pyinfraformat 25.7.10

Python library for Finnish Infraformat

pyinfraformat

Python library for reading, writing and analyzing Finnish borehole format Infraformat (version 2.5). Well suited for scientific and research applications.

Installation

Latest (stable) pyinfraformat can be installed with pip

python -m pip install pyinfraformat

The latest (unstable) version can be installed from git with pip (needs git-tools).

python -m pip install git+https://github.com/ahartikainen/pyinfraformat

Library can be installed also by git clone / downloading zip.

git clone https://github.com/ahartikainen/pyinfraformat
cd pyinfraformat
python -m pip install .

To install inplace for development work, use -e command.

python -m pip install -e .

Quickstart

Basic usage

import pyinfraformat as pif
pif.set_logger_level(50) # Suppress non-critical warnings, recommended for large files
holes = pif.from_infraformat("*.tek")
holes = holes.project("TM35FIN")
bounds = holes.bounds
holes.to_infraformat("holes_tm35fin.tek")

bounds = [6672242-200 ,  385795-200, 6672242 +200,  385795+200]
gtk_holes = pif.from_gtk_wfs(bounds, "TM35Fin")
print(gtk_holes) # View holes object
#Infraformat Holes -object:
#  Total of 203 holes
#    - PO ......... 161
#    - HP .........  13
#    - PA .........  12
#    - NO .........   2
#    - NE .........   1
#    - KE .........   5
#    - KR .........   9


html_map = gtk_holes.plot_map()
html_map.save("soundings.html")
html_map # View map in jupyter

hole_figure = gtk_holes[10].plot()
hole_figure # View hole in jupyter

Plot histograms from laboratory tests

import pandas as pd
bounds = [6672242-2000 ,  385795-2000, 6672242 +2000,  385795+2000]
gtk_holes = pif.from_gtk_wfs(bounds, "TM35FIN", maxholes=25_000)
laboratory_tests = gtk_holes.filter_holes(hole_type=["NO", "NE"], start="1990-01-01")
df = laboratory_tests.get_dataframe()
df['data_Soil type'] = df['data_Soil type'].astype("string")
clay_samples = df[df['data_Soil type'].str.endswith("Sa", na=False)].reset_index()
clay_samples['data_Laboratory w'] = pd.to_numeric(clay_samples['data_Laboratory w'])
fig = clay_samples['data_Laboratory w'].plot.hist(bins='fd')
fig.set_title("Clay samples water content, %")
fig