Commit d44ecee2 authored by Joseph Weston's avatar Joseph Weston

replace all references to 'args' with 'params' in tutorial

parent c0db02da
Pipeline #15423 passed with stages
in 41 minutes and 44 seconds
......@@ -143,7 +143,7 @@
normalized_fluxes = [flux / (2 * pi) for flux in fluxes]
data = []
for flux in fluxes:
smatrix = kwant.smatrix(syst, energy, args=[flux])
smatrix = kwant.smatrix(syst, energy, params=dict(phi=flux))
data.append(smatrix.transmission(1, 0))
- pyplot.figure()
......
......@@ -68,7 +68,7 @@
energies = []
for B in Bfields:
# Obtain the Hamiltonian as a dense matrix
ham_mat = syst.hamiltonian_submatrix(args=[B], sparse=True)
ham_mat = syst.hamiltonian_submatrix(params=dict(B=B), sparse=True)
# we only calculate the 15 lowest eigenvalues
ev = sla.eigsh(ham_mat.tocsc(), k=15, sigma=0,
......@@ -105,7 +105,7 @@
+ size = (_defs.figwidth_in, _defs.figwidth_in)
+
# Calculate the wave functions in the system.
ham_mat = syst.hamiltonian_submatrix(sparse=True, args=[B])
ham_mat = syst.hamiltonian_submatrix(sparse=True, params=dict(B=B))
evals, evecs = sorted_eigs(sla.eigsh(ham_mat.tocsc(), k=20, sigma=0))
# Plot the probability density of the 10th eigenmode.
......@@ -124,12 +124,12 @@
+ size = (_defs.figwidth_in, _defs.figwidth_in)
+
# Calculate the wave functions in the system.
ham_mat = syst.hamiltonian_submatrix(sparse=True, args=[B])
ham_mat = syst.hamiltonian_submatrix(sparse=True, params=dict(B=B))
evals, evecs = sorted_eigs(sla.eigsh(ham_mat.tocsc(), k=20, sigma=0))
# Calculate and plot the local current of the 10th eigenmode.
J = kwant.operator.Current(syst)
current = J(evecs[:, 9], args=[B])
current = J(evecs[:, 9], params=dict(B=B))
- kwant.plotter.current(syst, current, colorbar=False)
+ for extension in ('pdf', 'png'):
+ kwant.plotter.current(
......
......@@ -59,7 +59,7 @@
# Compute conductance
data = []
for welldepth in welldepths:
smatrix = kwant.smatrix(syst, energy, args=[-welldepth])
smatrix = kwant.smatrix(syst, energy, params=dict(pot=-welldepth))
data.append(smatrix.transmission(1, 0))
- pyplot.figure()
......
......@@ -160,9 +160,9 @@ energy eigenstates:
.. image:: /code/figure/discretizer_gs.*
Note in the above that we provided the function ``V`` to
``syst.hamiltonian_submatrix`` using ``params=dict(V=potential)``, rather than
via ``args``.
Note in the above that we pass the spatially varying potential *function*
to our system via a parameter called ``V``, because the symbol $V$
was used in the intial, symbolic, definition of the Hamiltonian.
In addition, the function passed as ``V`` expects two input parameters ``x``
and ``y``, the same as in the initial continuum Hamiltonian.
......
......@@ -184,10 +184,12 @@ Finally, we compute the transmission probability:
:start-after: #HIDDEN_BEGIN_sqvr
:end-before: #HIDDEN_END_sqvr
``kwant.smatrix`` allows us to specify a list, `args`, that will be passed as
additional arguments to the functions that provide the Hamiltonian matrix
elements. In this example we are able to solve the system for different depths
of the potential well by passing the potential value. We obtain the result:
``kwant.smatrix`` allows us to specify a dictionary, `params`, that contains
the additional arguments required by the Hamiltonian matrix elements.
In this example we are able to solve the system for different depths
of the potential well by passing the potential value (remember above
we defined our `onsite` function that takes a parameter named `pot`).
We obtain the result:
.. image:: /code/figure/quantum_well_result.*
......
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