xisf 0.9.6

Encoder/Decoder for XISF (Extensible Image Serialization Format)

xisf

XISF Encoder/Decoder (see https://pixinsight.com/xisf/).

This implementation is not endorsed nor related with PixInsight development team.

Copyright (C) 2021-2026 Sergio Díaz, sergiodiaz.eu

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

XISF Objects

class XISF()

Implements an baseline XISF Decoder and a simple baseline Encoder. It parses metadata from Image and Metadata XISF core elements. Image data is returned as a numpy ndarray (using the "channels-last" convention by default).

What's supported:

• Monolithic XISF files only
  • XISF data blocks with attachment, inline or embedded block locations
  • Planar pixel storage models, however it assumes 2D images only (with multiple channels)
  • UInt8/16/32 and Float32/64 pixel sample formats
  • Grayscale and RGB color spaces
• Decoding:
  • multiple Image core elements from a monolithic XISF file
  • Support all standard compression codecs defined in this specification for decompression (zlib/lz4[hc]/zstd + byte shuffling)
• Encoding:
  • Single image core element with an attached data block
  • Support all standard compression codecs defined in this specification for decompression (zlib/lz4[hc]/zstd + byte shuffling)
• "Atomic" properties (scalar types, String, TimePoint), Vector and Matrix (e.g. astrometric solutions)
• Metadata and FITSKeyword core elements

What's not supported (at least by now):

• Read pixel data in the normal pixel storage models
• Read pixel data in the planar pixel storage models other than 2D images
• Complex and Table properties
• Any other not explicitly supported core elements (Resolution, Thumbnail, ICCProfile, etc.)

Usage example:

from xisf import XISF
import matplotlib.pyplot as plt
xisf = XISF("file.xisf")
file_meta = xisf.get_file_metadata()    
file_meta
ims_meta = xisf.get_images_metadata()
ims_meta
im_data = xisf.read_image(0)
plt.imshow(im_data)
plt.show()
XISF.write(
    "output.xisf", im_data, 
    creator_app="My script v1.0", image_metadata=ims_meta[0], xisf_metadata=file_meta, 
    codec='lz4hc', shuffle=True
)

If the file is not huge and it contains only an image (or you're interested just in one of the images inside the file), there is a convenience method for reading the data and the metadata:

from xisf import XISF
import matplotlib.pyplot as plt    
im_data = XISF.read("file.xisf")
plt.imshow(im_data)
plt.show()

The XISF format specification is available at https://pixinsight.com/doc/docs/XISF-1.0-spec/XISF-1.0-spec.html

__init__

def __init__(fname)

Opens a XISF file and extract its metadata. To get the metadata and the images, see get_file_metadata(), get_images_metadata() and read_image().

Arguments:

• fname - filename

Returns:

XISF object.

get_images_metadata

def get_images_metadata()

Provides the metadata of all image blocks contained in the XISF File, extracted from the header ( core elements). To get the actual image data, see read_image().

It outputs a dictionary m_i for each image, with the following structure:

m_i = { 
    'geometry': (width, height, channels), # only 2D images (with multiple channels) are supported
    'location': (pos, size), # used internally in read_image()
    'dtype': np.dtype('...'), # derived from sampleFormat argument
    'compression': (codec, uncompressed_size, item_size), # optional
    'key': 'value', # other <Image> attributes are simply copied 
    ..., 
    'FITSKeywords': { <fits_keyword>: fits_keyword_values_list, ... }, 
    'XISFProperties': { <xisf_property_name>: property_dict, ... }
}

where:

fits_keyword_values_list = [ {'value': <value>, 'comment': <comment> }, ...]
property_dict = {'id': <xisf_property_name>, 'type': <xisf_type>, 'value': property_value, ...}

Returns:

list [ m_0, m_1, ..., m_{n-1} ] where m_i is a dict as described above.

get_file_metadata

def get_file_metadata()

Provides the metadata from the header of the XISF File (

Returns:

dictionary with one entry per property: { <xisf_property_name>: property_dict, ... } where:

property_dict = {'id': <xisf_property_name>, 'type': <xisf_type>, 'value': property_value, ...}

get_metadata_xml

def get_metadata_xml()

Returns the complete XML header as a xml.etree.ElementTree.Element object.

Returns:

• xml.etree.ElementTree.Element - complete XML XISF header

read_image

def read_image(n=0, data_format='channels_last')

Extracts an image from a XISF object.

Arguments:

• n - index of the image to extract in the list returned by get_images_metadata()
• data_format - channels axis can be 'channels_first' or 'channels_last' (as used in keras/tensorflow, pyplot's imshow, etc.), 0 by default.

Returns:

Numpy ndarray with the image data, in the requested format (channels_first or channels_last).

read

@staticmethod
def read(fname, n=0, image_metadata={}, xisf_metadata={})

Convenience method for reading a file containing a single image.

Arguments:

• fname string - filename
• n int, optional - index of the image to extract (in the list returned by get_images_metadata()). Defaults to 0.
• image_metadata dict, optional - dictionary that will be updated with the metadata of the image.
• xisf_metadata dict, optional - dictionary that will be updated with the metadata of the file.

Returns:

• [np.ndarray] - Numpy ndarray with the image data, in the requested format (channels_first or channels_last).

write

@staticmethod
def write(fname, im_data, creator_app=None, image_metadata={}, xisf_metadata={}, codec=None, shuffle=False, level=None)

Writes an image (numpy array) to a XISF file. Compression may be requested but it only will be used if it actually reduces the data size.

Arguments:

• fname - filename (will overwrite if existing)
• im_data - numpy ndarray with the image data
• creator_app - string for XISF:CreatorApplication file property (defaults to python version in None provided)
• image_metadata - dict with the same structure described for m_i in get_images_metadata(). Only 'FITSKeywords' and 'XISFProperties' keys are actually written, the rest are derived from im_data.
• xisf_metadata - file metadata, dict with the same structure returned by get_file_metadata()
• codec - compression codec ('zlib', 'lz4', 'lz4hc' or 'zstd'), or None to disable compression
• shuffle - whether to apply byte-shuffling before compression (ignored if codec is None). Recommended for 'lz4' ,'lz4hc' and 'zstd' compression algorithms.
• level - for zlib, 1..9 (default: 6); for lz4hc, 1..12 (default: 9); for zstd, 1..22 (default: 3). Higher means more compression.

Returns:

• bytes_written - the total number of bytes written into the output file.
• codec - The codec actually used, i.e., None if compression did not reduce the data block size so compression was not finally used.