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monkeybutter/3dim_hdf5.ipynb

Created October 23, 2015 04:06
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{
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import glob\n",
"\n",
"def get_paths(years, lats, lons):\n",
" path = \"/g/data2/rs0/tiles/EPSG4326_1deg_0.00025pixel/LS5_TM/\"\n",
" nbar_list = []\n",
" \n",
" for lon in lons:\n",
" for lat in lats:\n",
" for year in years:\n",
" part = glob.glob(path + \"{lon:03d}_{lat:04d}/{year}/LS5_TM_NBAR*.tif\".format(lon=lon, lat=lat, year=year))\n",
" nbar_list.extend(part)\n",
" \n",
" return nbar_list\n",
"\n",
"def get_paths2(lat, lon, year):\n",
" path = \"/g/data1/ep1_2/z00/prl900/hdf5_test/1A_hdf5/LS5_TM_NBAR_{lon:03d}_{lat:04d}_{year}*.h5\".format(lon=lon, lat=lat, year=year)\n",
" return glob.glob(path)\n",
"\n",
"def get_paths3(lat, lon, year):\n",
" path = \"/g/data1/ep1_2/z00/prl900/hdf5_test/1B_hdf5/LS5_TM_NBAR_{lon:03d}_{lat:04d}_{year}*.h5\".format(lon=lon, lat=lat, year=year)\n",
" return glob.glob(path)\n",
"\n",
"def get_paths4(path):\n",
" path = \"{}*.h5\".format(path)\n",
" return glob.glob(path)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from osgeo import gdal\n",
"import numpy as np\n",
"\n",
"def tiff2xyb(tiff_path):\n",
" image = gdal.Open(tiff_path)\n",
" \n",
" #cube = None\n",
"\n",
" for band in range(image.RasterCount):\n",
" if band == 0:\n",
" band_pack = image.GetRasterBand(band+1).ReadAsArray()\n",
" else:\n",
" band_pack = np.dstack((band_pack, image.GetRasterBand(band+1).ReadAsArray()))\n",
" \n",
" return band_pack\n",
"\n",
"\n",
"def tiff2bxy(tiff_path):\n",
" image = gdal.Open(tiff_path)\n",
" \n",
" #cube = None\n",
"\n",
" for band in range(image.RasterCount):\n",
" if band == 0:\n",
" band_pack = image.GetRasterBand(band+1).ReadAsArray()[np.newaxis,:]\n",
" else:\n",
" band_pack = np.vstack((band_pack, image.GetRasterBand(band+1).ReadAsArray()[np.newaxis,:]))\n",
" \n",
" return band_pack\n",
"\n",
"\n",
"def tiff2txyb(tiff_path):\n",
" image = gdal.Open(tiff_path)\n",
" \n",
" cube = None\n",
"\n",
" for band in range(image.RasterCount):\n",
" if band == 0:\n",
" band_pack = image.GetRasterBand(band+1).ReadAsArray()\n",
" else:\n",
" band_pack = np.dstack((band_pack, image.GetRasterBand(band+1).ReadAsArray()))\n",
"\n",
" if cube is None:\n",
" cube = band_pack[np.newaxis,:]\n",
" else:\n",
" cube = np.concatenate((cube, band_pack[np.newaxis,:]), axis=0)\n",
" \n",
" return cube\n",
"\n",
"def stacker_first(cube, layer):\n",
"\n",
" if cube is None:\n",
" cube = layer[np.newaxis,:]\n",
" else:\n",
" cube = np.concatenate((cube, layer[np.newaxis,:]), axis=0)\n",
" \n",
" return cube\n",
"\n",
"\n",
"def stacker_last(cube, layer):\n",
"\n",
" if cube is None:\n",
" cube = layer[:, :, :, np.newaxis]\n",
" else:\n",
" cube = np.concatenate((cube, layer[:, :, :, np.newaxis]), axis=3)\n",
" \n",
" return cube"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def time_stacker_first(paths):\n",
" cube = None\n",
"\n",
" for path in paths:\n",
" with h5py.File(path, 'r') as h5f:\n",
" layer = h5f[\"NBAR\"].value\n",
" cube = stacker_first(cube, layer)\n",
" \n",
" return cube\n",
"\n",
"def time_stacker_last(paths):\n",
" cube = None\n",
"\n",
" for path in paths:\n",
" with h5py.File(path, 'r') as h5f:\n",
" layer = h5f[\"NBAR\"].value\n",
" cube = stacker_last(cube, layer)\n",
"\n",
" return cube"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import h5py\n",
"\n",
"def pack_data(out_path, data):\n",
" with h5py.File(out_path, 'w') as h5f:\n",
" ds = h5f.create_dataset(\"NBAR\", data=data, compression='lzf', chunks=(100, 100, 1))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"149 -36 1990\n",
"149 -35 1989\n",
"149 -35 1990\n"
]
}
],
"source": [
"import os.path\n",
"\n",
"years = range(1988, 1998)\n",
"years = range(1989, 1991)\n",
"lats = range(-36, -34)\n",
"lons = range(148, 150)\n",
"\n",
"for lon in lons:\n",
" for lat in lats:\n",
" for year in years:\n",
" if not os.path.isfile('/g/data1/ep1_2/z00/prl900/hdf5_test/{lat:03d}_{lon:03d}_{year}.h5'.format(lat=abs(lat), lon=lon, year=year)): \n",
" print lon, lat, year\n",
" paths = get_paths3(lat, lon, year)\n",
" cube = time_stacker_first(paths)\n",
"\n",
"\n",
" with h5py.File('/g/data1/ep1_2/z00/prl900/hdf5_test/{lat:03d}_{lon:03d}_{year}.h5'.format(lat=abs(lat), lon=lon, year=year), 'w') as h5f:\n",
" h5f.create_dataset(\"NBAR\", data=cube, compression='lzf')\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_filename(path):\n",
" return path.split('/')[-1]\n",
"\n",
"def chunker(in_folder, out_folder):\n",
" exp_path = \"/g/data1/ep1_2/z00/prl900/hdf5_test/\"\n",
" \n",
" in_paths = get_paths4(exp_path + in_folder + '/')\n",
" \n",
" for in_path in in_paths:\n",
" if not os.path.isfile(exp_path + out_folder + '/' + get_filename(in_path)):\n",
" with h5py.File(in_path, 'r') as h5i:\n",
" cube = h5i[\"NBAR\"].value\n",
"\n",
" shape = cube.shape\n",
"\n",
" chunks = [] \n",
" for dim in shape:\n",
" if dim == 4000:\n",
" chunks.append(100)\n",
" \n",
" elif dim == 6:\n",
" chunks.append(1)\n",
"\n",
" else:\n",
" chunks.append(dim)\n",
" \n",
" \"\"\"\n",
" else if 6 < dim < 1000:\n",
" chunks.append(1)\n",
" \"\"\"\n",
"\n",
" with h5py.File(exp_path + out_folder + '/' + get_filename(in_path), 'w') as h5o:\n",
" h5o.create_dataset(\"NBAR\", data=cube, compression='lzf', chunks=tuple(chunks))\n",
"\n",
" \n",
"chunker('1A_TF', '1A_TF_C2')\n",
"chunker('1A_TL', '1A_TL_C2')\n",
"chunker('1B_TF', '1B_TF_C2')\n",
"chunker('1B_TL', '1B_TL_C2')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"#C1 x=100, y=100, b=[:], time=[:]\n",
"#C2 x=100, y=100, b=1, time=[:]\n",
"#C3 x=100, y=100, b=[:], time=1\n",
"#C4 x=100, y=100, b=1, time=1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6, 4000, 4000, 28)\n",
"(1, 100, 100, 10)\n",
"(6, 4000, 4000, 55)\n",
"(1, 100, 100, 10)\n",
"(6, 4000, 4000, 39)\n",
"(1, 100, 100, 10)\n",
"(6, 4000, 4000, 33)\n",
"(1, 100, 100, 10)\n",
"(6, 4000, 4000, 53)\n",
"(1, 100, 100, 10)\n",
"(6, 4000, 4000, 33)\n",
"(1, 100, 100, 10)\n",
"(6, 4000, 4000, 45)\n",
"(1, 100, 100, 10)\n",
"(6, 4000, 4000, 28)\n",
"(1, 100, 100, 10)\n"
]
}
],
"source": [
"import h5py\n",
"import os.path\n",
"import glob\n",
"\n",
"def get_files(path):\n",
" path = \"{}*.h5\".format(path)\n",
" return glob.glob(path)\n",
"\n",
"def get_filename(path):\n",
" return path.split('/')[-1]\n",
"\n",
"def repack(in_folder, out_folder):\n",
" exp_path = \"/g/data1/ep1_2/z00/prl900/hdf5_test/\"\n",
" in_paths = get_files(exp_path + in_folder + '/')\n",
" \n",
" for in_path in in_paths:\n",
" if not os.path.isfile(exp_path + out_folder + '/' + get_filename(in_path)):\n",
" with h5py.File(in_path, 'r') as h5i:\n",
" cube = h5i[\"NBAR\"].value\n",
" chunk_shape = (1, 100, 100, 10)\n",
" with h5py.File(exp_path + out_folder + '/' + get_filename(in_path), 'w') as h5o:\n",
" h5o.create_dataset(\"NBAR\", data=cube, compression='lzf', chunks=chunk_shape)\n",
"\n",
"col = '1B_TL'\n",
"repack(col, col + '_LZF_C3')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob\n",
"from random import randint\n",
"\n",
"def get_files(path):\n",
" path = \"{}*.h5\".format(path)\n",
" return glob.glob(path)\n",
"\n",
"#start & end both included\n",
"def get_rdm_list(number, start, end):\n",
" out = []\n",
" ready = False\n",
" \n",
" while not ready and len(out) < number:\n",
" n = randint(start, end)\n",
" if n not in out:\n",
" out.append(n)\n",
" \n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 26.4 s, sys: 817 ms, total: 27.2 s\n",
"Wall time: 37.7 s\n"
]
}
],
"source": [
"import h5py\n",
"from osgeo import gdal\n",
"\n",
"#image is composed of 8 tile\n",
"def get_image(path):\n",
" paths = get_files(path)\n",
" f_idxs = get_rdm_list(8, 0, len(paths)-1)\n",
" \n",
" for f_idx in f_idxs:\n",
" with h5py.File(paths[f_idx], 'r') as h5f:\n",
" shape = h5f[\"NBAR\"].shape\n",
" cube = h5f[\"NBAR\"][randint(0, 5), :, :, randint(0, shape[3]-1)]\n",
"\n",
"\n",
"%time get_image('/g/data1/ep1_2/z00/prl900/hdf5_test/1B_TL_LZF_C3/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"\n",
"# 1A_TF (T, 4000, 4000, 6)\n",
"res_1A_TF = [4.3, 4.51, 4.05, 3.38, 3.78, 5.03, 4.11, 4.08, 4.25, 3.86]\n",
"# 1A_TL (4000, 4000, 6, T)\n",
"res_1A_TL = [149.0, 127.0, 136.0, 152.0, over the top]\n",
"\n",
"# 1B_TF (T, 6, 4000, 4000)\n",
"res_1B_TF = [1.16, 1.32, 1,07, 1.19, 1.04, 1.37, 1.18, 1.14, 1.04, 1.01]\n",
"# 1B_TL (6, 4000, 4000, T)\n",
"res_1B_TL = [25.8, 22.3, 27.5, 23.0, 24.3, 22.6, 36.8, 28.4, 26.9, 32.7]\n",
"\n",
"\n",
"# 1A_TF_LZF (T, 4000, 4000, 6) chunked as [T, 4000, 4000, 1] !Unable to create dataset (Chunk size must be < 4gb)\n",
"res_1A_TF_LZF = [21.6, 21.4, 21.5, 21.3, 21.6, 21.1, 20.9, 21.6, 21.4, ]\n",
"# 1A_TL_LZF (4000, 4000, 6, T) chunked as [4000, 4000, 1, T]\n",
"res_1A_TL_LZF = [over the top]\n",
"\n",
"# 1B_TF_LZF (T, 6, 4000, 4000) chunked as [T, 1, 4000, 4000]\n",
"res_1B_TF_LZF = [38.2, 36.4, 39.6, 30.3, 32.7, 34.2, 33.9, 37.2, 33.5, 29.1]\n",
"# 1B_TL_LZF (6, 4000, 4000, T) chunked as [1, 4000, 4000, T]\n",
"res_1B_TL_LZF = [81.0, 74.0, 79.0, 86.0, 78.0]\n",
"\n",
"\n",
"# Spatial access optimised\n",
"# 1A_TF_LZF_C1 (T, 4000, 4000, 6) chunked as [1, 4000, 4000, 1] !Unable to create dataset (Chunk size must be < 4gb)\n",
"res_1A_TF_LZF_C1 = [21.7, 21.4, 21.5, 21.2, 21.3, 21.6, 21.1, 21.7, 21.2, 21.3]\n",
"# 1A_TL_LZF_C1 (4000, 4000, 6, T) chunked as [4000, 4000, 1, 1]\n",
"res_1A_TL_LZF_C1 = [21.7, 22.0, 21.7, 20.9, 22.7, 21.4, 21.6, 20.7, 21.8, 21.3]\n",
"\n",
"# 1B_TF_LZF_C1 (T, 6, 4000, 4000) chunked as [1, 1, 4000, 4000]\n",
"res_1B_TF_LZF_C1 = [0.90, 0.84, 0.61, 0.92, 0.73, 0.86, 0.70, 0.80, 0.59, 0.74]\n",
"# 1B_TL_LZF_C1 (6, 4000, 4000, T) chunked as [1, 4000, 4000, 1]\n",
"res_1B_TL_LZF_C1 = [21.0, 20.5, 21.5, 22.2, 21.7, 20.3, 21.2, 22.0, 21.4, 21.2]\n",
"\n",
"\n",
"# Time drill optimised\n",
"# 1A_TF_LZF_C2 (T, 4000, 4000, 6) chunked as [T, 25, 25, 1]\n",
"res_1A_TF_LZF_C2 = [159.0, 147.0, 157.0, 162.0, 137.0, 143.0, 152.0, 143.0, 149.0, 143.0]\n",
"# 1A_TL_LZF_C2 (4000, 4000, 6, T) chunked as [25, 25, 1, T]\n",
"res_1A_TL_LZF_C2 = [185.0, over the top]\n",
"\n",
"# 1B_TF_LZF_C2 (T, 6, 4000, 4000) chunked as [T, 1, 25, 25]\n",
"res_1B_TF_LZF_C2 = [20.0, 17.6, 22.3, 18.7, 19.4, 18.4, 21.5, 20.9, 18.7, 18.5]\n",
"# 1B_TL_LZF_C2 (6, 4000, 4000, T) chunked as [1, 25, 25, T]\n",
"res_1B_TL_LZF_C2 = [48.1, 53.8, 45.7, 46.8, 47.4]\n",
"\n",
"\n",
"# Compromise\n",
"# 1A_TF_LZF_C3 (T, 4000, 4000, 6) chunked as [10, 100, 100, 1]\n",
"res_1A_TF_LZF_C3 = [35.3, 34.2, 33.8, 32.0, 32.3, 33.8, 31.6, 32.3, 30.5, 34.9]\n",
"# 1A_TL_LZF_C3 (4000, 4000, 6, T) chunked as [100, 100, 1, 10]\n",
"res_1A_TL_LZF_C3 = [52.7, 49.5, 56.2, 46.1]\n",
"\n",
"# 1B_TF_LZF_C3 (T, 6, 4000, 4000) chunked as [10, 1, 100, 100]\n",
"res_1A_TF_LZF_C3 = [4.54, 4.6, 4.42, 4.55, 4.99, 4.29, 4.83, 3.88, 4.28, 4.41]\n",
"# 1B_TL_LZF_C3 (6, 4000, 4000, T) chunked as [1, 100, 100, 10]\n",
"res_1A_TL_LZF_C3 = [38.1, 37.7, 38.2, 36.4, 39.6 ]"
]
},
{
"cell_type": "code",
"execution_count": 327,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 5.68 s, sys: 1.92 s, total: 7.6 s\n",
"Wall time: 8.47 s\n"
]
}
],
"source": [
"#timeseries is composed of 80 tiles\n",
"def get_timeseries(path):\n",
" paths = get_files(path)\n",
" f_idxs = get_rdm_list(len(paths)/4, 0, len(paths)-1)\n",
" band = randint(0, 5)\n",
" x = randint(0, 3999)\n",
" y = randint(0, 3999)\n",
" \n",
" for f_idx in f_idxs:\n",
" with h5py.File(paths[f_idx], 'r') as h5f:\n",
" value = h5f[\"NBAR\"][band, x, y, :]\n",
" \n",
"%time get_timeseries('/g/data1/ep1_2/z00/prl900/hdf5_test/1B_TL_LZF/')"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"\n",
"# 1A_TF (T, 4000, 4000, 6)\n",
"res_1A_TF = [0.14, 12, 0.11, 0.13, 0.12, 0.09, 0.14, 0.11, 0.10, 0.11, 0.14]\n",
"# 1A_TL (4000, 4000, 6, T)\n",
"res_1A_TL = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n",
"\n",
"# 1B_TF (T, 6, 4000, 4000)\n",
"res_1B_TF = [0.46, 0.14, 0.13, 0.13, 0.34, 0.16, 0.51]\n",
"# 1B_TL (6, 4000, 4000, T)\n",
"res_1B_TL = [0.04, 0.05, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n",
"\n",
"\n",
"# 1A_TF_LZF (T, 4000, 4000, 6) chunked as [T, 4000, 4000, 1] !Unable to create dataset (Chunk size must be < 4gb)\n",
"res_1A_TF_LZF = [14.8, 6.5, 5.04, 11.2, 11.9, 7.95, 14.7, 6.33, 8.3, 8.45]\n",
"# 1A_TL_LZF (4000, 4000, 6, T) chunked as [4000, 4000, 1, T]\n",
"res_1A_TL_LZF = [11.6, 12.2, 13.2, 8.8, 6.81, 4.54, 11.3, 10.9, 9.02, 10.6]\n",
"\n",
"# 1B_TF_LZF (T, 6, 4000, 4000) chunked as [T, 1, 4000, 4000]\n",
"res_1B_TF_LZF = [14.4, 6.83, 5.66, 5.86, 10.2, 5.87, 4.8, 4.68, 11.4]\n",
"# 1B_TL_LZF (6, 4000, 4000, T) chunked as [1, 4000, 4000, T]\n",
"res_1B_TL_LZF = [13.9, 12.1, 13.0, 11.1, 17.4, 11.4, 7.51, 15.8, 8.47, 13.9, 12.1]\n",
"\n",
"\n",
"# Spatial access optimised\n",
"# 1A_TF_LZF_C1 (T, 4000, 4000, 6) chunked as [1, 4000, 4000, 1] !Unable to create dataset (Chunk size must be < 4gb)\n",
"res_1A_TF_LZF_C1 = [6.34, 4.44, 5.66, 6.09, 3.7, 4.5, 6.22, 4.88, 4.01, 3.95]\n",
"# 1A_TL_LZF_C1 (4000, 4000, 6, T) chunked as [4000, 4000, 1, 1]\n",
"res_1A_TL_LZF_C1 = [3.74, 4.37, 4.1, 3.97, 4.68, 3.71, 4.41, 4.05, 4.64, 3.83, 3.79]\n",
"\n",
"# 1B_TF_LZF_C1 (T, 6, 4000, 4000) chunked as [1, 1, 4000, 4000]\n",
"res_1B_TF_LZF_C1 = [6.24, 6.89, 7.09, 6.51, 5.26, 6.71, 5.48, 3.63, 5.81, 3.95, 3.74]\n",
"# 1B_TL_LZF_C1 (6, 4000, 4000, T) chunked as [1, 4000, 4000, 1]\n",
"res_1B_TL_LZF_C1 = [4.54, 3.69, 3.63, 3.86, 3.28, 3.02, 4.46, 3.16, 4.85, 4.51, 4.13]\n",
"\n",
"\n",
"# Time drill optimised\n",
"# 1A_TF_LZF_C2 (T, 4000, 4000, 6) chunked as [T, 25, 25, 1]\n",
"res_1A_TF_LZF_C2 = [0.19, 0.23, 0.21, 0.32, 0.18, 0.15, 0.13, 0.16, 0.16, 0.14]\n",
"# 1A_TL_LZF_C2 (4000, 4000, 6, T) chunked as [25, 25, 1, T]\n",
"res_1A_TL_LZF_C2 = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n",
"\n",
"# 1B_TF_LZF_C2 (T, 6, 4000, 4000) chunked as [T, 1, 25, 25]\n",
"res_1B_TF_LZF_C2 = [0.01, 0.02, 0.01, 0.02, 0.01, 0.02, 0.01, 0.02, 0.01, 0.01]\n",
"# 1B_TL_LZF_C2 (6, 4000, 4000, T) chunked as [1, 25, 25, T]\n",
"res_1B_TL_LZF_C2 = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n",
"\n",
"\n",
"# Compromise\n",
"# 1A_TF_LZF_C3 (T, 4000, 4000, 6) chunked as [10, 100, 100, 1]\n",
"res_1A_TF_LZF_C3 = [0.03, 0.08, 0.05, 0.07, 0.04, 0.06, 0.09, 0.05, 0.07]\n",
"# 1A_TL_LZF_C3 (4000, 4000, 6, T) chunked as [100, 100, 1, 10]\n",
"res_1A_TL_LZF_C3 = [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]\n",
"\n",
"# 1B_TF_LZF_C3 (T, 6, 4000, 4000) chunked as [10, 1, 100, 100]\n",
"res_1B_TF_LZF_C3 = [0.15, 0.05, 0.14, 0.04, 0.11, 0.14, 0.10, 0.10]\n",
"# 1B_TL_LZF_C3 (6, 4000, 4000, T) chunked as [1, 100, 100, 10]\n",
"res_1B_TL_LZF_C3 = [0.05, 0.05, 0.04, 0.05, 0.04, 0.05, 0.05, 0.04, 0.04, 0.05]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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