PhysicalConstant.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PhysicalConstant.jl explained in a Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This library contains Mathematical and Physical Constants to produce macro and micro-physics relations. It integrates with IJulia, outputting mathematical and Physical Constants to the notebook.\n",
    "\n",
    "search: ⊻ ⊋ ⊊ ⊉ ⊈ ⊇ ⊆ ≥ ≤ ≢ ≡ ≠ ≉ ≈ ∪ ∩ ∛ √ ∘ ∌ ∋ ∉ ∈ ℯ π ÷ ~ | ^ \\ > < : / - + "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note to the reader:\n",
    "\n",
    "-------------------\n",
    "\n",
    "In this tutorial we'll be using the following *Convention of Authorized caracters defined in PhysicalConstant Pkg*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* α\n",
    "* CmB\n",
    "* C_0\n",
    "* c_0\n",
    "* ƛe\n",
    "* m_e\n",
    "* m_p\n",
    "* m_H\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Useful list of Math Caracters\n",
    "\n",
    "Basic Math Symbols\n",
    "≠ ± ∓ ÷ × ∙ – √ ‰ ⊗ ⊕ ⊖ ⊘ ⊙ ≤ ≥ ≦ ≧ ≨ ≩ ≺ ≻ ≼ ≽ ⊏ ⊐ ⊑ ⊒ ² ³ °\n",
    "\n",
    "Geometry Symbols\n",
    "∠ ∟ ° ≅ ~ ‖ ⟂ ⫛\n",
    "\n",
    "Algebra Symbols\n",
    "≡ ≜ ≈ ∝ ∞ ≪ ≫ ⌊⌋ ⌈⌉ ∘∏ ∐ ∑ ⋀ ⋁ ⋂ ⋃ ⨀ ⨁ ⨂ 𝖕 𝖖 𝖗\n",
    "\n",
    "Set Theory Symbols\n",
    "∅ ∖ ∁ ↦ ↣ ∩ ∪ ⊆ ⊂ ⊄ ⊊ ⊇ ⊃ ⊅ ⊋ ⊖ ∈ ∉ ∋ ∌ ℕ ℤ ℚ ℝ ℂ ℵ ℶ ℷ ℸ 𝓟\n",
    "\n",
    "Logic Symbols\n",
    "¬ ∨ ∧ ⊕ → ← ⇒ ⇐ ↔ ⇔ ∀ ∃ ∄ ∴ ∵ ⊤ ⊥ ⊢ ⊨ ⫤ ⊣\n",
    "\n",
    "Calculus and Analysis Symbols\n",
    "∫ ∬ ∭ ∮ ∯ ∰ ∇ ∆ δ ∂ ℱ ℒ ℓ\n",
    "\n",
    "Greek Letters\n",
    "𝛢𝛼 𝛣𝛽 𝛤𝛾 𝛥𝛿 𝛦𝜀𝜖 𝛧𝜁 𝛨𝜂 𝛩𝜃𝜗 𝛪𝜄 𝛫𝜅 𝛬𝜆 𝛭𝜇 𝛮𝜈 𝛯𝜉 𝛰𝜊 𝛱𝜋 𝛲𝜌 𝛴𝜎 𝛵𝜏 𝛶𝜐 𝛷𝜙𝜑 𝛸𝜒 𝛹𝜓 𝛺𝜔"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre requisite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add Conda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "import Pkg;\n",
    "Pkg.add(\"Conda\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set the environment variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"/srv/julia/pkg/conda/3\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#*(Required)*\n",
    "#ENV[\"PYTHON\"] = \"/srv/conda/bin/python3\"\n",
    "ENV[\"PYTHON\"] = \"/srv/julia/pkg/conda/3\"\n",
    "# ENV[\"PYTHON\"] = \"C:\\\\Python37-x64\\\\python.exe\" # example for Windows\n",
    "# ENV[\"PYTHON\"] = \"/usr/bin/python3.7\"           # example for *nix\n",
    "#Pkg.build(\"PyCall\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add PyCall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#(Optional)\n",
    "import Pkg; Pkg.add(\"PyCall\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pkg update of PhysicalConstant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "\n",
    "using Pkg\n",
    "Pkg.update(\"PhysicalConstant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optional steps:\n",
    "* Install matplotlib\n",
    "* Install numpy\n",
    "* Install scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "#Pkg.build(\"PyCall\")\n",
    "using Conda\n",
    "#Conda.add(\"matplotlib\", :my_env)\n",
    "Conda.add(\"matplotlib\", \"/srv/julia/pkg/conda/3\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "using Conda\n",
    "Conda.add(\"numpy\", \"/srv/julia/pkg/conda/3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "using Conda\n",
    "Conda.add(\"scipy\", \"/srv/julia/pkg/conda/3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pkg Status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List all Conda modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "import Conda\n",
    "Conda.list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if PhysicalConstant is installed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1mProject \u001b[22m\u001b[39mPhysicalConstant v0.0.3\n",
      "\u001b[32m\u001b[1m    Status\u001b[22m\u001b[39m `~/Project.toml`\n",
      " \u001b[90m [8f4d0f93]\u001b[39m\u001b[92m + Conda v1.2.0\u001b[39m\n",
      " \u001b[90m [e30172f5]\u001b[39m\u001b[92m + Documenter v0.22.3\u001b[39m\n",
      " \u001b[90m [eff96d63]\u001b[39m\u001b[92m + Measurements v2.0.0\u001b[39m\n",
      " \u001b[90m [438e738f]\u001b[39m\u001b[92m + PyCall v1.91.2\u001b[39m\n",
      " \u001b[90m [d330b81b]\u001b[39m\u001b[92m + PyPlot v2.8.1\u001b[39m\n",
      " \u001b[90m [1986cc42]\u001b[39m\u001b[92m + Unitful v0.15.0\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "import Pkg; Pkg.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pkg Installation / Pkg De-installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To remove the package run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "using Pkg\n",
    "Pkg.rm(\"PhysicalConstant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To install the package run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "using Pkg\n",
    "Pkg.add(\"PhysicalConstant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install PyPlot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "import Pkg; Pkg.add(\"PyPlot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Update"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#*(Optional)*\n",
    "using Pkg\n",
    "Pkg.update(\"PhysicalConstant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, you will need to install the following dependencies if you do not already have them on your system.\n",
    "\n",
    "* Measurements and Unitful. These are required by PhysicalConstant. On Julia, you can get this by running `Pkg.add(Unitful)`. \n",
    "\n",
    "In addition, you may optionally want to use pdf2svg on Windows, you can download the binaries from http://www.cityinthesky.co.uk/opensource/pdf2svg/. Be sure to add pdf2svg to your path (and restart).\n",
    "* Pgf (version 3.0 or later). Install using your latex package manager (e.g., texlive or miktex).\n",
    "\n",
    "Once these things are installed, you should be able to run the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examples of CODATA2019 Constants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#*(Required)*\n",
    "using PhysicalConstant.CODATA2019\n",
    "using Measurements\n",
    "using Unitful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Planck's h constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Planck constant (h)\n",
       "Value                         = 6.62607004e-34 J s\n",
       "Standard uncertainty          = 8.1e-42 J s\n",
       "Relative standard uncertainty = 1.2e-8\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fine-structure constant (α)\n",
       "Value                         = 0.0072973525664\n",
       "Standard uncertainty          = 1.7e-12\n",
       "Relative standard uncertainty = 2.3e-10\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.FineStructureConstant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sanchez constant of gravitation (G)\n",
       "Value                         = 6.67545e-11 m^3 kg^-1 s^-2\n",
       "Standard uncertainty          = 3.1e-15 m^3 kg^-1 s^-2\n",
       "Relative standard uncertainty = 4.6e-5\n",
       "Reference                     = Francis M. Sanchez"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduceed Electron Compton Wavelength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduced Electron Compton Wavelength equation:\n",
    "\n",
    "ƛ$_{e}$=$\\hbar/m_{e}\\cdot c$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8615926754860414e-13 J s^2 kg^-1 m^-1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.PlanckConstantOver2pi/(CODATA2019.m_e*CODATA2019.c_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.861592676452718338399364915357181343914809630867925459864883008800725803939552e-13"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduced electron Compton wavelength\n",
    "big(2.4263102367*10^-12)/big(2pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5244956826076587e-11 J s^2 kg^-1 m^-1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(CODATA2019.h*2pi)/(CODATA2019.m_e*CODATA2019.c_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ΔνCs\n",
    "the unperturbed ground state hyperfine transition frequency of the caesium-133 atom in Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "UndefVarError",
     "evalue": "UndefVarError: ΔνCs not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: ΔνCs not defined",
      "",
      "Stacktrace:",
      " [1] getproperty(::Module, ::Symbol) at ./sysimg.jl:13",
      " [2] top-level scope at In[22]:1"
     ]
    }
   ],
   "source": [
    "Measurements.value(big(CODATA2019.ΔνCs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Electron Proton Hydrogen product\n",
    "\n",
    "$m_e \\cdot m_p \\cdot m_h$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "UndefVarError",
     "evalue": "UndefVarError: HydrogenMass not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: HydrogenMass not defined",
      "",
      "Stacktrace:",
      " [1] getproperty(::Module, ::Symbol) at ./sysimg.jl:13",
      " [2] top-level scope at In[23]:3",
      " [3] top-level scope at util.jl:156",
      " [4] top-level scope at In[23]:1"
     ]
    }
   ],
   "source": [
    "@time begin\n",
    "using PhysicalConstant.CODATA2019\n",
    "big(CODATA2019.ElectronMass*CODATA2019.ProtonMass*CODATA2019.HydrogenMass)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "UndefVarError",
     "evalue": "UndefVarError: MathPhysicalConstants not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: MathPhysicalConstants not defined",
      "",
      "Stacktrace:",
      " [1] top-level scope at In[24]:1"
     ]
    }
   ],
   "source": [
    "Measurements.value(big((MathPhysicalConstants.SI.e^128)-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate the visible Universe horizon radius"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the regular CODATA2019 Gravitational Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sanchez constant of gravitation (G)\n",
       "Value                         = 6.67545e-11 m^3 kg^-1 s^-2\n",
       "Standard uncertainty          = 3.1e-15 m^3 kg^-1 s^-2\n",
       "Relative standard uncertainty = 4.6e-5\n",
       "Reference                     = Francis M. Sanchez"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Sanchez's Gravitational Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sanchez constant of gravitation (G)\n",
       "Value                         = 6.67545e-11 m^3 kg^-1 s^-2\n",
       "Standard uncertainty          = 3.1e-15 m^3 kg^-1 s^-2\n",
       "Relative standard uncertainty = 4.6e-5\n",
       "Reference                     = Francis M. Sanchez"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.SanchezGravitationalConstant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's first determine the gravitational coupling constant $a_G$ : \n",
    "\n",
    "https://en.wikipedia.org/wiki/Gravitational_coupling_constant\n",
    "\n",
    "One determines the Gravitational Coupling Constant: \n",
    "### $a_{G}$ $=$ $\\hbar$$c$ $/$ $G$$m_{p}$$m_{H}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using PhysicalConstant.CODATA2019\n",
    "# $a_{G}$ = $\\hbar$$c$/$G$$m_{p}$$m_{H}$\n",
    "(CODATA2019.PlanckConstantOver2pi*CODATA2019.c_0)/(CODATA2019.G*CODATA2019.m_p*CODATA2019.m_H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# $a_{G}$ = $\\hbar$$c$/$G$$m_{p}$$m_{H}$\n",
    "CODATA2019.PlanckConstantOver2pi*CODATA2019.c_0/CODATA2019.SanchezGravitationalConstant*CODATA2019.MassProton*CODATA2019.m_H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visible Universe horizon radius calculation (distance)\n",
    "\n",
    "### $R$ $=$ $2a_{G}$ƛ$_{e}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the regular CODATA2019 Gravitational Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# R = 2a_{G} \\lambdabar_{e}\n",
    "R = 2*((CODATA2019.PlanckConstantOver2pi*CODATA2019.c_0)/(CODATA2019.Gg*CODATA2019.m_p*CODATA2019.m_H))*CODATA2019.ƛe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2*((CODATA2019.PlanckConstantOver2pi*CODATA2019.c_0)/(CODATA2019.NewtonianConstantOfGravitation*CODATA2019.m_p*CODATA2019.m_H))*CODATA2019.ƛe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Sanchez's Gravitational Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# R = 2a_{G} \\lambdabar_{e}\n",
    "R = 2*((CODATA2019.PlanckConstantOver2pi*CODATA2019.c_0)/(CODATA2019.SanchezGravitationalConstant*CODATA2019.m_p*CODATA2019.m_H))*CODATA2019.ƛe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the result in ly (LightYears) equivalent\n",
    "\n",
    "Notice that one obtain a distance equivalent to 1.3813524253217268e10 ly or 13.813524253217268 Gly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the regular CODATA2019 Gravitational Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1.3068334634480155e26/SI.LightYear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Sanchez's Gravitational Constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "1.3065645152410416e26/SI.LightYear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other PhysicalConstant Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#*(Required)*\n",
    "using PhysicalConstant.CODATA2019\n",
    "using Measurements\n",
    "using Unitful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bare Hydrogen radius (r_0)\n",
       "Value                         = 5.291772103e-11 m\n",
       "Standard uncertainty          = 1.2e-20 m\n",
       "Relative standard uncertainty = 2.3e-10\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.BareHydrogenRadius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bare Hydrogen radius (r_0)\n",
       "Value                         = 5.291772103e-11 m\n",
       "Standard uncertainty          = 1.2e-20 m\n",
       "Relative standard uncertainty = 2.3e-10\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.r_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gravitational velocity in interstellar vacuum (C_0)\n",
       "Value                         = 3.6993e44 m s^-1\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = J. Maruani in Dirac Electron"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.C_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CMB temperature in K (CmB)\n",
       "Value                         = 2.725820831 K^-1\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = Francis M. Sanchez"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.CmB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CMB temperature in K (CmB)\n",
       "Value                         = 2.725820831 K^-1\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = Francis M. Sanchez"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.CmbTemperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gravitational velocity in interstellar vacuum (C_0)\n",
       "Value                         = 3.6993e44 m s^-1\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = J. Maruani in Dirac Electron"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.GravitationalVelocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reduced Compton Electron Wavelength (ƛe)\n",
       "Value                         = 1.38064852e-23\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.RedComptonElectrWavlgth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reduced Compton Electron Wavelength (ƛe)\n",
       "Value                         = 1.38064852e-23\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.ƛe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8615926754860414e-13 J s^2 kg^-1 m^-1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(CODATA2019.ħ / (CODATA2019.c_0 * CODATA2019.m_e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "UndefVarError",
     "evalue": "UndefVarError: SpeedO not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: SpeedO not defined",
      "",
      "Stacktrace:",
      " [1] getproperty(::Module, ::Symbol) at ./sysimg.jl:13",
      " [2] top-level scope at In[44]:1"
     ]
    }
   ],
   "source": [
    "(CODATA2019.ħ / (CODATA2019.SpeedOfLightInVacuum * CODATA2019.m_e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Speed of light in vacuum (c_0)\n",
       "Value                         = 2.99792458e8 m s^-1\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.SpeedOfLightInVacuum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Speed of light in vacuum (c_0)\n",
       "Value                         = 2.99792458e8 m s^-1\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.c_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reduced Compton Electron Wavelength (ƛe)\n",
       "Value                         = 1.38064852e-23\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.ƛe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fine-structure constant (α)\n",
       "Value                         = 0.0072973525664\n",
       "Standard uncertainty          = 1.7e-12\n",
       "Relative standard uncertainty = 2.3e-10\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.FineStructureConstant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fine-structure constant (α)\n",
       "Value                         = 0.0072973525664\n",
       "Standard uncertainty          = 1.7e-12\n",
       "Relative standard uncertainty = 2.3e-10\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.α"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sanchez constant of gravitation (G)\n",
       "Value                         = 6.67545e-11 m^3 kg^-1 s^-2\n",
       "Standard uncertainty          = 3.1e-15 m^3 kg^-1 s^-2\n",
       "Relative standard uncertainty = 4.6e-5\n",
       "Reference                     = Francis M. Sanchez"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.SanchezGravitationalConstant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Newtonian constant of gravitation (Gg)\n",
       "Value                         = 6.67408e-11 m^3 kg^-1 s^-2\n",
       "Standard uncertainty          = 3.1e-15 m^3 kg^-1 s^-2\n",
       "Relative standard uncertainty = 4.6e-5\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.Gg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electron mass at rest (m_e)\n",
       "Value                         = 9.10938356e-31 kg\n",
       "Standard uncertainty          = 1.1e-38 kg\n",
       "Relative standard uncertainty = 1.2e-8\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.m_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Proton mass (m_p)\n",
       "Value                         = 1.672621898e-27 kg\n",
       "Standard uncertainty          = 2.1e-35 kg\n",
       "Relative standard uncertainty = 1.3e-8\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CODATA2019.m_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ℯ = 2.7182818284590..."
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ℯ for Euler's number \n",
    "CODATA2019.ℯ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Elementary charge (e)\n",
       "Value                         = 1.6021766208e-19 C\n",
       "Standard uncertainty          = 9.8e-28 C\n",
       "Relative standard uncertainty = 6.1e-9\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# e for Elementary charge\n",
    "CODATA2019.e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The ℯ^ℯ^ℯ^ℯ issue\n",
    "\n",
    "Why is Julia unable to render $ℯ^{ℯ}^{ℯ}^{ℯ}$ \n",
    "\n",
    "Is there a Limitation and/or a Hack ?\n",
    "\n",
    "Let's call it *The $\\e^{\\e}^{\\e}^{\\e}$ limitation Hack*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inf"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exception raised\n",
    "big(CODATA2019.ℯ^CODATA2019.ℯ^CODATA2019.ℯ^CODATA2019.ℯ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inf"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exception raised\n",
    "using PhysicalConstant.CODATA2019\n",
    "using Measurements\n",
    "using Unitful\n",
    "using PyCall\n",
    "#math = pyimport(\"math\")\n",
    "@pyimport numpy as np\n",
    "np.power(CODATA2019.ℯ, CODATA2019.ℯ^CODATA2019.ℯ^CODATA2019.ℯ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.764865179158049315959156489518625436695868996929735868921423813247391069467881e+17"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Workaround\n",
    "# 1st do big(CODATA2019.ℯ^CODATA2019.ℯ^CODATA2019.ℯ)\n",
    "a = big(CODATA2019.ℯ^CODATA2019.ℯ^CODATA2019.ℯ)\n",
    "b = a^ℯ\n",
    "# 3.8142791047602142207324504852294921875e+06^ℯ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "using PhysicalConstant.CODATA2019\n",
    "import PhysicalConstant.CODATA2019: h, ƛe, c_0, m_e, ħ\n",
    "using Measurements\n",
    "using Unitful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Reduced Compton Electron Wavelength (ƛe)\n",
       "Value                         = 1.38064852e-23\n",
       "Standard uncertainty          = (exact)\n",
       "Relative standard uncertainty = (exact)\n",
       "Reference                     = CODATA 2019"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ƛe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustrip(big(h)) == big\"6.626070040e-34\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustrip(big(ƛe)) == big\"1.38064852e-23\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ħ / (c_0 * m_e)) ≈ (ħ / float(c_0 * m_e))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try PhysicalConstant With PyCall, Numpy, Scipy and PyPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m registry at `/srv/julia/pkg/registries/General`\n",
      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m git-repo `https://github.com/JuliaRegistries/General.git`\n",
      "\u001b[2K\u001b[?25h\u001b[32m\u001b[1m Resolving\u001b[22m\u001b[39m package versions...\n",
      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m `~/Project.toml`\n",
      "\u001b[90m [no changes]\u001b[39m\n",
      "\u001b[32m\u001b[1m  Updating\u001b[22m\u001b[39m `~/Manifest.toml`\n",
      "\u001b[90m [no changes]\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "import Pkg; Pkg.add(\"PyPlot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Running `conda install -y numpy` in root environment\n",
      "└ @ Conda /srv/julia/pkg/packages/Conda/CpuvI/src/Conda.jl:112\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata: ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import Conda; Conda.add(\"numpy\", \"/srv/julia/pkg/conda/3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7071067811865475"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python\n",
    "#import numpy\n",
    "#print(np.pi)\n",
    "using PyCall\n",
    "#math = pyimport(\"math\")\n",
    "@pyimport numpy as np\n",
    "np.sin(np.pi / 4) # returns ≈ 1/√2 = 0.70710678...\n",
    "#math.pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Atiyah's $\\Gamma$ in Physics\n",
    "\n",
    "* Γ = γa/π"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5772156649015329"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "γ = np.euler_gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137.03599913815452"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = inv(CODATA2019.FineStructureConstant)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "π = np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.178097251911886"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Γ = γ*a/π"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using PyPlot\n",
    "using PyCall\n",
    "@pyimport math\n",
    "@pyimport numpy as np\n",
    "\n",
    "x = np.linspace(0,8*np.pi^2/log(2),30); y = np.sin(3*x +np.cos(x));\n",
    "scatter(x, y, color=\"blue\", linewidth=1.0, linestyle=\"-\")\n",
    "plot(x, y, color=\"red\", linewidth=1.0, linestyle=\"--\")\n",
    "#plt.title(\"A logarithm modulated sinusoid\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "using PyCall\n",
    "@pyimport math\n",
    "@pyimport numpy as np\n",
    "x = np.linspace(0, 34)\n",
    "#x = np.linspace(np.euler_gamma,8*np.pi^2/log(2),40)\n",
    "#y = np.exp(2)*x\n",
    "y = np.log(np.power(2,(x/4)))\n",
    "y1 = np.log(np.log(np.power(2,(x/4))))\n",
    "#y1 = np.exp(np.power(2,(x/4)))\n",
    "p = plot(x,y)\n",
    "p1 = plot(x,y1)\n",
    "xlabel(\"X\")\n",
    "ylabel(\"Y\")\n",
    "title(\"log(2^(x/4)) vs exp(2^(x/4)\")\n",
    "grid(\"on\")\n",
    "gcf() # Needed by IJulia to display plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 300x700 with 4 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1000x1000 with 3 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1000x1000 with 3 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject Text(0.5, 0.98, '3x1 Shared Axis')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using PyPlot\n",
    "using PyCall\n",
    "@pyimport math\n",
    "@pyimport numpy as np\n",
    "#####################\n",
    "##  2x2 Plot Grid  ##\n",
    "#####################\n",
    "fig = figure(\"pyplot_subplot_mixed\",figsize=(10,10)) # Create a new blank figure\n",
    "fig.set_figheight(7) # Doesn't work\n",
    "fig.set_figwidth(3) # Doesn't work\n",
    "subplot(221) # Create the 1st axis of a 2x2 arrax of axes\n",
    "grid(\"on\") # Create a grid on the axis\n",
    "title(\"221\") # Give the most recent axis a title\n",
    "subplot(222,polar=\"true\") # Create a plot and make it a polar plot, 2nd axis of 2x2 axis grid\n",
    "x = np.linspace(0, 2*np.pi)\n",
    "#x = np.linspace(np.euler_gamma,8*np.pi^2/log(2),40)\n",
    "#y = np.exp(2)*x\n",
    "y = np.log(np.power(2,(x/4)))\n",
    "p = plot(x,y)\n",
    "title(\"222\")\n",
    "ax = subplot(223,polar=\"true\") # Create a plot and make it a polar plot, 3rd axis of 2x2 axis grid\n",
    "ax.set_theta_zero_location(\"N\") # Set 0 degrees to the top of the plot\n",
    "ax.set_theta_direction(-1) # Switch the polar plot to clockwise\n",
    "title(\"223\")\n",
    "subplot(224) # Create the 4th axis of a 2x2 arrax of axes\n",
    "p = plot(x,y)\n",
    "xlabel(\"This is an X axis\")\n",
    "ylabel(\"This is a y axis\")\n",
    "title(\"224\")\n",
    "x = np.linspace(0, 34)\n",
    "#x = np.linspace(np.euler_gamma,8*np.pi^2/log(2),40)\n",
    "#y = np.exp(2)*x\n",
    "y = np.log(np.power(2,(x/4)))\n",
    "p = plot(x,y)\n",
    "fig.canvas.draw() # Update the figure\n",
    "suptitle(\"2x2 Subplot\")\n",
    "\n",
    "###################\n",
    "##  Column Plot  ##\n",
    "###################\n",
    "fig = figure(\"pyplot_subplot_column\",figsize=(10,10))\n",
    "subplot(311) # Create the 1st axis of a 3x1 array of axes\n",
    "title(\"311\")\n",
    "subplot(312) # Create the 2nd axis of a 3x1 arrax of axes\n",
    "p = plot(x,y)\n",
    "\n",
    "ax = gca() # Get the handle of the current axis\n",
    "ax.set_yscale(\"log\") # Set the y axis to a logarithmic scale\n",
    "grid(\"on\")\n",
    "ylabel(\"Log Scale\")\n",
    "title(\"312\")\n",
    "subplot(313) # Create the 3rd axis of a 3x1 array of axes\n",
    "p = plot(x,y)\n",
    "ax = gca()\n",
    "ax.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "xlabel(\"Log Scale\")\n",
    "title(\"313\")\n",
    "fig.canvas.draw() # Update the figure\n",
    "suptitle(\"3x1 Subplot\")\n",
    "\n",
    "###################\n",
    "##  Shared Axis  ##\n",
    "###################\n",
    "fig = figure(\"pyplot_subplot_touching\",figsize=(10,10))\n",
    "subplots_adjust(hspace=0.0) # Set the vertical spacing between axes\n",
    "subplot(311) # Create the 1st axis of a 3x1 array of axes\n",
    "p = plot(x,y)\n",
    "ax1 = gca()\n",
    "ax1.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "setp(ax1.get_xticklabels(),visible=false) # Disable x tick labels\n",
    "grid(\"on\")\n",
    "title(\"Title\")\n",
    "yticks(0.1:0.2:0.9) # Set the y-tick range and step size, 0.1 to 0.9 in increments of 0.2\n",
    "ylim(0.0,1.0) # Set the y-limits from 0.0 to 1.0\n",
    "subplot(312,sharex=ax1) # Create the 2nd axis of a 3x1 array of axes\n",
    "p = plot(x,y)\n",
    "ax2 = gca()\n",
    "ax2.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "setp(ax2.get_xticklabels(),visible=false) # Disable x tick labels\n",
    "grid(\"on\")\n",
    "ylabel(\"Log Scale\")\n",
    "yticks(0.1:0.2:0.9)\n",
    "ylim(0.0,1.0)\n",
    "subplot(313,sharex=ax2) # Create the 3rd axis of a 3x1 array of axes\n",
    "p = plot(x,y)\n",
    "ax3 = gca()\n",
    "ax3.set_xscale(\"log\") # Set the x axis to a logarithmic scale\n",
    "grid(\"on\")\n",
    "xlabel(\"Log Scale\")\n",
    "yticks(0.1:0.2:0.9)\n",
    "ylim(0.0,1.0)\n",
    "p = plot(x,y)\n",
    "fig.canvas.draw() # Update the figure\n",
    "suptitle(\"3x1 Shared Axis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.141592653589793115997963468544185161590576171875\n"
     ]
    }
   ],
   "source": [
    "using PyCall\n",
    "@pyimport numpy as np\n",
    "println(big((np.pi)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.718281828459045090795598298427648842334747314453125\n"
     ]
    }
   ],
   "source": [
    "using PyCall\n",
    "@pyimport numpy as np\n",
    "println(big(np.e))\n",
    "#println(big((np.e)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.57721566490153286554942724251304753124713897705078125\n"
     ]
    }
   ],
   "source": [
    "using PyCall\n",
    "@pyimport numpy as np\n",
    "#println(np.euler_gamma)\n",
    "println(big((np.euler_gamma)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.141592653589793\n"
     ]
    }
   ],
   "source": [
    "using PyCall\n",
    "@pyimport numpy as np\n",
    "println(np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gradually, more functionality will be added into this package."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.1.0",
   "language": "julia",
   "name": "julia-1.1"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}