{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trends with the SAGE II / OSIRIS / OMPS-LP Dataset\n",
    "\n",
    "Here we calculate trends using the SAGE II / OSIRIS / OMPS-LP dataset by regressing to deseasonalized relative monthly zonal mean anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from LOTUS_regression.regression import regress_all_bins\n",
    "from LOTUS_regression.predictors import load_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is in a single NetCDF4 file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "MERGED_FILE = r'/media/users/data/LOTUS/S2_OS_OMPS/MERGED_LOTUS.nc'\n",
    "\n",
    "mzm_data = xr.open_dataset(MERGED_FILE, engine='netcdf4')\n",
    "\n",
    "print(mzm_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictors = load_data('pred_baseline_pwlt.csv')\n",
    "\n",
    "print(predictors.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = regress_all_bins(predictors, mzm_data['relative_anomaly'], tolerance=0.1)\n",
    "\n",
    "# Convert to ~ percent\n",
    "results *= 100\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import LOTUS_regression.plotting.trends as trends\n",
    "trends.pre_post_with_confidence(results, x='mean_latitude', y='altitude', ylim=(18, 50), log_y=False, figsize=(16, 6),\n",
    "                                x_label='Latitude [$^\\circ$]', y_label='Altitude [km]', pre_title='Pre 1997',\n",
    "                                post_title='Post 1997')"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit Metadata",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}