pypbee 1.0.4

Performance-Based Earthquake Engineering toolkit

PyPBEE — Performance-Based Earthquake Engineering in Python

A modular, high-performance framework that lets researchers and engineers run the full Performance-Based Earthquake Engineering (PBEE) workflow — from site-specific seismic hazard analysis to demand, damage and (soon) loss hazard analysis — entirely in Python.

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Key Capabilities

• End-to-end PBEE pipeline
  Probabilistic Seismic Hazard Analysis (PSHA), Ground Motion Selection (GMS), Nonlinear Time-History Analysis (NLTHA), Probabilistic Seismic Demand Hazard Analysis (PSDemHA) and Probabilistic Seismic Damage Hazard Analysis (PSDamHA).
• Object-oriented core, plug-and-play extensions
  Clean template-method lifecycle (setup → run → wrap_up) and abstract entity classes (IM, EDP, DS, Structure) make it straightforward to add new intensity measures, demand parameters, fragilities, or analysis stages.
• Advanced Uncertainty Quantification
  Treat aleatory and epistemic sources (random FE parameters, parameter estimation uncertainty, model-form alternatives) with MultivariateNataf, Mixture, Latin Hypercube sampling, etc.
• Finite-element backends out of the box
  Adapters for OpenSeesTcl and OpenSeesPy; extendable to Abaqus, Ansys, …
• Scales from laptop to cluster
  Local multiprocessing via pathos or embarrassingly-parallel Slurm jobs on HPC/HTC systems.
• Built-in visualisation
  Hazard curves, conditional spectra, demand / damage hazard surfaces, deaggregation plots — all ready for publication.

PyPBEE Installation Guide

Supported Python Versions: 3.12

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Recommended: Create a Fresh Environment

It is highly recommended to install PyPBEE in a new isolated environment.

You can set up PyPBEE using five primary installation routes. The first four require forking and cloning the PyPBEE repository:

Route	Platform	Tool	Description
1. Windows (venv)	Windows	Virtual environment (venv)	Uses setup_venv.bat to create and configure a venv automatically
2. Windows (Conda)	Windows	Conda	Uses the provided environment.yml to set up a Conda environment
3. macOS (venv)	macOS	Virtual environment (venv)	Manual venv setup using python3 -m venv
4. macOS (Conda)	macOS	Conda	Uses the provided environment.yml to create a Conda environment
5. PyPI	All	pip	Install directly from PyPI

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Forking and Cloning the Repository

1. Install Git from git-scm.com
2. Go to https://github.com/angshuman311/PyPBEE and click Fork (top-right corner)
   This creates your own copy of the repository under your GitHub account.
3. Open Git Bash on Windows (or Terminal on macOS)
4. Navigate (cd) to the folder where you want the code to reside:

   cd path/to/your/projects
   

5. Clone your fork instead of the main repo:

   git clone https://github.com/<your-github-username>/PyPBEE
   

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Installing on Windows using virtual environment (venv): Using setup_venv.bat

1. Install Python >=3.12, <3.13 from python.org
2. Locate setup_venv.bat in your repository directory
3. Double-click setup_venv.bat
4. When prompted, browse to your python.exe location (from step 1)
5. The script will:
   • Create venv\pypbee inside the repository directory
   • Install all requirements from requirements.txt
6. Point your IDE's Python interpreter (for example, in VS Code, PyCharm, or Spyder) to the python.exe inside the virtual environment you just created (venv\pypbee)

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Installing on Windows (Conda)

1. Install the Anaconda distribution
2. Open Anaconda Prompt
3. Navigate to the cloned repository directory:

   cd path\to\your\PyPBEE
   

4. Create the environment from the provided file:

   conda env create -f environment.yml
   

5. Point your IDE's Python interpreter (e.g., in VS Code, PyCharm, or Spyder) to the conda environment you just created (pypbee)

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Installing on macOS using virtual environment (venv)

1. Install Python ≥3.12, <3.13 from python.org
2. Open Terminal
3. Navigate to your cloned repository directory:

   cd path/to/your/PyPBEE
   

4. Create a new virtual environment inside the repository directory:

   python3 -m venv venv/pypbee
   

5. Activate the environment:

   source venv/pypbee/bin/activate
   

6. Upgrade pip and install all required dependencies:

   python -m pip install --upgrade pip
   pip install -r requirements.txt
   

   Note: If you encounter issues when installing numba on macOS, use the alternate file:

   pip install -r requirements-no-numba.txt
   

7. Point your IDE's Python interpreter (for example, in VS Code, PyCharm, or Spyder) to the python binary inside the virtual environment you just created (venv/pypbee/bin/python)

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Installing on macOS (Conda)

1. Install the Anaconda distribution
2. Open Terminal
3. Navigate to the cloned repository directory:

   cd path/to/your/PyPBEE
   

4. Create the environment from the provided file:

   conda env create -f environment.yml
   

   Note: If installation errors related to numba occur, use the alternate environment file:

   conda env create -f environment-no-numba.yml
   

5. Activate the environment:

   conda activate pypbee
   

6. Point your IDE's Python interpreter (e.g., in VS Code, PyCharm, or Spyder) to the conda environment you just created (pypbee)

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Installing Directly from PyPI

PyPBEE is also available on PyPI.

⚠️ Important:
We strongly recommend installing PyPBEE inside a new, isolated virtual environment (using venv) or a Conda environment.
Do not install PyPBEE in your base Python or base Conda environment — this helps avoid version conflicts and dependency issues with other packages.

Step 1: Install Python (or Anaconda)

Before proceeding, ensure you have Python 3.12 ≤ version < 3.13 installed.
You can choose one of the two following options:

• Install Python 3.12 ≤ version < 3.13 directly from python.org/downloads.
  During installation on Windows, make sure to check “Add Python to PATH.”.
  To create a new environment:

  1. Open a terminal (macOS/Linux) or Command Prompt (Windows).
  2. Navigate (cd) to the folder where you want to create the virtual environment — typically inside your project directory.
     Example:

  cd path/to/your/project
  

  3. Create and activate the virtual environment:

  python -m venv pypbee-venv
  source pypbee-venv/bin/activate    # macOS/Linux
  pypbee-venv\Scripts\activate       # Windows
  

• Or install the Anaconda distribution (which includes Python and Conda).
  When creating the environment later, Conda will automatically install the correct Python version (>=3.12, <3.13).
  To create a new environment, do this inside an Anaconda Prompt (Windows) or a terminal (macOS/Linux):

  conda create -n pypbee python=3.12.10
  conda activate pypbee
  

Step 2: Install PyPBEE from PyPI

Without Numba:

pip install pypbee

With Numba acceleration enabled:

pip install "pypbee[numba]"

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Verifying Installation

Run Python and import PyPBEE:

import pypbee
print(pypbee.__version__)

If no errors appear, your environment is correctly set up.

Conceptual PSEUDO code

from pypbee.structure                               import OSB               # Ordinary Standard Bridge
from pypbee.avg_sa                                  import AvgSa
from pypbee.edp                                     import MaxColRebarStrain
from pypbee.ds                                      import DS
from pypbee.analysis                                import PrelimAnalysis, PSHA, GMS, NLTHA, PSDemHA, PSDamHA
from pypbee.utility                                 import Utility
from pypbee.pygmm_extension.boore_atkinson_2008     import BooreAtkinson2008
from pygmm.baker_jayaram_2008                       import calc_correls

# 1 ── define model‐params & site info ------------------------------------------------
# See files `osb_info_*.py` in `examples/Bridge_*/osb_info_*.py` for model_params and location_info definition
name = 'Bridge_A'
model_files_path = f'path/to/{name}'
model_params = Utility.import_attr_from_module(module_path=model_files_path, module_name=f"osb_info_{name}", attr='model_params')
location_info = Utility.import_attr_from_module(module_path=model_files_path, module_name=f"osb_info_{name}", attr='location_info')
local_python_path = '/path/to/PyPBEE/venv/pypbee/Scripts/python.exe'
model_work_dir_path = f'path/to/a/working/directory/for/results/data/storage/for/{name}'

# 2 ── create entities ---------------------------------------------------------------
structural_analysis_platform = OpenSeesPy(model_files_path, local_python_path)
osb         = OSB(name, location_info, model_files_path, model_work_dir_path, model_params, structural_analysis_platform)
im          = AvgSa(osb, gmm=BooreAtkinson2008, correl_func=calc_correls, define_range=['T_1_trans', 'T_1_trans'], range_multiplier=[1, 2.5])
edp_list    = [
                    MaxColRebarStrain(max_what='compression', frame_structure=osb, tag='1', recorder_file_storage='shared'),
                    MaxColRebarStrain(max_what='tension', frame_structure=osb, tag='2', recorder_file_storage='shared'),
                    MaxSpringDeformation(spring_type='shear_key', max_what='compression', frame_structure=osb, tag='4', recorder_file_storage='separate', normalize_with='D3')
              ]
ds_list     = [
                    DS(
                        edp=edp_list[0], predictor=lambda x: 0.004,
                        haz_req={'normalized_fragility_dist': lognorm(0.326, 0, 1.02),
                                'estimation_sample_size': 5},
                        ds_type='col_rebar_strain_damage'
                    )
                    DS(
                        edp=edp_list[1], predictor=lambda x: 0.03 + 700 * x[1] * x[2] / x[3] - 0.1 * x[8] / (x[4] * x[5]),
                        haz_req={'normalized_fragility_dist': lognorm(0.201, 0, 1.05),
                                'estimation_sample_size': 5},
                        ds_type='col_rebar_strain_damage'
                    )
                    DS(
                        edp=edp_list[2], predictor=lambda x: 1.0,
                        haz_req={'normalized_fragility_dist': lognorm(0.11, 0, 1.14),
                                'estimation_sample_size': 5},
                        ds_type='spring_deformation_damage',
                    )
              ]


# 3 ── assemble analyses -------------------------------------------------------------
pre         = PrelimAnalysis(osb, num_modes=8)
psha        = PSHA(im)
gms         = GMS(im)
nltha       = NLTHA(im, edp_list)
psdemha     = PSDemHA(edp_list, im)
psdamha     = PSDamHA(ds_list, im, sol_type='numerical')

# 4 ── run workflow ------------------------------------------------------------------
for a in (pre, psha, gms, nltha, psdemha, psdamha):
    a.setup(...)
    a.run(...)
    a.wrap_up(...)

Full, commented examples live in scripts/examples.

Contributing

1. Fork this repository and clone your fork locally.
2. Create a feature branch, make your changes (e.g., add a new IM, EDP, etc.), and push the branch to your fork.
3. Open a pull request describing what you changed and why it’s useful.
4. Bug reports and feature requests are equally welcome — open a GitHub issue.

Acknowledgements

Development supported by Caltrans (65A0594, Task 2880), PEER Transportation Systems Research Program (Project #1147-NCTRTE), and the Reissner Chair, UC San Diego. HPC tests used TACC resources.

License

This project is released under the MIT License.