def print_table(lst, colsep=' ', linesep='\n'):
width = [max(map(len, col)) for col in zip(*lst)]
return linesep.join(
colsep.join(
col.ljust(n) for n, col in zip(width, row)
) for row in lst
)
def print_table(lst, colsep=' ', linesep='\n'):
"""
?
Parameters
----------
lst : ?
?
colsep : ?
?
linesep : ?
?
Returns
-------
?
.. todo::
improve documentation.
"""
width = [max(map(len, col)) for col in zip(*lst)]
return linesep.join(
colsep.join(
col.ljust(n) for n, col in zip(width, row)
) for row in lst
)
def _extractDateURL(self, url):
"""Extracts the date from a particular url following the pattern"""
# remove the user and passwd from files if there:
url = url.replace("anonymous:[email protected]@", "")
# url_to_list substitutes '.' and '_' for '/' to then create
# a list of all the blocks in times - assuming they are all
# separated with either '.', '_' or '/'
url_to_list = lambda txt: re.sub(r'\.|_', '/', txt).split('/')
pattern_list = url_to_list(self.pattern)
url_list = url_to_list(url)
time_order = [
'%Y', '%y', '%b', '%B', '%m', '%d', '%j', '%H', '%I', '%M', '%S',
'%e', '%f'
]
final_date = []
final_pattern = []
# Find in directory and filename
for pattern_elem, url_elem in zip(pattern_list, url_list):
time_formats = [x for x in time_order if x in pattern_elem]
if len(time_formats) > 0:
# Find whether there's text that should not be here
toremove = re.split('%.', pattern_elem)
if len(toremove) > 0:
for bit in toremove:
if bit != '':
url_elem = url_elem.replace(bit, '', 1)
pattern_elem = pattern_elem.replace(bit, '', 1)
final_date.append(url_elem)
final_pattern.append(pattern_elem)
for time_bit in time_formats:
time_order.remove(time_bit)
# Find and remove repeated elements eg: %Y in ['%Y', '%Y%m%d']
# Make all as single strings
date_together = ''.join(final_date)
pattern_together = ''.join(final_pattern)
re_together = pattern_together
for k, v in six.iteritems(TIME_CONVERSIONS):
re_together = re_together.replace(k, v)
# Lists to contain the unique elements of the date and the pattern
final_date = list()
final_pattern = list()
re_together = re_together.replace('[A-Z]', '\\[A-Z]')
for p, r in zip(
pattern_together.split('%')[1:],
re_together.split('\\')[1:]):
if p == 'e':
continue
regexp = r'\{}'.format(r) if not r.startswith('[') else r
pattern = '%{}'.format(p)
date_part = re.search(regexp, date_together)
date_together = date_together[:date_part.start()] + \
date_together[date_part.end():]
if pattern not in final_pattern:
final_pattern.append('%{}'.format(p))
final_date.append(date_part.group())
return datetime.datetime.strptime(' '.join(final_date),
' '.join(final_pattern))
def _extractDateURL(self, url):
"""Extracts the date from a particular url following the pattern"""
# remove the user and passwd from files if there:
url = url.replace("anonymous:[email protected]@", "")
# url_to_list substitutes '.' and '_' for '/' to then create
# a list of all the blocks in times - assuming they are all
# separated with either '.', '_' or '/'
url_to_list = lambda txt: re.sub(r'\.|_', '/', txt).split('/')
pattern_list = url_to_list(self.pattern)
url_list = url_to_list(url)
time_order = ['%Y', '%y', '%b', '%B', '%m', '%d', '%j',
'%H', '%I', '%M', '%S', '%e', '%f']
final_date = []
final_pattern = []
# Find in directory and filename
for pattern_elem, url_elem in zip(pattern_list, url_list):
time_formats = [x for x in time_order if x in pattern_elem]
if len(time_formats) > 0:
# Find whether there's text that should not be here
toremove = re.split('%.', pattern_elem)
if len(toremove) > 0:
for bit in toremove:
if bit != '':
url_elem = url_elem.replace(bit, '', 1)
pattern_elem = pattern_elem.replace(bit, '', 1)
final_date.append(url_elem)
final_pattern.append(pattern_elem)
for time_bit in time_formats:
time_order.remove(time_bit)
# Find and remove repeated elements eg: %Y in ['%Y', '%Y%m%d']
# Make all as single strings
date_together = ''.join(final_date)
pattern_together = ''.join(final_pattern)
re_together = pattern_together
for k, v in six.iteritems(TIME_CONVERSIONS):
re_together = re_together.replace(k, v)
# Lists to contain the unique elements of the date and the pattern
final_date = list()
final_pattern = list()
re_together = re_together.replace('[A-Z]', '\\[A-Z]')
for p, r in zip(pattern_together.split('%')[1:], re_together.split('\\')[1:]):
if p == 'e':
continue
regexp = r'\{}'.format(r) if not r.startswith('[') else r
pattern = '%{}'.format(p)
date_part = re.search(regexp, date_together)
date_together = date_together[:date_part.start()] + \
date_together[date_part.end():]
if pattern not in final_pattern:
final_pattern.append('%{}'.format(p))
final_date.append(date_part.group())
return datetime.datetime.strptime(' '.join(final_date),
' '.join(final_pattern))
def add(self, fun, types, override=SILENT):
""" Add fun to the multimethod. It will be executed if get returns
values of the types passed as types. Must return tuples of same
length for any input.
Parameters
----------
fun : function
function to be added to the multimethod
types : tuple of classes
types for which the function is executed
override : SILENT, WARN or FAIL
control behaviour when overriding existing definitions.
If it is set to SILENT, prior definitions are silently
overridden, if it is set to WARN a TypeWarning
will be issued, and with FAIL a TypeError is raised when
attempting to override an existing definition.
"""
if override not in (SILENT, WARN, FAIL):
raise ValueError("Invalid value '{0}' for override.".format(override))
overriden = False
if override != SILENT:
for signature, _ in self.methods:
if all(issubclass(a, b) for a, b in zip(types, signature)):
overriden = True
if overriden and override == FAIL:
raise TypeError
elif overriden and override == WARN:
# pylint: disable=W0631
warn('Definition ({0}) overrides prior definition ({1}).'.format(_fmt_t(types),
_fmt_t(signature)),
TypeWarning, stacklevel=3)
self.methods.append((types, fun))
def fmt_argspec_types(fun, types, start=0):
args, varargs, keywords, defaults = correct_argspec(fun)
args = args[start:]
types = types[start:]
NULL = object()
if defaults is None:
defaults = []
defs = chain(repeat(NULL, len(args) - len(defaults)), defaults)
spec = []
for key, value, type_ in zip(args, defs, types):
# This is a work around for a bug introduced during Python 3 porting.
# for some reason the type was being passed in as a length 1 tuple.
# This extracts the type under that condition. SM 6/10/15
if isinstance(type_, tuple) and len(type_) == 1:
type_ = type_[0]
if value is NULL:
spec.append("{0}: {1}".format(key, type_.__name__))
else:
spec.append("{0}: {1} = {2}".format(key, type_.__name__, value))
if varargs is not None:
spec.append('*{!s}'.format(varargs))
if keywords is not None:
spec.append('**{!s}'.format(keywords))
return '(' + ', '.join(spec) + ')'
def add(self, fun, types, override=SILENT):
""" Add fun to the multimethod. It will be executed if get returns
values of the types passed as types. Must return tuples of same
length for any input.
Parameters
----------
fun : function
function to be added to the multimethod
types : tuple of classes
types for which the function is executed
override : SILENT, WARN or FAIL
control behaviour when overriding existing definitions.
If it is set to SILENT, prior definitions are silently
overridden, if it is set to WARN a TypeWarning
will be issued, and with FAIL a TypeError is raised when
attempting to override an existing definition.
"""
overriden = False
if override:
for signature, _ in self.methods:
if all(issubclass(a, b) for a, b in zip(types, signature)):
overriden = True
if overriden and override == FAIL:
raise TypeError
elif overriden and override == WARN:
# pylint: disable=W0631
warn('Definition ({0}) overrides prior definition ({1}).'.format(
_fmt_t(types), _fmt_t(signature)),
TypeWarning,
stacklevel=3)
elif overriden:
raise ValueError('Invalid value for override.')
self.methods.append((types, fun))
def generate_docs(self):
fns = (item[0] for item in chain(self.funcs, self.nones))
return '\n\n'.join(
"{0} -> :py:meth:`{1}`".format(sig, fun.__name__) for sig, fun in
# The 1 prevents the cls from incorrectly being shown in the
# documentation.
zip(self.get_signatures("create", -1), fns))
def matches_types(fun, types, args, kwargs):
""" See if args and kwargs match are instances of types. types are given
in the order they are defined in the function. kwargs are automatically
converted into that order. """
return all(
isinstance(obj, cls)
for obj, cls in zip(arginize(fun, args, kwargs), types))
def fmt_argspec_types(fun, types, start=0):
args, varargs, keywords, defaults = correct_argspec(fun)
args = args[start:]
types = types[start:]
NULL = object()
if defaults is None:
defaults = []
defs = chain(repeat(NULL, len(args) - len(defaults)), defaults)
spec = []
for key, value, type_ in zip(args, defs, types):
# This is a work around for a bug introduced during Python 3 porting.
# for some reason the type was being passed in as a length 1 tuple.
# This extracts the type under that condition. SM 6/10/15
if isinstance(type_, tuple) and len(type_) == 1:
type_ = type_[0]
if value is NULL:
spec.append("{0}: {1}".format(key, type_.__name__))
else:
spec.append("{0}: {1} = {2}".format(key, type_.__name__, value))
if varargs is not None:
spec.append('*{!s}'.format(varargs))
if keywords is not None:
spec.append('**{!s}'.format(keywords))
return '(' + ', '.join(spec) + ')'
def super(self, *args, **kwargs):
""" Like __call__, only that when you give it super(cls, obj) items,
it will skip the multimethod for cls and use the one for its parent
class. The normal __call__ does not consider this for performance
reasons. """
objs = self.get(*args, **kwargs)
types = tuple([
x.__thisclass__.__mro__[1] if isinstance(x, super) else type(x)
for x in objs
])
nargs = [x.__self__ if isinstance(x, super) else x for x in args]
for k, elem in six.iteritems(kwargs):
if isinstance(elem, super):
kwargs[k] = elem.__self__
# This code is duplicate for performance reasons.
cached = self.cache.get(types, None)
if cached is not None:
return cached(*nargs, **kwargs)
for signature, fun in reversed(self.methods):
if all(issubclass(ty, sig) for ty, sig in zip(types, signature)):
self.cache[types] = fun
return fun(*nargs, **kwargs)
raise TypeError
def super(self, *args, **kwargs):
""" Like __call__, only that when you give it super(cls, obj) items,
it will skip the multimethod for cls and use the one for its parent
class. The normal __call__ does not consider this for performance
reasons. """
objs = self.get(*args, **kwargs)
types = tuple(
[
x.__thisclass__.__mro__[1] if isinstance(x, super) else type(x)
for x in objs
]
)
nargs = [
x.__self__ if isinstance(x, super) else x
for x in args
]
for k, elem in six.iteritems(kwargs):
if isinstance(elem, super):
kwargs[k] = elem.__self__
# This code is duplicate for performance reasons.
cached = self.cache.get(types, None)
if cached is not None:
return cached(*nargs, **kwargs)
for signature, fun in reversed(self.methods):
if all(issubclass(ty, sig) for ty, sig in zip(types, signature)):
self.cache[types] = fun
return fun(*nargs, **kwargs)
raise TypeError
def generate_docs(self):
fns = (item[0] for item in chain(self.funcs, self.nones))
return '\n\n'.join("{0} -> :py:meth:`{1}`".format(sig, fun.__name__)
for sig, fun in
# The 1 prevents the cls from incorrectly being shown in the
# documentation.
zip(self.get_signatures("create", -1), fns)
)
def _extractDateURL(self, url):
"""Extracts the date from a particular url following the pattern"""
# url_to_list substitutes '.' and '_' for '/' to then create
# a list of all the blocks in times - assuming they are all
# separated with either '.', '_' or '/'
url_to_list = lambda txt: re.sub(r'\.|_', '/', txt).split('/')
pattern_list = url_to_list(self.pattern)
url_list = url_to_list(url)
time_order = [
'%Y', '%y', '%b', '%B', '%m', '%d', '%j', '%H', '%I', '%M', '%S'
]
final_date = []
final_pattern = []
# Find in directory and filename
for pattern_elem, url_elem in zip(pattern_list, url_list):
time_formats = [x for x in time_order if x in pattern_elem]
if len(time_formats) > 0:
final_date.append(url_elem)
final_pattern.append(pattern_elem)
for time_bit in time_formats:
time_order.remove(time_bit)
# Find and remove repeated elements eg: %Y in ['%Y', '%Y%m%d']
# Make all as single strings
date_together = ''.join(final_date)
pattern_together = ''.join(final_pattern)
re_together = pattern_together
for k, v in six.iteritems(TIME_CONVERSIONS):
re_together = re_together.replace(k, v)
# Create new empty lists
final_date = list()
final_pattern = list()
for p, r in zip(
pattern_together.split('%')[1:],
re_together.split('\\')[1:]):
regexp = '\\{}'.format(r)
pattern = '%{}'.format(p)
date_part = re.match(regexp, date_together)
date_together = date_together[:date_part.start()] + \
date_together[date_part.end():]
if pattern not in final_pattern:
final_pattern.append('%{}'.format(p))
final_date.append(date_part.group())
return datetime.datetime.strptime(' '.join(final_date),
' '.join(final_pattern))
def test_window(timerange_a):
timerange = sunpy.time.TimeRange(tbegin_str, tfin_str)
window = timerange.window(u.Quantity(12 * 60 * 60, 's'), u.Quantity(10, 's'))
expect = [sunpy.time.TimeRange('2012/1/1T00:00:00', '2012/1/1T00:00:10'),
sunpy.time.TimeRange('2012/1/1T12:00:00', '2012/1/1T12:00:10'),
sunpy.time.TimeRange('2012/1/2T00:00:00', '2012/1/2T00:00:10')]
assert isinstance(window, list)
# Doing direct comparisons seem to not work
assert all([wi.start == ex.start and wi.end == ex.end for wi, ex in zip(window, expect)])
def test_window_timedelta(timerange_a):
timerange = sunpy.time.TimeRange(tbegin_str, tfin_str)
window = timerange.window(datetime.timedelta(hours=12), datetime.timedelta(seconds=10))
expect = [sunpy.time.TimeRange('2012/1/1T00:00:00', '2012/1/1T00:00:10'),
sunpy.time.TimeRange('2012/1/1T12:00:00', '2012/1/1T12:00:10'),
sunpy.time.TimeRange('2012/1/2T00:00:00', '2012/1/2T00:00:10')]
assert isinstance(window, list)
# Doing direct comparisons seem to not work
assert all([wi.start == ex.start and wi.end == ex.end for wi, ex in zip(window, expect)])
def matches_types(fun, types, args, kwargs):
""" See if args and kwargs match are instances of types. types are given
in the order they are defined in the function. kwargs are automatically
converted into that order. """
return all(
isinstance(obj, cls) for obj, cls in zip(
arginize(fun, args, kwargs), types
)
)
def test_window_timedelta(timerange_a):
timerange = sunpy.time.TimeRange(tbegin_str,tfin_str)
window = timerange.window(datetime.timedelta(hours=12), datetime.timedelta(seconds=10))
expect = [sunpy.time.TimeRange('2012/1/1T00:00:00', '2012/1/1T00:00:10'),
sunpy.time.TimeRange('2012/1/1T12:00:00', '2012/1/1T12:00:10'),
sunpy.time.TimeRange('2012/1/2T00:00:00', '2012/1/2T00:00:10')]
assert isinstance(window, list)
#Doing direct comparisons seem to not work
assert all([wi.start == ex.start and wi.end == ex.end for wi, ex in zip(window, expect)])
def test_window(timerange_a):
timerange = sunpy.time.TimeRange(tbegin_str, tfin_str)
window = timerange.window(u.Quantity(12 * 60 * 60, 's'), u.Quantity(10, 's'))
expect = [sunpy.time.TimeRange('2012/1/1T00:00:00', '2012/1/1T00:00:10'),
sunpy.time.TimeRange('2012/1/1T12:00:00', '2012/1/1T12:00:10'),
sunpy.time.TimeRange('2012/1/2T00:00:00', '2012/1/2T00:00:10')]
assert isinstance(window, list)
#Doing direct comparisons seem to not work
assert all([wi.start == ex.start and wi.end == ex.end for wi, ex in zip(window, expect)])
def _extractDateURL(self, url):
"""Extracts the date from a particular url following the pattern"""
# url_to_list substitutes '.' and '_' for '/' to then create
# a list of all the blocks in times - assuming they are all
# separated with either '.', '_' or '/'
url_to_list = lambda txt: re.sub(r'\.|_', '/', txt).split('/')
pattern_list = url_to_list(self.pattern)
url_list = url_to_list(url)
time_order = ['%Y', '%y', '%b', '%B', '%m', '%d', '%j',
'%H', '%I', '%M', '%S']
final_date = []
final_pattern = []
# Find in directory and filename
for pattern_elem, url_elem in zip(pattern_list, url_list):
time_formats = [x for x in time_order if x in pattern_elem]
if len(time_formats) > 0:
final_date.append(url_elem)
final_pattern.append(pattern_elem)
for time_bit in time_formats:
time_order.remove(time_bit)
# Find and remove repeated elements eg: %Y in ['%Y', '%Y%m%d']
# Make all as single strings
date_together = ''.join(final_date)
pattern_together = ''.join(final_pattern)
re_together = pattern_together
for k, v in six.iteritems(TIME_CONVERSIONS):
re_together = re_together.replace(k, v)
# Create new empty lists
final_date = list()
final_pattern = list()
for p,r in zip(pattern_together.split('%')[1:], re_together.split('\\')[1:]):
regexp = '\\{}'.format(r)
pattern = '%{}'.format(p)
date_part = re.match(regexp, date_together)
date_together = date_together[:date_part.start()] + \
date_together[date_part.end():]
if pattern not in final_pattern:
final_pattern.append('%{}'.format(p))
final_date.append(date_part.group())
return datetime.datetime.strptime(' '.join(final_date),
' '.join(final_pattern))
def test_split(timerange_a):
expect = [
sunpy.time.TimeRange('2012/1/1T00:00:00', '2012/1/1T12:00:00'),
sunpy.time.TimeRange('2012/1/1T12:00:00', '2012/1/2T00:00:00')
]
split = timerange_a.split(n=2)
#Doing direct comparisons seem to not work
assert all([
wi.start == ex.start and wi.end == ex.end
for wi, ex in zip(split, expect)
])
def arginize(fun, a, kw):
""" Turn args and kwargs into args by considering the function
signature. """
args, varargs, keywords, defaults = correct_argspec(fun)
if varargs is not None:
raise ValueError
names = args[len(a):]
if defaults:
defs = dict(zip(args[-len(defaults):], defaults))
else:
defs = {}
return list(a) + [kw.get(name, defs.get(name, None)) for name in names]
def arginize(fun, a, kw):
""" Turn args and kwargs into args by considering the function
signature. """
args, varargs, keywords, defaults = correct_argspec(fun)
if varargs is not None:
raise ValueError
names = args[len(a):]
if defaults:
defs = dict(zip(args[-len(defaults):], defaults))
else:
defs = {}
return list(a) + [kw.get(name, defs.get(name, None)) for name in names]
def print_table(lst, colsep=' ', linesep='\n'):
"""
?
Parameters
----------
lst : ?
?
colsep : ?
?
linesep : ?
?
Returns
-------
?
.. todo::
improve documentation.
"""
width = [max(map(len, col)) for col in zip(*lst)]
return linesep.join(
colsep.join(col.ljust(n) for n, col in zip(width, row)) for row in lst)
def __call__(self, *args, **kwargs):
objs = self.get(*args, **kwargs)
# pylint: disable=W0141
types = tuple(map(type, objs))
# This code is duplicate for performance reasons.
cached = self.cache.get(types, None)
if cached is not None:
return cached(*args, **kwargs)
for signature, fun in reversed(self.methods):
if all(issubclass(ty, sig) for ty, sig in zip(types, signature)):
self.cache[types] = fun
return fun(*args, **kwargs)
raise TypeError('{0!r}'.format(types))
def __call__(self, *args, **kwargs):
objs = self.get(*args, **kwargs)
# pylint: disable=W0141
types = tuple(map(type, objs))
# This code is duplicate for performance reasons.
cached = self.cache.get(types, None)
if cached is not None:
return cached(*args, **kwargs)
for signature, fun in reversed(self.methods):
if all(issubclass(ty, sig) for ty, sig in zip(types, signature)):
self.cache[types] = fun
return fun(*args, **kwargs)
raise TypeError('{0!r}'.format(types))
def read(filepath, hdus=None, memmap=None, **kwargs):
"""
Read a fits file
Parameters
----------
filepath : `str`
The fits file to be read
hdu: `int` or iterable
The HDU indexes to read from the file
Returns
-------
pairs : `list`
A list of (data, header) tuples
Notes
-----
This routine reads all the HDU's in a fits file and returns a list of the
data and a FileHeader instance for each one.
Also all comments in the original file are concatenated into a single
'comment' key in the returned FileHeader.
"""
with fits.open(filepath, ignore_blank=True, memmap=memmap) as hdulist:
if hdus is not None:
if isinstance(hdus, int):
hdulist = hdulist[hdus]
elif isinstance(hdus, collections.Iterable):
hdulist = [hdulist[i] for i in hdus]
hdulist.verify('silentfix+warn')
headers = get_header(hdulist)
pairs = []
for i, (hdu, header) in enumerate(zip(hdulist, headers)):
try:
pairs.append(HDPair(hdu.data, header))
except (KeyError, ValueError) as e:
message = "Error when reading HDU {}. Skipping.\n".format(i)
for line in traceback.format_tb(sys.exc_info()[2]):
message += line
message += '\n'
message += repr(e)
warnings.warn(message, Warning, stacklevel=2)
return pairs
def read(filepath, hdus=None, memmap=None, **kwargs):
"""
Read a fits file
Parameters
----------
filepath : `str`
The fits file to be read
hdu: `int` or iterable
The HDU indexes to read from the file
Returns
-------
pairs : `list`
A list of (data, header) tuples
Notes
-----
This routine reads all the HDU's in a fits file and returns a list of the
data and a FileHeader instance for each one.
Also all comments in the original file are concatenated into a single
'comment' key in the returned FileHeader.
"""
with fits.open(filepath, memmap=memmap) as hdulist:
if hdus is not None:
if isinstance(hdus, int):
hdulist = hdulist[hdus]
elif isinstance(hdus, collections.Iterable):
hdulist = [hdulist[i] for i in hdus]
hdulist.verify('silentfix+warn')
headers = get_header(hdulist)
pairs = []
for i, (hdu, header) in enumerate(zip(hdulist, headers)):
try:
pairs.append(HDPair(hdu.data, header))
except (KeyError, ValueError) as e:
message = "Error when reading HDU {}. Skipping.\n".format(i)
for line in traceback.format_tb(sys.exc_info()[2]):
message += line
message += '\n'
message += repr(e)
warnings.warn(message, Warning, stacklevel=2)
return pairs
def linearize_freqs(self, delta_freq=None):
"""Rebin frequencies so that the frequency axis is linear.
Parameters
----------
delta_freq : float
Difference between consecutive values on the new frequency axis.
Defaults to half of smallest delta in current frequency axis.
Compare Nyquist-Shannon sampling theorem.
"""
if delta_freq is None:
# Nyquist–Shannon sampling theorem
delta_freq = _min_delt(self.freq_axis) / 2.
nsize = (self.freq_axis.max() - self.freq_axis.min()) / delta_freq + 1
new = np.zeros((nsize, self.shape[1]), dtype=self.data.dtype)
freqs = self.freq_axis - self.freq_axis.max()
freqs = freqs / delta_freq
midpoints = np.round((freqs[:-1] + freqs[1:]) / 2)
fillto = np.concatenate(
[midpoints - 1, np.round([freqs[-1]]) - 1]
)
fillfrom = np.concatenate(
[np.round([freqs[0]]), midpoints - 1]
)
fillto = np.abs(fillto)
fillfrom = np.abs(fillfrom)
for row, from_, to_ in zip(self, fillfrom, fillto):
new[from_: to_] = row
vrs = self._get_params()
vrs.update({
'freq_axis': np.linspace(
self.freq_axis.max(), self.freq_axis.min(), nsize
)
})
return self.__class__(new, **vrs)
def linearize_freqs(self, delta_freq=None):
"""Rebin frequencies so that the frequency axis is linear.
Parameters
----------
delta_freq : float
Difference between consecutive values on the new frequency axis.
Defaults to half of smallest delta in current frequency axis.
Compare Nyquist-Shannon sampling theorem.
"""
if delta_freq is None:
# Nyquist–Shannon sampling theorem
delta_freq = _min_delt(self.freq_axis) / 2.
nsize = (self.freq_axis.max() - self.freq_axis.min()) / delta_freq + 1
new = np.zeros((int(nsize), self.shape[1]), dtype=self.data.dtype)
freqs = self.freq_axis - self.freq_axis.max()
freqs = freqs / delta_freq
midpoints = np.round((freqs[:-1] + freqs[1:]) / 2)
fillto = np.concatenate(
[midpoints - 1, np.round([freqs[-1]]) - 1]
)
fillfrom = np.concatenate(
[np.round([freqs[0]]), midpoints - 1]
)
fillto = np.abs(fillto)
fillfrom = np.abs(fillfrom)
for row, from_, to_ in zip(self, fillfrom, fillto):
new[int(from_): int(to_)] = row
vrs = self._get_params()
vrs.update({
'freq_axis': np.linspace(
self.freq_axis.max(), self.freq_axis.min(), nsize
)
})
return self.__class__(new, **vrs)
def read(filepath, hdus=None, memmap=None, **kwargs):
"""
Read a fits file
Parameters
----------
filepath : `str`
The fits file to be read
hdu: `int` or iterable
The HDU indexes to read from the file
Returns
-------
pairs : `list`
A list of (data, header) tuples
Notes
-----
This routine reads all the HDU's in a fits file and returns a list of the
data and a FileHeader instance for each one.
Also all comments in the original file are concatenated into a single
'comment' key in the returned FileHeader.
"""
hdulist = fits.open(filepath, memmap=memmap)
if hdus is not None:
if isinstance(hdus, int):
hdulist = hdulist[hdus]
elif isinstance(hdus, collections.Iterable):
hdulist = [hdulist[i] for i in hdus]
try:
hdulist.verify('silentfix+warn')
headers = get_header(hdulist)
pairs = []
for hdu, header in zip(hdulist, headers):
pairs.append((hdu.data, header))
finally:
hdulist.close()
return pairs
def read(filepath, hdus=None, memmap=None, **kwargs):
"""
Read a fits file
Parameters
----------
filepath : `str`
The fits file to be read
hdu: `int` or iterable
The HDU indexes to read from the file
Returns
-------
pairs : `list`
A list of (data, header) tuples
Notes
-----
This routine reads all the HDU's in a fits file and returns a list of the
data and a FileHeader instance for each one.
Also all comments in the original file are concatenated into a single
'comment' key in the returned FileHeader.
"""
hdulist = fits.open(filepath, memmap=memmap)
if hdus is not None:
if isinstance(hdus, int):
hdulist = hdulist[hdus]
elif isinstance(hdus, collections.Iterable):
hdulist = [hdulist[i] for i in hdus]
try:
hdulist.verify('silentfix+warn')
headers = get_header(hdulist)
pairs = []
for hdu,header in zip(hdulist, headers):
pairs.append((hdu.data, header))
finally:
hdulist.close()
return pairs
def interpolate(self, frequency):
"""
Linearly interpolate intensity at unknown frequency using linear
interpolation of its two neighbours.
Parameters
----------
frequency : float or int
Unknown frequency for which to linearly interpolate the intensities.
freq_axis[0] >= frequency >= self_freq_axis[-1]
"""
lfreq, lvalue = None, None
for freq, value in zip(self.freq_axis, self.data[:, :]):
if freq < frequency:
break
lfreq, lvalue = freq, value
else:
raise ValueError("Frequency not in interpolation range")
if lfreq is None:
raise ValueError("Frequency not in interpolation range")
diff = frequency - freq # pylint: disable=W0631
ldiff = lfreq - frequency
return (ldiff * value + diff * lvalue) / (diff + ldiff) # pylint: disable=W0631
def interpolate(self, frequency):
"""
Linearly interpolate intensity at unknown frequency using linear
interpolation of its two neighbours.
Parameters
----------
frequency : float or int
Unknown frequency for which to linearly interpolate the intensities.
freq_axis[0] >= frequency >= self_freq_axis[-1]
"""
lfreq, lvalue = None, None
for freq, value in zip(self.freq_axis, self.data[:, :]):
if freq < frequency:
break
lfreq, lvalue = freq, value
else:
raise ValueError("Frequency not in interpolation range")
if lfreq is None:
raise ValueError("Frequency not in interpolation range")
diff = frequency - freq # pylint: disable=W0631
ldiff = lfreq - frequency
return (ldiff * value + diff * lvalue) / (diff + ldiff) # pylint: disable=W0631
def join_many(cls, specs, mk_arr=None, nonlinear=False,
maxgap=0, fill=JOIN_REPEAT):
"""Produce new Spectrogram that contains spectrograms
joined together in time.
Parameters
----------
specs : list
List of spectrograms to join together in time.
nonlinear : bool
If True, leave out gaps between spectrograms. Else, fill them with
the value specified in fill.
maxgap : float, int or None
Largest gap to allow in second. If None, allow gap of arbitrary
size.
fill : float or int
Value to fill missing values (assuming nonlinear=False) with.
Can be LinearTimeSpectrogram.JOIN_REPEAT to repeat the values for
the time just before the gap.
mk_array: function
Function that is called to create the resulting array. Can be set
to LinearTimeSpectrogram.memap(filename) to create a memory mapped
result array.
"""
# XXX: Only load header and load contents of files
# on demand.
mask = None
if mk_arr is None:
mk_arr = cls.make_array
specs = sorted(specs, key=lambda x: x.start)
freqs = specs[0].freq_axis
if not all(np.array_equal(freqs, sp.freq_axis) for sp in specs):
raise ValueError("Frequency channels do not match.")
# Smallest time-delta becomes the common time-delta.
min_delt = min(sp.t_delt for sp in specs)
dtype_ = max(sp.dtype for sp in specs)
specs = [sp.resample_time(min_delt) for sp in specs]
size = sum(sp.shape[1] for sp in specs)
data = specs[0]
start_day = data.start
xs = []
last = data
for elem in specs[1:]:
e_init = (
SECONDS_PER_DAY * (
get_day(elem.start) - get_day(start_day)
).days + elem.t_init
)
x = int((e_init - last.t_init) / min_delt)
xs.append(x)
diff = last.shape[1] - x
if maxgap is not None and -diff > maxgap / min_delt:
raise ValueError("Too large gap.")
# If we leave out undefined values, we do not want to
# add values here if x > t_res.
if nonlinear:
size -= max(0, diff)
else:
size -= diff
last = elem
# The non existing element after the last one starts after
# the last one. Needed to keep implementation below sane.
xs.append(specs[-1].shape[1])
# We do that here so the user can pass a memory mapped
# array if they'd like to.
arr = mk_arr((data.shape[0], size), dtype_)
time_axis = np.zeros((size,))
sx = 0
# Amount of pixels left out due to non-linearity. Needs to be
# considered for correct time axes.
sd = 0
for x, elem in zip(xs, specs):
diff = x - elem.shape[1]
e_time_axis = elem.time_axis
elem = elem.data
if x > elem.shape[1]:
if nonlinear:
x = elem.shape[1]
else:
# If we want to stay linear, fill up the missing
# pixels with placeholder zeros.
filler = np.zeros((data.shape[0], diff))
if fill is cls.JOIN_REPEAT:
filler[:, :] = elem[:, -1, np.newaxis]
else:
filler[:] = fill
minimum = e_time_axis[-1]
e_time_axis = np.concatenate([
e_time_axis,
np.linspace(
minimum + min_delt,
minimum + diff * min_delt,
diff
)
])
elem = np.concatenate([elem, filler], 1)
arr[:, sx:sx + x] = elem[:, :x]
if diff > 0:
if mask is None:
mask = np.zeros((data.shape[0], size), dtype=np.uint8)
mask[:, sx + x - diff:sx + x] = 1
time_axis[sx:sx + x] = e_time_axis[:x] + data.t_delt * (sx + sd)
if nonlinear:
sd += max(0, diff)
sx += x
params = {
'time_axis': time_axis,
'freq_axis': data.freq_axis,
'start': data.start,
'end': specs[-1].end,
't_delt': data.t_delt,
't_init': data.t_init,
't_label': data.t_label,
'f_label': data.f_label,
'content': data.content,
'instruments': _union(spec.instruments for spec in specs),
}
if mask is not None:
arr = ma.array(arr, mask=mask)
if nonlinear:
del params['t_delt']
return Spectrogram(arr, **params)
return common_base(specs)(arr, **params)
c = np.cos(angle); s = np.sin(angle)
rmatrix = np.array([[c, -s], [s, c]])
expected = np.rot90(original, k=k)
rot = affine_transform(original, rmatrix=rmatrix, use_scipy=True)
assert compare_results(expected, rot, allclose=False)
# TODO: Check incremental 360 degree rotation against original image
# Check derotated image against original
derot_matrix = np.array([[c, s], [-s, c]])
derot = affine_transform(rot, rmatrix=derot_matrix, use_scipy=True)
assert compare_results(original, derot, allclose=False)
dx_values, dy_values = list(range(-100, 101, 100))*3, list(range(-100, 101, 100))*3
dy_values.sort()
@pytest.mark.parametrize("dx, dy", list(zip(dx_values, dy_values)))
def test_shift(dx, dy):
# Rotation center for all translation tests.
image_center = np.array(original.shape)/2.0 - 0.5
# No rotation for all translation tests.
rmatrix = np.array([[1.0, 0.0], [0.0, 1.0]])
# Check a shifted shape against expected outcome
expected = np.roll(np.roll(original, dx, axis=1), dy, axis=0)
rcen = image_center + np.array([dx, dy])
shift = affine_transform(original, rmatrix=rmatrix, recenter=True, image_center=rcen)
ymin, ymax = max([0, dy]), min([original.shape[1], original.shape[1]+dy])
xmin, xmax = max([0, dx]), min([original.shape[0], original.shape[0]+dx])
compare_results(expected[ymin:ymax, xmin:xmax], shift[ymin:ymax, xmin:xmax])
def join_many(cls, specs, mk_arr=None, nonlinear=False,
maxgap=0, fill=JOIN_REPEAT):
"""Produce new Spectrogram that contains spectrograms
joined together in time.
Parameters
----------
specs : list
List of spectrograms to join together in time.
nonlinear : bool
If True, leave out gaps between spectrograms. Else, fill them with
the value specified in fill.
maxgap : float, int or None
Largest gap to allow in second. If None, allow gap of arbitrary
size.
fill : float or int
Value to fill missing values (assuming nonlinear=False) with.
Can be LinearTimeSpectrogram.JOIN_REPEAT to repeat the values for
the time just before the gap.
mk_array: function
Function that is called to create the resulting array. Can be set
to LinearTimeSpectrogram.memap(filename) to create a memory mapped
result array.
"""
# XXX: Only load header and load contents of files
# on demand.
mask = None
if mk_arr is None:
mk_arr = cls.make_array
specs = sorted(specs, key=lambda x: x.start)
freqs = specs[0].freq_axis
if not all(np.array_equal(freqs, sp.freq_axis) for sp in specs):
raise ValueError("Frequency channels do not match.")
# Smallest time-delta becomes the common time-delta.
min_delt = min(sp.t_delt for sp in specs)
dtype_ = max(sp.dtype for sp in specs)
specs = [sp.resample_time(min_delt) for sp in specs]
size = sum(sp.shape[1] for sp in specs)
data = specs[0]
start_day = data.start
xs = []
last = data
for elem in specs[1:]:
e_init = (
SECONDS_PER_DAY * (
get_day(elem.start) - get_day(start_day)
).days + elem.t_init
)
x = int((e_init - last.t_init) / min_delt)
xs.append(x)
diff = last.shape[1] - x
if maxgap is not None and -diff > maxgap / min_delt:
raise ValueError("Too large gap.")
# If we leave out undefined values, we do not want to
# add values here if x > t_res.
if nonlinear:
size -= max(0, diff)
else:
size -= diff
last = elem
# The non existing element after the last one starts after
# the last one. Needed to keep implementation below sane.
xs.append(specs[-1].shape[1])
# We do that here so the user can pass a memory mapped
# array if they'd like to.
arr = mk_arr((data.shape[0], size), dtype_)
time_axis = np.zeros((size,))
sx = 0
# Amount of pixels left out due to non-linearity. Needs to be
# considered for correct time axes.
sd = 0
for x, elem in zip(xs, specs):
diff = x - elem.shape[1]
e_time_axis = elem.time_axis
elem = elem.data
if x > elem.shape[1]:
if nonlinear:
x = elem.shape[1]
else:
# If we want to stay linear, fill up the missing
# pixels with placeholder zeros.
filler = np.zeros((data.shape[0], diff))
if fill is cls.JOIN_REPEAT:
filler[:, :] = elem[:, -1, np.newaxis]
else:
filler[:] = fill
minimum = e_time_axis[-1]
e_time_axis = np.concatenate([
e_time_axis,
np.linspace(
minimum + min_delt,
minimum + diff * min_delt,
diff
)
])
elem = np.concatenate([elem, filler], 1)
arr[:, sx:sx + x] = elem[:, :x]
if diff > 0:
if mask is None:
mask = np.zeros((data.shape[0], size), dtype=np.uint8)
mask[:, sx + x - diff:sx + x] = 1
time_axis[sx:sx + x] = e_time_axis[:x] + data.t_delt * (sx + sd)
if nonlinear:
sd += max(0, diff)
sx += x
params = {
'time_axis': time_axis,
'freq_axis': data.freq_axis,
'start': data.start,
'end': specs[-1].end,
't_delt': data.t_delt,
't_init': data.t_init,
't_label': data.t_label,
'f_label': data.f_label,
'content': data.content,
'instruments': _union(spec.instruments for spec in specs),
}
if mask is not None:
arr = ma.array(arr, mask=mask)
if nonlinear:
del params['t_delt']
return Spectrogram(arr, **params)
return common_base(specs)(arr, **params)
def print_table(lst, colsep=' ', linesep='\n'):
width = [max(map(len, col)) for col in zip(*lst)]
return linesep.join(
colsep.join(col.ljust(n) for n, col in zip(width, row)) for row in lst)
def test_split(timerange_a):
expect = [sunpy.time.TimeRange('2012/1/1T00:00:00', '2012/1/1T12:00:00'),
sunpy.time.TimeRange('2012/1/1T12:00:00', '2012/1/2T00:00:00')]
split = timerange_a.split(n=2)
#Doing direct comparisons seem to not work
assert all([wi.start == ex.start and wi.end == ex.end for wi, ex in zip(split, expect)])
