CDFpp (CDF++)

A modern, from-scratch C++20 implementation of NASA's CDF (Common Data Format) with full Python bindings.

Why another CDF library? NASA's official C implementation has no multi-thread support (global shared state), an aging C89 interface, and a license incompatible with most Linux distribution policies. CDFpp solves all three: it is thread-safe, idiomatic C++20, and MIT-licensed.

Highlights

Header-only C++20 library — just add the include path, no linking required
Complete read/write support — CDF versions 2.2 through 3.x, row and column major, compressed files and variables (GZip, RLE)
Python bindings (pycdfpp) via pybind11 — zero-copy NumPy integration, GIL-free I/O
SIMD-accelerated time conversions — AVX512/AVX2/SSE2 runtime dispatch for TT2000, EPOCH, EPOCH16
Lazy loading — variable data is read on first access, not at file open
Fast — up to ~4 GB/s read throughput; SIMD time conversions at up to 14 billion epochs/s
Runs everywhere — Linux, Windows, macOS (x86_64 + ARM64), and WebAssembly (Pyodide / emscripten-forge)
In-browser app — CDFpp Explorer inspects, plots, and ISTP-validates CDF files entirely client-side, no install

Packages & CI

	Linux x86_64	Linux aarch64	Windows x86_64	macOS x86_64	macOS ARM64	WASM (Pyodide)
Wheels
Tests

Also available on emscripten-forge for use in JupyterLite and other Emscripten-based environments.

CDFpp Explorer — CDF files in your browser

CDFpp compiles to WebAssembly, so the whole library runs client-side — no install, no server, and your files never leave your machine. CDFpp Explorer is a small web app built on it:

Inspect — browse variables and attributes grouped by ISTP VAR_TYPE, with live search and a value preview
Plot — line plots and spectrograms, ISTP-aware (DEPEND_0 time axis, DISPLAY_TYPE, SCALETYP, fill/valid masking), with CSV / JSON export
Validate — hand the file to AstraLint for ISTP conformance checking

👉 Open CDFpp Explorer

The WebAssembly wrapper lives in wacdfpp/ and ships TypeScript declarations (wacdfpp/cdfpp.d.ts) for embedding CDF read/write — with zero-copy typed arrays — in your own JavaScript app.

Installing

From PyPI

pip install pycdfpp

From source (C++ library)

meson setup build
ninja -C build
sudo ninja -C build install

From source (Python wheel)

python -m build .
# wheel is in dist/

Quick start (Python)

Reading a CDF file

import pycdfpp

cdf = pycdfpp.load("my_data.cdf")

# Variables expose numpy arrays (zero-copy when possible)
data = cdf["variable_name"].values

# Global attributes
print(cdf.attributes["Project"][0])

# Variable attributes
print(cdf["variable_name"].attributes["UNITS"].value)

# Iterate over all variables
for name, var in cdf.items():
    print(f"{name}: shape={var.shape}, type={var.type}")

Loading from memory

import pycdfpp

# Load from any bytes-like object (useful with HTTP responses, S3, etc.)
with open("my_data.cdf", "rb") as f:
    cdf = pycdfpp.load(f.read())

Time conversions

CDFpp handles all three CDF time types (EPOCH, EPOCH16, TT2000) and converts them to numpy datetime64[ns] or Python datetime:

import pycdfpp
import numpy as np

cdf = pycdfpp.load("my_data.cdf")

# Convert any CDF time variable to numpy datetime64 (fast, ~2ns/element)
times = pycdfpp.to_datetime64(cdf["Epoch"])

# Or to Python datetime objects
times_dt = pycdfpp.to_datetime(cdf["Epoch"])

# Format as strings (e.g. for PDS4 compliance)
time_strings = pycdfpp.to_time_string(cdf["Epoch"], "%Y-%m-%dT%H:%M:%SZ")
# array([b'2020-02-01T00:00:00.000000000Z', ...])

# Convert Python/numpy times to CDF types
tt2000_values = pycdfpp.to_tt2000(np.array(['2020-01-01', '2020-06-15'], dtype='datetime64[ns]'))
epoch_values = pycdfpp.to_epoch(np.array(['2020-01-01', '2020-06-15'], dtype='datetime64[ns]'))

Creating and writing CDF files

import pycdfpp
import numpy as np
from datetime import datetime

cdf = pycdfpp.CDF()

# Add global attributes (each entry is a list of values)
cdf.add_attribute("Project", ["MyMission"])
cdf.add_attribute("PI_name", ["Jane Doe"])

# Add a time variable with TT2000 encoding
times = np.arange('2020-01-01', '2020-01-02', dtype='datetime64[h]').astype('datetime64[ns]')
cdf.add_variable("Epoch", values=times, data_type=pycdfpp.DataType.CDF_TIME_TT2000)

# Add a data variable with variable attributes
cdf.add_variable("B_GSM",
    values=np.random.randn(24, 3).astype(np.float32),
    attributes={
        "FIELDNAM": ["Magnetic Field"],
        "UNITS": ["nT"],
        "DEPEND_0": ["Epoch"],
    })

# Save to disk
pycdfpp.save(cdf, "output.cdf")

# Or save to memory (returns bytes)
data = pycdfpp.save(cdf)

Compressed CDF files

import pycdfpp
import numpy as np

cdf = pycdfpp.CDF()
cdf.add_variable("data", values=np.zeros(10000, dtype=np.float64))

# Whole-file GZip compression
cdf.compression = pycdfpp.CompressionType.gzip_compression
pycdfpp.save(cdf, "compressed.cdf")

# Or per-variable compression
cdf2 = pycdfpp.CDF()
cdf2.add_variable("data",
    values=np.zeros(10000, dtype=np.float64),
    compression=pycdfpp.CompressionType.gzip_compression)

Filtering variables

import pycdfpp

cdf = pycdfpp.load("large_file.cdf")

# Keep only specific variables and attributes (returns a new CDF)
filtered = cdf.filter(variables=["Epoch", "B_GSM"], attributes=["Project"])

# Filter with a regex pattern
filtered = cdf.filter(variables="B_.*", attributes=".*")

# Filter with a callable
filtered = cdf.filter(variables=lambda var: var.name.startswith("B_"))

Cloning variables between CDF files

import pycdfpp

src = pycdfpp.load("source.cdf")
dst = pycdfpp.CDF()

# Clone a variable (deep copy, including its attributes)
dst.add_variable(src["Epoch"])
dst.add_variable(src["B_GSM"])

NumPy buffer protocol

Variables implement the Python buffer protocol, so they work directly with NumPy and any library that accepts array-like objects:

import pycdfpp
import numpy as np

cdf = pycdfpp.load("my_data.cdf")

# Direct numpy array construction (zero-copy for numeric types)
arr = np.array(cdf["B_GSM"])

Quick start (C++)

Reading

#include "cdfpp/cdf-io/cdf-io.hpp"
#include <iostream>

int main()
{
    // cdf::io::load returns std::optional<CDF>
    if (auto cdf = cdf::io::load("my_data.cdf"))
    {
        for (const auto& [name, variable] : cdf->variables)
            std::cout << name << " shape: " << variable.shape() << "\n";

        for (const auto& [name, attribute] : cdf->attributes)
            std::cout << name << "\n";
    }
}

Writing

#include "cdfpp/cdf-io/cdf-io.hpp"

int main()
{
    cdf::CDF my_cdf;

    // Save to file (returns bool)
    cdf::io::save(my_cdf, "output.cdf");

    // Or save to memory (returns a vector<char>)
    auto data = cdf::io::save(my_cdf);
}

Loading from memory

#include "cdfpp/cdf-io/cdf-io.hpp"
#include <vector>

void process(const std::vector<char>& buffer)
{
    if (auto cdf = cdf::io::load(buffer.data(), buffer.size()))
    {
        // ...
    }
}

Benchmarks

All benchmarks measured on a 16-core machine (5.1 GHz boost, 16 MB L3), release build (-O3). Source code in benchmarks/.

SIMD time conversions

Converting CDF time types to nanoseconds since 1970 (epochs/s, higher is better):

Conversion	64	1K	64K	1M	64M
TT2000 scalar	7.8e+08	8.7e+08	8.8e+08	8.6e+08	5.9e+08
TT2000 SIMD	2.5e+09	8.1e+09	5.3e+09	3.4e+09	1.5e+09
EPOCH scalar	2.2e+09	2.3e+09	2.2e+09	2.1e+09	1.1e+09
EPOCH SIMD	9.6e+09	1.4e+10	6.7e+09	3.8e+09	1.5e+09

SIMD vectorized TT2000 conversion peaks at ~8 billion epochs/s for L1/L2-resident data. EPOCH conversion (simpler, no leap-second table) peaks at ~14 billion epochs/s.

Leap-second lookup

Method	1K	64K	1M	64M
Branchless	9.1e+07	9.4e+07	9.5e+07	1.0e+08
Baseline	2.4e+08	2.4e+08	2.4e+08	2.4e+08

RLE compression (bytes/s)

Operation	1 KB	16 KB	64 KB	1 MB
Deflate	7.4e+08	6.9e+08	3.3e+08	2.7e+08
Inflate	1.8e+09	1.7e+09	1.8e+09	4.5e+08
Roundtrip	5.1e+08	5.0e+08	1.9e+08	1.7e+08

RLE inflate sustains ~1.8 GB/s for data that fits in cache.

Features & roadmap

Reading
- CDF versions 2.2 through 3.x
- Compressed files and variables (GZip, RLE)
- Row and column major
- Nested VXRs
- Lazy variable loading
- UTF-8 and ISO 8859-1 (Latin-1, auto-converted to UTF-8)
- In-memory loading (std::vector<char>, char*, Python bytes)
- DEC floating-point encoding (VAX, Alpha, Itanium)
- Pad values
Writing
- Uncompressed and compressed files/variables
- All numeric types, strings, datetime types
- Pad values
General
- libdeflate for faster GZip
- SIMD time conversions (AVX512/AVX2/SSE2 with runtime dispatch)
- Leap-second handling
- Python bindings with GIL-free I/O
- Documentation

Caveats

NRV variables shape: PyCDFpp exposes the record count as the first dimension, so NRV variables will have shape (0, ...) or (1, ...).

Reference invalidation: Python wrappers hold references into C++ containers. Adding or removing variables/attributes can invalidate them. Always re-fetch after mutation:

# UNSAFE - ref may dangle after add_variable
var = cdf["B_GSM"]
cdf.add_variable("new_var", values=np.zeros(10))
var.values  # potential segfault

# SAFE - re-fetch
cdf.add_variable("new_var", values=np.zeros(10))
var = cdf["B_GSM"]

See the full documentation for more details.

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SHA256	`4f098b18370199859ca392aed4a25f1b35d0fdb75c2b46cf318c32df11654f5b`
MD5	`a08a622976715f00e53f9eb1a1d126a7`
BLAKE2b-256	`f2214bf3e45ba39d9cf53326d3656281078b3e74f087e4e24104a827460f8678`

Algorithm	Hash digest
SHA256	`fa38514cdf92484a34f8d449ec15a218283360c176094f809e33856d31abbab0`
MD5	`7c83c634739bf8c4b158071defb4a367`
BLAKE2b-256	`e4a444c21a3d38f95c41e2a49eecf67dbd48503dab988878171b1ea9be13f01b`

Algorithm	Hash digest
SHA256	`758c73d663d21c784195b578f89a3f3b49a6504878047573e6225ddf7ba16d37`
MD5	`fa2c2c5eddd04b4f4f2945a83f83b4a5`
BLAKE2b-256	`8672f907700b8a2dc8bfe6e26ebb5c82828cc74d004992a108f9eec7d94fbf48`

Algorithm	Hash digest
SHA256	`3c976d01778a9c91309313405480cc55c43d1b4bbb65fe87f1d34449e69fb480`
MD5	`f9f313eede901385ca9ebe663e0847d5`
BLAKE2b-256	`815021f49cb0fe96fe1a29ed150b3e9f7557a1a3e61dd13d6f931a3fcb4d6686`

Algorithm	Hash digest
SHA256	`0d6d5fc51a4c662b210f04ac507769d63e8119b7d5796866b391dedeb2bb8bba`
MD5	`3f0fb7821a7a3404362664628026ab48`
BLAKE2b-256	`dfe18e5402504f4a2c1528f1364423361659a17b2daf1fe51212e2bc1120ed14`

Algorithm	Hash digest
SHA256	`7df7a1c040fa5d2f75d1d34fa31e1a555f3c75e17b2b40cb76fb41d8a207c811`
MD5	`3a7433fe69ef1332a115b7800f3aaa8b`
BLAKE2b-256	`00c20f056e9f2cdf2d24a6d3c8e12a7b44c4c2706cfbba27d17d845ae75e9c38`

Algorithm	Hash digest
SHA256	`510d226187dddcd667697469f9443b0a02dd2edb81222f328659c84936487f58`
MD5	`c1884bd1546c8d49a1f010cacd482fc5`
BLAKE2b-256	`d980c6619279b9df9ad85310ebecb219d408616f5287df9717125e3158e17b63`

Algorithm	Hash digest
SHA256	`5c1a1fc38c94c581aa192d3a547082ff19e04b0d24f1daa36a1c77e9662d872d`
MD5	`f5ec7d2a612a9e8894e49061e1db217e`
BLAKE2b-256	`16ec56c41df81d9dedff3d866611eaa8f4f82f2e836379c56107782586b6a732`

Algorithm	Hash digest
SHA256	`e80ba208808b65768a91df4f82afa73363ace545298ad962a251dfa3b5470ef4`
MD5	`77bcf0aa14a5f86f479718cda0d7062c`
BLAKE2b-256	`98ef0f8184fb5471f94c2548369fff8ccc44232c0f9cd6e100b21bc74d9545be`

Algorithm	Hash digest
SHA256	`fb89c4007c9f0332e1e2c043ff8508fcfdb41f918585aa5358c7cc19f420320c`
MD5	`2566422e08902f3fef277cf409745388`
BLAKE2b-256	`12508625e27049b2ba758a3be31423ad00fb19a649b15c16dffe2a558dd6e96e`

Algorithm	Hash digest
SHA256	`02de4061945a25400f9a97983ebfb0d9ed3bfd258518bda88a22b133016f2079`
MD5	`f4fcbf8875f5b776918b292cfb25c7e0`
BLAKE2b-256	`b00eca41b178e336c5b373030bed420c2517f7e4bebd0365e9fd1211fd2864b6`

Algorithm	Hash digest
SHA256	`0972ddd89aecfc83f3d0c094e5d5f1e4269cb881065c22702984e4a6a7d293c0`
MD5	`424bad13dba5d85a075c49ca8f5056c5`
BLAKE2b-256	`e04d69ba18567ecbcf97cc25d8cc334f894405df02498e6ee5b97b90a82b257e`

Algorithm	Hash digest
SHA256	`b50ce7d2e7d411e5aefdcfdf99100413fedaef06d1968077c0bed56163bf5c79`
MD5	`f4f458a7ace6d3e041f98873beb1bc9c`
BLAKE2b-256	`acbdd4f7183a0d900954ae2514942ae61f9dd9bf5b8d192135b7853ae842d91f`

Algorithm	Hash digest
SHA256	`6605ff004593492874f3ab807443ed87490931b6c5867d75a2a8bf193d33b00c`
MD5	`71f52ddef4c488dc0ebe6c7a91474ff7`
BLAKE2b-256	`062fc2efbe31daff99c49139eb7a3bd7f7e36f39acfeb1be0f042718ca0a3c02`

Algorithm	Hash digest
SHA256	`55fe45456fe09a178e0dae76de7e22eacac26a6c525df1f562a4b4c058d6ebeb`
MD5	`97e4a712e09f9d3fe17725b1234bcb82`
BLAKE2b-256	`5c7080cd87ce42ecafecefabfdcdf038d7c8e26da2c29c7e2f0ed1791833cd88`

Algorithm	Hash digest
SHA256	`826f922be06d76b43aa4f42b847b171fa500b66e4c369ce21d64bdc944e9cb2c`
MD5	`91e25b923d041a40be409b1147f462ed`
BLAKE2b-256	`5246ec5c09a35a4e1d17b3a42719efadab36cdf39de962780540ceab78bc8b25`

pycdfpp 0.11.0

CDFpp (CDF++)

Highlights

Packages & CI

CDFpp Explorer — CDF files in your browser

Installing

From PyPI

From source (C++ library)

From source (Python wheel)

Quick start (Python)

Reading a CDF file

Loading from memory

Time conversions

Creating and writing CDF files

Compressed CDF files

Filtering variables

Cloning variables between CDF files

NumPy buffer protocol

Quick start (C++)

Reading

Writing

Loading from memory

Benchmarks

SIMD time conversions

Leap-second lookup

RLE compression (bytes/s)

Features & roadmap

Caveats

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

Algorithm	Hash digest
SHA256	`c583d88d3e22ee141ba619823b6716f3b4566a703f37c0cd2528eec5b0af8c75`
MD5	`7cd6a6d8db8a4c48bb0d142f1855d47f`
BLAKE2b-256	`71fe395455e73bfacf7277a40e79464905ecc13aee05aaeda03e12fd27d9c8ef`

Algorithm	Hash digest
SHA256	`b98aa08a0f11a2a81a234327eb12df5c8b02e2f3db7c13252e9e10596c2e42bd`
MD5	`b4b2e5950c5e785a3c5f8b002fcacba4`
BLAKE2b-256	`616755c813e3fa4fc81643604965923adcf4b71452d8096f692ad9345cd7d1cc`

Algorithm	Hash digest
SHA256	`acae1dc6017a16256270dbe3def293daeeaa9cc77c553fad23515e698ad20ea6`
MD5	`37d4ee8a955b467a3586d4c50ac23cef`
BLAKE2b-256	`5c7d0747ccd5f3da97d955a80a0dc78d9bb855cf4b55c136fb4bf13306845bb2`

Algorithm	Hash digest
SHA256	`3db263adc6d03b660ab2101408454097fbe3c43436c2e95203205046502fcee5`
MD5	`83cadc067639531c62ffa75f9f5847d0`
BLAKE2b-256	`f6de2ae51fbe5f38734c18f1858e289b79c27b45d29856db1a050467af32081c`

Algorithm	Hash digest
SHA256	`f5c7c9178ef1e84050e62c1812dab72869191deacdced8a165327ea16536b293`
MD5	`17ac9aced7c89d9867b77cf8ec50443d`
BLAKE2b-256	`54d11daf1b773b96028f2beda506d1c9f1531dcbff75f329e707e5153c34358a`

Algorithm	Hash digest
SHA256	`e0d0e54dfd3639a95032087479e07d484d2fabe1a8df026f3ed3124b655b2198`
MD5	`c02f77788eb12eeaf8359457db225848`
BLAKE2b-256	`798dd664e9be75e707021a73c62f526c77745aa7f276b4ab1eaaea48f9d8134c`

Algorithm	Hash digest
SHA256	`adf15bbf66e6c2b4b3f6653f532cf8ad6fc96644ceed886eae48f223273e47b3`
MD5	`814b8fa81b6ced2d9fc2a90798f0c56b`
BLAKE2b-256	`e9c456103868c10922ce96425b7cb684813949f1c72fd026a11d7d649f27f028`

Algorithm	Hash digest
SHA256	`ba279b2ea8dbc93b7c522832318fba4da7164a5c763497563e2a64675754cc47`
MD5	`29c934770b30109a2cd00394acd5d5ff`
BLAKE2b-256	`a89d10f05f9f50bfb98d6a084233128f1f6ae585bea4806539d9b774b9b47367`

Algorithm	Hash digest
SHA256	`409f1ed1598b7ddbee9fea648ec97cb0c12f1702e719d9bbcbcd1f054cef277c`
MD5	`ba37fa143dd30df2f444cd0d88cb9189`
BLAKE2b-256	`a0e5a0227785f3e5c5f3e46c5c43e965543141ab98602985f0a0b03f88aece20`

Algorithm	Hash digest
SHA256	`d9327a2beadc03a83f585ca3bd976aaf327e4ecb96530045226383221aa3699e`
MD5	`4e3ec1a59ef0772f5f2e7d4f34ae9219`
BLAKE2b-256	`4268f275d4a94ecab69536de9bbd29daa31396d6a36bb204a6a6273a9f095142`