TempNet_06_randomisations_3_timeline_shufflings_instant-events.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temporal Networks v2.0 #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools as it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import RandTempNet.utils as tnu\n",
    "import RandTempNet.measures as tnm\n",
    "import RandTempNet.randomisations as tnr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Timeline shuffling - instant-events ##\n",
    "We here present randomization techniques which shuffle the timelines of contact of links in the static network, for instant-events temporal networks. All functions take a `link_timeline()` object as input, and return a `link_timeline()` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Events permutation $\\text{P}[\\mathcal{L}]$: `P__L(lks_data,t_i,t_f,dt)` ###\n",
    "This function randomly permutates the time stamps of events between `t_i` and `t_f` and among all timelines of a `link_timeline()` object `lks_data`. The initial time step `t_i`, final time step `t_f` and time step duration `dt` are required.\n",
    "\n",
    "**Note:** This function returns a `tij()` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lks_data = tnu.read_link_timeline(\"./data/lks.dat\")\n",
    "new_tij = tnr.P__L(lks_data, 0, 60480, 1)\n",
    "new_lks = tnu.tijtau_to_link_timeline(tnu.tij_to_tijtau(new_tij, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test of the preservation of the total number of events:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nC0 = sum(tnm.weights(lks_data).values())\n",
    "nC1 = sum(tnm.weights(new_lks).values())\n",
    "nC0 == nC1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test of the modification of the activity timeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TL0 = tnm.activity_timeline(\n",
    "    tnu.link_timeline_to_snapshot_sequence(lks_data, 1, 0, 60480)\n",
    ")\n",
    "TL1 = tnm.activity_timeline(\n",
    "    tnu.link_timeline_to_snapshot_sequence(new_lks, 1, 0, 60480)\n",
    ")\n",
    "\n",
    "tmin = 1000\n",
    "tmax = 2000\n",
    "\n",
    "fig = plt.figure(figsize=(8, 2))\n",
    "x = range(len(TL0))[tmin:tmax]\n",
    "plt.plot(x, TL0[tmin:tmax], \"k-\")\n",
    "plt.plot(x, TL1[tmin:tmax], \"r-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Events permutation with weigths preservation $\\text{P}[\\mathbf{w}]$: `P__w(lks_data,t_i,t_f,dt)` ###\n",
    "This function randomly assigns time stamps of $w$ events between `t_i` and `t_f` for every timeline of a `link_timeline()` object `lks_data`. The initial time step `t_i`, final time step `t_f` and time step duration `dt` are required.\n",
    "\n",
    "**Note:** This function returns a `tij()` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lks_data = tnu.read_link_timeline(\"./data/lks.dat\")\n",
    "new_tij = tnr.P__w(lks_data, 0, 60480, 1)\n",
    "new_lks = tnu.tijtau_to_link_timeline(tnu.tij_to_tijtau(new_tij, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test of the preservation of the weights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w0 = tnm.weights(lks_data)\n",
    "w1 = tnm.weights(new_lks)\n",
    "Test = np.array([w0[lk] == w1[lk] for lk in lks_data.links()])\n",
    "Test.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test of the modification of the activity timeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TL0 = tnm.activity_timeline(\n",
    "    tnu.link_timeline_to_snapshot_sequence(lks_data, 1, 0, 60480)\n",
    ")\n",
    "TL1 = tnm.activity_timeline(\n",
    "    tnu.link_timeline_to_snapshot_sequence(new_lks, 1, 0, 60480)\n",
    ")\n",
    "\n",
    "tmin = 1000\n",
    "tmax = 2000\n",
    "\n",
    "fig = plt.figure(figsize=(8, 2))\n",
    "x = range(len(TL0))[tmin:tmax]\n",
    "plt.plot(x, TL0[tmin:tmax], \"k-\")\n",
    "plt.plot(x, TL1[tmin:tmax], \"r-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Events permutation with weigths, initial time and final time preservation $\\text{P}[\\mathbf{w},\\mathbf{t}^1,\\mathbf{t}^w]$: `P__w_t1_tw(lks_data,t_i,t_f,dt)` ###\n",
    "This function randomly assigns time stamps of $w$ events between `t1` and `tw` for every timeline of a `link_timeline()` object `lks_data`. The initial time step `t_i`, final time step `t_f` and time step duration `dt` are required.\n",
    "\n",
    "**Note:** This function returns a `tij()` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lks_data = tnu.read_link_timeline(\"./data/lks.dat\")\n",
    "new_tij = tnr.P__w_t1_tw(lks_data, 0, 60480, 1)\n",
    "new_lks = tnu.tijtau_to_link_timeline(tnu.tij_to_tijtau(new_tij, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test of the preservation of the weights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w0 = tnm.weights(lks_data)\n",
    "w1 = tnm.weights(new_lks)\n",
    "Test = np.array([w0[lk] == w1[lk] for lk in lks_data.links()])\n",
    "Test.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test of the modification of the activity timeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TL0 = tnm.activity_timeline(\n",
    "    tnu.link_timeline_to_snapshot_sequence(lks_data, 1, 0, 60480)\n",
    ")\n",
    "TL1 = tnm.activity_timeline(\n",
    "    tnu.link_timeline_to_snapshot_sequence(new_lks, 1, 0, 60480)\n",
    ")\n",
    "\n",
    "tmin = 1000\n",
    "tmax = 2000\n",
    "\n",
    "fig = plt.figure(figsize=(8, 2))\n",
    "x = range(len(TL0))[tmin:tmax]\n",
    "plt.plot(x, TL0[tmin:tmax], \"k-\")\n",
    "plt.plot(x, TL1[tmin:tmax], \"r-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Permutation of intercontacts within timelines $\\text{P}[\\pi_\\mathcal{L}(\\Delta \\tau)]$, $\\text{P}[\\pi_\\mathcal{L}(\\Delta \\tau),\\mathbf{t}_1]$: ###\n",
    "These shufflings randomly permutate the intercontacts within each timeline of a `link_timeline()` object `lks_data`. For instant-events temporal networks, all contact durations are set to the time-step `dt`. For such shufflings, simply use the functions `P__pitau_pidtau(lks_data,t_i,t_f,dt)` and `P__pitau_pidtau_t1(lks_data,t_i,t_f,dt)` described in the next chapter with a `link_timeline()` object that contains an instant-events temporal network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}