diff --git a/doc/source/tutorial/discretize.rst b/doc/source/tutorial/discretize.rst
index 31c4b3bb3ca217888c3c0e1b53e444a270906705..9417d20b52e8e6448f32e64894928f3eefa56abc 100644
--- a/doc/source/tutorial/discretize.rst
+++ b/doc/source/tutorial/discretize.rst
@@ -202,7 +202,7 @@ energy eigenstates:
ham = syst.hamiltonian_submatrix(params=dict(V=potential), sparse=True)
evecs = scipy.sparse.linalg.eigsh(ham, k=10, which='SM')[1]
- kwant.plotter.map(syst, abs(evecs[:, n])**2, show=False)
+ kwant.plotter.density(syst, abs(evecs[:, n])**2, show=False)
.. jupyter-execute::
:hide-code:
@@ -281,7 +281,7 @@ and plot its dispersion using `kwant.plotter.bands`:
pyplot.show()
In the above we see the edge states of the quantum spin Hall effect, which
-we can visualize using `kwant.plotter.map`:
+we can visualize using `kwant.plotter.density`:
.. jupyter-execute::
@@ -298,8 +298,8 @@ we can visualize using `kwant.plotter.map`:
rho_sz = sum(spin_density(psi) for psi in wf(0)) # states from left lead
fig, (ax1, ax2) = pyplot.subplots(1, 2, sharey=True, figsize=(16, 4))
- kwant.plotter.map(syst, wf_sqr, ax=ax1)
- kwant.plotter.map(syst, rho_sz, ax=ax2)
+ kwant.plotter.density(syst, wf_sqr, ax=ax1)
+ kwant.plotter.density(syst, rho_sz, ax=ax2)
ax = ax1
im = [obj for obj in ax.get_children()
diff --git a/doc/source/tutorial/kpm.rst b/doc/source/tutorial/kpm.rst
index 7e1232581335aa1a77969899f59344e34c93eba7..f1d6650c44a2ae2b08016e74ffa357e6b643caf4 100644
--- a/doc/source/tutorial/kpm.rst
+++ b/doc/source/tutorial/kpm.rst
@@ -196,7 +196,7 @@ object that represents the density of states for this system.
fsyst = make_syst().finalized()
- spectrum = kwant.kpm.SpectralDensity(fsyst)
+ spectrum = kwant.kpm.SpectralDensity(fsyst, rng=0)
The `~kwant.kpm.SpectralDensity` can then be called like a function to obtain a
sequence of energies in the spectrum of the Hamiltonian, and the corresponding
@@ -319,7 +319,8 @@ and plot their respective local density of states.
# 'num_vectors' can be unspecified when using 'LocalVectors'
local_dos = kwant.kpm.SpectralDensity(fsyst_staggered, num_vectors=None,
vector_factory=vector_factory,
- mean=False)
+ mean=False,
+ rng=0)
energies, densities = local_dos()
.. jupyter-execute::
@@ -346,7 +347,7 @@ The simplest way to obtain a more accurate solution is to use the
.. jupyter-execute::
:hide-code:
- spectrum = kwant.kpm.SpectralDensity(fsyst)
+ spectrum = kwant.kpm.SpectralDensity(fsyst, rng=0)
original_dos = spectrum()
.. jupyter-execute::
@@ -399,7 +400,7 @@ shape as the system Hamiltonian.
# identity matrix
matrix_op = scipy.sparse.eye(len(fsyst.sites))
- matrix_spectrum = kwant.kpm.SpectralDensity(fsyst, operator=matrix_op)
+ matrix_spectrum = kwant.kpm.SpectralDensity(fsyst, operator=matrix_op, rng=0)
Or, to do the same calculation using `kwant.operator.Density`:
@@ -407,7 +408,7 @@ Or, to do the same calculation using `kwant.operator.Density`:
# 'sum=True' means we sum over all the sites
kwant_op = kwant.operator.Density(fsyst, sum=True)
- operator_spectrum = kwant.kpm.SpectralDensity(fsyst, operator=kwant_op)
+ operator_spectrum = kwant.kpm.SpectralDensity(fsyst, operator=kwant_op, rng=0)
Spectral density with random vectors
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -420,7 +421,7 @@ sum over all the sites of the system:
# 'sum=False' is the default, but we include it explicitly here for clarity.
kwant_op = kwant.operator.Density(fsyst, sum=False)
- local_dos = kwant.kpm.SpectralDensity(fsyst, operator=kwant_op)
+ local_dos = kwant.kpm.SpectralDensity(fsyst, operator=kwant_op, rng=0)
`~kwant.kpm.SpectralDensity` will properly handle this vector output,
and will average the local density obtained with random vectors.
@@ -499,11 +500,13 @@ vectors
# component 'xx'
s_factory = kwant.kpm.LocalVectors(fsyst_topo, where)
cond_xx = kwant.kpm.conductivity(fsyst_topo, alpha='x', beta='x', mean=True,
- num_vectors=None, vector_factory=s_factory)
+ num_vectors=None, vector_factory=s_factory,
+ rng=0)
# component 'xy'
s_factory = kwant.kpm.LocalVectors(fsyst_topo, where)
cond_xy = kwant.kpm.conductivity(fsyst_topo, alpha='x', beta='y', mean=True,
- num_vectors=None, vector_factory=s_factory)
+ num_vectors=None, vector_factory=s_factory,
+ rng=0)
energies = cond_xx.energies
cond_array_xx = np.array([cond_xx(e, temperature=0.01) for e in energies])
@@ -528,7 +531,8 @@ the random vectors.
s_factory = kwant.kpm.LocalVectors(fsyst_topo, where)
spectrum = kwant.kpm.SpectralDensity(fsyst_topo, num_vectors=None,
- vector_factory=s_factory)
+ vector_factory=s_factory,
+ rng=0)
plot_dos_and_curves(
(spectrum.energies, spectrum.densities * 8),
@@ -570,7 +574,8 @@ and which returns a vector in that Hilbert space:
yield np.rint(np.random.random_sample(n)) * 2 - 1
custom_factory = kwant.kpm.SpectralDensity(fsyst,
- vector_factory=binary_vectors())
+ vector_factory=binary_vectors(),
+ rng=0)
Aditionally, a `~kwant.kpm.LocalVectors` generator is also available, that
returns local vectors that correspond to the sites passed. Note that
@@ -613,8 +618,8 @@ methods for computing the local density of states, one using
def rho_alt(bra, ket):
return np.vdot(bra, ket)
- rho_spectrum = kwant.kpm.SpectralDensity(fsyst, operator=rho)
- rho_alt_spectrum = kwant.kpm.SpectralDensity(fsyst, operator=rho_alt)
+ rho_spectrum = kwant.kpm.SpectralDensity(fsyst, operator=rho, rng=0)
+ rho_alt_spectrum = kwant.kpm.SpectralDensity(fsyst, operator=rho_alt, rng=0)
__ operator_spectral_density_
diff --git a/doc/source/tutorial/operators.rst b/doc/source/tutorial/operators.rst
index 1646ae86e2cd5a47747843f6dd952144b0a92175..897c919c4b0b77e82b5ad39efec28c664df3d75a 100644
--- a/doc/source/tutorial/operators.rst
+++ b/doc/source/tutorial/operators.rst
@@ -165,7 +165,7 @@ inside the scattering region. The z component is shown by the color scale:
def plot_densities(syst, densities):
fig, axes = plt.subplots(1, len(densities), figsize=(13, 10))
for ax, (title, rho) in zip(axes, densities):
- kwant.plotter.map(syst, rho, ax=ax, a=4)
+ kwant.plotter.density(syst, rho, ax=ax)
ax.set_title(title)
plt.show()