{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import utide\n",
    "\n",
    "print(utide.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the data file to see what structure it has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"can1998.dtf\") as f:\n",
    "    lines = f.readlines()\n",
    "\n",
    "print(\"\".join(lines[:5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the fields are seconds, year, month, day, hour, elevation, flag.  We need a date parser function to combine the date and time fields into a single value to be used as the datetime index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "names = [\"seconds\", \"year\", \"month\", \"day\", \"hour\", \"elev\", \"flag\"]\n",
    "\n",
    "obs = pd.read_csv(\n",
    "    \"can1998.dtf\",\n",
    "    names=names,\n",
    "    skipinitialspace=True,\n",
    "    delim_whitespace=True,\n",
    "    na_values=\"9.990\",\n",
    ")\n",
    "\n",
    "\n",
    "date_cols = [\"year\", \"month\", \"day\", \"hour\"]\n",
    "index = pd.to_datetime(obs[date_cols])\n",
    "obs = obs.drop(date_cols, axis=1)\n",
    "obs.index = index\n",
    "\n",
    "obs.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although there are no elevations marked bad via special value, which should be `nan` after reading the file, the flag value of 2 indicates the values are unreliable, so we will mark them with `nan`, calculate the deviations of the elevations from their mean (stored in a new column called \"anomaly\"), and then interpolate to fill in the `nan` values in the anomaly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad = obs[\"flag\"] == 2\n",
    "corrected = obs[\"flag\"] == 1\n",
    "\n",
    "obs.loc[bad, \"elev\"] = np.nan\n",
    "obs[\"anomaly\"] = obs[\"elev\"] - obs[\"elev\"].mean()\n",
    "obs[\"anomaly\"] = obs[\"anomaly\"].interpolate()\n",
    "print(f\"{bad.sum()} points were flagged 'bad' and interpolated\")\n",
    "print(f\"{corrected.sum()} points were flagged 'corrected' and left unchanged\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can call solve to obtain the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = utide.solve(\n",
    "    obs.index,\n",
    "    obs[\"anomaly\"],\n",
    "    lat=-25,\n",
    "    method=\"ols\",\n",
    "    conf_int=\"MC\",\n",
    "    verbose=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The amplitudes and phases from the fit are now in the `coef` data structure (a Bunch), which can be used directly in the `reconstruct` function to generate a hindcast or forecast of the tides at the times specified in the `time` array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(coef.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tide = utide.reconstruct(obs.index, coef, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output from the reconstruction is also a Bunch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(tide.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = obs.index.to_pydatetime()\n",
    "\n",
    "fig, (ax0, ax1, ax2) = plt.subplots(figsize=(17, 5), nrows=3, sharey=True, sharex=True)\n",
    "\n",
    "ax0.plot(t, obs.anomaly, label=\"Observations\", color=\"C0\")\n",
    "ax1.plot(t, tide.h, label=\"Prediction\", color=\"C1\")\n",
    "ax2.plot(t, obs.anomaly - tide.h, label=\"Residual\", color=\"C2\")\n",
    "fig.legend(ncol=3, loc=\"upper center\");"
   ]
  }
 ],
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