test_ascii_file_handling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from backend.file_handler.handle_types import read_csv\n",
    "# file_path = \"sample_files/data.ascii\"\n",
    "file_path = \"C:/Users/debji/Downloads/lightcurve_ascii_sample.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'good': True,\n",
       " 'data': array([[153360960,       393],\n",
       "        [153360961,       387],\n",
       "        [153360962,       319],\n",
       "        ...,\n",
       "        [153397003,       350],\n",
       "        [153397004,       398],\n",
       "        [153397005,       359]])}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "dataR = []\n",
    "for line in open(file_path):\n",
    "    row = [int(x) for x in line.strip().split(' ') if len(x) > 0]\n",
    "    if len(row) == 2:\n",
    "        dataR.append(row)\n",
    "\n",
    "data = np.asarray(dataR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[153360960,       393],\n",
       "       [153360961,       387],\n",
       "       [153360962,       319],\n",
       "       ...,\n",
       "       [153397003,       350],\n",
       "       [153397004,       398],\n",
       "       [153397005,       359]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(dataR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([list([153360960, 393]), list([153360961, 387]),\n",
       "       list([153360962, 319]), ..., list([153397003, 350]),\n",
       "       list([153397004, 398]), list([153397005, 359])], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-fbe712f9f90e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
     ]
    }
   ],
   "source": [
    "data[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(file_path, sep = '\\t').to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['   153360961         387'],\n",
       "       ['   153360962         319'],\n",
       "       ['   153360963         403'],\n",
       "       ...,\n",
       "       ['   153397003         350'],\n",
       "       ['   153397004         398'],\n",
       "       ['   153397005         359']], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['   153360961         387']\n",
      " ['   153360962         319']\n",
      " ['   153360963         403']\n",
      " ...\n",
      " ['   153397003         350']\n",
      " ['   153397004         398']\n",
      " ['   153397005         359']]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr = np.array(data.tolist())[:,:2]\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[153360962, 319]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[int(x) for x in '   153360962         319 '.split(' ') if len(x) > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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