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 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and saving\n",
    "\n",
    "This notebook shows how the load data for use in the pipeline and how to save the results of a fit. The pipeline comes with the `iwutil` package, which contains utilities for loading and saving data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import iwutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import shutil"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate data\n",
    "\n",
    "Let's save some dummy data to a CSV and JSON files using the `iwutil.save` module. This module contains functions for saving data to a variety of formats. If the filename contains a \"/\" then the data is saved that subdirectory. If the subdirectory does not exist it will be created.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate some dummy data\n",
    "n_samples = 100\n",
    "x = np.linspace(0, 10, n_samples)\n",
    "y = 2 * x + np.random.normal(0, 1, n_samples)\n",
    "z = x**2 + np.random.normal(0, 2, n_samples)\n",
    "\n",
    "# Create a DataFrame and save to CSV in the tmp directory\n",
    "df = pd.DataFrame({\"x\": x, \"y\": y, \"z\": z})\n",
    "iwutil.save.csv(df, \"tmp/data.csv\")\n",
    "\n",
    "# Create a dictionary and save to JSON in the tmp directory\n",
    "metadata = {\"A\": [1, 2, 3], \"B\": [4, 5, 6], \"C\": [7, 8, 9]}\n",
    "iwutil.save.json(metadata, \"tmp/metadata.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data \n",
    "\n",
    "We can load the data into a pandas DataFrame using the `iwutil.read_df` function. This function will automatically detect the format of the file based on the filename extension (e.g. `.csv`, `.json`, `.parquet`).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = iwutil.read_df(\"tmp/data.csv\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = iwutil.read_df(\"tmp/metadata.json\")\n",
    "print(metadata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can delete the temporary directory and all of its contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shutil.rmtree(\"tmp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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