main_processing_simulate_versus_mode_basis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## our custom libraries\n",
    "sys.path.append('classes/')\n",
    "from classes.dataset import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the current blueprint:\n",
      "number of samples: 30, name of samples: logo\n",
      "number of samples: 120, name of samples: logo_full\n",
      "number of samples: 56, name of samples: lines_rayleigh\n",
      "number of samples: 96, name of samples: alphabet\n",
      "number of samples: 1764, name of samples: lines_pair\n",
      "number of samples: 60, name of samples: square\n",
      "number of samples: 441, name of samples: square_part\n",
      "number of samples: 1000, name of samples: rand_ellipse\n",
      "number of samples: 2000, name of samples: rand_ellipses\n",
      "number of samples: 1000, name of samples: rand_line\n",
      "number of samples: 2000, name of samples: rand_lines\n",
      "number of samples: 20000, name of samples: rand_matrix\n",
      "Total number of samples = 28567\n"
     ]
    }
   ],
   "source": [
    "data_folder = r'/Users/giorgio/Desktop/HGM_programs/data/'\n",
    "\n",
    "a = Dataset(\n",
    "    data_folder=data_folder,\n",
    "    HG_basis=6, #6, 11, 16, 21\n",
    "    DMD_basis=210\n",
    ")\n",
    "a.blueprint_load('blueprint.txt')\n",
    "a.blueprint_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.sources_load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "basis_to_simulate = [10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "sim over, go in peace\n"
     ]
    }
   ],
   "source": [
    "for basis in basis_to_simulate:\n",
    "    print(basis)\n",
    "    a.HG_basis = basis\n",
    "    a.sim_init()\n",
    "    a.sim_generate(save_temp=True)  # simulate all samples\n",
    "    a.sim_save(load_temp=True) # save single samples into datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.load('/Users/giorgio/Desktop/HGM_programs/data/sim_HG_reconstruction/6/alphabet.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fcd0cd39c70>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(t[0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.load('/Users/giorgio/Desktop/HGM_programs/data/sim_HG_reconstruction/16/alphabet.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fccf84db040>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(t[0])\n",
    "plt.colorbar()"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}