gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/fluidsim/references/output_analysis.md

Output and Analysis

Output Types

FluidSim automatically saves several types of output during simulations.

Physical Fields

File format: HDF5 (.h5)

Location: simulation_dir/state_phys_t*.h5

Contents: Velocity, vorticity, and other physical space fields at specific times

Access:

sim.output.phys_fields.plot()
sim.output.phys_fields.plot("vorticity")
sim.output.phys_fields.plot("vx")
sim.output.phys_fields.plot("div")  # check divergence

# Save manually
sim.output.phys_fields.save()

# Get data
vorticity = sim.state.state_phys.get_var("rot")

Spatial Means

File format: Text file (.txt)

Location: simulation_dir/spatial_means.txt

Contents: Volume-averaged quantities vs time (energy, enstrophy, etc.)

Access:

sim.output.spatial_means.plot()

# Load from file
from fluidsim import load_sim_for_plot
sim = load_sim_for_plot("simulation_dir")
sim.output.spatial_means.load()
spatial_means_data = sim.output.spatial_means

Spectra

File format: HDF5 (.h5)

Location: simulation_dir/spectra_*.h5

Contents: Energy and enstrophy spectra vs wavenumber

Access:

sim.output.spectra.plot1d()  # 1D spectrum
sim.output.spectra.plot2d()  # 2D spectrum

# Load spectra data
spectra = sim.output.spectra.load2d_mean()

Spectral Energy Budget

File format: HDF5 (.h5)

Location: simulation_dir/spect_energy_budg_*.h5

Contents: Energy transfer between scales

Access:

sim.output.spect_energy_budg.plot()

Post-Processing

Loading Simulations for Analysis

Fast Loading (Read-Only)

from fluidsim import load_sim_for_plot

sim = load_sim_for_plot("simulation_dir")

# Access all output types
sim.output.phys_fields.plot()
sim.output.spatial_means.plot()
sim.output.spectra.plot1d()

Use this for quick visualization and analysis. Does not initialize full simulation state.

Full State Loading

from fluidsim import load_state_phys_file

sim = load_state_phys_file("simulation_dir/state_phys_t10.000.h5")

# Can continue simulation
sim.time_stepping.start()

Visualization Tools

Built-in Plotting

FluidSim provides basic plotting through matplotlib:

# Physical fields
sim.output.phys_fields.plot("vorticity")
sim.output.phys_fields.animate("vorticity")

# Time series
sim.output.spatial_means.plot()

# Spectra
sim.output.spectra.plot1d()

Advanced Visualization

For publication-quality or 3D visualization:

ParaView: Open .h5 files directly

paraview simulation_dir/state_phys_t*.h5

VisIt: Similar to ParaView for large datasets

Custom Python:

import h5py
import matplotlib.pyplot as plt

# Load field manually
with h5py.File("state_phys_t10.000.h5", "r") as f:
    vx = f["state_phys"]["vx"][:]
    vy = f["state_phys"]["vy"][:]

# Custom plotting
plt.contourf(vx)
plt.show()

Analysis Examples

Energy Evolution

from fluidsim import load_sim_for_plot
import matplotlib.pyplot as plt

sim = load_sim_for_plot("simulation_dir")
df = sim.output.spatial_means.load()

plt.figure()
plt.plot(df["t"], df["E"], label="Kinetic Energy")
plt.xlabel("Time")
plt.ylabel("Energy")
plt.legend()
plt.show()

Spectral Analysis

sim = load_sim_for_plot("simulation_dir")

# Plot energy spectrum
sim.output.spectra.plot1d(tmin=5.0, tmax=10.0)  # average over time range

# Get spectral data
k, E_k = sim.output.spectra.load1d_mean(tmin=5.0, tmax=10.0)

# Check for power law
import numpy as np
log_k = np.log(k)
log_E = np.log(E_k)
# fit power law in inertial range

Parametric Study Analysis

When running multiple simulations with different parameters:

import os
import pandas as pd
from fluidsim import load_sim_for_plot

# Collect results from multiple simulations
results = []
for sim_dir in os.listdir("simulations"):
    if not os.path.isdir(f"simulations/{sim_dir}"):
        continue

    sim = load_sim_for_plot(f"simulations/{sim_dir}")

    # Extract key metrics
    df = sim.output.spatial_means.load()
    final_energy = df["E"].iloc[-1]

    # Get parameters
    nu = sim.params.nu_2

    results.append({
        "nu": nu,
        "final_energy": final_energy,
        "sim_dir": sim_dir
    })

# Analyze results
results_df = pd.DataFrame(results)
results_df.plot(x="nu", y="final_energy", logx=True)

Field Manipulation

sim = load_sim_for_plot("simulation_dir")

# Load specific time
sim.output.phys_fields.set_of_phys_files.update_times()
times = sim.output.phys_fields.set_of_phys_files.times

# Load field at specific time
field_file = sim.output.phys_fields.get_field_to_plot(time=5.0)
vorticity = field_file.get_var("rot")

# Compute derived quantities
import numpy as np
vorticity_rms = np.sqrt(np.mean(vorticity**2))
vorticity_max = np.max(np.abs(vorticity))

Output Directory Structure

simulation_dir/
├── params_simul.xml         # Simulation parameters
├── stdout.txt               # Standard output log
├── state_phys_t*.h5         # Physical fields at different times
├── spatial_means.txt        # Time series of spatial averages
├── spectra_*.h5            # Spectral data
├── spect_energy_budg_*.h5  # Energy budget data
└── info_solver.txt         # Solver information

Performance Monitoring

# During simulation, check progress
sim.output.print_stdout.complete_timestep()

# After simulation, review performance
sim.output.print_stdout.plot_deltat()  # plot time step evolution
sim.output.print_stdout.plot_clock_times()  # plot computation time

Data Export

Convert fluidsim output to other formats:

import h5py
import numpy as np

# Export to numpy array
with h5py.File("state_phys_t10.000.h5", "r") as f:
    vx = f["state_phys"]["vx"][:]
    np.save("vx.npy", vx)

# Export to CSV
df = sim.output.spatial_means.load()
df.to_csv("spatial_means.csv", index=False)