{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8d7c3cc5-d80b-41ab-b040-6ffafa9542dd",
   "metadata": {},
   "source": [
    "![](https://raw.githubusercontent.com/MomsFriendlyRobotCompany/the-collector/master/pics/header.jpg)\n",
    "# The Collector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "449a0a11-a9a5-49a0-bbd2-267df6658be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload library\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ecb4d894-42a0-4575-8f25-a9d17946691b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collector import Collector\n",
    "from collector import nuke\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "24c7ddb3-cfab-41c3-8eec-61070c3f3a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Collector()\n",
    "c.timestamp = False\n",
    "d = np.array([[1,2,3],[4.,5.,6.]])\n",
    "i = {\"b\":[1,2,3,4]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9ead77-cc7f-498b-8d6e-1e3d960f0760",
   "metadata": {},
   "source": [
    "## CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9ab7606a-b809-46e1-9648-f293cee66ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving 2 data points in csv to:\n",
      "--> test_test_now.csv\n",
      "Loaded 2 data points from:\n",
      "--> test_test_now.csv\n",
      "[[ True  True  True]\n",
      " [ True  True  True]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'data': [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.write(\"test_test_now.csv\",d)\n",
    "dd = c.read(\"test_test_now.csv\")\n",
    "# print(dd[\"info\"] == i)\n",
    "print(dd[\"data\"] == d)\n",
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babd8032-9de3-4881-a7e0-bb751be765f1",
   "metadata": {},
   "source": [
    "## Gzip'ed JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f935ed1c-e118-4428-912f-5ddd83db2f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving 2 data points in json gzip to:\n",
      "--> test_test_now.gzip\n",
      "Loaded 2 data points from:\n",
      "--> test_test_now.gzip\n",
      "True\n",
      "[[ True  True  True]\n",
      " [ True  True  True]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'info': {'b': [1, 2, 3, 4]}, 'data': [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.write(\"test_test_now.gzip\",d,i)\n",
    "dd = c.read(\"test_test_now.gzip\")\n",
    "print(dd[\"info\"] == i)\n",
    "print(dd[\"data\"] == d)\n",
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe08d63-dd86-4d0d-8f98-59fbb8f836f0",
   "metadata": {},
   "source": [
    "## JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c70441e4-395b-4353-a54c-59d3a2492037",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving 2 data points in json to:\n",
      "--> test_test_now.json\n",
      "Loaded 2 data points from:\n",
      "--> test_test_now.json\n",
      "True\n",
      "[[ True  True  True]\n",
      " [ True  True  True]]\n"
     ]
    }
   ],
   "source": [
    "c.write(\"test_test_now.json\",d,i)\n",
    "dd = c.read(\"test_test_now.json\")\n",
    "print(dd[\"info\"] == i)\n",
    "print(dd[\"data\"] == d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da293926-e270-4961-af13-f7f6c00e0322",
   "metadata": {},
   "source": [
    "## Pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ae01b97-c00b-4de2-87c4-913a3ff1d1d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving 2 data points in pickle to:\n",
      "--> 2023-08-08T18:53:53_test_test_now.pkl\n",
      "Loaded 2 data points from:\n",
      "--> 2023-08-08T18:53:53_test_test_now.pkl\n",
      "True\n",
      "[[ True  True  True]\n",
      " [ True  True  True]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'info': {'b': [1, 2, 3, 4]}, 'data': [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = c.write(\"test_test_now.pkl\",d,i)\n",
    "dd = c.read(fname)\n",
    "print(dd[\"info\"] == i)\n",
    "print(dd[\"data\"] == d)\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3313afb7-69a2-4478-92e0-bebf6dfc5707",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving 2 data points in pickle to:\n",
      "--> data/2023-08-08T18:57:52_test_test_now.pkl\n",
      "Loaded 2 data points from:\n",
      "--> data/2023-08-08T18:57:52_test_test_now.pkl\n",
      "True\n",
      "[[ True  True  True]\n",
      " [ True  True  True]]\n"
     ]
    }
   ],
   "source": [
    "c.timestamp = True\n",
    "fname = c.write(\"data/test_test_now.pkl\",d,i)\n",
    "dd = c.read(fname)\n",
    "print(dd[\"info\"] == i)\n",
    "print(dd[\"data\"] == d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b129886d-db09-424b-8bfd-0fafa3c3ed6a",
   "metadata": {},
   "source": [
    "## Nuke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "00ede705-3be4-45d4-8ad9-8ebcc246f9db",
   "metadata": {},
   "outputs": [],
   "source": [
    "nuke(recursive=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e25460e-dfad-448b-b345-d5757ee59a8a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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