Welcome to GEOLib+’s documentation!

GEOLib+ is a Python package . Release v0.4.1.

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Behold, the power of GEOLib+:

>>> import geolib_plus as glp

The User Guide

This part of the documentation, which is mostly prose, begins with some background information about GEOLib, then focuses on step-by-step instructions for getting the most out of GEOLib.

• Introduction
  • Philosophy
• Installation
  • GEOLib+ installation
  • Packages used
  • Get the Source Code

API

• Developer API
  • Overview
    • geolib_plus package
    • geolib_plus.bro_xml_cpt package
    • geolib_plus.gef_cpt package
    • geolib_plus.robertson_cpt_interpretation package
    • geolib_plus.plot_plotting package
    • geolib_plus.parameters_for_soil_models package
    • geolib_plus.shm package
    • geolib_plus.geolib_connectionspackage
  • Indices and tables

The Tutorials

This part of the documentation, which is all prose, gives some examples of using GEOLib+.

• Tutorials
  • Overview
    • Tutorial setup Cpt class interpretation using GEOLIB+
    • Tutorial Cpt interpretation using GEOLIB+
    • Tutorial Cpt plotting using GEOLIB+
    • Tutorial Custom Cpt interpretation using GEOLIB+
    • Tutorial defining and calculating parameters for the Soft Soil Creep model
    • Tutorial Defining and calculating parameters for the Hardening Soil model
    • Tutorial derive model parameters for sands using relative density input
    • Create a D-Foundations soil profile using GEOLIB+
    • Tutorial determine Nkt values using GEOLIB+
    • Tutorial determine Shansep parameters using GEOLIB+
    • Tutorial determine state parameters using GEOLIB+
  • Indices and tables
• Probabilistic Tutorials
  • Tutorial 1: Deriving Lognormal statistics from measurements
    • Step 1: Importing the dataset, and interpreting the data.
    • Step 2: Sample mean and sample standard deviation of the data set
    • Step 3: Fitted Lognormal Distribution using method of moments (MoM)
    • Step 4: Characteristic values
    • Step 5: Get the inputs for D-Stability, taking into account the statistical uncertainty
  • Tutorial 2: Fit an extreme value distribution for the water level
    • Step 1: Importing packages and data
    • Step 2: Importing the data from external source
    • Step 3: Preprocessing of the data.
    • Step 4: Visually checking the data
    • Step 5: Fitting a Generalized Extreme Value (GEV) distribution
    • Step 6: Optimizing the distribution parameters using Python
    • Step 7: Plot the result
    • Step 8: Force fitting a Gumbel distribution
  • Tutorial 3: Integrating the fragility curve with the PDF of the waterlevel
    • Step 1: Create base class DStabilityFragilityCurve for fragility curve operations
    • Step 2: Specify the fragility curve names and locations
    • Step 3: Plot the fragility points and the extrapolated fragility curve
    • Step 4: integrate the fragility curve with the probability density of water level
    • Step 5: Calculate the probability of exceedence
  • Tutorial 4: Combining 2 fragility curves for different scenario’s.
    • Import necessary python packages
    • Load base class for Fragility curve operations
    • Code for plotting
    • Plot the fragility points
    • Extrapolate fragility curves linearly along the given range, with the given discretisation
    • Plot the extrapolated fragility curves
    • Load the water level dependent scenario probability and densify and extrapolate
    • Sanity checks:
    • Plot the scenario probabilities
    • The combination of 2 fragility curves
    • Plot the combined fragility curve
    • Save the combined fragility curve to json (e.g. to import in the Probabilistic Toolkit)

Changelog

• Changelog

references

• References