source-code/hdf5/h5py.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.2.1'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5py.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Writing data to a file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Suppose you want to store the following numpy arrays in an HDF5 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_values = np.linspace(0.0, 1.0, 11)\n",
    "y_values = np.linspace(-1.0, 1.0, 21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the file for writing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5file = h5py.File('temp.h5', 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a group.  This is not required, but it may help you to organize your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_group = h5file.create_group('input')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a dataset in the group, the dataspace is determined by the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_dset = input_group.create_dataset('x', data=x_values)\n",
    "y_dset = input_group.create_dataset('y', data=y_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "h5file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HDF5 \"temp.h5\" {\n",
      "GROUP \"/\" {\n",
      "   GROUP \"input\" {\n",
      "      DATASET \"x\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 11 ) / ( 11 ) }\n",
      "         DATA {\n",
      "         (0): 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1\n",
      "         }\n",
      "      }\n",
      "      DATASET \"y\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 21 ) / ( 21 ) }\n",
      "         DATA {\n",
      "         (0): -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0,\n",
      "         (11): 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1\n",
      "         }\n",
      "      }\n",
      "   }\n",
      "}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!h5dump temp.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading data from a file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the file for reading, this time using a context manager."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.1\n",
      "0.2\n",
      "0.30000000000000004\n",
      "0.4\n",
      "0.5\n",
      "0.6000000000000001\n",
      "0.7000000000000001\n",
      "0.8\n",
      "0.9\n",
      "1.0\n",
      "<class 'h5py._hl.dataset.Dataset'> float64 (11,)\n"
     ]
    }
   ],
   "source": [
    "with h5py.File('temp.h5', 'r') as h5file:\n",
    "    x_dset = h5file['input']['x']\n",
    "    for value in x_dset:\n",
    "        print(value)\n",
    "    print(type(x_dset), x_dset.dtype, x_dset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that HDF5 file objects and groups behave like dictionaries.  The contents can be addressed by indexing and they have methods such as `keys`, `values` and `items`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modifying a file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you want to use the values of $x$ and $y$ to compute $e^{x^2 + y^2}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gaussian(x, y):\n",
    "    return np.exp(-(x**2 + y**2)/2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can open a file for both reading and writing using `r+` as the mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('temp.h5', 'r+') as h5file:\n",
    "    x = h5file['input/x'][:]\n",
    "    y = h5file['input/y'][:]\n",
    "    X, Y = np.meshgrid(x, y)\n",
    "    Z = gaussian(X, Y)\n",
    "    h5file.create_dataset('output/z', data=Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the dataset `z` is created in the new group `output`.  The latter is created implicitly since it doesn't exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HDF5 \"temp.h5\" {\n",
      "GROUP \"/\" {\n",
      "   GROUP \"input\" {\n",
      "      DATASET \"x\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 11 ) / ( 11 ) }\n",
      "         DATA {\n",
      "         (0): 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1\n",
      "         }\n",
      "      }\n",
      "      DATASET \"y\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 21 ) / ( 21 ) }\n",
      "         DATA {\n",
      "         (0): -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0,\n",
      "         (11): 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1\n",
      "         }\n",
      "      }\n",
      "   }\n",
      "   GROUP \"output\" {\n",
      "      DATASET \"z\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 21, 11 ) / ( 21, 11 ) }\n",
      "         DATA {\n",
      "         (0,0): 0.606531, 0.603506, 0.594521, 0.579842, 0.559898, 0.535261,\n",
      "         (0,6): 0.506617, 0.474734, 0.440432, 0.404542, 0.367879,\n",
      "         (1,0): 0.666977, 0.66365, 0.65377, 0.637628, 0.615697, 0.588605,\n",
      "         (1,6): 0.557106, 0.522046, 0.484325, 0.444858, 0.404542,\n",
      "         (2,0): 0.726149, 0.722527, 0.71177, 0.694197, 0.67032, 0.640824,\n",
      "         (2,6): 0.606531, 0.56836, 0.527292, 0.484325, 0.440432,\n",
      "         (3,0): 0.782705, 0.778801, 0.767206, 0.748264, 0.722527, 0.690734,\n",
      "         (3,6): 0.65377, 0.612626, 0.56836, 0.522046, 0.474734,\n",
      "         (4,0): 0.83527, 0.831104, 0.818731, 0.798516, 0.771052, 0.737123,\n",
      "         (4,6): 0.697676, 0.65377, 0.606531, 0.557106, 0.506617,\n",
      "         (5,0): 0.882497, 0.878095, 0.865022, 0.843665, 0.814647, 0.778801,\n",
      "         (5,6): 0.737123, 0.690734, 0.640824, 0.588605, 0.535261,\n",
      "         (6,0): 0.923116, 0.918512, 0.904837, 0.882497, 0.852144, 0.814647,\n",
      "         (6,6): 0.771052, 0.722527, 0.67032, 0.615697, 0.559898,\n",
      "         (7,0): 0.955997, 0.951229, 0.937067, 0.913931, 0.882497, 0.843665,\n",
      "         (7,6): 0.798516, 0.748264, 0.694197, 0.637628, 0.579842,\n",
      "         (8,0): 0.980199, 0.97531, 0.960789, 0.937067, 0.904837, 0.865022,\n",
      "         (8,6): 0.818731, 0.767206, 0.71177, 0.65377, 0.594521,\n",
      "         (9,0): 0.995012, 0.99005, 0.97531, 0.951229, 0.918512, 0.878095,\n",
      "         (9,6): 0.831104, 0.778801, 0.722527, 0.66365, 0.603506,\n",
      "         (10,0): 1, 0.995012, 0.980199, 0.955997, 0.923116, 0.882497,\n",
      "         (10,6): 0.83527, 0.782705, 0.726149, 0.666977, 0.606531,\n",
      "         (11,0): 0.995012, 0.99005, 0.97531, 0.951229, 0.918512, 0.878095,\n",
      "         (11,6): 0.831104, 0.778801, 0.722527, 0.66365, 0.603506,\n",
      "         (12,0): 0.980199, 0.97531, 0.960789, 0.937067, 0.904837, 0.865022,\n",
      "         (12,6): 0.818731, 0.767206, 0.71177, 0.65377, 0.594521,\n",
      "         (13,0): 0.955997, 0.951229, 0.937067, 0.913931, 0.882497, 0.843665,\n",
      "         (13,6): 0.798516, 0.748264, 0.694197, 0.637628, 0.579842,\n",
      "         (14,0): 0.923116, 0.918512, 0.904837, 0.882497, 0.852144, 0.814647,\n",
      "         (14,6): 0.771052, 0.722527, 0.67032, 0.615697, 0.559898,\n",
      "         (15,0): 0.882497, 0.878095, 0.865022, 0.843665, 0.814647, 0.778801,\n",
      "         (15,6): 0.737123, 0.690734, 0.640824, 0.588605, 0.535261,\n",
      "         (16,0): 0.83527, 0.831104, 0.818731, 0.798516, 0.771052, 0.737123,\n",
      "         (16,6): 0.697676, 0.65377, 0.606531, 0.557106, 0.506617,\n",
      "         (17,0): 0.782705, 0.778801, 0.767206, 0.748264, 0.722527, 0.690734,\n",
      "         (17,6): 0.65377, 0.612626, 0.56836, 0.522046, 0.474734,\n",
      "         (18,0): 0.726149, 0.722527, 0.71177, 0.694197, 0.67032, 0.640824,\n",
      "         (18,6): 0.606531, 0.56836, 0.527292, 0.484325, 0.440432,\n",
      "         (19,0): 0.666977, 0.66365, 0.65377, 0.637628, 0.615697, 0.588605,\n",
      "         (19,6): 0.557106, 0.522046, 0.484325, 0.444858, 0.404542,\n",
      "         (20,0): 0.606531, 0.603506, 0.594521, 0.579842, 0.559898, 0.535261,\n",
      "         (20,6): 0.506617, 0.474734, 0.440432, 0.404542, 0.367879\n",
      "         }\n",
      "      }\n",
      "   }\n",
      "}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!h5dump temp.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with h5py.File('temp.h5', 'r') as h5file:\n",
    "    plt.imshow(h5file['output/z'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperslabs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up to this point, you wrote or read entire datasets, but that is not necessary, and often not possible when working with large datasets.  You can use slicing to read (or write) just a subset of the data.  This is called hyperslabs in HDF5 parlance.  For example, you can read only the $z$ values in a rectangle around the origin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.84366482 0.81464732 0.77880078 0.73712337]\n",
      " [0.8824969  0.85214379 0.81464732 0.77105159]\n",
      " [0.91393119 0.8824969  0.84366482 0.79851622]\n",
      " [0.93706746 0.90483742 0.86502229 0.81873075]\n",
      " [0.95122942 0.91851228 0.87809543 0.83110428]\n",
      " [0.95599748 0.92311635 0.8824969  0.83527021]\n",
      " [0.95122942 0.91851228 0.87809543 0.83110428]\n",
      " [0.93706746 0.90483742 0.86502229 0.81873075]\n",
      " [0.91393119 0.8824969  0.84366482 0.79851622]\n",
      " [0.8824969  0.85214379 0.81464732 0.77105159]]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with h5py.File('temp.h5', 'r') as h5file:\n",
    "    z_dset = h5file['output/z']\n",
    "    idx = tuple(map(lambda x: x//2, z_dset.shape))\n",
    "    width = tuple(map(lambda x: x//4, z_dset.shape))\n",
    "    data = z_dset[idx[0] - width[0]:idx[0] + width[0], idx[1] - width[1]:idx[1] + width[1]]\n",
    "    print(data)\n",
    "    print(type(data))\n",
    "    plt.imshow(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the result of the slicing of an HDF5 dataset is a numpy array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use slices to write data to a dataset.  The data is clipped row by row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('temp.h5', 'a') as h5file:\n",
    "    threshold = 0.9\n",
    "    z_dset = h5file['output/z']\n",
    "    for row_nr in range(z_dset.shape[0]):\n",
    "        row = z_dset[row_nr, :]\n",
    "        row[row > threshold] = threshold\n",
    "        z_dset[row_nr, :] = row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJ4AAAD4CAYAAAAdKF88AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAARIElEQVR4nO2dfYxU53WHn9/OfsGCg20MtjF2rIq6RVEhEaWO3Eo4ri2MrJBWaQuqEjeNRGLZUlwlUt1WSvqnpSqplNiyQxtkW3L8USUkSEG2kZUqsRQ7XiP8QcGFEhLWYNZgPhfY3dk9/WPuomGZYc7OXfzO3D2PtJq59577vu8dftyv855zZGYEwUdNR+oBBDOTEF6QhBBekIQQXpCEEF6QhM7UA6hFqa/PuuZdNb2NOh/e5bUb93etscvQZtk30I6ys9Fy2d23jTa2PccQIzasettbUnhd867ixvv+obGh1T2ui+hw/q6lYafdWXfXdJ32iaTnpF95Pcd8B9R9ZMhl1zF4zN13+f3DDW1es5cv3Z+7txpIWi3pXUl7JT1UY7skfTfb/pakT+XpLygOTQtPUgl4FLgbWAqsl7R0ktndwJLsbwPwWLP9BcUizxlvJbDXzPaZ2QjwLLB2ks1a4Cmr8CowT9J1OfoMCkIe4S0CDlQtD2TrpmoTzEDyCK/Wnf3ku2iPTcVQ2iCpX1L/2JDvhjhoX/IIbwBYXLV8A3CwCRsAzGyjma0wsxWlvr4cwwragTzCex1YIulmSd3AOmDLJJstwBezp9tbgRNmdihHn0FBaPo9npmVJT0AvAiUgE1mtlPSV7PtjwNbgTXAXuAM8KX8Qw6KQK4XyGa2lYq4qtc9XvXdgPun3G6XMbxo1GE4hUbLvpN7xzmfXems/+V11ymfbbmv5G6z3Otrc7x7rsuup+Tv2yMaHbm0VfhqgySE8IIkhPCCJITwgiSE8IIkhPCCJITwgiSE8IIkhPCCJITwgiS0ZsxFd5nrF33Y0G5s3P//ZnjUd6hnznW77EbOdLn7Lvf5+h7r9R/PeJfP1jq8rr3Z7r57x65sbHQ8XGZBCxLCC5IQwguSEMILkhDCC5IQwguSkCege7Gkn0vaJWmnpK/VsFkl6YSkHdnfN/MNNygKed7jlYGvm9l2SXOBNyRtM7P/mWT3SzO7J0c/QQFp+oxnZofMbHv2/RSwiwjWDpxMi+dC0seBTwKv1dj8aUlvUomn/YaZ7azTxgYq+VWYe+1s/via3zXstzzuD1AZGvN5JI4O+2J6B4fmuPv+cJbPKzDc3etu09zBOT7PhabwW3aMNP6Nxvdd+pyW++FC0hzgR8CDZnZy0ubtwE1mtgz4HvCTeu1UB3TPurIn77CCFidvmrIuKqJ72sx+PHm7mZ00s9PZ961Al6T5efoMikGep1oBPwB2mdl36thcm9khaWXW39Fm+wyKQ557vNuALwBvS9qRrftn4EY4H9j9eeA+SWXgLLDOoqJLQL4UFq/Q4M7VzB4BHmm2j6C4hOciSEIIL0hCCC9IQggvSEIIL0hCSwb7zC2dY9UVuxvajZjfzXN8zOkKG73CZfebHv978P3dvipFB0sfc7d5jlkuO435fqOOUf85qPNcY9lY56VddXHGC5IQwguSEMILkhDCC5IQwguSEMILkhDCC5IQwguSEMILktCSnos5HWX+tLdx+fHRKcwpPe5MaXagy+c9uLLTX2FyVslRpWiKHBjzBfGMjPg8HKVzU6jsM9T4t7QGJnHGC5KQN9hnv6S3sywB/TW2S9J3Je2V9JakT+XpLygO03Gpvd3MjtTZdjewJPv7E+Cx7DOY4VzuS+1a4Cmr8CowT9J1l7nPoA3IKzwDXpL0RpYJYDKLgANVywPUSXNRXRr+6NHxnMMKWp28l9rbzOygpAXANkm7zewXVdtrPXrVfBQ1s43ARoDly7ojBLLg5DrjmdnB7HMQ2AysnGQyACyuWr6BSg6VYIaTJ5NAX5aeDEl9wF3AO5PMtgBfzJ5ubwVOmNmhpkcbFIY8l9qFwOYsQ0Un8EMze0HSV+F8JoGtwBpgL3AG+FK+4QZFIU8mgX3AshrrH6/6bsD9Ux9UB/NLvhgJL95H6WtKx112fR3DTY+lHmfH/EVbhkZ8adcOn/W1OerwRpy3PdXYa9IoHCY8F0ESQnhBEkJ4QRJCeEESQnhBEkJ4QRJCeEESQnhBEkJ4QRJCeEESWjLYJyV+V50/2Gdo/H2X3bHZfjfhB8O+ykLHZvuqCpVn+d11Y7McLrMI9glakRBekIQQXpCEEF6QhBBekIQQXpCEPDEXt2QZBCb+Tkp6cJLNKkknqmy+mXvEQSHIM/X9XWA5gKQS8B6VSLPJ/NLM7mm2n6CYTNel9g7g/8zst9PUXlBwpstzsQ54ps62T0t6k0o87TfMbGctoywTwQaAGxe1vkNlKsFIiztPuOz2d510t3l1j89zMrt3xGV3vLfX3fdYjyNNWQPnRu4znqRu4LPAf9XYvB24ycyWAd8DflKvHTPbaGYrzGzFNVf7c7UF7cl0XGrvBrab2UWZFM3spJmdzr5vBbok+WsxBYVlOoS3njqXWUnXKov4lrQy6+/oNPQZtDm5bqYkzQbuBL5Sta46k8DngfsklYGzwLosyDuY4eQSnpmdAa6etK46k8AjwCN5+giKSXgugiSE8IIkhPCCJITwgiS0vougAMzr8OV0nlfyx3H0lXweiZ6usq/BTn/e6XGPai635yIImiGEFyQhhBckIYQXJCGEFyQhhBckIYQXJCGEFyQhhBckIYQXJCFcZh8BXWqc1gugW2PuNjs7fLYlp7uukYvrQtv8c3njjBckoaHwJG2SNCjpnap1V0naJmlP9nllnX1XS3pX0l5JD03nwIP2xnPGewJYPWndQ8DLZrYEeDlbvoAsu8CjVKLQlgLrJS3NNdqgMDQUXlZx+8NJq9cCT2bfnwQ+V2PXlcBeM9tnZiPAs9l+QdD0Pd7CiYLH2eeCGjaLgANVywPZuppI2iCpX1L/B0f9N9lBe3I5Hy5qPSfVfRyKTAIzi2aFd1jSdQDZ52ANmwFgcdXyDVTypwRB08LbAtybfb8X+GkNm9eBJZJuzvKrrMv2CwLX65RngF8Bt0gakPRl4GHgTkl7qGQSeDizvV7SVgAzKwMPAC8Cu4Dn62WKCmYeDT0XZra+zqY7atgeBNZULW8FtjY9uoIw6szaMWL+e9vyuM92bNx5UZuKM6JRDjIH4bkIkhDCC5IQwguSEMILkhDCC5IQwguSEMILkhDCC5IQwguSEMILkhDBPh8Bx51uq+Nj/mpBQ2PdLrvhUec/cdl/DurwpNxr4IKLM16QhBBekIQQXpCEEF6QhBBekIQQXpCEZjMJ/Juk3ZLekrRZ0rw6++6X9LakHZL6p3HcQZvTbCaBbcAnzOyPgP8F/ukS+99uZsvNbEVzQwyKSFOZBMzspSyYB+BVKqGLQeBmOjwXfw88V2ebAS9JMuD7ZraxXiOSNgAbAG5c1PoOlSNj/io8B8rzXHaDo1e42zw67PNynDnn83B0nPPf7peGG9s0ymSWt1DyvwBl4Ok6JreZ2UFJC4BtknZnZ9CLyES5EWDFst4oplxwmn6qlXQvcA/wt/WqbmfhjpjZILCZSiKfIGhOeJJWA/8IfDar0l3Lpk/S3InvwF3AO7Vsg5lHs5kEHgHmUrl87pD0eGZ7PpMAsBB4RdKbwK+Bn5nZC5flKIK2o9lMAj+oY3s+k4CZ7QOW5RpdUFjCcxEkIYQXJCGEFyQhhBckofVdBB8xXo/Eu6Oz3G3uGbnWZfebM/PdbQ4OzXHZjZzpctl1nfWnHiudbWyjBnVd4owXJCGEFyQhhBckIYQXJCGEFyQhhBckIYQXJCGEFyQhhBckIYQXJKElXWZlxl2uK2/FHPCnCvMG5njdYAC7hq532e0/fZW7zQ9PznbZ6ZTvn7jrlN9l1nW68e+uBpVf44wXJKHZTAL/Kum9bNr7Dklr6uy7WtK7kvZKuqh8fDBzaTaTAMC/ZxkClmfF8i5AUgl4FLgbWAqsl7Q0z2CD4tBUJgEnK4G9ZrbPzEaAZ4G1TbQTFJA893gPZEl7Nkm6ssb2RcCBquWBbF1NJG2Q1C+p/+jRBpO5granWeE9BvwesBw4BHy7hk2tx6S6j0NmttHMVpjZiquvjmeeotPUv7CZHTazMTMbB/6D2hkCBoDFVcs3AAeb6S8oHs1mEriuavEvqJ0h4HVgiaSbJXUD64AtzfQXFI+GbxezTAKrgPmSBoBvAaskLady6dwPfCWzvR74TzNbY2ZlSQ8ALwIlYJOZ7bwcBxG0H5ctk0C2vBW46FVLI06Pd/LKuYUN7Uas5G7TW7zEmypsKoE5Xo/EwWMfc7c5erzXZddz3HdR6z7p7pqek40f/iLYJ2hJQnhBEkJ4QRJCeEESQnhBEkJ4QRJCeEESQnhBEkJ4QRJaMubi1Fgv/33yDxralcf9ngtvKXVv4RJvmjDwx0d4vREA3Ud9x95zzBdL0XPMPxWt51jj2vAqXzouI854QRJCeEESQnhBEkJ4QRJCeEESQnhBEkJ4QRI8U983USkPOmhmn8jWPQfckpnMA46b2fIa++4HTgFjQDnKwwcTeF4gP0GlWuNTEyvM7G8mvkv6NnDiEvvfbmZHmh1gUEw8MRe/kPTxWtskCfhr4DPTPK6g4OR1mf0ZcNjM9tTZbsBLkgz4flb+vSaSNgAbALoXXMHrH9zYsPMxZ+oxgOFR36GeOedzrXkr5oA/VZg3MAf8rrDeI75UbrOONsgrVkX3kcYp5DrKl3bB5RXeeuCZS2y/zcwOSlpApajy7iwXy0VkotwIMOf3r/UnvgvakqafaiV1An8JPFfPJgt3xMwGgc3UzjgQzEDyvE75c2C3mQ3U2iipT9Lcie/AXdTOOBDMQDyJGZ8BfgXcImlA0pezTeuYdJmVdL2kiQDuhcArkt4Efg38zMxemL6hB+1Ms5kEMLO/q7HufCYBM9sHLMs5vqCghOciSEIIL0hCCC9IQggvSEJLBvuMjnRy8D1Haq+pvGYu+/6PdZzz2XWdnUJBEmfxkimlCnMG53g9Ej2Hz7j77hg81tiofOmAoDjjBUkI4QVJCOEFSQjhBUkI4QVJCOEFSQjhBUkI4QVJCOEFSQjhBUloSZeZRkXPe45gGvO7rToap3QDoDTstDvr7pqu0z7fnqdiznlbR4468AXmgNMNllF+/3BDG7OcLjNJiyX9XNIuSTslfS1bf5WkbZL2ZJ+1atZGefigJp5LbRn4upn9IXArcH9W4v0h4GUzWwK8nC1fQJSHD+rhKQ1/yMy2Z99PAbuoVNpeCzyZmT0JfK7G7lEePqjJlB4usowCnwReAxaa2SGoiBNYUGOXKZWHD2YObuFJmgP8CHjQzLwzx9zl4SVtkNQvqX9syHdDHLQvLuFJ6qIiuqfN7MfZ6sMTlbqzz8Eau7rLw5vZRjNbYWYrSn2+zOtB++J5qhWVwsi7zOw7VZu2APdm3+8Fflpj9ygPH9TEc8a7DfgC8BlJO7K/NcDDwJ2S9gB3ZssXBHVb5WXORHn4XcDzUR4+AF9A9yvUvlcDuKOG/bSUhw+KjcxaLzGTpA+A305aPR8oUoLHIh1PrWO5ycyuqbdDSwqvFpL6i5TKtkjH08yxxCSBIAkhvCAJ7SS8umls25QiHc+Uj6Vt7vGCYtFOZ7ygQITwgiS0vPCKNpFU0n5Jb2ceoP7U45kqkjZJGpT0TtU616TgalpaeAWeSHq7mS1v0/d4TwCrJ61rOCl4Mi0tPGIiacuR1Sn5cNJqz6TgC2h14RVxIulEtaM3smpGRcAzKfgCWjLKrAr3RNI2wl3tqMi0+hnPPZG0XShotSPPpOALaHXhFWoiaYGrHXkmBV9AS19qzawsaWIiaQnY1OYTSRcCmyuTuukEfthu1Y6ySk+rgPmSBoBvUZkE/HxW9el3wF81bCdcZkEKWv1SGxSUEF6QhBBekIQQXpCEEF6QhBBekIQQXpCE/wfTEHUU4w6QxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with h5py.File('temp.h5') as h5file:\n",
    "    plt.imshow(h5file['output/z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HDF5 \"temp.h5\" {\n",
      "GROUP \"/\" {\n",
      "   GROUP \"input\" {\n",
      "      DATASET \"x\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 11 ) / ( 11 ) }\n",
      "         DATA {\n",
      "         (0): 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1\n",
      "         }\n",
      "      }\n",
      "      DATASET \"y\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 21 ) / ( 21 ) }\n",
      "         DATA {\n",
      "         (0): -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0,\n",
      "         (11): 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1\n",
      "         }\n",
      "      }\n",
      "   }\n",
      "   GROUP \"output\" {\n",
      "      DATASET \"z\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 21, 11 ) / ( 21, 11 ) }\n",
      "         DATA {\n",
      "         (0,0): 0.606531, 0.603506, 0.594521, 0.579842, 0.559898, 0.535261,\n",
      "         (0,6): 0.506617, 0.474734, 0.440432, 0.404542, 0.367879,\n",
      "         (1,0): 0.666977, 0.66365, 0.65377, 0.637628, 0.615697, 0.588605,\n",
      "         (1,6): 0.557106, 0.522046, 0.484325, 0.444858, 0.404542,\n",
      "         (2,0): 0.726149, 0.722527, 0.71177, 0.694197, 0.67032, 0.640824,\n",
      "         (2,6): 0.606531, 0.56836, 0.527292, 0.484325, 0.440432,\n",
      "         (3,0): 0.782705, 0.778801, 0.767206, 0.748264, 0.722527, 0.690734,\n",
      "         (3,6): 0.65377, 0.612626, 0.56836, 0.522046, 0.474734,\n",
      "         (4,0): 0.83527, 0.831104, 0.818731, 0.798516, 0.771052, 0.737123,\n",
      "         (4,6): 0.697676, 0.65377, 0.606531, 0.557106, 0.506617,\n",
      "         (5,0): 0.882497, 0.878095, 0.865022, 0.843665, 0.814647, 0.778801,\n",
      "         (5,6): 0.737123, 0.690734, 0.640824, 0.588605, 0.535261,\n",
      "         (6,0): 0.9, 0.9, 0.9, 0.882497, 0.852144, 0.814647, 0.771052,\n",
      "         (6,7): 0.722527, 0.67032, 0.615697, 0.559898,\n",
      "         (7,0): 0.9, 0.9, 0.9, 0.9, 0.882497, 0.843665, 0.798516, 0.748264,\n",
      "         (7,8): 0.694197, 0.637628, 0.579842,\n",
      "         (8,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.865022, 0.818731, 0.767206,\n",
      "         (8,8): 0.71177, 0.65377, 0.594521,\n",
      "         (9,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.878095, 0.831104, 0.778801,\n",
      "         (9,8): 0.722527, 0.66365, 0.603506,\n",
      "         (10,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.882497, 0.83527, 0.782705,\n",
      "         (10,8): 0.726149, 0.666977, 0.606531,\n",
      "         (11,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.878095, 0.831104, 0.778801,\n",
      "         (11,8): 0.722527, 0.66365, 0.603506,\n",
      "         (12,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.865022, 0.818731, 0.767206,\n",
      "         (12,8): 0.71177, 0.65377, 0.594521,\n",
      "         (13,0): 0.9, 0.9, 0.9, 0.9, 0.882497, 0.843665, 0.798516, 0.748264,\n",
      "         (13,8): 0.694197, 0.637628, 0.579842,\n",
      "         (14,0): 0.9, 0.9, 0.9, 0.882497, 0.852144, 0.814647, 0.771052,\n",
      "         (14,7): 0.722527, 0.67032, 0.615697, 0.559898,\n",
      "         (15,0): 0.882497, 0.878095, 0.865022, 0.843665, 0.814647, 0.778801,\n",
      "         (15,6): 0.737123, 0.690734, 0.640824, 0.588605, 0.535261,\n",
      "         (16,0): 0.83527, 0.831104, 0.818731, 0.798516, 0.771052, 0.737123,\n",
      "         (16,6): 0.697676, 0.65377, 0.606531, 0.557106, 0.506617,\n",
      "         (17,0): 0.782705, 0.778801, 0.767206, 0.748264, 0.722527, 0.690734,\n",
      "         (17,6): 0.65377, 0.612626, 0.56836, 0.522046, 0.474734,\n",
      "         (18,0): 0.726149, 0.722527, 0.71177, 0.694197, 0.67032, 0.640824,\n",
      "         (18,6): 0.606531, 0.56836, 0.527292, 0.484325, 0.440432,\n",
      "         (19,0): 0.666977, 0.66365, 0.65377, 0.637628, 0.615697, 0.588605,\n",
      "         (19,6): 0.557106, 0.522046, 0.484325, 0.444858, 0.404542,\n",
      "         (20,0): 0.606531, 0.603506, 0.594521, 0.579842, 0.559898, 0.535261,\n",
      "         (20,6): 0.506617, 0.474734, 0.440432, 0.404542, 0.367879\n",
      "         }\n",
      "      }\n",
      "   }\n",
      "}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!h5dump temp.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that indices for hyperslabs can be list, as you can do for numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can set attributes, i.e., annotations on groups as well as datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('temp.h5', 'a') as h5file:\n",
    "    h5file.attrs['experiment'] = 'by trial and error'\n",
    "    h5file['input'].attrs['description'] = 'x/y coordinates'\n",
    "    h5file['input/x'].attrs['units'] = 'km'\n",
    "    h5file['input/x'].attrs['description'] = 'x-coordinates'\n",
    "    h5file['input/y'].attrs['units'] = 'km'\n",
    "    h5file['input/y'].attrs['description'] = 'y-coordinates'\n",
    "    h5file['output'].attrs['description'] = 'fields'\n",
    "    h5file['output/z'].attrs['description'] = 'temperature'\n",
    "    h5file['output/z'].attrs['units'] = 'K'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HDF5 \"temp.h5\" {\n",
      "GROUP \"/\" {\n",
      "   ATTRIBUTE \"experiment\" {\n",
      "      DATATYPE  H5T_STRING {\n",
      "         STRSIZE H5T_VARIABLE;\n",
      "         STRPAD H5T_STR_NULLTERM;\n",
      "         CSET H5T_CSET_UTF8;\n",
      "         CTYPE H5T_C_S1;\n",
      "      }\n",
      "      DATASPACE  SCALAR\n",
      "      DATA {\n",
      "      (0): \"by trial and error\"\n",
      "      }\n",
      "   }\n",
      "   GROUP \"input\" {\n",
      "      ATTRIBUTE \"description\" {\n",
      "         DATATYPE  H5T_STRING {\n",
      "            STRSIZE H5T_VARIABLE;\n",
      "            STRPAD H5T_STR_NULLTERM;\n",
      "            CSET H5T_CSET_UTF8;\n",
      "            CTYPE H5T_C_S1;\n",
      "         }\n",
      "         DATASPACE  SCALAR\n",
      "         DATA {\n",
      "         (0): \"x/y coordinates\"\n",
      "         }\n",
      "      }\n",
      "      DATASET \"x\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 11 ) / ( 11 ) }\n",
      "         DATA {\n",
      "         (0): 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1\n",
      "         }\n",
      "         ATTRIBUTE \"description\" {\n",
      "            DATATYPE  H5T_STRING {\n",
      "               STRSIZE H5T_VARIABLE;\n",
      "               STRPAD H5T_STR_NULLTERM;\n",
      "               CSET H5T_CSET_UTF8;\n",
      "               CTYPE H5T_C_S1;\n",
      "            }\n",
      "            DATASPACE  SCALAR\n",
      "            DATA {\n",
      "            (0): \"x-coordinates\"\n",
      "            }\n",
      "         }\n",
      "         ATTRIBUTE \"units\" {\n",
      "            DATATYPE  H5T_STRING {\n",
      "               STRSIZE H5T_VARIABLE;\n",
      "               STRPAD H5T_STR_NULLTERM;\n",
      "               CSET H5T_CSET_UTF8;\n",
      "               CTYPE H5T_C_S1;\n",
      "            }\n",
      "            DATASPACE  SCALAR\n",
      "            DATA {\n",
      "            (0): \"km\"\n",
      "            }\n",
      "         }\n",
      "      }\n",
      "      DATASET \"y\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 21 ) / ( 21 ) }\n",
      "         DATA {\n",
      "         (0): -1, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0,\n",
      "         (11): 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1\n",
      "         }\n",
      "         ATTRIBUTE \"description\" {\n",
      "            DATATYPE  H5T_STRING {\n",
      "               STRSIZE H5T_VARIABLE;\n",
      "               STRPAD H5T_STR_NULLTERM;\n",
      "               CSET H5T_CSET_UTF8;\n",
      "               CTYPE H5T_C_S1;\n",
      "            }\n",
      "            DATASPACE  SCALAR\n",
      "            DATA {\n",
      "            (0): \"y-coordinates\"\n",
      "            }\n",
      "         }\n",
      "         ATTRIBUTE \"units\" {\n",
      "            DATATYPE  H5T_STRING {\n",
      "               STRSIZE H5T_VARIABLE;\n",
      "               STRPAD H5T_STR_NULLTERM;\n",
      "               CSET H5T_CSET_UTF8;\n",
      "               CTYPE H5T_C_S1;\n",
      "            }\n",
      "            DATASPACE  SCALAR\n",
      "            DATA {\n",
      "            (0): \"km\"\n",
      "            }\n",
      "         }\n",
      "      }\n",
      "   }\n",
      "   GROUP \"output\" {\n",
      "      ATTRIBUTE \"description\" {\n",
      "         DATATYPE  H5T_STRING {\n",
      "            STRSIZE H5T_VARIABLE;\n",
      "            STRPAD H5T_STR_NULLTERM;\n",
      "            CSET H5T_CSET_UTF8;\n",
      "            CTYPE H5T_C_S1;\n",
      "         }\n",
      "         DATASPACE  SCALAR\n",
      "         DATA {\n",
      "         (0): \"fields\"\n",
      "         }\n",
      "      }\n",
      "      DATASET \"z\" {\n",
      "         DATATYPE  H5T_IEEE_F64LE\n",
      "         DATASPACE  SIMPLE { ( 21, 11 ) / ( 21, 11 ) }\n",
      "         DATA {\n",
      "         (0,0): 0.606531, 0.603506, 0.594521, 0.579842, 0.559898, 0.535261,\n",
      "         (0,6): 0.506617, 0.474734, 0.440432, 0.404542, 0.367879,\n",
      "         (1,0): 0.666977, 0.66365, 0.65377, 0.637628, 0.615697, 0.588605,\n",
      "         (1,6): 0.557106, 0.522046, 0.484325, 0.444858, 0.404542,\n",
      "         (2,0): 0.726149, 0.722527, 0.71177, 0.694197, 0.67032, 0.640824,\n",
      "         (2,6): 0.606531, 0.56836, 0.527292, 0.484325, 0.440432,\n",
      "         (3,0): 0.782705, 0.778801, 0.767206, 0.748264, 0.722527, 0.690734,\n",
      "         (3,6): 0.65377, 0.612626, 0.56836, 0.522046, 0.474734,\n",
      "         (4,0): 0.83527, 0.831104, 0.818731, 0.798516, 0.771052, 0.737123,\n",
      "         (4,6): 0.697676, 0.65377, 0.606531, 0.557106, 0.506617,\n",
      "         (5,0): 0.882497, 0.878095, 0.865022, 0.843665, 0.814647, 0.778801,\n",
      "         (5,6): 0.737123, 0.690734, 0.640824, 0.588605, 0.535261,\n",
      "         (6,0): 0.9, 0.9, 0.9, 0.882497, 0.852144, 0.814647, 0.771052,\n",
      "         (6,7): 0.722527, 0.67032, 0.615697, 0.559898,\n",
      "         (7,0): 0.9, 0.9, 0.9, 0.9, 0.882497, 0.843665, 0.798516, 0.748264,\n",
      "         (7,8): 0.694197, 0.637628, 0.579842,\n",
      "         (8,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.865022, 0.818731, 0.767206,\n",
      "         (8,8): 0.71177, 0.65377, 0.594521,\n",
      "         (9,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.878095, 0.831104, 0.778801,\n",
      "         (9,8): 0.722527, 0.66365, 0.603506,\n",
      "         (10,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.882497, 0.83527, 0.782705,\n",
      "         (10,8): 0.726149, 0.666977, 0.606531,\n",
      "         (11,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.878095, 0.831104, 0.778801,\n",
      "         (11,8): 0.722527, 0.66365, 0.603506,\n",
      "         (12,0): 0.9, 0.9, 0.9, 0.9, 0.9, 0.865022, 0.818731, 0.767206,\n",
      "         (12,8): 0.71177, 0.65377, 0.594521,\n",
      "         (13,0): 0.9, 0.9, 0.9, 0.9, 0.882497, 0.843665, 0.798516, 0.748264,\n",
      "         (13,8): 0.694197, 0.637628, 0.579842,\n",
      "         (14,0): 0.9, 0.9, 0.9, 0.882497, 0.852144, 0.814647, 0.771052,\n",
      "         (14,7): 0.722527, 0.67032, 0.615697, 0.559898,\n",
      "         (15,0): 0.882497, 0.878095, 0.865022, 0.843665, 0.814647, 0.778801,\n",
      "         (15,6): 0.737123, 0.690734, 0.640824, 0.588605, 0.535261,\n",
      "         (16,0): 0.83527, 0.831104, 0.818731, 0.798516, 0.771052, 0.737123,\n",
      "         (16,6): 0.697676, 0.65377, 0.606531, 0.557106, 0.506617,\n",
      "         (17,0): 0.782705, 0.778801, 0.767206, 0.748264, 0.722527, 0.690734,\n",
      "         (17,6): 0.65377, 0.612626, 0.56836, 0.522046, 0.474734,\n",
      "         (18,0): 0.726149, 0.722527, 0.71177, 0.694197, 0.67032, 0.640824,\n",
      "         (18,6): 0.606531, 0.56836, 0.527292, 0.484325, 0.440432,\n",
      "         (19,0): 0.666977, 0.66365, 0.65377, 0.637628, 0.615697, 0.588605,\n",
      "         (19,6): 0.557106, 0.522046, 0.484325, 0.444858, 0.404542,\n",
      "         (20,0): 0.606531, 0.603506, 0.594521, 0.579842, 0.559898, 0.535261,\n",
      "         (20,6): 0.506617, 0.474734, 0.440432, 0.404542, 0.367879\n",
      "         }\n",
      "         ATTRIBUTE \"description\" {\n",
      "            DATATYPE  H5T_STRING {\n",
      "               STRSIZE H5T_VARIABLE;\n",
      "               STRPAD H5T_STR_NULLTERM;\n",
      "               CSET H5T_CSET_UTF8;\n",
      "               CTYPE H5T_C_S1;\n",
      "            }\n",
      "            DATASPACE  SCALAR\n",
      "            DATA {\n",
      "            (0): \"temperature\"\n",
      "            }\n",
      "         }\n",
      "         ATTRIBUTE \"units\" {\n",
      "            DATATYPE  H5T_STRING {\n",
      "               STRSIZE H5T_VARIABLE;\n",
      "               STRPAD H5T_STR_NULLTERM;\n",
      "               CSET H5T_CSET_UTF8;\n",
      "               CTYPE H5T_C_S1;\n",
      "            }\n",
      "            DATASPACE  SCALAR\n",
      "            DATA {\n",
      "            (0): \"K\"\n",
      "            }\n",
      "         }\n",
      "      }\n",
      "   }\n",
      "}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "!h5dump temp.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attributes behave like dictionaries, they can be indexed by name, and have methods such as `keys`, `values` and `items`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "description: \"temperature\"\n",
      "units: \"K\"\n"
     ]
    }
   ],
   "source": [
    "with h5py.File('temp.h5', 'r') as h5file:\n",
    "    for key, value in h5file['output/z'].attrs.items():\n",
    "        print(f'{key}: \"{value}\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimension scales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scales can be associated with dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File('temp.h5', 'a') as h5file:\n",
    "    h5file['input/x'].make_scale('x')\n",
    "    h5file['input/y'].make_scale('y')\n",
    "    h5file['output/z'].dims[0].label = 'y'\n",
    "    h5file['output/z'].dims[1].label = 'x'\n",
    "    h5file['output/z'].dims[0].attach_scale(h5file['input/y'])\n",
    "    h5file['output/z'].dims[1].attach_scale(h5file['input/x'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           x           y    temperature\n",
      "       0.000      -1.000:        0.60653\n",
      "       0.000      -0.900:        0.66698\n",
      "       0.000      -0.800:        0.72615\n",
      "       0.000      -0.700:        0.78270\n",
      "       0.000      -0.600:        0.83527\n",
      "       0.000      -0.500:        0.88250\n",
      "       0.000      -0.400:        0.90000\n",
      "       0.000      -0.300:        0.90000\n",
      "       0.000      -0.200:        0.90000\n",
      "       0.000      -0.100:        0.90000\n",
      "       0.000       0.000:        0.90000\n",
      "       0.000       0.100:        0.90000\n",
      "       0.000       0.200:        0.90000\n",
      "       0.000       0.300:        0.90000\n",
      "       0.000       0.400:        0.90000\n",
      "       0.000       0.500:        0.88250\n",
      "       0.000       0.600:        0.83527\n",
      "       0.000       0.700:        0.78270\n",
      "       0.000       0.800:        0.72615\n",
      "       0.000       0.900:        0.66698\n",
      "       0.000       1.000:        0.60653\n",
      "       0.100      -1.000:        0.60351\n",
      "       0.100      -0.900:        0.66365\n",
      "       0.100      -0.800:        0.72253\n",
      "       0.100      -0.700:        0.77880\n",
      "       0.100      -0.600:        0.83110\n",
      "       0.100      -0.500:        0.87810\n",
      "       0.100      -0.400:        0.90000\n",
      "       0.100      -0.300:        0.90000\n",
      "       0.100      -0.200:        0.90000\n",
      "       0.100      -0.100:        0.90000\n",
      "       0.100       0.000:        0.90000\n",
      "       0.100       0.100:        0.90000\n",
      "       0.100       0.200:        0.90000\n",
      "       0.100       0.300:        0.90000\n",
      "       0.100       0.400:        0.90000\n",
      "       0.100       0.500:        0.87810\n",
      "       0.100       0.600:        0.83110\n",
      "       0.100       0.700:        0.77880\n",
      "       0.100       0.800:        0.72253\n",
      "       0.100       0.900:        0.66365\n",
      "       0.100       1.000:        0.60351\n",
      "       0.200      -1.000:        0.59452\n",
      "       0.200      -0.900:        0.65377\n",
      "       0.200      -0.800:        0.71177\n",
      "       0.200      -0.700:        0.76721\n",
      "       0.200      -0.600:        0.81873\n",
      "       0.200      -0.500:        0.86502\n",
      "       0.200      -0.400:        0.90000\n",
      "       0.200      -0.300:        0.90000\n",
      "       0.200      -0.200:        0.90000\n",
      "       0.200      -0.100:        0.90000\n",
      "       0.200       0.000:        0.90000\n",
      "       0.200       0.100:        0.90000\n",
      "       0.200       0.200:        0.90000\n",
      "       0.200       0.300:        0.90000\n",
      "       0.200       0.400:        0.90000\n",
      "       0.200       0.500:        0.86502\n",
      "       0.200       0.600:        0.81873\n",
      "       0.200       0.700:        0.76721\n",
      "       0.200       0.800:        0.71177\n",
      "       0.200       0.900:        0.65377\n",
      "       0.200       1.000:        0.59452\n",
      "       0.300      -1.000:        0.57984\n",
      "       0.300      -0.900:        0.63763\n",
      "       0.300      -0.800:        0.69420\n",
      "       0.300      -0.700:        0.74826\n",
      "       0.300      -0.600:        0.79852\n",
      "       0.300      -0.500:        0.84366\n",
      "       0.300      -0.400:        0.88250\n",
      "       0.300      -0.300:        0.90000\n",
      "       0.300      -0.200:        0.90000\n",
      "       0.300      -0.100:        0.90000\n",
      "       0.300       0.000:        0.90000\n",
      "       0.300       0.100:        0.90000\n",
      "       0.300       0.200:        0.90000\n",
      "       0.300       0.300:        0.90000\n",
      "       0.300       0.400:        0.88250\n",
      "       0.300       0.500:        0.84366\n",
      "       0.300       0.600:        0.79852\n",
      "       0.300       0.700:        0.74826\n",
      "       0.300       0.800:        0.69420\n",
      "       0.300       0.900:        0.63763\n",
      "       0.300       1.000:        0.57984\n",
      "       0.400      -1.000:        0.55990\n",
      "       0.400      -0.900:        0.61570\n",
      "       0.400      -0.800:        0.67032\n",
      "       0.400      -0.700:        0.72253\n",
      "       0.400      -0.600:        0.77105\n",
      "       0.400      -0.500:        0.81465\n",
      "       0.400      -0.400:        0.85214\n",
      "       0.400      -0.300:        0.88250\n",
      "       0.400      -0.200:        0.90000\n",
      "       0.400      -0.100:        0.90000\n",
      "       0.400       0.000:        0.90000\n",
      "       0.400       0.100:        0.90000\n",
      "       0.400       0.200:        0.90000\n",
      "       0.400       0.300:        0.88250\n",
      "       0.400       0.400:        0.85214\n",
      "       0.400       0.500:        0.81465\n",
      "       0.400       0.600:        0.77105\n",
      "       0.400       0.700:        0.72253\n",
      "       0.400       0.800:        0.67032\n",
      "       0.400       0.900:        0.61570\n",
      "       0.400       1.000:        0.55990\n",
      "       0.500      -1.000:        0.53526\n",
      "       0.500      -0.900:        0.58860\n",
      "       0.500      -0.800:        0.64082\n",
      "       0.500      -0.700:        0.69073\n",
      "       0.500      -0.600:        0.73712\n",
      "       0.500      -0.500:        0.77880\n",
      "       0.500      -0.400:        0.81465\n",
      "       0.500      -0.300:        0.84366\n",
      "       0.500      -0.200:        0.86502\n",
      "       0.500      -0.100:        0.87810\n",
      "       0.500       0.000:        0.88250\n",
      "       0.500       0.100:        0.87810\n",
      "       0.500       0.200:        0.86502\n",
      "       0.500       0.300:        0.84366\n",
      "       0.500       0.400:        0.81465\n",
      "       0.500       0.500:        0.77880\n",
      "       0.500       0.600:        0.73712\n",
      "       0.500       0.700:        0.69073\n",
      "       0.500       0.800:        0.64082\n",
      "       0.500       0.900:        0.58860\n",
      "       0.500       1.000:        0.53526\n",
      "       0.600      -1.000:        0.50662\n",
      "       0.600      -0.900:        0.55711\n",
      "       0.600      -0.800:        0.60653\n",
      "       0.600      -0.700:        0.65377\n",
      "       0.600      -0.600:        0.69768\n",
      "       0.600      -0.500:        0.73712\n",
      "       0.600      -0.400:        0.77105\n",
      "       0.600      -0.300:        0.79852\n",
      "       0.600      -0.200:        0.81873\n",
      "       0.600      -0.100:        0.83110\n",
      "       0.600       0.000:        0.83527\n",
      "       0.600       0.100:        0.83110\n",
      "       0.600       0.200:        0.81873\n",
      "       0.600       0.300:        0.79852\n",
      "       0.600       0.400:        0.77105\n",
      "       0.600       0.500:        0.73712\n",
      "       0.600       0.600:        0.69768\n",
      "       0.600       0.700:        0.65377\n",
      "       0.600       0.800:        0.60653\n",
      "       0.600       0.900:        0.55711\n",
      "       0.600       1.000:        0.50662\n",
      "       0.700      -1.000:        0.47473\n",
      "       0.700      -0.900:        0.52205\n",
      "       0.700      -0.800:        0.56836\n",
      "       0.700      -0.700:        0.61263\n",
      "       0.700      -0.600:        0.65377\n",
      "       0.700      -0.500:        0.69073\n",
      "       0.700      -0.400:        0.72253\n",
      "       0.700      -0.300:        0.74826\n",
      "       0.700      -0.200:        0.76721\n",
      "       0.700      -0.100:        0.77880\n",
      "       0.700       0.000:        0.78270\n",
      "       0.700       0.100:        0.77880\n",
      "       0.700       0.200:        0.76721\n",
      "       0.700       0.300:        0.74826\n",
      "       0.700       0.400:        0.72253\n",
      "       0.700       0.500:        0.69073\n",
      "       0.700       0.600:        0.65377\n",
      "       0.700       0.700:        0.61263\n",
      "       0.700       0.800:        0.56836\n",
      "       0.700       0.900:        0.52205\n",
      "       0.700       1.000:        0.47473\n",
      "       0.800      -1.000:        0.44043\n",
      "       0.800      -0.900:        0.48432\n",
      "       0.800      -0.800:        0.52729\n",
      "       0.800      -0.700:        0.56836\n",
      "       0.800      -0.600:        0.60653\n",
      "       0.800      -0.500:        0.64082\n",
      "       0.800      -0.400:        0.67032\n",
      "       0.800      -0.300:        0.69420\n",
      "       0.800      -0.200:        0.71177\n",
      "       0.800      -0.100:        0.72253\n",
      "       0.800       0.000:        0.72615\n",
      "       0.800       0.100:        0.72253\n",
      "       0.800       0.200:        0.71177\n",
      "       0.800       0.300:        0.69420\n",
      "       0.800       0.400:        0.67032\n",
      "       0.800       0.500:        0.64082\n",
      "       0.800       0.600:        0.60653\n",
      "       0.800       0.700:        0.56836\n",
      "       0.800       0.800:        0.52729\n",
      "       0.800       0.900:        0.48432\n",
      "       0.800       1.000:        0.44043\n",
      "       0.900      -1.000:        0.40454\n",
      "       0.900      -0.900:        0.44486\n",
      "       0.900      -0.800:        0.48432\n",
      "       0.900      -0.700:        0.52205\n",
      "       0.900      -0.600:        0.55711\n",
      "       0.900      -0.500:        0.58860\n",
      "       0.900      -0.400:        0.61570\n",
      "       0.900      -0.300:        0.63763\n",
      "       0.900      -0.200:        0.65377\n",
      "       0.900      -0.100:        0.66365\n",
      "       0.900       0.000:        0.66698\n",
      "       0.900       0.100:        0.66365\n",
      "       0.900       0.200:        0.65377\n",
      "       0.900       0.300:        0.63763\n",
      "       0.900       0.400:        0.61570\n",
      "       0.900       0.500:        0.58860\n",
      "       0.900       0.600:        0.55711\n",
      "       0.900       0.700:        0.52205\n",
      "       0.900       0.800:        0.48432\n",
      "       0.900       0.900:        0.44486\n",
      "       0.900       1.000:        0.40454\n",
      "       1.000      -1.000:        0.36788\n",
      "       1.000      -0.900:        0.40454\n",
      "       1.000      -0.800:        0.44043\n",
      "       1.000      -0.700:        0.47473\n",
      "       1.000      -0.600:        0.50662\n",
      "       1.000      -0.500:        0.53526\n",
      "       1.000      -0.400:        0.55990\n",
      "       1.000      -0.300:        0.57984\n",
      "       1.000      -0.200:        0.59452\n",
      "       1.000      -0.100:        0.60351\n",
      "       1.000       0.000:        0.60653\n",
      "       1.000       0.100:        0.60351\n",
      "       1.000       0.200:        0.59452\n",
      "       1.000       0.300:        0.57984\n",
      "       1.000       0.400:        0.55990\n",
      "       1.000       0.500:        0.53526\n",
      "       1.000       0.600:        0.50662\n",
      "       1.000       0.700:        0.47473\n",
      "       1.000       0.800:        0.44043\n",
      "       1.000       0.900:        0.40454\n",
      "       1.000       1.000:        0.36788\n"
     ]
    }
   ],
   "source": [
    "with h5py.File('temp.h5', 'r') as h5file:\n",
    "    z_dset = h5file['output/z']\n",
    "    print(f'{z_dset.dims[1].label:>12s}{z_dset.dims[0].label:>12s}{z_dset.attrs[\"description\"]:>15s}')\n",
    "    x_values = z_dset.dims[1].keys()\n",
    "    for j, x in enumerate(z_dset.dims[1]['x']):\n",
    "        for i, y in enumerate(z_dset.dims[0]['y']):\n",
    "            print(f'{x:12.3f}{y:12.3f}:{z_dset[i, j]:15.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The advantage of adding dimension scales is that it links the data appropriately so that the correct $x$ and $yh$ values are used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "!rm temp.h5"
   ]
  }
 ],
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