def plot_time_frequency(spectrum, interpolation='bilinear',
background_color=None, clim=None, dbscale=True, **kwargs):
"""
Time-frequency plot. Modeled after image_nonuniform.py example
spectrum is a dataframe with frequencies in columns and time in rows
"""
if spectrum is None:
return None
times = spectrum.index
freqs = spectrum.columns
if dbscale:
z = 10 * np.log10(spectrum.T)
else:
z = spectrum.T
ax = plt.figure().add_subplot(111)
extent = (times[0], times[-1], freqs[0], freqs[-1])
im = NonUniformImage(ax, interpolation=interpolation, extent=extent)
if background_color:
im.get_cmap().set_bad(kwargs['background_color'])
else:
z[np.isnan(z)] = 0.0 # replace missing values with 0 color
if clim:
im.set_clim(clim)
if 'cmap' in kwargs:
im.set_cmap(kwargs['cmap'])
im.set_data(times, freqs, z)
ax.set_xlim(extent[0], extent[1])
ax.set_ylim(extent[2], extent[3])
ax.images.append(im)
if 'colorbar_label' in kwargs:
plt.colorbar(im, label=kwargs['colorbar_label'])
else:
plt.colorbar(im, label='Power (dB/Hz)')
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')
return plt.gcf()
def plot(self, data, fmt, xmin, xmax, ymin, ymax, zmin, zmax, **opts):
"""
Plot the data entry.
Raises NeXusError if the data cannot be plotted.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
raise NeXusError("Default plotting package (matplotlib) not available.")
over = opts.pop("over", False)
image = opts.pop("image", False)
log = opts.pop("log", False)
logx = opts.pop("logx", False)
logy = opts.pop("logy", False)
if fmt == '':
fmt = 'o'
if over:
plt.autoscale(enable=False)
else:
plt.autoscale(enable=True)
plt.clf()
signal = data.nxsignal
axes = data.nxaxes
errors = data.nxerrors
title = data.nxtitle
# Provide a new view of the data if there is a dimension of length 1
if 1 in signal.shape:
data, axes = _fixaxes(signal, axes)
else:
data = signal.nxdata
# Find the centers of the bins for histogrammed data
axis_data = centers(data, axes)
#One-dimensional Plot
if len(data.shape) == 1:
if hasattr(signal, 'units'):
if not errors and signal.units == 'counts':
errors = NXfield(np.sqrt(data))
if errors:
ebars = errors.nxdata
plt.errorbar(axis_data[0], data, ebars, fmt=fmt, **opts)
else:
plt.plot(axis_data[0], data, fmt, **opts)
if not over:
ax = plt.gca()
xlo, xhi = ax.set_xlim(auto=True)
ylo, yhi = ax.set_ylim(auto=True)
if xmin: xlo = xmin
if xmax: xhi = xmax
ax.set_xlim(xlo, xhi)
if ymin: ylo = ymin
if ymax: yhi = ymax
ax.set_ylim(ylo, yhi)
if logx: ax.set_xscale('symlog')
if log or logy: ax.set_yscale('symlog')
plt.xlabel(label(axes[0]))
plt.ylabel(label(signal))
plt.title(title)
#Two dimensional plot
else:
from matplotlib.image import NonUniformImage
from matplotlib.colors import LogNorm, Normalize
if len(data.shape) > 2:
slab = []
if image:
for _dim in data.shape[:-3]:
slab.append(0)
slab.extend([slice(None), slice(None), slice(None)])
else:
for _dim in data.shape[:-2]:
slab.append(0)
slab.extend([slice(None), slice(None)])
data = data[slab]
if 0 in slab:
print "Warning: Only the top 2D slice of the data is plotted"
if image:
x, y = axis_data[-2], axis_data[-3]
xlabel, ylabel = label(axes[-2]), label(axes[-3])
else:
x, y = axis_data[-1], axis_data[-2]
xlabel, ylabel = label(axes[-1]), label(axes[-2])
if not zmin:
zmin = np.nanmin(data[data>-np.inf])
if not zmax:
zmax = np.nanmax(data[data<np.inf])
if not image:
if log:
zmin = max(zmin, 0.01)
zmax = max(zmax, 0.01)
opts["norm"] = LogNorm(zmin, zmax)
else:
opts["norm"] = Normalize(zmin, zmax)
ax = plt.gca()
if image:
im = ax.imshow(data, **opts)
ax.set_aspect('equal')
else:
extent = (x[0],x[-1],y[0],y[-1])
im = NonUniformImage(ax, extent=extent, **opts)
im.set_data(x, y, data)
im.get_cmap().set_bad('k', 1.0)
ax.set_xlim(x[0], x[-1])
ax.set_ylim(y[0], y[-1])
ax.set_aspect('auto')
ax.images.append(im)
if not image:
plt.colorbar(im)
if 'origin' in opts and opts['origin'] == 'lower':
image = False
if xmin:
ax.set_xlim(left=xmin)
if xmax:
ax.set_xlim(right=xmax)
if ymin:
if image:
ax.set_ylim(top=ymin)
else:
ax.set_ylim(bottom=ymin)
if ymax:
if image:
ax.set_ylim(bottom=ymax)
else:
ax.set_ylim(top=ymax)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.title(title)
plt.gcf().canvas.draw_idle()
plt.ion()
plt.show()