0.3671875\n",
      " 0.375     0.3828125 0.390625  0.3984375 0.40625   0.4140625 0.421875\n",
      " 0.4296875 0.4375    0.4453125 0.453125  0.4609375 0.46875   0.4765625\n",
      " 0.484375  0.4921875 0.5       0.5078125 0.515625  0.5234375 0.53125\n",
      " 0.5390625 0.546875  0.5546875 0.5546875 0.5546875 0.5625    0.5625\n",
      " 0.5703125 0.578125  0.578125  0.5859375 0.59375   0.6015625 0.6015625\n",
      " 0.609375  0.609375  0.6171875 0.625     0.6328125 0.6328125 0.640625\n",
      " 0.6484375 0.65625   0.65625   0.65625   0.65625   0.6640625 0.6640625\n",
      " 0.6640625 0.671875  0.6796875 0.6796875 0.6796875 0.6875    0.6953125\n",
      " 0.703125  0.7109375 0.71875   0.7265625 0.7265625 0.7265625 0.734375\n",
      " 0.734375  0.7421875 0.7421875 0.75      0.75      0.75      0.7578125\n",
      " 0.765625  0.765625  0.765625  0.7734375 0.7734375 0.78125   0.7890625\n",
      " 0.7890625 0.7890625 0.7890625 0.796875  0.796875  0.8046875 0.8046875\n",
      " 0.8046875 0.8125    0.8203125 0.8203125 0.8203125 0.8203125 0.8203125\n",
      " 0.8203125 0.828125  0.828125  0.8359375 0.8359375 0.8359375 0.84375\n",
      " 0.84375   0.84375   0.84375   0.84375   0.8515625 0.8515625 0.8515625\n",
      " 0.8515625 0.859375  0.8671875 0.8671875 0.875     0.8828125 0.890625\n",
      " 0.8984375 0.8984375 0.8984375 0.90625   0.90625   0.9140625 0.9140625\n",
      " 0.921875  0.9296875 0.9296875 0.9296875 0.9296875 0.9375    0.9375\n",
      " 0.9375    0.9453125 0.953125  0.953125  0.9609375 0.9609375 0.9609375\n",
      " 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375\n",
      " 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375\n",
      " 0.96875   0.96875   0.9765625 0.9765625 0.9765625 0.9765625 0.9765625\n",
      " 0.9765625 0.9765625 0.9765625 0.9765625 0.9765625 0.9765625 0.9765625\n",
      " 0.9765625 0.984375  0.984375  0.984375  0.984375  0.984375  0.984375\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 0.9921875 1.\n",
      " 1.        1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625\n",
      " 0.0390625 0.046875  0.0546875 0.0625    0.0625    0.0703125 0.078125\n",
      " 0.0859375 0.09375   0.1015625 0.109375  0.109375  0.1171875 0.1171875\n",
      " 0.125     0.1328125 0.140625  0.1484375 0.15625   0.15625   0.15625\n",
      " 0.1640625 0.1640625 0.1640625 0.1640625 0.171875  0.1796875 0.1796875\n",
      " 0.1875    0.1953125 0.203125  0.2109375 0.21875   0.21875   0.2265625\n",
      " 0.2265625 0.234375  0.234375  0.2421875 0.2421875 0.2421875 0.2421875\n",
      " 0.25      0.2578125 0.265625  0.2734375 0.28125   0.28125   0.2890625\n",
      " 0.2890625 0.296875  0.296875  0.3046875 0.3125    0.3203125 0.328125\n",
      " 0.328125  0.328125  0.3359375 0.34375   0.3515625 0.359375  0.3671875\n",
      " 0.3671875 0.375     0.3828125 0.390625  0.390625  0.3984375 0.3984375\n",
      " 0.3984375 0.40625   0.4140625 0.421875  0.421875  0.421875  0.421875\n",
      " 0.4296875 0.4296875 0.4296875 0.4296875 0.4375    0.4453125 0.4453125\n",
      " 0.453125  0.4609375 0.46875   0.4765625 0.4765625 0.4765625 0.484375\n",
      " 0.484375  0.4921875 0.4921875 0.5       0.5       0.5078125 0.515625\n",
      " 0.5234375 0.5234375 0.53125   0.5390625 0.546875  0.5546875 0.5546875\n",
      " 0.5546875 0.5625    0.5703125 0.5703125 0.578125  0.5859375 0.59375\n",
      " 0.6015625 0.609375  0.6171875 0.6171875 0.625     0.6328125 0.640625\n",
      " 0.6484375 0.6484375 0.6484375 0.65625   0.65625   0.6640625 0.6640625\n",
      " 0.6640625 0.6640625 0.6640625 0.6640625 0.6640625 0.671875  0.6796875\n",
      " 0.6796875 0.6875    0.6875    0.6875    0.6875    0.6875    0.6953125\n",
      " 0.6953125 0.703125  0.7109375 0.71875   0.71875   0.7265625 0.734375\n",
      " 0.734375  0.734375  0.734375  0.734375  0.7421875 0.7421875 0.7421875\n",
      " 0.7421875 0.7421875 0.7421875 0.75      0.75      0.75      0.75\n",
      " 0.75      0.7578125 0.7578125 0.7578125 0.765625  0.765625  0.765625\n",
      " 0.765625  0.7734375 0.78125   0.7890625 0.796875  0.8046875 0.8046875\n",
      " 0.8046875 0.8046875 0.8046875 0.8125    0.8203125 0.828125  0.828125\n",
      " 0.8359375 0.84375   0.8515625 0.8515625 0.8515625 0.859375  0.859375\n",
      " 0.859375  0.8671875 0.875     0.875     0.875     0.875     0.8828125\n",
      " 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.8828125 0.890625  0.890625  0.890625  0.8984375 0.8984375 0.90625\n",
      " 0.90625   0.90625   0.90625   0.9140625 0.9140625 0.921875  0.921875\n",
      " 0.921875  0.921875  0.921875  0.921875  0.9296875 0.9296875 0.9296875\n",
      " 0.9375    0.9453125 0.9453125 0.9453125 0.9453125 0.953125  0.953125\n",
      " 0.9609375 0.9609375 0.9609375 0.9609375 0.96875   0.96875   0.96875\n",
      " 0.96875   0.96875   0.9765625 0.9765625 0.984375  0.9921875 1.\n",
      " 1.        1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625 0.0390625\n",
      " 0.0390625 0.046875  0.0546875 0.0546875 0.0625    0.0703125 0.078125\n",
      " 0.0859375 0.0859375 0.09375   0.1015625 0.109375  0.1171875 0.125\n",
      " 0.1328125 0.140625  0.1484375 0.15625   0.1640625 0.171875  0.171875\n",
      " 0.1796875 0.1875    0.1875    0.1953125 0.203125  0.203125  0.2109375\n",
      " 0.21875   0.21875   0.2265625 0.234375  0.2421875 0.2421875 0.25\n",
      " 0.25      0.2578125 0.2578125 0.265625  0.2734375 0.28125   0.2890625\n",
      " 0.296875  0.296875  0.3046875 0.3125    0.3203125 0.328125  0.3359375\n",
      " 0.34375   0.34375   0.3515625 0.3515625 0.3515625 0.359375  0.359375\n",
      " 0.3671875 0.3671875 0.375     0.3828125 0.390625  0.3984375 0.40625\n",
      " 0.40625   0.4140625 0.421875  0.4296875 0.4375    0.4453125 0.4453125\n",
      " 0.4453125 0.4453125 0.453125  0.453125  0.4609375 0.4609375 0.46875\n",
      " 0.4765625 0.4765625 0.484375  0.4921875 0.5       0.5078125 0.5078125\n",
      " 0.5078125 0.515625  0.5234375 0.5234375 0.53125   0.5390625 0.546875\n",
      " 0.546875  0.5546875 0.5625    0.5703125 0.578125  0.578125  0.5859375\n",
      " 0.59375   0.6015625 0.609375  0.6171875 0.625     0.6328125 0.640625\n",
      " 0.6484375 0.65625   0.65625   0.6640625 0.671875  0.6796875 0.6875\n",
      " 0.6953125 0.703125  0.703125  0.7109375 0.71875   0.7265625 0.734375\n",
      " 0.734375  0.734375  0.734375  0.734375  0.7421875 0.75      0.75\n",
      " 0.75      0.75      0.75      0.75      0.75      0.75      0.75\n",
      " 0.75      0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125\n",
      " 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.765625\n",
      " 0.765625  0.7734375 0.78125   0.78125   0.7890625 0.7890625 0.796875\n",
      " 0.796875  0.8046875 0.8046875 0.8046875 0.8046875 0.8125    0.8125\n",
      " 0.8125    0.8125    0.8203125 0.8203125 0.8203125 0.8203125 0.8203125\n",
      " 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125\n",
      " 0.828125  0.828125  0.828125  0.828125  0.8359375 0.8359375 0.8359375\n",
      " 0.8359375 0.8359375 0.8359375 0.8359375 0.8359375 0.84375   0.84375\n",
      " 0.84375   0.8515625 0.8515625 0.8515625 0.859375  0.8671875 0.8671875\n",
      " 0.8671875 0.8671875 0.8671875 0.8671875 0.8671875 0.8671875 0.875\n",
      " 0.875     0.875     0.875     0.875     0.875     0.8828125 0.8828125\n",
      " 0.8828125 0.890625  0.890625  0.8984375 0.90625   0.9140625 0.9140625\n",
      " 0.9140625 0.9140625 0.9140625 0.9140625 0.9140625 0.9140625 0.921875\n",
      " 0.921875  0.9296875 0.9296875 0.9296875 0.9296875 0.9296875 0.9375\n",
      " 0.9375    0.9375    0.9453125 0.9453125 0.953125  0.953125  0.9609375\n",
      " 0.9609375 0.96875   0.96875   0.9765625 0.9765625 0.9765625 0.984375\n",
      " 0.984375  0.984375  0.9921875 0.9921875 1.       ] [0.0390625 0.0390625 0.0390625 ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.0234375 0.03125\n",
      " 0.0390625 0.046875  0.0546875 0.0625    0.0703125 0.0703125 0.0703125\n",
      " 0.0703125 0.0703125 0.0703125 0.078125  0.0859375 0.0859375 0.0859375\n",
      " 0.09375   0.1015625 0.109375  0.1171875 0.125     0.1328125 0.140625\n",
      " 0.1484375 0.15625   0.1640625 0.171875  0.1796875 0.1796875 0.1875\n",
      " 0.1875    0.1875    0.1953125 0.203125  0.203125  0.2109375 0.21875\n",
      " 0.21875   0.2265625 0.2265625 0.2265625 0.234375  0.2421875 0.25\n",
      " 0.2578125 0.265625  0.2734375 0.2734375 0.2734375 0.2734375 0.2734375\n",
      " 0.28125   0.28125   0.2890625 0.296875  0.3046875 0.3046875 0.3125\n",
      " 0.3203125 0.328125  0.3359375 0.3359375 0.3359375 0.34375   0.3515625\n",
      " 0.359375  0.359375  0.359375  0.3671875 0.3671875 0.375     0.3828125\n",
      " 0.390625  0.3984375 0.40625   0.40625   0.4140625 0.4140625 0.421875\n",
      " 0.4296875 0.4296875 0.4375    0.4453125 0.453125  0.453125  0.4609375\n",
      " 0.46875   0.46875   0.4765625 0.484375  0.484375  0.484375  0.484375\n",
      " 0.4921875 0.4921875 0.5       0.5       0.5       0.5078125 0.515625\n",
      " 0.5234375 0.5234375 0.5234375 0.53125   0.5390625 0.5390625 0.546875\n",
      " 0.5546875 0.5625    0.5625    0.5625    0.5703125 0.5703125 0.5703125\n",
      " 0.578125  0.578125  0.5859375 0.5859375 0.5859375 0.59375   0.6015625\n",
      " 0.6015625 0.6015625 0.609375  0.6171875 0.625     0.625     0.6328125\n",
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      " 0.8671875 0.8671875 0.8671875 0.8671875 0.8671875 0.8671875 0.8671875\n",
      " 0.8671875 0.8671875 0.8671875 0.875     0.8828125 0.890625  0.8984375\n",
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      " 0.9609375 0.96875   0.96875   0.96875   0.96875   0.96875   0.96875\n",
      " 0.9765625 0.984375  0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
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      " 0.875     0.8828125 0.8828125 0.8828125 0.8828125 0.890625  0.890625\n",
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      " 0.984375  0.984375  0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
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      " 0.5546875 0.5625    0.5625    0.5625    0.5625    0.5703125 0.578125\n",
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      " 0.6796875 0.6796875 0.6796875 0.6796875 0.6875    0.6875    0.6875\n",
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      " 0.7265625 0.7265625 0.7265625 0.7265625 0.7265625 0.734375  0.734375\n",
      " 0.7421875 0.75      0.7578125 0.765625  0.7734375 0.78125   0.7890625\n",
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      " 0.84375   0.84375   0.8515625 0.8515625 0.8515625 0.859375  0.8671875\n",
      " 0.875     0.875     0.875     0.875     0.8828125 0.8828125 0.890625\n",
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      " 0.96875   0.9765625 0.9765625 0.9765625 0.9765625 0.9765625 0.9765625\n",
      " 0.9765625 0.984375  0.9921875 1.        1.       ] [0.015625 0.015625 0.015625 ... 1.       1.       1.      ]\n",
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      " 0.046875  0.0546875 0.0625    0.0703125 0.078125  0.078125  0.0859375\n",
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      " 0.109375  0.1171875 0.1171875 0.125     0.1328125 0.140625  0.140625\n",
      " 0.140625  0.140625  0.1484375 0.15625   0.15625   0.15625   0.1640625\n",
      " 0.1640625 0.1640625 0.1640625 0.1640625 0.1640625 0.1640625 0.1640625\n",
      " 0.1640625 0.171875  0.1796875 0.1875    0.1875    0.1953125 0.1953125\n",
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      " 0.234375  0.2421875 0.25      0.25      0.2578125 0.265625  0.265625\n",
      " 0.2734375 0.28125   0.28125   0.28125   0.28125   0.28125   0.2890625\n",
      " 0.296875  0.3046875 0.3046875 0.3046875 0.3125    0.3203125 0.3203125\n",
      " 0.328125  0.328125  0.3359375 0.34375   0.3515625 0.359375  0.359375\n",
      " 0.359375  0.359375  0.3671875 0.375     0.375     0.3828125 0.390625\n",
      " 0.3984375 0.40625   0.4140625 0.421875  0.4296875 0.4296875 0.4296875\n",
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      " 0.4609375 0.4609375 0.46875   0.4765625 0.4765625 0.4765625 0.484375\n",
      " 0.484375  0.4921875 0.4921875 0.4921875 0.5       0.5078125 0.515625\n",
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      " 0.53125   0.53125   0.5390625 0.546875  0.5546875 0.5625    0.5625\n",
      " 0.5625    0.5625    0.5703125 0.578125  0.578125  0.5859375 0.5859375\n",
      " 0.59375   0.59375   0.59375   0.59375   0.59375   0.6015625 0.609375\n",
      " 0.6171875 0.625     0.6328125 0.6328125 0.6328125 0.6328125 0.640625\n",
      " 0.6484375 0.65625   0.65625   0.6640625 0.6640625 0.671875  0.6796875\n",
      " 0.6875    0.6875    0.6875    0.6875    0.6875    0.6875    0.6953125\n",
      " 0.6953125 0.703125  0.7109375 0.71875   0.71875   0.71875   0.7265625\n",
      " 0.7265625 0.7265625 0.7265625 0.7265625 0.734375  0.734375  0.734375\n",
      " 0.734375  0.7421875 0.7421875 0.7421875 0.75      0.7578125 0.7578125\n",
      " 0.765625  0.7734375 0.78125   0.7890625 0.7890625 0.7890625 0.7890625\n",
      " 0.7890625 0.7890625 0.796875  0.796875  0.796875  0.796875  0.796875\n",
      " 0.796875  0.796875  0.796875  0.8046875 0.8125    0.8125    0.8125\n",
      " 0.8203125 0.828125  0.8359375 0.84375   0.8515625 0.8515625 0.8515625\n",
      " 0.859375  0.8671875 0.8671875 0.8671875 0.8671875 0.8671875 0.8671875\n",
      " 0.875     0.875     0.8828125 0.8828125 0.890625  0.8984375 0.90625\n",
      " 0.90625   0.9140625 0.921875  0.9296875 0.9296875 0.9296875 0.9296875\n",
      " 0.9296875 0.9375    0.9375    0.9375    0.9375    0.9375    0.9453125\n",
      " 0.9453125 0.953125  0.9609375 0.96875   0.9765625 0.9765625 0.9765625\n",
      " 0.9765625 0.9765625 0.984375  0.9921875 1.       ] [0.03125   0.03125   0.03125   ... 0.9765625 0.9765625 1.       ]\n",
      "[0.        0.0078125 0.015625  0.015625  0.015625  0.0234375 0.0234375\n",
      " 0.03125   0.03125   0.0390625 0.046875  0.0546875 0.0546875 0.0546875\n",
      " 0.0625    0.0703125 0.0703125 0.078125  0.078125  0.0859375 0.09375\n",
      " 0.1015625 0.109375  0.1171875 0.1171875 0.1171875 0.1171875 0.125\n",
      " 0.1328125 0.1328125 0.140625  0.1484375 0.15625   0.1640625 0.1640625\n",
      " 0.171875  0.171875  0.171875  0.1796875 0.1875    0.1875    0.1875\n",
      " 0.1875    0.1953125 0.203125  0.2109375 0.21875   0.2265625 0.234375\n",
      " 0.2421875 0.2421875 0.2421875 0.2421875 0.2421875 0.25      0.25\n",
      " 0.2578125 0.2578125 0.265625  0.265625  0.265625  0.2734375 0.28125\n",
      " 0.2890625 0.296875  0.3046875 0.3046875 0.3046875 0.3046875 0.3046875\n",
      " 0.3046875 0.3125    0.3125    0.3125    0.3203125 0.328125  0.328125\n",
      " 0.328125  0.3359375 0.3359375 0.3359375 0.3359375 0.3359375 0.34375\n",
      " 0.34375   0.3515625 0.359375  0.359375  0.3671875 0.375     0.375\n",
      " 0.3828125 0.390625  0.390625  0.390625  0.390625  0.3984375 0.40625\n",
      " 0.40625   0.4140625 0.4140625 0.4140625 0.4140625 0.421875  0.4296875\n",
      " 0.4296875 0.4375    0.4375    0.4453125 0.453125  0.453125  0.453125\n",
      " 0.453125  0.453125  0.453125  0.4609375 0.46875   0.46875   0.4765625\n",
      " 0.4765625 0.4765625 0.484375  0.4921875 0.5       0.5       0.5\n",
      " 0.5       0.5       0.5       0.5       0.5       0.5078125 0.5078125\n",
      " 0.5078125 0.515625  0.515625  0.5234375 0.53125   0.53125   0.5390625\n",
      " 0.5390625 0.546875  0.5546875 0.5625    0.5625    0.5703125 0.5703125\n",
      " 0.578125  0.578125  0.578125  0.578125  0.5859375 0.59375   0.6015625\n",
      " 0.6015625 0.609375  0.609375  0.609375  0.609375  0.6171875 0.625\n",
      " 0.625     0.6328125 0.640625  0.640625  0.6484375 0.6484375 0.6484375\n",
      " 0.65625   0.65625   0.6640625 0.6640625 0.671875  0.6796875 0.6875\n",
      " 0.6875    0.6953125 0.703125  0.703125  0.7109375 0.71875   0.71875\n",
      " 0.7265625 0.7265625 0.7265625 0.7265625 0.7265625 0.7265625 0.734375\n",
      " 0.734375  0.734375  0.734375  0.734375  0.734375  0.734375  0.734375\n",
      " 0.7421875 0.75      0.7578125 0.765625  0.7734375 0.78125   0.78125\n",
      " 0.7890625 0.796875  0.8046875 0.8125    0.8203125 0.828125  0.8359375\n",
      " 0.8359375 0.84375   0.8515625 0.8515625 0.8515625 0.8515625 0.8515625\n",
      " 0.8515625 0.859375  0.859375  0.859375  0.8671875 0.8671875 0.875\n",
      " 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.890625  0.8984375 0.8984375 0.8984375 0.90625   0.90625   0.9140625\n",
      " 0.9140625 0.921875  0.921875  0.921875  0.9296875 0.9375    0.9375\n",
      " 0.9453125 0.9453125 0.953125  0.953125  0.953125  0.9609375 0.9609375\n",
      " 0.96875   0.9765625 0.984375  0.9921875 1.       ] [0.015625  0.015625  0.015625  ... 0.9609375 0.9609375 1.       ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625 0.046875\n",
      " 0.0546875 0.0546875 0.0625    0.0625    0.0625    0.0703125 0.078125\n",
      " 0.078125  0.0859375 0.0859375 0.09375   0.1015625 0.1015625 0.109375\n",
      " 0.109375  0.1171875 0.1171875 0.125     0.1328125 0.140625  0.1484375\n",
      " 0.1484375 0.15625   0.15625   0.1640625 0.1640625 0.1640625 0.171875\n",
      " 0.1796875 0.1875    0.1875    0.1875    0.1953125 0.203125  0.203125\n",
      " 0.2109375 0.2109375 0.2109375 0.2109375 0.2109375 0.21875   0.21875\n",
      " 0.2265625 0.2265625 0.234375  0.2421875 0.25      0.25      0.2578125\n",
      " 0.265625  0.265625  0.2734375 0.28125   0.28125   0.2890625 0.296875\n",
      " 0.3046875 0.3125    0.3203125 0.3203125 0.328125  0.3359375 0.34375\n",
      " 0.34375   0.34375   0.3515625 0.359375  0.3671875 0.3671875 0.375\n",
      " 0.3828125 0.3828125 0.3828125 0.390625  0.390625  0.390625  0.3984375\n",
      " 0.40625   0.4140625 0.4140625 0.421875  0.4296875 0.4296875 0.4296875\n",
      " 0.4375    0.4453125 0.4453125 0.453125  0.453125  0.453125  0.453125\n",
      " 0.4609375 0.46875   0.46875   0.46875   0.46875   0.46875   0.4765625\n",
      " 0.4765625 0.4765625 0.4765625 0.4765625 0.4765625 0.484375  0.484375\n",
      " 0.484375  0.4921875 0.5       0.5       0.5078125 0.515625  0.5234375\n",
      " 0.5234375 0.53125   0.5390625 0.546875  0.5546875 0.5546875 0.5625\n",
      " 0.5625    0.5703125 0.5703125 0.5703125 0.5703125 0.5703125 0.578125\n",
      " 0.5859375 0.59375   0.6015625 0.609375  0.609375  0.6171875 0.6171875\n",
      " 0.6171875 0.6171875 0.625     0.6328125 0.640625  0.640625  0.6484375\n",
      " 0.6484375 0.6484375 0.65625   0.6640625 0.671875  0.6796875 0.6796875\n",
      " 0.6796875 0.6875    0.6953125 0.6953125 0.6953125 0.703125  0.703125\n",
      " 0.703125  0.7109375 0.7109375 0.7109375 0.7109375 0.7109375 0.71875\n",
      " 0.7265625 0.734375  0.734375  0.7421875 0.75      0.75      0.7578125\n",
      " 0.765625  0.765625  0.765625  0.7734375 0.7734375 0.7734375 0.7734375\n",
      " 0.7734375 0.7734375 0.78125   0.78125   0.78125   0.7890625 0.7890625\n",
      " 0.7890625 0.7890625 0.796875  0.796875  0.796875  0.796875  0.8046875\n",
      " 0.8125    0.8203125 0.8203125 0.8203125 0.828125  0.828125  0.828125\n",
      " 0.8359375 0.84375   0.8515625 0.8515625 0.8515625 0.8515625 0.8515625\n",
      " 0.8515625 0.859375  0.859375  0.859375  0.8671875 0.875     0.875\n",
      " 0.8828125 0.890625  0.890625  0.890625  0.8984375 0.8984375 0.8984375\n",
      " 0.8984375 0.90625   0.90625   0.9140625 0.9140625 0.9140625 0.921875\n",
      " 0.921875  0.9296875 0.9375    0.9453125 0.9453125 0.9453125 0.953125\n",
      " 0.953125  0.953125  0.953125  0.9609375 0.96875   0.96875   0.96875\n",
      " 0.9765625 0.9765625 0.9765625 0.984375  0.984375  0.9921875 0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 1.        1.       ] [0.0546875 0.0546875 0.0546875 ... 1.        1.        1.       ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.0234375 0.0234375 0.0234375\n",
      " 0.0234375 0.03125   0.0390625 0.0390625 0.046875  0.0546875 0.0625\n",
      " 0.0703125 0.078125  0.0859375 0.09375   0.09375   0.09375   0.09375\n",
      " 0.1015625 0.1015625 0.1015625 0.1015625 0.109375  0.1171875 0.125\n",
      " 0.1328125 0.140625  0.140625  0.140625  0.140625  0.140625  0.140625\n",
      " 0.140625  0.140625  0.140625  0.140625  0.1484375 0.1484375 0.15625\n",
      " 0.1640625 0.171875  0.1796875 0.1796875 0.1875    0.1953125 0.1953125\n",
      " 0.203125  0.2109375 0.21875   0.21875   0.2265625 0.2265625 0.234375\n",
      " 0.2421875 0.25      0.2578125 0.2578125 0.265625  0.2734375 0.28125\n",
      " 0.28125   0.2890625 0.296875  0.3046875 0.3125    0.3203125 0.3203125\n",
      " 0.328125  0.3359375 0.34375   0.34375   0.34375   0.3515625 0.3515625\n",
      " 0.359375  0.359375  0.359375  0.359375  0.359375  0.3671875 0.3671875\n",
      " 0.375     0.375     0.375     0.3828125 0.3828125 0.390625  0.3984375\n",
      " 0.3984375 0.40625   0.4140625 0.421875  0.4296875 0.4375    0.4375\n",
      " 0.4375    0.4453125 0.4453125 0.4453125 0.4453125 0.453125  0.4609375\n",
      " 0.4609375 0.46875   0.4765625 0.4765625 0.4765625 0.484375  0.484375\n",
      " 0.484375  0.484375  0.484375  0.4921875 0.4921875 0.4921875 0.4921875\n",
      " 0.5       0.5       0.5       0.5078125 0.515625  0.5234375 0.5234375\n",
      " 0.5234375 0.53125   0.53125   0.53125   0.53125   0.5390625 0.546875\n",
      " 0.546875  0.5546875 0.5625    0.5625    0.5625    0.5703125 0.5703125\n",
      " 0.5703125 0.578125  0.578125  0.578125  0.578125  0.5859375 0.59375\n",
      " 0.59375   0.59375   0.59375   0.59375   0.59375   0.6015625 0.609375\n",
      " 0.6171875 0.625     0.625     0.6328125 0.640625  0.6484375 0.65625\n",
      " 0.6640625 0.671875  0.671875  0.6796875 0.6796875 0.6875    0.6953125\n",
      " 0.6953125 0.6953125 0.703125  0.703125  0.7109375 0.7109375 0.7109375\n",
      " 0.7109375 0.7109375 0.71875   0.71875   0.71875   0.7265625 0.7265625\n",
      " 0.734375  0.7421875 0.7421875 0.7421875 0.7421875 0.7421875 0.7421875\n",
      " 0.75      0.75      0.7578125 0.765625  0.7734375 0.7734375 0.7734375\n",
      " 0.7734375 0.7734375 0.7734375 0.78125   0.78125   0.78125   0.78125\n",
      " 0.7890625 0.7890625 0.796875  0.8046875 0.8125    0.8125    0.8125\n",
      " 0.8125    0.8125    0.8203125 0.828125  0.8359375 0.84375   0.8515625\n",
      " 0.8515625 0.859375  0.859375  0.859375  0.859375  0.859375  0.8671875\n",
      " 0.875     0.8828125 0.8828125 0.8828125 0.890625  0.8984375 0.8984375\n",
      " 0.90625   0.9140625 0.9140625 0.921875  0.9296875 0.9296875 0.9375\n",
      " 0.9453125 0.9453125 0.953125  0.953125  0.953125  0.9609375 0.96875\n",
      " 0.96875   0.96875   0.9765625 0.984375  0.984375  0.9921875 0.9921875\n",
      " 1.        1.        1.        1.        1.       ] [0.0234375 0.0234375 0.0234375 ... 1.        1.        1.       ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625 0.046875\n",
      " 0.0546875 0.0625    0.0703125 0.0703125 0.078125  0.078125  0.0859375\n",
      " 0.09375   0.09375   0.1015625 0.109375  0.1171875 0.125     0.1328125\n",
      " 0.140625  0.140625  0.1484375 0.1484375 0.15625   0.1640625 0.171875\n",
      " 0.171875  0.1796875 0.1796875 0.1875    0.1953125 0.203125  0.203125\n",
      " 0.203125  0.203125  0.203125  0.203125  0.203125  0.203125  0.2109375\n",
      " 0.21875   0.21875   0.21875   0.2265625 0.2265625 0.2265625 0.2265625\n",
      " 0.2265625 0.234375  0.234375  0.234375  0.2421875 0.2421875 0.2421875\n",
      " 0.25      0.2578125 0.265625  0.265625  0.265625  0.2734375 0.2734375\n",
      " 0.28125   0.2890625 0.296875  0.296875  0.296875  0.3046875 0.3046875\n",
      " 0.3125    0.3125    0.3203125 0.328125  0.328125  0.328125  0.328125\n",
      " 0.3359375 0.3359375 0.34375   0.3515625 0.3515625 0.359375  0.359375\n",
      " 0.359375  0.359375  0.359375  0.359375  0.3671875 0.375     0.3828125\n",
      " 0.3828125 0.390625  0.390625  0.390625  0.390625  0.3984375 0.40625\n",
      " 0.4140625 0.4140625 0.421875  0.421875  0.4296875 0.4375    0.4453125\n",
      " 0.453125  0.453125  0.453125  0.453125  0.453125  0.453125  0.453125\n",
      " 0.4609375 0.4609375 0.46875   0.4765625 0.484375  0.4921875 0.5\n",
      " 0.5       0.5       0.5078125 0.515625  0.515625  0.5234375 0.53125\n",
      " 0.5390625 0.5390625 0.5390625 0.546875  0.546875  0.546875  0.546875\n",
      " 0.5546875 0.5625    0.5703125 0.5703125 0.5703125 0.5703125 0.5703125\n",
      " 0.578125  0.578125  0.578125  0.578125  0.5859375 0.59375   0.6015625\n",
      " 0.609375  0.609375  0.6171875 0.625     0.6328125 0.6328125 0.6328125\n",
      " 0.6328125 0.640625  0.6484375 0.6484375 0.6484375 0.6484375 0.65625\n",
      " 0.6640625 0.671875  0.6796875 0.6796875 0.6875    0.6953125 0.6953125\n",
      " 0.703125  0.703125  0.703125  0.703125  0.7109375 0.7109375 0.7109375\n",
      " 0.7109375 0.7109375 0.71875   0.7265625 0.734375  0.734375  0.7421875\n",
      " 0.7421875 0.75      0.75      0.7578125 0.7578125 0.765625  0.765625\n",
      " 0.765625  0.7734375 0.7734375 0.7734375 0.78125   0.78125   0.7890625\n",
      " 0.796875  0.796875  0.796875  0.8046875 0.8125    0.8203125 0.828125\n",
      " 0.828125  0.828125  0.828125  0.828125  0.828125  0.8359375 0.84375\n",
      " 0.8515625 0.8515625 0.859375  0.8671875 0.8671875 0.8671875 0.8671875\n",
      " 0.875     0.875     0.875     0.8828125 0.8828125 0.8828125 0.890625\n",
      " 0.8984375 0.8984375 0.90625   0.90625   0.90625   0.9140625 0.9140625\n",
      " 0.9140625 0.921875  0.9296875 0.9296875 0.9296875 0.9296875 0.9296875\n",
      " 0.9375    0.9375    0.9375    0.9453125 0.9453125 0.953125  0.9609375\n",
      " 0.96875   0.9765625 0.9765625 0.984375  0.984375  0.984375  0.9921875\n",
      " 0.9921875 1.        1.        1.        1.       ] [0.0703125 0.0703125 0.0703125 ... 1.        1.        1.       ]\n",
      "[0.        0.0078125 0.015625  0.015625  0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0703125 0.078125  0.0859375 0.09375\n",
      " 0.09375   0.1015625 0.1015625 0.109375  0.1171875 0.125     0.125\n",
      " 0.1328125 0.1328125 0.1328125 0.140625  0.1484375 0.1484375 0.15625\n",
      " 0.1640625 0.171875  0.171875  0.1796875 0.1875    0.1953125 0.203125\n",
      " 0.2109375 0.21875   0.21875   0.21875   0.2265625 0.234375  0.234375\n",
      " 0.2421875 0.25      0.25      0.2578125 0.2578125 0.265625  0.2734375\n",
      " 0.28125   0.2890625 0.2890625 0.296875  0.296875  0.3046875 0.3046875\n",
      " 0.3125    0.3125    0.3203125 0.328125  0.3359375 0.34375   0.3515625\n",
      " 0.3515625 0.3515625 0.3515625 0.3515625 0.3515625 0.359375  0.359375\n",
      " 0.3671875 0.3671875 0.375     0.3828125 0.3828125 0.3828125 0.390625\n",
      " 0.390625  0.390625  0.390625  0.3984375 0.3984375 0.40625   0.4140625\n",
      " 0.4140625 0.4140625 0.4140625 0.421875  0.4296875 0.4375    0.4375\n",
      " 0.4453125 0.453125  0.4609375 0.46875   0.4765625 0.4765625 0.484375\n",
      " 0.484375  0.484375  0.484375  0.484375  0.4921875 0.5       0.5\n",
      " 0.5078125 0.515625  0.515625  0.5234375 0.5234375 0.53125   0.53125\n",
      " 0.53125   0.53125   0.5390625 0.546875  0.546875  0.546875  0.5546875\n",
      " 0.5625    0.5625    0.5703125 0.5703125 0.5703125 0.5703125 0.5703125\n",
      " 0.5703125 0.5703125 0.5703125 0.578125  0.578125  0.578125  0.5859375\n",
      " 0.59375   0.6015625 0.6015625 0.609375  0.6171875 0.6171875 0.625\n",
      " 0.6328125 0.6328125 0.640625  0.640625  0.640625  0.640625  0.640625\n",
      " 0.640625  0.640625  0.6484375 0.6484375 0.65625   0.65625   0.6640625\n",
      " 0.6640625 0.6640625 0.6640625 0.6640625 0.671875  0.671875  0.671875\n",
      " 0.671875  0.671875  0.671875  0.671875  0.6796875 0.6875    0.6875\n",
      " 0.6953125 0.6953125 0.6953125 0.703125  0.7109375 0.7109375 0.71875\n",
      " 0.71875   0.7265625 0.7265625 0.7265625 0.734375  0.734375  0.734375\n",
      " 0.7421875 0.75      0.7578125 0.7578125 0.765625  0.765625  0.765625\n",
      " 0.7734375 0.78125   0.78125   0.78125   0.7890625 0.796875  0.796875\n",
      " 0.796875  0.8046875 0.8046875 0.8046875 0.8046875 0.8046875 0.8125\n",
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      " 0.9375    0.9609375 0.9765625 0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
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      " 0.140625  0.140625  0.1484375 0.15625   0.15625   0.1640625 0.171875\n",
      " 0.1796875 0.1875    0.1875    0.1875    0.1953125 0.203125  0.2109375\n",
      " 0.21875   0.2265625 0.234375  0.234375  0.2421875 0.2421875 0.2421875\n",
      " 0.25      0.2578125 0.265625  0.265625  0.2734375 0.28125   0.2890625\n",
      " 0.296875  0.3046875 0.3046875 0.3125    0.3203125 0.328125  0.3359375\n",
      " 0.3359375 0.34375   0.34375   0.3515625 0.359375  0.3671875 0.3671875\n",
      " 0.3671875 0.375     0.375     0.3828125 0.3828125 0.390625  0.390625\n",
      " 0.3984375 0.3984375 0.40625   0.40625   0.40625   0.40625   0.4140625\n",
      " 0.421875  0.421875  0.4296875 0.4375    0.4453125 0.4453125 0.4453125\n",
      " 0.4453125 0.453125  0.453125  0.4609375 0.46875   0.46875   0.4765625\n",
      " 0.484375  0.4921875 0.4921875 0.4921875 0.4921875 0.4921875 0.4921875\n",
      " 0.4921875 0.5       0.5078125 0.5078125 0.5078125 0.515625  0.515625\n",
      " 0.515625  0.5234375 0.53125   0.5390625 0.5390625 0.5390625 0.546875\n",
      " 0.5546875 0.5625    0.5625    0.5625    0.5625    0.5703125 0.578125\n",
      " 0.5859375 0.5859375 0.59375   0.59375   0.6015625 0.609375  0.609375\n",
      " 0.6171875 0.6171875 0.625     0.6328125 0.640625  0.640625  0.6484375\n",
      " 0.65625   0.6640625 0.671875  0.6796875 0.6796875 0.6796875 0.6875\n",
      " 0.6875    0.6875    0.6875    0.6875    0.6953125 0.6953125 0.6953125\n",
      " 0.6953125 0.703125  0.7109375 0.7109375 0.71875   0.71875   0.71875\n",
      " 0.7265625 0.7265625 0.734375  0.734375  0.734375  0.734375  0.7421875\n",
      " 0.7421875 0.75      0.75      0.75      0.75      0.75      0.7578125\n",
      " 0.7578125 0.7578125 0.765625  0.765625  0.765625  0.7734375 0.7734375\n",
      " 0.78125   0.7890625 0.7890625 0.796875  0.796875  0.796875  0.796875\n",
      " 0.796875  0.8046875 0.8125    0.8203125 0.8203125 0.8203125 0.828125\n",
      " 0.8359375 0.8359375 0.84375   0.84375   0.84375   0.8515625 0.8515625\n",
      " 0.8515625 0.8515625 0.8515625 0.8515625 0.8515625 0.859375  0.859375\n",
      " 0.8671875 0.875     0.8828125 0.890625  0.890625  0.890625  0.8984375\n",
      " 0.8984375 0.90625   0.9140625 0.921875  0.9296875 0.9296875 0.9375\n",
      " 0.9375    0.9453125 0.9453125 0.9453125 0.9453125 0.9453125 0.9453125\n",
      " 0.9453125 0.953125  0.9609375 0.96875   0.96875   0.96875   0.96875\n",
      " 0.96875   0.9765625 0.9765625 0.984375  0.984375  0.984375  0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 1.        1.       ] [0.046875 0.046875 0.046875 ... 1.       1.       1.      ]\n",
      "[0.        0.        0.0078125 0.0078125 0.015625  0.0234375 0.0234375\n",
      " 0.03125   0.0390625 0.0390625 0.0390625 0.0390625 0.046875  0.046875\n",
      " 0.046875  0.046875  0.046875  0.046875  0.0546875 0.0546875 0.0546875\n",
      " 0.0546875 0.0546875 0.0625    0.0703125 0.0703125 0.078125  0.078125\n",
      " 0.078125  0.078125  0.0859375 0.09375   0.1015625 0.109375  0.109375\n",
      " 0.109375  0.1171875 0.125     0.1328125 0.140625  0.1484375 0.1484375\n",
      " 0.1484375 0.1484375 0.15625   0.1640625 0.1640625 0.1640625 0.1640625\n",
      " 0.171875  0.1796875 0.1875    0.1875    0.1875    0.1953125 0.203125\n",
      " 0.203125  0.2109375 0.21875   0.21875   0.2265625 0.234375  0.234375\n",
      " 0.234375  0.234375  0.234375  0.2421875 0.2421875 0.25      0.2578125\n",
      " 0.2578125 0.265625  0.2734375 0.2734375 0.2734375 0.28125   0.28125\n",
      " 0.28125   0.28125   0.2890625 0.296875  0.3046875 0.3046875 0.3046875\n",
      " 0.3046875 0.3125    0.3203125 0.328125  0.3359375 0.34375   0.34375\n",
      " 0.3515625 0.359375  0.359375  0.359375  0.3671875 0.375     0.375\n",
      " 0.375     0.375     0.3828125 0.390625  0.3984375 0.40625   0.40625\n",
      " 0.40625   0.40625   0.4140625 0.421875  0.4296875 0.4375    0.4375\n",
      " 0.4453125 0.453125  0.453125  0.453125  0.4609375 0.4609375 0.4609375\n",
      " 0.46875   0.46875   0.4765625 0.4765625 0.4765625 0.4765625 0.4765625\n",
      " 0.484375  0.4921875 0.4921875 0.4921875 0.4921875 0.5       0.5078125\n",
      " 0.5078125 0.5078125 0.5078125 0.515625  0.515625  0.515625  0.5234375\n",
      " 0.5234375 0.5234375 0.53125   0.5390625 0.5390625 0.5390625 0.546875\n",
      " 0.546875  0.5546875 0.5546875 0.5625    0.5625    0.5625    0.5703125\n",
      " 0.578125  0.5859375 0.59375   0.6015625 0.609375  0.6171875 0.625\n",
      " 0.625     0.6328125 0.640625  0.6484375 0.65625   0.65625   0.65625\n",
      " 0.65625   0.65625   0.65625   0.65625   0.6640625 0.6640625 0.6640625\n",
      " 0.671875  0.6796875 0.6875    0.6953125 0.703125  0.703125  0.7109375\n",
      " 0.71875   0.71875   0.7265625 0.7265625 0.7265625 0.7265625 0.7265625\n",
      " 0.7265625 0.734375  0.734375  0.7421875 0.7421875 0.75      0.7578125\n",
      " 0.765625  0.7734375 0.78125   0.7890625 0.796875  0.8046875 0.8125\n",
      " 0.8125    0.8125    0.8125    0.8203125 0.828125  0.8359375 0.8359375\n",
      " 0.84375   0.8515625 0.8515625 0.8515625 0.8515625 0.859375  0.8671875\n",
      " 0.8671875 0.8671875 0.8671875 0.875     0.875     0.875     0.875\n",
      " 0.8828125 0.890625  0.890625  0.8984375 0.8984375 0.8984375 0.8984375\n",
      " 0.8984375 0.90625   0.90625   0.9140625 0.921875  0.921875  0.921875\n",
      " 0.9296875 0.9296875 0.9296875 0.9296875 0.9375    0.9453125 0.953125\n",
      " 0.9609375 0.96875   0.9765625 0.9765625 0.984375  0.984375  0.9921875\n",
      " 1.        1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.0078125 0.015625  0.015625  0.015625  0.0234375 0.03125\n",
      " 0.03125   0.0390625 0.0390625 0.046875  0.0546875 0.0546875 0.0625\n",
      " 0.0703125 0.078125  0.078125  0.0859375 0.09375   0.1015625 0.1015625\n",
      " 0.1015625 0.1015625 0.109375  0.1171875 0.1171875 0.1171875 0.1171875\n",
      " 0.125     0.1328125 0.1328125 0.1328125 0.1328125 0.1328125 0.140625\n",
      " 0.140625  0.140625  0.1484375 0.15625   0.15625   0.1640625 0.171875\n",
      " 0.1796875 0.1875    0.1953125 0.203125  0.2109375 0.21875   0.21875\n",
      " 0.2265625 0.234375  0.2421875 0.25      0.2578125 0.2578125 0.265625\n",
      " 0.2734375 0.28125   0.2890625 0.2890625 0.2890625 0.296875  0.296875\n",
      " 0.3046875 0.3125    0.3203125 0.3203125 0.3203125 0.3203125 0.328125\n",
      " 0.328125  0.3359375 0.3359375 0.3359375 0.3359375 0.34375   0.34375\n",
      " 0.3515625 0.359375  0.3671875 0.3671875 0.375     0.3828125 0.390625\n",
      " 0.3984375 0.40625   0.40625   0.4140625 0.4140625 0.421875  0.421875\n",
      " 0.4296875 0.4375    0.4453125 0.4453125 0.453125  0.453125  0.453125\n",
      " 0.4609375 0.46875   0.4765625 0.484375  0.484375  0.4921875 0.5\n",
      " 0.5       0.5       0.5078125 0.5078125 0.515625  0.515625  0.5234375\n",
      " 0.5234375 0.5234375 0.53125   0.53125   0.5390625 0.5390625 0.546875\n",
      " 0.546875  0.5546875 0.5625    0.5703125 0.5703125 0.5703125 0.578125\n",
      " 0.578125  0.5859375 0.59375   0.6015625 0.609375  0.609375  0.609375\n",
      " 0.6171875 0.625     0.6328125 0.6328125 0.6328125 0.640625  0.640625\n",
      " 0.6484375 0.6484375 0.6484375 0.6484375 0.6484375 0.65625   0.65625\n",
      " 0.6640625 0.6640625 0.671875  0.671875  0.671875  0.671875  0.671875\n",
      " 0.671875  0.6796875 0.6796875 0.6875    0.6953125 0.703125  0.7109375\n",
      " 0.7109375 0.71875   0.7265625 0.7265625 0.734375  0.7421875 0.7421875\n",
      " 0.75      0.75      0.75      0.75      0.75      0.7578125 0.7578125\n",
      " 0.765625  0.7734375 0.7734375 0.78125   0.7890625 0.7890625 0.7890625\n",
      " 0.7890625 0.7890625 0.796875  0.796875  0.796875  0.8046875 0.8125\n",
      " 0.8125    0.8125    0.8203125 0.828125  0.8359375 0.8359375 0.8359375\n",
      " 0.8359375 0.8359375 0.8359375 0.84375   0.84375   0.8515625 0.8515625\n",
      " 0.859375  0.859375  0.8671875 0.8671875 0.8671875 0.8671875 0.8671875\n",
      " 0.875     0.875     0.875     0.8828125 0.8828125 0.890625  0.890625\n",
      " 0.8984375 0.8984375 0.8984375 0.8984375 0.8984375 0.90625   0.9140625\n",
      " 0.9140625 0.921875  0.921875  0.921875  0.921875  0.921875  0.921875\n",
      " 0.921875  0.921875  0.9296875 0.9296875 0.9375    0.9375    0.9453125\n",
      " 0.9453125 0.953125  0.9609375 0.9609375 0.96875   0.9765625 0.984375\n",
      " 0.984375  0.984375  0.984375  0.9921875 0.9921875 0.9921875 1.\n",
      " 1.        1.        1.        1.        1.       ] [0.015625 0.015625 0.015625 ... 1.       1.       1.      ]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.0234375 0.0234375 0.03125\n",
      " 0.0390625 0.0390625 0.0390625 0.0390625 0.046875  0.046875  0.0546875\n",
      " 0.0625    0.0703125 0.0703125 0.078125  0.078125  0.0859375 0.09375\n",
      " 0.09375   0.09375   0.1015625 0.1015625 0.109375  0.1171875 0.1171875\n",
      " 0.1171875 0.1171875 0.125     0.125     0.1328125 0.140625  0.140625\n",
      " 0.140625  0.1484375 0.1484375 0.1484375 0.15625   0.1640625 0.1640625\n",
      " 0.1640625 0.171875  0.171875  0.171875  0.171875  0.171875  0.1796875\n",
      " 0.1796875 0.1796875 0.1796875 0.1796875 0.1796875 0.1796875 0.1796875\n",
      " 0.1875    0.1953125 0.1953125 0.203125  0.2109375 0.2109375 0.21875\n",
      " 0.2265625 0.2265625 0.234375  0.2421875 0.2421875 0.25      0.2578125\n",
      " 0.265625  0.2734375 0.28125   0.2890625 0.2890625 0.2890625 0.296875\n",
      " 0.296875  0.296875  0.296875  0.3046875 0.3046875 0.3125    0.3125\n",
      " 0.3203125 0.328125  0.328125  0.3359375 0.34375   0.3515625 0.3515625\n",
      " 0.359375  0.359375  0.3671875 0.375     0.3828125 0.3828125 0.390625\n",
      " 0.3984375 0.40625   0.40625   0.4140625 0.4140625 0.421875  0.421875\n",
      " 0.421875  0.4296875 0.4296875 0.4375    0.4375    0.4375    0.4375\n",
      " 0.4453125 0.453125  0.453125  0.4609375 0.46875   0.46875   0.46875\n",
      " 0.4765625 0.4765625 0.4765625 0.4765625 0.484375  0.484375  0.484375\n",
      " 0.4921875 0.4921875 0.5       0.5       0.5       0.5       0.5\n",
      " 0.5       0.5078125 0.515625  0.5234375 0.53125   0.53125   0.53125\n",
      " 0.53125   0.5390625 0.5390625 0.5390625 0.5390625 0.5390625 0.5390625\n",
      " 0.546875  0.5546875 0.5625    0.5703125 0.578125  0.5859375 0.59375\n",
      " 0.6015625 0.609375  0.609375  0.609375  0.6171875 0.6171875 0.6171875\n",
      " 0.625     0.625     0.625     0.625     0.625     0.625     0.6328125\n",
      " 0.6328125 0.640625  0.6484375 0.65625   0.65625   0.6640625 0.671875\n",
      " 0.671875  0.6796875 0.6796875 0.6796875 0.6796875 0.6875    0.6875\n",
      " 0.6953125 0.6953125 0.6953125 0.703125  0.703125  0.703125  0.703125\n",
      " 0.7109375 0.71875   0.7265625 0.7265625 0.734375  0.7421875 0.75\n",
      " 0.7578125 0.765625  0.7734375 0.78125   0.7890625 0.796875  0.796875\n",
      " 0.796875  0.8046875 0.8125    0.8125    0.8125    0.8203125 0.8203125\n",
      " 0.828125  0.828125  0.8359375 0.8359375 0.8359375 0.8359375 0.84375\n",
      " 0.8515625 0.859375  0.859375  0.859375  0.859375  0.859375  0.8671875\n",
      " 0.875     0.875     0.8828125 0.890625  0.8984375 0.8984375 0.8984375\n",
      " 0.8984375 0.90625   0.9140625 0.9140625 0.9140625 0.921875  0.9296875\n",
      " 0.9296875 0.9375    0.9375    0.9375    0.9453125 0.953125  0.953125\n",
      " 0.9609375 0.9609375 0.96875   0.96875   0.9765625 0.9765625 0.984375\n",
      " 0.984375  0.9921875 1.        1.        1.       ] [0.0234375 0.0234375 0.0234375 ... 1.        1.        1.       ]\n",
      "[0.        0.        0.        0.        0.0078125 0.0078125 0.015625\n",
      " 0.0234375 0.0234375 0.0234375 0.0234375 0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.046875  0.046875  0.0546875 0.0546875 0.0625    0.0625\n",
      " 0.0703125 0.0703125 0.0703125 0.078125  0.0859375 0.09375   0.09375\n",
      " 0.1015625 0.1015625 0.109375  0.1171875 0.125     0.1328125 0.140625\n",
      " 0.140625  0.140625  0.1484375 0.1484375 0.15625   0.15625   0.1640625\n",
      " 0.1640625 0.1640625 0.171875  0.171875  0.1796875 0.1796875 0.1796875\n",
      " 0.1875    0.1953125 0.1953125 0.203125  0.203125  0.2109375 0.21875\n",
      " 0.2265625 0.2265625 0.234375  0.2421875 0.2421875 0.2421875 0.25\n",
      " 0.25      0.25      0.25      0.25      0.25      0.25      0.2578125\n",
      " 0.265625  0.2734375 0.2734375 0.28125   0.2890625 0.2890625 0.296875\n",
      " 0.3046875 0.3046875 0.3046875 0.3125    0.3125    0.3125    0.3203125\n",
      " 0.3203125 0.328125  0.328125  0.3359375 0.3359375 0.3359375 0.3359375\n",
      " 0.34375   0.34375   0.34375   0.34375   0.3515625 0.359375  0.359375\n",
      " 0.3671875 0.375     0.375     0.375     0.3828125 0.3828125 0.3828125\n",
      " 0.390625  0.390625  0.3984375 0.3984375 0.40625   0.4140625 0.4140625\n",
      " 0.421875  0.421875  0.421875  0.4296875 0.4375    0.4453125 0.453125\n",
      " 0.453125  0.4609375 0.4609375 0.46875   0.4765625 0.4765625 0.4765625\n",
      " 0.4765625 0.484375  0.484375  0.484375  0.484375  0.484375  0.4921875\n",
      " 0.4921875 0.4921875 0.4921875 0.4921875 0.4921875 0.4921875 0.5\n",
      " 0.5       0.5       0.5078125 0.5078125 0.515625  0.5234375 0.53125\n",
      " 0.53125   0.53125   0.5390625 0.5390625 0.546875  0.546875  0.5546875\n",
      " 0.5625    0.5703125 0.578125  0.578125  0.578125  0.5859375 0.59375\n",
      " 0.59375   0.59375   0.59375   0.59375   0.6015625 0.609375  0.6171875\n",
      " 0.625     0.625     0.6328125 0.6328125 0.6328125 0.6328125 0.640625\n",
      " 0.640625  0.6484375 0.65625   0.6640625 0.6640625 0.6640625 0.671875\n",
      " 0.6796875 0.6875    0.6875    0.6953125 0.703125  0.703125  0.703125\n",
      " 0.703125  0.7109375 0.7109375 0.7109375 0.7109375 0.71875   0.7265625\n",
      " 0.734375  0.7421875 0.75      0.75      0.7578125 0.765625  0.765625\n",
      " 0.7734375 0.7734375 0.7734375 0.78125   0.78125   0.7890625 0.796875\n",
      " 0.8046875 0.8125    0.8203125 0.828125  0.828125  0.828125  0.8359375\n",
      " 0.84375   0.84375   0.8515625 0.8515625 0.859375  0.8671875 0.8671875\n",
      " 0.875     0.8828125 0.8828125 0.890625  0.890625  0.890625  0.8984375\n",
      " 0.90625   0.90625   0.90625   0.9140625 0.9140625 0.921875  0.921875\n",
      " 0.921875  0.9296875 0.9296875 0.9375    0.9375    0.9453125 0.953125\n",
      " 0.9609375 0.9609375 0.9609375 0.96875   0.96875   0.9765625 0.9765625\n",
      " 0.9765625 0.984375  0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.0078125 0.015625  0.0234375 0.03125   0.03125   0.03125\n",
      " 0.03125   0.03125   0.0390625 0.0390625 0.0390625 0.046875  0.046875\n",
      " 0.0546875 0.0625    0.0625    0.0703125 0.078125  0.078125  0.0859375\n",
      " 0.09375   0.09375   0.1015625 0.109375  0.1171875 0.125     0.125\n",
      " 0.1328125 0.140625  0.140625  0.140625  0.1484375 0.15625   0.1640625\n",
      " 0.1640625 0.1640625 0.1640625 0.1640625 0.1640625 0.1640625 0.171875\n",
      " 0.171875  0.1796875 0.1875    0.1875    0.1953125 0.203125  0.2109375\n",
      " 0.2109375 0.21875   0.21875   0.21875   0.21875   0.2265625 0.2265625\n",
      " 0.234375  0.234375  0.234375  0.2421875 0.25      0.2578125 0.265625\n",
      " 0.265625  0.265625  0.2734375 0.28125   0.28125   0.28125   0.28125\n",
      " 0.2890625 0.296875  0.3046875 0.3046875 0.3046875 0.3046875 0.3046875\n",
      " 0.3125    0.3125    0.3203125 0.3203125 0.328125  0.3359375 0.34375\n",
      " 0.3515625 0.359375  0.359375  0.359375  0.359375  0.359375  0.359375\n",
      " 0.359375  0.359375  0.359375  0.3671875 0.3671875 0.3671875 0.3671875\n",
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      " 0.3828125 0.390625  0.3984375 0.40625   0.40625   0.4140625 0.4140625\n",
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      " 0.5546875 0.5625    0.5703125 0.578125  0.5859375 0.59375   0.6015625\n",
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      " 0.53125   0.5390625 0.5390625 0.5390625 0.546875  0.5546875 0.5625\n",
      " 0.5625    0.5625    0.5703125 0.5703125 0.5703125 0.5703125 0.578125\n",
      " 0.578125  0.5859375 0.5859375 0.5859375 0.59375   0.59375   0.6015625\n",
      " 0.609375  0.609375  0.609375  0.6171875 0.625     0.625     0.625\n",
      " 0.625     0.6328125 0.6328125 0.640625  0.640625  0.6484375 0.65625\n",
      " 0.6640625 0.6640625 0.6640625 0.671875  0.6796875 0.6796875 0.6796875\n",
      " 0.6875    0.6875    0.6953125 0.6953125 0.703125  0.703125  0.703125\n",
      " 0.7109375 0.71875   0.71875   0.71875   0.7265625 0.7265625 0.7265625\n",
      " 0.7265625 0.734375  0.7421875 0.75      0.7578125 0.765625  0.7734375\n",
      " 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375\n",
      " 0.78125   0.78125   0.7890625 0.796875  0.796875  0.796875  0.8046875\n",
      " 0.8046875 0.8046875 0.8125    0.8203125 0.8203125 0.828125  0.8359375\n",
      " 0.8359375 0.8359375 0.8359375 0.84375   0.84375   0.8515625 0.8515625\n",
      " 0.859375  0.859375  0.859375  0.859375  0.859375  0.859375  0.8671875\n",
      " 0.8671875 0.875     0.8828125 0.8828125 0.8828125 0.8828125 0.890625\n",
      " 0.890625  0.890625  0.890625  0.890625  0.8984375 0.90625   0.90625\n",
      " 0.90625   0.9140625 0.921875  0.921875  0.9296875 0.9296875 0.9375\n",
      " 0.9375    0.9375    0.9453125 0.953125  0.9609375 0.96875   0.96875\n",
      " 0.9765625 0.984375  0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
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      " 0.359375  0.359375  0.359375  0.3671875 0.3671875 0.375     0.3828125\n",
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      " 0.46875   0.46875   0.46875   0.4765625 0.484375  0.4921875 0.4921875\n",
      " 0.4921875 0.4921875 0.4921875 0.4921875 0.4921875 0.5       0.5\n",
      " 0.5       0.5078125 0.515625  0.5234375 0.5234375 0.53125   0.53125\n",
      " 0.5390625 0.5390625 0.546875  0.5546875 0.5546875 0.5625    0.5625\n",
      " 0.5703125 0.5703125 0.5703125 0.5703125 0.5703125 0.5703125 0.578125\n",
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      " 0.59375   0.6015625 0.6015625 0.6015625 0.6015625 0.609375  0.6171875\n",
      " 0.625     0.6328125 0.640625  0.6484375 0.65625   0.6640625 0.6640625\n",
      " 0.671875  0.6796875 0.6875    0.6953125 0.6953125 0.703125  0.703125\n",
      " 0.7109375 0.7109375 0.71875   0.71875   0.7265625 0.7265625 0.734375\n",
      " 0.7421875 0.75      0.7578125 0.765625  0.7734375 0.7734375 0.78125\n",
      " 0.7890625 0.7890625 0.7890625 0.796875  0.8046875 0.8046875 0.8125\n",
      " 0.8203125 0.8203125 0.8203125 0.8203125 0.828125  0.8359375 0.8359375\n",
      " 0.84375   0.84375   0.84375   0.84375   0.84375   0.84375   0.8515625\n",
      " 0.859375  0.8671875 0.8671875 0.875     0.8828125 0.8828125 0.8828125\n",
      " 0.890625  0.890625  0.890625  0.890625  0.890625  0.890625  0.8984375\n",
      " 0.90625   0.9140625 0.9140625 0.9140625 0.9140625 0.921875  0.9296875\n",
      " 0.9375    0.9375    0.9453125 0.9453125 0.953125  0.953125  0.9609375\n",
      " 0.9609375 0.96875   0.9765625 0.9765625 0.9765625 0.9765625 0.9765625\n",
      " 0.984375  0.984375  0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
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      " 0.8359375 0.84375   0.8515625 0.859375  0.859375  0.859375  0.8671875\n",
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      " 0.90625   0.90625   0.90625   0.9140625 0.921875  0.921875  0.9296875\n",
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      " 0.2109375 0.2109375 0.21875   0.2265625 0.2265625 0.2265625 0.234375\n",
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      " 0.34375   0.34375   0.34375   0.3515625 0.359375  0.3671875 0.375\n",
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      " 0.609375  0.609375  0.6171875 0.6171875 0.625     0.6328125 0.6328125\n",
      " 0.6328125 0.6328125 0.6328125 0.640625  0.640625  0.6484375 0.6484375\n",
      " 0.6484375 0.65625   0.6640625 0.671875  0.6796875 0.6875    0.6953125\n",
      " 0.6953125 0.6953125 0.6953125 0.703125  0.703125  0.703125  0.7109375\n",
      " 0.71875   0.7265625 0.7265625 0.734375  0.734375  0.7421875 0.7421875\n",
      " 0.7421875 0.7421875 0.7421875 0.7421875 0.7421875 0.75      0.7578125\n",
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      " 0.796875  0.796875  0.796875  0.8046875 0.8046875 0.8046875 0.8046875\n",
      " 0.8125    0.8203125 0.828125  0.828125  0.8359375 0.84375   0.84375\n",
      " 0.8515625 0.8515625 0.8515625 0.8515625 0.859375  0.859375  0.8671875\n",
      " 0.8671875 0.875     0.875     0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.8828125 0.890625  0.8984375 0.90625   0.90625   0.9140625 0.921875\n",
      " 0.921875  0.921875  0.9296875 0.9296875 0.9375    0.9453125 0.9453125\n",
      " 0.9453125 0.953125  0.9609375 0.96875   0.9765625 0.9765625 0.984375\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 1.       ] [0.03125   0.03125   0.03125   ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.        0.0078125 0.0078125 0.0078125 0.0078125\n",
      " 0.0078125 0.0078125 0.015625  0.015625  0.015625  0.0234375 0.03125\n",
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      " 0.0703125 0.078125  0.078125  0.078125  0.0859375 0.0859375 0.09375\n",
      " 0.09375   0.09375   0.1015625 0.1015625 0.1015625 0.1015625 0.1015625\n",
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      " 0.2734375 0.28125   0.2890625 0.2890625 0.296875  0.296875  0.3046875\n",
      " 0.3046875 0.3046875 0.3046875 0.3125    0.3125    0.3203125 0.328125\n",
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      " 0.4609375 0.46875   0.4765625 0.484375  0.484375  0.484375  0.4921875\n",
      " 0.5       0.5078125 0.515625  0.515625  0.515625  0.5234375 0.53125\n",
      " 0.5390625 0.5390625 0.5390625 0.5390625 0.5390625 0.546875  0.5546875\n",
      " 0.5625    0.5703125 0.5703125 0.578125  0.5859375 0.5859375 0.59375\n",
      " 0.59375   0.59375   0.59375   0.6015625 0.609375  0.609375  0.6171875\n",
      " 0.625     0.6328125 0.640625  0.6484375 0.65625   0.6640625 0.6640625\n",
      " 0.6640625 0.671875  0.671875  0.671875  0.671875  0.6796875 0.6796875\n",
      " 0.6875    0.6875    0.6953125 0.6953125 0.6953125 0.703125  0.703125\n",
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      " 0.7734375 0.7734375 0.78125   0.78125   0.78125   0.78125   0.7890625\n",
      " 0.796875  0.796875  0.796875  0.796875  0.8046875 0.8125    0.8125\n",
      " 0.8203125 0.828125  0.8359375 0.84375   0.8515625 0.8515625 0.859375\n",
      " 0.859375  0.859375  0.859375  0.8671875 0.875     0.8828125 0.8828125\n",
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      " 0.9296875 0.9375    0.9375    0.9375    0.9453125 0.953125  0.953125\n",
      " 0.9609375 0.96875   0.96875   0.96875   0.9765625 0.9765625 0.9765625\n",
      " 0.984375  0.9921875 0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
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      " 0.046875  0.0546875 0.0546875 0.0546875 0.0546875 0.0546875 0.0546875\n",
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      " 0.09375   0.1015625 0.1015625 0.1015625 0.1015625 0.109375  0.1171875\n",
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      " 0.1640625 0.1640625 0.171875  0.1796875 0.1875    0.1875    0.1953125\n",
      " 0.1953125 0.1953125 0.1953125 0.1953125 0.203125  0.2109375 0.21875\n",
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      " 0.3671875 0.3671875 0.375     0.375     0.3828125 0.3828125 0.3828125\n",
      " 0.3828125 0.3828125 0.390625  0.3984375 0.3984375 0.3984375 0.40625\n",
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      " 0.6484375 0.6484375 0.6484375 0.65625   0.65625   0.6640625 0.6640625\n",
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      " 0.4453125 0.4453125 0.453125  0.4609375 0.46875   0.4765625 0.4765625\n",
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      " 0.703125  0.7109375 0.71875   0.7265625 0.7265625 0.7265625 0.734375\n",
      " 0.7421875 0.75      0.75      0.7578125 0.765625  0.765625  0.7734375\n",
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      " 0.8515625 0.859375  0.859375  0.8671875 0.8671875 0.8671875 0.8671875\n",
      " 0.8671875 0.8671875 0.8671875 0.8671875 0.875     0.875     0.875\n",
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      " 0.9765625 0.984375  0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
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      " 0.2109375 0.21875   0.2265625 0.2265625 0.234375  0.2421875 0.2421875\n",
      " 0.25      0.25      0.25      0.2578125 0.2578125 0.2578125 0.2578125\n",
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      " 0.34375   0.3515625 0.3515625 0.359375  0.3671875 0.3671875 0.3671875\n",
      " 0.3671875 0.375     0.3828125 0.3828125 0.3828125 0.390625  0.390625\n",
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      " 0.421875  0.4296875 0.4375    0.4453125 0.4453125 0.453125  0.453125\n",
      " 0.4609375 0.46875   0.46875   0.46875   0.4765625 0.4765625 0.484375\n",
      " 0.484375  0.4921875 0.5       0.5078125 0.515625  0.5234375 0.5234375\n",
      " 0.53125   0.5390625 0.546875  0.5546875 0.5546875 0.5546875 0.5546875\n",
      " 0.5546875 0.5546875 0.5625    0.5625    0.5703125 0.578125  0.578125\n",
      " 0.578125  0.578125  0.5859375 0.5859375 0.5859375 0.59375   0.6015625\n",
      " 0.6015625 0.609375  0.609375  0.609375  0.6171875 0.6171875 0.625\n",
      " 0.625     0.6328125 0.640625  0.6484375 0.6484375 0.65625   0.65625\n",
      " 0.6640625 0.6640625 0.6640625 0.6640625 0.671875  0.6796875 0.6875\n",
      " 0.6953125 0.6953125 0.703125  0.7109375 0.71875   0.7265625 0.7265625\n",
      " 0.734375  0.734375  0.734375  0.7421875 0.7421875 0.7421875 0.7421875\n",
      " 0.7421875 0.75      0.75      0.75      0.7578125 0.7578125 0.765625\n",
      " 0.7734375 0.78125   0.78125   0.7890625 0.796875  0.8046875 0.8125\n",
      " 0.8125    0.8203125 0.828125  0.8359375 0.8359375 0.84375   0.8515625\n",
      " 0.8515625 0.8515625 0.8515625 0.859375  0.859375  0.8671875 0.875\n",
      " 0.875     0.875     0.875     0.875     0.875     0.8828125 0.8828125\n",
      " 0.890625  0.890625  0.8984375 0.8984375 0.90625   0.9140625 0.921875\n",
      " 0.921875  0.9296875 0.9375    0.9375    0.9453125 0.953125  0.9609375\n",
      " 0.9609375 0.96875   0.96875   0.96875   0.96875   0.9765625 0.9765625\n",
      " 0.9765625 0.984375  0.984375  0.9921875 1.       ] [0.       0.       0.       ... 0.984375 0.984375 1.      ]\n",
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      " 0.0234375 0.03125   0.0390625 0.046875  0.046875  0.046875  0.046875\n",
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      " 0.09375   0.09375   0.09375   0.09375   0.1015625 0.109375  0.109375\n",
      " 0.1171875 0.1171875 0.1171875 0.125     0.125     0.1328125 0.1328125\n",
      " 0.140625  0.140625  0.1484375 0.15625   0.1640625 0.1640625 0.171875\n",
      " 0.1796875 0.1875    0.1875    0.1953125 0.203125  0.2109375 0.21875\n",
      " 0.21875   0.21875   0.2265625 0.2265625 0.234375  0.234375  0.2421875\n",
      " 0.25      0.25      0.25      0.2578125 0.2578125 0.265625  0.2734375\n",
      " 0.28125   0.28125   0.28125   0.2890625 0.2890625 0.296875  0.3046875\n",
      " 0.3046875 0.3125    0.3203125 0.328125  0.3359375 0.34375   0.3515625\n",
      " 0.3515625 0.3515625 0.359375  0.359375  0.3671875 0.375     0.3828125\n",
      " 0.390625  0.390625  0.3984375 0.3984375 0.40625   0.4140625 0.4140625\n",
      " 0.4140625 0.4140625 0.4140625 0.4140625 0.421875  0.4296875 0.4375\n",
      " 0.4453125 0.4453125 0.453125  0.4609375 0.46875   0.4765625 0.484375\n",
      " 0.484375  0.484375  0.484375  0.484375  0.484375  0.4921875 0.4921875\n",
      " 0.4921875 0.4921875 0.4921875 0.5       0.5       0.5078125 0.5078125\n",
      " 0.5078125 0.515625  0.515625  0.515625  0.515625  0.515625  0.515625\n",
      " 0.5234375 0.53125   0.53125   0.5390625 0.5390625 0.5390625 0.5390625\n",
      " 0.5390625 0.546875  0.5546875 0.5625    0.5625    0.5703125 0.5703125\n",
      " 0.578125  0.5859375 0.5859375 0.59375   0.6015625 0.6015625 0.6015625\n",
      " 0.6015625 0.609375  0.6171875 0.6171875 0.625     0.6328125 0.6328125\n",
      " 0.6328125 0.6328125 0.6328125 0.6328125 0.6328125 0.6328125 0.6328125\n",
      " 0.6328125 0.640625  0.640625  0.6484375 0.6484375 0.6484375 0.6484375\n",
      " 0.6484375 0.65625   0.6640625 0.671875  0.6796875 0.6875    0.6953125\n",
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      " 0.7421875 0.7421875 0.75      0.7578125 0.765625  0.765625  0.765625\n",
      " 0.765625  0.765625  0.765625  0.7734375 0.7734375 0.78125   0.78125\n",
      " 0.7890625 0.7890625 0.796875  0.8046875 0.8046875 0.8046875 0.8125\n",
      " 0.8125    0.8203125 0.8203125 0.828125  0.828125  0.8359375 0.84375\n",
      " 0.84375   0.84375   0.8515625 0.859375  0.859375  0.859375  0.859375\n",
      " 0.8671875 0.8671875 0.8671875 0.875     0.8828125 0.890625  0.890625\n",
      " 0.890625  0.890625  0.890625  0.8984375 0.8984375 0.90625   0.9140625\n",
      " 0.921875  0.9296875 0.9296875 0.9296875 0.9375    0.9453125 0.9453125\n",
      " 0.9453125 0.9453125 0.9453125 0.953125  0.9609375 0.9609375 0.9609375\n",
      " 0.9609375 0.9609375 0.9609375 0.96875   0.9765625 0.9765625 0.9765625\n",
      " 0.984375  0.984375  0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.0234375 0.03125\n",
      " 0.0390625 0.046875  0.046875  0.0546875 0.0546875 0.0546875 0.0546875\n",
      " 0.0625    0.0703125 0.0703125 0.0703125 0.0703125 0.0703125 0.078125\n",
      " 0.078125  0.0859375 0.0859375 0.09375   0.09375   0.09375   0.1015625\n",
      " 0.1015625 0.109375  0.109375  0.1171875 0.1171875 0.1171875 0.125\n",
      " 0.125     0.125     0.125     0.125     0.1328125 0.140625  0.1484375\n",
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      " 0.21875   0.2265625 0.234375  0.234375  0.2421875 0.2421875 0.25\n",
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      " 0.2890625 0.2890625 0.2890625 0.2890625 0.296875  0.3046875 0.3046875\n",
      " 0.3046875 0.3125    0.3125    0.3125    0.3125    0.3125    0.3203125\n",
      " 0.328125  0.3359375 0.34375   0.3515625 0.3515625 0.3515625 0.359375\n",
      " 0.3671875 0.375     0.3828125 0.390625  0.390625  0.390625  0.3984375\n",
      " 0.3984375 0.40625   0.4140625 0.4140625 0.4140625 0.421875  0.4296875\n",
      " 0.4375    0.4453125 0.4453125 0.453125  0.453125  0.4609375 0.46875\n",
      " 0.46875   0.46875   0.4765625 0.4765625 0.484375  0.4921875 0.4921875\n",
      " 0.5       0.5078125 0.5078125 0.515625  0.5234375 0.5234375 0.5234375\n",
      " 0.5234375 0.5234375 0.5234375 0.5234375 0.53125   0.53125   0.53125\n",
      " 0.53125   0.53125   0.5390625 0.5390625 0.5390625 0.5390625 0.546875\n",
      " 0.546875  0.5546875 0.5625    0.5625    0.5625    0.5703125 0.578125\n",
      " 0.5859375 0.59375   0.6015625 0.609375  0.6171875 0.6171875 0.6171875\n",
      " 0.6171875 0.6171875 0.625     0.625     0.625     0.6328125 0.640625\n",
      " 0.6484375 0.65625   0.6640625 0.6640625 0.671875  0.6796875 0.6796875\n",
      " 0.6875    0.6875    0.6953125 0.703125  0.7109375 0.7109375 0.7109375\n",
      " 0.7109375 0.71875   0.71875   0.7265625 0.734375  0.7421875 0.75\n",
      " 0.75      0.75      0.7578125 0.7578125 0.7578125 0.7578125 0.765625\n",
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      " 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375 0.78125\n",
      " 0.7890625 0.796875  0.8046875 0.8125    0.8203125 0.828125  0.8359375\n",
      " 0.8359375 0.8359375 0.84375   0.8515625 0.859375  0.859375  0.8671875\n",
      " 0.8671875 0.875     0.8828125 0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.890625  0.8984375 0.90625   0.90625   0.9140625 0.9140625 0.9140625\n",
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      " 0.9765625 0.9765625 0.9765625 0.9765625 0.984375  0.984375  0.984375\n",
      " 0.984375  0.9921875 0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
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      " 0.0078125 0.015625  0.0234375 0.0234375 0.03125   0.03125   0.03125\n",
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      " 0.0703125 0.078125  0.0859375 0.09375   0.09375   0.1015625 0.1015625\n",
      " 0.1015625 0.1015625 0.1015625 0.1015625 0.1015625 0.109375  0.109375\n",
      " 0.1171875 0.125     0.125     0.125     0.1328125 0.1328125 0.1328125\n",
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      " 0.234375  0.2421875 0.2421875 0.25      0.2578125 0.265625  0.2734375\n",
      " 0.2734375 0.28125   0.28125   0.2890625 0.2890625 0.296875  0.3046875\n",
      " 0.3046875 0.3046875 0.3046875 0.3125    0.3125    0.3203125 0.3203125\n",
      " 0.3203125 0.328125  0.328125  0.328125  0.328125  0.3359375 0.34375\n",
      " 0.3515625 0.3515625 0.359375  0.359375  0.3671875 0.3671875 0.375\n",
      " 0.375     0.375     0.3828125 0.3828125 0.3828125 0.390625  0.3984375\n",
      " 0.3984375 0.3984375 0.3984375 0.3984375 0.40625   0.4140625 0.4140625\n",
      " 0.421875  0.4296875 0.4375    0.4375    0.4453125 0.453125  0.453125\n",
      " 0.4609375 0.46875   0.4765625 0.4765625 0.4765625 0.4765625 0.4765625\n",
      " 0.4765625 0.484375  0.4921875 0.5       0.5078125 0.5078125 0.5078125\n",
      " 0.5078125 0.515625  0.5234375 0.5234375 0.5234375 0.5234375 0.5234375\n",
      " 0.5234375 0.53125   0.5390625 0.546875  0.5546875 0.5625    0.5625\n",
      " 0.5625    0.5625    0.5625    0.5625    0.5703125 0.5703125 0.578125\n",
      " 0.5859375 0.59375   0.6015625 0.6015625 0.609375  0.6171875 0.625\n",
      " 0.625     0.625     0.6328125 0.6328125 0.640625  0.640625  0.6484375\n",
      " 0.65625   0.65625   0.65625   0.65625   0.65625   0.6640625 0.6640625\n",
      " 0.671875  0.6796875 0.6796875 0.6875    0.6875    0.6953125 0.703125\n",
      " 0.703125  0.703125  0.7109375 0.71875   0.7265625 0.7265625 0.7265625\n",
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      " 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.765625\n",
      " 0.765625  0.765625  0.7734375 0.78125   0.7890625 0.796875  0.8046875\n",
      " 0.8125    0.8125    0.8125    0.8125    0.8203125 0.8203125 0.8203125\n",
      " 0.828125  0.828125  0.8359375 0.84375   0.8515625 0.859375  0.8671875\n",
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      " 1.        1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
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      " 0.046875  0.046875  0.0546875 0.0625    0.0625    0.0625    0.0625\n",
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      " 0.3671875 0.3671875 0.3671875 0.375     0.375     0.375     0.3828125\n",
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      " 0.5       0.5       0.5       0.5       0.5       0.5078125 0.515625\n",
      " 0.515625  0.515625  0.5234375 0.53125   0.5390625 0.5390625 0.546875\n",
      " 0.546875  0.546875  0.5546875 0.5625    0.5625    0.5703125 0.5703125\n",
      " 0.578125  0.5859375 0.59375   0.59375   0.59375   0.59375   0.59375\n",
      " 0.59375   0.59375   0.59375   0.6015625 0.609375  0.6171875 0.625\n",
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      " 0.7578125 0.765625  0.7734375 0.78125   0.7890625 0.796875  0.796875\n",
      " 0.8046875 0.8125    0.8125    0.8125    0.8203125 0.8203125 0.8203125\n",
      " 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125 0.8203125\n",
      " 0.8203125 0.828125  0.8359375 0.84375   0.84375   0.8515625 0.859375\n",
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      " 0.8984375 0.8984375 0.8984375 0.90625   0.90625   0.90625   0.90625\n",
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      " 0.984375  0.984375  0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.0078125 0.0078125 0.015625  0.0234375 0.03125\n",
      " 0.03125   0.0390625 0.0390625 0.046875  0.0546875 0.0625    0.0625\n",
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      " 0.0859375 0.09375   0.09375   0.09375   0.1015625 0.1015625 0.1015625\n",
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      " 0.234375  0.234375  0.2421875 0.25      0.2578125 0.2578125 0.2578125\n",
      " 0.2578125 0.2578125 0.2578125 0.265625  0.2734375 0.2734375 0.28125\n",
      " 0.2890625 0.2890625 0.2890625 0.296875  0.3046875 0.3125    0.3125\n",
      " 0.3203125 0.3203125 0.328125  0.328125  0.328125  0.328125  0.328125\n",
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      " 0.4921875 0.4921875 0.5       0.5       0.5078125 0.5078125 0.515625\n",
      " 0.515625  0.5234375 0.53125   0.53125   0.53125   0.5390625 0.546875\n",
      " 0.5546875 0.5546875 0.5546875 0.5625    0.5703125 0.5703125 0.5703125\n",
      " 0.5703125 0.5703125 0.578125  0.5859375 0.59375   0.59375   0.59375\n",
      " 0.59375   0.59375   0.59375   0.6015625 0.6015625 0.609375  0.6171875\n",
      " 0.6171875 0.625     0.625     0.6328125 0.640625  0.6484375 0.65625\n",
      " 0.6640625 0.671875  0.671875  0.6796875 0.6875    0.6953125 0.6953125\n",
      " 0.6953125 0.6953125 0.703125  0.7109375 0.71875   0.71875   0.7265625\n",
      " 0.7265625 0.734375  0.734375  0.734375  0.734375  0.7421875 0.75\n",
      " 0.75      0.75      0.75      0.75      0.7578125 0.765625  0.765625\n",
      " 0.7734375 0.78125   0.7890625 0.7890625 0.7890625 0.796875  0.8046875\n",
      " 0.8046875 0.8125    0.8203125 0.828125  0.828125  0.828125  0.8359375\n",
      " 0.8359375 0.8359375 0.8359375 0.84375   0.8515625 0.859375  0.859375\n",
      " 0.859375  0.859375  0.859375  0.859375  0.8671875 0.8671875 0.8671875\n",
      " 0.875     0.875     0.875     0.875     0.8828125 0.890625  0.890625\n",
      " 0.890625  0.8984375 0.90625   0.9140625 0.9140625 0.9140625 0.9140625\n",
      " 0.921875  0.921875  0.921875  0.9296875 0.9296875 0.9296875 0.9375\n",
      " 0.9453125 0.9453125 0.9453125 0.953125  0.953125  0.953125  0.953125\n",
      " 0.953125  0.9609375 0.96875   0.96875   0.9765625 0.984375  0.984375\n",
      " 0.9921875 1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.0078125 0.015625  0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.046875  0.0546875 0.0546875 0.0546875 0.0625    0.0625\n",
      " 0.0625    0.0625    0.0625    0.0625    0.0625    0.0625    0.0625\n",
      " 0.0703125 0.0703125 0.0703125 0.078125  0.0859375 0.0859375 0.0859375\n",
      " 0.09375   0.1015625 0.1015625 0.1015625 0.109375  0.109375  0.1171875\n",
      " 0.1171875 0.125     0.1328125 0.140625  0.1484375 0.15625   0.15625\n",
      " 0.15625   0.1640625 0.171875  0.171875  0.1796875 0.1875    0.1953125\n",
      " 0.1953125 0.1953125 0.1953125 0.1953125 0.1953125 0.203125  0.2109375\n",
      " 0.21875   0.21875   0.2265625 0.234375  0.234375  0.2421875 0.25\n",
      " 0.2578125 0.265625  0.2734375 0.2734375 0.28125   0.2890625 0.296875\n",
      " 0.296875  0.296875  0.296875  0.3046875 0.3046875 0.3046875 0.3046875\n",
      " 0.3125    0.3125    0.3125    0.3203125 0.3203125 0.3203125 0.328125\n",
      " 0.3359375 0.34375   0.34375   0.34375   0.3515625 0.359375  0.3671875\n",
      " 0.3671875 0.3671875 0.3671875 0.3671875 0.3671875 0.375     0.3828125\n",
      " 0.390625  0.3984375 0.40625   0.4140625 0.421875  0.4296875 0.4375\n",
      " 0.4453125 0.4453125 0.4453125 0.4453125 0.4453125 0.4453125 0.4453125\n",
      " 0.4453125 0.4453125 0.4453125 0.4453125 0.4453125 0.453125  0.4609375\n",
      " 0.46875   0.4765625 0.4765625 0.4765625 0.484375  0.4921875 0.4921875\n",
      " 0.5       0.5078125 0.5078125 0.515625  0.515625  0.515625  0.515625\n",
      " 0.5234375 0.5234375 0.53125   0.53125   0.5390625 0.546875  0.5546875\n",
      " 0.5546875 0.5546875 0.5625    0.5703125 0.578125  0.5859375 0.59375\n",
      " 0.59375   0.6015625 0.6015625 0.6015625 0.609375  0.609375  0.609375\n",
      " 0.609375  0.6171875 0.625     0.625     0.625     0.6328125 0.640625\n",
      " 0.640625  0.6484375 0.65625   0.6640625 0.671875  0.6796875 0.6875\n",
      " 0.6953125 0.6953125 0.703125  0.703125  0.703125  0.703125  0.703125\n",
      " 0.7109375 0.7109375 0.71875   0.71875   0.71875   0.7265625 0.734375\n",
      " 0.7421875 0.75      0.75      0.75      0.75      0.7578125 0.765625\n",
      " 0.7734375 0.7734375 0.7734375 0.78125   0.78125   0.78125   0.7890625\n",
      " 0.7890625 0.796875  0.796875  0.796875  0.796875  0.796875  0.796875\n",
      " 0.8046875 0.8046875 0.8125    0.8203125 0.828125  0.828125  0.828125\n",
      " 0.828125  0.8359375 0.84375   0.8515625 0.8515625 0.8515625 0.859375\n",
      " 0.859375  0.8671875 0.8671875 0.8671875 0.875     0.875     0.875\n",
      " 0.875     0.8828125 0.8828125 0.8828125 0.890625  0.8984375 0.90625\n",
      " 0.90625   0.90625   0.90625   0.9140625 0.921875  0.921875  0.921875\n",
      " 0.9296875 0.9296875 0.9375    0.9375    0.9453125 0.9453125 0.9453125\n",
      " 0.9453125 0.953125  0.953125  0.9609375 0.96875   0.9765625 0.984375\n",
      " 0.984375  0.9921875 0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.0078125 0.015625  0.015625  0.015625  0.015625  0.015625\n",
      " 0.0234375 0.0234375 0.03125   0.0390625 0.0390625 0.0390625 0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0703125 0.0703125 0.0703125 0.078125\n",
      " 0.0859375 0.0859375 0.09375   0.09375   0.1015625 0.109375  0.1171875\n",
      " 0.1171875 0.125     0.125     0.125     0.125     0.1328125 0.140625\n",
      " 0.140625  0.140625  0.140625  0.1484375 0.1484375 0.15625   0.15625\n",
      " 0.15625   0.15625   0.1640625 0.171875  0.1796875 0.1875    0.1875\n",
      " 0.1875    0.1875    0.1953125 0.1953125 0.1953125 0.1953125 0.203125\n",
      " 0.2109375 0.21875   0.2265625 0.2265625 0.2265625 0.2265625 0.234375\n",
      " 0.234375  0.234375  0.234375  0.2421875 0.25      0.25      0.2578125\n",
      " 0.2578125 0.2578125 0.2578125 0.265625  0.265625  0.2734375 0.2734375\n",
      " 0.28125   0.28125   0.2890625 0.296875  0.3046875 0.3046875 0.3125\n",
      " 0.3203125 0.3203125 0.3203125 0.328125  0.328125  0.3359375 0.3359375\n",
      " 0.3359375 0.34375   0.34375   0.3515625 0.359375  0.3671875 0.3671875\n",
      " 0.375     0.3828125 0.390625  0.3984375 0.3984375 0.40625   0.40625\n",
      " 0.40625   0.40625   0.40625   0.40625   0.40625   0.4140625 0.4140625\n",
      " 0.4140625 0.421875  0.421875  0.421875  0.421875  0.4296875 0.4296875\n",
      " 0.4375    0.4375    0.4453125 0.453125  0.4609375 0.4609375 0.46875\n",
      " 0.46875   0.4765625 0.4765625 0.484375  0.4921875 0.4921875 0.4921875\n",
      " 0.4921875 0.4921875 0.5       0.5       0.5       0.5       0.5\n",
      " 0.5078125 0.5078125 0.5078125 0.515625  0.515625  0.515625  0.5234375\n",
      " 0.5234375 0.5234375 0.5234375 0.5234375 0.53125   0.53125   0.53125\n",
      " 0.5390625 0.5390625 0.546875  0.546875  0.5546875 0.5625    0.5703125\n",
      " 0.5703125 0.578125  0.578125  0.578125  0.5859375 0.5859375 0.59375\n",
      " 0.6015625 0.6015625 0.609375  0.6171875 0.6171875 0.625     0.625\n",
      " 0.6328125 0.640625  0.6484375 0.65625   0.6640625 0.6640625 0.671875\n",
      " 0.6796875 0.6796875 0.6875    0.6953125 0.6953125 0.6953125 0.703125\n",
      " 0.703125  0.703125  0.7109375 0.7109375 0.7109375 0.71875   0.71875\n",
      " 0.7265625 0.7265625 0.734375  0.734375  0.7421875 0.75      0.7578125\n",
      " 0.765625  0.7734375 0.78125   0.7890625 0.796875  0.8046875 0.8125\n",
      " 0.8125    0.8203125 0.828125  0.8359375 0.84375   0.84375   0.8515625\n",
      " 0.8515625 0.859375  0.8671875 0.8671875 0.875     0.875     0.8828125\n",
      " 0.890625  0.890625  0.8984375 0.90625   0.9140625 0.9140625 0.921875\n",
      " 0.9296875 0.9296875 0.9375    0.9453125 0.9453125 0.9453125 0.953125\n",
      " 0.953125  0.953125  0.9609375 0.9609375 0.96875   0.96875   0.96875\n",
      " 0.9765625 0.9765625 0.9765625 0.9765625 0.9765625 0.984375  0.9921875\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 1.       ] [0.015625  0.015625  0.015625  ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.        0.        0.03125   0.03125   0.03125\n",
      " 0.0390625 0.046875  0.0625    0.1328125 0.1796875 0.21875   0.234375\n",
      " 0.2421875 0.28125   0.34375   0.375     0.3984375 0.4375    0.484375\n",
      " 0.5390625 0.5625    0.6171875 0.6484375 0.7109375 0.7265625 0.7421875\n",
      " 0.78125   0.7890625 0.84375   0.8984375 0.9140625 0.9375    0.96875\n",
      " 0.96875   0.96875   0.9765625 0.984375  0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.015625  0.03125   0.0546875 0.0546875 0.09375   0.109375\n",
      " 0.109375  0.109375  0.15625   0.1796875 0.1796875 0.2109375 0.25\n",
      " 0.3125    0.4296875 0.4609375 0.5234375 0.546875  0.5625    0.59375\n",
      " 0.59375   0.6484375 0.671875  0.6796875 0.765625  0.7890625 0.8046875\n",
      " 0.8359375 0.8515625 0.875     0.875     0.890625  0.8984375 0.953125\n",
      " 0.9609375 0.96875   0.984375  1.       ] [0.    0.002 0.004 ... 0.996 0.998 1.   ]\n",
      "[0.        0.0078125 0.015625  0.03125   0.0390625 0.046875  0.0546875\n",
      " 0.078125  0.09375   0.1015625 0.140625  0.140625  0.1484375 0.15625\n",
      " 0.171875  0.2109375 0.2265625 0.265625  0.2890625 0.3203125 0.3515625\n",
      " 0.390625  0.4609375 0.5078125 0.5625    0.625     0.6484375 0.6796875\n",
      " 0.75      0.8125    0.828125  0.8671875 0.8828125 0.8828125 0.9375\n",
      " 0.9375    0.9765625 0.984375  0.9921875 0.9921875 1.       ] [0.015625   0.01629167 0.01695833 ... 0.9921875  0.9921875  1.        ]\n",
      "[0.        0.015625  0.03125   0.046875  0.046875  0.0546875 0.0625\n",
      " 0.0703125 0.078125  0.125     0.15625   0.171875  0.1875    0.2109375\n",
      " 0.2265625 0.2734375 0.328125  0.34375   0.3984375 0.46875   0.5\n",
      " 0.5390625 0.578125  0.6328125 0.671875  0.734375  0.78125   0.8046875\n",
      " 0.8203125 0.859375  0.859375  0.90625   0.9296875 0.9375    0.9375\n",
      " 0.9453125 0.9453125 0.96875   0.9921875 0.9921875 1.        1.\n",
      " 1.       ] [0.00000000e+00 6.66666667e-04 1.33333333e-03 ... 1.00000000e+00\n",
      " 1.00000000e+00 1.00000000e+00]\n",
      "[0.        0.0078125 0.0234375 0.03125   0.046875  0.0859375 0.1171875\n",
      " 0.1484375 0.1875    0.1875    0.1953125 0.25      0.25      0.296875\n",
      " 0.296875  0.328125  0.375     0.3828125 0.4296875 0.4453125 0.4609375\n",
      " 0.5       0.515625  0.53125   0.5703125 0.5859375 0.640625  0.6796875\n",
      " 0.6953125 0.7109375 0.7265625 0.7265625 0.7578125 0.78125   0.8515625\n",
      " 0.8671875 0.8828125 0.90625   0.921875  0.9453125 0.9765625 0.984375\n",
      " 0.9921875 0.9921875 1.       ] [0.0078125 0.0088125 0.0098125 ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.0234375 0.03125   0.0703125 0.1015625 0.1015625 0.109375\n",
      " 0.109375  0.140625  0.1875    0.1875    0.1875    0.2265625 0.25\n",
      " 0.265625  0.3046875 0.34375   0.3828125 0.40625   0.4453125 0.5234375\n",
      " 0.5703125 0.6484375 0.6796875 0.7265625 0.765625  0.828125  0.8359375\n",
      " 0.859375  0.8984375 0.8984375 0.8984375 0.921875  0.9375    0.9453125\n",
      " 0.9609375 0.9765625 0.9765625 0.984375  1.       ] [0.0234375  0.02377083 0.02410417 ... 0.99866667 0.99933333 1.        ]\n",
      "[0.        0.0078125 0.015625  0.015625  0.03125   0.0390625 0.046875\n",
      " 0.0703125 0.09375   0.1015625 0.1484375 0.1484375 0.171875  0.203125\n",
      " 0.2421875 0.25      0.2890625 0.34375   0.375     0.4375    0.4765625\n",
      " 0.5       0.5625    0.6015625 0.65625   0.671875  0.6796875 0.7265625\n",
      " 0.7734375 0.8125    0.84375   0.875     0.90625   0.9140625 0.921875\n",
      " 0.9375    0.9453125 0.953125  0.9609375 0.96875   0.9921875 0.9921875\n",
      " 1.       ] [0.015625  0.015625  0.015625  ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.0078125 0.0234375 0.03125   0.0703125 0.109375  0.1171875\n",
      " 0.125     0.15625   0.171875  0.2421875 0.25      0.296875  0.3203125\n",
      " 0.328125  0.375     0.390625  0.4296875 0.484375  0.5       0.53125\n",
      " 0.5703125 0.625     0.65625   0.7109375 0.7109375 0.7578125 0.7578125\n",
      " 0.8046875 0.828125  0.8515625 0.8828125 0.90625   0.9140625 0.9140625\n",
      " 0.9140625 0.9609375 0.9765625 0.984375  0.9921875 1.        1.\n",
      " 1.       ] [0.03125 0.03225 0.03325 ... 1.      1.      1.     ]\n",
      "[0.        0.0078125 0.0234375 0.0390625 0.0546875 0.0859375 0.109375\n",
      " 0.125     0.15625   0.21875   0.21875   0.2421875 0.265625  0.3125\n",
      " 0.3515625 0.3984375 0.453125  0.5       0.5078125 0.546875  0.609375\n",
      " 0.6328125 0.640625  0.6640625 0.703125  0.75      0.7578125 0.8125\n",
      " 0.8828125 0.890625  0.921875  0.9375    0.953125  0.96875   0.9765625\n",
      " 0.9765625 0.984375  0.9921875 1.       ] [0.0234375 0.0244375 0.0254375 ... 0.998     0.999     1.       ]\n",
      "[0.        0.0078125 0.015625  0.015625  0.015625  0.0234375 0.0703125\n",
      " 0.0859375 0.0859375 0.09375   0.1015625 0.140625  0.1484375 0.171875\n",
      " 0.1796875 0.1953125 0.2109375 0.2421875 0.25      0.2578125 0.296875\n",
      " 0.34375   0.4140625 0.453125  0.5       0.5546875 0.59375   0.65625\n",
      " 0.6796875 0.703125  0.71875   0.7734375 0.7890625 0.8125    0.828125\n",
      " 0.84375   0.8671875 0.9140625 0.953125  0.9609375 0.96875   0.9765625\n",
      " 0.984375  1.        1.       ] [0.0078125 0.0083125 0.0088125 ... 1.        1.        1.       ]\n",
      "[0.        0.0078125 0.015625  0.015625  0.0234375 0.03125   0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0703125 0.078125  0.0859375 0.0859375\n",
      " 0.09375   0.1015625 0.1015625 0.109375  0.109375  0.109375  0.109375\n",
      " 0.1171875 0.1171875 0.1171875 0.125     0.125     0.125     0.125\n",
      " 0.125     0.125     0.1328125 0.140625  0.1484375 0.15625   0.15625\n",
      " 0.1640625 0.171875  0.171875  0.1796875 0.1875    0.1953125 0.1953125\n",
      " 0.1953125 0.1953125 0.1953125 0.203125  0.2109375 0.2109375 0.2109375\n",
      " 0.2109375 0.21875   0.21875   0.2265625 0.2265625 0.2265625 0.2265625\n",
      " 0.2265625 0.2265625 0.234375  0.234375  0.234375  0.234375  0.234375\n",
      " 0.2421875 0.2421875 0.2421875 0.2421875 0.2421875 0.25      0.25\n",
      " 0.2578125 0.265625  0.265625  0.265625  0.265625  0.265625  0.265625\n",
      " 0.2734375 0.2734375 0.2734375 0.2734375 0.2734375 0.28125   0.2890625\n",
      " 0.2890625 0.2890625 0.296875  0.296875  0.296875  0.3046875 0.3125\n",
      " 0.3203125 0.3203125 0.3203125 0.328125  0.328125  0.3359375 0.3359375\n",
      " 0.3359375 0.34375   0.34375   0.34375   0.3515625 0.359375  0.3671875\n",
      " 0.375     0.3828125 0.3828125 0.390625  0.390625  0.3984375 0.40625\n",
      " 0.4140625 0.421875  0.4296875 0.4375    0.4453125 0.453125  0.4609375\n",
      " 0.46875   0.46875   0.4765625 0.484375  0.4921875 0.5       0.5\n",
      " 0.5078125 0.515625  0.515625  0.515625  0.515625  0.515625  0.5234375\n",
      " 0.5234375 0.5234375 0.5234375 0.53125   0.53125   0.53125   0.53125\n",
      " 0.53125   0.5390625 0.546875  0.546875  0.5546875 0.5625    0.5625\n",
      " 0.5625    0.5625    0.5625    0.5625    0.5703125 0.578125  0.578125\n",
      " 0.5859375 0.59375   0.59375   0.59375   0.6015625 0.609375  0.609375\n",
      " 0.6171875 0.625     0.6328125 0.6328125 0.6328125 0.6328125 0.6328125\n",
      " 0.640625  0.640625  0.6484375 0.65625   0.65625   0.6640625 0.671875\n",
      " 0.6796875 0.6796875 0.6875    0.6875    0.6953125 0.6953125 0.703125\n",
      " 0.703125  0.7109375 0.71875   0.7265625 0.7265625 0.734375  0.7421875\n",
      " 0.75      0.7578125 0.7578125 0.7578125 0.765625  0.765625  0.7734375\n",
      " 0.78125   0.78125   0.78125   0.7890625 0.796875  0.8046875 0.8046875\n",
      " 0.8125    0.8125    0.8125    0.8203125 0.8203125 0.8203125 0.8203125\n",
      " 0.8203125 0.828125  0.8359375 0.84375   0.84375   0.84375   0.84375\n",
      " 0.8515625 0.859375  0.859375  0.8671875 0.875     0.875     0.8828125\n",
      " 0.890625  0.8984375 0.8984375 0.8984375 0.8984375 0.90625   0.9140625\n",
      " 0.921875  0.921875  0.9296875 0.9296875 0.9296875 0.9296875 0.9375\n",
      " 0.9375    0.9453125 0.9453125 0.953125  0.953125  0.9609375 0.9609375\n",
      " 0.9609375 0.9609375 0.9609375 0.96875   0.9765625 0.9765625 0.9765625\n",
      " 0.9765625 0.984375  0.9921875 0.9921875 1.       ] [0.015625  0.015625  0.015625  ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.0078125 0.0078125 0.015625  0.015625  0.0234375 0.03125\n",
      " 0.03125   0.03125   0.03125   0.03125   0.03125   0.03125   0.0390625\n",
      " 0.046875  0.046875  0.0546875 0.0625    0.0703125 0.078125  0.078125\n",
      " 0.0859375 0.09375   0.09375   0.1015625 0.1015625 0.1015625 0.109375\n",
      " 0.109375  0.1171875 0.125     0.1328125 0.140625  0.1484375 0.1484375\n",
      " 0.15625   0.15625   0.1640625 0.171875  0.1796875 0.1875    0.1953125\n",
      " 0.1953125 0.1953125 0.1953125 0.1953125 0.1953125 0.203125  0.2109375\n",
      " 0.21875   0.2265625 0.2265625 0.234375  0.234375  0.2421875 0.25\n",
      " 0.25      0.25      0.2578125 0.2578125 0.265625  0.2734375 0.2734375\n",
      " 0.2734375 0.28125   0.2890625 0.2890625 0.296875  0.296875  0.296875\n",
      " 0.3046875 0.3125    0.3203125 0.3203125 0.3203125 0.3203125 0.3203125\n",
      " 0.3203125 0.328125  0.328125  0.328125  0.3359375 0.34375   0.34375\n",
      " 0.3515625 0.3515625 0.3515625 0.359375  0.3671875 0.3671875 0.3671875\n",
      " 0.3671875 0.3671875 0.3671875 0.3671875 0.375     0.3828125 0.3828125\n",
      " 0.3828125 0.390625  0.390625  0.390625  0.3984375 0.3984375 0.3984375\n",
      " 0.40625   0.4140625 0.421875  0.421875  0.4296875 0.4375    0.4375\n",
      " 0.4375    0.4453125 0.4453125 0.4453125 0.453125  0.453125  0.453125\n",
      " 0.4609375 0.46875   0.46875   0.4765625 0.484375  0.4921875 0.4921875\n",
      " 0.4921875 0.5       0.5       0.5078125 0.5078125 0.515625  0.5234375\n",
      " 0.5234375 0.5234375 0.53125   0.53125   0.53125   0.53125   0.53125\n",
      " 0.53125   0.5390625 0.546875  0.5546875 0.5546875 0.5546875 0.5546875\n",
      " 0.5625    0.5625    0.5703125 0.5703125 0.5703125 0.5703125 0.578125\n",
      " 0.578125  0.5859375 0.5859375 0.5859375 0.5859375 0.59375   0.59375\n",
      " 0.59375   0.6015625 0.609375  0.6171875 0.625     0.625     0.6328125\n",
      " 0.640625  0.640625  0.6484375 0.6484375 0.65625   0.6640625 0.671875\n",
      " 0.671875  0.671875  0.671875  0.671875  0.6796875 0.6875    0.6875\n",
      " 0.6875    0.6875    0.6953125 0.703125  0.7109375 0.71875   0.7265625\n",
      " 0.734375  0.734375  0.734375  0.734375  0.734375  0.734375  0.734375\n",
      " 0.734375  0.734375  0.734375  0.7421875 0.7421875 0.75      0.75\n",
      " 0.7578125 0.7578125 0.7578125 0.765625  0.7734375 0.78125   0.7890625\n",
      " 0.796875  0.8046875 0.8125    0.8203125 0.828125  0.828125  0.8359375\n",
      " 0.84375   0.84375   0.84375   0.8515625 0.859375  0.8671875 0.8671875\n",
      " 0.875     0.875     0.875     0.8828125 0.890625  0.8984375 0.8984375\n",
      " 0.8984375 0.90625   0.90625   0.9140625 0.9140625 0.9140625 0.9140625\n",
      " 0.921875  0.921875  0.921875  0.9296875 0.9375    0.9375    0.9453125\n",
      " 0.9453125 0.953125  0.953125  0.9609375 0.9609375 0.96875   0.9765625\n",
      " 0.984375  0.984375  0.984375  0.9921875 1.       ] [0.0078125 0.0078125 0.0078125 ... 0.984375  0.984375  1.       ]\n",
      "[0.        0.        0.0078125 0.0078125 0.0078125 0.015625  0.015625\n",
      " 0.0234375 0.03125   0.0390625 0.0390625 0.046875  0.0546875 0.0625\n",
      " 0.0703125 0.078125  0.078125  0.078125  0.0859375 0.0859375 0.09375\n",
      " 0.1015625 0.1015625 0.1015625 0.109375  0.1171875 0.125     0.1328125\n",
      " 0.1328125 0.140625  0.140625  0.140625  0.1484375 0.15625   0.15625\n",
      " 0.1640625 0.171875  0.171875  0.171875  0.171875  0.171875  0.171875\n",
      " 0.171875  0.171875  0.171875  0.171875  0.171875  0.1796875 0.1796875\n",
      " 0.1796875 0.1875    0.1875    0.1875    0.1953125 0.203125  0.2109375\n",
      " 0.2109375 0.2109375 0.2109375 0.21875   0.2265625 0.2265625 0.234375\n",
      " 0.234375  0.2421875 0.25      0.2578125 0.2578125 0.2578125 0.265625\n",
      " 0.265625  0.2734375 0.2734375 0.2734375 0.28125   0.2890625 0.296875\n",
      " 0.296875  0.296875  0.3046875 0.3125    0.3203125 0.328125  0.3359375\n",
      " 0.3359375 0.34375   0.3515625 0.3515625 0.3515625 0.359375  0.359375\n",
      " 0.359375  0.3671875 0.3671875 0.375     0.375     0.3828125 0.390625\n",
      " 0.3984375 0.40625   0.40625   0.40625   0.4140625 0.421875  0.4296875\n",
      " 0.4375    0.4453125 0.4453125 0.453125  0.4609375 0.4609375 0.4609375\n",
      " 0.4609375 0.4609375 0.46875   0.46875   0.4765625 0.4765625 0.484375\n",
      " 0.484375  0.484375  0.484375  0.484375  0.4921875 0.4921875 0.5\n",
      " 0.5078125 0.515625  0.515625  0.515625  0.5234375 0.5234375 0.5234375\n",
      " 0.53125   0.5390625 0.546875  0.546875  0.546875  0.5546875 0.5546875\n",
      " 0.5546875 0.5546875 0.5546875 0.5546875 0.5546875 0.5546875 0.5546875\n",
      " 0.5546875 0.5546875 0.5625    0.5703125 0.578125  0.5859375 0.59375\n",
      " 0.59375   0.6015625 0.609375  0.609375  0.6171875 0.625     0.625\n",
      " 0.6328125 0.6328125 0.6328125 0.640625  0.6484375 0.6484375 0.65625\n",
      " 0.65625   0.6640625 0.671875  0.6796875 0.6875    0.6875    0.6953125\n",
      " 0.703125  0.703125  0.703125  0.703125  0.7109375 0.7109375 0.7109375\n",
      " 0.71875   0.7265625 0.734375  0.7421875 0.7421875 0.75      0.75\n",
      " 0.75      0.7578125 0.7578125 0.765625  0.765625  0.765625  0.765625\n",
      " 0.7734375 0.78125   0.78125   0.78125   0.78125   0.7890625 0.796875\n",
      " 0.796875  0.8046875 0.8125    0.8125    0.8203125 0.828125  0.828125\n",
      " 0.8359375 0.8359375 0.8359375 0.84375   0.84375   0.84375   0.8515625\n",
      " 0.859375  0.8671875 0.8671875 0.8671875 0.875     0.8828125 0.8828125\n",
      " 0.8828125 0.8828125 0.8828125 0.890625  0.890625  0.8984375 0.90625\n",
      " 0.90625   0.90625   0.90625   0.9140625 0.921875  0.921875  0.921875\n",
      " 0.9296875 0.9375    0.9453125 0.953125  0.953125  0.9609375 0.9609375\n",
      " 0.96875   0.96875   0.96875   0.9765625 0.9765625 0.984375  0.984375\n",
      " 0.9921875 1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.0078125 0.0078125 0.0078125 0.0078125 0.015625\n",
      " 0.015625  0.015625  0.015625  0.015625  0.015625  0.015625  0.015625\n",
      " 0.015625  0.015625  0.0234375 0.0234375 0.0234375 0.0234375 0.0234375\n",
      " 0.03125   0.0390625 0.0390625 0.046875  0.046875  0.0546875 0.0546875\n",
      " 0.0625    0.0625    0.0625    0.0625    0.0625    0.0625    0.0625\n",
      " 0.0625    0.0625    0.0703125 0.078125  0.0859375 0.09375   0.09375\n",
      " 0.09375   0.09375   0.1015625 0.1015625 0.1015625 0.1015625 0.1015625\n",
      " 0.1015625 0.109375  0.1171875 0.125     0.1328125 0.140625  0.140625\n",
      " 0.1484375 0.1484375 0.15625   0.1640625 0.171875  0.171875  0.171875\n",
      " 0.1796875 0.1796875 0.1796875 0.1796875 0.1796875 0.1875    0.1875\n",
      " 0.1953125 0.1953125 0.203125  0.203125  0.203125  0.2109375 0.2109375\n",
      " 0.2109375 0.21875   0.2265625 0.2265625 0.2265625 0.2265625 0.234375\n",
      " 0.2421875 0.25      0.25      0.25      0.2578125 0.2578125 0.265625\n",
      " 0.2734375 0.28125   0.2890625 0.296875  0.3046875 0.3125    0.3125\n",
      " 0.3203125 0.328125  0.3359375 0.34375   0.34375   0.3515625 0.359375\n",
      " 0.3671875 0.3671875 0.375     0.375     0.3828125 0.3828125 0.390625\n",
      " 0.390625  0.390625  0.3984375 0.40625   0.4140625 0.4140625 0.421875\n",
      " 0.4296875 0.4375    0.4375    0.4453125 0.453125  0.4609375 0.4609375\n",
      " 0.46875   0.4765625 0.4765625 0.4765625 0.484375  0.484375  0.4921875\n",
      " 0.5       0.5078125 0.5078125 0.515625  0.515625  0.5234375 0.53125\n",
      " 0.5390625 0.5390625 0.5390625 0.5390625 0.5390625 0.546875  0.5546875\n",
      " 0.5625    0.5625    0.5625    0.5703125 0.578125  0.5859375 0.59375\n",
      " 0.59375   0.6015625 0.609375  0.6171875 0.6171875 0.6171875 0.6171875\n",
      " 0.6171875 0.6171875 0.625     0.625     0.625     0.6328125 0.640625\n",
      " 0.6484375 0.6484375 0.65625   0.65625   0.6640625 0.6640625 0.671875\n",
      " 0.6796875 0.6875    0.6953125 0.6953125 0.6953125 0.6953125 0.703125\n",
      " 0.703125  0.703125  0.7109375 0.7109375 0.7109375 0.71875   0.71875\n",
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      " 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.765625\n",
      " 0.765625  0.765625  0.765625  0.765625  0.765625  0.7734375 0.78125\n",
      " 0.7890625 0.7890625 0.796875  0.796875  0.8046875 0.8125    0.8203125\n",
      " 0.828125  0.828125  0.8359375 0.84375   0.8515625 0.859375  0.859375\n",
      " 0.8671875 0.8671875 0.875     0.875     0.8828125 0.890625  0.8984375\n",
      " 0.8984375 0.8984375 0.90625   0.9140625 0.9140625 0.9140625 0.921875\n",
      " 0.921875  0.9296875 0.9296875 0.9375    0.9453125 0.953125  0.953125\n",
      " 0.953125  0.9609375 0.9609375 0.96875   0.9765625 0.984375  0.984375\n",
      " 0.9921875 0.9921875 0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.        0.        0.0078125 0.015625  0.015625\n",
      " 0.0234375 0.03125   0.0390625 0.0390625 0.0390625 0.046875  0.0546875\n",
      " 0.0625    0.0625    0.0625    0.0703125 0.078125  0.078125  0.078125\n",
      " 0.078125  0.0859375 0.09375   0.1015625 0.109375  0.109375  0.1171875\n",
      " 0.1171875 0.1171875 0.125     0.125     0.1328125 0.140625  0.140625\n",
      " 0.1484375 0.15625   0.1640625 0.171875  0.171875  0.1796875 0.1796875\n",
      " 0.1875    0.1875    0.1953125 0.1953125 0.203125  0.2109375 0.21875\n",
      " 0.21875   0.2265625 0.234375  0.2421875 0.2421875 0.25      0.25\n",
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      " 0.2890625 0.2890625 0.296875  0.3046875 0.3046875 0.3046875 0.3125\n",
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      " 0.3359375 0.34375   0.3515625 0.359375  0.3671875 0.3671875 0.375\n",
      " 0.375     0.375     0.3828125 0.3828125 0.3828125 0.390625  0.390625\n",
      " 0.3984375 0.40625   0.40625   0.4140625 0.4140625 0.4140625 0.421875\n",
      " 0.4296875 0.4296875 0.4375    0.4375    0.4453125 0.453125  0.4609375\n",
      " 0.46875   0.46875   0.4765625 0.4765625 0.484375  0.484375  0.4921875\n",
      " 0.4921875 0.4921875 0.5       0.5078125 0.5078125 0.515625  0.515625\n",
      " 0.5234375 0.53125   0.53125   0.5390625 0.546875  0.5546875 0.5625\n",
      " 0.5625    0.5703125 0.5703125 0.578125  0.578125  0.5859375 0.59375\n",
      " 0.6015625 0.609375  0.6171875 0.6171875 0.6171875 0.625     0.625\n",
      " 0.625     0.6328125 0.640625  0.640625  0.640625  0.640625  0.640625\n",
      " 0.640625  0.6484375 0.6484375 0.6484375 0.65625   0.6640625 0.6640625\n",
      " 0.6640625 0.6640625 0.6640625 0.6640625 0.671875  0.6796875 0.6875\n",
      " 0.6875    0.6875    0.6875    0.6875    0.6875    0.6953125 0.6953125\n",
      " 0.703125  0.703125  0.7109375 0.71875   0.71875   0.71875   0.7265625\n",
      " 0.7265625 0.734375  0.7421875 0.75      0.7578125 0.765625  0.7734375\n",
      " 0.78125   0.78125   0.7890625 0.796875  0.8046875 0.8046875 0.8046875\n",
      " 0.8046875 0.8125    0.8125    0.8125    0.8125    0.8203125 0.828125\n",
      " 0.828125  0.828125  0.828125  0.828125  0.8359375 0.84375   0.84375\n",
      " 0.84375   0.84375   0.8515625 0.8515625 0.859375  0.8671875 0.875\n",
      " 0.875     0.875     0.875     0.8828125 0.8828125 0.890625  0.8984375\n",
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      " 0.984375  0.984375  0.984375  0.984375  0.984375  0.9921875 1.\n",
      " 1.        1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
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      " 0.296875  0.296875  0.296875  0.3046875 0.3046875 0.3125    0.3203125\n",
      " 0.3203125 0.3203125 0.328125  0.328125  0.3359375 0.34375   0.34375\n",
      " 0.34375   0.34375   0.3515625 0.3515625 0.3515625 0.359375  0.359375\n",
      " 0.359375  0.3671875 0.375     0.3828125 0.390625  0.390625  0.3984375\n",
      " 0.3984375 0.40625   0.40625   0.4140625 0.4140625 0.4140625 0.421875\n",
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      " 0.453125  0.4609375 0.46875   0.46875   0.46875   0.4765625 0.4765625\n",
      " 0.4765625 0.4765625 0.484375  0.4921875 0.5       0.5078125 0.5078125\n",
      " 0.5078125 0.515625  0.5234375 0.5234375 0.5234375 0.53125   0.53125\n",
      " 0.53125   0.5390625 0.5390625 0.546875  0.5546875 0.5625    0.5703125\n",
      " 0.578125  0.578125  0.578125  0.5859375 0.59375   0.59375   0.59375\n",
      " 0.59375   0.6015625 0.6015625 0.6015625 0.609375  0.609375  0.6171875\n",
      " 0.6171875 0.6171875 0.6171875 0.625     0.6328125 0.640625  0.640625\n",
      " 0.6484375 0.6484375 0.65625   0.65625   0.65625   0.6640625 0.671875\n",
      " 0.6796875 0.6796875 0.6796875 0.6796875 0.6875    0.6953125 0.6953125\n",
      " 0.703125  0.7109375 0.7109375 0.71875   0.7265625 0.734375  0.7421875\n",
      " 0.75      0.7578125 0.765625  0.765625  0.7734375 0.78125   0.78125\n",
      " 0.78125   0.78125   0.78125   0.78125   0.7890625 0.796875  0.796875\n",
      " 0.8046875 0.8125    0.8203125 0.8203125 0.8203125 0.8203125 0.828125\n",
      " 0.828125  0.828125  0.8359375 0.84375   0.8515625 0.859375  0.8671875\n",
      " 0.875     0.8828125 0.8828125 0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.8828125 0.890625  0.8984375 0.90625   0.90625   0.9140625 0.9140625\n",
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      " 0.9375    0.9453125 0.9453125 0.953125  0.953125  0.953125  0.953125\n",
      " 0.953125  0.953125  0.953125  0.9609375 0.9609375 0.96875   0.96875\n",
      " 0.9765625 0.984375  0.984375  0.9921875 1.       ] [0.       0.       0.       ... 0.984375 0.984375 1.      ]\n",
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      " 0.0390625 0.046875  0.046875  0.046875  0.046875  0.0546875 0.0625\n",
      " 0.0703125 0.078125  0.078125  0.0859375 0.0859375 0.09375   0.1015625\n",
      " 0.109375  0.1171875 0.1171875 0.1171875 0.1171875 0.125     0.1328125\n",
      " 0.140625  0.1484375 0.15625   0.1640625 0.1640625 0.1640625 0.1640625\n",
      " 0.1640625 0.171875  0.171875  0.1796875 0.1796875 0.1875    0.1875\n",
      " 0.1953125 0.1953125 0.1953125 0.203125  0.2109375 0.21875   0.2265625\n",
      " 0.2265625 0.2265625 0.2265625 0.234375  0.234375  0.234375  0.234375\n",
      " 0.2421875 0.25      0.25      0.2578125 0.2578125 0.2578125 0.2578125\n",
      " 0.265625  0.2734375 0.2734375 0.28125   0.28125   0.28125   0.2890625\n",
      " 0.296875  0.3046875 0.3125    0.3125    0.3125    0.3203125 0.328125\n",
      " 0.3359375 0.34375   0.3515625 0.359375  0.3671875 0.3671875 0.375\n",
      " 0.3828125 0.390625  0.3984375 0.40625   0.4140625 0.421875  0.4296875\n",
      " 0.4296875 0.4296875 0.4296875 0.4296875 0.4296875 0.4296875 0.4375\n",
      " 0.4453125 0.453125  0.453125  0.453125  0.453125  0.453125  0.4609375\n",
      " 0.4609375 0.4609375 0.46875   0.46875   0.4765625 0.4765625 0.484375\n",
      " 0.484375  0.484375  0.484375  0.484375  0.484375  0.4921875 0.5\n",
      " 0.5078125 0.5078125 0.5078125 0.515625  0.5234375 0.53125   0.5390625\n",
      " 0.5390625 0.5390625 0.546875  0.546875  0.5546875 0.5625    0.5625\n",
      " 0.5625    0.5625    0.5625    0.5625    0.5703125 0.5703125 0.578125\n",
      " 0.578125  0.578125  0.578125  0.5859375 0.59375   0.59375   0.6015625\n",
      " 0.609375  0.6171875 0.6171875 0.625     0.625     0.6328125 0.6328125\n",
      " 0.6328125 0.640625  0.640625  0.6484375 0.6484375 0.65625   0.65625\n",
      " 0.6640625 0.6640625 0.671875  0.6796875 0.6875    0.6953125 0.6953125\n",
      " 0.703125  0.703125  0.7109375 0.7109375 0.7109375 0.7109375 0.7109375\n",
      " 0.71875   0.7265625 0.734375  0.734375  0.7421875 0.7421875 0.75\n",
      " 0.75      0.75      0.7578125 0.7578125 0.765625  0.765625  0.765625\n",
      " 0.765625  0.765625  0.765625  0.7734375 0.78125   0.78125   0.7890625\n",
      " 0.7890625 0.796875  0.796875  0.8046875 0.8046875 0.8046875 0.8125\n",
      " 0.8203125 0.8203125 0.828125  0.828125  0.828125  0.828125  0.828125\n",
      " 0.8359375 0.84375   0.84375   0.84375   0.84375   0.84375   0.84375\n",
      " 0.84375   0.8515625 0.8515625 0.8515625 0.8515625 0.8515625 0.8515625\n",
      " 0.8515625 0.859375  0.8671875 0.8671875 0.8671875 0.875     0.8828125\n",
      " 0.890625  0.8984375 0.90625   0.9140625 0.921875  0.921875  0.9296875\n",
      " 0.9375    0.9375    0.9453125 0.9453125 0.953125  0.953125  0.953125\n",
      " 0.9609375 0.9609375 0.9609375 0.9609375 0.96875   0.9765625 0.984375\n",
      " 0.984375  0.984375  0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.0078125 0.0078125 0.0078125 0.015625  0.015625  0.0234375\n",
      " 0.0234375 0.03125   0.03125   0.03125   0.03125   0.0390625 0.0390625\n",
      " 0.046875  0.0546875 0.0625    0.0625    0.0703125 0.0703125 0.078125\n",
      " 0.0859375 0.0859375 0.0859375 0.09375   0.09375   0.1015625 0.1015625\n",
      " 0.1015625 0.109375  0.109375  0.109375  0.1171875 0.1171875 0.1171875\n",
      " 0.1171875 0.125     0.1328125 0.140625  0.1484375 0.15625   0.1640625\n",
      " 0.1640625 0.1640625 0.1640625 0.171875  0.171875  0.1796875 0.1796875\n",
      " 0.1796875 0.1796875 0.1875    0.1953125 0.1953125 0.203125  0.2109375\n",
      " 0.21875   0.21875   0.21875   0.2265625 0.234375  0.2421875 0.25\n",
      " 0.25      0.25      0.25      0.25      0.25      0.25      0.2578125\n",
      " 0.265625  0.265625  0.2734375 0.2734375 0.28125   0.2890625 0.296875\n",
      " 0.296875  0.3046875 0.3125    0.3125    0.3203125 0.3203125 0.328125\n",
      " 0.328125  0.3359375 0.34375   0.34375   0.3515625 0.359375  0.359375\n",
      " 0.359375  0.359375  0.359375  0.3671875 0.375     0.3828125 0.3828125\n",
      " 0.390625  0.3984375 0.3984375 0.40625   0.4140625 0.421875  0.4296875\n",
      " 0.4375    0.4375    0.4453125 0.4453125 0.4453125 0.4453125 0.4453125\n",
      " 0.4453125 0.453125  0.4609375 0.46875   0.46875   0.46875   0.4765625\n",
      " 0.484375  0.484375  0.4921875 0.5       0.5078125 0.5078125 0.515625\n",
      " 0.515625  0.5234375 0.53125   0.5390625 0.546875  0.546875  0.5546875\n",
      " 0.5546875 0.5546875 0.5546875 0.5625    0.5703125 0.5703125 0.5703125\n",
      " 0.5703125 0.5703125 0.5703125 0.578125  0.5859375 0.5859375 0.5859375\n",
      " 0.5859375 0.59375   0.59375   0.6015625 0.6015625 0.6015625 0.609375\n",
      " 0.6171875 0.6171875 0.625     0.625     0.6328125 0.640625  0.640625\n",
      " 0.6484375 0.6484375 0.65625   0.6640625 0.671875  0.6796875 0.6796875\n",
      " 0.6796875 0.6796875 0.6796875 0.6875    0.6953125 0.6953125 0.6953125\n",
      " 0.6953125 0.6953125 0.6953125 0.6953125 0.703125  0.703125  0.7109375\n",
      " 0.71875   0.7265625 0.7265625 0.734375  0.734375  0.734375  0.7421875\n",
      " 0.75      0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125\n",
      " 0.765625  0.7734375 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375\n",
      " 0.7734375 0.78125   0.78125   0.78125   0.7890625 0.7890625 0.796875\n",
      " 0.796875  0.8046875 0.8046875 0.8125    0.8203125 0.828125  0.828125\n",
      " 0.8359375 0.84375   0.84375   0.84375   0.8515625 0.8515625 0.859375\n",
      " 0.8671875 0.875     0.8828125 0.890625  0.890625  0.8984375 0.8984375\n",
      " 0.90625   0.90625   0.90625   0.90625   0.9140625 0.9140625 0.921875\n",
      " 0.921875  0.921875  0.921875  0.9296875 0.9375    0.9453125 0.953125\n",
      " 0.953125  0.953125  0.9609375 0.96875   0.9765625 0.984375  0.984375\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 1.       ] [0.0078125 0.0078125 0.0078125 ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.0078125 0.015625  0.015625  0.015625  0.015625\n",
      " 0.015625  0.0234375 0.03125   0.03125   0.0390625 0.0390625 0.0390625\n",
      " 0.046875  0.046875  0.046875  0.0546875 0.0546875 0.0546875 0.0546875\n",
      " 0.0625    0.0625    0.0703125 0.0703125 0.078125  0.0859375 0.09375\n",
      " 0.09375   0.1015625 0.109375  0.1171875 0.125     0.125     0.125\n",
      " 0.125     0.125     0.1328125 0.1328125 0.1328125 0.1328125 0.140625\n",
      " 0.140625  0.140625  0.1484375 0.15625   0.1640625 0.171875  0.171875\n",
      " 0.1796875 0.1875    0.1875    0.1875    0.1875    0.1953125 0.1953125\n",
      " 0.1953125 0.1953125 0.1953125 0.203125  0.2109375 0.21875   0.2265625\n",
      " 0.2265625 0.2265625 0.2265625 0.2265625 0.2265625 0.2265625 0.2265625\n",
      " 0.234375  0.234375  0.234375  0.234375  0.234375  0.2421875 0.2421875\n",
      " 0.25      0.25      0.25      0.2578125 0.265625  0.2734375 0.28125\n",
      " 0.2890625 0.296875  0.3046875 0.3125    0.3203125 0.328125  0.3359375\n",
      " 0.34375   0.3515625 0.359375  0.3671875 0.3671875 0.375     0.3828125\n",
      " 0.3828125 0.3828125 0.3828125 0.3828125 0.3828125 0.3828125 0.3828125\n",
      " 0.3828125 0.3828125 0.3828125 0.390625  0.390625  0.3984375 0.40625\n",
      " 0.40625   0.4140625 0.4140625 0.421875  0.4296875 0.4296875 0.4296875\n",
      " 0.4296875 0.4375    0.4453125 0.4453125 0.453125  0.453125  0.4609375\n",
      " 0.4609375 0.46875   0.46875   0.46875   0.46875   0.4765625 0.4765625\n",
      " 0.484375  0.484375  0.4921875 0.5       0.5078125 0.515625  0.515625\n",
      " 0.515625  0.5234375 0.5234375 0.53125   0.5390625 0.546875  0.5546875\n",
      " 0.5546875 0.5625    0.5703125 0.578125  0.5859375 0.5859375 0.5859375\n",
      " 0.59375   0.59375   0.59375   0.6015625 0.609375  0.609375  0.6171875\n",
      " 0.625     0.6328125 0.640625  0.6484375 0.6484375 0.6484375 0.65625\n",
      " 0.6640625 0.671875  0.671875  0.6796875 0.6875    0.6875    0.6875\n",
      " 0.6953125 0.703125  0.703125  0.703125  0.703125  0.703125  0.7109375\n",
      " 0.71875   0.71875   0.71875   0.7265625 0.7265625 0.7265625 0.7265625\n",
      " 0.7265625 0.7265625 0.734375  0.7421875 0.7421875 0.75      0.7578125\n",
      " 0.7578125 0.7578125 0.7578125 0.7578125 0.7578125 0.765625  0.765625\n",
      " 0.7734375 0.7734375 0.78125   0.7890625 0.7890625 0.7890625 0.796875\n",
      " 0.8046875 0.8046875 0.8046875 0.8125    0.8125    0.8125    0.8203125\n",
      " 0.828125  0.828125  0.8359375 0.8359375 0.8359375 0.84375   0.84375\n",
      " 0.84375   0.8515625 0.859375  0.859375  0.8671875 0.875     0.8828125\n",
      " 0.890625  0.8984375 0.90625   0.9140625 0.921875  0.921875  0.9296875\n",
      " 0.9296875 0.9296875 0.9375    0.9375    0.9453125 0.9453125 0.9453125\n",
      " 0.9453125 0.9453125 0.953125  0.9609375 0.96875   0.9765625 0.984375\n",
      " 0.9921875 1.        1.        1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.0078125 0.0078125 0.0078125 0.0078125 0.0078125 0.0078125\n",
      " 0.015625  0.0234375 0.03125   0.03125   0.03125   0.0390625 0.046875\n",
      " 0.0546875 0.0625    0.0703125 0.0703125 0.0703125 0.078125  0.078125\n",
      " 0.0859375 0.09375   0.1015625 0.1015625 0.109375  0.109375  0.109375\n",
      " 0.109375  0.1171875 0.125     0.125     0.1328125 0.140625  0.140625\n",
      " 0.140625  0.140625  0.1484375 0.15625   0.15625   0.15625   0.1640625\n",
      " 0.171875  0.171875  0.1796875 0.1875    0.1875    0.1953125 0.203125\n",
      " 0.2109375 0.21875   0.2265625 0.234375  0.2421875 0.2421875 0.25\n",
      " 0.25      0.25      0.25      0.25      0.2578125 0.265625  0.265625\n",
      " 0.2734375 0.28125   0.28125   0.2890625 0.2890625 0.296875  0.296875\n",
      " 0.296875  0.3046875 0.3046875 0.3125    0.3125    0.3203125 0.3203125\n",
      " 0.3203125 0.3203125 0.3203125 0.3203125 0.3203125 0.3203125 0.3203125\n",
      " 0.3203125 0.328125  0.3359375 0.3359375 0.34375   0.3515625 0.359375\n",
      " 0.3671875 0.375     0.3828125 0.390625  0.390625  0.3984375 0.3984375\n",
      " 0.40625   0.4140625 0.4140625 0.4140625 0.421875  0.421875  0.4296875\n",
      " 0.4296875 0.4296875 0.4296875 0.4296875 0.4375    0.4453125 0.4453125\n",
      " 0.453125  0.453125  0.4609375 0.4609375 0.4609375 0.46875   0.4765625\n",
      " 0.4765625 0.484375  0.4921875 0.5       0.5       0.5       0.5\n",
      " 0.5       0.5078125 0.5078125 0.515625  0.515625  0.515625  0.515625\n",
      " 0.5234375 0.53125   0.53125   0.53125   0.53125   0.53125   0.5390625\n",
      " 0.5390625 0.5390625 0.5390625 0.546875  0.546875  0.5546875 0.5546875\n",
      " 0.5625    0.5703125 0.5703125 0.578125  0.5859375 0.59375   0.59375\n",
      " 0.6015625 0.6015625 0.609375  0.609375  0.6171875 0.625     0.625\n",
      " 0.625     0.625     0.625     0.6328125 0.6328125 0.640625  0.6484375\n",
      " 0.6484375 0.65625   0.6640625 0.6640625 0.671875  0.671875  0.671875\n",
      " 0.6796875 0.6796875 0.6875    0.6875    0.6875    0.6953125 0.703125\n",
      " 0.7109375 0.71875   0.7265625 0.7265625 0.7265625 0.734375  0.734375\n",
      " 0.734375  0.7421875 0.75      0.75      0.7578125 0.7578125 0.7578125\n",
      " 0.765625  0.7734375 0.78125   0.78125   0.7890625 0.796875  0.8046875\n",
      " 0.8046875 0.8046875 0.8125    0.8125    0.8125    0.8125    0.8125\n",
      " 0.8203125 0.828125  0.8359375 0.84375   0.84375   0.84375   0.8515625\n",
      " 0.8515625 0.8515625 0.8515625 0.8515625 0.859375  0.859375  0.859375\n",
      " 0.859375  0.8671875 0.875     0.8828125 0.890625  0.890625  0.890625\n",
      " 0.890625  0.890625  0.8984375 0.8984375 0.90625   0.9140625 0.9140625\n",
      " 0.921875  0.9296875 0.9375    0.9453125 0.953125  0.953125  0.953125\n",
      " 0.953125  0.9609375 0.9609375 0.9609375 0.9609375 0.96875   0.9765625\n",
      " 0.9765625 0.9765625 0.984375  0.9921875 1.       ] [0.0078125 0.0078125 0.0078125 ... 0.9765625 0.9765625 1.       ]\n",
      "[0.        0.        0.0078125 0.0078125 0.0078125 0.0078125 0.015625\n",
      " 0.0234375 0.0234375 0.03125   0.0390625 0.0390625 0.0390625 0.0390625\n",
      " 0.0390625 0.0390625 0.0390625 0.0390625 0.046875  0.0546875 0.0546875\n",
      " 0.0546875 0.0546875 0.0625    0.0625    0.0625    0.0625    0.0625\n",
      " 0.0625    0.0703125 0.0703125 0.078125  0.078125  0.0859375 0.09375\n",
      " 0.1015625 0.109375  0.109375  0.1171875 0.1171875 0.125     0.125\n",
      " 0.1328125 0.1328125 0.140625  0.1484375 0.15625   0.15625   0.1640625\n",
      " 0.171875  0.1796875 0.1796875 0.1796875 0.1875    0.1875    0.1875\n",
      " 0.1953125 0.203125  0.2109375 0.21875   0.21875   0.2265625 0.234375\n",
      " 0.234375  0.234375  0.234375  0.2421875 0.25      0.2578125 0.265625\n",
      " 0.265625  0.265625  0.265625  0.265625  0.265625  0.265625  0.265625\n",
      " 0.265625  0.265625  0.2734375 0.2734375 0.2734375 0.28125   0.28125\n",
      " 0.2890625 0.296875  0.3046875 0.3046875 0.3125    0.3125    0.3125\n",
      " 0.3203125 0.3203125 0.3203125 0.328125  0.328125  0.328125  0.3359375\n",
      " 0.34375   0.34375   0.34375   0.3515625 0.3515625 0.3515625 0.3515625\n",
      " 0.3515625 0.3515625 0.3515625 0.3515625 0.3515625 0.3515625 0.3515625\n",
      " 0.359375  0.3671875 0.3671875 0.375     0.375     0.375     0.3828125\n",
      " 0.390625  0.3984375 0.40625   0.40625   0.40625   0.4140625 0.4140625\n",
      " 0.4140625 0.4140625 0.4140625 0.421875  0.421875  0.421875  0.4296875\n",
      " 0.4296875 0.4375    0.4453125 0.4453125 0.453125  0.453125  0.453125\n",
      " 0.453125  0.4609375 0.46875   0.46875   0.4765625 0.484375  0.4921875\n",
      " 0.5       0.5078125 0.515625  0.515625  0.5234375 0.53125   0.5390625\n",
      " 0.546875  0.546875  0.5546875 0.5546875 0.5625    0.5703125 0.578125\n",
      " 0.5859375 0.59375   0.6015625 0.609375  0.609375  0.6171875 0.625\n",
      " 0.625     0.6328125 0.6328125 0.640625  0.6484375 0.6484375 0.6484375\n",
      " 0.65625   0.6640625 0.671875  0.6796875 0.6875    0.6953125 0.6953125\n",
      " 0.6953125 0.6953125 0.703125  0.7109375 0.7109375 0.71875   0.7265625\n",
      " 0.734375  0.734375  0.734375  0.7421875 0.7421875 0.75      0.75\n",
      " 0.75      0.7578125 0.765625  0.7734375 0.7734375 0.78125   0.7890625\n",
      " 0.796875  0.796875  0.8046875 0.8046875 0.8125    0.8125    0.8203125\n",
      " 0.8203125 0.8203125 0.828125  0.828125  0.828125  0.828125  0.828125\n",
      " 0.8359375 0.8359375 0.8359375 0.84375   0.8515625 0.859375  0.8671875\n",
      " 0.8671875 0.875     0.8828125 0.8828125 0.890625  0.8984375 0.8984375\n",
      " 0.90625   0.9140625 0.9140625 0.921875  0.921875  0.921875  0.921875\n",
      " 0.921875  0.9296875 0.9375    0.9453125 0.9453125 0.953125  0.9609375\n",
      " 0.9609375 0.96875   0.9765625 0.9765625 0.9765625 0.9765625 0.984375\n",
      " 0.9921875 0.9921875 0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.0078125 0.0078125 0.0078125 0.0078125 0.015625\n",
      " 0.0234375 0.03125   0.03125   0.03125   0.0390625 0.0390625 0.0390625\n",
      " 0.046875  0.046875  0.046875  0.046875  0.046875  0.046875  0.046875\n",
      " 0.0546875 0.0625    0.0703125 0.0703125 0.078125  0.078125  0.078125\n",
      " 0.078125  0.078125  0.078125  0.0859375 0.0859375 0.0859375 0.09375\n",
      " 0.1015625 0.109375  0.1171875 0.125     0.1328125 0.140625  0.140625\n",
      " 0.1484375 0.1484375 0.1484375 0.15625   0.1640625 0.1640625 0.171875\n",
      " 0.1796875 0.1875    0.1953125 0.1953125 0.203125  0.2109375 0.21875\n",
      " 0.21875   0.21875   0.2265625 0.2265625 0.234375  0.234375  0.2421875\n",
      " 0.2421875 0.2421875 0.25      0.25      0.2578125 0.265625  0.2734375\n",
      " 0.28125   0.2890625 0.296875  0.296875  0.3046875 0.3125    0.3203125\n",
      " 0.3203125 0.328125  0.328125  0.328125  0.328125  0.328125  0.3359375\n",
      " 0.34375   0.34375   0.34375   0.3515625 0.359375  0.3671875 0.3671875\n",
      " 0.3671875 0.375     0.3828125 0.3828125 0.3828125 0.3828125 0.3828125\n",
      " 0.3828125 0.3828125 0.3828125 0.3828125 0.3828125 0.3828125 0.3828125\n",
      " 0.3828125 0.390625  0.390625  0.3984375 0.40625   0.40625   0.40625\n",
      " 0.40625   0.4140625 0.4140625 0.4140625 0.421875  0.421875  0.4296875\n",
      " 0.4375    0.4453125 0.453125  0.4609375 0.46875   0.46875   0.46875\n",
      " 0.4765625 0.4765625 0.4765625 0.484375  0.484375  0.4921875 0.4921875\n",
      " 0.5       0.5078125 0.5078125 0.515625  0.515625  0.5234375 0.5234375\n",
      " 0.5234375 0.53125   0.5390625 0.5390625 0.5390625 0.5390625 0.546875\n",
      " 0.5546875 0.5546875 0.5625    0.5703125 0.578125  0.5859375 0.59375\n",
      " 0.6015625 0.609375  0.6171875 0.6171875 0.625     0.6328125 0.6328125\n",
      " 0.6328125 0.6328125 0.640625  0.6484375 0.6484375 0.65625   0.65625\n",
      " 0.6640625 0.6640625 0.671875  0.6796875 0.6875    0.6953125 0.703125\n",
      " 0.7109375 0.7109375 0.71875   0.71875   0.71875   0.71875   0.71875\n",
      " 0.7265625 0.7265625 0.734375  0.734375  0.7421875 0.75      0.75\n",
      " 0.75      0.7578125 0.765625  0.765625  0.765625  0.7734375 0.7734375\n",
      " 0.78125   0.7890625 0.7890625 0.796875  0.8046875 0.8046875 0.8046875\n",
      " 0.8046875 0.8125    0.8203125 0.8203125 0.828125  0.828125  0.828125\n",
      " 0.8359375 0.84375   0.84375   0.84375   0.84375   0.8515625 0.8515625\n",
      " 0.8515625 0.859375  0.8671875 0.8671875 0.875     0.875     0.875\n",
      " 0.8828125 0.890625  0.890625  0.890625  0.890625  0.890625  0.8984375\n",
      " 0.8984375 0.90625   0.90625   0.90625   0.9140625 0.921875  0.9296875\n",
      " 0.9296875 0.9296875 0.9375    0.9375    0.9453125 0.9453125 0.953125\n",
      " 0.953125  0.9609375 0.96875   0.9765625 0.984375  0.984375  0.984375\n",
      " 0.984375  0.9921875 0.9921875 0.9921875 1.       ] [0.        0.        0.        ... 0.9921875 0.9921875 1.       ]\n",
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      " 0.03125   0.0390625 0.046875  0.0546875 0.0546875 0.0625    0.0703125\n",
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      " 0.1015625 0.1015625 0.109375  0.1171875 0.1171875 0.1171875 0.1171875\n",
      " 0.1171875 0.1171875 0.1171875 0.125     0.1328125 0.1328125 0.140625\n",
      " 0.140625  0.140625  0.1484375 0.15625   0.1640625 0.1640625 0.171875\n",
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      " 0.2109375 0.2109375 0.21875   0.21875   0.2265625 0.2265625 0.2265625\n",
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      " 0.3203125 0.3203125 0.3203125 0.328125  0.328125  0.3359375 0.34375\n",
      " 0.3515625 0.359375  0.359375  0.3671875 0.3671875 0.375     0.3828125\n",
      " 0.3828125 0.3828125 0.390625  0.390625  0.390625  0.390625  0.3984375\n",
      " 0.40625   0.40625   0.40625   0.4140625 0.4140625 0.4140625 0.4140625\n",
      " 0.421875  0.421875  0.421875  0.421875  0.421875  0.4296875 0.4296875\n",
      " 0.4296875 0.4375    0.4375    0.4375    0.4375    0.4453125 0.4453125\n",
      " 0.453125  0.453125  0.453125  0.453125  0.4609375 0.4609375 0.46875\n",
      " 0.46875   0.46875   0.4765625 0.4765625 0.484375  0.4921875 0.5\n",
      " 0.5       0.5078125 0.5078125 0.515625  0.515625  0.5234375 0.5234375\n",
      " 0.5234375 0.5234375 0.5234375 0.53125   0.53125   0.5390625 0.546875\n",
      " 0.546875  0.5546875 0.5625    0.5625    0.5625    0.5703125 0.5703125\n",
      " 0.5703125 0.578125  0.5859375 0.5859375 0.59375   0.59375   0.6015625\n",
      " 0.6015625 0.609375  0.6171875 0.625     0.625     0.6328125 0.6328125\n",
      " 0.6328125 0.6328125 0.6328125 0.640625  0.640625  0.6484375 0.6484375\n",
      " 0.6484375 0.65625   0.6640625 0.6640625 0.671875  0.671875  0.6796875\n",
      " 0.6796875 0.6796875 0.6796875 0.6875    0.6875    0.6875    0.6875\n",
      " 0.6875    0.6953125 0.703125  0.7109375 0.7109375 0.7109375 0.71875\n",
      " 0.7265625 0.7265625 0.7265625 0.734375  0.7421875 0.7421875 0.75\n",
      " 0.75      0.7578125 0.7578125 0.765625  0.7734375 0.7734375 0.78125\n",
      " 0.78125   0.7890625 0.7890625 0.796875  0.796875  0.796875  0.796875\n",
      " 0.796875  0.796875  0.8046875 0.8046875 0.8125    0.8125    0.8203125\n",
      " 0.8203125 0.828125  0.828125  0.828125  0.8359375 0.8359375 0.84375\n",
      " 0.8515625 0.859375  0.8671875 0.875     0.875     0.8828125 0.8828125\n",
      " 0.8828125 0.890625  0.890625  0.890625  0.8984375 0.8984375 0.90625\n",
      " 0.9140625 0.9140625 0.921875  0.921875  0.9296875 0.9375    0.9375\n",
      " 0.9375    0.9453125 0.953125  0.953125  0.9609375 0.9609375 0.96875\n",
      " 0.9765625 0.984375  0.9921875 0.9921875 1.       ] [0.015625  0.015625  0.015625  ... 0.9921875 0.9921875 1.       ]\n",
      "[0.        0.        0.        0.        0.        0.        0.0078125\n",
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      " 0.046875  0.0546875 0.0625    0.0703125 0.0703125 0.0703125 0.0703125\n",
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      " 0.109375  0.1171875 0.1171875 0.1171875 0.125     0.125     0.1328125\n",
      " 0.1328125 0.140625  0.1484375 0.15625   0.15625   0.1640625 0.171875\n",
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      " 0.2734375 0.2734375 0.28125   0.28125   0.28125   0.28125   0.2890625\n",
      " 0.296875  0.296875  0.296875  0.296875  0.296875  0.296875  0.3046875\n",
      " 0.3125    0.3125    0.3125    0.3125    0.3203125 0.328125  0.3359375\n",
      " 0.3359375 0.3359375 0.3359375 0.3359375 0.3359375 0.3359375 0.34375\n",
      " 0.34375   0.3515625 0.3515625 0.3515625 0.3515625 0.359375  0.3671875\n",
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      " 0.40625   0.4140625 0.4140625 0.4140625 0.421875  0.4296875 0.4375\n",
      " 0.4453125 0.4453125 0.453125  0.4609375 0.46875   0.4765625 0.484375\n",
      " 0.484375  0.484375  0.484375  0.484375  0.4921875 0.5       0.5078125\n",
      " 0.515625  0.515625  0.5234375 0.53125   0.5390625 0.546875  0.546875\n",
      " 0.546875  0.5546875 0.5625    0.5703125 0.578125  0.5859375 0.5859375\n",
      " 0.59375   0.6015625 0.6015625 0.6015625 0.609375  0.6171875 0.6171875\n",
      " 0.6171875 0.625     0.6328125 0.640625  0.6484375 0.6484375 0.65625\n",
      " 0.65625   0.65625   0.6640625 0.671875  0.671875  0.6796875 0.6875\n",
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      " 0.859375  0.8671875 0.875     0.8828125 0.890625  0.890625  0.890625\n",
      " 0.8984375 0.90625   0.90625   0.9140625 0.9140625 0.921875  0.921875\n",
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      " 0.9765625 0.984375  0.9921875 1.        1.       ] [0. 0. 0. ... 1. 1. 1.]\n",
      "[0.        0.        0.        0.0078125 0.0078125 0.0078125 0.015625\n",
      " 0.015625  0.015625  0.0234375 0.03125   0.0390625 0.0390625 0.046875\n",
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      " 0.0859375 0.0859375 0.09375   0.1015625 0.1015625 0.109375  0.1171875\n",
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      " 0.375     0.3828125 0.390625  0.390625  0.390625  0.390625  0.390625\n",
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      " 0.5       0.5       0.5       0.5       0.5       0.5       0.5\n",
      " 0.5       0.5       0.5078125 0.515625  0.515625  0.5234375 0.5234375\n",
      " 0.5234375 0.5234375 0.53125   0.5390625 0.546875  0.546875  0.5546875\n",
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      " 0.8671875 0.875     0.875     0.8828125 0.8828125 0.8828125 0.890625\n",
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      " 0.0625    0.0625    0.0625    0.0625    0.0625    0.0625    0.0625\n",
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      " 0.125     0.1328125 0.140625  0.140625  0.140625  0.140625  0.140625\n",
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      " 0.1796875 0.1796875 0.1875    0.1875    0.1953125 0.1953125 0.203125\n",
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      " 0.4375    0.4453125 0.4453125 0.4453125 0.453125  0.453125  0.4609375\n",
      " 0.4609375 0.46875   0.4765625 0.484375  0.4921875 0.5       0.5078125\n",
      " 0.515625  0.515625  0.515625  0.515625  0.5234375 0.53125   0.5390625\n",
      " 0.5390625 0.546875  0.5546875 0.5546875 0.5625    0.5703125 0.5703125\n",
      " 0.5703125 0.5703125 0.578125  0.5859375 0.59375   0.6015625 0.6015625\n",
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      " 0.640625  0.640625  0.640625  0.6484375 0.65625   0.6640625 0.6640625\n",
      " 0.671875  0.671875  0.671875  0.671875  0.671875  0.6796875 0.6796875\n",
      " 0.6796875 0.6875    0.6953125 0.703125  0.703125  0.703125  0.703125\n",
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      " 0.0390625 0.0390625 0.0390625 0.0390625 0.0390625 0.0390625 0.046875\n",
      " 0.0546875 0.0546875 0.0546875 0.0546875 0.0546875 0.0625    0.0625\n",
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      " 0.109375  0.109375  0.1171875 0.125     0.125     0.125     0.125\n",
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      " 0.2109375 0.2109375 0.21875   0.2265625 0.2265625 0.2265625 0.234375\n",
      " 0.234375  0.2421875 0.2421875 0.2421875 0.25      0.2578125 0.265625\n",
      " 0.2734375 0.28125   0.2890625 0.2890625 0.2890625 0.2890625 0.2890625\n",
      " 0.296875  0.3046875 0.3046875 0.3046875 0.3125    0.3125    0.3125\n",
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      " 0.34375   0.34375   0.34375   0.34375   0.34375   0.34375   0.3515625\n",
      " 0.3515625 0.359375  0.3671875 0.375     0.3828125 0.3828125 0.390625\n",
      " 0.390625  0.390625  0.3984375 0.40625   0.40625   0.40625   0.40625\n",
      " 0.4140625 0.4140625 0.4140625 0.421875  0.421875  0.421875  0.421875\n",
      " 0.421875  0.421875  0.421875  0.4296875 0.4296875 0.4375    0.4375\n",
      " 0.4375    0.4453125 0.453125  0.4609375 0.46875   0.4765625 0.4765625\n",
      " 0.484375  0.4921875 0.4921875 0.4921875 0.5       0.5078125 0.515625\n",
      " 0.5234375 0.5234375 0.53125   0.53125   0.53125   0.53125   0.5390625\n",
      " 0.5390625 0.5390625 0.546875  0.5546875 0.5546875 0.5625    0.5703125\n",
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      " 0.65625   0.6640625 0.671875  0.6796875 0.6796875 0.6875    0.6953125\n",
      " 0.703125  0.703125  0.703125  0.703125  0.7109375 0.71875   0.7265625\n",
      " 0.7265625 0.734375  0.7421875 0.7421875 0.7421875 0.75      0.7578125\n",
      " 0.765625  0.765625  0.7734375 0.78125   0.7890625 0.7890625 0.796875\n",
      " 0.8046875 0.8046875 0.8046875 0.8046875 0.8046875 0.8125    0.8125\n",
      " 0.8125    0.8125    0.8125    0.8203125 0.828125  0.8359375 0.84375\n",
      " 0.8515625 0.8515625 0.8515625 0.8515625 0.859375  0.8671875 0.875\n",
      " 0.8828125 0.890625  0.890625  0.8984375 0.8984375 0.90625   0.90625\n",
      " 0.90625   0.9140625 0.921875  0.921875  0.921875  0.921875  0.921875\n",
      " 0.921875  0.921875  0.921875  0.921875  0.9296875 0.9296875 0.9296875\n",
      " 0.9296875 0.9296875 0.9296875 0.9375    0.9375    0.9375    0.9375\n",
      " 0.9453125 0.9453125 0.953125  0.9609375 0.96875   0.9765625 0.9765625\n",
      " 0.984375  0.984375  0.9921875 1.        1.       ] [0.0078125 0.0078125 0.0078125 ... 1.        1.        1.       ]\n",
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      " 0.2734375 0.28125   0.2890625 0.296875  0.296875  0.296875  0.296875\n",
      " 0.3046875 0.3046875 0.3046875 0.3046875 0.3125    0.3203125 0.328125\n",
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      " 0.359375  0.359375  0.3671875 0.3671875 0.375     0.3828125 0.390625\n",
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      " 0.4296875 0.4296875 0.4296875 0.4296875 0.4375    0.4453125 0.453125\n",
      " 0.453125  0.4609375 0.4609375 0.4609375 0.46875   0.46875   0.46875\n",
      " 0.4765625 0.484375  0.4921875 0.4921875 0.5       0.5078125 0.515625\n",
      " 0.515625  0.515625  0.5234375 0.5234375 0.53125   0.53125   0.53125\n",
      " 0.5390625 0.546875  0.5546875 0.5546875 0.5625    0.5625    0.5703125\n",
      " 0.5703125 0.578125  0.5859375 0.5859375 0.5859375 0.5859375 0.59375\n",
      " 0.59375   0.59375   0.6015625 0.609375  0.609375  0.6171875 0.625\n",
      " 0.6328125 0.640625  0.6484375 0.6484375 0.65625   0.6640625 0.6640625\n",
      " 0.671875  0.671875  0.671875  0.671875  0.6796875 0.6875    0.6953125\n",
      " 0.6953125 0.703125  0.7109375 0.71875   0.7265625 0.7265625 0.734375\n",
      " 0.7421875 0.75      0.75      0.7578125 0.7578125 0.7578125 0.7578125\n",
      " 0.765625  0.7734375 0.7734375 0.7734375 0.7734375 0.7734375 0.7734375\n",
      " 0.7734375 0.78125   0.78125   0.78125   0.7890625 0.796875  0.796875\n",
      " 0.796875  0.8046875 0.8125    0.8203125 0.8203125 0.828125  0.8359375\n",
      " 0.8359375 0.8359375 0.8359375 0.84375   0.8515625 0.859375  0.8671875\n",
      " 0.8671875 0.875     0.875     0.8828125 0.8828125 0.8828125 0.8828125\n",
      " 0.8828125 0.890625  0.890625  0.8984375 0.8984375 0.8984375 0.8984375\n",
      " 0.90625   0.9140625 0.921875  0.921875  0.9296875 0.9296875 0.9296875\n",
      " 0.9375    0.9375    0.9453125 0.953125  0.953125  0.953125  0.953125\n",
      " 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375 0.9609375 0.96875\n",
      " 0.96875   0.9765625 0.984375  0.9921875 1.       ] [0.015625 0.015625 0.015625 ... 0.96875  0.96875  1.      ]\n",
      "[0.        0.        0.        0.0078125 0.0078125 0.0078125 0.015625\n",
      " 0.0234375 0.03125   0.03125   0.03125   0.03125   0.0390625 0.0390625\n",
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      " 0.0859375 0.09375   0.1015625 0.109375  0.1171875 0.1171875 0.1171875\n",
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      " 0.1875    0.1953125 0.1953125 0.1953125 0.1953125 0.203125  0.203125\n",
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      " 0.3203125 0.3203125 0.3203125 0.3203125 0.328125  0.3359375 0.3359375\n",
      " 0.34375   0.34375   0.3515625 0.359375  0.359375  0.359375  0.3671875\n",
      " 0.3671875 0.3671875 0.375     0.375     0.3828125 0.3828125 0.3828125\n",
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      " 0.4765625 0.484375  0.484375  0.484375  0.4921875 0.5       0.5\n",
      " 0.5       0.5       0.5078125 0.515625  0.5234375 0.5234375 0.53125\n",
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      " 0.609375  0.6171875 0.6171875 0.625     0.6328125 0.6328125 0.6328125\n",
      " 0.640625  0.640625  0.6484375 0.65625   0.65625   0.6640625 0.671875\n",
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      " 0.859375  0.8671875 0.8671875 0.8671875 0.8671875 0.8671875 0.875\n",
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      " 0.3046875 0.3125    0.3203125 0.328125  0.3359375 0.3359375 0.34375\n",
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      " 0.3828125 0.3828125 0.3828125 0.3828125 0.390625  0.3984375 0.40625\n",
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      " 0.4375    0.4375    0.4453125 0.453125  0.4609375 0.4609375 0.4609375\n",
      " 0.46875   0.46875   0.4765625 0.484375  0.4921875 0.4921875 0.5\n",
      " 0.5078125 0.515625  0.515625  0.5234375 0.53125   0.5390625 0.5390625\n",
      " 0.5390625 0.5390625 0.5390625 0.546875  0.546875  0.5546875 0.5546875\n",
      " 0.5546875 0.5625    0.5703125 0.5703125 0.578125  0.578125  0.5859375\n",
      " 0.59375   0.59375   0.6015625 0.6015625 0.6015625 0.6015625 0.6015625\n",
      " 0.6015625 0.6015625 0.609375  0.609375  0.609375  0.609375  0.609375\n",
      " 0.609375  0.6171875 0.625     0.625     0.6328125 0.6328125 0.640625\n",
      " 0.640625  0.640625  0.640625  0.6484375 0.6484375 0.65625   0.6640625\n",
      " 0.671875  0.671875  0.6796875 0.6875    0.6953125 0.6953125 0.6953125\n",
      " 0.6953125 0.703125  0.7109375 0.7109375 0.7109375 0.7109375 0.71875\n",
      " 0.71875   0.71875   0.71875   0.71875   0.7265625 0.734375  0.7421875\n",
      " 0.7421875 0.75      0.75      0.75      0.75      0.75      0.7578125\n",
      " 0.765625  0.765625  0.7734375 0.78125   0.78125   0.78125   0.78125\n",
      " 0.78125   0.78125   0.7890625 0.7890625 0.7890625 0.796875  0.796875\n",
      " 0.796875  0.8046875 0.8046875 0.8046875 0.8046875 0.8125    0.8203125\n",
      " 0.8203125 0.828125  0.8359375 0.84375   0.8515625 0.8515625 0.859375\n",
      " 0.859375  0.8671875 0.875     0.875     0.8828125 0.8828125 0.890625\n",
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      " 0.9765625 0.984375  0.984375  0.9921875 1.       ] [0.0078125 0.0078125 0.0078125 ... 0.984375  0.984375  1.       ]\n"
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      "text/plain": [
       "<Figure size 4000x2800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data\n",
    "import matplotlib.gridspec as gridspec \n",
    "\n",
    "fontsize = 15\n",
    "tablefontsize = 20\n",
    "labelsize = 20\n",
    "density = True\n",
    "bins = 50\n",
    "SAMPLE = 256\n",
    "# fig, ax = plt.subplots(3, 3, figsize=(20, 22))\n",
    "fig = plt.figure(figsize=(40, 28))\n",
    "gs = gridspec.GridSpec(2 , 3, fig)\n",
    "k = 0\n",
    "plt.rcParams[\"axes.axisbelow\"] = True\n",
    "# downsample the data to plot the histograms to 250 points each\n",
    "scale = N // SAMPLE\n",
    "x_A_ = x_A[::scale]\n",
    "y_A_ = y_A[::scale]\n",
    "x_B_ = x_B[::scale]\n",
    "y_B_ = y_B[::scale]\n",
    "x_C_ = x_C[::scale]\n",
    "y_C_ = y_C[::scale]\n",
    "# plot variable A\n",
    "x_0_A, x_1_A = x_A_[y_A_ == 0], x_A_[y_A_ == 1]\n",
    "x_0_B, x_1_B = x_B_[y_B_ == 0], x_B_[y_B_ == 1]\n",
    "x_0_C, x_1_C = x_C_[y_C_ == 0], x_C_[y_C_ == 1]\n",
    "# x_min, x_max = np.min(x_A), np.max(x_A)\n",
    "# xs = np.linspace(x_min-1, x_max+1, 1000)\n",
    "\n",
    "# set the ticks size for all the plots\n",
    "k = 0\n",
    "for j in range(3):\n",
    "    ax = fig.add_subplot(gs[0, j]) # aspect=1.0\n",
    "    ax.tick_params(labelsize=labelsize - 1)\n",
    "    ax.set_title(string.ascii_uppercase[k], loc=\"left\", weight=\"bold\")\n",
    "    k += 1\n",
    "    if j ==0:\n",
    "        ax.hist(x_0_A[:SAMPLE // 2], density=density, alpha=0.6, color=PALETTE[1])\n",
    "        ax.hist(x_1_A[:SAMPLE // 2], density=density, alpha=0.3, color=PALETTE[0])\n",
    "        sns.lineplot(\n",
    "            pdf_A, x=\"x\", y=\"pdf_class0\", label=\"Control\", color=PALETTE[1], ax=ax\n",
    "        )\n",
    "        sns.lineplot(\n",
    "            pdf_A,\n",
    "            x=\"x\",\n",
    "            y=\"pdf_class1\",\n",
    "            label=\"Case\",\n",
    "            color=PALETTE[0],\n",
    "                ax=ax,\n",
    "                linestyle=\"--\",\n",
    "            )\n",
    "        # Set the axis labels\n",
    "        ax.set_title(\"Linear\")\n",
    "        ax.set_xlabel(\"Variable A\", fontsize=labelsize)\n",
    "        ax.set_ylabel(\"Likelihood\", fontsize=labelsize)\n",
    "        # remove the legend\n",
    "        ax.legend().remove()\n",
    "        # ax[0, 0].legend(fontsize=fontsize)\n",
    "        # set the xticks to just min and max\n",
    "        ax.set_xticks([-3, 0, 3])\n",
    "        ax.set_yticks([0.0, 0.3, 0.6])\n",
    "        ax.spines[\"left\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"right\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"top\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "    if j ==1:\n",
    "        ax.hist(x_0_B[:N // 2], density=density, alpha=0.6, color=PALETTE[1])\n",
    "        ax.hist(x_1_B[:N // 2], density=density, alpha=0.3, color=PALETTE[0])\n",
    "        sns.lineplot(\n",
    "            pdf_B, x=\"x\", y=\"pdf_class0\", label=\"Control\", color=PALETTE[1], ax=ax\n",
    "        )\n",
    "        sns.lineplot(\n",
    "            pdf_B,\n",
    "            x=\"x\",\n",
    "            y=\"pdf_class1\",\n",
    "            label=\"Case\",\n",
    "            color=PALETTE[0],\n",
    "            ax=ax,\n",
    "            linestyle=\"--\",\n",
    "        )\n",
    "        ax.set_title(\"Non-linear\")\n",
    "        ax.set_xlabel(\"Variable B\", fontsize=labelsize)\n",
    "        ax.set_ylabel(\"\")\n",
    "        # remove the legend\n",
    "        ax.legend().remove()\n",
    "        # ax[0, 1].legend(fontsize=fontsize)\n",
    "        # set the xtiks to integers\n",
    "        # ax[0, 1].set_xticks(np.arange(-6, 9, 2))\n",
    "        ax.set_xticks([-3, 0, 3, 6])\n",
    "        ax.set_yticks([0.0, 0.3, 0.6])\n",
    "        ax.spines[\"left\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"right\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"top\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "        \n",
    "    if j ==2:\n",
    "        ax.hist(x_0_C[:N // 2], density=density, alpha=0.6, color=PALETTE[1])\n",
    "        ax.hist(x_1_C[:N // 2], density=density, alpha=0.3, color=PALETTE[0])\n",
    "        sns.lineplot(\n",
    "            pdf_C, x=\"x\", y=\"pdf_class0\", label=\"Control\", color=PALETTE[1], ax=ax\n",
    "        )\n",
    "        sns.lineplot(\n",
    "            pdf_C,\n",
    "            x=\"x\",\n",
    "            y=\"pdf_class1\",\n",
    "            label=\"Case\",\n",
    "            color=PALETTE[0],\n",
    "            ax=ax,\n",
    "            linestyle=\"--\",\n",
    "        )\n",
    "    \n",
    "        ax.set_title(\"Null\")\n",
    "        ax.set_xlabel(\"Variable C\", fontsize=labelsize)\n",
    "        ax.set_ylabel(\"\")\n",
    "        ax.legend(fontsize=fontsize, frameon=False, loc=2)\n",
    "        ax.set_xticks([-3, 0, 3, 6])\n",
    "        ax.set_yticks([0.0, 0.3, 0.6])\n",
    "        ax.spines[\"left\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"right\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"top\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "# plot the ROC curve\n",
    "# convert the fpr, tpr, thresholds in a dataframe\n",
    "fpr_A, tpr_A, thresholds_A = metrics.roc_curve(\n",
    "    y_A, posterior_A, pos_label=1, drop_intermediate=False\n",
    ")\n",
    "fpr_B, tpr_B, thresholds_B = metrics.roc_curve(\n",
    "    y_B, posterior_B, pos_label=1, drop_intermediate=False\n",
    ")\n",
    "fpr_C, tpr_C, thresholds_C = metrics.roc_curve(\n",
    "    y_C, posterior_C, pos_label=1, drop_intermediate=False\n",
    ")\n",
    "df_roc_A = pd.DataFrame({\"fpr\": fpr_A, \"tpr\": tpr_A, \"thresholds\": thresholds_A})\n",
    "df_roc_B = pd.DataFrame({\"fpr\": fpr_B, \"tpr\": tpr_B, \"thresholds\": thresholds_B})\n",
    "df_roc_C = pd.DataFrame({\"fpr\": fpr_C, \"tpr\": tpr_C, \"thresholds\": thresholds_C})\n",
    "\n",
    "# plot the ROC curve\n",
    "# remove the padding from the subplots\n",
    "for j in range(3):\n",
    "    ax = fig.add_subplot(gs[1, j]) # aspect=1.0\n",
    "    ax.tick_params(labelsize=labelsize - 1)\n",
    "    ax.set_title(string.ascii_uppercase[k], loc=\"left\", weight=\"bold\")\n",
    "    k += 1\n",
    "    if j == 0:\n",
    "        ax.set_xlabel(\"False Positive Rate\", fontsize=labelsize)\n",
    "        ax.set_ylabel(\"True Positive Rate\", fontsize=labelsize)\n",
    "        # ax[1, 0].set_title(\"ROC Curve: Variable A\")\n",
    "        ax.plot(df_roc_A[\"fpr\"], df_roc_A[\"tpr\"], color=\"k\")\n",
    "        \n",
    "        \n",
    "        \n",
    "        for modelname in MODELNAME:\n",
    "            if modelname == 'might':\n",
    "                ax.plot(base_fpr,linear_might[0],label = MODELNAME_CAP['might'],color=PALETTE[0],linewidth=4)\n",
    "            else:\n",
    "                TPR = np.zeros((REPS,len(base_fpr)))\n",
    "                for rep in range(REPS):\n",
    "                    fpr_ori,tpr_ori = classifier_ROC(model_name = modelname,samplesize = 256, sim = 'trunk',dim = 4096,reps = rep)\n",
    "                    tpr = np.interp(base_fpr, fpr_ori, tpr_ori)\n",
    "                    print(tpr_ori,tpr)\n",
    "                    TPR[rep,:] = tpr\n",
    "                tpr_s = np.mean(TPR, axis=0)\n",
    "                ax.plot(base_fpr,tpr_s,label = MODELNAME_CAP[modelname])\n",
    "        ax.plot([0, 1], [0, 1], \"k--\", label=\"Random\", alpha=0.5)\n",
    "\n",
    "\n",
    "        # sa98 = max(\n",
    "        #     [\n",
    "        #         tpr_A\n",
    "        #         for (fpr_A, tpr_A) in zip(fpr_A, tpr_A)\n",
    "        #         if fpr_A <= 0.02\n",
    "        #     ]\n",
    "        # )\n",
    "        # ax[1, 0].axvline(\n",
    "        #     x=0.02,\n",
    "        #     color=PALETTE[-2],\n",
    "        #     ymin=0,\n",
    "        #     ymax=sa98,\n",
    "        #     #     label=\"S@\" + str(spec) + \" = \" + str(round(sa98, 2)),\n",
    "        #     linestyle=\"--\",\n",
    "        # )\n",
    "        # ax[1, 0].axhline(y=sa98, xmin=0, xmax=0.02, color=PALETTE[-2], linestyle=\"--\")\n",
    "\n",
    "        # set the ticks to 0, 1\n",
    "        ax.set_xticks([0, 1])\n",
    "        ax.set_yticks([0, 1])\n",
    "\n",
    "        ax.spines[\"left\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"right\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"top\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "        ax.set_ylim(bottom=0)\n",
    "        ax.set_xlim(left=0)\n",
    "        ax.set_ylim(top=1)\n",
    "        ax.set_xlim(right=1)\n",
    "        ax.set_axisbelow(True)\n",
    "    \n",
    "    if j == 1:\n",
    "\n",
    "        ax.set_xlabel(\"False Positive Rate\", fontsize=labelsize)\n",
    "        ax.set_ylabel(\"\")\n",
    "        # ax[1, 1].set_title(\"ROC Curve: Variable B\")\n",
    "        ax.plot(df_roc_B[\"fpr\"], df_roc_B[\"tpr\"], color=\"k\")\n",
    "\n",
    "        for modelname in MODELNAME:\n",
    "            if modelname == 'might':\n",
    "                ax.plot(base_fpr,nonlinear_might[0],label = MODELNAME_CAP['might'],color=PALETTE[0],linewidth=4)\n",
    "            else:\n",
    "                TPR = np.zeros((REPS,len(base_fpr)))\n",
    "                for rep in range(REPS):\n",
    "                    fpr_ori,tpr_ori = classifier_ROC(model_name = modelname,samplesize = 256, sim = 'trunk_mix',dim = 4096,reps = rep)\n",
    "                    tpr = np.interp(base_fpr, fpr_ori, tpr_ori)\n",
    "                    print(tpr_ori,tpr)\n",
    "                    TPR[rep,:] = tpr\n",
    "                tpr_s = np.mean(TPR, axis=0)\n",
    "                ax.plot(base_fpr,tpr_s,label = MODELNAME_CAP[modelname])\n",
    "\n",
    "        ax.plot([0, 1], [0, 1], \"k--\", label=\"Random\", alpha=0.5)\n",
    "\n",
    "        # sa98 = max(\n",
    "        #     [\n",
    "        #         tpr_B\n",
    "        #         for (fpr_B, tpr_B) in zip(fpr_B, tpr_B)\n",
    "        #         if fpr_B <= 0.02\n",
    "        #     ]\n",
    "        # )\n",
    "        # ax[1, 1].axvline(\n",
    "        #     x=0.02,\n",
    "        #     color=PALETTE[-2],\n",
    "        #     ymin=0,\n",
    "        #     ymax=sa98,\n",
    "        #     #     label=\"S@\" + str(spec) + \" = \" + str(round(sa98, 2)),\n",
    "        #     linestyle=\"--\",\n",
    "        # )\n",
    "        # ax[1, 1].axhline(y=sa98, xmin=0, xmax=0.02, color=PALETTE[-2], linestyle=\"--\")\n",
    "\n",
    "        ax.set_xticks([0, 1])\n",
    "        ax.set_yticks([0, 1])\n",
    "        ax.set_axisbelow(True)\n",
    "        ax.vlines(\n",
    "            x=0.005, ymin=0, ymax=statistics_B[\"S@98\"] - 0.005, color=\"k\", linestyle=\"-\"\n",
    "        )\n",
    "\n",
    "        ax.spines[\"left\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"right\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"top\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "        ax.set_ylim(bottom=0)\n",
    "        ax.set_xlim(left=0)\n",
    "        ax.set_ylim(top=1)\n",
    "        ax.set_xlim(right=1)\n",
    "        \n",
    "    if j ==2:\n",
    "        ax.set_xlabel(\"False Positive Rate\", fontsize=labelsize)\n",
    "        ax.set_ylabel(\"\")\n",
    "        # ax[1, 2].set_title(\"ROC Curve: Variable C\")\n",
    "        ax.plot(df_roc_C[\"fpr\"], df_roc_C[\"tpr\"], label=\"Truth\", color=\"k\")\n",
    "\n",
    "        for modelname in MODELNAME:\n",
    "            if modelname == 'might':\n",
    "                ax.plot(base_fpr,null_might[0],label = MODELNAME_CAP['might'],color=PALETTE[0],linewidth=4)\n",
    "            else:\n",
    "                TPR = np.zeros((REPS,len(base_fpr)))\n",
    "                for rep in range(REPS):\n",
    "                    fpr_ori,tpr_ori = classifier_ROC(model_name = modelname,samplesize = 256, sim = 'trunk_overlap',dim = 4096,reps = rep)\n",
    "                    tpr = np.interp(base_fpr, fpr_ori, tpr_ori)\n",
    "                    print(tpr_ori,tpr)\n",
    "                    TPR[rep,:] = tpr\n",
    "                tpr_s = np.mean(TPR, axis=0)\n",
    "                ax.plot(base_fpr,tpr_s,label = MODELNAME_CAP[modelname])\n",
    "\n",
    "\n",
    "\n",
    "        # ax[1, 2].axvline(\n",
    "        #     x=0.02,\n",
    "        #     color=PALETTE[-2],\n",
    "        #     ymin=0,\n",
    "        #     ymax=0.02,\n",
    "        #     #     label=\"S@\" + str(spec) + \" = \" + str(round(sa98, 2)),\n",
    "        #     linestyle=\"--\",\n",
    "        # )\n",
    "        # ax[1, 2].axhline(y=0.02, xmin=0, xmax=0.02, color=PALETTE[-2], linestyle=\"--\")\n",
    "                \n",
    "                \n",
    "        ax.plot([0, 1], [0, 1], \"k--\", label=\"Random\", alpha=0.5)\n",
    "        ax.legend(fontsize=fontsize, frameon=False, loc=2)\n",
    "        ax.set_xticks([0, 1])\n",
    "        ax.set_yticks([0, 1])\n",
    "        ax.set_ylim(bottom=0)\n",
    "\n",
    "        ax.spines[\"left\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"right\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"top\"].set_color(\"#dddddd\")\n",
    "        ax.spines[\"bottom\"].set_color(\"#dddddd\")\n",
    "\n",
    "        ax.set_ylim(bottom=0)\n",
    "        ax.set_xlim(left=0)\n",
    "        ax.set_ylim(top=1)\n",
    "        ax.set_xlim(right=1)\n",
    "        ax.set_axisbelow(True)\n",
    "\n",
    "# for j in range(3):\n",
    "#     ax_table = plt.subplot(gs[2,j])\n",
    "#     ax_table.axis(\"off\")\n",
    "#     if j ==0 :\n",
    "#         the_table = ax_table.table(cellText= statistics_linear.values,colLabels = ['TRUTH','MIGHT'],rowLabels = statistics_null.index,loc='center')\n",
    "    \n",
    "#     if j ==1 :\n",
    "#         the_table = ax_table.table(cellText= statistics_nonlinear.values,colLabels = ['TRUTH','MIGHT'],loc='center')\n",
    "    \n",
    "#     if j ==2 :\n",
    "#         the_table = ax_table.table(cellText= statistics_null.values,colLabels = ['TRUTH','MIGHT'],loc='center')\n",
    "    \n",
    "#     the_table.scale(1,6)\n",
    "#     the_table.auto_set_font_size(False)\n",
    "#     for cell in the_table._cells:\n",
    "#         the_table._cells[cell].set_fontsize(tablefontsize+10)\n",
    "\n",
    "\n"
   ]
  }
 ],
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