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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Example of DOV search methods for interpretations (informele hydrogeologische stratigrafie)
Use cases explained below
Get 'informele hydrogeologische stratigrafie' in a bounding box
Get 'informele hydrogeologische stratigrafie' with specific properties
Get 'informele hydrogeologische stratigrafie' in a bounding box based on specific properties
Select 'informele hydrogeologische stratigrafie' in a municipality and return date
Get 'informele hydrogeologische stratigrafie' based on fields not available in the standard output dataframe
Get 'informele hydrogeologische stratigrafie' data, returning fields not available in the standard output dataframe
Step1:  Get information about the datatype 'Informele hydrogeologische stratigrafie'
Step2:  A description is provided for the 'Informele stratigrafie' datatype
Step3:  The different fields that are available for objects of the 'Informele hydrogeologische stratigrafie' datatype can be requested with the get_fields() method
Step4:  You can get more information of a field by requesting it from the fields dictionary
Step5:  Example use cases
Get 'informele hydrogeologische stratigrafie' in a bounding box
Get data for all the 'informele hydrogeologisch stratigrafie' interpretations that are geographically located within the bounds of the specified box.
The coordinates are in the Belgian Lambert72 (EPSG
Step6:  The dataframe contains one 'informele stratigrafie' interpretation where three layers ('laag') were identified. The available data are flattened to represent unique attributes per row of the dataframe.
Using the pkey_interpretatie field one can request the details of this interpretation in a webbrowser
Step7:  Get 'informele hydrogeologische stratigrafie' with specific properties
Next to querying interpretations based on their geographic location within a bounding box, we can also search for interpretations matching a specific set of properties. For this we can build a query using a combination of the 'InformeleHydrogeologischeStratigrafie' fields and operators provided by the WFS protocol.
A list of possible operators can be found below
Step8:  In this example we build a query using the PropertyIsEqualTo operator to find all interpretations that are within the community (gemeente) of 'Herstappe'
Step9:  Once again we can use the pkey_interpretatie as a permanent link to the information of these interpretations
Step10:  Get 'informele hydrogeologische stratigrafie' in a bounding box based on specific properties
We can combine a query on attributes with a query on geographic location to get the interpretations within a bounding box that have specific properties.
The following example requests the interpretations of boreholes only, within the given bounding box.
(Note that the datatype of the literal parameter should be a string, regardless of the datatype of this field in the output dataframe.)
Step11:  We can look at one of the interpretations in a webbrowser using its pkey_interpretatie
Step12:  Select 'informele hydrogeologische stratigrafie' in a municipality and return date
We can limit the columns in the output dataframe by specifying the return_fields parameter in our search.
In this example we query all the 'informele stratigrafie' interpretations in the community of Rotselaar and return their date
Step13:  Get 'informele hydrogeologische stratigrafie' based on fields not available in the standard output dataframe
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 interpretations as illustrated below.
For example, make a selection of the interpretations in the municipality of Rotselaar, before 1/1/1990
Step14:  Get 'informele hydrogeologische stratigrafie' data, returning fields not available in the standard output dataframe
As denoted in the previous example, not all available fields are available in the default output frame to keep its size limited. However, you can request any available field by including it in the return_fields parameter of the search
Step15:  Visualize results
Using Folium, we can display the results of our search on a map.
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Python Code:
%matplotlib inline
import inspect, sys
# check pydov path
import pydov
Explanation: Example of DOV search methods for interpretations (informele hydrogeologische stratigrafie)
Use cases explained below
Get 'informele hydrogeologische stratigrafie' in a bounding box
Get 'informele hydrogeologische stratigrafie' with specific properties
Get 'informele hydrogeologische stratigrafie' in a bounding box based on specific properties
Select 'informele hydrogeologische stratigrafie' in a municipality and return date
Get 'informele hydrogeologische stratigrafie' based on fields not available in the standard output dataframe
Get 'informele hydrogeologische stratigrafie' data, returning fields not available in the standard output dataframe
End of explanation
from pydov.search.interpretaties import InformeleHydrogeologischeStratigrafieSearch
itp = InformeleHydrogeologischeStratigrafieSearch()
Explanation: Get information about the datatype 'Informele hydrogeologische stratigrafie'
End of explanation
itp.get_description()
Explanation: A description is provided for the 'Informele stratigrafie' datatype:
End of explanation
fields = itp.get_fields()
# print available fields
for f in fields.values():
    print(f['name'])
Explanation: The different fields that are available for objects of the 'Informele hydrogeologische stratigrafie' datatype can be requested with the get_fields() method:
End of explanation
fields['Datum']
Explanation: You can get more information of a field by requesting it from the fields dictionary:
* name: name of the field
* definition: definition of this field
* 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.
* notnull: whether the field is mandatory or not
* type: datatype of the values of this field
End of explanation
from pydov.util.location import Within, Box
df = itp.search(location=Within(Box(100000, 100000, 200000, 200000)))
df.head()
Explanation: Example use cases
Get 'informele hydrogeologische stratigrafie' in a bounding box
Get data for all the 'informele hydrogeologisch stratigrafie' interpretations that are geographically located within the bounds of the specified box.
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.
End of explanation
for pkey_interpretatie in set(df.pkey_interpretatie):
    print(pkey_interpretatie)
Explanation: The dataframe contains one 'informele stratigrafie' interpretation where three layers ('laag') were identified. The available data are flattened to represent unique attributes per row of the dataframe.
Using the pkey_interpretatie field one can request the details of this interpretation in a webbrowser:
End of explanation
[i for i,j in inspect.getmembers(sys.modules['owslib.fes'], inspect.isclass) if 'Property' in i]
Explanation: Get 'informele hydrogeologische stratigrafie' with specific properties
Next to querying interpretations based on their geographic location within a bounding box, we can also search for interpretations matching a specific set of properties. For this we can build a query using a combination of the 'InformeleHydrogeologischeStratigrafie' fields and operators provided by the WFS protocol.
A list of possible operators can be found below:
End of explanation
from owslib.fes import PropertyIsEqualTo
query = PropertyIsEqualTo(propertyname='gemeente',
                          literal='Rotselaar')
df = itp.search(query=query)
df.head()
Explanation: In this example we build a query using the PropertyIsEqualTo operator to find all interpretations that are within the community (gemeente) of 'Herstappe':
End of explanation
for pkey_interpretatie in set(df.pkey_interpretatie):
    print(pkey_interpretatie)
Explanation: Once again we can use the pkey_interpretatie as a permanent link to the information of these interpretations:
End of explanation
from owslib.fes import PropertyIsEqualTo
query = PropertyIsEqualTo(
            propertyname='Type_proef',
            literal='Boring')
df = itp.search(
    location=Within(Box(100000, 100000, 200000, 200000)),
    query=query
    )
df.head()
Explanation: Get 'informele hydrogeologische stratigrafie' in a bounding box based on specific properties
We can combine a query on attributes with a query on geographic location to get the interpretations within a bounding box that have specific properties.
The following example requests the interpretations of boreholes only, within the given bounding box.
(Note that the datatype of the literal parameter should be a string, regardless of the datatype of this field in the output dataframe.)
End of explanation
for pkey_interpretatie in set(df.pkey_interpretatie):
    print(pkey_interpretatie)
Explanation: We can look at one of the interpretations in a webbrowser using its pkey_interpretatie:
End of explanation
query = PropertyIsEqualTo(propertyname='gemeente',
                          literal='Rotselaar')
df = itp.search(query=query,
                return_fields=('Datum',))
df.head()
df.describe()
Explanation: Select 'informele hydrogeologische stratigrafie' in a municipality and return date
We can limit the columns in the output dataframe by specifying the return_fields parameter in our search.
In this example we query all the 'informele stratigrafie' interpretations in the community of Rotselaar and return their date:
End of explanation
from owslib.fes import And, PropertyIsEqualTo, PropertyIsLessThan
query = And([PropertyIsEqualTo(propertyname='gemeente',
                               literal='Rotselaar'),
             PropertyIsLessThan(propertyname='Datum', 
                                 literal='1990-01-01')]
            )
df = itp.search(query=query,
                return_fields=('pkey_interpretatie', 'Datum'))
df.head()
Explanation: Get 'informele hydrogeologische stratigrafie' based on fields not available in the standard output dataframe
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 interpretations as illustrated below.
For example, make a selection of the interpretations in the municipality of Rotselaar, before 1/1/1990:
End of explanation
query = PropertyIsEqualTo(
            propertyname='gemeente',
            literal='Rotselaar')
df = itp.search(query=query,
                return_fields=('pkey_interpretatie', 'pkey_boring',
                               'x', 'y', 'Z_mTAW', 'gemeente', 'Auteurs', 'Proefnummer'))
df.head()
Explanation: Get 'informele hydrogeologische stratigrafie' data, returning fields not available in the standard output dataframe
As denoted in the previous example, not all available fields are available in the default output frame to keep its size limited. However, you can request any available field by including it in the return_fields parameter of the search:
End of explanation
# import the necessary modules (not included in the requirements of pydov!)
import folium
from folium.plugins import MarkerCluster
from pyproj import Transformer
# convert the coordinates to lat/lon for folium
def convert_latlon(x1, y1):
    transformer = Transformer.from_crs("epsg:31370", "epsg:4326", always_xy=True)
    x2,y2 = transformer.transform(x1, y1)
    return x2, y2
df['lon'], df['lat'] = zip(*map(convert_latlon, df['x'], df['y'])) 
# convert to list
loclist = df[['lat', 'lon']].values.tolist()
# initialize the Folium map on the centre of the selected locations, play with the zoom until ok
fmap = folium.Map(location=[df['lat'].mean(), df['lon'].mean()], zoom_start=12)
marker_cluster = MarkerCluster().add_to(fmap)
for loc in range(0, len(loclist)):
    folium.Marker(loclist[loc], popup=df['Proefnummer'][loc]).add_to(marker_cluster)
fmap
Explanation: Visualize results
Using Folium, we can display the results of our search on a map.
End of explanation
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