{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example of DOV search methods for observations (observaties)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/DOV-Vlaanderen/pydov/master?filepath=docs%2Fnotebooks%2Fsearch_observaties.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use cases explained below\n", "* Get observations in a bounding box\n", "* Get observations with specific properties\n", "* Get observations in a bounding box based on specific properties\n", "* Select observations in a municipality and return depth\n", "* Get observations based on fields not available in the standard output dataframee" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import inspect, sys\n", "import warnings; warnings.simplefilter('ignore')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# check pydov path\n", "import pydov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get information about the datatype 'Observatie'" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "from pydov.search.observatie import ObservatieSearch\n", "observatie = ObservatieSearch()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A description is provided for the 'Observatie' datatype:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Observaties zijn gekoppeld aan een monster en geven de waarde van een kenmerk/parameter weer die in een laboratorium of in het veld bepaald kunnen zijn. De observaties kunnen zeer divers zijn variërend van een kwantitatieve waarde uit een meting zoals watergehalte (%), kwalitatieve waarde uit een beschrijving of classificatie zoals grondsoort, gecodeerde waarde, zoals kalkreactie met HCL (ja/nee), een meetreeks zoals een korrelverdeling, een textuurmeting, een tijdmeetreeks zoals de temperatuurmeting met de gazondolk. De geografische ligging (X en Y (mL72) nemen ze over van het gekoppeld monster. De gegevens van de observaties kunnen worden geëxporteerd in een rapport.'" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observatie.get_description()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The different fields that are available for objects of the 'Observatie' datatype can be requested with the get_fields() method:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "id\n", "pkey_observatie\n", "pkey_parent\n", "parameter\n", "parametergroep\n", "observatietype\n", "detectieconditie\n", "resultaat\n", "eenheid\n", "fenomeentijd\n", "resultaattijd\n", "methode\n", "uitvoerder\n", "diepte_van_m\n", "diepte_tot_m\n", "herkomst\n", "opdracht\n", "geom\n" ] } ], "source": [ "fields = observatie.get_fields()\n", "\n", "# print available fields\n", "for f in fields.values():\n", " print(f['name'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can get more information of a field by requesting it from the fields dictionary:\n", "\n", "* *name*: name of the field\n", "* *definition*: definition of this field\n", "* *cost*: currently this is either 1 or 10, depending on the datasource of the field. It is an indication of the expected time it will take to retrieve this field in the output dataframe.\n", "* *notnull*: whether the field is mandatory or not\n", "* *type*: datatype of the values of this field\n", "* *codelist*: optionally, a codelist that describes the possible values of this field\n", "\n", "Alternatively, you can list all the fields and their details by inspecting the `get_fields()` output or the search instance itself in a notebook:" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", "
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\n", " pydov.search.observatie.ObservatieSearch\n", "
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Observaties zijn gekoppeld aan een monster en geven de waarde van een kenmerk/parameter weer die in een laboratorium of in het veld bepaald kunnen zijn. De observaties kunnen zeer divers zijn variërend van een kwantitatieve waarde uit een meting zoals watergehalte (%), kwalitatieve waarde uit een beschrijving of classificatie zoals grondsoort, gecodeerde waarde, zoals kalkreactie met HCL (ja/nee), een meetreeks zoals een korrelverdeling, een textuurmeting, een tijdmeetreeks zoals de temperatuurmeting met de gazondolk. De geografische ligging (X en Y (mL72) nemen ze over van het gekoppeld monster. De gegevens van de observaties kunnen worden geëxporteerd in een rapport.

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id - Uniek referentienummer in de databank.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
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pkey_observatie - Permanente URL die verwijst naar de gegevens van de observatie op de website.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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pkey_parent - Permanente URL die verwijst naar de gegevens van het gekoppeld object op de website.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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parameter - Het geobserveerd kenmerk.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
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parametergroep - Indicatieve indeling van parameters in groepen. Bijvoorbeeld onderkenningsproeven, chemische analyses, kationen, anionen, zware metalen, farmaceutische stoffen, ...

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
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observatietype - Type van de observatie: numerieke waarde, keuzelijst, meetreeks, tijdmeetreeks.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
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detectieconditie - De detectieconditie is groter of kleiner dan een numerieke waarde.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
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resultaat - De waarde van een observatie.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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eenheid - De eenheid waarin een resultaat of waarde van een observatie wordt uitgedrukt.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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fenomeentijd - Het moment vanaf wanneer het resultaat van de observatie van toepassing is (het moment waarop geobserveerd of bemonsterd werd).

  • type: date
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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resultaattijd - Het moment waarop het resultaat van de observatie werd bekomen (bvb datum/tijd van de laboanalyse is 1 dag later dan de monstername).

  • type: date
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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methode - De methode gebruikt om het resultaat van de observatie te bekomen.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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uitvoerder - De agent (persoon of organisatie) die de observatie heeft uitgevoerd.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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diepte_van_m - Minimum diepte van het monster ten opzichte van het aanvangspeil van het bemonsterd object, in meter, waarvoor de observatie van toepassing is.

  • type: float
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
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diepte_tot_m - Maximum diepte van het monster ten opzichte van het aanvangspeil van het bemonsterd object, in meter, waarvoor de observatie van toepassing is.

  • type: float
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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herkomst - De plaats waar de observatie is uitgevoerd: observatie in het labo of op het veld.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
\n", " \n", " \n", "
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opdracht - Opdracht waaraan een observatie gekoppeld is.

  • type: string
  • notnull: False
  • query: True
  • cost: 1
  • multivalue: False
\n", "
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geom - None

  • type: geometry
  • notnull: False
  • query: False
  • cost: 1
  • multivalue: False
\n", "
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\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observatie" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example use cases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get observations in a bounding box" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get data for all the observations that are geographically located within the bounds of the specified box.\n", "\n", "The coordinates are in the Belgian Lambert72 (EPSG:31370) coordinate system and are given in the order of lower left x, lower left y, upper right x, upper right y." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparameterdetectieconditieresultaateenheidmethodeuitvoerderherkomst
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2018-01-097.58.00Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...NaNNaNNaNHydrometerVO - Afdeling GeotechniekLABO
1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2018-01-097.58.00Onderkenning-grondsoortGrondsoort volgens ASTM, de beschrijving (ASTM...NaNClayey sandNaNOnbekendVO - Afdeling GeotechniekLABO
2https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2018-01-097.58.00Onderkenning - proevenConsistentiegrenzen - Uitrolgrens (Consistenti...NaN23%OnbekendVO - Afdeling GeotechniekLABO
3https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2018-01-094.54.75Onderkenning-grondsoortGrondsoort volgens ASTM, de code (ASTM_code)NaNML-OnbekendVO - Afdeling GeotechniekLABO
4https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2018-01-094.54.75Volumemassa-watergehalteWatergehalte (watergehalte)NaN51.7%Gewichtsverlies na drogen in droogstoofVO - Afdeling GeotechniekLABO
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/201... 2018-01-09 \n", "1 https://www.dov.vlaanderen.be/data/monster/201... 2018-01-09 \n", "2 https://www.dov.vlaanderen.be/data/monster/201... 2018-01-09 \n", "3 https://www.dov.vlaanderen.be/data/monster/201... 2018-01-09 \n", "4 https://www.dov.vlaanderen.be/data/monster/201... 2018-01-09 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 7.5 8.00 Onderkenningsproeven-korrelverdeling \n", "1 7.5 8.00 Onderkenning-grondsoort \n", "2 7.5 8.00 Onderkenning - proeven \n", "3 4.5 4.75 Onderkenning-grondsoort \n", "4 4.5 4.75 Volumemassa-watergehalte \n", "\n", " parameter detectieconditie \\\n", "0 Korrelverdeling d.m.v. hydrometer/areometer (K... NaN \n", "1 Grondsoort volgens ASTM, de beschrijving (ASTM... NaN \n", "2 Consistentiegrenzen - Uitrolgrens (Consistenti... NaN \n", "3 Grondsoort volgens ASTM, de code (ASTM_code) NaN \n", "4 Watergehalte (watergehalte) NaN \n", "\n", " resultaat eenheid methode \\\n", "0 NaN NaN Hydrometer \n", "1 Clayey sand NaN Onbekend \n", "2 23 % Onbekend \n", "3 ML - Onbekend \n", "4 51.7 % Gewichtsverlies na drogen in droogstoof \n", "\n", " uitvoerder herkomst \n", "0 VO - Afdeling Geotechniek LABO \n", "1 VO - Afdeling Geotechniek LABO \n", "2 VO - Afdeling Geotechniek LABO \n", "3 VO - Afdeling Geotechniek LABO \n", "4 VO - Afdeling Geotechniek LABO " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.util.location import Within, Box\n", "\n", "df = observatie.search(location=Within(Box(114000, 172310, 114005, 172315, epsg=31370)), max_features = 10)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataframe contains several observations made at the same location\n", "\n", "Using the *pkey_observatie* field one can request the details of these obsevrations in a webbrowser:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://www.dov.vlaanderen.be/data/observatie/2022-1754871\n", "https://www.dov.vlaanderen.be/data/observatie/2022-4675419\n", "https://www.dov.vlaanderen.be/data/observatie/2022-6135892\n", "https://www.dov.vlaanderen.be/data/observatie/2022-2531554\n", "https://www.dov.vlaanderen.be/data/observatie/2022-2805721\n", "https://www.dov.vlaanderen.be/data/observatie/2022-5086066\n", "https://www.dov.vlaanderen.be/data/observatie/2022-2028882\n", "https://www.dov.vlaanderen.be/data/observatie/2022-2531553\n", "https://www.dov.vlaanderen.be/data/observatie/2022-1846538\n", "https://www.dov.vlaanderen.be/data/observatie/2022-2760180\n" ] } ], "source": [ "for pkey_observatie in set(df.pkey_observatie):\n", " print(pkey_observatie)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get observations with specific properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next to querying observations based on their geographic location within a bounding box, we can also search for observations matching a specific set of properties. For this we can build a query using a combination of the 'Observatie' fields and operators provided by the WFS protocol.\n", "\n", "A list of possible operators can be found below:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['PropertyIsBetween',\n", " 'PropertyIsEqualTo',\n", " 'PropertyIsGreaterThan',\n", " 'PropertyIsGreaterThanOrEqualTo',\n", " 'PropertyIsLessThan',\n", " 'PropertyIsLessThanOrEqualTo',\n", " 'PropertyIsLike',\n", " 'PropertyIsNotEqualTo',\n", " 'PropertyIsNull',\n", " 'SortProperty']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[i for i,j in inspect.getmembers(sys.modules['owslib.fes2'], inspect.isclass) if 'Property' in i]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we build a query using the *PropertyIsEqualTo* operator to find all observations concerning the parameter \"Watergehalte (watergehalte)\":" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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4https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-01-2931.031.45Volumemassa-watergehalteWatergehalte (watergehalte)NaN23.8%OnbekendVO - Afdeling GeotechniekLABO
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/201... 1967-03-14 \n", "1 https://www.dov.vlaanderen.be/data/monster/202... 2025-02-27 \n", "2 https://www.dov.vlaanderen.be/data/monster/202... 2025-02-27 \n", "3 https://www.dov.vlaanderen.be/data/monster/202... 2020-01-29 \n", "4 https://www.dov.vlaanderen.be/data/monster/202... 2020-01-29 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 0.6 0.78 Volumemassa-watergehalte \n", "1 20.8 20.85 Volumemassa-watergehalte \n", "2 4.8 4.85 Volumemassa-watergehalte \n", "3 22.0 22.50 Volumemassa-watergehalte \n", "4 31.0 31.45 Volumemassa-watergehalte \n", "\n", " parameter detectieconditie resultaat eenheid \\\n", "0 Watergehalte (watergehalte) NaN 15.5 % \n", "1 Watergehalte (watergehalte) NaN 29.2 % \n", "2 Watergehalte (watergehalte) NaN 22.0 % \n", "3 Watergehalte (watergehalte) NaN 25.8 % \n", "4 Watergehalte (watergehalte) NaN 23.8 % \n", "\n", " methode \\\n", "0 Gewichtsverlies na drogen in droogstoof \n", "1 Gewichtsverlies na drogen in droogstoof \n", "2 Gewichtsverlies na drogen in droogstoof \n", "3 Onbekend \n", "4 Onbekend \n", "\n", " uitvoerder herkomst \n", "0 Rijksinstituut voor Grondmechanica LABO \n", "1 VO - Afdeling Geotechniek LABO \n", "2 VO - Afdeling Geotechniek LABO \n", "3 VO - Afdeling Geotechniek LABO \n", "4 VO - Afdeling Geotechniek LABO " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from owslib.fes2 import PropertyIsEqualTo\n", "\n", "query = PropertyIsEqualTo(propertyname='parameter',\n", " literal='Watergehalte (watergehalte)')\n", "df = observatie.search(query=query, max_features = 10)\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again we can use the *pkey_observatie* as a permanent link to the information of these observations:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://www.dov.vlaanderen.be/data/observatie/2022-3228509\n", "https://www.dov.vlaanderen.be/data/observatie/2026-53354083\n", "https://www.dov.vlaanderen.be/data/observatie/2024-35758874\n", "https://www.dov.vlaanderen.be/data/observatie/2026-53353988\n", "https://www.dov.vlaanderen.be/data/observatie/2025-53353978\n", "https://www.dov.vlaanderen.be/data/observatie/2024-35758957\n", "https://www.dov.vlaanderen.be/data/observatie/2026-53354032\n", "https://www.dov.vlaanderen.be/data/observatie/2025-53353951\n", "https://www.dov.vlaanderen.be/data/observatie/2026-53354052\n", "https://www.dov.vlaanderen.be/data/observatie/2026-53354173\n" ] } ], "source": [ "for pkey_observatie in set(df.pkey_observatie):\n", " print(pkey_observatie)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get observations in a bounding box based on specific properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can combine a query on attributes with a query on geographic location to get the observations within a bounding box that have specific properties.\n", "\n", "The following example requests the observations where the parameter 'Watergehalte (watergehalte)' is greater than 30 and within the given bounding box.\n", "\n", "(Note that the datatype of the *literal* parameter should be a string, regardless of the datatype of this field in the output dataframe.)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparameterdetectieconditieresultaateenheidmethodeuitvoerderherkomst
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2018-01-094.54.75Volumemassa-watergehalteWatergehalte (watergehalte)NaN51.7%Gewichtsverlies na drogen in droogstoofVO - Afdeling GeotechniekLABO
1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2018-01-097.58.00Volumemassa-watergehalteWatergehalte (watergehalte)NaN36.3%Gewichtsverlies na drogen in droogstoofVO - Afdeling GeotechniekLABO
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/201... 2018-01-09 \n", "1 https://www.dov.vlaanderen.be/data/monster/201... 2018-01-09 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 4.5 4.75 Volumemassa-watergehalte \n", "1 7.5 8.00 Volumemassa-watergehalte \n", "\n", " parameter detectieconditie resultaat eenheid \\\n", "0 Watergehalte (watergehalte) NaN 51.7 % \n", "1 Watergehalte (watergehalte) NaN 36.3 % \n", "\n", " methode uitvoerder herkomst \n", "0 Gewichtsverlies na drogen in droogstoof VO - Afdeling Geotechniek LABO \n", "1 Gewichtsverlies na drogen in droogstoof VO - Afdeling Geotechniek LABO " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from owslib.fes2 import PropertyIsGreaterThanOrEqualTo, And\n", "\n", "query = And([PropertyIsGreaterThanOrEqualTo(propertyname='resultaat',literal='30'),\n", " PropertyIsEqualTo(propertyname='parameter', literal='Watergehalte (watergehalte)')])\n", "\n", "df = observatie.search(\n", " location=Within(Box(114000, 172310, 114005, 172315, epsg=31370)),\n", " query=query,\n", " max_features = 10\n", " )\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look at one of theobservations in a webbrowser using its *pkey_observatie*:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://www.dov.vlaanderen.be/data/observatie/2022-2531554\n", "https://www.dov.vlaanderen.be/data/observatie/2022-2760180\n" ] } ], "source": [ "for pkey_observatie in set(df.pkey_observatie):\n", " print(pkey_observatie)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Select observations with specific conditions and return the results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can limit the columns in the output dataframe by specifying the *return_fields* parameter in our search.\n", "\n", "In this example we query all the observations that have a value (resultaat) greater than 10 for parameter 'Watergehalte (watergehalte)' and\treturn its value (resultaat):" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " resultaat\n", "0 15.5\n", "1 29.2\n", "2 22.0\n", "3 25.8\n", "4 23.8" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = And([PropertyIsGreaterThanOrEqualTo(propertyname='resultaat',literal='10'),\n", " PropertyIsEqualTo(propertyname='parameter', literal='Watergehalte (watergehalte)')])\n", "\n", "df = observatie.search(query=query,\n", " return_fields=('resultaat',),\n", " max_features=10)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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resultaat
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" ], "text/plain": [ " resultaat\n", "count 10\n", "unique 10\n", "top 15.5\n", "freq 1" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By discarding the observations with a resultaat less than 20, we get a different result:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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resultaat
count6
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" ], "text/plain": [ " resultaat\n", "count 6\n", "unique 6\n", "top 15.5\n", "freq 1" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.resultaat.astype(float) < 25.0].describe()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.astype(float).boxplot()\n", "ax.set_ylabel(\"Water content(%)\");\n", "ax.set_title(\"Distribution of water content\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get observations based on fields not available in the standard output dataframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To keep the output dataframe size acceptable, not all available WFS fields are included in the standard output. However, one can use this information to select observations as illustrated below.\n", "\n", "For example, make a selection of the observations that have an 'opdracht':" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_observatieopdracht
0https://www.dov.vlaanderen.be/data/observatie/...Curieuzeneuzen in de tuin,Curieuzeneuzen in de...
1https://www.dov.vlaanderen.be/data/observatie/...Curieuzeneuzen in de tuin,Curieuzeneuzen in de...
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4https://www.dov.vlaanderen.be/data/observatie/...Curieuzeneuzen in de tuin 2020,Curieuzeneuzen ...
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " opdracht \n", "0 Curieuzeneuzen in de tuin,Curieuzeneuzen in de... \n", "1 Curieuzeneuzen in de tuin,Curieuzeneuzen in de... \n", "2 Curieuzeneuzen in de tuin,Curieuzeneuzen in de... \n", "3 Curieuzeneuzen in de tuin,Curieuzeneuzen in de... \n", "4 Curieuzeneuzen in de tuin 2020,Curieuzeneuzen ... " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from owslib.fes2 import Not\n", "from owslib.fes2 import PropertyIsNull\n", "\n", "query = Not([PropertyIsNull(propertyname='opdracht')])\n", "\n", "df = observatie.search(query=query, max_features = 10,\n", " return_fields=('pkey_observatie', 'opdracht'))\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Select observations with extra details\n", "\n", "We can ask extra info from an observation from the XML. In this example we want the details of an observation" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/010] ..........\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparameterdetectieconditieresultaateenheidmethodeuitvoerderherkomstbetrouwbaarheidgeobserveerd_object_typegeobserveerd_object_naamgeobserveerd_object_permkey
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1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/bodemlocati...2024-12-18NaNNaNBodem_fysisch_vochtDiepte (grond)watertafel t.o.v. maaiveld (wate...NaN100cmBodemhydrologie, veldhandleiding voor archeolo...Steegmans, Joris (Aron bv)VELDBbodemlocatieARCH_2024L222_LB12024-035950
2https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/bodemlocati...2024-12-18NaNNaNBodem_terreinVegetatie (vegetatie)NaNGras-NaNSteegmans, Joris (Aron bv)VELDBbodemlocatieARCH_2024L222_LB102024-035951
3https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/bodemlocati...2024-12-18NaNNaNBodem_boringDiameter van de boor (boor_diameter)NaN7.0cmCode van Goede Praktijk voor Archeologie en Me...Steegmans, Joris (Aron bv)VELDBbodemlocatieARCH_2024L222_LB122024-035953
4https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/bodemlocati...2024-12-18NaNNaNBodem_boringType van de boring (boor_type)NaNEdelmanNaNNaNSteegmans, Joris (Aron bv)VELDBbodemlocatieARCH_2024L222_LB132024-035954
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/bodemlocati... 2024-12-18 \n", "1 https://www.dov.vlaanderen.be/data/bodemlocati... 2024-12-18 \n", "2 https://www.dov.vlaanderen.be/data/bodemlocati... 2024-12-18 \n", "3 https://www.dov.vlaanderen.be/data/bodemlocati... 2024-12-18 \n", "4 https://www.dov.vlaanderen.be/data/bodemlocati... 2024-12-18 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 NaN NaN Bodem_terrein \n", "1 NaN NaN Bodem_fysisch_vocht \n", "2 NaN NaN Bodem_terrein \n", "3 NaN NaN Bodem_boring \n", "4 NaN NaN Bodem_boring \n", "\n", " parameter detectieconditie \\\n", "0 Vegetatie (vegetatie) NaN \n", "1 Diepte (grond)watertafel t.o.v. maaiveld (wate... NaN \n", "2 Vegetatie (vegetatie) NaN \n", "3 Diameter van de boor (boor_diameter) NaN \n", "4 Type van de boring (boor_type) NaN \n", "\n", " resultaat eenheid methode \\\n", "0 Gras - NaN \n", "1 100 cm Bodemhydrologie, veldhandleiding voor archeolo... \n", "2 Gras - NaN \n", "3 7.0 cm Code van Goede Praktijk voor Archeologie en Me... \n", "4 Edelman NaN NaN \n", "\n", " uitvoerder herkomst betrouwbaarheid \\\n", "0 Steegmans, Joris (Aron bv) VELD B \n", "1 Steegmans, Joris (Aron bv) VELD B \n", "2 Steegmans, Joris (Aron bv) VELD B \n", "3 Steegmans, Joris (Aron bv) VELD B \n", "4 Steegmans, Joris (Aron bv) VELD B \n", "\n", " geobserveerd_object_type geobserveerd_object_naam \\\n", "0 bodemlocatie ARCH_2024L222_LB1 \n", "1 bodemlocatie ARCH_2024L222_LB1 \n", "2 bodemlocatie ARCH_2024L222_LB10 \n", "3 bodemlocatie ARCH_2024L222_LB12 \n", "4 bodemlocatie ARCH_2024L222_LB13 \n", "\n", " geobserveerd_object_permkey \n", "0 2024-035950 \n", "1 2024-035950 \n", "2 2024-035951 \n", "3 2024-035953 \n", "4 2024-035954 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.search.observatie import ObservatieSearch\n", "from pydov.types.observatie import Observatie, ObservatieDetails\n", "\n", "observatie = ObservatieSearch(\n", " objecttype=Observatie.with_extra_fields(ObservatieDetails)\n", ")\n", "\n", "df = observatie.search(max_features=10)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get observations with data from the subtype 'SecundaireParameter'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another available subtype within observations search: 'SecundaireParameter'" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/001] c\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparameterdetectieconditieresultaateenheidmethodeuitvoerderherkomstsecundaireparameter_parametersecundaireparameter_resultaatsecundaireparameter_eenheid
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/bodemdiepte...2015-12-10NaNNaNBodem_fysisch_vochtKsat (ksat)NaN0.00E00m/sKsat Open-end-methodeBodemkundige Dienst van BelgiëVELDtemp_bodem5.0°C
1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/bodemdiepte...2015-12-10NaNNaNBodem_fysisch_vochtKsat (ksat)NaN0.00E00m/sKsat Open-end-methodeBodemkundige Dienst van BelgiëVELDtemp_water5.0°C
2https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/bodemdiepte...2015-12-10NaNNaNBodem_fysisch_vochtKsat (ksat)NaN0.00E00m/sKsat Open-end-methodeBodemkundige Dienst van BelgiëVELDproefvlak_diepte60.0cm
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/bodemdiepte... 2015-12-10 \n", "1 https://www.dov.vlaanderen.be/data/bodemdiepte... 2015-12-10 \n", "2 https://www.dov.vlaanderen.be/data/bodemdiepte... 2015-12-10 \n", "\n", " diepte_van_m diepte_tot_m parametergroep parameter \\\n", "0 NaN NaN Bodem_fysisch_vocht Ksat (ksat) \n", "1 NaN NaN Bodem_fysisch_vocht Ksat (ksat) \n", "2 NaN NaN Bodem_fysisch_vocht Ksat (ksat) \n", "\n", " detectieconditie resultaat eenheid methode \\\n", "0 NaN 0.00E00 m/s Ksat Open-end-methode \n", "1 NaN 0.00E00 m/s Ksat Open-end-methode \n", "2 NaN 0.00E00 m/s Ksat Open-end-methode \n", "\n", " uitvoerder herkomst secundaireparameter_parameter \\\n", "0 Bodemkundige Dienst van België VELD temp_bodem \n", "1 Bodemkundige Dienst van België VELD temp_water \n", "2 Bodemkundige Dienst van België VELD proefvlak_diepte \n", "\n", " secundaireparameter_resultaat secundaireparameter_eenheid \n", "0 5.0 °C \n", "1 5.0 °C \n", "2 60.0 cm " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.search.observatie import ObservatieSearch\n", "from pydov.types.observatie import Observatie, SecundaireParameter\n", "from owslib.fes2 import PropertyIsLike\n", "\n", "observatie = ObservatieSearch(\n", " objecttype=Observatie.with_subtype(SecundaireParameter))\n", "query = PropertyIsLike(propertyname='pkey_observatie',\n", " literal='%/2019-000555')\n", "df = observatie.search(query=query, max_features = 10)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using Geopandas GeoDataFrame, we can easily display the results of our search on a map." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] } ], "source": [ "import geopandas as gpd\n", "from pydov.search.fields import GeometryReturnField\n", "\n", "from pydov.search.fields import GeometryReturnField\n", "\n", "query = And([PropertyIsGreaterThanOrEqualTo(propertyname='resultaat',literal='10'),\n", " PropertyIsEqualTo(propertyname='parameter', literal='Watergehalte (watergehalte)')])\n", "\n", "df = observatie.search(query=query, location=Within(Box(100000, 100000, 500000, 500000, epsg=31370)),\n", " return_fields=('pkey_observatie','resultaat',GeometryReturnField('geom', epsg=31370)), max_features = 100)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pkey_observatieresultaatgeom
0https://www.dov.vlaanderen.be/data/observatie/...15.5POINT (154823 177008)
1https://www.dov.vlaanderen.be/data/observatie/...29.2POINT (101458.94 190074.92)
2https://www.dov.vlaanderen.be/data/observatie/...22.0POINT (101458.94 190074.92)
3https://www.dov.vlaanderen.be/data/observatie/...25.8POINT (147404.2 222441.4)
4https://www.dov.vlaanderen.be/data/observatie/...23.8POINT (147404.2 222441.4)
............
95https://www.dov.vlaanderen.be/data/observatie/...29.3POINT (150362.19 201546.7)
96https://www.dov.vlaanderen.be/data/observatie/...46.6POINT (153011.06 196404.89)
97https://www.dov.vlaanderen.be/data/observatie/...42.1POINT (152832.03 196327.5)
98https://www.dov.vlaanderen.be/data/observatie/...46.0POINT (153132.55 196323.71)
99https://www.dov.vlaanderen.be/data/observatie/...37.6POINT (152980.98 196326.26)
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100 rows × 3 columns

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" ], "text/plain": [ " pkey_observatie resultaat \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... 15.5 \n", "1 https://www.dov.vlaanderen.be/data/observatie/... 29.2 \n", "2 https://www.dov.vlaanderen.be/data/observatie/... 22.0 \n", "3 https://www.dov.vlaanderen.be/data/observatie/... 25.8 \n", "4 https://www.dov.vlaanderen.be/data/observatie/... 23.8 \n", ".. ... ... \n", "95 https://www.dov.vlaanderen.be/data/observatie/... 29.3 \n", "96 https://www.dov.vlaanderen.be/data/observatie/... 46.6 \n", "97 https://www.dov.vlaanderen.be/data/observatie/... 42.1 \n", "98 https://www.dov.vlaanderen.be/data/observatie/... 46.0 \n", "99 https://www.dov.vlaanderen.be/data/observatie/... 37.6 \n", "\n", " geom \n", "0 POINT (154823 177008) \n", "1 POINT (101458.94 190074.92) \n", "2 POINT (101458.94 190074.92) \n", "3 POINT (147404.2 222441.4) \n", "4 POINT (147404.2 222441.4) \n", ".. ... \n", "95 POINT (150362.19 201546.7) \n", "96 POINT (153011.06 196404.89) \n", "97 POINT (152832.03 196327.5) \n", "98 POINT (153132.55 196323.71) \n", "99 POINT (152980.98 196326.26) \n", "\n", "[100 rows x 3 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf = gpd.GeoDataFrame(df, geometry='geom', crs='EPSG:31370')\n", "gdf.explore()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find observations with fraction measurements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observations of type 'Textuurmeting' contain multiple values per observation: they are the result of fraction measurements and include the different intervals and their respective mass percentage.\n", "\n", "While these observations are included when using the standard `ObservatieSearch` class, by default the measurements themselves are not included there. You can include them by adding the `Fractiemeting` subtype:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparameterdetectieconditieresultaateenheidmethodeuitvoerderherkomstfractiemeting_ondergrensfractiemeting_bovengrensfractiemeting_waarde
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.000.010.000
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10https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO1.502.000.528
11https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO2.004.002.220
12https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO4.008.003.641
13https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO8.0016.005.457
14https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO16.0031.007.419
15https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO31.0063.0010.702
16https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO63.00125.0021.422
17https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO125.00250.0031.117
18https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO250.00500.0015.191
19https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO500.001000.001.091
20https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...NaNNaN%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO1000.002000.000.000
\n", "
" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "5 https://www.dov.vlaanderen.be/data/observatie/... \n", "6 https://www.dov.vlaanderen.be/data/observatie/... \n", "7 https://www.dov.vlaanderen.be/data/observatie/... \n", "8 https://www.dov.vlaanderen.be/data/observatie/... \n", "9 https://www.dov.vlaanderen.be/data/observatie/... \n", "10 https://www.dov.vlaanderen.be/data/observatie/... \n", "11 https://www.dov.vlaanderen.be/data/observatie/... \n", "12 https://www.dov.vlaanderen.be/data/observatie/... \n", "13 https://www.dov.vlaanderen.be/data/observatie/... \n", "14 https://www.dov.vlaanderen.be/data/observatie/... \n", "15 https://www.dov.vlaanderen.be/data/observatie/... \n", "16 https://www.dov.vlaanderen.be/data/observatie/... \n", "17 https://www.dov.vlaanderen.be/data/observatie/... \n", "18 https://www.dov.vlaanderen.be/data/observatie/... \n", "19 https://www.dov.vlaanderen.be/data/observatie/... \n", "20 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "1 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "2 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "3 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "4 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "5 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "6 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "7 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "8 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "9 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "10 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "11 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "12 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "13 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "14 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "15 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "16 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "17 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "18 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "19 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "20 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 NaN NaN Bodem_fysisch_textuur \n", "1 NaN NaN Bodem_fysisch_textuur \n", "2 NaN NaN Bodem_fysisch_textuur \n", "3 NaN NaN Bodem_fysisch_textuur \n", "4 NaN NaN Bodem_fysisch_textuur \n", "5 NaN NaN Bodem_fysisch_textuur \n", "6 NaN NaN Bodem_fysisch_textuur \n", "7 NaN NaN Bodem_fysisch_textuur \n", "8 NaN NaN Bodem_fysisch_textuur \n", "9 NaN NaN Bodem_fysisch_textuur \n", "10 NaN NaN Bodem_fysisch_textuur \n", "11 NaN NaN Bodem_fysisch_textuur \n", "12 NaN NaN Bodem_fysisch_textuur \n", "13 NaN NaN Bodem_fysisch_textuur \n", "14 NaN NaN Bodem_fysisch_textuur \n", "15 NaN NaN Bodem_fysisch_textuur \n", "16 NaN NaN Bodem_fysisch_textuur \n", "17 NaN NaN Bodem_fysisch_textuur \n", "18 NaN NaN Bodem_fysisch_textuur \n", "19 NaN NaN Bodem_fysisch_textuur \n", "20 NaN NaN Bodem_fysisch_textuur \n", "\n", " parameter detectieconditie \\\n", "0 Textuurfracties laser volumeprocent (textuurme... NaN \n", "1 Textuurfracties laser volumeprocent (textuurme... NaN \n", "2 Textuurfracties laser volumeprocent (textuurme... NaN \n", "3 Textuurfracties laser volumeprocent (textuurme... NaN \n", "4 Textuurfracties laser volumeprocent (textuurme... NaN \n", "5 Textuurfracties laser volumeprocent (textuurme... NaN \n", "6 Textuurfracties laser volumeprocent (textuurme... NaN \n", "7 Textuurfracties laser volumeprocent (textuurme... NaN \n", "8 Textuurfracties laser volumeprocent (textuurme... NaN \n", "9 Textuurfracties laser volumeprocent (textuurme... NaN \n", "10 Textuurfracties laser volumeprocent (textuurme... NaN \n", "11 Textuurfracties laser volumeprocent (textuurme... NaN \n", "12 Textuurfracties laser volumeprocent (textuurme... NaN \n", "13 Textuurfracties laser volumeprocent (textuurme... NaN \n", "14 Textuurfracties laser volumeprocent (textuurme... NaN \n", "15 Textuurfracties laser volumeprocent (textuurme... NaN \n", "16 Textuurfracties laser volumeprocent (textuurme... NaN \n", "17 Textuurfracties laser volumeprocent (textuurme... NaN \n", "18 Textuurfracties laser volumeprocent (textuurme... NaN \n", "19 Textuurfracties laser volumeprocent (textuurme... NaN \n", "20 Textuurfracties laser volumeprocent (textuurme... NaN \n", "\n", " resultaat eenheid methode \\\n", "0 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "1 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "2 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "3 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "4 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "5 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "6 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "7 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "8 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "9 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "10 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "11 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "12 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "13 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "14 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "15 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "16 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "17 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "18 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "19 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "20 NaN % Textuurfracties volumeprocent afgeleid op basi... \n", "\n", " uitvoerder herkomst \\\n", "0 Université Catholique de Louvain, Earth and Cl... LABO \n", "1 Université Catholique de Louvain, Earth and Cl... LABO \n", "2 Université Catholique de Louvain, Earth and Cl... LABO \n", "3 Université Catholique de Louvain, Earth and Cl... LABO \n", "4 Université Catholique de Louvain, Earth and Cl... LABO \n", "5 Université Catholique de Louvain, Earth and Cl... LABO \n", "6 Université Catholique de Louvain, Earth and Cl... LABO \n", "7 Université Catholique de Louvain, Earth and Cl... LABO \n", "8 Université Catholique de Louvain, Earth and Cl... LABO \n", "9 Université Catholique de Louvain, Earth and Cl... LABO \n", "10 Université Catholique de Louvain, Earth and Cl... LABO \n", "11 Université Catholique de Louvain, Earth and Cl... LABO \n", "12 Université Catholique de Louvain, Earth and Cl... LABO \n", "13 Université Catholique de Louvain, Earth and Cl... LABO \n", "14 Université Catholique de Louvain, Earth and Cl... LABO \n", "15 Université Catholique de Louvain, Earth and Cl... LABO \n", "16 Université Catholique de Louvain, Earth and Cl... LABO \n", "17 Université Catholique de Louvain, Earth and Cl... LABO \n", "18 Université Catholique de Louvain, Earth and Cl... LABO \n", "19 Université Catholique de Louvain, Earth and Cl... LABO \n", "20 Université Catholique de Louvain, Earth and Cl... LABO \n", "\n", " fractiemeting_ondergrens fractiemeting_bovengrens fractiemeting_waarde \n", "0 0.00 0.01 0.000 \n", "1 0.01 0.05 0.000 \n", "2 0.05 0.10 0.000 \n", "3 0.10 0.20 0.000 \n", "4 0.20 0.30 0.000 \n", "5 0.30 0.40 0.000 \n", "6 0.40 0.50 0.014 \n", "7 0.50 0.75 0.348 \n", "8 0.75 1.00 0.322 \n", "9 1.00 1.50 0.529 \n", "10 1.50 2.00 0.528 \n", "11 2.00 4.00 2.220 \n", "12 4.00 8.00 3.641 \n", "13 8.00 16.00 5.457 \n", "14 16.00 31.00 7.419 \n", "15 31.00 63.00 10.702 \n", "16 63.00 125.00 21.422 \n", "17 125.00 250.00 31.117 \n", "18 250.00 500.00 15.191 \n", "19 500.00 1000.00 1.091 \n", "20 1000.00 2000.00 0.000 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.search.observatie import ObservatieSearch\n", "from pydov.types.observatie import Observatie, Fractiemeting\n", "\n", "from owslib.fes2 import PropertyIsEqualTo\n", "\n", "search = ObservatieSearch(\n", " objecttype=Observatie.with_subtype(Fractiemeting)\n", ")\n", "\n", "df = search.search(\n", " query=PropertyIsEqualTo('observatietype', 'Textuurmeting'),\n", " max_features=1)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make it easier to find the observations with fraction measurements, and immediately retrieve the data, you can use the `ObservatieFractiemetingSearch` class instead. This will return only observations of the type 'Textuurmeting' (hence: containing fraction measurements) and will include the `Fractiemeting` subtype by default:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/001] c\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparametereenheidmethodeuitvoerderherkomstfractiemeting_ondergrensfractiemeting_bovengrensfractiemeting_waarde
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.000.010.000
1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.010.050.000
2https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.050.100.000
3https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.100.200.000
4https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.200.300.000
5https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.300.400.000
6https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.400.500.014
7https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.500.750.348
8https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO0.751.000.322
9https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO1.001.500.529
10https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO1.502.000.528
11https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO2.004.002.220
12https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO4.008.003.641
13https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO8.0016.005.457
14https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO16.0031.007.419
15https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO31.0063.0010.702
16https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO63.00125.0021.422
17https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO125.00250.0031.117
18https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO250.00500.0015.191
19https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO500.001000.001.091
20https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-06-01NaNNaNBodem_fysisch_textuurTextuurfracties laser volumeprocent (textuurme...%Textuurfracties volumeprocent afgeleid op basi...Université Catholique de Louvain, Earth and Cl...LABO1000.002000.000.000
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "5 https://www.dov.vlaanderen.be/data/observatie/... \n", "6 https://www.dov.vlaanderen.be/data/observatie/... \n", "7 https://www.dov.vlaanderen.be/data/observatie/... \n", "8 https://www.dov.vlaanderen.be/data/observatie/... \n", "9 https://www.dov.vlaanderen.be/data/observatie/... \n", "10 https://www.dov.vlaanderen.be/data/observatie/... \n", "11 https://www.dov.vlaanderen.be/data/observatie/... \n", "12 https://www.dov.vlaanderen.be/data/observatie/... \n", "13 https://www.dov.vlaanderen.be/data/observatie/... \n", "14 https://www.dov.vlaanderen.be/data/observatie/... \n", "15 https://www.dov.vlaanderen.be/data/observatie/... \n", "16 https://www.dov.vlaanderen.be/data/observatie/... \n", "17 https://www.dov.vlaanderen.be/data/observatie/... \n", "18 https://www.dov.vlaanderen.be/data/observatie/... \n", "19 https://www.dov.vlaanderen.be/data/observatie/... \n", "20 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "1 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "2 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "3 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "4 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "5 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "6 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "7 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "8 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "9 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "10 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "11 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "12 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "13 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "14 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "15 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "16 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "17 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "18 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "19 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "20 https://www.dov.vlaanderen.be/data/monster/202... 2020-06-01 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 NaN NaN Bodem_fysisch_textuur \n", "1 NaN NaN Bodem_fysisch_textuur \n", "2 NaN NaN Bodem_fysisch_textuur \n", "3 NaN NaN Bodem_fysisch_textuur \n", "4 NaN NaN Bodem_fysisch_textuur \n", "5 NaN NaN Bodem_fysisch_textuur \n", "6 NaN NaN Bodem_fysisch_textuur \n", "7 NaN NaN Bodem_fysisch_textuur \n", "8 NaN NaN Bodem_fysisch_textuur \n", "9 NaN NaN Bodem_fysisch_textuur \n", "10 NaN NaN Bodem_fysisch_textuur \n", "11 NaN NaN Bodem_fysisch_textuur \n", "12 NaN NaN Bodem_fysisch_textuur \n", "13 NaN NaN Bodem_fysisch_textuur \n", "14 NaN NaN Bodem_fysisch_textuur \n", "15 NaN NaN Bodem_fysisch_textuur \n", "16 NaN NaN Bodem_fysisch_textuur \n", "17 NaN NaN Bodem_fysisch_textuur \n", "18 NaN NaN Bodem_fysisch_textuur \n", "19 NaN NaN Bodem_fysisch_textuur \n", "20 NaN NaN Bodem_fysisch_textuur \n", "\n", " parameter eenheid \\\n", "0 Textuurfracties laser volumeprocent (textuurme... % \n", "1 Textuurfracties laser volumeprocent (textuurme... % \n", "2 Textuurfracties laser volumeprocent (textuurme... % \n", "3 Textuurfracties laser volumeprocent (textuurme... % \n", "4 Textuurfracties laser volumeprocent (textuurme... % \n", "5 Textuurfracties laser volumeprocent (textuurme... % \n", "6 Textuurfracties laser volumeprocent (textuurme... % \n", "7 Textuurfracties laser volumeprocent (textuurme... % \n", "8 Textuurfracties laser volumeprocent (textuurme... % \n", "9 Textuurfracties laser volumeprocent (textuurme... % \n", "10 Textuurfracties laser volumeprocent (textuurme... % \n", "11 Textuurfracties laser volumeprocent (textuurme... % \n", "12 Textuurfracties laser volumeprocent (textuurme... % \n", "13 Textuurfracties laser volumeprocent (textuurme... % \n", "14 Textuurfracties laser volumeprocent (textuurme... % \n", "15 Textuurfracties laser volumeprocent (textuurme... % \n", "16 Textuurfracties laser volumeprocent (textuurme... % \n", "17 Textuurfracties laser volumeprocent (textuurme... % \n", "18 Textuurfracties laser volumeprocent (textuurme... % \n", "19 Textuurfracties laser volumeprocent (textuurme... % \n", "20 Textuurfracties laser volumeprocent (textuurme... % \n", "\n", " methode \\\n", "0 Textuurfracties volumeprocent afgeleid op basi... \n", "1 Textuurfracties volumeprocent afgeleid op basi... \n", "2 Textuurfracties volumeprocent afgeleid op basi... \n", "3 Textuurfracties volumeprocent afgeleid op basi... \n", "4 Textuurfracties volumeprocent afgeleid op basi... \n", "5 Textuurfracties volumeprocent afgeleid op basi... \n", "6 Textuurfracties volumeprocent afgeleid op basi... \n", "7 Textuurfracties volumeprocent afgeleid op basi... \n", "8 Textuurfracties volumeprocent afgeleid op basi... \n", "9 Textuurfracties volumeprocent afgeleid op basi... \n", "10 Textuurfracties volumeprocent afgeleid op basi... \n", "11 Textuurfracties volumeprocent afgeleid op basi... \n", "12 Textuurfracties volumeprocent afgeleid op basi... \n", "13 Textuurfracties volumeprocent afgeleid op basi... \n", "14 Textuurfracties volumeprocent afgeleid op basi... \n", "15 Textuurfracties volumeprocent afgeleid op basi... \n", "16 Textuurfracties volumeprocent afgeleid op basi... \n", "17 Textuurfracties volumeprocent afgeleid op basi... \n", "18 Textuurfracties volumeprocent afgeleid op basi... \n", "19 Textuurfracties volumeprocent afgeleid op basi... \n", "20 Textuurfracties volumeprocent afgeleid op basi... \n", "\n", " uitvoerder herkomst \\\n", "0 Université Catholique de Louvain, Earth and Cl... LABO \n", "1 Université Catholique de Louvain, Earth and Cl... LABO \n", "2 Université Catholique de Louvain, Earth and Cl... LABO \n", "3 Université Catholique de Louvain, Earth and Cl... LABO \n", "4 Université Catholique de Louvain, Earth and Cl... LABO \n", "5 Université Catholique de Louvain, Earth and Cl... LABO \n", "6 Université Catholique de Louvain, Earth and Cl... LABO \n", "7 Université Catholique de Louvain, Earth and Cl... LABO \n", "8 Université Catholique de Louvain, Earth and Cl... LABO \n", "9 Université Catholique de Louvain, Earth and Cl... LABO \n", "10 Université Catholique de Louvain, Earth and Cl... LABO \n", "11 Université Catholique de Louvain, Earth and Cl... LABO \n", "12 Université Catholique de Louvain, Earth and Cl... LABO \n", "13 Université Catholique de Louvain, Earth and Cl... LABO \n", "14 Université Catholique de Louvain, Earth and Cl... LABO \n", "15 Université Catholique de Louvain, Earth and Cl... LABO \n", "16 Université Catholique de Louvain, Earth and Cl... LABO \n", "17 Université Catholique de Louvain, Earth and Cl... LABO \n", "18 Université Catholique de Louvain, Earth and Cl... LABO \n", "19 Université Catholique de Louvain, Earth and Cl... LABO \n", "20 Université Catholique de Louvain, Earth and Cl... LABO \n", "\n", " fractiemeting_ondergrens fractiemeting_bovengrens fractiemeting_waarde \n", "0 0.00 0.01 0.000 \n", "1 0.01 0.05 0.000 \n", "2 0.05 0.10 0.000 \n", "3 0.10 0.20 0.000 \n", "4 0.20 0.30 0.000 \n", "5 0.30 0.40 0.000 \n", "6 0.40 0.50 0.014 \n", "7 0.50 0.75 0.348 \n", "8 0.75 1.00 0.322 \n", "9 1.00 1.50 0.529 \n", "10 1.50 2.00 0.528 \n", "11 2.00 4.00 2.220 \n", "12 4.00 8.00 3.641 \n", "13 8.00 16.00 5.457 \n", "14 16.00 31.00 7.419 \n", "15 31.00 63.00 10.702 \n", "16 63.00 125.00 21.422 \n", "17 125.00 250.00 31.117 \n", "18 250.00 500.00 15.191 \n", "19 500.00 1000.00 1.091 \n", "20 1000.00 2000.00 0.000 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.search.observatie import ObservatieFractiemetingSearch\n", "\n", "s = ObservatieFractiemetingSearch()\n", "df = s.search(max_features=1)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find observations with measurement series\n", "\n", "Observations of type 'Meetreeks' contain multiple values per observation: they are the result of a series of measurements and include a list of points (meetpunten) and their corresponding values (meetwaarden). Both the points and the values have a parameter and a unit.\n", "\n", "While these observations are included when using the standard `ObservatieSearch` class, by default the measurements themselves are not included there. You can include them by adding the `Meetreeks` subtype:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparameterdetectieconditieresultaateenheidmethodeuitvoerderherkomstmeetreeks_meetpunt_parametermeetreeks_meetpuntmeetreeks_meetpunt_eenheidmeetreeks_meetwaarde_parametermeetreeks_meetwaardemeetreeks_meetwaarde_eenheid
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...NaNNaNNaNOnbekendVO - Afdeling GeotechniekLABODiameter0.001718mmFractie met grotere diameter98.3%
1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...NaNNaNNaNOnbekendVO - Afdeling GeotechniekLABODiameter0.007332mmFractie met grotere diameter95.0%
2https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...NaNNaNNaNOnbekendVO - Afdeling GeotechniekLABODiameter0.010916mmFractie met grotere diameter94.4%
3https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...NaNNaNNaNOnbekendVO - Afdeling GeotechniekLABODiameter0.015932mmFractie met grotere diameter94.0%
4https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...NaNNaNNaNOnbekendVO - Afdeling GeotechniekLABODiameter0.026385mmFractie met grotere diameter92.4%
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "1 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "2 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "3 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "4 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "1 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "2 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "3 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "4 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "\n", " parameter detectieconditie \\\n", "0 Korrelverdeling d.m.v. hydrometer/areometer (K... NaN \n", "1 Korrelverdeling d.m.v. hydrometer/areometer (K... NaN \n", "2 Korrelverdeling d.m.v. hydrometer/areometer (K... NaN \n", "3 Korrelverdeling d.m.v. hydrometer/areometer (K... NaN \n", "4 Korrelverdeling d.m.v. hydrometer/areometer (K... NaN \n", "\n", " resultaat eenheid methode uitvoerder herkomst \\\n", "0 NaN NaN Onbekend VO - Afdeling Geotechniek LABO \n", "1 NaN NaN Onbekend VO - Afdeling Geotechniek LABO \n", "2 NaN NaN Onbekend VO - Afdeling Geotechniek LABO \n", "3 NaN NaN Onbekend VO - Afdeling Geotechniek LABO \n", "4 NaN NaN Onbekend VO - Afdeling Geotechniek LABO \n", "\n", " meetreeks_meetpunt_parameter meetreeks_meetpunt meetreeks_meetpunt_eenheid \\\n", "0 Diameter 0.001718 mm \n", "1 Diameter 0.007332 mm \n", "2 Diameter 0.010916 mm \n", "3 Diameter 0.015932 mm \n", "4 Diameter 0.026385 mm \n", "\n", " meetreeks_meetwaarde_parameter meetreeks_meetwaarde \\\n", "0 Fractie met grotere diameter 98.3 \n", "1 Fractie met grotere diameter 95.0 \n", "2 Fractie met grotere diameter 94.4 \n", "3 Fractie met grotere diameter 94.0 \n", "4 Fractie met grotere diameter 92.4 \n", "\n", " meetreeks_meetwaarde_eenheid \n", "0 % \n", "1 % \n", "2 % \n", "3 % \n", "4 % " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.search.observatie import ObservatieSearch\n", "from pydov.types.observatie import Observatie, Meetreeks\n", "\n", "from owslib.fes2 import PropertyIsEqualTo\n", "\n", "search = ObservatieSearch(\n", " objecttype=Observatie.with_subtype(Meetreeks)\n", ")\n", "\n", "df = search.search(\n", " query=PropertyIsEqualTo('observatietype', 'Meetreeks'),\n", " max_features=1)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make it easier to find the observations with measurement series, and immediately retrieve the data, you can use the `ObservatieMeetreeksSearch` class instead. This will return only observations of the type 'Meetreeks' (hence: containing measurement series) and will include the `Meetreeks` subtype by default:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n", "[000/001] c\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparametermethodeuitvoerderherkomstmeetreeks_meetpunt_parametermeetreeks_meetpuntmeetreeks_meetpunt_eenheidmeetreeks_meetwaarde_parametermeetreeks_meetwaardemeetreeks_meetwaarde_eenheid
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...OnbekendVO - Afdeling GeotechniekLABODiameter0.001718mmFractie met grotere diameter98.3%
1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...OnbekendVO - Afdeling GeotechniekLABODiameter0.007332mmFractie met grotere diameter95.0%
2https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...OnbekendVO - Afdeling GeotechniekLABODiameter0.010916mmFractie met grotere diameter94.4%
3https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...OnbekendVO - Afdeling GeotechniekLABODiameter0.015932mmFractie met grotere diameter94.0%
4https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2020-02-0622.022.5Onderkenningsproeven-korrelverdelingKorrelverdeling d.m.v. hydrometer/areometer (K...OnbekendVO - Afdeling GeotechniekLABODiameter0.026385mmFractie met grotere diameter92.4%
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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "1 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "2 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "3 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "4 https://www.dov.vlaanderen.be/data/monster/202... 2020-02-06 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "1 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "2 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "3 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "4 22.0 22.5 Onderkenningsproeven-korrelverdeling \n", "\n", " parameter methode \\\n", "0 Korrelverdeling d.m.v. hydrometer/areometer (K... Onbekend \n", "1 Korrelverdeling d.m.v. hydrometer/areometer (K... Onbekend \n", "2 Korrelverdeling d.m.v. hydrometer/areometer (K... Onbekend \n", "3 Korrelverdeling d.m.v. hydrometer/areometer (K... Onbekend \n", "4 Korrelverdeling d.m.v. hydrometer/areometer (K... Onbekend \n", "\n", " uitvoerder herkomst meetreeks_meetpunt_parameter \\\n", "0 VO - Afdeling Geotechniek LABO Diameter \n", "1 VO - Afdeling Geotechniek LABO Diameter \n", "2 VO - Afdeling Geotechniek LABO Diameter \n", "3 VO - Afdeling Geotechniek LABO Diameter \n", "4 VO - Afdeling Geotechniek LABO Diameter \n", "\n", " meetreeks_meetpunt meetreeks_meetpunt_eenheid \\\n", "0 0.001718 mm \n", "1 0.007332 mm \n", "2 0.010916 mm \n", "3 0.015932 mm \n", "4 0.026385 mm \n", "\n", " meetreeks_meetwaarde_parameter meetreeks_meetwaarde \\\n", "0 Fractie met grotere diameter 98.3 \n", "1 Fractie met grotere diameter 95.0 \n", "2 Fractie met grotere diameter 94.4 \n", "3 Fractie met grotere diameter 94.0 \n", "4 Fractie met grotere diameter 92.4 \n", "\n", " meetreeks_meetwaarde_eenheid \n", "0 % \n", "1 % \n", "2 % \n", "3 % \n", "4 % " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.search.observatie import ObservatieMeetreeksSearch\n", "\n", "s = ObservatieMeetreeksSearch()\n", "df = s.search(max_features=1)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Working with water samples and observations\n", "\n", "For further analysis and visualisation of the time series data, we can use the data analysis library pandas and visualisation library matplotlib." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Query the data of a specific filter using its permanent key:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_monsternaampkey_parentsmateriaalklassedatum_monsternamediepte_van_mdiepte_tot_mmonstertypemonstersamenstellingbemonsteringsprocedurebemonsteringsinstrumentbemonstering_door
0https://www.dov.vlaanderen.be/data/monster/201...4-0076-F1/M1/C2013(https://www.dov.vlaanderen.be/data/filter/199...grondwater2014-02-25NaN13.0vloeistofEnkelvoudig monsterNaN(pomp,)DE WATERGROEP (VROEGER: VMW - VLAAMSE MAATSCHA...
1https://www.dov.vlaanderen.be/data/monster/201...4-0076-F1/M2014(https://www.dov.vlaanderen.be/data/filter/199...grondwater2014-09-30NaN13.0vloeistofEnkelvoudig monsterNaN(pomp,)EUROFINS
2https://www.dov.vlaanderen.be/data/monster/201...4-0076-F1/M2015(https://www.dov.vlaanderen.be/data/filter/199...grondwater2015-09-30NaN13.0vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.
3https://www.dov.vlaanderen.be/data/monster/201...4-0076-F1/M2016(https://www.dov.vlaanderen.be/data/filter/199...grondwater2016-07-12NaN13.0vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.
4https://www.dov.vlaanderen.be/data/monster/201...4-0076-F1/M2017(https://www.dov.vlaanderen.be/data/filter/199...grondwater2017-03-27NaN13.0vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.
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" ], "text/plain": [ " pkey_monster naam \\\n", "0 https://www.dov.vlaanderen.be/data/monster/201... 4-0076-F1/M1/C2013 \n", "1 https://www.dov.vlaanderen.be/data/monster/201... 4-0076-F1/M2014 \n", "2 https://www.dov.vlaanderen.be/data/monster/201... 4-0076-F1/M2015 \n", "3 https://www.dov.vlaanderen.be/data/monster/201... 4-0076-F1/M2016 \n", "4 https://www.dov.vlaanderen.be/data/monster/201... 4-0076-F1/M2017 \n", "\n", " pkey_parents materiaalklasse \\\n", "0 (https://www.dov.vlaanderen.be/data/filter/199... grondwater \n", "1 (https://www.dov.vlaanderen.be/data/filter/199... grondwater \n", "2 (https://www.dov.vlaanderen.be/data/filter/199... grondwater \n", "3 (https://www.dov.vlaanderen.be/data/filter/199... grondwater \n", "4 (https://www.dov.vlaanderen.be/data/filter/199... grondwater \n", "\n", " datum_monstername diepte_van_m diepte_tot_m monstertype \\\n", "0 2014-02-25 NaN 13.0 vloeistof \n", "1 2014-09-30 NaN 13.0 vloeistof \n", "2 2015-09-30 NaN 13.0 vloeistof \n", "3 2016-07-12 NaN 13.0 vloeistof \n", "4 2017-03-27 NaN 13.0 vloeistof \n", "\n", " monstersamenstelling bemonsteringsprocedure bemonsteringsinstrument \\\n", "0 Enkelvoudig monster NaN (pomp,) \n", "1 Enkelvoudig monster NaN (pomp,) \n", "2 Enkelvoudig monster NaN (pomp,) \n", "3 Enkelvoudig monster NaN (pomp,) \n", "4 Enkelvoudig monster NaN (pomp,) \n", "\n", " bemonstering_door \n", "0 DE WATERGROEP (VROEGER: VMW - VLAAMSE MAATSCHA... \n", "1 EUROFINS \n", "2 Eurofins Analytico B.V. \n", "3 Eurofins Analytico B.V. \n", "4 Eurofins Analytico B.V. " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.search.monster import MonsterSearch\n", "from pydov.search.observatie import ObservatieSearch\n", "\n", "from owslib.fes2 import PropertyIsLike\n", "\n", "monster = MonsterSearch()\n", "observatie = ObservatieSearch()\n", "\n", "query = PropertyIsLike(\n", " propertyname='pkey_parents',\n", " literal='%/data/filter/1991-001040|%')\n", "\n", "df_monsters = monster.search(query=query)\n", "df_monsters.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the related observations:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_mdiepte_tot_mparametergroepparameterdetectieconditieresultaateenheidmethodeuitvoerderherkomst
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2012-11-26NaNNaNAnionenFluoride (F)<0.100mg/lOnbekendCHEMIPHARLABO
1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2012-11-26NaNNaNFysico-chemische parametersElektrische geleidbaarheid (EC)NaN2050µS/cmOnbekendCHEMIPHARLABO
2https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2012-11-26NaNNaNAnionenBicarbonaat (HCO3)NaN976.0mg/lOnbekendCHEMIPHARLABO
3https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2012-11-26NaNNaNAnionenNitriet (NO2)<0.010mg/lOnbekendCHEMIPHARLABO
4https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2012-11-26NaNNaNKationenAmmonium (NH4)NaN0.310mg/lOnbekendCHEMIPHARLABO
\n", "
" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/201... 2012-11-26 \n", "1 https://www.dov.vlaanderen.be/data/monster/201... 2012-11-26 \n", "2 https://www.dov.vlaanderen.be/data/monster/201... 2012-11-26 \n", "3 https://www.dov.vlaanderen.be/data/monster/201... 2012-11-26 \n", "4 https://www.dov.vlaanderen.be/data/monster/201... 2012-11-26 \n", "\n", " diepte_van_m diepte_tot_m parametergroep \\\n", "0 NaN NaN Anionen \n", "1 NaN NaN Fysico-chemische parameters \n", "2 NaN NaN Anionen \n", "3 NaN NaN Anionen \n", "4 NaN NaN Kationen \n", "\n", " parameter detectieconditie resultaat eenheid \\\n", "0 Fluoride (F) < 0.100 mg/l \n", "1 Elektrische geleidbaarheid (EC) NaN 2050 µS/cm \n", "2 Bicarbonaat (HCO3) NaN 976.0 mg/l \n", "3 Nitriet (NO2) < 0.010 mg/l \n", "4 Ammonium (NH4) NaN 0.310 mg/l \n", "\n", " methode uitvoerder herkomst \n", "0 Onbekend CHEMIPHAR LABO \n", "1 Onbekend CHEMIPHAR LABO \n", "2 Onbekend CHEMIPHAR LABO \n", "3 Onbekend CHEMIPHAR LABO \n", "4 Onbekend CHEMIPHAR LABO " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.util.query import Join\n", "\n", "df_observaties = observatie.search(\n", " query=Join(df_monsters, on='pkey_parent', using='pkey_monster')\n", ")\n", "\n", "df_observaties.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The date is still stored as a string type. Transforming to a data type using the available pandas function `to_datetime` and using these dates as row index:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "df_observaties['fenomeentijd'] = pd.to_datetime(df_observaties['fenomeentijd'])\n", "df_observaties['resultaat'] = pd.to_numeric(df_observaties['resultaat'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For many usecases, it is useful to create a pivoted table, showing the value per parameter:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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parameterAfwijkingBalans% (%AfwijkBalans)Aluminium (Al)Ammonium (NH4)Arseen (As)Bicarbonaat (HCO3)Boor (B)Bromide (Br)Cadmium (Cd)Calcium (Ca)Carbonaat (CO3)...Opgeloste zuurstof (O2)Redoxpotentiaal (Eh°)Som anionen (SomAN)Som kationen (SomKAT)Sulfaat (SO4)Temperatuur (T)Totaal organische koolstof (TOC)Zink (Zn)Zuurtegraad (pH)Zuurtegraad in het labo (pH(Lab.))
fenomeentijd
2006-05-24NaN0.0500.3605.000863.4NaNNaN0.500399.0NaN...0.5120.0NaNNaN488.013.958.016.06.70NaN
2009-03-13NaNNaN0.3705.000945.6NaNNaN0.500394.71.0...0.3130.0NaNNaN431.012.78.610.06.766.80
2009-12-23NaNNaN0.29012.467939.4NaNNaN0.500447.21.0...1.034.0NaNNaN433.610.88.924.97.046.90
2011-03-28NaNNaN0.33012.9981030.3NaNNaN0.519331.11.0...0.3189.0NaNNaN290.112.010.013.06.706.90
2011-11-04NaNNaN0.3108.400958.3112.0NaN0.500345.01.0...0.1307.0NaNNaN385.512.18.310.96.737.40
2012-11-26NaNNaN0.310NaN976.0NaNNaNNaN360.01.0...0.4179.0NaNNaN313.012.77.8NaN6.617.18
2014-02-25NaNNaN0.6702.000969.2100.0NaN0.030330.01.0...0.2176.0NaNNaN190.012.47.55.06.827.10
2014-09-30NaN0.0200.3062.300976.0100.0NaN0.030280.01.0...0.2176.0NaNNaN143.212.45.85.06.826.90
2015-09-30-6.0490.0490.3255.000963.8115.5NaN0.500299.41.0...0.2149.522.34619.797171.212.09.247.36.686.80
2016-07-12NaN0.0200.0655.000931.5138.7NaN0.400335.51.0...3.0173.722.52622.431224.012.28.816.57.416.79
2017-03-27-7.3050.0970.4815.000994.997.3NaN0.400289.21.0...0.2137.223.02119.886218.412.38.010.06.766.77
2018-09-030.8350.0200.3165.000979.1134.9NaN0.400354.31.0...0.2199.323.19723.588222.812.39.110.06.876.88
2019-06-132.0650.0280.2765.0001012.0133.20.620.400334.01.0...0.9213.621.97422.900187.714.29.510.06.936.75
2020-06-090.2330.0200.2135.000965.0179.70.580.400291.61.0...0.2113.420.45720.552134.313.611.310.06.636.79
2021-09-02NaN0.0200.3455.000936.4120.30.610.400266.21.0...0.4128.518.48418.56270.112.710.710.06.606.78
2024-11-06-4.9590.0200.5045.000952.2121.30.580.400239.41.0...2.7266.018.82917.05069.612.910.010.07.206.89
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16 rows × 40 columns

\n", "
" ], "text/plain": [ "parameter AfwijkingBalans% (%AfwijkBalans) Aluminium (Al) \\\n", "fenomeentijd \n", "2006-05-24 NaN 0.050 \n", "2009-03-13 NaN NaN \n", "2009-12-23 NaN NaN \n", "2011-03-28 NaN NaN \n", "2011-11-04 NaN NaN \n", "2012-11-26 NaN NaN \n", "2014-02-25 NaN NaN \n", "2014-09-30 NaN 0.020 \n", "2015-09-30 -6.049 0.049 \n", "2016-07-12 NaN 0.020 \n", "2017-03-27 -7.305 0.097 \n", "2018-09-03 0.835 0.020 \n", "2019-06-13 2.065 0.028 \n", "2020-06-09 0.233 0.020 \n", "2021-09-02 NaN 0.020 \n", "2024-11-06 -4.959 0.020 \n", "\n", "parameter Ammonium (NH4) Arseen (As) Bicarbonaat (HCO3) Boor (B) \\\n", "fenomeentijd \n", "2006-05-24 0.360 5.000 863.4 NaN \n", "2009-03-13 0.370 5.000 945.6 NaN \n", "2009-12-23 0.290 12.467 939.4 NaN \n", "2011-03-28 0.330 12.998 1030.3 NaN \n", "2011-11-04 0.310 8.400 958.3 112.0 \n", "2012-11-26 0.310 NaN 976.0 NaN \n", "2014-02-25 0.670 2.000 969.2 100.0 \n", "2014-09-30 0.306 2.300 976.0 100.0 \n", "2015-09-30 0.325 5.000 963.8 115.5 \n", "2016-07-12 0.065 5.000 931.5 138.7 \n", "2017-03-27 0.481 5.000 994.9 97.3 \n", "2018-09-03 0.316 5.000 979.1 134.9 \n", "2019-06-13 0.276 5.000 1012.0 133.2 \n", "2020-06-09 0.213 5.000 965.0 179.7 \n", "2021-09-02 0.345 5.000 936.4 120.3 \n", "2024-11-06 0.504 5.000 952.2 121.3 \n", "\n", "parameter Bromide (Br) Cadmium (Cd) Calcium (Ca) Carbonaat (CO3) ... \\\n", "fenomeentijd ... \n", "2006-05-24 NaN 0.500 399.0 NaN ... \n", "2009-03-13 NaN 0.500 394.7 1.0 ... \n", "2009-12-23 NaN 0.500 447.2 1.0 ... \n", "2011-03-28 NaN 0.519 331.1 1.0 ... \n", "2011-11-04 NaN 0.500 345.0 1.0 ... \n", "2012-11-26 NaN NaN 360.0 1.0 ... \n", "2014-02-25 NaN 0.030 330.0 1.0 ... \n", "2014-09-30 NaN 0.030 280.0 1.0 ... \n", "2015-09-30 NaN 0.500 299.4 1.0 ... \n", "2016-07-12 NaN 0.400 335.5 1.0 ... \n", "2017-03-27 NaN 0.400 289.2 1.0 ... \n", "2018-09-03 NaN 0.400 354.3 1.0 ... \n", "2019-06-13 0.62 0.400 334.0 1.0 ... \n", "2020-06-09 0.58 0.400 291.6 1.0 ... \n", "2021-09-02 0.61 0.400 266.2 1.0 ... \n", "2024-11-06 0.58 0.400 239.4 1.0 ... \n", "\n", "parameter Opgeloste zuurstof (O2) Redoxpotentiaal (Eh°) \\\n", "fenomeentijd \n", "2006-05-24 0.5 120.0 \n", "2009-03-13 0.3 130.0 \n", "2009-12-23 1.0 34.0 \n", "2011-03-28 0.3 189.0 \n", "2011-11-04 0.1 307.0 \n", "2012-11-26 0.4 179.0 \n", "2014-02-25 0.2 176.0 \n", "2014-09-30 0.2 176.0 \n", "2015-09-30 0.2 149.5 \n", "2016-07-12 3.0 173.7 \n", "2017-03-27 0.2 137.2 \n", "2018-09-03 0.2 199.3 \n", "2019-06-13 0.9 213.6 \n", "2020-06-09 0.2 113.4 \n", "2021-09-02 0.4 128.5 \n", "2024-11-06 2.7 266.0 \n", "\n", "parameter Som anionen (SomAN) Som kationen (SomKAT) Sulfaat (SO4) \\\n", "fenomeentijd \n", "2006-05-24 NaN NaN 488.0 \n", "2009-03-13 NaN NaN 431.0 \n", "2009-12-23 NaN NaN 433.6 \n", "2011-03-28 NaN NaN 290.1 \n", "2011-11-04 NaN NaN 385.5 \n", "2012-11-26 NaN NaN 313.0 \n", "2014-02-25 NaN NaN 190.0 \n", "2014-09-30 NaN NaN 143.2 \n", "2015-09-30 22.346 19.797 171.2 \n", "2016-07-12 22.526 22.431 224.0 \n", "2017-03-27 23.021 19.886 218.4 \n", "2018-09-03 23.197 23.588 222.8 \n", "2019-06-13 21.974 22.900 187.7 \n", "2020-06-09 20.457 20.552 134.3 \n", "2021-09-02 18.484 18.562 70.1 \n", "2024-11-06 18.829 17.050 69.6 \n", "\n", "parameter Temperatuur (T) Totaal organische koolstof (TOC) Zink (Zn) \\\n", "fenomeentijd \n", "2006-05-24 13.9 58.0 16.0 \n", "2009-03-13 12.7 8.6 10.0 \n", "2009-12-23 10.8 8.9 24.9 \n", "2011-03-28 12.0 10.0 13.0 \n", "2011-11-04 12.1 8.3 10.9 \n", "2012-11-26 12.7 7.8 NaN \n", "2014-02-25 12.4 7.5 5.0 \n", "2014-09-30 12.4 5.8 5.0 \n", "2015-09-30 12.0 9.2 47.3 \n", "2016-07-12 12.2 8.8 16.5 \n", "2017-03-27 12.3 8.0 10.0 \n", "2018-09-03 12.3 9.1 10.0 \n", "2019-06-13 14.2 9.5 10.0 \n", "2020-06-09 13.6 11.3 10.0 \n", "2021-09-02 12.7 10.7 10.0 \n", "2024-11-06 12.9 10.0 10.0 \n", "\n", "parameter Zuurtegraad (pH) Zuurtegraad in het labo (pH(Lab.)) \n", "fenomeentijd \n", "2006-05-24 6.70 NaN \n", "2009-03-13 6.76 6.80 \n", "2009-12-23 7.04 6.90 \n", "2011-03-28 6.70 6.90 \n", "2011-11-04 6.73 7.40 \n", "2012-11-26 6.61 7.18 \n", "2014-02-25 6.82 7.10 \n", "2014-09-30 6.82 6.90 \n", "2015-09-30 6.68 6.80 \n", "2016-07-12 7.41 6.79 \n", "2017-03-27 6.76 6.77 \n", "2018-09-03 6.87 6.88 \n", "2019-06-13 6.93 6.75 \n", "2020-06-09 6.63 6.79 \n", "2021-09-02 6.60 6.78 \n", "2024-11-06 7.20 6.89 \n", "\n", "[16 rows x 40 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pivot = df_observaties.pivot_table(columns=df_observaties.parameter, values='resultaat', index='fenomeentijd')\n", "pivot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For plotting, the default plotting functionality of Pandas can be used:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "parameters = ['Nitraat (NO3)', 'Nitriet (NO2)', 'Ammonium (NH4)']\n", "ax = pivot[parameters].plot.line(style='.-', figsize=(12, 5))\n", "ax.set_xlabel('');\n", "ax.set_ylabel('concentration (mg/l)');\n", "ax.set_title('Concentration nitrite, nitrate and ammonium for filter id 1991-001040');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combine search in filters and samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this example, we will first search filters, and later search all samples and observations for this selection. We will select filters in the primary network located in Kalmthout." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_filtergw_idfilternummer
0https://www.dov.vlaanderen.be/data/filter/1975...1-05151
1https://www.dov.vlaanderen.be/data/filter/1981...1-04101
2https://www.dov.vlaanderen.be/data/filter/1975...1-01711
3https://www.dov.vlaanderen.be/data/filter/1981...1-04103
4https://www.dov.vlaanderen.be/data/filter/1981...1-04091
5https://www.dov.vlaanderen.be/data/filter/1981...1-04152
6https://www.dov.vlaanderen.be/data/filter/1981...1-07101
7https://www.dov.vlaanderen.be/data/filter/1981...1-04153
8https://www.dov.vlaanderen.be/data/filter/1981...1-04102
9https://www.dov.vlaanderen.be/data/filter/1981...1-07103
10https://www.dov.vlaanderen.be/data/filter/1981...1-04154
11https://www.dov.vlaanderen.be/data/filter/1981...1-07102
12https://www.dov.vlaanderen.be/data/filter/1981...1-04092
13https://www.dov.vlaanderen.be/data/filter/1981...1-04151
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" ], "text/plain": [ " pkey_filter gw_id filternummer\n", "0 https://www.dov.vlaanderen.be/data/filter/1975... 1-0515 1\n", "1 https://www.dov.vlaanderen.be/data/filter/1981... 1-0410 1\n", "2 https://www.dov.vlaanderen.be/data/filter/1975... 1-0171 1\n", "3 https://www.dov.vlaanderen.be/data/filter/1981... 1-0410 3\n", "4 https://www.dov.vlaanderen.be/data/filter/1981... 1-0409 1\n", "5 https://www.dov.vlaanderen.be/data/filter/1981... 1-0415 2\n", "6 https://www.dov.vlaanderen.be/data/filter/1981... 1-0710 1\n", "7 https://www.dov.vlaanderen.be/data/filter/1981... 1-0415 3\n", "8 https://www.dov.vlaanderen.be/data/filter/1981... 1-0410 2\n", "9 https://www.dov.vlaanderen.be/data/filter/1981... 1-0710 3\n", "10 https://www.dov.vlaanderen.be/data/filter/1981... 1-0415 4\n", "11 https://www.dov.vlaanderen.be/data/filter/1981... 1-0710 2\n", "12 https://www.dov.vlaanderen.be/data/filter/1981... 1-0409 2\n", "13 https://www.dov.vlaanderen.be/data/filter/1981... 1-0415 1" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pydov.search.grondwaterfilter import GrondwaterFilterSearch\n", "from pydov.search.monster import MonsterSearch\n", "from pydov.util.query import FuzzyJoin\n", "\n", "from owslib.fes2 import And\n", "\n", "filter = GrondwaterFilterSearch()\n", "monster = MonsterSearch()\n", "\n", "gemeente = 'Kalmthout'\n", "filter_query = And([PropertyIsLike(propertyname='meetnet',\n", " literal='meetnet 1 %'),\n", " PropertyIsEqualTo(propertyname='gemeente',\n", " literal=gemeente)])\n", "\n", "df_filters = filter.search(query=filter_query, return_fields=['pkey_filter', 'gw_id', 'filternummer'])\n", "df_filters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find all samples linked to the filters:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_monsternaampkey_parentsmateriaalklassedatum_monsternamediepte_van_mdiepte_tot_mmonstertypemonstersamenstellingbemonsteringsprocedurebemonsteringsinstrumentbemonstering_doorpkey_filtergw_idfilternummer
0https://www.dov.vlaanderen.be/data/monster/201...1-0171-F1/M2014(https://www.dov.vlaanderen.be/data/filter/197...grondwater2014-07-17NaN27.5vloeistofEnkelvoudig monsterNaN(pomp,)Bodemkundige Dienst van Belgiëhttps://www.dov.vlaanderen.be/data/filter/1975...1-01711
1https://www.dov.vlaanderen.be/data/monster/201...1-0171-F1/M2015(https://www.dov.vlaanderen.be/data/filter/197...grondwater2015-08-26NaN27.5vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.https://www.dov.vlaanderen.be/data/filter/1975...1-01711
2https://www.dov.vlaanderen.be/data/monster/201...1-0171-F1/M2016(https://www.dov.vlaanderen.be/data/filter/197...grondwater2016-07-05NaN27.5vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.https://www.dov.vlaanderen.be/data/filter/1975...1-01711
3https://www.dov.vlaanderen.be/data/monster/201...1-0171-F1/M2017(https://www.dov.vlaanderen.be/data/filter/197...grondwater2017-03-10NaN27.5vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.https://www.dov.vlaanderen.be/data/filter/1975...1-01711
4https://www.dov.vlaanderen.be/data/monster/201...1-0171-F1/M2018(https://www.dov.vlaanderen.be/data/filter/197...grondwater2018-08-07NaN27.5vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.https://www.dov.vlaanderen.be/data/filter/1975...1-01711
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" ], "text/plain": [ " pkey_monster naam \\\n", "0 https://www.dov.vlaanderen.be/data/monster/201... 1-0171-F1/M2014 \n", "1 https://www.dov.vlaanderen.be/data/monster/201... 1-0171-F1/M2015 \n", "2 https://www.dov.vlaanderen.be/data/monster/201... 1-0171-F1/M2016 \n", "3 https://www.dov.vlaanderen.be/data/monster/201... 1-0171-F1/M2017 \n", "4 https://www.dov.vlaanderen.be/data/monster/201... 1-0171-F1/M2018 \n", "\n", " pkey_parents materiaalklasse \\\n", "0 (https://www.dov.vlaanderen.be/data/filter/197... grondwater \n", "1 (https://www.dov.vlaanderen.be/data/filter/197... grondwater \n", "2 (https://www.dov.vlaanderen.be/data/filter/197... grondwater \n", "3 (https://www.dov.vlaanderen.be/data/filter/197... grondwater \n", "4 (https://www.dov.vlaanderen.be/data/filter/197... grondwater \n", "\n", " datum_monstername diepte_van_m diepte_tot_m monstertype \\\n", "0 2014-07-17 NaN 27.5 vloeistof \n", "1 2015-08-26 NaN 27.5 vloeistof \n", "2 2016-07-05 NaN 27.5 vloeistof \n", "3 2017-03-10 NaN 27.5 vloeistof \n", "4 2018-08-07 NaN 27.5 vloeistof \n", "\n", " monstersamenstelling bemonsteringsprocedure bemonsteringsinstrument \\\n", "0 Enkelvoudig monster NaN (pomp,) \n", "1 Enkelvoudig monster NaN (pomp,) \n", "2 Enkelvoudig monster NaN (pomp,) \n", "3 Enkelvoudig monster NaN (pomp,) \n", "4 Enkelvoudig monster NaN (pomp,) \n", "\n", " bemonstering_door \\\n", "0 Bodemkundige Dienst van België \n", "1 Eurofins Analytico B.V. \n", "2 Eurofins Analytico B.V. \n", "3 Eurofins Analytico B.V. \n", "4 Eurofins Analytico B.V. \n", "\n", " pkey_filter gw_id filternummer \n", "0 https://www.dov.vlaanderen.be/data/filter/1975... 1-0171 1 \n", "1 https://www.dov.vlaanderen.be/data/filter/1975... 1-0171 1 \n", "2 https://www.dov.vlaanderen.be/data/filter/1975... 1-0171 1 \n", "3 https://www.dov.vlaanderen.be/data/filter/1975... 1-0171 1 \n", "4 https://www.dov.vlaanderen.be/data/filter/1975... 1-0171 1 " ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_monsters = monster.search(\n", " query=FuzzyJoin(df_filters, on='pkey_parents', using='pkey_filter'))\n", "\n", "df_monsters['pkey_filter'] = df_monsters.pkey_parents.apply(lambda x: x[0])\n", "df_monsters = df_monsters.merge(df_filters, on='pkey_filter')\n", "\n", "df_monsters.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find all NH4 observations linked to the samples:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[000/001] .\n" ] }, { "data": { "text/html": [ "
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pkey_observatiepkey_parentfenomeentijddiepte_van_m_xdiepte_tot_m_xparametergroepparameterdetectieconditieresultaateenheid...diepte_van_m_ydiepte_tot_m_ymonstertypemonstersamenstellingbemonsteringsprocedurebemonsteringsinstrumentbemonstering_doorpkey_filtergw_idfilternummer
0https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2016-07-05NaNNaNKationenAmmonium (NH4)NaN0.098mg/l...NaN14.5vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.https://www.dov.vlaanderen.be/data/filter/1981...1-04091
1https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2010-12-06NaNNaNKationenAmmonium (NH4)NaN0.210mg/l...NaN54.0vloeistofEnkelvoudig monsterNaN(pomp,)EUROFINShttps://www.dov.vlaanderen.be/data/filter/1981...1-04153
2https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/201...2016-07-05NaNNaNKationenAmmonium (NH4)NaN0.184mg/l...NaN17.6vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.https://www.dov.vlaanderen.be/data/filter/1981...1-04101
3https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2025-03-13NaNNaNKationenAmmonium (NH4)NaN0.598mg/l...NaN45.0vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.https://www.dov.vlaanderen.be/data/filter/1981...1-04102
4https://www.dov.vlaanderen.be/data/observatie/...https://www.dov.vlaanderen.be/data/monster/202...2025-03-13NaNNaNKationenAmmonium (NH4)NaN0.757mg/l...NaN82.0vloeistofEnkelvoudig monsterNaN(pomp,)Eurofins Analytico B.V.https://www.dov.vlaanderen.be/data/filter/1981...1-04103
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5 rows × 28 columns

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" ], "text/plain": [ " pkey_observatie \\\n", "0 https://www.dov.vlaanderen.be/data/observatie/... \n", "1 https://www.dov.vlaanderen.be/data/observatie/... \n", "2 https://www.dov.vlaanderen.be/data/observatie/... \n", "3 https://www.dov.vlaanderen.be/data/observatie/... \n", "4 https://www.dov.vlaanderen.be/data/observatie/... \n", "\n", " pkey_parent fenomeentijd \\\n", "0 https://www.dov.vlaanderen.be/data/monster/201... 2016-07-05 \n", "1 https://www.dov.vlaanderen.be/data/monster/201... 2010-12-06 \n", "2 https://www.dov.vlaanderen.be/data/monster/201... 2016-07-05 \n", "3 https://www.dov.vlaanderen.be/data/monster/202... 2025-03-13 \n", "4 https://www.dov.vlaanderen.be/data/monster/202... 2025-03-13 \n", "\n", " diepte_van_m_x diepte_tot_m_x parametergroep parameter \\\n", "0 NaN NaN Kationen Ammonium (NH4) \n", "1 NaN NaN Kationen Ammonium (NH4) \n", "2 NaN NaN Kationen Ammonium (NH4) \n", "3 NaN NaN Kationen Ammonium (NH4) \n", "4 NaN NaN Kationen Ammonium (NH4) \n", "\n", " detectieconditie resultaat eenheid ... diepte_van_m_y diepte_tot_m_y \\\n", "0 NaN 0.098 mg/l ... NaN 14.5 \n", "1 NaN 0.210 mg/l ... NaN 54.0 \n", "2 NaN 0.184 mg/l ... NaN 17.6 \n", "3 NaN 0.598 mg/l ... NaN 45.0 \n", "4 NaN 0.757 mg/l ... NaN 82.0 \n", "\n", " monstertype monstersamenstelling bemonsteringsprocedure \\\n", "0 vloeistof Enkelvoudig monster NaN \n", "1 vloeistof Enkelvoudig monster NaN \n", "2 vloeistof Enkelvoudig monster NaN \n", "3 vloeistof Enkelvoudig monster NaN \n", "4 vloeistof Enkelvoudig monster NaN \n", "\n", " bemonsteringsinstrument bemonstering_door \\\n", "0 (pomp,) Eurofins Analytico B.V. \n", "1 (pomp,) EUROFINS \n", "2 (pomp,) Eurofins Analytico B.V. \n", "3 (pomp,) Eurofins Analytico B.V. \n", "4 (pomp,) Eurofins Analytico B.V. \n", "\n", " pkey_filter gw_id filternummer \n", "0 https://www.dov.vlaanderen.be/data/filter/1981... 1-0409 1 \n", "1 https://www.dov.vlaanderen.be/data/filter/1981... 1-0415 3 \n", "2 https://www.dov.vlaanderen.be/data/filter/1981... 1-0410 1 \n", "3 https://www.dov.vlaanderen.be/data/filter/1981... 1-0410 2 \n", "4 https://www.dov.vlaanderen.be/data/filter/1981... 1-0410 3 \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_observaties = observatie.search(query=And([\n", " Join(df_monsters, on='pkey_parent', using='pkey_monster'),\n", " PropertyIsEqualTo('parameter', 'Ammonium (NH4)'),\n", " PropertyIsEqualTo('herkomst', 'LABO')\n", "]))\n", "df_observaties = df_observaties.merge(df_monsters, left_on='pkey_parent', right_on='pkey_monster')\n", "\n", "df_observaties.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And plot the results:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_observaties['fenomeentijd'] = pd.to_datetime(df_observaties['fenomeentijd'])\n", "df_observaties['resultaat'] = pd.to_numeric(df_observaties['resultaat'])\n", "\n", "trends_sel = df_observaties.set_index('fenomeentijd')\n", "trends_sel['label'] = trends_sel['gw_id'] + ' F' + trends_sel['filternummer']\n", "\n", "# By pivoting, we get each location in a different column\n", "trends_sel_pivot = trends_sel.pivot_table(columns='label', values='resultaat', index='fenomeentijd')\n", "# trends_sel_pivot.index = pd.to_datetime(trends_sel_pivot.index)\n", "\n", "# resample to yearly values and plot data\n", "ax = trends_sel_pivot.resample('A').median().plot.line(style='.-', figsize=(12, 5))\n", "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", "\n", "ax.set_title(f'Long term evolution of NH4 in {gemeente}');\n", "ax.set_xlabel('year');\n", "ax.set_ylabel('concentration (mg/l)');" ] } ], "metadata": { "kernelspec": { "display_name": ".venv (3.13.5)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.5" } }, "nbformat": 4, "nbformat_minor": 4 }