Process_in_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DM3 to Pandas DataFrame in heating experiments\n",
    "This file is used to load DM3 data file, and create a Pandas DataFrame to store this information. We would like to store all the related DM3 information in one DataFrame.<br>\n",
    "\n",
    "The features of DataFrame are: 'Temperature', 'Nanoflake #', 'Thickness', 'Layer', 'EELS Spectrum', 'Peak Position'\n",
    "- 'Temperature', 'Nanoflake #' are from filenames, \n",
    "- 'Thickness' is from simple process, \n",
    "- 'Layer' needs to be calculated based on 'Thickness' after all the data is stored,\n",
    "- 'EELS Spectrum' is a list of one principle axis in decomposition (NMF) of all the EELS in this thickness. The determination of same thickness comes from K-means clustering,\n",
    "- 'Peak Position' is from the curve fitting of 'EELS Spectrum'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "WARNING:hyperspy_gui_traitsui:The module://ipykernel.pylab.backend_inline matplotlib backend is not supported by the installed traitsui version and the ETS toolkit has been set to null. To set the ETS toolkit independently from the matplotlib backend, set it before importing matplotlib.\n",
      "WARNING:hyperspy_gui_traitsui:The traitsui GUI elements are not available.\n"
     ]
    }
   ],
   "source": [
    "#Import packages\n",
    "\n",
    "# Ensure that this code works on both python 2 and python 3\n",
    "from __future__ import division, print_function, absolute_import, unicode_literals\n",
    "\n",
    "# basic numeric computation:\n",
    "import numpy as np\n",
    "import scipy.integrate as si\n",
    "import math\n",
    "\n",
    "# basic system operation:\n",
    "import os\n",
    "from copy import deepcopy\n",
    "\n",
    "# The package used for creating and manipulating HDF5 files:\n",
    "import h5py\n",
    "import hyperspy.api as hs\n",
    "\n",
    "# To extract EELS ZLP, put 'eels.py' from hyperspy_tools in the python path and then import here:\n",
    "from eels import extract_ZLP\n",
    "\n",
    "# Plotting and visualization:\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "# multivariate analysis:\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.preprocessing import normalize\n",
    "import pandas as pd\n",
    "\n",
    "# finally import pycroscopy:\n",
    "import pycroscopy as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n"
     ]
    }
   ],
   "source": [
    "center = hs.load('Center2.dm3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I save T150 information in 250.txt\n",
    "x250, y250 = np.loadtxt('150.txt', delimiter='\\t', usecols=(0, 1), unpack=True)\n",
    "x150, y150 = np.loadtxt('250.txt', delimiter='\\t', usecols=(0, 1), unpack=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1be3bda0>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c0bdfd0>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Position (nm)')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1be21ef0>,\n",
       "  <matplotlib.axis.XTick at 0x1bd29390>,\n",
       "  <matplotlib.axis.XTick at 0x1be7ab38>,\n",
       "  <matplotlib.axis.XTick at 0x1c093048>,\n",
       "  <matplotlib.axis.XTick at 0x1c0936a0>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'$E_p$ (eV)')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1bd9ee10>,\n",
       "  <matplotlib.axis.YTick at 0x1be7de80>,\n",
       "  <matplotlib.axis.YTick at 0x1be19d30>],\n",
       " <a list of 3 Text yticklabel objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1be19048>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAH1CAYAAABr1D+LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xd8VGX2x/HPE2roVQHpihQRELCg\nCEgTKwiISizYcPGnq2Bf1LUgouuisioWFBWxoLJrRREUVMQC6rKCIksJCkiRLAKBAMnz++MQkkAS\nUmZy70y+79drXjNzc3PnEOPkzFPOcd57REREREQSgg5ARERERMJBiaGIiIiIAEoMRURERGQvJYYi\nIiIiAigxFBEREZG9lBiKiIiICKDEUERERET2Cm1i6Jy7zTn3jXPuD+fcRufcO865tvmc/7Rzzjvn\nbizAtbs75xY653Y651Y45/4U2ehFREREYk9oE0OgB/AEcCLQE9gDzHLO1dr/ROfcYOBYYO3BLuqc\nawa8D3wBHAPcD/zDOTcoYpGLiIiIxCAXK51PnHNVgC3AAO/9O9mON8GSvN7ADOAx7/1D+VznAWCg\n975FtmOTgKO8912iFb+IiIhI2IV5xHB/VbF4UzIPOOfKAq8AY7z3PxbwOl2Amfsd+xDo7JwrF4lA\nRURERGJR2aADKIRHge+B+dmO3Q387r2fWIjr1ANm7XdsPfazqAOsy/4F59xwYDhA5cqVO7Vq1aqQ\nYYuIiIiUvIULF27y3tctzPfERGLonBsPdAW6eu/T9x7rDgwDOhThkvvPn7s8juO9fxp4GqBz585+\nwYIFRXg5ERERkZLlnEsu7PeEfirZOfcwcAHQ03u/ItuXTgHqA+ucc3ucc3uAJsADzrlf87nkb9io\nYXaHYJtbfo9c5CIiIiKxJdQjhs65R4HzgR7e+5/2+/ITwBv7HfsQW3P4TD6XnQ8M2O9YH2CB9353\nMcIVERERiWmhTQydc48DF2FJXIpzLnOUb5v3fpv3fgOwYb/v2Q385r1fmu3YiwDe+4v3HnoSuMY5\n9wjwFHASNiV9QRT/OSIiIiKhF+ap5KuxncizsQ0hmbeDFrDeT+O9NwC89yuB04Fu2GaW0cCfvfdv\nRiBmERERkZgV2hFD7707+FkHfE/TXI71yOXYXKBjkQITERERiVNhHjEUERERkRIU2hFDERGR0mbL\nli1s2rSJXbt2BR2KhFj58uWpU6cO1atXj/i1lRiKiIiEwM6dO1m/fj0NGzYkMTER5wq9okpKAe89\nO3bs4Ndff6VChQpUrFgxotfXVLKIiEgIbNy4kbp161KpUiUlhZIn5xyVKlWiTp06bNy4MeLXV2Io\nIiISAjt37qRKlSpBhyExomrVquzcuTPi11ViKCIiEgJ79uyhbFmt8JKCKVu2LHv27In4dZUYioiI\nhISmkKWgovW7osRQRERERAAlhiIiIiKylxJDERERiTjn3EFvTZs2jXocv/76K9deey1dunTZt+N7\n1apVB5w3Z86cXGOsUaPGAef+8ssvDB48mOrVq1OtWjUGDhzI6tWrDzhv8+bNXHTRRbRs2ZJWrVrx\nwAMPHHDO/PnzGTJkCA0aNKB8+fLUrl2bPn368MILL5Cenh6Rn0FhaJWriIiIRNz8+fNzPD/nnHNo\n3749d911175jFSpUiHoc//3vf5k2bRqdOnXi5JNPZubMmfmeP2HCBI499th9z/ffEJSamkrPnj2p\nUKECL7zwAs45br/9dk455RQWLVpE5cqV9507YMAABg0axJQpU9i+fTtt2rThtNNOo127dgA88sgj\njBo1ip49e/LAAw/QpEkTUlJSmDlzJiNGjKBGjRr0798/gj+Ng1NiKCIiIhF3wgkn5HheoUIF6tSp\nc8DxaOvWrRvr168HYNKkSQdNDFu3bp1vjM888wwrVqxg6dKlHHHEEQC0a9eOFi1a8NRTTzFq1CgA\n5s6dS2pqKtdddx0AlStXpnnz5vti+fTTTxk1ahTXXHMNEyZMyPEa/fv3Z9SoUWzfvr1o/+hi0FSy\niIiIxK2EhMimOm+//TYnnHDCvqQQoFmzZpx00km89dZb+459//33dOrUad/ztLQ0li5dytFHHw3A\nuHHjqFWrFg8++GCur3P44YfvG1ksSUoMRURE4tDUqdC0KSQk2P3UqUFHVHDee/bs2XPQWzTW4CUl\nJVGmTBlq167N0KFDD1g7uHjxYtq2bXvA9x111FEsWbJk3/NGjRqxePFi0tPTycjI4Oabb+b000+n\nXr16pKenM2fOHPr27RvxlnbFpcRQREQkzkydCsOHQ3IyeG/3w4fHTnI4d+5cypUrd9Bbr169Ivaa\n1atX54YbbmDSpEl8/PHH3HHHHcyaNYsuXbqwYcOGfedt3ryZmjVrHvD9tWrVIiUlZd/zAQMGcPTR\nR9O2bVvatGlDQkICjz/+OACbNm1ix44dNGnSJGLxR4rWGIqIiIRYfnWMn3rKEj6Ap5+Gq67K+9zU\nVBg9GpKSoFMn+Pbb3M+78kq7FsDChXZuSevUqRPffPPNQc+rWrVqxF7zmGOO4Zhjjtn3vHv37nTr\n1o3jjjuOCRMmMGbMmH1fy624tPc+x/OEhAQmTpwYsfhKihJDERGRUiKXiiqhVKVKFTp06HDQ86Ld\nKaZjx44ceeSROZLUmjVrsnnz5gPOTUlJyXUkMTe1a9cmMTGR5OTkiMUaKZpKFhERCTHv875ljhaC\nPc48ntcMZePGdr9wYd7XzBwthGBGCyGYqeS8eO9zJKBHHXUUixcvPuC8JUuW0KZNmwJds2zZsvTo\n0YOPPvqItLS0iMUaCRoxFBERiTP33WeJYmpq1rFKlex4LAhiKjk3CxYs4Oeff2bIkCH7jp199tnc\neOONrFixgubNmwOwatUq5s2bx7hx4wp87VtvvZUePXpw0003HVCuBmDlypVs3bq1xHcmKzEUERGJ\nM0lJdj96tE0fN25sSWHm8bCrWrUqnTt3jtj13njjDQAWLlwIwIwZM6hbty5169ale/fugO1Gbtas\nGR07dqRGjRp899133H///Rx22GFce+21+6515ZVX8thjj9G/f3/GjBmDc4477riDRo0acVV+izz3\n061bN8aPH8+oUaP48ccfGTZsGI0bNyYlJYXZs2czadIkXn755RJPDN3+iyUlb507d/YLFiwIOgwR\nEYlDP/74I61btw46jKhp2rQpXbt25aWXXirx185rLWL37t2ZM2cOAPfffz+vvPIKycnJpKamUq9e\nPU477TTuvvtu6tevn+P7Vq9ezciRI/noo4/w3tOrVy8eeeSRIrX4++KLL3j44Yf5/PPP2bRp076k\n+OKLL2bo0KH51mE82O+Mc26h975QGbYSw0JQYigiItES74mhRF40EkNtPhERERERQImhiIiIiOyl\nxFBEREREACWGIiIiIrKXEkMRERERAZQYioiIiMheSgxFREREBFBiKCExdSo0bQoJCXY/dWrQEYmI\niJQ+aokngZs6NWdPz+TkrMbwsdK+SUREJB5oxFACN3p0zkbvYM9Hjw4mHhERkdJKiaEEbvXqwh0X\nERGR6FBiKIFr1Cj34zVqgFp5i4jEJufcQW9NmzaNehxvvPEGgwYNokmTJiQmJtKyZUtuu+02tm7d\nmuO8OXPm5BpjjRo1DrjmL7/8wuDBg6levTrVqlVj4MCBrM5lNGPz5s1cdNFFtGzZklatWvHAAw8c\ncM78+fMZMmQIDRo0oHz58tSuXZs+ffrwwgsvkJ6eHrkfRAFpjaEE7pJL4N57DzyekgLDhsGTT0Ji\nYomHJSIixTB//vwcz8855xzat2/PXXfdte9YhQoVoh7HQw89ROPGjRk7diwNGzbku+++46677uKT\nTz7hiy++ICEh5xjZhAkTOPbYY/c9L1s2Z6qUmppKz549qVChAi+88ALOOW6//XZOOeUUFi1aROXK\nlfedO2DAAAYNGsSUKVPYvn07bdq04bTTTqNdu3YAPPLII4waNYqePXvywAMP0KRJE1JSUpg5cyYj\nRoygRo0a9O/fP4o/nQMpMZTAZWTYfdWqsG0bNG4MZ50Fzz0HL74IS5bA9Ol5jyyKiEj4nHDCCTme\nV6hQgTp16hxwPNreeecd6tatu+959+7dqVWrFpdccglz5syhZ8+eOc5v3bp1vjE+88wzrFixgqVL\nl3LEEUcA0K5dO1q0aMFTTz3FqFGjAJg7dy6pqalcd911AFSuXJnmzZuzfv16AD799FNGjRrFNddc\nw4QJE3K8Rv/+/Rk1ahTbt28v/g+gkDSVLIGbMcPuX3/dksRVq+Af/4D586FZM1iwADp3hs8+CzRM\nERGJQdmTwkyZI4Jr1qwp9PXefvttTjjhhH1JIUCzZs046aSTeOutt/Yd+/777+nUqdO+52lpaSxd\nupSjjz4agHHjxlGrVi0efPDBXF/n8MMP3zeyWJKUGEqg1q+Hb7+FihWhW7ecX2vXDr75Bnr3hg0b\nbNRQREQKYd066N4dfvst6EgKxXvPnj17Dnor6hq8uXPnAjY6uL+kpCTKlClD7dq1GTp06AFrBxcv\nXkzbtm0P+L6jjjqKJUuW7HveqFEjFi9eTHp6OhkZGdx8882cfvrp1KtXj/T0dObMmUPfvn2pWLFi\nkf4N0aLEUAL14Yd2f8opua8jrF3bRhSfeALy+FAlIiJ5ufde+PxzuOeeoCMplLlz51KuXLmD3nr1\n6lXoa69Zs4Y777yT3r1707lz533Hq1evzg033MCkSZP4+OOPueOOO5g1axZdunRhw4YN+87bvHkz\nNWvWPOC6tWrVIiUlZd/zAQMGcPTRR9O2bVvatGlDQkICjz/+OACbNm1ix44dNGnSpNDxR5vWGEqg\nWreGq66Ck07K+5yyZWHEiKznv/8O//d/MH48NGgQ/RhFRALlXN5fe+qprI4ATz9tb6i5mTjRbhUr\nwo4d0KmTTdfk5sor7VoACxfauSWsU6dOfPPNNwc9r2rVqoW67rZt2+jfvz9ly5Zl8uTJOb52zDHH\ncMwxx+x73r17d7p168Zxxx3HhAkTGDNmzL6vuVz+m/j9ymgkJCQwceLEQsUXBkoMJVDHHmu3wrjh\nBnjtNZg716aXu3SJTmwiInElKQkeeijoKAqkSpUqdOjQ4aDn5Zag5WXnzp2cffbZrFixgrlz59Kw\nYcODfk/Hjh058sgjcySpNWvWZPPmzQecm5KSkutIYm5q165NYmIiycnJBY6/pGgqWWLO3/4GPXrY\nkpnu3WHSpKAjEhGJIu/zvmWOFoI9zv61P/3JGtBXrGj31apBvXp27sKFeV8zc7QQAhkthMhPJe/e\nvZtBgwbx9ddf8/777+/bAFIQ3vscCehRRx3F4sWLDzhvyZIltGnTpkDXLFu2LD169OCjjz4iLS2t\nwLGUBI0YSmCmT4ft2+GMM6BWrYJ/X926MHMm3HQTPPqozXp8+y088giULx+9eEVEYsr69ZYcDh9u\nyd66dUFHVGCRnErOyMggKSmJ2bNn89577xWqXM6CBQv4+eefGTJkyL5jZ599NjfeeCMrVqygefPm\nAKxatYp58+Yxbty4Al/71ltvpUePHtx0000HlKsBWLlyJVu3bi3xnclu/zlxyVvnzp39ggULgg4j\nbhx/PHz9NbzzDpx5ZtGu8fzz9r6XlgZdu8KsWVAC9VJFRCLuxx9/zHWXbLxo2rQpXbt25aWXXirR\n1x0xYgRPPvkko0eP5sz9/tg0bNhw35RyUlISzZo1o2PHjtSoUYPvvvuO+++/n0qVKvHtt99Sp04d\nALZv30779u1JTExkzJgxOOe444472Lp1K4sWLaJKlSoFji2zwHWvXr0YNmwYjRs3JiUlhdmzZzNp\n0iRefvnlfAtcH+x3xjm30HvfOc8TcuO9162At06dOnmJjI0bvXfO+/Llvd+2rXjX+uor7w87zPub\nbopMbCIiQViyZEnQIURVkyZNfFJSUiCvC+R6++tf/7rvvLFjx/qjjz7aV6tWzZctW9Y3bNjQX3nl\nlX7t2rUHXDM5OdkPHDjQV61a1VepUsX379/fr1y5skjxzZs3zw8ePNjXq1fPly1b1tesWdP36dPH\nT5kyxaenp+f7vQf7nQEW+ELmOhoxLASNGEbOyy/bOujeveGjj4p/vY0boWZN28EM1k6vgGuARURC\nId5HDCXyojFiqM0nEogPPrD7fv0ic726dbOSwg0boH17uO462L07MtcXEREpDZQYSonLyMhKDE87\nLfLX/+or27E8YQKceqqNJoqIiMjBKTGUEvfdd5asNWpkBa4j7ayzYM4cq8rwySdWJ/G77yL/OiIi\nIvFGiaGUuB07bAfxWWflX9C/OE480cp0HX88JCdbZ5VXXonOa4mIiMQLJYZS4rp2hc8+g8cei+7r\nNGhg3VEuu8yS0aFD4YcfovuaIiIisUwFriUw0RotzK5CBeuM0qmTTV+3bRv91xQREYlVSgylRC1Z\nAjt3QocO1qGpJDgHV1+d89g331jSWMIF5UVE8uX3a78mkpdolRvUVLKUqL//3UbvHnkkuBh++w0G\nDIAuXeD114OLQ0Qku3LlyrFjx46gw5AYsWPHDsqVKxfx6yoxlBLjfVaZmgL2PY+K6tXt9VNTYcgQ\n6N8fmjSxEcymTWHq1JKNZ+pUe92gXl9EwuGQQw5hzZo1pKamRm00SGKf957U1FTWrFnDIYccEvHr\naypZSsx//gNr10L9+sFO4SYmwgsv2MjlyJHw9ttZX0tOtn7zYJ1ZIsH7rPWUe/bYVHpGht1ee81i\nyBwkiMbri0hsqFatGgBr165lt6rzSz7KlSvHoYceuu93JpKUGEqJmTHD7vv1K5mNJ/lxzjqjjB1r\nnVKyS02F0aNhzRqYPBnS0+2WkZF136yZ7azOdNhh8McfOc/JvH/oIbjhBjtv2rSDJ3yZr6/EUKT0\nqVatWlT+2IsUlBJDKTGRboMXCXl1RVm92r7200+5fz0xMefzrVth27bcz01Pz3pcrhxUqgRlytjU\n8ZYteb++iIhISXNax1BwnTt39gsWLAg6jJj0xx9Qu7aNoG3aBDVrBh2RadrUpm/316QJfPklpKRY\nAlemTFYyV6aMJXiHHpp1/v/+Z1/L7dz8Rkfze/1Vq4r5jxMRkVLNObfQe9+5MN+jEUMpET//bJs+\nWrYMT1IIcN99tqYvNTXrWKVKdrxePbsVRI0akX99ERGRkqbEUEpE586wfn3eU7dByVzHN3q0Td82\nbmxJWUmt79v/9evXt04tWl8oIiJB0FRyIWgqWaLpyy+ttuLhh9sIa0kVABcRkfhUlKlk/emRqNuy\nxdYYSv6OPdZ2Ny9fDp9+GnQ0IiJSGikxlKh79lnbeDJ2bNCRhFuZMnDppfb42WeDjUVEREonJYYS\ndR98YIWdmzULOpLwy0wM33jDdjqLiIiUJCWGElXbt8PcuVaypU+foKMJv+bNoWdP647y8stBRyMi\nIqWNEkOJqjlzYNcuOO44qFMn6GhiwxVX2L2mk0VEpKQpMZSoyt4GTwrmnHOsbM2RR2b1UBYRESkJ\nqmMoURXGNnhhV7GidT0pXz7oSEREpLTRiKFETXKylV6pVctKsUjBKSkUEZEgaMRQoqZJE1izBpYu\ntVIsUjh79sD771srwe7dg45GRERKAyWGElUNGthNCu+FF2wjSs+eMHt20NGIiEhpoKlkiQp1Wiy+\nwYMhMRE+/tim5EVERKJNiaFExYcfwhFHwEMPBR1J7KpeHc491x5PnhxsLCIiUjooMZSo+OADG+VK\nSQk6kth2+eV2//zztuZQREQkmpQYSlRklqk57bRg44h1J58MLVrYJp4PPww6GhERiXdKDCXiVq60\nncjVq8MJJwQdTWxzDi67zB6rE4qIiESbEkOJuMzRwj59oKz2vRfbJZdAq1Zw4olBRyIiIvFOf7Yl\n4tQGL7Lq14clS2z0UEREJJo0YigRlZZm5VUATj012FjiiZJCEREpCRoxlIgqUwb+9S9YuBAaNgw6\nmvjy66+2O7lfP+jcOehoREQkHikxlIgqWxZ697abRNbjj8O4cbBiBTz3XNDRiIhIPNJUskiMyNyd\n/Npr8McfwcYiIiLxSYmhRMwvv8DZZ9t0p0ReixbQrRukplpyKCIiEmlKDCViPvgA3nnH1hhKdGR2\nQlFNQxERiQYlhhIxmfULVaYmegYPhmrV4Kuv4Icfgo5GRETijRJDiYjdu2HWLHusxDB6KlWCoUPt\nsUYNRUQk0rQrWSJi/nzbENGqFTRtGnQ08e2KK2D9ejjzzKAjERGReKPEUCIicxr5tNOCjaM06NQJ\npk8POgoREYlHmkqWiFAbPBERkdinxFCKzXu49FKb2uzWLehoSo9Zs+DccyE5OehIREQkXigxlGJz\nDv78ZytVU7Fi0NGUHs8+C2+8AZMnBx2JiIjECyWGIjHqiivsfvJkSE8PNhYREYkPSgylWNLT4c47\nYe5cm1KWknPKKdCsGaxeDbNnBx2NiIjEAyWGUixffw333mujV84FHU3pkpBgazsBJk0KNhYREYkP\nSgylWDJ3I6tMTTCGDbOE/F//gk2bgo5GRERinRJDKRa1wQtWo0b2s9+9G156KehoREQk1qnAtRTZ\nxo2wYAFUqAA9egQdTel1/fVw/PEwcGDQkYiISKxTYihFNnOmbTjp3t16+Eow+va1m4iISHFpKlmK\nTG3wRERE4osSQymyww7LWuMmwcrIgAcfhM6dYdu2oKMREZFYpcRQimzcOGvH1rJl0JFIQgK89RYs\nXAivvx50NCIiEquUGEqxOKf6hWFx+eV2/+yzwcYhIiKxS4mhFMmMGbBhQ9BRSHZDhkCVKjBvHvz0\nU9DRiIhILFJiKIW2eTOceSY0bgypqUFHI5mqVIHzz7fHzz0XbCwiIhKblBhKoX30kW12OPFElakJ\nm8zp5BdesKLXIiIihaHEUApNZWrC6/jjoU0bm+Z/772goxERkVijAtdSKBkZaoMXZs7B2LH23+mM\nM4KORkREYo0SQymURYvgt9+shmHbtkFHI7np3z/oCEREJFZpKlkKZcYMu+/XT2VqYkF6etARiIhI\nLFFiKIWydq0VU9b6wnBLSYGkJGjf3qaVRURECkKJoRTKP/4BmzbB6acHHYnkp1o1+OwzWLwY5s4N\nOhoREYkVSgyl0GrWhMTEoKOQ/JQpA5deao8nTQo2FhERiR1KDKXAfv1V05Kx5NJLbR3om2/a1LKI\niMDUqdC0qS2LatrUnksWJYZSIN5Dly7QoAGsXh10NFIQTZtCr16QlqY3PhERsPfC4cMhOdn+riUn\n23O9R2ZRYigFsnixjRgCNGwYbCxScFdcYffPPhtsHCIiQUpLg2XLYNSoA1u5pqbC6NHBxBVGqmMo\nBZJZ1PrUU234XWLDgAFQqxZ8/z0sXQotWwYdkYhI5KWlwS+/wKpVNhLYp48d//13aNfOKmrkRzNh\nWfQnXgoks36hytTElgoV4MUX7ZOykkIRCYOirPFLS8vZ/33aNLjwQuja1WaxEhOhRQtLCG+9Neu8\nmjUtOSxTxl6rQoXcr9+4seq+ZtKIoRzUtm1W+iQhIetTmMQOtcYTkbDIXOOXOZ2bucYPYNAgmDfP\nRv1WrYKVK7Mer10Ln3wC3bvbud9+mzOhTEiw5K5pUxshzH58+XI49FAoW/bA1weoVAnuusu+79xz\n4cYboUqVaP0Ewk+JoRzUxx/bJ7UTToDatYOORorKe9iyBWrUCDoSESmtRo/Oe41f7952y01CAmzY\nkPV80CBo1coSwaZNrU1ruXK5f+9hh2U9TkrKimP1aksm77vPRhyXLIG774ann4Z774Vhw2yksbRR\nYigH9ckndt+vX7BxSNH95z9wwQVQpw7MmRN0NCJSGmVk2AhhblavhkMOsUoKhx2WlfA1a5Z74nfs\nsXYriqSkrAQxu08/hRtugG++sY17jzwCDz1ka+tLE+e9DzqGmNG5c2e/YMGCoMMocenpsGAB1K9v\nn64k9vzxh/33S0219YZHHBF0RCJSmnz0ka39+/bb3L/epIlNGQctI8PWL956a1YSe8UV8MwzwcZV\nVM65hd77zoX5Hm0+kYMqUwaOP15JYSyrVs3WzgA891ywsYhI6fLNN9C3ryWFNWpA+fI5v16pkk3n\nhkFCApx/Pvz0E/ztb1C9et7T2/FKiaHkSwPK8ePyy+3++edhz55AQxGROJd9PeCxx9oH03HjYM0a\n+3DapIl1ZmrSxNb05Ta1G6SKFW0TyooVMGRI1vHRo+Gvf7VNmfFKU8mFUBqnks8+2/4HeOwxaNMm\n6GikOLy3xdo//wxvvw1nnRV0RCISb9atg3vusaL6X38NHToEHVHk/PabzZzt3g316tm/87LLwr1B\nRVPJElE7dti6kE8+sU0LEtucyxo1VCcUEYmkLVtsNO2II+DJJ21t+mefBR1VZNWrZ1U6jjvOksTh\nwy3x/eCD+JpdU2IoeZozB3buhE6dbLeYxL6LL7ZaXl9+aYm/iEhx7NwJf/87NG8OY8faBrcBA+CH\nH+Daa4OOLvK6drX3z1dftd3SP/xgjR9OPTV+3lOVGEqeMtvgqdtJ/Mj8xJucbHW7RESK4/bbbS3e\n5s3QrRt88QX885/QunXQkUWPc3DeebZB5aGHbENNpUrx856qxFDylNkGT/UL48vJJ+fdFkpEJD/e\nw8aNWc+vv96qVrz3ns0ydekSWGglrkIFq3v43//aOvxMX30Fd94JW7cGF1txKDGUXC1fbvXuatSw\n/+kl/vzxx8Eby4uIZPr8c5tK7dkzq69ww4Y2tXr66TaSVhrVrm0/B7DE+cYbrXNKixa24zrWqkAo\nMZRcZU4j9+1ra9Ikvrz5phW8/stfgo5ERMLuhx+sisHJJ9tU8W+/2cCBHMg5K8tz/PGwfj1cdZVt\nUJkxI3Y2qCgxlFwNGmQ7V//0p6AjkWjo0MEWiU+bZrsJRUT2l5wMl1wC7drBu+9C5cpWw2/FCit9\nJbk76SSYPx9ee81a+i1ebCOqffvCww/bppWEBLufOjXoaA+kOoaFUBrrGEr8OuUUWxP05JP2qVZE\nJFN6uu00Xr3aehRfdZVtNDn00KAjiy1pafD44za1vGOHJYTZdy9XqhTdAt+qYygiBXbFFXavmoYi\nArB9u80kgBVtvuUWGDrUdt+CNKhOAAAgAElEQVT+4x9KCouiQgUYNcrW7VevfmBJm9RUq/8YJkoM\n5QAPPWT1p378MehIJJoGDrQ3qm++gUWLgo5GRErK1Kk5pzNffBEmTrTi1OPHZ5139dV2bvPmQUUa\nP2rVyrmbO7vVq0s2loNRYigHmDzZtt5n73Up8ScxMWv6QqOGIqXD1KnWsSM52TZDJCfDsGGWBP72\nG8ydGzubJGJN48aFOx4UJYaSw+rVsGQJVK0KJ54YdDQSbZkt8p55JtyLoUUkMkaPzpouzuS9VZ94\n4w2YObP0lp2JtvvuszWF2VWqZMfDRImh5JBZpqZ3b1twLPHtxx+hYkVb95I5ejB8uJJDkXiV17Rl\nerpVo1BSGD1JSbbRpEkT+zk3aRLdjSdFpcRQclC3k9Jl9GjrdZpdGBdDi0hkxMp0ZrxKSoJVqyAj\nw+7DlhSCEkPJZtcumDXLHisxLB3yGj0I22JoEYmMWJnOlOAoMZR9vvgCtm2DNm306bG00OiBSOmx\ncaNNF8fCdKYER4mhALamLPONYd06rTErLXIbPUhM1OiBSLzJyIAhQ+C44+DYY8M/nSnBUWIo+8oX\nrF1rz1NStAGhtMi+GDrTqafqD4VIvBk/3jodrV8PNWoEHY2EmVriFUK8tsRr0iT3NWVNmtinSSkd\nPv8cTj4ZateGX36xkUMRiX3ff28jhbt3W8/jM84IOiIpKWqJJ4W2aZM2IIg56STo3Bl+/x2mTAk6\nGhGJhB07bAZg924YMUJJoRycEsNSbMEC6NQp769rA0Lp4hyMHGmPH3nE1h+JSGy79VZrWtCypbU7\nFTkYJYalkPfW6eKkk2xUsHnzA6cNVb6gdDr3XGjdGvr2PbC+oYjEloULYcIE62oydeqBG81EclM2\n6ACkZO3YAddcA889Z89HjICHH7ZWSKNHW6LYuLElhdqAUPqUKwc//GDt8UQktnXsaH3Qt2zJf3ZI\nJDttPimEeNh8sn69vVls3gxPPQUXXxx0RCIiIhINRdl8ohHDUubQQ2H6dKhQATp0CDoaCaudO+Hl\nl62E0e23Bx2NiBTG22/bmsKWLYOORGKRRgwLIRZHDDMy4N57rUH6PfcEHY3EimXL7I9K+fK2vOCQ\nQ4KOSEQKYvlyaN/e3vsXLYIjjgg6IgmSytVIDps3w5lnwl13wdix9oYhUhAtWtjvTloaTJwYdDQi\nUhB79sCFF8L27XD22XD44UFHJLFIiWGc+vZbW2w8Y4YVLH7/fb1JSOFklq554gntUBaJBWPHwpdf\nQsOG9oHOuaAjklikxDAOPfccnHiidS3p3NlKFvTtG3RUEmt69LApqQ0b4JVXgo5GRPLz5ZdZy4Ve\neAFq1gw2HoldSgzjzIQJcPnlNgU4fDh89lnOPrgiBeUcjBpljx9+2Opfikj4bNtmU8jp6XDDDdCz\nZ9ARSSxTYhhnzj/fFhs/95yVo6lYMeiIJJadfz7Uqwf/+Q/Mnh10NCKSm6+/hjVr4Oij1ZhAik/l\nauLAl1/alHHZsrZ7dMkSK1QsUlzly9u6Je+ha9egoxGR3PTsaUuGwEqRiRSHRgxjWEYGjBlj6wmz\n15pTUiiRdOmlcNllGn0WCbM2bewmUlxKDGNUSgr07w933GHPK1fWGjCJvt27g45ARMDe7887zzaa\n6L1fIkmJYQz6/nubOn73Xdt59v77liCqNIFES0aGla9p1Ah+/z3oaETkiSdg2jTbILZ5c9DRSDxR\nYhhjXnwRunSBFSus5/G330K/fkFHJfEuIQGWLrVe2089FXQ0IqXbkiVw4432+OmnrVatSKQoMYwh\nGRkwdaoVG778cpg3D5o2DToqKS0yC14/9hjs2hVsLCKl1a5dkJRkfwcuvRQGDQo6Iok3SgxjSEKC\nJYYvvgiTJmkzgJSs3r2hbVtYtw5eey3oaERKpzvvtOVEzZvDo48GHY3EIyWGITdrln0izFz0X6cO\nXHRRsDFJ6eQcXH+9PVbBa5GSN2cOPPigDRJMmQJVqwYdkcQjJYYhlZFh9eNOPRWmT4fJk4OOSMSm\nsOrWhe++g7lzg45GpHSpWRNat4bRo61MmUg0KDEMialTbb1gQoLt/Dz2WPufPyPDpg4uvzzoCEVs\n+cLVV9vjDz8MNhaR0qZ9e1iwIKtMmUg0qPNJCEydan2NU1Pt+a+/2i0xEV5/Hc44I9j4RLL7v/+D\nM8+0kkkiEn2//goNG9rjxMRgY5H4pxHDEBg9OispzK5mTSWFEj516yopFCkpq1fbpq+LL4a0tKCj\nkdJAiWEIrF6d+/F160o2DpHC+v57+N//go5CJD6lp1tCuGWL3cqXDzqiOLJuHXTvDr/9FnQkoaPE\nMAQaNy7ccZEwuPVWOOYYK7ArIpE3frxt8jr0UCtRpu5WEXTvvfD553DPPUFHEjpKDEPgvvugUqWc\nxypVsuMiYdWtm93/4x/qoSwSad99Z8uMAJ57zpZwSAQkJlqGPXGi7e6cONGea/HmPkoMQyApyUZd\nmjSx388mTex5UlLQkYnkrV8/aNnSFsa/+WbQ0YjEjx077P1/926rAnD66UFHFEcy+8lmqlDBftgr\nVwYXU8goMQyJpCRYtco+wKxapaRQwi8hIatN3vjxKngtEikPPgg//gitWsHf/hZ0NHGmbFn44Yes\n52lploHXqxdcTCGjxFBEiuyii6BWLfjmG/jii6CjEYkPN9wAV11lpcz2X2YkxXTHHdZwumFDOOUU\nO/b998HGFDJKDEWkyCpVgj/9yR4//HCwsYjEiypV4Mknc854SgSkp8OGDTZqOHMmvP++7er56aeg\nIwsVJYYiUiz/939QowbUr6/pZJGi8t6Swe3bg44kjpUpYz1mlyyx3oIVK1pbsczt3ps3q/4WSgxF\npJgaNIC1a213ssppiBTN5MkwYgT06GFrzSWKWrQ48NiaNXDyyXD22bb7pxRTYigixaZKDyJF99//\nwp//bI+vu842dkkE7dwJ559vjabzsmePVRH/7DM477xSXYOrUL9+zvRxzj3onPvSObfWObfLObfF\nObfMOfe6c26Ec+6waAUsIuG0axdMmQKvvhp0JCKxY88euPBCm0I+7zxVpIiK8ePhtdds2jiv9S5N\nmti6w1q14J137NxSOnRboMTQOVfJOXcrsAL4ALgROA6oAWwA9gDNgUHA48BK59ybzrkToxK1SKSp\nPVKxzZxp7btuu83+2IlI3qZOhaZNoVw5+OorqFkzq9ayRNDatTB2rD0ePz7/H3CbNrYhpXJl+5R7\nww2lcuH0QRND59ylwDJgLLADuBvoA9Tw3lfy3jf03tcGygJtgMuAN4HTgM+cc6855wrd3M05d5tz\n7hvn3B/OuY3OuXecc233O+de59xPzrntzrkU59zsgyWjzrkezjmfy61VYWOUOKL2SMV2+ulwxBFW\nh/Nf/wo6GpHwmjoVhg+H5OSsY6mplpNIhN12mw3HDhgAvXod/Pzjj7cNKuXKwSOPwP33Rz/GkCnI\niOGzwFfA8d77Nt77e7z3s733f2Q/yZufvPfPe+8vAOoB1wNdgWFFiK0H8ARwItATG5Wc5Zyrle2c\npcD/AUfvfZ2VwAfOuUMLcP2jgPrZbsuKEKPEOrVHipiEBFsfBSpdI5Kf0aMtEcwuLS2rBZ5EyNdf\nw4svQvny8NBDBf++vn3hpZfsb8GmTaVu1ND5g/yDnXMdvfffFvkFnKsINPXeF6tQkHOuCrAFGOC9\nfyePc6rtPaef9/7DPM7pAXwC1PXebypMDJ07d/YL8lu8KrFn7Vro2ROWLs065px9unz1VXtDkQLb\ntg0aNbKKD19+aR++RSSnhITccw3nSu2ytsjzHk480d6IbrkFxo0r/DW+/RaOOSam5/edcwu9950L\n8z0HHTEsTlK49/t3Fjcp3KsqFm9Kbl90zpUHhgN/AAUpY77AObdu7/TzKRGIT2LRo49mJYVly9q9\n95bZZE8K09NLPrYYVKUKXHmlPdaooUjuGuexuCqv41IEP/1kre/q1Sv6UGzHjllJ4YYNttyoFCjo\n5pOznXNBb6B/FEv45mc/6Jw70zm3DdgJjAT6eO/X53OddcAIbKPMQGw6erZzrltuJzvnhjvnFjjn\nFmzcuDEC/wwJlQYN7H/8006zUgZXXw2nngoTJmSd88030KyZNS1NyfVziWRz7bVWR/aNN2D16qCj\nEQmfW2+12srZVaoE990XTDxxqXVr+PlnmDYNqlYt3rU2boSuXaFfP5uejnMHnUoGcM5lAGuAycCz\n3vvkg3xLRDnnxgPnA1299yv2+1plbI1gHeBKoBfQxXu/rhDXfx/Y470/O7/zNJUcp1autMQvLzfc\nYLvZwN69hw2zxXRHHlki4cWi226z6g8XX6xeryL7e+ghuOkmy1e2bbORwvvuU6ma0MrIgEsusXWH\ntWrZyGHr1kFHVSBRmUreazbQALgdWO6cm+GcO8c5V6awQRaWc+5h4AKg5/5JIYD3frv3/r/e+y+9\n95cDu4ErCvkyXwG5lEKXuDR+fM41hfklhWAjhe+9B3362IrxJ56Ali3hzDNhzpyohhqr7r/feigr\nKRQ50Guv2f3kyZZzrFqlpDBikpPh8ccjWzMrIQGeew7OOMPa5vXpk3NLeZwpUGLove+D1Skci03F\nngq8AfzinLvPOdc8GsE55x4FhmJJYUHXKSYAFQr5Uh2wf5fEuzFjbASwV68DtwXmJSHBarHMnAn/\n+Q9ccQVUqGDJ4ltvRTfeOFDKNvSJ5Gv5clu1UqWKva1IhN18M1xzjU1bRFK5cjYt3bWrtc/r29em\nmONQgdcNeu+Tvfe3A02A/sC72PTtbcDPzrmZzrnBzrmykQjMOfc4cCk2WpjinKu391Zl79erOefG\nOOeOd841ds51cs49BzQEpmW7zovOuRezPb/eOTfAOdfCOXeUc+5+YADwWCTilhAbNw7uuMPWFD7w\nQNGGs9q2hWeegV9+sSTz2muzvjZtml1/nT5jgE2R3XwzHHec9u6IZJq296/T2WerKlbEffqp/YAT\nE7N6DEZSpUrWFaVdO1u/2K9fwQcYYkihN5R47zO89+947/sDjbHp5WSgN/AasMY594BzrrhTs1dj\nO5FnY6N5mbcb9359D1aL8J9YDcJ3gNpAN+/9omzXabz3lqk88BCwCPgMq394hvd+ejHjlTB76CH7\nBOkcPP988edt6ta1nW7N9w6We2+J4pgxtrjuootg4cJihx3LEhPh9ddtdOTdd4OORiQcMqeRzzsv\n2DjiTno6XH+9Pb7lFqubFQ01asCHH8Lhh1tiGIfZfYE2nxToQs71wjZ/9MeSL++9j8joYVho80mM\neuQRGDnSHj/3HFx6aeRfw3uYN89e65//zCpGdvLJ9mbVv79t1S1lMn/03btrOabI0qXQqhVUrw7r\n19uKFImQSZOsVlajRlaqJtoLnLdssf+QIRfNzScFMRdrhfdvwO29iQRr0aKspPDpp6OTFIKNRHbt\najVa/vtfGDUKqlWDzz6DQYPglVei87ohd9lltvNy7lyrFStSmv30k+USAwYoKYyoLVuyahU++GDJ\n7HrLnhSuWWPtVONkQXWxE0PnXEvn3N+wcjavAsdhrenuKO61RYqtXTt47DHbSZxZeTnamjWDv/8d\nfv3V6iF26QKDB2d9ffp0Sx5LgWrVbK8OqOC1SP/+NlL44INBR4Kthe7eHX77LehIiu+ZZ6wA9Ukn\nlfwcfXq61b7961+tQGUcKNJU8t42d0OwsjAnYaODu4G3gWe89zMjGWRYaCo5hmzdWvyiptGwZQs0\nbGhN3c86y6aZe/SwN+fzz7cFSPXqBR1lRK1aZctxEhLs8WGHBR2RiHD11fDUU3DVVfbBOZalp9sy\noU6drFtJSZsxw3YT7dljGxtvvrnkY8hD1KeSnXMd9u4WXocVu+4KrMB2Jjfy3p8br0mhxJBnn7WF\nPD/+GHQkB0pNhXPPtdIHb79tfZo7dLDNMJ9/btMRcaZpUzjnHHvPnK4tXlJK/fCDfR4MXGKiLX2Z\nONHWQk+caM9jeRNFmTI2IxREUgjWOevFF+3neMst9jcohhW0Jd5VzrkFwEKsnVwi8DrQ23vfwnv/\ngPd+QxTjFCmY55+3N4i1a+GTT4KO5kD169sn29Wr4e677diiRRZrvLxJ5+Luu21vzjXXBB2JSMnz\n3j4cHXKIJYiBWrECLrgg57ETTrAOULHm++9tyU4YXHBBVivV4cNtE2KMKuiI4USgI1YW5iagoff+\nfO/9x1GLTKSwXnrJdjt4b8P5V18ddER5O/RQuPNOezM+4YSsRu2JiTZ6GItv0vk46ig48cSsf6ZI\nafLdd7asuEoVm8wIVP36tswm+/+MX35p06GxZM8ee69s2RK++CLoaMw118Bdd9mH/PPPt1qHMaig\nieErwCne+1be+7977zdFMyiRQnvlFetl6b01HQ3RGo98NW1qU8nO2TbFtDTbsZGYGLdVoX/6Kaua\nj0hpkFm7cPBgKBuGIm4bN8KIETbidvzxduzyy202I1Y8+SQsWWKJbqdOQUeT5c47LUG84w5oEZud\ndotVx9A5Vxk4Eqjivf8sYlGFlDafhNS0aTaMn5Fhc5Z33hl0RIUzcKC9uQ0fbiV11qyBlBSoXBle\nftkKqsaJK66w5Tfvvad2YFI6eG918FetsrJN3boFHVEuHnjAdtSeeiq8/77tFAuz33+3pCslBf71\nL9vuHSbeh2Z6pMTqGDrnGjrn3gRSgAXAJ9m+1tU5t8Q516Mo1xYptN9/t6TwzjtjLykE25Hx+OPQ\nvr3d338/LF5sUzvHHWefiuNEy5Z2P358sHGIlJSvv7aksEEDK3UauNRUm1XJXlj0llvg1VdtXVzY\nk0Kw0jApKdC7t+0GDpvsSWFyMvTpY21UY0ShfwOcc/WBr8jqlzyfnMWsvwIOAdTwR0rGiBHw1Ve2\ntiMetG5tfeTat4dly2yq5623go4qIq680gZCZ8+2PTci8e7VV+1+yJCQ5Fxz5sDtt1uZmuzOOy9r\n01taWnjXHP7wg00jJyRYcdSQjMzl6aabYNYs6NsXNsXGKryi/Jr+FUv8envvBwIfZf+i93431oP4\npOKHJ5KHGTNylqM57rjwv0EURtOmtqD6vPNg2zZrlXD33TG/OK9GDdsfBCp4LaXD8uV2H5reyJkJ\n32mn5f71jAwrqXX66ba0JWxuvNHWX//pT9C2bdDRHNxTT1mcP/1kP9OtW4OO6KCKkhieDrztvZ+T\nzzmrgQZFikjkYGbMsESpRw8rSxOvKlWyTTXjxlnSe9ddcdFa77rr7J/z8svx0XRBJD9vv5018B8K\nmYlhXot8ExLglFPs8VVX2ehcmDzwgK0pzCz3FXY1a8KHH1pHrG++sTXlaWlBR5WvoiSGh2Jla/Kz\nG6hchGuL5O/DD60g2K5dVg6gfv2gI4quzIKp778PF154YP2xGHT44bYsaNcuK9soEu+OOCIkExrL\nltkQZu3acOyxeZ83cmTWkP6IEeH6H7V9e9twUqdO0JEUXIMGMHOmlSmbNQsGDbJdSCH9ZFyUxHAz\n0Ogg5xwJhPNfLLFr1iwbKUxLsxqFjzwSknfbEtCvH0yZkrVIad06+Oij/L8nxEaOzKrOIxKP0tND\n2Hzp/fftvm9f6xaSn+uvt/dYsPfbxx+PbmwHk5wc7OsX1xFH2MBG9epWliHEna6KkhjOA852zuXa\n0NU51wLoR7adyiLF9vHHNsy0c6dNb/zjH6UnKdzfrl32ibNfP3jwQSuNEGO6dbOqPOPGBR2JSHTM\nnQtt2thn2dA42PrC/V13XVY3j2uuCW4T3Pr1cPTRFveOHcHEEAknnABbtthj70Pb6aooieHfgIrA\nXOfcaUAlsJqGe5+/A2QAf49YlFK6/fabJYU7dlghvCeeCMn2voCULWv1xjIybJp56FArQRFDnLPZ\nLJF4lVnU+qijgo0jh4YNoW5de/8oqGuvtQ/i/foV7vsiafRo27RRrlzokqhCWbHC3q8rVbLnlSqF\nstNVof+6eu+/AoYDTbFyNTfu/dIfe583Ay733i+OUIxS2tWrB3//u1Xmf+qp0p0Ugv37//pXqzlW\npYrVwzjpJCuWFmOmTLG/UwkJthF76tSgIxIpvt274Y037PH55wcbSw6TJtkH7UMOKdz3XXONTX9W\nrGjPS7I6wsKF1pGlXDn7OxDL6te3zlY7d9rPcudOe14v1wnYwBTpL6z3fjLQFpgAfA0sB74FngDa\nee/19i7Ft2dP1uOrroJnnlFSmN2AAVa/8YgjrLXVscdajbIYMXWq5fqbNtmsSnKyNX9RciixbvZs\n2LzZSpKGrqJKUd9DM78vLc1mcB59NHIx5cV7m872Hv7855htMZfD+vVWaufLL+0+hBtQity10Xu/\nDBgZwVhEsnz1FVx0kY2KZc7FlNY1hflp08ZaKwwdCh98AJ99ZmV8YsDo0Taykl1qqh1PSgomJpFI\nyJxGPu+8EL1tvf++tV6pVq1415k1y0YP33vPRg5HRjENmDYN5s2zaYU77oje65Sk6dOzHge9oScP\nGn6R8FmwwNayLFsGjz0WdDThV7MmvPsuvPiiZVUxYvXqwh0XiQVpafZ5FkJU1HrZMjjjDDjyyOJP\nA59xRlZtw1GjotffMjXVuoaAtfCrXj06ryMHOGhi6Jwr9krPSFxDSoF166BTJ+jVy3ZuDR5si57l\n4MqUsRHWzOmeX36x6Z41a4KNKx+NGxfuuEgsWLLEcq927aBVq6Cj2StzN3KPHpFZjnPVVbbeG+CG\nG6K39u/CC60yeGa7JCkRBfkNWemcu845V6GwF3fOtXfOvUXWBhWRvF13nTV2/+MPK2L98su2A1cK\nb+RIeOcd6NzZWuuF0H33ZW3Oy1Shgh0XiVXHHAMbNmRtPgmFwpapKYjhw7Na5t14I/ztb5G7Ntib\nw9ixNpV8sJqLElEFSQxnAuOBdc65ic65U/IbAXTONXfOjXDOzcc2pLRHNQ0lP4mJthDn9dezjv3z\nn8VfC1OaPfmktbX67TcbJXjmmaAjOkBSkv1dadIk69hRR2l9ocS+ihVDtE9ix46sTWn9+kX22lde\naTudnYNPPrGq3pGwa1fWYyWFJe6giaH3/mLgeGABVqZmFrDFOfdv59wHzrlXnHP/dM596pxbj7XL\nexwrZzMaaOm9/zxq/wKJfStWQMeOWc8TE0NZ2ymm1KljVfavu852eAwfbq2tsr/hhkBSklXZ+e03\n6NABLr446IhEim7t2hDWX54zx8qidOxoLdki7fLLbSPK9OmRSeK++AKaN7cyXBKIAi028N4v8N73\nBVphBa7/DbQB+gLnAf2BrntPnw4MBRp778d579X0SvJXvz4cd5x96szskxbC2k4xp1w5a2k1ebL9\nXJ980lphRepTfQQdeih8953lsSKxauRIKxH47rtBR5JNNKaR93faaVk1DnfuzNqWXVgZGfYmsGaN\nLdaUQBRqAdfeEjW3AjjnKgGHAbWBHcAG7/26iEcopcP69TailbluZZ1+lSJm2DArazNwoO0o1NSM\nSMRt324JYWqqdW8LjV9/tfvTT4/+a3lvmwbfew+WL4e//KVw3//ii1aV4rDDrKuTBKI4dQxTsWnj\nZZELR0qdN96wMgQxUNspph13HCxaZKVtMm3YUPgOCFG2fDm88IL9bWnXLuhoRArunXcsKezSJee6\n2cBNn25z3CXx/7pzcO65VjNx9GgbAbz99oJ979atcNtt9njcOKhcOXpxSr5Ux1CC87//WeX3vn3h\ncy1DjbpatbKq7a5caaOIN9yQs8NMwCZMgHvvDeVeGZF8ZS9qHToNGpRchYdLLrFPd85ZUep77y3Y\n940da4uNTzjBCvZLYJQYSnDGjIHff4fu3a3Xr5SchQutVuT48bZTcfFi++8QcHumYcPs/uWXbamp\nSCz44w9bypc5YBYaa9fa9G5Ju+gimxZOSIA774S7787//OXLswplP/qoWp8GTD99CcayZTY85Jy9\nIYSmb1QpMXiwlZc45BBr7Nqli7XTu+eeQMPq0MGmkDdvDtkCfpF8vPWWfZDp1s0G50Jhxw44/HDr\ndhLEVukLL8xKDu+6yz7t5WXTJqtsf9FFtuxFAqXEUIJx881WRmXYsJylaqTkdO1qo4Zg63u8h4kT\nLUlPDKZZkXNw6aX2+PnnAwlBpNC++cbuQzWNPHeu7RCuVi2w/59JSoIpU6wL06BBeZ93/PHwww82\nWCCBU2IoJe+TT+Bf/7LFxWpzEayVK230MFMIakgmJdlyqBkzAp/ZFimQCRPgp5/ggguCjiSbkihT\nUxBDh9r7fYW9zdN27859ertCBahRo2Rjk1wpMZSSN3as3d96q9UwlODUr2/FsDNljjAEWEOybl04\n80wrt/jSS4GFIVIoLVuGLK8JS2IIWUuFdu6Es86ydYdr19o096WX2lSyhIYa0UrJe/11W2B8ww1B\nRyJgNSQvvBD+/Gebvw1BDckrr7RWqSeeGHQkIvlbs8bK7oXK8uW2jrtGDZumDYv582HWLOvK9Pbb\nFuOyZfZJML+pZilRzkdwx5JzrgLWHQVgqfd+Z8QuHgKdO3f2CxYsCDoMEREJgY0bbdC9UyeYN6/k\nKsIc1GOPwbXXwpAhRe9CEi3ly9t08v4qVgxhP8HY55xb6L3vXJjvidhUsnOuD/AL8C3wHfC7c26S\ncy5cFXQlOJ98ov/xw27nTqu9ISIH9eabtuShdu0QJYVglQagZLqdFFZysm18y1SxYuDrmiWnSK4x\nfAyYC7QAGgMX7X38rXNOC8lKu1WrbK1Lq1ZKPMLq8cftL1xIdgZ6b2vWL7jA8lWRsAltUetXX4WP\nPrIp2rCpXx/atrV1h+XLw65dga9rlpwimRg2Bm7z3q/w3v/qvZ/uve8OfIIljVKa3XKLFfrq2tXe\nBCR8Gja0nl7vvx90JID93bjnHvsb9847QUcjktO6dVYRpnx5GDAg6Gj2U6EC9O5tH/TCaP16GDEC\nvv7aul+p/ECoRHLw+yanlNcAACAASURBVD9AQ+C/+x0fAyyM4OtIrJk3D6ZNs1Io48YFHY3kpVcv\nKFcOvvzSdglm360ckGHD4LvvYPLkkHWUkFLvjTdsVPu006zde2h4H/6GAdOnZz1+/PHg4pBcRXLE\n8GXgGefcEfsdrwukRPB1JJZkZMDIkfb4xhuhUaNg45G8ValibfG8t12DITB0qOWqH35o1S1EwiK0\n08idOllB6Q0bgo5EYlQkE8PxwOHAEufcm865251zdwLPAKpLUlpNnWptAerXt24nEm5nnGH3IZlO\nrlPHyp5lZKimoYTH//4H//63TYKcdVbQ0WSzfLkNsX/2GdSqFXQ0EqMimRjWBfoCfwF2AOcDdwBH\nAH91zr3qnPuLcy6Eq2ElKnbsgNtus8djx9qIlIRb5i7GDz6w7ZYhMGyY3U+enHvDBJGSVqOGLZOb\nPTtkb2uZRa379AnZNmmJJQVKDPcmdK3yO8d7/7v3fpb3/iHv/YXe+7ZAFaAL8DCwCTgd0Of+0qJi\nRStkPWgQXHxx0NFIQbRoAYcfDps3w8JwLA3u1w8OPdRajn39ddDRiJhKlaBLl6Cj2E+Yup1IzCpQ\ngWvnXAZwl/f+nmzHEr33paoonQpcS6kwdy40aGBJYkg8/LBVtbj0UjhElVElQFu32rrXihWDjmQ/\nO3fa9PGOHbYgV+1GhZIvcH2zcy7X1a3OuXrOucrFuLbEOpUfiF3du4cqKQTbv3TLLUoKJXiPPWa/\nhxMnBh3JfubOtaSwQwclhVIsxV1jmFeRpKuA/xXz2hKrvv4aGjfWZpN4oEV9Ijm89pqNGoauP7Km\nkSVCIrn5pCSvLWHlPYwaZb0ww15LS/I2ZQq0bw/PPRd0JPts3mwFr6+8MuhIpLRautR2I1evDqee\nGnQ0+7n2WnjkETj//KAjkRin5E0i6/XXraB13bowenTQ0UhRbdsGixaFpmxNpvvug2efhV9/DToS\nKY0yaxcOGGDNRULl8MPhuuugXbugI5EYp8RQImfnzqzp4zFj1PoulmWWrfnoI9v1EQK1akH//jYo\nPWVK0NFIaRTaotYiEVSYxFCLjSR/Dz8Myclw9NFw+eVBRyPF0aQJHHWULaaaNy/oaPa59FK7V01D\nKWk//ABLltgHlN69g45mPyNHwq23WgNnkWIqTGJ4u3NugXNuonPuMqBxtIKSGPTbb1bEGixBLFMm\n2Hik+DJHDUM0ndynj224XLYM5s8POhopTWbNsvuBA61cTWjs3AlPPQUPPBB0JBInCpoYzga2Ah2x\nHcfPAMMAnHNznXMTnHOXO+c6O+fCtvJCSsKePdC3r/WH6tUr6GgkEkKYGJYtCxddZI+ffz7QUKSU\nuf56GzUMXbGFTz9VmRqJqAIVuN53snPNgc7ZbscA1fd+OfNCGcA2oJr3Pq6GjVTgugDS0kK4KluK\nZPduqF3bppNXroSmTYOOCIAff4Q2bWwJ67p11oFCpNQaOdJ2I992W9asjcheRSlwXahmit77FcAK\nYFq2Fz2SnMliByxZ1Aqg0iDzg0VmaRolhfGjXDl48EEbhTj00KCj2ad1a/jLX6BrV/26ScnYssVK\n1ISS6hdKhBVqxLBAF3TOAa2BTt77uNo7qBHDXLz1Ftx/v/VEPv74oKMREYko760STM2a8M471i0y\nNFauhObNLWvdtMnWWohkE/URw4Lwlmku2XuTeLZrF9x4I/z3v9btRImhiMSZr7+2/CstLVQD5yZz\ntLBPHyWFEjGqYyhF9/jjlhS2agV/+lPQ0Ui0fPABDB0Kn3wSdCQ5LFgAgwfbbLdItGTWLjz33BAW\nWzjmGBg+XN1OJKL0EUOKZtMmuPtue/z3v4esfoNE1Lx58MorthHllFOCjmafTZvgzTfh++/hppvU\ngVEiLyMDpu1dUR/KotZduthNJII0YihFc9ddtiK7b18teo532cvWhKiqdJ8+tt5r+XL4/POgo5F4\n9MUXsGYNNG4MJ5wQdDQiJUOJoRTekiXw5JOQkGCjhRqqiW/HHWejhStWwM8/Bx3NPmXKwMUX22PV\nNJRoyJxGHjIkhG9zL71kvSFTUoKOROKMEkMpvJ9+suJxV10FbdsGHY1EW5ky0K+fPQ5RsWuAYcPs\nfto02L490FAkzniftbcjlEv4xoyxT0b/+U/QkUicUWIohTdwoG06GTMm6EikpGROJ7/3XrBx7Kdl\nS1titW2brTcUiRTn4N//hunToWPHoKPZz8qVsHSpVXnXGkOJMCWGUjSHHGLd5KV0OPVUWzrw6afW\nCSVELr3U7jWdLJFWuTKcc04Ip5Gzl6nRxj+JMO1KloJ7/nn44w8YMUJvRqVN7dq2dKBhQ+uLHSJD\nhsCGDVk9lEWKKz3dfs1D21nngw/sXhv/JAoi3vkknpXqzicpKdCiBfz+O7z7LpxxRtARiYhExcyZ\n9oHjuuuyqnKFRlqafVDbvh1+/RUOOyzoiCTEitL5RFPJUjD33mtJYY8eWevNRELG+1BV1JEY9eqr\nVo0rdFPIAJ99Zklhu3ZKCiUqlBjKwS1bBo89Zu+SDz8c0ndLKRE//AD33WcL30PmqadsM8qnnwYd\nicSyXbvgn/+0x6Esag1w0klw1llBRyFxSmsM5eBuugl274bLLoMOHYKORoL08MPw3HO2EeW224KO\nJodffrHPMJMnQ/fuQUcjsWrmTPjf/2xArnXroKPJRe/edtPQuESJRgwlfx9/DG+9BVWqqDyN5OyC\nEjKXXGL3b7xh5WtEiiKzqHVoRwszaeZGokSJoeQvswbIbbdB/fqBhiIh0KcPlC1rvcJC1nGhRQvo\n2tWWX73+etDRSCzascM+B0NIE8Nvv7VbRkbQkUgcU2Io+Zs82VovjRwZdCQSBtWqwckn2x+mmTOD\njuYAmZ1QVNNQimLOHCvT2akTHH540NHk4q67LLgpU4KOROKYEkPJX5kykJQEiYlBRyJhEdIuKADn\nnmu/qp9+CsuXBx2NxJp+/WDRIltKGzppaba0B2yNoUiUKDGU3E2bBqtXBx2FhFFmYjhjRuimtKpV\ng8GD7fErrwQbi8Qe5+Doo21QPHRUpkZKiHYly4FWrrQ2EmXK2LCL1hZKdq1b23RWq1ZW7K1mzaAj\nymHUKEsO1RRCCmPXLihfPugo8pHZBq9fv2DjkLinxFAOdMst9i6ZlKSkUA7kHIS4A1CHDqqqJIV3\nwQVW8ujJJ6Fjx6CjyUVmYqhPPBJlSgwlp88/ty2diYlw//1BRyNSLGlpIe53K6Hxxx+2ZHbXLjj0\n0KCjyUVyMvz4I1StasWtRaJIawwly5o1WZ9Gb7oJGjUKNh4Jt61brUXE778HHckB9uyxEaAGDeyP\nvkh+3nrLPkScfHJIl+8tWwa1atmmk3Llgo5G4pwSQ8ly8cVWGbhSJbj55qCjkbC74AIYOBDefTfo\nSA5QtiysWwebN6umoRxc6Ita9+4NGzbAM88EHYmUAkoMS6OMDJuamDkTVqywaWPnskohpKZapxOV\nqPn/9u47TMry+v/4+7B0EVFEiriA2LtxVRClKVhQIXYl9l6isUdNjD1EbHwNakgsqNjFghVLjKKi\nghoriixFI0WQokhZ4P79cWZ/O6y7sAuzcz8z83ld11wz88zMM2eXYffsXc6RVSlfBJ/ALiigmoZS\nM3Pn+o/CevXg0ENjR7MKRUXQsmXsKKQAKDEsBCNGwJ//DEccATvu6Elfx46w774+FVhaCsccU7El\nr2lT33gyeXLUsCXhysvWvPyyz90mzGGHwTrr+LLZiRNjRyNJ9dRT3gq+V6+Eri/84QdftiGSJUoM\nc11ZGXz9NYwaBTfdBKeeWlHIrdwf/+h9jh9/3Ku3LlrkPwG7d4eNNvKdx82b+y/3xo1h8WK/36ZN\nnK9JcsOmm1aUrHnnndjR/EqzZhX/FYYPjxuLJNfIkX6d2GnkQYN8pPCuu2JHIgVCu5KTZPp0OOoo\nX/CSnpSF4OtL6tevmEp46invXzxpUtWjNQsX+nAJwGmn+Xa7Lbf0yxZbwHrrrfz8mTPhjDP8ucOG\neSwiq3PAATBhgk8nd+8eO5pfOfFETwqHD4err/bZOJF0Dz/sf1fvu2/sSKrx0ks+ALDVVrEjkQJh\nIYTYMeSMkpKSMK4u67eddZb/Vdinj/+S/eqrisv8+f6X46WX+nOffRb69/fbxcUVSV954tezZ8Kr\ntUpeeO01Xxi//fY+Gp0wK1bAZpv5qoj33oPddosdkSTFiBFwxRXe4Km4GK6/3lfQJMq0adChg5ep\nmTNHO5Kl1sxsfAihpDav0YhhEjRp4tO35UaP9ku65s29nkK5Hj3g449h8819TaBIDHvu6b+0iopW\nHqVOiHr14J57/Bf/ppvGjkaSYsQInxz55Re/P3Wq34eEJYflRa1VpkaySIlhEpSWwkUXwdNP+0+q\nevV81O/kk32IY4stfE2gWcVr1lvPN5KIxNSokY9qtGgRO5Jq9ewZOwJJmiuuqEgKy/3yix9PZGKo\nbieSRUoMk6B888fixb75Y+lS3yJ30UWxIxNZvQQnhelC8NIkG2wQOxKJbdq02h2PYulSX6oBSgwl\nq7QrOSnKN3+MHevXM2bEjkik5kLw3fHpyx0S5IsvYLvt4MADY0eyshEjvHJUvXp+PWJE7Ijy3/Ll\n1S+/Li7Obiyr9OGH3nBgu+2gffvY0UgB0YhhUpTXTAAYOjReHCJrYsAA3xA1erRvnkqYDh3g2289\nQfzqK9+jFVvOrHPLM1dfXfXfL02b+gaUxOjSxduU/u9/sSORAqMRQxFZe9tu69cJ7YKyzjpe3x2S\n0wllVevcpG68/LKXdK1XDy6/3P9gMPPrYcMSmJC3awe77ho7CikwSgxFZO2Vd0FJaGIIFS3y7r/f\npxNjGTsW5s3LkXVueWT5cjjvPF/1cM01Pjo4ZYqXNJoyJWFJYcwPqBQ8JYYisva6dPFNKF9/Dd98\nEzuaKnXr5jUNv/8eXn01u++9YgU8/7xXmeraFf7xj+rXsyVqnVseKSryEcOLL/beAIl2993+Yb37\n7tiRSAFSYigia69+/YrWEQkdNTSrGDW8997svOfSpT5CucMOvvHlzTe90lRRkY9YVS5B2qRJwta5\n5ZkOHeDGG30qOdFefNG7WmnkUCJI+n8PEckVOTCdfOyxniCOGeNdxurSE09A585w/PHw+eew8cbe\nznzaNK9ENXCgr2vr0KHiNb17J2xKMw8884x/33OmydfSpRVD2ipTIxEoMRSRzNhvP8+6xo2r+6xr\nDRUXwxtveE35umgkkZ58NGoE330H22zjI5SlpXDhhV6ytNzAgb6+7b33/P6YMfDTT5mPq1CVlnpi\nfvHFKxd+SLQxY7xMzbbbwiabxI5GCpASQxHJjI02grff9vIaCW7f1b175tuIT5zo5UdPP73iWL9+\nvqbt0099CntV77nbbt5dcP58GDUqs7EVqiVLfCf6/PneVv6QQ2JHVEPqdiKRqY6hiGRO166xI6ix\nBQt8YLNlyzU/xwcfwN/+5qNRIXg+fMMNsOGGvo6tb9+an2vwYD9HDn0LE+3CC2H8eC8cfu+9K3cU\nTTQlhhKZRgxFJPOWLEn0oq577oE2bXwjQm2F4L+7e/Xykb4nn/SE8JRT4JNPPClcE126KCnMlEcf\n9T4BDRvC44/D+uvHjqiGvv3WF6Q2a+ZDyCIRKDEUkcw65RRvSPzFF7EjqdZWW8GiRfDAA7BsWe1e\nO2WKTxO/8YavF7z0Upg8Gf75Tz9vJkycmJnzFKKvv/aPIMCtt0JJSdx4aqV1a3jlFRgyJPPrHURq\nSImhiGTWsmXewiPBu5O7doUttoDp072L36osXOglZ8oHQDt1grPP9tHGadNg0CBvUJEpRx/tsb37\nbubOWUgaN/b2wkceCWeeGTuaWmrYEPbZB046KXYkUsCUGIpIZvXr59cJTgzTaxpW1yJv1iy48krf\nyXz88SsXxb79dt/put56mY+tUye/vuWWzJ+7EBQXw3/+47Whc2ZdoUiCKDEUkczq08crOI8Z41tC\nE6q8puEzz8CPP1YcnzTJRwQ7dIBrr/XHunb14tPZcM45vmZx5Eifopaa+ewz7zADPvC2zjpx46m1\nMWPg4IO9AKZIREoMRSSzWrTw/nPLlvl6qYRq395LxS1d6juTO3b0WbwttoA77oDFi+Ggg/z39Tvv\nZG8vQLt2Pp28YoUvNZPV++wz3wjUv7/ve8pJzz7rtYrGjo0diRQ4JYYiknk50AVlxAjfqFBu6lRv\nWQc+zfz55/67ulu37Md2wQV+fffdMG9e9t8/l/z8Mxx+uG8matkyh/dsqEyNJIQSQxHJvPTEsHx+\nL2GuuMJHC9OVlUHbtl73bptt4sQFsOOOsPfenvT885/x4ki6ELyo+IQJPvo7dGiOriv89lsf9lSZ\nGkkAJYYiknnbbQd33glvveWVnhNo2rSqj3//fXbjqM6FF/oOW7XIq96wYfDQQ76e8PHHc3BdYbmX\nXvLrvff2XooiEanziYhknpn3iEuw4mKfPq7qeBLsu68PJK1pwex89+GHcO65fnvYMNh667jxrBVN\nI0uCJPNPeRGROnb99dC06crHmjb140lQr56SwlW5+WZfCnD66XDMMbGjWQtLl1bUQlJiKAmgxFBE\n6s7VV8POO8Ps2bEj+ZWBA32kqUMHH+Ds0MHvDxwYO7KVlZX5dOkHH8SOJFnuvdf7S992W+xI1tKy\nZV4X6dRTkzNcLQXNQoL7mSZNSUlJGDduXOwwRHJH375esubBB5OXceWIG2/0tnsHHujVTEREasrM\nxocQatUYUiOGIlJ3yncnP/983Dhy2Akn+H6E556Dr76KHU1cY8fCb38Lc+bEjkQkfykxFJG6U54Y\nvvQSLF8eN5YctdFGcNxxfjvnp03Xwpw5cMQR8PTT8Pe/x44mQ77/Hi67TI2xJVGUGIpI3dl8c+jc\nGebOhffeix1Nzjr/fL++775ELtescytWeHL87bfQpYvnUnnhhRdg0CC/iCSEEkMRqTtm0K+f305w\nF5Sk23prH3xdvBjuuit2NNn3t7/5x2eDDeDRR3O4u0llI0f69R57xI1DJI0SQxGpWznQHi8XlLfJ\n+/vfc7gf8Br4z3/gT3/y2w88kEcbd9PL1Hz2WdxYRNKowLWI1K0ePbzY9X77xY4kp/XuDYcc4teF\nUkxi1iw46iifSr7ssoq/MXJekyY+/FvuwQf90rixN30WiUjlampB5WpERLKnrMwTwnHjfHCtfr4M\nZbz7LvTsWdGsu2lT3259003Qpk3U0CS/rEm5mnz5byYiInmmQQPPlZYuzaOkEODWWyuSwsaNffSw\neXMlhZIIWmMoInUvBJ8qO+GEil+IskbmzvVRtKOOih1J3Xn//ZVrFebNZpNyd9/tu/VPPtmLM55x\nBsyYETsqEUBTybWiqWSRtbD99r7I/vXXoVev2NHkrLlzoX17+OUX+OQT/7bmk+++8y6KTZrAW295\nq8K8EYLv1BfJEnU+EZHk0u7kjFh/fTjpJL99yy1xY8m0sjIfCZ0920v0bLJJ7IgyaOhQL8ZYSFvK\nJScpMRSR7FB7vIz5wx984GnECJg+PXY0mXPFFfD227Dxxr7yoF6+/IYaPRrOO8+/qFdeiR2NyCrl\ny387EUm6PfbwBfZffgmTJ8eOJqd17gwDBvgI29ChsaPJjGefhcGDoagIHnkEWrWKHVGGfPklHH64\nt4S8/HI48MDYEYmskhJDEcmOBg2gb1+//eKLcWPJA+UFr++809cb5rLJk+H44/32oEGw555x48mY\n2bM9EVywAA49FK69NnZEIqulxFBEskfTyRnTrRvsthv8+KOPtuWaESOgY0efLt5lF5g3Dw46CC68\nMHZkGbJkiVckLy31L/D++/NoblzyWT5VhhKRpNt/f59S3nvv2JHkPDOv8RcC7LVX7GhqZ8QIOO20\nipHOuXOhUSMfXMubTbuDB/u26nbt4JlnvIi1SA5QuZpaULkaEZG117EjTJ366+MdOsCUKdmOpo78\n8ovXJzzvPB8xFIlAnU9ERArUlCmecOWCadNqdzwnNW3q08ciOUYLHkQku0KAjz6CIUP8tqy1gQOh\nUycYPz52JDWz0UZVHy8uzm4cGffxx76LJtd3A0lBU2IoItm1YoXvTv7DH+Crr2JHkxfatvXrXCh4\n/eGHMH/+r483bQrXX5/9eDJm+nTfPXP//b6+UCRHKTEUkewqKoL99vPb6oKSEeee69/WRx+Fb7+N\nHU31Pv/c/yZYvNh3VBcX+2aTDh1g2DAf+cxJixZB//7ez69bN/jjH2NHJLLGlBiKSPapPV5GFRdX\n1FC+/fbY0VTtm2+gTx+YMwf69fMNu1On+gDylCk5nBSuWAEnnAAffOCLPJ96yrdYi+QoJYYikn19\n+3pNtzffhJ9+ih1NXiiv/zdsWDK/pePGwcyZ0KsXPP44NGwYO6IMufpqeOwxWHddeO65PGrZIoVK\niaGIZF/LltCli/d0e+212NHkhZISr2c4fz7cfXfsaH7tqKO8rvmzz0KTJrGjyZAXX4RrrvE/ch57\nDLbdNnZEImtNiaGIxNGvn19rOjljLrzQR+Jmz44difvxR/jss4r7++0HzZrFiyfjeveGY4+FW2+t\nWDcrkuNUx1BE4jjgALj55jzLFOI68EBft9emTexIvD3wfvvBxInwyis+opl3GjWC4cNjRyGSURox\nFJE4dtwRZs3KjRorOaKoKBlJ4S+/eJL6wQewwQYV5XTywk8/wfnnVyzkNMujPn4iSgxFJBYzz2Qk\n45Ytgyee8Dri2bZkCfz2t77reOONfQnpxhtnP446sXw5HH003HYbnHRS7GhE6oQSQxGJa+JEHz2c\nMSN2JHlj8GAvX5PtgtFlZb7JZPRo727y2mu506avRi65xHfQbLAB/PWvsaMRqRNKDEUknsWLYZtt\n4JNP4PLLY0eTN44/Hho08JJ6paXZe9+TT4ann4YWLTw53HLL7L13nfvXv3zZQ4MGMHIkbLZZ7IhE\n6oQSQxGJo0kTvyxb5vfvvdenl/Omlkk87drBMcd47eUhQ7L3vn37elL40ks+CJw3/v1vOPNMv33X\nXdCjR9x4ROqQEkMRiaO01LOXyl0iDjoIJk+OE1MeOf98v777bpg7Nzvv+bvf+T/d7rtn5/2yYsoU\nOPRQ/wPmoou0tlDynhJDEYmjbVto3twXppUnh2beFmPLLX33hKyxHXeEffaBhQvhn/+su/e58UYY\nP77ifosWdfdeUWy8sS/YPPhgGDQodjQidc5CCLFjyBklJSVh3LhxscMQyR+HHOIJ4mmneS+3b76B\n1q19odrEiX4bIASVBFkDL70E++/vuU1paebb0N10E1x8May/vp8/75LCciH4HzB508dPCoWZjQ8h\n1KqKqApci0g8I0dW3B46tOL2jz/6zk/wX8hdu8KAAXDeed6TVmpk3329jnifPr7eMJPuvNOTQvDq\nLXmVFIbgn8ff/c6/MDMlhVIwNJUsIslTnhSC96MdPx7+/Gfo3NmzkMWL48WWQ8y8usof/gCNG2fu\nvPffD2ed5bfvuAOOOy5z506E//s/+P3voVcvr10oUkCUGIpIsh18MLz+OnTpAj/84LsqNt/cy4eU\n72iWrHnySTjxRL99440Vm3XzxgsvwAUX+O1LLlERdik4SgxFJPl69YJ33oFRo2CHHeC77+DUU70Z\nr6zW3Lnwl79UJHRrasYMn11dsQKuvLJiKjlvfPaZV+hescK/YUcfHTsikaxTYigiucHMG/B+9BE8\n9JAXGE7/xb10qa8Nk19ZtsxH9+67DyZMWPPztGnj08iXXAJXXZWp6BJi1iz/fP30Exx5pCeGIgVI\niaGI5JZ69Twh/OILb/FR7i9/gT33hP/8J15sCdWqVcU6wNtuq/3ry8oqbh9+OPztb3m2Sby8wfPU\nqbDbbhXF1kUKkBJDEclNDRpA/VRhhbIyePhhn27u2dO346q01ErKC14PH+5LNWvqv/+FrbaCsWPr\nJq5EKCqCXXeF9u29VJK670gBU2IoIrmvQQP49FO45hovmj16tP+iP/RQH1kUttoK+vXzDd133lmz\n10yY4KVuSktXriaUN6ZP9/Z2s2f7UOrHH3tdTZECpsRQRPLDuut6SZvSUl8E17ix10ncbjslhykX\nXujXQ4euvuLP5MneOeWHH7wH8r/+VffxZd3pp8OYMf4HBUDLlnHjEUkAdT6pBXU+Eckh338P113n\nvW5feKHi+IIFPqpYgEKA3/zGB8YefRSOOKLq5/3vf9C9u+fYe+3lHVSaNs1urHWqSZOqM+PGjWHR\nouzHI1JH1qTziUYMRSQ/tWvn1Zefe67i2H//68f/+EfvrgIV04kzZsSJM4vM4Oab4dVXfRNJVX74\nwUcKS0uhpMS/fXmVFELFCGG5pk1h4EAfJhUpcEoMRSS/1Uv7Mffii7BwoW+r7dTJRxT//OeVpxPz\nXO/esPfe1W+6ff99b1O93XY+UphXg6sh+L/zJZf4fTMfJVy82L/QNm3ixieSAJpKrgVNJYvkgQ8+\ngCuugFdeqfrxAppO/P57H0Ct7KWXYKed8ixPWr4czjkH7rrL/1jYYQfYYw847TQYNsxHjtN7d4vk\ngTWZSlZiWAtKDEXyyBNPwBlnwJw5fr9BA190d9NNeZYRVe2442DECF9vuNlmMGmSjxLmrWeegQED\noFEjeOQRvy2S57TGUESkpg47zC9mflm2rKCmE1u08M5vXbr4ErsddvA2d3mrf38vgv7KK0oKRVZB\niaGIFK5Zs+DMM31h3ZlnFsQGlHKdOvn1L7/4dQgweLCPIuaN//3Ph0LLXXWVb7MWkWppKrkWNJUs\nkudCgH/8Aw45BDbaKHY0dapjR+8AV1mHDl7hJ+dNmOAdcOrX9444rVvHjkgk6zSVLCKyNgYP9pHD\nQw7x/rl5bNq02h3PKWPHet/sadM8wS9vnSgiq6XEUESk3LHHer/ct9+GU0/1EcQ8VVxcu+M544UX\nvCbPnDneA/C1054WwQAAFlhJREFU19TRRKQWlBiKiJRr2xZGjYJ11oEHHoBBg2JHVGeuv/7Xhaub\nNvXjOWv4cDj4YC83dMIJ8NRTeVidW6RuKTEUEUm3006+A8MMLr88b2vbDRzo5fs6dPAvtUMHvz9w\nYOzI1tDHH3syuHw5XHYZ3HOPlyASkVrR5pNa0OYTkQJy441w6aXeV/ett2CXXWJHJKtz1VWw/vpw\n3nmxIxFJhDXZfKIVuSIiVbn4Yt/Z+uGH0KpV7GikKkuWeMeSjh39/lVXxYxGJC8oMRQRqYqZt09b\nuhSaNYsdjVT200++e/yrr7wcTfv2sSMSyQtaYygiUp2GDSuSwhDg8ce9XYjENXMm9OwJr77qo4bl\nbQ1FZK0pMRQRqYmzz/ZeynndNy4HTJoE3br5FH/nzj5auOOOsaMSyRtKDEVEamLAACgq8nouDz4Y\nO5rC9OGHsMcenhz+5jdeb7Jz59hRieQVJYYiIjXRty8MGeK3Tz7ZkxLJnvLp41mzYJ994I031OZO\npA4oMRQRqamzz/bL0qXw29/mSVPhHNG6tdeVPPpoeP55WHfd2BGJ5CXVMawF1TEUEZYt81Zro0fD\nttv6GrfmzWNHlb9mz4YNN/TbIfilnsY0RGpiTeoY6n+XiEht1K8Pjz0GW2/t7dYWLYodUX4KwTuY\nbL89TJ7sx8yUFIrUMdUxFBGprfXW8xHDli29M4pkVlkZnHYa3Hefb/gZPx46dYodlUhB0J9eIiJr\non37iqRwxQp477248eSLhQt9B/h99/mI7KhRcNhhsaMSKRhKDEVE1sby5XD44V5b77XXYkeT2+bM\ngb33hhde8NHY11+H/fePHZVIQVFiKCKyNoqKYPPNPUE87DBv0Sa1t2QJdO/uI6/FxV4OaPfdY0cl\nUnCUGIqIrK0bbvDpz3nz4KCD4McfY0eUG6ZPhx49YMYMaNQITj/dN5u8+y5suWXs6EQKkhJDEZG1\nVa8ePPAA7LQTTJzoI4dLl8aOKvmuvRbGjIFrrvH7554L778P7drFjUukgCU2MTSzy8zsAzNbYGY/\nmNkoM9uu0nOuNbMJZrbQzOaa2WtmtkcNzt3DzMab2WIzKzWzM+ruKxGRgtCsmW+UaNMG/v1vL4St\nOrFVa9LES8/ceadv3LnzTr/fpAk0bhw7OpGCltjEEOgJ3AHsAfQGlgGvmtkGac/5Cjgb2B7YE5gM\nvGRm1fZJMrNOwAvAO8DOwF+B283s0Dr4GkSkkLRvD88+68nNhAmqcVid0lLo06fifv36MHBgRb1C\nEYkmsXUMQwj7pt83s2OB+UA3YFTqOQ9Wes4FwMnATsDL1Zz6DOD7EMLvU/e/NLPdgYuAJzP2BYhI\nYdp1Vx8x3HlnXzcnv7ZoEbz1lt8uKvJRw+bNfbRVRKJK8ohhZevi8c6t6kEzawicBiwAPl7FeboC\noysdexkoMbMGGYhTRApdly4VSeHy5fDtt3HjSZJ587yl4OLFPsI6diyccYZvQBGR6BI7YliFIXjC\n9276QTM7EHgEaApMB/qEEGau4jxtgFcrHZuJfy82TJ0j/fyn4QknxcXFaxG+iBScn3+Go46CTz/1\nTRWtq13lUhjKynxjzoQJ3mf67be9i0xJrVq5ikgdyokRQzO7BV9DeGgIYXmlh/+NTx3vAbwEPGZm\nbVdzysorwq2a44QQhoUQSkIIJa1atap98CJSuOrX96LN06Z5OZvFi2NHFNcrr3gR8Nat4fnnPSkU\nkURJfGJoZrcCRwO9QwillR8PISwMIXwTQhgbQjgZKANOWcUpZ+Cjhuk2wje3zMlQ2CIivgnl6ae9\nYPPYsXDSSYW9U/mAA+CRR3yDTocOsaMRkSokOjE0syHAMXhSOKGGL6sHrGrF97vAPpWO9QHGhRDK\nah+liMgqtG7tZWyaNYOHH4brrosdUfaVpf1oPfJI2G23eLGIyColNjE0s6HAifho4Vwza5O6NEs9\n3tzMrjOz3c2s2Mx2MbN7gPbAY2nnud/M7k879V1AezO7zcy2NrNTgBOAm7L1tYlIgdlhB3joIa/V\nd+WV8Nhjq39Nvhg71ruYjBsXOxIRqYHEJobAWfhO5NfwDSHll4tSjy8DtgWeAibiJWxaAt1DCJ+k\nnac4dQEghDAZOADojm9muQI4N4SgUjUiUncOOggGD/bbDz5YGFPKU6ZA//5en3D48NjRiEgNWCiE\nH04ZUlJSEsbpr14RWVMh+MjhEUdAgzyvjjVvHnTrBl984cWsn38+/79mkYQxs/EhhFpt+0/yiKGI\nSH4x8w4f5QlSWRlMmgQ9euRXHb+yMjj8cE8Kt9kGHn9cSaFIjlBiKCISw9y5sP/+sM8+MGYMXHNN\n7IgyIwTvE/3qq7DRRipLI5JjNJVcC5pKFpGMadwYliyp+ngu91geN853HTdqBG+8AbvvHjsikYKl\nqWQRkVwxeTL07l1xv149OOQQP57LSkrgqad8g42SQpGco8RQRCSGtm1hiy183SHAihXw4ovw5Zdx\n41pTK1ZU3O7fHw49NF4sIrLGlBiKiMQycyaceaa3idt4Y59C3mef3Kv5N3Wq12ocMyZ2JCKylurH\nDkBEpGCNHFlxe+pUuOoqmDgRdtklWki1Nn8+9OsHn38OgwbBc8/FjkhE1oISQxGRJCgqgmuv9SnZ\n8unlSZNg9uzkrtUrL0vz+edelubBB2NHJCJrSVPJIiJJUi/1Y3nRIjjsMNhrL7j99uR1SgkBzjkH\nXnkFWrXykcIWLWJHJSJrSYmhiEgSFRVB9+4+KnfuuXDkkbBgQeyoKtx8Mwwb5uV1nn0WOnWKHZGI\nZIASQxGRJGrYEIYMgcceg3XX9e4hu+4Kn34aOzKYNg0uv9xv338/dOkSNx4RyRglhiIiSXb44b5L\nefvt4euvfb3hAw/Ejam4GEaNgltu8fhEJG8oMRQRSbottoCxY+HEE33tYawp5fR1jvvuC+efHycO\nEakzSgxFRHJB06Zwzz1e8/CssyqOL12anfdfsMDXPL76anbeT0SiUGIoIpJLeveuKGczcSJ07rxy\nPcS6sGwZHHGEF7C+4AJYvrxu309EolFiKCKSq+69F777ztvPXXCB72DOtBDg97+Hl1/2sjRPP+07\npkUkLykxFBHJVddfD7fdBvXrw623Qs+enihm0q23wl13QaNG8MwzsOmmmT2/iCSKEkMRkVxlBued\nB2++Ce3bwzvvwM47w+jRmTn/00/DRRf57eHDoWvXzJxXRBJLiaGISK7r2hU++sh3Cs+eDf37w8yZ\na3fOBQt8F3QIcN11XmBbRPKeeiWLiOSDDTeEF17w6eVWraB167U7X/PmPnU8cmRFMWsRyXsWktZ/\nM8FKSkrCuHHjYochIlJzo0bBBhtAt26xIxGRLDOz8SGEktq8RlPJIiL5qrQUBg6EHj28t/HqBgLK\ny9I891x24hORxFFiKCKSrzbZBE4/3esOXnQRHHIIzJtX9XND8I0sjz8Op5wCP/+c3VhFJBGUGIqI\n5KsGDWDwYHjqKVhvPd9lvMsuvlGlsiFD4I47oGFDePJJaNYs+/GKSHRKDEVE8t2AAfDhh17KprTU\ndzE/8og/Nn06bLddRd/j++7TekSRAqbEUESkEGy6qdc5PP10v7/FFn597rnw+ed++5pr4Oij48Qn\nIomgXcm1oF3JIpIXJk3yUcLFi3/9WOPGsGhR9mMSkYzTrmQREVm9zp19SvmYY7zVHUCTJr6DefLk\nuLGJSFRKDEVEClHbtl7EuqzMRwmXLPH7bdrEjkxEIlJiKCJSqGbOhDPOgLFj/XrGjNgRiUhkaokn\nIlKoRo6suD10aLw4RCQxNGIoIiIiIoASQxERERFJUWIoIiIiIoASQxERERFJUWIoIiIiIoASQxER\nERFJUWIoIiIiIoASQxERERFJUWIoIiIiIoASQxERERFJUWIoIiIiIoASQxERERFJUWIoIiIiIoAS\nQxERERFJUWIoIiIiIgBYCCF2DDnDzH4Aptbx22wIzK7j95Dcos+EpNPnQSrTZ0LSpX8eOoQQWtXm\nxUoME8bMxoUQSmLHIcmhz4Sk0+dBKtNnQtKt7edBU8kiIiIiAigxFBEREZEUJYYiIiIiAigxFBER\nEZEUJYbJMyx2AJI4+kxIOn0epDJ9JiTdWn0etCtZRERERACNGIqIiIhIihJDEREREQGUGIqIiIhI\nihLDBDGzs8xsspktNrPxZrZX7Jgk+8zsMjP7wMwWmNkPZjbKzLaLHZckg5ldbmbBzP4eOxaJx8za\nmtnw1M+IxWb2hZn1iB2XxGFmRWZ2bVoOMdnMrjOz+rU9lxLDhDCzI4EhwA3AzsA7wItmVhw1MImh\nJ3AHsAfQG1gGvGpmG8QMSuIzsy7AqcAnsWOReMysBfA2YEA/YGvg98CsmHFJVJcCZwPnAlsB56Xu\nX1bbE2lXckKY2XvAJyGEU9OOTQSeCCHU+h9W8oeZNQPmAwNCCKNixyNxmNl6wId4Yngl8FkI4Zy4\nUUkMZnYD0COE0C12LJIMZvYcMCeEcHzaseFAyxDCgbU5l0YME8DMGgK7AKMrPTQaHzWSwrYu/n91\nbuxAJKph+B+Kr8cORKIbALxnZo+a2Swz+9jMzjEzix2YRDMG6GVmWwGY2Tb4jNMLtT1RreeepU5s\nCBQBMysdnwnsk/1wJGGGAB8D78YOROIws1OBzYBjY8ciibApcBZwKzAI2Am4PfWY1p4Wpr/hgwhf\nmNlyPL+7PoRwR21PpMQwWSrP61sVx6SAmNktwJ7AniGE5bHjkewzsy3xtcd7hRCWxo5HEqEeMC5t\nmdFHZrY5vqZMiWFhOhI4DjgG+Bz/Y2GImU0OIdxdmxMpMUyG2cByoE2l4xvx61FEKRBmditwFNAr\nhFAaOx6Jpis+q/BZ2kxhEdDdzM4A1gkhLIkVnEQxHfii0rEv8Q0HUpgGAzeFEB5J3f/UzDrgm09q\nlRhqjWECpEYBxgN9Kj3UB9+dLAXGzIbgf/n1DiFMiB2PRPU0sD0+AlB+GQc8krqtUcTC8zawZaVj\nWwBTI8QiydAUH2BKt5w1yPM0YpgctwAPmNn7+H/6M4B2wF1Ro5KsM7Oh+FqyAcBcMysfSf45hPBz\nvMgkhhDCPGBe+jEzWwj8GEL4LE5UEtmtwDtmdgXwKF7i7Fzg8qhRSUyjgD+a2WR8Knln4ALg/tqe\nSOVqEsTMzgIuAdoCnwHnhxDejBuVZJuZVfef8uoQwlXZjEWSyczeQOVqCpqZ9cPXnm4JTMPXFt4e\n9Eu9IJnZusC1wG/xZWjT8VmFa0IIi2t1Ln2GRERERAS0xlBEREREUpQYioiIiAigxFBEREREUpQY\nioiIiAigxFBEREREUpQYioiIiAigxFBEREREUpQYiogAZnaVmQUz61mL13RMvea+uotszZnZG2b2\nqZkl5me9mW1sZovM7NrYsYjIryXmh4WIFKZUYpV+WW5ms83sdTMbmJD43ogdR22Z2WFAD+AvIYQV\nseMpF0L4H97q80Iz2yR2PCKyMnU+EZGo0loAXp26boC3+RoAFAG3hhAuyEIcGwIbAtNCCL9Uiu8/\nIYSeVbymAdAZmB9CmF7XMdaUmRnwJWDAVklrk2Zm7fA2bveEEE6LHY+IVFBiKCJRlSeGIQSrdHxv\n4JXU3U1DCFOyHFp5HNUmhkllZn2A0cAVIYQbYsdTFTN7EegOtAshzI8dj4g4TSWLSCKFEF4DJuCj\nXrumP2ZmR5jZm2Y2P7Ve7VMzu8zMGlU+j5ntYGYPm9kUM1tiZj+Y2YdmdltqxK/8eSutMTSzE9JG\nM3tUmu6+KvWcatcYmllbMxuaet+lqfcdaWa7VPHcE1LnOcHMeqXWBv5kZgvM7Hkz27qW376TU9eP\nZuK9zOy+1Gs6mdk5ZvaFmS1OfW2Xp0YoMbPDzex9M1toZrPM7O9m1riaGB8BmgJH1fJrE5E6VD92\nACIiq1A+ivj/pzbM7AbgMmA28BDwM7A/cAOwr5n1CSGUpZ67A/Be6vXPApOB5sBmwFnAn4Cyat77\nY3x6+y/AVOC+tMfeWGXQZp2AMUA74HXgYWAT4HCgn5kdGkJ4roqXHgj0B17E1+FtAxwA7Gpm24QQ\nZq/qfVPvbUBvYEYIYdIqnrom73UT0BMYhY9IHgxcDzQ0sx+BQcDTwFtAH+BsfDnAmVWc6+3UdR/g\nH6v7ukQkS0IIuuiiiy7RLnjSFqo4vg+wInXpkDrWNfX8aUCbtOfWx5OVAFyedvzm1LH+VZx/faBe\n2v2rUs/tWUV8b1QTe8fU4/dVOv5y6vgVlY7vASwD5gDN0o6fkHr+MmDvSq/5a+qxS2r4/dwq9fxR\n1Txe6/fCk+IATAE2TjveAk/QFwI/AFunPdYI+AJYAmxUTSxzgVmxP4O66KJLxUVTySKSCKmp3KvM\n7HozewJ4CR8xvC2EMDX1tJNS19eFEGaUvzaEsAy4EE8iT6ni9IsqHwghzA11sFvXzNoDffHk9cZK\n7/kOPnq4AXBIFS9/JPgUerphqevdahhCcep6dZth1uS9rg2+qxiAEMI8fCS2KXBnCOHLtMeW4FPZ\nDYHqpsJnAK1WMd0sIlmmxFBEkuIvqctl+FToW8CxYeUdyb9JXb9e+cUhhK+B74BOZtYidfhRYDnw\ntJndb2bHmVnnuvoCUnZOXb8VUlPalbxe6XnpxlVx7NvU9fo1fP+Wqeu5q3nemrxXVa/5PnU9vorH\nypPI9tWc78fU9YbVPC4iWabEUEQSIYRgqUu9EMIGIYReIYQHKz1tvdR1daNh09OfF0J4H9gLT8YO\nA4YD35jZBDM7OsNfQm1jbFHFY/MqH0iNhoKv1auJ8tHR1Y3Crcl7VbV7eFkNHmtQxWMATVLXvxrR\nFZE4lBiKSC4pTz7aVPN420rPI4TwbgjhQHwUrBtwLdAaeMjM9klCjBk2K3XdcpXPSoaWePL44+qe\nKCLZocRQRHLJR6nrnpUfMLPN8CnLyam1bysJISwJIbwTQrgSODd1uH8N3nMFNR+tS49xTzOrqvJD\nr9T1h7U4Z218jk+fb1VH588IM1sH2Bj4JISggroiCaHEUERyyT2p6z+ZWavyg2ZWhJdSqQfcnXZ8\nLzNbj19rnbr+pYrHKpuDl5qpkRDCd3hh7o7AH9IfM7PdgWPw9X9P1fSctRG8WPTHwA5m1mR1z49o\nNzzh/nfsQESkguoYikjOCCG8Y2Y3ApcAn6V2Ly/E6xhuh9cOHJz2kguBvqlex6V4zcNtU8+fS8Uu\n3FV5DTjKzEbhGyyWAW+GEN5cxWvOwOv0DTazvvimjfI6hiuAE0MIP9Xoi14zTwK74Jt4nq/D91kb\nfVPXT0aNQkRWosRQRHJKCOFSM/sIOAc4Dt/YMAkvVn1zCGFp2tPvwBPA3fH1hfXxnct3pJ47ldU7\nD6/htzdeALoeXvi62sQwhFBqZiWpmA7Ap74X4CV4rg8hfFDTr3cN3Y3XZTyOBCaGZlYP+B3w3xDC\nu7HjEZEK6pUsIpKHzOwfwPFAx/Saj0lgZgfh9Q+PrWLnuYhEpMRQRCQPmVlrYCIwPITw+9jxlEu1\n7BuPb5DZTRtPRJJFm09ERPJQCGEmPl37fWrqNina4KOFpyopFEkejRiKiIiICKARQxERERFJUWIo\nIiIiIoASQxERERFJUWIoIiIiIoASQxERERFJUWIoIiIiIgD8Pw9Tep/Du+a+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1be3b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "plt.plot(x150,y150,'--o',color='b',label=\"T = 150$^o$C\")\n",
    "plt.plot(x250,y250,'--*',color='r',label='T = 250$^o$C')\n",
    "plt.xlabel('Position (nm)', fontsize=20)\n",
    "plt.xticks([0,2,4,6,8])\n",
    "plt.ylabel('$E_p$ (eV)', fontsize=20)\n",
    "plt.yticks([23,23.5,24])\n",
    "plt.legend()\n",
    "plt.savefig('line_scan.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([], dtype=float64), <a list of 0 Text xticklabel objects>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([], dtype=float64), <a list of 0 Text yticklabel objects>)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS8AAAElCAYAAAC1RWS+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzaPCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0dfba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myim = plt.imshow(raw,cmap='jet',extent=[0,1,0,1],interpolation='nearest',vmin=18,vmax=24.5)\n",
    "cbar = plt.colorbar(ticks=[20,21,22,23,24],boundaries=np.linspace(19.8,24.2,70))\n",
    "cbar.xlim=[19,25]\n",
    "cbar.ax.tick_params(labelsize=20) \n",
    "plt.gca().set_xticks([])\n",
    "plt.xticks()\n",
    "plt.gca().set_yticks([])\n",
    "plt.yticks()\n",
    "\n",
    "plt.savefig('center.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Pandas DataFrame to store all the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create Pandas DataFrame\n",
    "tmd = pd.DataFrame(columns=['Temperature', 'Nanoflake_Number', 'Thickness', 'Thickness_List', 'Layer', 'Spectrum_Axis', 'EELS_Spectrum', 'Peak_Position'])\n",
    "temp_list = [150,250,350]\n",
    "nflake_list = [1,2,3,3.5,4,5,6,7,8,9,10,11]\n",
    "cluster_list = [2,2,2,2,2,7,4,3,4,4,2,2]\n",
    "weight = 5\n",
    "\n",
    "#define a function to return the k number for Kmeans clustering of thickness\n",
    "def get_k_number(a):\n",
    "    for i in range(1,6):\n",
    "        estimator = KMeans(n_clusters=i);#构造聚类器\n",
    "        estimator.fit(a)#聚类\n",
    "        if estimator.inertia_ < 0.15: # 获取聚类准则的总和\n",
    "            return i\n",
    "    if  estimator.inertia_ >= 0.15:\n",
    "        return 5\n",
    "    \n",
    "\n",
    "#define a function to give a new feature of thickness, variance:\n",
    "def get_t_variance(tmat):\n",
    "    num_rows = np.shape(tmat)[0]\n",
    "    num_cols = np.shape(tmat)[1]\n",
    "    stdmat = np.zeros((num_rows-2,num_cols-2))\n",
    "    for i in range(1,num_rows-1):\n",
    "        for j in range(1,num_cols-1):\n",
    "            row = np.array([[i-1,i,i+1]]).astype(int)\n",
    "            col = np.array([[j-1,j,j+1]]).astype(int)\n",
    "            rowmat = np.transpose(np.dot(np.ones((3,1)).astype(int),row))\n",
    "            colmat = np.dot(np.ones((3,1)).astype(int),col)\n",
    "            test = np.reshape(tmat[rowmat,colmat],(-1))\n",
    "            stdmat[i-1,j-1] = np.std(test)\n",
    "    return stdmat\n",
    "\n",
    "def get_thickness(raw,zlp):\n",
    "    new_axes = raw.axes_manager['Energy loss']\n",
    "    x0 = np.linspace(new_axes.offset, new_axes.offset+new_axes.scale*(new_axes.size-1), new_axes.size)\n",
    "    th=lograt_mat(raw,zlp,x0)\n",
    "    return th\n",
    "    \n",
    "def lograt(r0,z0,x0):\n",
    "    t0 = si.simps(z0,x0)\n",
    "    t1 = si.simps(r0,x0)\n",
    "    return math.log(t1/t0)\n",
    "\n",
    "def lograt_mat(raw,zlp,x0):\n",
    "    thick = np.ones(np.shape(raw)[0:2])\n",
    "    for i in range(np.shape(raw)[0]):\n",
    "        for j in range(np.shape(raw)[1]):\n",
    "            r0 = raw.data[i,j,:]\n",
    "            z0 = zlp.data[i,j,:]\n",
    "            thick[i,j]=lograt(r0,z0,x0)\n",
    "    return thick\n",
    "\n",
    "def getindex(number,data_shape):\n",
    "    id1 = number//(data_shape[1])\n",
    "    id2 = number%(data_shape[1])\n",
    "    return (id1,id2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n",
      "WARNING:hyperspy.signal:<EELSSpectrum, title: EELS Spectrum Image, dimensions: (22, 22|2048)> data is replaced by its optimized copy\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44a0b39c60a24a3883388a54515ad646",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e991baec84f4cf8ae4117ca74086293",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a25dbdc860ad4976b8cea77c4329095e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\misc\\math_tools.py:61: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  return np.issubdtype(number, np.float)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58ae30bc2b7845929058632e2cd85746",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "affeb2a8c33b4d21addccfa922459971",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19f13016216f43d9bfbf11dc01192b7e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13284f2478954aa0abc27d14b36cf886",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\Lib\\Xuan\\eels.py:151: RuntimeWarning: invalid value encountered in less\n",
      "  inelastic.data[inelastic.data < 0] = 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5a4e80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19fd9978>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19fd9c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19576780>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n",
      "WARNING:hyperspy.signal:<EELSSpectrum, title: EELS Spectrum Image, dimensions: (22, 22|2048)> data is replaced by its optimized copy\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae8001c8714a4bdf94fd5e7a94102475",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c2ac87f890141d2b568fba12b9f8ad3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "686f229b29984a468895ccdbb0ca8009",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\misc\\math_tools.py:61: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  return np.issubdtype(number, np.float)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "806ae2b854f24c3ca5a387afdf27022b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ac819d04ce244dcb3847ca6a920027b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74199c593f954236a77f99e220a4ae47",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69948113ab794cb287c7576dafc5ea13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5a4e80>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c645940>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19fd9c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c5ebe48>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "NotImplementedError",
     "evalue": "Currently we only support reading DM versions 3 and 4 but this file seems to be version -1991752634 ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-70cc7ce4ebc1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mfolder_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0mfile_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfolder_path\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfolder_name\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'/EELS Spectrum Image.dm4'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m         \u001b[0mraw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msignal_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'EELS'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[1;31m#general process:alignment, thickness, zlp substraction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(filenames, signal_type, stack, stack_axis, new_axis_name, lazy, **kwds)\u001b[0m\n\u001b[0;32m    241\u001b[0m             objects = [load_single_file(filename, lazy=lazy,\n\u001b[0;32m    242\u001b[0m                                         **kwds)\n\u001b[1;32m--> 243\u001b[1;33m                        for filename in filenames]\n\u001b[0m\u001b[0;32m    244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    245\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjects\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    241\u001b[0m             objects = [load_single_file(filename, lazy=lazy,\n\u001b[0;32m    242\u001b[0m                                         **kwds)\n\u001b[1;32m--> 243\u001b[1;33m                        for filename in filenames]\n\u001b[0m\u001b[0;32m    244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    245\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjects\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py\u001b[0m in \u001b[0;36mload_single_file\u001b[1;34m(filename, signal_type, **kwds)\u001b[0m\n\u001b[0;32m    284\u001b[0m                                 \u001b[0mreader\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m                                 \u001b[0msignal_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msignal_type\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m                                 **kwds)\n\u001b[0m\u001b[0;32m    287\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py\u001b[0m in \u001b[0;36mload_with_reader\u001b[1;34m(filename, reader, signal_type, **kwds)\u001b[0m\n\u001b[0;32m    293\u001b[0m     \u001b[0mlazy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'lazy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m     file_data_list = reader.file_reader(filename,\n\u001b[1;32m--> 295\u001b[1;33m                                         **kwds)\n\u001b[0m\u001b[0;32m    296\u001b[0m     \u001b[0mobjects\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\hyperspy\\io_plugins\\digital_micrograph.py\u001b[0m in \u001b[0;36mfile_reader\u001b[1;34m(filename, record_by, order, lazy)\u001b[0m\n\u001b[0;32m    986\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    987\u001b[0m         \u001b[0mdm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDigitalMicrographReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 988\u001b[1;33m         \u001b[0mdm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    989\u001b[0m         images = [ImageObject(imdict, f, order=order, record_by=record_by)\n\u001b[0;32m    990\u001b[0m                   for imdict in dm.get_image_dictionaries()]\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\hyperspy\\io_plugins\\digital_micrograph.py\u001b[0m in \u001b[0;36mparse_file\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     78\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mparse_file\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_header\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     81\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtags_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"root\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m         \u001b[0mnumber_of_root_tags\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_tag_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\hyperspy\\io_plugins\\digital_micrograph.py\u001b[0m in \u001b[0;36mparse_header\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     93\u001b[0m                 \u001b[1;34m\"Currently we only support reading DM versions 3 and 4 but \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m                 \u001b[1;34m\"this file \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m                 \"seems to be version %s \" % self.dm_version)\n\u001b[0m\u001b[0;32m     96\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mskipif4\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m         \u001b[0mfilesizeB\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miou\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_long\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"big\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: Currently we only support reading DM versions 3 and 4 but this file seems to be version -1991752634 "
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAACoCAYAAACYG5eLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAEOVJREFUeJzt3V9slGd2x/Hf8WDzJyaGYBwDgeCQ\nQLKLEiLcXCSbCEViK1WqtOpN2kir5aJNK6SqK6261TZRxPaiXe3FdnORqE1u+kdatermZlfqSqRV\n01W7q3SxFDVRkgUFHP7YGBMgGIIxxk8v5nHj+D0nzMCM32Hm+5Esw5mH8fOaR2dez/HzHEspCQAg\ndZU9AQBoFSREAMhIiACQkRABICMhAkBGQgSAjIQIABkJEQCyhiZEM9tvZsfMbNrMRszsyUY+Pzob\n6wvNtqxRT2Rmz0h6SdJ+Sf+VP//MzL6UUjoe/btKpZKWLStOY2Zmxh2/atWqQuzatWvu2O7u7hpm\n/pnZ2dlCrKurvteMFStWuPGrV6/W/Nxzc3NufPny5W48un4zK8R6enrcsd61fxFvjtH8vHlI0tmz\nZ8+mlNbX8vVudn319/enrVu31vIl0GZGRkZqXl/zrFFb98zsLUn/m1L6gwWxI5J+nFL6TvTvli9f\nnjZu3FiIj46OuuN3795diJ05c8Ydu369/73wErAknT17thDzErAkRd+37du3u/Fjx44VYlHy/PTT\nT934tm3b3Hh0/d4LwoYNG9yx58+fd+PRdU5PTxdiQ0NDNc9Dkl577bWRlNKw++AiN7u+hoeH06FD\nh2r5EmgzZlbz+prXkB+ZzaxH0m5JBxc9dFDS4434GuhcrC8slUa9h9gvqSJpYlF8QtLg4sFm9pyZ\nHTKzQ9evX2/QFNDGbnp9TU5OLsX80CYaXWVe/POVOTGllF5NKQ2nlIYrlUqDp4A2Vvf6it42ATyN\nSohnJV1X8dV6QMVXdaBerC8siYZUmVNKM2Y2ImmvpH9Z8NBeSa/f4N+6VdJ169a540+fPl2IRW/a\nR9XaqII9MDBQiK1du9YdG1V27733Xjd++fLlQiwqQkTzjgofmzdvduNe0aavr88dOzhY+MlTkj9v\nSfLe6oiquVHxqFa3sr6AejTs124k/UDSP5rZ/0j6b0l/JGmjpL9p4NdA52J9oekalhBTSv9sZusk\nvSBpg6R3Jf1WSumjRn0NdC7WF5ZCI+8QlVJ6RdIrjXxOYB7rC83GXmYAyEiIAJA19Efmm5FScvf5\nRlVPb19stL0uqqhGlVNvC2FUNZ6amnLj0e9VetfT29vrjj137pwbj6rSFy5cqHl8NO/oexhViL3x\n0W8GbNq0yY0D9Yj2xDcSd4gAkJEQASAjIQJARkIEgKz0ooqZuW/ce4UWSbrjjjsKseicQG+sFG/1\n27FjRyEWHeI6NjZW19f0trVFJ/3s2rXLjUcFkSje399fiEXX88QTT7jxkydPuvF77rmnEPPOfJT8\ncyZRnnqLE9GZmN7z1DO2FXGHCAAZCREAMhIiAGQkRADISIgAkJVeZe7q6nK3gUWVY68jXbTNL9pG\nF20l8/pvRGOHh/1mXuPj427ca9G5cuVKd2y0FS/aRhd10vO20kUH3kbbGaMOgN71RFsl7777bjeO\nWFSVLaOKW89z3y7V5Ah3iACQkRABICMhAkBGQgSAjIQIAFnpVeaUkqanpwtxLyZJ9913XyEWHaj6\n0EMPufGoheju3bsLsaNHj7pjr1y54sajQ1K9/dPRHt+onafXJlWK9xB7XzPayxy1Mo2qht7cowNv\no8NnO009e3/reQ40DneIAJCREAEgIyECQEZCBICMhAgAWelV5kqlojVr1hTiUUV1/fr1hVhPT487\nNjqROmotev78+UKs3haaUdXw0qVLhdj999/vjo32FUeV96iK632vIjMzM248qpp71xnNLzr9vF11\n0t7fdsMdIgBkJEQAyEiIAJCREAEgK72o0t3d7RYu7rrrLne81+YzejPfa5UpxVv3vANYo2KDVwiS\n4qKKd0hqdI3Rc0StT70th5K/vTA6ZPb48eNufOfOnW58YmKiEIsKYdF2wdsdBZH2054rFQBuAgkR\nADISIgBkJEQAyEiIAJCVXmXu6elxDyf12lxKftU3aq0ZtfN87LHH3Lh3GOz27dvdsd5WPCmutHrb\nC6NDZpct8/9b7rzzTje+evVqNz43N1eIffzxx+7Yhx9+2I2fOXPGjXu8yrMUt0m93ZXREhTNxR0i\nAGQkRADISIgAkJEQASAjIQJAVnqVuVKpuNXTvr4+d7xXUV25cqU7NmrnGbXt9OYxOzvrjo3adk5N\nTbnx0dFRN+6JDo4dHx9341E7U6+yHVV8T5w44cajav8nn3xSiEX/Z7c7qsadgztEAMhIiACQkRAB\nICMhAkBWU0I0s6fM7CdmdsrMkpntW/S4mdkBMxszsytm9qaZfbkpM0bbYX2hVdRaZe6V9K6kf8gf\ni31b0rck7ZP0a0kvSnrDzHaklPyya9bd3e2eJh21EO3t7S3Eoj2lFy9erPk5ItFpz2+//bYbj1p/\neq1Po33C0Z5g77RwKa6Ee6KWoNE+6ajK7J28He2Tjk4GX6Bp66teVJQ7W013iCmlf00p/XlK6ceS\nPndigFVX0DclfS+l9HpK6V1J35C0WtKzjZ4w2g/rC62iEe8hDkkalHRwPpBSuiLp55Ieb8Dzo7Ox\nvrBkGpEQB/PnxT/nTSx47HPM7DkzO2Rmh6JjtIDsltbX5ORkUyeH9tLIKvPiN/LMiVUHpvRqSmk4\npTRcz/t56Gg3tb6i93QBTyO27p3OnwclLdz/NaDiq3rB3Nyce+hr9Ga+t8CjQkHUbjRqLeoVFqI3\n2aNCgbddLhofbZeL2qdGRYvoOr3ClFfckeIDb7ds2eLG33///UJsaGjIHfvee++58Rrd0voC6tGI\nO8Rjqi7avfMBM1sh6UlJv2jA86Ozsb6wZGq6QzSzXknzJw50SdpiZrsknUspHTezH0p63sw+kHRY\n0guSLkn6URPmjDbD+kKrqPVH5mFJ/7Hg79/NH3+v6u+GfV/SSkkvS1or6S1JX23074ihbbG+0BJq\nSogppTdVfRM7ejxJOpA/gLqwvtAq2MsMAFnpB8RK/tazqGLpte6MtvlFleAoPjY2VohFVePu7m43\nvmbNGjfuVbYffPBBd+z09LQbf+SRR9z4Rx995Ma9FqrR4bjRNsfTp0+7ca8SHh1Uu3HjRjdeJrbo\nwcMdIgBkJEQAyEiIAJCREAEgIyECQFZ6ldnM3Dai58+fd8d7e5mjNqSXL192414LTcmvEEdV5lWr\nVrnx6BBXby7RXuboObwKuyT3gF1JOnz4cCEWXU/0PYzm4h0QGz139H8JtBruEAEgIyECQEZCBICM\nhAgAGQkRALKWqDJ7+4Kj/cbevuWohWa0PzfahzwwMFCITU35J0xF7UmjyrZ3PZVKxR17/Pjxmucn\nxSeAL1tW+39v9P2OTun2TjSPTjmvoQ1p04yMjLBvGTXjDhEAMhIiAGQkRADISIgAkJEQASBriSqz\nV22NqrheX+HoVOeoB/HEhN/O99y5c4VYtA9306ZNbjyq7HqnSY+Pj7tjN2zY4MZHR0fdeNTHua+v\nrxCLKtvRPuSosu1VlKM94tG+b6DVcIcIABkJEQAyEiIAZCREAMhKL6p0d3e7B5xevXrVHe8dWLp5\n82Z3bPQmf39/vxv3iiqDg4PuWO+AVCneRui1Co3GRlvNHn30UTd+9OhRN+4Vpq5du+aOjVqFRkUl\nr6gStU+N4kCr4Q4RADISIgBkJEQAyEiIAJCREAEgK73K3NXV5W7tig4V9Q5PjbbRRaJ2nlu2bCnE\nxsbG3LGnTp2q67m9qvSZM2fcsVEV/J133nHjUatQ73DXtWvXumOj64wq4RcuXCjE5ubm3LFRi1Og\n1XCHCAAZCREAMhIiAGQkRADISIgAkJVeZY5MTk66ca9ae+LEibqeO9qHfPLkyUIsqpxGLTejPdhe\n1dyrakvx3uSomh4dNLtt27ZCzNtTLcWV6qhq7u0/jw6w9VrHAq2IO0QAyEiIAJCREAEgIyECQEZC\nBICs9CpzV1eXent7ax7v7fM9cuSIO3br1q1ufHZ2tuavF50wXU+70Wh8dJL0mjVr3HjUKjQa/+GH\nHxZi0d7kqFWod4q45FfTo1O3vT3VQCviDhEAMhIiAGQkRADIbpgQzew7ZvYrM7toZpNm9lMz27lo\njJnZATMbM7MrZvammX25edNGu2B9oZXUUlTZI+kVSb+SZJL+QtK/mdmXUkrz77h/W9K3JO2T9GtJ\nL0p6w8x2pJSmvujJZ2Zm3K13U1P+P7t8+XIh9sADD7hjo0NcowKC99zRlraoIDI0NOTGvcLPunXr\n3LHHjh1z496hrFLcbnVgYKAQ87YnSv5WvC/ifQ+jlqXRdWZ71MT1BdTjhgkxpfSbC/9uZl+X9Imk\nJyT91KpNhL8p6XsppdfzmG9IOiPpWUl/2+hJo32wvtBKbuY9xNX5383fDgxJGpR0cH5ASumKpJ9L\nevxWJ4iOw/pCaW4mIb4k6W1Jv8x/H8yfJxaNm1jwGFAr1hdKU9cvZpvZDyR9RdJXUkqLz3RKi4c7\nsfnneU7Sc9IN319CB2nG+gLqUfMdopn9taTfk/R0SmnhgX2n8+fFr9YDKr6qS5JSSq+mlIZTSsOr\nV6+uZ75oU81aX42fKdpZTXeIZvaSpN+VtCel9MGih4+pumj3qloplJmtkPSkpD+96YkFW+MuXrxY\niEUtNKNDXKOqbPX9+xt/PSmuVHd3d7vxHTt2FGLe1jpJWr9+vRvv6+tz45VKxY13dRVf77xDY6W4\nah4deOu1UI22BU5MuHnr/5WxvgDPDROimb0s6euSvibpvJnNv1JfSildSiklM/uhpOfN7ANJhyW9\nIOmSpB81ad5oE6wvtJJa7hD358//vij+XUkH8p+/L2mlpJclrZX0lqSv8jtiqAHrCy2jlt9DLP4c\nWRyTVF28B259SugkrC+0EvYyA0BGQgSAzKo/jZQ4AbNJSfO9Mfsl+Sesto9OuEaptuu8N6Xkl9Qb\npAPXl9QZ19mU9VV6QlzIzA61+++OdcI1Sq15na04p2bohOts1jXyIzMAZCREAMhIiACQkRABIGu1\nhPhq2RNYAp1wjVJrXmcrzqkZOuE6m3KNLVVlBoAytdodIgCUhoQIABkJEQCylkiIZrbfzI6Z2bSZ\njZjZk2XP6VaY2VNm9hMzO2Vmycz2LXr8tu8zfDv1U2Z9sb5qVXpCNLNnVG0s9JeSHpX0C0k/M7Mt\npU7s1vRKelfSn0i64jw+32f4jyX9hqotNd8ws9upn8IeVfspPy7paUmzqvZTvmvBmNKvk/XF+qpL\nSqnUD1UP+3xtUeyIpL8qe24Nur5LkvYt+LtJGpf0/ILYSklTkv6w7PnewnX2Srou6bdb6TpZX6yv\nej5KvUM0sx5Ju7Wg5252UO3bc7dd+wy3XD9l1lcV66t2Zf/I3C+pos7quduufYZbsZ8y6+szt/s1\nL8n6qqsvcxPV3HO3jbTNNTeqn3ITtcIcllrbXPNSrq+y7xDPqvq+QM09d9tA3X2GW1kj+yk3Aevr\nM7flNS/1+io1IaaUZiSNqNpzd6G9qlYD29HCPsOSPtdn+La65txP+VlVF+sX9VOeH7+k18n6qmJ9\n1aEFqkfPSJqR9PuSHlL1vYJLqh7/Xfr8bqEitit/fCrpxfznLfnxP5N0UdLvSNop6Z8kjUlaXfbc\n67jGl/M1PK3qq/T8R++CMaVfJ+uL9VXX1y37wvOF7Zc0Kumqqq/oT5U9p1u8nj2qvo+x+OPv8uOm\nakvNcUnTkv5T0s6y513nNXrXlyQdWDCmJa6T9cX6qvWD024AICu7qAIALYOECAAZCREAMhIiAGQk\nRADISIgAkJEQASAjIQJARkIEgOz/AIhFmpX37AwXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196aef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start 'for' loop to load and process the data\n",
    "folder_path = 'D:/Books/research/IMAGE/20180110_TMD/'\n",
    "for t in temp_list:\n",
    "    for fl in nflake_list:\n",
    "        if t == temp_list[-1] and fl == nflake_list[-1]:\n",
    "            break\n",
    "        #load the data\n",
    "        folder_name = str(t) + '_' + str(fl)\n",
    "        file_name = folder_path + folder_name + '/EELS Spectrum Image.dm4'\n",
    "        raw = hs.load(file_name,signal_type='EELS')\n",
    "        \n",
    "        #general process:alignment, thickness, zlp substraction\n",
    "        num_rows = np.shape(raw.data)[0]\n",
    "        num_cols = np.shape(raw.data)[1]\n",
    "        raw.align_zero_loss_peak(subpixel=True,show_progressbar=None,print_stats=False)\n",
    "        \n",
    "        \n",
    "        try:\n",
    "            zlp, sig = extract_ZLP(raw,range_factor=(-10, 8),return_inelastic=True,print_output=False)\n",
    "        except OverflowError:\n",
    "            raw = raw/10\n",
    "            zlp, sig = extract_ZLP(raw,range_factor=(-10, 8),return_inelastic=True,print_output=False)\n",
    "        \n",
    "        \n",
    "        new_axes = sig.axes_manager['Energy loss']\n",
    "        x0 = np.linspace(new_axes.offset, new_axes.offset+new_axes.scale*(new_axes.size-1), new_axes.size)\n",
    "        thickmat = get_thickness(raw,zlp)\n",
    "                \n",
    "        sig_data = np.reshape(sig.data,(-1,sig.data.shape[2]))\n",
    "        sig_data[np.isnan(sig_data)] = 0\n",
    "        \n",
    "        #for i in range(np.shape(sig_data)[0]):\n",
    "        #    temp_data = deepcopy(sig_data[i,:])\n",
    "        #    temp_data[np.isnan(temp_data)] = 0\n",
    "        #    sig_data[i,:] = deepcopy(temp_data)\n",
    "            \n",
    "        \n",
    "        #divide the data into clusters based on the thickness by k-means\n",
    "        sp = np.shape(thickmat)\n",
    "        task = []\n",
    "        for i in range(sp[0]):\n",
    "            for j in range(sp[1]):\n",
    "                task.append([i,j,thickmat[i,j]])\n",
    "        t1=np.array(task)\n",
    "\n",
    "        t1[np.isnan(t1)]=0\n",
    "        t2=normalize(t1,axis=0)\n",
    "        t3=deepcopy(t2)\n",
    "        t3[:,2]=t3[:,2]*weight\n",
    "\n",
    "        #num_clusters = get_k_number(task)\n",
    "        num_clusters = cluster_list[nflake_list.index(fl)]\n",
    "        estimator = KMeans(n_clusters=num_clusters)#构造聚类器\n",
    "        estimator.fit(t3)#聚类\n",
    "        label_pred = estimator.labels_ #获取聚类标签\n",
    "        centroids = estimator.cluster_centers_ #获取聚类中心\n",
    "        #print('KMeans error: ',estimator.inertia_) # 获取聚类准则的总和\n",
    "        \n",
    "        #test clustering\n",
    "        plt.subplot(1,2,1)\n",
    "        plt.imshow(thickmat)\n",
    "        plt.subplot(1,2,2)\n",
    "        lb=np.reshape(label_pred,np.shape(thickmat))\n",
    "        plt.imshow(lb)\n",
    "        ulabel = np.unique(label_pred)\n",
    "        take_num = 0\n",
    "        for im in ulabel:\n",
    "            if centroids[im,2] == min(centroids[:,2]):\n",
    "                take_num = im\n",
    "        label_num = list(range(num_clusters))\n",
    "        label_num.pop(take_num)\n",
    "        for i in label_num:\n",
    "            sig_temp = sig_data[label_pred == i, :]\n",
    "            thick_val = np.mean(t1[label_pred == i, 2])\n",
    "            model = NMF(n_components=4, init='random', random_state=0)\n",
    "            W = model.fit_transform(sig_temp)\n",
    "            H = model.components_\n",
    "            spec = H[0,:]\n",
    "            \n",
    "            tmd = tmd.append({'Temperature': t,\n",
    "                             'Nanoflake_Number': fl,\n",
    "                             'Thickness': thick_val,\n",
    "                             'Thickness_List': list(t1[label_pred == i, 2]),\n",
    "                             'Spectrum_Axis': x0,\n",
    "                             'EELS_Spectrum': list(spec)}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmd.to_csv(folder_path+'/TMD_new.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmd = pd.DataFrame(columns=['Temperature', 'Nanoflake_Number', 'Thickness', 'Layer', 'EELS_Spectrum', 'Peak_Position'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4.,  5., 10.,  7.,  3.,  3.,  4.,  5.,  1.,  0.,  4.,  1.,  1.,\n",
       "         0.,  2.,  0.,  2.,  1.,  2.,  2.,  1.,  1.,  1.,  0.,  0.,  2.,\n",
       "         0.,  3.,  2.,  0.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
       " array([0.03369653, 0.03962061, 0.04554468, 0.05146875, 0.05739282,\n",
       "        0.06331689, 0.06924096, 0.07516503, 0.08108911, 0.08701318,\n",
       "        0.09293725, 0.09886132, 0.10478539, 0.11070946, 0.11663353,\n",
       "        0.12255761, 0.12848168, 0.13440575, 0.14032982, 0.14625389,\n",
       "        0.15217796, 0.15810203, 0.16402611, 0.16995018, 0.17587425,\n",
       "        0.18179832, 0.18772239, 0.19364646, 0.19957053, 0.2054946 ,\n",
       "        0.21141868, 0.21734275, 0.22326682, 0.22919089, 0.23511496,\n",
       "        0.24103903, 0.2469631 , 0.25288718, 0.25881125, 0.26473532,\n",
       "        0.27065939, 0.27658346, 0.28250753, 0.2884316 , 0.29435568,\n",
       "        0.30027975, 0.30620382, 0.31212789, 0.31805196, 0.32397603,\n",
       "        0.3299001 ]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAE5CAYAAADoVlnZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADNpJREFUeJzt3XuMZvVdx/H3B4hVA153EKqZjg0R\nU40pOkbEcpEEm7jRNBpbWgMladhUEG1aUpe2GmovWaxpSyMbs41pKdrYBC8BFxPU2mLB0C62KStF\nG7uLsSIsQhUMl7D9+sd8sZvZwc7ld57Z2b5fyWTnOc95fueXw+TNeW7npKqQJMEJmz0BSTpWGERJ\nagZRkppBlKRmECWpGURJagZRkppBlKRmECWpGURJaidt9gS2bdtWCwsLmz0NSceZe+6555GqmlvL\nYzY9iAsLC+zbt2+zpyHpOJPkgbU+xqfMktQMoiQ1gyhJzSBKUjOIktQMoiS1VQUxyXlJbkny5SSV\n5LJl9yfJtUn+PcmTST6R5IcmmbEkTWS1R4gnA/uBXweeXOH+NwNvAq4Cfhx4GPirJKeMmKQkzcKq\nglhVt1XVW6rqZuCrR96XJMAbgF1V9SdVtR94LXAK8JrRE5akqYx4DfH7gdOA259bUFVPAncA5wwY\nX5JmYsRX907rfx9atvwh4HtXekCSHcAOgPn5+QFTWNnCzr1HLTu4a/tk25O0tY18l3n5BZ6zwrKl\nFav2VNViVS3Oza3pu9eSNJkRQfyP/ve0ZctP5eijRkk6Zo0I4gGWonjRcwuSfDNwLnDXgPElaSZW\n9RpikpOBM/rmCcB8kpcCj1bVvyZ5P/DWJPcD/wy8DXgC+OgEc5akSaz2TZVF4G+PuP32/rkRuAz4\nHeBbgBuA7wTuBn6mqh4fNlNJmtiqglhVn2DpTZLnu7+Aa/tHkrYkv8ssSc0gSlIziJLUDKIkNYMo\nSc0gSlIziJLUDKIkNYMoSc0gSlIziJLUDKIkNYMoSc0gSlIziJLUDKIkNYMoSc0gSlIziJLUDKIk\nNYMoSc0gSlIziJLUDKIkNYMoSc0gSlIziJLUDKIkNYMoSc0gSlIziJLUDKIkNYMoSc0gSlIziJLU\nDKIkNYMoSc0gSlIziJLUDKIkNYMoSW1IEJOcmOQdSQ4kear/fWeSk0aML0mzMCpYvwFcCbwWuBf4\nEeBG4GngHYO2IUmTGhXEc4Bbq+rWvn0wyS3ATwwaX5ImN+o1xE8BP53kBwGSvAS4ELht0PiSNLlR\nR4jXAacA9yU53OO+q6p2r7Rykh3ADoD5+flBU5CkjRl1hPgq4FLgNcCP9u9XJHndSitX1Z6qWqyq\nxbm5uUFTkKSNGXWE+B7gd6vqj/v2vUleBFwD/MGgbUjSpEYdIX4rcHjZssMDx5ekyY06QrwV2Jnk\nAPCPwFnAG4GPDBpfkiY3KohXsfR5w93AqcCDwAeB3x40viRNbkgQq+px4A39I0lbkq/xSVIziJLU\nDKIkNYMoSc0gSlIziJLUDKIkNYMoSc0gSlIziJLUDKIkNYMoSc0gSlIziJLUDKIkNYMoSc0gSlIz\niJLUDKIktVEXmdoyFnbuPWrZwV3bN2Emko41HiFKUjOIktQMoiQ1gyhJzSBKUjOIktQMoiQ1gyhJ\nzSBKUjOIktQMoiQ1gyhJzSBKUjOIktQMoiQ1gyhJzSBKUjOIktQMoiQ1gyhJzSBKUhsWxCSnJ7kx\nyaEkTyW5L8n5o8aXpKkNuQxpku8A7gQ+BWwHDgEvBh4eMb4kzcKo6zK/GXiwqi49YtmBQWNL0kyM\nesr8CuDuJB9L8nCSzyX51SQZNL4kTW5UEF8MXAF8CXg5cD2wC7hypZWT7EiyL8m+Q4cODZqCJG3M\nqCCeAPxDVV1TVZ+tqg8BH+B5glhVe6pqsaoW5+bmBk1BkjZmVBAfBO5btuwLwPyg8SVpcqOCeCdw\n5rJlPwA8MGh8SZrcqCC+Dzg7yVuTnJHkl4BfA24YNL4kTW5IEKvqMyy90/xKYD/wLuA3gd0jxpek\nWRj1OUSqai+wd9R4kjRrfpdZkppBlKRmECWpGURJagZRkppBlKRmECWpGURJagZRkppBlKRmECWp\nGURJagZRkppBlKRmECWpGURJagZRkppBlKRmECWpDbumymZb2Lk5l3NZabsHd23fhJlI2iiPECWp\nGURJagZRkppBlKRmECWpGURJagZRkppBlKRmECWpGURJagZRkppBlKRmECWpGURJagZRkppBlKRm\nECWpGURJagZRkppBlKQ2SRCTvCVJJfm9KcaXpCkMD2KSs4HLgc+PHluSpjQ0iEm+Hfgj4HXAYyPH\nlqSpjT5C3APcXFUfHzyuJE1u2IXqk1wOnAFcMmpMSZqlIUFMcibwbuDcqnpmFevvAHYAzM/Pr3l7\nCzv3rvkxI4ze7krjHdy1feg2JK3eqKfMPwlsA/YneTbJs8D5wBV9+wVHrlxVe6pqsaoW5+bmBk1B\nkjZm1FPmPwf2LVv2IeCLLB05ft2jRknabEOCWFVfAb5y5LIk/wM8WlX7R2xDkqbmN1UkqQ17l3m5\nqrpgqrElaQoeIUpSM4iS1AyiJDWDKEnNIEpSM4iS1AyiJDWDKEnNIEpSM4iS1AyiJDWDKEnNIEpS\nM4iS1AyiJDWDKEnNIEpSM4iS1AyiJDWDKEltsotMbSULO/du9hQ00PP99zy4a/uMZ6KtxiNESWoG\nUZKaQZSkZhAlqRlESWoGUZKaQZSkZhAlqRlESWoGUZKaQZSkZhAlqRlESWoGUZKaQZSkZhAlqRlE\nSWoGUZKaQZSkZhAlqQ0JYpJrknwmyX8nOZTk1iQ/PGJsSZqVUUeIFwC7gXOAC4Fngb9O8l2Dxpek\nyQ25DGlVvfzI20kuAf4L+Cng1hHbkKSpTfUa4ik99mMTjS9Jw00VxOuBzwF/P9H4kjTckKfMR0ry\nXuBlwMuq6vDzrLMD2AEwPz8/egrf0BZ27j1q2cFd24eut5KVHjvaauc81eN1/Bt6hJjkfcCrgQur\n6kvPt15V7amqxapanJubGzkFSVq3YUeISa4HLgYuqKr7R40rSbMyJIhJbgAuAV4BPJbktL7riap6\nYsQ2JGlqo54yX8HSO8t/Azx4xM/Vg8aXpMmN+hxiRowjSZvJ7zJLUjOIktQMoiQ1gyhJzSBKUjOI\nktQMoiQ1gyhJzSBKUjOIktQMoiQ1gyhJzSBKUjOIktQMoiQ1gyhJzSBKUjOIktQMoiS14Req18Yu\niL6ZF4Zf7bZHP3YrXCx+9EXuR4+3ke3OaturtVn7BjxClKT/YxAlqRlESWoGUZKaQZSkZhAlqRlE\nSWoGUZKaQZSkZhAlqRlESWoGUZKaQZSkZhAlqRlESWoGUZKaQZSkZhAlqRlESWoGUZKaQZSkNjSI\nSa5IciDJU0nuSXLuyPElaUrDgpjkVcD1wLuBs4C7gL9MMj9qG5I0pZFHiG8EPlxVH6yqL1TVVcCD\nwK8M3IYkTWZIEJN8E/BjwO3L7rodOGfENiRpaqmqjQ+SvBD4MnB+Vd1xxPLfAn65qs5ctv4OYEff\nPBP4pw1P4viyDXhksydxHHK/TuNY3a8vqqq5tTzgpMETWF7XrLCMqtoD7Bm87eNGkn1VtbjZ8zje\nuF+ncTzt11GvIT4CHAZOW7b8VOChQduQpEkNCWJVPQPcA1y07K6LWHq3WZKOeSOfMr8XuCnJp4E7\ngdcDLwR+f+A2JGkyw4JYVR9L8t3A24DTgf3Az1bVA6O2IUlTGvqmSlXtBnaPHPMblG84TcP9Oo3j\nZr8O+diNJB0PPLmDJDWDKEnNIEpSM4ibYC2nSUtyepKPJrk/yeEkH57hVLeUNe7XX0hye5JDSR5P\ncneSn5/lfLeCNe7T85PcleQ/kzzZf7NXz3K+G2UQZ2wdp0l7AUvfBNoF3D2TSW5B69iv5wMfB7b3\n+rcBf+Y5PL9mHfv0CeADwHnAS4B3Am9PcsUMpjuE7zLPWJK7gc9X1eVHLPsicHNVXfN1HvsXwCNV\nddm0s9x6NrJfj1j/08DfVdWbJprmljJon/4p8HRVvXqiaQ7lEeIMeZq0aQzcr6cAj42a11Y2Yp8m\nOavX/eTY2U3HIM7WNuBEjj7hxUMcfWIMrd6G92uSK4HvA24aO7Uta937NMm/JXka2Afsrqot8/Xd\n0af/0uqs6jRpWrN17dckvwi8B7jYr5oeZT379FzgZOBs4LokB6pqS/yPxiDOlqdJm8a692vH8Cbg\n0qq6ZZrpbUnr3qdVdaB/vTfJ9wDXskWOvH3KPEOeJm0a692vSV4J/CFwWVXdPN0Mt56Bf6snsPRJ\niS3BI8TZ+39Pk5bkIwBVdelzD0jy0v7124Cv9u1nquq+WU78GLem/ZrkYpaOWq4G7kjy3JHQM1X1\n6Iznfqxa6z69CjjA1y4Jch5L+3fLnPDFIM7YKk6TttJnvD677PbPAQ8AC1PNc6tZx359PUt//+/v\nn+d8Erhg2tluDevYpycC17H0d/ks8C/ATrbQOVH9HKIkNV9DlKRmECWpGURJagZRkppBlKRmECWp\nGURJagZRkppBlKT2v4/h4M8aAOCXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19bf5f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind, plt.hist(tmd['Thickness'],50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Nanoflake_Number</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>Thickness_List</th>\n",
       "      <th>Layer</th>\n",
       "      <th>Spectrum_Axis</th>\n",
       "      <th>EELS_Spectrum</th>\n",
       "      <th>Peak_Position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>0.051779</td>\n",
       "      <td>[0.04964418256944714, 0.05025928332922756, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.611673711845267, -10.561673711100209, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>0.051953</td>\n",
       "      <td>[0.04008605391897694, 0.04153889935995845, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.564876190486771, -10.514876189741713, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>0.051799</td>\n",
       "      <td>[0.036499892053164236, 0.03700958004260841, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.548553876193964, -10.498553875448906, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.052271</td>\n",
       "      <td>[0.037202066101265564, 0.03860911272269586, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.613533216005207, -10.563533215260149, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150</td>\n",
       "      <td>4</td>\n",
       "      <td>0.049739</td>\n",
       "      <td>[0.04412852450012425, 0.04557972751704245, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.951852015047162, -10.901852014302104, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>0.157282</td>\n",
       "      <td>[0.14879464363892514, 0.15226728761238875, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.182500151731073, -10.132500150986015, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>0.055273</td>\n",
       "      <td>[0.06599271046914475, 0.05907118794274354, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.182500151731073, -10.132500150986015, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>0.034734</td>\n",
       "      <td>[0.03950695348725538, 0.036343847738604065, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.182500151731073, -10.132500150986015, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>0.120578</td>\n",
       "      <td>[0.13486726993454, 0.12202916798542426, 0.1201...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.182500151731073, -10.132500150986015, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>0.079299</td>\n",
       "      <td>[0.09432813224530383, 0.08776116700366823, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.182500151731073, -10.132500150986015, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>150</td>\n",
       "      <td>5</td>\n",
       "      <td>0.202029</td>\n",
       "      <td>[0.18352363062715762, 0.1925449313093487, 0.18...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.182500151731073, -10.132500150986015, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>150</td>\n",
       "      <td>6</td>\n",
       "      <td>0.076064</td>\n",
       "      <td>[0.06445018373872953, 0.061785374769888594, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.816406411177013, -10.766406410431955, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>150</td>\n",
       "      <td>6</td>\n",
       "      <td>0.043823</td>\n",
       "      <td>[0.03093123325900168, 0.032361796346999386, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.816406411177013, -10.766406410431955, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>150</td>\n",
       "      <td>6</td>\n",
       "      <td>0.123022</td>\n",
       "      <td>[0.10209316456066493, 0.13139892992330487, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.816406411177013, -10.766406410431955, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>0.096543</td>\n",
       "      <td>[0.1403360244771739, 0.11826446549460676, 0.11...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.88775268749268, -10.837752686747622, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>150</td>\n",
       "      <td>7</td>\n",
       "      <td>0.198882</td>\n",
       "      <td>[0.21608601727580787, 0.18750495965651753, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.88775268749268, -10.837752686747622, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>150</td>\n",
       "      <td>8</td>\n",
       "      <td>0.042892</td>\n",
       "      <td>[0.03851380164251307, 0.04046399408615765, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.732083493253837, -10.682083492508779, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>150</td>\n",
       "      <td>8</td>\n",
       "      <td>0.150345</td>\n",
       "      <td>[0.1159235401711209, 0.1313578978672453, 0.134...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.732083493253837, -10.682083492508779, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>150</td>\n",
       "      <td>8</td>\n",
       "      <td>0.075515</td>\n",
       "      <td>[0.06125878766537881, 0.061976098463828184, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.732083493253837, -10.682083492508779, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>150</td>\n",
       "      <td>9</td>\n",
       "      <td>0.132557</td>\n",
       "      <td>[0.20378151569656747, 0.19503297029376984, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.750529260724468, -10.70052925997941, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>150</td>\n",
       "      <td>9</td>\n",
       "      <td>0.075702</td>\n",
       "      <td>[0.09690011996773011, 0.0855341906779702, 0.08...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.750529260724468, -10.70052925997941, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>150</td>\n",
       "      <td>9</td>\n",
       "      <td>0.282093</td>\n",
       "      <td>[0.3648006746931767, 0.33001411027259925, 0.30...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.750529260724468, -10.70052925997941, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>150</td>\n",
       "      <td>10</td>\n",
       "      <td>0.059908</td>\n",
       "      <td>[0.04896291689489782, 0.05630235078722392, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.639791825211917, -10.58979182446686, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>150</td>\n",
       "      <td>11</td>\n",
       "      <td>0.047189</td>\n",
       "      <td>[0.05616652311901246, 0.053191361442421335, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.900619997142638, -10.85061999639758, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>0.046076</td>\n",
       "      <td>[0.03823848930472759, 0.04036575383059291, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.423140651184665, -10.373140650439607, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>250</td>\n",
       "      <td>2</td>\n",
       "      <td>0.040599</td>\n",
       "      <td>[0.02924062506228201, 0.03137073187170781, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.539979495900907, -10.489979495155849, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>250</td>\n",
       "      <td>3</td>\n",
       "      <td>0.043080</td>\n",
       "      <td>[0.028958346005192957, 0.02867692964922076, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.517458834408297, -10.467458833663239, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>250</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.052375</td>\n",
       "      <td>[0.03699898302549691, 0.03432674767258898, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.831921649011514, -10.781921648266456, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>250</td>\n",
       "      <td>4</td>\n",
       "      <td>0.046810</td>\n",
       "      <td>[0.03331707202115937, 0.037639671184944865, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.296487756735406, -10.246487755990348, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>250</td>\n",
       "      <td>5</td>\n",
       "      <td>0.144395</td>\n",
       "      <td>[0.12550184859082267, 0.13368437690003493, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.819474784002734, -9.769474783257676, -9.71...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>250</td>\n",
       "      <td>8</td>\n",
       "      <td>0.071030</td>\n",
       "      <td>[0.05983102815436843, 0.06555670080210907, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.022449128937964, -9.972449128192906, -9.9...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>250</td>\n",
       "      <td>8</td>\n",
       "      <td>0.039222</td>\n",
       "      <td>[0.04678394370452075, 0.0443562024202056, 0.04...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.022449128937964, -9.972449128192906, -9.9...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>250</td>\n",
       "      <td>9</td>\n",
       "      <td>0.074700</td>\n",
       "      <td>[0.09702776215444152, 0.07968005925582318, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.801136509684676, -9.751136508939618, -9.70...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>250</td>\n",
       "      <td>9</td>\n",
       "      <td>0.132221</td>\n",
       "      <td>[0.17279476543124703, 0.15686400567295278, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.801136509684676, -9.751136508939618, -9.70...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>250</td>\n",
       "      <td>9</td>\n",
       "      <td>0.246592</td>\n",
       "      <td>[0.3049676622824629, 0.22278105282161703, 0.21...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.801136509684676, -9.751136508939618, -9.70...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>250</td>\n",
       "      <td>10</td>\n",
       "      <td>0.066649</td>\n",
       "      <td>[0.06514836999609229, 0.06529112110841695, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.57342121018783, -10.523421209442771, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>250</td>\n",
       "      <td>11</td>\n",
       "      <td>0.050962</td>\n",
       "      <td>[0.05362042771073763, 0.052216448148048614, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.524736998936064, -10.474736998191005, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>0.055125</td>\n",
       "      <td>[0.05250048358583511, 0.053500541448039694, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.078202629515763, -10.028202628770705, -9....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>350</td>\n",
       "      <td>2</td>\n",
       "      <td>0.034542</td>\n",
       "      <td>[0.02903772918341221, 0.03266601761681158, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.664876177075726, -9.614876176330668, -9.56...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>350</td>\n",
       "      <td>3</td>\n",
       "      <td>0.044177</td>\n",
       "      <td>[0.03338067668102486, 0.034461786773377426, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.203615854525061, -10.153615853780003, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>350</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.046373</td>\n",
       "      <td>[0.0363252311433362, 0.03538186571469515, 0.04...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.19710758996527, -10.147107589220212, -10....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>350</td>\n",
       "      <td>4</td>\n",
       "      <td>0.037641</td>\n",
       "      <td>[0.027817644312505857, 0.030599097678379997, 0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.317045608281411, -10.267045607536353, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>350</td>\n",
       "      <td>5</td>\n",
       "      <td>0.045428</td>\n",
       "      <td>[0.052923443225843164, 0.0481171101037723, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.504264847506933, -9.454264846761875, -9.40...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>350</td>\n",
       "      <td>5</td>\n",
       "      <td>0.118687</td>\n",
       "      <td>[0.1383204271095276, 0.12990420048555076, 0.11...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.504264847506933, -9.454264846761875, -9.40...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>350</td>\n",
       "      <td>5</td>\n",
       "      <td>0.083901</td>\n",
       "      <td>[0.09952881997164742, 0.09562007527228812, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.504264847506933, -9.454264846761875, -9.40...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>350</td>\n",
       "      <td>5</td>\n",
       "      <td>0.079582</td>\n",
       "      <td>[0.07348193593982243, 0.07303070489165728, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.504264847506933, -9.454264846761875, -9.40...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>350</td>\n",
       "      <td>5</td>\n",
       "      <td>0.061615</td>\n",
       "      <td>[0.07019547998346375, 0.06292731656915845, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.504264847506933, -9.454264846761875, -9.40...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>350</td>\n",
       "      <td>5</td>\n",
       "      <td>0.163370</td>\n",
       "      <td>[0.1573528692835371, 0.15247509364806885, 0.15...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.504264847506933, -9.454264846761875, -9.40...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>350</td>\n",
       "      <td>6</td>\n",
       "      <td>0.069530</td>\n",
       "      <td>[0.04899354142881104, 0.0613532879047974, 0.06...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.986467090958714, -9.936467090213656, -9.88...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>350</td>\n",
       "      <td>6</td>\n",
       "      <td>0.187744</td>\n",
       "      <td>[0.14738813590400376, 0.14700231515520892, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.986467090958714, -9.936467090213656, -9.88...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>350</td>\n",
       "      <td>6</td>\n",
       "      <td>0.095374</td>\n",
       "      <td>[0.08695605997255734, 0.09102881648334726, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.986467090958714, -9.936467090213656, -9.88...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>350</td>\n",
       "      <td>7</td>\n",
       "      <td>0.111217</td>\n",
       "      <td>[0.16313291554245193, 0.13080301000332994, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.04591681636249, -9.995916815617433, -9.94...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>350</td>\n",
       "      <td>7</td>\n",
       "      <td>0.220433</td>\n",
       "      <td>[0.22095311335510595, 0.22277176188627776, 0.2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.04591681636249, -9.995916815617433, -9.94...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>0.143349</td>\n",
       "      <td>[0.110881288759185, 0.11889795468982557, 0.111...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.622934027690409, -9.572934026945351, -9.52...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>0.074741</td>\n",
       "      <td>[0.061118246804216576, 0.06024460338206817, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.622934027690409, -9.572934026945351, -9.52...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>350</td>\n",
       "      <td>8</td>\n",
       "      <td>0.047400</td>\n",
       "      <td>[0.0470197998536438, 0.04713801439482922, 0.04...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.622934027690409, -9.572934026945351, -9.52...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>350</td>\n",
       "      <td>9</td>\n",
       "      <td>0.097991</td>\n",
       "      <td>[0.12024232782838436, 0.12078746161425483, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.527786519684058, -9.477786518939, -9.42778...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>350</td>\n",
       "      <td>9</td>\n",
       "      <td>0.149309</td>\n",
       "      <td>[0.22417168765951284, 0.19350191700810557, 0.1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.527786519684058, -9.477786518939, -9.42778...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>350</td>\n",
       "      <td>9</td>\n",
       "      <td>0.329900</td>\n",
       "      <td>[0.3553741665786681, 0.35414970119837796, 0.35...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.527786519684058, -9.477786518939, -9.42778...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>350</td>\n",
       "      <td>10</td>\n",
       "      <td>0.067177</td>\n",
       "      <td>[0.06575426900657717, 0.06544444445402305, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-9.788947514287734, -9.738947513542676, -9.68...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature Nanoflake_Number  Thickness  \\\n",
       "0          150                1   0.051779   \n",
       "1          150                2   0.051953   \n",
       "2          150                3   0.051799   \n",
       "3          150              3.5   0.052271   \n",
       "4          150                4   0.049739   \n",
       "5          150                5   0.157282   \n",
       "6          150                5   0.055273   \n",
       "7          150                5   0.034734   \n",
       "8          150                5   0.120578   \n",
       "9          150                5   0.079299   \n",
       "10         150                5   0.202029   \n",
       "11         150                6   0.076064   \n",
       "12         150                6   0.043823   \n",
       "13         150                6   0.123022   \n",
       "14         150                7   0.096543   \n",
       "15         150                7   0.198882   \n",
       "16         150                8   0.042892   \n",
       "17         150                8   0.150345   \n",
       "18         150                8   0.075515   \n",
       "19         150                9   0.132557   \n",
       "20         150                9   0.075702   \n",
       "21         150                9   0.282093   \n",
       "22         150               10   0.059908   \n",
       "23         150               11   0.047189   \n",
       "24         250                1   0.046076   \n",
       "25         250                2   0.040599   \n",
       "26         250                3   0.043080   \n",
       "27         250              3.5   0.052375   \n",
       "28         250                4   0.046810   \n",
       "29         250                5   0.144395   \n",
       "..         ...              ...        ...   \n",
       "41         250                8   0.071030   \n",
       "42         250                8   0.039222   \n",
       "43         250                9   0.074700   \n",
       "44         250                9   0.132221   \n",
       "45         250                9   0.246592   \n",
       "46         250               10   0.066649   \n",
       "47         250               11   0.050962   \n",
       "48         350                1   0.055125   \n",
       "49         350                2   0.034542   \n",
       "50         350                3   0.044177   \n",
       "51         350              3.5   0.046373   \n",
       "52         350                4   0.037641   \n",
       "53         350                5   0.045428   \n",
       "54         350                5   0.118687   \n",
       "55         350                5   0.083901   \n",
       "56         350                5   0.079582   \n",
       "57         350                5   0.061615   \n",
       "58         350                5   0.163370   \n",
       "59         350                6   0.069530   \n",
       "60         350                6   0.187744   \n",
       "61         350                6   0.095374   \n",
       "62         350                7   0.111217   \n",
       "63         350                7   0.220433   \n",
       "64         350                8   0.143349   \n",
       "65         350                8   0.074741   \n",
       "66         350                8   0.047400   \n",
       "67         350                9   0.097991   \n",
       "68         350                9   0.149309   \n",
       "69         350                9   0.329900   \n",
       "70         350               10   0.067177   \n",
       "\n",
       "                                       Thickness_List  Layer  \\\n",
       "0   [0.04964418256944714, 0.05025928332922756, 0.0...    NaN   \n",
       "1   [0.04008605391897694, 0.04153889935995845, 0.0...    NaN   \n",
       "2   [0.036499892053164236, 0.03700958004260841, 0....    NaN   \n",
       "3   [0.037202066101265564, 0.03860911272269586, 0....    NaN   \n",
       "4   [0.04412852450012425, 0.04557972751704245, 0.0...    NaN   \n",
       "5   [0.14879464363892514, 0.15226728761238875, 0.1...    NaN   \n",
       "6   [0.06599271046914475, 0.05907118794274354, 0.0...    NaN   \n",
       "7   [0.03950695348725538, 0.036343847738604065, 0....    NaN   \n",
       "8   [0.13486726993454, 0.12202916798542426, 0.1201...    NaN   \n",
       "9   [0.09432813224530383, 0.08776116700366823, 0.0...    NaN   \n",
       "10  [0.18352363062715762, 0.1925449313093487, 0.18...    NaN   \n",
       "11  [0.06445018373872953, 0.061785374769888594, 0....    NaN   \n",
       "12  [0.03093123325900168, 0.032361796346999386, 0....    NaN   \n",
       "13  [0.10209316456066493, 0.13139892992330487, 0.1...    NaN   \n",
       "14  [0.1403360244771739, 0.11826446549460676, 0.11...    NaN   \n",
       "15  [0.21608601727580787, 0.18750495965651753, 0.1...    NaN   \n",
       "16  [0.03851380164251307, 0.04046399408615765, 0.0...    NaN   \n",
       "17  [0.1159235401711209, 0.1313578978672453, 0.134...    NaN   \n",
       "18  [0.06125878766537881, 0.061976098463828184, 0....    NaN   \n",
       "19  [0.20378151569656747, 0.19503297029376984, 0.1...    NaN   \n",
       "20  [0.09690011996773011, 0.0855341906779702, 0.08...    NaN   \n",
       "21  [0.3648006746931767, 0.33001411027259925, 0.30...    NaN   \n",
       "22  [0.04896291689489782, 0.05630235078722392, 0.0...    NaN   \n",
       "23  [0.05616652311901246, 0.053191361442421335, 0....    NaN   \n",
       "24  [0.03823848930472759, 0.04036575383059291, 0.0...    NaN   \n",
       "25  [0.02924062506228201, 0.03137073187170781, 0.0...    NaN   \n",
       "26  [0.028958346005192957, 0.02867692964922076, 0....    NaN   \n",
       "27  [0.03699898302549691, 0.03432674767258898, 0.0...    NaN   \n",
       "28  [0.03331707202115937, 0.037639671184944865, 0....    NaN   \n",
       "29  [0.12550184859082267, 0.13368437690003493, 0.1...    NaN   \n",
       "..                                                ...    ...   \n",
       "41  [0.05983102815436843, 0.06555670080210907, 0.0...    NaN   \n",
       "42  [0.04678394370452075, 0.0443562024202056, 0.04...    NaN   \n",
       "43  [0.09702776215444152, 0.07968005925582318, 0.0...    NaN   \n",
       "44  [0.17279476543124703, 0.15686400567295278, 0.1...    NaN   \n",
       "45  [0.3049676622824629, 0.22278105282161703, 0.21...    NaN   \n",
       "46  [0.06514836999609229, 0.06529112110841695, 0.0...    NaN   \n",
       "47  [0.05362042771073763, 0.052216448148048614, 0....    NaN   \n",
       "48  [0.05250048358583511, 0.053500541448039694, 0....    NaN   \n",
       "49  [0.02903772918341221, 0.03266601761681158, 0.0...    NaN   \n",
       "50  [0.03338067668102486, 0.034461786773377426, 0....    NaN   \n",
       "51  [0.0363252311433362, 0.03538186571469515, 0.04...    NaN   \n",
       "52  [0.027817644312505857, 0.030599097678379997, 0...    NaN   \n",
       "53  [0.052923443225843164, 0.0481171101037723, 0.0...    NaN   \n",
       "54  [0.1383204271095276, 0.12990420048555076, 0.11...    NaN   \n",
       "55  [0.09952881997164742, 0.09562007527228812, 0.0...    NaN   \n",
       "56  [0.07348193593982243, 0.07303070489165728, 0.0...    NaN   \n",
       "57  [0.07019547998346375, 0.06292731656915845, 0.0...    NaN   \n",
       "58  [0.1573528692835371, 0.15247509364806885, 0.15...    NaN   \n",
       "59  [0.04899354142881104, 0.0613532879047974, 0.06...    NaN   \n",
       "60  [0.14738813590400376, 0.14700231515520892, 0.1...    NaN   \n",
       "61  [0.08695605997255734, 0.09102881648334726, 0.0...    NaN   \n",
       "62  [0.16313291554245193, 0.13080301000332994, 0.1...    NaN   \n",
       "63  [0.22095311335510595, 0.22277176188627776, 0.2...    NaN   \n",
       "64  [0.110881288759185, 0.11889795468982557, 0.111...    NaN   \n",
       "65  [0.061118246804216576, 0.06024460338206817, 0....    NaN   \n",
       "66  [0.0470197998536438, 0.04713801439482922, 0.04...    NaN   \n",
       "67  [0.12024232782838436, 0.12078746161425483, 0.1...    NaN   \n",
       "68  [0.22417168765951284, 0.19350191700810557, 0.1...    NaN   \n",
       "69  [0.3553741665786681, 0.35414970119837796, 0.35...    NaN   \n",
       "70  [0.06575426900657717, 0.06544444445402305, 0.0...    NaN   \n",
       "\n",
       "                                        Spectrum_Axis  \\\n",
       "0   [-10.611673711845267, -10.561673711100209, -10...   \n",
       "1   [-10.564876190486771, -10.514876189741713, -10...   \n",
       "2   [-10.548553876193964, -10.498553875448906, -10...   \n",
       "3   [-10.613533216005207, -10.563533215260149, -10...   \n",
       "4   [-10.951852015047162, -10.901852014302104, -10...   \n",
       "5   [-10.182500151731073, -10.132500150986015, -10...   \n",
       "6   [-10.182500151731073, -10.132500150986015, -10...   \n",
       "7   [-10.182500151731073, -10.132500150986015, -10...   \n",
       "8   [-10.182500151731073, -10.132500150986015, -10...   \n",
       "9   [-10.182500151731073, -10.132500150986015, -10...   \n",
       "10  [-10.182500151731073, -10.132500150986015, -10...   \n",
       "11  [-10.816406411177013, -10.766406410431955, -10...   \n",
       "12  [-10.816406411177013, -10.766406410431955, -10...   \n",
       "13  [-10.816406411177013, -10.766406410431955, -10...   \n",
       "14  [-10.88775268749268, -10.837752686747622, -10....   \n",
       "15  [-10.88775268749268, -10.837752686747622, -10....   \n",
       "16  [-10.732083493253837, -10.682083492508779, -10...   \n",
       "17  [-10.732083493253837, -10.682083492508779, -10...   \n",
       "18  [-10.732083493253837, -10.682083492508779, -10...   \n",
       "19  [-10.750529260724468, -10.70052925997941, -10....   \n",
       "20  [-10.750529260724468, -10.70052925997941, -10....   \n",
       "21  [-10.750529260724468, -10.70052925997941, -10....   \n",
       "22  [-10.639791825211917, -10.58979182446686, -10....   \n",
       "23  [-10.900619997142638, -10.85061999639758, -10....   \n",
       "24  [-10.423140651184665, -10.373140650439607, -10...   \n",
       "25  [-10.539979495900907, -10.489979495155849, -10...   \n",
       "26  [-10.517458834408297, -10.467458833663239, -10...   \n",
       "27  [-10.831921649011514, -10.781921648266456, -10...   \n",
       "28  [-10.296487756735406, -10.246487755990348, -10...   \n",
       "29  [-9.819474784002734, -9.769474783257676, -9.71...   \n",
       "..                                                ...   \n",
       "41  [-10.022449128937964, -9.972449128192906, -9.9...   \n",
       "42  [-10.022449128937964, -9.972449128192906, -9.9...   \n",
       "43  [-9.801136509684676, -9.751136508939618, -9.70...   \n",
       "44  [-9.801136509684676, -9.751136508939618, -9.70...   \n",
       "45  [-9.801136509684676, -9.751136508939618, -9.70...   \n",
       "46  [-10.57342121018783, -10.523421209442771, -10....   \n",
       "47  [-10.524736998936064, -10.474736998191005, -10...   \n",
       "48  [-10.078202629515763, -10.028202628770705, -9....   \n",
       "49  [-9.664876177075726, -9.614876176330668, -9.56...   \n",
       "50  [-10.203615854525061, -10.153615853780003, -10...   \n",
       "51  [-10.19710758996527, -10.147107589220212, -10....   \n",
       "52  [-10.317045608281411, -10.267045607536353, -10...   \n",
       "53  [-9.504264847506933, -9.454264846761875, -9.40...   \n",
       "54  [-9.504264847506933, -9.454264846761875, -9.40...   \n",
       "55  [-9.504264847506933, -9.454264846761875, -9.40...   \n",
       "56  [-9.504264847506933, -9.454264846761875, -9.40...   \n",
       "57  [-9.504264847506933, -9.454264846761875, -9.40...   \n",
       "58  [-9.504264847506933, -9.454264846761875, -9.40...   \n",
       "59  [-9.986467090958714, -9.936467090213656, -9.88...   \n",
       "60  [-9.986467090958714, -9.936467090213656, -9.88...   \n",
       "61  [-9.986467090958714, -9.936467090213656, -9.88...   \n",
       "62  [-10.04591681636249, -9.995916815617433, -9.94...   \n",
       "63  [-10.04591681636249, -9.995916815617433, -9.94...   \n",
       "64  [-9.622934027690409, -9.572934026945351, -9.52...   \n",
       "65  [-9.622934027690409, -9.572934026945351, -9.52...   \n",
       "66  [-9.622934027690409, -9.572934026945351, -9.52...   \n",
       "67  [-9.527786519684058, -9.477786518939, -9.42778...   \n",
       "68  [-9.527786519684058, -9.477786518939, -9.42778...   \n",
       "69  [-9.527786519684058, -9.477786518939, -9.42778...   \n",
       "70  [-9.788947514287734, -9.738947513542676, -9.68...   \n",
       "\n",
       "                                        EELS_Spectrum  Peak_Position  \n",
       "0   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "1   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "2   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "3   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "4   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "5   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "6   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "7   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "8   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "9   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "10  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "11  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "12  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "13  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "14  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "15  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "16  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "17  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "18  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "19  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "20  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "21  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "22  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "23  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "24  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "25  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "26  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "27  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "28  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "29  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "..                                                ...            ...  \n",
       "41  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "42  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "43  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "44  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "45  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "46  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "47  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "48  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "49  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "50  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "51  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "52  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "53  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "54  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "55  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "56  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "57  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "58  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "59  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "60  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "61  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "62  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "63  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "64  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "65  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "66  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "67  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "68  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "69  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "70  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...            NaN  \n",
       "\n",
       "[71 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmdlist = [isimn for isim in tmd['Thickness_List'] for isimn in isim]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 651.,  803., 1013., 1088.,  909.,  702.,  471.,  432.,  343.,\n",
       "         234.,  224.,  172.,  183.,  147.,  134.,  128.,  131.,  131.,\n",
       "         105.,   98.,   82.,   99.,  103.,   81.,   61.,   66.,   51.,\n",
       "          59.,   87.,   79.,   99.,   78.,   67.,   58.,   59.,   52.,\n",
       "          58.,   52.,   44.,   44.,   34.,   43.,   27.,   26.,   24.,\n",
       "          26.,   23.,   23.,   14.,   29.]),\n",
       " array([0.04  , 0.0432, 0.0464, 0.0496, 0.0528, 0.056 , 0.0592, 0.0624,\n",
       "        0.0656, 0.0688, 0.072 , 0.0752, 0.0784, 0.0816, 0.0848, 0.088 ,\n",
       "        0.0912, 0.0944, 0.0976, 0.1008, 0.104 , 0.1072, 0.1104, 0.1136,\n",
       "        0.1168, 0.12  , 0.1232, 0.1264, 0.1296, 0.1328, 0.136 , 0.1392,\n",
       "        0.1424, 0.1456, 0.1488, 0.152 , 0.1552, 0.1584, 0.1616, 0.1648,\n",
       "        0.168 , 0.1712, 0.1744, 0.1776, 0.1808, 0.184 , 0.1872, 0.1904,\n",
       "        0.1936, 0.1968, 0.2   ]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAE5CAYAAAAZXQJUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bed74a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(tmdlist,50,(0.04,0.2))\n",
    "plt.yscale('log', nonposy='clip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x27dbcda0>]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x27dbc0f0>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAE5CAYAAAAp0RN1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21282780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=[0.0496, 0.0944, 0.136]\n",
    "b=[1,2,3]\n",
    "thick_eff=np.polyfit(b,a,1)\n",
    "xa=np.linspace(0,6,100)\n",
    "plt.plot(xa,thick_eff[0]*xa+thick_eff[1])\n",
    "plt.scatter(b,a,color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmd['Layer']=(tmd['Thickness']-thick_eff[1])/thick_eff[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result=pd.DataFrame(index=tmd[tmd['Temperature'] == 150].index,columns=['Nanoflake_Number','Layer','Peak_Position','Temperature_List','Shift','Normalized_Shift'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for ifl in tmd[tmd['Temperature'] == 150].index:\n",
    "    resulttmd['Peak_Position'][tmd['Nanoflake_Number'] == ifl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pp1 = tmd['Peak_Position'][tmd['Temperature'] == 150]\n",
    "pp2 = tmd['Peak_Position'][tmd['Temperature'] == 250]\n",
    "pp3 = tmd['Peak_Position'][tmd['Temperature'] == 350]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271deb00>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271ad128>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271ad518>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271ada90>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271ade80>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271ab1d0>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271ab3c8>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271abba8>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271b60f0>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271b6048>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2132cc88>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271b6e10>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271b6f60>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271b8588>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271b8ba8>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x271b8fd0>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22d9e470>]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAE5CAYAAADoVlnZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x256f6390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ip in range(np.shape(ppr2)[0]):\n",
    "    plt.plot(ppr2[ip,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ppr = np.vstack((pp1,pp2,pp3))\n",
    "ppr = np.transpose(ppr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:34: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "D:\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:785: OptimizeWarning: Covariance of the parameters could not be estimated\n",
      "  category=OptimizeWarning)\n",
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "def gauss(x, a, b, c, d): return a*np.exp(-((x-b)/c)**2) + d;\n",
    "\n",
    "def gauss_fit(x0,y0,rg):\n",
    "    offset = x0[0]\n",
    "    scale = (x0[-1]-x0[0])/(np.shape(x0)[0]-1)\n",
    "    a4 = min(y0);\n",
    "    def FWHM(X,Y):\n",
    "        half_max = max(Y) / 2.\n",
    "        d = Y - half_max\n",
    "        indx = np.where(d > 0)\n",
    "        return abs(X[indx][-1] - X[indx][0])\n",
    "    a3 = FWHM(x0,np.array(y0))/2.35;\n",
    "    #a3 = ((max(x0)-min(x0))/10)**2;\n",
    "    a1 = max(y0)-min(y0);\n",
    "    a2 = (rg[0]+rg[1])/2;\n",
    "    ind = np.round((rg-offset)/scale).astype('int')\n",
    "    x1 = x0[ind[0]:ind[1]]\n",
    "    y1 = y0[ind[0]:ind[1]]\n",
    "\n",
    "    pg, pgv = curve_fit(gauss, x1, y1, [a1, a2, a3, a4],maxfev=5000)\n",
    "    return pg[1]\n",
    "    \n",
    "rg=[20,30]\n",
    "#def find_rg(X,Y):\n",
    "#    d = Y - max(Y)/2\n",
    "#    indx = np.where(d > 0)\n",
    "#    return [X[indx][0],X[indx][-1]]\n",
    "\n",
    "for icase in tmd.index:\n",
    "    x0 = tmd['Spectrum_Axis'][icase]\n",
    "    y0 = tmd['EELS_Spectrum'][icase]\n",
    "    try:\n",
    "        tmd['Peak_Position'][icase] = gauss_fit(x0,y0,rg)\n",
    "    except RuntimeError:\n",
    "        tmd['Peak_Position'][icase] = 0\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "half_max = max(np.array(y0)) / 2\n",
    "d = y0 - half_max\n",
    "ind = np.where(d > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Nanoflake_Number</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>Thickness_List</th>\n",
       "      <th>Layer</th>\n",
       "      <th>Spectrum_Axis</th>\n",
       "      <th>EELS_Spectrum</th>\n",
       "      <th>Peak_Position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>0.051779</td>\n",
       "      <td>[0.04964418256944714, 0.05025928332922756, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.611673711845267, -10.561673711100209, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>25.194752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>0.046052</td>\n",
       "      <td>[0.03823848930472759, 0.04036575383059291, 0.0...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.423140651184665, -10.373140650439607, -10...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>24.864017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>350</td>\n",
       "      <td>1</td>\n",
       "      <td>0.055125</td>\n",
       "      <td>[0.05250048358583511, 0.053500541448039694, 0....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[-10.078202629515763, -10.028202628770705, -9....</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature Nanoflake_Number  Thickness  \\\n",
       "0          150                1   0.051779   \n",
       "24         250                1   0.046052   \n",
       "48         350                1   0.055125   \n",
       "\n",
       "                                       Thickness_List  Layer  \\\n",
       "0   [0.04964418256944714, 0.05025928332922756, 0.0...    NaN   \n",
       "24  [0.03823848930472759, 0.04036575383059291, 0.0...    NaN   \n",
       "48  [0.05250048358583511, 0.053500541448039694, 0....    NaN   \n",
       "\n",
       "                                        Spectrum_Axis  \\\n",
       "0   [-10.611673711845267, -10.561673711100209, -10...   \n",
       "24  [-10.423140651184665, -10.373140650439607, -10...   \n",
       "48  [-10.078202629515763, -10.028202628770705, -9....   \n",
       "\n",
       "                                        EELS_Spectrum  Peak_Position  \n",
       "0   [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...      25.194752  \n",
       "24  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...      24.864017  \n",
       "48  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...       0.000000  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for ifl in nflake_list:\n",
    "    tmd['Peak_Position'][tmd['Nanoflake_Number'] == ifl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmd[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     25.194752\n",
       "24    24.864017\n",
       "48     0.000000\n",
       "Name: Peak_Position, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmd['Peak_Position'][tmd['Nanoflake_Number'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pg, pgv = curve_fit(gauss, x0, y0, [a1, a2, a3, a4],maxfev=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f41f0f0>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b48a198>]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAE5CAYAAADoVlnZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0GrqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f81d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x0,y0,marker='.')\n",
    "xg = np.linspace(rg[0], rg[1], new_axes.size*10)\n",
    "plt.plot(xg,gauss(xg,pg[0],pg[1],pg[2],pg[3]),'r')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2862: PerformanceWarning: \n",
      "your performance may suffer as PyTables will pickle object types that it cannot\n",
      "map directly to c-types [inferred_type->mixed-integer,key->block1_values] [items->['Temperature', 'Nanoflake_Number', 'EELS_Spectrum']]\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "## Save the DataFrame\n",
    "store = pd.HDFStore(folder_path +'/TMD_new.h5')\n",
    "\n",
    "store['TMD'] = tmd  # save it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hdf = px.ioHDF5(folder_path + folder_name +'/TMD.h5')\n",
    "h5_file = hdf.file\n",
    "\n",
    "print('Contents of data file:')\n",
    "print('----------------------')\n",
    "px.hdf_utils.print_tree(h5_file)\n",
    "print('----------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n",
      "WARNING:hyperspy.signal:<EELSSpectrum, title: EELS Spectrum Image, dimensions: (44, 15|2048)> data is replaced by its optimized copy\n"
     ]
    }
   ],
   "source": [
    "t=150\n",
    "fl=5\n",
    "folder_path = 'D:/Books/research/IMAGE/20180110_TMD/'\n",
    "folder_name = str(t) + '_' + str(fl)\n",
    "file_name = folder_path + folder_name + '/EELS Spectrum Image.dm4'\n",
    "raw = hs.load(file_name,signal_type='EELS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:/Books/research/IMAGE/20180110_TMD/thickness_150_3.dm4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n"
     ]
    }
   ],
   "source": [
    "folder_path = 'D:/Books/research/IMAGE/20180110_TMD/'\n",
    "file_name = folder_path + 'thickness_150_3.dm4'\n",
    "print(file_name)\n",
    "thickmat = hs.load(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80a08fe6f4a04e328519835cc4248b16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1d922dad4224f50a7846ee841a7c1c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80fcfefcf1b84030b1afc0981990bf5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\misc\\math_tools.py:61: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  return np.issubdtype(number, np.float)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "761ac2a86ae84235a154248674515bde",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28b01d82b78c46f98f29e48526f5bf63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d025c83e8cd34e609c34f1528fde39df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb96b69ad7424ea7a136e71a441a10a6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19ce9ba8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a5da390>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKsAAABVCAYAAAA2cBRwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c84940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw.align_zero_loss_peak(subpixel=True,show_progressbar=None,print_stats=False)\n",
    "\n",
    "for isim in range(3):\n",
    "    try:\n",
    "        zlp, sig = extract_ZLP(raw,range_factor=(-10, 8),return_inelastic=True,print_output=False)\n",
    "    except OverflowError:\n",
    "        raw = raw/10\n",
    "        zlp, sig = extract_ZLP(raw,range_factor=(-10, 8),return_inelastic=True,print_output=False)\n",
    "    if sum(sum(np.sum(np.isnan(sig.data),axis=2))) < 8:\n",
    "        break\n",
    "\n",
    "thickmat = get_thickness(raw,zlp)\n",
    "\n",
    "sig_data = np.reshape(sig.data,(-1,sig.data.shape[2]))\n",
    "sig_data[np.isnan(sig_data)] = 0\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(thickmat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=6, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.figure.Figure at 0x19cb1fd0>,\n",
       " array([<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019C5E8D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019DC7EB8>],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x194a00f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19e05b00>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19e456a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19ddbac8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3IAAACrCAYAAAAjDq6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAArz/0Sdr4gh3vgFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19cb1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#divide the data into clusters based on kmeans the thickness\n",
    "sp = np.shape(thickmat)\n",
    "task = []\n",
    "for i in range(sp[0]):\n",
    "    for j in range(sp[1]):\n",
    "        task.append([i,j,thickmat[i,j]])\n",
    "t=np.array(task)\n",
    "\n",
    "t[np.isnan(t)]=0\n",
    "t2=normalize(t,axis=0)\n",
    "t3=deepcopy(t2)\n",
    "t3[:,2]=t3[:,2]*4\n",
    "\n",
    "#num_clusters = get_k_number(task)\n",
    "num_clusters = 6\n",
    "estimator = KMeans(n_clusters=num_clusters)#构造聚类器\n",
    "estimator.fit(t3)#聚类\n",
    "label_pred = estimator.labels_ #获取聚类标签\n",
    "centroids = estimator.cluster_centers_ #获取聚类中心\n",
    "#print('KMeans error: ',estimator.inertia_) # 获取聚类准则的总和\n",
    "\n",
    "#test clustering\n",
    "plt.subplots(1,2,figsize=(15,15))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(thickmat)\n",
    "plt.subplot(1,2,2)\n",
    "lb=np.reshape(label_pred,np.shape(thickmat))\n",
    "plt.imshow(lb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.integrate as si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n",
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\misc\\math_tools.py:61: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  return np.issubdtype(number, np.float)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd2f5255da904099aa22724d8432ee8d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1947e65fe3e54dab808359df1082cf55",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eeab0fce17894e22b23049ee0cc6ef87",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a61f7e25eb0479eab20920e86f24cca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\Lib\\Xuan\\eels.py:151: RuntimeWarning: invalid value encountered in less\n",
      "  inelastic.data[inelastic.data < 0] = 0\n"
     ]
    }
   ],
   "source": [
    "zlp, sig = extract_ZLP(raw,range_factor=(-10, 8),return_inelastic=True,print_output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 214.58952491  234.05152262  275.89404409 ...  -42.17545529 -123.14618029\n",
      " -156.58046418]\n"
     ]
    }
   ],
   "source": [
    "z0 = zlp.data[0,0,:]\n",
    "r0 = raw.data[0,0,:]\n",
    "print(z0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def lograt(r0,z0,x0):\n",
    "    t0 = si.simps(z0,x0)\n",
    "    t1 = si.simps(r0,x0)\n",
    "    return math.log(t1/t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def lograt_mat(raw,zlp,x0):\n",
    "    thick = np.ones(np.shape(raw)[0:2])\n",
    "    for i in range(np.shape(raw)[0]):\n",
    "        for j in range(np.shape(raw)[1]):\n",
    "            r0 = raw.data[i,j,:]\n",
    "            z0 = zlp.data[i,j,:]\n",
    "            thick[i,j]=lograt(r0,z0,x0)\n",
    "    return thick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2033,)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_axes = raw.axes_manager['Energy loss']\n",
    "x0 = np.linspace(new_axes.offset, new_axes.offset+new_axes.scale*(new_axes.size-1), new_axes.size)\n",
    "th=lograt_mat(raw,zlp,x0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c43ea58>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAACXCAYAAAABMXD2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c018208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getindex(number,data_shape):\n",
    "    id1 = number//(data_shape[1])\n",
    "    id2 = number%(data_shape[1])\n",
    "    return (id1,id2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sp = np.shape(th)\n",
    "task = []\n",
    "for i in range(sp[0]):\n",
    "    for j in range(sp[1]):\n",
    "        task.append([i,j,th[i,j]])\n",
    "t=np.array(task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t[np.isnan(t)]=0\n",
    "t2=normalize(t,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "ts = preprocessing.scale(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from skimage.filters import sobel\n",
    "from skimage.morphology import watershed\n",
    "elevation_map = sobel(th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.00628661, 0.01000932, 0.00878695, 0.00621744,\n",
       "       0.00842881, 0.00623491, 0.00414671, 0.00526201, 0.00556569,\n",
       "       0.00763648, 0.01134952, 0.01377074, 0.01531775, 0.01791521,\n",
       "       0.02250773, 0.02767116, 0.03095942, 0.03452303, 0.04033501,\n",
       "       0.04556038, 0.05474318, 0.06241111, 0.06661327, 0.06921901,\n",
       "       0.06604867, 0.06097254, 0.05795283, 0.05170378, 0.04270499,\n",
       "       0.03848158, 0.03349239, 0.02606167, 0.01852373, 0.01200386,\n",
       "       0.01189599, 0.0128543 , 0.00806757, 0.00917298, 0.00315636,\n",
       "       0.00905738, 0.02088011, 0.05286701, 0.        ])"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elevation_map[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t2[:,2]=t2[:,2]/6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err=[]\n",
    "for i in range(10):\n",
    "    t3=deepcopy(t2)\n",
    "    t3[:,2]=t3[:,2]*i\n",
    "    estimator.fit(t3)\n",
    "    err.append(estimator.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d1a2f98>]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD+CAYAAAA09s7qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nORW9iEiUU9GLiES5/w9qJFCR0z5sDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cfc53c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(10),err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimator = KMeans(n_clusters=7, init='k-means++', n_init=50, max_iter=3000, \n",
    "                   tol=0.0001, precompute_distances=True, verbose=0, random_state=None, \n",
    "                   copy_x=True, n_jobs=1, algorithm='full')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'H:/20180110_TMD/150_5/EELS Spectrum Image.dm4'"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='full', copy_x=True, init='k-means++', max_iter=3000,\n",
       "    n_clusters=7, n_init=50, n_jobs=1, precompute_distances=True,\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3=deepcopy(t2)\n",
    "t3[:,2]=t3[:,2]*4\n",
    "estimator.fit(t3)\n",
    "label_pred = estimator.labels_ #获取聚类标签\n",
    "centroids = estimator.cluster_centers_ #获取聚类中心"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d42b198>"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAACXCAYAAAABMXD2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACpRJREFUeJzt3W+MHHUdx/HPhwLXy0ECJ39SKBHO\nELXXKJJKTDBEsZCCxmJCE9AHPCBpYiTRGKMlJqaamKCJ/x4YTUWkUQGvKoEQgjSA4YkBrlKgB2gL\nllDacJJCxASKlK8Pdk6X697s7c7u/OZ+934lm52Znb3ft7/ufm527ntzjggBAPJxXOoCAACDRbAD\nQGYIdgDIDMEOAJkh2AEgMwQ7AGSGYAeAzBDsAJAZgh0AMnN8nYONjIzE2NhYnUMCWXvjjTdSl9DR\nm2++mbqExlm5cuWCj42OjpY+99VXX30lIk5f7FiVgt32Bkk/kbRC0s0RcVPZ/mNjY1q/fn2VIQG0\nmZmZSV1CR08//XTqEhpnYmJiwccmJydLn7tjx44Xehmr71MxtldI+qmkKyStkXSt7TX9fj0AwGBU\nOcd+kaR9EfF8RLwl6Q5JGwdTFgCgX1WC/WxJL7atHyi2vYvtzbanbU8fOXKkwnAAgMWoEuzusO2Y\nawBHxLaIWBcR60ZGRioMBwBYjCrBfkDSOW3rqyUdrFYOAKCqKl0xj0k63/Z5kl6SdI2kzw+kKqBm\nVbtLhtkFsmbNwj0JdJ+gk76DPSLetn2DpD+p1e54S0Q0s/cKAJaRSn3sEXGvpHsHVAsAYAC4pAAA\nZIZgB4DMEOwAkBmCHQAyU+vVHbE8LMcLU23durWvxxaDlkb0iiN2AMgMwQ4AmSHYASAzBDsAZIZg\nB4DMEOwAkBmCHQAy44hj/jbG0IyPjwd/zHrxmtoP3k2VvuuqPd9LUZV/c6rnondll1/u9l63vSsi\n1i12LI7YASAzBDsAZIZgB4DMEOwAkBmCHQAyQ7ADQGYIdgDITKP62Jdq3/awDLMfnP7nPAzz/7nq\n2Hi3TZs2LfjY1NRU6XPpYweAZY5gB4DMEOwAkBmCHQAyQ7ADQGYIdgDITK3tjqOjozExMVHbeHO6\ntQ02tW2r7DKfVVVppQSk5r5vUun2fq3Szk27IwAsc8dXebLt/ZJel3RU0tu9fEcBAAxHpWAvfDIi\nXhnA1wEADACnYgAgM1WDPSTdb3uX7c2ddrC92fa07emjR49WHA4A0E3VUzEXR8RB22dI2mn72Yh4\nuH2HiNgmaZvU6oqpOB4AoItKR+wRcbC4n5V0p6SLBlEUAKB/fR+x2x6TdFxEvF4sXy7pO2XPOeus\ns+h9zUCV/nr655eHlJcTHqay1/7k5GSNlZSrcirmTEl32p77OrdFxH0DqQoA0Le+gz0inpf04QHW\nAgAYANodASAzBDsAZIZgB4DMEOwAkBmCHQAyM4iLgGEJ6taLPqx+81TjYvCWai96FWW96lNTUzVW\nUo4jdgDIDMEOAJkh2AEgMwQ7AGSGYAeAzBDsAJCZWtsdR0dH+7605czMzICrQZkql+YFlmorZLfX\nfZNaGstwxA4AmSHYASAzBDsAZIZgB4DMEOwAkBmCHQAyQ7ADQGYcEbUNtnbt2hhWHyh97ssDl/Wt\nT7de9GH1qg+zB75bn3pTc8T2rohYt9j9OWIHgMwQ7ACQGYIdADJDsANAZgh2AMgMwQ4AmSHYASAz\ntV6PvYqm9pdisOhTb45h9pOn6lXv9+9BLDVdj9ht32J71vaetm3jtnfa3lvcnzrcMgEAi7WYUzG3\nStowb9sWSQ9ExPmSHijWAQAN0DXYI+JhSYfnbd4oaXuxvF3SVQOuCwDQp35/eHpmRBySpOL+jIV2\ntL3Z9rTt6cOH539/AAAM2tC7YiJiW0Ssi4h14+Pjwx4OAJa9foP9ZdurJKm4nx1cSQCAKvptd7xb\n0nWSbiru7xpYRVjWul1WlXbI/HV7DXRDa/Ti2h1vl/QXSe+3fcD29WoF+mW290q6rFgHADRA1yP2\niLh2gYc+NeBaAAADwCUFACAzBDsAZIZgB4DMEOwAkBmCHQAys2Qu2wtI5T3O9LjXq+zSu8O8LC99\n6t1xxA4AmSHYASAzBDsAZIZgB4DMEOwAkBmCHQAyQ7ADQGboY8eSQq/64GzatGloz5+cnCx9btnv\nI9CnXh1H7ACQGYIdADJDsANAZgh2AMgMwQ4AmSHYASAztDsuYd1aynK0HP/NS1FZO6NES+N8g35d\nc8QOAJkh2AEgMwQ7AGSGYAeAzBDsAJAZgh0AMkOwA0BmHBH1DWb/U9ILbZtOk/RKbQUsHnX1hrp6\nQ129oS7pvRFx+mJ3rjXYjxncno6IdckKWAB19Ya6ekNdvaGu3nEqBgAyQ7ADQGYIdgDIDMEOAJlJ\nHezbEo+/EOrqDXX1hrp6Q109StoVAwAYvNRH7ACAASPYASAzSYLd9gbbf7O9z/aWFDV0Ynu/7ads\n77Y9nbiWW2zP2t7Ttm3c9k7be4v7UxtS11bbLxXzttv2lTXXdI7th2w/Y3vG9peL7Unnq6Su1PO1\n0vajtp8o6vp2sf08248U8/U72yc2pK5bbf+jbb4uqLOutvpW2H7c9j3FetL5KhURtd4krZD0nKQJ\nSSdKekLSmrrrWKC2/ZJOS11HUcslki6UtKdt2/clbSmWt0j6XkPq2irpawnnapWkC4vlkyX9XdKa\n1PNVUlfq+bKkk4rlEyQ9IuljkqYkXVNs/7mkLzakrlslXZ1qvtrq+6qk2yTdU6wnna+yW4oj9osk\n7YuI5yPiLUl3SNqYoI5Gi4iHJR2et3mjpO3F8nZJV9ValBasK6mIOBQRfy2WX5f0jKSzlXi+SupK\nKlr+XayeUNxC0qWSfl9sTzFfC9WVnO3Vkj4t6eZi3Uo8X2VSBPvZkl5sWz+gBrzYCyHpftu7bG9O\nXUwHZ0bEIakVGpLOSFxPuxtsP1mcqqn9FNEc2+dK+ohaR3uNma95dUmJ56s4rbBb0qyknWp9in4t\nIt4udknyvpxfV0TMzdd3i/n6ke2RuuuS9GNJX5f0TrH+HjVgvhaSItjdYVsjvitLujgiLpR0haQv\n2b4kdUFLxM8kvU/SBZIOSfpBiiJsnyTpD5K+EhH/SlFDJx3qSj5fEXE0Ii6QtFqtT9Ef7LRbvVUd\nW5fttZJulPQBSR+VNC7pG3XWZPszkmYjYlf75g67NiXHkgT7AUnntK2vlnQwQR3HiIiDxf2spDvV\nesE3ycu2V0lScT+buB5JUkS8XLwh35H0CyWYN9snqBWev42IPxabk89Xp7qaMF9zIuI1SX9W61z2\nKbaPLx5K+r5sq2tDcUorIuKIpF+p/vm6WNJnbe9X69TxpWodwTdmvuZLEeyPSTq/+InyiZKukXR3\ngjrexfaY7ZPnliVdLmlP+bNqd7ek64rl6yTdlbCW/5kLz8LnVPO8Fec7fynpmYj4YdtDSedroboa\nMF+n2z6lWB6VtF6t8/8PSbq62C3FfHWq69m2b85W6zx2rfMVETdGxOqIOFetvHowIr6gxPNVKtFP\nl69Uq0PgOUnfTP0T5KKmCbU6dJ6QNJO6Lkm3q/Ux/T9qfcq5Xq3zeg9I2lvcjzekrl9LekrSk2qF\n6aqaa/q4Wh+Dn5S0u7hdmXq+SupKPV8fkvR4Mf4eSd8qtk9IelTSPkk7JI00pK4Hi/naI+k3Kjpn\nUtwkfUL/74pJOl9lNy4pAACZ4TdPASAzBDsAZIZgB4DMEOwAkBmCHQAyQ7ADQGYIdgDIDMEOAJn5\nLwp+ZW6FsTtQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d3aa5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lb=np.reshape(label_pred,np.shape(th))\n",
    "plt.imshow(lb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.29012546],\n",
       "       [0.        , 0.00155887, 0.29711087],\n",
       "       [0.        , 0.00311774, 0.29990173],\n",
       "       ...,\n",
       "       [0.06624738, 0.06391366, 0.40960236],\n",
       "       [0.06624738, 0.06547253, 0.3616219 ],\n",
       "       [0.06624738, 0.0670314 , 0.28817023]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c882780>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAACXCAYAAAABMXD2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c817518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l = 100\n",
    "x, y = np.indices((l, l))\n",
    "\n",
    "center1 = (28, 24)\n",
    "center2 = (40, 50)\n",
    "center3 = (67, 58)\n",
    "center4 = (24, 70)\n",
    "\n",
    "radius1, radius2, radius3, radius4 = 16, 14, 15, 14\n",
    "\n",
    "circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2\n",
    "circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2\n",
    "circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import image\n",
    "# #############################################################################\n",
    "# 4 circles\n",
    "img = circle1 + circle2 + circle3 + circle4\n",
    "\n",
    "# We use a mask that limits to the foreground: the problem that we are\n",
    "# interested in here is not separating the objects from the background,\n",
    "# but separating them one from the other.\n",
    "mask = img.astype(bool)\n",
    "\n",
    "img = img.astype(float)\n",
    "img += 1 + 0.2 * np.random.randn(*img.shape)\n",
    "\n",
    "# Convert the image into a graph with the value of the gradient on the\n",
    "# edges.\n",
    "graph = image.img_to_graph(img, mask=mask)\n",
    "\n",
    "# Take a decreasing function of the gradient: we take it weakly\n",
    "# dependent from the gradient the segmentation is close to a voronoi\n",
    "graph.data = np.exp(-graph.data / graph.data.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sig_data = np.reshape(sig.data,(-1,sig.data.shape[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n",
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\misc\\math_tools.py:61: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  return np.issubdtype(number, np.float)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b334f18b6feb44139382bc20bd91ebc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a524cac6d3864a9991e71ad3455b0a2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "222265b9b7a44f7aa5e35f1023602b81",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee3fb4f670e64319bf557bdd98263464",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\Lib\\Xuan\\eels.py:151: RuntimeWarning: invalid value encountered in less\n",
      "  inelastic.data[inelastic.data < 0] = 0\n"
     ]
    }
   ],
   "source": [
    "zlp, sig = extract_ZLP(raw,range_factor=(-10, 8),return_inelastic=True,print_output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0,    0,    0,    0,    0,    0, 1750,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0, 1750, 1750, 1750, 1750, 1750, 1750,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0, 1750, 1750, 1750, 1750, 1750, 1750,    0,    0,    0,    0,\n",
       "           0,    0],\n",
       "       [   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "           0, 1750, 1750, 1750, 1750, 1750, 1750,    0,    0,    0,    0,\n",
       "           0,    0]])"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.isnan(sig.data),axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}