{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## `evalhyd-python` demonstration"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import numpy\n",
    "import evalhyd"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Deterministic evaluation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Define streamflow observations"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# shape: {1, time: 4}\n",
    "obs = numpy.array(\n",
    "    [[4.7, 4.3, 5.5, 2.7]]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Define streamflow predictions"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# shape: {series: 1, time: 4}\n",
    "prd = numpy.array(\n",
    "    [[5.3, 4.2, 5.7, 2.3]]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Compute Nash-Sutcliffe efficiency"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shape: {series: 1, subsets: 1, samples: 1}\n",
    "evalhyd.evald(obs, prd, [\"NSE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Probabilistic evaluation"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Define streamflow observations"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# shape: {sites: 1, time: 5}\n",
    "obs = numpy.array(\n",
    "    [[4.7, 4.3, 5.5, 2.7, 4.1]]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Define streamflow predictions"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# shape: {sites: 1, lead times: 1, members: 3, time: 5}\n",
    "prd = numpy.array(\n",
    "    [[[[5.3, 4.2, 5.7, 2.3, 3.1],\n",
    "       [4.3, 4.2, 4.7, 4.3, 3.3],\n",
    "       [5.3, 5.2, 5.7, 2.3, 3.9]]]]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Define streamflow thresholds"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# shape: {sites: 1, thresholds: 2}\n",
    "thr = numpy.array(\n",
    "    [[4., 5.]]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Compute Brier score"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# shape: {sites: 1, lead times: 1, subsets: 1, samples: 1, thresholds: 2}\n",
    "evalhyd.evalp(obs, prd, [\"BS\"], thr, events=\"high\")"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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