topological-axis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install git+git://github.com/mkrphys/ipython-tikzmagic.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext tikzmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%tikz -f svg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%tikz \\draw (0,0) rectangle (1,1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "init_cell": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from ipypublish.scripts.ipynb_latex_setup import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true
    }
   },
   "source": [
    "# Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true
    }
   },
   "source": [
    "## General"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true
    }
   },
   "source": [
    "topological-axis.\n",
    "\n",
    "A list:\n",
    "\n",
    "- something\n",
    "- something else\n",
    "\n",
    "A numbered list\n",
    "\n",
    "1. something\n",
    "2. something else"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {}
   },
   "source": [
    "This is a long section of text, which we only want in a document (not a presentation)\n",
    "some text\n",
    "some more text\n",
    "some more text\n",
    "some more text\n",
    "some more text\n",
    "some more text\n",
    "some more text\n",
    "some more text\n",
    "some more text\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true,
     "slideonly": true
    }
   },
   "source": [
    "This is an abbreviated section of the document text, which we only want in a presentation\n",
    "\n",
    "- summary of document text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true
    }
   },
   "source": [
    "## References and Citations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true
    }
   },
   "source": [
    "References to \\cref{fig:example}, \\cref{tbl:example}, \\cref{eqn:example_sympy} and \\cref{code:example_mpl}.\n",
    "\n",
    "Referencing multiple items: \\cref{fig:example,fig:example_h,fig:example_v}.\n",
    "\n",
    "A latex citation.\\cite{Back_to_Cosmos_2019}\n",
    "\n",
    "A html citation.<cite data-cite=\"Back to Cosmos, Sanchez et Al. \">(Back to Cosmos, Sanchez et Al. Progress in Physics, 2019)</cite> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true
    }
   },
   "source": [
    "## Todo notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true
    }
   },
   "source": [
    "\\todo[inline]{an inline todo}\n",
    "\n",
    "Some text.\\todo{a todo in the margins}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ipub": {
     "text": {
      "format": {
       "backgroundcolor": "\\color{blue!10}"
      }
     }
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The topological axis follow the law $$exp(2^{d/4})$$,\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\"\"\n",
    "The topological axis follows the law $$exp(2^{d/4})$$,\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Images and Figures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Displaying a plot with its code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "caption": "fig:example_mpl"
    }
   },
   "source": [
    "A matplotlib figure, with the caption set in the markdowncell above the figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "caption": "code:example_mpl"
    }
   },
   "source": [
    "The plotting code for a matplotlib figure (\\cref{fig:example_mpl})."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.ion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ipub": {
     "code": {
      "asfloat": true,
      "caption": "a",
      "label": "code:example_mpl",
      "widefigure": false
     },
     "figure": {
      "caption": "",
      "label": "fig:example_mpl",
      "widefigure": false
     }
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.text(1.25, .25, \"$ {T^2}={L^3}={M^5}={F^7}$\", fontsize=30, horizontalalignment='center')\n",
    "ax.text(.5, .5, \"$ {2 \\hbar^2}/{Gm_em_pm_n}$\", fontsize=30, horizontalalignment='center')\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Tables (with pandas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "caption": "code:example_pd"
    }
   },
   "source": [
    "The plotting code for a pandas Dataframe table (\\cref{tbl:example})."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "URL= 'https://github.com/LaGuer/topological-axis/master/crpso.csv'\n",
    "from urllib.request import urlretrieve\n",
    "urlretrieve (URL, 'crpso.csv')\n",
    "df = pd.read_csv(\"crpso.csv\", dtype = {\"Gly\" : \"float64\",\"m\" : \"float64\"}, skiprows = [50,51], sep = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>E(d)</th>\n",
       "      <th>$$E^0$$</th>\n",
       "      <th>$$E^1$$</th>\n",
       "      <th>$$E^2$$</th>\n",
       "      <th>$$E^3$$</th>\n",
       "      <th>$$E^4$$</th>\n",
       "      <th>...</th>\n",
       "      <th>$$E^7$$</th>\n",
       "      <th>$$E^8$$</th>\n",
       "      <th>$$E^9$$</th>\n",
       "      <th>$$E^{10}$$</th>\n",
       "      <th>$$E^{11}$$</th>\n",
       "      <th>$$E^{12}$$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$$K_{d+}$$</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>419</td>\n",
       "      <td>419</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$$K_{d-}$$</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>419</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$$K_{d+}+K_{d...</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>112</td>\n",
       "      <td>165</td>\n",
       "      <td>292</td>\n",
       "      <td>419</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$$\\sum{K_{d}}$$</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>250</td>\n",
       "      <td>415</td>\n",
       "      <td>707</td>\n",
       "      <td>1126</td>\n",
       "      <td>1836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$$K(d−1)+ + K...</td>\n",
       "      <td>-</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>283</td>\n",
       "      <td>389</td>\n",
       "      <td>749</td>\n",
       "      <td>1003</td>\n",
       "      <td>1839</td>\n",
       "      <td>2421</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               E(d)  $$E^0$$   $$E^1$$   $$E^2$$   $$E^3$$  $$E^4$$  ...  \\\n",
       "0        $$K_{d+}$$        1         1         5         5       19  ...   \n",
       "1        $$K_{d-}$$        -         1         1         5        5  ...   \n",
       "2  $$K_{d+}+K_{d...        -         1         3         5       12  ...   \n",
       "3   $$\\sum{K_{d}}$$        -         1         4         9       21  ...   \n",
       "4  $$K(d−1)+ + K...        -         7        11        29       43  ...   \n",
       "\n",
       "   $$E^7$$  $$E^8$$  $$E^9$$  $$E^{10}$$  $$E^{11}$$  $$E^{12}$$  \n",
       "0       59      165      165         419         419        1001  \n",
       "1       59       59      165         165         419         419  \n",
       "2       59      112      165         292         419         710  \n",
       "3      138      250      415         707        1126        1836  \n",
       "4      283      389      749        1003        1839        2421  \n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display = pd.options.display\n",
    "display.max_columns = 13\n",
    "display.max_rows = 5\n",
    "display.max_colwidth = 17\n",
    "display.width = None\n",
    "#print(df)\n",
    "df.iloc[:,[0,1,2,3,4,5,6,7,8,9,10,11,12,13]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Equations (with ipython or sympy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ipub": {
     "equation": {
      "label": "eqn:example_ipy"
     }
    }
   },
   "outputs": [],
   "source": [
    "Latex('$$ 2\\hbar^2 / Gm_em_pm_n $$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "caption": "code:example_sym"
    }
   },
   "source": [
    "The plotting code for a sympy equation (\\cref{eqn:example_sympy})."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ipub": {
     "code": {
      "asfloat": true,
      "caption": "",
      "label": "code:example_sym",
      "placement": "H",
      "widefigure": false
     },
     "equation": {
      "environment": "equation",
      "label": "eqn:example_sympy"
     }
    }
   },
   "outputs": [],
   "source": [
    "f = sym.Function('f')\n",
    "y,n = sym.symbols(r'y \\alpha')\n",
    "f = y(n)-2*y(n-1/sym.pi)-5*y(n-2)\n",
    "sym.rsolve(f,y(n),[1,4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Embed interactive HTML (like ipywidgets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ipub": {
     "slide": true
    }
   },
   "source": [
    "Interactive HTML was created using <a href=\"https://ipyvolume.readthedocs.io\">ipyvolume</a>\n",
    "and will render below in .html type outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "!pip install ipyvolume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ipub": {
     "embed_html": {
      "filepath": "../html/embed.html"
     },
     "figure": {
      "caption": "An example of embedded html"
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x980 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3913bebda0445d7a937d04e84a8cba7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Figure(camera=PerspectiveCamera(fov=46.0, position=(0.0, 0.0, 2.0), quaternion=(0.0, 0.0, 0.0, …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import numpy as np\n",
    "import ipyvolume.pylab as p3\n",
    "p3.figure()\n",
    "p3.figure()\n",
    "#%%time\n",
    "#%matplotlib inline\n",
    "#%matplotlib notebook\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "w = 6\n",
    "h = 7\n",
    "d = 140\n",
    "plt.figure(figsize=(w, h), dpi=d)\n",
    "\n",
    "# x Periodicity\n",
    "x = [10, 14, 18, 22, 23.333, 25.178, 26]\n",
    "y = [1.72515, 2.426, 3.12, 3.755, 4.078, 4.363, 4.506]\n",
    "plt.scatter(x, y,  color=\"red\", lw=1, marker='x', label=r\"Macro-physics, $y = \\ln\\ln\\frac{\\lambdabar_{Macro}}{\\lambdabar_e}$\")\n",
    "\n",
    "# + Periodicity\n",
    "x2 = [10, 14, 22, 23.333, 25.178, 30]\n",
    "y2 = [1.72155, 2.428, 3.78446, 4.048, 4.368, 5.253]\n",
    "plt.scatter(x2, y2,  color=\"black\", lw=2, marker='+', label=r\"Micro-physics, $y = \\ln\\ln\\frac{\\lambdabar_e}{\\lambdabar_{micro}}$\")\n",
    "\n",
    "# Green Periodicity\n",
    "x3 = [14, 30, 31.556]\n",
    "y3 = [2.51872, 5.199, 5.4321]\n",
    "plt.scatter(x3, y3,  color=\"green\", lw=2, marker='.', label=r\"Holophysics\")\n",
    "\n",
    "# Slope\n",
    "x4 = [0, 32]\n",
    "y4 = [0, 5.5452]\n",
    "plt.plot(x4, y4, color=\"grey\", lw=1, linestyle=':', label=r\"Slope ln2/4\")\n",
    "\n",
    "# Slope\n",
    "# Atiyah\n",
    "x5 = [-1, 0, 1, 2, 3, 4, 5, 5.3, 5.7, 6, 7, 7.38, 8]\n",
    "#x5 = np.arange[0.3 , 7.38, 0.3]\n",
    "x6 = [5.33, 7.38, 8.]\n",
    "new_tick_location = [2, 6, 10, 14, 18, 22, 26, 30]\n",
    "\n",
    "#offset = -40\n",
    "#new_fixed_axis = par2.get_grid_helper().new_fixed_axis\n",
    "#par2.axis[\"bottom\"] = new_fixed_axis(loc=\"bottom\",\n",
    "#                                     axes=par2,\n",
    "#                                     offset=(0, offset))\n",
    "\n",
    "\n",
    "#y5 = [0, 5.5452]\n",
    "#plt.plot(x4, y4, color=\"grey\", lw=1, linestyle=':', label=r\"Slope ln2/4\")\n",
    "#plt.yscale('log')\n",
    "#plt.loglog(y, y2, y3)\n",
    "plt.text(18, 1.00, r' MicroPhysics', fontsize=18, color=\"black\")\n",
    "plt.text(0, 0., r' STRING=2', fontsize=8, color=\"grey\")\n",
    "plt.text(6, 1., r' Gluons', fontsize=8, color=\"black\")\n",
    "plt.text(10, 1.55, r' Mean Pion', fontsize=8, color=\"black\")\n",
    "plt.text(0, 1.70, r' Bohr Atom Diameter', fontsize=8, color=\"red\")\n",
    "plt.text(-1, 2.35, r' Rydberg Reduced Wavelength/pi', fontsize=8, color=\"red\")\n",
    "plt.text(14, 2.20, r' Half Mean Weak Boson', fontsize=8, color=\"black\")\n",
    "plt.text(14, 2.52, r'Higgs Boson', fontsize=8, color=\"green\")\n",
    "plt.text(1, 3.09, r' Cosmic Wavelength hc/2kT T=2.73 K', fontsize=8, color=\"red\")\n",
    "plt.text(12, 3.78, r' White Dwarf Diameter', fontsize=8, color=\"red\")\n",
    "plt.text(22, 3.6, r' X Boson GUT', fontsize=8, color=\"black\")\n",
    "plt.text(24, 4.00, r' Photon', fontsize=8, color=\"black\")\n",
    "plt.text(11, 4.00, r' Kotov cycle: Stellar System', fontsize=8, color=\"red\")\n",
    "plt.text(26, 4.30, r' Graviton/a', fontsize=8, color=\"black\")\n",
    "plt.text(13, 4.30, r' Milankovitch cycle: Galaxy', fontsize=8, color=\"red\")\n",
    "plt.text(16.7, 4.6, r' Hubble Diameter x pi', fontsize=8, color=\"red\")\n",
    "plt.text(28, 5.00, r'Large Cycle', fontsize=8, color=\"green\")\n",
    "plt.text(28, 5.25, r'HOL', fontsize=8, color=\"black\")\n",
    "plt.text(27, 5.55, r'GrandCosmos', fontsize=8, color=\"green\")\n",
    "plt.text(-1, 3.50, r' MacroPhysics', fontsize=18, color=\"red\")\n",
    "plt.grid(True)\n",
    "plt.legend(loc=2) # upper left corner\n",
    "plt.xlabel('Number of peculiar dimensions in the bosonic string theory: n = 4k+2')\n",
    "plt.ylabel(r'Macro: lnln$\\frac{\\lambdabar_{Macro}}{\\lambdabar_e}$', fontsize=10, color=\"red\")\n",
    "plt.title('Topological Axis')\n",
    "plt.arrow(1,30,0,0, head_width=0.05, head_length=0.1, fc='k', ec='k')\n",
    "ax2 = plt.twinx()\n",
    "#ax2.plot(x, exp(2)**x, lw=1, color=\"m\")\n",
    "#T=arange(len(x))+0.5\n",
    "#ax.set_yticks(T)\n",
    "ax2.set_ylabel(r\"Micro: $\\ln\\ln\\frac{\\lambdabar_e}{\\lambda_{micro}}$\", fontsize=10, color=\"black\")\n",
    "\n",
    "#for label in ax2.get_yticklabels():\n",
    "#    label.set_color(\"red\")\n",
    "ax3 = plt.twiny()\n",
    "#ax3.plot(x4, y4, lw=1, color=\"b\")\n",
    "ax3.set_xlabel(r\"Characteristic lengths of physics and cosmology follow the law: $exp(2^{{n}/{4}})$\", fontsize=6, color=\"blue\")\n",
    "\n",
    "for label in ax2.get_xticklabels():\n",
    "    label.set_color(\"pink\")\n",
    "plt.xticks(x5,x5)\n",
    "ax2.xaxis.set_label_position('top') \n",
    "ax2.yaxis.set_label_position('right') \n",
    "ax2.tick_params(axis='x', colors=\"C1\")\n",
    "ax2.tick_params(axis='y', colors=\"C1\")\n",
    "ax2.set_ylim(10e-5,10e105)\n",
    "ax2.set_yscale('log',basey=10)\n",
    "ax2.spines['right'].set_position(('outward', 40))\n",
    "ax2.spines['bottom'].set_position(('outward', 40))\n",
    "my_xticks = ['-1', '0', '1', '2','$\\pi$', '4', '5', '$16/3$', '$e^{\\pi}/{4}$', '6', '7', '$e^2$', '']\n",
    "plt.xticks(x5, my_xticks, fontsize=6)\n",
    "\n",
    "# Turn on the frame for the twin axis, but then hide all \n",
    "# but the bottom spine\n",
    "ax2.set_frame_on(True)\n",
    "ax2.patch.set_visible(False)\n",
    "#for sp in ax2.spines.itervalues():\n",
    "#    sp.set_visible(False)\n",
    "#ax2.spines[\"bottom\"].set_visible(True)\n",
    "ax2.set_xticks(new_tick_location)\n",
    "ax2.set_xticklabels(new_tick_location)\n",
    "#ax2.axis[\"bottom\"].label.set_color(p1.get_color())\n",
    "#ax2.axis[\"bottom\"].label.set_color(p3.get_color())\n",
    "#ax2.set_xlabel(r\"Modified x-axis: $1/(1+X)$\")\n",
    "#my_x1ticks = ['-1', '0', '1', '2','3', '4', '5', '$16/3$', '$e^{\\pi}/{4}$', '6', '7', '$e^2$','']\n",
    "#plt.xticks(x5, my_x1ticks, fontsize=6)\n",
    "#plt.arrow(-1, -1010, 4, 10e10, head_width=0.06, head_length=0.1, fc='k', ec='k');\n",
    "#par2.axis[\"top\"].toggle(all=False)\n",
    "#par2.set_xlabel(\"Velocity\")\n",
    "#p1, = host.plot([0, 1, 2], [0, 1, 2], label=\"Density\")\n",
    "#p3, = par2.plot([50, 30, 15], [0, 1, 2], label=\"Velocity\")\n",
    "#par2.set_xlim(1, 65)\n",
    "#host.legend()\n",
    "#host.axis[\"bottom\"].label.set_color(p1.get_color())\n",
    "#par2.axis[\"bottom\"].label.set_color(p3.get_color())\n",
    "ax2.grid()\n",
    "ax3.grid()\n",
    "plt.show()\n",
    "p3.ylim(-3,3)\n",
    "p3.show()\n",
    "p3.save('embed.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tikz LaTeX "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext tikzmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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    "%%tikz -s 400,400 -sc 1. -f svg\n",
    "\\draw [style=help lines, step=2]                (-31,-31)    grid        (+31,+31);\n",
    "%%%\\draw (-31.0,-31.0){A}\n",
    "\n",
    " \n",
    "\\draw [->] (-31.0,-31.0) node[left]{} -- (-31.,31.) node[right]{}; % scale Y\n",
    "\\draw [->] (31.0,-31.0) node[left]{} -- (31.,31.) node[right]{}; % \n",
    "\\draw [->] (-31.0,-31.0) node[left]{} -- (31.8,-31.0) node[right]{}; % scale d\n",
    "\\draw [->] (-31.0,-34.) node[left]{} -- (31.8,-34.) node[right]{};   % scale k\n",
    "\\draw [->] (-34.0,-31.) node[left]{} -- (-34.,34.) node[right]{};   % scale Y'\n",
    "\n",
    "\\draw [-,color=blue] (-31.0,-31.8) node[left]{} -- (30.8,30.8) node[right]{};   % Diagonal line (Slope)\n",
    "\n",
    "\\draw [-,color=blue,dashed] (-34.0,-2.8) node[left]{} -- (-2.8,-2.8) node[right]{};   % Scalar Boson horizontal line\n",
    "\\draw [-,color=green,dashed] (-34.0,0.) node[left]{} -- (0.,0.) node[right]{};   % bicodon horizontal line\n",
    "\\draw [-,color=red,dashed] (-34.0,31.) node[left]{} -- (31.,31.) node[right]{};   % Cosmos horizontal line\n",
    "\n",
    "\\draw [-,color=blue,dashed] (-28.0,-32.) node[left]{} -- (-28.,-28.) node[right]{};   % String vertical line\n",
    "\\draw [-,color=blue,dashed] (-20.0,-32.) node[left]{} -- (-20.,-20.) node[right]{};   % Gluon vertical line\n",
    "\\draw [-,color=blue,dashed] (-12.0,-32.) node[left]{} -- (-12.,-12.) node[right]{};   % ATOM DIAMETER vertical line\n",
    "\\draw [-,color=blue,dashed] (-10.0,-32.) node[left]{} -- (-10.,-10.) node[right]{};   % Kaon vertical line\n",
    "\\draw [-,color=blue,dashed] (-4.0,-32.) node[left]{} -- (-4.,-4.) node[right]{};   % Half Mean Weak Boson vertical line\n",
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    "\\draw [-,color=blue,dashed] (0.,-32.) node[left]{} -- (0.,0.) node[right]{};   % Bicodon vertical line\n",
    "\\draw [-,color=blue,dashed] (4.1,-32.) node[left]{} -- (4.1,4.1) node[right]{};   % Thermal Photon vertical line\n",
    "\\draw [-,color=blue,dashed] (12.1,-32.) node[left]{} -- (12.1,12.1) node[right]{};   % Boson X GUT vertical line\n",
    "\\draw [-,color=blue,dashed] (16.,-32.) node[left]{} -- (16.,16.) node[right]{};   % Photon vertical line\n",
    "\\draw [-,color=blue,dashed] (18.25,-32.) node[left]{} -- (18.25,18.25) node[right]{};   % Galaxy group vertical line\n",
    "\\draw [-,color=blue,dashed] (20.0,-32.) node[left]{} -- (20.0,20.) node[right]{};   % Gravitonde vertical line\n",
    "\\draw [-,color=blue,dashed] (28.0,-32.) node[left]{} -- (28.0,28.0) node[right]{};   % Super Period vertical line\n",
    "\\draw [-,color=blue,dashed] (29.,-32.) node[left]{} -- (29.,29.0) node[right]{};   % Topon vertical line\n",
    "\\draw [-,color=red,dashed] (31.0,-36.) node[left]{} -- (31.,31.) node[right]{};   % Cosmos vertical line\n",
    "\n",
    "\\draw [red] ( 31.0, 31.0) node[anchor=south east] {\\Huge{COSMOS}};\n",
    "\\draw [black!40!green] ( 0.0, 0.0) node[anchor=south east] {\\Huge{DNA BI-CODON}};\n",
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    "\\draw [red] ( -8.0, 26.0) node[anchor=south east] {\\Huge{MACRO-PHYSICS}};\n",
    "\\draw [black] ( 24.0, -24.0) node[anchor=south east] {\\Huge{MACRO-PHYSICS}};\n",
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    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
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   "source": [
    "%%tikz -p pgfplots -f svg\n",
    "\\begin{axis}[\n",
    "  xlabel=$x$,\n",
    "  ylabel={$f(x) = \\exp(2^{x/4})$}\n",
    "]\n",
    "%%%\\addplot [green,domain=0:34, samples=60]{ln(2^(x /4))};\n",
    "%%%\\addplot [blue,domain=0:34, samples=60]{ln(ln(2^(x /4)))};\n",
    "%%%\\addplot [blue,domain=-30:30, samples=60]{log2(x^(x/4))};\n",
    "\\addplot [red, domain=1:30, samples=2]{exp(2^(x/4))};\n",
    "%%%\\addplot [cyan, domain=1:30, samples=2]{exp(2)^(x/4)};\n",
    "\\end{axis}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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   "source": [
    "%%tikz -p pgfplots -f svg\n",
    "\\begin{axis}[\n",
    "  xlabel=$x$,\n",
    "  ylabel={$f(x) = \\exp(2^{x/4})$}\n",
    "]\n",
    "%%%\\addplot [green,domain=0:34, samples=60]{ln(2^(x /4))};\n",
    "%%%\\addplot [blue,domain=0:34, samples=60]{ln(ln(2^(x /4)))};\n",
    "%%%\\addplot [blue,domain=-30:30, samples=60]{log2(x^(x/4))};\n",
    "\\addplot [red, domain=1:30, samples=3]{exp(2^(x/4))};\n",
    "%%%\\addplot [cyan, domain=1:30, samples=2]{exp(2)^(x/4)};\n",
    "\\end{axis}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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     "metadata": {
      "isolated": "true"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%tikz -p pgfplots -f svg\n",
    "\\begin{axis}[\n",
    "  xlabel=$x$,\n",
    "  ylabel={$f(x) = \\ln(2^{x/4})$}\n",
    "]\n",
    "\\addplot [green,domain=0:34, samples=60]{ln(2)^(x /4)};\n",
    "\\addplot [blue,domain=0:34, samples=60]{ln(2^(x /4))};\n",
    "%%%\\addplot [blue,domain=-30:30, samples=60]{log2(x^(x/4))};\n",
    "%%%\\addplot [red, domain=1:30, samples=2]{exp(2^(x/4))};\n",
    "\\end{axis}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert ipynb to latex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!jupyter-nbconvert topological-axis.ipynb --to latex ## produce tex file and folder of png/pdf files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!nbpublish topological-axis.ipynb # produce tex file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert tex to pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use any of the following command and run it in terminal\n",
    "lualatex --shell-escape topological-axis.tex\n",
    "xelatex --shell-escape topological-axis.tex\n",
    "pdflatex --shell-escape topological-axis.tex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Tikz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%tikz -s 400,400 -sc 1. -f svg\n",
    "%%%#\\begin{tikzpicture}[scale=0.9, line cap=round,line join=round,>=triangle \n",
    "%%%#45,x=0.25cm,y=0.25cm]\n",
    "\\draw[step=1cm,gray,very thin] (-31,-31) grid (31,31);\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\\draw [->] (-31.0,-31.0) node[left]{} -- (-31.,31.) node[right]{}; % scale Y\n",
    "\\draw [->] (31.0,-31.0) node[left]{} -- (31.,31.) node[right]{}; % \n",
    "\\draw [->] (-31.0,-31.0) node[left]{} -- (31.8,-31.0) node[right]{}; % scale d\n",
    "\\draw [->] (-31.0,-34.) node[left]{} -- (31.8,-34.) node[right]{};   % scale k\n",
    "\\draw [->] (-34.0,-31.) node[left]{} -- (-34.,34.) node[right]{};   % scale Y'\n",
    "%%%# Blue Dashed Horizontal lines\n",
    "\\draw [-,color=blue,dashed] (-34.0,-2.8) node[left]{} -- (-2.8,-2.8) node[right]{};   % Scalar Boson horizontal line\n",
    "\\draw [-,color=green,dashed] (-34.0,0.) node[left]{} -- (0.,0.) node[right]{};   % bicodon horizontal line\n",
    "\\draw [-,color=red,dashed] (-34.0,31.) node[left]{} -- (31.,31.) node[right]{};   % Cosmos horizontal line\n",
    "%%%# Blue Dashed Vertical lines\n",
    "\\draw [-,color=blue,dashed] (-28.0,-32.) node[left]{} -- (-28.,-28.) node[right]{};   % String vertical line\n",
    "\\draw [-,color=blue,dashed] (-20.0,-32.) node[left]{} -- (-20.,-20.) node[right]{};   % Gluon vertical line\n",
    "\\draw [-,color=blue,dashed] (-12.0,-32.) node[left]{} -- (-12.,-12.) node[right]{};   % ATOM DIAMETER vertical line\n",
    "\\draw [-,color=blue,dashed] (-10.0,-32.) node[left]{} -- (-10.,-10.) node[right]{};   % Kaon vertical line\n",
    "\\draw [-,color=blue,dashed] (-4.0,-32.) node[left]{} -- (-4.,-4.) node[right]{};   % Half Mean Weak Boson vertical line\n",
    "\\draw [-,color=blue,dashed] (-3.0,-32.) node[left]{} -- (-3.,-3.) node[right]{};   % Scalar Boson vertical line\n",
    "\\draw [-,color=blue,dashed] (0.,-32.) node[left]{} -- (0.,0.) node[right]{};   % Bicodon vertical line\n",
    "\\draw [-,color=blue,dashed] (4.1,-32.) node[left]{} -- (4.1,4.1) node[right]{};   % Thermal Photon vertical line\n",
    "\\draw [-,color=blue,dashed] (12.1,-32.) node[left]{} -- (12.1,12.1) node[right]{};   % Boson X GUT vertical line\n",
    "\\draw [-,color=blue,dashed] (16.,-32.) node[left]{} -- (16.,16.) node[right]{};   % Photon vertical line\n",
    "\\draw [-,color=blue,dashed] (18.25,-32.) node[left]{} -- (18.25,18.25) node[right]{};   % Galaxy group vertical line\n",
    "\\draw [-,color=blue,dashed] (20.0,-32.) node[left]{} -- (20.0,20.) node[right]{};   % Gravitonde vertical line\n",
    "\\draw [-,color=blue,dashed] (28.0,-32.) node[left]{} -- (28.0,28.0) node[right]{};   % Super Period vertical line\n",
    "\\draw [-,color=blue,dashed] (29.,-32.) node[left]{} -- (29.,29.0) node[right]{};   % Topon vertical line\n",
    "\\draw [-,color=red,dashed] (31.0,-36.) node[left]{} -- (31.,31.) node[right]{};   % Cosmos vertical line\n",
    "\n",
    "\\draw[color=blue] (A) -- (Y);\n",
    "\\node [anchor = text=blue] at (-8.,35.75) {\\Large{TOPOLOGICAL AXIS}};\n",
    "\\node [anchor = text=black] at (-6.,33.75) {\\tiny{Caracteristics length follow the law: $exp(2^{d/4})$}};\n",
    "\\node [anchor = text=black] at (-10.,32.75) {\\tiny{Unit length: electron Compton reduced wavelength $\\hbar m_e c=\\lambdabar_e$}};\n",
    "\\node [anchor = text=black] at (-12.,31.75) {\\tiny{Super Period unit: $\\lambdabar_e/c$. Photonde and Gravitonde masses unit: $2\\pi m_N$}};\n",
    "\\node [anchor = east,text=red] at (31.75,31.75) {\\Large{COSMOS}};\n",
    "\\node [anchor = south] at (-28.,-33.0) {$2$};\n",
    "\\node [anchor = south] at (-28.,-36.0) {$0$};\n",
    "\\node [anchor = north] at (-28.,-28.0) {$STRING$};\n",
    "\\node [anchor = south] at (-20.,-33.0) {$6$};\n",
    "\\node [anchor = south] at (-20.,-36.0) {$1$};\n",
    "\\node [anchor = east] at (-20.,-20.0) {$GLUON$};\n",
    "\\node [anchor = south] at (-12.,-33.0) {$10$};\n",
    "\\node [anchor = south] at (-10.,-33.0) {$11$};\n",
    "\\node [anchor = south] at (-12.,-36.0) {$2$};\n",
    "\\node [anchor = east] at (-12.,-12.) {MEAN BOSON PI};\n",
    "\\node [anchor = west,color=red] at (-12.,-12.) {ATOM DIAMETER};\n",
    "\\node [anchor = east] at (-10.,-10.) {KAON};\n",
    "\\node [anchor = south] at (-4.25,-33.0) {$14$};\n",
    "\\node [anchor = south] at (-4.25,-36.0) {$3$};\n",
    "\\node [anchor = east] at (-4.5,-4.5) {HALF MEAN WEAK BOSON};\n",
    "\\node [anchor = west,color=red] at (-4.5,-4.5){HALF RYDBERG WAVELENGTH};\n",
    "\\node [anchor = south] at (-2.75,-31.0) {$\\gamma\\Gamma$};\n",
    "\\node [anchor = east,color=black] at (-3.0,-3.0){SCALAR BOSON};\n",
    "\\node [anchor = west,color=black] at (-3.0,-3.0){$125GeV$};\n",
    "\\node [anchor = south,color=black!40!green] at (0.,-33.0) {\\textbf{16}};\n",
    "\\node [anchor = east,color=black!40!green] at (0.,0.0) {DNA BI-CODON};\n",
    "\\node [anchor = west,color=black!40!green] at (0.,0.0) {$\\times$ $\\phi_9^2$};\n",
    "\\node [anchor = south] at (4.25,-33.0) {$18$};\n",
    "\\node [anchor = south] at (4.25,-36.0) {$4$};\n",
    "\\node [cross,anchor = west,color=red] at (4.25,4.25){THERMAL PHOTON: $\\hbar c/2k$};\n",
    "\\node [cross,anchor = west,color=red] at (3.25,3.25){T=2.73 Kelvin $\\approx T_{mam}/j$};\n",
    "\\node [anchor = south] at (12.25,-33.0) {$22$};\n",
    "\\node [anchor = south] at (12.25,-36.0) {$5$};\n",
    "\\node [anchor = east,color=black] at (12.25,12.250){BOSON $X_{GUT}:2.3 \\times 10^{16}$GeV};\n",
    "\\node [anchor = east,color=black!40!green] at (16.,16.){$PHOTONDE$};\n",
    "\\node [anchor = west,color=red] at (16.,16.){Kotov Length $\\times$ $2\\pi$a};\n",
    "\\node [anchor = west,color=red] at (18.25,18.250){Galaxy group};\n",
    "\\node [anchor = south,color=red] at (18.25,-31.0) {$\\Gamma$};\n",
    "\\node [anchor = east,color=black!40!green] at (20.,20.0){$GRAVITONDE$};\n",
    "\\node [anchor = west,color=red] at (20.,20.0){Hubble Radius $\\times$ 6};\n",
    "\\node [anchor = south] at (16.25,-33.0) {\\textbf{24}};\n",
    "\\node [anchor = east,color=black!40!green] at (28.25,28.250){$Super Period$};\n",
    "\\node [anchor = east,color=black] at (29.25,29.250){$TOPON$\\tiny{:Visible universe wavelength}};\n",
    "\\node [anchor = east,color=black] at (-8.25,24.250){\\LARGE{MICROPHYSICS}};\n",
    "\\node [anchor = east,color=black] at (-8.25,22.250){$Y=\\ln\\ln(\\lambdabar_e(n)/\\lambdabar_{MICRO})$};\n",
    "\\node [anchor = east,color=red] at (22.25,-20.250){\\LARGE{MACROPHYSICS}};\n",
    "\\node [anchor = east,color=red] at (22.25,-22.250){$Y=\\ln\\ln(\\lambdabar_{MACRO}(n)/\\lambdabar_{e})$};\n",
    "\\node [anchor = south] at (20.25,-33.0) {$26$};\n",
    "\\node [anchor = south] at (20.25,-36.0) {$6$};\n",
    "\\node [anchor = south] at (28.25,-33.0) {$30$};\n",
    "\\node [anchor = south] at (28.25,-36.0) {$7$};\n",
    "\\node [anchor = south] at (31.25,-36.0) {$e^2$};\n",
    "\\node [anchor = south] at (29.25,-31.0) {$2e^e$};\n",
    "\\node [anchor = south] at (32.6,-33.00) {$d$};\n",
    "\\node [anchor = south] at (32.6,-36.00) {$k$};\n",
    "%%%# Y AXIS\n",
    "\\node [anchor = north] at (-31.55,-31.0) {$0$};\n",
    "\\node [anchor = north] at (-31.55,-19.0) {$1$};\n",
    "\\node [anchor = north] at (-31.55,-7.0) {$2$};\n",
    "\\node [anchor = north] at (-31.55, 5.0) {$3$};\n",
    "\\node [anchor = north] at (-28.55, 1.0) {$\\pi-1/e$};\n",
    "\\node [anchor = north] at (-31.55, 16.0) {$4$};\n",
    "\\node [anchor = north] at (-31.55, 26.0) {$5$};\n",
    "\\node [anchor = north] at (-31.55, 32.4) {$2e$};\n",
    "%%%## Y' AXIS\n",
    "\\node [anchor = north] at (-34.25,-31.0) {$e$};\n",
    "\\node [anchor = north] at (-34.25,-20.0) {$10$};\n",
    "\\node [anchor = north] at (-34.25,-12.0) {$100$};\n",
    "\\node [anchor = north] at (-34.25,-2.0) {$495^2$};\n",
    "\\node [anchor = north] at (-34.25,4.0) {$10^{10}$};\n",
    "\\node [anchor = north] at (-34.25,12.0) {$10^{20}$};\n",
    "\\node [anchor = north] at (-34.25,20.0) {$10^{40}$};\n",
    "\\node [anchor = north] at (-34.25,28.0) {$10^{80}$};\n",
    "\\node [anchor = north] at (-34.25,36.0) {$Y=\\ln\\ln(y)$};\n",
    "%%%# SLOPE\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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