star_wrapper_example.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd8e2c6d",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2af0b866",
   "metadata": {},
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   "source": [
    "# imagine this is the dataframe, 34k rows\n",
    "\n",
    "files = ['obj1', 'obj2', 'obj3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "44e6b718",
   "metadata": {},
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     "text": [
      "obj1_g.txt obj1_v.txt\n",
      "obj2_g.txt obj2_v.txt\n",
      "obj3_g.txt obj3_v.txt\n"
     ]
    }
   ],
   "source": [
    "for k in range(len(files)):\n",
    "    # these are the files you might read?\n",
    "    print(files[k] + '_g.txt', files[k] + '_v.txt' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d706d0f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create empty storage arrays to fill with the 4 key numbers\n",
    "mean_skew_all = np.zeros(len(files), dtype=float)\n",
    "std_skew_all = np.zeros(len(files), dtype=float)\n",
    "YN1_all = np.zeros(len(files), dtype=float)\n",
    "YN2_all = np.zeros(len(files), dtype=float)\n",
    "\n",
    "# a loop first over g files first\n",
    "for k in range(len(files)):\n",
    "    # 1. read in the file\n",
    "    print(files[k] + '_g.txt')\n",
    "    \n",
    "    # do all the LC stuff you're doing... blah blah blah\n",
    "    for j in range(100):\n",
    "        # do our trials, the stuff you're already doing\n",
    "        \n",
    "    # save our 4 numbers for the k'th star\n",
    "    YN1_all[k] = the yes/no calculation\n",
    "    YN2_all[k] = ...\n",
    "\n",
    "# combine these storage arrays into a NEW data frame    \n",
    "df_out_g = pd.DataFrame(data={'mean_skew_all':mean_skew_all, 'std_skew_all':std_skew_all,\n",
    "                              'YN1':YN1_all, 'YN2':YN2_all})\n",
    "\n",
    "# save the outputs to a file for future you\n",
    "df_out_g.to_csv('g-band.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9cc044f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can read the output in and now move on with your analysis\n",
    "df_out_g = pd.read_csv('g-band.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15392da0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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