.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_03_generating_data.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_03_generating_data.py: =============================== 03 Generating Data with PyCFAST =============================== This example demonstrates how to use PyCFAST with NumPy to generate simulation data by running multiple CFAST fire simulations with varying parameters. We'll create a simple parametric study by varying fire characteristics and analyze the trends in the results. .. GENERATED FROM PYTHON SOURCE LINES 12-20 Step 1: Import Libraries ------------------------- We'll import: - **NumPy**: For generating parameter ranges and arrays - **Pandas**: For organizing and analyzing simulation results - **Matplotlib**: For visualizing the generated data - **PyCFAST**: For parsing and running CFAST models (see :func:`~pycfast.parsers.parse_cfast_file` and :meth:`~pycfast.CFASTModel.run`) .. GENERATED FROM PYTHON SOURCE LINES 20-28 .. code-block:: Python import os import matplotlib.pyplot as plt import numpy as np from pycfast.parsers import parse_cfast_file .. GENERATED FROM PYTHON SOURCE LINES 29-34 Step 2: Load Base Model ----------------------- We start with an existing CFAST model as our template. We'll use `USN_Hawaii_Test_03.in `_. This model serves as the foundation that we'll modify systematically to generate our dataset. .. GENERATED FROM PYTHON SOURCE LINES 34-37 .. code-block:: Python model = parse_cfast_file("data/USN_Hawaii_Test_03.in") .. GENERATED FROM PYTHON SOURCE LINES 38-39 The parsed model is displayed below. .. GENERATED FROM PYTHON SOURCE LINES 39-42 .. code-block:: Python model .. raw:: html

🔥

CFAST Model

USN_Hawaii_Test_03_parsed.in

Total Components: 5

Simulation: HawaiiTest 3

Duration: 10 min

Compartments (1)

Bay 1: 97.6×74.0×14.8 m

Fires (1)

Hawaii_03 in Bay 1: Hawaii_03_Fire

Devices (1)

Targ 1 in Bay 1: PLATE

Materials (2)

CONCRETE: Concrete Normal Weight (6 in)
STEELSHT: Steel Plain Carbon (1/16 in)

.. GENERATED FROM PYTHON SOURCE LINES 43-50 Step 3: Generate Parameter Combinations ---------------------------------------- We use NumPy to create systematic parameter variations. For this study, we'll vary two key fire parameters: - **Heat of combustion**: Energy released per unit mass of fuel (affects fire intensity) - **Radiative fraction**: Portion of fire energy released as radiation (affects heat transfer) .. GENERATED FROM PYTHON SOURCE LINES 50-68 .. code-block:: Python n_samples = 10 heat_of_combustion_values = np.linspace(15, 5000, n_samples) # MJ/kg radiative_fraction_values = np.linspace(0.1, 0.9, n_samples) # Fraction parameter_combinations = list( zip(heat_of_combustion_values, radiative_fraction_values, strict=False) ) print(f"Generated {len(parameter_combinations)} parameter combinations") print( f"Heat of combustion range: {heat_of_combustion_values[0]:.2f} - {heat_of_combustion_values[-1]:.2f} MJ/kg" ) print( f"Radiative fraction range: {radiative_fraction_values[0]:.2f} - {radiative_fraction_values[-1]:.2f}" ) .. rst-class:: sphx-glr-script-out .. code-block:: none Generated 10 parameter combinations Heat of combustion range: 15.00 - 5000.00 MJ/kg Radiative fraction range: 0.10 - 0.90 .. GENERATED FROM PYTHON SOURCE LINES 69-75 Step 4: Run Parametric Study ---------------------------- Now we'll systematically modify the model parameters and run simulations to generate our dataset. This creates a structured dataset linking input parameters to simulation outputs. .. GENERATED FROM PYTHON SOURCE LINES 75-99 .. code-block:: Python all_runs = [] for i, (hoc, rf) in enumerate(parameter_combinations): print( f"Running simulation {i + 1}/{len(parameter_combinations)}: hoc={hoc}, rf={rf}" ) # Update fire parameters using :meth:`~pycfast.CFASTModel.update_fire_params` temp_model = model.update_fire_params( fire="Hawaii_03_Fire", heat_of_combustion=hoc, radiative_fraction=rf ) # Run the simulation with :meth:`~pycfast.CFASTModel.run` and save the output outputs = temp_model.run(file_name=f"data_gen_sim_{i:03d}.in") all_runs.append( { "simulation_id": i, "hoc": hoc, # heat of combustion "rf": rf, # radiative fraction "outputs": outputs, } ) .. rst-class:: sphx-glr-script-out .. code-block:: none Running simulation 1/10: hoc=15.0, rf=0.1 Running simulation 2/10: hoc=568.8888888888889, rf=0.18888888888888888 Running simulation 3/10: hoc=1122.7777777777778, rf=0.2777777777777778 Running simulation 4/10: hoc=1676.6666666666667, rf=0.3666666666666667 Running simulation 5/10: hoc=2230.5555555555557, rf=0.4555555555555556 Running simulation 6/10: hoc=2784.4444444444443, rf=0.5444444444444445 Running simulation 7/10: hoc=3338.3333333333335, rf=0.6333333333333333 Running simulation 8/10: hoc=3892.2222222222226, rf=0.7222222222222222 Running simulation 9/10: hoc=4446.111111111111, rf=0.8111111111111111 Running simulation 10/10: hoc=5000.0, rf=0.9 .. GENERATED FROM PYTHON SOURCE LINES 100-107 Step 5: Analyze Generated Data ------------------------------- Now we visualize our generated dataset to understand how the parameter variations affect fire behavior. Each line represents a different combination of heat of combustion and radiative fraction values. .. GENERATED FROM PYTHON SOURCE LINES 107-145 .. code-block:: Python plt.figure(figsize=(12, 7)) colors = plt.get_cmap("viridis")(np.linspace(0, 1, len(all_runs))) for idx, run in enumerate(all_runs): upper_layer = run["outputs"]["compartments"]["ULT_1"] time = run["outputs"]["compartments"]["Time"] plt.plot( time, upper_layer, label=(f"$H_c$={run['hoc']:.0f} MJ/kg, $f_{{rad}}$={run['rf']:.2f}"), linewidth=2, alpha=0.85, color=colors[idx], ) plt.xlabel("Time (s)", fontsize=16, fontweight="bold") plt.ylabel("Upper Layer Temperature (°C)", fontsize=16, fontweight="bold") plt.title( "Upper Layer Temperature for Different Fire Parameters", fontsize=18, fontweight="bold", ) plt.legend( loc="upper left", fontsize=12, frameon=False, ncol=2, title="Simulations", title_fontsize=14, ) plt.grid(True, which="both", linestyle="--", linewidth=0.7, alpha=0.5) plt.minorticks_on() plt.tick_params(axis="both", which="major", labelsize=14, length=7, width=2) plt.tick_params(axis="both", which="minor", labelsize=12, length=4, width=1) plt.tight_layout() plt.show() .. image-sg:: /auto_examples/images/sphx_glr_plot_03_generating_data_001.png :alt: Upper Layer Temperature for Different Fire Parameters :srcset: /auto_examples/images/sphx_glr_plot_03_generating_data_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 146-149 Cleanup ------- CFAST generates multiple output files during each simulation run. .. GENERATED FROM PYTHON SOURCE LINES 149-160 .. code-block:: Python files_removed = 0 for fname in os.listdir("."): if fname.startswith("data_gen_sim_"): try: os.remove(fname) files_removed += 1 except Exception as e: print(f"Could not remove {fname}: {e}") print(f"Cleanup complete. Removed {files_removed} simulation files.") .. rst-class:: sphx-glr-script-out .. code-block:: none Cleanup complete. Removed 120 simulation files. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.920 seconds) .. _sphx_glr_download_auto_examples_plot_03_generating_data.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_03_generating_data.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_03_generating_data.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_03_generating_data.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_