Struct npyz::NpyFile[−][src]

pub struct NpyFile<R: Read> { /* fields omitted */ }

Expand description

Object for reading an npy file.

This type represents a partially read npy file, where the header has been parsed and we are ready to begin parsing data.

use std::fs::File;
use std::io;

let file = io::BufReader::new(File::open("./test-data/c-order.npy")?);
let npy = npyz::NpyFile::new(file)?;

// Helper methods for inspecting the layout of the data.
assert_eq!(npy.shape(), &[2, 3, 4]);
assert_eq!(npy.strides(), &[12, 4, 1]);
assert_eq!(npy.order(), npyz::Order::C);

// Get the data!
let data: Vec<i64> = npy.into_vec()?;
assert_eq!(data.len(), 24);

Working with large files

For large files, it may be undesirable to read all of the data into a Vec. The NpyFile::data method allows you to iterate over the data instead.

let npy = npyz::NpyFile::new(file)?;

let mut sum = 0;
for x in npy.data::<i64>()? {
    sum += x?;  // items are Result
}
println!("{}", sum);

Migrating from `NpyData`

At construction, since &[u8] impls Read, you can still use them as input.

was:
    npyz::NpyData::<i64>::from_bytes(&bytes).to_vec()
now:
    npyz::NpyFile::new(&bytes[..]).into_vec::<i64>()

If you were using the iterator API of NpyData, this is now on NpyReader, which requires us to call NpyFile::data:

was:
    let iter = npyz::NpyData::<i64>::new(&bytes)?;
now:
    let iter = npyz::NpyFile::new(&bytes[..]).data::<i64>.map_err(invalid_data)?;

where the following function has been used to paper over the fact that NpyFile::data has a different Error type:

fn invalid_data<S: ToString>(err: S) -> std::io::Error {
    std::io::Error::new(std::io::ErrorKind::InvalidData, err.to_string())
}

NpyData::is_empty is gone due to possible ambiguity between NpyReader::len and NpyReader::total_len. Use the one that is appropriate for what you are doing.

If you were using NpyData::get… well, honestly, you should first consider whether you could just iterate over the reader instead. But if you were using NpyData::get because you genuinely need random access, then there is NpyReader::read_at.

was:
    // note:  0 <= i < arr.len()
    arr.get(i)
now:
    // note:  0 <= i < reader.total_len()
    reader.read_at(i)?

Struct npyz::NpyFile[−][src]

Working with large files

Migrating from NpyData

Implementations

Auto Trait Implementations

Blanket Implementations

Migrating from `NpyData`