jreadey/adcirc_smoke_test.ipynb

## adcirc_smoke_test.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import h5pyd as h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jreadey@hdfgroup.org                 folder   2019-05-04 14:15:37 /home/jreadey/ESIP/\n",
      "jreadey@hdfgroup.org    54.8K        domain   2019-05-04 18:07:10 /home/jreadey/ESIP/adcirc_01.nc\n",
      "2 items\n"
     ]
    }
   ],
   "source": [
    "! hsls -H -v /home/jreadey/ESIP/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File(\"/home/jreadey/ESIP/adcirc_01.nc\", \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['adcirc_mesh',\n",
       " 'depth',\n",
       " 'element',\n",
       " 'ibtype',\n",
       " 'ibtypee',\n",
       " 'max_nvdll',\n",
       " 'max_nvell',\n",
       " 'mesh',\n",
       " 'nbdv',\n",
       " 'nbou',\n",
       " 'nbvv',\n",
       " 'nele',\n",
       " 'neta',\n",
       " 'node',\n",
       " 'nope',\n",
       " 'nvdll',\n",
       " 'nvel',\n",
       " 'nvell',\n",
       " 'nvertex',\n",
       " 'time',\n",
       " 'x',\n",
       " 'y',\n",
       " 'zeta']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "adcirc_mesh (1,)\n",
      "depth (9228245,)\n",
      "element (18300169, 3)\n",
      "ibtype (1190,)\n",
      "ibtypee (1,)\n",
      "max_nvdll ()\n",
      "max_nvell ()\n",
      "mesh (1,)\n",
      "nbdv (292,)\n",
      "nbou (1190,)\n",
      "nbvv (157605,)\n",
      "nele (18300169,)\n",
      "neta (292,)\n",
      "node (9228245,)\n",
      "nope (1,)\n",
      "nvdll (1,)\n",
      "nvel (157605,)\n",
      "nvell (1190,)\n",
      "nvertex (3,)\n",
      "time (720,)\n",
      "x (9228245,)\n",
      "y (9228245,)\n",
      "zeta (720, 9228245)\n"
     ]
    }
   ],
   "source": [
    "for name in f:\n",
    "    dset = f[name]\n",
    "    print(name, dset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "depth = f['depth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9228245,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 488 ms, sys: 256 ms, total: 744 ms\n",
      "Wall time: 1.45 s\n"
     ]
    }
   ],
   "source": [
    "%time arr = depth[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-69.478249, 8012.2879, 59.06915539064633)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.min(), arr.max(), arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f08f5c86d68>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(arr.shape[0]), arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"%matplotlib inline\n",
	"import h5pyd as h5py\n",
	"import numpy as np\n",
	"import matplotlib.pyplot as plt\n",
	"import matplotlib.image as mpimg"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"jreadey@hdfgroup.org folder 2019-05-04 14:15:37 /home/jreadey/ESIP/\n",
	"jreadey@hdfgroup.org 54.8K domain 2019-05-04 18:07:10 /home/jreadey/ESIP/adcirc_01.nc\n",
	"2 items\n"
	]
	}
	],
	"source": [
	"! hsls -H -v /home/jreadey/ESIP/"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"f = h5py.File(\"/home/jreadey/ESIP/adcirc_01.nc\", \"r\")"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"['adcirc_mesh',\n",
	" 'depth',\n",
	" 'element',\n",
	" 'ibtype',\n",
	" 'ibtypee',\n",
	" 'max_nvdll',\n",
	" 'max_nvell',\n",
	" 'mesh',\n",
	" 'nbdv',\n",
	" 'nbou',\n",
	" 'nbvv',\n",
	" 'nele',\n",
	" 'neta',\n",
	" 'node',\n",
	" 'nope',\n",
	" 'nvdll',\n",
	" 'nvel',\n",
	" 'nvell',\n",
	" 'nvertex',\n",
	" 'time',\n",
	" 'x',\n",
	" 'y',\n",
	" 'zeta']"
	]
	},
	"execution_count": 4,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"list(f)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"adcirc_mesh (1,)\n",
	"depth (9228245,)\n",
	"element (18300169, 3)\n",
	"ibtype (1190,)\n",
	"ibtypee (1,)\n",
	"max_nvdll ()\n",
	"max_nvell ()\n",
	"mesh (1,)\n",
	"nbdv (292,)\n",
	"nbou (1190,)\n",
	"nbvv (157605,)\n",
	"nele (18300169,)\n",
	"neta (292,)\n",
	"node (9228245,)\n",
	"nope (1,)\n",
	"nvdll (1,)\n",
	"nvel (157605,)\n",
	"nvell (1190,)\n",
	"nvertex (3,)\n",
	"time (720,)\n",
	"x (9228245,)\n",
	"y (9228245,)\n",
	"zeta (720, 9228245)\n"
	]
	}
	],
	"source": [
	"for name in f:\n",
	" dset = f[name]\n",
	" print(name, dset.shape)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [],
	"source": [
	"depth = f['depth']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"(9228245,)"
	]
	},
	"execution_count": 7,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"depth.shape"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"CPU times: user 488 ms, sys: 256 ms, total: 744 ms\n",
	"Wall time: 1.45 s\n"
	]
	}
	],
	"source": [
	"%time arr = depth[:]"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 9,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"(-69.478249, 8012.2879, 59.06915539064633)"
	]
	},
	"execution_count": 9,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"arr.min(), arr.max(), arr.mean()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x7f08f5c86d68>]"
	]
	},
	"execution_count": 10,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.plot(range(arr.shape[0]), arr)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"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": "3.6.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}