jreadey/4dset_ex.ipynb

## 4dset_ex.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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = h5py.File(\"/home/jreadey/4DStem/stack2_60x60+30nmss.h5\", \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['4D-STEM_data', 'log']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = f['4D-STEM_data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['datacube', 'metadata']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "datacube = data['datacube']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['datacube', 'dim1', 'dim2', 'dim3', 'dim4']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(datacube)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "datacube = datacube['datacube']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h5pyd._hl.dataset.Dataset"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(datacube)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 57, 170, 170)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datacube.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datacube.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "790704000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.prod(datacube.shape) * 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8 ms, sys: 0 ns, total: 8 ms\n",
      "Wall time: 206 ms\n"
     ]
    }
   ],
   "source": [
    "%time arr = datacube[0,0,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 159449, 1250.9576816608997)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.min(), arr.max(), arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f486a0a61d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, figsize=(10,10),dpi=72)\n",
    "plt.imshow(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
}