Correctly handle compressed fits files

.github/workflows/test-suite.yml

@@ -34,11 +34,6 @@ jobs:
         uses: KyleMayes/install-llvm-action@v1
         with:
           version: "10.0"
-      - name: cache poetry install
-        uses: actions/cache@v2
-        with:
-          path: ~/.local
-          key: poetry-1.5.1-0
 
       - uses: snok/install-poetry@v1
         with:

CHANGELOG.md

@@ -8,6 +8,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 #### Added
 
+- Added support for compressed FITS files [#694](https://github.com/askap-vast/vast-pipeline/pull/694)
 - Added links to Data Central DAS and the Fink Broker to the source page [#697](https://github.com/askap-vast/vast-pipeline/pull/697/)
 - Added `n_new_sources` column to run model to store the number of new sources in a pipeline run [#676](https://github.com/askap-vast/vast-pipeline/pull/676).
 - Added `MAX_CUTOUT_IMAGES` to the pipeline settings to limit the number of postage stamps displayed on the source detail page [#658](https://github.com/askap-vast/vast-pipeline/pull/658).
@@ -41,6 +42,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 #### Changed
 
+- Updated all FITS loading to use a wrapper that can handle compressed FITS files [#694](https://github.com/askap-vast/vast-pipeline/pull/694)
 - Downgrade ci-docs to python 3.8 [#702](https://github.com/askap-vast/vast-pipeline/pull/702)
 - Update Gr1N poetry to v8, force python 3.8.10 [#701](https://github.com/askap-vast/vast-pipeline/pull/701)
 - Updated path to test data in github actions and docs [#699](https://github.com/askap-vast/vast-pipeline/pull/699)
@@ -112,7 +114,8 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 #### List of PRs
 
-- [#702](https://github.com/askap-vast/vast-pipeline/pull/702): fix: Downgrade ci-docs to python 3.8 
+- [#694](https://github.com/askap-vast/vast-pipeline/pull/694): feat: Handle compressed fits files.
+- [#702](https://github.com/askap-vast/vast-pipeline/pull/702): fix: Downgrade ci-docs to python 3.8.
 - [#701](https://github.com/askap-vast/vast-pipeline/pull/701): fix: Update Gr1N poetry to v8, force python 3.8.10.
 - [#699](https://github.com/askap-vast/vast-pipeline/pull/699): docs, feat: Add new regression data download URL and updates to Github Actions.
 - [#697](https://github.com/askap-vast/vast-pipeline/pull/697/): feat: Added links to Data Central DAS and the Fink Broker to the source page.

docs/using/runconfig.md

@@ -151,8 +151,7 @@ Boolean. Astropy warnings are suppressed in the logging output if set to `True`.
 
 **`inputs.image`**
 Line entries or epoch headed entries. 
-The full paths to the image FITS files to be processed. 
-Epoch mode is activated by including an extra key value with the epoch name, see the example below for a demonstration.
+The full paths to the image FITS files to be processed - these can be regular FITS files, or FITS files that use a [`CompImageHDU`](https://docs.astropy.org/en/stable/io/fits/api/images.html#astropy.io.fits.CompImageHDU). In principle the pipeline also supports [`.fits.fz`](https://heasarc.gsfc.nasa.gov/fitsio/fpack/) files, although this is not officially supported. Epoch mode is activated by including an extra key value with the epoch name, see the example below for a demonstration.
 Refer to [this section](../design/association.md#epoch-based-association) of the documentation for more information on epoch based association.
 
 <!-- markdownlint-disable MD046 -->

vast_pipeline/image/main.py

@@ -19,6 +19,7 @@
 
 from vast_pipeline import models
 from vast_pipeline.survey.translators import tr_selavy
+from vast_pipeline.image.utils import open_fits
 
 
 logger = logging.getLogger(__name__)
@@ -33,6 +34,7 @@ class Image(object):
         path (str): The system path to the image.
 
     """
+
     def __init__(self, path: str) -> None:
         """
         Initiliase an image object.
@@ -80,7 +82,7 @@ class FitsImage(Image):
 
     entire_image = True
 
-    def __init__(self, path: str, hdu_index: int=0) -> None:
+    def __init__(self, path: str, hdu_index: int = 0) -> None:
         """
         Initialise a FitsImage object.
 
@@ -107,7 +109,7 @@ def __init__(self, path: str, hdu_index: int=0) -> None:
 
     def __get_header(self, hdu_index: int) -> fits.Header:
         """
-        Retrieves the header from teh FITS image.
+        Retrieves the header from the FITS image.
 
         Args:
             hdu_index:
@@ -116,13 +118,14 @@ def __get_header(self, hdu_index: int) -> fits.Header:
         Returns:
             The FITS header as an astropy.io.fits.Header object.
         """
+
         try:
-            with fits.open(self.path) as hdulist:
+            with open_fits(self.path) as hdulist:
                 hdu = hdulist[hdu_index]
         except Exception:
             raise IOError((
-                'Could not read this FITS file: '
-                f'{os.path.basename(self.path)}'
+                'Could not read FITS file: '
+                f'{self.path}'
             ))
 
         return hdu.header.copy()
@@ -223,7 +226,8 @@ def __get_radius_pixels(
             The radius of the image in pixels.
         """
         if self.entire_image:
-            # a large circle that *should* include the whole image (and then some)
+            # a large circle that *should* include the whole image
+            # (and then some)
             diameter = np.hypot(header[fits_naxis1], header[fits_naxis2])
         else:
             # We simply place the largest circle we can in the centre.
@@ -244,10 +248,11 @@ def __get_frequency(self, header: fits.Header) -> None:
         self.freq_eff = None
         self.freq_bw = None
         try:
-            if ('ctype3' in header) and (header['ctype3'] in ('FREQ', 'VOPT')):
+            freq_keys = ('FREQ', 'VOPT')
+            if ('ctype3' in header) and (header['ctype3'] in freq_keys):
                 self.freq_eff = header['crval3']
                 self.freq_bw = header['cdelt3'] if 'cdelt3' in header else 0.0
-            elif ('ctype4' in header) and (header['ctype4'] in ('FREQ', 'VOPT')):
+            elif ('ctype4' in header) and (header['ctype4'] in freq_keys):
                 self.freq_eff = header['crval4']
                 self.freq_bw = header['cdelt4'] if 'cdelt4' in header else 0.0
             else:
@@ -271,6 +276,7 @@ class SelavyImage(FitsImage):
             associated with the image.
         config (Dict): The image configuration settings.
     """
+
     def __init__(
         self,
         path: str,
@@ -313,7 +319,8 @@ def read_selavy(self, dj_image: models.Image) -> pd.DataFrame:
             Dataframe containing the cleaned and processed Selavy components.
         """
         # TODO: improve with loading only the cols we need and set datatype
-        if self.selavy_path.endswith(".xml") or self.selavy_path.endswith(".vot"):
+        if self.selavy_path.endswith(
+                ".xml") or self.selavy_path.endswith(".vot"):
             df = Table.read(
                 self.selavy_path, format="votable", use_names_over_ids=True
             ).to_pandas()
@@ -460,12 +467,12 @@ def read_selavy(self, dj_image: models.Image) -> pd.DataFrame:
             .agg('sum')
         )
 
-        df['flux_int_isl_ratio'] =  (
+        df['flux_int_isl_ratio'] = (
             df['flux_int'].values
             / island_flux_totals.loc[df['island_id']]['flux_int'].values
         )
 
-        df['flux_peak_isl_ratio'] =  (
+        df['flux_peak_isl_ratio'] = (
             df['flux_peak'].values
             / island_flux_totals.loc[df['island_id']]['flux_peak'].values
         )

vast_pipeline/image/utils.py

@@ -7,7 +7,9 @@
 import numpy as np
 import pandas as pd
 
-from typing import Tuple
+from typing import Tuple, Union, Optional
+from pathlib import Path
+from astropy.io import fits
 
 
 logger = logging.getLogger(__name__)
@@ -81,7 +83,7 @@ def calc_error_radius(ra, ra_err, dec, dec_err) -> float:
             np.deg2rad(i),
             dec_1,
             np.deg2rad(j)
-        )) for i,j in zip(ra_offsets, dec_offsets)
+        )) for i, j in zip(ra_offsets, dec_offsets)
     ]
 
     seps = np.column_stack(seps)
@@ -190,7 +192,7 @@ def calc_condon_flux_errors(
                    (1. + (theta_B / major)**2)**alpha_maj2 *
                    (1. + (theta_b / minor)**2)**alpha_min2 *
                    snr**2)
-        rho_sq3 = ((major * minor / (4.* theta_B * theta_b)) *
+        rho_sq3 = ((major * minor / (4. * theta_B * theta_b)) *
                    (1. + (theta_B / major)**2)**alpha_maj3 *
                    (1. + (theta_b / minor)**2)**alpha_min3 *
                    snr**2)
@@ -210,9 +212,9 @@ def calc_condon_flux_errors(
 
         # ra and dec errors
         errorra = np.sqrt((error_par_major * np.sin(theta))**2 +
-                            (error_par_minor * np.cos(theta))**2)
+                          (error_par_minor * np.cos(theta))**2)
         errordec = np.sqrt((error_par_major * np.cos(theta))**2 +
-                            (error_par_minor * np.sin(theta))**2)
+                           (error_par_minor * np.sin(theta))**2)
 
         errormajor = np.sqrt(2) * major / rho1
         errorminor = np.sqrt(2) * minor / rho2
@@ -238,11 +240,40 @@ def calc_condon_flux_errors(
         help1 = (errormajor / major)**2
         help2 = (errorminor / minor)**2
         help3 = theta_B * theta_b / (major * minor)
-        errorflux = np.abs(flux_int) * np.sqrt(errorpeaksq / flux_peak**2 + help3 * (help1 + help2))
+        help4 = np.sqrt(errorpeaksq / flux_peak**2 + help3 * (help1 + help2))
+        errorflux = np.abs(flux_int) * help4
 
         # need to return flux_peak if used.
         return errorpeak, errorflux, errormajor, errorminor, errortheta, errorra, errordec
 
     except Exception as e:
-        logger.debug("Error in the calculation of Condon errors for a source", exc_info=True)
+        logger.debug(
+            "Error in the calculation of Condon errors for a source",
+            exc_info=True)
         return 0., 0., 0., 0., 0., 0., 0.
+
+
+def open_fits(fits_path: Union[str, Path], memmap: Optional[bool] = True):
+    """
+    This function opens both compressed and uncompressed fits files.
+
+    Args:
+        fits_path: Path to the fits file
+        memmap: Open the fits file with mmap.
+
+    Returns:
+        HDUList loaded from the fits file
+    """
+
+    if isinstance(fits_path, Path):
+        fits_path = str(fits_path)
+
+    hdul = fits.open(fits_path, memmap=memmap)
+
+    # This is a messy way to check, but I can't think of a better one
+    if len(hdul) == 1:
+        return hdul
+    elif type(hdul[1]) == fits.hdu.compressed.CompImageHDU:
+        return fits.HDUList(hdul[1:])
+    else:
+        return hdul