refactor into separate cells

8a0de460 · Thibault Hallouin · 1548ec44 · 8a0de460
Commit 8a0de460 authored 2 years ago by Thibault Hallouin
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with 128 additions and 33 deletions
+128 -33
--- a/evalhyd-python.ipynb
+++ b/evalhyd-python.ipynb
@@ -3,7 +3,7 @@
  {
   "cell_type": "markdown",
   "source": [
-    "### Deterministic evaluation"
+    "## `evalhyd-python` demonstration"
   ],
   "metadata": {
    "collapsed": false
@@ -15,15 +15,59 @@
   "outputs": [],
   "source": [
    "import numpy\n",
-    "\n",
+    "import evalhyd"
-    "# define streamflow observations\n",
+   ],
-    "#   > shape: {1, time: 4}\n",
+   "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",
-    ")\n",
+    ")"
-    "\n",
+   ],
-    "# define streamflow predictions\n",
+   "metadata": {
-    "#   > shape: {series: 1, time:4}\n",
+    "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",
    ")"
@@ -32,16 +76,22 @@
    "collapsed": false
   }
  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Compute Nash-Sutcliffe efficiency"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
-    "import evalhyd\n",
+    "# shape: {series: 1, subsets: 1, samples: 1}\n",
-    "\n",
-    "# compute Nash-Sutcliffe efficiency\n",
-    "#   > shape: {series: 1, subsets: 1, samples: 1}\n",
    "evalhyd.evald(obs, prd, [\"NSE\"])"
   ]
  },
@@ -54,36 +104,84 @@
    "collapsed": false
   }
  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Define streamflow observations"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
-    "import numpy\n",
+    "# shape: {sites: 1, time: 5}\n",
-    "\n",
-    "# define streamflow observations\n",
-    "#   > shape: {sites: 1, time: 5}\n",
    "obs = numpy.array(\n",
    "    [[4.7, 4.3, 5.5, 2.7, 4.1]]\n",
-    ")\n",
+    ")"
-    "\n",
+   ],
-    "# define streamflow predictions\n",
+   "metadata": {
-    "#   > shape: {sites: 1, lead times: 1, members: 3, time: 5}\n",
+    "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",
-    ")\n",
+    ")"
-    "\n",
-    "# define streamflow thresholds\n",
-    "#   > shape: {sites: 1, thresholds: 2}\n",
-    "thr = numpy.array([[4., 5.]])"
   ],
   "metadata": {
-    "collapsed": false,
+    "collapsed": false
-    "pycharm": {
+   }
-     "is_executing": true
+  },
-    }
+  {
+   "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
   }
  },
  {
@@ -91,10 +189,7 @@
   "execution_count": null,
   "outputs": [],
   "source": [
-    "import evalhyd\n",
+    "# shape: {sites: 1, lead times: 1, subsets: 1, samples: 1, thresholds: 2}\n",
-    "\n",
-    "# compute Brier score\n",
-    "#   > shape: {sites: 1, lead times: 1, subsets: 1, samples: 1, thresholds: 2}\n",
    "evalhyd.evalp(obs, prd, [\"BS\"], thr, events=\"high\")"
   ],
   "metadata": {

 %% Cell type:markdown id: tags:
-### Deterministic evaluation
+## `evalhyd-python` demonstration
 %% Cell type:code id: tags:
 ``` python
 import numpy
+import evalhyd
+```
+%% Cell type:markdown id: tags:
+### Deterministic evaluation
-# define streamflow observations
+%% Cell type:markdown id: tags:
-#   > shape: {1, time: 4}
+Define streamflow observations
+%% Cell type:code id: tags:
+``` python
+# shape: {1, time: 4}
 obs = numpy.array(
    [[4.7, 4.3, 5.5, 2.7]]
 )
+```
+%% Cell type:markdown id: tags:
+Define streamflow predictions
+%% Cell type:code id: tags:
-# define streamflow predictions
+``` python
-#   > shape: {series: 1, time:4}
+# shape: {series: 1, time: 4}
 prd = numpy.array(
    [[5.3, 4.2, 5.7, 2.3]]
 )
 ```
+%% Cell type:markdown id: tags:
+Compute Nash-Sutcliffe efficiency
 %% Cell type:code id: tags:
 ``` python
-import evalhyd
+# shape: {series: 1, subsets: 1, samples: 1}
-# compute Nash-Sutcliffe efficiency
-#   > shape: {series: 1, subsets: 1, samples: 1}
 evalhyd.evald(obs, prd, ["NSE"])
 ```
 %% Cell type:markdown id: tags:
 ### Probabilistic evaluation
+%% Cell type:markdown id: tags:
+Define streamflow observations
 %% Cell type:code id: tags:
 ``` python
-import numpy
+# shape: {sites: 1, time: 5}
-# define streamflow observations
-#   > shape: {sites: 1, time: 5}
 obs = numpy.array(
    [[4.7, 4.3, 5.5, 2.7, 4.1]]
 )
+```
-# define streamflow predictions
+%% Cell type:markdown id: tags:
-#   > shape: {sites: 1, lead times: 1, members: 3, time: 5}
+Define streamflow predictions
+%% Cell type:code id: tags:
+``` python
+# shape: {sites: 1, lead times: 1, members: 3, time: 5}
 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]]]]
 )
-# define streamflow thresholds
-#   > shape: {sites: 1, thresholds: 2}
-thr = numpy.array([[4., 5.]])
 ```
+%% Cell type:markdown id: tags:
+Define streamflow thresholds
 %% Cell type:code id: tags:
 ``` python
-import evalhyd
+# shape: {sites: 1, thresholds: 2}
+thr = numpy.array(
+    [[4., 5.]]
+)
+```
-# compute Brier score
+%% Cell type:markdown id: tags:
-#   > shape: {sites: 1, lead times: 1, subsets: 1, samples: 1, thresholds: 2}
+Compute Brier score
+%% Cell type:code id: tags:
+``` python
+# shape: {sites: 1, lead times: 1, subsets: 1, samples: 1, thresholds: 2}
 evalhyd.evalp(obs, prd, ["BS"], thr, events="high")
 ```