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   "source": [
    "# Exercise 3: Update a `gwrefpy`model with new data\n",
    "\n",
    "This notebook introduces how to create and fit a simple `gwrefpy` model. \n",
    "\n",
    "This notebook can be downloaded from the source code [here](https://github.com/andersretznerSGU/gwrefpy/blob/main/docs/tutorial/exercises/1_exerciese1.ipynb).\n",
    "\n",
    "```{warning}\n",
    "This exercise is not completed yet.\n",
    "```"
   ],
   "id": "da019a3e0a3820ec"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1. Import the gwrefpy package",
   "id": "402830e86ed13f04"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gwrefpy as gr\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 2. Create some sample data",
   "id": "74d8f6918785d91d"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# Create some example data\n",
    "n_days = 100\n",
    "dates = pd.date_range(\"2020-01-01\", periods=n_days, freq=\"D\")\n",
    "\n",
    "# Observed and reference values with some noise\n",
    "values_obs1 = ( 25.75 + 0.7 * np.sin(np.linspace(0, 4 * np.pi, n_days)) + np.random.normal(0, 0.1, n_days))\n",
    "values_obs1 = pd.Series(values_obs1, index=dates)\n",
    "values_ref1 = (18.75 + 0.3 * np.sin(np.linspace(0, 4 * np.pi, n_days)) + np.random.normal(0, 0.05, n_days))\n",
    "values_ref1 = pd.Series(values_ref1, index=dates)"
   ],
   "id": "ed232a2ee85d4dbd"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 3. Create a Well objects",
   "id": "30c4c7cf4957476c"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "# Create your well objects here",
   "id": "de002510d9d1a6d1"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 4. Create a Model object",
   "id": "428ed7f690f31e80"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "# Create your model object here",
   "id": "7eb7bf69b43128c5"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 5. Fit the model",
   "id": "9638b4e81e91cb57"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "# Perform the model fitting here",
   "id": "b21ff8970a3bed8c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 6. Plot the results",
   "id": "2411337ab0ebf279"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "# Plot the results here",
   "id": "bbad3bade29c6b91"
  }
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