import fnmatch
from os import listdir, path
import logging
import numpy as np
import numpy.ma as ma
from astropy.table import Table, vstack
import astropy.io.fits as fits
logger = logging.getLogger(__name__)
__all__ = ['ImageFileCollection']
[docs]class ImageFileCollection(object):
"""
Representation of a collection of image files.
The class offers a table summarizing values of
keywords in the FITS headers of the files in the collection and offers
convenient methods for iterating over the files in the collection. The
generator methods use simple filtering syntax and can automate storage
of any FITS files modified in the loop using the generator.
Parameters
----------
location : str, optional
path to directory containing FITS files
keywords : list of str or '*', optional
Keywords that should be used as column headings in the summary table.
If the value is or includes '*' then all keywords that appear in any
of the FITS headers of the files in the collection become table
columns.
info_file : str, optional
Path to file that contains a table of information about FITS files.
Attributes
----------
location
keywords
files
summary_info
Raises
------
ValueError
Raised if keywords are set to a combination of '*' and any other
value.
"""
def __init__(self, location=None, keywords=None, info_file=None):
self._location = location
self._files = []
if location:
self._files = self._fits_files_in_directory()
self._summary_info = {}
if keywords is None:
keywords = []
if info_file is not None:
try:
info_path = path.join(self.location, info_file)
except AttributeError:
info_path = info_file
try:
self._summary_info = Table.read(info_path,
format='ascii',
delimiter=',')
self._summary_info = Table(self._summary_info,
masked=True)
except IOError:
if location:
logger.warning('Unable to open table file %s, will try '
'initializing from location instead',
info_path)
else:
raise
if keywords:
self.keywords = keywords
@property
[docs] def summary_info(self):
"""
astropy.table.Table of values of FITS keywords for files in the collection.
Each keyword is a column heading. In addition, there is a column
called 'file' that contains the name of the FITS file. The directory
is not included as part of that name.
"""
return self._summary_info
@property
[docs] def location(self):
"""
str, Path name to directory containing FITS files
"""
return self._location
@property
def keywords(self):
"""
list of str, Keywords currently in the summary table.
Setting the keywords causes the summary table to be regenerated unless
the new keywords are a subset of the old.
"""
if self.summary_info:
return self.summary_info.keys()
else:
return []
@keywords.setter
[docs] def keywords(self, keywords=None):
# since keywords are drawn from self.summary_info, setting
# summary_info sets the keywords.
if keywords is None:
self._summary_info = []
return
logging.debug('keywords in setter before pruning: %s', keywords)
# remove duplicates and force a copy
new_keys = list(set(keywords))
logging.debug('keywords after pruning %s', new_keys)
full_new_keys = list(set(new_keys))
full_new_keys.append('file')
full_new_set = set(full_new_keys)
current_set = set(self.keywords)
if full_new_set.issubset(current_set):
logging.debug('table columns before trimming: %s',
' '.join(current_set))
cut_keys = current_set.difference(full_new_set)
logging.debug('will try removing columns: %s',
' '.join(cut_keys))
for key in cut_keys:
self._summary_info.remove_column(key)
logging.debug('after removal column names are: %s',
' '.join(self.keywords))
else:
logging.debug('should be building new table...')
self._summary_info = self._fits_summary(header_keywords=new_keys)
@property
[docs] def files(self):
"""
list of str, Unfiltered list of FITS files in location.
"""
return self._files
[docs] def values(self, keyword, unique=False):
"""
List of values for a keyword.
Parameters
----------
keyword : str
Keyword (i.e. table column) for which values are desired.
unique : bool, optional
If True, return only the unique values for the keyword.
Returns
-------
list
Values as a list.
"""
if keyword not in self.keywords:
raise ValueError(
'keyword %s is not in the current summary' % keyword)
if unique:
return list(set(self.summary_info[keyword]))
else:
return list(self.summary_info[keyword])
[docs] def files_filtered(self, **kwd):
"""Determine files whose keywords have listed values.
`**kwd` is list of keywords and values the files must have.
The value '*' represents any value.
A missing keyword is indicated by value ''
Example:
>>> keys = ['imagetyp','filter']
>>> collection = ImageFileCollection('test/data', keywords=keys)
>>> collection.files_filtered(imagetyp='LIGHT', filter='R')
>>> collection.files_filtered(imagetyp='*', filter='')
NOTE: Value comparison is case *insensitive* for strings.
"""
# force a copy by explicitly converting to a list
current_file_mask = list(self.summary_info['file'].mask)
self._find_keywords_by_values(**kwd)
filtered_files = self.summary_info['file'].compressed()
self.summary_info['file'].mask = current_file_mask
return filtered_files
def _table_from_fits_header(self, file_name, input_summary=None,
missing_marker=None):
"""
Construct a table whose columns are the header keywords
Parameters
----------
file_name : str
Name of FITS file.
input_summary : dict
Existing dictionary to which new values should be appended.
Returns
-------
file_table : astropy.table.Table
"""
from collections import OrderedDict
def _add_val_to_table(key, value, table, n_prev):
try:
table[key.lower()].append(value)
except KeyError:
table[key.lower()] = [missing_marker] * n_previous
table[key.lower()].append(value)
if input_summary is None:
summary = OrderedDict()
n_previous = 0
else:
summary = input_summary
n_previous = len(summary['file'])
h = fits.getheader(file_name)
assert 'file' not in h
# Try opening header before this so that file name is only added if
# file is valid FITS
try:
summary['file'].append(path.basename(file_name))
except KeyError:
summary['file'] = [path.basename(file_name)]
missing_in_this_file = [k for k in summary if (k not in h and
k != 'file')]
multi_entry_keys = {'comment': [],
'history': []}
for k, v in h.iteritems():
if k == '':
continue
if k.lower() in ['comment', 'history']:
multi_entry_keys[k.lower()].append(str(v))
# Accumulate these is a separate dictionary until the
# end to avoid adding multiple entries to summary.
continue
else:
val = v
_add_val_to_table(k, val, summary, n_previous)
for k, v in multi_entry_keys.iteritems():
if v:
joined = ','.join(v)
_add_val_to_table(k, joined, summary, n_previous)
for missing in missing_in_this_file:
summary[missing].append(missing_marker)
return summary
def _set_column_name_case_to_match_keywords(self, header_keys,
summary_table):
key_name_dict = {k.lower(): k for k in header_keys
if k != k.lower()}
for lcase, user_case in key_name_dict.iteritems():
try:
summary_table.rename_column(lcase, user_case)
except KeyError:
pass
def _fits_summary(self, header_keywords=None):
"""
Generate a summary table of keywords from FITS headers.
Parameters
----------
header_keywords : list of str or '*', optional
Keywords whose value should be extracted from FITS headers.
Default value is ``None``.
"""
from astropy.table import MaskedColumn
if not self.files:
return None
# Get rid of any duplicate keywords, also forces a copy.
header_keys = set(header_keywords)
header_keys.add('file')
file_name_column = MaskedColumn(name='file', data=self.files)
if not header_keys or (header_keys == set(['file'])):
summary_table = Table(masked=True)
summary_table.add_column(file_name_column)
return summary_table
summary_dict = None
missing_marker = None
for file_name in file_name_column:
file_path = path.join(self.location, file_name)
try:
summary_dict = self._table_from_fits_header(
file_path, input_summary=summary_dict,
missing_marker=missing_marker)
except IOError as e:
logger.warning('Unable to get FITS header for file %s: %s',
file_path, e)
continue
summary_table = Table(summary_dict, masked=True)
for column in summary_table.colnames:
summary_table[column].mask = [v is missing_marker
for v in summary_table[column]]
self._set_column_name_case_to_match_keywords(header_keys,
summary_table)
missing_columns = header_keys - set(summary_table.colnames)
missing_columns -= set(['*'])
length = len(summary_table)
for column in missing_columns:
all_masked = MaskedColumn(name=column, data=np.zeros(length),
mask=np.ones(length))
summary_table.add_column(all_masked)
if '*' not in header_keys:
summary_table.keep_columns(header_keys)
if not summary_table.masked:
summary_table = Table(summary_table, masked=True)
return summary_table
def _find_keywords_by_values(self, **kwd):
"""
Find files whose keywords have given values.
`**kwd` is list of keywords and values the files must have.
The value '*' represents any value.
A missing keyword is indicated by value ''
Example:
>>> keys = ['imagetyp','filter']
>>> collection = ImageFileCollection('test/data', keywords=keys)
>>> collection.files_filtered(imagetyp='LIGHT', filter='R')
>>> collection.files_filtered(imagetyp='*', filter='')
NOTE: Value comparison is case *insensitive* for strings.
"""
keywords = kwd.keys()
values = kwd.values()
if (set(keywords).issubset(set(self.keywords))):
# we already have the information in memory
use_info = self.summary_info
else:
# we need to load information about these keywords.
use_info = self._fits_summary(header_keywords=keywords)
matches = np.array([True] * len(use_info))
for key, value in zip(keywords, values):
logger.debug('Key %s, value %s', key, value)
logger.debug('Value in table %s', use_info[key])
value_missing = use_info[key].mask
logger.debug('Value missing: %s', value_missing)
value_not_missing = np.logical_not(value_missing)
if value == '*':
have_this_value = value_not_missing
elif value is not None:
if isinstance(value, basestring):
# need to loop explicitly over array rather than using
# where to correctly do string comparison.
have_this_value = np.array([False] * len(use_info))
for idx, file_key_value in enumerate(use_info[key]):
if value_not_missing[idx]:
value_matches = (file_key_value.lower() ==
value.lower())
else:
value_matches = False
have_this_value[idx] = (value_not_missing[idx] &
value_matches)
else:
have_this_value = value_not_missing
tmp = (use_info[key][value_not_missing] == value)
have_this_value[value_not_missing] = tmp
have_this_value &= value_not_missing
else:
# this case--when value==None--is asking for the files which
# are missing a value for this keyword
have_this_value = value_missing
matches &= have_this_value
# the numpy convention is that the mask is True for values to
# be omitted, hence use ~matches.
logger.debug('Matches: %s', matches)
self.summary_info['file'].mask = ma.nomask
self.summary_info['file'][~matches] = ma.masked
def _fits_files_in_directory(self, extensions=None,
compressed=True):
"""
Get names of FITS files in directory, based on filename extension.
Parameters
----------
extension : list of str, optional
List of filename extensions that are FITS files. Default is
``['fit', 'fits']``
compressed : bool, optional
If ``True``, compressed files should be included in the list
(e.g. `.fits.gz`)
Returns
-------
list
*Names* of the files (with extension), not the full pathname.
"""
full_extensions = extensions or ['fit', 'fits']
if compressed:
with_gz = [extension + '.gz' for extension in full_extensions]
full_extensions.extend(with_gz)
all_files = listdir(self.location)
files = []
for extension in full_extensions:
files.extend(fnmatch.filter(all_files, '*' + extension))
return files
def _generator(self, return_type,
save_with_name="", save_location='',
clobber=False,
do_not_scale_image_data=True,
return_fname=False,
**kwd):
"""
Generator that yields each {name} in the collection.
If any of the parameters ``save_with_name``, ``save_location`` or
``clobber`` evaluates to ``True`` the generator will write a copy of
each FITS file it is iterating over. In other words, if
``save_with_name`` and/or ``save_location`` is a string with non-zero
length, and/or ``clobber`` is ``True``, a copy of each FITS file will
be made.
Parameters
----------
save_with_name : str
string added to end of file name (before extension) if
FITS file should be saved after iteration. Unless
`save_location` is set, files will be saved to location of
the source files `self.location`
save_location : str
Directory in which to save FITS files; implies that FITS
files will be saved. Note this provides an easy way to
copy a directory of files--loop over the {name} with
`save_location` set.
clobber : bool
If ``True``, overwrite input FITS files.
do_not_scale_image_data : bool
If ``True``, prevents fits from scaling images. Default is
``{default_scaling}``.
return_fname : bool, default is False
If True, return the tuple (header, file_name) instead of just
header.
kwd : dict
Any additional keywords are used to filter the items returned; see
Examples for details.
Returns
-------
{return_type}
If ``return_fname`` is ``False``, yield the next {name} in the
collection
({return_type}, str)
If ``return_fname`` is ``True``, yield a tuple of
({name}, `file path`) for next the item in the collection.
"""
# store mask so we can reset at end--must COPY, otherwise
# current_mask just points to the mask of summary_info
if not self.summary_info:
return
current_mask = {}
for col in self.summary_info.columns:
current_mask[col] = self.summary_info[col].mask
if kwd:
self._find_keywords_by_values(**kwd)
for full_path in self._paths():
no_scale = do_not_scale_image_data
hdulist = fits.open(full_path,
do_not_scale_image_data=no_scale)
return_options = {'header': hdulist[0].header,
'hdu': hdulist[0],
'data': hdulist[0].data}
try:
yield (return_options[return_type] # pragma: no branch
if (not return_fname) else
(return_options[return_type], full_path))
except KeyError:
raise ValueError('No generator for {}'.format(return_type))
if save_location:
destination_dir = save_location
else:
destination_dir = path.dirname(full_path)
basename = path.basename(full_path)
if save_with_name:
base, ext = path.splitext(basename)
basename = base + save_with_name + ext
new_path = path.join(destination_dir, basename)
if (new_path != full_path) or clobber:
try:
hdulist.writeto(new_path, clobber=clobber)
except IOError:
logger.error('Error writing file %s', new_path)
raise
hdulist.close()
# reset mask
for col in self.summary_info.columns:
self.summary_info[col].mask = current_mask[col]
def _paths(self):
"""
Full path to each file.
"""
unmasked_files = self.summary_info['file'].compressed()
return [path.join(self.location, file_) for file_ in unmasked_files]
headers.__doc__ = _generator.__doc__.format(name='header',
default_scaling='True',
return_type='astropy.io.fits.Header')
[docs] def hdus(self, do_not_scale_image_data=False, **kwd):
return self._generator('hdu',
do_not_scale_image_data=do_not_scale_image_data,
**kwd)
hdus.__doc__ = _generator.__doc__.format(name='HDU',
default_scaling='False',
return_type='astropy.io.fits.HDU')
[docs] def data(self, do_not_scale_image_data=False, **kwd):
return self._generator('data',
do_not_scale_image_data=do_not_scale_image_data,
**kwd)
data.__doc__ = _generator.__doc__.format(name='image',
default_scaling='False',
return_type='numpy.ndarray')