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Overview

The evaluator module provides the Evaluator class and handler classes for managing task evaluation and output. Handlers specify what to compute and when, while the evaluator coordinates the actual computation.

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Evaluator

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Evaluator Class

Evaluator(dist, vars)
Coordinates evaluation of expressions through various handlers.

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Parameters

  • dist (Distributor): Problem distributor
  • vars (dict): Variables for parsing task strings

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Methods

add_file_handler(filename, …) - Create file output handler 
handler = evaluator.add_file_handler('snapshots', sim_dt=0.1)
add_dictionary_handler(…) - Create dictionary handler 
handler = evaluator.add_dictionary_handler(iter=10)
add_system_handler(…) - Create system handler 
handler = evaluator.add_system_handler(iter=1)
**evaluate_scheduled(kw) - Evaluate all scheduled handlers 
evaluator.evaluate_scheduled(
    iteration=solver.iteration,  
    sim_time=solver.sim_time,
    wall_time=time.time(),
    timestep=dt
)
**evaluate_group(group, kw) - Evaluate specific group 
evaluator.evaluate_group('analysis')

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Handlers

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Handler Base Class

All handlers share common parameters:

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Scheduling Parameters

  • sim_dt (float, optional): Simulation time cadence
  • wall_dt (float, optional): Wall time cadence (seconds)
  • iter (int, optional): Iteration cadence
  • custom_schedule (function, optional): Custom scheduling function
  • group (str, optional): Handler group name

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Methods

add_task(task, layout=‘g’, name=None, scales=None) - Add a task to evaluate 
handler.add_task("u", name='velocity')
handler.add_task("dx(u)", layout='g', scales=2)
add_tasks(tasks, …) - Add multiple tasks 
handler.add_tasks(["u", "v", "T"])

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FileHandler

add_file_handler(filename, max_writes=None, mode='overwrite', parallel='gather', **kw)
Handler that writes tasks to HDF5 files.

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Parameters

  • filename (str): Base filename/path for output
  • max_writes (int, optional): Maximum writes per file (default: unlimited)
  • mode (str, optional): ‘overwrite’ or ‘append’ (default: ‘overwrite’)
  • parallel (str, optional): ‘gather’, ‘virtual’, or ‘mpio’ (default: ‘gather’)
  • Scheduling parameters (sim_dt, wall_dt, iter, etc.)

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Example: Basic File Output

import dedalus.public as d3
import time

# Problem setup
solver = problem.build_solver(d3.RK222)

# Create file handler
snapshots = solver.evaluator.add_file_handler(
    'snapshots',  
    sim_dt=0.1,
    max_writes=50
)

# Add tasks
snapshots.add_task("u", name='velocity')
snapshots.add_task("T", name='temperature')  
snapshots.add_task("div(u)", name='divergence')

# Timestepping (file handler is called automatically)
dt = 0.001
while solver.proceed:
    solver.step(dt)

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Example: Multiple Handlers

import dedalus.public as d3

# Snapshots every 0.1 sim time
snapshots = solver.evaluator.add_file_handler(
    'snapshots',
    sim_dt=0.1,  
    max_writes=50
)
snapshots.add_task("u")
snapshots.add_task("T")

# Analysis outputs every 0.01 sim time
analysis = solver.evaluator.add_file_handler(
    'analysis',  
    sim_dt=0.01
)
analysis.add_task("sqrt(u@u)", name='speed')
analysis.add_task("dx(u) - dy(v)", name='vorticity')

# Checkpoints every 1.0 wall time hours  
checkpoint = solver.evaluator.add_file_handler(
    'checkpoint',
    wall_dt=3600,
    max_writes=1,
    mode='overwrite'
)  
checkpoint.add_task("u")
checkpoint.add_task("T")

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Parallel Output Methods

‘gather’ (default): Gather to process 0, write single file
  • Simple, portable
  • Memory intensive for large problems
‘virtual’: Write per-process files, create virtual HDF5 dataset
  • Scalable
  • Requires HDF5 1.10+
‘mpio’: Parallel MPI-IO to single file
  • Scalable
  • Requires parallel HDF5 build
# Use virtual datasets for large parallel runs
handler = solver.evaluator.add_file_handler(
    'data',
    parallel='virtual',
    sim_dt=0.1
)

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DictionaryHandler

add_dictionary_handler(**kw)
Handler that stores outputs in a dictionary for immediate access.

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Example

import dedalus.public as d3

# Create dictionary handler  
analysis = solver.evaluator.add_dictionary_handler(iter=10)
analysis.add_task("sqrt(u@u)", name='speed')
analysis.add_task("integ(T)", name='avg_temp')

# Timestepping
while solver.proceed:
    solver.step(dt)  
    
    if solver.iteration % 10 == 0:
        # Access evaluated tasks
        speed = analysis['speed']
        avg_T = analysis['avg_temp']
        print(f"Max speed: {np.max(speed['g'])}")
        print(f"Avg temp: {avg_T['g'].flat[0]}")

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SystemHandler

add_system_handler(**kw)
Handler for internal system use (e.g., RHS evaluation in solvers).

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Example

# Typically used internally by solvers
F_handler = solver.evaluator.add_system_handler(iter=1, group='F')
for eq in problem.eqs:
    F_handler.add_task(eq['F'])
F_handler.build_system()

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Task Specification

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String Expressions

Most flexible - parsed using problem namespace: 
handler.add_task("u", name='velocity')  
handler.add_task("dx(u)", name='du_dx')
handler.add_task("sqrt(u@u + v@v)", name='speed')
handler.add_task("integ(T, 'x')/Lx", name='x_average')

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Field Objects

Direct field references: 
handler.add_task(u, name='velocity')

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Operator Expressions

Pre-built expressions: 
import dedalus.public as d3

ω = d3.curl(u)
handler.add_task(ω, name='vorticity')

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Layout and Scales

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Layout Parameter

Specify evaluation layout: 
# Grid space (default)
handler.add_task("u", layout='g')

# Coefficient space
handler.add_task("u", layout='c')

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Scales Parameter

Specify dealiasing scales: 
# Default (1x)
handler.add_task("u")

# 2x dealiasing
handler.add_task("u", scales=2)  

# Per-axis scales
handler.add_task("u", scales=(1, 2, 1))

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Custom Scheduling

Define custom output schedules: 
import dedalus.public as d3

def custom_schedule(iteration, sim_time, wall_time, timestep):
    # Output on specific iterations
    return iteration in [100, 500, 1000, 5000]

handler = solver.evaluator.add_file_handler(
    'custom',  
    custom_schedule=custom_schedule
)

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Reading Output

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Reading HDF5 Files

import h5py
import numpy as np

# Open output file  
with h5py.File('snapshots/snapshots_s1.h5', 'r') as f:
    # List tasks
    print(list(f['tasks'].keys()))
    
    # Read data
    u = f['tasks']['velocity']  
    t = f['scales']['sim_time']
    
    # Access specific writes
    u_0 = u[0]  # First write
    u_last = u[-1]  # Last write
    
    # Access metadata  
    grid = f['tasks']['velocity'].dims[1][0][:]

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Post-processing Script

import h5py
import numpy as np
import matplotlib.pyplot as plt

# Merge multiple output files if needed  
from dedalus.tools import post
post.merge_process_files('snapshots', cleanup=True)

# Read merged data
with h5py.File('snapshots/snapshots_s1.h5', 'r') as f:
    u = f['tasks']['velocity'][:]  
    x = f['tasks']['velocity'].dims[1][0][:]
    t = f['scales']['sim_time'][:]
    
    # Make plot
    plt.figure()
    for i in range(0, len(t), 10):
        plt.plot(x, u[i], label=f't={t[i]:.2f}')  
    plt.legend()
    plt.xlabel('x')
    plt.ylabel('u')
    plt.savefig('evolution.png')

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See Also

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