{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting\n",
    "PyProBE includes compatibility with multiple open plotting libraries in Python. This example will go through interacting with each of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by importing PyProBE and the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pyprobe\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "data_directory = \"../../../tests/sample_data/neware\"\n",
    "\n",
    "# Create a cell object\n",
    "cell = pyprobe.Cell(info=info_dictionary)\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": [
    "## Pandas/Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Matplotlib](https://matplotlib.org/) is one of the most popular plotting libraries in Python. [Pandas](https://pandas.pydata.org/) dataframes include a built-in `plot()` method to construct plots using Matplotlib, which is also integrated into PyProBE `Result` objects. \n",
    "\n",
    "Call `plot()` on any result object and control the method exactly as you would expect to with Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\").plot(\n",
    "    x=\"Time [s]\", y=\"Voltage [V]\", grid=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HvPlot\n",
    "Plots created using Matplotlib sometimes need a lot of work to look good, and they are not particularly interactive. [HvPlot](https://hvplot.holoviz.org/index.html) provides a high-level interface similar to plotting in pandas with the additional flexibility of specifying a more interactive backend. Note hvplot is an optional dependency, it can be installed seperately to enable this functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\").hvplot(\n",
    "    x=\"Time [s]\", y=\"Voltage [V]\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "Seaborn is a high-level plotting library built on top of matplotlib. It provides a clean interface for dataframes, while maintaining much of matplotlib's flexibility.\n",
    "\n",
    "In PyProBE you can import a version of Seaborn customized to work with PyProBE objects directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyprobe.plot import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This version of Seaborn can be interacted with as normal. The only difference is you are able to pass PyProBE `Result` objects directly to Seaborn plotting functions in their `data` argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.lineplot(\n",
    "    data=cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\"),\n",
    "    x=\"Time [s]\",\n",
    "    y=\"Voltage [V]\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Being built on matplotlib, Seaborn can interact natively with customisation features like subplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(8, 6))\n",
    "sns.lineplot(\n",
    "    data=cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\"),\n",
    "    x=\"Time [s]\",\n",
    "    y=\"Voltage [V]\",\n",
    "    ax=ax[0],\n",
    ")\n",
    "sns.lineplot(\n",
    "    data=cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\"),\n",
    "    x=\"Time [s]\",\n",
    "    y=\"Current [A]\",\n",
    "    ax=ax[1],\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, plots with two y axes can be created as usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.lineplot(\n",
    "    data=cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\").cycle(-1),\n",
    "    x=\"Time [s]\",\n",
    "    y=\"Voltage [V]\",\n",
    "    ax=ax,\n",
    ")\n",
    "ax2 = ax.twinx()\n",
    "sns.lineplot(\n",
    "    data=cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\").cycle(-1),\n",
    "    x=\"Time [s]\",\n",
    "    y=\"Current [A]\",\n",
    "    ax=ax2,\n",
    "    color=\"r\",\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for all the plotting methods demonstrated, built-in unit conversion is also present:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.lineplot(\n",
    "    data=cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\").cycle(-1),\n",
    "    x=\"Time [hr]\",\n",
    "    y=\"Voltage [V]\",\n",
    "    ax=ax,\n",
    ")\n",
    "ax2 = ax.twinx()\n",
    "sns.lineplot(\n",
    "    data=cell.procedure[\"Sample\"].experiment(\"Break-in Cycles\").cycle(-1),\n",
    "    x=\"Time [hr]\",\n",
    "    y=\"Current [mA]\",\n",
    "    ax=ax2,\n",
    "    color=\"r\",\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}