def plot_gaussian(X, axis=-1, scale=2, **kwargs):
"""
Plot Gaussian node as a 1-D function
Parameters
----------
X : node
Node with Gaussian moments.
axis : int
The index of the time axis.
"""
X = X._convert(GaussianMoments)
u_X = X.get_moments()
x = u_X[0]
xx = misc.get_diag(u_X[1], ndim=len(X.dims[0]))
std = scale * np.sqrt(xx - x**2)
#std = scale * np.sqrt(np.einsum('...ii->...i', xx) - x**2)
return _timeseries_mean_and_error(x, std, axis=axis, **kwargs)
def plot_gaussian(X, axis=-1, scale=2, **kwargs):
"""
Plot Gaussian node as a 1-D function
Parameters
----------
X : node
Node with Gaussian moments.
axis : int
The index of the time axis.
"""
X = X._ensure_moments(X, GaussianMoments, ndim=0)
u_X = X.get_moments()
x = u_X[0]
xx = misc.get_diag(u_X[1], ndim=len(X.dims[0]))
std = scale * np.sqrt(xx - x**2)
#std = scale * np.sqrt(np.einsum('...ii->...i', xx) - x**2)
return _timeseries_mean_and_error(x, std, axis=axis, **kwargs)
def gaussian_hinton(X, rows=None, cols=None, scale=1, fig=None):
"""
Plot the Hinton diagram of a Gaussian node
"""
if fig is None:
fig = plt.gcf()
# Get mean and second moment
X = X._convert(GaussianMoments)
(x, xx) = X.get_moments()
ndim = len(X.dims[0])
shape = X.get_shape(0)
size = len(X.get_shape(0))
# Compute standard deviation
xx = misc.get_diag(xx, ndim=ndim)
std = np.sqrt(xx - x**2)
# Force explicit elements when broadcasting
x = x * np.ones(shape)
std = std * np.ones(shape)
if rows is None:
rows = np.nan
if cols is None:
cols = np.nan
# Preprocess the axes to 0,...,ndim
if rows < 0:
rows += size
if cols < 0:
cols += size
if rows < 0 or rows >= size:
raise ValueError("Row axis invalid")
if cols < 0 or cols >= size:
raise ValueError("Column axis invalid")
# Remove non-row and non-column axes that have length 1
squeezed_shape = list(shape)
for i in reversed(range(len(shape))):
if shape[i] == 1 and i != rows and i != cols:
squeezed_shape.pop(i)
if i < cols:
cols -= 1
if i < rows:
rows -= 1
x = np.reshape(x, squeezed_shape)
std = np.reshape(std, squeezed_shape)
size = np.ndim(x)
if np.isnan(cols):
if rows != size - 1:
cols = size - 1
else:
cols = size - 2
if np.isnan(rows):
if cols != size - 1:
rows = size - 1
else:
rows = size - 2
# Put the row and column axes to the end
axes = [i for i in range(size) if i not in (rows, cols)] + [rows, cols]
x = np.transpose(x, axes=axes)
std = np.transpose(std, axes=axes)
vmax = np.max(np.abs(x) + scale * std)
if scale == 0:
_subplots(_hinton, (x, 2), fig=fig, kwargs=dict(vmax=vmax))
else:
def plotfunc(z, e, **kwargs):
return _hinton(z, error=e, **kwargs)
_subplots(plotfunc, (x, 2), (scale * std, 2),
fig=fig,
kwargs=dict(vmax=vmax))
def gaussian_hinton(X, rows=None, cols=None, scale=1, fig=None):
"""
Plot the Hinton diagram of a Gaussian node
"""
if fig is None:
fig = plt.gcf()
# Get mean and second moment
X = X._ensure_moments(X, GaussianMoments, ndim=0)
(x, xx) = X.get_moments()
ndim = len(X.dims[0])
shape = X.get_shape(0)
size = len(X.get_shape(0))
# Compute standard deviation
xx = misc.get_diag(xx, ndim=ndim)
std = np.sqrt(xx - x**2)
# Force explicit elements when broadcasting
x = x * np.ones(shape)
std = std * np.ones(shape)
if rows is None:
rows = np.nan
if cols is None:
cols = np.nan
# Preprocess the axes to 0,...,ndim
if rows < 0:
rows += size
if cols < 0:
cols += size
if rows < 0 or rows >= size:
raise ValueError("Row axis invalid")
if cols < 0 or cols >= size:
raise ValueError("Column axis invalid")
# Remove non-row and non-column axes that have length 1
squeezed_shape = list(shape)
for i in reversed(range(len(shape))):
if shape[i] == 1 and i != rows and i != cols:
squeezed_shape.pop(i)
if i < cols:
cols -= 1
if i < rows:
rows -= 1
x = np.reshape(x, squeezed_shape)
std = np.reshape(std, squeezed_shape)
if np.ndim(x) < 2:
cols += 2 - np.ndim(x)
rows += 2 - np.ndim(x)
x = np.atleast_2d(x)
std = np.atleast_2d(std)
size = np.ndim(x)
if np.isnan(cols):
if rows != size - 1:
cols = size - 1
else:
cols = size - 2
if np.isnan(rows):
if cols != size - 1:
rows = size - 1
else:
rows = size - 2
# Put the row and column axes to the end
axes = [i for i in range(size) if i not in (rows, cols)] + [rows, cols]
x = np.transpose(x, axes=axes)
std = np.transpose(std, axes=axes)
vmax = np.max(np.abs(x) + scale*std)
if scale == 0:
_subplots(_hinton, (x, 2), fig=fig, kwargs=dict(vmax=vmax))
else:
def plotfunc(z, e, **kwargs):
return _hinton(z, error=e, **kwargs)
_subplots(plotfunc, (x, 2), (scale*std, 2), fig=fig, kwargs=dict(vmax=vmax))
def gaussian_hinton(X, rows=None, cols=None, scale=1):
"""
Plot the Hinton diagram of a Gaussian node
"""
# Get mean and second moment
X = X._convert(GaussianMoments)
(x, xx) = X.get_moments()
ndim = len(X.dims[0])
shape = X.get_shape(0)
size = len(X.get_shape(0))
# Compute standard deviation
xx = misc.get_diag(xx, ndim=ndim)
std = np.sqrt(xx - x**2)
# Force explicit elements when broadcasting
x = x * np.ones(shape)
std = std * np.ones(shape)
if rows is None:
rows = np.nan
if cols is None:
cols = np.nan
# Preprocess the axes to 0,...,ndim
if rows < 0:
rows += size
if cols < 0:
cols += size
if rows < 0 or rows >= size:
raise ValueError("Row axis invalid")
if cols < 0 or cols >= size:
raise ValueError("Column axis invalid")
# Remove non-row and non-column axes that have length 1
squeezed_shape = list(shape)
for i in reversed(range(len(shape))):
if shape[i] == 1 and i != rows and i != cols:
squeezed_shape.pop(i)
if i < cols:
cols -= 1
if i < rows:
rows -= 1
x = np.reshape(x, squeezed_shape)
std = np.reshape(std, squeezed_shape)
# Make explicit four axes
cols = cols + (4 - np.ndim(x))
rows = rows + (4 - np.ndim(x))
x = misc.atleast_nd(x, 4)
std = misc.atleast_nd(std, 4)
size = np.ndim(x)
if np.isnan(cols):
if rows != size - 1:
cols = size - 1
else:
cols = size - 2
if np.isnan(rows):
if cols != size - 1:
rows = size - 1
else:
rows = size - 2
# Put the row and column axes to the end
axes = [i for i in range(size) if i not in (rows, cols)] + [rows, cols]
x = np.transpose(x, axes=axes)
std = np.transpose(std, axes=axes)
if np.ndim(x) != 4:
raise ValueError("Can not plot arrays with over 4 axes")
M = np.shape(x)[0]
N = np.shape(x)[1]
vmax = np.max(np.abs(x) + scale*std)
#plt.subplots(M, N, sharey=True, sharex=True, fig_kw)
ax = [plt.subplot(M, N, i*N+j+1) for i in range(M) for j in range(N)]
for i in range(M):
for j in range(N):
plt.subplot(M, N, i*N+j+1)
#plt.subplot(M, N, i*N+j+1, sharey=ax[0], sharex=ax[0])
if scale == 0:
_hinton(x[i,j], vmax=vmax)
else:
_hinton(x[i,j], vmax=vmax, error=scale*std[i,j])
