pycdfpp 0.3.9

A modern C++ header only cdf library

CDFpp (CDF++)

A NASA's CDF modern C++ library. This is not a C++ wrapper but a full C++ implementation. Why? CDF files are still used for space physics missions but few implementations are available. The main one is NASA's C implementation available here but it lacks multi-threads support, has an old C interface and has a license which isn't compatible with most Linux distributions policy. There are also Java and Python implementations which are not usable in C++.

List of features and roadmap:

• read uncompressed file headers
• read uncompressed attributes
• read uncompressed variables
• read variable attributes
• loads cdf files from memory (std::vector or char*)
• handles both row and column major files
• read variables with nested VXRs
• read compressed file (GZip, RLE)
• read compressed variables (GZip, RLE)
• write uncompressed headers
• write uncompressed attributes
• write uncompressed variables
• write compressed attributes
• write compressed file variables
• handle leap seconds
• Python wrappers
• Documentation
• Examples
• Benchmarks

If you want to understand how it works, how to use the code or what works, you may have to read tests.

Installing

From PyPi

pip3 install --user pycdfpp

From sources

meson build
cd build
ninja
sudo ninja install

Basic usage

Python

Basic example from a local file:

import pycdfpp
cdf = pycdfpp.load("some_cdf.cdf")
cdf_var_data = cdf["var_name"].values #builds a numpy view or a list of strings
attribute_name_first_value = cdf.attributes['attribute_name'][0]

Note that you can also load in memory files:

import pycdfpp
import requests
import matplotlib.pyplot as plt
tha_l2_fgm = pycdfpp.load(requests.get("https://spdf.gsfc.nasa.gov/pub/data/themis/tha/l2/fgm/2016/tha_l2_fgm_20160101_v01.cdf").content)
plt.plot(tha_l2_fgm["tha_fgl_gsm"])
plt.show()

Datetimes handling:

import pycdfpp
import os
# Due to an issue with pybind11 you have to force your timezone to UTC for 
# datetime conversion (not necessary for numpy datetime64)
os.environ['TZ']='UTC'

mms2_fgm_srvy = pycdfpp.load("mms2_fgm_srvy_l2_20200201_v5.230.0.cdf")

# to convert any CDF variable holding any time type to python datetime:
epoch_dt = pycdfpp.to_datetime(mms2_fgm_srvy["Epoch"])

# same with numpy datetime64:
epoch_dt64 = pycdfpp.to_datetime64(mms2_fgm_srvy["Epoch"])

# note that using datetime64 is ~100x faster than datetime (~2ns/element on an average laptop)

C++

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

std::ostream& operator<<(std::ostream& os, const cdf::Variable::shape_t& shape)
{
    os << "(";
    for (auto i = 0; i < static_cast<int>(std::size(shape)) - 1; i++)
        os << shape[i] << ',';
    if (std::size(shape) >= 1)
        os << shape[std::size(shape) - 1];
    os << ")";
    return os;
}

int main(int argc, char** argv)
{
    auto path = std::string(DATA_PATH) + "/a_cdf.cdf";
    // cdf::io::load returns a optional<CDF>
    if (const auto my_cdf = cdf::io::load(path); my_cdf)
    {
        std::cout << "Attribute list:" << std::endl;
        for (const auto& [name, attribute] : my_cdf->attributes)
        {
            std::cout << "\t" << name << std::endl;
        }
        std::cout << "Variable list:" << std::endl;
        for (const auto& [name, variable] : my_cdf->variables)
        {
            std::cout << "\t" << name << " shape:" << variable.shape() << std::endl;
        }
        return 0;
    }
    return -1;
}