{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 12: Channel Flow with Navier-Stokes\n",
    "\n",
    "The only difference between this step and Step 11 is that we add a source term to the u-momentum equation, to mimic the effect of a pressure-driven channel flow. her's how the Navier-Stokes equations get modified to account for it:\n",
    "\n",
    "\n",
    "$$  \\frac{\\partial u}{ \\partial t} + u \\frac{\\partial u}{ \\partial x} +  v\\frac{\\partial u}{ \\partial y} = -  \\frac{1}{ \\rho } \\frac{\\partial p}{ \\partial x} + \\nu \\left ( \\frac{\\partial^2 u}{ \\partial x^2} + \\frac{\\partial^2 u}{ \\partial y^2} \\right ) + F $$\n",
    "\n",
    "$$  \\frac{\\partial v}{ \\partial t} + u \\frac{\\partial v}{ \\partial x} +  v\\frac{\\partial v}{ \\partial y} = -  \\frac{1}{ \\rho } \\frac{\\partial p}{ \\partial y} + \\nu \\left ( \\frac{\\partial^2 v}{ \\partial x^2} + \\frac{\\partial^2 v}{ \\partial y^2} \\right ) $$\n",
    "\n",
    "\n",
    "$$ \\frac{\\partial^2 p}{\\partial x^2} + \\frac{\\partial^2 p}{\\partial y^2} = -\\rho \\left ( \\frac{\\partial u}{ \\partial x}  \\frac{\\partial u}{ \\partial x} + 2  \\frac{\\partial u}{ \\partial y}  \\frac{\\partial v}{ \\partial x} +  \\frac{\\partial v}{ \\partial y }  \\frac{\\partial v}{ \\partial y} \\right ) $$\n",
    "\n",
    "\n",
    "## Discretization\n",
    "\n",
    "We already discretized these equations in the previous step but now we must compensate for the new addition for the new F term. For the \n",
    "\n",
    "$$ \\frac{u^{n+1}_{i,j} - u^n_{i,j}}{\\Delta t} + u^n_{i,j} \\frac{u^{n}_{i,j} - u^n_{i-1,j}}{\\Delta x} + v^n_{i,j} \\frac{u^{n}_{i,j} - u^n_{i,j-1}}{\\Delta y} = - \\frac{1}{\\rho} \\frac{p^{n}_{i+1,j}-p^{n}_{i-1,j}}{ 2 \\Delta x} + \\nu \\left ( \\frac{u^{n}_{i+1,j}-2 u^{n}_{i,j} + u^{n}_{i-1,j}}{ \\Delta x^2} + \\frac{u^{n}_{i,j+1}-2 u^{n}_{i,j} + u^{n}_{i,j-1}}{ \\Delta y^2} \\right ) +F_{i,j}\n",
    "$$\n",
    "\n",
    "Similarly for the v equation:\n",
    "\n",
    "$$ \\frac{v^{n+1}_{i,j} - v^n_{i,j}}{\\Delta t} + u^n_{i,j} \\frac{v^{n}_{i,j} - v^n_{i-1,j}}{\\Delta x} + v^n_{i,j} \\frac{v^{n}_{i,j} - v^n_{i,j-1}}{\\Delta y} = - \\frac{1}{\\rho} \\frac{p^{n}_{i,j+1}-p^{n}_{i,j-1}}{ 2 \\Delta y} + \\nu \\left ( \\frac{v^{n}_{i+1,j}-2 v^{n}_{i,j} + v^{n}_{i-1,j}}{ \\Delta x^2} + \\frac{v^{n}_{i,j+1}-2 v^{n}_{i,j} + v^{n}_{i,j-1}}{ \\Delta y^2} \\right )\n",
    "$$\n",
    "\n",
    "Finally the discretized pressure-poisson equation can be written like so:\n",
    "\n",
    "$$ \\frac{p^n_{i+1,j} - 2 p^n_{i,j} + p^n_{i-1,j}}{\\Delta x^2} + \\frac{p^n_{i,j+1} - 2 p^n_{i,j} + p^n_{i,j-1}}{\\Delta y^2} = \\rho \\left [ \\frac{1}{\\Delta t} \\left ( \\frac{u^n_{i+1,j} -  u^n_{i-1,j}}{2 \\Delta x} + \\frac{v^n_{i+1,j} -  v^n_{i-1,j}}{2 \\Delta y} \\right ) - \\frac{u_{i+1,j} - u_{i-1,j}}{2 \\Delta x} \\frac{u_{i+1,j} u_{i-1,j}}{2 \\Delta x} - 2\\frac{u_{i,j+1} - u_{i,j-1}}{2 \\Delta y} \\frac{v_{i+1,j} - v_{i-1,j}}{2 \\Delta x} - \\frac{v_{i,j+1} - v_{i,j-1}}{2 \\Delta y} \\frac{v_{i,j+1} - v_{i,j-1}}{2 \\Delta y} \\right ]\n",
    "$$\n",
    "\n",
    "As always, we shall write these equations in a rearranged form in the way that the iterations need to proceed in the code. First the momentum equations for the velocity at the next time step:\n",
    "\n",
    "The momentum in the u direction:\n",
    "\n",
    "$$ u^{n+1}_{i,j} = u^{n}_{i,j} - u^{n}_{i,j} \\frac{\\Delta t}{\\Delta x} \\left ( u^n_{i,j} - u^n_{i-1,j} \\right ) - v^n_{i,j} \\frac{\\Delta t}{\\Delta y} \\left ( u^n_{i,j} - u^n_{i,j-1} \\right ) - \\frac{\\Delta t}{ \\rho 2 \\Delta x} \\left ( p^n_{i-1,j} - p^n_{i-1,j} \\right ) + \\nu \\left ( \\frac{\\Delta t}{\\Delta x ^2} \\left ( u^n_{i+1,j} - 2 u^n_{i,j} + u^{n}{i-1, j} \\right ) + \\frac{\\Delta t}{\\Delta y^2} \\left ( u^n_{i,j+1} - 2u^n_{i,j} + u^n_{i,j-1} \\right ) \\right ) + F \\Delta t\n",
    "$$\n",
    "\n",
    "The momentum in the v direction:\n",
    "\n",
    "$$ v^{n+1}_{i,j} = v^{n}_{i,j} - u^{n}_{i,j} \\frac{\\Delta t}{\\Delta x} \\left ( v^n_{i,j} - v^n_{i-1,j} \\right ) - v^n_{i,j} \\frac{\\Delta t}{\\Delta y} \\left ( v^n_{i,j} -  v^n_{i,j-1} \\right ) - \\frac{\\Delta t}{ \\rho 2 \\Delta y} \\left ( p^n_{i,j+1} - p^n_{i,j-1} \\right ) + \\nu \\left ( \\frac{\\Delta t}{\\Delta x ^2} \\left ( v^n_{i+1,j} - 2 v^n_{i,j} + v^{n}{i-1, j} \\right ) + \\frac{\\Delta t}{\\Delta y^2} \\left ( v^n_{i,j+1} - 2v^n_{i,j} + v^n_{i,j-1} \\right ) \\right )\n",
    "$$\n",
    "\n",
    "Last but not least, the Pressure-poisson equation:\n",
    "\n",
    "$$p^n_{i,j} = \\frac{\\left ( p^n_{i+1,j} + p^n_{i-1,j} \\right )\\Delta y^2 + \\left ( p^n_{i,j+1} + p^n_{i,j-1} \\right ) \\Delta x^2  }{2 \\left ( \\Delta x^2 + \\Delta y^2 \\right )} - \\frac{\\rho \\Delta x^2 \\Delta y^2}{2\\left (\\Delta x^2 + \\Delta y^2 \\right )} \\times \\left [ \\frac{1}{\\Delta t} \\left ( \\frac{u_{i+1, j} - u_{i-1,j}}{2 \\Delta x} + \\frac{v_{i, j+1} - v_{i,j-1}}{2 \\Delta y} \\right ) - \\frac{u_{i+1,j} - u_{i-1,j}}{2 \\Delta x} \\frac{u_{i+1,j} - u_{i-1,j}}{2 \\Delta x} - 2 \\frac{u_{i,j+1} - u_{i,j-1}}{2 \\Delta y} \\frac{v_{i+1,j} - v_{i-1,j}}{2 \\Delta x} - \\frac{v_{i,j+1} - v_{i,j-1}}{2 \\Delta y} \\frac{v_{i,j+1} - v_{i,j-1}}{2 \\Delta y} \\right ]\n",
    "$$\n",
    "\n",
    "## Initial and Boundary Conditions\n",
    "\n",
    "The initial conditions are that $ u,v,p = 0$ everywhere and the boundary conditions are as follows:\n",
    "\n",
    "$ u,v,p \\ \\text{are periodic on} \\ x = 0,2 $\n",
    "\n",
    "$ u,v = 0 \\ \\text{at} \\ y = 0,2 $\n",
    "\n",
    "$ \\frac{\\partial p}{\\partial y} = 0 \\ \\text{at} \\ y = 0,2$\n",
    "\n",
    "$ F = 1$ everywhere.\n",
    "\n",
    "We now have all we need to begin the simulation! Let's start by importing all the libraries that we shall use and then declare some key functions and variables.\n",
    "\n",
    "## Libraries and Variable declarations\n",
    "\n",
    "### Lib import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding inline command to make plots appear under comments\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In step 11 we isolated a part of our tansposed equation ot make it easier to read and we're going to do the same here. One thing to note is that since we have periodic boundary conditions throughout hte grid we need to explicityly calculate the values at the leading and trailing edge of our `u` vector.\n",
    "\n",
    "## Function Declarations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_up_b(rho, dt, dx, dy, u , v):\n",
    "    b = np.zeros_like(u)\n",
    "    b[1:-1, 1:-1] = (rho * (1 / dt * ((u[1:-1, 2:] - u[1:-1, 0:-2]) / (2 * dx) +\n",
    "                                      (v[2:, 1:-1] - v[0:-2, 1:-1]) / (2 * dy)) -\n",
    "                            ((u[1:-1, 2:] - u[1:-1, 0:-2]) / (2 * dx))**2 -\n",
    "                            2 * ((u[2:, 1:-1] - u[0:-2, 1:-1]) / (2 * dy) *\n",
    "                                 (v[1:-1, 2:] - v[1:-1, 0:-2]) / (2 * dx))-\n",
    "                            ((v[2:, 1:-1] - v[0:-2, 1:-1]) / (2 * dy))**2))\n",
    "    \n",
    "    # Periodic BC Pressure @ x = 2\n",
    "    b[1:-1, -1] = (rho * (1 / dt * ((u[1:-1, 0] - u[1:-1,-2]) / (2 * dx) +\n",
    "                                    (v[2:, -1] - v[0:-2, -1]) / (2 * dy)) -\n",
    "                          ((u[1:-1, 0] - u[1:-1, -2]) / (2 * dx))**2 -\n",
    "                          2 * ((u[2:, -1] - u[0:-2, -1]) / (2 * dy) *\n",
    "                               (v[1:-1, 0] - v[1:-1, -2]) / (2 * dx)) -\n",
    "                          ((v[2:, -1] - v[0:-2, -1]) / (2 * dy))**2))\n",
    "\n",
    "    # Periodic BC Pressure @ x = 0\n",
    "    b[1:-1, 0] = (rho * (1 / dt * ((u[1:-1, 1] - u[1:-1, -1]) / (2 * dx) +\n",
    "                                   (v[2:, 0] - v[0:-2, 0]) / (2 * dy)) -\n",
    "                         ((u[1:-1, 1] - u[1:-1, -1]) / (2 * dx))**2 -\n",
    "                         2 * ((u[2:, 0] - u[0:-2, 0]) / (2 * dy) *\n",
    "                              (v[1:-1, 1] - v[1:-1, -1]) / (2 * dx))-\n",
    "                         ((v[2:, 0] - v[0:-2, 0]) / (2 * dy))**2))\n",
    "    return b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also define a periodic pressure poisson iterative function as we did in step 11. Once again we include the periodic boundary conditions at the leading and trailing edge. We also ahve to specify boundary conditions at the top and bottom of our grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pressure_poisson_periodic(p, dx, dy):\n",
    "    pn = np.empty_like(p)\n",
    "    \n",
    "    for q in range(nit):\n",
    "        pn = p.copy()\n",
    "        p[1:-1, 1:-1] = (((pn[1:-1, 2:] + pn[1:-1, 0:-2]) * dy**2 +\n",
    "                          (pn[2:, 1:-1] + pn[0:-2, 1:-1]) * dx**2) /\n",
    "                         (2 * (dx**2 + dy**2)) -\n",
    "                         dx**2 * dy**2 / (2 * (dx**2 + dy**2)) * b[1:-1, 1:-1])\n",
    "\n",
    "        # Periodic BC Pressure @ x = 2\n",
    "        p[1:-1, -1] = (((pn[1:-1, 0] + pn[1:-1, -2])* dy**2 +\n",
    "                        (pn[2:, -1] + pn[0:-2, -1]) * dx**2) /\n",
    "                       (2 * (dx**2 + dy**2)) -\n",
    "                       dx**2 * dy**2 / (2 * (dx**2 + dy**2)) * b[1:-1, -1])\n",
    "\n",
    "        # Periodic BC Pressure @ x = 0\n",
    "        p[1:-1, 0] = (((pn[1:-1, 1] + pn[1:-1, -1])* dy**2 +\n",
    "                       (pn[2:, 0] + pn[0:-2, 0]) * dx**2) /\n",
    "                      (2 * (dx**2 + dy**2)) -\n",
    "                      dx**2 * dy**2 / (2 * (dx**2 + dy**2)) * b[1:-1, 0])\n",
    "        \n",
    "        # Wall boundary conditions, pressure\n",
    "        p[-1, :] =p[-2, :]  # dp/dy = 0 at y = 2\n",
    "        p[0, :] = p[1, :]  # dp/dy = 0 at y = 0\n",
    "    \n",
    "    return p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variable Declarations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx = 41\n",
    "ny = 41\n",
    "nt = 10\n",
    "nit = 50\n",
    "c = 1\n",
    "dx = 2 / (nx - 1) \n",
    "dy = 2 / (ny - 1) \n",
    "\n",
    "#Initializing arrays\n",
    "x = np.linspace(0, 2, nx)\n",
    "y = np.linspace(0, 2, ny)\n",
    "X,Y = np.meshgrid(x,y)\n",
    "\n",
    "rho = 1\n",
    "nu = .1\n",
    "F = 1\n",
    "dt = 0.01\n",
    "\n",
    "#Initializing tu,v and pressure arrays\n",
    "u  = np.zeros((nx, ny))\n",
    "un  = np.zeros((nx, ny))\n",
    "\n",
    "v  = np.zeros((nx, ny))\n",
    "vn  = np.zeros((nx, ny))\n",
    "\n",
    "p  = np.ones((nx, ny))\n",
    "pn  = np.ones((nx, ny))\n",
    "\n",
    "b  = np.zeros((nx, ny))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving channel flow  in 2D\n",
    "\n",
    "Next comes the actual core code of our ocmputation and we will reach back to step 9 when we worked on laplace's equation to use a trick we developed then. Since we're only interested in what the grid will look like once we have reached a near-steady-state solution we could either specify a number of timesteps `nt` and increment that value until we're satisfied, or we can tell our code to run until the difference between two consecutive iterations gets samall.\n",
    "\n",
    "There are also 8 separate boundary conditions that must be met at each iteration. The code below writes them all explicitely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "udiff = 1\n",
    "stepcount = 0\n",
    "\n",
    "while udiff > .001:\n",
    "    un = u.copy()\n",
    "    vn = v.copy()\n",
    "\n",
    "    b = build_up_b(rho, dt, dx, dy, u, v)\n",
    "    p = pressure_poisson_periodic(p, dx, dy)\n",
    "\n",
    "    u[1:-1, 1:-1] = (un[1:-1, 1:-1] -\n",
    "                     un[1:-1, 1:-1] * dt / dx * \n",
    "                    (un[1:-1, 1:-1] - un[1:-1, 0:-2]) -\n",
    "                     vn[1:-1, 1:-1] * dt / dy * \n",
    "                    (un[1:-1, 1:-1] - un[0:-2, 1:-1]) -\n",
    "                     dt / (2 * rho * dx) * \n",
    "                    (p[1:-1, 2:] - p[1:-1, 0:-2]) +\n",
    "                     nu * (dt / dx**2 * \n",
    "                    (un[1:-1, 2:] - 2 * un[1:-1, 1:-1] + un[1:-1, 0:-2]) +\n",
    "                     dt / dy**2 * \n",
    "                    (un[2:, 1:-1] - 2 * un[1:-1, 1:-1] + un[0:-2, 1:-1])) + \n",
    "                     F * dt)\n",
    "\n",
    "    v[1:-1, 1:-1] = (vn[1:-1, 1:-1] -\n",
    "                     un[1:-1, 1:-1] * dt / dx * \n",
    "                    (vn[1:-1, 1:-1] - vn[1:-1, 0:-2]) -\n",
    "                     vn[1:-1, 1:-1] * dt / dy * \n",
    "                    (vn[1:-1, 1:-1] - vn[0:-2, 1:-1]) -\n",
    "                     dt / (2 * rho * dy) * \n",
    "                    (p[2:, 1:-1] - p[0:-2, 1:-1]) +\n",
    "                     nu * (dt / dx**2 *\n",
    "                    (vn[1:-1, 2:] - 2 * vn[1:-1, 1:-1] + vn[1:-1, 0:-2]) +\n",
    "                     dt / dy**2 * \n",
    "                    (vn[2:, 1:-1] - 2 * vn[1:-1, 1:-1] + vn[0:-2, 1:-1])))\n",
    "\n",
    "    # Periodic BC u @ x = 2     \n",
    "    u[1:-1, -1] = (un[1:-1, -1] - un[1:-1, -1] * dt / dx * \n",
    "                  (un[1:-1, -1] - un[1:-1, -2]) -\n",
    "                   vn[1:-1, -1] * dt / dy * \n",
    "                  (un[1:-1, -1] - un[0:-2, -1]) -\n",
    "                   dt / (2 * rho * dx) *\n",
    "                  (p[1:-1, 0] - p[1:-1, -2]) + \n",
    "                   nu * (dt / dx**2 * \n",
    "                  (un[1:-1, 0] - 2 * un[1:-1,-1] + un[1:-1, -2]) +\n",
    "                   dt / dy**2 * \n",
    "                  (un[2:, -1] - 2 * un[1:-1, -1] + un[0:-2, -1])) + F * dt)\n",
    "\n",
    "    # Periodic BC u @ x = 0\n",
    "    u[1:-1, 0] = (un[1:-1, 0] - un[1:-1, 0] * dt / dx *\n",
    "                 (un[1:-1, 0] - un[1:-1, -1]) -\n",
    "                  vn[1:-1, 0] * dt / dy * \n",
    "                 (un[1:-1, 0] - un[0:-2, 0]) - \n",
    "                  dt / (2 * rho * dx) * \n",
    "                 (p[1:-1, 1] - p[1:-1, -1]) + \n",
    "                  nu * (dt / dx**2 * \n",
    "                 (un[1:-1, 1] - 2 * un[1:-1, 0] + un[1:-1, -1]) +\n",
    "                  dt / dy**2 *\n",
    "                 (un[2:, 0] - 2 * un[1:-1, 0] + un[0:-2, 0])) + F * dt)\n",
    "\n",
    "    # Periodic BC v @ x = 2\n",
    "    v[1:-1, -1] = (vn[1:-1, -1] - un[1:-1, -1] * dt / dx *\n",
    "                  (vn[1:-1, -1] - vn[1:-1, -2]) - \n",
    "                   vn[1:-1, -1] * dt / dy *\n",
    "                  (vn[1:-1, -1] - vn[0:-2, -1]) -\n",
    "                   dt / (2 * rho * dy) * \n",
    "                  (p[2:, -1] - p[0:-2, -1]) +\n",
    "                   nu * (dt / dx**2 *\n",
    "                  (vn[1:-1, 0] - 2 * vn[1:-1, -1] + vn[1:-1, -2]) +\n",
    "                   dt / dy**2 *\n",
    "                  (vn[2:, -1] - 2 * vn[1:-1, -1] + vn[0:-2, -1])))\n",
    "\n",
    "    # Periodic BC v @ x = 0\n",
    "    v[1:-1, 0] = (vn[1:-1, 0] - un[1:-1, 0] * dt / dx *\n",
    "                 (vn[1:-1, 0] - vn[1:-1, -1]) -\n",
    "                  vn[1:-1, 0] * dt / dy *\n",
    "                 (vn[1:-1, 0] - vn[0:-2, 0]) -\n",
    "                  dt / (2 * rho * dy) * \n",
    "                 (p[2:, 0] - p[0:-2, 0]) +\n",
    "                  nu * (dt / dx**2 * \n",
    "                 (vn[1:-1, 1] - 2 * vn[1:-1, 0] + vn[1:-1, -1]) +\n",
    "                  dt / dy**2 * \n",
    "                 (vn[2:, 0] - 2 * vn[1:-1, 0] + vn[0:-2, 0])))\n",
    "\n",
    "\n",
    "    # Wall BC: u,v = 0 @ y = 0,2\n",
    "    u[0, :] = 0\n",
    "    u[-1, :] = 0\n",
    "    v[0, :] = 0\n",
    "    v[-1, :]=0\n",
    "    \n",
    "    udiff = (np.sum(u) - np.sum(un)) / np.sum(u)\n",
    "    stepcount += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code above also includes a new variable called stepcout that keeps track of how many iterations the loop went through before the stop condition was met."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "499\n"
     ]
    }
   ],
   "source": [
    "print(stepcount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (11,7), dpi=100)\n",
    "plt.quiver(X[::3, ::3], Y[::3, ::3], u[::3, ::3], v[::3, ::3]);\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y');\n",
    "plt.title('Channel Flow Velocity plot');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "\n",
    "This final image brings this module to a close and showcases how far we have come. Animating this would not be very useful since there is very little transient activity and we only care about the steady state solution. Hence we will skip the work on making an animation for this last step."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}