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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sharing Data\n",
    "\n",
    "PyProBE makes sharing data simple and straightforward. This is a simple example to demonstrate the process.\n",
    "\n",
    "First we will import some sample data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "from pprint import pprint\n",
    "\n",
    "import pyprobe\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Describe the cell. Required fields are 'Name'.\n",
    "info_dictionary = {\n",
    "    \"Name\": \"Sample cell\",\n",
    "    \"Chemistry\": \"NMC622\",\n",
    "    \"Nominal Capacity [Ah]\": 0.04,\n",
    "    \"Cycler number\": 1,\n",
    "    \"Channel number\": 1,\n",
    "}\n",
    "\n",
    "# Create a cell object\n",
    "cell = pyprobe.Cell(info=info_dictionary)\n",
    "\n",
    "data_directory = \"../../../tests/sample_data/neware\"\n",
    "\n",
    "cell.add_procedure(\n",
    "    procedure_name=\"Sample\",\n",
    "    folder_path=data_directory,\n",
    "    filename=\"sample_data_neware.parquet\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use the `archive()` method of the cell object. This stores all attributes of the `cell` object into a single folder. The data is stored as `.parquet` files and the metadata is stored in `.json` files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell.archive(path=\"sample_archive\")\n",
    "os.listdir(\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can choose to compress the folder by adding `.zip` to the path:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell.archive(path=\"sample_archive.zip\")\n",
    "os.listdir(\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can then retrieve the archived object with the `pyprobe.load_archive()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_cell = pyprobe.load_archive(\"sample_archive.zip\")\n",
    "pprint(saved_cell.info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "saved_cell.procedure[\"Sample\"].plot(x=\"Time [hr]\", y=\"Voltage [V]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up after example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shutil.rmtree(\"sample_archive\")"
   ]
  }
 ],
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