def __new__(cls, input_, sortby="date"):
"""Creates a new Map instance"""
# Directory of files
if isinstance(input_, basestring):
filepaths = []
fits_arr = []
data = []
headers = []
# directory
if os.path.isdir(input_):
for filename in os.listdir(input_):
filepaths.append(os.path.join(input_, filename))
# glob string
else:
from glob import glob
filepaths = glob(input_)
# read in files
for filepath in filepaths:
fits = pyfits.open(filepath)
# append normalized header tags for use during sorting
found_header_match = False
for subcls in BaseMap.__subclasses__(): #pylint: disable=E1101
if subcls.is_datasource_for(fits[0].header):
found_header_match = True
fits.norm_header = subcls.get_properties(fits[0].header)
if not found_header_match:
raise UnrecognizedDataSouceError
fits_arr.append(fits)
# sort data
if sortby and hasattr(cls, '_sort_by_%s' % sortby):
fits_arr.sort(key=getattr(cls, '_sort_by_%s' % sortby)())
# create data cube
for fits in fits_arr:
data.append(fits[0].data)
headers.append(fits[0].header)
obj = np.asarray(data).view(cls)
obj._headers = headers
# List of data or filepaths
elif isinstance(input_, list):
obj = np.asarray(input_).view(cls)
# ndarray
elif isinstance(input_, np.ndarray):
obj = input_
return obj
def __getitem__(self, key):
"""Overiding indexing operation"""
if self.ndim is 3 and isinstance(key, int):
data = np.ndarray.__getitem__(self, key)
header = self._headers[key]
for cls in BaseMap.__subclasses__():
if cls.is_datasource_for(header):
return cls(data, header)
raise UnrecognizedDataSouceError
else:
return np.ndarray.__getitem__(self, key)
def Map(input_):
"""Map class factory
Attempts to determine the type of data associated with input and returns
an instance of either the generic BaseMap class or a subclass of BaseMap
such as AIAMap, EUVIMap, etc.
Parameters
----------
input_ : filepath, data array
The data source used to create the map object. This can be either a
filepath to an image, a 2d list, or an ndarray.
Returns
-------
out : BaseMap
Returns a BaseMap or BaseMap subclass instance
Notes
-----
PyFITS
[1] Due to the way PyFITS works with images the header dictionary may
differ depending on whether is accessed before or after the fits[0].data
is requested. If the header is read before the data then the original
header will be returned. If the header is read after the data has been
accessed then the data will have been scaled and a modified header
reflecting these changes will be returned: BITPIX may differ and
BSCALE and B_ZERO may be dropped in the modified version.
[2] The verify('fix') call attempts to handle violations of the FITS
standard. For example, nan values will be converted to "nan" strings.
Attempting to cast a pyfits header to a dictionary while it contains
invalid header tags will result in an error so verifying it early on
makes the header easier to work with later.
References
----------
| http://stackoverflow.com/questions/456672/class-factory-in-python
| http://stsdas.stsci.edu/download/wikidocs/The_PyFITS_Handbook.pdf
"""
if isinstance(input_, basestring):
fits = pyfits.open(input_)
fits.verify('silentfix')
data = fits[0].data
header = fits[0].header
for cls in BaseMap.__subclasses__():
if cls.is_datasource_for(header):
return cls(data, header)
raise UnrecognizedDataSouceError
else:
return BaseMap(input_)
