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):
if t_delt is None:
t_delt = min_delt(freq_axis)
super(LinearTimeSpectrogram, self).__init__(
data, time_axis, freq_axis, start, end, t_init, t_label, f_label,
content, instruments
)
self.t_delt = t_delt
def __init__(self, arr, delt=None):
self.arr = arr
if delt is None:
# Nyquist–Shannon sampling theorem
delt = min_delt(arr.freq_axis) / 2.
self.delt = delt
midpoints = (self.arr.freq_axis[:-1] + self.arr.freq_axis[1:]) / 2
self.midpoints = np.concatenate([midpoints, arr.freq_axis[-1:]])
self.max_mp_delt = np.min(delta(self.midpoints))
self.freq_axis = np.arange(self.arr.freq_axis[0],
self.arr.freq_axis[-1], -self.delt)
self.time_axis = self.arr.time_axis
self.shape = (len(self), arr.shape[1])
def __init__(self, arr, delt=None):
self.arr = arr
if delt is None:
# Nyquist–Shannon sampling theorem
delt = min_delt(arr.freq_axis) / 2.
self.delt = delt
midpoints = (self.arr.freq_axis[:-1] + self.arr.freq_axis[1:]) / 2
self.midpoints = np.concatenate([midpoints, arr.freq_axis[-1:]])
self.max_mp_delt = np.min(delta(self.midpoints))
self.freq_axis = np.arange(
self.arr.freq_axis[0], self.arr.freq_axis[-1], -self.delt
)
self.time_axis = self.arr.time_axis
self.shape = (len(self), arr.shape[1])
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):
if t_delt is None:
t_delt = min_delt(freq_axis)
super(LinearTimeSpectrogram,
self).__init__(data, time_axis, freq_axis, start, end, t_init,
t_label, f_label, content, instruments)
self.t_delt = t_delt
def linearize_freqs(self, delta_freq=None):
""" Rebin frequencies so that the frequency axis is linear.
Parameters
----------
delta_freq : float
Difference between consecutive values on the new frequency axis.
Defaults to half of smallest delta in current frequency axis.
Compare Nyquist-Shannon sampling theorem.
"""
if delta_freq is None:
# Nyquist–Shannon sampling theorem
delta_freq = min_delt(self.freq_axis) / 2.
nsize = (self.freq_axis.max() - self.freq_axis.min()) / delta_freq + 1
new = np.zeros((nsize, self.shape[1]), dtype=self.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 izip(self, fillfrom, fillto):
new[from_: to_] = row
vrs = self._get_params()
vrs.update({
'freq_axis': np.linspace(
self.freq_axis.max(), self.freq_axis.min(), nsize
)
})
return self.__class__(new, **vrs)
def linearize_freqs(self, delta_freq=None):
""" Rebin frequencies so that the frequency axis is linear.
Parameters
----------
delta_freq : float
Difference between consecutive values on the new frequency axis.
Defaults to half of smallest delta in current frequency axis.
Compare Nyquist-Shannon sampling theorem.
"""
if delta_freq is None:
# Nyquist–Shannon sampling theorem
delta_freq = min_delt(self.freq_axis) / 2.
nsize = (self.freq_axis.max() - self.freq_axis.min()) / delta_freq + 1
new = np.zeros((nsize, self.shape[1]), dtype=self.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 izip(self, fillfrom, fillto):
new[from_:to_] = row
vrs = self._get_params()
vrs.update({
'freq_axis':
np.linspace(self.freq_axis.max(), self.freq_axis.min(), nsize)
})
return self.__class__(new, **vrs)
def plot(self, figure=None, overlays=[], colorbar=True, min_=None, max_=None,
linear=True, showz=True, yres=DEFAULT_YRES,
max_dist=None, **matplotlib_args):
"""
Plot spectrogram onto figure.
Parameters
----------
figure : matplotlib.figure.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.
min_ : float
Clip intensities lower than min_ before drawing.
max_ : float
Clip intensities higher than max_ 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(min_, max_), delt)
freqs = np.arange(
self.freq_axis[0], self.freq_axis[-1], -data.delt
)
else:
data = np.array(self.clip_values(min_, max_))
freqs = self.freq_axis
newfigure = figure is None
if figure is None:
figure = plt.figure(frameon=True, FigureClass=SpectroFigure)
axes = figure.add_subplot(111)
else:
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:
figure.gca().format_coord = self._mk_format_coord(
data, figure.gca().format_coord)
if colorbar:
if newfigure:
figure.colorbar(im).set_label("Intensity")
else:
if len(figure.axes) > 1:
Colorbar(figure.axes[1], im).set_label("Intensity")
for overlay in overlays:
figure, axes = overlay(figure, axes)
for ax in figure.axes:
ax.autoscale()
figure._init(self, freqs)
return figure
def plot(self,
figure=None,
overlays=[],
colorbar=True,
min_=None,
max_=None,
linear=True,
showz=True,
yres=DEFAULT_YRES,
max_dist=None,
**matplotlib_args):
"""
Plot spectrogram onto figure.
Parameters
----------
figure : matplotlib.figure.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.
min_ : float
Clip intensities lower than min_ before drawing.
max_ : float
Clip intensities higher than max_ 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(min_, max_), delt)
freqs = np.arange(self.freq_axis[0], self.freq_axis[-1],
-data.delt)
else:
data = np.array(self.clip_values(min_, max_))
freqs = self.freq_axis
newfigure = figure is None
if figure is None:
figure = plt.figure(frameon=True, FigureClass=SpectroFigure)
axes = figure.add_subplot(111)
else:
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:
figure.gca().format_coord = self._mk_format_coord(
data,
figure.gca().format_coord)
if colorbar:
if newfigure:
figure.colorbar(im).set_label("Intensity")
else:
if len(figure.axes) > 1:
Colorbar(figure.axes[1], im).set_label("Intensity")
for overlay in overlays:
figure, axes = overlay(figure, axes)
for ax in figure.axes:
ax.autoscale()
figure._init(self, freqs)
return figure
