Merge pull request #4 from DHI/modelskill

Rename fmskill -> modelskill

Makefile

@@ -0,0 +1,2 @@
+all:
+	jupyter-book build mini_book

README.md

@@ -1,4 +1,4 @@
-# Python for marine modelers using [MIKE IO](https://github.com/DHI/mikeio) and [FMskill](https://github.com/DHI/fmskill)
+# Python for marine modelers using [MIKE IO](https://github.com/DHI/mikeio) and [ModelSkill](https://github.com/DHI/modelskill)
 
 [https://dhi.github.io/book-learn-mikeio-fmskill/](https://dhi.github.io/book-learn-mikeio-fmskill/)
 
@@ -10,7 +10,7 @@ This course has been designed for MIKE 21/3 modelers with some Python experience
 
 ## CONTENT
 
-The course comprises typical data processing tasks in connection with MIKE 21/3 modelling, preparing input data, converting forcing data from other formats, creating roughness maps, concatenating files, extracting data from output files (e.g. vertical profiles), visualization, assessing the skill of MIKE 21/3 by comparing to station and satellite track observation data. We will use Jupyter notebooks with Python packages such as NumPy, pandas and xarray as well DHI's own MIKE IO and FMskill packages. 
+The course comprises typical data processing tasks in connection with MIKE 21/3 modelling, preparing input data, converting forcing data from other formats, creating roughness maps, concatenating files, extracting data from output files (e.g. vertical profiles), visualization, assessing the skill of MIKE 21/3 by comparing to station and satellite track observation data. We will use Jupyter notebooks with Python packages such as NumPy, pandas and xarray as well DHI's own MIKE IO and ModelSkill packages. 
 
 ## BENEFITS
 

mini_book/_config.yml

@@ -1,7 +1,7 @@
 # Book settings
 # Learn more at https://jupyterbook.org/customize/config.html
 
-title: Python for marine modelers using MIKE IO and FMSkill
+title: Python for marine modelers using MIKE IO and ModelSkill
 author: Jesper Mariegaard and  Henrik Andersson
 logo: images/logo.png
 

mini_book/_toc.yml

@@ -22,12 +22,12 @@ parts:
     - file: output_visualisation
     - file: output_statistics
     - file: export_to_file
-  - caption: FMskill
+  - caption: ModelSkill
     chapters: 
     - file: point_observations
       sections:
       - file: exercises/exercise_point_observations
-    - file: fmskill_model_results
+    - file: modelskill_model_results
     - file: model_skill
       sections:
       - file: exercises/exercise_basic_model_skill
@@ -38,5 +38,5 @@ parts:
     - file: multi_model_comparison
   - caption: Assignments
     chapters: 
-    - file: exercises/fmskill_assignment
+    - file: exercises/modelskill_assignment
     - file: exercises/final_assignment

mini_book/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": {
-  "orig_nbformat": 4,
+  "interpreter": {
+   "hash": "f4041ee05ab07c15354d6207e763f17a216c3f5ccf08906343c2b4fd3fa7a6fb"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.6 64-bit",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python",
-   "version": "3.9.6",
-   "mimetype": "text/x-python",
    "codemirror_mode": {
     "name": "ipython",
     "version": 3
    },
-   "pygments_lexer": "ipython3",
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
    "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.6 64-bit"
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
   },
-  "interpreter": {
-   "hash": "f4041ee05ab07c15354d6207e763f17a216c3f5ccf08906343c2b4fd3fa7a6fb"
-  }
+  "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}