06-26-17_test1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import h5py\n",
    "import nbodykit.lab as nlab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ifile = '/Users/ding/Documents/playground/WiggleNowiggle/1test_data/normal-fof00100_0.6452_0.168.hdf5'\n",
    "f = h5py.File(ifile, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: KeysView(<HDF5 file \"normal-fof00100_0.6452_0.168.hdf5\" (mode r)>)\n",
      "['FOFGroups']\n"
     ]
    }
   ],
   "source": [
    "# List all groups\n",
    "print(\"Keys: %s\" % f.keys())\n",
    "group = list(f.keys())\n",
    "print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ ([  9.99988258e-01,   9.99988377e-01,   6.48897898e-04], [ -1.44284144e-07,   1.25406046e-07,   1.43052944e-07],     0)\n",
      " ([  2.58767426e-01,   3.38956803e-01,   7.74578005e-02], [ -4.20922972e-03,  -4.52705048e-04,   5.28158911e-04], 64700)\n",
      " ([  1.80212960e-01,   1.37286350e-01,   6.39832854e-01], [  1.25438161e-03,  -7.21376273e-04,  -3.95847077e-04], 63277)\n",
      " ...,\n",
      " ([  5.03986776e-01,   2.72752553e-01,   9.67745930e-02], [  4.05300641e-03,  -1.11695495e-03,   4.07119654e-03],    33)\n",
      " ([  5.07516824e-02,   3.13175380e-01,   3.72966170e-01], [ -2.91355164e-03,   1.18933572e-03,  -2.79745017e-03],    33)\n",
      " ([  3.60247374e-01,   2.44766977e-02,   1.90057501e-01], [  9.23227984e-04,   7.36368471e-04,   4.00221488e-03],    33)] [('Position', '<f4', (3,)), ('Velocity', '<f4', (3,)), ('Length', '<i4')]\n"
     ]
    }
   ],
   "source": [
    "dset = f[group[0]][:]\n",
    "print(dset, dset.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "print(f[group[0]].attrs['BoxSize'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.37998380e+03   1.37998396e+03   8.95479099e-01]\n",
      " [  3.57099048e+02   4.67760388e+02   1.06891765e+02]\n",
      " [  2.48693884e+02   1.89455163e+02   8.82969339e+02]\n",
      " ..., \n",
      " [  6.95501751e+02   3.76398523e+02   1.33548938e+02]\n",
      " [  7.00373217e+01   4.32182025e+02   5.14693315e+02]\n",
      " [  4.97141376e+02   3.37778428e+01   2.62279352e+02]]\n"
     ]
    }
   ],
   "source": [
    "x_y_z = dset['Position']*f[group[0]].attrs['BoxSize']\n",
    "print(x_y_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12696653, 3)\n"
     ]
    }
   ],
   "source": [
    "v_xyz = dset['Velocity']*f[group[0]].attrs['BoxSize']\n",
    "print(v_xyz.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "x_v = np.hstack((x_y_z, v_xyz))\n",
    "print(x_v.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<built-in method dump of numpy.ndarray object at 0x1107d50d0>\n"
     ]
    }
   ],
   "source": [
    "print(dset.dump)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_v_cat = nlab.ArrayCatalog(dset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Length', 'Position', 'Selection', 'Velocity', 'Weight']\n"
     ]
    }
   ],
   "source": [
    "print(x_v_cat.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = nlab.FFTPower(x_v_cat, mode='1d', BoxSize=1380., Nmesh=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'comm'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-81-df17b9c585a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnlab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFFTPower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'1d'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBoxSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1380.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNmesh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/nbodykit/algorithms/fftpower.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, first, mode, Nmesh, BoxSize, second, los, Nmu, dk, kmin, poles)\u001b[0m\n\u001b[1;32m    204\u001b[0m             \u001b[0mpoles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m         \u001b[0mFFTPowerBase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNmesh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBoxSize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;31m# save meta-data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/nbodykit/algorithms/fftpower.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, first, second, Nmesh, BoxSize, kmin, dk)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;31m# grab comm from first source\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfirst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;31m# if input is CatalogSource, use defaults to make it into a mesh\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'comm'"
     ]
    }
   ],
   "source": [
    "#r = nlab.FFTPower(dset, mode='1d', BoxSize=1380., Nmesh=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DataSet: dims: (k: 64), variables: ('k', 'power', 'modes')>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.00644902,  0.01115458,  0.01561628,  0.02017146,\n",
       "        0.02492702,  0.02959412,  0.03402964,  0.03848839,  0.04309683,\n",
       "        0.04770974,  0.05220016,  0.05676944,  0.06145674,  0.06598348,\n",
       "        0.07045739,  0.07503438,  0.07957931,  0.08415473,  0.08871564,\n",
       "        0.09324763,  0.09780306,  0.10234642,  0.10691264,  0.11144127,\n",
       "        0.11599882,  0.12061477,  0.12519467,  0.12971907,  0.13424624,\n",
       "        0.1387587 ,  0.14332536,  0.14789404,  0.15247116,  0.15705065,\n",
       "        0.16158943,  0.16613519,  0.17067724,  0.17522835,  0.17977761,\n",
       "        0.18431173,  0.18886541,  0.19342445,  0.19800479,  0.202579  ,\n",
       "        0.20713416,  0.21166358,  0.21619375,  0.22077055,  0.22532619,\n",
       "        0.22987954,  0.23444724,  0.23900943,  0.24357905,  0.24810361,\n",
       "        0.25264894,  0.25720737,  0.26173761,  0.26629377,  0.27084813,\n",
       "        0.27541488,  0.27997044,  0.28451892,  0.28909183])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.power['k']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ding/miniconda3/lib/python3.6/site-packages/numpy/core/numeric.py:531: 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 0x129509780>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+oKDN5/zoNBGRuPnPFcVU1jby/esm98qRZU8lZli4+/fdPd/dRwG3Asvd/fa2bcxsFrAY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\niIjEpLAQEZGYFBYiIhKTwkJERGL6f1VmD61eLtx6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1294edb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(r.power['k'], r.power['power'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}