{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d264116-f5d0-43e5-952b-8cd2530b5d8c",
   "metadata": {},
   "source": [
    "## Ries data\n",
    "\n",
    "[Ries et. al.](https://doi.org/10.6094/UNIFR/151460) data contains data of a experimental campaign on 21 experimental sites with 6 distinct experiments on each site.\n",
    "The data is published in an institutional repository, without table descriptive metadata interpreted by this repository.\n",
    "Metadata for each table column is contained within the data *.tsv files in comment rows, which were extracted and parsed to fricitonless descriptors.\n",
    "Concepts from controlled vacabularies were assigned to the fricitonless descriptor from a user defined mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0eaf39a-b2d6-4fb7-8412-9dd95801f437",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset_loader import *\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88d8d490-f543-4680-9b4d-9fb20c2fcbec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints',\n",
       " 'agrov.dump',\n",
       " 'application_examples.md',\n",
       " 'concept_unit_method.md',\n",
       " 'dataset_loader.py',\n",
       " 'example_datasets',\n",
       " 'requirement_template.ipynb',\n",
       " 'reuse',\n",
       " 'runoff.ipynb',\n",
       " 'runoff_TUBAF.ipynb',\n",
       " '__pycache__']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d8740bd3-6de9-4df7-b9b9-9f2d3cdf3d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'resources': [{'name': '1_site_data',\n",
       "                'type': 'table',\n",
       "                'path': 'tables/1_site_data.csv',\n",
       "                'scheme': 'file',\n",
       "                'format': 'csv',\n",
       "                'mediatype': 'text/csv',\n",
       "                'encoding': 'utf-8',\n",
       "                'schema': {'fields': [{'name': 'Site_number',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' ID number of '\n",
       "                                                      'experiment location ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Site_name',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Name of experiment '\n",
       "                                                      'location ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_330988'},\n",
       "                                      {'name': 'Elevation',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' Elevation ',\n",
       "                                       'unit': 'mabovesealevel',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_316'},\n",
       "                                      {'name': 'Slope',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' Average slope at the '\n",
       "                                                      'experimental plot ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_fa256eab'},\n",
       "                                      {'name': 'Slope_aspect',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' Slope aspect of the '\n",
       "                                                      'experimental plot ',\n",
       "                                       'unit': '°',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_1832474d'},\n",
       "                                      {'name': 'Land_use',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Land use at the '\n",
       "                                                      'experimental plot ',\n",
       "                                       'unit': 'pasturearableland',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Vegetation',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Type of vegetation on '\n",
       "                                                      'the experimental plot ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': 'https://agrovoc.fao.org/browse/agrovoc/en/page/c_8176'},\n",
       "                                      {'name': 'Vegetation_height',\n",
       "                                       'type': 'number',\n",
       "                                       'concept': 'https://agrovoc.fao.org/browse/agrovoc/en/page/c_61f3cae5'},\n",
       "                                      {'name': 'Plant_coverage',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Estimated percentage '\n",
       "                                                      'of plant coverage ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Geology_GUEK300',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Sand_10cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of sand in 10 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Silt_10cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of silt in 30 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Clay_10cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of clay in 50 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_15619'},\n",
       "                                      {'name': 'Sand_30cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of sand in 10 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Silt_30cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of silt in 30 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Clay_30cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of clay in 50 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_15619'},\n",
       "                                      {'name': 'Sand_50cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of sand in 10 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Silt_50cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of silt in 30 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Clay_50cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Fraction of clay in 50 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_15619'},\n",
       "                                      {'name': 'Bulk_density_10cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Bulk density in 10 cm '\n",
       "                                                      'depth ',\n",
       "                                       'unit': 'g/cm3',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_7167'},\n",
       "                                      {'name': 'Bulk_density_30cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Bulk density in 30 cm '\n",
       "                                                      'depth ',\n",
       "                                       'unit': 'g/cm3',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_7167'},\n",
       "                                      {'name': 'Bulk_density_50cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Bulk density in 50 cm '\n",
       "                                                      'depth ',\n",
       "                                       'unit': 'g/cm3',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_7167'},\n",
       "                                      {'name': 'Stone_content_10cm',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' Estimated stone '\n",
       "                                                      'content from 0 to 10 cm '\n",
       "                                                      'depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Stone_content_30cm',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' Estimated stone '\n",
       "                                                      'content from 10 to 30 '\n",
       "                                                      'cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'MP_density_2mm_10cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Macropore density of '\n",
       "                                                      'size 2-5 mm at 10 cm '\n",
       "                                                      'depth ',\n",
       "                                       'unit': '-/m2',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_35318'},\n",
       "                                      {'name': 'MP_density_2mm_30cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Macropore density of '\n",
       "                                                      'size 2-5 mm at 30 cm '\n",
       "                                                      'depth ',\n",
       "                                       'unit': '-/m2',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_35318'},\n",
       "                                      {'name': 'MP_density_5mm_10cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Macropore density of '\n",
       "                                                      'size >5 mm at 10 cm '\n",
       "                                                      'depth ',\n",
       "                                       'unit': '-/m2',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_35318'},\n",
       "                                      {'name': 'MP_density_5mm_30cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Macropore density of '\n",
       "                                                      'size >5 mm at 30 cm '\n",
       "                                                      'depth ',\n",
       "                                       'unit': '-/m2',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_35318'},\n",
       "                                      {'name': 'SOM_10cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Soil organic matter at '\n",
       "                                                      '10 cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_389fe908'},\n",
       "                                      {'name': 'SOM_30cm',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Soil organic matter at '\n",
       "                                                      '30 cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_389fe908'},\n",
       "                                      {'name': 'SOM_50cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Soil organic matter at '\n",
       "                                                      '50 cm depth ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_389fe908'},\n",
       "                                      {'name': 'Total_porosity_10cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Total porosity in 10 '\n",
       "                                                      'cm depth',\n",
       "                                       'unit': 'cm3/cm3',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_7184'},\n",
       "                                      {'name': 'Total_porosity_30cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Total porosity in 30 '\n",
       "                                                      'cm depth',\n",
       "                                       'unit': 'cm3/cm3',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_7184'},\n",
       "                                      {'name': 'Total_porosity_50cm',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Total porosity in 50 '\n",
       "                                                      'cm depth',\n",
       "                                       'unit': 'cm3/cm3',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_7184'},\n",
       "                                      {'name': 'lon',\n",
       "                                       'type': 'number',\n",
       "                                       'description': 'longitude',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_64a2abf9'},\n",
       "                                      {'name': 'lat',\n",
       "                                       'type': 'number',\n",
       "                                       'description': 'latitude',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_64a2abf9'}],\n",
       "                           'missingValues': ['', 'NA'],\n",
       "                           'primaryKey': ['Site_number'],\n",
       "                           'foreignKeys': []}},\n",
       "               {'name': '2_event_data',\n",
       "                'type': 'table',\n",
       "                'path': 'tables/2_event_data.csv',\n",
       "                'scheme': 'file',\n",
       "                'format': 'csv',\n",
       "                'mediatype': 'text/csv',\n",
       "                'encoding': 'utf-8',\n",
       "                'schema': {'fields': [{'name': 'Site_number',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' ID number of '\n",
       "                                                      'experiment location ',\n",
       "                                       'unit': '1to23',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Experiment_number',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' ID number of '\n",
       "                                                      'experiment ',\n",
       "                                       'unit': '1to6',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Experiment_start',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Starting time of the '\n",
       "                                                      'experiment ',\n",
       "                                       'unit': 'yyyy-mm-ddhh',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Experiment_end',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Ending time of the '\n",
       "                                                      'experiment ',\n",
       "                                       'unit': 'yyyy-mm-ddhh',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Land_use',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Land use at the '\n",
       "                                                      'experimental plot ',\n",
       "                                       'unit': 'pasturearableland',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Experiment_duration',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' Duration of '\n",
       "                                                      'experiment ',\n",
       "                                       'unit': 'min',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Return_period',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Return period of the '\n",
       "                                                      'simulated rainfall '\n",
       "                                                      'event of the respective '\n",
       "                                                      'duration ',\n",
       "                                       'unit': 'years',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Target_intensity',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Target intensity '\n",
       "                                                      'according to local '\n",
       "                                                      'rainfall statistics and '\n",
       "                                                      'maximum observed '\n",
       "                                                      'rainfall values ',\n",
       "                                       'unit': 'mm/h',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Data_quality_comment',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Comment on data '\n",
       "                                                      'quality ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Selected_subplots',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Subplot selected for '\n",
       "                                                      'evaluation of '\n",
       "                                                      'experiment results '\n",
       "                                                      'according to data '\n",
       "                                                      'quality comment ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_1',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 1 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_2',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 2 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_3',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 3 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_4',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 4 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_5',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 5 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_6',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 6 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_1',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 1 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_2',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 2 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_3',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 3 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_4',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 4 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_5',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 5 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_6',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 6 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_7',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 7 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_8',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 8 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_9',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 9 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_10',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 10 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_C_11',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall collector 11 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_subplot_A',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Interpolated rainfall '\n",
       "                                                      'for subplot A ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_subplot_B',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Interpolated rainfall '\n",
       "                                                      'for subplot B ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_subplot_C',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Interpolated rainfall '\n",
       "                                                      'for subplot C ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_mean_selected',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Mean interpolated '\n",
       "                                                      'rainfall for selected '\n",
       "                                                      'subplots according to '\n",
       "                                                      'data quality ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_OF_subplot_A',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Cumulative overland '\n",
       "                                                      'flow for the experiment '\n",
       "                                                      'at subplot A ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_OF_subplot_B',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Cumulative overland '\n",
       "                                                      'flow for the experiment '\n",
       "                                                      'at subplot B ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_OF_subplot_C',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Cumulative overland '\n",
       "                                                      'flow for the experiment '\n",
       "                                                      'at subplot C ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_SSF_subplot_B',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Cumulative subsurface '\n",
       "                                                      'up to 40 cm depth flow '\n",
       "                                                      'for the experiment at '\n",
       "                                                      'subplot B ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_OF_mean_selected',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Mean cumulative '\n",
       "                                                      'overland flow for '\n",
       "                                                      'selected subplots '\n",
       "                                                      'according to data '\n",
       "                                                      'quality ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'RC_OF_subplot_A',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Overland flow '\n",
       "                                                      'coefficient for the '\n",
       "                                                      'experiment at subplot '\n",
       "                                                      'A ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': 'https://dbpedia.org/resource/Runoff_curve_number'},\n",
       "                                      {'name': 'RC_OF_subplot_B',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Overland flow '\n",
       "                                                      'coefficient for the '\n",
       "                                                      'experiment at subplot '\n",
       "                                                      'B ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': 'https://dbpedia.org/resource/Runoff_curve_number'},\n",
       "                                      {'name': 'RC_OF_subplot_C',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Overland flow '\n",
       "                                                      'coefficient for the '\n",
       "                                                      'experiment at subplot '\n",
       "                                                      'C ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': 'https://dbpedia.org/resource/Runoff_curve_number'},\n",
       "                                      {'name': 'RC_SSF_subplot_B',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Subsurface runoff '\n",
       "                                                      'coefficient for the '\n",
       "                                                      'experiment at subplot '\n",
       "                                                      'B ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': 'https://dbpedia.org/resource/Runoff_curve_number'},\n",
       "                                      {'name': 'RC_OF_mean_selected',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Mean surface runoff '\n",
       "                                                      'coefficient for '\n",
       "                                                      'selected subplots '\n",
       "                                                      'according to data '\n",
       "                                                      'quality ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': 'https://dbpedia.org/resource/Runoff_curve_number'}],\n",
       "                           'missingValues': ['', 'NA'],\n",
       "                           'primaryKey': ['Site_number', 'Experiment_number'],\n",
       "                           'foreignKeys': [{'fields': ['Site_number'],\n",
       "                                            'reference': {'resource': '1_site_data',\n",
       "                                                          'fields': ['Site_number']}}]}},\n",
       "               {'name': '3_experiment_time_series',\n",
       "                'type': 'table',\n",
       "                'path': 'tables/3_experiment_time_series.csv',\n",
       "                'scheme': 'file',\n",
       "                'format': 'csv',\n",
       "                'mediatype': 'text/csv',\n",
       "                'encoding': 'utf-8',\n",
       "                'schema': {'fields': [{'name': 'Date_time',\n",
       "                                       'type': 'datetime',\n",
       "                                       'description': ' Date and time in '\n",
       "                                                      'Central European Summer '\n",
       "                                                      'Time (CEST) = UTC +2 '\n",
       "                                                      'hours ',\n",
       "                                       'format': '%Y-%m-%d %H:%M',\n",
       "                                       'unit': 'yyyy-mm-ddhh',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Site_number',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' ID number of '\n",
       "                                                      'experiment location ',\n",
       "                                       'unit': '1to23',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Experiment_number',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' ID number of '\n",
       "                                                      'experiment ',\n",
       "                                       'unit': '1to6',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Land_use',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Land use at the '\n",
       "                                                      'experimental plot ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Selected_subplots',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Subplot selected for '\n",
       "                                                      'evaluation of '\n",
       "                                                      'experiment results '\n",
       "                                                      'according to data '\n",
       "                                                      'quality comment ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Experiment_duration',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Duration of '\n",
       "                                                      'experiment ',\n",
       "                                       'unit': 'min',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Return_period',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Return period of the '\n",
       "                                                      'simulated rainfall '\n",
       "                                                      'event of the respective '\n",
       "                                                      'duration ',\n",
       "                                       'unit': 'years',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Experiment_time_step_min',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Consecutive time step '\n",
       "                                                      'vector in minutes since '\n",
       "                                                      'begin of experiment ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Experiment_time_step_plus10min',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Consecutive time step '\n",
       "                                                      'vector in minutes since '\n",
       "                                                      'begin of experiment + '\n",
       "                                                      '10 minutes for '\n",
       "                                                      'recession ',\n",
       "                                       'unit': '-',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Solar_radiation',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Incoming solar '\n",
       "                                                      'radiation ',\n",
       "                                       'unit': 'W/m^2',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Wind_direction',\n",
       "                                       'type': 'integer',\n",
       "                                       'description': ' Wind direction '\n",
       "                                                      'measured in 2 m height ',\n",
       "                                       'unit': 'deg.',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Wind_speed',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Wind speed measured in '\n",
       "                                                      '2 m height ',\n",
       "                                       'unit': 'm/s',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Air_temperature',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Air temperature ',\n",
       "                                       'unit': 'deg.C',\n",
       "                                       'concept': 'http://aims.fao.org/aos/agrovoc/c_230'},\n",
       "                                      {'name': 'Relative_humidity',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Relative humidity ',\n",
       "                                       'unit': '%',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_1',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 1 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_2',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 2 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_3',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 3 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_4',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 4 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_5',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 5 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_RG_6',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Observed rainfall '\n",
       "                                                      'during experiment at '\n",
       "                                                      'rainfall gauge 6 ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_subplot_A',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Interpolated rainfall '\n",
       "                                                      'for subplot A ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_subplot_B',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Interpolated rainfall '\n",
       "                                                      'for subplot B ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_subplot_C',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Interpolated rainfall '\n",
       "                                                      'for subplot C ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'P_mean_selected',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Mean interpolated '\n",
       "                                                      'rainfall for selected '\n",
       "                                                      'subplots according to '\n",
       "                                                      'data quality ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM1_5cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM1_10cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM1_20cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM1_30cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM1_40cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM2_5cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM2_10cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM2_20cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM2_30cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'SM2_40cm',\n",
       "                                       'type': 'string',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'WT1_height',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Depth of water table '\n",
       "                                                      'at piezometer WT1 ',\n",
       "                                       'unit': 'cm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'WT2_height',\n",
       "                                       'type': 'string',\n",
       "                                       'description': ' Depth of water table '\n",
       "                                                      'at piezometer WT2 ',\n",
       "                                       'unit': 'cm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_OF_subplot_A',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Cumulative overland '\n",
       "                                                      'flow for the experiment '\n",
       "                                                      'at subplot A ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_OF_subplot_B',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Cumulative overland '\n",
       "                                                      'flow for the experiment '\n",
       "                                                      'at subplot B ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_OF_subplot_C',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Cumulative overland '\n",
       "                                                      'flow for the experiment '\n",
       "                                                      'at subplot C ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_SSF_subplot_B',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Cumulative subsurface '\n",
       "                                                      'up to 40 cm depth flow '\n",
       "                                                      'for the experiment at '\n",
       "                                                      'subplot B ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''},\n",
       "                                      {'name': 'Q_OF_mean_selected',\n",
       "                                       'type': 'number',\n",
       "                                       'description': ' Mean cumulative '\n",
       "                                                      'overland flow for '\n",
       "                                                      'selected subplots '\n",
       "                                                      'according to data '\n",
       "                                                      'quality ',\n",
       "                                       'unit': 'mm',\n",
       "                                       'concept': ''}],\n",
       "                           'missingValues': ['', 'NA'],\n",
       "                           'primaryKey': ['Date_time', 'Site_number'],\n",
       "                           'foreignKeys': [{'fields': ['Site_number'],\n",
       "                                            'reference': {'resource': '1_site_data',\n",
       "                                                          'fields': ['Site_number']}}]}}]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RIES = load_sp_datapackage({\"sourcedir\": \"example_datasets/RIES/\"}, 'RIES')\n",
    "#sorted(get_dataset_concepts(TUBAF, vocab = optio))\n",
    "RIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f209f48-25d6-45a8-814b-66945e4292c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Experiment No')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select mean overland flow Runoff coefficients from RIES data\n",
    "a = view_sp_resource(RIES.resources[1], fields = ['RC_OF_mean_selected'])\n",
    "# without .open() and .close() the first call gets only the last experiments - for whatever frictionless reason\n",
    "a.open()\n",
    "a.close()\n",
    "\n",
    "# convert this to pandas and draw a figure\n",
    "b = a.to_pandas()\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "ax1 = fig.add_subplot(111)\n",
    "for x in b.index.levels[0]:\n",
    "    site_data = b.xs(x, level=\"Site_number\")\n",
    "    #site_data = b[b.index.get_level_values(\"Site_number\") == x].reset_index()\n",
    "    ax1.plot(site_data, label = x)\n",
    "    #ax1.plot(site_data.Experiment_number, site_data.RC_OF_mean_selected, label=x)\n",
    "ax1.grid()\n",
    "ax1.legend(title = \"experimental site\")\n",
    "ax1.set_ylabel(\"Runoff coefficient of Experiment in series [-]\")\n",
    "ax1.set_xlabel(\"Experiment No\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ac4a455-6268-4c96-bf78-2e78e1f8dd53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': '3_experiment_time_series',\n",
       " 'type': 'table',\n",
       " 'data': [],\n",
       " 'scheme': '',\n",
       " 'format': 'inline',\n",
       " 'mediatype': 'text/csv',\n",
       " 'extrapaths': [],\n",
       " 'schema': {'fields': [{'name': 'Date_time',\n",
       "                        'type': 'datetime',\n",
       "                        'description': ' Date and time in Central European '\n",
       "                                       'Summer Time (CEST) = UTC +2 hours ',\n",
       "                        'format': '%Y-%m-%d %H:%M',\n",
       "                        'unit': 'yyyy-mm-ddhh',\n",
       "                        'concept': ''},\n",
       "                       {'name': 'Site_number',\n",
       "                        'type': 'integer',\n",
       "                        'description': ' ID number of experiment location ',\n",
       "                        'unit': '1to23',\n",
       "                        'concept': ''},\n",
       "                       {'name': 'Experiment_number',\n",
       "                        'type': 'string',\n",
       "                        'description': ' ID number of experiment ',\n",
       "                        'unit': '1to6',\n",
       "                        'concept': ''},\n",
       "                       {'name': 'P_mean_selected',\n",
       "                        'type': 'number',\n",
       "                        'description': ' Mean interpolated rainfall for '\n",
       "                                       'selected subplots according to data '\n",
       "                                       'quality ',\n",
       "                        'unit': 'mm',\n",
       "                        'concept': ''},\n",
       "                       {'name': 'Q_OF_mean_selected',\n",
       "                        'type': 'number',\n",
       "                        'description': ' Mean cumulative overland flow for '\n",
       "                                       'selected subplots according to data '\n",
       "                                       'quality ',\n",
       "                        'unit': 'mm',\n",
       "                        'concept': ''}],\n",
       "            'missingValues': ['', 'NA'],\n",
       "            'primaryKey': ['Date_time', 'Site_number'],\n",
       "            'foreignKeys': [{'fields': ['Site_number'],\n",
       "                             'reference': {'resource': '1_site_data',\n",
       "                                           'fields': ['Site_number']}}]}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_time = view_sp_resource(RIES.resources[2],\n",
    "                          fields = ['Experiment_number', 'P_mean_selected', 'Q_OF_mean_selected'],\n",
    "                          #row_filters = ['P_mean_selected > 1.5']\n",
    "                         )\n",
    "# without .open() and .close() the first call gets only the last experiments - for whatever frictionless reason\n",
    "a_time.open()\n",
    "a_time.close()\n",
    "a_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63cabc58-74e2-47fb-b61f-a4071c3eaaeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Experiment_number</th>\n",
       "      <th>P_mean_selected</th>\n",
       "      <th>Q_OF_mean_selected</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Site_number</th>\n",
       "      <th>Date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>2016-08-16 00:01:00</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-16 00:02:00</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-16 00:03:00</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-16 00:04:00</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-16 00:05:00</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Experiment_number  P_mean_selected  \\\n",
       "Site_number Date_time                                                \n",
       "1           2016-08-16 00:01:00              None              0.0   \n",
       "            2016-08-16 00:02:00              None              0.0   \n",
       "            2016-08-16 00:03:00              None              0.0   \n",
       "            2016-08-16 00:04:00              None              0.0   \n",
       "            2016-08-16 00:05:00              None              0.0   \n",
       "\n",
       "                                 Q_OF_mean_selected  \n",
       "Site_number Date_time                                \n",
       "1           2016-08-16 00:01:00                 0.0  \n",
       "            2016-08-16 00:02:00                 0.0  \n",
       "            2016-08-16 00:03:00                 0.0  \n",
       "            2016-08-16 00:04:00                 0.0  \n",
       "            2016-08-16 00:05:00                 0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_time = a_time.to_pandas()\n",
    "b_time = b_time.reorder_levels(['Site_number','Date_time']).sort_index()\n",
    "b_time.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "132dab0c-7865-4188-8166-cfbb933861d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Date')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "ax1 = fig.add_subplot(111)\n",
    "for x in b_time.index.levels[0]:\n",
    "    site_data = b_time.xs(x, level=\"Site_number\")\n",
    "    #site_data = b[b.index.get_level_values(\"Site_number\") == x].reset_index()\n",
    "    ax1.plot(site_data.index, site_data.P_mean_selected, label = x)\n",
    "    ax1.plot(site_data.index, site_data.Q_OF_mean_selected, label = x, linestyle = 'dashdot')\n",
    "#ax1.grid()\n",
    "ax1.legend(title = \"experimental site\")\n",
    "ax1.set_ylabel(\"P and Q_OF mean of selected plots []\")\n",
    "ax1.set_xlabel(\"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfb48167-5ea1-4f93-8d62-3904d81a6057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date_time'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "tst = pd.to_datetime('2017-10-19 09:30')\n",
    "tnd = tst+pd.Timedelta(hours=3)\n",
    "\n",
    "site_data.loc[tst:tnd].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "990c9871-1464-4e1b-8f7c-7666141d0898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>P_mean_selected</th>\n",
       "      <th>Q_OF_mean_selected</th>\n",
       "      <th>RC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Site_number</th>\n",
       "      <th>Experiment_number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>66.702</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132.738</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118.112</td>\n",
       "      <td>32.044</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.483</td>\n",
       "      <td>4.430</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60.251</td>\n",
       "      <td>22.724</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">23</th>\n",
       "      <th>2</th>\n",
       "      <td>61.270</td>\n",
       "      <td>50.930</td>\n",
       "      <td>83.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.980</td>\n",
       "      <td>40.550</td>\n",
       "      <td>88.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.271</td>\n",
       "      <td>28.454</td>\n",
       "      <td>85.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               P_mean_selected  Q_OF_mean_selected    RC\n",
       "Site_number Experiment_number                                           \n",
       "1           1                           66.702               0.114   0.2\n",
       "            2                          132.738               0.117   0.1\n",
       "            3                          118.112              32.044  27.1\n",
       "            4                           60.483               4.430   7.3\n",
       "            5                           60.251              22.724  37.7\n",
       "...                                        ...                 ...   ...\n",
       "23          2                           61.270              50.930  83.1\n",
       "            3                           45.980              40.550  88.2\n",
       "            4                           33.271              28.454  85.5\n",
       "            5                            0.000               0.000   NaN\n",
       "            6                            0.000               0.000   NaN\n",
       "\n",
       "[138 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg = b_time.pivot_table(values=[\"P_mean_selected\",\"Q_OF_mean_selected\"], columns=['Site_number','Experiment_number'], aggfunc='sum', dropna=True)\n",
    "agg = agg.T\n",
    "agg['RC'] = round(agg.Q_OF_mean_selected/ agg.P_mean_selected * 100, 1)\n",
    "agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0523196e-5ef6-4306-91a8-46413ee86500",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True False  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True False False False\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True False False]\n",
      "[ 0.2  0.1 27.1  7.3 37.7 35.5  0.9 36.4 67.1 69.2 82.4 79.6  1.1 38.4\n",
      " 82.8 73.5 99.6 80.4  0.7 41.9  nan 69.3 89.9 66.5 50.6 77.1 87.2 79.1\n",
      " 98.1 91.9 37.6 49.9 57.6 59.4 64.  55.7 10.7 31.5 56.5 55.6 51.3 58.3\n",
      " 73.2 78.2 84.8  nan  nan  nan 52.1 77.5 91.9 84.2 97.3 86.1  0.2  0.6\n",
      "  0.8  8.4  0.6  0.5  1.  19.3 62.  67.8 73.7 69.   0.   4.3 30.8 44.1\n",
      " 33.9 48.5 17.3 22.  39.2 54.4 58.9 68.3  0.1  0.   0.   4.3  7.7 33.1\n",
      "  4.8 37.2 57.4 61.7 61.2 66.4  0.   1.4 29.4 46.3 19.  40.7  0.1 45.4\n",
      " 81.3 78.8 89.  79.5  1.5 30.3 73.5 68.9 95.9 80.9 17.4 70.7 75.2 75.9\n",
      " 87.8 67.8 71.1 89.4 89.  82.8 95.5 85.6 29.2 82.9 77.9 69.2 90.4 61.5\n",
      " 66.5 53.9 62.7 65.6 74.2 50.3 54.3 83.1 88.2 85.5  nan  nan]\n",
      "[ 0.2  0.1 27.1  7.3 37.7 35.5  0.9 36.4 67.1 69.2 82.4 79.6  1.1 38.4\n",
      " 82.8 73.5 99.6 80.4  0.7 41.9  nan 69.3 89.9 66.5 50.6 77.1 87.2 79.1\n",
      " 98.1 91.9 37.6 49.9 57.6 59.4 64.  55.7 10.7 31.5 56.5 55.6 51.3 58.3\n",
      " 73.2 78.2 84.8  nan  nan  nan 52.1 77.5 91.9 84.2 97.3 86.1  0.2  0.6\n",
      "  0.8  8.4  0.6  0.5  1.  19.3 62.  67.8 73.7 69.   0.   4.3 30.8 44.1\n",
      " 33.9 48.5 17.3 22.  39.2 54.4 58.9 68.3  0.1  0.   0.   4.3  7.7 33.1\n",
      "  4.8 37.2 57.4 61.7 61.2 66.4  0.   1.4 29.4 46.3 19.  40.7  0.1 45.4\n",
      " 81.3 78.8 89.  79.5  1.5 30.3 73.5 68.9 95.9 80.9 17.4 70.7 75.2 75.9\n",
      " 87.8 67.8 71.1 89.4 89.  82.8 95.5 85.6 29.2 82.9 77.9 69.2 90.4 61.5\n",
      " 66.5 53.9 62.7 65.6 74.2 50.3 54.3 83.1 88.2 85.5  nan  nan]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JL\\AppData\\Local\\Temp\\ipykernel_57876\\3709138711.py:2: RuntimeWarning: invalid value encountered in divide\n",
      "  plt.plot(agg.RC.values/b.RC_OF_mean_selected.values)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All recalculated RC values divided by given values equal 1, so recalculation was successfull \n",
    "plt.plot(agg.RC.values/b.RC_OF_mean_selected.values)\n",
    "print(agg.RC.values == b.RC_OF_mean_selected.values)\n",
    "print(agg.RC.values)\n",
    "print(b.RC_OF_mean_selected.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c01f633-83e7-4c9a-9781-a253aa84d145",
   "metadata": {},
   "outputs": [],
   "source": [
    "TUBAF = load_sp_datapackage({\"sourcedir\": \"catalogue/temp_1/\"})\n",
    "multitable = load_sp_datapackage({\"sourcedir\": \"catalogue/temp_2/\"})\n",
    "Ries = load_sp_datapackage({\"sourcedir\": \"catalogue/temp_4/\"})\n",
    "view_sp_resource(TUBAF.resources[0], fields = ['SigP'], row_filters = ['SigP >9', 'SigP < 12']).to_pandas()\n",
    "\n",
    "get_dataset_concepts(TUBAF)\n",
    "get_sp_data(TUBAF, fielddefinition=[{'name': 'SigP'}])\n",
    "view = merge_foreign_keys(\n",
    "    get_sp_data(\n",
    "        TUBAF,\n",
    "        fielddefinition=[\n",
    "            {'name': 'SigP',\n",
    "             'row_filters': ['No == \"8\"']\n",
    "             },\n",
    "            {'unit': 'g/l',\n",
    "             'row_filters': ['not sedconc == None', 'No == \"8\"']\n",
    "             }\n",
    "            ]\n",
    "        )\n",
    "    )\n",
    "\n",
    "view = get_sp_data(\n",
    "    TUBAF,\n",
    "    fielddefinition=[\n",
    "        {'name': 'SigP',\n",
    "         'row_filters': ['SigP > 10']\n",
    "         },\n",
    "        {'unit': 'g/l',\n",
    "         'row_filters': ['not sedconc == None and sedconc >=300']\n",
    "         }\n",
    "        ]\n",
    "    )\n",
    "\n",
    "\n",
    "view = merge_foreign_keys(\n",
    "    get_sp_data(\n",
    "        TUBAF,\n",
    "        fielddefinition=[\n",
    "            {'name': 'SigP',\n",
    "             'row_filters': ['No == \"8\"']\n",
    "             },\n",
    "            {'unit': 'g/l',\n",
    "             'row_filters': ['not sedconc == None', 'No == \"8\"']\n",
    "             }\n",
    "            ]\n",
    "        )\n",
    "    )\n",
    "\n",
    "try:\n",
    "    view.extract()\n",
    "except:\n",
    "    view.extract()\n",
    "\n",
    "# model requirements can be defined by row constraints\n",
    "#from frictionless import validate, checks, transform\n",
    "#validate(TUBAF.resources[0],\n",
    "#         checks=[\n",
    "#             checks.row_constraint(formula=\"fSi + mSi + cSi == SILT\"),\n",
    "#             checks.row_constraint(formula=\"fSa + mSa + cSa == SAND\"),\n",
    "#             checks.row_constraint(formula=\"SILT + SAND + CLAY == 100\")\n",
    "#             ]\n",
    "#         )\n",
    "TUBAF.resources[1].to_pandas().head()"
   ]
  }
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