deploy: 9590ec5

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: d67d185dc58ecfd435f64d446be4e821
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/exercises/exercise_basic_model_skill.ipynb

@@ -2,156 +2,156 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Exercise: Basic model skill"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "source": [
-    "import fmskill"
-   ],
+   "metadata": {},
    "outputs": [],
-   "metadata": {}
+   "source": [
+    "import modelskill"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "You want do to a simple comparison between model and observation using the fmskill.compare method, but the following code snippet doesn't work.\n",
     "\n",
     "Change the code below, so that it works as intended. Hint: look at the documentation\n",
     "```\n",
     "help(fmskill.compare)\n",
     "```"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fn_mod = '../data/SW/ts_storm_4.dfs0'\n",
     "fn_obs = '../data/SW/eur_Hm0.dfs0'\n",
     "\n",
-    "c = fmskill.compare(fn_obs, fn_mod)"
-   ],
-   "outputs": [],
-   "metadata": {}
+    "c = modelskillill.compare(fn_obs, fn_mod)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "When you have fixed the above snippet, you can continue to do the skill assessment"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# plot a timeseries of the comparison\n",
     "# * remove the default title\n",
     "# * set the limits of the y axis to cover the 0-6m interval"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Your colleague, who is very skilled at Excel, wants to make a plot like this one:\n",
     "\n",
     "![](../images/excel_chart.png)"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Use the .df property on the comparer object to save the obs and model timeseries as an Excel file (\"skill.xlsx\")\n",
     "# you might get an error \"No module named 'openpyxl'\", the solution is to run `pip install openpyxl`"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# calculate the default skill metrics using the skill method\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# calculate the skill using the mean absolute percentage error and max error, use the metrics argument\n",
     "# c.skill(metrics=[__,__])"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# import the hit_ratio metric from fmskill.metrics\n",
+    "# import the hit_ratio metric from modelskillill.metrics\n",
     "# and calculate the ratio when the deviation between model and observation is less than 0.5 m\n",
     "# hint: use the Observation and Model columns of the dataframe from the .df property you used above\n",
     "\n",
     "# is the hit ratio ~0.95 ? Does it match with your expectation based on the timeseries plot?\n",
     "# what about a deviation of less than 0.1m? Pretty accurate wave model..."
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# hit_ratio(c.df.Observation, __, a=__)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# compare the distribution of modelled and observed values, using the .hist method\n",
     "# change the number of bins to 10"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
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+  "interpreter": {
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+   "display_name": "Python 3.9.6 64-bit",
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   "language_info": {
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    "codemirror_mode": {
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