OpenPMD API (openpmd/openpmd-api)

OpenPMD API

https://github.com/openpmd/openpmd-api
Admin
:floppy_disk: C++ & Python API for Scientific I/O

Tokens:81,031
Snippets:697
Trust Score:8.6
Update:3 months ago
Show doc for...
Context Summary (auto-generated)
Raw
# openPMD-api

openPMD-api is a C++ and Python library for scientific I/O that implements the openPMD (Open Standard for Particle-Mesh Data) metadata schema. It provides a unified, portable interface for reading and writing scientific simulation data, particularly particle and mesh data from physics simulations. The library supports multiple I/O backends including HDF5, ADIOS2, and JSON/TOML, enabling both serial and MPI-parallel workflows across different platforms.

The core functionality centers around the `Series` class, which represents an openPMD data file or collection of files. Each Series contains `Iteration` objects representing time steps, with each Iteration containing `meshes` (field data on grids) and `particles` (particle species data). The library provides both random-access and streaming/linear access patterns, making it suitable for both post-processing analysis and in-situ data processing in running simulations.

## Core API

### Series - Opening Data Files

The Series class is the entry point for all openPMD I/O operations. It manages file handles, iterations, and backend configuration.

```cpp
#include <openPMD/openPMD.hpp>

// Open a series for reading
openPMD::Series read_series("data%T.h5", openPMD::Access::READ_ONLY);
std::cout << "openPMD version: " << read_series.openPMD() << "\n";
std::cout << "Number of iterations: " << read_series.snapshots().size() << "\n";

// Open a series for writing with linear access (streaming-compatible)
openPMD::Series write_series("output.bp", openPMD::Access::CREATE_LINEAR);
write_series.setAuthor("My Simulation");
write_series.setSoftware("MyCode", "1.0.0");

// Open with JSON/TOML configuration
openPMD::Series configured_series(
    "data.h5",
    openPMD::Access::READ_ONLY,
    R"({"defer_iteration_parsing": true})"
);

// MPI-parallel opening
// MPI_Comm comm = MPI_COMM_WORLD;
// openPMD::Series parallel_series("data.bp", openPMD::Access::CREATE_LINEAR, comm);
```

### Series (Python)

Python bindings provide an equivalent interface with Pythonic conventions.

```python
import openpmd_api as io

# Open series for reading
series = io.Series("data%T.h5", io.Access.read_only)
print(f"openPMD version: {series.openPMD}")
print(f"Iterations: {list(series.snapshots().keys())}")

# Open for writing
series = io.Series("output.h5", io.Access.create_linear)
series.set_author("My Simulation")
series.set_software("MyCode", "1.0.0")

# With deferred parsing for large datasets
series = io.Series("data%T.h5", io.Access.read_only, {
    "defer_iteration_parsing": True
})

# Always close when done
series.close()
```

### Snapshots/Iterations - Accessing Time Steps

Snapshots (iterations) represent discrete time steps in a simulation. Use `series.snapshots()` to access them.

```cpp
#include <openPMD/openPMD.hpp>

openPMD::Series series("data%T.h5", openPMD::Access::READ_ONLY);

// Iterate over all iterations
for (auto const& [step, iteration] : series.snapshots()) {
    std::cout << "Iteration " << step << "\n";
    std::cout << "  Time: " << iteration.time<double>() << "\n";
    std::cout << "  Meshes: " << iteration.meshes.size() << "\n";
    std::cout << "  Particle species: " << iteration.particles.size() << "\n";

    // Close iteration to free resources (important for streaming)
    iteration.close();
}

// Access specific iteration
auto& iteration_100 = series.snapshots()[100];
iteration_100.open();  // Required with defer_iteration_parsing
// ... work with data ...
iteration_100.close();
```

### Snapshots/Iterations (Python)

```python
import openpmd_api as io

series = io.Series("data%T.h5", io.Access.read_only, {
    "defer_iteration_parsing": True
})

# Iterate over all snapshots
for idx, iteration in series.snapshots().items():
    iteration.open()  # Required with defer_iteration_parsing
    print(f"Iteration {idx}: time={iteration.time}")
    print(f"  Meshes: {list(iteration.meshes.keys())}")
    print(f"  Particles: {list(iteration.particles.keys())}")
    iteration.close()

# Access specific iteration
i = series.snapshots()[100].open()
print(f"Meshes in iteration 100: {list(i.meshes.keys())}")
i.close()

series.close()
```

### Writing Mesh Data

Mesh records store field data on structured grids (e.g., electromagnetic fields, density).

```cpp
#include <openPMD/openPMD.hpp>
#include <vector>
#include <numeric>

openPMD::Series series("mesh_data.h5", openPMD::Access::CREATE_LINEAR);

// Create iteration and mesh
auto& iteration = series.snapshots()[1];
openPMD::Mesh rho = iteration.meshes["rho"];

// Set mesh attributes
rho.setGeometry(openPMD::Mesh::Geometry::cartesian);
rho.setAxisLabels({"x", "y", "z"});
rho.setGridSpacing({1.0, 1.0, 1.0});
rho.setGridGlobalOffset({0.0, 0.0, 0.0});
rho.setGridUnitSI(1.0e-6);  // grid in micrometers

// Prepare data (100x100x50 grid)
std::vector<double> data(100 * 100 * 50);
std::iota(data.begin(), data.end(), 0.0);

// Define dataset and store
openPMD::Dataset dataset(openPMD::determineDatatype<double>(), {100, 100, 50});
rho.resetDataset(dataset);
rho.storeChunk(data, {0, 0, 0}, {100, 100, 50});

iteration.close();
series.close();
```

### Writing Mesh Data (Python)

```python
import numpy as np
import openpmd_api as io

series = io.Series("mesh_data.h5", io.Access.create_linear)

# Create iteration and mesh
iteration = series.snapshots()[1]
rho = iteration.meshes["rho"]

# Prepare data
data = np.arange(100 * 100 * 50, dtype=np.float64).reshape(100, 100, 50)

# Define dataset
dataset = io.Dataset(data.dtype, data.shape)
rho.reset_dataset(dataset)

# Store using slice notation
rho[()] = data  # Store entire array
# Or: rho[0:50, 0:50, :] = data[0:50, 0:50, :]  # Partial store

iteration.close()
series.close()
```

### Reading Mesh Data

Load field data from openPMD files with support for partial reads and lazy loading.

```cpp
#include <openPMD/openPMD.hpp>

openPMD::Series series("data%T.h5", openPMD::Access::READ_ONLY);

auto& iteration = series.snapshots()[100].open();
auto E_x = iteration.meshes["E"]["x"];

// Get metadata
openPMD::Extent extent = E_x.getExtent();
std::cout << "Shape: (" << extent[0] << ", " << extent[1] << ", " << extent[2] << ")\n";
std::cout << "Datatype: " << E_x.getDatatype() << "\n";

// Load full dataset
auto all_data = E_x.loadChunk<double>();
series.flush();  // Execute deferred load
std::cout << "First value: " << all_data.get()[0] << "\n";

// Load partial chunk
openPMD::Offset offset = {10, 10, 0};
openPMD::Extent chunk_extent = {20, 20, 5};
auto chunk = E_x.loadChunk<double>(offset, chunk_extent);
series.flush();
// Access chunk.get()[index]

iteration.close();
series.close();
```

### Reading Mesh Data (Python)

```python
import openpmd_api as io

series = io.Series("data%T.h5", io.Access.read_only)
iteration = series.snapshots()[100]

E_x = iteration.meshes["E"]["x"]
print(f"Shape: {E_x.shape}")
print(f"Dtype: {E_x.dtype}")

# Load full dataset
all_data = E_x.load_chunk()
series.flush()
print(f"Data shape: {all_data.shape}")
print(f"First 5 values: {all_data.flat[:5]}")

# Load partial chunk using slice notation
chunk = E_x[10:30, 10:30, 0:5]
series.flush()
print(f"Chunk shape: {chunk.shape}")

iteration.close()
series.close()
```

### Writing Particle Data

Store particle species data including positions, momenta, and custom attributes.

```python
import numpy as np
from openpmd_api import Access, Dataset, Series, Unit_Dimension

series = Series("particles.h5", Access.create)
iteration = series.snapshots()[0]

# Create particle species
electrons = iteration.particles["electrons"]
n_particles = 1000

# Position data
position_x = np.random.rand(n_particles).astype(np.float32)
position_y = np.random.rand(n_particles).astype(np.float32)
position_z = np.random.rand(n_particles).astype(np.float32)

dataset = Dataset(position_x.dtype, position_x.shape)

# Set up position components
for dim, data in [("x", position_x), ("y", position_y), ("z", position_z)]:
    electrons["position"][dim].reset_dataset(dataset)
    electrons["position"][dim].store_chunk(data)

# Position offset (required by openPMD)
offset_data = np.arange(n_particles, dtype=np.uint64)
offset_dataset = Dataset(offset_data.dtype, offset_data.shape)
for dim in ["x", "y", "z"]:
    electrons["positionOffset"][dim].reset_dataset(offset_dataset)
    electrons["positionOffset"][dim].store_chunk(offset_data)

# Weighting (constant value for all particles)
electrons["weighting"] \
    .reset_dataset(Dataset(np.dtype("float32"), [n_particles])) \
    .make_constant(1.0e-15)

# Custom record with unit dimension
electrons["displacement"].unit_dimension = {Unit_Dimension.M: 1}
electrons["displacement"].unit_SI = 1.e-6
electrons["displacement"].reset_dataset(Dataset(np.dtype("float64"), [n_particles]))
electrons["displacement"].make_constant(42.43)

iteration.close()
series.close()
```

### Reading Particle Data

Load particle data with support for pandas/dask DataFrames.

```python
import openpmd_api as io

series = io.Series("data%T.h5", io.Access.read_only)
iteration = series.snapshots()[100]

electrons = iteration.particles["electrons"]

# Load specific component
charge = electrons["charge"].load_chunk()
series.flush()
print(f"First 5 charges: {charge[:5]}")

# Load position data
pos_x = electrons["position"]["x"].load_chunk()
pos_y = electrons["position"]["y"].load_chunk()
momentum_z = electrons["momentum"]["z"].load_chunk()
series.flush()

# Convert to pandas DataFrame (requires pandas)
df = electrons.to_df()
print(df.head())
print(df.columns)

# With custom attributes
df = electrons.to_df(attributes=["particleShape"])

# Load only first 100 particles
import numpy as np
df = electrons.to_df(slice=np.s_[:100])

# Convert all iterations to DataFrame
df_all = series.to_df("electrons", attributes=["particleShape"])

iteration.close()
series.close()
```

### Parallel I/O with MPI

Write and read data in parallel using MPI domain decomposition.

```cpp
#include <openPMD/openPMD.hpp>
#include <mpi.h>
#include <vector>

int main(int argc, char *argv[]) {
    MPI_Init(&argc, &argv);

    int mpi_size, mpi_rank;
    MPI_Comm_size(MPI_COMM_WORLD, &mpi_size);
    MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);

    // Each rank writes a 10x300 slice
    std::vector<float> local_data(10 * 300, float(mpi_rank));

    // Open with MPI communicator
    openPMD::Series series(
        "parallel_data.bp",
        openPMD::Access::CREATE_LINEAR,
        MPI_COMM_WORLD
    );

    series.snapshots()[1].open();
    auto mymesh = series.snapshots()[1].meshes["mymesh"];

    // Global dataset: [mpi_size * 10, 300]
    openPMD::Extent global_extent = {10ul * mpi_size, 300};
    openPMD::Dataset dataset(openPMD::determineDatatype<float>(), global_extent);
    mymesh.resetDataset(dataset);

    // Each rank writes its slice
    openPMD::Offset offset = {10ul * mpi_rank, 0};
    openPMD::Extent local_extent = {10, 300};
    mymesh.storeChunk(local_data, offset, local_extent);

    series.snapshots()[1].close();
    series.close();

    MPI_Finalize();
    return 0;
}
```

### Parallel I/O with MPI (Python)

```python
from mpi4py import MPI
import numpy as np
import openpmd_api as io

comm = MPI.COMM_WORLD
mpi_size = comm.Get_size()
mpi_rank = comm.Get_rank()

# Local data for this rank
local_data = np.full((10, 300), float(mpi_rank), dtype=np.float32)

# Open with MPI communicator
series = io.Series("parallel_data.bp", io.Access.create_linear, comm)

iteration = series.snapshots()[1]
mymesh = iteration.meshes["mymesh"]

# Global dataset definition
global_extent = [10 * mpi_size, 300]
dataset = io.Dataset(local_data.dtype, global_extent)
mymesh.reset_dataset(dataset)

# Each rank writes its portion
offset = [10 * mpi_rank, 0]
mymesh.store_chunk(local_data, offset, local_data.shape)

iteration.close()
series.close()
```

### Streaming I/O

Use ADIOS2 SST engine for producer-consumer streaming workflows.

```cpp
// Writer (producer)
#include <openPMD/openPMD.hpp>

openPMD::Series series("stream.sst", openPMD::Access::CREATE_LINEAR, R"({
    "adios2": {
        "engine": {
            "parameters": {
                "DataTransport": "WAN"
            }
        }
    }
})");

for (size_t i = 0; i < 100; ++i) {
    auto iteration = series.snapshots()[i];
    auto positions = iteration.particles["e"]["position"];

    std::vector<double> data(1000);
    std::iota(data.begin(), data.end(), i * 1000.0);

    openPMD::Dataset dataset(openPMD::Datatype::DOUBLE, {1000});
    for (auto dim : {"x", "y", "z"}) {
        positions[dim].resetDataset(dataset);
        positions[dim].storeChunk(data, {0}, {1000});
    }
    iteration.close();  // Publishes this step
}
series.close();
```

### Streaming I/O (Python Reader)

```python
import openpmd_api as io

config = {
    'adios2': {
        'engine': {
            'parameters': {
                'DataTransport': 'WAN'
            }
        }
    }
}

series = io.Series("stream.sst", io.Access_Type.read_linear, config)

# Iterate through streamed iterations
for idx, iteration in series.snapshots().items():
    print(f"Received iteration {idx}")

    positions = iteration.particles["e"]["position"]
    for dim in ["x", "y", "z"]:
        data = positions[dim].load_chunk()

    iteration.close()  # Releases step, advances to next

series.close()
```

### Compression Configuration

Configure compression for HDF5 and ADIOS2 backends using JSON/TOML.

```cpp
#include <openPMD/openPMD.hpp>

// ADIOS2 with Blosc compression
std::string adios2_config = R"(
    backend = "adios2"

    [[adios2.dataset.operators]]
    type = "blosc"
    parameters.doshuffle = "BLOSC_BITSHUFFLE"
    parameters.clevel = 5
)";

openPMD::Series series_bp("compressed.bp", openPMD::Access::CREATE_LINEAR, adios2_config);

// HDF5 with zlib compression
std::string hdf5_config = R"(
    backend = "hdf5"

    [hdf5.dataset]
    chunks = "auto"

    [hdf5.dataset.permanent_filters]
    type = "zlib"
    aggression = 5
)";

openPMD::Series series_h5("compressed.h5", openPMD::Access::CREATE_LINEAR, hdf5_config);

// Dataset-specific compression (meshes vs particles)
std::string mixed_config = R"(
    backend = "adios2"

    [[adios2.dataset]]
    select = "meshes/.*"
    [adios2.dataset.cfg.operators]
    type = "blosc"
    parameters.clevel = 9

    [[adios2.dataset]]
    select = "particles/.*"
    [adios2.dataset.cfg.operators]
    type = "bzip2"
    parameters.clevel = 5
)";
```

### Dask Integration

Use Dask for parallel, out-of-core analysis of large datasets.

```python
import openpmd_api as io
import dask.array as da

series = io.Series("data%T.h5", io.Access.read_only)
iteration = series.snapshots()[400]

# Convert mesh to dask array
E = iteration.meshes["E"]
E_x = E["x"].to_dask_array()
E_y = E["y"].to_dask_array()
E_z = E["z"].to_dask_array()

# Lazy computation of field intensity
intensity = E_x**2 + E_y**2 + E_z**2
max_intensity = intensity.max().compute()  # Triggers computation
print(f"Maximum intensity: {max_intensity}")

# Find location of maximum
idx_max = da.argwhere(intensity == max_intensity).compute()[0]
print(f"Location of maximum: {idx_max}")

# Convert particles to dask DataFrame
electrons = iteration.particles["electrons"]
df = electrons.to_dask(attributes=["particleShape"])

# Compute mean momentum
mean_momentum_z = df["momentum_z"].mean().compute()
print(f"Mean momentum_z: {mean_momentum_z}")

# Histogram computation
h, bins = da.histogram(
    df["momentum_z"].to_dask_array(),
    bins=50,
    range=[-8.0e-23, 8.0e-23],
    weights=df["weighting"].to_dask_array()
)
histogram = h.compute()

series.close()
```

### Span API for Zero-Copy Writing

Use the Span API to write directly into backend buffers, avoiding memory copies.

```cpp
#include <openPMD/openPMD.hpp>
#include <numeric>

openPMD::Series series("span_write.bp", openPMD::Access::CREATE_LINEAR);

for (size_t i = 0; i < 10; ++i) {
    auto& iteration = series.snapshots()[i];
    auto& E = iteration.meshes["E"];

    for (auto dim : {"x", "y"}) {
        auto& component = E[dim];
        component.resetDataset({openPMD::Datatype::FLOAT, {100, 100}});

        // Get a view into the backend buffer
        auto buffer_view = component.storeChunk<float>({0, 0}, {100, 100})
                                    .currentBuffer();

        // Fill the buffer directly
        std::iota(buffer_view.begin(), buffer_view.end(), float(i * 10000));
    }

    iteration.close();
}
series.close();
```

## Summary

openPMD-api provides a comprehensive solution for scientific data I/O in high-performance computing environments. The primary use cases include storing simulation output from particle-in-cell codes, plasma physics simulations, and other mesh/particle-based scientific applications. The library excels at handling large-scale datasets through MPI-parallel I/O, supports both HDF5 for archival storage and ADIOS2 for streaming workflows, and integrates seamlessly with Python data science tools like pandas and dask for analysis.

The typical integration pattern involves creating a `Series` object at simulation startup, writing `Iteration` objects at each time step containing `meshes` (field data) and `particles` (particle species), and closing the series at simulation end. For analysis, the pattern is reversed: open an existing series, iterate through snapshots, load chunks of data as needed, and perform computations. The deferred I/O model (where data operations are queued and executed on `flush()`) enables optimizations like batching operations and provides a consistent interface across different backends. For streaming scenarios, the linear access pattern with `CREATE_LINEAR`/`READ_LINEAR` modes ensures proper synchronization between data producers and consumers.