{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example usage\n",
    "\n",
    "To use `sc2_datasets` in a project:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Extracting unpack: 100%|██████████| 1/1 [00:00<00:00, 200.00it/s]\n",
      "Extracting 2022_TestReplaypack: 100%|██████████| 1/1 [00:00<00:00, 333.36it/s]\n"
     ]
    }
   ],
   "source": [
    "from sc2_datasets.available_replaypacks import EXAMPLE_SYNTHETIC_REPLAYPACKS\n",
    "from sc2_datasets.torch.sc2_egset_dataset import SC2EGSetDataset\n",
    "\n",
    "# Initialize the dataset:\n",
    "sc2_egset_dataset = SC2EGSetDataset(\n",
    "    unpack_dir=\"../tests/test_output/unpack\",  # Specify existing directory path, where the data will be unpacked.\n",
    "    download_dir=\"../tests/test_output/download\",  # Specify existing directory path, where the data will be downloaded.\n",
    "    download=True,\n",
    "    names_urls=EXAMPLE_SYNTHETIC_REPLAYPACKS,  # Use a synthetic replaypack containing 1 replay.\n",
    ")\n",
    "\n",
    "# Iterate over instances:\n",
    "for i in range(len(sc2_egset_dataset)):\n",
    "    sc2_egset_dataset[i]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.0 ('venv': venv)",
   "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": "3.10.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "ddabeebea1640b0163a92815eef996f97fed6bbf3bb1d5b76776c493c09f748f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}