Figure9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d348d272-bed8-4440-9952-c3c34ec51f95",
   "metadata": {},
   "source": [
    "### Figure 9: evolution of momenta in time for different $\\chi$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4747a350-918c-4411-a27f-c6b8dd4f562a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import log, log10, sin, cos, exp, sqrt, pi\n",
    "import pandas as pd\n",
    "import glob as glob\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "from matplotlib import cm\n",
    "\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4897ecc1-f017-4bf7-b712-da33cdbefe0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x700 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 3, figsize=(6, 7), layout='constrained')\n",
    "mrksz = 2\n",
    "\n",
    "################################################################\n",
    "### fig 9 11\n",
    "f = open('dataWPD/fig9_11/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[0,0].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "\n",
    "### fig 9 12\n",
    "f = open('dataWPD/fig9_12/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[0,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='k', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[0,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "\n",
    "### fig 9 13\n",
    "f = open('dataWPD/fig9_13/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[0,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='k', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[0,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][2]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[0,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='b', label='WPD', markersize=mrksz)\n",
    "\n",
    "################################################################\n",
    "### fig 9 21\n",
    "f = open('dataWPD/fig9_21/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[1,0].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "\n",
    "### fig 9 22\n",
    "f = open('dataWPD/fig9_22/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[1,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='k', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[1,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][2]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[1,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='b', label='WPD', markersize=mrksz)\n",
    "\n",
    "### fig 9 23\n",
    "f = open('dataWPD/fig9_23/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[1,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='k', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[1,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][2]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[1,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='b', label='WPD', markersize=mrksz)\n",
    "\n",
    "################################################################\n",
    "### fig 9 31\n",
    "f = open('dataWPD/fig9_31/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[2,0].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='k', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[2,0].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "\n",
    "### fig 9 32\n",
    "f = open('dataWPD/fig9_32/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[2,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='k', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[2,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][2]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[2,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='b', label='WPD', markersize=mrksz)\n",
    "\n",
    "### fig 9 33\n",
    "f = open('dataWPD/fig9_33/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[2,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[2,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='b', label='WPD', markersize=mrksz)\n",
    "\n",
    "################################################################\n",
    "### fig 9 41\n",
    "f = open('dataWPD/fig9_41/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[3,0].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='k', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[3,0].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "\n",
    "### fig 9 42\n",
    "f = open('dataWPD/fig9_42/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[3,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "\n",
    "### fig 9 43\n",
    "f = open('dataWPD/fig9_43/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[3,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='r', label='WPD', markersize=mrksz)\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axs[3,2].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.', color='b', label='WPD', markersize=mrksz)\n",
    "\n",
    "for i in range(3): axs[3,i].set_xlabel(r'$\\omega_c~t$')\n",
    "for i in range(4): axs[i,0].set_ylabel(r'$10^{-2}~\\Delta \\langle \\gamma \\rangle / \\gamma_0$')\n",
    "for i in range(2): axs[i,1].set_ylabel(r'$10^{-3}~ \\sigma_\\gamma^2/\\gamma_0^2$')\n",
    "for i in range(2): axs[i+2,1].set_ylabel(r'$10^{-2}~ \\sigma_\\gamma^2/\\gamma_0^2$')\n",
    "for i in range(4): axs[i,2].set_ylabel(r'$10^{'+str(np.array([-6,-5,-3,-2])[i])+'}~ \\mu_3/\\gamma_0^3$')\n",
    "for i in range(3): axs[0,i].set_xlim([0,20]); axs[1,i].set_xlim([0,20]); axs[0,i].set_xticks(np.arange(0,21,5)); axs[1,i].set_xticks(np.arange(0,21,5))\n",
    "for i in range(3): axs[2,i].set_xlim([0,5]); axs[2,i].set_xticks(np.arange(0,6,1));\n",
    "for i in range(3): axs[3,i].set_xlim([0,3]); axs[3,i].set_xticks(np.arange(0,4,1));\n",
    "for i in range(4): axs[i,0].set_ylim([-7,1]); axs[i,0].set_yticks(np.arange(-7,2,2));\n",
    "for i in axs:\n",
    "    for ax in i:\n",
    "        ax.set_box_aspect(1)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
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