initial commit

Author: SCA-ZMT

.github/badges/.blank

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+
+name: Coverage
+
+on:
+  push:
+    branches: [ "**" ]
+
+jobs:
+  compute_coverage:
+      runs-on: ubuntu-latest
+      
+      steps:
+      - uses: actions/checkout@v4
+      - name: Set up Pyton
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          if [ -f extra_requirements.txt ]; then pip install -r extra_requirements.txt; fi
+      - name: Compute coverage
+        run: |
+          python -m pytest --cov=sar_sampling --cov-report=term-missing --local-badge-output-dir=${{ github.workspace }}/.github/badges > coverage.txt
+      - name: Push changes
+        run: |
+          git config user.name "github-actions"
+          git config user.email "[email protected]"
+          if git diff --quiet; then
+            git status
+            echo "No files to update"
+          else
+            git add -u
+            git commit -m "chore: update coverage data"
+            git push
+          fi

.github/badges/coverage.svg

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+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Test
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+jobs:
+  run_tests:
+
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        python -m pytest --import-mode=prepend tests

.github/badges/tests.svg

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+**/__pycache__/
+**/*.pyc
+.venv
+.coverage
+*.egg-info/

.github/workflows/coverage.yml

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+[![Pytest](.github/badges/tests.svg) ![Coverage](.github/badges/coverage.svg)](
+    ./coverage.txt
+)
+
+# SAR Sampling
+
+A Python package for generating structured samples for SAR (Specific Absorption Rate) testing scenarios using Latin Hypercube sampling with domain-specific constraints and mappings.
+
+## Overview
+
+SAR Sampling is designed to create realistic test scenarios for SAR testing by combining Latin Hypercube sampling with domain-specific knowledge about antenna configurations, frequency/radius mappings, and modulation schemes. The package processes input tables containing antenna data and generates structured samples that maintain the statistical properties of the original data while providing comprehensive coverage of the test space.
+
+## Features
+
+- **Latin Hypercube Sampling**: Implements various LHS methods including classic, centered, maximin, and centermaximin
+- **Domain-Specific Constraints**: Handles SAR-specific requirements for antenna configurations, frequencies, distances, and modulation schemes
+- **Voronoi Diagram Integration**: Uses spatial distribution analysis for frequency-radius mappings
+- **Flexible Input Formats**: Supports both pandas DataFrames and CSV files
+- **Configurable Sampling**: Customizable domains, weights, and sampling methods
+
+## Installation
+
+### Prerequisites
+
+The package requires Python 3.10+ and the following dependencies:
+- numpy
+- pandas
+- scipy
+- shapely
+
+### Install from source
+
+```bash
+git clone <repository-url>
+pip install <project-root-dir>
+```
+
+## Quick Start
+
+### Input Data Format
+
+The package expects input data in a specific format. Here's an example:
+
+```csv
+antenna,frequency,2mm,5mm,10mm,21mm,M1,M2,M3,M4
+D750,750,,,,15.4,21.5,1,1,1,0
+D835,835,4.6,,15.0,20.8,1,1,0,0
+D900,900,4.7,,14.6,20.4,1,1,0,0
+D1450,1450,5.4,9.4,,18.3,1,1,0,0
+```
+
+**Column requirements:**
+- `antenna`: Unique identifier for each antenna configuration
+- `frequency`: Frequency value in MHz
+- Distance columns: Must end with 'mm' (e.g., '2mm', '5mm', '10mm')
+- Modulation columns: Must start with 'M' (e.g., 'M1', 'M2', 'M3')
+
+### Basic Run
+
+```python
+from sar_sampling import SarSampler
+import pandas as pd
+
+# Create or load input data from csv
+input_data = {
+    'antenna': ['D750', 'D835', 'D900', 'D1450'],
+    'frequency': [750, 835, 900, 1450],
+    '10mm': [15.4, 15.0, 14.6, None],
+    '21mm': [21.5, 20.8, 20.4, 18.3],
+    'M1': [1, 1, 1, 1],
+    'M2': [1, 1, 1, 1],
+}
+input_df = pd.DataFrame(input_data)
+
+# Initialize sampler
+sampler = SarSampler(input_df)
+
+# Generate samples
+sample = sampler(n_samples=100, method='maximin', seed=42)
+
+# Access results
+print(sample)
+
+# save to file
+sample.to_csv('sample.csv')
+```
+
+## Sampling Methods
+
+The package supports several Latin Hypercube sampling methods:
+
+- **'classic'**: Standard Latin Hypercube sampling
+- **'center'/'c'**: Centered Latin Hypercube sampling
+- **'maximin'/'m'**: Maximin distance optimization
+- **'centermaximin'/'cm'**: Centered with maximin optimization
+
+## Core Components
+
+### SarSampler
+
+The main class for SAR-specific sampling:
+
+```python
+from sar_sampling import SarSampler
+
+# Initialize with input table
+sampler = SarSampler(input_table, config={})
+
+# Generate samples
+samples = sampler(n_samples=100, method='maximin', seed=42)
+```
+
+**Parameters:**
+- `input_table`: DataFrame or CSV path containing antenna configurations
+- `config`: Optional configuration overrides
+
+**Required input columns:**
+- `antenna`: Antenna identifiers
+- `frequency`: Frequency values
+- Distance columns ending with 'mm' (e.g., '10mm', '21mm')
+- Modulation columns starting with 'M' (e.g., 'M1', 'M2')
+
+### LhSampler
+
+General-purpose Latin Hypercube sampler:
+
+```python
+from sar_sampling import LhSampler
+
+# Define domains
+domains = {
+    'a': [0, 10],      # Continuous uniform on [0, 10]
+    'b': [0, 20],      # Continuous uniform on [0, 20]
+    'c': [1, 2, 3]  # Discrete values
+}
+
+# Initialize sampler
+sampler = LhSampler(domains)
+
+# Generate samples
+samples = sampler(100, method='maximin')
+```
+
+### lhs Function
+
+Direct Latin Hypercube sampling function:
+
+```python
+from sar_sampling import lhs
+import numpy as np
+
+# Generate 100 samples in 3 dimensions
+samples = lhs(dim=3, size=100, method='maximin', seed=42)
+```
+
+### Sample Class
+
+Container for structured data with variable classification:
+
+```python
+from sar_sampling import Sample
+
+# Create sample with variable classification
+sample = Sample(df, xvar=['x', 'y'], zvar='output')
+
+# Access data
+x_data = sample.xdata()  # Independent variables
+z_data = sample.zdata()  # Dependent variables
+```
+
+## Examples
+
+### Basic Usage
+
+See `examples/example.py` for a complete working example:
+
+```python
+from sar_sampling import SarSampler
+import pandas as pd
+
+# Load sample data
+input_df = pd.read_csv('data/input_table.csv')
+
+# Create sampler
+sampler = SarSampler(input_df)
+
+# Generate samples
+samples = sampler(1000, method='maximin', seed=123)
+
+# Analyze results
+print(f"Generated {samples.size()} samples")
+print(f"X range: {samples.data['x'].min():.1f} to {samples.data['x'].max():.1f}")
+print(f"Frequency range: {samples.data['frequency'].min():.0f} to {samples.data['frequency'].max():.0f}")
+
+# Save results
+samples.to_csv('output_samples.csv')
+```

.github/workflows/python-tests.yml

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+python -m pytest --cov=sar_sampling ${@}