Crate fitsio [−] [src]

fitsio - a thin wrapper around the cfitsio C library.

This library wraps the low level cfitsio bindings: fitsio-sys and provides a more native experience for rust users.

The main interface to a fits file is FitsFile. All file manipulation and reading starts with this class.

Opening a file:

use fitsio::FitsFile;

// let filename = ...;
let fptr = FitsFile::open(filename).unwrap();

Alternatively a new file can be created on disk with the companion method create:

use fitsio::FitsFile;

// let filename = ...;
let fptr = FitsFile::create(filename).unwrap();

From this point, the current HDU can be queried and changed, or fits header cards can be read or file contents can be read.

To open a fits file in read/write mode (to allow changes to the file), the edit must be used. This opens a file which already exists on disk for editing.

HDU access

HDU information belongs to the FitsHdu object. HDUs can be fetched by String/str or integer (0-indexed). The HduInfo object contains information about the current HDU:

let hdu = fptr.hdu(0).unwrap();
// image HDU
if let HduInfo::ImageInfo { shape, .. } = hdu.info {
   println!("Image is {}-dimensional", shape.len());
   println!("Found image with shape {:?}", shape);
}

// tables
if let HduInfo::TableInfo { column_descriptions, num_rows, .. } = hdu.info {
    println!("Table contains {} rows", num_rows);
    println!("Table has {} columns", column_descriptions.len());
}

Creating new HDUs

Images

New fits images are created with the create_image method. This method requires the extension name, and an ImageDescription object, which defines the shape and type of the desired image:

let image_description = ImageDescription {
    data_type: ImageType::FLOAT_IMG,
    dimensions: &[100, 100],
};
let hdu = fptr.create_image("EXTNAME".to_string(), &image_description).unwrap();

Tables

Similar to creating new images, new tables are created with the create_table method. This requires an extension name, and a slice of ColumnDescriptions:

let first_description = ColumnDescription::new("A")
    .with_type(ColumnDataType::Int)
    .create().unwrap();
let second_description = ColumnDescription::new("B")
    .with_type(ColumnDataType::Long)
    .create().unwrap();
let descriptions = [first_description, second_description];
let hdu = fptr.create_table("EXTNAME".to_string(), &descriptions).unwrap();

Column descriptions

Columns are described with the ColumnDescription struct. This encapsulates: the name of the column, and the data format.

The fits specification allows scalar or vector columns, and the data format is described the ColumnDataDescription struct, which in turn encapsulates the number of elements per row element (typically 1), the width of the column (for strings), and the data type, which is one of the ColumnDataType members

For the common case of a scalar column, a ColumnDataDescription object can be constructed with the scalar method:

let desc = ColumnDataDescription::scalar(ColumnDataType::Int);
assert_eq!(desc.repeat, 1);
assert_eq!(desc.width, 1);

Vector columns can be constructed with the vector method:

let desc = ColumnDataDescription::vector(ColumnDataType::Int, 100);
assert_eq!(desc.repeat, 100);
assert_eq!(desc.width, 1);

These impl From<...> for String such that the traditional fits column description string can be obtained:

let desc = ColumnDataDescription::scalar(ColumnDataType::Int);
assert_eq!(String::from(desc), "1J".to_string());

Header keys

Header keys are read through the read_key function, and is generic over types that implement the ReadsKey trait:

let int_value: i64 = fptr.hdu(0).unwrap().read_key(&mut fptr, "INTTEST").unwrap();

// Alternatively
let int_value = fptr.hdu(0).unwrap().read_key::<i64>(&mut fptr, "INTTEST").unwrap();

// Or let the compiler infer the types (if possible)

Header cards can be written through the method write_key. It takes a key name and value. See the WritesKey trait for supported data types.

fptr.hdu(0).unwrap().write_key(&mut fptr, "foo", 1i64).unwrap();
assert_eq!(fptr.hdu(0).unwrap().read_key::<i64>(&mut fptr, "foo").unwrap(), 1i64);

Reading file data

Images

Image data can be read through either read_section which reads contiguous pixels between a start index and end index, or read_region which reads rectangular chunks from the image.

// Read the first 100 pixels
let first_row: Vec<i32> = hdu.read_section(&mut fptr, 0, 100).unwrap();

// Read a square section of the image

let xcoord = 0..10;
let ycoord = 0..10;
let chunk: Vec<i32> = hdu.read_region(&mut fptr, &[&ycoord, &xcoord]).unwrap();

Some convenience methods are available for reading rows of the image. This is typically useful as it's an efficient access method:

let start_row = 0;
let num_rows = 10;
let first_few_rows: Vec<f32> = hdu.read_rows(&mut fptr, start_row, num_rows).unwrap();

// 10 rows of 100 columns
assert_eq!(first_few_rows.len(), 1000);

The whole image can also be read into memory:

let image_data: Vec<f32> = hdu.read_image(&mut fptr, ).unwrap();

// 100 rows of 100 columns
assert_eq!(image_data.len(), 10_000);

Tables

Columns can be read using the read_col function, which can convert data types on the fly. See the ReadsCol trait for supported data types.

let integer_data: Vec<i32> = hdu.and_then(|hdu| hdu.read_col(&mut fptr, "intcol")).unwrap();

The columns method returns an iterator over all of the columns in a table.

Writing file data

When writing to the file, all methods are attached to the FitsHdu object to which data is to be written.

let hdu = fptr.hdu(1);

Images

Image data is written through two methods on the HDU object: write_section and write_region.

write_section requires a start index and end index and data to write. The data parameter needs to be a slice, meaning any contiguous memory storage method (e.g. Vec) can be passed.

let data_to_write: Vec<f64> = vec![1.0, 2.0, 3.0];
hdu.write_section(&mut fptr, 0, data_to_write.len(), &data_to_write).unwrap();

write_region takes a slice of ranges with which the data is to be written, and the data to write.

let data_to_write: Vec<f64> = vec![1.0, 2.0, 3.0, 4.0];
let ranges = [&(0..1), &(0..1)];
hdu.write_region(&mut fptr, &ranges, &data_to_write).unwrap();

Tables

Inserting columns

Two methods on the HDU object allow for adding new columns: append_column and insert_column. append_column adds a new column as the last column member, and is generally preferred as it does not require shifting of data within the file.

let column_description = ColumnDescription::new("abcdefg")
.with_type(ColumnDataType::Int)
.create().unwrap();
hdu.append_column(&mut fptr, &column_description).unwrap();

Deleting columns

The HDU object has the method delete_column which removes a column. The column can either be accessed by integer or name

let newhdu = hdu.delete_column(&mut fptr, "bar").unwrap();
// or
let newhdu = hdu.delete_column(&mut fptr, 0).unwrap();

Raw fits file access

If this library does not support the particular use case that is needed, the raw fitsfile pointer can be accessed:

extern crate fitsio_sys;

let fptr = FitsFile::open(filename).unwrap();

/* Find out the number of HDUs in the file */
let mut num_hdus = 0;
let mut status = 0;

unsafe {
    let fitsfile = fptr.as_raw();

    /* Use the unsafe fitsio-sys low level library to call a function that is possibly not
    implemented in this crate */
    fitsio_sys::ffthdu(fitsfile, &mut num_hdus, &mut status);
}
assert_eq!(num_hdus, 2);

This (unsafe) pointer can then be used with the underlying fitsio-sys library directly.

Reexports

pub use self::fitsfile::FitsFile;
pub use self::fitsfile::FitsHdu;
pub use self::types::HduInfo;
pub use self::errors::Error;
pub use self::errors::Result;

Modules

columndescription
errors
fitsfile
types