def plot_spectral_estimate(f, sdf, sdf_ests, limits=None, elabels=()):
"""
Plot an estimate of a spectral transform against the ground truth.
Utility file used in building the documentation
"""
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax_limits = (sdf.min() - 2*np.abs(sdf.min()),
sdf.max() + 1.25*np.abs(sdf.max()))
ax.plot(f, sdf, 'c', label='True S(f)')
if not elabels:
elabels = ('',) * len(sdf_ests)
colors = 'bgkmy'
for e, l, c in zip(sdf_ests, elabels, colors):
ax.plot(f, e, color=c, linewidth=2, label=l)
if limits is not None:
ax.fill_between(f, limits[0], y2=limits[1], color=(1, 0, 0, .3),
alpha=0.5)
ax.set_ylim(ax_limits)
ax.legend()
return fig
def plot_spectral_estimate(f, sdf, sdf_ests, limits=None, elabels=()):
"""
Plot an estimate of a spectral transform against the ground truth.
Utility file used in building the documentation
"""
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax_limits = (sdf.min() - 2 * np.abs(sdf.min()),
sdf.max() + 1.25 * np.abs(sdf.max()))
ax.plot(f, sdf, 'c', label='True S(f)')
if not elabels:
elabels = ('', ) * len(sdf_ests)
colors = 'bgkmy'
for e, l, c in zip(sdf_ests, elabels, colors):
ax.plot(f, e, color=c, linewidth=2, label=l)
if limits is not None:
ax.fill_between(f,
limits[0],
y2=limits[1],
color=(1, 0, 0, .3),
alpha=0.5)
ax.set_ylim(ax_limits)
ax.legend()
return fig
def plot_tseries(time_series, fig=None, axis=0,
xticks=None, xunits=None, yticks=None, yunits=None,
xlabel=None, ylabel=None, yerror=None, error_alpha=0.1,
time_unit=None, **kwargs):
"""plot a timeseries object
Arguments
---------
time_series: a nitime time-series object
fig: a figure handle, opens a new figure if None
subplot: an axis number (if there are several in the figure to be opened),
defaults to 0.
xticks: optional, list, specificies what values to put xticks on. Defaults
to the matlplotlib-generated.
yticks: optional, list, specificies what values to put xticks on. Defaults
to the matlplotlib-generated.
xlabel: optional, list, specificies what labels to put on xticks
ylabel: optional, list, specificies what labels to put on yticks
yerror: optional, UniformTimeSeries with the same sampling_rate and number
of samples and channels as time_series, the error will be displayed as a
shading above and below the plotted time-series
"""
if fig is None:
fig = plt.figure()
if not fig.get_axes():
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.get_axes()[axis]
#Make sure that time displays on the x axis with the units you want:
#If you want to change the time-unit on the visualization from that used to
#represent the time-series:
if time_unit is not None:
tu = time_unit
conv_fac = ts.time_unit_conversion[time_unit]
#Otherwise, get the information from your input:
else:
tu = time_series.time_unit
conv_fac = time_series.time._conversion_factor
this_time = time_series.time / float(conv_fac)
ax.plot(this_time, time_series.data.T, **kwargs)
if xlabel is None:
ax.set_xlabel('Time (%s)' % tu)
else:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if yerror is not None:
if len(yerror.data.shape) == 1:
this_e = yerror.data[np.newaxis, :]
else:
this_e = yerror.data
delta = this_e
e_u = time_series.data + delta
e_d = time_series.data - delta
for i in range(e_u.shape[0]):
ax.fill_between(this_time, e_d[i], e_u[i], alpha=error_alpha)
return fig
def plot_tseries(time_series,
fig=None,
axis=0,
xticks=None,
xunits=None,
yticks=None,
yunits=None,
xlabel=None,
ylabel=None,
yerror=None,
error_alpha=0.1,
time_unit=None,
**kwargs):
"""plot a timeseries object
Arguments
---------
time_series: a nitime time-series object
fig: a figure handle, opens a new figure if None
subplot: an axis number (if there are several in the figure to be opened),
defaults to 0.
xticks: optional, list, specificies what values to put xticks on. Defaults
to the matlplotlib-generated.
yticks: optional, list, specificies what values to put xticks on. Defaults
to the matlplotlib-generated.
xlabel: optional, list, specificies what labels to put on xticks
ylabel: optional, list, specificies what labels to put on yticks
yerror: optional, UniformTimeSeries with the same sampling_rate and number
of samples and channels as time_series, the error will be displayed as a
shading above and below the plotted time-series
"""
if fig is None:
fig = plt.figure()
if not fig.get_axes():
ax = fig.add_subplot(1, 1, 1)
else:
ax = fig.get_axes()[axis]
#Make sure that time displays on the x axis with the units you want:
#If you want to change the time-unit on the visualization from that used to
#represent the time-series:
if time_unit is not None:
tu = time_unit
conv_fac = ts.time_unit_conversion[time_unit]
#Otherwise, get the information from your input:
else:
tu = time_series.time_unit
conv_fac = time_series.time._conversion_factor
this_time = time_series.time / float(conv_fac)
ax.plot(this_time, time_series.data.T, **kwargs)
if xlabel is None:
ax.set_xlabel('Time (%s)' % tu)
else:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if yerror is not None:
if len(yerror.data.shape) == 1:
this_e = yerror.data[np.newaxis, :]
else:
this_e = yerror.data
delta = this_e
e_u = time_series.data + delta
e_d = time_series.data - delta
for i in range(e_u.shape[0]):
ax.fill_between(this_time, e_d[i], e_u[i], alpha=error_alpha)
return fig