Example of DOV search methods for CPT measurements (sonderingen)

Binder

Use cases explained below

  • Get CPT measurements in a bounding box

  • Get CPT measurements with specific properties

  • Get CPT measurements in a bounding box based on specific properties

  • Select CPT measurements in a municipality and return depth

  • Get CPT measurements based on fields not available in the standard output dataframe

  • Get CPT measurements data, returning fields not available in the standard output dataframe

  • Get CPT measurements in a municipality and where groundwater related data are available

[1]:

%matplotlib inline
import inspect, sys

[2]:

import pydov

Get information about the datatype ‘Sondering’

[3]:

from pydov.search.sondering import SonderingSearch
sondering = SonderingSearch()

A description is provided for the ‘Sondering’ datatype:

[4]:

sondering.get_description()

[4]:

'In DOV worden de resultaten van sonderingen ter beschikking gesteld. Bij het uitvoeren van de sondering wordt een sondeerpunt met conus bij middel van buizen statisch de grond ingedrukt. Continu of met bepaalde diepte-intervallen wordt de weerstand aan de conuspunt, de plaatselijke wrijvingsweerstand en/of de totale indringingsweerstand opgemeten. Eventueel kan aanvullend de waterspanning in de grond rond de conus tijdens de sondering worden opgemeten met een waterspanningsmeter. Het op diepte drukken van de sondeerbuizen gebeurt met een indrukapparaat. De nodige reactie voor het indrukken van de buizen wordt geleverd door een verankering en/of door het gewicht van de sondeerwagen. De totale indrukcapaciteit varieert van 25 kN tot 250 kN, afhankelijk van apparaat en opstellingswijze.'

The different fields that are available for objects of the ‘Sondering’ datatype can be requested with the get_fields() method:

[5]:

fields = sondering.get_fields()

# print available fields
for f in fields.values():
    print(f['name'])

id
sondeernummer
pkey_sondering
weerstandsdiagram
meetreeks
x
y
start_sondering_mtaw
gemeente
diepte_sondering_van
diepte_sondering_tot
datum_aanvang
uitvoerder
conus
sondeermethode
apparaat
informele_stratigrafie
formele_stratigrafie
hydrogeologische_stratigrafie
opdrachten
eerste_invoer
geom
datum_gw_meting
diepte_gw_m
lengte
diepte
qc
Qt
fs
u
i
mv_mtaw

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

[6]:

fields['diepte_sondering_tot']

[6]:

{'name': 'diepte_sondering_tot',
 'definition': 'Maximumdiepte van de sondering ten opzichte van het aanvangspeil, in meter.',
 'type': 'float',
 'notnull': False,
 'query': True,
 'cost': 1}

Optionally, if the values of the field have a specific domain the possible values are listed as values:

[7]:

fields['conus']['values']

[7]:

{'E': None, 'M1': None, 'M2': None, 'M4': None, 'U': None, 'onbekend': None}

Example use cases

Get CPT measurements in a bounding box

Get data for all the CPT measurements 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.

[8]:

from pydov.util.location import Within, Box

df = sondering.search(location=Within(Box(152999, 206930, 153050, 207935)))
df.head()

[000/001] .
[000/001] .

[8]:
pkey_sondering sondeernummer x y mv_mtaw start_sondering_mtaw diepte_sondering_van diepte_sondering_tot datum_aanvang uitvoerder ... apparaat datum_gw_meting diepte_gw_m lengte diepte qc Qt fs u i
0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII 153008.0 206985.0 NaN 15.8 0.0 36.0 1973-03-21 Rijksinstituut voor Grondmechanica ... 100 kN NaN NaN 0.2 NaN 1.6 2.06 NaN NaN NaN
1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII 153008.0 206985.0 NaN 15.8 0.0 36.0 1973-03-21 Rijksinstituut voor Grondmechanica ... 100 kN NaN NaN 0.4 NaN 3.6 4.26 NaN NaN NaN
2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII 153008.0 206985.0 NaN 15.8 0.0 36.0 1973-03-21 Rijksinstituut voor Grondmechanica ... 100 kN NaN NaN 0.6 NaN 2.6 3.46 NaN NaN NaN
3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII 153008.0 206985.0 NaN 15.8 0.0 36.0 1973-03-21 Rijksinstituut voor Grondmechanica ... 100 kN NaN NaN 0.8 NaN 4.0 5.66 NaN NaN NaN
4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-72/555-SXVIII 153008.0 206985.0 NaN 15.8 0.0 36.0 1973-03-21 Rijksinstituut voor Grondmechanica ... 100 kN NaN NaN 1.0 NaN 3.0 6.53 NaN NaN NaN

5 rows × 21 columns

The dataframe contains one CPT measurement where multiple measurement points. The available data are flattened to represent unique attributes per row of the dataframe.

Using the pkey_sondering field one can request the details of this borehole in a webbrowser:

[9]:

for pkey_sondering in set(df.pkey_sondering):
    print(pkey_sondering)

https://www.dov.vlaanderen.be/data/sondering/1973-016812

Get CPT measurements with specific properties

Next to querying CPT based on their geographic location within a bounding box, we can also search for CPT measurements matching a specific set of properties. For this we can build a query using a combination of the ‘Sondering’ fields and operators provided by the WFS protocol.

A list of possible operators can be found below:

[10]:

[i for i,j in inspect.getmembers(sys.modules['owslib.fes2'], inspect.isclass) if 'Property' in i]

[10]:

['PropertyIsBetween',
 'PropertyIsEqualTo',
 'PropertyIsGreaterThan',
 'PropertyIsGreaterThanOrEqualTo',
 'PropertyIsLessThan',
 'PropertyIsLessThanOrEqualTo',
 'PropertyIsLike',
 'PropertyIsNotEqualTo',
 'PropertyIsNull',
 'SortProperty']

In this example we build a query using the PropertyIsEqualTo operator to find all CPT measuremetns that are within the community (gemeente) of ‘Herstappe’:

[11]:

from owslib.fes2 import PropertyIsEqualTo

query = PropertyIsEqualTo(propertyname='gemeente',
                          literal='Elsene')
df = sondering.search(query=query)

df.head()

[000/001] .
[000/029] .............................

[11]:
pkey_sondering sondeernummer x y mv_mtaw start_sondering_mtaw diepte_sondering_van diepte_sondering_tot datum_aanvang uitvoerder ... apparaat datum_gw_meting diepte_gw_m lengte diepte qc Qt fs u i
0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 169796.0 NaN 56.3 0.0 4.5 1975-05-20 Rijksinstituut voor Grondmechanica ... 25 kN NaN 1.97 1.0 NaN 3.3 NaN NaN NaN NaN
1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 169796.0 NaN 56.3 0.0 4.5 1975-05-20 Rijksinstituut voor Grondmechanica ... 25 kN NaN 1.97 1.1 NaN 2.9 NaN NaN NaN NaN
2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 169796.0 NaN 56.3 0.0 4.5 1975-05-20 Rijksinstituut voor Grondmechanica ... 25 kN NaN 1.97 1.2 NaN 2.7 NaN NaN NaN NaN
3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 169796.0 NaN 56.3 0.0 4.5 1975-05-20 Rijksinstituut voor Grondmechanica ... 25 kN NaN 1.97 1.3 NaN 2.4 NaN NaN NaN NaN
4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-75/194-S1 150310.0 169796.0 NaN 56.3 0.0 4.5 1975-05-20 Rijksinstituut voor Grondmechanica ... 25 kN NaN 1.97 1.4 NaN 3.6 NaN NaN NaN NaN

5 rows × 21 columns

Once again we can use the pkey_sondering as a permanent link to the information of these CPT measurements:

[12]:

for pkey_sondering in set(df.pkey_sondering):
    print(pkey_sondering)

https://www.dov.vlaanderen.be/data/sondering/1976-013900
https://www.dov.vlaanderen.be/data/sondering/1976-030140
https://www.dov.vlaanderen.be/data/sondering/1971-023323
https://www.dov.vlaanderen.be/data/sondering/1976-030148
https://www.dov.vlaanderen.be/data/sondering/1971-023091
https://www.dov.vlaanderen.be/data/sondering/1980-024719
https://www.dov.vlaanderen.be/data/sondering/1974-016927
https://www.dov.vlaanderen.be/data/sondering/1992-000337
https://www.dov.vlaanderen.be/data/sondering/1992-000335
https://www.dov.vlaanderen.be/data/sondering/1971-022776
https://www.dov.vlaanderen.be/data/sondering/1980-024720
https://www.dov.vlaanderen.be/data/sondering/1976-014638
https://www.dov.vlaanderen.be/data/sondering/1971-023319
https://www.dov.vlaanderen.be/data/sondering/1971-022775
https://www.dov.vlaanderen.be/data/sondering/1976-013899
https://www.dov.vlaanderen.be/data/sondering/1975-014064
https://www.dov.vlaanderen.be/data/sondering/1971-023320
https://www.dov.vlaanderen.be/data/sondering/1976-014640
https://www.dov.vlaanderen.be/data/sondering/1976-030150
https://www.dov.vlaanderen.be/data/sondering/1975-014063
https://www.dov.vlaanderen.be/data/sondering/1992-000336
https://www.dov.vlaanderen.be/data/sondering/1971-023321
https://www.dov.vlaanderen.be/data/sondering/1974-016926
https://www.dov.vlaanderen.be/data/sondering/1976-013898
https://www.dov.vlaanderen.be/data/sondering/1971-023322
https://www.dov.vlaanderen.be/data/sondering/1976-030128
https://www.dov.vlaanderen.be/data/sondering/1992-000339
https://www.dov.vlaanderen.be/data/sondering/1971-022777
https://www.dov.vlaanderen.be/data/sondering/1992-000338

Get CPT measurements in a bounding box based on specific properties

We can combine a query on attributes with a query on geographic location to get the CPT measurements within a bounding box that have specific properties.

The following example requests the CPT measurements with a depth greater than or equal to 2000 meters 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.)

[13]:

from owslib.fes2 import PropertyIsGreaterThanOrEqualTo

query = PropertyIsGreaterThanOrEqualTo(
            propertyname='diepte_sondering_tot',
            literal='20')

df = sondering.search(
    location=Within(Box(200000, 211000, 205000, 214000)),
    query=query
    )

df.head()

[000/001] .
[000/021] .....................

[13]:
pkey_sondering sondeernummer x y mv_mtaw start_sondering_mtaw diepte_sondering_van diepte_sondering_tot datum_aanvang uitvoerder ... apparaat datum_gw_meting diepte_gw_m lengte diepte qc Qt fs u i
0 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 202348.46 213129.09 NaN 26.27 1.5 20.0 2007-05-10 VO - Afdeling Geotechniek ... 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.55 1.55 0.60 NaN -1.0 NaN 0.2
1 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 202348.46 213129.09 NaN 26.27 1.5 20.0 2007-05-10 VO - Afdeling Geotechniek ... 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.60 1.60 0.48 NaN 0.0 NaN 0.2
2 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 202348.46 213129.09 NaN 26.27 1.5 20.0 2007-05-10 VO - Afdeling Geotechniek ... 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.65 1.65 0.57 NaN 0.0 NaN 0.2
3 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 202348.46 213129.09 NaN 26.27 1.5 20.0 2007-05-10 VO - Afdeling Geotechniek ... 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.70 1.70 0.86 NaN 0.0 NaN 0.3
4 https://www.dov.vlaanderen.be/data/sondering/2... GEO-07/054-S3 202348.46 213129.09 NaN 26.27 1.5 20.0 2007-05-10 VO - Afdeling Geotechniek ... 200 kN - TRACK-TRUCK 2007-05-10 10:40:00 NaN 1.75 1.75 0.90 NaN 0.0 NaN 0.3

5 rows × 21 columns

We can look at one of the CPT measurements in a webbrowser using its pkey_sondering:

[14]:

for pkey_sondering in set(df.pkey_sondering):
    print(pkey_sondering)

https://www.dov.vlaanderen.be/data/sondering/2008-077592
https://www.dov.vlaanderen.be/data/sondering/2009-000053
https://www.dov.vlaanderen.be/data/sondering/2008-077566
https://www.dov.vlaanderen.be/data/sondering/2008-077579
https://www.dov.vlaanderen.be/data/sondering/2008-077545
https://www.dov.vlaanderen.be/data/sondering/2015-054999
https://www.dov.vlaanderen.be/data/sondering/2009-000052
https://www.dov.vlaanderen.be/data/sondering/2008-077577
https://www.dov.vlaanderen.be/data/sondering/2008-077565
https://www.dov.vlaanderen.be/data/sondering/2007-049200
https://www.dov.vlaanderen.be/data/sondering/2010-062407
https://www.dov.vlaanderen.be/data/sondering/2008-077556
https://www.dov.vlaanderen.be/data/sondering/2008-077564
https://www.dov.vlaanderen.be/data/sondering/2008-077580
https://www.dov.vlaanderen.be/data/sondering/2009-000054
https://www.dov.vlaanderen.be/data/sondering/2015-055496
https://www.dov.vlaanderen.be/data/sondering/2008-077557
https://www.dov.vlaanderen.be/data/sondering/2015-054995
https://www.dov.vlaanderen.be/data/sondering/2008-077581
https://www.dov.vlaanderen.be/data/sondering/2008-077591
https://www.dov.vlaanderen.be/data/sondering/2007-049201

Select CPT measurements in a municipality and return depth

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 CPT measurements in the city of Ghent and return their depth:

[15]:

query = PropertyIsEqualTo(propertyname='gemeente',
                          literal='Gent')
df = sondering.search(query=query,
                      return_fields=('diepte_sondering_tot',))
df.head()

[000/001] .

[15]:
diepte_sondering_tot
0 2.7
1 1.4
2 7.6
3 11.5
4 18.6 
[16]:

df.describe()

[16]:
diepte_sondering_tot
count 3798.000000
mean 18.741095
std 8.455745
min 0.660000
25% 11.700000
50% 19.200000
75% 24.800000
max 52.600000 
[17]:

ax = df.boxplot()
ax.set_title('Distribution depth CPT measurements in Ghent');
ax.set_ylabel("depth (m)")

[17]:

Text(0, 0.5, 'depth (m)')
../_images/notebooks_search_sonderingen_37_1.png

Get CPT measurements 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 CPT measurements as illustrated below.

For example, make a selection of the CPT measurements in municipality the of Antwerp, using a conustype ‘U’:

[18]:

from owslib.fes2 import And

query = And([PropertyIsEqualTo(propertyname='gemeente',
                               literal='Antwerpen'),
             PropertyIsEqualTo(propertyname='conus',
                               literal='U')]
            )
df = sondering.search(query=query,
                      return_fields=('pkey_sondering', 'sondeernummer', 'x', 'y', 'diepte_sondering_tot', 'datum_aanvang'))
df.head()

[000/001] .

[18]:
pkey_sondering sondeernummer x y diepte_sondering_tot datum_aanvang
0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E 152740.0 215493.0 29.70 1993-03-02
1 https://www.dov.vlaanderen.be/data/sondering/2... GEO-02/111-S1 150347.3 214036.4 29.95 2002-12-17
2 https://www.dov.vlaanderen.be/data/sondering/2... GEO-04/123-SKD4-E 146437.7 222317.5 4.45 2004-07-12
3 https://www.dov.vlaanderen.be/data/sondering/2... GEO-04/123-SKD6-E 146523.9 222379.7 7.40 2004-07-14
4 https://www.dov.vlaanderen.be/data/sondering/2... GEO-04/123-SKD5-E 146493.4 222298.8 1.65 2004-07-16

Get CPT 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:

[19]:

query = And([PropertyIsEqualTo(propertyname='gemeente', literal='Gent'),
             PropertyIsEqualTo(propertyname='conus', literal='U')])

df = sondering.search(query=query,
                      return_fields=('pkey_sondering', 'sondeernummer', 'diepte_sondering_tot',
                                     'conus', 'x', 'y'))

df.head()

[000/001] .

[19]:
pkey_sondering sondeernummer diepte_sondering_tot conus x y
0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SV 33.80 U 110241.6 204692.2
1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SI 15.65 U 110062.5 205051.4
2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SII 26.50 U 110107.0 204965.3
3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIII 16.50 U 110152.4 204876.1
4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIV 16.70 U 110197.8 204787.0 
[20]:

df

[20]:
pkey_sondering sondeernummer diepte_sondering_tot conus x y
0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SV 33.80 U 110241.60 204692.20
1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SI 15.65 U 110062.50 205051.40
2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SII 26.50 U 110107.00 204965.30
3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIII 16.50 U 110152.40 204876.10
4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIV 16.70 U 110197.80 204787.00
5 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SIX 27.60 U 110479.50 205240.70
6 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SVI 16.80 U 110288.50 204608.80
7 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SVII 26.70 U 110334.30 204519.80
8 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SX 27.50 U 110685.00 204845.50
9 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SXI 25.60 U 109941.50 204346.90
10 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/020-SXII 26.50 U 110412.20 204398.10
11 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/096-SIX(CPT9) 17.60 U 105018.00 190472.00
12 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/096-SVII(CPT7) 26.05 U 105046.00 190550.00
13 https://www.dov.vlaanderen.be/data/sondering/1... GEO-94/096-SVIII(CPT8) 24.75 U 104997.00 190521.00
14 https://www.dov.vlaanderen.be/data/sondering/1... GEO-97/002-S2 29.90 U 105376.60 189104.30
15 https://www.dov.vlaanderen.be/data/sondering/1... GEO-97/002-S3 5.90 U 105391.30 189083.70
16 https://www.dov.vlaanderen.be/data/sondering/1... GEO-97/002-S1 30.60 U 105399.30 189065.20
17 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S1_U 22.98 U 108697.19 203441.72
18 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S2_U 22.98 U 108709.48 203436.67
19 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S2 22.98 U 108709.48 203436.67
20 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S1 22.98 U 108697.19 203441.72
21 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S3_U 22.98 U 108734.23 203429.66
22 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S3 22.98 U 108734.23 203429.66
23 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S4_U 22.98 U 108747.25 203424.10
24 https://www.dov.vlaanderen.be/data/sondering/2... GEO-23/042-S4 22.98 U 108747.25 203424.10
25 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S1 18.05 U 106104.10 188699.40
26 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S2 17.30 U 106045.30 188708.40
27 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S3 18.70 U 106100.50 188743.80
28 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S5 17.30 U 106130.00 188712.00
29 https://www.dov.vlaanderen.be/data/sondering/2... GEO-01/162-S4 17.00 U 106077.50 188686.00

Resistivity plot

The data for the reporting of resistivity plots with the online application, see for example this report, is also accessible with the pydov package. Querying the data for this specific sondering:

[21]:

query = PropertyIsEqualTo(propertyname='pkey_sondering',
                          literal='https://www.dov.vlaanderen.be/data/sondering/1993-001275')
df_sond = sondering.search(query=query)

df_sond.head()

[000/001] .
[000/001] .

[21]:
pkey_sondering sondeernummer x y mv_mtaw start_sondering_mtaw diepte_sondering_van diepte_sondering_tot datum_aanvang uitvoerder ... apparaat datum_gw_meting diepte_gw_m lengte diepte qc Qt fs u i
0 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E 152740.0 215493.0 NaN 6.25 0.0 29.7 1993-03-02 MVG - Afdeling Geotechniek ... 200 kN NaN NaN 0.6 NaN 11.60 NaN 130.0 69.0 NaN
1 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E 152740.0 215493.0 NaN 6.25 0.0 29.7 1993-03-02 MVG - Afdeling Geotechniek ... 200 kN NaN NaN 0.7 NaN 6.30 NaN 100.0 29.0 NaN
2 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E 152740.0 215493.0 NaN 6.25 0.0 29.7 1993-03-02 MVG - Afdeling Geotechniek ... 200 kN NaN NaN 0.8 NaN 6.22 NaN 120.0 -4.0 NaN
3 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E 152740.0 215493.0 NaN 6.25 0.0 29.7 1993-03-02 MVG - Afdeling Geotechniek ... 200 kN NaN NaN 0.9 NaN 4.92 NaN 120.0 -48.0 NaN
4 https://www.dov.vlaanderen.be/data/sondering/1... GEO-93/023-SII-E 152740.0 215493.0 NaN 6.25 0.0 29.7 1993-03-02 MVG - Afdeling Geotechniek ... 200 kN NaN NaN 1.0 NaN 4.40 NaN 80.0 -35.0 NaN

5 rows × 21 columns

We have the depth (length) available, together with the measured values for each depth of the variables (in dutch):

  • qc: Opgemeten waarde van de conusweerstand, uitgedrukt in MPa.

  • Qt: Opgemeten waarde van de totale weerstand, uitgedrukt in kN.

  • fs: Opgemeten waarde van de plaatelijke kleefweerstand uitgedrukt in kPa.

  • u: Opgemeten waarde van de porienwaterspanning, uitgedrukt in kPa.

  • i: Opgemeten waarde van de inclinatie, uitgedrukt in graden.

To recreate the resistivity plot, we also need the resistivity number (wrijvingsgetal rf), see DOV documentation.

\begin{equation} R_f = \frac{f_s}{q_c} \end{equation}

Notice: \(f_s\) is provide in kPa and \(q_c\) in MPa.

Adding rf to the dataframe:

[22]:

df_sond["rf"] = df_sond["fs"]/df_sond["qc"]/10

Recreate the resistivity plot:

[23]:

import matplotlib.pyplot as plt

[24]:

def make_patch_spines_invisible(ax):
    ax.set_frame_on(True)
    ax.patch.set_visible(False)
    for sp in ax.spines.values():
        sp.set_visible(False)

[25]:

# Determine Lengte or Depth
# If diepte is available, the y-axis will be Diepte
# Else the y-axis will be Lengte
if df_sond['diepte'].isnull().values.any():
    # IsNan
    y_type = "lengte"
    y_axis = "Length (m)"
else:
    y_type = "diepte"
    y_axis = "Depth (m)"


fig, ax0 = plt.subplots(figsize=(8, 12))

# Prepare the individual axis
ax_qc = ax0.twiny()
ax_fs = ax0.twiny()
ax_u = ax0.twiny()
ax_rf = ax0.twiny()

for i, ax in enumerate([ax_qc, ax_fs, ax_u]):
    ax.spines["top"].set_position(("axes", 1+0.05*(i+1)))
    make_patch_spines_invisible(ax)
    ax.spines["top"].set_visible(True)

# Plot the data on the axis
df_sond.plot(x="rf", y=y_type, label="rf", ax=ax_rf, color='purple', legend=False)
df_sond.plot(x="qc", y=y_type, label="qc (MPa)", ax=ax_qc, color='black', legend=False)
df_sond.plot(x="fs", y=y_type, label="fs (kPa)", ax=ax_fs, color='green', legend=False)
df_sond.plot(x="u", y=y_type, label="u (kPa)", ax=ax_u, color='red',
        legend=False, xlim=(-100, 300)) # ! 300 is hardocded here for the example

# styling and configuration
ax_rf.xaxis.label.set_color('purple')
ax_fs.xaxis.label.set_color('green')
ax_u.xaxis.label.set_color('red')

ax0.axes.set_visible(False)
ax_qc.axes.yaxis.set_visible(False)
ax_fs.axes.yaxis.set_visible(False)
for i, ax in enumerate([ax_rf, ax_qc, ax_fs, ax_u, ax0]):
    ax.spines["right"].set_visible(False)
    ax.spines["bottom"].set_visible(False)
    ax.xaxis.label.set_fontsize(15)
    ax.xaxis.set_label_coords(-0.05, 1+0.05*i)
    ax.spines['left'].set_position(('outward', 10))
    ax.spines['left'].set_bounds(0, 30)
ax_rf.set_xlim(0, 46)

ax_u.set_title("Resistivity plot CPT measurement GEO-93/023-SII-E", fontsize=12)

ax0.invert_yaxis()
ax_rf.invert_xaxis()
ax_u.set_ylabel(y_axis, fontsize=12)
fig.legend(loc='lower center', ncol=4)
fig.tight_layout()
../_images/notebooks_search_sonderingen_55_0.png

Visualize locations

Using Folium, we can display the results of our search on a map.

[26]:

# import the necessary modules (not included in the requirements of pydov!)
import folium
from folium.plugins import MarkerCluster
from pyproj import Transformer

[27]:

# 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()

[28]:

# 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=11)
marker_cluster = MarkerCluster().add_to(fmap)
for loc in range(0, len(loclist)):
    folium.Marker(loclist[loc], popup=df['sondeernummer'][loc]).add_to(marker_cluster)
fmap

[28]:
Make this Notebook Trusted to load map: File -> Trust Notebook