A structure plot presents the crystal structure of a model by drawing lattice sites as circles and hoppings as lines which connect the circles. At first glance, this seems like a combination of the standard scatter and line plots found in matplotlib, but the specific requirements of tight-binding complicate the implementation. This is why pybinding has its own specialized structure plotting functions. While these functions are based on matplotlib, they offer additional options which will be explained here.
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A few different classes in Pybinding use structure plots. These are .Lattice, .Model, .System, .Lead and .StructureMap. They all represent some kind of spatial structure with sites and hoppings. Note that most of these classes are components of the main .Model. Calling their plot methods will draw the structure which they represent. The following pseudo-code presents a few possibilities:
model = pb.Model(...) # specify model
model.attach_lead(...) # specify leads
model.lattice.plot() # just the unit cell
model.plot() # the main system and leads
model.system.plot() # only the main system
model.leads[0].plot() # only lead 0In the following sections we'll present a few features of the structure plotting API. The examples will involve mainly Model.plot, but all of these methods share the same common API.
The structure plot usually draws lines for all hoppings. We can see an example here with the third-nearest-neighbor model of graphene. Note the huge number of hoppings in the figure below. The extra information may be useful for calculations, but it is not always desirable for figures because of the extra noise. To filter out some of the lines, we can pass the draw_only argument as a list of hopping names. For example, if we only want the first-nearest neighbors:
from pybinding.repository import graphene
plt.figure(figsize=(8, 3)) model = pb.Model(graphene.monolayer(nearest_neighbors=3), graphene.hexagon_ac(1))
plt.subplot(121, title="Unfiltered: all 3 hoppings") model.plot()
plt.subplot(122, title="Filtered: shows only nearest") model.plot(hopping={'draw_only': ['t']})
We can also select hoppings in any combination:
plt.figure(figsize=(8, 3))
plt.subplot(121, title="$t$ and $t{nn}$") model.plot(hopping={'draw_only': ['t', 't_nn']})
plt.subplot(122, title="$t$ and $t{nnn}$") model.plot(hopping={'draw_only': ['t', 't_nnn']})
The site radius is given in data units (nanometers in this example). Colors are passed as a list of colors or a matplotlib colormap.
plt.figure(figsize=(8, 3)) model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(0.5))
plt.subplot(121, title="Default") model.plot()
plt.subplot(122, title="Customized") model.plot(site={'radius': 0.04, 'cmap': ['blue', 'red']})
By default, all hopping kinds (nearest, next-nearest, etc.) are shown using the same line color, but they can be colorized using the cmap parameter.
plt.figure(figsize=(8, 3)) model = pb.Model(graphene.monolayer(nearest_neighbors=3), pb.rectangle(0.6))
plt.subplot(121, title="Default") model.plot()
plt.subplot(122, title="Customized") model.plot(hopping={'width': 2, 'cmap': 'auto'})
By default, pybinding plots will remove the right and top axes spines. To recover those lines call the .pltutils.respine function.
model = pb.Model(graphene.monolayer(), graphene.hexagon_ac(1)) model.plot() pb.pltutils.respine()