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)
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 EOVSASpectrogram(LinearTimeSpectrogram):
_create = ConditionalDispatch.from_existing(LinearTimeSpectrogram._create)
create = classmethod(_create.wrapper())
COPY_PROPERTIES = LinearTimeSpectrogram.COPY_PROPERTIES + [
('bg', REFERENCE)
]
@classmethod
def read(cls, filename, **kwargs):
"""
Read in FITS file and return a new SWavesSpectrogram.
"""
fl = fits.open(filename, **kwargs)
data = fl[0].data
freq_axis = fl[1].data.sfreq
tdata = fl[2].data
mjd = tdata.mjd
sec = tdata.time / 1000.
start = Time(mjd[0] + sec[0] / 86400., format='mjd').datetime
end = Time(mjd[-1] + sec[-1] / 86400., format='mjd').datetime
time_axis = sec - sec[0]
header = fl[0].header
t_delt = 1.0
t_init = (start - get_day(start)).seconds
content = 'EOVSA'
t_label = 'Time [UT]'
f_label = 'Frequency [GHz]'
#reverse the frequency axis
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)
def __init__(self, data, time_axis, freq_axis, start, end, t_init, t_delt,
t_label, f_label, content):
# Because of how object creation works, there is no avoiding
# unused arguments in this case.
# pylint: disable=W0613
super(EOVSASpectrogram,
self).__init__(data, time_axis, freq_axis, start, end, t_init,
t_delt, t_label, f_label, content, set(["EOVSA"]))
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 {
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))
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(list(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(list(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 = int((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 '{!s} z={!s}'.format(fmt_coord(x, y), pixel)
return format_coord
class CallistoSpectrogram(LinearTimeSpectrogram):
"""
Class used for dynamic spectra coming from the Callisto network.
Attributes
----------
header : `~astropy.io.fits.Header`
main header of the FITS file
axes_header : `~astropy.io.fits.Header`
header for the axes table
swapped : bool
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 = {
'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):
"""
Returns the 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):
"""
Reads 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 = {header["INSTRUME"]}
fl.close()
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 : `~astropy.io.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):
"""
Returns a 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 = list(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 : `~sunpy.time.parse_time` compatible
start of the measurement
end : `~sunpy.time.parse_time` compatible
end of the measurement
"""
kw = {
'maxgap': None,
'fill': cls.JOIN_REPEAT,
}
kw.update(kwargs)
if SUNPY_LT_1:
start = parse_time(start)
end = parse_time(end)
else:
start = parse_time(start).datetime
end = parse_time(end).datetime
urls = query(start, end, [instrument])
data = list(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.values()])
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 : `radiospectra.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 : `radiospectra.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 * np.polyval(f1, two.freq_axis)[:, np.newaxis] +
np.polyval(f2, two.freq_axis)[:, np.newaxis])
def extend(self, minutes=15, **kwargs):
"""
Requests subsequent files from the server.
If minutes is negative, retrieve preceding files.
"""
if len(self.instruments) != 1:
raise ValueError
instrument = next(iter(self.instruments))
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):
"""
Returns CallistoSpectrogram read from URL.
Parameters
----------
url : str
URL to retrieve the data from
Returns
-------
newSpectrogram : `radiospectra.CallistoSpectrogram`
"""
return cls.read(url)
def test_types():
f = ConditionalDispatch()
f.add(lambda x: 2 * x, lambda x: x % 2 == 0, [int])
with pytest.raises(TypeError):
f(2.0)