1 Day Force Steps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import pickle\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class flow(object):\n",
    "    def __init__(self, nx=256, ny=256, lx=1.0e6, ly=2.0e6, beta=6.0e-10, kappa=100.0, w=4.0e-6):\n",
    "        x = np.linspace(-np.pi, np.pi, nx, endpoint=False)\n",
    "        y = np.linspace(-np.pi, np.pi, ny, endpoint=False)\n",
    "        self.xx, self.yy = np.meshgrid(x,y)\n",
    "        \n",
    "        kx = np.fft.rfftfreq(nx, lx/nx)\n",
    "        ky = np.fft.fftfreq(ny, ly/ny)\n",
    "        self.kkx, self.kky = np.meshgrid(kx,ky)\n",
    "        \n",
    "        self.ksq = self.kkx**2 + self.kky**2\n",
    "        self.ksq[0,0] += 1.0e-15\n",
    "        self.kmax = np.min(np.max(kx), np.max(ky))\n",
    "        \n",
    "        self.nx = nx\n",
    "        self.ny = ny\n",
    "        self.lx = lx\n",
    "        self.ly = ly\n",
    "        self.beta = beta\n",
    "        self.kappa = kappa\n",
    "        self.w = w\n",
    "        \n",
    "        self.psihat = np.zeros((nx,int(ny/2+1)), dtype=complex)\n",
    "        fzero = 1.0e-4\n",
    "        thick = 1.0e3\n",
    "        famp = self.w * fzero/thick\n",
    "        phases = np.random.uniform(-np.pi, np.pi, size=self.psihat.shape)\n",
    "        \n",
    "        forcing = np.zeros_like(self.psihat, dtype=complex)\n",
    "        forcing[np.abs(self.ksq - self.kmax**2/81) < 3e-11] = famp\n",
    "        self.forcefield = forcing * (np.cos(phases) + np.sin(phases)*(0.0+1.0j))\n",
    "        self.psihat += self.forcefield\n",
    "\n",
    "        self.psi = np.fft.irfft2(self.psihat)\n",
    "        \n",
    "        self.qhat = -self.psihat*self.ksq\n",
    "        self.q = np.fft.irfft2(self.qhat)\n",
    "        \n",
    "    def psihatshape(self):\n",
    "        return self.psihat.shape\n",
    "    \n",
    "    def plot_psi(self):\n",
    "        return plt.contour(2*self.xx, 2*self.yy, self.psi)\n",
    "        \n",
    "    def plot_q(self):\n",
    "        return plt.contour(self.xx, self.yy, self.q)\n",
    "        \n",
    "        \n",
    "    def get_psihat_from_qhat(self, qhat):\n",
    "        \"\"\"What it says on the tin.        \n",
    "        \"\"\"\n",
    "        \n",
    "        psihat = -qhat/self.ksq\n",
    "        return psihat\n",
    "        \n",
    "    def waveterm(self, psihat):\n",
    "        \"\"\"Compute the beta wave term.\n",
    "        \n",
    "        Assume that we start and end in Fourier space.\n",
    "        \"\"\"\n",
    "        return self.beta*psihat*self.kkx*(0.0+1.0j)\n",
    "    \n",
    "    def dissipation(self, qhat):\n",
    "        \"\"\"Dissipation term, all in Fourier space.\"\"\"\n",
    "        \n",
    "        return -self.kappa*qhat*self.ksq\n",
    "    \n",
    "    def forcing(self, t):\n",
    "        \"\"\"Forcing is in the form of random phases in a k-space anulus.\n",
    "        \n",
    "        The magnitude (and variables) come from the ocean problem, and probably aren't appropriate for\n",
    "        atmospheric simulation.\n",
    "        \"\"\"\n",
    "#        fzero = 1.0e-4\n",
    "#        thick = 1.0e3\n",
    "#        famp = self.w * fzero/thick\n",
    "\n",
    "#        phases = np.random.uniform(-np.pi, np.pi, size=self.qhat.shape)        \n",
    "#        return self.forcefield * (np.cos(phases) + np.sin(phases)*(0.0+1.0j))\n",
    "        return self.forcefield\n",
    "\n",
    "    def nlterm(self, qhat, psihat):\n",
    "        \"\"\"Compute the jacobian determinant.\"\"\"\n",
    "        \n",
    "        # dealias\n",
    "        qhat[self.ksq>4/9*self.kmax**2] = 0.0\n",
    "        psihat[self.ksq>4/9*self.kmax**2] = 0.0\n",
    "        qhat[0,0] = 0.0\n",
    "        psihat[0,0] = 0.0\n",
    "        \n",
    "        psihat_x = psihat*self.kkx*(0.0+1.0j)\n",
    "        psihat_y = psihat*self.kky*(0.0+1.0j)\n",
    "        qhat_x = qhat*self.kkx*(0.0+1.0j)\n",
    "        qhat_y = qhat*self.kky*(0.0+1.0j)\n",
    "        \n",
    "        psi_x = np.fft.irfft2(psihat_x)\n",
    "        psi_y = np.fft.irfft2(psihat_y)\n",
    "        q_x = np.fft.irfft2(qhat_x)\n",
    "        q_y = np.fft.irfft2(qhat_y)\n",
    "        \n",
    "        jac = psi_x*q_y - psi_y*q_x\n",
    "        \n",
    "        jachat = np.fft.rfft2(jac)\n",
    "        return jachat\n",
    "    \n",
    "    def rhs(self, q_reshaped, t):\n",
    "        \"\"\"The time derivative, ready for the integrator.\"\"\"\n",
    "        \n",
    "        qhat = self.unmunge(q_reshaped)\n",
    "        \n",
    "        psihat = self.get_psihat_from_qhat(qhat)\n",
    "        nlterm = self.nlterm(qhat, psihat)\n",
    "        waveterm = self.waveterm(psihat)\n",
    "        dissipation = self.dissipation(qhat)\n",
    "        forcing = self.forcing(t)\n",
    "        \n",
    "        return self.munge(forcing + dissipation - waveterm - nlterm)\n",
    "    \n",
    "    def munge(self, qhat):\n",
    "        \"\"\"format a complex k-space field for odeint\"\"\"\n",
    "        \n",
    "        r = qhat.real\n",
    "        i = qhat.imag\n",
    "        z = np.array([r,i])\n",
    "        return z.reshape(-1)\n",
    "    \n",
    "    def unmunge(self, munged):\n",
    "        \"\"\"Return the 1d real sequence to its 2d complex state\"\"\"\n",
    "        \n",
    "        z = munged.reshape((2,self.nx,int(self.ny/2+1)))\n",
    "        r = z[0]\n",
    "        i = z[1]\n",
    "        return r + (0+1.0j)*i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.5/site-packages/numpy/core/_methods.py:29: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return umr_minimum(a, axis, None, out, keepdims)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f67af41ba58>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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RTghttPE4/+BSrhg4haTWQ/fP5HyUJIk0Mcn119olbYgXLMcYk+cssM0H8xhQFNy4KWcf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0fQKx1GMzGymltE3yBrijUoaX/2iHtAkydL60FNbkyXsNDR8+nuffzGYOfQjpbWsqNN0Lzrdk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\nBhiSN8CQvAGGAkjHotNzCq3ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67b1730860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "foo = flow(nx = 128, ny=128)\n",
    "foo.plot_psi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mungeany(array):\n",
    "    r = array.real\n",
    "    i = array.imag\n",
    "    z = np.array([r, i])\n",
    "    return z.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'real'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-61-bb8bce44be79>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0modeint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrhs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mt0\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m     \u001b[0minit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmunge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m     \u001b[0mday\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[0mt0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-49-93cb0f46e984>\u001b[0m in \u001b[0;36mmunge\u001b[1;34m(self, qhat)\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;34m\"\"\"format a complex k-space field for odeint\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mqhat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m         \u001b[0mi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mqhat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m         \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'real'"
     ]
    }
   ],
   "source": [
    "#linspace, keep all frames\n",
    "totaldays = 100\n",
    "day = 1\n",
    "\n",
    "t0 = np.linspace(0, 86400, 10)\n",
    "result = odeint(foo.rhs, foo.munge(foo.qhat), t0).tolist()\n",
    "init = foo.munge(foo.qhat)\n",
    "\n",
    "while day < totaldays:\n",
    "    t = np.linspace(day*86400, (day+1)*86400, 10)\n",
    "    result = result + odeint(foo.rhs, init, t).tolist()\n",
    "    t0.tolist().append(t.tolist())\n",
    "    day += 1\n",
    "t0 = np.array(t0)\n",
    "result = np.array(result)\n",
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7ff0cd2b0588>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y4sbTL6rEnHlowLB/l4NaDyt5jXwKWMJ5+PFjSnDshccrlFHbmoZVcWg24dv5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6AAAg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\nAe0+SH1W6Z6FoYwNzEspBt/F876kF4wvZSOb+MyoxoClmiLzG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/HfSR9/GoMVSYhOQWct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klMUsgoR6N1J1xaqZT9Kj4DQXpkN9xaB3PLkvyJ27iGaxVxJncfVL546BPGj4Pgb1nv8dNJ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\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oFs1bzk2jWkKESFBBHXU00nTW0UOJEh0c9VzYxcGmiiramMsSltJ6DgRKP338gUfQ0bmC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\nvHPIIYccDkP8P+S66o/fQ1xhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0cd280ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contour(foo.xx, foo.yy, np.fft.irfft2(foo.unmunge(result[99])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timestamp: 2016-12-03 01:32:59\n",
      "Timestamp: 2016-12-03 01:42:40\n",
      "Timestamp: 2016-12-03 01:49:08\n",
      "Timestamp: 2016-12-03 01:55:34\n",
      "Timestamp: 2016-12-03 01:55:34\n",
      "Timestamp: 2016-12-03 01:55:35\n",
      "Timestamp: 2016-12-03 02:02:02\n",
      "Timestamp: 2016-12-03 02:08:28\n",
      "Timestamp: 2016-12-03 02:14:57\n",
      "Timestamp: 2016-12-03 02:21:25\n",
      "Timestamp: 2016-12-03 02:27:52\n",
      "Timestamp: 2016-12-03 02:34:20\n",
      "Timestamp: 2016-12-03 02:40:48\n",
      "Timestamp: 2016-12-03 02:47:16\n",
      "Timestamp: 2016-12-03 02:53:43\n",
      "Timestamp: 2016-12-03 03:03:24\n",
      "Timestamp: 2016-12-03 03:09:52\n",
      "Timestamp: 2016-12-03 03:16:19\n",
      "Timestamp: 2016-12-03 03:22:47\n",
      "Timestamp: 2016-12-03 03:29:14\n",
      "Timestamp: 2016-12-03 03:35:42\n",
      "Timestamp: 2016-12-03 03:42:10\n",
      "Timestamp: 2016-12-03 03:48:37\n",
      "Timestamp: 2016-12-03 03:55:05\n",
      "Timestamp: 2016-12-03 04:01:32\n",
      "Timestamp: 2016-12-03 04:07:59\n",
      "Timestamp: 2016-12-03 04:14:27\n",
      "Timestamp: 2016-12-03 04:20:54\n",
      "Timestamp: 2016-12-03 04:27:22\n",
      "Timestamp: 2016-12-03 04:33:49\n",
      "Timestamp: 2016-12-03 04:40:16\n",
      "Timestamp: 2016-12-03 04:46:44\n",
      "Timestamp: 2016-12-03 04:53:11\n",
      "Timestamp: 2016-12-03 04:53:11\n",
      "Timestamp: 2016-12-03 04:59:39\n",
      "Timestamp: 2016-12-03 05:06:06\n",
      "Timestamp: 2016-12-03 05:12:33\n",
      "Timestamp: 2016-12-03 05:19:01\n",
      "Timestamp: 2016-12-03 05:25:28\n",
      "Timestamp: 2016-12-03 05:31:55\n",
      "Timestamp: 2016-12-03 05:38:22\n",
      "Timestamp: 2016-12-03 05:44:50\n",
      "Timestamp: 2016-12-03 05:51:17\n",
      "Timestamp: 2016-12-03 05:57:44\n",
      "Timestamp: 2016-12-03 06:04:11\n",
      "Timestamp: 2016-12-03 06:10:38\n",
      "Timestamp: 2016-12-03 06:17:05\n",
      "Timestamp: 2016-12-03 06:23:33\n",
      "Timestamp: 2016-12-03 06:30:00\n",
      "Timestamp: 2016-12-03 06:36:28\n",
      "Timestamp: 2016-12-03 06:42:55\n",
      "Timestamp: 2016-12-03 06:49:36\n",
      "Timestamp: 2016-12-03 06:56:03\n",
      "Timestamp: 2016-12-03 07:02:30\n",
      "Timestamp: 2016-12-03 07:08:58\n",
      "Timestamp: 2016-12-03 07:15:25\n",
      "Timestamp: 2016-12-03 07:21:52\n",
      "Timestamp: 2016-12-03 07:28:19\n",
      "Timestamp: 2016-12-03 07:34:46\n",
      "Timestamp: 2016-12-03 07:41:14\n",
      "Timestamp: 2016-12-03 07:47:41\n",
      "Timestamp: 2016-12-03 07:54:09\n",
      "Timestamp: 2016-12-03 08:00:37\n",
      "Timestamp: 2016-12-03 08:07:05\n",
      "Timestamp: 2016-12-03 08:13:32\n",
      "Timestamp: 2016-12-03 08:20:00\n",
      "Timestamp: 2016-12-03 08:23:15\n",
      "Timestamp: 2016-12-03 08:29:42\n",
      "Timestamp: 2016-12-03 08:36:09\n",
      "Timestamp: 2016-12-03 08:42:37\n",
      "Timestamp: 2016-12-03 08:49:04\n",
      "Timestamp: 2016-12-03 08:55:32\n",
      "Timestamp: 2016-12-03 08:58:46\n",
      "Timestamp: 2016-12-03 09:05:14\n",
      "Timestamp: 2016-12-03 09:11:41\n",
      "Timestamp: 2016-12-03 09:18:08\n",
      "Timestamp: 2016-12-03 09:24:36\n",
      "Timestamp: 2016-12-03 09:31:03\n",
      "Timestamp: 2016-12-03 09:31:03\n",
      "Timestamp: 2016-12-03 09:40:44\n",
      "Timestamp: 2016-12-03 09:47:11\n",
      "Timestamp: 2016-12-03 09:53:39\n",
      "Timestamp: 2016-12-03 10:00:06\n",
      "Timestamp: 2016-12-03 10:06:34\n",
      "Timestamp: 2016-12-03 10:09:48\n",
      "Timestamp: 2016-12-03 10:13:02\n",
      "Timestamp: 2016-12-03 10:19:29\n",
      "Timestamp: 2016-12-03 10:25:57\n",
      "Timestamp: 2016-12-03 10:29:11\n",
      "Timestamp: 2016-12-03 10:32:25\n",
      "Timestamp: 2016-12-03 10:38:52\n",
      "Timestamp: 2016-12-03 10:45:20\n",
      "Timestamp: 2016-12-03 10:51:47\n",
      "Timestamp: 2016-12-03 10:55:01\n",
      "Timestamp: 2016-12-03 10:58:15\n",
      "Timestamp: 2016-12-03 11:04:43\n",
      "Timestamp: 2016-12-03 11:07:57\n",
      "Timestamp: 2016-12-03 11:14:25\n",
      "Timestamp: 2016-12-03 11:20:53\n",
      "Timestamp: 2016-12-03 11:27:20\n"
     ]
    }
   ],
   "source": [
    "#munge, keep last frame in day\n",
    "#days = np.linspace(1, 5, 5)\n",
    "days = np.array(np.linspace(1, 100, 100))\n",
    "result = np.zeros(16640)\n",
    "init = foo.munge(foo.qhat)\n",
    "for day in days:\n",
    "    t = np.linspace(day*86400, (day+1)*86400, 100)\n",
    "    integration = odeint(foo.rhs, init, t)\n",
    "    result = np.vstack([result,integration[-1]])\n",
    "    init = result[-1]\n",
    "    with open(\"results.pkl\", \"wb\") as f:\n",
    "        pickle.dump(result, f)\n",
    "    print('Timestamp: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 16640)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 129)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foo.unmunge(result[-1]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = pickle.load(open(\"results.pkl\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 16640)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eAAAC4gkAICCeAAAC4gkAICCeAAAC4gkAICCeAAAC4gkAICCeAAAC4gkAICCeAAAC4gkA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\nPAEABP4H5DXHwZ3uFNEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67aeeb9e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = plt.axes()  \n",
    "plt.xlabel(r'x')\n",
    "plt.ylabel(r'y')\n",
    "\n",
    "totalmax = np.max([np.max(np.abs(np.fft.irfft2(foo.unmunge(frame)))) for frame in result])\n",
    "levels = np.linspace(-totalmax, totalmax, 10)\n",
    "\n",
    "# animation function\n",
    "def animate(i): \n",
    "    z = np.fft.irfft2(foo.unmunge(result[i]))\n",
    "    ax.clear()\n",
    "    \n",
    "    cont = plt.contour(foo.xx, foo.yy, z, levels)\n",
    "    return cont  \n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, frames=len(days)+1, blit=False)\n",
    "mywriter = animation.FFMpegWriter()\n",
    "\n",
    "anim.save(\"dayatatime.mp4\", writer=mywriter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = plt.axes()  \n",
    "plt.xlabel(r'x')\n",
    "plt.ylabel(r'y')\n",
    "\n",
    "totalmax = np.max([np.max(np.abs(foo.unmunge(frame))) for frame in result])\n",
    "levels = np.linspace(-totalmax, totalmax, 10)\n",
    "\n",
    "# animation function\n",
    "def animate(i): \n",
    "    z = foo.unmunge(result[i])\n",
    "    ax.clear()\n",
    "    \n",
    "    cont = plt.contour(foo.kkx, foo.kky, z, levels)\n",
    "    return cont  \n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, frames=len(t), blit=False)\n",
    "mywriter = animation.FFMpegWriter()\n",
    "\n",
    "anim.save(\"kspace.mp4\", writer=mywriter, fps=45, dpi=100, bitrate=4000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on ndarray object:\n",
      "\n",
      "class ndarray(builtins.object)\n",
      " |  ndarray(shape, dtype=float, buffer=None, offset=0,\n",
      " |          strides=None, order=None)\n",
      " |  \n",
      " |  An array object represents a multidimensional, homogeneous array\n",
      " |  of fixed-size items.  An associated data-type object describes the\n",
      " |  format of each element in the array (its byte-order, how many bytes it\n",
      " |  occupies in memory, whether it is an integer, a floating point number,\n",
      " |  or something else, etc.)\n",
      " |  \n",
      " |  Arrays should be constructed using `array`, `zeros` or `empty` (refer\n",
      " |  to the See Also section below).  The parameters given here refer to\n",
      " |  a low-level method (`ndarray(...)`) for instantiating an array.\n",
      " |  \n",
      " |  For more information, refer to the `numpy` module and examine the\n",
      " |  the methods and attributes of an array.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  (for the __new__ method; see Notes below)\n",
      " |  \n",
      " |  shape : tuple of ints\n",
      " |      Shape of created array.\n",
      " |  dtype : data-type, optional\n",
      " |      Any object that can be interpreted as a numpy data type.\n",
      " |  buffer : object exposing buffer interface, optional\n",
      " |      Used to fill the array with data.\n",
      " |  offset : int, optional\n",
      " |      Offset of array data in buffer.\n",
      " |  strides : tuple of ints, optional\n",
      " |      Strides of data in memory.\n",
      " |  order : {'C', 'F'}, optional\n",
      " |      Row-major (C-style) or column-major (Fortran-style) order.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  T : ndarray\n",
      " |      Transpose of the array.\n",
      " |  data : buffer\n",
      " |      The array's elements, in memory.\n",
      " |  dtype : dtype object\n",
      " |      Describes the format of the elements in the array.\n",
      " |  flags : dict\n",
      " |      Dictionary containing information related to memory use, e.g.,\n",
      " |      'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.\n",
      " |  flat : numpy.flatiter object\n",
      " |      Flattened version of the array as an iterator.  The iterator\n",
      " |      allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for\n",
      " |      assignment examples; TODO).\n",
      " |  imag : ndarray\n",
      " |      Imaginary part of the array.\n",
      " |  real : ndarray\n",
      " |      Real part of the array.\n",
      " |  size : int\n",
      " |      Number of elements in the array.\n",
      " |  itemsize : int\n",
      " |      The memory use of each array element in bytes.\n",
      " |  nbytes : int\n",
      " |      The total number of bytes required to store the array data,\n",
      " |      i.e., ``itemsize * size``.\n",
      " |  ndim : int\n",
      " |      The array's number of dimensions.\n",
      " |  shape : tuple of ints\n",
      " |      Shape of the array.\n",
      " |  strides : tuple of ints\n",
      " |      The step-size required to move from one element to the next in\n",
      " |      memory. For example, a contiguous ``(3, 4)`` array of type\n",
      " |      ``int16`` in C-order has strides ``(8, 2)``.  This implies that\n",
      " |      to move from element to element in memory requires jumps of 2 bytes.\n",
      " |      To move from row-to-row, one needs to jump 8 bytes at a time\n",
      " |      (``2 * 4``).\n",
      " |  ctypes : ctypes object\n",
      " |      Class containing properties of the array needed for interaction\n",
      " |      with ctypes.\n",
      " |  base : ndarray\n",
      " |      If the array is a view into another array, that array is its `base`\n",
      " |      (unless that array is also a view).  The `base` array is where the\n",
      " |      array data is actually stored.\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  array : Construct an array.\n",
      " |  zeros : Create an array, each element of which is zero.\n",
      " |  empty : Create an array, but leave its allocated memory unchanged (i.e.,\n",
      " |          it contains \"garbage\").\n",
      " |  dtype : Create a data-type.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  There are two modes of creating an array using ``__new__``:\n",
      " |  \n",
      " |  1. If `buffer` is None, then only `shape`, `dtype`, and `order`\n",
      " |     are used.\n",
      " |  2. If `buffer` is an object exposing the buffer interface, then\n",
      " |     all keywords are interpreted.\n",
      " |  \n",
      " |  No ``__init__`` method is needed because the array is fully initialized\n",
      " |  after the ``__new__`` method.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  These examples illustrate the low-level `ndarray` constructor.  Refer\n",
      " |  to the `See Also` section above for easier ways of constructing an\n",
      " |  ndarray.\n",
      " |  \n",
      " |  First mode, `buffer` is None:\n",
      " |  \n",
      " |  >>> np.ndarray(shape=(2,2), dtype=float, order='F')\n",
      " |  array([[ -1.13698227e+002,   4.25087011e-303],\n",
      " |         [  2.88528414e-306,   3.27025015e-309]])         #random\n",
      " |  \n",
      " |  Second mode:\n",
      " |  \n",
      " |  >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n",
      " |  ...            offset=np.int_().itemsize,\n",
      " |  ...            dtype=int) # offset = 1*itemsize, i.e. skip first element\n",
      " |  array([2, 3])\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __abs__(self, /)\n",
      " |      abs(self)\n",
      " |  \n",
      " |  __add__(self, value, /)\n",
      " |      Return self+value.\n",
      " |  \n",
      " |  __and__(self, value, /)\n",
      " |      Return self&value.\n",
      " |  \n",
      " |  __array__(...)\n",
      " |      a.__array__(|dtype) -> reference if type unchanged, copy otherwise.\n",
      " |      \n",
      " |      Returns either a new reference to self if dtype is not given or a new array\n",
      " |      of provided data type if dtype is different from the current dtype of the\n",
      " |      array.\n",
      " |  \n",
      " |  __array_prepare__(...)\n",
      " |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n",
      " |  \n",
      " |  __array_wrap__(...)\n",
      " |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n",
      " |  \n",
      " |  __bool__(self, /)\n",
      " |      self != 0\n",
      " |  \n",
      " |  __contains__(self, key, /)\n",
      " |      Return key in self.\n",
      " |  \n",
      " |  __copy__(...)\n",
      " |      a.__copy__([order])\n",
      " |      \n",
      " |      Return a copy of the array.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      order : {'C', 'F', 'A'}, optional\n",
      " |          If order is 'C' (False) then the result is contiguous (default).\n",
      " |          If order is 'Fortran' (True) then the result has fortran order.\n",
      " |          If order is 'Any' (None) then the result has fortran order\n",
      " |          only if the array already is in fortran order.\n",
      " |  \n",
      " |  __deepcopy__(...)\n",
      " |      a.__deepcopy__() -> Deep copy of array.\n",
      " |      \n",
      " |      Used if copy.deepcopy is called on an array.\n",
      " |  \n",
      " |  __delitem__(self, key, /)\n",
      " |      Delete self[key].\n",
      " |  \n",
      " |  __divmod__(self, value, /)\n",
      " |      Return divmod(self, value).\n",
      " |  \n",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __float__(self, /)\n",
      " |      float(self)\n",
      " |  \n",
      " |  __floordiv__(self, value, /)\n",
      " |      Return self//value.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getitem__(self, key, /)\n",
      " |      Return self[key].\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __iadd__(self, value, /)\n",
      " |      Return self+=value.\n",
      " |  \n",
      " |  __iand__(self, value, /)\n",
      " |      Return self&=value.\n",
      " |  \n",
      " |  __ifloordiv__(self, value, /)\n",
      " |      Return self//=value.\n",
      " |  \n",
      " |  __ilshift__(self, value, /)\n",
      " |      Return self<<=value.\n",
      " |  \n",
      " |  __imatmul__(self, value, /)\n",
      " |      Return self@=value.\n",
      " |  \n",
      " |  __imod__(self, value, /)\n",
      " |      Return self%=value.\n",
      " |  \n",
      " |  __imul__(self, value, /)\n",
      " |      Return self*=value.\n",
      " |  \n",
      " |  __index__(self, /)\n",
      " |      Return self converted to an integer, if self is suitable for use as an index into a list.\n",
      " |  \n",
      " |  __int__(self, /)\n",
      " |      int(self)\n",
      " |  \n",
      " |  __invert__(self, /)\n",
      " |      ~self\n",
      " |  \n",
      " |  __ior__(self, value, /)\n",
      " |      Return self|=value.\n",
      " |  \n",
      " |  __ipow__(self, value, /)\n",
      " |      Return self**=value.\n",
      " |  \n",
      " |  __irshift__(self, value, /)\n",
      " |      Return self>>=value.\n",
      " |  \n",
      " |  __isub__(self, value, /)\n",
      " |      Return self-=value.\n",
      " |  \n",
      " |  __iter__(self, /)\n",
      " |      Implement iter(self).\n",
      " |  \n",
      " |  __itruediv__(self, value, /)\n",
      " |      Return self/=value.\n",
      " |  \n",
      " |  __ixor__(self, value, /)\n",
      " |      Return self^=value.\n",
      " |  \n",
      " |  __le__(self, value, /)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self, /)\n",
      " |      Return len(self).\n",
      " |  \n",
      " |  __lshift__(self, value, /)\n",
      " |      Return self<<value.\n",
      " |  \n",
      " |  __lt__(self, value, /)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __matmul__(self, value, /)\n",
      " |      Return self@value.\n",
      " |  \n",
      " |  __mod__(self, value, /)\n",
      " |      Return self%value.\n",
      " |  \n",
      " |  __mul__(self, value, /)\n",
      " |      Return self*value.\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __neg__(self, /)\n",
      " |      -self\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  __or__(self, value, /)\n",
      " |      Return self|value.\n",
      " |  \n",
      " |  __pos__(self, /)\n",
      " |      +self\n",
      " |  \n",
      " |  __pow__(self, value, mod=None, /)\n",
      " |      Return pow(self, value, mod).\n",
      " |  \n",
      " |  __radd__(self, value, /)\n",
      " |      Return value+self.\n",
      " |  \n",
      " |  __rand__(self, value, /)\n",
      " |      Return value&self.\n",
      " |  \n",
      " |  __rdivmod__(self, value, /)\n",
      " |      Return divmod(value, self).\n",
      " |  \n",
      " |  __reduce__(...)\n",
      " |      a.__reduce__()\n",
      " |      \n",
      " |      For pickling.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __rfloordiv__(self, value, /)\n",
      " |      Return value//self.\n",
      " |  \n",
      " |  __rlshift__(self, value, /)\n",
      " |      Return value<<self.\n",
      " |  \n",
      " |  __rmatmul__(self, value, /)\n",
      " |      Return value@self.\n",
      " |  \n",
      " |  __rmod__(self, value, /)\n",
      " |      Return value%self.\n",
      " |  \n",
      " |  __rmul__(self, value, /)\n",
      " |      Return value*self.\n",
      " |  \n",
      " |  __ror__(self, value, /)\n",
      " |      Return value|self.\n",
      " |  \n",
      " |  __rpow__(self, value, mod=None, /)\n",
      " |      Return pow(value, self, mod).\n",
      " |  \n",
      " |  __rrshift__(self, value, /)\n",
      " |      Return value>>self.\n",
      " |  \n",
      " |  __rshift__(self, value, /)\n",
      " |      Return self>>value.\n",
      " |  \n",
      " |  __rsub__(self, value, /)\n",
      " |      Return value-self.\n",
      " |  \n",
      " |  __rtruediv__(self, value, /)\n",
      " |      Return value/self.\n",
      " |  \n",
      " |  __rxor__(self, value, /)\n",
      " |      Return value^self.\n",
      " |  \n",
      " |  __setitem__(self, key, value, /)\n",
      " |      Set self[key] to value.\n",
      " |  \n",
      " |  __setstate__(...)\n",
      " |      a.__setstate__(version, shape, dtype, isfortran, rawdata)\n",
      " |      \n",
      " |      For unpickling.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      version : int\n",
      " |          optional pickle version. If omitted defaults to 0.\n",
      " |      shape : tuple\n",
      " |      dtype : data-type\n",
      " |      isFortran : bool\n",
      " |      rawdata : string or list\n",
      " |          a binary string with the data (or a list if 'a' is an object array)\n",
      " |  \n",
      " |  __sizeof__(...)\n",
      " |      __sizeof__() -> int\n",
      " |      size of object in memory, in bytes\n",
      " |  \n",
      " |  __str__(self, /)\n",
      " |      Return str(self).\n",
      " |  \n",
      " |  __sub__(self, value, /)\n",
      " |      Return self-value.\n",
      " |  \n",
      " |  __truediv__(self, value, /)\n",
      " |      Return self/value.\n",
      " |  \n",
      " |  __xor__(self, value, /)\n",
      " |      Return self^value.\n",
      " |  \n",
      " |  all(...)\n",
      " |      a.all(axis=None, out=None, keepdims=False)\n",
      " |      \n",
      " |      Returns True if all elements evaluate to True.\n",
      " |      \n",
      " |      Refer to `numpy.all` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.all : equivalent function\n",
      " |  \n",
      " |  any(...)\n",
      " |      a.any(axis=None, out=None, keepdims=False)\n",
      " |      \n",
      " |      Returns True if any of the elements of `a` evaluate to True.\n",
      " |      \n",
      " |      Refer to `numpy.any` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.any : equivalent function\n",
      " |  \n",
      " |  argmax(...)\n",
      " |      a.argmax(axis=None, out=None)\n",
      " |      \n",
      " |      Return indices of the maximum values along the given axis.\n",
      " |      \n",
      " |      Refer to `numpy.argmax` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.argmax : equivalent function\n",
      " |  \n",
      " |  argmin(...)\n",
      " |      a.argmin(axis=None, out=None)\n",
      " |      \n",
      " |      Return indices of the minimum values along the given axis of `a`.\n",
      " |      \n",
      " |      Refer to `numpy.argmin` for detailed documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.argmin : equivalent function\n",
      " |  \n",
      " |  argpartition(...)\n",
      " |      a.argpartition(kth, axis=-1, kind='introselect', order=None)\n",
      " |      \n",
      " |      Returns the indices that would partition this array.\n",
      " |      \n",
      " |      Refer to `numpy.argpartition` for full documentation.\n",
      " |      \n",
      " |      .. versionadded:: 1.8.0\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.argpartition : equivalent function\n",
      " |  \n",
      " |  argsort(...)\n",
      " |      a.argsort(axis=-1, kind='quicksort', order=None)\n",
      " |      \n",
      " |      Returns the indices that would sort this array.\n",
      " |      \n",
      " |      Refer to `numpy.argsort` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.argsort : equivalent function\n",
      " |  \n",
      " |  astype(...)\n",
      " |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n",
      " |      \n",
      " |      Copy of the array, cast to a specified type.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      dtype : str or dtype\n",
      " |          Typecode or data-type to which the array is cast.\n",
      " |      order : {'C', 'F', 'A', 'K'}, optional\n",
      " |          Controls the memory layout order of the result.\n",
      " |          'C' means C order, 'F' means Fortran order, 'A'\n",
      " |          means 'F' order if all the arrays are Fortran contiguous,\n",
      " |          'C' order otherwise, and 'K' means as close to the\n",
      " |          order the array elements appear in memory as possible.\n",
      " |          Default is 'K'.\n",
      " |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n",
      " |          Controls what kind of data casting may occur. Defaults to 'unsafe'\n",
      " |          for backwards compatibility.\n",
      " |      \n",
      " |            * 'no' means the data types should not be cast at all.\n",
      " |            * 'equiv' means only byte-order changes are allowed.\n",
      " |            * 'safe' means only casts which can preserve values are allowed.\n",
      " |            * 'same_kind' means only safe casts or casts within a kind,\n",
      " |              like float64 to float32, are allowed.\n",
      " |            * 'unsafe' means any data conversions may be done.\n",
      " |      subok : bool, optional\n",
      " |          If True, then sub-classes will be passed-through (default), otherwise\n",
      " |          the returned array will be forced to be a base-class array.\n",
      " |      copy : bool, optional\n",
      " |          By default, astype always returns a newly allocated array. If this\n",
      " |          is set to false, and the `dtype`, `order`, and `subok`\n",
      " |          requirements are satisfied, the input array is returned instead\n",
      " |          of a copy.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      arr_t : ndarray\n",
      " |          Unless `copy` is False and the other conditions for returning the input\n",
      " |          array are satisfied (see description for `copy` input parameter), `arr_t`\n",
      " |          is a new array of the same shape as the input array, with dtype, order\n",
      " |          given by `dtype`, `order`.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Starting in NumPy 1.9, astype method now returns an error if the string\n",
      " |      dtype to cast to is not long enough in 'safe' casting mode to hold the max\n",
      " |      value of integer/float array that is being casted. Previously the casting\n",
      " |      was allowed even if the result was truncated.\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      ComplexWarning\n",
      " |          When casting from complex to float or int. To avoid this,\n",
      " |          one should use ``a.real.astype(t)``.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([1, 2, 2.5])\n",
      " |      >>> x\n",
      " |      array([ 1. ,  2. ,  2.5])\n",
      " |      \n",
      " |      >>> x.astype(int)\n",
      " |      array([1, 2, 2])\n",
      " |  \n",
      " |  byteswap(...)\n",
      " |      a.byteswap(inplace)\n",
      " |      \n",
      " |      Swap the bytes of the array elements\n",
      " |      \n",
      " |      Toggle between low-endian and big-endian data representation by\n",
      " |      returning a byteswapped array, optionally swapped in-place.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      inplace : bool, optional\n",
      " |          If ``True``, swap bytes in-place, default is ``False``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      out : ndarray\n",
      " |          The byteswapped array. If `inplace` is ``True``, this is\n",
      " |          a view to self.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> A = np.array([1, 256, 8755], dtype=np.int16)\n",
      " |      >>> map(hex, A)\n",
      " |      ['0x1', '0x100', '0x2233']\n",
      " |      >>> A.byteswap(True)\n",
      " |      array([  256,     1, 13090], dtype=int16)\n",
      " |      >>> map(hex, A)\n",
      " |      ['0x100', '0x1', '0x3322']\n",
      " |      \n",
      " |      Arrays of strings are not swapped\n",
      " |      \n",
      " |      >>> A = np.array(['ceg', 'fac'])\n",
      " |      >>> A.byteswap()\n",
      " |      array(['ceg', 'fac'],\n",
      " |            dtype='|S3')\n",
      " |  \n",
      " |  choose(...)\n",
      " |      a.choose(choices, out=None, mode='raise')\n",
      " |      \n",
      " |      Use an index array to construct a new array from a set of choices.\n",
      " |      \n",
      " |      Refer to `numpy.choose` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.choose : equivalent function\n",
      " |  \n",
      " |  clip(...)\n",
      " |      a.clip(min=None, max=None, out=None)\n",
      " |      \n",
      " |      Return an array whose values are limited to ``[min, max]``.\n",
      " |      One of max or min must be given.\n",
      " |      \n",
      " |      Refer to `numpy.clip` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.clip : equivalent function\n",
      " |  \n",
      " |  compress(...)\n",
      " |      a.compress(condition, axis=None, out=None)\n",
      " |      \n",
      " |      Return selected slices of this array along given axis.\n",
      " |      \n",
      " |      Refer to `numpy.compress` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.compress : equivalent function\n",
      " |  \n",
      " |  conj(...)\n",
      " |      a.conj()\n",
      " |      \n",
      " |      Complex-conjugate all elements.\n",
      " |      \n",
      " |      Refer to `numpy.conjugate` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.conjugate : equivalent function\n",
      " |  \n",
      " |  conjugate(...)\n",
      " |      a.conjugate()\n",
      " |      \n",
      " |      Return the complex conjugate, element-wise.\n",
      " |      \n",
      " |      Refer to `numpy.conjugate` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.conjugate : equivalent function\n",
      " |  \n",
      " |  copy(...)\n",
      " |      a.copy(order='C')\n",
      " |      \n",
      " |      Return a copy of the array.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      order : {'C', 'F', 'A', 'K'}, optional\n",
      " |          Controls the memory layout of the copy. 'C' means C-order,\n",
      " |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n",
      " |          'C' otherwise. 'K' means match the layout of `a` as closely\n",
      " |          as possible. (Note that this function and :func:numpy.copy are very\n",
      " |          similar, but have different default values for their order=\n",
      " |          arguments.)\n",
      " |      \n",
      " |      See also\n",
      " |      --------\n",
      " |      numpy.copy\n",
      " |      numpy.copyto\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n",
      " |      \n",
      " |      >>> y = x.copy()\n",
      " |      \n",
      " |      >>> x.fill(0)\n",
      " |      \n",
      " |      >>> x\n",
      " |      array([[0, 0, 0],\n",
      " |             [0, 0, 0]])\n",
      " |      \n",
      " |      >>> y\n",
      " |      array([[1, 2, 3],\n",
      " |             [4, 5, 6]])\n",
      " |      \n",
      " |      >>> y.flags['C_CONTIGUOUS']\n",
      " |      True\n",
      " |  \n",
      " |  cumprod(...)\n",
      " |      a.cumprod(axis=None, dtype=None, out=None)\n",
      " |      \n",
      " |      Return the cumulative product of the elements along the given axis.\n",
      " |      \n",
      " |      Refer to `numpy.cumprod` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.cumprod : equivalent function\n",
      " |  \n",
      " |  cumsum(...)\n",
      " |      a.cumsum(axis=None, dtype=None, out=None)\n",
      " |      \n",
      " |      Return the cumulative sum of the elements along the given axis.\n",
      " |      \n",
      " |      Refer to `numpy.cumsum` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.cumsum : equivalent function\n",
      " |  \n",
      " |  diagonal(...)\n",
      " |      a.diagonal(offset=0, axis1=0, axis2=1)\n",
      " |      \n",
      " |      Return specified diagonals. In NumPy 1.9 the returned array is a\n",
      " |      read-only view instead of a copy as in previous NumPy versions.  In\n",
      " |      a future version the read-only restriction will be removed.\n",
      " |      \n",
      " |      Refer to :func:`numpy.diagonal` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.diagonal : equivalent function\n",
      " |  \n",
      " |  dot(...)\n",
      " |      a.dot(b, out=None)\n",
      " |      \n",
      " |      Dot product of two arrays.\n",
      " |      \n",
      " |      Refer to `numpy.dot` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.dot : equivalent function\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> a = np.eye(2)\n",
      " |      >>> b = np.ones((2, 2)) * 2\n",
      " |      >>> a.dot(b)\n",
      " |      array([[ 2.,  2.],\n",
      " |             [ 2.,  2.]])\n",
      " |      \n",
      " |      This array method can be conveniently chained:\n",
      " |      \n",
      " |      >>> a.dot(b).dot(b)\n",
      " |      array([[ 8.,  8.],\n",
      " |             [ 8.,  8.]])\n",
      " |  \n",
      " |  dump(...)\n",
      " |      a.dump(file)\n",
      " |      \n",
      " |      Dump a pickle of the array to the specified file.\n",
      " |      The array can be read back with pickle.load or numpy.load.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      file : str\n",
      " |          A string naming the dump file.\n",
      " |  \n",
      " |  dumps(...)\n",
      " |      a.dumps()\n",
      " |      \n",
      " |      Returns the pickle of the array as a string.\n",
      " |      pickle.loads or numpy.loads will convert the string back to an array.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      None\n",
      " |  \n",
      " |  fill(...)\n",
      " |      a.fill(value)\n",
      " |      \n",
      " |      Fill the array with a scalar value.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      value : scalar\n",
      " |          All elements of `a` will be assigned this value.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> a = np.array([1, 2])\n",
      " |      >>> a.fill(0)\n",
      " |      >>> a\n",
      " |      array([0, 0])\n",
      " |      >>> a = np.empty(2)\n",
      " |      >>> a.fill(1)\n",
      " |      >>> a\n",
      " |      array([ 1.,  1.])\n",
      " |  \n",
      " |  flatten(...)\n",
      " |      a.flatten(order='C')\n",
      " |      \n",
      " |      Return a copy of the array collapsed into one dimension.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      order : {'C', 'F', 'A', 'K'}, optional\n",
      " |          'C' means to flatten in row-major (C-style) order.\n",
      " |          'F' means to flatten in column-major (Fortran-\n",
      " |          style) order. 'A' means to flatten in column-major\n",
      " |          order if `a` is Fortran *contiguous* in memory,\n",
      " |          row-major order otherwise. 'K' means to flatten\n",
      " |          `a` in the order the elements occur in memory.\n",
      " |          The default is 'C'.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y : ndarray\n",
      " |          A copy of the input array, flattened to one dimension.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      ravel : Return a flattened array.\n",
      " |      flat : A 1-D flat iterator over the array.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> a = np.array([[1,2], [3,4]])\n",
      " |      >>> a.flatten()\n",
      " |      array([1, 2, 3, 4])\n",
      " |      >>> a.flatten('F')\n",
      " |      array([1, 3, 2, 4])\n",
      " |  \n",
      " |  getfield(...)\n",
      " |      a.getfield(dtype, offset=0)\n",
      " |      \n",
      " |      Returns a field of the given array as a certain type.\n",
      " |      \n",
      " |      A field is a view of the array data with a given data-type. The values in\n",
      " |      the view are determined by the given type and the offset into the current\n",
      " |      array in bytes. The offset needs to be such that the view dtype fits in the\n",
      " |      array dtype; for example an array of dtype complex128 has 16-byte elements.\n",
      " |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n",
      " |      between 0 and 12 bytes.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      dtype : str or dtype\n",
      " |          The data type of the view. The dtype size of the view can not be larger\n",
      " |          than that of the array itself.\n",
      " |      offset : int\n",
      " |          Number of bytes to skip before beginning the element view.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.diag([1.+1.j]*2)\n",
      " |      >>> x[1, 1] = 2 + 4.j\n",
      " |      >>> x\n",
      " |      array([[ 1.+1.j,  0.+0.j],\n",
      " |             [ 0.+0.j,  2.+4.j]])\n",
      " |      >>> x.getfield(np.float64)\n",
      " |      array([[ 1.,  0.],\n",
      " |             [ 0.,  2.]])\n",
      " |      \n",
      " |      By choosing an offset of 8 bytes we can select the complex part of the\n",
      " |      array for our view:\n",
      " |      \n",
      " |      >>> x.getfield(np.float64, offset=8)\n",
      " |      array([[ 1.,  0.],\n",
      " |         [ 0.,  4.]])\n",
      " |  \n",
      " |  item(...)\n",
      " |      a.item(*args)\n",
      " |      \n",
      " |      Copy an element of an array to a standard Python scalar and return it.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \\*args : Arguments (variable number and type)\n",
      " |      \n",
      " |          * none: in this case, the method only works for arrays\n",
      " |            with one element (`a.size == 1`), which element is\n",
      " |            copied into a standard Python scalar object and returned.\n",
      " |      \n",
      " |          * int_type: this argument is interpreted as a flat index into\n",
      " |            the array, specifying which element to copy and return.\n",
      " |      \n",
      " |          * tuple of int_types: functions as does a single int_type argument,\n",
      " |            except that the argument is interpreted as an nd-index into the\n",
      " |            array.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      z : Standard Python scalar object\n",
      " |          A copy of the specified element of the array as a suitable\n",
      " |          Python scalar\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      When the data type of `a` is longdouble or clongdouble, item() returns\n",
      " |      a scalar array object because there is no available Python scalar that\n",
      " |      would not lose information. Void arrays return a buffer object for item(),\n",
      " |      unless fields are defined, in which case a tuple is returned.\n",
      " |      \n",
      " |      `item` is very similar to a[args], except, instead of an array scalar,\n",
      " |      a standard Python scalar is returned. This can be useful for speeding up\n",
      " |      access to elements of the array and doing arithmetic on elements of the\n",
      " |      array using Python's optimized math.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.random.randint(9, size=(3, 3))\n",
      " |      >>> x\n",
      " |      array([[3, 1, 7],\n",
      " |             [2, 8, 3],\n",
      " |             [8, 5, 3]])\n",
      " |      >>> x.item(3)\n",
      " |      2\n",
      " |      >>> x.item(7)\n",
      " |      5\n",
      " |      >>> x.item((0, 1))\n",
      " |      1\n",
      " |      >>> x.item((2, 2))\n",
      " |      3\n",
      " |  \n",
      " |  itemset(...)\n",
      " |      a.itemset(*args)\n",
      " |      \n",
      " |      Insert scalar into an array (scalar is cast to array's dtype, if possible)\n",
      " |      \n",
      " |      There must be at least 1 argument, and define the last argument\n",
      " |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster\n",
      " |      than ``a[args] = item``.  The item should be a scalar value and `args`\n",
      " |      must select a single item in the array `a`.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \\*args : Arguments\n",
      " |          If one argument: a scalar, only used in case `a` is of size 1.\n",
      " |          If two arguments: the last argument is the value to be set\n",
      " |          and must be a scalar, the first argument specifies a single array\n",
      " |          element location. It is either an int or a tuple.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Compared to indexing syntax, `itemset` provides some speed increase\n",
      " |      for placing a scalar into a particular location in an `ndarray`,\n",
      " |      if you must do this.  However, generally this is discouraged:\n",
      " |      among other problems, it complicates the appearance of the code.\n",
      " |      Also, when using `itemset` (and `item`) inside a loop, be sure\n",
      " |      to assign the methods to a local variable to avoid the attribute\n",
      " |      look-up at each loop iteration.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.random.randint(9, size=(3, 3))\n",
      " |      >>> x\n",
      " |      array([[3, 1, 7],\n",
      " |             [2, 8, 3],\n",
      " |             [8, 5, 3]])\n",
      " |      >>> x.itemset(4, 0)\n",
      " |      >>> x.itemset((2, 2), 9)\n",
      " |      >>> x\n",
      " |      array([[3, 1, 7],\n",
      " |             [2, 0, 3],\n",
      " |             [8, 5, 9]])\n",
      " |  \n",
      " |  max(...)\n",
      " |      a.max(axis=None, out=None)\n",
      " |      \n",
      " |      Return the maximum along a given axis.\n",
      " |      \n",
      " |      Refer to `numpy.amax` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.amax : equivalent function\n",
      " |  \n",
      " |  mean(...)\n",
      " |      a.mean(axis=None, dtype=None, out=None, keepdims=False)\n",
      " |      \n",
      " |      Returns the average of the array elements along given axis.\n",
      " |      \n",
      " |      Refer to `numpy.mean` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.mean : equivalent function\n",
      " |  \n",
      " |  min(...)\n",
      " |      a.min(axis=None, out=None, keepdims=False)\n",
      " |      \n",
      " |      Return the minimum along a given axis.\n",
      " |      \n",
      " |      Refer to `numpy.amin` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.amin : equivalent function\n",
      " |  \n",
      " |  newbyteorder(...)\n",
      " |      arr.newbyteorder(new_order='S')\n",
      " |      \n",
      " |      Return the array with the same data viewed with a different byte order.\n",
      " |      \n",
      " |      Equivalent to::\n",
      " |      \n",
      " |          arr.view(arr.dtype.newbytorder(new_order))\n",
      " |      \n",
      " |      Changes are also made in all fields and sub-arrays of the array data\n",
      " |      type.\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      new_order : string, optional\n",
      " |          Byte order to force; a value from the byte order specifications\n",
      " |          below. `new_order` codes can be any of:\n",
      " |      \n",
      " |          * 'S' - swap dtype from current to opposite endian\n",
      " |          * {'<', 'L'} - little endian\n",
      " |          * {'>', 'B'} - big endian\n",
      " |          * {'=', 'N'} - native order\n",
      " |          * {'|', 'I'} - ignore (no change to byte order)\n",
      " |      \n",
      " |          The default value ('S') results in swapping the current\n",
      " |          byte order. The code does a case-insensitive check on the first\n",
      " |          letter of `new_order` for the alternatives above.  For example,\n",
      " |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n",
      " |      \n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      new_arr : array\n",
      " |          New array object with the dtype reflecting given change to the\n",
      " |          byte order.\n",
      " |  \n",
      " |  nonzero(...)\n",
      " |      a.nonzero()\n",
      " |      \n",
      " |      Return the indices of the elements that are non-zero.\n",
      " |      \n",
      " |      Refer to `numpy.nonzero` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.nonzero : equivalent function\n",
      " |  \n",
      " |  partition(...)\n",
      " |      a.partition(kth, axis=-1, kind='introselect', order=None)\n",
      " |      \n",
      " |      Rearranges the elements in the array in such a way that value of the\n",
      " |      element in kth position is in the position it would be in a sorted array.\n",
      " |      All elements smaller than the kth element are moved before this element and\n",
      " |      all equal or greater are moved behind it. The ordering of the elements in\n",
      " |      the two partitions is undefined.\n",
      " |      \n",
      " |      .. versionadded:: 1.8.0\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      kth : int or sequence of ints\n",
      " |          Element index to partition by. The kth element value will be in its\n",
      " |          final sorted position and all smaller elements will be moved before it\n",
      " |          and all equal or greater elements behind it.\n",
      " |          The order all elements in the partitions is undefined.\n",
      " |          If provided with a sequence of kth it will partition all elements\n",
      " |          indexed by kth of them into their sorted position at once.\n",
      " |      axis : int, optional\n",
      " |          Axis along which to sort. Default is -1, which means sort along the\n",
      " |          last axis.\n",
      " |      kind : {'introselect'}, optional\n",
      " |          Selection algorithm. Default is 'introselect'.\n",
      " |      order : str or list of str, optional\n",
      " |          When `a` is an array with fields defined, this argument specifies\n",
      " |          which fields to compare first, second, etc.  A single field can\n",
      " |          be specified as a string, and not all fields need be specified,\n",
      " |          but unspecified fields will still be used, in the order in which\n",
      " |          they come up in the dtype, to break ties.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.partition : Return a parititioned copy of an array.\n",
      " |      argpartition : Indirect partition.\n",
      " |      sort : Full sort.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      See ``np.partition`` for notes on the different algorithms.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> a = np.array([3, 4, 2, 1])\n",
      " |      >>> a.partition(a, 3)\n",
      " |      >>> a\n",
      " |      array([2, 1, 3, 4])\n",
      " |      \n",
      " |      >>> a.partition((1, 3))\n",
      " |      array([1, 2, 3, 4])\n",
      " |  \n",
      " |  prod(...)\n",
      " |      a.prod(axis=None, dtype=None, out=None, keepdims=False)\n",
      " |      \n",
      " |      Return the product of the array elements over the given axis\n",
      " |      \n",
      " |      Refer to `numpy.prod` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.prod : equivalent function\n",
      " |  \n",
      " |  ptp(...)\n",
      " |      a.ptp(axis=None, out=None)\n",
      " |      \n",
      " |      Peak to peak (maximum - minimum) value along a given axis.\n",
      " |      \n",
      " |      Refer to `numpy.ptp` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.ptp : equivalent function\n",
      " |  \n",
      " |  put(...)\n",
      " |      a.put(indices, values, mode='raise')\n",
      " |      \n",
      " |      Set ``a.flat[n] = values[n]`` for all `n` in indices.\n",
      " |      \n",
      " |      Refer to `numpy.put` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.put : equivalent function\n",
      " |  \n",
      " |  ravel(...)\n",
      " |      a.ravel([order])\n",
      " |      \n",
      " |      Return a flattened array.\n",
      " |      \n",
      " |      Refer to `numpy.ravel` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.ravel : equivalent function\n",
      " |      \n",
      " |      ndarray.flat : a flat iterator on the array.\n",
      " |  \n",
      " |  repeat(...)\n",
      " |      a.repeat(repeats, axis=None)\n",
      " |      \n",
      " |      Repeat elements of an array.\n",
      " |      \n",
      " |      Refer to `numpy.repeat` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.repeat : equivalent function\n",
      " |  \n",
      " |  reshape(...)\n",
      " |      a.reshape(shape, order='C')\n",
      " |      \n",
      " |      Returns an array containing the same data with a new shape.\n",
      " |      \n",
      " |      Refer to `numpy.reshape` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.reshape : equivalent function\n",
      " |  \n",
      " |  resize(...)\n",
      " |      a.resize(new_shape, refcheck=True)\n",
      " |      \n",
      " |      Change shape and size of array in-place.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      new_shape : tuple of ints, or `n` ints\n",
      " |          Shape of resized array.\n",
      " |      refcheck : bool, optional\n",
      " |          If False, reference count will not be checked. Default is True.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      None\n",
      " |      \n",
      " |      Raises\n",
      " |      ------\n",
      " |      ValueError\n",
      " |          If `a` does not own its own data or references or views to it exist,\n",
      " |          and the data memory must be changed.\n",
      " |      \n",
      " |      SystemError\n",
      " |          If the `order` keyword argument is specified. This behaviour is a\n",
      " |          bug in NumPy.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      resize : Return a new array with the specified shape.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      This reallocates space for the data area if necessary.\n",
      " |      \n",
      " |      Only contiguous arrays (data elements consecutive in memory) can be\n",
      " |      resized.\n",
      " |      \n",
      " |      The purpose of the reference count check is to make sure you\n",
      " |      do not use this array as a buffer for another Python object and then\n",
      " |      reallocate the memory. However, reference counts can increase in\n",
      " |      other ways so if you are sure that you have not shared the memory\n",
      " |      for this array with another Python object, then you may safely set\n",
      " |      `refcheck` to False.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      Shrinking an array: array is flattened (in the order that the data are\n",
      " |      stored in memory), resized, and reshaped:\n",
      " |      \n",
      " |      >>> a = np.array([[0, 1], [2, 3]], order='C')\n",
      " |      >>> a.resize((2, 1))\n",
      " |      >>> a\n",
      " |      array([[0],\n",
      " |             [1]])\n",
      " |      \n",
      " |      >>> a = np.array([[0, 1], [2, 3]], order='F')\n",
      " |      >>> a.resize((2, 1))\n",
      " |      >>> a\n",
      " |      array([[0],\n",
      " |             [2]])\n",
      " |      \n",
      " |      Enlarging an array: as above, but missing entries are filled with zeros:\n",
      " |      \n",
      " |      >>> b = np.array([[0, 1], [2, 3]])\n",
      " |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n",
      " |      >>> b\n",
      " |      array([[0, 1, 2],\n",
      " |             [3, 0, 0]])\n",
      " |      \n",
      " |      Referencing an array prevents resizing...\n",
      " |      \n",
      " |      >>> c = a\n",
      " |      >>> a.resize((1, 1))\n",
      " |      Traceback (most recent call last):\n",
      " |      ...\n",
      " |      ValueError: cannot resize an array that has been referenced ...\n",
      " |      \n",
      " |      Unless `refcheck` is False:\n",
      " |      \n",
      " |      >>> a.resize((1, 1), refcheck=False)\n",
      " |      >>> a\n",
      " |      array([[0]])\n",
      " |      >>> c\n",
      " |      array([[0]])\n",
      " |  \n",
      " |  round(...)\n",
      " |      a.round(decimals=0, out=None)\n",
      " |      \n",
      " |      Return `a` with each element rounded to the given number of decimals.\n",
      " |      \n",
      " |      Refer to `numpy.around` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.around : equivalent function\n",
      " |  \n",
      " |  searchsorted(...)\n",
      " |      a.searchsorted(v, side='left', sorter=None)\n",
      " |      \n",
      " |      Find indices where elements of v should be inserted in a to maintain order.\n",
      " |      \n",
      " |      For full documentation, see `numpy.searchsorted`\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.searchsorted : equivalent function\n",
      " |  \n",
      " |  setfield(...)\n",
      " |      a.setfield(val, dtype, offset=0)\n",
      " |      \n",
      " |      Put a value into a specified place in a field defined by a data-type.\n",
      " |      \n",
      " |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n",
      " |      bytes into the field.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      val : object\n",
      " |          Value to be placed in field.\n",
      " |      dtype : dtype object\n",
      " |          Data-type of the field in which to place `val`.\n",
      " |      offset : int, optional\n",
      " |          The number of bytes into the field at which to place `val`.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      None\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      getfield\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.eye(3)\n",
      " |      >>> x.getfield(np.float64)\n",
      " |      array([[ 1.,  0.,  0.],\n",
      " |             [ 0.,  1.,  0.],\n",
      " |             [ 0.,  0.,  1.]])\n",
      " |      >>> x.setfield(3, np.int32)\n",
      " |      >>> x.getfield(np.int32)\n",
      " |      array([[3, 3, 3],\n",
      " |             [3, 3, 3],\n",
      " |             [3, 3, 3]])\n",
      " |      >>> x\n",
      " |      array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],\n",
      " |             [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],\n",
      " |             [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])\n",
      " |      >>> x.setfield(np.eye(3), np.int32)\n",
      " |      >>> x\n",
      " |      array([[ 1.,  0.,  0.],\n",
      " |             [ 0.,  1.,  0.],\n",
      " |             [ 0.,  0.,  1.]])\n",
      " |  \n",
      " |  setflags(...)\n",
      " |      a.setflags(write=None, align=None, uic=None)\n",
      " |      \n",
      " |      Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.\n",
      " |      \n",
      " |      These Boolean-valued flags affect how numpy interprets the memory\n",
      " |      area used by `a` (see Notes below). The ALIGNED flag can only\n",
      " |      be set to True if the data is actually aligned according to the type.\n",
      " |      The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE\n",
      " |      can only be set to True if the array owns its own memory, or the\n",
      " |      ultimate owner of the memory exposes a writeable buffer interface,\n",
      " |      or is a string. (The exception for string is made so that unpickling\n",
      " |      can be done without copying memory.)\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      write : bool, optional\n",
      " |          Describes whether or not `a` can be written to.\n",
      " |      align : bool, optional\n",
      " |          Describes whether or not `a` is aligned properly for its type.\n",
      " |      uic : bool, optional\n",
      " |          Describes whether or not `a` is a copy of another \"base\" array.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Array flags provide information about how the memory area used\n",
      " |      for the array is to be interpreted. There are 6 Boolean flags\n",
      " |      in use, only three of which can be changed by the user:\n",
      " |      UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n",
      " |      \n",
      " |      WRITEABLE (W) the data area can be written to;\n",
      " |      \n",
      " |      ALIGNED (A) the data and strides are aligned appropriately for the hardware\n",
      " |      (as determined by the compiler);\n",
      " |      \n",
      " |      UPDATEIFCOPY (U) this array is a copy of some other array (referenced\n",
      " |      by .base). When this array is deallocated, the base array will be\n",
      " |      updated with the contents of this array.\n",
      " |      \n",
      " |      All flags can be accessed using their first (upper case) letter as well\n",
      " |      as the full name.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> y\n",
      " |      array([[3, 1, 7],\n",
      " |             [2, 0, 0],\n",
      " |             [8, 5, 9]])\n",
      " |      >>> y.flags\n",
      " |        C_CONTIGUOUS : True\n",
      " |        F_CONTIGUOUS : False\n",
      " |        OWNDATA : True\n",
      " |        WRITEABLE : True\n",
      " |        ALIGNED : True\n",
      " |        UPDATEIFCOPY : False\n",
      " |      >>> y.setflags(write=0, align=0)\n",
      " |      >>> y.flags\n",
      " |        C_CONTIGUOUS : True\n",
      " |        F_CONTIGUOUS : False\n",
      " |        OWNDATA : True\n",
      " |        WRITEABLE : False\n",
      " |        ALIGNED : False\n",
      " |        UPDATEIFCOPY : False\n",
      " |      >>> y.setflags(uic=1)\n",
      " |      Traceback (most recent call last):\n",
      " |        File \"<stdin>\", line 1, in <module>\n",
      " |      ValueError: cannot set UPDATEIFCOPY flag to True\n",
      " |  \n",
      " |  sort(...)\n",
      " |      a.sort(axis=-1, kind='quicksort', order=None)\n",
      " |      \n",
      " |      Sort an array, in-place.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      axis : int, optional\n",
      " |          Axis along which to sort. Default is -1, which means sort along the\n",
      " |          last axis.\n",
      " |      kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n",
      " |          Sorting algorithm. Default is 'quicksort'.\n",
      " |      order : str or list of str, optional\n",
      " |          When `a` is an array with fields defined, this argument specifies\n",
      " |          which fields to compare first, second, etc.  A single field can\n",
      " |          be specified as a string, and not all fields need be specified,\n",
      " |          but unspecified fields will still be used, in the order in which\n",
      " |          they come up in the dtype, to break ties.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.sort : Return a sorted copy of an array.\n",
      " |      argsort : Indirect sort.\n",
      " |      lexsort : Indirect stable sort on multiple keys.\n",
      " |      searchsorted : Find elements in sorted array.\n",
      " |      partition: Partial sort.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      See ``sort`` for notes on the different sorting algorithms.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> a = np.array([[1,4], [3,1]])\n",
      " |      >>> a.sort(axis=1)\n",
      " |      >>> a\n",
      " |      array([[1, 4],\n",
      " |             [1, 3]])\n",
      " |      >>> a.sort(axis=0)\n",
      " |      >>> a\n",
      " |      array([[1, 3],\n",
      " |             [1, 4]])\n",
      " |      \n",
      " |      Use the `order` keyword to specify a field to use when sorting a\n",
      " |      structured array:\n",
      " |      \n",
      " |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n",
      " |      >>> a.sort(order='y')\n",
      " |      >>> a\n",
      " |      array([('c', 1), ('a', 2)],\n",
      " |            dtype=[('x', '|S1'), ('y', '<i4')])\n",
      " |  \n",
      " |  squeeze(...)\n",
      " |      a.squeeze(axis=None)\n",
      " |      \n",
      " |      Remove single-dimensional entries from the shape of `a`.\n",
      " |      \n",
      " |      Refer to `numpy.squeeze` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.squeeze : equivalent function\n",
      " |  \n",
      " |  std(...)\n",
      " |      a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
      " |      \n",
      " |      Returns the standard deviation of the array elements along given axis.\n",
      " |      \n",
      " |      Refer to `numpy.std` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.std : equivalent function\n",
      " |  \n",
      " |  sum(...)\n",
      " |      a.sum(axis=None, dtype=None, out=None, keepdims=False)\n",
      " |      \n",
      " |      Return the sum of the array elements over the given axis.\n",
      " |      \n",
      " |      Refer to `numpy.sum` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.sum : equivalent function\n",
      " |  \n",
      " |  swapaxes(...)\n",
      " |      a.swapaxes(axis1, axis2)\n",
      " |      \n",
      " |      Return a view of the array with `axis1` and `axis2` interchanged.\n",
      " |      \n",
      " |      Refer to `numpy.swapaxes` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.swapaxes : equivalent function\n",
      " |  \n",
      " |  take(...)\n",
      " |      a.take(indices, axis=None, out=None, mode='raise')\n",
      " |      \n",
      " |      Return an array formed from the elements of `a` at the given indices.\n",
      " |      \n",
      " |      Refer to `numpy.take` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.take : equivalent function\n",
      " |  \n",
      " |  tobytes(...)\n",
      " |      a.tobytes(order='C')\n",
      " |      \n",
      " |      Construct Python bytes containing the raw data bytes in the array.\n",
      " |      \n",
      " |      Constructs Python bytes showing a copy of the raw contents of\n",
      " |      data memory. The bytes object can be produced in either 'C' or 'Fortran',\n",
      " |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n",
      " |      unless the F_CONTIGUOUS flag in the array is set, in which case it\n",
      " |      means 'Fortran' order.\n",
      " |      \n",
      " |      .. versionadded:: 1.9.0\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      order : {'C', 'F', None}, optional\n",
      " |          Order of the data for multidimensional arrays:\n",
      " |          C, Fortran, or the same as for the original array.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      s : bytes\n",
      " |          Python bytes exhibiting a copy of `a`'s raw data.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([[0, 1], [2, 3]])\n",
      " |      >>> x.tobytes()\n",
      " |      b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n",
      " |      >>> x.tobytes('C') == x.tobytes()\n",
      " |      True\n",
      " |      >>> x.tobytes('F')\n",
      " |      b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n",
      " |  \n",
      " |  tofile(...)\n",
      " |      a.tofile(fid, sep=\"\", format=\"%s\")\n",
      " |      \n",
      " |      Write array to a file as text or binary (default).\n",
      " |      \n",
      " |      Data is always written in 'C' order, independent of the order of `a`.\n",
      " |      The data produced by this method can be recovered using the function\n",
      " |      fromfile().\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      fid : file or str\n",
      " |          An open file object, or a string containing a filename.\n",
      " |      sep : str\n",
      " |          Separator between array items for text output.\n",
      " |          If \"\" (empty), a binary file is written, equivalent to\n",
      " |          ``file.write(a.tobytes())``.\n",
      " |      format : str\n",
      " |          Format string for text file output.\n",
      " |          Each entry in the array is formatted to text by first converting\n",
      " |          it to the closest Python type, and then using \"format\" % item.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      This is a convenience function for quick storage of array data.\n",
      " |      Information on endianness and precision is lost, so this method is not a\n",
      " |      good choice for files intended to archive data or transport data between\n",
      " |      machines with different endianness. Some of these problems can be overcome\n",
      " |      by outputting the data as text files, at the expense of speed and file\n",
      " |      size.\n",
      " |  \n",
      " |  tolist(...)\n",
      " |      a.tolist()\n",
      " |      \n",
      " |      Return the array as a (possibly nested) list.\n",
      " |      \n",
      " |      Return a copy of the array data as a (nested) Python list.\n",
      " |      Data items are converted to the nearest compatible Python type.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      none\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y : list\n",
      " |          The possibly nested list of array elements.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The array may be recreated, ``a = np.array(a.tolist())``.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> a = np.array([1, 2])\n",
      " |      >>> a.tolist()\n",
      " |      [1, 2]\n",
      " |      >>> a = np.array([[1, 2], [3, 4]])\n",
      " |      >>> list(a)\n",
      " |      [array([1, 2]), array([3, 4])]\n",
      " |      >>> a.tolist()\n",
      " |      [[1, 2], [3, 4]]\n",
      " |  \n",
      " |  tostring(...)\n",
      " |      a.tostring(order='C')\n",
      " |      \n",
      " |      Construct Python bytes containing the raw data bytes in the array.\n",
      " |      \n",
      " |      Constructs Python bytes showing a copy of the raw contents of\n",
      " |      data memory. The bytes object can be produced in either 'C' or 'Fortran',\n",
      " |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n",
      " |      unless the F_CONTIGUOUS flag in the array is set, in which case it\n",
      " |      means 'Fortran' order.\n",
      " |      \n",
      " |      This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      order : {'C', 'F', None}, optional\n",
      " |          Order of the data for multidimensional arrays:\n",
      " |          C, Fortran, or the same as for the original array.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      s : bytes\n",
      " |          Python bytes exhibiting a copy of `a`'s raw data.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([[0, 1], [2, 3]])\n",
      " |      >>> x.tobytes()\n",
      " |      b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n",
      " |      >>> x.tobytes('C') == x.tobytes()\n",
      " |      True\n",
      " |      >>> x.tobytes('F')\n",
      " |      b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n",
      " |  \n",
      " |  trace(...)\n",
      " |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n",
      " |      \n",
      " |      Return the sum along diagonals of the array.\n",
      " |      \n",
      " |      Refer to `numpy.trace` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.trace : equivalent function\n",
      " |  \n",
      " |  transpose(...)\n",
      " |      a.transpose(*axes)\n",
      " |      \n",
      " |      Returns a view of the array with axes transposed.\n",
      " |      \n",
      " |      For a 1-D array, this has no effect. (To change between column and\n",
      " |      row vectors, first cast the 1-D array into a matrix object.)\n",
      " |      For a 2-D array, this is the usual matrix transpose.\n",
      " |      For an n-D array, if axes are given, their order indicates how the\n",
      " |      axes are permuted (see Examples). If axes are not provided and\n",
      " |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n",
      " |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      axes : None, tuple of ints, or `n` ints\n",
      " |      \n",
      " |       * None or no argument: reverses the order of the axes.\n",
      " |      \n",
      " |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n",
      " |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n",
      " |      \n",
      " |       * `n` ints: same as an n-tuple of the same ints (this form is\n",
      " |         intended simply as a \"convenience\" alternative to the tuple form)\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      out : ndarray\n",
      " |          View of `a`, with axes suitably permuted.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      ndarray.T : Array property returning the array transposed.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> a = np.array([[1, 2], [3, 4]])\n",
      " |      >>> a\n",
      " |      array([[1, 2],\n",
      " |             [3, 4]])\n",
      " |      >>> a.transpose()\n",
      " |      array([[1, 3],\n",
      " |             [2, 4]])\n",
      " |      >>> a.transpose((1, 0))\n",
      " |      array([[1, 3],\n",
      " |             [2, 4]])\n",
      " |      >>> a.transpose(1, 0)\n",
      " |      array([[1, 3],\n",
      " |             [2, 4]])\n",
      " |  \n",
      " |  var(...)\n",
      " |      a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n",
      " |      \n",
      " |      Returns the variance of the array elements, along given axis.\n",
      " |      \n",
      " |      Refer to `numpy.var` for full documentation.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.var : equivalent function\n",
      " |  \n",
      " |  view(...)\n",
      " |      a.view(dtype=None, type=None)\n",
      " |      \n",
      " |      New view of array with the same data.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      dtype : data-type or ndarray sub-class, optional\n",
      " |          Data-type descriptor of the returned view, e.g., float32 or int16. The\n",
      " |          default, None, results in the view having the same data-type as `a`.\n",
      " |          This argument can also be specified as an ndarray sub-class, which\n",
      " |          then specifies the type of the returned object (this is equivalent to\n",
      " |          setting the ``type`` parameter).\n",
      " |      type : Python type, optional\n",
      " |          Type of the returned view, e.g., ndarray or matrix.  Again, the\n",
      " |          default None results in type preservation.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      ``a.view()`` is used two different ways:\n",
      " |      \n",
      " |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n",
      " |      of the array's memory with a different data-type.  This can cause a\n",
      " |      reinterpretation of the bytes of memory.\n",
      " |      \n",
      " |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n",
      " |      returns an instance of `ndarray_subclass` that looks at the same array\n",
      " |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the\n",
      " |      memory.\n",
      " |      \n",
      " |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n",
      " |      bytes per entry than the previous dtype (for example, converting a\n",
      " |      regular array to a structured array), then the behavior of the view\n",
      " |      cannot be predicted just from the superficial appearance of ``a`` (shown\n",
      " |      by ``print(a)``). It also depends on exactly how ``a`` is stored in\n",
      " |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n",
      " |      defined as a slice or transpose, etc., the view may give different\n",
      " |      results.\n",
      " |      \n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n",
      " |      \n",
      " |      Viewing array data using a different type and dtype:\n",
      " |      \n",
      " |      >>> y = x.view(dtype=np.int16, type=np.matrix)\n",
      " |      >>> y\n",
      " |      matrix([[513]], dtype=int16)\n",
      " |      >>> print(type(y))\n",
      " |      <class 'numpy.matrixlib.defmatrix.matrix'>\n",
      " |      \n",
      " |      Creating a view on a structured array so it can be used in calculations\n",
      " |      \n",
      " |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n",
      " |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n",
      " |      >>> xv\n",
      " |      array([[1, 2],\n",
      " |             [3, 4]], dtype=int8)\n",
      " |      >>> xv.mean(0)\n",
      " |      array([ 2.,  3.])\n",
      " |      \n",
      " |      Making changes to the view changes the underlying array\n",
      " |      \n",
      " |      >>> xv[0,1] = 20\n",
      " |      >>> print(x)\n",
      " |      [(1, 20) (3, 4)]\n",
      " |      \n",
      " |      Using a view to convert an array to a recarray:\n",
      " |      \n",
      " |      >>> z = x.view(np.recarray)\n",
      " |      >>> z.a\n",
      " |      array([1], dtype=int8)\n",
      " |      \n",
      " |      Views share data:\n",
      " |      \n",
      " |      >>> x[0] = (9, 10)\n",
      " |      >>> z[0]\n",
      " |      (9, 10)\n",
      " |      \n",
      " |      Views that change the dtype size (bytes per entry) should normally be\n",
      " |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n",
      " |      \n",
      " |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n",
      " |      >>> y = x[:, 0:2]\n",
      " |      >>> y\n",
      " |      array([[1, 2],\n",
      " |             [4, 5]], dtype=int16)\n",
      " |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
      " |      Traceback (most recent call last):\n",
      " |        File \"<stdin>\", line 1, in <module>\n",
      " |      ValueError: new type not compatible with array.\n",
      " |      >>> z = y.copy()\n",
      " |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n",
      " |      array([[(1, 2)],\n",
      " |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  T\n",
      " |      Same as self.transpose(), except that self is returned if\n",
      " |      self.ndim < 2.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([[1.,2.],[3.,4.]])\n",
      " |      >>> x\n",
      " |      array([[ 1.,  2.],\n",
      " |             [ 3.,  4.]])\n",
      " |      >>> x.T\n",
      " |      array([[ 1.,  3.],\n",
      " |             [ 2.,  4.]])\n",
      " |      >>> x = np.array([1.,2.,3.,4.])\n",
      " |      >>> x\n",
      " |      array([ 1.,  2.,  3.,  4.])\n",
      " |      >>> x.T\n",
      " |      array([ 1.,  2.,  3.,  4.])\n",
      " |  \n",
      " |  __array_finalize__\n",
      " |      None.\n",
      " |  \n",
      " |  __array_interface__\n",
      " |      Array protocol: Python side.\n",
      " |  \n",
      " |  __array_priority__\n",
      " |      Array priority.\n",
      " |  \n",
      " |  __array_struct__\n",
      " |      Array protocol: C-struct side.\n",
      " |  \n",
      " |  base\n",
      " |      Base object if memory is from some other object.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      The base of an array that owns its memory is None:\n",
      " |      \n",
      " |      >>> x = np.array([1,2,3,4])\n",
      " |      >>> x.base is None\n",
      " |      True\n",
      " |      \n",
      " |      Slicing creates a view, whose memory is shared with x:\n",
      " |      \n",
      " |      >>> y = x[2:]\n",
      " |      >>> y.base is x\n",
      " |      True\n",
      " |  \n",
      " |  ctypes\n",
      " |      An object to simplify the interaction of the array with the ctypes\n",
      " |      module.\n",
      " |      \n",
      " |      This attribute creates an object that makes it easier to use arrays\n",
      " |      when calling shared libraries with the ctypes module. The returned\n",
      " |      object has, among others, data, shape, and strides attributes (see\n",
      " |      Notes below) which themselves return ctypes objects that can be used\n",
      " |      as arguments to a shared library.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      None\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      c : Python object\n",
      " |          Possessing attributes data, shape, strides, etc.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.ctypeslib\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Below are the public attributes of this object which were documented\n",
      " |      in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n",
      " |      as well as documented private attributes):\n",
      " |      \n",
      " |      * data: A pointer to the memory area of the array as a Python integer.\n",
      " |        This memory area may contain data that is not aligned, or not in correct\n",
      " |        byte-order. The memory area may not even be writeable. The array\n",
      " |        flags and data-type of this array should be respected when passing this\n",
      " |        attribute to arbitrary C-code to avoid trouble that can include Python\n",
      " |        crashing. User Beware! The value of this attribute is exactly the same\n",
      " |        as self._array_interface_['data'][0].\n",
      " |      \n",
      " |      * shape (c_intp*self.ndim): A ctypes array of length self.ndim where\n",
      " |        the basetype is the C-integer corresponding to dtype('p') on this\n",
      " |        platform. This base-type could be c_int, c_long, or c_longlong\n",
      " |        depending on the platform. The c_intp type is defined accordingly in\n",
      " |        numpy.ctypeslib. The ctypes array contains the shape of the underlying\n",
      " |        array.\n",
      " |      \n",
      " |      * strides (c_intp*self.ndim): A ctypes array of length self.ndim where\n",
      " |        the basetype is the same as for the shape attribute. This ctypes array\n",
      " |        contains the strides information from the underlying array. This strides\n",
      " |        information is important for showing how many bytes must be jumped to\n",
      " |        get to the next element in the array.\n",
      " |      \n",
      " |      * data_as(obj): Return the data pointer cast to a particular c-types object.\n",
      " |        For example, calling self._as_parameter_ is equivalent to\n",
      " |        self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a\n",
      " |        pointer to a ctypes array of floating-point data:\n",
      " |        self.data_as(ctypes.POINTER(ctypes.c_double)).\n",
      " |      \n",
      " |      * shape_as(obj): Return the shape tuple as an array of some other c-types\n",
      " |        type. For example: self.shape_as(ctypes.c_short).\n",
      " |      \n",
      " |      * strides_as(obj): Return the strides tuple as an array of some other\n",
      " |        c-types type. For example: self.strides_as(ctypes.c_longlong).\n",
      " |      \n",
      " |      Be careful using the ctypes attribute - especially on temporary\n",
      " |      arrays or arrays constructed on the fly. For example, calling\n",
      " |      ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory\n",
      " |      that is invalid because the array created as (a+b) is deallocated\n",
      " |      before the next Python statement. You can avoid this problem using\n",
      " |      either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will\n",
      " |      hold a reference to the array until ct is deleted or re-assigned.\n",
      " |      \n",
      " |      If the ctypes module is not available, then the ctypes attribute\n",
      " |      of array objects still returns something useful, but ctypes objects\n",
      " |      are not returned and errors may be raised instead. In particular,\n",
      " |      the object will still have the as parameter attribute which will\n",
      " |      return an integer equal to the data attribute.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> import ctypes\n",
      " |      >>> x\n",
      " |      array([[0, 1],\n",
      " |             [2, 3]])\n",
      " |      >>> x.ctypes.data\n",
      " |      30439712\n",
      " |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))\n",
      " |      <ctypes.LP_c_long object at 0x01F01300>\n",
      " |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents\n",
      " |      c_long(0)\n",
      " |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents\n",
      " |      c_longlong(4294967296L)\n",
      " |      >>> x.ctypes.shape\n",
      " |      <numpy.core._internal.c_long_Array_2 object at 0x01FFD580>\n",
      " |      >>> x.ctypes.shape_as(ctypes.c_long)\n",
      " |      <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>\n",
      " |      >>> x.ctypes.strides\n",
      " |      <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>\n",
      " |      >>> x.ctypes.strides_as(ctypes.c_longlong)\n",
      " |      <numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>\n",
      " |  \n",
      " |  data\n",
      " |      Python buffer object pointing to the start of the array's data.\n",
      " |  \n",
      " |  dtype\n",
      " |      Data-type of the array's elements.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      None\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      d : numpy dtype object\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.dtype\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x\n",
      " |      array([[0, 1],\n",
      " |             [2, 3]])\n",
      " |      >>> x.dtype\n",
      " |      dtype('int32')\n",
      " |      >>> type(x.dtype)\n",
      " |      <type 'numpy.dtype'>\n",
      " |  \n",
      " |  flags\n",
      " |      Information about the memory layout of the array.\n",
      " |      \n",
      " |      Attributes\n",
      " |      ----------\n",
      " |      C_CONTIGUOUS (C)\n",
      " |          The data is in a single, C-style contiguous segment.\n",
      " |      F_CONTIGUOUS (F)\n",
      " |          The data is in a single, Fortran-style contiguous segment.\n",
      " |      OWNDATA (O)\n",
      " |          The array owns the memory it uses or borrows it from another object.\n",
      " |      WRITEABLE (W)\n",
      " |          The data area can be written to.  Setting this to False locks\n",
      " |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE\n",
      " |          from its base array at creation time, but a view of a writeable\n",
      " |          array may be subsequently locked while the base array remains writeable.\n",
      " |          (The opposite is not true, in that a view of a locked array may not\n",
      " |          be made writeable.  However, currently, locking a base object does not\n",
      " |          lock any views that already reference it, so under that circumstance it\n",
      " |          is possible to alter the contents of a locked array via a previously\n",
      " |          created writeable view onto it.)  Attempting to change a non-writeable\n",
      " |          array raises a RuntimeError exception.\n",
      " |      ALIGNED (A)\n",
      " |          The data and all elements are aligned appropriately for the hardware.\n",
      " |      UPDATEIFCOPY (U)\n",
      " |          This array is a copy of some other array. When this array is\n",
      " |          deallocated, the base array will be updated with the contents of\n",
      " |          this array.\n",
      " |      FNC\n",
      " |          F_CONTIGUOUS and not C_CONTIGUOUS.\n",
      " |      FORC\n",
      " |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n",
      " |      BEHAVED (B)\n",
      " |          ALIGNED and WRITEABLE.\n",
      " |      CARRAY (CA)\n",
      " |          BEHAVED and C_CONTIGUOUS.\n",
      " |      FARRAY (FA)\n",
      " |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n",
      " |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n",
      " |      names are only supported in dictionary access.\n",
      " |      \n",
      " |      Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by\n",
      " |      the user, via direct assignment to the attribute or dictionary entry,\n",
      " |      or by calling `ndarray.setflags`.\n",
      " |      \n",
      " |      The array flags cannot be set arbitrarily:\n",
      " |      \n",
      " |      - UPDATEIFCOPY can only be set ``False``.\n",
      " |      - ALIGNED can only be set ``True`` if the data is truly aligned.\n",
      " |      - WRITEABLE can only be set ``True`` if the array owns its own memory\n",
      " |        or the ultimate owner of the memory exposes a writeable buffer\n",
      " |        interface or is a string.\n",
      " |      \n",
      " |      Arrays can be both C-style and Fortran-style contiguous simultaneously.\n",
      " |      This is clear for 1-dimensional arrays, but can also be true for higher\n",
      " |      dimensional arrays.\n",
      " |      \n",
      " |      Even for contiguous arrays a stride for a given dimension\n",
      " |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n",
      " |      or the array has no elements.\n",
      " |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n",
      " |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n",
      " |      Fortran-style contiguous arrays is true.\n",
      " |  \n",
      " |  flat\n",
      " |      A 1-D iterator over the array.\n",
      " |      \n",
      " |      This is a `numpy.flatiter` instance, which acts similarly to, but is not\n",
      " |      a subclass of, Python's built-in iterator object.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      flatten : Return a copy of the array collapsed into one dimension.\n",
      " |      \n",
      " |      flatiter\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.arange(1, 7).reshape(2, 3)\n",
      " |      >>> x\n",
      " |      array([[1, 2, 3],\n",
      " |             [4, 5, 6]])\n",
      " |      >>> x.flat[3]\n",
      " |      4\n",
      " |      >>> x.T\n",
      " |      array([[1, 4],\n",
      " |             [2, 5],\n",
      " |             [3, 6]])\n",
      " |      >>> x.T.flat[3]\n",
      " |      5\n",
      " |      >>> type(x.flat)\n",
      " |      <type 'numpy.flatiter'>\n",
      " |      \n",
      " |      An assignment example:\n",
      " |      \n",
      " |      >>> x.flat = 3; x\n",
      " |      array([[3, 3, 3],\n",
      " |             [3, 3, 3]])\n",
      " |      >>> x.flat[[1,4]] = 1; x\n",
      " |      array([[3, 1, 3],\n",
      " |             [3, 1, 3]])\n",
      " |  \n",
      " |  imag\n",
      " |      The imaginary part of the array.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.sqrt([1+0j, 0+1j])\n",
      " |      >>> x.imag\n",
      " |      array([ 0.        ,  0.70710678])\n",
      " |      >>> x.imag.dtype\n",
      " |      dtype('float64')\n",
      " |  \n",
      " |  itemsize\n",
      " |      Length of one array element in bytes.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([1,2,3], dtype=np.float64)\n",
      " |      >>> x.itemsize\n",
      " |      8\n",
      " |      >>> x = np.array([1,2,3], dtype=np.complex128)\n",
      " |      >>> x.itemsize\n",
      " |      16\n",
      " |  \n",
      " |  nbytes\n",
      " |      Total bytes consumed by the elements of the array.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Does not include memory consumed by non-element attributes of the\n",
      " |      array object.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.zeros((3,5,2), dtype=np.complex128)\n",
      " |      >>> x.nbytes\n",
      " |      480\n",
      " |      >>> np.prod(x.shape) * x.itemsize\n",
      " |      480\n",
      " |  \n",
      " |  ndim\n",
      " |      Number of array dimensions.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([1, 2, 3])\n",
      " |      >>> x.ndim\n",
      " |      1\n",
      " |      >>> y = np.zeros((2, 3, 4))\n",
      " |      >>> y.ndim\n",
      " |      3\n",
      " |  \n",
      " |  real\n",
      " |      The real part of the array.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.sqrt([1+0j, 0+1j])\n",
      " |      >>> x.real\n",
      " |      array([ 1.        ,  0.70710678])\n",
      " |      >>> x.real.dtype\n",
      " |      dtype('float64')\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.real : equivalent function\n",
      " |  \n",
      " |  shape\n",
      " |      Tuple of array dimensions.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      May be used to \"reshape\" the array, as long as this would not\n",
      " |      require a change in the total number of elements\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.array([1, 2, 3, 4])\n",
      " |      >>> x.shape\n",
      " |      (4,)\n",
      " |      >>> y = np.zeros((2, 3, 4))\n",
      " |      >>> y.shape\n",
      " |      (2, 3, 4)\n",
      " |      >>> y.shape = (3, 8)\n",
      " |      >>> y\n",
      " |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
      " |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
      " |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])\n",
      " |      >>> y.shape = (3, 6)\n",
      " |      Traceback (most recent call last):\n",
      " |        File \"<stdin>\", line 1, in <module>\n",
      " |      ValueError: total size of new array must be unchanged\n",
      " |  \n",
      " |  size\n",
      " |      Number of elements in the array.\n",
      " |      \n",
      " |      Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's\n",
      " |      dimensions.\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n",
      " |      >>> x.size\n",
      " |      30\n",
      " |      >>> np.prod(x.shape)\n",
      " |      30\n",
      " |  \n",
      " |  strides\n",
      " |      Tuple of bytes to step in each dimension when traversing an array.\n",
      " |      \n",
      " |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n",
      " |      is::\n",
      " |      \n",
      " |          offset = sum(np.array(i) * a.strides)\n",
      " |      \n",
      " |      A more detailed explanation of strides can be found in the\n",
      " |      \"ndarray.rst\" file in the NumPy reference guide.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Imagine an array of 32-bit integers (each 4 bytes)::\n",
      " |      \n",
      " |        x = np.array([[0, 1, 2, 3, 4],\n",
      " |                      [5, 6, 7, 8, 9]], dtype=np.int32)\n",
      " |      \n",
      " |      This array is stored in memory as 40 bytes, one after the other\n",
      " |      (known as a contiguous block of memory).  The strides of an array tell\n",
      " |      us how many bytes we have to skip in memory to move to the next position\n",
      " |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to\n",
      " |      move to the next column, but 20 bytes (5 values) to get to the same\n",
      " |      position in the next row.  As such, the strides for the array `x` will be\n",
      " |      ``(20, 4)``.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      numpy.lib.stride_tricks.as_strided\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n",
      " |      >>> y\n",
      " |      array([[[ 0,  1,  2,  3],\n",
      " |              [ 4,  5,  6,  7],\n",
      " |              [ 8,  9, 10, 11]],\n",
      " |             [[12, 13, 14, 15],\n",
      " |              [16, 17, 18, 19],\n",
      " |              [20, 21, 22, 23]]])\n",
      " |      >>> y.strides\n",
      " |      (48, 16, 4)\n",
      " |      >>> y[1,1,1]\n",
      " |      17\n",
      " |      >>> offset=sum(y.strides * np.array((1,1,1)))\n",
      " |      >>> offset/y.itemsize\n",
      " |      17\n",
      " |      \n",
      " |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n",
      " |      >>> x.strides\n",
      " |      (32, 4, 224, 1344)\n",
      " |      >>> i = np.array([3,5,2,2])\n",
      " |      >>> offset = sum(i * x.strides)\n",
      " |      >>> x[3,5,2,2]\n",
      " |      813\n",
      " |      >>> offset / x.itemsize\n",
      " |      813\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __hash__ = None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nx = 256\n",
    "lx = 1.0e6\n",
    "\n",
    "help(np.fft.rfftfreq(nx, lx/nx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "writer = animation.FFMpegWriter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ffmpeg'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "writer.bin_path()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ffmpeg version 3.2 Copyright (c) 2000-2016 the FFmpeg developers\r\n",
      "built with gcc 4.8 (Ubuntu 4.8.4-2ubuntu1~14.04.3)\r\n",
      "configuration: --extra-libs=-ldl --prefix=/opt/ffmpeg --mandir=/usr/share/man --enable-avresample --disable-debug --enable-nonfree --enable-gpl --enable-version3 --enable-libopencore-amrnb --enable-libopencore-amrwb --disable-decoder=amrnb --disable-decoder=amrwb --enable-libpulse --enable-libfreetype --enable-gnutls --enable-libx264 --enable-libx265 --enable-libfdk-aac --enable-libvorbis --enable-libmp3lame --enable-libopus --enable-libvpx --enable-libspeex --enable-libass --enable-avisynth --enable-libsoxr --enable-libxvid --enable-libvidstab --enable-libwavpack --enable-nvenc\r\n",
      "libavutil      55. 34.100 / 55. 34.100\r\n",
      "libavcodec     57. 64.100 / 57. 64.100\r\n",
      "libavformat    57. 56.100 / 57. 56.100\r\n",
      "libavdevice    57.  1.100 / 57.  1.100\r\n",
      "libavfilter     6. 65.100 /  6. 65.100\r\n",
      "libavresample   3.  1.  0 /  3.  1.  0\r\n",
      "libswscale      4.  2.100 /  4.  2.100\r\n",
      "libswresample   2.  3.100 /  2.  3.100\r\n",
      "libpostproc    54.  1.100 / 54.  1.100\r\n"
     ]
    }
   ],
   "source": [
    "!ffmpeg -version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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