discard examples as now redundant with online docs

keeping them presents a high chance of them being outdated every other time, so it is safer to rely only on the online docs as demonstration

discard examples as now redundant with online docs
keeping them presents a high chance of them being outdated every other time, so it is safer to rely only on the online docs as demonstration
0c8d660c · Thibault Hallouin · 2eb109d6 · 2eb109d6 · 2eb109d6
Commit 0c8d660c authored 2 years ago by Thibault Hallouin
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--- a/examples/example_determinist.ipynb
+++ b/examples/example_determinist.ipynb
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "import evalhyd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Calculate Nash-Sutcliffe efficiency on 1D arrays"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(4,) (4,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# streamflow observations\n",
-    "obs = numpy.array(\n",
-    "    [4.7, 4.3, 5.5, 2.7]\n",
-    ")\n",
-    "# streamflow predictions\n",
-    "prd = numpy.array(\n",
-    "    [5.3, 4.2, 5.7, 2.3]\n",
-    ")\n",
-    "\n",
-    "print(obs.shape, prd.shape)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.86298077]\n"
-     ]
-    }
-   ],
-   "source": [
-    "nse, = evalhyd.evald(obs, prd, ['NSE'])\n",
-    "print(nse)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Calculate Nash-Sutcliffe efficiency on 2D arrays"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1, 4) (3, 4)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# streamflow observations\n",
-    "obs = numpy.array(\n",
-    "    [[4.7, 4.3, 5.5, 2.7]]\n",
-    ")\n",
-    "# streamflow predictions\n",
-    "prd = numpy.array(\n",
-    "    [[5.3, 4.2, 5.7, 2.3],\n",
-    "     [4.3, 4.2, 4.7, 4.3],\n",
-    "     [5.3, 5.2, 5.7, 2.3]]\n",
-    ")\n",
-    "\n",
-    "print(obs.shape, prd.shape)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.86298077]\n",
-      " [0.18990385]\n",
-      " [0.67067308]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "nse, = evalhyd.evald(obs, prd, ['NSE'])\n",
-    "print(nse)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
-%% Cell type:code id: tags:
-
-``` python
-import numpy
-import evalhyd
-```
-
-%% Cell type:markdown id: tags:
-
-## Calculate Nash-Sutcliffe efficiency on 1D arrays
-
-%% Cell type:code id: tags:
-
-``` python
-# streamflow observations
-obs = numpy.array(
-    [4.7, 4.3, 5.5, 2.7]
-)
-# streamflow predictions
-prd = numpy.array(
-    [5.3, 4.2, 5.7, 2.3]
-)
-
-print(obs.shape, prd.shape)
-```
-
-%% Output
-
-    (4,) (4,)
-
-%% Cell type:code id: tags:
-
-``` python
-nse, = evalhyd.evald(obs, prd, ['NSE'])
-print(nse)
-```
-
-%% Output
-
-    [0.86298077]
-
-%% Cell type:markdown id: tags:
-
-## Calculate Nash-Sutcliffe efficiency on 2D arrays
-
-%% Cell type:code id: tags:
-
-``` python
-# streamflow observations
-obs = numpy.array(
-    [[4.7, 4.3, 5.5, 2.7]]
-)
-# streamflow predictions
-prd = numpy.array(
-    [[5.3, 4.2, 5.7, 2.3],
-     [4.3, 4.2, 4.7, 4.3],
-     [5.3, 5.2, 5.7, 2.3]]
-)
-
-print(obs.shape, prd.shape)
-```
-
-%% Output
-
-    (1, 4) (3, 4)
-
-%% Cell type:code id: tags:
-
-``` python
-nse, = evalhyd.evald(obs, prd, ['NSE'])
-print(nse)
-```
-
-%% Output
-
-    [[0.86298077]
-     [0.18990385]
-     [0.67067308]]
--- a/examples/example_probabilist.ipynb
+++ b/examples/example_probabilist.ipynb
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "import pandas\n",
-    "import evalhyd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Calculate Brier score and Brier skill score at once"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1, 5) (1, 1, 3, 5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# streamflow observations (1 site)\n",
-    "obs = numpy.array(\n",
-    "    [[4.7, 4.3, 5.5, 2.7, 4.1]]\n",
-    ")\n",
-    "# streamflow predictions (1 site, 1 lead time, 3 forecast members)\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",
-    ")\n",
-    "# streamflow thresholds (2)\n",
-    "thr = [4., 5.]\n",
-    "\n",
-    "print(obs.shape, prd.shape)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [],
-   "source": [
-    "bs, bss = evalhyd.evalp(obs, prd, ['BS', 'BSS'], thr)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         Brier score\n",
-      "q > 4.0     0.222222\n",
-      "q > 5.0     0.133333\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    pandas.DataFrame(\n",
-    "        bs[0, 0, 0], [f\"q > {thr[0]}\", f\"q > {thr[1]}\"],\n",
-    "        [\"Brier score\"]\n",
-    "    )\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         Brier skill score\n",
-      "q > 4.0          -0.388889\n",
-      "q > 5.0           0.166667\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    pandas.DataFrame(\n",
-    "        bss[0, 0, 0], [f\"q > {thr[0]}\", f\"q > {thr[1]}\"],\n",
-    "        [\"Brier skill score\"]\n",
-    "    )\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Calculate decompositions of Brier score"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [],
-   "source": [
-    "bs_crd, bs_lbd = evalhyd.evalp(obs, prd, ['BS_CRD', 'BS_LBD'], [4., 5.])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### calibration-refinement decomposition"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         reliability  resolution  uncertainty\n",
-      "q > 4.0     0.222222        0.16         0.16\n",
-      "q > 5.0     0.033333        0.06         0.16\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    pandas.DataFrame(\n",
-    "        bs_crd[0, 0, 0], [f\"q > {thr[0]}\", f\"q > {thr[1]}\"],\n",
-    "        [\"reliability\", \"resolution\", \"uncertainty\"]\n",
-    "    )\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### bs = rel - res + unc?"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "True"
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.allclose(\n",
-    "    bs, bs_crd[..., 0] - bs_crd[..., 1] + bs_crd[..., 2]\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### likelihood-base rate decomposition"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "         type 2 bias  discrimination  sharpness\n",
-      "q > 4.0     0.072222        0.027778   0.177778\n",
-      "q > 5.0     0.072222        0.027778   0.088889\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    pandas.DataFrame(\n",
-    "        bs_lbd[0, 0, 0], [f\"q > {thr[0]}\", f\"q > {thr[1]}\"],\n",
-    "        [\"type 2 bias\", \"discrimination\", \"sharpness\"]\n",
-    "    )\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### bs = t2b - dis + shr?"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "True"
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numpy.allclose(\n",
-    "    bs, bs_lbd[..., 0] - bs_lbd[..., 1] + bs_lbd[..., 2]\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
-%% Cell type:code id: tags:
-
-``` python
-import numpy
-import pandas
-import evalhyd
-```
-
-%% Cell type:markdown id: tags:
-
-## Calculate Brier score and Brier skill score at once
-
-%% Cell type:code id: tags:
-
-``` python
-# streamflow observations (1 site)
-obs = numpy.array(
-    [[4.7, 4.3, 5.5, 2.7, 4.1]]
-)
-# streamflow predictions (1 site, 1 lead time, 3 forecast members)
-prd = numpy.array(
-    [[[[5.3, 4.2, 5.7, 2.3, 3.1],
-       [4.3, 4.2, 4.7, 4.3, 3.3],
-       [5.3, 5.2, 5.7, 2.3, 3.9]]]]
-)
-# streamflow thresholds (2)
-thr = [4., 5.]
-
-print(obs.shape, prd.shape)
-```
-
-%% Output
-
-    (1, 5) (1, 1, 3, 5)
-
-%% Cell type:code id: tags:
-
-``` python
-bs, bss = evalhyd.evalp(obs, prd, ['BS', 'BSS'], thr)
-```
-
-%% Cell type:code id: tags:
-
-``` python
-print(
-    pandas.DataFrame(
-        bs[0, 0, 0], [f"q > {thr[0]}", f"q > {thr[1]}"],
-        ["Brier score"]
-    )
-)
-```
-
-%% Output
-
-             Brier score
-    q > 4.0     0.222222
-    q > 5.0     0.133333
-
-%% Cell type:code id: tags:
-
-``` python
-print(
-    pandas.DataFrame(
-        bss[0, 0, 0], [f"q > {thr[0]}", f"q > {thr[1]}"],
-        ["Brier skill score"]
-    )
-)
-```
-
-%% Output
-
-             Brier skill score
-    q > 4.0          -0.388889
-    q > 5.0           0.166667
-
-%% Cell type:markdown id: tags:
-
-## Calculate decompositions of Brier score
-
-%% Cell type:code id: tags:
-
-``` python
-bs_crd, bs_lbd = evalhyd.evalp(obs, prd, ['BS_CRD', 'BS_LBD'], [4., 5.])
-```
-
-%% Cell type:markdown id: tags:
-
-### calibration-refinement decomposition
-
-%% Cell type:code id: tags:
-
-``` python
-print(
-    pandas.DataFrame(
-        bs_crd[0, 0, 0], [f"q > {thr[0]}", f"q > {thr[1]}"],
-        ["reliability", "resolution", "uncertainty"]
-    )
-)
-```
-
-%% Output
-
-             reliability  resolution  uncertainty
-    q > 4.0     0.222222        0.16         0.16
-    q > 5.0     0.033333        0.06         0.16
-
-%% Cell type:markdown id: tags:
-
-#### bs = rel - res + unc?
-
-%% Cell type:code id: tags:
-
-``` python
-numpy.allclose(
-    bs, bs_crd[..., 0] - bs_crd[..., 1] + bs_crd[..., 2]
-)
-```
-
-%% Output
-
-True
-
-%% Cell type:markdown id: tags:
-
-### likelihood-base rate decomposition
-
-%% Cell type:code id: tags:
-
-``` python
-print(
-    pandas.DataFrame(
-        bs_lbd[0, 0, 0], [f"q > {thr[0]}", f"q > {thr[1]}"],
-        ["type 2 bias", "discrimination", "sharpness"]
-    )
-)
-```
-
-%% Output
-
-             type 2 bias  discrimination  sharpness
-    q > 4.0     0.072222        0.027778   0.177778
-    q > 5.0     0.072222        0.027778   0.088889
-
-%% Cell type:markdown id: tags:
-
-#### bs = t2b - dis + shr?
-
-%% Cell type:code id: tags:
-
-``` python
-numpy.allclose(
-    bs, bs_lbd[..., 0] - bs_lbd[..., 1] + bs_lbd[..., 2]
-)
-```
-
-%% Output
-
-True