Metadata-Version: 2.4
Name: pymodal
Version: 0.2.0
Summary: Modal analysis data management, simulation and storage tool
Home-page: https://github.com/grcarmenaty/pymodal
Download-URL: https://github.com/grcarmenaty/pymodal/archive/0.2.0.tar.gz
Author: Guillermo Reyes Carmenaty
Author-email: grcarmenaty@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: numpy>=1.18.1
Requires-Dist: scipy>=1.4.1
Requires-Dist: matplotlib>=3.1.3
Requires-Dist: pandas>=1.0.1
Requires-Dist: Pint>=0.20.1
Requires-Dist: pyFRF>=0.40
Requires-Dist: h5py>=3.9.0
Requires-Dist: audiomentations>=0.31.0
Requires-Dist: torch>=2.0.1
Provides-Extra: dev
Requires-Dist: pytest>=3.7; extra == "dev"
Requires-Dist: pytest-cov>=2.10.0; extra == "dev"
Requires-Dist: docutils>=0.16; extra == "dev"
Requires-Dist: doc8>=0.8.0; extra == "dev"
Requires-Dist: flake8>=3.7.9; extra == "dev"
Requires-Dist: black>=23.1.0; extra == "dev"
Provides-Extra: mcp
Requires-Dist: mcp>=1.0; extra == "mcp"
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=================
PyModal Library
=================
------------------------------------------------------
Simulate, load, store and represent your modal data
------------------------------------------------------

Introduction
============

This library is a work in progress developed by the Applied Mechanics Lab, 
IQS School of Engineering, as part of the research in Structural Health Monitoring (SHM). 
This library enables the user to store and process Frequency Response Functions (FRF). 
The library includes a wide range of frequency-based damage indicators found in the literature. 
Currently, Transmissibility Functions (TF) are being implemented in the library.
The library also includes tools for building certain geometries in ANSYS and get their FRFs. 
If you plan on using the ANSYS module, be sure to have a working ANSYS installation.

Features
----------------
- Basic usage: Load, store and plot FRFs.

- Post-processing FRFs: Modify properties of FRF.

- Frequency-based correlation indicators: Indicators used for damage detection, similarity and model updating purposes.

- ANSYS

Installation
============

In order to install this module, just run

.. code-block:: bash
    
    pip install pymodal

in your terminal. This will also potentially install all the requirements, which
you can find in `requirements.txt <https://github.com/grcarmenaty/pymodal/blob/master/requirements.txt>`_, although they will be included here as
well for clarity's sake:

- numpy
- scipy
- matplotlib
- pandas
- pyansys

Dev Installation
----------------

If you wish to try and add some features yourself or modify some of the existing
ones, clone the repository and, in the same folder where the repo is cloned,
run the following command:

.. code-block:: bash
    
    pip install -e .[dev]

This will also potentially install all the development requirements, which
you can find in `requirements-dev.txt <https://github.com/grcarmenaty/pymodal/blob/master/requirements-dev.txt>`_, although they are included here as
well for clarity's sake:

- pytest
- docutils
- doc8
- flake8

Basic usage
============

**Make an instance of FRF class:**

At least one of the following must be specified: Resolution, Bandwidth or Maximum frequency.  
If not specified, minimum frequency is assumed to be 0 Hz.

.. code:: python

   frf_data = pymodal.FRF(
       frf,
       resolution,
       bandwidth,
       max_freq,
       min_freq,
       name,
       part,
       modal_frequencies
       )
       
Generate instance named **frf_data** from **frf** numpy array.
       
**Add new FRFs to existing instance:**

.. code:: python

   def extend(self, frf: list, name: list = None)

**Save instance as zip file:**

.. code:: python

   def extend(self, frf: list, name: list = None)

**Plot FRF:** Plot all FRFs together with varying colors unless otherwise specified.

.. code:: python

    def plot(self,
             ax: list = None,
             fontsize: float = 12,
             title: str = 'Frequency Response',
             title_size: float = None,
             major_locator: int = 4,
             minor_locator: int = 4,
             fontname: str = 'serif',
             color: list = None,
             ylabel: str = None,
             bottom_ylim: float = None,
             decimals_y: int = 1,
             decimals_x: int = 1):
             
Use slice to only plot specific FRFs.

.. code:: python

    frf_data[0].plot()
    plt.show()

Post-processing FRFs
====================


**Change resolution**  

.. code:: python

   frf.change_resolution(frequencies=[0,100])

**Change FRF lines**  

**Change frequency range**  

**Extract real part of FRF**  

**Extract imaginary part of FRF**  

**Calculate magnitude of FRF**  

**Calculate phase of FRF**  

**Extract modal frequencies**  

**Extract mode shapes**  

**Synthetic FRFs**  

**Silhouette**  

**Transmissibility matrix**  

Frequency-based damage indicators
=================================
Currently, the pymodal library holds the following damage indicators:

**Frequency Response Function RMS [FRFRMS]:**

.. code:: python

    def get_FRFRMS(self, ref:int)
    
https://www.sciencedirect.com/science/article/abs/pii/S1270963802011938

**Global Amplitude Criterion [GAC]:**

.. code:: python

    def get_GAC(self, ref:int, frf: list = None)

**Average Integration Global Amplitude Criterion (AIGAC):**

.. code:: python

    def get_AIGAC(self, ref:int)

**Frequency Domain Assurance Criterion (FDAC):**


**Response Vector Assurance Criterion (RVAC)**


**Detection and Relative Quantification (DRQ)**


**Detection and Relative Quantification curvature-based (DRQ'')**


**Frequency Response Function Scale Factor (FRFSF)**


**Coefficient of Determination (R^2)**


**ODS difference indicator (∆ODS)**


**Frequency Response Function Similarity Metric (FRFSM)**


**Complex Frequency Domain Assurance Crietrion [CFDAC]**


**Spectral Correlation Index [SCI]**
https://www.sciencedirect.com/science/article/abs/pii/S0888327018306551?via%3Dihub

.. code:: python

    def get_SCI(self, ref:int, part: str = 'abs')
    


MCP server
============

Pymodal ships an `MCP <https://modelcontextprotocol.io>`_ server that exposes
collection construction, signal processing, FRF computation, SHM-indicator
math and PyTorch-dataset hand-off as tools an LLM agent can call directly.

Install the optional dependency:

.. code-block:: bash

    pip install -e .[mcp]

Run the server (stdio transport):

.. code-block:: bash

    python -m pymodal.mcp
    # or, after install:
    pymodal-mcp

Configure your MCP client (Claude Desktop, Claude Code, …) to launch
``python -m pymodal.mcp`` as a server. Every tool takes file paths in and
writes file paths out, so a typical agent flow is

``create_timeseries_collection`` → ``add_gaussian_noise`` →
``timeseries_to_frf`` → ``compute_indicator`` → ``split_collection`` →
``torch_dataset_summary``.

Use ``list_indicators`` and ``list_collection_classes`` for in-tool discovery.

ANSYS
============
