FFT_with_numpy_scipy_skcuda-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import scipy.fftpack\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pycuda.autoinit\n",
    "import pycuda.gpuarray as gpuarray\n",
    "import skcuda.fft as cu_fft\n",
    "import math\n",
    "import cmath\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "f1=50\n",
    "A1=1\n",
    "phase1=0\n",
    "f2=78\n",
    "A2=0\n",
    "phase2=0\n",
    "f3=143\n",
    "A3=0\n",
    "phase3=0\n",
    "w1=2*math.pi*f1\n",
    "w2=2*math.pi*f2\n",
    "w3=2*math.pi*f3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n"
     ]
    }
   ],
   "source": [
    "N=1<<7\n",
    "print(N)\n",
    "ys=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ys=[]\n",
    "for j in range(0,N):\n",
    "    #math.sin(2*math.pi*f1*i)\n",
    "    i=j/60\n",
    "    y = A1*math.sin(2*math.pi*f1*i+phase1)+A2*math.sin(2*math.pi*f2*i+phase2)+A3*math.sin(2*math.pi*f3*i+phase3)\n",
    "    ys.append(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff5d4324748>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IioxqlomNDPBMiyjXVyE7J78+06nDeGqD2jbGTahE2qc1eq9+3zWid/0ZXWHNjm7e+f9b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\nvfXWsN4x9NZbb7311rD/H3l1cwafoxDfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d43da898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_np = np.array(ys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xf=np.fft.fft(x_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -0.86602540 +0.00000000e+00j,  -0.86916246 +4.25964711e-02j,\n",
       "        -0.87865750 +8.57107752e-02j,  -0.89476784 +1.29883913e-01j,\n",
       "        -0.91794183 +1.75705436e-01j,  -0.94885084 +2.23843635e-01j,\n",
       "        -0.98843995 +2.75083950e-01j,  -1.03800481 +3.30380583e-01j,\n",
       "        -1.09930722 +3.90928965e-01j,  -1.17475034 +4.58271517e-01j,\n",
       "        -1.26764949 +5.34457737e-01j,  -1.38266274 +6.22295819e-01j,\n",
       "        -1.52650041 +7.25764772e-01j,  -1.70914732 +8.50722158e-01j,\n",
       "        -1.94608687 +1.00618986e+00j,  -2.26263378 +1.20685702e+00j,\n",
       "        -2.70314271 +1.47839784e+00j,  -3.35296894 +1.87015157e+00j,\n",
       "        -4.39995984 +2.49062553e+00j,  -6.35406957 +3.63429040e+00j,\n",
       "       -11.25524228 +6.47895256e+00j, -45.62320472 +2.63358236e+01j,\n",
       "        23.14087820 -1.33510330e+01j,   9.39870931 -5.40326249e+00j,\n",
       "         5.96947755 -3.41001770e+00j,   4.41510129 -2.49954125e+00j,\n",
       "         3.53015908 -1.97585620e+00j,   2.96012930 -1.63425472e+00j,\n",
       "         2.56321420 -1.39284685e+00j,   2.27160620 -1.21246233e+00j,\n",
       "         2.04880063 -1.07200498e+00j,   1.87340412 -9.59104270e-01j,\n",
       "         1.73205081 -8.66025404e-01j,   1.61596170 -7.87682720e-01j,\n",
       "         1.51913383 -7.20594107e-01j,   1.43732451 -6.62294434e-01j,\n",
       "         1.36745105 -6.10988919e-01j,   1.30722048 -5.65339245e-01j,\n",
       "         1.25489255 -5.24326654e-01j,   1.20912326 -4.87161528e-01j,\n",
       "         1.16885868 -4.53222025e-01j,   1.13326119 -4.22011425e-01j,\n",
       "         1.10165717 -3.93127849e-01j,   1.07349914 -3.66242361e-01j,\n",
       "         1.04833799 -3.41082850e-01j,   1.02580221 -3.17421999e-01j,\n",
       "         1.00558225 -2.95068165e-01j,   0.98741848 -2.73858390e-01j,\n",
       "         0.97109190 -2.53652968e-01j,   0.95641692 -2.34331193e-01j,\n",
       "         0.94323558 -2.15787977e-01j,   0.93141302 -1.97931150e-01j,\n",
       "         0.92083386 -1.80679278e-01j,   0.91139921 -1.63959879e-01j,\n",
       "         0.90302432 -1.47707963e-01j,   0.89563664 -1.31864813e-01j,\n",
       "         0.88917422 -1.16376971e-01j,   0.88358447 -1.01195370e-01j,\n",
       "         0.87882303 -8.62746053e-02j,   0.87485300 -7.15722907e-02j,\n",
       "         0.87164419 -5.70485057e-02j,   0.86917259 -4.26652977e-02j,\n",
       "         0.86741993 -2.83862362e-02j,   0.86637341 -1.41760012e-02j,\n",
       "         0.86602540 +0.00000000e+00j,   0.86637341 +1.41760012e-02j,\n",
       "         0.86741993 +2.83862362e-02j,   0.86917259 +4.26652977e-02j,\n",
       "         0.87164419 +5.70485057e-02j,   0.87485300 +7.15722907e-02j,\n",
       "         0.87882303 +8.62746053e-02j,   0.88358447 +1.01195370e-01j,\n",
       "         0.88917422 +1.16376971e-01j,   0.89563664 +1.31864813e-01j,\n",
       "         0.90302432 +1.47707963e-01j,   0.91139921 +1.63959879e-01j,\n",
       "         0.92083386 +1.80679278e-01j,   0.93141302 +1.97931150e-01j,\n",
       "         0.94323558 +2.15787977e-01j,   0.95641692 +2.34331193e-01j,\n",
       "         0.97109190 +2.53652968e-01j,   0.98741848 +2.73858390e-01j,\n",
       "         1.00558225 +2.95068165e-01j,   1.02580221 +3.17421999e-01j,\n",
       "         1.04833799 +3.41082850e-01j,   1.07349914 +3.66242361e-01j,\n",
       "         1.10165717 +3.93127849e-01j,   1.13326119 +4.22011425e-01j,\n",
       "         1.16885868 +4.53222025e-01j,   1.20912326 +4.87161528e-01j,\n",
       "         1.25489255 +5.24326654e-01j,   1.30722048 +5.65339245e-01j,\n",
       "         1.36745105 +6.10988919e-01j,   1.43732451 +6.62294434e-01j,\n",
       "         1.51913383 +7.20594107e-01j,   1.61596170 +7.87682720e-01j,\n",
       "         1.73205081 +8.66025404e-01j,   1.87340412 +9.59104270e-01j,\n",
       "         2.04880063 +1.07200498e+00j,   2.27160620 +1.21246233e+00j,\n",
       "         2.56321420 +1.39284685e+00j,   2.96012930 +1.63425472e+00j,\n",
       "         3.53015908 +1.97585620e+00j,   4.41510129 +2.49954125e+00j,\n",
       "         5.96947755 +3.41001770e+00j,   9.39870931 +5.40326249e+00j,\n",
       "        23.14087820 +1.33510330e+01j, -45.62320472 -2.63358236e+01j,\n",
       "       -11.25524228 -6.47895256e+00j,  -6.35406957 -3.63429040e+00j,\n",
       "        -4.39995984 -2.49062553e+00j,  -3.35296894 -1.87015157e+00j,\n",
       "        -2.70314271 -1.47839784e+00j,  -2.26263378 -1.20685702e+00j,\n",
       "        -1.94608687 -1.00618986e+00j,  -1.70914732 -8.50722158e-01j,\n",
       "        -1.52650041 -7.25764772e-01j,  -1.38266274 -6.22295819e-01j,\n",
       "        -1.26764949 -5.34457737e-01j,  -1.17475034 -4.58271517e-01j,\n",
       "        -1.09930722 -3.90928965e-01j,  -1.03800481 -3.30380583e-01j,\n",
       "        -0.98843995 -2.75083950e-01j,  -0.94885084 -2.23843635e-01j,\n",
       "        -0.91794183 -1.75705436e-01j,  -0.89476784 -1.29883913e-01j,\n",
       "        -0.87865750 -8.57107752e-02j,  -0.86916246 -4.25964711e-02j])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cmoman/anaconda3/envs/snowflakes/lib/python3.5/site-packages/numpy/core/numeric.py:482: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff5d427a240>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aOuq0rW/aj0Mze9DMnp3i31YAd/+0u68F7gV+q9EGmNltZrbDzHYMDAw0/hfM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\nBMcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5d42b0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_gpu = gpuarray.to_gpu(x_np)\n",
    "batch_size = 16\n",
    "xf_gpu = gpuarray.empty((batch_size, N//2+1), np.complex64)\n",
    "plan_forward = cu_fft.Plan(N, np.float32, np.complex64, batch_size)\n",
    "cu_fft.fft(x_gpu, xf_gpu, plan_forward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cmoman/anaconda3/envs/snowflakes/lib/python3.5/site-packages/numpy/core/numeric.py:482: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff5c70dffd0>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/hyRvBe5j+HXyKuCBqvqTJL/EKp99VJL3AH9cVTf0ZfYkv8rwVQIML8v8dVV9qi/zAyS5\nEvg8sAF4FriV7uuIHswPaywMkqSlraVLSZKkZTAMkqSGYZAkNQyDJKlhGCRJDcMgSWoYBklSwzBI\nkhr/C9rZJHZ89ESiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5c71569b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xf_gpu.get()[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing fft/ifft..\n",
      "Success status:  True\n",
      "Testing in-place fft..\n",
      "Success status:  True\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import pycuda.autoinit\n",
    "import pycuda.gpuarray as gpuarray\n",
    "\n",
    "import skcuda.fft as cu_fft\n",
    "\n",
    "print('Testing fft/ifft..')\n",
    "N = 4096 * 16\n",
    "batch_size = 16\n",
    "\n",
    "x = np.asarray(np.random.rand(batch_size, N), np.float32)\n",
    "xf = np.fft.fft(x)\n",
    "y = np.real(np.fft.ifft(xf))\n",
    "\n",
    "x_gpu = gpuarray.to_gpu(x)\n",
    "xf_gpu = gpuarray.empty((batch_size, N//2+1), np.complex64)\n",
    "plan_forward = cu_fft.Plan(N, np.float32, np.complex64, batch_size)\n",
    "cu_fft.fft(x_gpu, xf_gpu, plan_forward)\n",
    "\n",
    "y_gpu = gpuarray.empty_like(x_gpu)\n",
    "plan_inverse = cu_fft.Plan(N, np.complex64, np.float32, batch_size)\n",
    "cu_fft.ifft(xf_gpu, y_gpu, plan_inverse, True)\n",
    "\n",
    "print('Success status: ', np.allclose(y, y_gpu.get(), atol=1e-6))\n",
    "\n",
    "print('Testing in-place fft..')\n",
    "x = np.asarray(np.random.rand(batch_size, N) +\n",
    "               1j * np.random.rand(batch_size, N), np.complex64)\n",
    "x_gpu = gpuarray.to_gpu(x)\n",
    "\n",
    "plan = cu_fft.Plan(N, np.complex64, np.complex64, batch_size)\n",
    "cu_fft.fft(x_gpu, x_gpu, plan)\n",
    "\n",
    "cu_fft.ifft(x_gpu, x_gpu, plan, True)\n",
    "\n",
    "print('Success status: ', np.allclose(x, x_gpu.get(), atol=1e-6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(temp_fft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'col' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-5484df5cd031>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'col' is not defined"
     ]
    }
   ],
   "source": [
    "temp[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.to_xarray of DATE\n",
       "1994-01-01     9.886111\n",
       "1994-01-02    11.707895\n",
       "1994-01-03    12.661111\n",
       "1994-01-04    11.894737\n",
       "1994-01-05    10.283333\n",
       "1994-01-06    11.789474\n",
       "1994-01-07     8.491667\n",
       "1994-01-08     8.684211\n",
       "1994-01-09    11.207500\n",
       "1994-01-10     9.727778\n",
       "1994-01-11     9.613889\n",
       "1994-01-12    11.869444\n",
       "1994-01-13    12.226471\n",
       "1994-01-14    11.558333\n",
       "1994-01-15    10.920000\n",
       "1994-01-16     9.465789\n",
       "1994-01-17     5.467647\n",
       "1994-01-18     6.405263\n",
       "1994-01-19     5.965789\n",
       "1994-01-20     6.534211\n",
       "1994-01-21     8.687500\n",
       "1994-01-22     9.672500\n",
       "1994-01-23     9.458333\n",
       "1994-01-24    10.826471\n",
       "1994-01-25    14.192105\n",
       "1994-01-26    10.785294\n",
       "1994-01-27    11.558333\n",
       "1994-01-28    10.836111\n",
       "1994-01-29     9.405556\n",
       "1994-01-30    10.657895\n",
       "                ...    \n",
       "2013-12-02     9.112500\n",
       "2013-12-03    14.356250\n",
       "2013-12-04     9.000000\n",
       "2013-12-05    14.691667\n",
       "2013-12-06     9.425000\n",
       "2013-12-07    10.891667\n",
       "2013-12-08    12.390000\n",
       "2013-12-09    10.621429\n",
       "2013-12-10     7.588889\n",
       "2013-12-11    10.555000\n",
       "2013-12-12    10.471429\n",
       "2013-12-13    14.300000\n",
       "2013-12-14    14.183333\n",
       "2013-12-15    13.666667\n",
       "2013-12-16    11.268750\n",
       "2013-12-17     8.690000\n",
       "2013-12-18    15.435714\n",
       "2013-12-19    15.070000\n",
       "2013-12-20    16.200000\n",
       "2013-12-21    10.716667\n",
       "2013-12-22    12.025000\n",
       "2013-12-24    23.350000\n",
       "2013-12-25    27.600000\n",
       "2013-12-26    11.892857\n",
       "2013-12-27    10.283333\n",
       "2013-12-28    11.591667\n",
       "2013-12-29    19.383333\n",
       "2013-12-30     8.561111\n",
       "2013-12-31     8.433333\n",
       "2014-01-01     6.550000\n",
       "dtype: float64>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t_gpu = gpuarray.to_gpu(temp2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tf_gpu = gpuarray.empty((batch_size, N//2+1), np.complex64)\n",
    "plan_forward = cu_fft.Plan(N, np.float32, np.complex64, batch_size)\n",
    "cu_fft.fft(t_gpu, tf_gpu, plan_forward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[             nan            +nanj,\n",
       "                     nan            +nanj,\n",
       "                     nan            +nanj, ...,\n",
       "                     nan            +nanj,\n",
       "                     nan            +nanj,\n",
       "                     nan            +nanj],\n",
       "       [  3.03602887e+38 +7.09884336e+31j,\n",
       "          1.01715311e+38 -1.63889615e+37j,\n",
       "          3.58744486e+37 -3.45342808e+37j, ...,\n",
       "          3.58744790e+37 +3.45343213e+37j,\n",
       "          1.01715321e+38 +1.63889780e+37j,\n",
       "          3.03602846e+38 -2.40853614e+31j],\n",
       "       [  7.45717087e+01 +7.62939453e-06j,\n",
       "          7.45018845e+01 -3.65767002e+00j,\n",
       "          7.42907562e+01 -7.32403183e+00j, ...,\n",
       "          2.88473845e+00 -3.97625089e+00j,\n",
       "          2.42204428e+00 -2.00461221e+00j,\n",
       "          2.26749611e+00 -2.47955322e-05j],\n",
       "       ..., \n",
       "       [  3.62651636e+03 +3.33547592e-04j,\n",
       "         -1.28534861e+01 +1.14129791e+01j,\n",
       "         -1.98933372e+01 -3.07429962e+01j, ...,\n",
       "         -1.29579830e+01 +2.67077885e+01j,\n",
       "         -4.98164034e+00 -1.71239471e+01j,\n",
       "         -8.11666965e+00 +8.58306885e-06j],\n",
       "       [  3.67032422e+03 +3.39508057e-04j,\n",
       "         -1.41340313e+01 +2.16874619e+01j,\n",
       "         -4.04637642e+01 +6.85342979e+00j, ...,\n",
       "         -5.09021235e+00 -3.91452946e-02j,\n",
       "          5.78681564e+00 +1.94270802e+01j,\n",
       "         -5.96878510e+01 -8.58306885e-06j],\n",
       "       [  3.62055933e+03 +3.39508057e-04j,\n",
       "         -2.34016495e+01 -1.97339592e+01j,\n",
       "         -1.10985079e+01 +3.57445221e+01j, ...,\n",
       "          4.90827179e+00 +3.92434096e+00j,\n",
       "          1.39224319e+01 +9.38501263e+00j,\n",
       "          5.53606129e+00 +9.53674316e-07j]], dtype=complex64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf_gpu.get()"
   ]
  },
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   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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