def plot_2d_to_axis(
spectrum: Spectrum2D,
ax: Axes,
cmap: Union[str, Colormap] = 'viridis',
interpolation: str = 'nearest',
norm: Optional[Normalize] = None,
) -> NonUniformImage:
"""Plot Spectrum2D object to Axes
Parameters
----------
spectrum
2D data object for plotting as NonUniformImage. The x_tick_labels
attribute will be used to mark labelled points.
ax
Matplotlib axes to which image will be drawn
cmap
Matplotlib colormap or registered colormap name
interpolation
Interpolation method: 'nearest' or 'bilinear' for a pixellated or
smooth result
norm
Matplotlib normalization object; set this in order to ensure separate
plots are on the same colour scale.
"""
x_unit = spectrum.x_data_unit
y_unit = spectrum.y_data_unit
z_unit = spectrum.z_data_unit
x_bins = spectrum.get_bin_edges('x').to(x_unit).magnitude
y_bins = spectrum.get_bin_edges('y').to(y_unit).magnitude
image = NonUniformImage(ax,
interpolation=interpolation,
extent=(min(x_bins), max(x_bins), min(y_bins),
max(y_bins)),
cmap=cmap)
if norm is not None:
image.set_norm(norm)
image.set_data(
spectrum.get_bin_centres('x').to(x_unit).magnitude,
spectrum.get_bin_centres('y').to(y_unit).magnitude,
spectrum.z_data.to(z_unit).magnitude.T)
ax.images.append(image)
ax.set_xlim(min(x_bins), max(x_bins))
ax.set_ylim(min(y_bins), max(y_bins))
_set_x_tick_labels(ax, spectrum.x_tick_labels, spectrum.x_data)
return image
def plot2d(x, y, z, ax=None, cmap='RdGy', norm=None, **kw):
""" Plot dataset using NonUniformImage class
Parameters
----------
x : (nx,)
y : (ny,)
z : (nx,nz)
"""
from matplotlib.image import NonUniformImage
if ax is None:
fig = plt.gcf()
ax = fig.add_subplot(111)
xlim = (x.min(), x.max())
ylim = (y.min(), y.max())
im = NonUniformImage(ax,
interpolation='bilinear',
extent=xlim + ylim,
cmap=cmap)
if norm is not None:
im.set_norm(norm)
im.set_data(x, y, z, **kw)
ax.images.append(im)
#plt.colorbar(im)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
def update(z):
return im.set_data(x, y, z, **kw)
return im, update
def test_nonuniformimage_setnorm():
ax = plt.gca()
im = NonUniformImage(ax)
im.set_norm(plt.Normalize())
def test_nonuniformimage_setnorm():
ax = plt.gca()
im = NonUniformImage(ax)
im.set_norm(plt.Normalize())
def tsys_show_dynspec(out,idx=None,ampscl=None,domedian=True,frq='linear'):
''' Given "standard" output of rd_tsys_multi(), possibly
calibrated using calibration.sp_apply_cal() and/or
background-subtracted using calibration.sp_bg_subtract(),
make a nice image plot of the dynamic spectrum. The
plot can contain multiple panels if domedian is False,
or plot a single spectrum representing the median of
multiple antennas. Only linear frequency scale is supported
at this time.
'''
from matplotlib.image import NonUniformImage
from matplotlib.dates import AutoDateLocator, DateFormatter
from matplotlib import colors
from matplotlib.pyplot import locator_params
from util import Time
nant, npol, nf, nt = out['tsys'].shape
if idx is None:
idx_ = np.arange(nt)
else:
idx_ = idx
ut = Time(out['ut_mjd'][idx_],format='mjd')
utd = (ut.mjd - int(ut[0].mjd) + 1).astype('float')
good = np.where(out['fghz'] > 2.0)[0]
if frq == 'linear':
fghz = out['fghz'][good]
else:
fghz = np.log10(out['fghz'][good])
locator = AutoDateLocator(interval_multiples=True) #maxticks=7,minticks=3,
tsys = out['tsys'][:,:,good,idx_]
if domedian:
medtsys = np.nanmedian(np.nanmedian(tsys,0),0)
fig = plt.figure()
fig.suptitle('EOVSA Total Power Data for '+ut[0].iso[:10],fontsize=14)
# Plot X-feed
ax = fig.add_subplot(211)
ax.xaxis_date()
ax.set_ylabel('Frequency [GHz]')
ax.set_title('Median Total Power')
extent=[utd[0],utd[-1],fghz[0],fghz[-1]]
# extent=[ut[0],ut[-1],fghz[0],fghz[-1]]
im = NonUniformImage(ax,extent=extent)
if ampscl != None:
im.set_norm(colors.Normalize(vmin=ampscl[0],vmax=ampscl[1]))
im.set_data(utd,fghz,medtsys)
#fig.colorbar(im,ax)
ax.images.append(im)
ax.set_xlim(extent[0],extent[1])
ax.set_ylim(extent[2],extent[3])
ax.xaxis.set_major_locator(locator)
#ax.xaxis.set_minor_locator(MinuteLocator(interval=10))
# Set up date formatting
fmt = DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(fmt)
labels = (10**ax.get_yticks()).astype('str')
for i in range(len(labels)):
labels[i] = labels[i][:4]
ax.set_yticklabels(labels)
# Repeat for Y-feed
ax = fig.add_subplot(212)
ax.xaxis_date()
ax.set_xlabel('Start time '+ut[0].iso[:19]+' UT')
ax.set_title('Median of Y-poln')
ax.set_ylabel('Frequency [GHz]')
im = NonUniformImage(ax,extent=extent)
if ampscl != None:
im.set_norm(colors.Normalize(vmin=ampscl[0],vmax=ampscl[1]))
im.set_data(utd,fghz,medytsys)
#fig.colorbar(im,ax)
ax.images.append(im)
ax.set_xlim(extent[0],extent[1])
ax.set_ylim(extent[2],extent[3])
ax.xaxis.set_major_locator(locator)
# Set up date formatting
ax.xaxis.set_major_formatter(fmt)
ax.set_yticklabels(labels)
plt.draw()
plt.show()
def tsys_show_dynspec(out, idx=None, ampscl=None, domedian=True, frq='linear'):
''' Given "standard" output of rd_tsys_multi(), possibly
calibrated using calibration.sp_apply_cal() and/or
background-subtracted using calibration.sp_bg_subtract(),
make a nice image plot of the dynamic spectrum. The
plot can contain multiple panels if domedian is False,
or plot a single spectrum representing the median of
multiple antennas. Only linear frequency scale is supported
at this time.
'''
from matplotlib.image import NonUniformImage
from matplotlib.dates import AutoDateLocator, DateFormatter
from matplotlib import colors
from matplotlib.pyplot import locator_params
from util import Time
nant, npol, nf, nt = out['tsys'].shape
if idx is None:
idx_ = np.arange(nt)
else:
idx_ = idx
ut = Time(out['ut_mjd'][idx_], format='mjd')
utd = (ut.mjd - int(ut[0].mjd) + 1).astype('float')
good = np.where(out['fghz'] > 2.0)[0]
if frq == 'linear':
fghz = out['fghz'][good]
else:
fghz = np.log10(out['fghz'][good])
locator = AutoDateLocator(interval_multiples=True) #maxticks=7,minticks=3,
tsys = out['tsys'][:, :, good, idx_]
if domedian:
medtsys = np.nanmedian(np.nanmedian(tsys, 0), 0)
fig = plt.figure()
fig.suptitle('EOVSA Total Power Data for ' + ut[0].iso[:10],
fontsize=14)
# Plot X-feed
ax = fig.add_subplot(211)
ax.xaxis_date()
ax.set_ylabel('Frequency [GHz]')
ax.set_title('Median Total Power')
extent = [utd[0], utd[-1], fghz[0], fghz[-1]]
# extent=[ut[0],ut[-1],fghz[0],fghz[-1]]
im = NonUniformImage(ax, extent=extent)
if ampscl != None:
im.set_norm(colors.Normalize(vmin=ampscl[0], vmax=ampscl[1]))
im.set_data(utd, fghz, medtsys)
#fig.colorbar(im,ax)
ax.images.append(im)
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])
ax.xaxis.set_major_locator(locator)
#ax.xaxis.set_minor_locator(MinuteLocator(interval=10))
# Set up date formatting
fmt = DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(fmt)
labels = (10**ax.get_yticks()).astype('str')
for i in range(len(labels)):
labels[i] = labels[i][:4]
ax.set_yticklabels(labels)
# Repeat for Y-feed
ax = fig.add_subplot(212)
ax.xaxis_date()
ax.set_xlabel('Start time ' + ut[0].iso[:19] + ' UT')
ax.set_title('Median of Y-poln')
ax.set_ylabel('Frequency [GHz]')
im = NonUniformImage(ax, extent=extent)
if ampscl != None:
im.set_norm(colors.Normalize(vmin=ampscl[0], vmax=ampscl[1]))
im.set_data(utd, fghz, medytsys)
#fig.colorbar(im,ax)
ax.images.append(im)
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])
ax.xaxis.set_major_locator(locator)
# Set up date formatting
ax.xaxis.set_major_formatter(fmt)
ax.set_yticklabels(labels)
plt.draw()
plt.show()
