vrproject

commit 74857e67ea3433d3104f1da0e4a7d219e7920f40
parent b48e97b045d5992785eb7af970340b3383a0b932
Author: miksa234 <milutin@popovic.xyz>
Date:   Thu, 21 Apr 2022 17:27:28 +0200

changes

Diffstat:
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nodes_edges/dynamic/edges_2006-1.csv
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4
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nodes_edges/dynamic/edges_2007-1.csv
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25
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nodes_edges/dynamic/edges_2008-1.csv
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747
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nodes_edges/dynamic/edges_2009-1.csv
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1280
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nodes_edges/dynamic/edges_2010-1.csv
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2184
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nodes_edges/dynamic/edges_step2006-1.csv
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1
-
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nodes_edges/dynamic/nodes_2006-1.csv
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139
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nodes_edges/dynamic/nodes_2007-1.csv
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490
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nodes_edges/dynamic/nodes_2008-1.csv
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1504
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nodes_edges/dynamic/nodes_2009-1.csv
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nodes_edges/dynamic/nodes_2010-1.csv
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plots/dynamic/2006-1.html
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2
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plots/dynamic/2007-1.html
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plots/dynamic/2008-1.html
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2
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plots/dynamic/2009-1.html
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2
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plots/dynamic/2010-1.html
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2
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src/.ipynb_checkpoints/network_dynamic-checkpoint.ipynb
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249
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src/__pycache__/csv_to_network.cpython-310.pyc
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0

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src/__pycache__/nxplot_3d.cpython-310.pyc
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0

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src/csv_to_network.py
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6
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src/network_dynamic.ipynb
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271
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src/network_static.ipynb
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55
++++++++++++++++++++++++++++++++++++++-----------------
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src/nxplot_3d.py
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2
+-

23 files changed, 15014 insertions(+), 943 deletions(-)
diff --git a/nodes_edges/dynamic/edges_2006-1.csv b/nodes_edges/dynamic/edges_2006-1.csv
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diff --git a/nodes_edges/dynamic/edges_2007-1.csv b/nodes_edges/dynamic/edges_2007-1.csv
@@ -1,2 +1,23 @@
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diff --git a/nodes_edges/dynamic/edges_2008-1.csv b/nodes_edges/dynamic/edges_2008-1.csv
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diff --git a/nodes_edges/dynamic/edges_2009-1.csv b/nodes_edges/dynamic/edges_2009-1.csv
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diff --git a/nodes_edges/dynamic/edges_step2006-1.csv b/nodes_edges/dynamic/edges_step2006-1.csv
@@ -1 +0,0 @@
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diff --git a/nodes_edges/dynamic/nodes_2006-1.csv b/nodes_edges/dynamic/nodes_2006-1.csv
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diff --git a/nodes_edges/dynamic/nodes_2007-1.csv b/nodes_edges/dynamic/nodes_2007-1.csv
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diff --git a/nodes_edges/dynamic/nodes_2008-1.csv b/nodes_edges/dynamic/nodes_2008-1.csv
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diff --git a/nodes_edges/dynamic/nodes_2009-1.csv b/nodes_edges/dynamic/nodes_2009-1.csv
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diff --git a/nodes_edges/dynamic/nodes_2010-1.csv b/nodes_edges/dynamic/nodes_2010-1.csv
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diff --git a/src/.ipynb_checkpoints/network_dynamic-checkpoint.ipynb b/src/.ipynb_checkpoints/network_dynamic-checkpoint.ipynb
@@ -1,6 +1,251 @@
 {
- "cells": [],
- "metadata": {},
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3c5889e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from collections import defaultdict\n",
+    "from datetime import datetime\n",
+    "\n",
+    "from scipy.optimize import curve_fit\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import networkx as nx\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib \n",
+    "\n",
+    "matplotlib.rcParams.update({'font.size': 15})\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import plotly\n",
+    "import plotly.graph_objs as pgo\n",
+    "\n",
+    "from nxplot_3d import get_trace_nodes_3D_legend\n",
+    "from nxplot_3d import get_trace_edges_3D\n",
+    "from nxplot_3d import plot_3D\n",
+    "\n",
+    "from export_import import export_data\n",
+    "from export_import import read_data\n",
+    "\n",
+    "from csv_to_network import get_graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "59ff0f16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path= '../data/dynamic/'\n",
+    "file_names =  sorted(os.listdir(path), key=lambda x: datetime.strptime(x, '%Y-%m.csv'))\n",
+    "dframes = {}\n",
+    "\n",
+    "for fname in file_names:\n",
+    "   dframes[fname[:-4]] = pd.read_csv(path+ fname, sep='|', keep_default_na=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9cbeb80b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def at_each_step(dframes, do_to_graph, untill='2007-2', args=[], get_out=False):\n",
+    "    out = []\n",
+    "    for t in dframes:\n",
+    "        if t == untill:\n",
+    "            break\n",
+    "        elif t == '2005-3' :\n",
+    "            DG_T = get_graph(dframes[t], cut=0)\n",
+    "            DG_T.remove_nodes_from([''])\n",
+    "        else:\n",
+    "            DG_T.add_edges_from(dframes[t].to_numpy(dtype='str'))\n",
+    "            DG_T.remove_nodes_from([''])\n",
+    "        if get_out == True: \n",
+    "            out.append(do_to_graph(DG_T, t, *args))\n",
+    "        else:\n",
+    "            do_to_graph(DG_T, t, *args)\n",
+    "    if get_out == True:\n",
+    "        return out\n",
+    "    return DG_T\n",
+    "    \n",
+    "    \n",
+    "def get_graph_untill(DG_T, t):\n",
+    "    if t[-2:] == '-1':\n",
+    "        max_deg = max(DG_T.degree(), key=lambda x: x[1])[1]\n",
+    "        if max_deg == 0:\n",
+    "            max_deg = 1\n",
+    "        posDG3D = nx.spring_layout(DG_T, dim=3, k=1/np.sqrt(DG_T.number_of_nodes())*10)\n",
+    "        partition = {node: 1 for node in DG_T.nodes()}\n",
+    "        cmap = cm.get_cmap('Reds', max(partition.values()) + 1)\n",
+    "        export_data(DG_T, posDG3D, partition, cmap, path='../nodes_edges/dynamic/', step=t, max_deg=max_deg)\n",
+    "        G, d_hoverinfo, d_nodecolors, d_nodesize, posG3D = read_data(f'../nodes_edges/dynamic/nodes_{t}.csv',\n",
+    "                                                                     f'../nodes_edges/dynamic/edges_{t}.csv')\n",
+    "        node_trace = get_trace_nodes_3D_legend(posG3D, d_hoverinfo, d_nodecolors, d_nodesize)\n",
+    "        edge_trace = get_trace_edges_3D(G, posG3D, color = '#C7C7C7', opac = 0.4, linewidth=0.5)\n",
+    "        data = [*node_trace, edge_trace]\n",
+    "        plot_3D(data, path='../plots/dynamic/', fname = t, scheme='dark')\n",
+    "            \n",
+    "#at_each_step(dframes, get_graph_untill)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "id": "30c77b43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_k_i_t(DG_T, t, all_nodes):\n",
+    "    add_degrees = DG_T.in_degree\n",
+    "    all_nodes.update({node: [*all_nodes[node], int(deg)] for node, deg in add_degrees})\n",
+    "    return all_nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "id": "49a5f571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_DG_T = get_graph(pd.concat([dframes[_] for _ in dframes], ignore_index=True), cut=0)\n",
+    "all_DG_T.remove_nodes_from([''])\n",
+    "all_nodes = {node: [] for node in all_DG_T.nodes()}\n",
+    "all_nodes = at_each_step(dframes, get_k_i_t, untill=None, args=[all_nodes], get_out=True)\n",
+    "all_nodes = sorted(list(all_nodes[0].items()), key=lambda x: max(x[1]), reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 392,
+   "id": "9a4dbfd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/miksa/.local/lib/python3.10/site-packages/scipy/optimize/minpack.py:833: OptimizeWarning: Covariance of the parameters could not be estimated\n",
+      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f48c1e96c50>"
+      ]
+     },
+     "execution_count": 392,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def fit_lin(x, β, B):\n",
+    "    return β * x + B \n",
+    "time_steps = len(file_names)\n",
+    "plt.figure(figsize=[10, 8])\n",
+    "cmap = cm.get_cmap('tab10').colors; j=0\n",
+    "β_i = {}\n",
+    "k_i = []\n",
+    "for node, k_i_t in [all_nodes[i] for i in range(len(all_nodes)) if max(all_nodes[i][1]) != 0]:\n",
+    "    try:\n",
+    "        time_creation = time_steps - len(k_i_t)\n",
+    "        time_seq = np.arange(time_creation, time_steps)\n",
+    "        log_k_i_t = np.log(k_i_t, where=(np.array(k_i_t) > 0))\n",
+    "        popt, pcov = curve_fit(fit_lin, time_seq, log_k_i_t)\n",
+    "        if j < 10:\n",
+    "            plt.plot(time_seq, log_k_i_t, '.', label=node + f' β_i = {round(popt[0], 5)}', color=cmap[j])\n",
+    "            j+=1\n",
+    "        if popt[0] < 0:\n",
+    "            β_i[node] =  0\n",
+    "            k_i.append(k_i_t[-1])\n",
+    "        elif popt[0] > 1:\n",
+    "            continue\n",
+    "        else:\n",
+    "            β_i[node] =  popt[0]\n",
+    "            k_i.append(k_i_t[-1])\n",
+    "        \n",
+    "    except:\n",
+    "        continue\n",
+    "    \n",
+    "#    plt.plot(time_seq, fit_lin(time_seq, *popt), color=cmap[j])\n",
+    "plt.legend(loc='best')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "419735c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "865c5701",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "497bceea",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bd4fa6d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.4"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/src/__pycache__/csv_to_network.cpython-310.pyc b/src/__pycache__/csv_to_network.cpython-310.pyc
Binary files differ.
diff --git a/src/__pycache__/nxplot_3d.cpython-310.pyc b/src/__pycache__/nxplot_3d.cpython-310.pyc
Binary files differ.
diff --git a/src/csv_to_network.py b/src/csv_to_network.py
@@ -8,12 +8,12 @@ def get_graph(data:pd.DataFrame, cut:int, direction=""):
                                  create_using=nx.DiGraph())
     DG.remove_nodes_from(['.', 'NaN'])
     if direction == "":
-        to_remove = [n[0] for n in DG.degree() if n[1] <= cut]
+        to_remove = [n[0] for n in DG.degree() if n[1] < cut]
         DG.remove_nodes_from(to_remove)
     elif direction == "in":
-        to_remove = [n[0] for n in DG.in_degree() if n[1] <= cut]
+        to_remove = [n[0] for n in DG.in_degree() if n[1] < cut]
         DG.remove_nodes_from(to_remove)
     elif direction == "out":
-        to_remove = [n[0] for n in DG.in_degree() if n[1] <= cut]
+        to_remove = [n[0] for n in DG.in_degree() if n[1] < cut]
         DG.remove_nodes_from(to_remove)
     return DG
diff --git a/src/network_dynamic.ipynb b/src/network_dynamic.ipynb
@@ -3,11 +3,12 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a9051d1e",
+   "id": "3c5889e6",
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
+    "from collections import defaultdict\n",
     "from datetime import datetime\n",
     "\n",
     "from scipy.optimize import curve_fit\n",
@@ -38,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "60cbd801",
+   "id": "59ff0f16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,150 +48,172 @@
     "dframes = {}\n",
     "\n",
     "for fname in file_names:\n",
-    "   dframes[fname] = pd.read_csv(path+ fname, sep='|', keep_default_na=False)\n",
-    "\n",
-    "dtgraphs = {}; i = 0\n",
-    "for fname in dframes:\n",
-    "    dtgraphs[fname[:-4]] = get_graph(dframes[fname], cut=0)\n",
-    "    dtgraphs[fname[:-4]].remove_node('')\n",
-    "    if i != 0:\n",
-    "        dtgraphs[fname[:-4]] = nx.compose(dtgraphs[fname[:-4]], dtgraphs[file_names[i][:-4]])\n",
-    "    i += 1"
+    "   dframes[fname[:-4]] = pd.read_csv(path+ fname, sep='|', keep_default_na=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9cbeb80b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def at_each_step(dframes, do_to_graph, untill='2007-2', args=[], get_out=False):\n",
+    "    out = []\n",
+    "    for t in dframes:\n",
+    "        if t == untill:\n",
+    "            break\n",
+    "        elif t == '2005-3' :\n",
+    "            DG_T = get_graph(dframes[t], cut=0)\n",
+    "            DG_T.remove_nodes_from([''])\n",
+    "        else:\n",
+    "            DG_T.add_edges_from(dframes[t].to_numpy(dtype='str'))\n",
+    "            DG_T.remove_nodes_from([''])\n",
+    "        if get_out == True: \n",
+    "            out.append(do_to_graph(DG_T, t, *args))\n",
+    "        else:\n",
+    "            do_to_graph(DG_T, t, *args)\n",
+    "    if get_out == True:\n",
+    "        return out\n",
+    "    return DG_T\n",
+    "    \n",
+    "    \n",
+    "def get_graph_untill(DG_T, t):\n",
+    "    if t[-2:] == '-1':\n",
+    "        max_deg = max(DG_T.degree(), key=lambda x: x[1])[1]\n",
+    "        if max_deg == 0:\n",
+    "            max_deg = 1\n",
+    "        posDG3D = nx.spring_layout(DG_T, dim=3, k=1/np.sqrt(DG_T.number_of_nodes())*10)\n",
+    "        partition = {node: 1 for node in DG_T.nodes()}\n",
+    "        cmap = cm.get_cmap('Reds', max(partition.values()) + 1)\n",
+    "        export_data(DG_T, posDG3D, partition, cmap, path='../nodes_edges/dynamic/', step=t, max_deg=max_deg)\n",
+    "        G, d_hoverinfo, d_nodecolors, d_nodesize, posG3D = read_data(f'../nodes_edges/dynamic/nodes_{t}.csv',\n",
+    "                                                                     f'../nodes_edges/dynamic/edges_{t}.csv')\n",
+    "        node_trace = get_trace_nodes_3D_legend(posG3D, d_hoverinfo, d_nodecolors, d_nodesize)\n",
+    "        edge_trace = get_trace_edges_3D(G, posG3D, color = '#C7C7C7', opac = 0.4, linewidth=0.5)\n",
+    "        data = [*node_trace, edge_trace]\n",
+    "        plot_3D(data, path='../plots/dynamic/', fname = t, scheme='dark')\n",
+    "            \n",
+    "#at_each_step(dframes, get_graph_untill)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "236e03cf",
+   "execution_count": 263,
+   "id": "30c77b43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_k_i_t(DG_T, t, all_nodes):\n",
+    "    add_degrees = DG_T.in_degree\n",
+    "    all_nodes.update({node: [*all_nodes[node], int(deg)] for node, deg in add_degrees})\n",
+    "    return all_nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "id": "49a5f571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_DG_T = get_graph(pd.concat([dframes[_] for _ in dframes], ignore_index=True), cut=0)\n",
+    "all_DG_T.remove_nodes_from([''])\n",
+    "all_nodes = {node: [] for node in all_DG_T.nodes()}\n",
+    "all_nodes = at_each_step(dframes, get_k_i_t, untill=None, args=[all_nodes], get_out=True)\n",
+    "all_nodes = sorted(list(all_nodes[0].items()), key=lambda x: max(x[1]), reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 392,
+   "id": "9a4dbfd1",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Opening in existing browser session.\n"
-     ]
-    },
-    {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[28968:28968:0100/000000.734084:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process.\n",
-      "[29037:29037:0420/165846.125911:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process.\n"
+      "/home/miksa/.local/lib/python3.10/site-packages/scipy/optimize/minpack.py:833: OptimizeWarning: Covariance of the parameters could not be estimated\n",
+      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Opening in existing browser session.\n"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f48c1e96c50>"
+      ]
+     },
+     "execution_count": 392,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[29138:29138:0420/165847.845271:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Opening in existing browser session.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[29208:29208:0420/165849.488837:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Opening in existing browser session.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[29277:29277:0420/165851.038571:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Opening in existing browser session.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[29386:29386:0420/165853.590850:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Opening in existing browser session.\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3160\u001b[0m, in \u001b[0;36mndim\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m   3159\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3160\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mndim\u001b[49m\n\u001b[1;32m   3161\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'ndim'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_deg \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m      5\u001b[0m     max_deg \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m----> 6\u001b[0m posDG3D \u001b[38;5;241m=\u001b[39m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspring_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtgraphs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqrt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtgraphs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgname\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumber_of_nodes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m partition \u001b[38;5;241m=\u001b[39m {node: \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m dtgraphs[gname]\u001b[38;5;241m.\u001b[39mnodes()}\n\u001b[1;32m      8\u001b[0m cmap \u001b[38;5;241m=\u001b[39m cm\u001b[38;5;241m.\u001b[39mget_cmap(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mReds\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28mmax\u001b[39m(partition\u001b[38;5;241m.\u001b[39mvalues()) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[0;32m<class 'networkx.utils.decorators.argmap'> compilation 8:4\u001b[0m, in \u001b[0;36margmap_spring_layout_5\u001b[0;34m(G, k, pos, fixed, iterations, threshold, weight, scale, center, dim, seed)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpath\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m splitext\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcontextlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m contextmanager\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnetworkx\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnx\u001b[39;00m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/networkx/drawing/layout.py:481\u001b[0m, in \u001b[0;36mspring_layout\u001b[0;34m(G, k, pos, fixed, iterations, threshold, weight, scale, center, dim, seed)\u001b[0m\n\u001b[1;32m    479\u001b[0m         nnodes, _ \u001b[38;5;241m=\u001b[39m A\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    480\u001b[0m         k \u001b[38;5;241m=\u001b[39m dom_size \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39msqrt(nnodes)\n\u001b[0;32m--> 481\u001b[0m     pos \u001b[38;5;241m=\u001b[39m \u001b[43m_sparse_fruchterman_reingold\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[43m        \u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos_arr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfixed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterations\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\n\u001b[1;32m    483\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m    485\u001b[0m     A \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mto_numpy_array(G, weight\u001b[38;5;241m=\u001b[39mweight)\n",
-      "File \u001b[0;32m<class 'networkx.utils.decorators.argmap'> compilation 16:4\u001b[0m, in \u001b[0;36margmap__sparse_fruchterman_reingold_13\u001b[0;34m(A, k, pos, fixed, iterations, threshold, dim, seed)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpath\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m splitext\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcontextlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m contextmanager\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnetworkx\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnx\u001b[39;00m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/networkx/drawing/layout.py:622\u001b[0m, in \u001b[0;36m_sparse_fruchterman_reingold\u001b[0;34m(A, k, pos, fixed, iterations, threshold, dim, seed)\u001b[0m\n\u001b[1;32m    620\u001b[0m distance \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(distance \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0.01\u001b[39m, \u001b[38;5;241m0.01\u001b[39m, distance)\n\u001b[1;32m    621\u001b[0m \u001b[38;5;66;03m# the adjacency matrix row\u001b[39;00m\n\u001b[0;32m--> 622\u001b[0m Ai \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(\u001b[43mA\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetrowview\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtoarray())\n\u001b[1;32m    623\u001b[0m \u001b[38;5;66;03m# displacement \"force\"\u001b[39;00m\n\u001b[1;32m    624\u001b[0m displacement[:, i] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    625\u001b[0m     delta \u001b[38;5;241m*\u001b[39m (k \u001b[38;5;241m*\u001b[39m k \u001b[38;5;241m/\u001b[39m distance \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m-\u001b[39m Ai \u001b[38;5;241m*\u001b[39m distance \u001b[38;5;241m/\u001b[39m k)\n\u001b[1;32m    626\u001b[0m )\u001b[38;5;241m.\u001b[39msum(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/scipy/sparse/lil.py:187\u001b[0m, in \u001b[0;36mlil_matrix.getrowview\u001b[0;34m(self, i)\u001b[0m\n\u001b[1;32m    184\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgetrowview\u001b[39m(\u001b[38;5;28mself\u001b[39m, i):\n\u001b[1;32m    185\u001b[0m     \u001b[38;5;124;03m\"\"\"Returns a view of the 'i'th row (without copying).\u001b[39;00m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 187\u001b[0m     new \u001b[38;5;241m=\u001b[39m \u001b[43mlil_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m     new\u001b[38;5;241m.\u001b[39mrows[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrows[i]\n\u001b[1;32m    189\u001b[0m     new\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata[i]\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/scipy/sparse/lil.py:104\u001b[0m, in \u001b[0;36mlil_matrix.__init__\u001b[0;34m(self, arg1, shape, dtype, copy)\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m A\u001b[38;5;241m.\u001b[39mdata\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg1,\u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m--> 104\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43misshape\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg1\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    105\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m shape \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    106\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minvalid use of shape parameter\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/scipy/sparse/sputils.py:232\u001b[0m, in \u001b[0;36misshape\u001b[0;34m(x, nonneg)\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    231\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m isintlike(M) \u001b[38;5;129;01mand\u001b[39;00m isintlike(N):\n\u001b[0;32m--> 232\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mndim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mM\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m np\u001b[38;5;241m.\u001b[39mndim(N) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    233\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nonneg \u001b[38;5;129;01mor\u001b[39;00m (M \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m N \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m    234\u001b[0m                 \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[0;32m<__array_function__ internals>:5\u001b[0m, in \u001b[0;36mndim\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
-      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3162\u001b[0m, in \u001b[0;36mndim\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m   3160\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m a\u001b[38;5;241m.\u001b[39mndim\n\u001b[1;32m   3161\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m-> 3162\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43masarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mndim\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "for gname in dtgraphs:\n",
-    "    if gname[-2:] == '-1':\n",
-    "        max_deg = max(dtgraphs[gname].degree(), key=lambda x: x[1])[1]\n",
-    "        if max_deg == 0:\n",
-    "            max_deg = 1\n",
-    "        posDG3D = nx.spring_layout(dtgraphs[gname], dim=3, k=1/np.sqrt(dtgraphs[gname].number_of_nodes())*10)\n",
-    "        partition = {node: 1 for node in dtgraphs[gname].nodes()}\n",
-    "        cmap = cm.get_cmap('Reds', max(partition.values()) + 1)\n",
-    "        export_data(dtgraphs[gname], posDG3D, partition, cmap, path='../nodes_edges/dynamic/', step=gname, max_deg=max_deg)\n",
-    "        G, d_hoverinfo, d_nodecolors, d_nodesize, posG3D = read_data(f'../nodes_edges/dynamic/nodes_{gname}.csv',\n",
-    "                                                                     f'../nodes_edges/dynamic/edges_{gname}.csv')\n",
-    "        node_trace = get_trace_nodes_3D_legend(posG3D, d_hoverinfo, d_nodecolors, d_nodesize)\n",
-    "        edge_trace = get_trace_edges_3D(G, posG3D, color = '#C7C7C7', opac = 0.4, linewidth=0.5)\n",
-    "        data = [*node_trace, edge_trace]\n",
-    "        plot_3D(data, path='../plots/dynamic/', fname = gname, scheme='dark')\n",
-    "    if gname == '2019-2':\n",
-    "        break"
+    "def fit_lin(x, β, B):\n",
+    "    return β * x + B \n",
+    "time_steps = len(file_names)\n",
+    "plt.figure(figsize=[10, 8])\n",
+    "cmap = cm.get_cmap('tab10').colors; j=0\n",
+    "β_i = {}\n",
+    "k_i = []\n",
+    "for node, k_i_t in [all_nodes[i] for i in range(len(all_nodes)) if max(all_nodes[i][1]) != 0]:\n",
+    "    try:\n",
+    "        time_creation = time_steps - len(k_i_t)\n",
+    "        time_seq = np.arange(time_creation, time_steps)\n",
+    "        log_k_i_t = np.log(k_i_t, where=(np.array(k_i_t) > 0))\n",
+    "        popt, pcov = curve_fit(fit_lin, time_seq, log_k_i_t)\n",
+    "        if j < 10:\n",
+    "            plt.plot(time_seq, log_k_i_t, '.', label=node + f' β_i = {round(popt[0], 5)}', color=cmap[j])\n",
+    "            j+=1\n",
+    "        if popt[0] < 0:\n",
+    "            β_i[node] =  0\n",
+    "            k_i.append(k_i_t[-1])\n",
+    "        elif popt[0] > 1:\n",
+    "            continue\n",
+    "        else:\n",
+    "            β_i[node] =  popt[0]\n",
+    "            k_i.append(k_i_t[-1])\n",
+    "        \n",
+    "    except:\n",
+    "        continue\n",
+    "    \n",
+    "#    plt.plot(time_seq, fit_lin(time_seq, *popt), color=cmap[j])\n",
+    "plt.legend(loc='best')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7cb21da",
+   "id": "419735c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "865c5701",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "497bceea",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -198,7 +221,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8a80eba3",
+   "id": "6bd4fa6d",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/src/network_static.ipynb b/src/network_static.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "id": "4c9bab78",
    "metadata": {},
    "outputs": [],
@@ -39,7 +39,7 @@
     "\n",
     "from csv_to_network import get_graph\n",
     "\n",
-    "path = \"../data/\""
+    "path = \"../data/static/\""
    ]
   },
   {
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "9ec366d6",
    "metadata": {},
    "outputs": [],
@@ -375,30 +375,51 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Opening in existing browser session.\n"
+     "ename": "NameError",
+     "evalue": "name 'data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plot_3D(\u001b[43mdata\u001b[49m,\n\u001b[1;32m      2\u001b[0m         path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./plots/\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;66;03m# set a folder of choice\u001b[39;00m\n\u001b[1;32m      3\u001b[0m         fname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstatic\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;66;03m# set a name for your plot file\u001b[39;00m\n\u001b[1;32m      4\u001b[0m         scheme\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdark\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;66;03m# or 'light' = plot background choice \u001b[39;00m\n\u001b[1;32m      5\u001b[0m        )\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
      ]
-    },
+    }
+   ],
+   "source": [
+    "plot_3D(data,\n",
+    "        path='./plots/static', # set a folder of choice\n",
+    "        fname = 'static', # set a name for your plot file\n",
+    "        scheme='dark', # or 'light' = plot background choice \n",
+    "       )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7c724fa8",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "text/plain": [
-       "'./plots/test.html'"
+       "361743"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "plot_3D(data,\n",
-    "        path='./plots/', # set a folder of choice\n",
-    "        fname = 'static', # set a name for your plot file\n",
-    "        scheme='dark', # or 'light' = plot background choice \n",
-    "       )"
-   ]
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3231d7e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/nxplot_3d.py b/src/nxplot_3d.py
@@ -159,7 +159,7 @@ def get_trace_edges_3D(G, posG, color = '#C7C7C7', opac = 0.1, linewidth=0.1):
 
 
 
-def plot_3D(data,path,fname, scheme='light',annotat=None, show_leg=True):
+def plot_3D(data,path,fname, scheme='light',annotat=None, show_leg=True, auto_open=True):
     '''
     Create a 3D plot from traces using plotly.
     Input: