{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convergence of MOU and rDCM derived EC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment setup & index configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path\n",
    "from os.path import dirname, join as pjoin\n",
    "import numpy as np\n",
    "\n",
    "import scipy.linalg as spl\n",
    "import scipy.io as sio\n",
    "from scipy import stats, odr\n",
    "from scipy.stats import sem\n",
    "import scipy.stats as stt\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patheffects as PathEffects\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "import seaborn as sb\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import networkx as nx\n",
    "from pathlib import Path\n",
    "\n",
    "# Jupyter magic commands\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load MOU Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current shapes before removal:\n",
      "model_fit: (3, 156)\n",
      "model_error: (3, 156)\n",
      "J_mod: (3, 156, 24, 24)\n",
      "Sigma_mod: (3, 156, 24, 24)\n"
     ]
    }
   ],
   "source": [
    "#out_path = '/home/nicolas/Documents/RetNet/ret-EC/new_results/'\n",
    "save_path = \"/home/nicolas/Documents/GitHubProjects/retNet/temp/results_MOU/\"\n",
    "\n",
    "# Index dictionary\n",
    "conf = {\n",
    "        'tasks'   : ['vfm','movie','rest'], \n",
    "        'eccen'   : ['fovea','para-fovea'], \n",
    "        'fovea'   : [ \n",
    "                    [0,2,4,6,8,10,12,14,16,18,20,22],  #  < fovea\n",
    "                    [1,3,5,7,9,11,13,15,17,19,21,23],   #  > fovea\n",
    "                    ],\n",
    "        'roiname' : ['V1','V2','V3'], \n",
    "        'rois'    : [\n",
    "                    [0,1,2,3,12,13,14,15],   # V1\n",
    "                    [4,5,6,7,16,17,18,19],   # V2\n",
    "                    [8,9,10,11,20,21,22,23]  # V3\n",
    "                    ],\n",
    "        'task'    : [\n",
    "                    [0,1],     # retinotopy\n",
    "                    [2,3,4,5], # movie watching\n",
    "                    [6,7,8,9]  # resting state\n",
    "                    ],\n",
    "\n",
    "        'hemis'   : ['left', 'right'],\n",
    "        'flows'   : ['outflow', 'inflow']\n",
    "        }\n",
    "\n",
    "# Define the conditions and the corresponding filenames\n",
    "conditions = conf['tasks']\n",
    "model = 'new_2021'  # Replace with your actual model name\n",
    "\n",
    "# Initialize lists to store the loaded data\n",
    "model_fit_list = []\n",
    "model_error_list = []\n",
    "J_mod_list = []\n",
    "Sigma_mod_list = []\n",
    "tau_x_list = []\n",
    "tau_list = []\n",
    "\n",
    "# Load the MOU results\n",
    "appendix = model \n",
    "model_fit = np.load(os.path.join(save_path, 'Fit_' + appendix + '.npy'))\n",
    "model_error = np.load(os.path.join(save_path, 'Error_' + appendix + '.npy'))\n",
    "J_mod = np.load(os.path.join(save_path, 'J_mod_' + appendix + '.npy'))\n",
    "Sigma_mod = np.load(os.path.join(save_path, 'Sigma_mod_' + appendix + '.npy'))\n",
    "\n",
    "\n",
    "\n",
    "# Print shapes of all arrays to determine the correct axis for subject removal\n",
    "print(\"Current shapes before removal:\")\n",
    "print(f\"model_fit: {model_fit.shape}\")\n",
    "print(f\"model_error: {model_error.shape}\")\n",
    "print(f\"J_mod: {J_mod.shape}\")\n",
    "print(f\"Sigma_mod: {Sigma_mod.shape}\")\n",
    "#print(f\"tau_x: {tau_x.shape}\")\n",
    "#print(f\"tau: {tau.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load rDCM results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading results from: /home/nicolas/Documents/GitHubProjects/retNet/temp/results_rDCM\n",
      "EC_avg_dcm shape: (3, 156, 24, 24)\n",
      "diagonals_dcm shape: (3, 156, 24)\n",
      "F_avg_dcm shape: (3, 156)\n"
     ]
    }
   ],
   "source": [
    "model = 'rDCM_2024' \n",
    "\n",
    "out_path = \"../../../temp/results_rDCM/\"\n",
    "print(\"Loading results from:\", Path(out_path).resolve())\n",
    "\n",
    "# Load pre-averaged results directly\n",
    "EC_avg_dcm = np.load(os.path.join(out_path, \"EC_avg_\" + model + \".npy\"))\n",
    "diagonals_dcm = np.load(os.path.join(out_path, \"diagonals_\" + model + \".npy\"))\n",
    "F_avg_dcm = np.load(os.path.join(out_path, \"F_avg_\" + model + \".npy\"))\n",
    "\n",
    "print('EC_avg_dcm shape:', EC_avg_dcm.shape)       # Expected: (3, 156, 24, 24)\n",
    "print('diagonals_dcm shape:', diagonals_dcm.shape) # Expected: (3, 156, 24)\n",
    "print('F_avg_dcm shape:', F_avg_dcm.shape)         # Expected: (10, 156) or (3, 156) if you saved F_avg\n",
    "\n",
    "\n",
    "out_pth = '/media/nicolas/KINGSTON/retNet_final/output'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize EC matrices \n",
    "\n",
    "To make EC matrices comparable across participants and conditions, each matrix is scaled by its L1 norm. This preserves relative differences in connection strengths while removing variations in overall magnitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_connectivity_matrices(EC_data, normalization_type='L1', verbose=True):\n",
    "    \"\"\"\n",
    "    Normalize effective connectivity matrices using L1 or L2 normalization.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    EC_data : ndarray\n",
    "        Effective connectivity data with shape (n_conditions, n_subjects, n_rois, n_rois)\n",
    "    normalization_type : str, optional\n",
    "        Type of normalization to apply: 'L1' or 'L2' (default: 'L2')\n",
    "        - 'L1': Normalize by sum of values (Manhattan norm)\n",
    "        - 'L2': Normalize by Euclidean norm\n",
    "    verbose : bool, optional\n",
    "        Whether to print normalization information (default: True)\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    EC_normalized : ndarray\n",
    "        Normalized connectivity matrices with same shape as input\n",
    "    norm_name : str\n",
    "        Name of the normalization method used\n",
    "    \n",
    "    Notes\n",
    "    -----\n",
    "    - Diagonal elements are set to zero before and after normalization\n",
    "    - If the norm is zero, the matrix is returned unchanged\n",
    "    \"\"\"\n",
    "    # Get dimensions\n",
    "    n_cond, n_sub, n_rois, _ = EC_data.shape\n",
    "    \n",
    "    # Initialize output array\n",
    "    EC_normalized = np.zeros((n_cond, n_sub, n_rois, n_rois))\n",
    "    \n",
    "    # Normalize each condition and subject\n",
    "    for i_cond in range(n_cond):\n",
    "        for i_sub in range(n_sub):\n",
    "\n",
    "            # Get connectivity matrix for this condition and subject\n",
    "            EC = EC_data[i_cond, i_sub, :, :].copy()\n",
    "            np.fill_diagonal(EC, 0)  # zero out diagonal\n",
    "            ECflat  = EC.flatten() # flatten the matrix\n",
    "            \n",
    "\n",
    "            # Get off-diagonal elements (link weights)\n",
    "            EClinks = EC[~np.eye(EC.shape[0],dtype=bool)].reshape(EC.shape[0],-1) # ignore diagonal\n",
    "            EClinks = EClinks.flatten() # flatten the links\n",
    "            \n",
    "            if normalization_type == 'L1':\n",
    "\n",
    "                norm = sum(EClinks)                \n",
    "                ECnorm  = ECflat / norm\n",
    "                ECnorm  = ECnorm.reshape((n_rois, n_rois))\n",
    "\n",
    "            elif normalization_type == 'L2':\n",
    "\n",
    "                norm = np.sqrt(sum(EClinks)**2)                \n",
    "                ECnorm  = ECflat / norm\n",
    "                ECnorm  = ECnorm.reshape((n_rois, n_rois))\n",
    "           \n",
    "            \n",
    "            # Ensure diagonal remains zero\n",
    "            np.fill_diagonal(ECnorm, 0)\n",
    "            \n",
    "            # Store the normalized matrix\n",
    "            EC_normalized[i_cond, i_sub, :, :] = ECnorm\n",
    "    \n",
    "    # Print information if verbose\n",
    "    if verbose:\n",
    "        print(f'EC shape: {EC_normalized.shape}, normalized using {normalization_type} norm')\n",
    "    \n",
    "    return EC_normalized\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EC shape: (3, 156, 24, 24), normalized using L1 norm\n",
      "EC shape: (3, 156, 24, 24), normalized using L1 norm\n"
     ]
    }
   ],
   "source": [
    "EC_normed_MOU = normalize_connectivity_matrices(\n",
    "    EC_data=J_mod,\n",
    "    normalization_type='L1',\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "EC_normed_rDCM = normalize_connectivity_matrices(\n",
    "    EC_data=EC_avg_dcm,\n",
    "    normalization_type='L1',\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mou = EC_normed_MOU.reshape((3*156,24*24))\n",
    "X_dcm = EC_normed_rDCM.reshape((3*156,24*24))\n",
    "\n",
    "X_mou= X_mou / np.std(X_mou)\n",
    "X_dcm = X_dcm / np.std(X_dcm)\n",
    "\n",
    "y = np.repeat(np.arange(3).reshape(3,1),156,axis=1).reshape((3*156))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MOU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_mou = EC_normed_MOU.reshape((3*156,24*24))\n",
    "X_dcm = EC_normed_rDCM.reshape((3*156,24*24))\n",
    "\n",
    "X_mou= X_mou / np.std(X_mou)\n",
    "X_dcm = X_dcm / np.std(X_dcm)\n",
    "\n",
    "y = np.repeat(np.arange(3).reshape(3,1),156,axis=1).reshape((3*156))\n",
    "\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3,3), dpi=300, facecolor=\"w\")\n",
    "n = 9\n",
    "colors = plt.cm.YlGnBu_r(np.linspace(0.2,0.8,n))\n",
    "\n",
    "x_pca = pca.fit_transform(X_mou)\n",
    "\n",
    "cols = ['r', 'g', 'b']\n",
    "for i in range(156*3):\n",
    "    ax.scatter(x_pca[i,0], x_pca[i,1], c=colors[int(y[i])*3]) \n",
    "ax.set_xlabel('PC1', fontsize=18, weight='bold')\n",
    "ax.set_ylabel('PC2', fontsize=18, weight='bold')\n",
    "\n",
    "ax.legend(['vfm','movie','rest '], frameon=False,bbox_to_anchor=(0.98, 0.98),fontsize=12,  title=\"\", loc=2, prop={'size':12})\n",
    "\n",
    "fname = out_pth + '/PCA_MOU.svg' \n",
    "plt.savefig(fname, transparent=None, dpi='figure', format='svg',\n",
    "    facecolor='auto', edgecolor='auto',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rDCM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pca = PCA(n_components=2)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3,3), dpi=300, facecolor=\"w\")\n",
    "n = 9\n",
    "colors = plt.cm.YlGnBu_r(np.linspace(0.2,0.8,n))\n",
    "\n",
    "x_pca = pca.fit_transform(X_dcm)\n",
    "\n",
    "cols = ['r', 'g', 'b']\n",
    "for i in range(156*3):\n",
    "    ax.scatter(x_pca[i,0], x_pca[i,1], c=colors[int(y[i])*3]) \n",
    "\n",
    "ax.set_xlabel('PC1', fontsize=18, weight='bold')\n",
    "ax.set_ylabel('PC2', fontsize=18, weight='bold')\n",
    "\n",
    "ax.legend(['vfm','movie','rest '], frameon=False,bbox_to_anchor=(0.98, 0.98),fontsize=12,  title=\"\", loc=2, prop={'size':12})\n",
    "\n",
    "fname = out_pth + '/PCA_rDCM.svg' \n",
    "plt.savefig(fname, transparent=None, dpi='figure', format='svg',\n",
    "    facecolor='auto', edgecolor='auto',bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MOU vs rDCM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 156, 24, 24)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EC_normed_MOU.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 156, 24, 24)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EC_normed_rDCM.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "\n",
    "n = 9\n",
    "colors = plt.cm.YlGnBu_r(np.linspace(0.2, 0.8, n))\n",
    "colors_cb = [colors[0], colors[3], colors[6]]  \n",
    "labels = ['vfm', 'movie', 'rest']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(3, 3), dpi=300, facecolor=\"w\")\n",
    "\n",
    "for i_cond in range(3):\n",
    "    x = EC_normed_MOU[i_cond, :, :, :].flatten()\n",
    "    y = EC_normed_rDCM[i_cond, :, :, :].flatten()\n",
    "    r, p = stt.pearsonr(x, y)\n",
    "    \n",
    "    # Format p-value (use proper LaTeX for < symbol)\n",
    "    if p < 0.001:\n",
    "        p_str = r'$p\\textless 0.001$'\n",
    "    else:\n",
    "        p_str = f'$p={p:.3f}$'\n",
    "    \n",
    "    # Rasterize scatter points to reduce SVG size\n",
    "    ax.scatter(x, y, c=colors_cb[i_cond], alpha=0.3, s=30, \n",
    "               edgecolors='none', label=f'{labels[i_cond]} ($r={r:.2f}$, {p_str})',\n",
    "               rasterized=True)\n",
    "\n",
    "ax.set_xlabel('MOU ', fontsize=18, weight='bold')\n",
    "ax.set_ylabel('rDCM ', fontsize=18, weight='bold')\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "\n",
    "# Add diagonal reference line\n",
    "lims = [min(ax.get_xlim()[0], ax.get_ylim()[0]), \n",
    "        max(ax.get_xlim()[1], ax.get_ylim()[1])]\n",
    "ax.plot(lims, lims, '--k', alpha=0.5, linewidth=1)\n",
    "\n",
    "ax.legend(['vfm','movie','rest '], frameon=False,bbox_to_anchor=(0.98, 0.98),fontsize=12,  title=\"\", loc=2, prop={'size':12})\n",
    "\n",
    "fname = out_pth + '/MOU_vs_rDCM_scatter.svg'\n",
    "plt.savefig(fname, transparent=None, dpi='figure', format='svg',\n",
    "            facecolor='auto', edgecolor='auto', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "\n",
    "n = 9\n",
    "colors = plt.cm.YlGnBu_r(np.linspace(0.2, 0.8, n))\n",
    "colors_cb = [colors[0], colors[3], colors[6]]  \n",
    "labels = ['vfm', 'movie', 'rest']\n",
    "\n",
    "# Create a minimal figure just for the legend\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "fig = plt.figure(figsize=(6, 0.5), dpi=300, facecolor=\"w\")\n",
    "ax = fig.add_subplot(111)\n",
    "ax.axis('off')  # Hide the axes\n",
    "\n",
    "# Create legend elements\n",
    "legend_elements = [Patch(facecolor=colors_cb[i], edgecolor='none', label=labels[i]) for i in range(3)]\n",
    "\n",
    "# Add horizontal legend\n",
    "legend = ax.legend(handles=legend_elements, loc='center', ncol=3, \n",
    "                   frameon=False, fontsize=14)\n",
    "\n",
    "fname = out_pth + '/Legend_only.svg' \n",
    "plt.savefig(fname, transparent=True, dpi='figure', format='svg',\n",
    "            facecolor='none', edgecolor='none', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "\n",
    "n = 9\n",
    "colors = plt.cm.YlGnBu_r(np.linspace(0.2, 0.8, n))\n",
    "colors_cb = [colors[0], colors[3], colors[6]]  \n",
    "labels = ['vfm', 'movie', 'rest']\n",
    "\n",
    "# Create a minimal figure just for the legend\n",
    "from matplotlib.patches import Patch\n",
    "\n",
    "fig = plt.figure(figsize=(1.5, 3), dpi=300, facecolor=\"w\")\n",
    "ax = fig.add_subplot(111)\n",
    "ax.axis('off')  # Hide the axes\n",
    "\n",
    "# Create legend elements\n",
    "legend_elements = [Patch(facecolor=colors_cb[i], edgecolor='none', label=labels[i]) for i in range(3)]\n",
    "\n",
    "# Add vertical legend\n",
    "legend = ax.legend(handles=legend_elements, loc='center', ncol=1, \n",
    "                   frameon=False, fontsize=14)\n",
    "\n",
    "fname = out_pth + '/Legend_only.svg' \n",
    "plt.savefig(fname, transparent=True, dpi='figure', format='svg',\n",
    "            facecolor='none', edgecolor='none', bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variability in within-hemisphere connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variability in callosal connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Callosal connections are less affected by local vascular signal spread across V1–V3 and therefore exhibit lower variability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Within-hemisphere correlations:\n",
      "vfm: PearsonRResult(statistic=0.9260894211209972, pvalue=5.5985517659497575e-62)\n",
      "movie: PearsonRResult(statistic=0.9719730280502413, pvalue=3.573373597055484e-91)\n",
      "rest: PearsonRResult(statistic=0.9286876445442753, pvalue=4.8414587374714395e-63)\n",
      "\n",
      "Between-hemisphere correlations:\n",
      "vfm: PearsonRResult(statistic=0.9529489598409404, pvalue=1.7263373652392927e-75)\n",
      "movie: PearsonRResult(statistic=0.9612103843626386, pvalue=2.570281820838483e-81)\n",
      "rest: PearsonRResult(statistic=0.9359738591161524, pvalue=2.9824675050069767e-66)\n"
     ]
    }
   ],
   "source": [
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.weight'] = 'bold'\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "\n",
    "n_rois = 12\n",
    "n_cond = 3\n",
    "n_sub = 156\n",
    "\n",
    "# Set all colors to black\n",
    "colors_cb = ['k', 'k', 'k']  # Black for all conditions\n",
    "labels = ['vfm', 'movie', 'rest']\n",
    "\n",
    "# Prepare data for both within and between hemisphere connections\n",
    "xm_within = np.zeros((3,12,12))\n",
    "ym_within = np.zeros((3,12,12))\n",
    "xm_between = np.zeros((3,12,12))\n",
    "ym_between = np.zeros((3,12,12))\n",
    "\n",
    "# Compute within-hemisphere data\n",
    "for i_cond in range(n_cond):\n",
    "    for p in range(12):\n",
    "        for q in range(12):\n",
    "            x_within = EC_normed_MOU[i_cond, :10, p, q].flatten()\n",
    "            y_within = EC_normed_rDCM[i_cond, :10, p, q].flatten()\n",
    "            xm_within[i_cond,p,q] = np.mean(x_within)\n",
    "            ym_within[i_cond,p,q] = np.mean(y_within)\n",
    "            \n",
    "            x_between = EC_normed_MOU[i_cond, :, p, q+12 if q<12 else q-12].flatten()\n",
    "            y_between = EC_normed_rDCM[i_cond, :, p, q+12 if q<12 else q-12].flatten()\n",
    "            xm_between[i_cond,p,q] = np.mean(x_between)\n",
    "            ym_between[i_cond,p,q] = np.mean(y_between)\n",
    "\n",
    "# Find global axis limits across all conditions\n",
    "all_x = np.concatenate([xm_within.flatten(), xm_between.flatten()])\n",
    "all_y = np.concatenate([ym_within.flatten(), ym_between.flatten()])\n",
    "xlim = [all_x.min() - 0.0001, all_x.max() + 0.0001]\n",
    "ylim = [all_y.min() - 0.0001, all_y.max() + 0.0001]\n",
    "\n",
    "# Create figure with gridspec - tighter spacing\n",
    "fig = plt.figure(figsize=(6, 4), dpi=300, facecolor=\"w\")\n",
    "gs = fig.add_gridspec(2, 3, hspace=0.08, wspace=0.08, \n",
    "                      left=0.12, right=0.92, top=0.94, bottom=0.12)\n",
    "\n",
    "# Row 1: Within-hemisphere connections\n",
    "for i_cond in range(n_cond):\n",
    "    ax = fig.add_subplot(gs[0, i_cond])\n",
    "    \n",
    "    # Only add title to top row\n",
    "    ax.set_title(labels[i_cond], fontsize=22, weight='bold', pad=10)\n",
    "    \n",
    "    for p in range(12):\n",
    "        for q in range(12):\n",
    "            x = EC_normed_MOU[i_cond, :10, p, q].flatten()\n",
    "            y = EC_normed_rDCM[i_cond, :10, p, q].flatten()\n",
    "            xerr = sem(x)\n",
    "            yerr = sem(y)\n",
    "            \n",
    "            ax.errorbar(xm_within[i_cond,p,q], ym_within[i_cond,p,q], \n",
    "                       xerr=xerr, yerr=yerr, fmt='o', capsize=3, \n",
    "                       color='k', alpha=0.5, \n",
    "                       markersize=3, linewidth=0.8, rasterized=True)\n",
    "    \n",
    "    # Make subplot square\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    \n",
    "    # Add diagonal and set limits\n",
    "    ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], '--k', alpha=0.5, linewidth=1)\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(ylim)\n",
    "    \n",
    "    # Only show x-label on bottom row \n",
    "    ax.set_xticklabels([])\n",
    "    \n",
    "    # Only show y-axis on leftmost column\n",
    "    if i_cond == 0:\n",
    "        ax.set_ylabel('rDCM', fontsize=22, weight='bold')\n",
    "    else:\n",
    "        ax.set_yticklabels([])\n",
    "    \n",
    "    ax.tick_params(axis='both', labelsize=14)\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_linewidth(1)\n",
    "\n",
    "# Row 2: Between-hemisphere (callosal) connections\n",
    "for i_cond in range(n_cond):\n",
    "    ax = fig.add_subplot(gs[1, i_cond])\n",
    "    \n",
    "    for p in range(12):\n",
    "        for q in range(12):\n",
    "            x = EC_normed_MOU[i_cond, :, p, q].flatten()\n",
    "            y = EC_normed_rDCM[i_cond, :, p, q].flatten()\n",
    "            xerr = sem(x)\n",
    "            yerr = sem(y)\n",
    "            \n",
    "            ax.errorbar(xm_between[i_cond,p,q], ym_between[i_cond,p,q], \n",
    "                       xerr=xerr, yerr=yerr, fmt='o', capsize=3, \n",
    "                       color='k', alpha=0.5, \n",
    "                       markersize=3, linewidth=0.8, rasterized=True)\n",
    "    \n",
    "    # Make subplot square\n",
    "    ax.set_aspect('equal', adjustable='box')\n",
    "    \n",
    "    # Add diagonal and set limits\n",
    "    ax.plot([xlim[0], xlim[1]], [ylim[0], ylim[1]], '--k', alpha=0.5, linewidth=1)\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(ylim)\n",
    "    \n",
    "    # Only show x-label on bottom row\n",
    "    ax.set_xlabel('MOU', fontsize=22, weight='bold')\n",
    "    \n",
    "    # Only show y-axis on leftmost column\n",
    "    if i_cond == 0:\n",
    "        ax.set_ylabel('rDCM', fontsize=22, weight='bold')\n",
    "    else:\n",
    "        ax.set_yticklabels([])\n",
    "    \n",
    "    ax.tick_params(axis='both', labelsize=14)\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_linewidth(1)\n",
    "\n",
    "# Add row labels on the right\n",
    "fig.text(0.94, 0.73, 'within ', fontsize=22, weight='bold', \n",
    "         rotation=270, va='center', ha='left')\n",
    "fig.text(0.94, 0.27, 'between', fontsize=22, weight='bold', \n",
    "         rotation=270, va='center', ha='left')\n",
    "\n",
    "fname = out_pth + '/Hemis_Alignment_grid.svg' \n",
    "plt.savefig(fname, transparent=None, dpi='figure', format='svg',\n",
    "            facecolor='auto', edgecolor='auto', bbox_inches='tight')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nWithin-hemisphere correlations:\")\n",
    "for i_cond in range(n_cond):\n",
    "    print(f\"{labels[i_cond]}: {stt.pearsonr(xm_within[i_cond,:,:].flatten(), ym_within[i_cond,:,:].flatten())}\")\n",
    "\n",
    "print(\"\\nBetween-hemisphere correlations:\")\n",
    "for i_cond in range(n_cond):\n",
    "    print(f\"{labels[i_cond]}: {stt.pearsonr(xm_between[i_cond,:,:].flatten(), ym_between[i_cond,:,:].flatten())}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model convergence\n",
    "\n",
    "MOU and rDCM estimates are robustly correlated and stabilize > participants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2   4   8  16  32  64 128]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9ECwDAACg7EzT1O2FeGo/4vFl46aTexcnQ+PJHbjnntUiOasdWcPilpq0EdRL4XGt3UpXMQAAWgqAY4kV+/yu6P5ddjwj9J2NLgbD6Y9nY4oTAFcMmzU9AM6yt2+O7t/6WvtS6OtQfU3a46Ux0VUOW7nfGoBNtG93nZ499VS5y9iwa7em9RMnvpT13J+d/Ee6d3d9iSsqvn2768pdQsFOnDiRCpbD4bCCwWDOcdOBQEAul2tdHcDFsN6AuBR7QWNzECwDAABgU8zHEorMRxWZvRsWp4fDybA4MrfYZRzbgR/M1jtsqf2IW5dGTucKi5uqHLJZCYoBAJVtIRrPv8/vbO4u3/TQeGY2quml0HhmLqpYfOf9O6NS2ayW1L69dcm9fdMfp3X+Zo6Kztw3OD00rq2xq4ov5QFYB7vNqra9u8pdxqa6d3d9xb/HrSa5b3Fyr2W/358zWPb7/fJ6vaUsDzscwTIAAAAKkjBNTc7HMvYnHk8bN303MF4Mi+9E4+UuueTsFksqHE7uR9xamyU4rnGoucahGjsdLACA7Sm61AG8nn1+l69JjZCeiyka23nbV1Qqq9WSfcRzxuOVewHXLxsVnT5Cuq7GoSoHATAAoPJ5vd5UsDw0NCTDMFZ0+YZCIYVCIR07dqwMFWKnIlgGAADYwWai8bv7Ec9FdWs2c2/i9PB4Yj6qHdhUrMYqe9r46Sz7FacFyY1Vdj7oBABsKbF4Yt2hb3rH793Hi6OiCYArh8Wide3zW7c06rl+eafwUjdxdZWNfxcBALBO3d3dcjqdMgxD0uLI6+V7Lfv9fvX29jJWGiVFsAwAAFBBYglTRqp7eCE1cvpW2uP0IHl2B34oXGW1qLW2KmM/4paazP2Jk0Fyc7VDDpu13CUDAHaAeDyR2rc31z6/07PRpTHPmV2+GY+XrVmI7ry/6ytZct/e9D1862qX7e2bZZ/fu+HvstC4xq4aAmAAALYkt9ud2mvZ7/evCJYDgYBGRkbKURp2MIJlAACALcw0Td1Z6ioeXxoxneokXrZ3cWQuKmM+Vu6SS84iqbkmffy0Qy21afsTpz1vrXWozs6HpwCA9UskzOydvsnHOTt97+7/mwyN00dJzy/svC0kKlldjT373r5Lj+uXgt3l+/zWL+v6TQ+Na6rsslr5NwwAADvFiRMnUsFyOBxWKBRSZ2enpMVQ2eVypZ4DpUKwDAAAUGIL8cTdgHh2IbU3ca7gOLoD50/X2W1L3cNp46fT9idOD4ud1Q7Z+JAVALBMImEudu6mB7+zMc3ML417Tjs+vRQIL4a+i8dnk8fTH89GNUcAXFFqq+0Zge7d0Neu2mrHUuhrV+1SJ3BG12+O0Li2mgAYAABsnNPpVHd3d2qv5ZMnT2pwcFDSYgezx+MpZ3nYoQiWAQAANihhmpqaj60YPz2eJSyOzC7odnTnfSBtsyi1N/HdgDj381q7rdwlAwBKxDTN1OjmbPv/Zuv4TXYFZ3YKZ4bGsztwikclq6myLYW+efb2zbLPb12WNfVLncK11TbZ2PICAABsYR6PJxUsDw0NyTAMRSIRhUIhnT17tszVYSciWAYAAMhiNhZP7Uc8vmx/4vG5haXx04tB8sRcVPGd11Ssxip7xt7Eyc7i1izPG6vtsjJ+GgC2NdM0NbcQzwh+s+3tu1rH7/Lx0bPzMZk78O/RSlVdZVsKdLOEvel7+66yJhka19YsBsQEwAAAYCfq7e2V0+mUYRiSpDNnzmhkZCR1HCg1gmUAAIBlPvKFEc3FE+Uuo+QcVotaa6tWjJtOhsety55X8QEvAGxJpmlqfiFe8N6+qcc5OobTfyYArhxVDuuyUHf1Tt/k4/ra5P7Bd/f/TY6BtvPvAwAAgKJyu92pvZZ9Pp8ikUhqJHYxJEPrQkUikayPsTMQLAMAgB1laj6mr16N6Euv3cy5ppJCZWf1YldxRmCcFg6nn9vlsMlCVzEAlIxpmlqIJtL2+V0Md9ND31SomzX0XTYKOu1xIkECXCkcduuqoW8y3M0X+tYtC4AddgJgAACA7cDj8aSC5XA4LJfLpe7u7g3dMxwOZ328WddeuHAh43koFJLL5Vr1ulAotObXwuYiWAYAABXv9ckZnbsS0bNjEYWuT27rsdU1Nqtaa5f2I15l/LSzxiG7laAYADbKNE0txBI5u39nZqNLoW/uoDe1Ztn6OAFwxbDbLFnD32SQm763b/rj+qVx0Iuh7+J1yeO11XZVOWzlfmsAgArWtneXLn3xp8tdBoA8kkFycq9lj8ez5nuEw2ENDQ1JkoaHh1P3kqRgMKiuri51d3ertbVV0uII7mTwu/za9LA3FAqpp6dHPT09K64LBAIyDEPj4+MKBAIZ9Rw/flzDw8Pq6OiQ0+mU2+1e8Vrnz5/PqDMcDmfU2d3drc7OzjX/WmBjCJYBAEDFiSYSev76lM5diejcWESvT82Wu6ScrBapuXoxFG6tqVJrWji82E2c+byOD5cBIK+FaDz/Pr/Zwt9kN3BaaDwzG80YJR3bzt9KQgab1ZJ1b99kkFu/FA4vhruZnb7Lj6ePiSYABgAAwGbxeDypkDUZwq5FKBSS1+uVJDmdzhX7M4fD4VQQLEmdnZ2pgDgYDMrr9aauWX7thQsXdOHCBRmGkRES+3w+hcPhnNedOXNmxTXhcDjva6XX6fP5CJbLgGAZAABUhMn5qL5yZULnrkT0tasTmlqIla2WXQ5bxv7EramAOG2/4qXnTdV2WRk/DWAHii51AK9ln9/p2ehS6Lu4dnbpfPrjaKxytjPY6axWy1LHr121NY6Vj5d3+i7r+l3sGl4WINc6VGW3svUDAAAAtpVkJ3Bvb++KsLXQ601zfV+Wdbvd6wqzR0dH13xNd3f3uutEaRAsAwCAbck0TYUnZ5e6ksf1/I0pbdY0UbvVsjhqOtk5XHt3r+K7x5eC5GqHqtmzEEAFicUTBYW+2fYCnl46Pju39DhthDQBcOWwWJS5t2+WfX7TO3sXR0UvjYzOcby+xqEqBwEwAAAAkLSeoBYoNoJlAACwbUTjCY1cn9SzYxGduxLR2O25TXmdX3l/hw4116eC5IYqGx9sA6goiYSpazenc55/2vs3WojGNT0X1UKUALhSLAbAS5292fb2zbI/cEbwW5M9NK7m70kAAAAA2BEIlgEAwJY2MRfVV64u7pX8tasTuhONF+W+99RW6ebsQtZzH9jfov0NNUV5HQAop1g8obG3buvS2KQuXjY0emVSl8YMjY5Nam4h95+n1yMzJawS2SSD3NTevsv2+c0Mf+8Gv/W1jqXQd3Ft+mjoGgJgAAAAAMAGECwDAIAtxTRNjRozqa7kb98szohri6RHdzfoaHuLHm9vVb3dqn/0Fxc2fmMA2AIWonG9fm1Kly4vBseXxiY1Ojap8NVJRk5vssXRz2mdvanQd/VO31yhcU2VXVYrATAAAAAAYGshWAYAAGW3EE/o/FuTqf2Sr96ZL8p96+w2ff9+p462teiDbS3aXVuVOnd1k8ZoA8BmmpuPKXx1aik8NnTp8qRGr0zq9TenFN+sjeYrRE2VbbHDN0fom3qctiZb6Ls4QnrpeDUBMAAAAABg5yBYBgAAZTE+u6CvXJnQs1fG9fWrhmZixRlxvX9XtY62t+poW4uO7GtSlc1alPsCQCndmYkqfHVSly4vdh9fXBpfPXb9tswKz4+rq2wF7+1bmzye/jhLaFxbbZONvw8AAAAAANgQgmUAAFASpmnq1YkZPTs2ri9fieg7N2+rGNmIRdK79zTq8bYWPd7eog5nHftHAtg2Ju/Ma3QpOL40djdIvnZrutylrarKYU3t4Ztrb9+6GnvWNdnC32Q3sJ0AGAAAAACALYlgGQAAbJr5WELPvWUsjbiO6Np0cUZc1zts+sD+Zn2orUUfamtRc42jKPcFgM0yPjm3NLp6KUAeMzR6ZVI3IrPlLi0r78906b49u1S/vHM4LTQmAAYAAAAAYGchWAYAAEV1c2ZBX14Kkr9xbUKzsURR7tvWUJPqSu7a2yQHgQaALcY0Td2IzC7tfzyZESRP3C7OF2vWy2KR2vc26OD9TTrY7tTBtiY11lfpX/3O32dd/8M/8KDa9u4qcZUAAAAAyqFt7y5d+uJPl7sMANsAwTIAANgQ0zT13ci0nh0b17krEb14605R7mu1SO/d05jaL9nVVMuIawBbQiJh6tqtaV0aM3Tx8mRGkHxnJlrW2mxWix68r1EH2xcD5I6ln137G1VTnfl//65cL86f1wAAAAAAYGcgWAYAAGs2F4vrH64ZOjcW0ZevRHR9ZqEo921w2PSBthYdbWvRB9ua1VTNiGsA5ROPJzR2/U5mB/LYpEbHJjU7HytrbQ67Va79TakO5I62xccP3tuoKoetrLUBAAAAAIDKRLAMAAAKcn16Xl++EtGzYxF985qhuXhxRlw/2Firo+2LYfJ79zbKYWXENYDSWojG9ca12xq9Mrk0unoxQA5fndRCtDh/1q1XTZUt1XW8+KNJB9ub1L6vgT2OAQAAAABASREsAwCArBKmqZfH7+jZpa7kl8eLMzLVZpE69zbp8aUw+cGmuqLcd632N9ToxZ/5YFleG0B5zC/EFb662HF8aczQxaWf33hzSrG4Wdba6msdOtjepEPtzowu5P17dslqZRsAAAAAAABQfgTLAAAgZSaaOeL65mxxRlw3Vtn1wbZmHW1r0Qf2t6ixmn+CANg807NRha9MZoyvvjRmaOz6HSUS5Q2Qm3ZV6dD9d7uPk93I+1rr2EceAAAAAABsaXyqCwDADndtel7nxsZ1bmnE9UKRQhdXU62Otrfq8fYWvfueRtnpuANQZFN3FnTpiqFLlyc1euVugHz1xnS5S9NuZ83d0dXJILmtSa3OGgJkAAAAAACwLREsAwCwwyRMUy/euq1nxyI6NxbR9yaKE8DYLRYd3teU2i/5/sbaotwXACJTc0t7H09m7IN8PTJb7tK0b3dd2t7HTh1qd6qjvUnOhupylwYAAAAAAFBUBMsAAOwAM9G4vvbmRGrEdWQuWpT7Oqvt+lBbi462t+j772tWQxX/tACwPqZp6ubE7Irx1ZfGJhWZnCtrbRaL1LZn17IO5Ca52prUUFdV1toAAAAAAABKhU9/AQCoUFdvz+nclcWu5OfeMhQt0ojrQ846fai9RY+3t+pduxtkY8Q1gDUwTVPXbk3r4uXF4PhuB/KkpqaLs6/7etmsFt1/b0Oq+zgZJLvamlTL3vAAAAAAAGCH49MRAAAqRDxh6ju3buvcWETnxsZ10Zgpyn0dVouO7GvS4+2t+lBbi9oaaopyXwCVLR5P6MqNO3c7j5f2QR69Mqnp2VhZa3PYrTqwv1EdbUsB8v1OHWpv0gP3NaraYStrbQAAAAAAAFsVwTIAANvYnYWYvv7mhJ4di+grVyKamC9OWNNS40gbce1UvYN/MgDILhpL6PJbt1P7Hi8GyZMKX53U/EK8rLVVV9mWwuOmjH2Q2/c1yGG3lrU2AAAAAACA7YZPiQEA2GbGbs/q2bHFEdcXrk8qVqQR1w811+vx9hY93t6iR3Y3yGphxDWAu+ajcb1+dUqXxgxdHFscXz16ZVKvvzmlaCxR1trqa+062O5M60Be/Hn/PfWy2QiQAQAAAAAAioFgGQCALS6WMPXtm1M6NxbRs2PjCk/OFuW+VVaLHrvXqaPtLTra1qJ7dzHiGoA0Ox/L2Pc42YV8+a3bShTpiyzr1VhfpUP3L3Yed6Ttg3zv7jpZ+DIMAAAAAADApiJYBgBgC5qaj+lrb07o2bFxffXqhCaLNOJ6d61DR9tadbS9Re+/16k69hIFdqzbMwsaTQuOk/sgX715R2Z582O1NtXoYDJAbmvSoaV9kHc7awiQAQAAAAAAyoRgGQCALeKNqVk9Ozauc2MRha5PKVakZOcdLfU62t6qx9tb9M7WXYy4BnaYiam5pQ7kyaUx1otB8vXxmXKXpr2tdTrYvhgcJzuQO9qb1NLIBAUAAAAAAICthmAZAIAyiSYSev76lM5dWdwv+fWp4oy4rrZZ9f57nXq8vUUfamvR3vrqotwXwNZlmqZuGXOZ3cdL+yCPT86Vuzy17dmV2vd4sQt58eeG+qpylwYAAAAAAIACESwDAFBCk/NRffXqhJ4di+hrVyc0tVCcEdd76qp0tK1FR9tb9Ni9TtXaGXENVCLTNHXt1owujRkrxlhP3lkoa21Wq0X372vQwbTO40PtTrnaGlVX4yhrbQAAAAAAANg4gmUAADaRaZp6bXJWz16J6Mtj43r+xpTiRdq79OHWXXq8vUVH21v1jpZ69h0FKkgiYerKjTtp4XEyQJ7U9Gy0rLXZbRY9eF/jYvfx/U4dWtoH+cD+JlVX8aUWAAAAAACASkWwDABAkUXjCY1cn9S5KxE9OxbR2O3ijKGttVv1ffc260PtLTra1qJ76hghC2x3sXhCl6/d1uiVSV28fLf7OHxlUnML8bLWVuWwqqPt7vjqZBfyA/c2ymG3lrU2FEfb3l269MWfLncZAAAAAMrs6u05Pfn581nPPfPUEe1vqClxRQC2KoJlAACKYGIuqq9cXdwr+WtXJ3QnWpxAaF99tY62tejx9hYd2dekGkZcA9vSfDSuN96c0sWxSY2mja9+7eqUorFEWWurq7HfDZCX9kHuaGtS+95dstkIkAEAAAAAALCIYBkAgHUwTVOjxoyeHYvo3JWIvn1zSokijLi2SHp0d4OOti/ul/y2ZkZcA9vJ7HxM4SuLI6tHrxi6dHkxQH7j2m3Fi/GHxAY01DuWuo/vdiAfur9J+1rrZbXy5wwAAAAAAADyI1gGAKBAC/GEzr+1OOL6y2MRXblTnBHXdXabvn+/U0fbWvTBthbtrmXENbDV3ZmJLgbHY5MaHZvUxaV9kK9cvyOzvPmxmhurF0Pj9iYdvP9ukHxPcy1fVAEAAAAAAMC6ESwDAJDH+OyCvnJlQueujOtrVw3NxIoz4vq++mo93t6io+2tOrKvSVWMmwW2pMk787p0+W5wPDq22I187dZ0uUvT3pZadaR1Hx9sb1JHu1OtTex9BQAAAAAAgOIjWK5ggUBAfr9f4XA4day7u1sej0fd3d2b/vqGYSgQCOj06dMKh8MyDENOp7NoNQSDQfn9foVCIYXDYblcLnV2dpbs/QGoTKZp6tWJGT07Nq4vX4noOzdvqxjNhxZJ797TqMfbFkdcH3TW0TkIbBGmaSoyOaeLS/seX7psaHRpnPXNidlyl6f9e+rV0ZYWIN/fpINtTjXuYroBAAAAAAAASodguQKFQiE98cQTamlpkdfrldvtliSFw2H5/X719PSot7dXp06dktPp3JQahoaGdPz4cRmGIZ/Pp97eXrW0tCgcDsvr9aqnp0fd3d0aHh5e871DoZD6+voUDofldrs1ODgol8ulcDisCxcuqK+vT263Wz6fbxPeGYBiuXp7Tk9+/nzWc888dUT7G0rXcTcfS+i5twyduxLRubGIrk3PF+W+9Q6bPrC/WR9qa9EH25rVUkMIBJSTaZq6HplJ7Xt8aSlIHh2b1MTt4vy+Xy+LRbp/X4MOtjvV0d6kQ0sdyK62JtXXOspaGwAAAAAAACARLFecYDConp4euVwujYyMZATHLpdLPp9PHR0d8ng8CoVCK9YUg9fr1cDAgCRpZGREnZ2dqXOdnZ0aHh6Wx+NRIBBQV1eXRkZGCr53IBCQx+OR0+nMeu/Ozk4dO3ZMBw4cSIXXAJDNrZmFxSD5SkTfeHNCs7FEUe7b1lCjx9ta9Hh7i7r2NsnBiGug5BIJU2/enF4Kj43UKOvRK5O6MxMta212m0UP3Ne42Hnc1rTYhXy/Uwfua1RNNf80BwAAAAAAwNbFp1cVxDAM9fX1SZIGBwdzBsZut1sjIyMKBAJ64okn1hTsriYQCKRCZb/fnxH8pvP7/Tpz5oxCoZA8Ho/8fn9B9/Z4PJIWA2uXy7VizdDQkLxerwzDkN/vJ1gGkGKapr4bmda5KxE9OzauF2/dKcp9rRbpvXsadbS9VUfbWuRqqmXE9TZx5fodPX7881nPPXvqKbXt3VXiirBWsXhCV67f0cXLd7uPL40ZCl+Z0ux8rKy1OexWdSwFx+n7ID9wb4OqHLay1gYAAAAAAACsB8FyBUmOnk527ubj9XoVCAQUCoU0NDSk3t7eDb++YRip4NfpdKZGcOdy4sSJVB0ejydvzckAWloMpbOFypJ08uTJ1J7SoVBoPW8DQAWZi8X1zWuTqf2Sr88sFOW+DQ6bPtDWoqNLI66bqhlTC2ymhWhcb1y7nTG++tLlSYWvTipapGkD61VbbVdH+1KAvLQP8qH7nWrbu0t2JhYAAAAAAABsGcmtViORiI4dO1ZQ0yMyESxXCMMwNDQ0JEkFdem6XK7UvsQnT54sSrDs9XpTjwupIX2N3+/P+xs42YntcrlWDayTcoXPACrbjZl5nRtbHHH9D28amosXJ3R6oLFWj7cvhsnv3dsoh5XACCi2ufmYXntzSpcyOpAn9ca1KcXiZllr21XnWBxfvRQiLz526r576mW1MqUAAAAAAABgq3viiSdkGIYkpbZrLTRzwiKC5QoRCARSj48cOVLQNclgORQKKRwObziIPXPmTMa9C3n9pEAgkDNYDgQCqS7kZNdyLoODg/L5fJIyg24AlSthmnp5/E4qTH55vDgjrm0WqXNvUypMfrCprij3BSBNz0YVvjKpS2OLex8nO5Cv3LijRKK8AbKzoVqHlvY9Xhxl7dTB+5u0t6WOMfcAAAAAAADblGEYqVA5qZhbxe4UBMsV4vTp06nHufZWXi492A0Ggxv6VkY4HF7xG3I1y+sMBoNZO52TQbGkVTurXS4XowuAHWAmGtc3rxl6diyiL1+J6OZscUZcN1bZ9cG2Zh1ta9EH9reosZq/JoGNmLqzoEtXFkPj9DHWb96cLndpuqe5Nq3zeHEf5EPtTWppqiFABgAAAAAAqDBOpzPVcJnU09NTxoq2Jz4xrxDp+wm3tLQUdE16sLvRb2UsD5VbW1vXfI9QKLQiWA4Gg6nf5Mnf9AB2pmvT8zo3Nq5zVyL65puGForU1XigqVaPt7fqaFuL3rOnUXZG2gJrNj45p0tjhkbHMgPkG5HZcpeme3fXLwbI9y+NsW5zqqO9Sc6G6nKXBgAAAAAAgBIaHh6W1+tVOByWx+MpyjaxOw3BcgVI/3aFVPjewunh74ULF4paU6GcTmcqlD5//vyK88PDw6nHhw8fLlVZALaAhGnqxVu3dW4somfHIvreRHE6HO0Wiw7va9LRpRHX9zfWFuW+QKUzTVM3IrOLAfKVSV26bOjiUoA8MTVf1tosFqlt7y4dbHfqUKoDuUmutiY11FWVtTYAAAAAAABsDS6XS4ODg+UuY1sjWK4Aax1BnZTesbzee2S7lySNj48XdF366y4PyKXFjuVsrzEwMKDTp08rFAqlOpmffvpp9ff3r6luAFvTf7nwmi5cn1RkLlqU+zmr7fpgW4seb2/R99/XrIYq/voDckkkTF27NZ3WeZzsQjZ0e7o4vyfXy2a16IF7G1L7Hi+OsXbqwP5G1TK6HgAAAADWbHJhVp999atZz33soQ+oqYov5AMA7uITOEiSIpHIhq53uVwZ3cfrke3abCO+u7q61N3drcHBQblcLhmGoTNnzsjj8ejkyZM6depUUccX3LhxQzdv3lzTNZcuXSra6wM70d++cWvD9zjorEt1Jb/7nkbZGHENZIjHExq7ficVICfHWI9emdTMXKystTnsVh3Y35ja/zgZJD9wb6OqHbay1gYAAAAAAADsVATLFWB5KLy8e7gQG+1YlqRjx44pEAhIygyEc1m+Zvn7WF6T0+lUX1/fis5kp9Mpt9utw4cPq6urS319ffL7/XK73et8J5k+9alP6ZOf/GRR7gVg89itFr0vNeK6VW0NNeUuCdgSorGE3rg2tdh5fHlpjPVSgLwQTZS1tpoqm1xtTakA+dDSPsjt+xpkt1nLWhsAAAAAAACATATLFaAYoXAxeL3eVLCcPsI6F7/fn/f88tHYybHXucZdd3Z2yu12KxAIyOPx6PDhw+rs7CywegCl9uaduQ3fo6XGoQ+1tehoe4u+/z6n6h38tYada34hrteupo+uXvz59TenFIubZa2tvtax1Hl8d3z1wfYm7d+zS1amCQAAAAAAAADbAp/Ao2hcLpf6+/s1MDAgaTFc7u7uzrk+GAzK5XJl3VtZWtnBHAwGNTIykreGvr6+VLjd19en0dHRtbwFACUSNmb07859d13XPtRcr8eXRlw/ek+DrBZCKewsM3NRha9M6eKYkdaBPKnLb91WIlHeALlpV5UO3e9UR1tTxj7I+1rrZOH3KgAAAAAAALCtESxXgPWMvt4sPp9PoVBIwWBQHo8nZ7Db09Mjn8+nkydPpo65XK6MNck9lZOcTueqHcjpQXY4HF413C7EJz7xCfX19a3pmkuXLunHf/zHN/S6QKX6buSO3H/7oiJz0YLWV1kteuxeZ2q/5Ht3MeIaO8Pt6YW07uOlAPnypK7cuFPu0rTbWaOD7U51tKeNsW53qtVZQ4AMAAAAAABQgHA4LL/fL8MwFIlEFA6HU49PnTql3t7e1NqhoSH5/X5duHBBhmHI6XTK5XLp6aefltvtzpoTGYahQCCg4eHhjOsOHz4sj8eTcf/1CIVCqZqStbtcLnV2durpp5/e8P2LJfnrMDo6qkgkIsMwFA6HFYlE5Ha75fP51ny/M2fOaHBwUOFwWOFwOON/j97e3hV5VyUhWK4AywPY5B8Oa1HMcHp4eFher1cDAwPq6OiQ3+9PhbuhUEher1ednZ3q7e2V1+tNXZctSE53+PDhgl4/vQt6cHBww8Hynj17tGfPng3dA8CiF2/dlvtvX9TUQizvupYahx5vb9Hj7a16/71O1TlsJaoQKL2JqbnM8dWXDV26Mqnr4zPlLk37dtctBsdpHcgdbU1qbuQLHgAAAAAAxBKmrk/Pl7uMDXsrz3vId2472VtfLfsW244rGAymJsDmYhiGjh8/LsMw5PV6UzlJMBiU1+tN/RgZGclozBsaGpLX65XH45Hf75fL5VIoFNLp06c1MDCQasobHh5ec93hcFh9fX0KhULq7e2Vz+fT4cOH5XQ6FQ6HNTQ0pOPHj+v48eMrAvL099Xc3Cwpfz7V0tKSs4HRYrHkvNblcqUm4IbD4YwsaiMCgYC8Xq8Mw1B/f3/q1zb5vpP/e/h8vpzbum53BMsVoBih8PJQd6N8Pl/qDyyPx5Maa3348GF5vd5U2Js+7nq1juX1fMOjkL2eAZRG6PqkPhF8SXei8VXX/smPvFvtjbUlqAooDdM0dcuY06XLxuII66UgeXRsUuOTG99vfKPa9+5K60BeDJE72prUUF9V7tIAAAAAANiyrk/P68nPny93GZvqp7/0nXKXUBTPPHVE+xu21hfl3W633G53qqM2W/jZ1dUln8+3Ipzt7e1Vd3e3Dhw4IMMw9MQTT2hkZEQul0tDQ0M6efKkRkZGMvKjzs5OdXZ2qrW1VV6vV8FgUH19fRocHCy45qGhIfX19cnpdGp4eHhFY19yy1S3262+vj719fWpt7d3xWs4nU75fD6Nj48rGAwqFAqtOH/s2DF1dXXlrKW/v1+BQECGYWQc7+7uzphC29nZKdM0U53GHo+n4Pebrq+vT0NDQ3I6nRodHc3IrJLvu7e3Vx0dHfJ6vRodHZXf71/Xa21lBMsVYHkAG4lECgqb03+zbcY4bZfLJZ/Pl3eMQHoNPT09GeeW11Rojenrlu/TDKA8vnnN0P919iXNxhIFrWffZGxXpmnq2q2Z1PjqZAfy6JVJTd5ZKGttVqtFD9zboIPtTepoWxxfffB+p1z7G1VX4yhrbQAAAAAAADuV0+lUf3+/Tp8+nRGw9vX15R1Z7XQ65Xa7NTAwIMMwUo1+x48f12uvvZYzU+nv70+F2ENDQwqFQqtuQyotdusmQ9mzZ8/mvSYZPHd1dWloaEg9PT0ruqPTO3o9Ho8CgUDqeTIkz8fn8+nEiROpcL2zs1Nnz57N+b6Tv16Dg4NrbkpMhsrS4nvPVZvL5dLg4KD6+voUCATU0dFRcZ3LBMsVYPlvkuXfzsglfXxAOea9J8dVJ2UbWd3Z2Zn6g7TQ9wVga/nKlYj+zd+/ovl4YaEysB3E4wldvTm9GB5fztwHeXo2/6j3zeawW/XgfY2pzuPkzw/ub1Q1Y+UBAAAAAAC2pOS4akmpkHm1buKenp7UOO1AIKBQKCSfz7dqo153d3cqXA0Gg6sGy6FQKBUq9/f3FxRES4vblXZ0dKRGd+dqRPT5fBnBcjgcLii3cjqdqfeSL1ROt9Y8bGhoKBUqu93uVd97co/l5AjuXHtgb1cEyxUiPYAttEs3fd2RI0c2pa580r9509nZmfU31uHDh9f8vtID6EreIB3YDoJv3NK/O/ddxRJmuUsB1iUWT+jytdt3u4/HDF28PKnw1UnNL6w+1n0zVVfZ5NrfpEP33x1dffB+p+7f1yCH3VrW2gAAAAAAALA26dNph4aGCtr/OD0DMQxDkUhEbrd7Tdfl2sM4Xfpo6RMnTqy6Pv11ksHvwMCAnn766azBbHpALEl+vz9rM2I2wWCwoDB9vY4fP556XOhezb29vRmBfyV1LRMsV4ju7u5UALu8EziX9HW5RilspvQ/FHP9QZQcFyAV/r7SJTeyB1B6/yd8Qye+8j3FyZSxDf36H/6Drt6c1utvTila4Aj3zVJXY0/rPr67D3Lbnl2y2QiQAQAAAAAAKk0yaF3N8q1Sn3766YLvn7RaU9/Q0FAqn8nVJJhPX19fKjA+efJkzi7s5L7Pydc0DGPV1wqFQjIMo6AwfT2SY8alxV+zQpsZ0zvJT58+TbCMrefEiROp/0hHRkYKuiYZRLtcrqJ19nq9XgUCAR0+fFiDg4N5f9OfOXMm9fq5gu3u7m45nU4ZhlFQsLx8Xfq3aACUzl9cfEu/9rWLypcpP3F/q85eHi9ZTcBaPDtyteSv2Vhfldr3OH2M9b2762Vh33EAAAAAAIAdY71Nc4WOqE632jakJ0+eTD0utIs4Xfo1+QLj9DxIKqzT1+/3b1qonLx/en2FSv/fL316byUgWK4Q6WMCzpw5k/EfezbpG5MX0ro/NDQkl8uV9w+lcDicCreDwaBOnjyZc15+IBBI/eGw2h4BJ06ckNfrlWEYq24if+HChdTjzs7Odf0hB2Bj/uyVN/Xb38w/PqXvoX362CNtBMsoG9M09bVvvVmW125pqskIjg8tdSHf01xLgAwAAAAAO4RpmoqbphJmQvGlHzEzoXgioYRp3n2e/LF0POPY0vH40vq8x1LHM8/FEuXd5mm721tfrWeeKv02k8X21vS8fvpL38l67o//0bu0r766xBUV395t9h7WO9Z5eQfzRiVzmaSOjo4132N5Y+OZM2dyhsFutzuVM/n9/lWD5UAgUHCz5Votb2RcS4Om0+nMCMkL3TN6OyBYriB+v19dXV0yDENDQ0N5x1sng+fOzs683+YwDENdXV2p3zw+ny/nb+Tlf2C1trbmvGcyzC5kk/f+/n75/X6Fw+G8YxKkzG+PnDp1Ku99ARTfH714Rb974bW8a/7FO+7TL7/PpTfvzJeoKiDT629O6TdPPadzm9yVvLeldjE8TutA7mhvUktjzaa+LgAAAAAgUyI9ZE2kh62rHE8kVoazprnm47FEIiNATh7H9me3WrS/obL/f/6++uqKf49b0XoD4mLvM5zezCetLVzNZXh4OGculT6dNxwOKxgM5mwgDAQCqzZEbkR6g6a09lC9paUlFSyv1hW+nRAsVxCXy6VTp06pr69PfX19Gh0dzfqbPBAIaGhoSE6nc9Vu4WAwmPGNjJMnT+YMlpPz5SORiI4dO5Z1XTKoNgxD/f39OTualxseHlZXV5eGhoY0MDCQ9d4DAwMaGhqStNgFvVl/mABYyTRN+b8zpv/+/Bt51/3so236N50P0pWJspiZi+pTZ17QZ77wUlH3Td6/pz7VfZzcB/lge5Ma6quK9hoAAAAAsB0ku3Dj5t0gNZYKaBe7c5d34a4IeHMdX8vaZcfNvJt1AQByKWSL0kKkd+/mC1nTp/NKi82EuYJlv99f0ETe9Vq+97TH45HH41nzfZLdy5WCYLnC9Pb2anh4WH19ferq6pLP50t98yPZKRwIBNTd3b3qHsjSym+3rPZtlGQALN0dn+10OhUKhXT+/HkNDAykAu18HdXLuVwujYyMqKenR16vV8PDw/J4POrs7FQ4HJbf70+9HqEyUFqmaer3Q6/rMy9cybvu/3rvA/K8q51QGSVnmqb+5mtv6Hc+e15v3ZpZ1z2sVova9+66GyDf79ShdqcO7G9Ufa2jyBUDAAAAwOo2qws3ZqZ31y4ezzx2tws3nuU4AKBybEan7WphtdfrTQXLufZkDoVCCoVCOnbsWNHrS1r+3oeHh9l+VQTLFam7u1uvvfZaaq/l9G9sdHd3r+k//u7ubvX392tgYCAV2ubjcrk0MTGhgYEBnTx5UuFwOPWb/vDhwxvaSN3lcml0dFSBQECDg4M6fvx4xr3XGlYD2DjTNPV/PxfWn7ySf6/af3f4gH7mkbYSVQXcdfGyod8IfFPf+M5ba7ruQ5379a5Drakg+cD+JlVX2TapSgAoj8mFWX321a9mPfexhz6gpqraElcEAMDWlN6FmwxS07twC9nHdvUu3LUfJ8IFAJTaeoPm9OtWa3js7u7O6HAOBAIrptj6/X719vZuaidwJXUZFxPBcoVyOp1yu93rDnHT+Xy+gkdWJ/X396+6qfp6Fet9AdiYhGnqN75xSUOv5g/s/tP7O/TP3n7fiuP7G2r04s98cLPKww53e2ZBf/Bn39bn/voVxeJr/7jlN/71+9W2d9cmVAYAAAAgn0TOztrCu3DvdtdmduHGlx3PPJbZhbv8OAAAO9HyKbbLx0MX457ZuN3u1F7Lfr9/Rd4UCAQ0MjKy4VryWb7PdbHGgm93BMsAgDWLJUz9ytde1V+N3si5xiLpN37gkP7JoX2lKww7XiJh6i+fDcv3Rxd0y5grdzkAAADAlrW8CzcztC1mF26+EcwrjxPhApkskmwW690fVqtsFkvmsVzHrelrLFmPz8djOvfW98r9NgFsUcu3HV1Px/Lya44cObLqNSdOnEgFy+FwWKFQKFVLIBCQy+Xa9C1Ri/HeKxHBMgBgTaLxhLxf/p7+9o1bOdfYLNLJD75NP+LaU8LKsNO9NDquT/q/qdB3bxa0vrmhWhO35ze5KgAAACBfF26yu9bM0nG78lh82fFY2n0Sy7pw07tzY8teM3kcQCZrlsDWbrWuOG63LB2zZh6zLTtuT7smedy+LPi1WiyZx5YFxVaLdVPf8+TCLMEygJxcLpdcLleqW3d0dHTN91je6VvIVq1Op1Pd3d2pvZZPnjyZ2qrV7/fL4/GsuY61Wv7ez58/v+mvuR0QLAMACjYfS+iXzr2iZ8dyjzyxWy363aNvV/cDu0tYGXYy4/a8/uv/el5//syrSiRW/3DMZrXop37s7ep94pB+7Be+WIIKAQAAUCpZu3BzddrShQuUTUYXrjVLR+1au3Cty4PZXMfzX2OxWMr9SwMAW47H45HX65WkVNC7FunXrKXT2OPxpK4dGhqSYRiKRCIKhUI6e/bsmutYj42+d0nq6emR3+8vaAT4dkCwDAAoyGwsrp//u5f1jTeNnGuqbVb9/g++Qx9sa8m5BiiWeDyhweFL+i//M1Rw5/Fjj+7Tr7nfp4ceaNaV63c2uUIAAIDKtnwP20K6cOOJpePLunDTj8eWBbwJ08w8tnTv5V24yeMAMiW7XgvpwrUtO25PC3OTx1ftuM3VhZsWIG92Fy4AoHj6+/t18uRJGYahcDiscDi8ppD09OnTqcc+n6/g63p7e+V0OlMjqM+cOaORkZHU8VJIf++GYSgYDBbUcZ0UCoUUDAYrJlSWCJYBAAWYjsb0ieBLGrk+lXNNrd2q//7Ew3rsXmfpCsOO9a3v3dQn/d/UC5fGC1q/t7VO/+Fjh/UjH3iQb6ADAIBtZ01duBlha65u21W6cLMEttmOE+ECmQrqwl31+Aa6cHMc5/8DAcD2EIlEsj7eCk6dOqW+vj5JktfrTY2lXk0oFFIoFJK0GBT39vau6XXdbndqr2Wfz6dIJFLwaxeikH2TBwcH1dPTI2nxvY+MjBR8/+PHj6u/v3+95W1JBMsAgLwm56P618Mv6Tu3budcs8th06e6H1bn3qYSVoadaNyY1X/+XEhDwUsFrXfYrfrYR96pTxx7l+prHZtcHQAAqATpXbix1F606R23ZtoetXe7cFN73C7rwk111xYcCq8c10wXLrBSri7c5cftqSD27nF7Rpi7eLyQLtzVgmK6cAEAG5G+F/HyfYlzuXDhQsbzUChUUHdsMuwt9LV6e3vV39+vgYEBDQ0NaWhoaNWQ2DAMPfHEE5IWR2CvJxD2eDypYDnZKb2WjuFs0n/NCnnv3d3d8vl88nq9CoVC8nq9BXVee71eGYaxpi7t7YBgGQCQU2RuQe6/fVHfjUznXNNYZVfgw4/okd0NJawMO00sntCf/s339Ht/8rxuT0cLuuaD771Pv+p+nw7s5wsPAABsRWYqQF1rVy1duEApbYUuXLvFuixEpgsXALD9hcNhDQ0NSZKGh4czwt5QKKSenp5Up2xvb28qMA4EAjIMQ+Pj4woEAhn3PH78uIaHh9XR0SGn0ym3273itc6fP5+xX3A4HFZXV5e6u7vV2tqq7u7urPsg+3w+tba2yuv1qq+vT4ODgznD5XA4rJ6eHhmGoc7OznXviZwMkpP1ejyeNd8j369zMBhUX1+fjhw5Ikk5u4uTx71eb0YHdS4ejyc1urvSWEyTr70CxfbSSy/pkUceST1/8cUX9fDDD5exImDtbs4s6ON/+4JGjZmca1pqHDr14Uf1tpb6ElaGnea5F9/SJ/3P6XtvTBS0vm3PLv3Hjx9R92PteT9sunL9jh4//vms55499ZTa9u5aV70AsF1MLszqs69+Neu5jz30ATVV1Za4Imym5V24Kztuc3fhxk1TiSxduGsfzZx5nC5cYKVkyJrRcWu1rji+3i7cFeFvtuCXLlxgR+HfhHyWK0lXb8/pyc+fz3rumaeOaH9DTYkr2hkCgYA8Hs+q+wUbhiG/358KiTs6OhQOh/NeZxiGnE6nJiYWP08LBoPq6ekp6LV8Pl/e8c3BYFAej0fhcFjd3d3yeDypIDocDmtwcDAVeK92r0IMDQ2lxnBPTEyseX/l5PWr/XpJi19+zScUCun48eOpznCPx6Pu7u7UXtDBYFAnT57U4cOH5ff7K2pv5SQ6lgEAK1ybntfHn3lBb0zN5lyzp65Kn/7wo3I560pYGXaS6+Mz8v3RBX3x3GsFra+ussnz1CNy/9NHVFPNP3EAAOVhZoSnZu7u2Q104d4dz7x6F25yLREukCl7F27ucJUuXAAAUGxutzsVFq/F6Ojomq/p7u5eNTRdy71GR0cVDAY1ODgor9erSCQiwzDkcrnkcrnk9/t17NixNYfA2SS7tXt7e9d1v97e3qK9987OTo2MjKTeu9/vl9frlbTYXd3Z2anBwcENj+veyvjUFQCQYez2rH72Sy/ozen5nGvura/WZ558VPc3Vv63VlF6C9G4/vivXtF/P/1tTc/GCrqm5/3t+g8fO6L2fYxkB4CdwjRNmTJXdOEmu27Tu3A30lW7+vHMc3ThAiulB7Op7tpVunCTx5d34a4IadfVhbu4fy4AAADWr7u7u2QB6nrC9M1Uyve+1RAsAwBSwsaMPv63L+jGzELONe0NNfrsk4/q3l2MwEHxfeX5q/rNwHMKX50qaP2D9zXqV46/T0e79m9yZQCwsxXWhZurg3bl+thSALu8CzcZzK5cu7ILN24myv3LAmw5FllydMquLXTdaBdu5ihnunABAACASkGwDACQJL06Ma2PP/OCInPRnGtcTbX69JOPak9ddQkrw05w9cYd/c5nzuuZb1wuaH1djV0/9/S79DP/+J2qdtg2uToAqAwL8ZimY/OLP6ILujV/J+faPxt9bqkbmC5cIJfVunAzOm7zdtvShQsAAABgeyBYBgDopVu35R5+UZPzuccOv625XqeefEQtNVUlrAyVbn4hrlN/8aL+x9ALml+IF3TNj33wQXk/elj37q7f5OoAYOszTVPz8ZjupALjeU3HFtIC5LvPo4nC/pyVpNl47uklQKnl7sJda2dt4Wvv7n2b2YVrSztOFy4AAACAnYZgGQB2uOdvTOlfD7+oO9HcHzY/urtBf9jzsJqqHSWsDJXMNE393fkr+q1PP6ext3J3zKU7dL9Tv+Z5TO9/dN8mVwcA5WeapmbiC5qOLg+Jl4LitMeMhEYxLe+etWbpwi2s25YuXAAAtoOmqlr94iM95S4DALBNECwDwA723DVDP3f2Jc3Gcn8g3bmnUZ/qfli7qvgrA8Xx+ptT+q1PP6dnL1wtaP2uOof+zT9/j/7Fj7xdDrt1k6sDgM0VNxOaSQuG7yyNpV7eYTwTW5Apxk9XsuJ14d7tqk0/Zl8KZJd34drSjtOFCwAAAABYC1ICANihvnZ1Qj//dy9rPp47VH7/vU79tx96p+rYwxZFMDMX1f8YfEGf/t8vKZrnywzpnnqiQ//+X3Zpd3PtJlcHABsTS8RzhsTpz2fj0XKXuiMVr9uWLlwAAAAAwM5FsAwAO9DfXR7XLz37iqKJ3J1QH2pr0e89/g5V0yGKDTJNU3/ztTf0O589r7duzRR0zcMdLfo1z2PqfPueTa4OAHIzTVMLifjKMdTLQ+PovOYTsXKXuyWkunBXdNTm67bN03Frtaw4ZrMuddwW0IWbPE4XLgAAAJDb/oYavfgzHyx3GQC2AYJlANhh/ua1m/rlL39X8TzTNXseaNXAh94uh41QGRtz8bKh3wh8U9/4zlsFrXc2VOuXfuq9OtZzSDb++wOwSUzT1Fw8uiwkzj6WOlah+xdXWe2qtTk0GZ3Nev5D+x5Sg6Nm9S5c68oQFwAAAAAAVCaCZQDYQb5w8bp+9euvKk+jsn7UdY9++wNvk93KB8NYv9szC/qDP/u2PvfXryiW71sMSywW6SeefEi/+JPvVXNjTQkqlNr27tKlL/50SV4LQGkkTFOzsYXFgDhLV3Hy8UxsXnGzMvcvrrE5VG+v1i5HtertVaq3V6veUb34c9pzh9WmyYVZffbVr2a9z8HGPWqqYhsCAAAAAABwF8EyAOwQf/7dN/Vb/zCad81Th/bpV7/voGyEylgn0zT1hWfDGvijEd2cyN4Ft9x73naPfv1fPaZHOlo3uToA21U8kcgyhnrl85nYgioxLrZIqssTEqc/t1mZ9gAAAAAAADYHwTIA7ACfe+mqBs6H86755++4T7/8PhcjLLFuL4cj+qT/mxp55UZB61ubauT9mS79+A92yMqXGYAdKZqI5wyJp2MLurP0eC4eLXepm8JqsSyFwkvBsGPl4132atXaq/j7GQAAAAAAlB3BMgBUOP+3L+sPnn8j75qPPdKmX+x6UBY+tMY6GLfn9Xt/8rz+7EuvKpFvzvoSm9Win/qxt+sXfuI9aqivKkGFAErJNE0tJGKLQXHG3sUrx1IvJGLlLndT2C3WtJC4WvWOKu1KPb4bHtfYHPzdCwAAAAAAtg2CZQCoUKZp6r89/4ZOfWcs77pPvOd+/et3388H21izeDyhweAl/ZfPhTRxe76ga973yF79uucxPfRA8yZXB6DYTNPUbDyaY+/i+YwgOWYmyl3upqi22rN2FS8fS11ltfH3KgAAAAAAqDgEywBQgUzT1MD5sP7ny2/mXfdvux7Uxx5tL1FVqCTf+t5NfdL/Tb1wabyg9Xtb6/QfPnZYP/IBOuOBrSZhJjQTW8gIhpcHxckQOWFW4g7GUq3NkdlhnBEa3+04tltt5S4VAAAAAACgbAiWAaDCJExTv/mNSxp89a286/7DYx365++4r0R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",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 9\n",
    "colors = plt.cm.YlGnBu_r(np.linspace(0.2,0.8,n))\n",
    "\n",
    "\n",
    "# stats\n",
    "R2_cond = []\n",
    "for i_cond in range(n_cond):\n",
    "    R2_cond.append([])\n",
    "    for i_sub in range(156):\n",
    "        R2_cond[i_cond].append(stt.pearsonr(EC_normed_rDCM[i_cond,i_sub,:,:].flatten(), EC_normed_MOU[i_cond,i_sub,:,:].flatten())[0])\n",
    "\n",
    "plt.figure()\n",
    "for i_cond in range(n_cond):\n",
    "    plt.violinplot(R2_cond[i_cond], positions=[i_cond])\n",
    "plt.axis(ymin=0.0, ymax=1.0)\n",
    "\n",
    "#print(EC_dcm.shape)\n",
    "\n",
    "n_rep = 100\n",
    "v_n = np.array([2,4,8,16,32,64,128])\n",
    "print(v_n)\n",
    "R2_cond_av = np.zeros([n_cond,n_rep, v_n.size])\n",
    "for i_rep in range(n_rep):\n",
    "    for i_n, n in enumerate(v_n):\n",
    "        #print(n)\n",
    "        ind_sub = np.random.choice(np.arange(156), size=n, replace=False)\n",
    "        for i_cond in range(n_cond):\n",
    "            av_EC_dcm = EC_normed_rDCM[i_cond,ind_sub,:,:].mean(0)\n",
    "            av_EC_mou = EC_normed_MOU[i_cond,ind_sub,:,:].mean(0)\n",
    "            R2_cond_av[i_cond,i_rep,i_n] = stt.pearsonr(av_EC_dcm.flatten(), av_EC_mou.flatten())[0]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4), dpi=300, facecolor=\"w\")\n",
    "\n",
    "for i_cond in range(n_cond):\n",
    "    y = R2_cond_av[i_cond,:,:].mean(0)\n",
    "    yerr = sem(R2_cond_av[i_cond,:,:].flatten())\n",
    "    ax.errorbar(v_n, y, yerr=yerr*1.96, label=i_cond,color=colors[i_cond*3])\n",
    "\n",
    "#ax.legend(['vfm','movie','rs'],loc='upper left', bbox_to_anchor=(1, 1))\n",
    "ax.set_ylabel('Pearson\\'s $\\mathit{R}$', fontsize=18)\n",
    "ax.set_xlabel('Subjects ($\\mathit{n}$)', fontsize=18)\n",
    "ax.legend(['vfm','movie','rest '], frameon=False,bbox_to_anchor=(0.98, 0.98),fontsize=12,  title=\"\", loc=2, prop={'size':12})\n",
    "\n",
    "fname = out_pth + 'MOU-rDCM_Alignment.png' \n",
    "plt.savefig(fname, transparent=None, dpi='figure', format='png',\n",
    "    facecolor='auto', edgecolor='auto',bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "#plt.axis(ymin=0.0, ymax=1.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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voQ68hw8fti1Xq9Uj7afRaESlUklEkAS9tL61Ga87wqzrw1faAvgdb9a3Yn1ju7X8eUebwW9/6/rX+7iqKgsAAAAASBZhVhdjY2MxNzcXT548idXV1dje3o7V1dX4i7/4i/jBD37Q7+Gdmn7O21QoFNrmOGs0GkcKtD788MPIZrNx+/btPb877YArCfNgJWEM9Maz9a+i2bEuPXK5622/fPV16LWxuR3/7jev2n6/uzLLfFkAAAAAQNIMZJvB82ZiYqLfQzgRi4uLcevWrVYAMzs7GysrKwfevl6vx/z8fCwtLUWpVNrz+/3utyTcn0kYw2H96Ec/iunp6UNt8+mnn8Yf/MEfnMyABtzz9fZAaih1Ia4OXep62xe75sv6d795Gdvb7THY39sdZl1RmQUAAAAAJIswi8RKp9Px8ccfx927d6PRaES9Xo/Z2dkol8sH2n56ejpmZmYin893DbO0HuytDz74ID744IN+D+Nc2G4248Xmm7Z1o0MjMXyh+yX9y11h1ue/am8x+M3xK3Hl0tfbaTMIAAAAACSNNoMD6qTayZ12AJTNZmN5eTkymUxERCwsLMTs7Ow7t2k0GpHL5SIiWsHX6urqnttNTk4eeBxJuD+TMAaSYXN7K17tCbMuxdCFi11v/+Ll120Gf/m3X7b9bneLwaGLqRi9LMwCAAAAAJLlXIRZf/M3fxP/6l/9q3j48GH80R/9Udy/fz9+9KMfvXe7f/7P/3n85Cc/iefPn5/CKI/upMKIbgHQTqh0mjKZTKysrMTc3FxEvA20xsfHY2FhIer1ekS8DXpqtVrMzs7G+Ph4ZDKZ+Pjjj9+53/1a+SXh/kzCGEiujeZWvNxcb1t3dWgkhruEWZtb2/F6fbO1/HlHmPXtthaD5ssCAAAAAJJnYNsM/uVf/mWUy+WoVCp7ftdsNiOVSsWf/umfvnMfv/nNb2Jubi5mZmaiWCzGP/kn/yRu3LhxUkM+stMMI27evHlqx+pUKpVabQYrlUrXCq1sNhtLS0uRz+fb1u+EXrt13mZHEu7PJIyB5Nrc3o5XHWHW6NClGL6w9/sJX77aiObfTZHVbDbj879tbzP4nd+63vr52qiqLAAAAAAgeQauMusv//Iv4+///b8fU1NTUalUotlstv47rB//+Mexuroa/8V/8V/Ej3/847h161b8m3/zb05g1MfXWcnTrQLnsLq1tet3S7pMJhOlUilWVlZibW0tlpeXY2lpKZaXl1vL3UKqzr8lk8m8829Jwv2ZhDGQTBtd2gxeGx6JC6nuYdaOp42v4qs3W22/391m0HxZAAAAAEASDVSYdf/+/ZiamoqVlZVWeJVKpVq/P0qolU6no1wux+rqavz+7/9+ZLPZ+PM///OejrsXOlvm9WJ+padPn+5Zd/v27WPvt1fS6XRks9nI5/ORzWb3DWW63RfZbPad+07C/ZmEMZBMrzbfxPp2eyg1Njza9bZfvvq6gquzxeDVK8ORvn6ptazNIAAAAACQRAPTZvAf/aN/FNVqtdVCcCe4SqfTcfv27chkMjE5ORnpdDqazWb80R/90aH2n06nY3FxMWZnZ6NQKES1Wo3f+73fO6G/5vCy2WzXVnrH0S08OYvzKz158mTPuqmpqXduk4T7MwljIJmevXm9Z93YyJWut32xqzJrT4vBb11rBf6plDaDAAAAAEAyDUSYdf/+/VhaWmp9KHvr1q1W6HTr1q09t3/27Nmhw6wd5XI5IiIKhUJ89tlniZlD686dO3vmB2s0GsdqIdfZ1u59rfmSqlar7Vl37969d26ThPszCWMgmZ5vtodZly8Ox6WLey/nzWYzXr7+Osz6ZUdl1rd3tRgcvTwcFy6kAgAAAAAgac58m8GPP/44FhcXIyJibGwsFhcX49NPP40//uM/7hpk9UK5XI7x8fH4J//kn5zI/o+i2zxRx63q6dz+rFbwLC0ttS0XCoX3BjhJuD+TMAaSZ3N7O77cWG9bd3VoJIYvXNxz21dfbcbW9tetVT//1d7KrB2qsgAAAACApDrzYdbs7GxEvG3J9tlnn8UPf/jDUznuj3/84yiXy/H8+fNTOd77dJsDqlt7vcPoDD6mp6ePtb9+qVarbcsPHz587zZJuD+TMAaSZ7O5Fa8228Os0aFLXcOsF7vmy3r5eiNWn79p+/13fut66+frwiwAAAAAIKHOdJj113/911Gv1yOXy8WTJ09ibGzs1I6908JwpyosCQqFQtvyysrKkffVaDT2zK/0vtZ8J6FSqUSlUuk619NBLCwstC1nMpmuIVE3Sbg/kzAGkmVzeytebbaHUqNDIzHUJcz68tX+LQaHLqbit26OtpavjY70eKQAAAAAAL1xpsOsjz76KFKpVN8CpWw2G48fP+7LsbvprDjqrEg6jM5tD9Kar5cajUaMj4/H9PR0TE9Px61bt7rOffU+O3Oc7TjMv5Uk3J9JGAPJsrG9FS8397YZHErtvZy/ePl1mPV5R5j1733zWly8+PU22gwCAAAAAEl1psOsarUahUIhvvvd7/bl+Hfu3Dn2HEa9lM1m26qOjhL+7OicZ2qnneNhNBqNI1dWffjhh23bNBqN+PDDDw+1j1qt1nYfzMzMHLgqKyIZ92cSxkCyvNnaitcdYdaN4cuRSqX23PbL11/f7pd/u/98WZeGL8al4b2VXQAAAAAASXCmw6xGoxFTU1N9O34mk0lUmBWxt5Kns83eQe3eLpvNRj6fP9T2tVotbt26deTKqm7362Hv693zQaXT6SiVSofaPiIZ92cSxkByfLn5Oraj2bbuxsjontu9Wd+K9Y3t1nJnZda3f+vrMEtVFgAAAACQZGc6zKrX65HJZPp6/KQpFAptlTxHCXDm5+fblo/SxvHu3bt7Kqvu3r174O0nJib2rLt///6Bty8Wi22Pz8cff3yklnpJuD+TMAaS49n667bli6lUXB+6tOd2X776uiprc2s7/t0XL9t+/51vXW/9fP2q+bIAAAAAgOQ602FWv+f7WVlZ6WuYtp/FxcXWfVOv1/cEGe/SaDSiWCy2lkul0qH/xnq93rWtYKPROHAAuLuqKuJti8C5ubkDbbuwsND2Ny8vLx+qvWCnft+fSRkD/ddsNuPF+ldt60aHLsXIxaE9t33x6uv5sv7dFy9ja7u9muvbH+yqzLqiMgsAAAAASK4zHWZlMpljzSF0XI8fP05kKJDJZOLjjz9uLReLxahUKu/drrN6qlQqHThA2q1bVdVBfrdbPp+PbDYb6XQ6lpaWolwuH2i7YrHYmgtqZ9vjBFkR/b8/kzIG+m+zuR0vO+bLGh0aiaELey/lX+4KszpbDH4jfTmuXP46ALt+VZgFAAAAACTXmQ+zPvroo74c+6c//Wnf5+x6l2w2G8vLy62wbXp6+p3VPLVaLXK5XCscPE7okU6no1Ao7FlfKBQOVU230wpvamoqpqeno1qtdr1do9GIhYWFGB8fb/2N+Xw+Pvvss57NC9XP+zNJY6C/Nra39oRZV4dGYujCxT23fbGrzeDnf/ui7Xff3tVicOhiKq5c2lvZBQAAAACQFKlms9l8/82SqVKpxP3796Narcbv/d7vHXi7Z8+excTERGxtbR352L/zO78Tn332WaysrMR3v/vdI+/nNBSLxVhYWIhGoxHpdDru3bsXuVwuIt62SqxWq63Ao1Ao9KwN3ezsbDx+/Dgi3rYJPMpcT41GIx48eNBWhZROp1vj62xpmMlkolgsxszMzPEG/w79uj+TNoZe+PnPfx7f//73W8s/+9nP4nvf+14fR5Rsz9Zfx5//ohZr669a676f/nvx+//e/yAu7qrO2trajv9b7f8XO1f3f/F/qsV/9/9ttH7/v/if3or/+f/0VkREjF0bidv/w2+dyvgBAAAAgLOp35/lnukwK+Jt27oLFy7EZ599FtevX3//BnH8MOv+/fuxuLgYuVwuPvnkkyPtox8qlUp89NFHUa/XWyHQTjCUz+fj/v37x27Jd1Lq9XpUKpX45JNP2safyWRa/83Ozp7q+JNwfyZhDMfR7wvgWfP0qy/jo/on8WZ7s7Xuf/LNTPxH35psu92zL9/Ek//21xHxdp6t//W/+L/Hq6++3uZH9/5H8T/+B9+MiLdzZ/0H3x0/hdEDAAAAAGdVvz/LPfO9pR48eBD/9X/9X0cul4ulpaX49//9f/9Ej/dHf/RHsbi4GKlU6kiVRv1UKBS6tv87CzKZTOJa5CXh/kzCGDg9rzfX24KsiIgbI5f33G73fFmrz75qC7IiIr6zq83gtVHzZQEAAAAAyXam58yKeDsP0K1bt2JlZSWy2Wz8+Z//+Ykc5/nz53Hnzp1YWFiIVCoVhUIhfv/3f/9EjgXQzbON13vWjY1c2bPuxcvd82V92fa70ctDMX7jUmtZmAUAAAAAJN2ZD7MiIhYXF6PZbEaj0YhCoRB37tzpWaj1i1/8Iv7oj/4oxsfHo1arRbPZjHQ6HY8ePerJ/gEO6tn6V23Lly4MxZWLI3tu9+XrryuzOsOs73zrWqRSqYiISKUirl0RZgEAAAAAyXbm2wxGRGSz2Xj8+HHcu3cvUqlU1Gq1Vuu1fD4f09PTcfv27chkMnHjxo337u9v/uZvolqtxtLSUlSr1Yh4O+9MRMT4+Hg8efLkQPsB6JWt7e14ufGmbd3o0KUYunCxbV2z2WxrM/jLv33R9vtv/9bXLQavXBqKixcH4jsNAAAAAMAAG4gwK+Lt3EG7A62d8KlarbYCqR3pdDoiIu7cuRMREY1GI1ZXV6PRaOzZb7PZbFUxpNPpqFarcevWrZP7QwC62GhuxcvN9jDr6tBIDHeEWesb27G13Wwtf/6rvZVZO65f3VvVBQAAAACQNAMTZkW8DbQ+/fTTmJ6ejr/+679uhVA7wdaOtbW1iIhYXl5+5/5SqVQrGMtms/Hxxx/H2NjYyQwe4B02t7fi1eZ627rRrmHWVuvnV19txNNn7a0Jv/OtryuztBgEAAAAAM6CgesvlclkYnl5OX784x/H2NhYq7Kq23879vt9s9mMsbGxKJVK8eTJE0EW0Dcb21vxaqs9zLo6NBJDqfbL+O4w65cd82UNXUzFb31jtLWsMgsAAAAAOAsGLszaMTc3F6urq/Fnf/ZncevWrWg2m23/RURbqNX5+50Qa3V1Nf74j/+4n38KQGxsbcWrjjaD10cutwXzERFvNrZbP3/eEWb99jevxtCuObKujarMAgAAAACSb6DaDHYzMzMTMzMz8dlnn0W1Wo2lpaWo1+vRaDSiXq9HxNu5sDKZTExMTMTU1FQUCgXzYgGJ8mLzTWx1tEwdGxndc7uNza8rsz7/1Yu23337g6/nyxoZvhCXhttbFAIAAAAAJNHAh1k7bt26FQ8ePIgHDx70eygAh/Z8/VXb8oVIxfWhy3tut76rMuuXv26vzPrOb309X9b1US0GAQAAAICzYWDbDAIMimazGc83vmpbNzo0EiMX91ZW7cyZtbm1Hf/ui5dtv/vOt76uzNJiEAAAAAA4K4RZAAm32dyOl5vrbetGh0Zi+EK3MOttZdavfvMqNrfa2xJ+W5gFAAAAAJxBZ6LN4C9+8Yuo1+sxMTERmUwmbty40e8hAZyaje2teLX5pm3d6NClGOoSZu3MmfX537bPl3Vz7HKMXv46wNJmEAAAAAA4KxIZZj1//jweP34c5XI5arXant+n0+mYnZ2NmZmZ+O53v3v6AwQ4RZvbW10rs4ZS+1dmff63nfNlfV2VdfFCKkYvJ/LyDwAAAACwR+LaDD569CjGx8djdnY2lpeXo9ls7vlvbW0tSqVSTE5Oxj/9p/+030MGOFFvK7Paw6xrwyMxdGHvJXxj822Y9ctftVdmfftb17/ednQ4UqnUCYwUAAAAAKD3EhNmPX/+PO7cuRN/+Id/2AqtIiJSqVTX/3ZuUyqV4h/8g38Q//bf/ts+/wUAJ+PN1mZ8tbXRtu7G8JU9t9vY3I7tv7s27qnM2j1f1hXzZQEAAAAAZ0ciwqxnz55FLpeLWq0WzWazLbTaz+5Q69NPP41cLifQAgbSs/XXe9alR0b3rNuZL2vt+Zt49dVm2+92h1nXr5ovCwAAAAA4OxIRZuVyuVhZWYmIaAuwurUY3F21tXP7VCoVq6urkcvl4vnz56c+foCT9GzjVdvyyIWLcWVobyD19XxZ7S0GRy8PxcTY5dbytVGVWQAAAADA2THU7wHcv38/6vV6K8TaCarS6XTk8/nIZDIxOTnZun2j0YiVlZWoVqtRr9cjoj3QunfvXvw3/81/c/p/CMAJ2Gpux5cbb9rWjQ5diuELF/fcdn3jbWXW579qbzH47Q+uta6xqZQ2gwAAAADA2dLXMOunP/1pLC4utgVZhUIhHj58GD/4wQ/eu/2zZ8/iv/qv/qt49OhRNBqNiIhYWlqKn/zkJ/Gf/+f/+UkOHeBUbG5vxcvN9bZ1o0MjMXRhb2HtTmXWL3/dEWb91tctBq9cGoqLFxNRlAsAAAAAcCB9/USzWCy2gqyxsbFYWlqKx48fHyjI2tmmVCpFvV6Pu3fvRsTbQGxubu7Exgxwmja2t+NVR5h1dWgkhlN7K7N25sz6/FftbQa/863rrZ+vj5ovCwAAAAA4W/oWZv30pz9ttQlMp9Px2WeftQKpw0qn07G0tBQPHjyIiLetCH/yk5/0bKwA/bK5vRWvNve2GRzq2mZwO15/tRm/aXzVtv473/q6Mst8WQAAAADAWdO3MOvDDz9s/by8vBxjY2PH3me5XI4f/OAH0Ww248c//vGx9wfQbxvbm3vaDF4fvhwX/q6qdbf1ja09LQYvXkjFb3/z6tfbCrMAAAAAgDOmL2HWs2fPolarRSqVirm5ufjud7/bs30vLi5GRES9Xo9f/OIXPdsvQD+83HwTW83ttnVjw1e63nZjczs+/9v2FoO//c2rMbRrjqxr2gwCAAAAAGdMX8KsarXa+vnhw4c93Xcmk4kf/vCHe44DcBY11ttbBqYi4sbI5a63fbOxFb/8VXtl1u4WgyPDF+LSyN72hAAAAAAASdaXMGtpaSkiIvL5fNy4caPn+//DP/zDaDabreMAnEXNZjOeb7xuW3dlaCRGLg51vf36xnZ8/rftYda3v3W99fO1K6qyAAAAAICzpy9h1pMnTyKVSsXU1NSJ7P/27dsR8bbVIMBZtdXcjpcb7fNljQ6NxHBqb3XV9nYz3qxvxn//xf6VWdevmi8LAAAAADh7+hJmNRqNiHhbmXUSxsbGIpPJtI4DcBZtbG/Fq803beuuDl2K4Qt7w6yNze341dNXsbnVbFv/7V1h1rVRYRYAAAAAcPb0JcxaXV2NiIh0On1ix8hkMq3jAJxFb8OsvZVZQxf2XrrXN7fi81+9aFs3ceNSXL3ydYB1fVSbQQAAAADg7OlrZdZ3v/vdEztGOp1WmQWcaZvN7XjZpTJrqEtl1vrGdvzy1x0tBn/r6/myLl5Ixejl7nNtAQAAAAAkWV/CrNMwMTHR7yEAHMubrY14vbXRtm5s+HLX265vbMXnv2oPs3a3GLx6ZThSqVTvBwkAAAAAcMIGNsw6yRaGAKfh+frrPevGRka73vbN+mZ8/rcdlVnf+roy67r5sgAAAACAM2pgwyyAs+7Z+ldty8Opi3F1+FLX2/7q6at4+bq9ius7uyqzrpkvCwAAAAA4o4RZAAm03WzGi432MGt0aCSGLnS/bP+7L161LQ9dvBA301+3JLymMgsAAAAAOKOEWQAJtLG9Fa8219vWjQ6NxHDqYtfbP33W3pLw+tWv58hKpYRZAAAAAMDZJcw6ovv378c//sf/uN/DAAbU5vZWvNx807ZudOhSDF3oHmatPmu/7Y2rX7cVvDIyFEMXXe4BAAAAgLNpqN8DOKuWlpbi2bNn/R4GMKA2mnsrs64OjcTwPm0GV5+3tyS8vivM2v0zAAAAAMBZ46v6R9RoNCKdTvd7GMCA2tzejldb7WHWteFLcSHV/bLdeNFemXV9V1tBLQYBAAAAgLNMmAWQQK823sTG9lbburGRK11vu7m1Hc+/bA++2iqzhFkAAAAAwBkmzDqCzz77rN9DAAZcY/31nnU39gmz1je24sWr9jBr95xZ10a1GQQAAAAAzq6+hll/9Vd/1c/DH9ns7GxERExMTPR5JMCger7RHmZduTgcly92r7Ba39iO5y87KrP+rhprZPhCXBq5eDKDBAAAAAA4BUP9PHg+n498Ph+ZTKbn+15aWoqIiH/0j/5RT/a3uroaERH1ej0ajUZP9gnQzeb2Vny52T4H1ujQpRjaZ76sjc3tePFyo23dTpvBa1dUZQEAAAAAZ1tfw6xmsxnVavVM7L/ZbPZkPwDvs7m9Ha822yutrg6NxPCF7hVWX73Z3LfN4DXzZQEAAAAAZ1xfw6xUKnXiIVGv959KpU5kvwA7Nppbe8Ks0XeEWavPv4rOS9JOZdb1q8IsAAAAAOBs63tl1k44BMBbm9tb8aqjzeDV4UtxcZ82g1+svt6zbqciS5tBAAAAAOCsG/jKrJMggANO0putzT2VWTeGr+x77fmi0R5mjV4eiqGLF+JCKhVXr/T1Mg8AAAAAcGx9r8wql8tx+/btSKfT/RzKgTQajfjkk0/iD//wDwVawIl5vv5VdMb8YyOX973908ZXbcvXd82X5VoFAAAAAJx1fQ2zqtVq/P7v/34/h3BoP/jBDyKTycR//B//x7G6utrv4QAD6PlGe6XVxdSFuDp0ad/brz7vCLN2WgyOmi8LAAAAADj7uk/Ackpu377dz8MfWT6f7/cQgAHVbDbj+UZ7ODU6NBIjF/b/7sHq8/b5tW78XWXW9VHzZQEAAAAAZ19fw6wbN2708/AAibOxvbVnvqyrQyMxdOFi19s3m8149mV7mLW7zSAAAAAAwFnX1zALgHabze14tdkeTo0OXYrhC90v1+ub2/HiZXv4JcwCAAAAAAaJMOuIxsbGotFo9HsYwIA5bGXW+sZWvHi50bbuxtXhGL00FEMXXeIBAAAAgLNv/0lYTtDa2lo/DttTv/jFL/o9BGAAbWxvxcuOMOva8KW4mNqnMmujS2XW6IiqLAAAAABgYPQlzBobG+vHYXtqEP4GIHleb67H+vZm27obw1f2vf3G5lY879JmcKfVIAAAAADAWacHFUCCPNt4tWfd2Mj+YdaLlxvx1fpW27rrV4fj2hWVWQAAAADAYBBmASTIs/Wv2pYvXxyOyxf3D6Z+03i9Z931UZVZAAAAAMDgEGYBJMTm9na83HjTtm50aCSGL1zcd5sv1trDrIsXUjF2bSQujey/DQAAAADAWSLMAkiIzeZWvNxsn/9qdGgkht4RZnVWZpkvCwAAAAAYNMIsgITY3N6KVx1h1tWhS++szFp91t6W8PrV4bg+KswCAAAAAAaHMAsgITa2t+LVZnubwatDIzGU2v9Svfa8/fbXR0fi2uj+c2wBAAAAAJw1wiyAhNjY2luZdX34cqRSqX23We0Is25cHVGZBQAAAAAMFGEWQEI83/wqtqPZtm5s5Mq+t9/a2o7nX+4Ns65eGTqR8QEAAAAA9IMwCyAhnq2/blu+kErF9aHL+95+fXM7XrzaaFs3fuPSOyu5AAAAAADOGmEWQAI0m814sf5V27rRiyMxfPHivtusb2zFi5ftbQknxvYPvwAAAAAAziJhFkACbDa34+Vme8vAq0OXYvjC/mHWxsZ2vHjVHmbdFGYBAAAAAANGmAWQABvbW/Fqsz2YGh0aiaF3hFlvNjbj+cv2NoM308IsAAAAAGCwCLMAEmBzeytebe0Ns4ZT+4dZq8/exPZ2s23dN9JXTmR8AAAAAAD9IswCSICN7a09bQavDV+Oixf2v0z/eu3VnnXfHBdmAQAAAACDRZgFkACvt9bjzdZm27qxkXe3DPxN46u25cuXLsa10eGejw0AAAAAoJ+EWQAJ8Hz9qz3rbgy/u8rqaeN1++1HR2JkeP+2hAAAAAAAZ5EwCyABnq23B1MjF4biytDIO7dZfd7elvD61ZEYGRJmAQAAAACDRZgF0Gdbze34smO+rKtDIzF84d3B1Nrz9mqu61dHYnjYZR0AAAAAGCw+9QTos83trXi1ud62bnTo0jvDrGazGY0X7dvcuDocI0Mu6wAAAADAYPGpJ0CfbWxvxasulVlDqf0v0Rub2/H8ZXuYNXbtUqRSqRMZIwAAAABAvwizAPpsc3s7Xu6pzHp3m8H1je140RFmjd+4dCLjAwAAAADoJ2EWQJ+tb2/uaTN4ffjyO6us1je34sXLjbZ1E2OXT2R8AAAAAAD9JMwC6LOXG29iq7ndtu7GyJV3brOxsR0vXrUHYDeFWQAAAADAABJmAfRZY/1V23IqUnFj+N1h1svXG/Hqq822dd8cf/c2AAAAAABnkTALoI+azWY83/iqbd3o0Ehcurj/fFkREV80Xu9ZJ8wCAAAAAAaRMAugjzab23vmyxodGomhC+8Js9baw6wLqZQ2gwAAAADAQBJmAfTRdnM7XnaEWVeHRmI49e4w6zcdlVnXrg7HpZGhno8PAAAAAKDfhFkAfdSMiFebb9rWjQ5dem9l1mqjvTXhjdGRGBl2SQcAAAAABo9PPgH6rLPN4NWhkRi68O7L8+rz9gDs+tXhGB5ySQcAAAAABo9PPgH6aGt7O15vbbStuzF85b3bNV60V2ZdHx2JkeF3V3MBAAAAAJxFwiyAPnqx8dWedelL7w6ztreb8ezL9mqusWsjMXTRJR0AAAAAGDw++QToo+cbr9uWhy9cjNGhS+/cZn1jK168bK/mSl9/9zYAAAAAAGeVMAugj553VGYdZL6s9Y3teP6qvTJrfOxyz8cGAAAAAJAEwiyAPnq+3h5mjQ5diuEL7577an1jK150tBm8OaYyCwAAAAAYTMIsgD7qbDM4OjQSw6l3h1lvNrbixavOMOvd82wBAAAAAJxVwiyAPnqxpzJrJIbeU5m19uJNbG4129Z9Y1yYBQAAAAAMJmEWQJ80m809lVk3hi7HhVTqndt9sfZqz7oPhFkAAAAAwIASZgH0yVdbG7G+vdW27sbI+0Opp2vt1VyXRi7GjWsjPR0bAAAAAEBSCLMA+uTZentVVioiboxcfu92v3nWHmZdHx2O4aF3tyYEAAAAADirhFkAffKso8XglYsjceni8Hu3W+0Ms66OxMiwyzkAAAAAMJh8+gnQJ52VWaNDIzGcen+F1drz9jDrxtWRGFGZBQAAAAAMKGEWQJ90hllXhy7F0IX3X5YbL9bblq+PDqvMAgAAAAAGlk8/Afqka2XWhXdXWG1sbsfzl+1h1ti1S5FKpXo+PgAAAACAJBBmAfTJnsqs4ZG4mHr3ZXl9YytedIRZ42OXej42AAAAAICkEGYB9MFWcztebLTPfXV9+Mp7K6zWN7bixav2MOvmjcs9Hx8AAAAAQFIIswD64MXGV9GMZtu69MiV9273ts3gRtu6m+n3bwcAAAAAcFYJswD64HlHi8Gh1IW4OjTy3u1evd6Ml687wqwxlVkAAAAAwOASZgH0Qed8WaNDl2L4wtB7t/ui8XrPum+Oq8wCAAAAAAaXMAugD0YuDsW/N5qO0b+rxro6NBLDFy6+d7sv1l61Laci4hvCLAAAAABggL2/DACAnvsPxn4r/oOx34qvtjbi375Yja3mVgwdIMx62viqbfna6HBcHnn/dgAAAAAAZ5UwC6DPhi5ciKG4EMMX3l8s+/RZe5h1/epIjAwLswAAAACAwaXNIEACXEyl4kLq/Zfk1edv2pbfhlku5QAAAADA4PIJKEACHKTFYEREoyPMujE6HMNDKrMAAAAAgMElzAJIgOEDhFnb281ofNkRZl0bieEhl3IAAAAAYHD5BBQgAQ4SZm1sbseLl+tt68auXTqpIQEAAAAAJIIwCyABhg4wX9b6xla8eNUeZk2MCbMAAAAAgMEmzAJIgINUZq1vbMWLlxtt6yZuXDmpIQEAAAAAJIIwCyABhg4QZr3Z2IrnHW0Gv5G+fFJDAgAAAABIBGEWQJ+l4mBtBp9/uR4bm9tt674xrjILAAAAABhswiyAPhu6cCFSqdR7b/frtdd71n0gzAIAAAAABpwwC6DPDjJfVkTEFx1h1vDQhbhxbeQkhgQAAAAAkBjCLIA+O8h8WRERTxtftS3fuDoSl4YPti0AAAAAwFklzALos+HUwQKp1eftlVnXrw7HiDALAAAAABhwwiyAPhu6cLBL8eqzN23L10dHhFkAAAAAwMATZgH02UHbDD77sj3MunF1JIaHXMYBAAAAgMHmU1CAPhs+QJi1sbkdz79cb1s3du1SXLiQOqlhAQAAAAAkgjALoI8upi7ExdT7L8Ubm1vx/NVG27r0jUsnNSwAAAAAgMQQZgH00dABgqyIiPWN7Xjxsr0ya0KYBQAAAACcA8IsgD5KpQ7WJnB9Y2tPmHVz7PJJDAkAAAAAIFGEWQBnwFfrW/FlR5vBm2lhFgAAAAAw+IRZAGfAb9ZeR7Nj3TfHR/syFgAAAACA0yTMAjgDvlh7vWfdN8ev9GEkAAAAAACnS5gFcAY8ffZV2/LVK8MxenmoT6MBAAAAADg9wiyAM+A3jfbKrBtXh2N4yCUcAAAAABh8PgmFd5ifn49UKhXVarXfQ+GcW+2ozLo+OhIjwxf7NBoAAAAAgNMjzDpHqtVqTE9PRy6Xi/Hx8UilUjE5ORlTU1MxPz8fjUaj30M8kEajEQsLCzE9PR2Tk5Otv2V8fDxyuVzMzs5GpVLpybFWVlYiImJ6ejqKxWJUq9Wo1+unfl/V6/UYHx+PYrEY9Xr9SPtoNBoxPz/fuo84W9aev2lbvn5VmAUAAAAAnA+pZrPZ7PcgOFnz8/NRLpejXq9HOp2OfD4fd+7ciXQ6HSsrK1GtVqNWq0VERD6fj3K5HJlMps+j3qvRaESxWIyFhYXWukwmE5lMJtLpdDQajXjy5EkraEqn0/Hw4cOYm5s78jFzuVzrvjlJc3NzUSqV9v19tVqNqamp1nImk4l8Ph+5XC5u377dug92q9frUavV4pNPPml7jCMilpeXI5vN9vzvOKyf//zn8f3vf7+1/LOf/Sy+973v9XFEydRsNuN/9c+W4v/xN/+ute73bn87Hv1v7/ZxVAAAAADAedHvz3KHTu1InLp6vR5TU1OtSp65ubl4+PDhntAj4m1YMjs7G9VqNSYnJ6NUKh0rBOq1naqyRqMR2Ww2SqVS5PP5rret1+tRKpViYWEhisVilMvlWFxcPFJ4c9QqqMO6efPmoW5fr9fbQr3DOOp9Qf+sb2zHi1cbbevS10f6NBoAAAAAgNOlzeCAqtVqkcvlWmHM4uJilEqlrkFWxNuKrJWVlVZAVCwWE9OKbmFhIaampmJiYiKWlpZieXl53yAr4m3VUrlcjuXl5chkMlGv1yOXyx163qtGo3Eq7QQzmcypBIfpdDqWlpaiUCic+LHorfXNrXj+cr1t3fiNy30aDQAAAADA6RJmDaBGoxF3795tBTHlcvnAAcbS0lKrxeDCwkLMz8+f1DAPZKdiLJPJvDfE6pTNZluBVkS0VakdxGlVZS0tLZ34MQqFQnz22WeHuv9IjvWN7XjREWZNjAmzAAAAAIDzQZvBAbTTji/ibaAzMzNzqO3L5XJrfqZisRiFQqFvc2hNT09HxNvAZ7+qsnfZqUaanJxs7W95eflA2z558qRtPxMTE60xTExMHHosu+1UiZVKpRO9b/P5fJRKJW0Fz7iNza148bK9zeA3hFkAAAAAwDkhzBow1Wq1rZ3ew4cPD72PfD4f2Ww2arVaRBwuAOqlYrEYjUbj2GHaThu/+fn5qNVqUa1WD1ShtLKyEhHR8/nDKpVKawyH3e/c3FxMTU3F0tJS1Gq1ePLkSSu43AncstlsTE1Nxb17944UAJI8z75cjzcbW23rbo4LswAAAACA80GYNWCKxWLr53Q6feT5ke7fv98Ks2q1WtRqtVOv7llYWGiN5bhmZ2dbLRPL5fKBwqydv7+XQVaj0YgHDx5ExNt5zA7r5s2bkc/ntQs8Z75Ye71n3TfHR/swEgAAAACA02fOrAGyEzrtOE7g0RmClcvlI+/rKGq1WlvF0XFlMpnWfg46F1a9Xu95aLTTAnJxcVHVFAf2m44wa+hiKsavX+rTaAAAAAAATpcwa4B0Bk537tw58r462/rtVEmdlt2B00HDp/fZ+ZsOE2b1shptYWEhqtVqFAqFI1fMcT49fdYeZl0fHYlLwxf7NBoAAAAAgNMlzBogjx8/bls+bhDTGWhVKpVj7e8wdgdOS0tLPdnnTqXXxMTEgY9/nECwc3+zs7ORTqfj0aNHPdkn58fTZ2/alq9fG4kRYRYAAAAAcE4IswZEvV5vhTU7OsOow+oMw3oVKh3E7hZ8lUqlrX3iUe0EVAe5X3aO16vKrOnp6YgI7QU5krVnX7Ut3xgdiZFhl28AAAAA4HzwaeiAqFare9YdN8zqrGDqdoyT0nnsnbmmjmr32HeCpXcpFAqxsrJy7PswIqJYLEatVou5ubmez8HF+bD2oqMy6+qwyiwAAAAA4NwQZg2Ik6ia6qwg6tXcVQfRGfrU6/XI5XJHHkOxWGz9fO/evQNt04sgq1arxfz8fGQymSiVSsfeH+fP5tZ2PP9yvW3d2LWRuHAh1acRAQAAAACcLmHWgOgMeXrRyu7mzZt71vWi3d9BpNPproHW5ORkzM/PH2pfCwsLrXGXSqVTbfO3u70gHMX6xla8eNUeZqWvX+7TaAAAAAAATp8wa0B0hlmdbfqOolvoc5rVWburqTrX53K5AwVrtVqttZ98Ph9zc3M9HeO7TE9PR71ej1Kp1LO5tzh/Nja348XLjbZ1Ezcu9Wk0AAAAAACnT5g1II4zn9RhnHarwUKh0PV3tVotcrncvoFXxNux3r17NxqNRuTz+RNpxbifarUalUolstnsiQZojUYj5ufnY2pqKiYnJyOVSsX4+HjkcrmYnp4+1XnOOBnrG9vxvKMyayKtMgsAAAAAOD+EWQOgW5DVi1Z63aq7nj59euz9Hsbi4uI7566an5+PycnJPVValUolcrlcNBqNmJubO9UgK+Lr9oKPHj06kf3X6/WYnp6O8fHxKBaLkU6nY3Z2NhYXF6NUKsXt27ejUqnE1NRUjI+Px8LCwomMg5P3Zn0zvuyozPpG+kqfRgMAAAAAcPqEWQNgdXX11I51mpVZO5aXl9/Zpq9er0cul4vZ2dlWyLN7rqpSqXRaQ42It20QG41GFAqFE2kvWKlUYnJyMqrVaiwuLkaz2YzFxcWYm5uLQqEQMzMzUS6XY2VlJbLZbDQajZidnY2pqalTq+Cjd54++yq2m822dd8cF2YBAAAAAOeHMGsADHpAkU6nY3l5+b3t+hYWFmJycjIqlUrMzc3F2travm0KT8pO27+IOJEQrVwux/T0dMzMzLz378tkMm1BYLVabbVd5Oz4YvX1nnXfVJkFAAAAAJwjQ/0eAMd3mpVZ/QxCSqVS3L9/P6anp99bIdavce7M4ZXNZt/ZHvGo6vV6q/LqoBYXF2NycjIi3s41dvfu3VheXu752H7961/HF198cahtPv30056PY9B80WgPs0YvD8XVK8N9Gg0AAAAAwOkTZrGvXsy71WvZbDaWl5cjl8u9M9BaWFiIx48fx6NHj06tOqter7fmppqdnT2RY+Tz+UMFWRFvK7RKpVIraKvValEsFnteOfanf/qn8Sd/8ic93Sdv2wzudn10JIaHFdUCAAAAAOeHT0Q5UxYWFuLWrVtRr9cjn8+/s/qp0Wi0zZ910naHQ/fu3evpvndCrKWlpSNtPzMz07Y8Pz/fl/nPOLzVzjDr6nCMDF3s02gAAAAAAE6fMIt9JWlupXq9HrlcrlXxtLi4GEtLS7GysvLeubQqlUrkcrkT/XsajUarKiubzZ5IVVtnIHUY6XS6NXfWjp1KLZJt7fmbtuXrV0diRGUWAAAAAHCOaDM4ACYmJvo9hBNVqVRa1VWFQiEWFxfbfn+QubRqtVrcunUrPv744z2hTi98+OGHrZ/z+XzP998Lt2/fjlqt1lquVCrRaDR6Frz96Ec/OnQV3Keffhp/8Ad/0JPjD6JmsxnPvmwPs25cHYnhIWEWAAAAAHB+CLNItPn5+VYFUbcga0c2m42VlZW223dqNBqRy+ViZWXlne0Jj2KnKisi4s6dOz3dd6/kcrk96x4/fnysiq/dPvjgg/jggw96si/eWt/cjucv19vWpa+PRCqV6tOIAAAAAABOn6/3D6iTaql3Eu3z9rOwsNAKpmZmZvYNsnabm5uLlZWVd1ZfTU1N9WyMERHVarXt/j6Jyq9e6FbBd9Q5uDgdGxvb8eLlRtu68RuX+zQaAAAAAID+EGYNgJMKmFZXV/es63VF035qtVprfqxsNhvlcvnA22YymVheXt53m3q9HvPz8z0ZZ0TsOc5p3UeH1e3fSbVaPf2BcGDrm1vxoqMya+LGpT6NBgAAAACgP4RZA+A0w5ObN2+eynF2z7306NGjI+1jZmZm3yqt3XNcHUej0YhKpdJaPs3KtcPqVpl1UhV89Mb6xlY8f9UeZt1MX+nTaAAAAAAA+kOYNSA6Q5RuVVWH1S3oOI2wplKpRL1ej4iIfD5/rLZ9O1Va+Xy+bX2j0ehJVVLnPm7fvn3sfZ62nfua5Pny1UZ89Warbd030toMAgAAAADnizBrQHRW3fSi4ubp06d71p1GWLO7bd/OnFnHtbS0tKeCrRfzRXXuo9dVcvPz8zE+Ph6pVCqmp6eP9bjuF0SqzkquL9Ze71n3wfhoH0YCAAAAANA/wqwBcZzqpf10CzlOo6Xh7mqnzoqq41hcXGxb7kVF0uPHj9uWJycnj73PHZVKJYrFYutxqFQqbe0XD2u/0Cqpc3wR8ZtGe5h14UIqJsbMmQUAAAAAnC/CrAFx586dPeuOW3HT2aowk8mceJvB3QFTr0OWbDbbFo4d9/6p1+t79tHL++ejjz7as64XrRE7JXmer/PuN42v2pZvXB2OS8NDfRoNAAAAAEB/CLMGRLcKpuNWHnVufxoVPLuPeRIhy9TUVM/2fxLB0m69bv/X7d+DICvZnnZUZl0fHYnhYZdtAAAAAOB88anogOjWZvDJkyfH2mdn+HGcFncHtXvur5OYy2l3IHfccG55eXnPus65y46j2/hmZmaOvL/OSruIiHv37h15f5y8tedv2pavXx2JkeGLfRoNAAAAAEB/CLMGSKFQaFteWVk58r4ajcaeMOk0go/dAU4v5rTqtDvQ6daa8TBOutJpZ/6tbDYb2Ww25ubmolwuH3l/3cK33ZVqJM+eMGt0OEaGXLYBAAAAgPPFp6ID5OHDh23Lx2mD17ltoVA4lZZ06XS6LdDqdSu/3QFfZ/h3WMetfHufndaRs7Ozsby8HKVS6Vj767wv0+n0se8DTs7W1nY8f7netu7GtUtx8aLLNgAAAABwvvhUdIDsVPDsqNVqR97X0tJS2/Ls7Oyh99FoNKJSqUSlUjlUy8DdxzpOJVI3CwsLEXH8ICuiexvEXrYZzGazkclkenIf1Ov1PZVkxw3HOFnrm3vDrIkbl/o0GgAAAACA/hFmDZjO6qyd8Oawdm+XzWZbVUIHVavV4tatWzE9PR3T09Nx69atA4dru+eFqlQqxwrldltYWGgFUMcNck5iPq9uSqVS1Gq1qFQqx9pPsVhsW85kMseaf4uTt7GxHS9etYdZ48IsAAAAAOAcEmYNmEKh0FaddZTQZn5+vm15cXHx0Pu4e/duW+DTaDTi7t27B9o2nU63VSNNT08fOzyq1+utiq9SqdTWyvAods+9tVuvWzHuPJ7T09NHDvW6hWGdlXckz/rGVrzorMwau9yn0QAAAAAA9I8wawAtLi62QpV6vb4nnHqXRqPRVsVzlOCnXq93DZ8ajcaeVnf7mZmZaVUO1ev1yOVyRw60arVa5HK51n7n5uaOtJ/dTqsyK+LrMDGXyx060OoWIi4uLh47zOPkvdnYjOcvN9rWfSN9pU+jAQAAAADoH2HWAMpkMvHxxx+3lovF4oHa1HUGH6VS6UjBz7vmjTrMnFLlcrkt0Lp169ah2+3Nz8+3grCZmZmezcG1X2XWSchkMq1Kqlwud+BwcqfV4+7gbWlpqSfzhXHyVp+9ie3tZtu6b44LswAAAACA80eYNaCy2WwsLy+3KnCmp6ffGYLsVC/tVP4cNciKeNtqr1tgUigUDt2Gr1wutyrNGo1GTE9Px+TkZCwsLOxb5VWr1aJYLMb4+HirymxxcbFnQVbE/qHcYcK6w8jn87G8vBzpdLr1t83Pz3etEKvVajE9Pd1WzZbJZGJlZeXQc5/RP180Xu9ZpzILAAAAADiPUs1ms/n+m3GWFYvFWFhYiEajEel0Ou7du9dqu7eyshLVarUVYhUKhZ7MKRURMTs7G48fP46It+39jjJ/145GoxELCwvx4Ycf7glwMplMK+zqDLjm5ubi4cOHPZ/LKiJifHy8bSw7gdFJKxaLbcFkOp1uhWidf386nY5SqdSqcEuan//85/H973+/tfyzn/0svve97/VxRMnx0V/8d/G/+T/8P1vLl0cuRrX8v4zfunm1j6MCAAAAAM6jfn+WK8w6RyqVSnz00UdRr9db81ql0+nIZDKRz+fj/v37kc1m+z3M96pWq7G0tBTVarUtwNr5W27fvh3T09MnXoVUq9XiwYMHUa/X4/bt21Eul09tLqpGoxGPHz+OxcXFqNfrsbq6Go1GoxXsnZXHs98XwCT7l4v/7/gX/+e/bi1/MH4l/q//+/8kbo5d7uOoAAAAAIDzqN+f5QqzgL7p9wUwyf53/+r/Ff/H/8v/p7U8+e2xqPzz/1lcHx3p46gAAAAAgPOo35/lmjMLIIFWn3/Vtnz96nCMDF/s02gAAAAAAPpHmAWQQI0X623L16+OxMiQSzYAAAAAcP74ZBQgYdY3tuLFy/Ywa+zapUilUn0aEQAAAABA/wizABJmfWM7nneEWRM3LvVpNAAAAAAA/SXMAkiYjc2tePFqo23dxI3LfRoNAAAAAEB/CbMAEmZ9Y3tPm8GbaWEWAAAAAHA+CbMAEubl64149dVm27pvpq/0aTQAAAAAAP0lzAJImN80Xu9Z980JYRYAAAAAcD4JswAS5ou19jArlTJnFgAAAABwfgmzABLmN42v2pavj47E5UtDfRoNAAAAAEB/CbMAEmb1WUeYdXU4hodcrgEAAACA88mnowAJs/p8b2XWyLDLNQAAAABwPvl0FCBh1p6/aVu+cXUkRoYv9mk0AAAAAAD9JcwCSJDt7WY8+7IjzLo2EkMXXa4BAAAAgPPJp6MACbK+sRUvXm60rUtfv9Sn0QAAAAAA9J8wCyBBNja34/mr9bZ1Ezcu92k0AAAAAAD9J8wCSJC3lVkdYdaYyiwAAAAA4PwSZgEkyFfre9sM3hy70qfRAAAAAAD0nzALIEEaL97E5tZ227pvjmszCAAAAACcX8IsgAT5TeP1nnXfGFeZBQAAAACcX8IsgAT5YrU9zLo0fDHGrpkzCwAAAAA4v4RZAAnym2dftS1fvzocI8MX+zQaAAAAAID+E2YBJMjanjBrJEaGXaoBAAAAgPPLJ6QACbL6/E3b8vXRkRgZUpkFAAAAAJxfwiyABGl82RFmXR2O4SGXagAAAADg/PIJKUBCbGxux/OX623rxq5digsXUn0aEQAAAABA/wmzABJifWMrXnSEWRM3LvVpNAAAAAAAySDMAkiIjc3tLmHW5T6NBgAAAAAgGYRZAAmxvrEVz19utK27OSbMAgAAAADON2EWQEK8+mozXr5uD7O+MX6lT6MBAAAAAEgGYRZAQvxm7fWedd8cV5kFAAAAAJxvwiyAhPhNoz3MSkXExJjKLAAAAADgfBNmASTEbxpftS1fHR2O0ctDfRoNAAAAAEAyCLMAEqKzMuvG1ZEYGb7Yp9EAAAAAACSDMAsgIVafv2lbvj46HMNDLtMAAAAAwPnmU1KAhGh0hlkqswAAAAAAhFkASbC93YxnX7aHWTeujqjMAgAAAADOPZ+SAiTAxuZ2PH+13rYuff1Sn0YDAAAAAJAcwiyABFjf2IoXLzfa1o3fEGYBAAAAAAizABJgfXM7Xrxsr8yauHG5T6MBAAAAAEgOYRZAArxZ34znHWHWN8av9Gk0AAAAAADJIcwCSIDnX67HxuZ227pvpIVZAAAAAADCLIAE+KLxes+6Dya0GQQAAAAAEGYBJMAXa1+1LQ8PXYgbVy/1aTQAAAAAAMkhzAJIgKcdlVnXR4fj8sjFPo0GAAAAACA5hFkACbD6rL0y6/rVkRgZFmYBAAAAAAizABJg7fmbtuXrV0dieMglGgAAAADAJ6UACbD2or0y64bKLAAAAACAiBBmAfTd5tZ2PP9yvW3d2LWRuHAh1acRAQAAAAAkhzALoM/WN7bi+auNtnXj1y/1aTQAAAAAAMkizALos/WN7Xjxsr0ya2Lscp9GAwAAAACQLMIsgD7b2NyOF6+EWQAAAAAA3QizAPrs9ZvN+LKjzeA3hFkAAAAAABEhzALou6eN19Fstq/75sRofwYDAAAAAJAwwiyAPvti7fWedSqzAAAAAADeEmYB9NlvGl+1LV+9MhSjV4b7NBoAAAAAgGQRZgH02dNGe2XW9dGRGBl2eQYAAAAAiBBmAfTd6vM3bcvXr47EyPDFPo0GAAAAACBZhFkAfbb2vL3N4PWrwzE85PIMAAAAABAhzALoq2azGY0v19vW3VCZBQAAAADQIswC6KP1ze148bI9zEpfv9Sn0QAAAAAAJI8wC6CP1je24nlHmDV+43KfRgMAAAAAkDzCLIA+am7Hnsqsm8IsAAAAAIAWYRZAn714tdG2PJEWZgEAAAAA7BBmAfTR6zeb8WZ9q23dN4RZAAAAAAAtwiyAPlp7/mbPug/Gr/RhJAAAAAAAySTMAuijtedftS0PXUzF+I1LfRoNAAAAAEDyCLMA+ujps/Yw6/roSFwaHurTaAAAAAAAkkeYBdBHqx2VWdevjsTI8MU+jQYAAAAAIHmEWQB91Dln1vWrwzEy7NIMAAAAALDDJ6YAfbTapc3g8JBLMwAAAADADp+YAvRRZ5vBsWsjcfGiSzMAAAAAwA6fmAL0UWebwfSNS30aCQAAAABAMgmzAPqoszJr4sblPo0EAAAAACCZhFkAfdQ5Z9bNtDALAAAAAGA3YRZAn2xvN/e0Gbw5JswCAAAAANhNmAXQJ8++fBNb2822dd8cH+3TaAAAAAAAkkmYBdAnTztaDEZEfFObQQAAAACANsIsgD7pDLOuXBqKq6PDfRoNAAAAAEAyCbMA+uRpoz3Mun51OEaGL/ZpNAAAAAAAySTMAuiTzsqs66MjMTIkzAIAAAAA2E2YBdAnTxuv25ZvXBuJ4WGXZQAAAACA3XxqCtAnnZVZN66OxMiQyzIAAAAAwG4+NQXok845s8aujUQqlerTaAAAAAAAkkmYBdAnnZVZ4zcu92kkAAAAAADJJcwC6JPOMGti7FKfRgIAAAAAkFzCLIA+efrsddvyzbErfRoJAAAAAEByCbMA+uDN+la8eLnRtu4baW0GAQAAAAA6CbMA+qCzxWBExAcTo30YCQAAAABAsgmzAPpgtSPMunAhFRM3zJkFAAAAANBJmAXQB53zZV0fHY5LI0N9Gg0AAAAAQHIJswD64GmjvTLr+tWRGBl2SQYAAAAA6KQMAKAPfvubV+M/+d1M/Hr1Vfzy11/Gb98cjZHhi/0eFgAAAABA4gizAPrgP/oPfzv+o//wt+P5l+vxyX/7txERMTykMgsAAAAAoJNPTgES4OKFVAxddEkGAAAAAOjkk1OABFCVBQAAAADQnU9PARLAfFkAAAAAAN0JswASYGTY5RgAAAAAoBufngIkgMosAAAAAIDuhFkACWDOLAAAAACA7nx6CpAAKrMAAAAAALoTZgEkgDmzAAAAAAC68+kpQAIMD6nMAgAAAADoRpgFkAAqswAAAAAAuvPpKUACmDMLAAAAAKA7YRZAAowMuRwDAAAAAHTj01OAPhseuhCpVKrfwwAAAAAASCRhFkCfDavKAgAAAADYl09QAfrskvmyAAAAAAD2JcwC6LMRYRYAAAAAwL6EWQB9ps0gAAAAAMD+fIIK0Gcjwy7FAAAAAAD78QkqQJ9pMwgAAAAAsD9hFkCfCbMAAAAAAPYnzALoM3NmAQAAAADszyeoAH1mziwAAAAAgP35BBWgz4aHtBkEAAAAANiPMAugjy5cSGkzCAAAAADwDj5BBeijYS0GAQAAAADeyaeoAH00oioLAAAAAOCdfIoK0EepVKrfQwAAAAAASDRhFgAAAAAAAIklzAIAAAAAACCxhFkAAAAAAAAkljALAAAAAACAxBJmAQAAAAAAkFjCLAAAAAAAABJLmAUAAAAAAEBiCbPOkWq1GtPT05HL5WJ8fDxSqVRMTk7G1NRUzM/PR6PR6PcQD6TRaMTCwkJMT0/H5ORk628ZHx+PXC4Xs7OzUalUena8er0e4+PjUSwWo16vH3nM8/PzrfH1yqA8pgAAAAAAsJ9Us9ls9nsQnKz5+fkol8tRr9cjnU5HPp+PO3fuRDqdjpWVlahWq1Gr1SIiIp/PR7lcjkwm0+dR79VoNKJYLMbCwkJrXSaTiUwmE+l0OhqNRjx58qQV4KTT6Xj48GHMzc0d67jVajWmpqbajpnP5yOXy8Xt27dbx9+tXq9HrVaLTz75pO3+jYhYXl6ObDZ7rDENymP685//PL7//e+3ln/2s5/F9773vT6OCAAAAACATv3+LHfo1I7EqavX6zE1NdWqJpqbm4uHDx/uCV4i3gY2s7OzUa1WY3JyMkql0rFDoF7aqUBqNBqRzWajVCpFPp/vett6vR6lUikWFhaiWCxGuVyOxcXFYwdIu/e/O1A7jOOOY5AeUwAAAAAAOAhtBgdUrVaLXC7XCj0WFxejVCp1DT0i3lbvrKystAKiYrHY03Z4x7GwsBBTU1MxMTERS0tLsby8vG+QFfG2cqpcLsfy8nJkMpmo1+uRy+WiWq2e4qjbpdPpWFpaikKhcOR9DNJjCgAAAAAAByXMGkCNRiPu3r3bardXLpcPHKIsLS212tEtLCzE/Pz8SQ3zQHaqizKZzHtDrE7ZbLYVaEVEW0XTaSoUCvHZZ58dauydBukxBQAAAACAwxBmDaCddnwRbwOdmZmZQ21fLpdbPxeLxb4EQDump6cj4m0gs18F0rvsVER17u805PP5WF5ejsXFxSONfbdBekwBAAAAAOAwhFkDplqttrXTe/jw4aH3kc/n2+Z1Os0AaLdisRiNRiMKhUKrsugoMplMa66oWq125HaDc3NzsbS0FHNzc5HP59sCqnQ6HZlMJgqFQpTL5VhbW4ulpaWezNM1SI8pAAAAAAAc1lC/B0BvFYvF1s/pdPrIczTdv38/arVaRLwNgGq1Wk+CmcNYWFhojeW4ZmdnW+31yuXykVr+3bx5M/L5/LHaBR7FID2mAAAAAABwWCqzBshOQLHjOKFLZ2Cyu03daajVaq22esdt0RfxtjprZz9nqcXeID2mAAAAAABwFMKsAdIZTty5c+fI++ps67dTJXVadgdOvQqfdv6msxRmDdJjCgAAAAAARyHMGiCPHz9uWz5uC7nO8KNSqRxrf4exO3BaWlrqyT53Kr0mJiZ6sr/TMEiPKQAAAAAAHIUwa0DU6/VWWLOjM7g4rM7gpFeh0kHsbi1YqVTaWu0d1U5Adtz75bQM2mMKAAAAAABHIcwaENVqdc+64wYfnRVM3Y5xUjqPPT09vSfYOYzdY5+enj7yfk7ToD2mAAAAAABwFMKsAXESFTa7q6MiTneuqXw+v+fYuVzuyGMoFoutn+/du3essZ2WQXtMAQAAAADgKIRZA6IzlOgMLY7i5s2be9b1ot3fQaTT6a6B1uTkZMzPzx9qXwsLC61xl0qlntw3p2HQHlMAAAAAADgKYdaA6Aw+OtvJHUW38OQ0K3l2V1N1rs/lcgcKYWq1Wms/+Xw+5ubmejrGkzSIjykAAAAAAByWMGtAHGc+qcM47VaDhUKh6+9qtVrkcrl9A6+It2O9e/duNBqNyOfzPW/b12g0Yn5+PqampmJycjJSqVSMj49HLpeL6enpY89HNYiPKQAAAAAAHJYwawB0Cz160ZKuWyXQ06dPj73fw1hcXIxMJrPv7+fn52NycnJPlValUolcLheNRiPm5uZ6GmTV6/WYnp6O8fHxKBaLkU6nY3Z2NhYXF6NUKsXt27ejUqnE1NRUjI+Px8LCwqGPMciPKQAAAAAAHIYwawCsrq6e2rH6UcWzvLwc2Wx239/X6/XI5XIxOzvbCpqmp6cjIloBU69UKpWYnJyMarUai4uL0Ww2Y3FxMebm5qJQKMTMzEyUy+VYWVmJbDYbjUYjZmdnY2pq6lCVVoP+mAIAAAAAwEEJswbAabWj65d0Oh3Ly8vvne9qYWEhJicno1KpxNzcXKytre3bpvAoyuVyTE9Px8zMzHv3nclk2kK4arXaanl4EIP+mAIAAAAAwEEN9XsAHN9pVvH0M2QplUpx//79mJ6efm810UmMs16vtyqvDmpxcTEmJycj4u08X3fv3o3l5eX3bncWH9Nf//rX8cUXXxxqm08//bQnxwYAAAAAYHAJs9hXL+Zo6rVsNhvLy8uRy+XeGWgtLCzE48eP49GjRz2rzsrn84cKsiLeVmiVSqUoFosR8TbQKhaLPW19eBgn+Zj+6Z/+afzJn/zJie0fAAAAAIDzSZtBzpSFhYW4detW1Ov1yOfzkclk9r1to9Fomz/rqHZCrKWlpSNtPzMz07Y8Pz9vnioAAAAAADggYRb7StK8TfV6PXK5XMzOzkbE2/Z9S0tLsbKy8t65tCqVSuRyuWP9PZ2B1GGk0+nW3Fk7diq1TluSHlMAAAAAADgIbQYHwMTERL+HcKIqlUqruqpQKMTi4mLb7w8yl1atVotbt27Fxx9/vCdYOg23b9+OWq3WWq5UKtFoNPZt+3cWH9Mf/ehHh66C+/TTT+MP/uAPTmZAAAAAAAAMBGEWiTY/P9+qYuoWZO3IZrOxsrLSdvtOjUYjcrlcrKysvLM94UnI5XJ71j1+/PhYFV9J88EHH8QHH3zQ72EAAAAAADBgtBkcUCfVTm6/SqKTsLCw0AqmZmZm9g2ydpubm4uVlZV3Vl9NTU31bIwH1a3S6rBzcA3CYwoAAAAAAIclzBoAJxVGrK6u7ll3WhVNtVqtNT9WNpuNcrl84G0zmUwsLy/vu029Xo/5+fmejPOguj1G1Wr1ULfvhX4+pgAAAAAAcBTCrAFwmmHEzZs3T+U4u+deevTo0ZH2MTMzs2+V1ocffnjksR1Ft8qsd1VaDeJjCgAAAAAARyHMGhCdlTzdKnAOq1vYchot6SqVStTr9YiIyOfz72wZ+D47VVr5fL5tfaPReGdl1GnZ+Tu7GaTHFAAAAAAAjkqYNSA6K396Mb/S06dP96y7ffv2sff7PrvbA+7MmXVcS0tLe6qd3jdn1fz8fIyPj0cqlYrp6elj3af7BUbv2ucgPaYAAAAAAHBUwqwBcZzqpf10C09Oo/3d7oqpzoqq41hcXGxbfldVVKVSiWKx2LoPKpVKW+vDw9oviHrX/TlIjykAAAAAABzVUL8HQG/cuXMnKpVK27pGo3GsFnKdbe0ymcyJt6TbHTD1OmTJZrORz+dbYdm7Kp0++uijPetOoi3hu+7PQXlM3+XNmzdty59++mmfRgIAAAAAwH46P7vt/Gz3pAmzBkS3CqZ6vX6s6p7OyqXTqODZfcyTCFmmpqZaodS79t+Lln67dasCe9/fNyiP6bt8/vnnbct/8Ad/0J+BAAAAAABwYJ9//vmJdBfbjzaDA6LbP5onT54ca5+dwcdx2uwd1O55onodKEW0hzfvCnK6/W5mZubIx+2siIqIuHfv3ju3GZTHFAAAAAAAjkOYNUAKhULb8srKypH31Wg09oRJ7wtfemF3iPSuOa2OaneodOfOnX1vNzk5GRFvA6VsNhtzc3NRLpePfNzl5eU966ampt673SA8pgAAAAAAcBzaDA6Qhw8fts2xdJw5njq3LRQKpzK3Ujqdjkwm0wqyqtVq13Z7R7U7DOoMinbbOebs7OyxKrJ2dN6f6XT6ncffMQiP6bv87u/+bvzrf/2vW8vf+c534tKlS/0b0Ht8+umnba0Q//W//tfxO7/zO/0bEHAinOtwfjjf4fxwvsP54FyH88P5fvrevHnTNm3M7/7u757q8YVZA2SniqhWq0VEtP5/FEtLS23Ls7Ozh95Ho9FoBSj5fP7Awcns7GwUi8WIiCiXyz0NsxYWFiLi3UFWxNv7MpPJRLlcPnaYVa/X91SZlUqlA22btMe019LpdPyn/+l/2u9hHNnv/M7vxPe+971+DwM4Yc51OD+c73B+ON/hfHCuw/nhfD8dpzlHVidtBgfMw4cP25Z3wpvD2r1dNps9dKBUq9Xi1q1bMT09HdPT03Hr1q0DBzG7w6NKpXKsAGe3hYWFVpu9g4RJpVIparVaW2XUUewEczsymcyhArKkPKYAAAAAANAPwqwBUygU2tLRg1YA7TY/P9+2vLi4eOh93L17t21+pkajEXfv3j3Qtul0um1+qunp6T1zPR1WvV5vVSKVSqW2ubn2s3NfTk9PHzlQ6xaGdVZIHXQcO/r1mAIAAAAAQD8IswbQ4uJiq6VfvV7fE2S8S6PRaKskOmjws1u9Xu8aPjUajT3t9vYzMzPTql6q1+uRy+WOHGjVarXI5XKt/c7NzR14253QJ5fLHTrQ6hbgLS4uHvr+3Nmun48pAAAAAAD0izBrAGUymfj4449by8Vi8UCt8jrDl1KpdKjgZ8fExMSRftdp93xV9Xo9bt26deiWf/Pz860gbGZmpq3i6yAymUyrkiqXyx04RNpps7g7gFtaWnrvXF3vGkc/H1MAAAAAAOgXYdaAymazsby83KrAmZ6efmcQs1O9tFN9dJzQI51Odw1tCoVCq7rooMrlcqsqqdFoxPT0dExOTsbCwsK+VV61Wi2KxWKMj4+3KpIWFxcPHWTtyOfzsby8HOl0urXf+fn5rpVitVotpqen2yrJMplMrKysHHuOqn4+pgAAAAAA0C+pZrPZ7PcgOFnFYjEWFhai0WhEOp2Oe/futdruraysRLVabQUehUKhZ23oZmdn4/HjxxHxtr3fUeZ62tFoNGJhYSE+/PDDPSFSJpNphV2dAdfc3Fw8fPjw0CHaforFYluAlE6nW9VmncdOp9NRKpVa1WW91K/H9Lz7+c9/Ht///vdbyz/72c/ie9/7Xh9HBJwE5zqcH853OD+c73A+ONfh/HC+nz/CrHOkUqnERx99FPV6vTWvVTqdjkwmE/l8Pu7fvx/ZbLbfw3yvarUaS0tLUa1W2wKsnb/l9u3bMT09fexKqP00Go14/PhxLC4uRr1ej9XV1Wg0Gq1Q7TTvy0F5TM8KT5JwPjjX4fxwvsP54XyH88G5DueH8/38Ger3ADg9hULhyHM2JUk+nz+xoOog0ul0zMzMnEjF1WENymMKAAAAAAD7MWcWAAAAAAAAiSXMAgAAAAAAILGEWQAAAAAAACSWObMADuib3/xm/LN/9s/aloHB41yH88P5DueH8x3OB+c6nB/O9/Mn1Ww2m/0eBAAAAAAAAHSjzSAAAAAAAACJJcwCAAAAAAAgsYRZAAAAAAAAJJYwCwAAAAAAgMQSZgEAAAAAAJBYwiwAAAAAAAASS5gFAAAAAABAYgmzAAAAAAAASCxhFgAAAAAAAIklzAIAAAAAACCxhFkAAAAAAAAkljALAAAAAACAxBJmAQAAAKdmfn4+UqlUVKvVfg8FAIAzQpgF0EW1Wo3p6enI5XIxPj4eqVQqJicnY2pqKubn56PRaPR7iHDuVavVmJ2dbTtPx8fHY3JyMqanp2NhYSHq9XrPjlev12N8fDyKxeKR99toNGJ+fj5yuVzMzs72bGwwKJJ8nnltAL2zsrISERHT09NRLBajWq1GvV4/9fMoydccOCumpqZifHz81I6XhOfjJIwB+uE0z3fv9+km1Ww2m/0eBEBSzM/PR7lcjnq9Hul0OvL5fNy5cyfS6XSsrKxEtVqNWq0WERH5fD7K5XJkMpk+jxrOl0qlEg8ePDjwm8R8Ph/FYjHy+fyxjlutVmNqaqq1nMlkIp/PRy6Xi9u3b0cmk4l0Ot22Tb1ej1qtFp988knb9SMiYnl5ObLZ7LHGBIMmieeZ1wbQe7lcru1cPSlzc3NRKpX2/X0SrzlwVtRqtXjw4EHrHFhbW9tzvvRSEp6PkzAG6IfTPN+93+edmgA0V1ZWmplMphkRzYhozs3NNdfW1rredmlpqe22pVLpdAcL51g+n2+dezMzM83l5eW236+srDRLpVLbObrzX6FQ2Pe8PoilpaU9+zzqf4uLi8e8J2AwJek889oATk46ne7Zuf6u/953LibpmgNnxcrKSrNQKOw5B47zOvt9x+v383ESxgD9cNrnu/f7vI8wCzj3lpeX295QH/RJp/NJFjg5a2trzWw224yIZj6fb66srLx3m7m5uT0vKjOZzIG27aYXL27T6XRzaWnpSMeH8yAp55nXBnBy1tbWTiXIymQy7x1LUq45kGQrKyvNxcXF5tzcXOv1eLf/TuLD7SQ8HydhDHBa+nW+e7/PQQmzgHNtbW2t7YVpuVw+1Pa+cQWnY+eF7WHfCJbL5a4vMI/y4vu4L26P+00xOA+ScJ55bQAna3l5+VTCrIN8mJWEaw4k0buqJwuFwqlUaiTh+TgJY4CTloTz3ft9DsqcWcC5NjU1FdVqNSIistlsLC8vH2r7zp66KysremJDjxWLxZifn498Ph9LS0tH3n63TCbTmnz+oDrP94PK5/NRKpX0y4YDSMJ55rUBnKyFhYXWpOjpdDomJiZac1BMTEwca987526pVIq5ubkD3b7f1xxIolQqFRFvz9FMJhO3b9+OqampyOfzkU6nY35+PorFYts2vZ5DJwnPx0kYA5y0fp/v3u9zGEP9HgBAv1Sr1dYL04iIhw8fHnof+Xw+stlsa5LH6enpQ7/ABfZXr9djfn4+MpnMkV7YRrz9QKtSqUS9Xt+z34N80NVpbm4upqamYmlpKWq1Wjx58qQ1Oe3Oh3LZbDampqbi3r17JzoRNgyqfp1nXhvAydv5cOmggdNBVSqVqFarkc/nD71fz+3Qrt/fe0/C83ESxgCnoZ/nu/f7HJYwCzi3dn+zJJ1OR6FQONJ+7t+/33pxWqvVolar+UYG9EipVIqI2PNNsMMqFoutb4Hv+PDDD2NmZubQLz5v3rwZ+Xw+8vn8scYE7K9f55nXBnDyds6NXgZZjUYjHjx4EBERi4uLh97eczskSxKej5MwBhh03u9zWBf6PQCAfth5EbnjOE9SnS9qy+XykfcFtFtYWIiIiJmZmWPtp9v2jUYjHj9+fKz9AoPDawM4HfV6vecfEE1PT0ej0YjFxUXfkIYzLgnPx0kYA5wH3u9zWMIs4FzqfAF5586dI++rs+f1zpMxcDw7bT3y+Xzbm8mj6vYNyKN8exsYTF4bwOmo1+s9rUpYWFiIarUahULhyJUTQHIk4fk4CWOAQef9PkchzALOpc5vZxz3DXXnC9RKpXKs/QHR6pldrVYjl8tFKpU61pu/bt+o3N0HHzjfvDaAk7czn8VxPhju3N/s7Gyk0+l49OhRT/YJ9FcSno+TMAYYdN7vcxTCLODcqdfrrckbd3S+uDyszhe3R524Evja7glcd8zOznZdfxCTk5PHHRIwoLw2gNOx883rXlVmTU9PR0RoLwgDIgnPx0kYA5wH3u9zFMIs4Nzp9s2M4744nZiYeO8xgMPpfBO546jnV+d5uuOoL5aBweG1AZyOQqEQKysrxz6/It5O9l6r1WJubs4k7TAgkvB8nIQxwHng/T5HIcwCzp2T+BZU5zdBPVnC8e33Dev9XqQCHJXXBnB6ehFk1Wq1mJ+fj0wmE6VSqQejApIgCc/HSRgDnAfe73MUwizg3Ol84diLliQ3b97cs64XE1jCeTY7O7tnXTqdPvK3r1dXV7uu92IZ8NoAzpbd7QWBwZGE5+MkjAHOA+/3OQphFnDudL447cUTW7cXuL5tBceTz+djcXGx9Q3ufD4fH3/88ZHfUK6srHRdb44NwGsDODump6ejXq9HqVTq2dxbQDIk4fk4CWOA88D7fY5CmAWcO/v15e01L07h+Hbm1mg2m7G0tHSsD626ffuxF3NsNBqNmJ+fj6mpqZicnIxUKhXj4+ORy+VienpaT3zogZM+z7w2gLOhWq1GpVKJbDYbc3NzJ3Ycz+3QH0l4Pk7CGOC88H6fwxJmAedKtxemvfiWRrdvaz19+vTY+wV6p9uLzKmpqSPvr16vx/T0dIyPj0exWIx0Oh2zs7OxuLgYpVIpbt++HZVKJaampmJ8fDwWFhaOM3w4l07jPPPaAM6OnfaCjx49OpH9e26H/knC83ESxgAcjff758NQvwcAcJr266F7EnzTCpJjvxeWR/1Wd6VSienp6Uin07G4uBiFQqHr7YrFYkxPT0etVmu98F1cXNTqAA7gtM4zrw3gbCgWi9FoNKJQKJxIe0HP7dBfSXg+TsIYgMPzfv/8UJkFnCun1TIASJZSqbRn3VFf2JbL5Zieno6ZmZlYW1vb94VtREQmk4nl5eXWh27VajXu3r3rWgTvcZrnmfMRkm+nxU9E9+f04/LcDv2XhHMoCWMADs/7/fNDmAWcK6f5TStPXpAMtVptzzcfM5nMkT8Mq9frMTMzE+Vy+cDbLC4uto3n7t27Rzo2nBeneZ55bQDJVywWIyIim822JorvJc/t0H9JeD5OwhiAw/F+/3wRZgH0gBJiSK4HDx7sWbf7xeZh5fP5Q72wjdj7YrpWq7U+mAP2GoTzzGsD6I16vd5qHzQ7O3sixxiEaw7QXRKej5MwBhhU3u+fL8IsAGBgLSwsRK1Wa1u3uLh4pLk2dl7ULi0tHWksMzMzbcvz8/N65UMH5xnQafeHQ/fu3evpvl1zAODs8n7//BFmAfSAFgGQPI1GY883uEul0jt7Xr9P5wvUw0in03teVPu2Fuw1KOeZ1wZwfI1Go1WVlc1mT6S6YVCuOUB3SXg+TsIYYNB4v38+CbOAc2ViYqLfQwBOyfT0dNtyqVQ68iSwvXL79u225Uql4s0t9NhhzzOvDSC5Pvzww9bP+Xy+jyPZn+d26I0kPB8nYQzAwXi/fz4JswCAgVMsFqNarbaWk/DCNiIil8vtWff48eM+jAQGl/MMBsdOVVZExJ07d/o4kv255gDA6fJ+//wSZgHn3kl9S8Ikr9AfCwsLMT8/31pOygvbiO7f9jxqT26gu16cZ14bQP9Vq9W2c/Eo81+cBs/tcHKS8HychDEAX/N+/3wb6vcAAE7TSb1gXF1d3bMuk8mcyLGA/dVqtba+2eVy+Vh9r3ut2zVo9zfKgOM77HnmtQEkU7lcbltO6vnjuR16IwnPx0kYA7A/7/cRZgHnymm+YLx58+apHQuIqNfrcffu3dby4uLisSZ/PQndvqmlhzb01mHPM68NIHkajUZUKpXWcpIrGDy3Q28k4fk4CWMAuvN+nwhtBoFzqPPNcLdvSR1WtyenJL/phkHTaDQil8u1zsWlpaXEvbB9l3q93u8hwMB713nmtQEkS+e3mDsnVD8LPLfD4SXh+TgJYwDaeb/PDmEWcO50flOiF9+SePr06Z51Z/FNN5xFu1/YptPpWF5ejnw+37P9z8/Px/j4eKRSqZienj7WNWO/N62+rcV51+/zzGsDSJbO+SV6XS3R72sO0F0Sno+TMAbga97vs5swCzh3TmLy6G5PTHpgw+m4e/du1Ov11gvbXp7jlUolisVi6xyvVCoxPT195P3t9yLW9YLzLAnnmdcGkCyPHz9uW56cnOzZvpNwzQG6S8LzcRLGAHzN+312E2YB586dO3f2rDvutyQ6Ww9kMhltA+AUTE1NRa1Wi0wmE5999lnPXyR+9NFHe9adxASurhecZ0k4z7w2gOSo1+t7zr9enjtJuOYA3SXh+TgJYwDe8n6fTsIs4NzpVo583P61ndv71gWcvOnp6ahWq5HJZGJ5efnQLxArlUqkUql3nv+9bgfQ7Vhe2HLeJeE889oAkuMkPkTaLQnXHKC7JDwfJ2EMgPf7dCfMAs6dbiXJT548OdY+O5+wjlOWDLzf7OxsVCqVyGazR3phGxHxySefRMS730x2+93MzMyhj7Wj2wTS9+7dO/L+YBAk4Tzz2gCSY3l5ec+6zjlsjiMJ1xyguyQ8HydhDHDeeb/PfoRZwLlUKBTalldWVo68r0ajsefbHJ6s4OTMz8/HwsJCZLPZ+Pjjj4/8TaeddgXvsjNHRzabjWw2G3Nzc1Eul490vIjuH9BNTU0deX8wCJJynnltAMlw0t9qTso1B+guCc/HSRgDnFfe7/MuwizgXHr48GHb8nHamXRuWygUlBHDCVlYWIhisXisb2jtePLkyXsnj91pMzI7OxvLy8tRKpWOfLyIvdeLdDq9580ynDdJOc+8NoBkOG4FxPsk5ZoDdJeE5+MkjAHOI+/3eR9hFnAu7XzrYketVjvyvpaWltqWZ2dnj7wvYH/VajVmZ2cjn893/cbTYexMLv++b2pls9nIZDLH+nbW7mN2ftv8uC+WYRAk5Tzz2gCSodv8Fb1sM5iUaw7QXRKej5MwBjhvvN/nIIRZwLnV+W2rhYWFI+1n93bZbLbrhLHA8dRqtZiamopCobDnDeFR7LyovHPnzoFuW6vVolKpHOuYxWKxbTmTyRyrHzcMkqScZ14bQH/1eiL2/STlmgODpNtcMUeVhOfjJIwBkqqX53uE9/scXKrZbDb7PQiAfsnlcq1vWWUymUP3wp6fn297wlpZWXnvNz+Aw6nX65HL5aLRaBzrzd/q6mo0Go22b0stLy+/t/VAxNfXioPevlOtVotcLte2zvUC2iXlPPPaAPqnXq+35q/Y7STOo6Rcc+AsKhaLMT8/37au1//+k/B8nIQxQL+d9Pnu/T6H0gQ4x1ZWVprpdLoZEc2IaJZKpQNvu7a21trusNsCB7O2ttZ2jvb6v4NaWVlpbbO8vHzsv2FxcfGwdwUMvKScZ14bQP8sLy93fb5eWVnp+bGScs2Bsyifz+85T3t9DiTh+TgJY4B+O8nz3ft9DkuYBZx7nW+aD/Kks7a21sxms16YwglaW1trZjKZE3thm8lkDjWepaWlQ5/zy8vLe17YLi0tHeXugHMhKeeZ1wbQH7uvAScdZnUez3M7vN/y8nJzZmam63maTqeb5XK5p+drEp6PkzAG6IeTPt+93+cohFkAzbdPQLufRN/1xHWY2wJHVygUTuyFbUQ08/n8oce0+8VqOp1ulkql5traWtfbdY4/k8mc2IdxMEiScp55bQCnb7/KrG7XgF4eMwnXHEiSubm5Zjqdbv131Nfbu/dx2GqHHUl4Pk7CGOCk9Ot8936fozBnFsAuxWIxFhYWotFoRDqdjnv37rX63q6srES1Wm31zC4UClEqlfTAhRMyNTUV1Wr1xPY/NzfXmhj2sDr7hqfT6ZiYmIiIaOvRvfO7Uqlk8lc4pKScZ14bwOkaHx+PRqPRWj7KPDVHkZRrDiTBzvmQTqePva+d8/moc9HsHlO/n4+TMAbotX6d797vcxTCLIAuKpVKfPTRR1Gv16Ner7derGYymcjn83H//v1jvRAHzr5GoxGPHz+OxcXFqNfrrQlnM5lMpNNp1wrogSSdZ14bwOmo1Wrx4MGDqNfrcfv27SiXy6f2YXCSrjlAd0l4Pk7CGICT5TVBMgmzAAAAAAAASKwL/R4AAAAAAAAA7EeYBQAAAAAAQGIJswAAAAAAAEgsYRYAAAAAAACJJcwCAAAAAAAgsYRZAAAAAAAAJJYwCwAAAAAAgMQSZgEAAAAAAJBYwiwAAAAAAAASS5gFAAAAAABAYgmzAAAAAAAASCxhFgAAAAAAAIklzAIAAAAAACCxhFkAAAAAAAAkljALAAAAAACAxBJmAQAAAAAAkFjCLAAAAAAAABJLmAUAAAAAAEBiCbMAAAAAAABILGEWAAAAAAAAiSXMAgAAAAAAILGEWQAAAAAAACSWMAsAAAAAAIDEEmYBAAAAAACQWMIsAAAAAAAAEkuYBQAAAAAAQGIJswAAAAAAAEgsYRYAAAAAAACJJcwCAAAAzq1isRjT09P9HsaJqFQqkcvlotFo9HsoAADHkmo2m81+DwIAAADgtE1NTcWTJ09ieXk5MplMv4dzInb+xo8//jiy2Wy/hwMAcCTCLAAAAODcyeVyUa/XBzrI2pHL5aJWq8Xy8rJACwA4k4RZAAAAwLkyNTUV1Wo1lpaWIp/P93s4J67RaMStW7ciIs5FeAcADB5hFgAAkCj1ej0qlUosLS1FvV6P1dXVaDQakclkIp1ORyaTifv370ehUHjvvhYWFqLRaMTc3NwpjLy36vV61Ov1aDQaUa/X4+nTp23Lq6urPpSGI5ieno5KpRLlcjlmZmb6PZxTU6vVIpfLRTqdjs8++yzS6XS/hwQAcGDCLAAAIBGq1WoUi8Wo1Wpt63fCmp1Qa7dCoRCPHj3a90PZ8fHxyOfzsbi4eBJDPlE7lSPvsrKyIsyCQ5ifn49isRiFQuFMXheOa2FhIWZnZyObzcby8nK/hwMAcGAX+j0AAADgfGs0GjE1NRVTU1NRq9UinU7H3NxcrKysRLPZjJWVlVhZWYm1tbVoNpuxvLwcpVIpMplMVCqVGB8fj4WFhT373anKOquy2Wxks1lhFfRIrVaLYrEY6XQ6Hj161O/h9MXMzEzk8/nWfQEAcFYIswAAgL6p1+uRy+VaFUhzc3OxtrbWCqu6yWazrbBrcXExMplMzM7OxtTUVNvtSqXSiY//JJVKpVheXm4FeWexHVq1Wo3x8fFIpVIxPj7+3kozOCmNRiPu3r0bERGLi4vnusXezt8/Pz/vnAQAzgxhFgAA0BeNRiNyuVzU6/WIePsB62EDqEKhECsrK1EoFKJarcb09HRERFQqldZ+B0E6nY5yuXymPoCv1+sxNTXVqo7bqcAbpMeFs+PBgwfRaDQin89HPp/v93D6Kp1Ox8OHDyMiWtdMAICkE2YBAAB9kcvlWkFHuVyOQqFw5H0tLi7G3NxcVCqVmJ2djQ8//LBHo0yWs/QhfKVSOdR6OCnVarX1765cLvd5NMkwNzcXmUwmGo1GzM7O9ns4AADvJcwCAABOXbFYbFXoFAqFnrTQK5VKkc/nY2FhIWq12rH3B0lRr9cjlUr5d31EO2HNzMyMOeh22amEdc0EAM4CYRYAAHCqGo1GzM/Pt5YfPXrUs30P+lw4ExMT/R7Cge0XUJ7Fub/6TWvGo1tYWGjdf2d9Hr1eKxQKkc1mI+LtFwwAAJJMmAUAAJyq3R+aFgqFnoZP6XS6p+EYR5dOp2N5ebn1YXk2m43l5eWBDhtPyk47Tg5v53rT62vNoNipWqtWq6qzAIBEE2YBAACn6vHjx62f79+/3/P9FwoFrcQSYifAajabbcEWh6My62gWFhZaQeDDhw/7O5iEmpmZaYV8qrMAgCQTZgEAAKemVqu1VZmcVLihnRiDZGVlpd9DOJN2rgOZTEaQ+g737t2LiLfVWYJTACCphFkAAMCp6fyg9KQqqAqFwonsF/pBwHB4tVqtdb/ttNKju90VWeVyuY8jAQDYnzALAAA4Naf5oXw+nz+1Y8FJevLkSb+HcObsDmVmZmb6OJLk2125trCw0OfRAAB0J8wCAABOzdOnT9uWTzLcmp6ePrF9w2npbM3JwezMzZfP51tzQrG/nfkLG41GVCqVPo8GAGAvYRYAANA31Wr1xPZ9+/btE9s3nJaPPvqo30M4cyqVSisAFGofzO7WrP7NAQBJJMwCAABOzc2bN9uWFxcXT+xYO22z4Cybn5/v9xDOnN0tBrUbPZhMJtOaw1BlFgCQRMIsAADg1HR+sFytVk+01eDOh7NwFgmyjman4jOdTrsGHMLu67NACwBIGmEWAABwarpVS83Ozp7Y8fL5vA+zOZNqtVoUi8V+D+PMqdVqrZ9VZR3O1NRU6+elpaU+jgQAYC9hFgAAcKp2z80S8baK4qQ+tC+Xy1EqlU5k33BS6vV63L17t9/DOJN2z8O3O5zh/XZ/2eAk5zMEADiKoX4PAAAAOF8ePny4p4XV/Px83Lx5M+bm5k59PI1GI548eRKNRiPq9Xo8ffo06vV6a3l1dTUePnx44LHVarXWdo1GI1ZWVmJ1dbW1z9XV1VhbWzvRv6larcbi4mI8efKkddydOXGy2WzMzs6eSMVavV5vu++63ZczMzM9DRgrlUp89NFHbcfOZDKRTqcjn8/H/fv3T2T+tEajEY8fP47FxcW2x3fnfp6amopCoXDo+7lSqcSDBw+i0Wj0fMz72flblpeX2/7NRHzdqi+fz+/5d1Ov12N6ejqWl5dPbazvs7ui6Pbt26dyzIWFhSiXy1Gr1drOsYNUhtVqtSiXy61/v/V6ve0+f/jwYaTT6ZP/IyL2PLaNRuPUjg0A8D6pZrPZ7PcgAACA82V6errrnCz5fD7K5fKptgYsFovvnZuoVCodKMxqNBoxPj7+ztuk0+kjh1mzs7OxsLDQWl5ZWWm7rxYWFlpBUT6fj6mpqchkMq2wZXFxsVVxkc1m49GjRz0NenK5XFubt27m5uZ6EmYVi8VYWFiIRqMR2Wy2LbSq1+uxtLTU+jeWyWSiXC73pO1cvV6PYrHY2nc6nY579+5FLpeLiLePSb1eb/2+UCjEo0eP3hkKVCqV+OSTT6JSqfRkDrlCoRCLi4uH+luy2Wzbv5mJiYnWber1epTL5ahWqzEzMxPlcjkajUbkcrmo1+uRpI8VUqlU6+fTGNfOObkTPj158qR1DuzcV93sVKTu3Dabzcbt27cjnU5HtVptO4/K5XLMzMyc+N8S0X4OLy0tadUIACRHEwAA4JStra010+l0MyK6/pfP55uLi4vNtbW1Ex9LuVxuZrPZZiaT2Xc8pVLpQPtaW1trZrPZZjab3ffvS6fTRx7rzMxM275WVlZax83n881MJtNcWlp65z5WVlaa+Xy+tY+ZmZkjj6fb+N53X87NzR3rGMvLy639p9Ppd/69KysrzWw22zp2oVA41rFLpVLb47i4uPjO25fL5dZY97vtysrKvvfVUf87yN+5uLh4oPuwc6yZTKb1b3zneEmxvLzcGlMmkznx4+38e8jn823rd9833a4dO+dxOp3e99rSea6/799ar+w+7nHPVQCAXkrOq04AAOBcWVpaOtAH89lstjk3N3fgD9yPa25u7shhVqfdwctJhVkrKyvNdDp96KBm99+ZzWZPJDjcCUx6FWbt3l8mkznwmHeHC53Bw0EVCoUj3V87QWNENMvl8qGOuTt0jIjm8vLyEUa+1865d5j7cLfd92eSwqxyuXzsx/mgdoeQnffh7tCzM3Teue8O8m9o97XjNMK5ZrN97Cd9HwIAHMaFAAAA6IN8Pt82v81+arVazM/Px9TUVKRSqcjlcm3tuXqtVCr1rLVWNpvt6fxQner1ekxNTUU+nz9QW7ndSqVSq3VZrVaLXC7X83maCoVCz9qjVSqVmJ6ejoi3rf2WlpYOPJ/Pxx9/3LpttVpt7eegpqam2loW7t7f++yMdWZmJmZnZ0/s3+1BNRqN1t+/uLh4pDmRDvtv7bSsrKy0fj7pVqXFYjEi3rbN7LwPO5d3WnvevXs3arVazMzMxPLy8nvv+93Xod2tK0/S7rajvWh5CQDQK8IsAACgb3YCrcN88LwTbuVyuRgfH4/p6enWh8W90st5pE5yzpndocRR7J6frF6vHzrkOYhe7LNzbI8ePTrUv5l0Oh2PHj1qLVcqlQP/m5mdnW277WFCtN3K5XKk0+kTuY8PY2eesUwmc+R/55lM5kBzyJ223eHL5OTkiR2n0Wi0gqXZ2dmuv99taWkppqeno1arteYFPIjOv+Eg4f9x7T6vhFkAQJIIswAAgL7K5/OxsrISpVLp0CHBzofKU1NTMTk5GQsLCz0Z082bN3uyn4i9VRq91Gg0jv0B9+7KsWq1GvPz88cdVpuJiYlj72N3AJTNZqNQKBx6H4VCoe2D+m4hRKdKpdL2b2pubu5YFT+Li4tRr9d7Hr4exk6QctzA9iD332nbHb6cZGXWzr+JfD7f9ThPnz5tW65UKlGpVCKTyRzqfO28dpxGuNR5vva6WhMA4KiEWQAAQCLMzc3F2tpaqyXbYUOger0es7OzMTU1dW4+gJ2ZmTn2h/adIU+xWExURcb8/Hxba76HDx8eeV+7A5j3hUqNRiMePHjQtu6oLSPn5+djcnIypqamIqK/bfp2HtvjhoyZTObEW/kd1u5/tycZIn/00UcRsX/V4X6tJA/7uHdex07jvOxHgAYAcBDCLAAAIFF22nCtra3F8vLyoeewqlarcevWrXPxIexOOHJcO/P/7DjJeb4O68MPP2xbPkpV1n7bvqvd24cfftgWJhx17q9arbYnIHzy5MmR9nVcu/+eXozhJFtoHsX/v727P04bC/cA/O6dW4CSrWBxB5CtILgDcCoIdIAnFWSgA0gFMXQAqSCBDlAHsdUB948dcUGWv/iU4+eZ2VmbEOlIHDGz57fvezav7xAVgQ+dIw+rrq6uSt9Tdm97vd6Lq+GKFV7HDOgeOs/t7e1JzgkA8BRhFgAAUFn1ej16vV5Mp9NYrVYxn8+ftSicZVk0Go03U6G1r+KifL6v0rkVx7FveFKr1bYW6vN9jx4696Zd2+qVVX+d695uXvtisdg78D3mvlQvVbynxwp+8s+zOJdyaZqWjmWXgLhY4XWqSrjNIPAt/E8BAMDrIMwCAABejXq9Hv1+P+bzedzd3cV4PH4w4MiyLD5+/HjiEb5OSZLcWyg/1P5j+ygGAA+1dXuJDx8+bP1e1hKuLMzbdY+psr+3735V+9gMYNrt9l7BWpXaDBYriI5VmZXvefVQhWBZeLlra8xihdep7vfmHKlCqA0AECHMAgAAXqkkSaLVasV0Oo3pdFpaJbFYLGIwGJx+cK9QMWDJF+3PJU3Te1UhxSBqF8VAoKwlXHFvo33Cp2azuRV87FqlcyibVXiLxSIajcaje4c9ptlsnnX/r02nqszK5+SnT59K/7zsuen1ei8+T5Zl967pHJVwxVaHAADnIswCAABevWazGXd3d6XVEsX9oChXDHl2DTgOpez8h6hMKQYCy+XyyXPvG6KNx+NYLpcxnU7j7u7urBVNxechTdO4vLyMi4uLGAwGL6rEyQPlt2Q8Hsfd3d2DAWdx7ux6f25ubu69dqo9yjar2lRmAQBVIcwCAAD+GOPxuHSRuQot86qurOqjrAXfqZRVuByi2qZ4jOJifdk1NxqNvc9bq9VOFkY8NY6yyrA0TeP6+jrevXsXjUYjrq+vYzKZvJowo9hm8FiSJHlwHpbtl/VQBddTivO/rBUoAMBb8r/nHgAAAMAh/fjxI/7555+tReXhcBidTud8g3oFyhbK0zQ92/5OxRaDSZIcJVgphiDF80Ycb/+lc+n1evH79+8HW3AuFoutUC8P4i4vL99cJdZLlFUT7hpgFo9VhSAUAOCchFkAAMAfJd+TqNvtrl87Z4XRa3aqapcyxeAqy7J49+7dwc9TDKrKrvlY+y+dU7/fj8vLy+h2u6UB3qY0TWM0GsVoNIokSeLLly877QP1pytWU9VqtZ3mTlmF1+Xl5R4j292fOPcBgNdJm0EAAOBkBoNBtNvto5+nrArr3HtAVV1Z9VHZflKnUgxY6vV6rFarg/8zHA63zlNW/fWnVWblms1mLJfLB9tzlsmyLK6vr+Pi4uLJEOytOVQ11WQyuffa1dXVTscCAPhTCLMAAICTWS6XJ1sALwZaFt5f7u+//z73EE7u9+/f5x7CybVarZjP53F3dxfj8Tg6nc6T4VaaptFoNCrzXJ07cDxkNdX379+3fq/X6yetkNqsTnyL3wEAQDUJswAAgJM6Vcu/4kLyMfZb+pNUrb1e8dyn+vwuLi7uvVaVwObYkiSJVqsVw+Ew5vN5rFarmM/n0e/3S6uMsiyLRqNRiWfrXPMld6j9srIsu/cd+enTp53HtS9tBgGAqhBmAQAAJ3eKheZarbb1+1usuHmJss+keA9PqXjuU+3fVVbhc869w3ZRFojsql6vR6/Xi+l0Gnd3d/cqHrMsi8+fPx/kXPt4zt5nx1TcLytJkp2CoJubm3uvtVqtXYe1kyqEkwAARcIsAADg5E5R6VIMQ7TLelzZ4v85W7cVz32qBfayAO+1Le6PRqPSvemyLNtrr6skSWI4HN4LbiaTydnvUdUqs3bdL6u4h1utVjt5qLw5P84ZaAMAbBJmAQAAJ1fWkuvQiovb52qXde5F/udaLpf3Xntq36Rj2nW/oX2VXXPZvXmNbm9vI03TvZ+/ZrMZ4/F467XRaLTXMQ9h8xk/ZWXWofbLKquo63a7j77/2N8vwiwAoCqEWQAAwMkVqw+OoVh98uHDh6Ofs8xraVFXXEQ/dWuzorLzn2q/tWJVzSnC11MqBlG7aLVaW/epCoHfZvByyn3ODrVfVlmLwWJbx9xisYh3796VVuDtoxiOCbMAgKoQZgEAACeXpunRg4niYva5qoxeS2VWcUH+XJVRuVqtdq+a7lShUvHaTxmMnMKh7mO/31//XIV7tBm8nDJcK9sva5cQqBjy1+v1BytKv3//vn7PIW1+jueqZgUAKCPMAgAAzuL6+vqox9+sPnlplcQhF3FfQ1VPMVhMkuTBipBT+vLly9bvxdBgX9fX1zEYDO69Xnbtk8nkYOdtt9s7zf/nVvn9/v37yfccIkzeDFKqUIFYlcqsXaqyygL+x1oM5vPx0KGz/bIAgKoSZgEAAGcxm82OGvRstux6aXDw/v37g43j58+fBzvWseRVHrliiHQuvV5vK1iczWYHCymyLIvBYFAaXJaFeYdqjZllWUwmk7i4uHjyvcWxPbfKL8uyJ4OI4me+q3yMVQg+/v333/XPpwqzDrVfVllYenV19eA50zSNJEl2Cs4es3nfztWaFQCgjDALAAA4m3a7fZQ2fKPRaH3cer2+d2XWcypdyuTBxbEcIpDIQ51ckiTR6/X2Pu6hFIO1zdZ2+8gDzocCg2IAeqggbTQaPXreTcWA6LnVT2maPhkuHWpe5s9ZFcKszUqxU4VZh9ovq1h12Gw2H6wQzYPVY1RPbobvjUbj4McHANiVMAsAADibLMui0WgcNNDKsmwriNgl/ChWJOw6vpubm3t7Px3yWieTyd6L9l+/ft36fbM9YxX0er2tkGIzqNxVlmUxGo3uVX5tqtVq9+bOIVpjfv36NTqdzrNaWW5WGkU8fx+oNE2frPxK03TvQGtz7n369GmvYx3C5rOWZdlJ9qs71H5Zxee43W4/+N48fH6sDeGuNlsdHrrqCwBgH8IsAADgLJIkiX6/H2maRqPROFglxWa1V6/X22lBtrggvWs7xOvr6+j3+wdtW5hrtVrRbDYfXfR+SpqmW1VZu96vY/vx48dW+PPx48e9jvf58+dIkuTJdorF+zGZTPYKgPIw7LkBa6vV2vr9OfNwsVhEmqb3/u5j49lVfi9qtdpW4HhOm0H0r1+/jn6+4j5Xh3p+HgrE8sq+Vqt1lGq4/Ht411AOAOBYhFkAAMBZfPnyJXq9XvR6vXUlSb5Qu6t2u71e8K/X63u1pNusesj3qHnpWD58+FAaKhwiuOt2uzEejyNN051CiSzLtvb2abVaB2vhd2hJkmwFWovFYueqlG63G5PJ5F5A9pDxeLwV1LTb7XsBxnNMJpMYDAbx7du3Z503tzl/8qDqMcPhMOr1+rOCiF3nTsR/8yev6qvSvNmc08WqqUPLsuze57HLflkR98Orsv2qNqtOv337ttN5HqMqCwCoMmEWAABwcpv7MvX7/fUeMN1uNxqNxotDrcViEY1GY10p0mw2Yz6f7zXGXq+3tcD8kgX76+vrmM1mD7bs27c9WK1WW++pMx6PYzAYbFVYPSXLsvj48eN6Ib7X61WuvWBRvV6P+Xy+DoJGo9GLq9K63W6MRqN7AdVjkiSJ+Xy+tbj/8ePHFwVak8kk2u129Pv9Z1VMbSqGFo+FT1mWxc3NzZMVZxH/XddwOIzBYLBTiJxXQPZ6vRdf0zEVw79jOtR+WRH3Q7CywDO/58Ph8EWB6HNtXs+uoRwAwNGsAAAATqTT6awiYtXr9e792XK5XDWbzVVErCJilSTJqtVqrYbD4Wo+n6/u7u7uvX88Hm/9nYeOvavlcrlKkmR97H6//+j77+7uVq1Wa1Wr1VbL5XL9eq1W2xrj5vV1Op1Vp9N51njy+1ccx3w+Xx+veJ+KhsPh+pqSJFmNx+NnnXtX8/n8oJ9Pfo/z49VqtSevYTwer2q12ipJktV0Ot353L1eb+taOp3Oo/d7c6zD4XDn847H463zls3Du7u7Vb1eXzWbzQePs1wu18fI78NwOFxFxLPmzmq1Wk2n0/V8PuSzdkibz+wxbc7D/Hnax+a4N78/NufRMe/55nfp5vkBAKrgr9VqtTpeVAYAAPD/8sqYu7u7BysLZrNZDIfDF+9N1Gw2YzgcHnyflyzL4vPnz1v7A7Varfj333/X50rTNKbTaYxGo2i1WvdayV1cXDzZHu45/2n22P3LW5CNRqP1Xlq1Wi3ev38faZrGz58/YzQabe0ndor2cHnVXO5Q553NZnF9fb2uvkmSJK6uruLi4iJqtdr6mvPWfJ1OJ/r9/t4VLWmaRrfb3api2ZwPt7e3sVwuY7FYxGw2O9i8nM1mW/vB1ev1aDab8e+//64/21qt9mj7xLydZ6vV2qrEWywW0W6313ttXV5erufO7e1tZFkWP3/+jMlkEmmarlt4VrUVXf6cRPzXavBY42y321vfU8PhMDqdzs7H2/yM8++ZNE1jNptFlmXR7/fXFa3H8Ndff0XEf99xy+XyaOcBANiFMAsAADiZwWAQv3//fnaYkQcC0+k00jRdL6wnSRLv37+PWq0W7XY7rq6ujtJ2a1Pewi3fpyoPp5IkWbf9+/TpU2n7uouLi7i9vY3379+vx77577///vtZi9T5ovZjbd3yvYzyECdN0/UYa7VafPr06aRt4Y4VZuXSNI3JZLKeI/nnkl/vsebHQ/MhP2+9Xo9ut3vwcHUymcRwOLz32Xa73SeDlCzLotFobLVr3LRYLGI4HMavX78iTdN7z9rl5WU0m81nt2g8l805d8zQNg8Bb29v4+rqat0udR95KD2bzdbB4YcPH+L6+vrgc2nTbDZbtxY8dmgGALALYRYAAABHc+wwC8q8e/duXeGkyuhpm9Vsj1XOAgCcy/+cewAAAAAAh5RXqaVpum5FycNubm4i4r92rYIsAKCKhFkAAADAH+XLly/rnw/R/u9PNplM1nuxdbvd8w4GAOABwiwAAADgj5IkyXpvuLx9HuXysG/zngEAVI0wCwAAgKO5vb099xB4ozarswRa5dI0jdlsFhHb9wsAoGqEWQAAABxN3r4sd3FxcZ6B8ObU6/VoNpsREdHv9888mmrarMrq9XpnHg0AwMOEWQAAAJxMrVY79xB4Q/IQa7MCif9kWRaDwSAiVGUBANUnzAIAAOBFFotFXF5eRqPRWC+GPyRN063f80oZOIV6vb7eB6rb7Z55NNXy9evXiPgvYFaVBQBU3V+r1Wp17kEAAADwOmRZFu/evdt6rdfrPdjG7fLycl0RU6/XYz6fH32MsCnLsvjnn38iy7IYDofR6XTOPaSz23yOp9OpkBkAqDyVWQAAADzbzc3Nvdcmk8mD7//169f6Z63MOIckSdZh6/X19ZlHUw2fP3+OiIhWqyXIAgBeBWEWAAAAe0mSpPT1xWIRWZZFxH+tzPJ2b3BqnU4nWq1WZFn25tsNzmazmEwmkSRJfPv27dzDAQB4FmEWAAAAz1ZWxfFQxVW+J09ExHg8PtqY4Dm+ffsWSZLEaDRat758a7Isi3a7HRERP378eDCIBgCoGmEWAAAAz1ar1dYt25Ikiel0WlpxNRqN1u0Hx+Nx1Ov1k44TipIkiR8/fkRERLvdXlcNviWfP39e7x3mmQQAXpO/VqvV6tyDAAAA4HUZDAZxfX0dtVotut1u1Ov1eP/+faRpGt+/f1+3MRuPx/bkoVImk0m02+1oNpsxnU7PPZyTyZ/ZTqcTw+Hw3MMBAHgRYRYAAAA7ybIsRqNRTKfTSNM00jSNJEniw4cP0W63o9PpnHuIUGo0GkW3230zwc5sNovLy8totVpafgIAr5IwCwAAAHhz8kqlfr8fvV7v3MM5msViEY1GQ5AFALxqwiwAAADgTcpbDg6Hwz+ykjBN02g0GnF1dfUmKtAAgD/X/5x7AAAAAADn0Gq1YrlcnnsYR5NlWXz79k2QBQC8eiqzAAAAAAAAqCyVWQAAAAAAAFSWMAsAAAAAAIDKEmYBAAAAAABQWcIsAAAAAAAAKkuYBQAAAAAAQGUJswAAAAAAAKgsYRYAAAAAAACVJcwCAAAAAACgsoRZAAAAAAAAVJYwCwAAAAAAgMoSZgEAAAAAAFBZwiwAAAAAAAAqS5gFAAAAAABAZQmzAAAAAAAAqCxhFgAAAAAAAJUlzAIAAAAAAKCyhFkAAAAAAABUljALAAAAAACAyhJmAQAAAAAAUFnCLAAAAAAAACpLmAUAAAAAAEBlCbMAAAAAAACoLGEWAAAAAAAAlSXMAgAAAAAAoLKEWQAAAAAAAFSWMAsAAAAAAIDKEmYBAAAAAABQWcIsAAAAAAAAKkuYBQAAAAAAQGX9HwJ3p1LQZHMmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4), dpi=300, facecolor=\"w\")\n",
    "\n",
    "for i_cond in range(n_cond):\n",
    "    y = R2_cond_av[i_cond,:,:].mean(0)\n",
    "    yerr = sem(R2_cond_av[i_cond,:,:].flatten())\n",
    "    # Shaded error region\n",
    "    ax.fill_between(v_n, y - yerr*2.58, y + yerr*2.58, alpha=0.3, color=colors[i_cond*3],edgecolor='none')\n",
    "    # Line plot\n",
    "    ax.plot(v_n, y, color=colors[i_cond*3], linewidth=1)\n",
    "\n",
    "ax.set_ylabel('Pearson\\'s $\\mathit{R}$', fontsize=18)\n",
    "ax.set_xlabel('Subjects ($\\mathit{n}$)', fontsize=18)\n",
    "\n",
    "ax.tick_params(axis='both', labelsize=16)\n",
    "\n",
    "\n",
    "fname = out_pth + '/MOU-rDCM_Alignment.svg' \n",
    "plt.savefig(fname, transparent=None, dpi='figure', format='svg',\n",
    "    facecolor='auto', edgecolor='auto',bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group‑level convergence emerges around 20 subjects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x71651b7942e0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph from average connectivity\n",
    "i_cond = 0\n",
    "plt.scatter(EC_normed_MOU[i_cond,:,:,:].mean(0), EC_normed_rDCM[i_cond,:,:,:].mean(0))\n",
    "#plt.plot([-2,4],[-2,4],'--k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.013506272637285257, 0.020202785256314314, 0.021362378310677537, 0.04115052982149307, 0.04913767790120809, 0.030076682395583247, 0.004719187114599733, 0.018653914800659654, 0.06409645314033396, 0.04924536916238663, 0.06356894923825612, 0.05776392488176349]\n"
     ]
    }
   ],
   "source": [
    "G_av_mou = []\n",
    "G_av_dcm = []\n",
    "\n",
    "for i_cond in range(n_cond):\n",
    "\n",
    "    G_av_mou.append(nx.DiGraph())\n",
    "    for i in range(n_rois): # source\n",
    "        for j in range(n_rois): # target\n",
    "            if i != j:\n",
    "                G_av_mou[i_cond].add_weighted_edges_from([(i,j,EC_normed_MOU[i_cond,:,i,j].mean(0))])\n",
    "\n",
    "    G_av_dcm.append(nx.DiGraph())\n",
    "    for i in range(n_rois): # source\n",
    "        for j in range(n_rois): # target\n",
    "            if i != j:\n",
    "                G_av_dcm[i_cond].add_weighted_edges_from([(i,j,EC_normed_rDCM[i_cond,:,i,j].mean(0))])\n",
    "\n",
    "print(list(nx.clustering(G_av_mou[0], weight='weight').values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([0.27163866051169433, 0.2128763870062628, 0.2745444287633638, 0.2241297072856641, 0.32667753209726086, 0.2708271495092676, 0.34530712467501273, 0.300103953826289, 0.31951409009037685, 0.26053051373217617, 0.3247938447393352, 0.30077553198897755])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.eigenvector_centrality_numpy(G_av_mou[i_cond], weight='weight').values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['r', 'g', 'b']\n",
    "bins = np.linspace(-0.3, 0.3, 30)\n",
    "\n",
    "# dominating eigenvector (in previous version I forgot to transpose)\n",
    "plt.figure()\n",
    "for i_cond in range(n_cond):\n",
    "    eigval_mou, eigvec_mou = np.linalg.eig(EC_normed_MOU[i_cond,:,:,:].mean(0).T)\n",
    "    i_max = np.argmax(np.real(eigval_mou))\n",
    "    evc_mou = np.abs(eigvec_mou[:,i_max])\n",
    "    eigval_dcm, eigvec_dcm = np.linalg.eig(EC_normed_rDCM[i_cond,:,:,:].mean(0).T)\n",
    "    i_max = np.argmax(np.real(eigval_dcm))\n",
    "    evc_dcm = np.abs(eigvec_dcm[:,i_max])\n",
    "    plt.scatter(evc_mou, evc_dcm,\n",
    "                marker='.', s=50, color=cols[i_cond], label=conf['tasks'][i_cond])\n",
    "plt.plot([0.0,0.35],[0.0,0.35],'--k')\n",
    "plt.xlabel('mou')\n",
    "plt.ylabel('dcm')\n",
    "plt.legend(fontsize=12)\n",
    "plt.title('eigenvector centrality (own version)')\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "# asymmetry for each node\n",
    "def asym_nodes(M):\n",
    "    return np.abs(M-M.T).sum(0) / np.abs(M+M.T).sum(0)\n",
    "    \n",
    "plt.figure()\n",
    "for i_cond in range(n_cond):\n",
    "    plt.scatter(asym_nodes(EC_normed_MOU[i_cond,:,:,:].mean(0)),\n",
    "                asym_nodes(EC_normed_rDCM[i_cond,:,:,:].mean(0)),\n",
    "                marker='.', s=50, color=cols[i_cond], label=conf['tasks'][i_cond])\n",
    "plt.plot([0.0,0.35],[0.0,0.35],'--k')\n",
    "plt.xlabel('mou')\n",
    "plt.ylabel('dcm')\n",
    "plt.legend(fontsize=12)\n",
    "plt.title('asymmetry')\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "# eigenvector centrality from networkx, some instability for movie DCM\n",
    "plt.figure()\n",
    "for i_cond in range(n_cond):\n",
    "    plt.scatter(list(nx.eigenvector_centrality_numpy(G_av_mou[i_cond], weight='weight').values()),\n",
    "                list(nx.eigenvector_centrality_numpy(G_av_dcm[i_cond], weight='weight').values()),\n",
    "                marker='.', s=50, color=cols[i_cond], label=conf['tasks'][i_cond])\n",
    "plt.plot([0.0,0.35],[0.0,0.35],'--k')\n",
    "plt.xlabel('mou')\n",
    "plt.ylabel('dcm')\n",
    "plt.legend(fontsize=12)\n",
    "plt.title('eigenvector centrality')\n",
    "plt.tight_layout()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = ['r', 'g', 'b']\n",
    "bins = np.linspace(-0.3, 0.3, 30)\n",
    "\n",
    "plt.figure()\n",
    "for i_cond in range(n_cond):\n",
    "    plt.scatter(list(nx.clustering(G_av_mou[i_cond], weight='weight').values()),\n",
    "                list(nx.clustering(G_av_dcm[i_cond], weight='weight').values()),\n",
    "                marker='.', color=cols[i_cond], label=conf['tasks'][i_cond])\n",
    "plt.plot([0.0,0.2],[0.0,0.2],'--k')\n",
    "plt.xlabel('mou')\n",
    "plt.ylabel('dcm')\n",
    "plt.legend(fontsize=12)\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lamidec",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}