groundmeas 1.3.0

No project description provided

groundmeas: Grounding System Measurements & Analysis

groundmeas is a comprehensive Python package designed for the management, analysis, and visualization of earthing (grounding) system measurements. It provides a robust toolset for engineers and researchers to handle field data, perform complex physical analysis, and generate interactive reports.

See the full documentation here: https://ce1ectric.github.io/groundmeas/

---

📚 Physical Background

Understanding the behavior of grounding systems is critical for electrical safety and system performance. groundmeas implements several standard and advanced methods for analyzing measurement data.

Earthing Impedance ($Z_E$)

The earthing impedance is a frequency-dependent parameter defined as the ratio of the Earth Potential Rise (EPR) to the current flowing into the earth ($I_E$). $$ Z_E(f) = \frac{V_{EPR}(f)}{I_E(f)} $$

Soil Resistivity ($\rho$)

Soil resistivity is a key factor influencing the grounding resistance. It is typically measured using the Wenner or Schlumberger method and varies with depth and frequency.

The Rho-f Model

To characterize the frequency dependence of a grounding system, groundmeas implements the Rho-f Model. This empirical model correlates the earthing impedance with soil resistivity ($\rho$) and frequency ($f$) using a linear regression approach with complex coefficients:

$$ Z(\rho, f) = k_1 \cdot \rho + (k_2 + j \cdot k_3) \cdot f + (k_4 + j \cdot k_5) \cdot \rho \cdot f $$

Where $k_1 \dots k_5$ are coefficients determined by fitting the model to measured data. This allows for the prediction of impedance behavior under varying soil conditions and frequencies.

Determination of Earthing Impedance (Fall-of-Potential)

When measuring impedance using the Fall-of-Potential method, the challenge is to determine the "true" impedance from a distance vs. impedance profile. groundmeas offers multiple algorithms to extract this value:

1. Maximum: Returns the highest measured value (conservative approach).
2. 62% Rule: Interpolates the value at 62% of the distance to the current injection point (valid for homogeneous soil).
3. Minimum Gradient: Identifies the flat portion of the curve where the gradient ($\Delta Z / \Delta d$) is minimal.
4. Minimum Standard Deviation: Uses a sliding window to find the region with the lowest variance (flattest part of the curve).
5. Inverse Extrapolation: Fits a function $1/Z = a \cdot (1/d) + b$ to extrapolate the value at infinite distance ($d \to \infty$).

---

🚀 Features

• Data Management: robust SQLite database with SQLModel (Pydantic) ORM for Measurements, MeasurementItems, and Locations.
• Interactive Dashboard: A Streamlit-based web interface with:
  • Map Visualization: Geospatial view of measurement locations using Folium.
  • Interactive Plots: Plotly-based charts for Impedance vs. Frequency, Voltage profiles, and more.
  • Engineering Notation: Automatic formatting of axis labels (e.g., $k\Omega$, $mA$).
• CLI (Command Line Interface): A powerful Typer-based CLI for all operations.
• Analytics: Built-in functions for:
  • Split factor calculation (shield currents).
  • Touch voltage ($V_t$) and Prospective Touch Voltage ($V_{tp}$) analysis.
  • Complex number processing (Real/Imaginary/Magnitude/Angle).
• Import/Export: Support for JSON import/export, including batch processing of folders.

---

📦 Installation

Prerequisites: Python 3.12+

Using Poetry (Recommended)

git clone https://github.com/Ce1ectric/groundmeas.git
cd groundmeas
poetry install
poetry shell

Using Pip

pip install groundmeas

---

🖥️ Usage

1. The Command Line Interface (CLI)

The gm-cli tool is the main entry point for managing your data.

Setup Database: By default, groundmeas looks for a database. You can specify one via the --db flag or set a default path in ~/.config/groundmeas/config.json.

Common Commands:

• Dashboard: Launch the interactive visualization tool.

  gm-cli dashboard
  

• Import Data: Import measurements from a JSON file or a folder of JSON files.

  gm-cli import-json ./data/measurements/
  

• List Data: View stored measurements.

  gm-cli list-measurements
  

• Analytics: Calculate the characteristic impedance using the minimum gradient method.

  gm-cli distance-profile 1 --algorithm minimum_gradient
  

• Help: See all available commands.

  gm-cli --help
  

2. The Interactive Dashboard

The dashboard provides a user-friendly interface to explore your data.

1. Run gm-cli dashboard.
2. Map View: Select measurement points on the map. Use "Multi-select mode" to compare multiple datasets.
3. Analysis Tabs:
   • Impedance vs Frequency: Compare frequency responses ($Z_E$).
   • Rho-f Model: Fit and visualize the model parameters.
   • Voltage / EPR: Analyze Earth Potential Rise and Touch Voltages ($V_t$, $V_{tp}$).
   • Value vs Distance: Visualize Fall-of-Potential curves with filtering by frequency.

3. Python API

You can use groundmeas directly in your Python scripts or Jupyter notebooks.

from groundmeas import connect_db, read_measurements_by, impedance_over_frequency

# Connect to DB
connect_db("groundmeas.db")

# Fetch data
measurements, _ = read_measurements_by(location_id=1)

# Analyze
for meas in measurements:
    z_f = impedance_over_frequency(meas["id"])
    print(f"Measurement {meas['id']}: {z_f}")

---

📂 Project Structure

groundmeas/
├── src/groundmeas/
│   ├── __init__.py              # Public API exports
│   ├── core/
│   │   ├── db.py                # DB connection + CRUD
│   │   └── models.py            # SQLModel data definitions
│   ├── services/
│   │   ├── analytics.py         # Physical models and algorithms
│   │   ├── export.py            # JSON/CSV/XML export helpers
│   │   └── vision_import.py     # OCR import pipeline
│   ├── visualization/
│   │   ├── plots.py             # Matplotlib plots
│   │   ├── vis_plotly.py        # Plotly figures
│   │   └── map_vis.py           # Folium map generation
│   └── ui/
│       ├── cli.py               # Typer CLI entry point
│       └── dashboard.py         # Streamlit dashboard
├── tests/                       # Pytest suite
└── pyproject.toml               # Project configuration and dependencies

---

🤝 Contributing

1. Fork the repository.
2. Create a feature branch (git checkout -b feat/new-feature).
3. Commit your changes.
4. Push to the branch.
5. Open a Pull Request.

📄 License

MIT License