diff --git a/scripts/will/entropy_metrics/entropy_metrics.ipynb b/scripts/will/entropy_metrics/entropy_metrics.ipynb
new file mode 100644
index 0000000..3e63c4d
--- /dev/null
+++ b/scripts/will/entropy_metrics/entropy_metrics.ipynb
@@ -0,0 +1,1218 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../../')\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from lcs import *\n",
+    "import networkx as nx\n",
+    "from scipy.stats import beta\n",
+    "import time\n",
+    "from numpy.linalg import eigh\n",
+    "import random\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from tqdm import tqdm\n",
+    "random.seed(1234)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "def calc_entropy(mat,normalized = True):\n",
+    "    c_1d = mat.ravel()#turn into 1d distribution\n",
+    "    p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+    "    #p_1d = c_1d #do not normalize\n",
+    "    p_1d_masked = p_1d[p_1d>0]#remove the zeros\n",
+    "    entropy = -np.sum(p_1d_masked * np.log2(p_1d_masked))#calculate the entropy\n",
+    "    if normalized == True:\n",
+    "        return entropy\n",
+    "    else:\n",
+    "        return entropy*mat.sum()\n",
+    "    \n",
+    "\n",
+    "def degree_infection_hist(my_A,my_c_i):\n",
+    "    degree_mat = my_A.sum(axis = 1)\n",
+    "    max_degree = degree_mat.max()\n",
+    "    degree_df = pd.DataFrame(degree_mat,columns=['degree'])\n",
+    "\n",
+    "    ci_df = pd.DataFrame(my_c_i)\n",
+    "    ci_degree =  degree_df.join(ci_df)\n",
+    "    #max column\n",
+    "    max_col = my_c_i[my_c_i.any(axis = 0)].shape[0]\n",
+    "\n",
+    "    #ci_degree.set_index_degree = ci_degree.set_index('degree')\n",
+    "    ci_degree.sort_index(inplace = True)\n",
+    "    ci_degree_hist = ci_degree.groupby('degree').sum()\n",
+    "    ci_degree_hist_cut = ci_degree_hist.iloc[:,:max_col]\n",
+    "    ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+    "    ci_degree_hist_mat = ci_degree_hist_cut.to_numpy()\n",
+    "    degree_list = ci_degree_hist_cut.index.to_numpy()\n",
+    "    infection_count = ci_degree_hist_cut.columns.values\n",
+    "\n",
+    "    return ci_degree_hist_mat,degree_list,infection_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def a_prime(f,a,b):\n",
+    "    return (f+a+b-1)/(f*(b/a+1))\n",
+    "def b_prime(a,b,a_prime):\n",
+    "    return b*a_prime/a\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "#x0 = np.zeros(n)\n",
+    "#x0[random.randrange(n)] = 1\n",
+    "\n",
+    "gamma0 = 0.1\n",
+    "nu = eigh(A)[0][-1]\n",
+    "b0 = 2 * gamma0 / nu\n",
+    "#b0 = 0.1\n",
+    "\n",
+    "f_range = np.linspace(1-b0-gamma0,10,20)#the range of spreading rates\n",
+    "gamma_range = a_prime(f_range,gamma0,b0)#generate the corresponding recovery rates\n",
+    "b_range = b_prime(gamma0,b0,gamma_range)#generate the corresponding transmission rates\n",
+    "\n",
+    "\n",
+    "fig,ax = plt.subplots(nrows = 3 )\n",
+    "ax[0].plot(f_range,gamma_range)\n",
+    "ax[0].set(xlabel = 'f',ylabel = '$\\gamma(f)$')\n",
+    "\n",
+    "ax[1].plot(f_range,b_range)\n",
+    "ax[1].set(xlabel = 'f',ylabel = r'$\\beta(f)$')\n",
+    "\n",
+    "ax[2].plot(f_range,1-gamma_range-b_range)\n",
+    "ax[2].set(xlabel = 'f',ylabel = r'$1- \\beta(f) - \\alpha(f)$')\n",
+    "\n",
+    "plt.suptitle('Infection Paramters adjusted for constant $R_0$')\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# simple contagion\n",
+    "random.seed(1243)\n",
+    "\n",
+    "nsamples = 20000\n",
+    "\n",
+    "G = nx.barabasi_albert_graph(100,4)\n",
+    "A = nx.adjacency_matrix(G, weight=None).todense()\n",
+    "n = np.size(A, axis=0)\n",
+    "\n",
+    "x0 = np.ones(n)\n",
+    "sc = lambda nu, b: 1 - (1 - b) ** nu\n",
+    "c = sc(np.arange(n), b0)\n",
+    "\n",
+    "x = contagion_process(A, gamma0, c, x0, tmin=0, tmax=100)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x295f5cb90>"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.spy(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x296030510>"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.imshow(c_i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/1265461989.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "18\n",
+      "19\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "gamma0 = 0.1\n",
+    "nu = eigh(A)[0][-1]\n",
+    "b0 = 2 * gamma0 / nu\n",
+    "#b0 = 0.1\n",
+    "\n",
+    "f_range = np.linspace(1-b0-gamma0,10,20)#the range of spreading rates\n",
+    "gamma_range = a_prime(f_range,gamma0,b0)#generate the corresponding recovery rates\n",
+    "b_range = b_prime(gamma0,b0,gamma_range)#generate the corresponding transmission rates\n",
+    "\n",
+    "\n",
+    "x_list = []\n",
+    "c_list = []\n",
+    "n_iter = 100\n",
+    "entropy_res = np.zeros((len(f_range),n_iter))\n",
+    "entropy_no_norm_res = np.zeros((len(f_range),n_iter))\n",
+    "\n",
+    "for i,f in enumerate(f_range):\n",
+    "    print(i)\n",
+    "    for j in range(n_iter):\n",
+    "        gamma_i = gamma_range[i]\n",
+    "        b_i = b_range[i]\n",
+    "        sc = lambda nu, b: 1 - (1 - b) ** nu\n",
+    "        c = sc(np.arange(n), b_i)\n",
+    "        x = contagion_process(A, gamma_i, c, x0, tmin=0, tmax=100)\n",
+    "        #x_list.append(x)\n",
+    "        c_i, nu_i = count_all_infection_events(x,A)\n",
+    "        #c_list.append(c_i)\n",
+    "        entropy_res[i,j] = calc_entropy(c_i)\n",
+    "        entropy_no_norm_res[i,j] = calc_entropy(c_i,normalized=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "/Users/willthompson/miniconda3/envs/complex_inference/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
+      "  self._figure.tight_layout(*args, **kwargs)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/1764011045.py:14: UserWarning: The figure layout has changed to tight\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def make_long_df(entropy_res,f_range):\n",
+    "    df = pd.DataFrame(entropy_res.T)\n",
+    "    df.columns = f_range\n",
+    "    df = df.melt(var_name='f', value_name='entropy')\n",
+    "    return df \n",
+    "df_entropy = make_long_df(entropy_res,f_range)\n",
+    "df_entropy_no_norm = make_long_df(entropy_no_norm_res,f_range)\n",
+    "\n",
+    "df_joined = df_entropy.merge(df_entropy_no_norm,on='f',suffixes=('_norm','_no_norm'))\n",
+    "df_joined_melt = df_joined.melt(id_vars='f',value_vars=['entropy_norm','entropy_no_norm'],var_name='entropy_type',value_name='entropy')\n",
+    "\n",
+    "sns.relplot(df_joined_melt,x = 'f',y = 'entropy',kind = 'line',row = 'entropy_type',facet_kws={'sharey': False},aspect = 2)\n",
+    "plt.suptitle(\"Entropy for $m_l$ for different $f$\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Measure Entropy as function of Alpha and Gamma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "0it [00:00, ?it/s]/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: divide by zero encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:21: RuntimeWarning: invalid value encountered in divide\n",
+      "  p_1d = c_1d / np.sum(c_1d)#normalize the distribution\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "2it [00:00, 12.46it/s]/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "4it [00:00,  5.35it/s]/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "5it [00:01,  2.95it/s]/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "6it [00:02,  2.36it/s]/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "7it [00:02,  1.85it/s]/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "8it [00:03,  1.47it/s]/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "9it [00:04,  1.22it/s]/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n",
+      "10it [00:06,  1.56it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "#G = nx.karate_club_graph()\n",
+    "#G = nx.barabasi_albert_graph(100,4)\n",
+    "G = nx.erdos_renyi_graph(200,0.1)\n",
+    "A = nx.adjacency_matrix(G, weight=None).todense()\n",
+    "n = np.size(A, axis=0)\n",
+    "\n",
+    "x0 = np.ones(n)\n",
+    "#x0 = np.zeros(n)\n",
+    "#x0[random.randrange(n)] = 1\n",
+    "\n",
+    "gamma0 = 0.1\n",
+    "nu = eigh(A)[0][-1]\n",
+    "b0 = 2 * gamma0 / nu\n",
+    "t0 = 100\n",
+    "\n",
+    "sc = lambda nu, b: 1 - (1 - b) ** nu\n",
+    "c = sc(np.arange(n), b0)\n",
+    "\n",
+    "\n",
+    "gamma_range = np.linspace(0.01,0.4,10)\n",
+    "b_range = np.linspace(0.01,0.4,10)\n",
+    "n_iter = 1\n",
+    "\n",
+    "entropy_res = np.zeros((len(gamma_range),len(b_range),n_iter))\n",
+    "entropy_no_norm_res = np.zeros((len(gamma_range),len(b_range),n_iter))\n",
+    "\n",
+    "degree_c_i_entropy_res = np.zeros((len(gamma_range),len(b_range),n_iter))\n",
+    "degree_c_i_entropy_no_norm_res = np.zeros((len(gamma_range),len(b_range),n_iter))\n",
+    "\n",
+    "results = dict()\n",
+    "\n",
+    "for i,gamma_i in tqdm(enumerate(gamma_range)):\n",
+    "    for j,b_i in enumerate(b_range):\n",
+    "\n",
+    "        t_i = gamma_i/gamma0 * t0\n",
+    "\n",
+    "        for k in range(n_iter):\n",
+    "            \n",
+    "            c = sc(np.arange(n), b_i)\n",
+    "            x = contagion_process(A, gamma_i, c, x0, tmin=0, tmax=t_i)\n",
+    "            c_i, nu_i = count_all_infection_events(x,A)\n",
+    "            #c_list.append(c_i)\n",
+    "            degree_c_i_mat,_,_ = degree_infection_hist(A,c_i)\n",
+    "\n",
+    "\n",
+    "            entropy_res[i,j,k] = calc_entropy(c_i)\n",
+    "            entropy_no_norm_res[i,j,k] = calc_entropy(c_i,normalized=False)\n",
+    "\n",
+    "            degree_c_i_entropy_res[i,j,k] = calc_entropy(degree_c_i_mat)\n",
+    "            degree_c_i_entropy_no_norm_res[i,j,k] = calc_entropy(degree_c_i_mat,normalized=False)\n",
+    "\n",
+    "            results[f\"{i}_{j}_{k}\"] = x\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 2000x2000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.ticker as ticker\n",
+    "mean_entropty = np.mean(entropy_res,axis=2)\n",
+    "mean_entropty_no_norm_res = np.mean(entropy_no_norm_res,axis=2)\n",
+    "\n",
+    "\n",
+    "degree_c_i_mean_entropty = np.mean(degree_c_i_entropy_res,axis=2)\n",
+    "degree_c_i_mean_entropty_no_norm_res = np.mean(degree_c_i_entropy_no_norm_res,axis=2)\n",
+    "#formatter = ticker.FuncFormatter(lambda x, pos: f'{x:.2f}')\n",
+    "formatter = ticker.FormatStrFormatter('%.2f')\n",
+    "fig,ax = plt.subplots(ncols = 2,nrows = 2,figsize = (20,20))\n",
+    "\n",
+    "im1 = ax[0,0].imshow(mean_entropty, extent=[min(b_range), max(b_range), max(gamma_range), min(gamma_range)])\n",
+    "ax[0,0].set(ylabel = r'$\\gamma$',xlabel = r'$\\beta$',title = \"Entropy for $C_i$\")\n",
+    "#ax[0,0].set_xticks(np.arange(len(b_range)))\n",
+    "#ax[0,0].set_xticklabels(b_range)\n",
+    "#ax[0,0].set_yticks(np.arange(len(gamma_range)))\n",
+    "#ax[0,0].set_yticklabels(gamma_range)\n",
+    "#ax[0,0].xaxis.set_major_formatter(formatter)\n",
+    "#ax[0,0].yaxis.set_major_formatter(formatter)\n",
+    "fig.colorbar(im1, ax=ax[0,0])\n",
+    "\n",
+    "im2 = ax[0,1].imshow(mean_entropty_no_norm_res, extent=[min(b_range), max(b_range), max(gamma_range), min(gamma_range)])\n",
+    "ax[0,1].set(ylabel = r'$\\gamma$',xlabel = r'$\\beta$',title = \"Non Normalized Entropy for $C_i$\")\n",
+    "#ax[0,1].set_xticks(np.arange(len(b_range)))\n",
+    "#ax[0,1].set_xticklabels(b_range)\n",
+    "#ax[0,1].set_yticks(np.arange(len(gamma_range)))\n",
+    "#ax[0,1].set_yticklabels(gamma_range)\n",
+    "ax[0,1].xaxis.set_major_formatter(formatter)\n",
+    "ax[0,1].yaxis.set_major_formatter(formatter)\n",
+    "fig.colorbar(im2, ax=ax[0,1])\n",
+    "\n",
+    "im3 = ax[1,0].imshow(degree_c_i_mean_entropty, extent=[min(b_range), max(b_range), max(gamma_range), min(gamma_range)])\n",
+    "ax[1,0].set(ylabel = r'$\\gamma$',xlabel = r'$\\beta$',title = \"Entropy for Degree Mat\")\n",
+    "#ax[1,0].set_xticks(np.arange(len(b_range)))\n",
+    "#ax[1,0].set_xticklabels(b_range)\n",
+    "#ax[1,0].set_yticks(np.arange(len(gamma_range)))\n",
+    "#ax[1,0].set_yticklabels(gamma_range)\n",
+    "ax[1,0].xaxis.set_major_formatter(formatter)\n",
+    "ax[1,0].yaxis.set_major_formatter(formatter)\n",
+    "fig.colorbar(im3, ax=ax[1,0])\n",
+    "\n",
+    "im4 = ax[1,1].imshow(degree_c_i_mean_entropty_no_norm_res, extent=[min(b_range), max(b_range), max(gamma_range), min(gamma_range)])\n",
+    "ax[1,1].set(ylabel = r'$\\gamma$',xlabel = r'$\\beta$',title = \"Non Normalized Entropy for Degree Mat\")\n",
+    "#ax[1,1].set_xticks(np.arange(len(b_range)))\n",
+    "#ax[1,1].set_xticklabels(b_range)\n",
+    "#ax[1,1].set_yticks(np.arange(len(gamma_range)))\n",
+    "#ax[1,1].set_yticklabels(gamma_range)\n",
+    "ax[1,1].xaxis.set_major_formatter(formatter)\n",
+    "ax[1,1].yaxis.set_major_formatter(formatter)\n",
+    "fig.colorbar(im4, ax=ax[1,1])\n",
+    "\n",
+    "# Add a single colorbar for all subplots\n",
+    "#fig.colorbar(im, ax=ax.ravel().tolist())\n",
+    "\n",
+    "plt.suptitle(r\"Mean Entropy of $m_l$ for different $\\gamma$ and $\\beta$\")\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What do the distributions look like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'Gamma Index: 0.3566666666666667, Beta Index: 0.01')"
+      ]
+     },
+     "execution_count": 370,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 2000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "fig,ax = plt.subplots(ncols = 3,figsize = (20,5))\n",
+    "\n",
+    "\n",
+    "gamma_index = 8\n",
+    "beta_index = 0\n",
+    "\n",
+    "key = f'{gamma_index}_{beta_index}_0'\n",
+    "x_key = results[key]\n",
+    "c_i,_ = count_all_infection_events(x_key,A)\n",
+    "degree_infection_hist_mat,degree_list,infection_count = degree_infection_hist(A,c_i)\n",
+    "ax[0].spy(x_key)\n",
+    "ax[1].imshow(c_i)\n",
+    "ax[2].imshow(degree_infection_hist_mat,extent = [degree_list.min(),degree_list.max(),degree_list.min(),degree_list.max()])\n",
+    "\n",
+    "ax[0].set(title = \"Contagion Time Series\")\n",
+    "ax[1].set(title = \"$C_i$ Heatmap\")\n",
+    "ax[2].set(title = \"$Degree Infection Heatmap\")\n",
+    "plt.suptitle(\"Gamma Index: {}, Beta Index: {}\".format(gamma_range[gamma_index],b_range[beta_index]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 364,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d8/_nh0kys17c383rmrhqbdm9y00000gn/T/ipykernel_85933/431954665.py:45: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  ci_degree_hist_cut.sort_values(by = 'degree',ascending = False,inplace = True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0.98, 'Gamma Index: 0.09666666666666666, Beta Index: 0.3566666666666667')"
+      ]
+     },
+     "execution_count": 364,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "gamma_index = 2\n",
+    "beta_index = 8\n",
+    "\n",
+    "key = f'{gamma_index}_{beta_index}_0'\n",
+    "x_key = results[key]\n",
+    "c_i,_ = count_all_infection_events(x_key,A)\n",
+    "degree_infection_hist_mat,degree_list,infection_count = degree_infection_hist(A,c_i)\n",
+    "ax[0].spy(x_key)\n",
+    "ax[1].imshow(c_i)\n",
+    "ax[2].imshow(degree_infection_hist_mat,extent = [degree_list.min(),degree_list.max(),degree_list.min(),degree_list.max()])\n",
+    "\n",
+    "ax[0].set(title = \"Contagion Time Series\")\n",
+    "ax[1].set(title = \"$C_i$ Heatmap\")\n",
+    "ax[2].set(title = \"$Degree Infection Heatmap\")\n",
+    "plt.suptitle(\"Gamma Index: {}, Beta Index: {}\".format(gamma_range[gamma_index],b_range[beta_index]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Degree Distribution')"
+      ]
+     },
+     "execution_count": 218,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(degree_mat,bins = 20)\n",
+    "plt.title(\"Degree Distribution\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "complex_inference",
+   "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": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}