density_target_hdf5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the densities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import csv\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "\n",
    "epoch_list = [12,15,19,23,27]\n",
    "\n",
    "density_dict = collections.defaultdict(dict) # initialize empty dict of dicts \n",
    "for r in epoch_list: \n",
    "    for j in os.listdir('./datasets/density50/'+str(r)): \n",
    "        den=[]\n",
    "        with open('./datasets/density50/'+str(r)+'/'+str(j)) as csv_file:\n",
    "            csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "            line_count = 0\n",
    "            for row in csv_reader:        \n",
    "                if line_count == 0:\n",
    "                    #print(f'Column names are {\", \".join(row)}')\n",
    "                    line_count += 1  \n",
    "                    continue\n",
    "                else: \n",
    "                    den= np.append(den,row)   #list of strings\n",
    "                line_count += 1  \n",
    "        Den=list(map(float,den))              #list of float values\n",
    "        density= np.reshape(Den,(50,50,50))\n",
    "        \n",
    "        i = int(j.split('.csv')[0])\n",
    "        density_dict[r][i] = density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 50, 50)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f06dc9d0e80>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "print(density.shape)\n",
    "plt.figure()\n",
    "plt.imshow(density_dict[27][0][:,:,20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "MR_dict = collections.defaultdict(dict) # initialize empty dict of dicts \n",
    "SR_dict = collections.defaultdict(dict)\n",
    "\n",
    "for r in [12,15,19,23,27]: \n",
    "    with open('./datasets/targets/'+str(r)+'/labels.csv') as csv_file:\n",
    "        csv_reader = list(csv.reader(csv_file, delimiter=','))\n",
    "        MR=np.asarray(csv_reader[1])\n",
    "        SR=np.asarray(csv_reader[3])\n",
    "        MR=MR.astype(np.float)\n",
    "        SR=SR.astype(np.float)\n",
    "        for k in density_dict[r].keys():\n",
    "            MR_dict[r][k] = MR[k]\n",
    "            SR_dict[r][k] = SR[k]\n",
    "    #             row_count= sum(1 for row_ in csv_reader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save in hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/mquesnel/Courses/DeepLearning/datasets/merger_train_dens134.hdf5 created\n"
     ]
    }
   ],
   "source": [
    "path_to_dataset = \"/home/mquesnel/Courses/DeepLearning/datasets/\"\n",
    "filename = \"merger_train_dens134.hdf5\"\n",
    "\n",
    "with h5py.File(path_to_dataset+filename, 'w') as hdf:\n",
    "    print(\"{} created\".format(path_to_dataset+filename))\n",
    "    # Create groups in hdf5 file:\n",
    "    dens_grp = hdf.create_group('density')\n",
    "    ratio_grp = hdf.create_group('ratio')\n",
    "    # MR and SR are in subgroups of the target group:\n",
    "    mr_grp = ratio_grp.create_group('MR')\n",
    "    sr_grp = ratio_grp.create_group('SR')\n",
    "    \n",
    "    # Add attributs to the hdf5 main object:\n",
    "    hdf.attrs['nb_mergers'] = len(density_dict)\n",
    "\n",
    "    i = 0\n",
    "    for epoch in density_dict.keys():\n",
    "        for ind in density_dict[epoch].keys():\n",
    "            # Create and fill the datasets:\n",
    "            mr_grp.create_dataset(str(i), data = MR_dict[epoch][ind])\n",
    "            sr_grp.create_dataset(str(i), data = SR_dict[epoch][ind])\n",
    "\n",
    "            dens_grp.create_dataset(str(i), data = density_dict[epoch][ind])\n",
    "            \n",
    "            i+=1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read hdf5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "density_out = {}\n",
    "target = {}\n",
    "path_to_dataset2 = \"/home/mquesnel/Courses/DeepLearning/datasets/\"\n",
    "filename2 = \"merger_train_dens658.h5\"\n",
    "with h5py.File(path_to_dataset2+filename2, 'r') as f:\n",
    "    for ind in range(658):\n",
    "        density_out[str(ind)] = np.array(f['Density'][str(ind)])\n",
    "        target[str(ind)] = np.array(f['Ratio'][str(ind)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.18918922 0.0826211 ]\n"
     ]
    }
   ],
   "source": [
    "print(target['1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "density_out = {}\n",
    "MR_out = {}\n",
    "SR_out = {}\n",
    "with h5py.File(path_to_dataset+filename, 'r') as f:\n",
    "    for ind in range(76):\n",
    "        density_out[str(ind)] = np.array(f['density'][str(ind)])\n",
    "        MR_out[str(ind)] = np.array(f['ratio']['MR'][str(ind)])\n",
    "        SR_out[str(ind)] = np.array(f['ratio']['SR'][str(ind)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.18918921701943123\n",
      "0.08262109616010802\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f06dc628c40>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ind = 0\n",
    "print(MR_out[str(ind)])\n",
    "print(SR_out[str(ind)])\n",
    "plt.figure()\n",
    "plt.imshow(density_out[str(ind)][:,:,18])"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}