Answer:
This is very easy. The following example shows you this.
Preparation
The web requests to the Geo-Zone Tool using Python require:
- Editor or IDE (Integrated Development Environment) for writing the script
- Python – python.org
- Python Library Requests
- Python Library - pandas (optional)
To run a web request of the Geo-Zone Tool, you need the information for the Geo-Zone Tool. This is explained in the following article using the example of the query URL structure:
→
Controlling WebService (API)
In this example, the following information is used, which you can replace with your own in the script:
- Language: en (English)
- Login: [email protected]
- Hash: 123456ABCD
- Map: wind-DIN-EN-1991-1-4 (wind load according to the German Annex EC1)
- Location: Dlubal, Tiefenbach (headquarters of Dlubal GmbH)
- Position: 49.4353975, 12.5894907 (Latitude, Longitude)
Executing Web Request and Reading Data
The following script queries the web service of the Geo-Zone Tool and documents the required times and content.
…
#%% Import
# Library for reading out timestamp (standard library, optional)
import datetime as dt
# Library to run web request
import requests
#%% Set parameters
# URL Webservice Geo-Zone Tool
urlgz = 'https://external-crm.dlubal.com/loadzones/data.aspx'
# Parameters for query (replace with your own values)
pargz = {
'language': 'en',
'login': '[email protected]',
'hash': '123456ABCD',
'map': 'wind-DIN-EN-1991-1-4',
'place': 'Dlubal, Tiefenbach',
'position': '49.4353975,12.5894907'
}
# Set time for canceling the request
reto=10 # s
#%% Run query
# Timestamp before retrieval
cdt1 = dt.datetime.now()
# Web query via requests
rgz = requests.get(urlgz, params=pargz, timeout=reto)
# Timestamp after retrieval
cdt2 = dt.datetime.now()
# Query duration in seconds
dur=(cdt2-cdt1).total_seconds()
# HTTP status code of the request
sgz=rgz.status_code
# Contents description of the query
hgz=rgz.headers['content-type']
# Content of the web request as text
tgz = rgz.text
#%% Console output of the web request
txt=[]
txt.append(f"Timestamp: {cdt1}") # Time YYYY-MM-DD HH:MM:SS.SSSSSS
txt.append(f"Duration: {dur} s") # Duration of the query
txt.append(f"Status code: {rgz.status_code}") # HTTP status code (normal: 200)
txt.append(f"Header: {hgz}") # Content description (normal: text/html; charset=utf-8)
txt.append(f"Text output of request:\n{tgz}") # Output of Geo-Zone Tool
print('\n'.join(txt))
…This leads to the following results, for example:
Timestamp: 2024-08-22 13:24:32.727006
Duration: 2.214527
Status code: 200
Header: text/html; charset=utf-8
Text output of request:
Result 1,Result 2,Zone,Latitude,Longitude,Elevation,Street,ZIP,City,Standard,Annex,Note(s),Legal notice
22.5 m/s,0.32 kN/m²,1,49.4353975,12.5894907,520.69384765625,Am Zellweg 2,93464,Tiefenbach,EN 1991-1-4,DIN EN 1991-1-4,,All data without guaranteeAddition: Preparing Web Request Content
The following script converts the text obtained from the Geo-Zone Tool web service into a tabular form. Furthermore, the result values are separated from their units and finally saved as a CSV and an Excel file.
…
#%% Import
# String functions of standard library for import and export
from io import StringIO
# Library for data processing
import pandas as pd
#%% Functions
def rsep_val_unit(indf, cnstart='Result',):
"""
Separates Dlubal Geo-Zone-Tool request Dataframe columns with results by value and unit.
Parameters
----------
indf : pandas.DataFrame
Input data.
cnstart: string, optional
Identifier at start of column name containing results.
Returns
-------
outdf : pandas.DataFrame
Output data.
"""
tmp2 = indf.loc(axis=1)[indf.columns.str.startswith(cnstart)]
tmp3 = pd.DataFrame()
for i in tmp2.columns:
tmp3[[(i, 'value'), (i, 'unit')]] = tmp2[i].str.split(
' ', n=1, expand=True)
outdf = pd.concat(
[tmp3, indf.loc(axis=1)[~indf.columns.str.startswith(cnstart)]], axis=1)
return outdf
#%% Run conversion
# Convert the output of Geo-Zone tool into "tabular" dataframe
dfgz=pd.read_csv(StringIO(rgz.text))
# DataFrame with results separated by value and unit
dfgz_rs=rsep_val_unit(dfgz)
#%% Saving
# as a CSV file
dfgz_rs.to_csv("Dlubal_GZT_request.csv")
# as an Excel file
dfgz_rs.to_excel("Dlubal_GZT_request.xlsx")
#%% Console output of conversion
print(f"Original Dataframe:\n{dfgz.to_string()}")
print(f"Manipulated Dataframe:\n{dfgz_rs.to_string()}")
print("Exemplary Output:\n"
+ f" The first result has the value {dfgz_rs.iloc[0,0]}."
+ f" (in {dfgz_rs.iloc[0,1]})")
…This results in the following, for example:
Original Dataframe:
Result 1 Result 2 Zone Latitude Longitude Elevation Street ZIP City Standard Annex Note(s) Legal notice
0 22.5 m/s 0.32 kN/m² 1 49.435398 12.589491 520.693848 Am Zellweg 2 93464 Tiefenbach EN 1991-1-4 DIN EN 1991-1-4 NaN All data without guarantee
Manipulated Dataframe:
(Result 1, value) (Result 1, unit) (Result 2, value) (Result 2, unit) Zone Latitude Longitude Elevation Street ZIP City Standard Annex Note(s) Legal notice
0 22.5 m/s 0.32 kN/m² 1 49.435398 12.589491 520.693848 Am Zellweg 2 93464 Tiefenbach EN 1991-1-4 DIN EN 1991-1-4 NaN All data without guarantee
Example output:
The first result has the value 22.5. (in m/s)
