def test_is_time():
assert time.is_time(datetime.utcnow()) is True
assert time.is_time('2017-02-14 08:08:12.999',
"%Y-%m-%d %H:%M:%S.%f") is True
assert time.is_time(None) is False
assert time.is_time('2016-14-14 19:08', "%Y-%b-%d %H:%M:%S") is False
def get_properties(cls, header):
"""Parses a map header and determines default properties."""
if is_time(header.get('date-obs',[])): # Hack! check FITS standard is a time
date = header.get('date-obs')
# Check commonly used but non-standard FITS keyword for observation time is a time
elif is_time(header.get('date_obs',[])): # Horrible [] hack
date = header.get('date_obs')
else:
date = None
return {
"cmap": cm.gray, # @UndefinedVariable
"date": parse_time(date) if date is not None else 'N/A',
"detector": header.get('detector', ''),
"dsun": header.get('dsun_obs', constants.au),
"exposure_time": header.get('exptime', 0.),
"instrument": header.get('instrume', ''),
"measurement": header.get('wavelnth', ''),
"observatory": header.get('telescop', ''),
"name": header.get('telescop', '') + " " +
str(header.get('wavelnth', '')),
"nickname": header.get('detector', ''),
"rsun_meters": header.get('rsun_ref', constants.radius),
"rsun_arcseconds": header.get('rsun_obs', header.get('solar_r',
header.get('radius',
constants.average_angular_size))),
"coordinate_system": {
'x': header.get('ctype1', 'HPLN-TAN'),
'y': header.get('ctype2', 'HPLT-TAN')
},
"carrington_longitude": header.get('crln_obs', 0.),
"heliographic_latitude": header.get('hglt_obs',
header.get('crlt_obs',
header.get('solar_b0', 0.))),
"heliographic_longitude": header.get('hgln_obs', 0.),
"reference_coordinate": {
'x': header.get('crval1', 0.),
'y': header.get('crval2', 0.),
},
"reference_pixel": {
'x': header.get('crpix1', (header.get('naxis1') + 1) / 2.),
'y': header.get('crpix2', (header.get('naxis2') + 1) / 2.)
},
"scale": {
'x': header.get('cdelt1', 1.),
'y': header.get('cdelt2', 1.),
},
"units": {
'x': header.get('cunit1', 'arcsec'),
'y': header.get('cunit2', 'arcsec')
},
"rotation_angle": {
'x': header.get('crota1', 0.),
'y': header.get('crota2', 0.)
}
}
def _validate(self, params):
"""Filters out images that are known to have problems using information
in their metadata"""
# Make sure the time can be understood
if not is_time(params['date']):
raise BadImage("DATE")
# AIA
if params['detector'] == "AIA":
if params['header'].get("IMG_TYPE") == "DARK":
raise BadImage("DARK")
if float(params['header'].get('PERCENTD')) < 50:
raise BadImage("PERCENTD")
if str(params['header'].get('WAVE_STR')).endswith("_OPEN"):
raise BadImage("WAVE_STR")
# LASCO
if params['instrument'] == "LASCO":
hcomp_sf = params['header'].get('hcomp_sf')
if ((params['detector'] == "C2" and hcomp_sf == 32) or
(params['detector'] == "C3" and hcomp_sf == 64)):
raise BadImage("WrongMask")
def _validate(self, params):
"""Filters out images that are known to have problems using information
in their metadata"""
# Make sure the time can be understood
if not is_time(params['date']):
raise BadImage("DATE")
# AIA
if params['detector'] == "AIA":
if params['header'].get("IMG_TYPE") == "DARK":
raise BadImage("DARK")
if float(params['header'].get('PERCENTD')) < 50:
raise BadImage("PERCENTD")
if str(params['header'].get('WAVE_STR')).endswith("_OPEN"):
raise BadImage("WAVE_STR")
# LASCO
if params['instrument'] == "LASCO":
hcomp_sf = params['header'].get('hcomp_sf')
if ((params['detector'] == "C2" and hcomp_sf == 32)
or (params['detector'] == "C3" and hcomp_sf == 64)):
raise BadImage("WrongMask")
def _fix_date(self):
# Check commonly used but non-standard FITS keyword for observation time
# and correct the keyword if we can. Keep updating old one for
# backwards compatibility.
if is_time(self.meta.get('date_obs', None)):
self.meta['date-obs'] = self.meta['date_obs']
def _fix_date(self):
# Check commonly used but non-standard FITS keyword for observation time
# and correct the keyword if we can. Keep updating old one for
# backwards compatibility.
if is_time(self.meta.get('date_obs', None)):
self.meta['date-obs'] = self.meta['date_obs']
def time_is_time(self):
t = is_time('1995-12-31 23:59:60')
def mem_is_time(self):
t = is_time('1995-11-31 23:59:60')
return t
# Because this has *args, **kwargs as its signature we need to disable the
# check of ConditionalDispatch that makes sure the function and the
# conditional need to have the same signature - but they still do have to.
LightCurve._cond_dispatch.add(
run_cls("from_time"),
lambda cls, time: sunpy.time.is_time(time),
# type is here because the class parameter is a class,
# i.e. an instance of type (which is the base meta-class).
[type, (basestring, datetime, tuple)],
False
)
LightCurve._cond_dispatch.add(
run_cls("from_range"),
lambda cls, time, **kwargs: is_time(time),
[type, (basestring, datetime, tuple)],
False
)
LightCurve._cond_dispatch.add(
run_cls("from_timerange"),
lambda cls, timerange, **kwargs: True,
[type, TimeRange],
False
)
LightCurve._cond_dispatch.add(
run_cls("from_file"),
lambda cls, filename: os.path.exists(os.path.expanduser(filename)),
[type, basestring],
# What's happening here is the following: The ConditionalDispatch is just an
# unbound callable object, that is, it does not know which class it is attached
# to. What we do against that is return a wrapper and make that a classmethod -
# thus we get the class as whose member it is called as as the first argument,
# this is why in the type signature we always have type as the first type.
# We then use run_cls, which just returns a wrapper that interprets the first
# argument as the class the function should be called of. So,
# x = run_cls("foo") returns something that turns x(cls, 1) into cls.foo(1).
# Because this has *args, **kwargs as its signature we need to disable the
# check of ConditionalDispatch that makes sure the function and the
# conditional need to have the same signature - but they still do have to.
LightCurve._cond_dispatch.add(
run_cls("from_time"),
lambda cls, time: is_time(time),
# type is here because the class parameter is a class,
# i.e. an instance of type (which is the base meta-class).
[type, (basestring, datetime, tuple)],
False,
)
LightCurve._cond_dispatch.add(
run_cls("from_range"),
lambda cls, time1, time2, **kwargs: is_time(time1) and is_time(time2),
[type, (basestring, datetime, tuple), (basestring, datetime, tuple)],
False,
)
LightCurve._cond_dispatch.add(
run_cls("from_timerange"), lambda cls, timerange, **kwargs: True, [type, TimeRange], False
# What's happening here is the following: The ConditionalDispatch is just an
# unbound callable object, that is, it does not know which class it is attached
# to. What we do against that is return a wrapper and make that a classmethod -
# thus we get the class as whose member it is called as as the first argument,
# this is why in the type signature we always have type as the first type.
# We then use run_cls, which just returns a wrapper that interprets the first
# argument as the class the function should be called of. So,
# x = run_cls("foo") returns something that turns x(cls, 1) into cls.foo(1).
# Because this has *args, **kwargs as its signature we need to disable the
# check of ConditionalDispatch that makes sure the function and the
# conditional need to have the same signature - but they still do have to.
LightCurve._cond_dispatch.add(
run_cls("from_time"),
lambda cls, time, **kwargs: is_time(time),
# type is here because the class parameter is a class,
# i.e. an instance of type (which is the base meta-class).
[type, (basestring, datetime, tuple)],
False)
LightCurve._cond_dispatch.add(
run_cls("from_range"),
lambda cls, time1, time2, **kwargs: is_time(time1) and is_time(time2),
[type, (basestring, datetime, tuple),
(basestring, datetime, tuple)], False)
LightCurve._cond_dispatch.add(run_cls("from_timerange"),
lambda cls, timerange, **kwargs: True,
[type, TimeRange], False)
# What's happening here is the following: The ConditionalDispatch is just an
# unbound callable object, that is, it does not know which class it is attached
# to. What we do against that is return a wrapper and make that a classmethod -
# thus we get the class as whose member it is called as as the first argument,
# this is why in the type signature we always have type as the first type.
# We then use run_cls, which just returns a wrapper that interprets the first
# argument as the class the function should be called of. So,
# x = run_cls("foo") returns something that turns x(cls, 1) into cls.foo(1).
# Because this has *args, **kwargs as its signature we need to disable the
# check of ConditionalDispatch that makes sure the function and the
# conditional need to have the same signature - but they still do have to.
LightCurve._cond_dispatch.add(
run_cls("from_time"),
lambda cls, time: is_time(time),
# type is here because the class parameter is a class,
# i.e. an instance of type (which is the base meta-class).
[type, (basestring, datetime, tuple)],
False)
LightCurve._cond_dispatch.add(run_cls("from_range"),
lambda cls, time, **kwargs: is_time(time),
[type, (basestring, datetime, tuple)], False)
LightCurve._cond_dispatch.add(run_cls("from_timerange"),
lambda cls, timerange, **kwargs: True,
[type, TimeRange], False)
LightCurve._cond_dispatch.add(
run_cls("from_file"),
def test_is_time():
assert time.is_time(datetime.utcnow()) is True
assert time.is_time('2017-02-14 08:08:12.999', "%Y-%m-%d %H:%M:%S.%f") is True
assert time.is_time(None) is False
assert time.is_time('2016-14-14 19:08', "%Y-%b-%d %H:%M:%S") is False
def get_properties(cls, header):
"""Parses a map header and determines default properties."""
if is_time(header.get('date-obs',
[])): # Hack! check FITS standard is a time
date = header.get('date-obs')
# Check commonly used but non-standard FITS keyword for observation time is a time
elif is_time(header.get('date_obs', [])): # Horrible [] hack
date = header.get('date_obs')
else:
date = None
return {
"cmap":
cm.gray, # @UndefinedVariable
"date":
parse_time(date) if date is not None else 'N/A',
"detector":
header.get('detector', ''),
"dsun":
header.get('dsun_obs', constants.au),
"exposure_time":
header.get('exptime', 0.),
"instrument":
header.get('instrume', ''),
"measurement":
header.get('wavelnth', ''),
"observatory":
header.get('telescop', ''),
"name":
header.get('telescop', '') + " " + str(header.get('wavelnth', '')),
"nickname":
header.get('detector', ''),
"rsun_meters":
header.get('rsun_ref', constants.radius),
"rsun_arcseconds":
header.get(
'rsun_obs',
header.get(
'solar_r',
header.get('radius', constants.average_angular_size))),
"coordinate_system": {
'x': header.get('ctype1', 'HPLN-TAN'),
'y': header.get('ctype2', 'HPLT-TAN')
},
"carrington_longitude":
header.get('crln_obs', 0.),
"heliographic_latitude":
header.get('hglt_obs',
header.get('crlt_obs', header.get('solar_b0', 0.))),
"heliographic_longitude":
header.get('hgln_obs', 0.),
"reference_coordinate": {
'x': header.get('crval1', 0.),
'y': header.get('crval2', 0.),
},
"reference_pixel": {
'x': header.get('crpix1', (header.get('naxis1') + 1) / 2.),
'y': header.get('crpix2', (header.get('naxis2') + 1) / 2.)
},
"scale": {
'x': header.get('cdelt1', 1.),
'y': header.get('cdelt2', 1.),
},
"units": {
'x': header.get('cunit1', 'arcsec'),
'y': header.get('cunit2', 'arcsec')
},
"rotation_angle": {
'x': header.get('crota1', 0.),
'y': header.get('crota2', 0.)
}
}