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Fix Documentation 404 Errors
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‎.ai/steering/commands.md‎

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# Command Execution Instructions
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## Overview
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When the user asks you to execute a command, look for the corresponding file in the `.ai/commands/` directory and follow the instructions contained within that file.
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## Command Execution Process
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1. **Command Request**: Commands can be triggered in multiple ways:
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- Natural language: "run fix-tests", "execute fix-examples"
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- Slash commands: `/fix-tests`, `/fix-examples`, `/fix-docs`
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- Direct command names: "fix-tests", "fix-examples"
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2. **Locate Command File**: Find the corresponding `.md` file in `.ai/commands/` directory:
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- Command: "fix-tests" → File: `.ai/commands/fix-tests.md`
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- Command: "fix-examples" → File: `.ai/commands/fix-examples.md`
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- Command: "fix-docs" → File: `.ai/commands/fix-docs.md`
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3. **Read and Execute**: Read the command file and follow the step-by-step instructions provided.
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4. **Handle Arguments**: Commands may contain `$ARGUMENTS` placeholders:
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- **Arguments Provided**: If arguments are already provided in the user's prompt, substitute them directly
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- **Arguments Missing**: If arguments are needed but not provided, ask the user for clarification in a follow-up question
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## Example Usage
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**User Request**: "Execute fix-tests for the algorithms module"
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- **Action**: Read `.ai/commands/fix-tests.md`
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- **Arguments**: `$ARGUMENTS = "algorithms module"` (provided in prompt)
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- **Execution**: Follow the instructions in the file, substituting the arguments where needed
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**User Request**: "Run fix-examples"
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- **Action**: Read `.ai/commands/fix-examples.md`
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- **Arguments**: If the command file requires `$ARGUMENTS` but none provided, ask user: "Which examples would you like me to focus on?"
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## Command File Structure
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Command files in `.ai/commands/` typically contain:
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- Step-by-step instructions
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- Code snippets to execute
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- Conditional logic based on results
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- Argument placeholders (`$ARGUMENTS`)
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- Expected outcomes and next steps
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## Best Practices
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- Always read the entire command file before starting execution
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- Follow the instructions sequentially
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- Substitute arguments appropriately
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- If a step fails, continue with any error handling instructions provided in the command file
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- Report progress and results back to the user as you execute each major step

‎.ai/steering/product.md‎

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# Product Overview
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pymoo is an open-source Python framework for multi-objective optimization that provides state-of-the-art single and multi-objective algorithms. The framework offers comprehensive features for optimization including:
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- Multi-objective optimization algorithms (NSGA-II, NSGA-III, MOEA/D, etc.)
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- Single-objective optimization methods
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- Visualization tools for optimization results
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- Decision making utilities
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- Performance indicators and metrics
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- Constraint handling mechanisms
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- Gradient-based optimization support
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The project targets researchers, engineers, and developers working on optimization problems, providing both ease of use for beginners and advanced customization for experts. It's designed to be modular, extensible, and performance-oriented with compiled extensions for speed-critical operations.

‎.ai/README.md‎ renamed to ‎.ai/steering/readme.md‎

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3. Using intelligent caching to avoid re-running successful tests
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4. Final verification
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### Project Commands Collection
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The `.ai/commands/` directory contains a collection of predefined commands and workflows that should be executed when requested. These commands encapsulate common development tasks, debugging procedures, and project-specific operations. When asked to perform standard operations like testing, building, or fixing issues, reference and follow these command templates to ensure consistent execution patterns.
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### Key File Structure
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```

‎.ai/steering/structure.md‎

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# Project Structure
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## Root Directory Layout
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```
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pymoo/ # Main package directory
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├── algorithms/ # Optimization algorithms (MOO, SOO)
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├── core/ # Core framework classes and interfaces
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├── problems/ # Test problems and problem definitions
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├── operators/ # Genetic operators (crossover, mutation, selection)
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├── indicators/ # Performance metrics and indicators
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├── visualization/ # Plotting and visualization tools
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├── gradient/ # Gradient computation utilities
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├── constraints/ # Constraint handling mechanisms
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├── termination/ # Termination criteria
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├── util/ # Utility functions and helpers
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└── functions/ # Compiled performance-critical functions
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examples/ # Usage examples and tutorials
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tests/ # Test suite
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docs/ # Documentation source
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benchmark/ # Performance benchmarking scripts
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```
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## Core Architecture Patterns
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### Problem Definition
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- Base class: `pymoo.core.problem.Problem`
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- Elementwise problems: `ElementwiseProblem` for single-point evaluation
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- Meta problems: `MetaProblem` for problem composition
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- All problems define `n_var`, `n_obj`, `n_constr`, `xl`, `xu` attributes
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### Algorithm Structure
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- Base class: `pymoo.core.algorithm.Algorithm`
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- Algorithms follow ask-and-tell pattern
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- Modular design with interchangeable operators
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- Support for both population-based and single-point methods
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### Operator Pattern
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- All operators inherit from `pymoo.core.operator.Operator`
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- Crossover, mutation, selection, sampling operators
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- Configurable and composable design
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### Data Structures
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- `Population`: Collection of individuals
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- `Individual`: Single solution with variables, objectives, constraints
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- `Result`: Optimization result container
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## File Organization Conventions
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### Algorithm Files
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- Multi-objective: `pymoo/algorithms/moo/`
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- Single-objective: `pymoo/algorithms/soo/`
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- Each algorithm in separate module with descriptive name
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### Problem Files
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- Multi-objective: `pymoo/problems/multi/`
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- Single-objective: `pymoo/problems/single/`
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- Many-objective: `pymoo/problems/many/`
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- Dynamic problems: `pymoo/problems/dynamic/`
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### Example Structure
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- Organized by category in `examples/` subdirectories
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- Each example is self-contained and executable
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- Integration tests run examples as validation
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### Test Organization
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- Mirror package structure in `tests/`
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- Separate test categories using pytest markers
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- Performance tests in `benchmark/` directory
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## Import Conventions
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- Main API accessible via `from pymoo.optimize import minimize`
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- Algorithm imports: `from pymoo.algorithms.moo.nsga2 import NSGA2`
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- Problem imports: `from pymoo.problems import get_problem`
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- Utility imports follow full module path

‎.ai/steering/tech.md‎

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# Technology Stack
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## Core Technologies
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- **Python**: 3.9+ (supports 3.9, 3.10, 3.11, 3.12, 3.13)
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- **Build System**: setuptools with Cython compilation
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- **Package Management**: pip/PyPI distribution
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## Key Dependencies
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- **numpy** (>=1.19.3): Core numerical operations
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- **scipy** (>=1.1): Scientific computing algorithms
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- **matplotlib** (>=3): Visualization and plotting
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- **autograd** (>=1.4): Automatic differentiation
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- **cma** (>=3.2.2): Covariance Matrix Adaptation
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- **moocore** (>=0.1.7): Multi-objective optimization core utilities
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- **Cython**: Performance-critical compiled extensions
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## Optional Dependencies
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- **Parallelization**: joblib, dask, ray
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- **Development**: pytest, jupyter, pandas, numba
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- **Optimization**: optuna
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## Build Commands
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### Installation
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```bash
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# Standard installation
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pip install -U pymoo
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# Development installation
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git clone https://github.com/anyoptimization/pymoo
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cd pymoo
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pip install .
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# With optional dependencies
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pip install pymoo[full] # All features
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pip install pymoo[parallelization] # Parallel computing
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pip install pymoo[visualization] # Enhanced plotting
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```
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### Development Commands
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```bash
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# Clean build artifacts
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make clean
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# Clean compiled extensions
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make clean-ext
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# Compile Cython extensions
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make compile
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python setup.py build_ext --inplace
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# Create distribution
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make dist
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# Install from source
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make install
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```
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### Testing
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```bash
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# Run all tests
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pytest
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# Run specific test categories
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pytest -m "not long" # Skip long-running tests
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pytest -m examples # Run example integration tests
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pytest -m gradient # Run gradient computation tests
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```
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### Verification
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```bash
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# Check if compiled extensions are working
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python -c "from pymoo.functions import is_compiled;print('Compiled Extensions: ', is_compiled())"
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```

‎docs/Makefile‎

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# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

‎docs/serve.sh‎

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python3 -m http.server --directory build/html
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python3 -m http.server --directory build/html 8080
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‎docs/source/_theme/newsletter.html‎

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<div class="mc-field-group">
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<label for="mce-EMAIL">Email Address <span class="asterisk">*</span>
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</label>
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<input type="email" value="" name="EMAIL" class="required email" id="mce-EMAIL">
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<input type="email" value="" name="EMAIL" class="required email" id="mce-EMAIL" autocomplete="email">
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</div>
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<div class="mc-field-group">
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<label for="mce-FNAME">First Name </label>

‎docs/source/_theme/static/sphinx-book-theme.7d483ff0a819d6edff12ce0b1ead3928.js‎

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}
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}
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var scrollToActive = () => {
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var navbar = document.getElementById('site-navigation')
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var navbar = document.getElementById('bd-docs-nav')
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if (!navbar) return;
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var active_pages = navbar.querySelectorAll(".active")
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var active_page = active_pages[active_pages.length - 1]
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if (active_page !== undefined && active_page.offsetTop > ($(window).height() * .5)) {

‎docs/source/conf.py‎

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copybutton_remove_prompts = True
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copybutton_copy_empty_lines = False
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# ===========================================================================
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# nbsphinx
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# ===========================================================================
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# Disable RequireJS to avoid conflicts with clipboard.min.js
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nbsphinx_requirejs_path = ""
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