def test_nocond():
f = ConditionalDispatch()
# Multiply even numbers by two.
f.add(lambda x: 2 * x, lambda x: x % 2 == 0)
with pytest.raises(TypeError) as exc_info:
f(3)
assert "Your input did not fulfill the condition for any function." in str(exc_info.value)
class Parent(object):
_create = ConditionalDispatch()
@classmethod
def read(cls, filename):
raise NotImplementedError
@classmethod
def read_many(cls, filenames):
return list(map(cls.read, filenames))
@classmethod
def from_glob(cls, pattern):
""" Read out files using glob (e.g., ~/BIR_2011*) pattern. Returns
list of objects made from all matched files.
"""
return cls.read_many(glob.glob(pattern))
@classmethod
def from_single_glob(cls, singlepattern):
""" Read out a single file using glob (e.g., ~/BIR_2011*) pattern.
If more than one file matches the pattern, raise ValueError.
"""
matches = glob.glob(os.path.expanduser(singlepattern))
if len(matches) != 1:
raise ValueError("Invalid number of matches: {0:d}".format(len(matches)))
return cls.read(matches[0])
@classmethod
def from_files(cls, filenames):
""" Return list of object read from given list of
filenames. """
filenames = list(map(os.path.expanduser, filenames))
return cls.read_many(filenames)
@classmethod
def from_file(cls, filename):
""" Return object from file. """
filename = os.path.expanduser(filename)
return cls.read(filename)
@classmethod
def from_dir(cls, directory):
""" Return list that contains all files in the directory read in. """
directory = os.path.expanduser(directory)
return cls.read_many(
(os.path.join(directory, elem) for elem in os.listdir(directory))
)
@classmethod
def from_url(cls, url):
""" Return object read from URL.
Parameters
----------
url : str
URL to retrieve the data from
"""
path = download_file(url, get_and_create_download_dir())
return cls.read(path)
def test_else2():
# This verifies else branches do not catch cases that are covered
# by cases added later.
f = ConditionalDispatch()
# Because gcd(2, 3) == 1, 2 | x and 3 | x are mutually exclusive.
f.add(lambda x: 2 * x, lambda x: x % 2 == 0)
f.add(lambda x: 3 * x)
f.add(lambda x: 4 * x, lambda x: x % 3 == 0)
assert f(2) == 4
assert f(3) == 12
assert f(5) == 15
def test_else():
f = ConditionalDispatch()
# Multiply even numbers by two.
f.add(lambda x: 2 * x, lambda x: x % 2 == 0)
f.add(lambda x: 3 * x)
assert f(2) == 4
assert f(3) == 9
def oddeven(request):
f = ConditionalDispatch()
# Multiply even numbers by two.
f.add(lambda x: 2 * x, lambda x: x % 2 == 0)
# Multiply odd numbers by three.
f.add(lambda x: 3 * x, lambda x: x % 2 == 1)
return f
class SWavesSpectrogram(LinearTimeSpectrogram):
_create = ConditionalDispatch.from_existing(LinearTimeSpectrogram._create)
create = classmethod(_create.wrapper())
COPY_PROPERTIES = LinearTimeSpectrogram.COPY_PROPERTIES + [
('bg', REFERENCE)
]
@staticmethod
def swavesfile_to_date(filename):
_, name = os.path.split(filename)
date = name.split('_')[2]
return datetime.datetime(
int(date[0:4]), int(date[4:6]), int(date[6:])
)
@classmethod
def read(cls, filename, **kwargs):
"""Read in FITS file and return a new SWavesSpectrogram. """
data = np.genfromtxt(filename, skip_header=2)
time_axis = data[:, 0] * 60.
data = data[:, 1:].transpose()
header = np.genfromtxt(filename, skip_footer=time_axis.size)
freq_axis = header[0, :]
bg = header[1, :]
start = cls.swavesfile_to_date(filename)
end = start + datetime.timedelta(seconds=time_axis[-1])
t_delt = 60.
t_init = (start - get_day(start)).seconds
content = ''
t_label = 'Time [UT]'
f_label = 'Frequency [KHz]'
freq_axis = freq_axis[::-1]
data = data[::-1, :]
return cls(data, time_axis, freq_axis, start, end, t_init, t_delt,
t_label, f_label, content, bg)
def __init__(self, data, time_axis, freq_axis, start, end,
t_init, t_delt, t_label, f_label, content, bg):
# Because of how object creation works, there is no avoiding
# unused arguments in this case.
# pylint: disable=W0613
super(SWavesSpectrogram, self).__init__(
data, time_axis, freq_axis, start, end,
t_init, t_delt, t_label, f_label,
content, set(["SWAVES"])
)
self.bg = bg
class LightCurve(object):
"""
LightCurve(filepath)
A generic light curve object.
Parameters
----------
args : filepath, url, or start and end dates
The input for a LightCurve object should either be a filepath, a URL,
or a date range to be queried for the particular instrument.
Attributes
----------
meta : string, dict
The comment string or header associated with the light curve input
data : pandas.DataFrame
An pandas DataFrame prepresenting one or more fields as they vary with
respect to time.
Examples
--------
>>> import sunpy
>>> import datetime
>>> import numpy as np
>>> base = datetime.datetime.today()
>>> dates = [base - datetime.timedelta(minutes=x) for x in range(0, 24 * 60)]
>>> intensity = np.sin(np.arange(0, 12 * np.pi, step=(12 * np.pi) / 24 * 60))
>>> light_curve = sunpy.lightcurve.LightCurve.create(
... {"param1": intensity}, index=dates
... )
>>> light_curve.peek()
References
----------
| http://pandas.pydata.org/pandas-docs/dev/dsintro.html
"""
_cond_dispatch = ConditionalDispatch()
create = classmethod(_cond_dispatch.wrapper())
def __init__(self, data, meta=None):
self.data = pandas.DataFrame(data)
if meta == '' or meta is None:
self.meta = OrderedDict()
else:
self.meta = OrderedDict(meta)
@property
def header(self):
"""
Return the lightcurves metadata
.. deprecated:: 0.4.0
Use .meta instead
"""
warnings.warn(
"""lightcurve.header has been renamed to lightcurve.meta
for compatability with map, please use meta instead""", Warning)
return self.meta
@classmethod
def from_time(cls, time, **kwargs):
'''Called by Conditional Dispatch object when valid time is passed as input to create method.'''
date = parse_time(time)
url = cls._get_url_for_date(date, **kwargs)
filepath = cls._download(
url, kwargs, err="Unable to download data for specified date")
return cls.from_file(filepath)
@classmethod
def from_range(cls, start, end, **kwargs):
'''Called by Conditional Dispatch object when start and end time are passed as input to create method.'''
url = cls._get_url_for_date_range(parse_time(start), parse_time(end))
filepath = cls._download(
url,
kwargs,
err="Unable to download data for specified date range")
result = cls.from_file(filepath)
result.data = result.data.truncate(start, end)
return result
@classmethod
def from_timerange(cls, timerange, **kwargs):
'''Called by Conditional Dispatch object when time range is passed as input to create method.'''
url = cls._get_url_for_date_range(timerange)
filepath = cls._download(
url,
kwargs,
err="Unable to download data for specified date range")
result = cls.from_file(filepath)
result.data = result.data.truncate(timerange.start(), timerange.end())
return result
@classmethod
def from_file(cls, filename):
'''Used to return Light Curve object by reading the given filename
Parameters:
filename: Path of the file to be read.
'''
filename = os.path.expanduser(filename)
meta, data = cls._parse_filepath(filename)
if data.empty:
raise ValueError("No data found!")
else:
return cls(data, meta)
@classmethod
def from_url(cls, url, **kwargs):
'''
Downloads a file from the given url, reads and returns a Light Curve object.
Parameters:
url : string
Uniform Resource Locator pointing to the file.
kwargs :Dict
Dict object containing other related parameters to assist in download.
'''
try:
filepath = cls._download(url, kwargs)
except (urllib2.HTTPError, urllib2.URLError, ValueError):
err = ("Unable to read location %s.") % url
raise ValueError(err)
return cls.from_file(filepath)
@classmethod
def from_data(cls, data, index=None, meta=None):
'''
Called by Conditional Dispatch object to create Light Curve object when corresponding data is passed
to create method.
'''
return cls(pandas.DataFrame(data, index=index), meta)
@classmethod
def from_yesterday(cls):
return cls.from_url(cls._get_default_uri())
@classmethod
def from_dataframe(cls, dataframe, meta=None):
'''
Called by Conditional Dispatch object to create Light Curve object when Pandas DataFrame is passed
to create method.
'''
return cls(dataframe, meta)
def plot(self, axes=None, **plot_args):
"""Plot a plot of the light curve
Parameters
----------
axes: matplotlib.axes object or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
**plot_args : dict
Any additional plot arguments that should be used
when plotting the image.
"""
#Get current axes
if axes is None:
axes = plt.gca()
axes = self.data.plot(ax=axes, **plot_args)
return axes
def peek(self, **kwargs):
"""Displays the light curve in a new figure"""
figure = plt.figure()
self.plot(**kwargs)
figure.show()
return figure
@staticmethod
def _download(uri, kwargs, err='Unable to download data at specified URL'):
"""Attempts to download data at the specified URI"""
_filename = os.path.basename(uri).split("?")[0]
# user specifies a download directory
if "directory" in kwargs:
download_dir = os.path.expanduser(kwargs["directory"])
else:
download_dir = sunpy.config.get("downloads", "download_dir")
# overwrite the existing file if the keyword is present
if "overwrite" in kwargs:
overwrite = kwargs["overwrite"]
else:
overwrite = False
# If the file is not already there, download it
filepath = os.path.join(download_dir, _filename)
if not (os.path.isfile(filepath)) or (overwrite
and os.path.isfile(filepath)):
try:
response = urllib2.urlopen(uri)
except (urllib2.HTTPError, urllib2.URLError):
raise urllib2.URLError(err)
with open(filepath, 'wb') as fp:
shutil.copyfileobj(response, fp)
else:
warnings.warn(
"Using existing file rather than downloading, use overwrite=True to override.",
RuntimeWarning)
return filepath
@classmethod
def _get_default_uri(cls):
"""Default data to load when none is specified"""
msg = "No default action set for %s"
raise NotImplementedError(msg % cls.__name__)
@classmethod
def _get_url_for_date(cls, date, **kwargs):
"""Returns a URL to the data for the specified date"""
msg = "Date-based downloads not supported for for %s"
raise NotImplementedError(msg % cls.__name__)
@classmethod
def _get_url_for_date_range(cls, *args, **kwargs):
"""Returns a URL to the data for the specified date range"""
msg = "Date-range based downloads not supported for for %s"
raise NotImplementedError(msg % cls.__name__)
@staticmethod
def _parse_csv(filepath):
"""Place holder method to parse CSV files."""
msg = "Generic CSV parsing not yet implemented for LightCurve"
raise NotImplementedError(msg)
@staticmethod
def _parse_fits(filepath):
"""Place holder method to parse FITS files."""
msg = "Generic FITS parsing not yet implemented for LightCurve"
raise NotImplementedError(msg)
@classmethod
def _parse_filepath(cls, filepath):
"""Check the file extension to see how to parse the file"""
filename, extension = os.path.splitext(filepath)
if extension.lower() in (".csv", ".txt"):
return cls._parse_csv(filepath)
else:
return cls._parse_fits(filepath)
def truncate(self, a, b=None):
"""Returns a truncated version of the timeseries object"""
if isinstance(a, TimeRange):
time_range = a
else:
time_range = TimeRange(a, b)
truncated = self.data.truncate(time_range.start(), time_range.end())
return self.__class__.create(truncated, self.meta.copy())
def extract(self, a):
"""Extract a set of particular columns from the DataFrame"""
# TODO allow the extract function to pick more than one column
if isinstance(self, pandas.Series):
return self
else:
return LightCurve(self.data[a], self.meta.copy())
def time_range(self):
"""Returns the start and end times of the LightCurve as a TimeRange
object"""
return TimeRange(self.data.index[0], self.data.index[-1])
class Spectrogram(Parent):
"""
Spectrogram Class.
.. warning:: This module is under development! Use at your own risk.
Attributes
----------
data : `~numpy.ndarray`
two-dimensional array of the image data of the spectrogram.
time_axis : `~numpy.ndarray`
one-dimensional array containing the offset from the start
for each column of data.
freq_axis : `~numpy.ndarray`
one-dimensional array containing information about the
frequencies each row of the image corresponds to.
start : `~datetime.datetime`
starting time of the measurement
end : `~datetime.datetime`
end time of the measurement
t_init : int
offset from the start of the day the measurement began. If None
gets automatically set from start.
t_label : str
label for the time axis
f_label : str
label for the frequency axis
content : str
header for the image
instruments : str array
instruments that recorded the data, may be more than one if
it was constructed using combine_frequencies or join_many.
"""
# Contrary to what pylint may think, this is not an old-style class.
# pylint: disable=E1002,W0142,R0902
# This needs to list all attributes that need to be
# copied to maintain the object and how to handle them.
COPY_PROPERTIES = [
('time_axis', COPY),
('freq_axis', COPY),
('instruments', COPY),
('start', REFERENCE),
('end', REFERENCE),
('t_label', REFERENCE),
('f_label', REFERENCE),
('content', REFERENCE),
('t_init', REFERENCE),
]
_create = ConditionalDispatch.from_existing(Parent._create)
@property
def shape(self):
return self.data.shape
@property
def dtype(self):
return self.data.dtype
def _get_params(self):
"""Implementation detail."""
return dict(
(name, getattr(self, name)) for name, _ in self.COPY_PROPERTIES
)
def _slice(self, y_range, x_range):
"""Return new spectrogram reduced to the values passed
as slices. Implementation detail."""
data = self.data[y_range, x_range]
params = self._get_params()
soffset = 0 if x_range.start is None else x_range.start
soffset = int(soffset)
eoffset = self.shape[1] if x_range.stop is None else x_range.stop # pylint: disable=E1101
eoffset -= 1
eoffset = int(eoffset)
params.update({
'time_axis': self.time_axis[
x_range.start:x_range.stop:x_range.step
] - self.time_axis[soffset],
'freq_axis': self.freq_axis[
y_range.start:y_range.stop:y_range.step],
'start': self.start + datetime.timedelta(
seconds=self.time_axis[soffset]),
'end': self.start + datetime.timedelta(
seconds=self.time_axis[eoffset]),
't_init': self.t_init + self.time_axis[soffset],
})
return self.__class__(data, **params)
def _with_data(self, data):
new = copy(self)
new.data = data
return new
def __init__(self, data, time_axis, freq_axis, start, end, t_init=None,
t_label="Time", f_label="Frequency", content="",
instruments=None):
# Because of how object creation works, there is no avoiding
# unused arguments in this case.
self.data = data
if t_init is None:
diff = start - get_day(start)
t_init = diff.seconds
if instruments is None:
instruments = set()
self.start = start
self.end = end
self.t_label = t_label
self.f_label = f_label
self.t_init = t_init
self.time_axis = time_axis
self.freq_axis = freq_axis
self.content = content
self.instruments = instruments
def time_formatter(self, x, pos):
"""This returns the label for the tick of value x at
a specified pos on the time axis."""
# Callback, cannot avoid unused arguments.
# pylint: disable=W0613
x = int(x)
if x >= len(self.time_axis) or x < 0:
return ""
return self.format_time(
self.start + datetime.timedelta(
seconds=float(self.time_axis[x])
)
)
@staticmethod
def format_time(time):
"""Override to configure default plotting."""
return time.strftime("%H:%M:%S")
@staticmethod
def format_freq(freq):
"""Override to configure default plotting."""
return "{freq:0.1f}".format(freq=freq)
def peek(self, *args, **kwargs):
"""
Plot spectrum onto current axes.
Parameters
----------
*args : dict
**kwargs : dict
Any additional plot arguments that should be used
when plotting.
Returns
-------
fig : `~matplotlib.Figure`
A plot figure.
"""
figure()
ret = self.plot(*args, **kwargs)
plt.show()
return ret
def plot(self, figure=None, overlays=[], colorbar=True, vmin=None,
vmax=None, linear=True, showz=True, yres=DEFAULT_YRES,
max_dist=None, **matplotlib_args):
"""
Plot spectrogram onto figure.
Parameters
----------
figure : `~matplotlib.Figure`
Figure to plot the spectrogram on. If None, new Figure is created.
overlays : list
List of overlays (functions that receive figure and axes and return
new ones) to be applied after drawing.
colorbar : bool
Flag that determines whether or not to draw a colorbar. If existing
figure is passed, it is attempted to overdraw old colorbar.
vmin : float
Clip intensities lower than vmin before drawing.
vmax : float
Clip intensities higher than vmax before drawing.
linear : bool
If set to True, "stretch" image to make frequency axis linear.
showz : bool
If set to True, the value of the pixel that is hovered with the
mouse is shown in the bottom right corner.
yres : int or None
To be used in combination with linear=True. If None, sample the
image with half the minimum frequency delta. Else, sample the
image to be at most yres pixels in vertical dimension. Defaults
to 1080 because that's a common screen size.
max_dist : float or None
If not None, mask elements that are further than max_dist away
from actual data points (ie, frequencies that actually have data
from the receiver and are not just nearest-neighbour interpolated).
"""
# [] as default argument is okay here because it is only read.
# pylint: disable=W0102,R0914
if linear:
delt = yres
if delt is not None:
delt = max(
(self.freq_axis[0] - self.freq_axis[-1]) / (yres - 1),
_min_delt(self.freq_axis) / 2.
)
delt = float(delt)
data = _LinearView(self.clip_values(vmin, vmax), delt)
freqs = np.arange(
self.freq_axis[0], self.freq_axis[-1], -data.delt
)
else:
data = np.array(self.clip_values(vmin, vmax))
freqs = self.freq_axis
figure = plt.gcf()
if figure.axes:
axes = figure.axes[0]
else:
axes = figure.add_subplot(111)
params = {
'origin': 'lower',
'aspect': 'auto',
}
params.update(matplotlib_args)
if linear and max_dist is not None:
toplot = ma.masked_array(data, mask=data.make_mask(max_dist))
pass
else:
toplot = data
im = axes.imshow(toplot, **params)
xa = axes.get_xaxis()
ya = axes.get_yaxis()
xa.set_major_formatter(
FuncFormatter(self.time_formatter)
)
if linear:
# Start with a number that is divisible by 5.
init = (self.freq_axis[0] % 5) / data.delt
nticks = 15.
# Calculate MHz difference between major ticks.
dist = (self.freq_axis[0] - self.freq_axis[-1]) / nticks
# Round to next multiple of 10, at least ten.
dist = max(round(dist, -1), 10)
# One pixel in image space is data.delt MHz, thus we can convert
# our distance between the major ticks into image space by dividing
# it by data.delt.
ya.set_major_locator(
IndexLocator(
dist / data.delt, init
)
)
ya.set_minor_locator(
IndexLocator(
dist / data.delt / 10, init
)
)
def freq_fmt(x, pos):
# This is necessary because matplotlib somehow tries to get
# the mid-point of the row, which we do not need here.
x = x + 0.5
return self.format_freq(self.freq_axis[0] - x * data.delt)
else:
freq_fmt = _list_formatter(freqs, self.format_freq)
ya.set_major_locator(MaxNLocator(integer=True, steps=[1, 5, 10]))
ya.set_major_formatter(
FuncFormatter(freq_fmt)
)
axes.set_xlabel(self.t_label)
axes.set_ylabel(self.f_label)
# figure.suptitle(self.content)
figure.suptitle(
' '.join([
get_day(self.start).strftime("%d %b %Y"),
'Radio flux density',
'(' + ', '.join(self.instruments) + ')',
])
)
for tl in xa.get_ticklabels():
tl.set_fontsize(10)
tl.set_rotation(30)
figure.add_axes(axes)
figure.subplots_adjust(bottom=0.2)
figure.subplots_adjust(left=0.2)
if showz:
axes.format_coord = self._mk_format_coord(
data, figure.gca().format_coord)
if colorbar:
if len(figure.axes) > 1:
Colorbar(figure.axes[1], im).set_label("Intensity")
else:
figure.colorbar(im).set_label("Intensity")
for overlay in overlays:
figure, axes = overlay(figure, axes)
for ax in figure.axes:
ax.autoscale()
if isinstance(figure, SpectroFigure):
figure._init(self, freqs)
return axes
def __getitem__(self, key):
only_y = not isinstance(key, tuple)
if only_y:
return self.data[int(key)]
elif isinstance(key[0], slice) and isinstance(key[1], slice):
return self._slice(key[0], key[1])
elif isinstance(key[1], slice):
# return Spectrum( # XXX: Right class
# super(Spectrogram, self).__getitem__(key),
# self.time_axis[key[1].start:key[1].stop:key[1].step]
# )
return np.array(self.data[key])
elif isinstance(key[0], slice):
return Spectrum(
self.data[key],
self.freq_axis[key[0].start:key[0].stop:key[0].step]
)
return self.data[int(key)]
def clip_freq(self, vmin=None, vmax=None):
"""Return a new spectrogram only consisting of frequencies
in the interval [vmin, vmax].
Parameters
----------
vmin : float
All frequencies in the result are greater or equal to this.
vmax : float
All frequencies in the result are smaller or equal to this.
"""
left = 0
if vmax is not None:
while self.freq_axis[left] > vmax:
left += 1
right = len(self.freq_axis) - 1
if vmin is not None:
while self.freq_axis[right] < vmin:
right -= 1
return self[left:right + 1, :]
def auto_find_background(self, amount=0.05):
"""Automatically find the background. This
is done by first subtracting the average value in each channel and then
finding those times which have the lowest standard deviation.
Parameters
----------
amount : float
The percent amount (out of 1) of lowest standard deviation to
consider.
"""
# pylint: disable=E1101,E1103
data = self.data.astype(to_signed(self.dtype))
# Subtract average value from every frequency channel.
tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
# Get standard deviation at every point of time.
# Need to convert because otherwise this class's __getitem__
# is used which assumes two-dimensionality.
sdevs = np.asarray(np.std(tmp, 0))
# Get indices of values with lowest standard deviation.
cand = sorted(range(self.shape[1]), key=lambda y: sdevs[y])
# Only consider the best 5 %.
return cand[:max(1, int(amount * len(cand)))]
def auto_const_bg(self):
"""Automatically determine background."""
realcand = self.auto_find_background()
bg = np.average(self.data[:, realcand], 1)
return bg.reshape(self.shape[0], 1)
def subtract_bg(self):
"""Perform constant background subtraction."""
return self._with_data(self.data - self.auto_const_bg())
def randomized_auto_const_bg(self, amount):
"""Automatically determine background. Only consider a randomly
chosen subset of the image.
Parameters
----------
amount : int
Size of random sample that is considered for calculation of
the background.
"""
cols = [randint(0, self.shape[1] - 1) for _ in range(amount)]
# pylint: disable=E1101,E1103
data = self.data.astype(to_signed(self.dtype))
# Subtract average value from every frequency channel.
tmp = (data - np.average(self.data, 1).reshape(self.shape[0], 1))
# Get standard deviation at every point of time.
# Need to convert because otherwise this class's __getitem__
# is used which assumes two-dimensionality.
tmp = tmp[:, cols]
sdevs = np.asarray(np.std(tmp, 0))
# Get indices of values with lowest standard deviation.
cand = sorted(range(amount), key=lambda y: sdevs[y])
# Only consider the best 5 %.
realcand = cand[:max(1, int(0.05 * len(cand)))]
# Average the best 5 %
bg = np.average(self[:, [cols[r] for r in realcand]], 1)
return bg.reshape(self.shape[0], 1)
def randomized_subtract_bg(self, amount):
"""Perform randomized constant background subtraction.
Does not produce the same result every time it is run.
Parameters
----------
amount : int
Size of random sample that is considered for calculation of
the background.
"""
return self._with_data(self.data - self.randomized_auto_const_bg(amount))
def clip_values(self, vmin=None, vmax=None, out=None):
"""
Clip intensities to be in the interval [vmin, vmax].
Any values greater than the maximum will be assigned the maximum,
any values lower than the minimum will be assigned the minimum.
If either is left out or None, do not clip at that side of the interval.
Parameters
----------
min : int or float
New minimum value for intensities.
max : int or float
New maximum value for intensities
"""
# pylint: disable=E1101
if vmin is None:
vmin = int(self.data.min())
if vmax is None:
vmax = int(self.data.max())
return self._with_data(self.data.clip(vmin, vmax, out))
def rescale(self, vmin=0, vmax=1, dtype=np.dtype('float32')):
"""
Rescale intensities to [vmin, vmax].
Note that vmin ≠ vmax and spectrogram.min() ≠ spectrogram.max().
Parameters
----------
vmin : float or int
New minimum value in the resulting spectrogram.
vmax : float or int
New maximum value in the resulting spectrogram.
dtype : `numpy.dtype`
Data-type of the resulting spectrogram.
"""
if vmax == vmin:
raise ValueError("Maximum and minimum must be different.")
if self.data.max() == self.data.min():
raise ValueError("Spectrogram needs to contain distinct values.")
data = self.data.astype(dtype) # pylint: disable=E1101
return self._with_data(
vmin + (vmax - vmin) * (data - self.data.min()) / # pylint: disable=E1101
(self.data.max() - self.data.min()) # pylint: disable=E1101
)
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 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 freq_overlap(self, other):
"""Get frequency range present in both spectrograms. Returns
(min, max) tuple.
Parameters
----------
other : Spectrogram
other spectrogram with which to look for frequency overlap
"""
lower = max(self.freq_axis[-1], other.freq_axis[-1])
upper = min(self.freq_axis[0], other.freq_axis[0])
if lower > upper:
raise ValueError("No overlap.")
return lower, upper
def time_to_x(self, time):
"""Return x-coordinate in spectrogram that corresponds to the
passed `~datetime.datetime` value.
Parameters
----------
time : `~sunpy.time.parse_time` compatible str
`~datetime.datetime` to find the x coordinate for.
"""
diff = time - self.start
diff_s = SECONDS_PER_DAY * diff.days + diff.seconds
if self.time_axis[-1] < diff_s < 0:
raise ValueError("Out of bounds")
for n, elem in enumerate(self.time_axis):
if diff_s < elem:
return n - 1
# The last element is the searched one.
return n
def at_freq(self, freq):
return self[np.nonzero(self.freq_axis == freq)[0], :]
@staticmethod
def _mk_format_coord(spec, fmt_coord):
def format_coord(x, y):
shape = list(map(int, spec.shape))
xint, yint = int(x), int(y)
if 0 <= xint < shape[1] and 0 <= yint < shape[0]:
pixel = spec[yint][xint]
else:
pixel = ""
return '{0!s} z={1!s}'.format(fmt_coord(x, y), pixel)
return format_coord
class CallistoSpectrogram(LinearTimeSpectrogram):
""" Classed used for dynamic spectra coming from the Callisto network.
Attributes
----------
header : fits.Header
main header of the FITS file
axes_header : fits.Header
header foe the axes table
swapped : boolean
flag that specifies whether originally in the file the x-axis was
frequency
"""
# XXX: Determine those from the data.
SIGMA_SUM = 75
SIGMA_DELTA_SUM = 20
_create = ConditionalDispatch.from_existing(LinearTimeSpectrogram._create)
create = classmethod(_create.wrapper())
# Contrary to what pylint may think, this is not an old-style class.
# pylint: disable=E1002,W0142,R0902
# This needs to list all attributes that need to be
# copied to maintain the object and how to handle them.
COPY_PROPERTIES = LinearTimeSpectrogram.COPY_PROPERTIES + [
('header', REFERENCE),
('swapped', REFERENCE),
('axes_header', REFERENCE)
]
# List of instruments retrieved in July 2012 from
# http://soleil.i4ds.ch/solarradio/data/2002-20yy_Callisto/
INSTRUMENTS = set([
'ALASKA', 'ALMATY', 'BIR', 'DARO', 'HB9SCT', 'HUMAIN',
'HURBANOVO', 'KASI', 'KENYA', 'KRIM', 'MALAYSIA', 'MRT1',
'MRT2', 'OOTY', 'OSRA', 'SWMC', 'TRIEST', 'UNAM'
])
def save(self, filepath):
""" Save modified spectrogram back to filepath.
Parameters
----------
filepath : str
path to save the spectrogram to
"""
main_header = self.get_header()
data = fits.PrimaryHDU(self, header=main_header)
## XXX: Update axes header.
freq_col = fits.Column(
name="frequency", format="D8.3", array=self.freq_axis
)
time_col = fits.Column(
name="time", format="D8.3", array=self.time_axis
)
cols = fits.ColDefs([freq_col, time_col])
table = fits.new_table(cols, header=self.axes_header)
hdulist = fits.HDUList([data, table])
hdulist.writeto(filepath)
def get_header(self):
""" Return updated header. """
header = self.header.copy()
if self.swapped:
header['NAXIS2'] = self.shape[1] # pylint: disable=E1101
header['NAXIS1'] = self.shape[0] # pylint: disable=E1101
else:
header['NAXIS1'] = self.shape[1] # pylint: disable=E1101
header['NAXIS2'] = self.shape[0] # pylint: disable=E1101
return header
@classmethod
def read(cls, filename, **kwargs):
""" Read in FITS file and return a new CallistoSpectrogram.
Any unknown (i.e. any except filename) keyword arguments get
passed to fits.open.
Parameters
----------
filename : str
path of the file to read
"""
fl = fits.open(filename, **kwargs)
data = fl[0].data
axes = fl[1]
header = fl[0].header
start = _parse_header_time(
header['DATE-OBS'], header.get('TIME-OBS', header.get('TIME$_OBS'))
)
end = _parse_header_time(
header['DATE-END'], header.get('TIME-END', header.get('TIME$_END'))
)
swapped = "time" not in header["CTYPE1"].lower()
# Swap dimensions so x-axis is always time.
if swapped:
t_delt = header["CDELT2"]
t_init = header["CRVAL2"] - t_delt * header["CRPIX2"]
t_label = header["CTYPE2"]
f_delt = header["CDELT1"]
f_init = header["CRVAL1"] - t_delt * header["CRPIX1"]
f_label = header["CTYPE1"]
data = data.transpose()
else:
t_delt = header["CDELT1"]
t_init = header["CRVAL1"] - t_delt * header["CRPIX1"]
t_label = header["CTYPE1"]
f_delt = header["CDELT2"]
f_init = header["CRVAL2"] - t_delt * header["CRPIX2"]
f_label = header["CTYPE2"]
# Table may contain the axes data. If it does, the other way of doing
# it might be very wrong.
if axes is not None:
try:
# It's not my fault. Neither supports __contains__ nor .get
tm = axes.data['time']
except KeyError:
tm = None
try:
fq = axes.data['frequency']
except KeyError:
fq = None
if tm is not None:
# Fix dimensions (whyever they are (1, x) in the first place)
time_axis = np.squeeze(tm)
else:
# Otherwise, assume it's linear.
time_axis = \
np.linspace(0, data.shape[1] - 1) * t_delt + t_init # pylint: disable=E1101
if fq is not None:
freq_axis = np.squeeze(fq)
else:
freq_axis = \
np.linspace(0, data.shape[0] - 1) * f_delt + f_init # pylint: disable=E1101
content = header["CONTENT"]
instruments = set([header["INSTRUME"]])
return cls(
data, time_axis, freq_axis, start, end, t_init, t_delt,
t_label, f_label, content, instruments,
header, axes.header, swapped
)
def __init__(self, data, time_axis, freq_axis, start, end,
t_init=None, t_delt=None, t_label="Time", f_label="Frequency",
content="", instruments=None, header=None, axes_header=None,
swapped=False):
# Because of how object creation works, there is no avoiding
# unused arguments in this case.
# pylint: disable=W0613
super(CallistoSpectrogram, self).__init__(
data, time_axis, freq_axis, start, end,
t_init, t_delt, t_label, f_label,
content, instruments
)
self.header = header
self.axes_header = axes_header
self.swapped = swapped
@classmethod
def is_datasource_for(cls, header):
""" Check if class supports data from the given FITS file.
Parameters
----------
header : fits.Header
main header of the FITS file
"""
return header.get('instrume', '').strip() in cls.INSTRUMENTS
def remove_border(self):
""" Remove duplicate entries on the borders. """
left = 0
while self.freq_axis[left] == self.freq_axis[0]:
left += 1
right = self.shape[0] - 1
while self.freq_axis[right] == self.freq_axis[-1]:
right -= 1
return self[left-1:right+2, :]
@classmethod
def read_many(cls, filenames, sort_by=None):
""" Return list of CallistoSpectrogram objects read from filenames.
Parameters
----------
filenames : list of str
list of paths to read from
sort_by : str
optional attribute of the resulting objects to sort from, e.g.
start to sort by starting time.
"""
objs = map(cls.read, filenames)
if sort_by is not None:
objs.sort(key=lambda x: getattr(x, sort_by))
return objs
@classmethod
def from_range(cls, instrument, start, end, **kwargs):
""" Automatically download data from instrument between start and
end and join it together.
Parameters
----------
instrument : str
instrument to retrieve the data from
start : parse_time compatible
start of the measurement
end : parse_time compatible
end of the measurement
"""
kw = {
'maxgap': None,
'fill': cls.JOIN_REPEAT,
}
kw.update(kwargs)
start = parse_time(start)
end = parse_time(end)
urls = query(start, end, [instrument])
data = map(cls.from_url, urls)
freq_buckets = defaultdict(list)
for elem in data:
freq_buckets[tuple(elem.freq_axis)].append(elem)
try:
return cls.combine_frequencies(
[cls.join_many(elem, **kw) for elem in freq_buckets.itervalues()]
)
except ValueError:
raise ValueError("No data found.")
def _overlap(self, other):
""" Find frequency and time overlap of two spectrograms. """
one, two = self.intersect_time([self, other])
ovl = one.freq_overlap(two)
return one.clip_freq(*ovl), two.clip_freq(*ovl)
@staticmethod
def _to_minimize(a, b):
"""
Function to be minimized for matching to frequency channels.
"""
def _fun(p):
if p[0] <= 0.2 or abs(p[1]) >= a.max():
return float("inf")
return a - (p[0] * b + p[1])
return _fun
def _homogenize_params(self, other, maxdiff=1):
"""
Return triple with a tuple of indices (in self and other, respectively),
factors and constants at these frequencies.
Parameters
----------
other : CallistoSpectrogram
Spectrogram to be homogenized with the current one.
maxdiff : float
Threshold for which frequencies are considered equal.
"""
pairs_indices = [
(x, y) for x, y, d in minimal_pairs(self.freq_axis, other.freq_axis)
if d <= maxdiff
]
pairs_data = [
(self[n_one, :], other[n_two, :]) for n_one, n_two in pairs_indices
]
# XXX: Maybe unnecessary.
pairs_data_gaussian = [
(gaussian_filter1d(a, 15), gaussian_filter1d(b, 15))
for a, b in pairs_data
]
# If we used integer arithmetic, we would accept more invalid
# values.
pairs_data_gaussian64 = np.float64(pairs_data_gaussian)
least = [
leastsq(self._to_minimize(a,b), [1, 0])[0]
for a, b in pairs_data_gaussian64
]
factors = [x for x, y in least]
constants = [y for x, y in least]
return pairs_indices, factors, constants
def homogenize(self, other, maxdiff=1):
""" Return overlapping part of self and other as (self, other) tuple.
Homogenize intensities so that the images can be used with
combine_frequencies. Note that this works best when most of the
picture is signal, so use :py:meth:`in_interval` to select the subset
of your image before applying this method.
Parameters
----------
other : CallistoSpectrogram
Spectrogram to be homogenized with the current one.
maxdiff : float
Threshold for which frequencies are considered equal.
"""
one, two = self._overlap(other)
pairs_indices, factors, constants = one._homogenize_params(
two, maxdiff
)
# XXX: Maybe (xd.freq_axis[x] + yd.freq_axis[y]) / 2.
pairs_freqs = [one.freq_axis[x] for x, y in pairs_indices]
# XXX: Extrapolation does not work this way.
# XXX: Improve.
f1 = np.polyfit(pairs_freqs, factors, 3)
f2 = np.polyfit(pairs_freqs, constants, 3)
return (
one,
two * polyfun_at(f1, two.freq_axis)[:, np.newaxis] +
polyfun_at(f2, two.freq_axis)[:, np.newaxis]
)
def extend(self, minutes=15, **kwargs):
""" Request subsequent files from the server. If minutes is negative,
retrieve preceding files. """
if len(self.instruments) != 1:
raise ValueError
instrument = iter(self.instruments).next()
if minutes > 0:
data = CallistoSpectrogram.from_range(
instrument,
self.end, self.end + datetime.timedelta(minutes=minutes)
)
else:
data = CallistoSpectrogram.from_range(
instrument,
self.start - datetime.timedelta(minutes=-minutes), self.start
)
data = data.clip_freq(self.freq_axis[-1], self.freq_axis[0])
return CallistoSpectrogram.join_many([self, data], **kwargs)
@classmethod
def from_url(cls, url):
""" Return CallistoSpectrogram read from URL.
Parameters
----------
url : str
URL to retrieve the data from
"""
return cls.read(url)
class LightCurve(object):
"""
LightCurve(filepath)
A generic light curve object.
Parameters
----------
args : filepath, url, or start and end dates
The input for a LightCurve object should either be a filepath, a URL,
or a date range to be queried for the particular instrument.
Attributes
----------
data : pandas.DataFrame
An pandas DataFrame prepresenting one or more fields as they vary with
respect to time.
header : string, dict
The comment string or header associated with the light curve input
Examples
--------
import sunpy
import datetime
base = datetime.datetime.today()
dates = [base - datetime.timedelta(minutes=x) for x in range(0, 24 * 60)]
light_curve = sunpy.lightcurve.LightCurve.create(
{"param1": range(24 * 60)}, index=dates)
light_curve.show()
References
----------
| http://pandas.pydata.org/pandas-docs/dev/dsintro.html
"""
_cond_dispatch = ConditionalDispatch()
create = classmethod(_cond_dispatch.wrapper())
def __init__(self, data, header=None):
self.data = data
self.header = header
@classmethod
def from_time(cls, time, **kwargs):
date = parse_time(time)
url = cls._get_url_for_date(date)
filepath = cls._download(
url, kwargs, err="Unable to download data for specified date")
return cls.from_file(filepath)
@classmethod
def from_range(cls, from_, to, **kwargs):
url = cls._get_url_for_date_range(from_, to)
filepath = cls._download(
url,
kwargs,
err="Unable to download data for specified date range")
return cls.from_file(filepath)
@classmethod
def from_timerange(cls, timerange, **kwargs):
url = cls._get_url_for_date_range(timerange)
filepath = cls._download(
url,
kwargs,
err="Unable to download data for specified date range")
return cls.from_file(filepath)
@classmethod
def from_file(cls, filename):
filename = os.path.expanduser(filename)
header, data = cls._parse_filepath(filename)
return cls(data, header)
@classmethod
def from_url(cls, url, **kwargs):
try:
filepath = cls._download(url, kwargs)
except (urllib2.HTTPError, urllib2.URLError, ValueError):
err = ("Unable to read location. Did you "
"specify a valid filepath or URL?")
raise ValueError(err)
return cls.from_file(filepath)
@classmethod
def from_data(cls, data, index=None, header=None):
return cls(pandas.DataFrame(data, index=index), header)
@classmethod
def from_yesterday(cls):
return cls.from_url(cls._get_default_uri())
@classmethod
def from_dataframe(cls, dataframe, header=None):
return cls(dataframe, header)
def plot(self, axes=None, **plot_args):
"""Plot a plot of the light curve
Parameters
----------
axes: matplotlib.axes object or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
**plot_args : dict
Any additional plot arguments that should be used
when plotting the image.
"""
#Get current axes
if axes is None:
axes = plt.gca()
axes = self.data.plot(ax=axes, **plot_args)
return axes
def peek(self, **kwargs):
"""Displays the light curve in a new figure"""
figure = plt.figure()
self.plot(**kwargs)
figure.show()
return figure
@staticmethod
def _download(uri, kwargs, err='Unable to download data at specified URL'):
"""Attempts to download data at the specified URI"""
_filename = os.path.basename(uri).split("?")[0]
# user specifies a download directory
if "directory" in kwargs:
download_dir = os.path.expanduser(kwargs["directory"])
else:
download_dir = sunpy.config.get("downloads", "download_dir")
# overwrite the existing file if the keyword is present
if "overwrite" in kwargs:
overwrite = kwargs["overwrite"]
else:
overwrite = False
# If the file is not already there, download it
filepath = os.path.join(download_dir, _filename)
if not (os.path.isfile(filepath)) or (overwrite
and os.path.isfile(filepath)):
try:
response = urllib2.urlopen(uri)
except (urllib2.HTTPError, urllib2.URLError):
raise urllib2.URLError(err)
with open(filepath, 'wb') as fp:
shutil.copyfileobj(response, fp)
return filepath
@classmethod
def _get_default_uri(cls):
"""Default data to load when none is specified"""
msg = "No default action set for %s"
raise NotImplementedError(msg % cls.__name__)
@classmethod
def _get_url_for_date(cls, date):
"""Returns a URL to the data for the specified date"""
msg = "Date-based downloads not supported for for %s"
raise NotImplementedError(msg % cls.__name__)
@classmethod
def _get_url_for_date_range(cls, *args, **kwargs):
"""Returns a URL to the data for the specified date range"""
msg = "Date-range based downloads not supported for for %s"
raise NotImplementedError(msg % cls.__name__)
@staticmethod
def _parse_csv(filepath):
"""Place holder method to parse CSV files."""
msg = "Generic CSV parsing not yet implemented for LightCurve"
raise NotImplementedError(msg)
@staticmethod
def _parse_fits(filepath):
"""Place holder method to parse FITS files."""
msg = "Generic FITS parsing not yet implemented for LightCurve"
raise NotImplementedError(msg)
@classmethod
def _parse_filepath(cls, filepath):
filename, extension = os.path.splitext(filepath)
if extension.lower() in (".csv", ".txt"):
return cls._parse_csv(filepath)
else:
return cls._parse_fits(filepath)
def truncate(self, a, b=None):
"""Returns a truncated version of the timeseries object"""
if isinstance(a, TimeRange):
time_range = a
else:
time_range = TimeRange(a, b)
truncated = self.data.truncate(time_range.start(), time_range.end())
return LightCurve(truncated, self.header.copy())
def extract(self, a):
"""Extract a set of particular columns from the DataFrame"""
# TODO allow the extract function to pick more than one column
if isinstance(self, pandas.Series):
return self
else:
return LightCurve(self.data[a], self.header.copy())
def time_range(self):
"""Returns the start and end times of the LightCurve as a TimeRange
object"""
return TimeRange(self.data.index[0], self.data.index[-1])
class LightCurve(object):
"""
LightCurve(filepath)
A generic light curve object.
Attributes
----------
meta : `str` or `dict`
The comment string or header associated with the data.
data : `~pandas.DataFrame`
An pandas DataFrame prepresenting one or more fields as a function of time.
Examples
--------
>>> import sunpy
>>> import datetime
>>> import numpy as np
>>> base = datetime.datetime.today()
>>> dates = [base - datetime.timedelta(minutes=x) for x in range(0, 24 * 60)]
>>> intensity = np.sin(np.arange(0, 12 * np.pi, step=(12 * np.pi) / 24 * 60))
>>> light_curve = sunpy.lightcurve.LightCurve.create({"param1": intensity}, index=dates)
>>> light_curve.peek() # doctest: +SKIP
References
----------
* `Pandas Documentation <http://pandas.pydata.org/pandas-docs/dev/dsintro.html>`_
"""
_cond_dispatch = ConditionalDispatch()
create = classmethod(_cond_dispatch.wrapper())
def __init__(self, data, meta=None):
self.data = pandas.DataFrame(data)
if meta == '' or meta is None:
self.meta = OrderedDict()
else:
self.meta = OrderedDict(meta)
@property
def header(self):
"""
Return the lightcurves metadata
.. deprecated:: 0.4.0
Use .meta instead
"""
warnings.warn(
"""lightcurve.header has been renamed to lightcurve.meta
for compatibility with map, please use meta instead""", Warning)
return self.meta
@classmethod
def from_time(cls, time, **kwargs):
"""
Called by Conditional Dispatch object when valid time is passed as
input to create method.
"""
date = parse_time(time)
url = cls._get_url_for_date(date, **kwargs)
filepath = cls._download(
url, kwargs, err="Unable to download data for specified date")
return cls.from_file(filepath)
@classmethod
def from_range(cls, start, end, **kwargs):
"""Called by Conditional Dispatch object when start and end time are
passed as input to create method.
:param start:
:param end:
:param kwargs:
:return:
"""
url = cls._get_url_for_date_range(parse_time(start), parse_time(end),
**kwargs)
filepath = cls._download(
url,
kwargs,
err="Unable to download data for specified date range")
result = cls.from_file(filepath)
result.data = result.data.truncate(start, end)
return result
@classmethod
def from_timerange(cls, timerange, **kwargs):
"""
Called by Conditional Dispatch object when time range is passed as
input to create method.
"""
url = cls._get_url_for_date_range(timerange, **kwargs)
filepath = cls._download(
url,
kwargs,
err="Unable to download data for specified date range")
result = cls.from_file(filepath)
result.data = result.data.truncate(timerange.start, timerange.end)
return result
@classmethod
def from_file(cls, filename):
"""Used to return Light Curve object by reading the given filename.
Parameters
----------
filename: `str`
Path of the file to be read.
Returns
-------
Lightcurve object.
"""
filename = os.path.expanduser(filename)
meta, data = cls._parse_filepath(filename)
if data.empty:
raise ValueError("No data found!")
else:
return cls(data, meta)
@classmethod
def from_url(cls, url, **kwargs):
"""
Called by Conditional Dispatch object to create Light Curve object when
given a url. Downloads a file from the given url, attemps to read it
and returns a Light Curve object.
Parameters
----------
url : str
A url given as a string.
"""
try:
filepath = cls._download(url, kwargs)
except (urllib.error.HTTPError, urllib.error.URLError, ValueError):
err = "Unable to read location {!s}.".format(url)
raise ValueError(err)
return cls.from_file(filepath)
@classmethod
def from_data(cls, data, index=None, meta=None):
"""
Called by Conditional Dispatch object to create Light Curve object when
corresponding data is passed to create method.
Parameters
----------
data : `~numpy.ndarray`
The data array
index : `~datetime.datetime` array
The time values
"""
return cls(pandas.DataFrame(data, index=index), meta)
@classmethod
def from_yesterday(cls):
"""
Called by Conditional Dispatch object if no input if given
"""
return cls.from_url(cls._get_default_uri())
@classmethod
def from_dataframe(cls, dataframe, meta=None):
"""
Called by Conditional Dispatch object to create Light Curve object when
Pandas DataFrame is passed to create method.
Parameters
----------
dataframe : `~pandas.DataFrame`
The data.
meta : `str` or `dict`
The metadata.
"""
return cls(dataframe, meta)
def plot(self, axes=None, **plot_args):
"""Plot a plot of the light curve
Parameters
----------
axes : `~matplotlib.axes.Axes` or None
If provided the image will be plotted on the given axes. Otherwise
the current axes will be used.
**plot_args : `dict`
Any additional plot arguments that should be used
when plotting.
Returns
-------
axes : `~matplotlib.axes.Axes`
The plot axes.
"""
# Get current axes
if axes is None:
axes = plt.gca()
axes = self.data.plot(ax=axes, **plot_args)
return axes
def peek(self, **kwargs):
"""Displays the light curve in a new figure.
Parameters
----------
**kwargs : `dict`
Any additional plot arguments that should be used
when plotting.
Returns
-------
fig : `~matplotlib.Figure`
A plot figure.
"""
figure = plt.figure()
self.plot(**kwargs)
figure.show()
return figure
@staticmethod
def _download(uri, kwargs, err='Unable to download data at specified URL'):
"""Attempts to download data at the specified URI.
Parameters
----------
**kwargs : uri
A url
"""
_filename = os.path.basename(uri).split("?")[0]
# user specifies a download directory
if "directory" in kwargs:
download_dir = os.path.expanduser(kwargs["directory"])
else:
download_dir = config.get("downloads", "download_dir")
# overwrite the existing file if the keyword is present
if "overwrite" in kwargs:
overwrite = kwargs["overwrite"]
else:
overwrite = False
# If the file is not already there, download it
filepath = os.path.join(download_dir, _filename)
if not (os.path.isfile(filepath)) or (overwrite
and os.path.isfile(filepath)):
try:
response = urllib.request.urlopen(uri)
except (urllib.error.HTTPError, urllib.error.URLError):
raise urllib.error.URLError(err)
with open(filepath, 'wb') as fp:
shutil.copyfileobj(response, fp)
else:
warnings.warn(
"Using existing file rather than downloading, use "
"overwrite=True to override.", RuntimeWarning)
return filepath
@classmethod
def _get_default_uri(cls):
"""Default data to load when none is specified."""
msg = "No default action set for {}"
raise NotImplementedError(msg.format(cls.__name__))
@classmethod
def _get_url_for_date(cls, date, **kwargs):
"""Returns a URL to the data for the specified date."""
msg = "Date-based downloads not supported for for {}"
raise NotImplementedError(msg.format(cls.__name__))
@classmethod
def _get_url_for_date_range(cls, *args, **kwargs):
"""Returns a URL to the data for the specified date range."""
msg = "Date-range based downloads not supported for for {}"
raise NotImplementedError(msg.format(cls.__name__))
@staticmethod
def _parse_csv(filepath):
"""Place holder method to parse CSV files."""
msg = "Generic CSV parsing not yet implemented for LightCurve"
raise NotImplementedError(msg)
@staticmethod
def _parse_fits(filepath):
"""Place holder method to parse FITS files."""
msg = "Generic FITS parsing not yet implemented for LightCurve"
raise NotImplementedError(msg)
@classmethod
def _parse_filepath(cls, filepath):
"""Check the file extension to see how to parse the file."""
filename, extension = os.path.splitext(filepath)
if extension.lower() in (".csv", ".txt"):
return cls._parse_csv(filepath)
else:
return cls._parse_fits(filepath)
def truncate(self, a, b=None):
"""Returns a truncated version of the timeseries object.
Parameters
----------
a : `sunpy.time.TimeRange`
A time range to truncate to.
Returns
-------
newlc : `~sunpy.lightcurve.LightCurve`
A new lightcurve with only the selected times.
"""
if isinstance(a, TimeRange):
time_range = a
else:
time_range = TimeRange(a, b)
truncated = self.data.truncate(time_range.start, time_range.end)
return self.__class__.create(truncated, self.meta.copy())
def extract(self, column_name):
"""Returns a new lightcurve with the chosen column.
Parameters
----------
column_name : `str`
A valid column name
Returns
-------
newlc : `~sunpy.lightcurve.LightCurve`
A new lightcurve with only the selected column.
"""
# TODO allow the extract function to pick more than one column
if isinstance(self, pandas.Series):
return self
else:
return LightCurve(self.data[column_name], self.meta.copy())
def time_range(self):
"""Returns the start and end times of the LightCurve as a `~sunpy.time.TimeRange`
object"""
return TimeRange(self.data.index[0], self.data.index[-1])
def test_types():
f = ConditionalDispatch()
f.add(lambda x: 2 * x, lambda x: x % 2 == 0, [int])
with pytest.raises(TypeError):
f(2.0)
class Map(np.ndarray, Parent):
"""
Map(data, header)
A spatially-aware data array based on the SolarSoft Map object
Parameters
----------
data : numpy.ndarray, list
A 2d list or ndarray containing the map data
header : dict
A dictionary of the original image header tags
Attributes
----------
carrington_longitude : str
Carrington longitude (crln_obs)
center : dict
X and Y coordinate of the center of the map in units.
Usually represents the offset between the center of the Sun and the
center of the map.
cmap : matplotlib.colors.Colormap
A Matplotlib colormap to be applied to the data
coordinate_system : dict
Coordinate system used for x and y axes (ctype1/2)
date : datetime
Image observation time
detector : str
Detector name
dsun : float
The observer distance from the Sun.
exptime : float
Exposure time of the image in seconds.
heliographic_latitude : float
Heliographic latitude in degrees
heliographic_longitude : float
Heliographic longitude in degrees
instrument : str
Instrument name
measurement : str, int
Measurement name. In some instances this is the wavelength of image.
name: str
Human-readable description of map-type
nickname : str
An abbreviated human-readable description of the map-type; part of
the Helioviewer data model
observatory : str
Observatory name
reference_coordinate : float
Reference point WCS axes in data units (crval1/2)
reference_pixel : float
Reference point axes in pixels (crpix1/2)
rsun_arcseconds : float
Radius of the sun in arcseconds
rsun_meters : float
Radius of the sun in meters
scale : dict
Image scale along the x and y axes in units/pixel (cdelt1/2).
units : dict
Image coordinate units along the x and y axes (cunit1/2).
Methods
-------
std()
Return the standard deviation of the map data
mean()
Return the mean of the map data
min()
Return the minimum value of the map data
max()
Return the maximum value of the map data
resample(dimension, method)
Returns a new map that has been resampled up or down
superpixel(dimension, method)
Returns a new map consisting of superpixels formed from the
original data.
save()
Save the map to a fits file.
submap(range_a, range_b, units)
Returns a submap of the map with the specified range
plot()
Return a matplotlib plot figure object
show()
Display a matplotlib plot to the screen
get_header()
Returns the original header from when the map was first created.
Examples
--------
>>> aia = sunpy.Map(sunpy.AIA_171_IMAGE)
>>> aia.T
AIAMap([[ 0.3125, 1. , -1.1875, ..., -0.625 , 0.5625, 0.5 ],
[-0.0625, 0.1875, 0.375 , ..., 0.0625, 0.0625, -0.125 ],
[-0.125 , -0.8125, -0.5 , ..., -0.3125, 0.5625, 0.4375],
...,
[ 0.625 , 0.625 , -0.125 , ..., 0.125 , -0.0625, 0.6875],
[-0.625 , -0.625 , -0.625 , ..., 0.125 , -0.0625, 0.6875],
[ 0. , 0. , -1.1875, ..., 0.125 , 0. , 0.6875]])
>>> aia.units['x']
'arcsec'
>>> aia.show()
>>> import matplotlib.cm as cm
>>> import matplotlib.colors as colors
>>> aia.show(cmap=cm.hot, norm=colors.Normalize(1, 2048))
See Also
--------
numpy.ndarray Parent class for the Map object
References
----------
| http://docs.scipy.org/doc/numpy/reference/arrays.classes.html
| http://docs.scipy.org/doc/numpy/user/basics.subclassing.html
| http://docs.scipy.org/doc/numpy/reference/ufuncs.html
| http://www.scipy.org/Subclasses
"""
_create = ConditionalDispatch.from_existing(Parent._create)
create = classmethod(_create.wrapper())
def __new__(cls, data, header):
"""Creates a new Map instance"""
if isinstance(data, np.ndarray):
obj = data.view(cls)
elif isinstance(data, list):
obj = np.asarray(data).view(cls)
else:
raise TypeError('Invalid input')
return obj
def __init__(self, data, header):
self._original_header = header
# Set naxis1 and naxis2 if not specified
if header.get('naxis1') is None:
header['naxis1'] = self.shape[1]
if header.get('naxis2') is None:
header['naxis2'] = self.shape[0]
# Parse header and set map attributes
for attr, value in list(self.get_properties(header).items()):
setattr(self, attr, value)
# Validate properties
self._validate()
@classmethod
def get_properties(cls, header):
"""Parses a map header and determines default properties."""
return {
"cmap":
cm.gray, # @UndefinedVariable
"date":
parse_time(header.get('date-obs', None)),
"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 __array_finalize__(self, obj):
"""Finishes instantiation of the new map object"""
if obj is None:
return
if hasattr(obj, '_original_header'):
properties = [
'_original_header', 'cmap', 'date', 'detector', 'dsun',
'exposure_time', 'instrument', 'measurement', 'name',
'observatory', 'rsun_arcseconds', 'rsun_meters', 'scale',
'units', 'reference_coordinate', 'reference_pixel',
'coordinate_system', 'heliographic_latitude',
'heliographic_longitude', 'carrington_longitude',
'rotation_angle'
]
for attr in properties:
setattr(self, attr, getattr(obj, attr))
def __array_wrap__(self, out_arr, context=None):
"""Returns a wrapped instance of a Map object"""
return np.ndarray.__array_wrap__(self, out_arr, context)
def __getitem__(self, key):
"""Overiding indexing operation to ensure that header is updated"""
if isinstance(key, tuple) and type(key[0]) is slice:
x_range = [key[1].start, key[1].stop]
y_range = [key[0].start, key[0].stop]
return self.submap(y_range, x_range, units="pixels")
else:
return np.ndarray.__getitem__(self, key)
def __add__(self, other):
"""Add two maps. Currently does not take into account the alignment
between the two maps."""
result = np.ndarray.__add__(self, other)
return result
def __repr__(self):
if not hasattr(self, 'observatory'):
return np.ndarray.__repr__(self)
return ("""SunPy Map
---------
Observatory:\t %s
Instrument:\t %s
Detector:\t %s
Measurement:\t %s
Obs Date:\t %s
dt:\t\t %f
Dimension:\t [%d, %d]
[dx, dy] =\t [%f, %f]
""" % (self.observatory, self.instrument, self.detector, self.measurement,
self.date.strftime("%Y-%m-%d %H:%M:%S"), self.exposure_time,
self.shape[1], self.shape[0], self.scale['x'], self.scale['y']) +
np.ndarray.__repr__(self))
def __sub__(self, other):
"""Subtract two maps. Currently does not take into account the
alignment between the two maps.
numpy dtype nums:
1 int8
2 uint8
3 int16
4 uint16
"""
# if data is stored as unsigned, cast up (e.g. uint8 => int16)
if self.dtype.kind == "u":
self = self.astype(to_signed(self.dtype))
if other.dtype.kind == "u":
other = other.astype(to_signed(other.dtype))
result = np.ndarray.__sub__(self, other)
def norm():
mean = result.mean()
std = result.std()
vmin = max(result.min(), mean - 6 * std)
vmax = min(result.max(), mean + 6 * std)
return colors.Normalize(vmin, vmax)
result.norm = norm
result.cmap = cm.gray # @UndefinedVariable
return result
@property
def xrange(self):
"""Return the X range of the image in arcsec from edge to edge."""
xmin = self.center['x'] - self.shape[1] / 2 * self.scale['x']
xmax = self.center['x'] + self.shape[1] / 2 * self.scale['x']
return [xmin, xmax]
@property
def yrange(self):
"""Return the Y range of the image in arcsec from edge to edge."""
ymin = self.center['y'] - self.shape[0] / 2 * self.scale['y']
ymax = self.center['y'] + self.shape[0] / 2 * self.scale['y']
return [ymin, ymax]
@property
def center(self):
"""Returns the offset between the center of the Sun and the center of
the map."""
return {
'x':
wcs.get_center(self.shape[1], self.scale['x'],
self.reference_pixel['x'],
self.reference_coordinate['x']),
'y':
wcs.get_center(self.shape[0], self.scale['y'],
self.reference_pixel['y'],
self.reference_coordinate['y'])
}
def _draw_limb(self, fig, axes):
"""Draws a circle representing the solar limb"""
circ = patches.Circle([0, 0],
radius=self.rsun_arcseconds,
fill=False,
color='white')
axes.add_artist(circ)
return fig, axes
def _draw_grid(self, fig, axes, grid_spacing=20):
"""Draws a grid over the surface of the Sun"""
# define the number of points for each latitude or longitude line
num_points = 20
hg_longitude_deg = np.linspace(-90, 90, num=num_points)
hg_latitude_deg = np.arange(-90, 90, grid_spacing)
# draw the latitude lines
for lat in hg_latitude_deg:
hg_latitude_deg_mesh, hg_longitude_deg_mesh = np.meshgrid(
lat * np.ones(num_points), hg_longitude_deg)
x, y = wcs.convert_hg_hpc(self.rsun_meters,
self.dsun,
self.heliographic_latitude,
self.heliographic_longitude,
hg_longitude_deg_mesh,
hg_latitude_deg_mesh,
units='arcsec')
axes.plot(x, y, color='white', linestyle='dotted')
hg_longitude_deg = np.arange(-90, 90, grid_spacing)
hg_latitude_deg = np.linspace(-90, 90, num=num_points)
# draw the longitude lines
for lon in hg_longitude_deg:
hg_longitude_deg_mesh, hg_latitude_deg_mesh = np.meshgrid(
lon * np.ones(num_points), hg_latitude_deg)
x, y = wcs.convert_hg_hpc(self.rsun_meters,
self.dsun,
self.heliographic_latitude,
self.heliographic_longitude,
hg_longitude_deg_mesh,
hg_latitude_deg_mesh,
units='arcsec')
axes.plot(x, y, color='white', linestyle='dotted')
return fig, axes
def _validate(self):
"""Validates the meta-information associated with a Map.
This function includes very basic validation checks which apply to
all of the kinds of files that SunPy can read. Datasource-specific
validation should be handled in the relevant file in the
sunpy.map.sources package."""
if (self.dsun <= 0 or self.dsun >= 40 * constants.au):
raise InvalidHeaderInformation("Invalid value for DSUN")
def std(self, *args, **kwargs):
"""overide np.ndarray.std()"""
return np.array(self, copy=False, subok=False).std(*args, **kwargs)
def mean(self, *args, **kwargs):
"""overide np.ndarray.mean()"""
return np.array(self, copy=False, subok=False).mean(*args, **kwargs)
def min(self, *args, **kwargs):
"""overide np.ndarray.min()"""
return np.array(self, copy=False, subok=False).min(*args, **kwargs)
def max(self, *args, **kwargs):
"""overide np.ndarray.max()"""
return np.array(self, copy=False, subok=False).max(*args, **kwargs)
def data_to_pixel(self, value, dim):
"""Convert pixel-center data coordinates to pixel values"""
if dim not in ['x', 'y']:
raise ValueError("Invalid dimension. Must be one of 'x' or 'y'.")
size = self.shape[dim == 'x'] # 1 if dim == 'x', 0 if dim == 'y'.
return (value - self.center[dim]) / self.scale[dim] + ((size - 1) / 2.)
def get_header(self, original=False):
"""Returns an updated MapHeader instance"""
header = self._original_header.copy()
# If requested, return original header as-is
if original:
return header
# Bit-depth
#
# 8 Character or unsigned binary integer
# 16 16-bit twos-complement binary integer
# 32 32-bit twos-complement binary integer
# -32 IEEE single precision floating point
# -64 IEEE double precision floating point
#
if not header.has_key('bitpix'):
bitdepth = 8 * self.dtype.itemsize
if self.dtype.kind == "f":
bitdepth = -bitdepth
header['bitpix'] = bitdepth
# naxis
header['naxis'] = self.ndim
header['naxis1'] = self.shape[1]
header['naxis2'] = self.shape[0]
# dsun
if header.has_key('dsun_obs'):
header['dsun_obs'] = self.dsun
# rsun_obs
if header.has_key('rsun_obs'):
header['rsun_obs'] = self.rsun_arcseconds
elif header.has_key('solar_r'):
header['solar_r'] = self.rsun_arcseconds
elif header.has_key('radius'):
header['radius'] = self.rsun_arcseconds
# cdelt
header['cdelt1'] = self.scale['x']
header['cdelt2'] = self.scale['y']
# crpix
header['crval1'] = self.reference_coordinate['x']
header['crval2'] = self.reference_coordinate['y']
# crval
header['crpix1'] = self.reference_pixel['x']
header['crpix2'] = self.reference_pixel['y']
return header
def resample(self, dimensions, method='linear'):
"""Returns a new Map that has been resampled up or down
Arbitrary resampling of the Map to new dimension sizes.
Uses the same parameters and creates the same co-ordinate lookup points
as IDL''s congrid routine, which apparently originally came from a
VAX/VMS routine of the same name.
Parameters
----------
dimensions : tuple
Dimensions that new Map should have.
Note: the first argument corresponds to the 'x' axis and the second
argument corresponds to the 'y' axis.
method : {'neighbor' | 'nearest' | 'linear' | 'spline'}
Method to use for resampling interpolation.
* neighbor - Closest value from original data
* nearest and linear - Uses n x 1-D interpolations using
scipy.interpolate.interp1d
* spline - Uses ndimage.map_coordinates
Returns
-------
out : Map
A new Map which has been resampled to the desired dimensions.
References
----------
| http://www.scipy.org/Cookbook/Rebinning (Original source, 2011/11/19)
"""
# Note: because the underlying ndarray is transposed in sense when
# compared to the Map, the ndarray is transposed, resampled, then
# transposed back
# Note: "center" defaults to True in this function because data
# coordinates in a Map are at pixel centers
# Make a copy of the original data and perform resample
data = resample(np.asarray(self).copy().T,
dimensions,
method,
center=True)
# Update image scale and number of pixels
header = self._original_header.copy()
# Note that 'x' and 'y' correspond to 1 and 0 in self.shape,
# respectively
scale_factor_x = (float(self.shape[1]) / dimensions[0])
scale_factor_y = (float(self.shape[0]) / dimensions[1])
# Create new map instance
new_map = self.__class__(data.T, header)
# Update metadata
new_map.scale['x'] *= scale_factor_x
new_map.scale['y'] *= scale_factor_y
new_map.reference_pixel['x'] = (dimensions[0] + 1) / 2.
new_map.reference_pixel['y'] = (dimensions[1] + 1) / 2.
new_map.reference_coordinate['x'] = self.center['x']
new_map.reference_coordinate['y'] = self.center['y']
return new_map
def superpixel(self, dimensions, method='sum'):
"""Returns a new map consisting of superpixels formed from the
original data. Useful for increasing signal to noise ratio in images.
Parameters
----------
dimensions : tuple
One superpixel in the new map is equal to (dimension[0],
dimension[1]) pixels of the original map
Note: the first argument corresponds to the 'x' axis and the second
argument corresponds to the 'y' axis.
method : {'sum' | 'average'}
What each superpixel represents compared to the original data
* sum - add up the original data
* average - average the sum over the number of original pixels
Returns
-------
out : Map
A new Map which has superpixels of the required size.
References
----------
| http://mail.scipy.org/pipermail/numpy-discussion/2010-July/051760.html
"""
# Note: because the underlying ndarray is transposed in sense when
# compared to the Map, the ndarray is transposed, resampled, then
# transposed back
# Note: "center" defaults to True in this function because data
# coordinates in a Map are at pixel centers
# Make a copy of the original data and perform reshaping
reshaped = reshape_image_to_4d_superpixel(
np.asarray(self).copy().T, dimensions)
if method == 'sum':
data = reshaped.sum(axis=3).sum(axis=1)
elif method == 'average':
data = ((reshaped.sum(axis=3).sum(axis=1)) /
np.float32(dimensions[0] * dimensions[1]))
#data = resample(np.asarray(self).copy().T, dimensions,
# method, center=True)
# Update image scale and number of pixels
header = self._original_header.copy()
# Note that 'x' and 'y' correspond to 1 and 0 in self.shape,
# respectively
new_nx = self.shape[1] / dimensions[0]
new_ny = self.shape[0] / dimensions[1]
# Create new map instance
new_map = self.__class__(data.T, header)
# Update metadata
new_map.scale['x'] = dimensions[0] * self.scale['x']
new_map.scale['y'] = dimensions[1] * self.scale['y']
new_map.reference_pixel['x'] = (new_nx + 1) / 2.
new_map.reference_pixel['y'] = (new_ny + 1) / 2.
new_map.reference_coordinate['x'] = self.center['x']
new_map.reference_coordinate['y'] = self.center['y']
return new_map
def save(self, filepath):
"""Saves the SunPy Map object to a file.
Currently SunPy can only save files in the FITS format. In the future
support will be added for saving to other formats.
Parameters
----------
filepath : string
Location to save file to.
"""
pyfits_header = self.get_header().as_pyfits_header()
hdu = pyfits.PrimaryHDU(self, header=pyfits_header)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto(os.path.expanduser(filepath))
def submap(self, range_a, range_b, units="data"):
"""Returns a submap of the map with the specified range
Parameters
----------
range_a : list
The range of the Map to select across either the x axis.
range_b : list
The range of the Map to select across either the y axis.
units : {'data' | 'pixels'}, optional
The units for the supplied ranges.
Returns
-------
out : Map
A new map instance is returned representing to specified sub-region
Examples
--------
>>> aia.submap([-5,5],[-5,5])
AIAMap([[ 341.3125, 266.5 , 329.375 , 330.5625, 298.875 ],
[ 347.1875, 273.4375, 247.4375, 303.5 , 305.3125],
[ 322.8125, 302.3125, 298.125 , 299. , 261.5 ],
[ 334.875 , 289.75 , 269.25 , 256.375 , 242.3125],
[ 273.125 , 241.75 , 248.8125, 263.0625, 249.0625]])
>>> aia.submap([0,5],[0,5], units='pixels')
AIAMap([[ 0.3125, -0.0625, -0.125 , 0. , -0.375 ],
[ 1. , 0.1875, -0.8125, 0.125 , 0.3125],
[-1.1875, 0.375 , -0.5 , 0.25 , -0.4375],
[-0.6875, -0.3125, 0.8125, 0.0625, 0.1875],
[-0.875 , 0.25 , 0.1875, 0. , -0.6875]])
"""
if units is "data":
# Check edges (e.g. [:512,..] or [:,...])
if range_a[0] is None:
range_a[0] = self.xrange[0]
if range_a[1] is None:
range_a[1] = self.xrange[1]
if range_b[0] is None:
range_b[0] = self.yrange[0]
if range_b[1] is None:
range_b[1] = self.yrange[1]
#x_pixels = [self.data_to_pixel(elem, 'x') for elem in range_a]
x_pixels = [
np.ceil(self.data_to_pixel(range_a[0], 'x')),
np.floor(self.data_to_pixel(range_a[1], 'x')) + 1
]
#y_pixels = [self.data_to_pixel(elem, 'y') for elem in range_b]
y_pixels = [
np.ceil(self.data_to_pixel(range_b[0], 'y')),
np.floor(self.data_to_pixel(range_b[1], 'y')) + 1
]
elif units is "pixels":
# Check edges
if range_a[0] is None:
range_a[0] = 0
if range_a[1] is None:
range_a[1] = self.shape[0]
if range_b[0] is None:
range_b[0] = 0
if range_b[1] is None:
range_b[1] = self.shape[0]
x_pixels = range_a
y_pixels = range_b
else:
raise ValueError("Invalid unit. Must be one of 'data' or 'pixels'")
# Make a copy of the header with updated centering information
header = self._original_header.copy()
# Get ndarray representation of submap
data = np.asarray(self)[y_pixels[0]:y_pixels[1],
x_pixels[0]:x_pixels[1]]
# Instantiate new instance and update metadata
new_map = self.__class__(data.copy(), header)
new_map.reference_pixel['x'] = self.reference_pixel['x'] - x_pixels[0]
new_map.reference_pixel['y'] = self.reference_pixel['y'] - y_pixels[0]
return new_map
@toggle_pylab
def plot(self,
figure=None,
overlays=None,
draw_limb=True,
gamma=None,
draw_grid=False,
colorbar=True,
basic_plot=False,
**matplot_args):
"""Plots the map object using matplotlib
Parameters
----------
overlays : list
List of overlays to include in the plot
draw_limb : bool
Whether the solar limb should be plotted.
draw_grid : bool
Whether solar meridians and parallels
grid_spacing : float
Set the spacing between meridians and parallels for the grid
gamma : float
Gamma value to use for the color map
colorbar : bool
Whether to display a colorbar next to the plot
basic_plot : bool
If true, the data is plotted by itself at it's natural scale; no
title, labels, or axes are shown.
**matplot_args : dict
Matplotlib Any additional imshow arguments that should be used
when plotting the image.
"""
if overlays is None:
overlays = []
if draw_limb:
overlays = overlays + [self._draw_limb]
# TODO: need to be able to pass the grid spacing to _draw_grid from the
# plot command.
if draw_grid:
overlays = overlays + [self._draw_grid]
# Create a figure and add title and axes
if figure is None:
figure = plt.figure(frameon=not basic_plot)
# Basic plot
if basic_plot:
axes = plt.Axes(figure, [0., 0., 1., 1.])
axes.set_axis_off()
figure.add_axes(axes)
# Normal plot
else:
axes = figure.add_subplot(111)
axes.set_title("%s %s" % (self.name, self.date))
# x-axis label
if self.coordinate_system['x'] == 'HG':
xlabel = 'Longitude [%s]' % self.units['x']
else:
xlabel = 'X-position [%s]' % self.units['x']
# y-axis label
if self.coordinate_system['y'] == 'HG':
ylabel = 'Latitude [%s]' % self.units['y']
else:
ylabel = 'Y-position [%s]' % self.units['y']
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
# Determine extent
extent = self.xrange + self.yrange
# Matplotlib arguments
params = {"cmap": self.cmap, "norm": self.norm()}
params.update(matplot_args)
if gamma is not None:
params['cmap'] = copy(params['cmap'])
params['cmap'].set_gamma(gamma)
im = axes.imshow(self, origin='lower', extent=extent, **params)
if colorbar and not basic_plot:
figure.colorbar(im)
for overlay in overlays:
figure, axes = overlay(figure, axes)
return figure
def show(self,
figure=None,
overlays=None,
draw_limb=False,
gamma=1.0,
draw_grid=False,
colorbar=True,
basic_plot=False,
**matplot_args):
"""Displays map on screen. Arguments are same as plot()."""
self.plot(figure, overlays, draw_limb, gamma, draw_grid, colorbar,
basic_plot, **matplot_args).show()
def norm(self):
"""Default normalization method"""
return None
@classmethod
def parse_file(cls, filepath):
"""Reads in a map file and returns a header and data array"""
return read_file(filepath)
@classmethod
def read(cls, filepath):
"""Map class factory
Attempts to determine the type of data associated with input and
returns an instance of either the generic Map class or a subclass
of Map such as AIAMap, EUVIMap, etc.
Parameters
----------
filepath : string
Path to a valid FITS or JPEG 2000 file of a type supported by SunPy
Returns
-------
out : Map
Returns a Map instance for the particular type of data loaded.
"""
data, header = cls.parse_file(filepath)
if cls.__name__ is not "Map":
return cls(data, header)
for cls in Map.__subclasses__():
if cls.is_datasource_for(header):
return cls(data, header)
return Map(data, header)
@classmethod
def read_header(cls, filepath):
"""Attempts to detect the datasource type and returns meta-information
for that particular datasource."""
header = read_file_header(filepath)
for cls in Map.__subclasses__():
if cls.is_datasource_for(header):
properties = cls.get_properties(header)
properties['header'] = header
return properties
class LightCurve(object):
"""
LightCurve(filepath)
A generic light curve object.
Parameters
----------
args : filepath, url, or start and end dates
The input for a LightCurve object should either be a filepath, a URL,
or a date range to be queried for the particular instrument.
Attributes
----------
data : pandas.DataFrame
An pandas DataFrame prepresenting one or more fields as they vary with
respect to time.
header : string, dict
The comment string or header associated with the light curve input
Examples
--------
>>> import sunpy
>>> import datetime
>>> base = datetime.datetime.today()
>>> dates = [base - datetime.timedelta(minutes=x) for x in
range(0, 24 * 60)]
>>> light_curve = sunpy.lightcurve.LightCurve({"param1": range(24 * 60)},
index=dates)
>>> light_curve.show()
References
----------
| http://pandas.pydata.org/pandas-docs/dev/dsintro.html
"""
_cond_dispatch = ConditionalDispatch()
create = classmethod(_cond_dispatch.wrapper())
def __init__(self, data, header=None):
self.data = data
self.header = header
@classmethod
def from_time(cls, time, **kwargs):
date = sunpy.time.parse_time(time)
url = cls._get_url_for_date(date)
filepath = cls._download(
url, kwargs, err="Unable to download data for specified date")
return cls.from_file(filepath)
@classmethod
def from_range(cls, from_, to, **kwargs):
url = cls._get_url_for_date_range(from_, to)
filepath = self._download(
url,
kwargs,
err="Unable to download data for specified date range")
return cls.from_file(filepath)
@classmethod
def from_timerange(cls, timerange, **kwargs):
url = cls._get_url_for_date_range(timerange)
err = "Unable to download data for specified date range"
filepath = self._download(url, err, kwargs)
return cls.from_file(filepath)
@classmethod
def from_file(cls, filename):
filename = os.path.expanduser(filename)
header, data = cls._parse_filepath(filename)
return cls(data, header)
@classmethod
def from_url(cls, url, **kwargs):
try:
filepath = cls._download(url, kwargs)
except (urllib2.HTTPError, urllib2.URLError, ValueError):
err = ("Unable to read location. Did you "
"specify a valid filepath or URL?")
raise ValueError(err)
return cls.from_file(filepath)
@classmethod
def from_data(cls, data, index=None, header=None):
return cls(pandas.DataFrame(data, index=index), header)
@classmethod
def from_yesterday(cls):
return cls.from_url(cls._get_default_uri())
@classmethod
def from_dataframe(cls, dataframe, header=None):
return cls(dataframe, header)
def plot(self, **kwargs):
"""Plot a plot of the light curve"""
axes = self.data.plot(**kwargs)
return axes.get_figure()
def show(self, **kwargs):
"""Shows a plot of the light curve"""
fig = self.plot(**kwargs)
fig.show()
return fig
@staticmethod
def _download(uri, kwargs, err='Unable to download data at specified URL'):
"""Attempts to download data at the specified URI"""
_filename = os.path.basename(uri).split("?")[0]
# user specifies a download directory
if "directory" in kwargs:
download_dir = os.path.expanduser(kwargs["directory"])
else:
download_dir = sunpy.config.get("downloads", "download_dir")
# overwrite the existing file if the keyword is present
if "overwrite" in kwargs:
overwrite = kwargs["overwrite"]
else:
overwrite = False
# If the file is not already there, download it
filepath = os.path.join(download_dir, _filename)
if not (os.path.isfile(filepath)) or (overwrite
and os.path.isfile(filepath)):
try:
response = urllib2.urlopen(uri)
except (urllib2.HTTPError, urllib2.URLError):
raise urllib2.URLError(err)
with open(filepath, 'wb') as fp:
shutil.copyfileobj(response, fp)
return filepath
@classmethod
def _get_default_uri(cls):
"""Default data to load when none is specified"""
msg = "No default action set for %s"
raise NotImplementedError(msg % cls.__name__)
@classmethod
def _get_url_for_date(cls, date):
"""Returns a URL to the data for the specified date"""
msg = "Date-based downloads not supported for for %s"
raise NotImplementedError(msg % cls.__name__)
@classmethod
def _get_url_for_date_range(cls, *args, **kwargs):
"""Returns a URL to the data for the specified date range"""
msg = "Date-range based downloads not supported for for %s"
raise NotImplementedError(msg % cls.__name__)
@staticmethod
def _parse_csv(filepath):
"""Place holder method to parse CSV files."""
msg = "Generic CSV parsing not yet implemented for LightCurve"
raise NotImplementedError(msg)
@staticmethod
def _parse_fits(filepath):
"""Place holder method to parse FITS files."""
msg = "Generic FITS parsing not yet implemented for LightCurve"
raise NotImplementedError(msg)
@classmethod
def _parse_filepath(cls, filepath):
filename, extension = os.path.splitext(filepath)
if extension.lower() in (".csv", ".txt"):
return cls._parse_csv(filepath)
else:
return cls._parse_fits(filepath)
def discrete_boxcar_average(self, seconds=1):
"""Computes a discrete boxcar average for the DataFrame"""
date_range = pandas.DateRange(self.data.index[0],
self.data.index[-1],
offset=pandas.datetools.Second(seconds))
grouped = self.data.groupby(date_range.asof)
subsampled = grouped.mean()
return LightCurve(subsampled, self.header.copy())
def truncate(self, start=None, end=None):
"""Returns a truncated version of the Lyra object"""
if start is None:
start = self.data.index[0]
if end is None:
end = self.data.index[-1]
truncated = self.data.truncate(sunpy.time.parse_time(start),
sunpy.time.parse_time(end))
return LightCurve(truncated, self.header.copy())
class Map(np.ndarray, Parent):
"""
Map(data, header)
A spatially-aware data array based on the SolarSoft Map object
Parameters
----------
data : numpy.ndarray, list
A 2d list or ndarray containing the map data
header : dict
A dictionary of the original image header tags
Attributes
----------
original_header : dict
Dictionary representation of the original FITS header
carrington_longitude : str
Carrington longitude (crln_obs)
center : dict
X and Y coordinate of the center of the map in units.
Usually represents the offset between the center of the Sun and the
center of the map.
cmap : matplotlib.colors.Colormap
A Matplotlib colormap to be applied to the data
coordinate_system : dict
Coordinate system used for x and y axes (ctype1/2)
date : datetime
Image observation time
detector : str
Detector name
dsun : float
The observer distance from the Sun.
exptime : float
Exposure time of the image in seconds.
heliographic_latitude : float
Heliographic latitude in degrees
heliographic_longitude : float
Heliographic longitude in degrees
instrument : str
Instrument name
measurement : str, int
Measurement name. In some instances this is the wavelength of image.
name: str
Human-readable description of map-type
nickname : str
An abbreviated human-readable description of the map-type; part of
the Helioviewer data model
observatory : str
Observatory name
reference_coordinate : float
Reference point WCS axes in data units (crval1/2)
reference_pixel : float
Reference point axes in pixels (crpix1/2)
rsun_arcseconds : float
Radius of the sun in arcseconds
rsun_meters : float
Radius of the sun in meters
scale : dict
Image scale along the x and y axes in units/pixel (cdelt1/2).
units : dict
Image coordinate units along the x and y axes (cunit1/2).
Methods
-------
std()
Return the standard deviation of the map data
mean()
Return the mean of the map data
min()
Return the minimum value of the map data
max()
Return the maximum value of the map data
resample(dimension, method)
Returns a new map that has been resampled up or down
superpixel(dimension, method)
Returns a new map consisting of superpixels formed from the
original data.
save()
Save the map to a fits file.
submap(range_a, range_b, units)
Returns a submap of the map with the specified range
plot()
Return a matplotlib imageaxes instance, like plt.imshow()
peek()
Display a matplotlib plot to the screen
draw_limb()
Draw a line on the image where the solar limb is.
draw_grid()
Draw a lon/lat grid on a map plot.
get_header()
Returns the original header from when the map was first created.
Examples
--------
>>> aia = sunpy.make_map(sunpy.AIA_171_IMAGE)
>>> aia.T
AIAMap([[ 0.3125, 1. , -1.1875, ..., -0.625 , 0.5625, 0.5 ],
[-0.0625, 0.1875, 0.375 , ..., 0.0625, 0.0625, -0.125 ],
[-0.125 , -0.8125, -0.5 , ..., -0.3125, 0.5625, 0.4375],
...,
[ 0.625 , 0.625 , -0.125 , ..., 0.125 , -0.0625, 0.6875],
[-0.625 , -0.625 , -0.625 , ..., 0.125 , -0.0625, 0.6875],
[ 0. , 0. , -1.1875, ..., 0.125 , 0. , 0.6875]])
>>> aia.units['x']
'arcsec'
>>> aia.peek()
See Also
--------
numpy.ndarray Parent class for the Map object
References
----------
| http://docs.scipy.org/doc/numpy/reference/arrays.classes.html
| http://docs.scipy.org/doc/numpy/user/basics.subclassing.html
| http://docs.scipy.org/doc/numpy/reference/ufuncs.html
| http://www.scipy.org/Subclasses
"""
_create = ConditionalDispatch.from_existing(Parent._create)
create = classmethod(_create.wrapper())
def __new__(cls, data, header):
"""Creates a new Map instance"""
if isinstance(data, np.ndarray):
obj = data.view(cls)
elif isinstance(data, list):
obj = np.asarray(data).view(cls)
else:
raise TypeError('Invalid input')
return obj
def __init__(self, data, header):
self._original_header = header
# Set naxis1 and naxis2 if not specified
if header.get('naxis1') is None:
header['naxis1'] = self.shape[1]
if header.get('naxis2') is None:
header['naxis2'] = self.shape[0]
# Parse header and set map attributes
for attr, value in list(self.get_properties(header).items()):
setattr(self, attr, value)
# Validate properties
self._validate()
@classmethod
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 __array_finalize__(self, obj):
"""Finishes instantiation of the new map object"""
if obj is None:
return
if hasattr(obj, '_original_header'):
properties = [
'_original_header', 'cmap', 'date', 'detector', 'dsun',
'exposure_time', 'instrument', 'measurement', 'name',
'observatory', 'rsun_arcseconds', 'rsun_meters', 'scale',
'units', 'reference_coordinate', 'reference_pixel',
'coordinate_system', 'heliographic_latitude',
'heliographic_longitude', 'carrington_longitude',
'rotation_angle'
]
for attr in properties:
setattr(self, attr, getattr(obj, attr))
def __array_wrap__(self, out_arr, context=None):
"""Returns a wrapped instance of a Map object"""
return np.ndarray.__array_wrap__(self, out_arr, context)
def __getitem__(self, key):
"""Overriding indexing operation to ensure that header is updated. Note
that the indexing follows the ndarray row-column order, which is
reversed from calling Map.submap()"""
if isinstance(key, tuple):
# Used when asking for a 2D sub-array
# The header will be updated
if type(key[1]) is slice:
x_range = [key[1].start, key[1].stop]
else:
x_range = [key[1], key[1] + 1]
if type(key[0]) is slice:
y_range = [key[0].start, key[0].stop]
else:
y_range = [key[0], key[0] + 1]
return self.submap(x_range, y_range, units="pixels")
else:
# Typically used by np.ndarray.__repr__() due to indexing with [-1]
# The header will not be updated properly!
return np.ndarray.__getitem__(self, key)
def __add__(self, other):
"""Add two maps. Currently does not take into account the alignment
between the two maps."""
result = np.ndarray.__add__(self, other)
return result
def __repr__(self):
if not hasattr(self, 'observatory'):
return np.ndarray.__repr__(self)
return ("""SunPy Map
---------
Observatory:\t %s
Instrument:\t %s
Detector:\t %s
Measurement:\t %s
Obs Date:\t %s
dt:\t\t %f
Dimension:\t [%d, %d]
[dx, dy] =\t [%f, %f]
""" % (self.observatory, self.instrument, self.detector, self.measurement,
self.date, self.exposure_time, self.shape[1], self.shape[0],
self.scale['x'], self.scale['y']) + np.ndarray.__repr__(self))
def __sub__(self, other):
"""Subtract two maps. Currently does not take into account the
alignment between the two maps.
numpy dtype nums:
1 int8
2 uint8
3 int16
4 uint16
"""
# if data is stored as unsigned, cast up (e.g. uint8 => int16)
if self.dtype.kind == "u":
self = self.astype(to_signed(self.dtype))
if other.dtype.kind == "u":
other = other.astype(to_signed(other.dtype))
result = np.ndarray.__sub__(self, other)
def norm():
mean = result.mean()
std = result.std()
vmin = max(result.min(), mean - 6 * std)
vmax = min(result.max(), mean + 6 * std)
return colors.Normalize(vmin, vmax)
result.norm = norm
result.cmap = cm.gray # @UndefinedVariable
return result
@property
def xrange(self):
"""Return the X range of the image in arcsec from edge to edge."""
xmin = self.center['x'] - self.shape[1] / 2. * self.scale['x']
xmax = self.center['x'] + self.shape[1] / 2. * self.scale['x']
return [xmin, xmax]
@property
def yrange(self):
"""Return the Y range of the image in arcsec from edge to edge."""
ymin = self.center['y'] - self.shape[0] / 2. * self.scale['y']
ymax = self.center['y'] + self.shape[0] / 2. * self.scale['y']
return [ymin, ymax]
@property
def center(self):
"""Returns the offset between the center of the Sun and the center of
the map."""
return {
'x':
wcs.get_center(self.shape[1], self.scale['x'],
self.reference_pixel['x'],
self.reference_coordinate['x']),
'y':
wcs.get_center(self.shape[0], self.scale['y'],
self.reference_pixel['y'],
self.reference_coordinate['y'])
}
def draw_limb(self, axes=None):
"""Draws a circle representing the solar limb
Parameters
----------
axes: matplotlib.axes object or None
Axes to plot limb on or None to use current axes.
Returns
-------
matplotlib.axes object
"""
if not axes:
axes = plt.gca()
if hasattr(self, 'center'):
circ = patches.Circle([self.center['x'], self.center['y']],
radius=self.rsun_arcseconds,
fill=False,
color='white',
zorder=100)
else:
print("Assuming center of Sun is center of image")
circ = patches.Circle([0, 0],
radius=self.rsun_arcseconds,
fill=False,
color='white',
zorder=100)
axes.add_artist(circ)
return axes
def draw_grid(self, axes=None, grid_spacing=20):
"""Draws a grid over the surface of the Sun
Parameters
----------
axes: matplotlib.axes object or None
Axes to plot limb on or None to use current axes.
grid_spacing: float
Spacing (in degrees) for longitude and latitude grid.
Returns
-------
matplotlib.axes object
"""
if not axes:
axes = plt.gca()
x, y = self.pixel_to_data()
rsun = self.rsun_meters
dsun = self.dsun
b0 = self.heliographic_latitude
l0 = self.heliographic_longitude
units = [self.units.get('x'), self.units.get('y')]
#TODO: This function could be optimized. Does not need to convert the entire image
# coordinates
lon_self, lat_self = wcs.convert_hpc_hg(rsun, dsun, units[0], units[1],
b0, l0, x, y)
# define the number of points for each latitude or longitude line
num_points = 20
#TODO: The following code is ugly. Fix it.
lon_range = [lon_self.min(), lon_self.max()]
lat_range = [lat_self.min(), lat_self.max()]
if np.isfinite(lon_range[0]) == False:
lon_range[0] = -90 + self.heliographic_longitude
if np.isfinite(lon_range[1]) == False:
lon_range[1] = 90 + self.heliographic_longitude
if np.isfinite(lat_range[0]) == False:
lat_range[0] = -90 + self.heliographic_latitude
if np.isfinite(lat_range[1]) == False:
lat_range[1] = 90 + self.heliographic_latitude
hg_longitude_deg = np.linspace(lon_range[0],
lon_range[1],
num=num_points)
hg_latitude_deg = np.arange(lat_range[0], lat_range[1] + grid_spacing,
grid_spacing)
# draw the latitude lines
for lat in hg_latitude_deg:
hg_latitude_deg_mesh, hg_longitude_deg_mesh = np.meshgrid(
lat * np.ones(num_points), hg_longitude_deg)
x, y = wcs.convert_hg_hpc(self.rsun_meters,
self.dsun,
self.heliographic_latitude,
self.heliographic_longitude,
hg_longitude_deg_mesh,
hg_latitude_deg_mesh,
units='arcsec')
axes.plot(x, y, color='white', linestyle='dotted', zorder=100)
hg_longitude_deg = np.arange(lon_range[0], lon_range[1] + grid_spacing,
grid_spacing)
hg_latitude_deg = np.linspace(lat_range[0],
lat_range[1],
num=num_points)
# draw the longitude lines
for lon in hg_longitude_deg:
hg_longitude_deg_mesh, hg_latitude_deg_mesh = np.meshgrid(
lon * np.ones(num_points), hg_latitude_deg)
x, y = wcs.convert_hg_hpc(self.rsun_meters,
self.dsun,
self.heliographic_latitude,
self.heliographic_longitude,
hg_longitude_deg_mesh,
hg_latitude_deg_mesh,
units='arcsec')
axes.plot(x, y, color='white', linestyle='dotted', zorder=100)
axes.set_ylim(self.yrange)
axes.set_xlim(self.xrange)
return axes
def _validate(self):
"""Validates the meta-information associated with a Map.
This function includes very basic validation checks which apply to
all of the kinds of files that SunPy can read. Datasource-specific
validation should be handled in the relevant file in the
sunpy.map.sources package."""
if (self.dsun <= 0 or self.dsun >= 40 * constants.au):
raise InvalidHeaderInformation("Invalid value for DSUN")
def std(self, *args, **kwargs):
"""overide np.ndarray.std()"""
return np.array(self, copy=False, subok=False).std(*args, **kwargs)
def mean(self, *args, **kwargs):
"""overide np.ndarray.mean()"""
return np.array(self, copy=False, subok=False).mean(*args, **kwargs)
def min(self, *args, **kwargs):
"""overide np.ndarray.min()"""
return np.array(self, copy=False, subok=False).min(*args, **kwargs)
def max(self, *args, **kwargs):
"""overide np.ndarray.max()"""
return np.array(self, copy=False, subok=False).max(*args, **kwargs)
def data_to_pixel(self, value, dim):
"""Convert pixel-center data coordinates to pixel values"""
#TODO: This function should be renamed. It is confusing as data
# coordinates are in something like arcsec but this function just changes how you
# count pixels
if dim not in ['x', 'y']:
raise ValueError("Invalid dimension. Must be one of 'x' or 'y'.")
size = self.shape[dim == 'x'] # 1 if dim == 'x', 0 if dim == 'y'.
return (value - self.center[dim]) / self.scale[dim] + ((size - 1) / 2.)
def pixel_to_data(self, x=None, y=None):
"""Convert from pixel coordinates to data coordinates (e.g. arcsec)"""
width = self.shape[1]
height = self.shape[0]
if (x is not None) & (x > width - 1):
raise ValueError("X pixel value larger than image width (%s)." %
width)
if (x is not None) & (y > height - 1):
raise ValueError("Y pixel value larger than image height (%s)." %
height)
if (x is not None) & (x < 0):
raise ValueError("X pixel value cannot be less than 0.")
if (x is not None) & (y < 0):
raise ValueError("Y pixel value cannot be less than 0.")
scale = np.array([self.scale.get('x'), self.scale.get('y')])
crpix = np.array(
[self.reference_pixel.get('x'),
self.reference_pixel.get('y')])
crval = np.array([
self.reference_coordinate.get('x'),
self.reference_coordinate.get('y')
])
coordinate_system = [
self.coordinate_system.get('x'),
self.coordinate_system.get('y')
]
x, y = wcs.convert_pixel_to_data(width,
height,
scale[0],
scale[1],
crpix[0],
crpix[1],
crval[0],
crval[1],
coordinate_system[0],
x=x,
y=y)
return x, y
def get_header(self, original=False):
"""Returns an updated MapHeader instance"""
header = self._original_header.copy()
# If requested, return original header as-is
if original:
return header
# Bit-depth
#
# 8 Character or unsigned binary integer
# 16 16-bit twos-complement binary integer
# 32 32-bit twos-complement binary integer
# -32 IEEE single precision floating point
# -64 IEEE double precision floating point
#
if not header.has_key('bitpix'):
bitdepth = 8 * self.dtype.itemsize
if self.dtype.kind == "f":
bitdepth = -bitdepth
header['bitpix'] = bitdepth
# naxis
header['naxis'] = self.ndim
header['naxis1'] = self.shape[1]
header['naxis2'] = self.shape[0]
# dsun
if header.has_key('dsun_obs'):
header['dsun_obs'] = self.dsun
# rsun_obs
if header.has_key('rsun_obs'):
header['rsun_obs'] = self.rsun_arcseconds
elif header.has_key('solar_r'):
header['solar_r'] = self.rsun_arcseconds
elif header.has_key('radius'):
header['radius'] = self.rsun_arcseconds
# cdelt
header['cdelt1'] = self.scale['x']
header['cdelt2'] = self.scale['y']
# crpix
header['crval1'] = self.reference_coordinate['x']
header['crval2'] = self.reference_coordinate['y']
# crval
header['crpix1'] = self.reference_pixel['x']
header['crpix2'] = self.reference_pixel['y']
return header
def resample(self, dimensions, method='linear'):
"""Returns a new Map that has been resampled up or down
Arbitrary resampling of the Map to new dimension sizes.
Uses the same parameters and creates the same co-ordinate lookup points
as IDL''s congrid routine, which apparently originally came from a
VAX/VMS routine of the same name.
Parameters
----------
dimensions : tuple
Dimensions that new Map should have.
Note: the first argument corresponds to the 'x' axis and the second
argument corresponds to the 'y' axis.
method : {'neighbor' | 'nearest' | 'linear' | 'spline'}
Method to use for resampling interpolation.
* neighbor - Closest value from original data
* nearest and linear - Uses n x 1-D interpolations using
scipy.interpolate.interp1d
* spline - Uses ndimage.map_coordinates
Returns
-------
out : Map
A new Map which has been resampled to the desired dimensions.
References
----------
| http://www.scipy.org/Cookbook/Rebinning (Original source, 2011/11/19)
"""
# Note: because the underlying ndarray is transposed in sense when
# compared to the Map, the ndarray is transposed, resampled, then
# transposed back
# Note: "center" defaults to True in this function because data
# coordinates in a Map are at pixel centers
# Make a copy of the original data and perform resample
data = sunpy_image_resample(np.asarray(self).copy().T,
dimensions,
method,
center=True)
# Update image scale and number of pixels
header = self._original_header.copy()
# Note that 'x' and 'y' correspond to 1 and 0 in self.shape,
# respectively
scale_factor_x = (float(self.shape[1]) / dimensions[0])
scale_factor_y = (float(self.shape[0]) / dimensions[1])
# Create new map instance
new_map = self.__class__(data.T, header)
# Update metadata
new_map.scale['x'] *= scale_factor_x
new_map.scale['y'] *= scale_factor_y
new_map.reference_pixel['x'] = (dimensions[0] + 1) / 2.
new_map.reference_pixel['y'] = (dimensions[1] + 1) / 2.
new_map.reference_coordinate['x'] = self.center['x']
new_map.reference_coordinate['y'] = self.center['y']
return new_map
def superpixel(self, dimensions, method='sum'):
"""Returns a new map consisting of superpixels formed from the
original data. Useful for increasing signal to noise ratio in images.
Parameters
----------
dimensions : tuple
One superpixel in the new map is equal to (dimension[0],
dimension[1]) pixels of the original map
Note: the first argument corresponds to the 'x' axis and the second
argument corresponds to the 'y' axis.
method : {'sum' | 'average'}
What each superpixel represents compared to the original data
* sum - add up the original data
* average - average the sum over the number of original pixels
Returns
-------
out : Map
A new Map which has superpixels of the required size.
References
----------
| http://mail.scipy.org/pipermail/numpy-discussion/2010-July/051760.html
"""
# Note: because the underlying ndarray is transposed in sense when
# compared to the Map, the ndarray is transposed, resampled, then
# transposed back
# Note: "center" defaults to True in this function because data
# coordinates in a Map are at pixel centers
# Make a copy of the original data and perform reshaping
reshaped = reshape_image_to_4d_superpixel(
np.asarray(self).copy().T, dimensions)
if method == 'sum':
data = reshaped.sum(axis=3).sum(axis=1)
elif method == 'average':
data = ((reshaped.sum(axis=3).sum(axis=1)) /
np.float32(dimensions[0] * dimensions[1]))
#data = resample(np.asarray(self).copy().T, dimensions,
# method, center=True)
# Update image scale and number of pixels
header = self._original_header.copy()
# Note that 'x' and 'y' correspond to 1 and 0 in self.shape,
# respectively
new_nx = self.shape[1] / dimensions[0]
new_ny = self.shape[0] / dimensions[1]
# Create new map instance
new_map = self.__class__(data.T, header)
# Update metadata
new_map.scale['x'] = dimensions[0] * self.scale['x']
new_map.scale['y'] = dimensions[1] * self.scale['y']
new_map.reference_pixel['x'] = (new_nx + 1) / 2.
new_map.reference_pixel['y'] = (new_ny + 1) / 2.
new_map.reference_coordinate['x'] = self.center['x']
new_map.reference_coordinate['y'] = self.center['y']
return new_map
def rotate(self,
angle,
scale=1.0,
rotation_centre=None,
recentre=True,
missing=0.0,
interpolation='bicubic',
interp_param=-0.5):
"""Returns a new rotated, rescaled and shifted map.
Parameters
---------
angle: float
The angle to rotate the image by (radians)
scale: float
A scale factor for the image, default is no scaling
rotation_centre: tuple
The point in the image to rotate around (Axis of rotation).
Default: Centre of the array
recentre: bool, or array-like
Move the centroid (axis of rotation) to the centre of the array
or recentre coords.
Default: True, recentre to the centre of the array.
missing: float
The numerical value to fill any missing points after rotation.
Default: 0.0
interpolation: {'nearest' | 'bilinear' | 'spline' | 'bicubic'}
Interpolation method to use in the transform.
Spline uses the
scipy.ndimage.interpolation.affline_transform routine.
nearest, bilinear and bicubic all replicate the IDL rot() function.
Default: 'bicubic'
interp_par: Int or Float
Optional parameter for controlling the interpolation.
Spline interpolation requires an integer value between 1 and 5 for
the degree of the spline fit.
Default: 3
BiCubic interpolation requires a flaot value between -1 and 0.
Default: 0.5
Other interpolation options ingore the argument.
Returns
-------
New rotated, rescaled, translated map
Notes
-----
Apart from interpolation='spline' all other options use a compiled
C-API extension. If for some reason this is not compiled correctly this
routine will fall back upon the scipy implementation of order = 3.
For more infomation see:
http://sunpy.readthedocs.org/en/latest/guide/troubleshooting.html#crotate-warning
"""
#Interpolation parameter Sanity
assert interpolation in ['nearest', 'spline', 'bilinear', 'bicubic']
#Set defaults based on interpolation
if interp_param is None:
if interpolation is 'spline':
interp_param = 3
elif interpolation is 'bicubic':
interp_param = 0.5
else:
interp_param = 0 #Default value for nearest or bilinear
#Make sure recenter is a vector with shape (2,1)
if not isinstance(recentre, bool):
recentre = np.array(recentre).reshape(2, 1)
#Define Size and centre of array
centre = (np.array(self.shape) - 1) / 2.0
#If rotation_centre is not set (None or False),
#set rotation_centre to the centre of the image.
if rotation_centre is None:
rotation_centre = centre
else:
#Else check rotation_centre is a vector with shape (2,1)
rotation_centre = np.array(rotation_centre).reshape(2, 1)
#Recentre to the rotation_centre if recentre is True
if isinstance(recentre, bool):
#if rentre is False then this will be (0,0)
shift = np.array(rotation_centre) - np.array(centre)
else:
#Recentre to recentre vector otherwise
shift = np.array(recentre) - np.array(centre)
image = np.asarray(self).copy()
#Calulate the parameters for the affline_transform
c = np.cos(angle)
s = np.sin(angle)
mati = np.array([[c, s], [-s, c]]) / scale # res->orig
centre = np.array([centre]).transpose() # the centre of rotn
shift = np.array([shift]).transpose() # the shift
kpos = centre - np.dot(mati, (centre + shift))
# kpos and mati are the two transform constants, kpos is a 2x2 array
rsmat, offs = mati, np.squeeze((kpos[0, 0], kpos[1, 0]))
if interpolation == 'spline':
# This is the scipy call
data = scipy.ndimage.interpolation.affine_transform(
image,
rsmat,
offset=offs,
order=interp_param,
mode='constant',
cval=missing)
else:
#Use C extension Package
if not 'Crotate' in globals():
warnings.warn(
""""The C extension sunpy.image.Crotate is not
installed, falling back to the interpolation='spline' of order=3""", Warning)
data = scipy.ndimage.interpolation.affine_transform(
image,
rsmat,
offset=offs,
order=3,
mode='constant',
cval=missing)
#Set up call parameters depending on interp type.
if interpolation == 'nearest':
interp_type = Crotate.NEAREST
elif interpolation == 'bilinear':
interp_type = Crotate.BILINEAR
elif interpolation == 'bicubic':
interp_type = Crotate.BICUBIC
#Make call to extension
data = Crotate.affine_transform(image,
rsmat,
offset=offs,
kernel=interp_type,
cubic=interp_param,
mode='constant',
cval=missing)
#Return a new map
#Copy Header
header = self._original_header.copy()
# Create new map instance
new_map = self.__class__(data, header)
return new_map
def save(self, filepath):
"""Saves the SunPy Map object to a file.
Currently SunPy can only save files in the FITS format. In the future
support will be added for saving to other formats.
Parameters
----------
filepath : string
Location to save file to.
"""
pyfits_header = self.get_header().as_pyfits_header()
hdu = pyfits.PrimaryHDU(self, header=pyfits_header)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto(os.path.expanduser(filepath))
def submap(self, range_a, range_b, units="data"):
"""Returns a submap of the map with the specified range
Parameters
----------
range_a : list
The range of the Map to select across either the x axis.
range_b : list
The range of the Map to select across either the y axis.
units : {'data' | 'pixels'}, optional
The units for the supplied ranges.
Returns
-------
out : Map
A new map instance is returned representing to specified sub-region
Examples
--------
>>> aia.submap([-5,5],[-5,5])
AIAMap([[ 341.3125, 266.5 , 329.375 , 330.5625, 298.875 ],
[ 347.1875, 273.4375, 247.4375, 303.5 , 305.3125],
[ 322.8125, 302.3125, 298.125 , 299. , 261.5 ],
[ 334.875 , 289.75 , 269.25 , 256.375 , 242.3125],
[ 273.125 , 241.75 , 248.8125, 263.0625, 249.0625]])
>>> aia.submap([0,5],[0,5], units='pixels')
AIAMap([[ 0.3125, -0.0625, -0.125 , 0. , -0.375 ],
[ 1. , 0.1875, -0.8125, 0.125 , 0.3125],
[-1.1875, 0.375 , -0.5 , 0.25 , -0.4375],
[-0.6875, -0.3125, 0.8125, 0.0625, 0.1875],
[-0.875 , 0.25 , 0.1875, 0. , -0.6875]])
"""
if units is "data":
# Check edges (e.g. [:512,..] or [:,...])
if range_a[0] is None:
range_a[0] = self.xrange[0]
if range_a[1] is None:
range_a[1] = self.xrange[1]
if range_b[0] is None:
range_b[0] = self.yrange[0]
if range_b[1] is None:
range_b[1] = self.yrange[1]
#x_pixels = [self.data_to_pixel(elem, 'x') for elem in range_a]
x_pixels = [
np.ceil(self.data_to_pixel(range_a[0], 'x')),
np.floor(self.data_to_pixel(range_a[1], 'x')) + 1
]
#y_pixels = [self.data_to_pixel(elem, 'y') for elem in range_b]
y_pixels = [
np.ceil(self.data_to_pixel(range_b[0], 'y')),
np.floor(self.data_to_pixel(range_b[1], 'y')) + 1
]
elif units is "pixels":
# Check edges
if range_a[0] is None:
range_a[0] = 0
if range_a[1] is None:
range_a[1] = self.shape[1]
if range_b[0] is None:
range_b[0] = 0
if range_b[1] is None:
range_b[1] = self.shape[0]
x_pixels = range_a
y_pixels = range_b
else:
raise ValueError("Invalid unit. Must be one of 'data' or 'pixels'")
# Make a copy of the header with updated centering information
header = self._original_header.copy()
# Get ndarray representation of submap
data = np.asarray(self)[y_pixels[0]:y_pixels[1],
x_pixels[0]:x_pixels[1]]
# Instantiate new instance and update metadata
new_map = self.__class__(data.copy(), header)
new_map.reference_pixel['x'] = self.reference_pixel['x'] - x_pixels[0]
new_map.reference_pixel['y'] = self.reference_pixel['y'] - y_pixels[0]
return new_map
@toggle_pylab
def plot(self, gamma=None, annotate=True, axes=None, **imshow_args):
""" Plots the map object using matplotlib, in a method equivalent
to plt.imshow() using nearest neighbour interpolation.
Parameters
----------
gamma : float
Gamma value to use for the color map
annotate : bool
If true, the data is plotted at it's natural scale; with
title and axis labels.
axes: matplotlib.axes object or None
If provided the image will be plotted on the given axes. Else the
current matplotlib axes will be used.
**imshow_args : dict
Any additional imshow arguments that should be used
when plotting the image.
Examples
--------
#Simple Plot with color bar
plt.figure()
aiamap.plot()
plt.colorbar()
#Add a limb line and grid
aia.plot()
aia.draw_limb()
aia.draw_grid()
"""
#Get current axes
if not axes:
axes = plt.gca()
# Normal plot
if annotate:
axes.set_title("%s %s" % (self.name, self.date))
# x-axis label
if self.coordinate_system['x'] == 'HG':
xlabel = 'Longitude [%s]' % self.units['x']
else:
xlabel = 'X-position [%s]' % self.units['x']
# y-axis label
if self.coordinate_system['y'] == 'HG':
ylabel = 'Latitude [%s]' % self.units['y']
else:
ylabel = 'Y-position [%s]' % self.units['y']
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
# Determine extent
extent = self.xrange + self.yrange
cmap = copy(self.cmap)
if gamma is not None:
cmap.set_gamma(gamma)
#make imshow kwargs a dict
kwargs = {
'origin': 'lower',
'cmap': cmap,
'norm': self.norm(),
'extent': extent,
'interpolation': 'nearest'
}
kwargs.update(imshow_args)
ret = axes.imshow(self, **kwargs)
#Set current image (makes colorbar work)
plt.sci(ret)
return ret
@toggle_pylab
def peek(self,
draw_limb=True,
draw_grid=False,
gamma=None,
colorbar=True,
basic_plot=False,
**matplot_args):
"""Displays the map in a new figure
Parameters
----------
draw_limb : bool
Whether the solar limb should be plotted.
draw_grid : bool or number
Whether solar meridians and parallels are plotted. If float then sets
degree difference between parallels and meridians.
gamma : float
Gamma value to use for the color map
colorbar : bool
Whether to display a colorbar next to the plot
basic_plot : bool
If true, the data is plotted by itself at it's natural scale; no
title, labels, or axes are shown.
**matplot_args : dict
Matplotlib Any additional imshow arguments that should be used
when plotting the image.
"""
# Create a figure and add title and axes
figure = plt.figure(frameon=not basic_plot)
# Basic plot
if basic_plot:
axes = plt.Axes(figure, [0., 0., 1., 1.])
axes.set_axis_off()
figure.add_axes(axes)
matplot_args.update({'annotate': False})
# Normal plot
else:
axes = figure.gca()
im = self.plot(axes=axes, **matplot_args)
if colorbar and not basic_plot:
figure.colorbar(im)
if draw_limb:
self.draw_limb(axes=axes)
if isinstance(draw_grid, bool):
if draw_grid:
self.draw_grid(axes=axes)
elif isinstance(draw_grid, (int, long, float)):
self.draw_grid(axes=axes, grid_spacing=draw_grid)
else:
raise TypeError("draw_grid should be bool, int, long or float")
figure.show()
return figure
def norm(self):
"""Default normalization method. Not yet implemented."""
return None
@classmethod
def parse_file(cls, filepath):
"""Reads in a map file and returns a header and data array"""
return read_file(filepath)
@classmethod
def read(cls, filepath):
"""Map class factory
Attempts to determine the type of data associated with input and
returns an instance of either the generic Map class or a subclass
of Map such as AIAMap, EUVIMap, etc.
Parameters
----------
filepath : string
Path to a valid FITS or JPEG 2000 file of a type supported by SunPy
Returns
-------
out : Map
Returns a Map instance for the particular type of data loaded.
"""
data, header = cls.parse_file(filepath)
if cls.__name__ is not "Map":
return cls(data, header)
for cls in Map.__subclasses__():
if cls.is_datasource_for(header):
return cls(data, header)
return Map(data, header)
@classmethod
def read_header(cls, filepath):
"""Attempts to detect the datasource type and returns meta-information
for that particular datasource."""
header = read_file_header(filepath)
for cls in Map.__subclasses__():
if cls.is_datasource_for(header):
properties = cls.get_properties(header)
properties['header'] = header
return properties
