def _timeseries_mean_and_error(y, std, *args, axis=-1, center=True, **kwargs):
# TODO/FIXME: You must multiply by ones(plates) in order to plot
# broadcasted plates properly
shape = list(np.shape(y))
# Get and remove the length of the time axis
T = shape.pop(axis)
# Move time axis to first
y = np.rollaxis(y, axis)
if std is not None:
std = np.rollaxis(std, axis)
y = np.reshape(y, (T, -1))
if std is not None:
std = np.reshape(std, (T, -1))
# Remove 1s
shape = [s for s in shape if s > 1]
# Calculate number of rows and columns
shape = utils.multiply_shapes(shape, (1, 1))
if len(shape) > 2:
raise Exception("Can plot only in 2 dimensions (rows and columns)")
(M, N) = shape
# Prefer plotting to rows
if M == 1:
M = N
N = 1
# Plot each timeseries
ax0 = plt.subplot(M, N, 1)
for i in range(M * N):
if i > 0:
# Share x axis between all subplots
ax = plt.subplot(M, N, i + 1, sharex=ax0)
else:
ax = ax0
# Autoscale the axes to data and use tight y and x axes
ax.autoscale(enable=True, tight=True)
ax.set_ylim(auto=True)
if i < (M - 1) * N:
# Remove x tick labels from other than the last row
plt.setp(ax.get_xticklabels(), visible=False)
if std is None:
plt.plot(y[:, i], *args, **kwargs)
else:
if len(args) > 0:
raise Exception("Can't handle extra arguments")
errorplot(y=y[:, i], error=std[:, i], **kwargs)
if center:
# Center the zero level on y-axis
ylim = ax.get_ylim()
vmax = np.max(np.abs(ylim))
ax.set_ylim([-vmax, vmax])
# Remove height space between subplots
plt.subplots_adjust(hspace=0)
def _plates_from_parent(self, index):
return tuple(utils.multiply_shapes(self.parents[index].plates,
tiles))
def _plates_from_parent(self, index):
return tuple(utils.multiply_shapes(self.parents[index].plates,
tiles))
def _timeseries_mean_and_error(y, std, *args, axis=-1, center=True, **kwargs):
# TODO/FIXME: You must multiply by ones(plates) in order to plot
# broadcasted plates properly
shape = list(np.shape(y))
# Get and remove the length of the time axis
T = shape.pop(axis)
# Move time axis to first
y = np.rollaxis(y, axis)
if std is not None:
std = np.rollaxis(std, axis)
y = np.reshape(y, (T, -1))
if std is not None:
std = np.reshape(std, (T, -1))
# Remove 1s
shape = [s for s in shape if s > 1]
# Calculate number of rows and columns
shape = utils.multiply_shapes(shape, (1,1))
if len(shape) > 2:
raise Exception("Can plot only in 2 dimensions (rows and columns)")
(M, N) = shape
# Prefer plotting to rows
if M == 1:
M = N
N = 1
# Plot each timeseries
ax0 = plt.subplot(M, N, 1)
for i in range(M*N):
if i > 0:
# Share x axis between all subplots
ax = plt.subplot(M, N, i+1, sharex=ax0)
else:
ax = ax0
# Autoscale the axes to data and use tight y and x axes
ax.autoscale(enable=True, tight=True)
ax.set_ylim(auto=True)
if i < (M-1)*N - 1:
# Remove x tick labels from other than the last row
ax.set_xticklabels([])
if std is None:
plt.plot(y[:,i], *args, **kwargs)
else:
if len(args) > 0:
raise Exception("Can't handle extra arguments")
errorplot(y=y[:,i], error=std[:,i], **kwargs)
if center:
# Center the zero level on y-axis
ylim = ax.get_ylim()
vmax = np.max(np.abs(ylim))
ax.set_ylim([-vmax, vmax])
# Remove height space between subplots
plt.subplots_adjust(hspace=0)