165 lines
6.3 KiB
Markdown
165 lines
6.3 KiB
Markdown
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# GridFire Python Usage Guide
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This tutorial walks you through installing GridFire’s Python bindings, choosing engines and views thoughtfully, running a simulation, and visualizing your results.
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---
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## 1. Installation
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### 1.1 PyPI Release
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The quickest way to get started is:
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```bash
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pip install gridfire
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```
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### 1.2 Development from Source
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If you want the cutting-edge features or need to hack the C++ backend:
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```bash
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git clone https://github.com/4DSTAR/GridFire.git
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cd GridFire
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# Create a virtualenv to isolate dependencies
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python3 -m venv .venv && source .venv/bin/activate
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# Install Python bindings (meson-python & pybind11 under the hood)
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pip install .
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```
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You can also build manually with Meson (generally end users will not need to do this):
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```bash
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meson setup build-python
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meson compile -C build_gridfire
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```
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---
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## 2. Why These Engines and Views?
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GridFire’s design [balances physical fidelity and performance](../README.md). Here’s why we pick each component:
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1. **GraphEngine**: Constructs the **full** reaction network from Reaclib rates and composition. Use this when:
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- You need maximum physical accuracy (no reactions are omitted).
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- You are exploring new burning pathways or validating against literature.
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2. **MultiscalePartitioningEngineView**: Implements the Hix & Thielemann partitioning strategy:
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- **Fast reactions** vs **slow reactions** are split onto separate kernels.
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- This reduces stiffness by isolating processes on very different timescales.
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- Choose when your network spans orders of magnitude in timescales (e.g., rapid proton captures vs. slow beta decays).
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3. **AdaptiveEngineView**: Dynamically culls low-flow reactions at runtime:
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- At each timestep, reactions with negligible contribution are temporarily removed.
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- This greatly accelerates large networks without significant loss of accuracy.
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- Ideal for long integrations where the active set evolves over time.
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4. **Leading-Edge Views**:
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- `NetworkPrimingEngineView` to inject seed species and study ignition phenomena.
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- `DefinedEngineView` to freeze the network to a user-specified subset (e.g., focus on the CNO cycle).
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By composing these views in sequence, you can tailor accuracy vs performance for your scientific question. Commonly one might use a flow like **GraphEngine → Partitioning → Adaptive** to capture both full-network physics and manageable stiffness.
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---
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## 3. Step-by-Step Example
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Adapted from [`tests/python/test.py`](../../tests/python/test.py). Comments explain each choice.
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```python
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import matplotlib.pyplot as plt
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from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
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from gridfire.solver import DirectNetworkSolver
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from gridfire.type import NetIn
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from fourdst.composition import Composition
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# 1. Define your composition (e.g., M-dwarf surface mix)
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symbols = ["H-1","He-3","He-4","C-12","N-14","O-16","Ne-20","Mg-24"]
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abundances = [0.708,2.94e-5,0.276,0.003,0.0011,9.62e-3,1.62e-3,5.16e-4]
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comp = Composition()
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comp.registerSymbols(symbols)
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comp.setMassFraction(symbols, abundances)
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comp.finalize(normalize=True) # scale to total mass = 1
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# 2. Prepare the NetIn object
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netIn = NetIn()
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netIn.composition = comp
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netIn.temperature = 1.5e7 # Kelvin
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netIn.density = 1.6e2 # g/cm³
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netIn.tMax = 3.15e7 # seconds to evolve (~1yr)
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netIn.dt0 = 1e-12 # initial timestep
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# 3. Construct the full network engine
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build_depth = 2 # shallow test network for speed; Full = -1
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baseEngine = GraphEngine(comp, buildDepth=build_depth)
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baseEngine.setUseReverseReactions(False) # At these temps we can turn off reverse reactions
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# 4. Partition fast/slow reactions (reduces stiffness)
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partitionedEngine = MultiscalePartitioningEngineView(baseEngine)
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# 5. Adaptively cull negligible flows (improves speed)
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adaptiveEngine = AdaptiveEngineView(partitionedEngine)
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# 6. Choose an ODE solver (implicit Rosenbrock4)
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solver = DirectNetworkSolver(adaptiveEngine, absTol=1e-12, relTol=1e-8)
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# 7. Run the integration
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netOut = solver.evaluate(netIn)
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# 8. Final result:
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print(f"Final H-1 fraction: {netOut.composition.getMassFraction('H-1')}")
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```
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**Why these choices?**
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- **buildDepth=2**: In Emily’s preliminary tests, depth=2 captures key reaction loops without the overhead of a full network.
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- **Partition & Adaptive Views**: Partitioning reduces stiffness between rapid charged-particle captures and slower β-decays; adaptive culling keeps the working set minimal.
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- **Implicit solver**: Rosenbrock4 handles stiff systems robustly, letting you push to longer `tMax`.
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---
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## 4. Visualizing Reaction Networks
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GridFire engines and views provide built-in export methods for Graphviz DOT and CSV formats:
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```python
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# Export the base network to DOT for Graphviz
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baseEngine.exportToDot('network.dot')
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# Optionally generate a PNG (shell):
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# dot -Tpng network.dot -o network.png
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# Export a partitioned view of the network
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partitionedEngine.exportToDot('partitioned.dot')
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# Export to CSV for programmatic analysis
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baseEngine.exportToCSV('network.csv')
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```
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You can then use tools like Graphviz or pandas:
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```bash
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# Convert DOT to PNG
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dot -Tpng network.dot -o network.png
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```
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```python
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import pandas as pd
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# Load and inspect reaction list
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df = pd.read_csv('network.csv')
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print(df.head())
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```
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For time-series data, record intermediates with an observer and save with pandas or numpy:
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```python
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import pandas as pd
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# Build a DataFrame of time vs species fraction
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df = pd.DataFrame({'time': t, 'H-1': X_H1})
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df.to_csv('H1_evolution.csv', index=False)
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```
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Then plot in pandas or Excel for custom figures.
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---
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## 5. Beyond the Basics
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- **Custom Partition Functions**: In Python, subclass `gridfire.partition.PartitionFunction`, override `evaluate`, `supports`, and `clone` to implement new weighting schemes.
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- **Parameter Studies**: Loop over `buildDepth`, solver tolerances, or initial compositions to get a sense of the sensitity of the network to input conditions or build a monte carlo grid.
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- **Error Handling**:
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```python
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try:
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results = solver.evaluate(netIn)
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except GridFireRuntimeError as e:
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print('Fatal engine error:', e)
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except GridFireValueError as e:
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print('Invalid input:', e)
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```
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For full API details, consult the docstrings in `src/python/` and the C++ implementation in `src/lib/`. Enjoy exploring nuclear astrophysics with GridFire!
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