.
FatbandsPlot
[1]:
import sisl
import sisl.viz
For this notebook we will create a toy “Boron nitride” tight binding:
[2]:
# First, we create the geometry
BN = sisl.geom.graphene(atoms=["B", "N"])
# Create a hamiltonian with different on-site terms
H = sisl.Hamiltonian(BN)
H[0, 0] = 2
H[1, 1] = -2
H[0, 1] = -2.7
H[1, 0] = -2.7
H[0, 1, (-1, 0)] = -2.7
H[0, 1, (0, -1)] = -2.7
H[1, 0, (1, 0)] = -2.7
H[1, 0, (0, 1)] = -2.7
Note that we could have obtained this hamiltonian from any other source. Then we generate a path for the band structure:
[3]:
band = sisl.BandStructure(H, [[0., 0.], [2./3, 1./3],
[1./2, 1./2], [1., 1.]], 301,
[r'$\Gamma$', 'K', 'M', r'$\Gamma$'])
And finally we just ask for the fatbands plot:
[4]:
fatbands = band.plot.fatbands()
fatbands
We only see the bands here, but this is a fatbands plot, and it is ready to accept your requests on what to draw!
Requesting specific weights
The fatbands that the plot draws are controlled by the groups setting.
[5]:
print(fatbands.get_param("groups").help)
The different groups that are displayed in the fatbands
Each item is a dict.
Structure of the dict: {
'name':
'species':
'atoms':
Structure of the dict: {
'index':
Structure of the dict: {
'in':
}
'fx':
'fy':
'fz':
'x':
'y':
'z':
'Z':
'neighbours':
Structure of the dict: {
'range':
'R':
'neigh_tag':
}
'tag':
'seq':
}
'orbitals':
'spin':
'normalize':
'color':
'scale':
}
This setting works exactly like the requests setting in PdosPlot, which is documented here. Therefore we won’t give an extended description of it, but just quickly show that you can autogenerate the groups:
[6]:
fatbands.split_groups(on="species")
Or write them yourself if you want the maximum flexibility:
[7]:
fatbands.update_settings(groups=[
{"species": "N", "color": "blue", "name": "Nitrogen"},
{"species": "B", "color": "red", "name": "Boron"}
])
Scaling fatbands
The visual appeal of fatbands depends a lot on the size of your plot, therefore there’s one global scale setting that scales all fatbands at the same time:
[8]:
fatbands.update_settings(scale=2)
You can also use the scale_fatbands method, which additionally lets you choose if you want to rescale from the current size or just set the value of scale:
[9]:
fatbands.scale_fatbands(0.5, from_current=True)
Use BandsPlot settings
All settings of BandsPlot work as well for FatbandsPlot. Even spin texture!
We hope you enjoyed what you learned!
This next cell is just to create the thumbnail for the notebook in the docs
[10]:
thumbnail_plot = fatbands
if thumbnail_plot:
thumbnail_plot.show("png")
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [10], in <cell line: 3>()
1 thumbnail_plot = fatbands
3 if thumbnail_plot:
----> 4 thumbnail_plot.show("png")
File ~/checkouts/readthedocs.org/user_builds/sisl/checkouts/v0.12.2/sisl/viz/plot.py:1176, in Plot.show(self, listen, return_figWidget, *args, **kwargs)
1173 except Exception as e:
1174 warn(e)
-> 1176 return self._backend.show(*args, **kwargs)
File ~/checkouts/readthedocs.org/user_builds/sisl/checkouts/v0.12.2/sisl/viz/backends/plotly/backend.py:42, in PlotlyBackend.show(self, *args, **kwargs)
41 def show(self, *args, **kwargs):
---> 42 return self.figure.show(*args, **kwargs)
File ~/checkouts/readthedocs.org/user_builds/sisl/conda/v0.12.2/lib/python3.10/site-packages/plotly/basedatatypes.py:3398, in BaseFigure.show(self, *args, **kwargs)
3365 """
3366 Show a figure using either the default renderer(s) or the renderer(s)
3367 specified by the renderer argument
(...)
3394 None
3395 """
3396 import plotly.io as pio
-> 3398 return pio.show(self, *args, **kwargs)
File ~/checkouts/readthedocs.org/user_builds/sisl/conda/v0.12.2/lib/python3.10/site-packages/plotly/io/_renderers.py:388, in show(fig, renderer, validate, **kwargs)
385 fig_dict = validate_coerce_fig_to_dict(fig, validate)
387 # Mimetype renderers
--> 388 bundle = renderers._build_mime_bundle(fig_dict, renderers_string=renderer, **kwargs)
389 if bundle:
390 if not ipython_display:
File ~/checkouts/readthedocs.org/user_builds/sisl/conda/v0.12.2/lib/python3.10/site-packages/plotly/io/_renderers.py:296, in RenderersConfig._build_mime_bundle(self, fig_dict, renderers_string, **kwargs)
293 if hasattr(renderer, k):
294 setattr(renderer, k, v)
--> 296 bundle.update(renderer.to_mimebundle(fig_dict))
298 return bundle
File ~/checkouts/readthedocs.org/user_builds/sisl/conda/v0.12.2/lib/python3.10/site-packages/plotly/io/_base_renderers.py:127, in ImageRenderer.to_mimebundle(self, fig_dict)
126 def to_mimebundle(self, fig_dict):
--> 127 image_bytes = to_image(
128 fig_dict,
129 format=self.format,
130 width=self.width,
131 height=self.height,
132 scale=self.scale,
133 validate=False,
134 engine=self.engine,
135 )
137 if self.b64_encode:
138 image_str = base64.b64encode(image_bytes).decode("utf8")
File ~/checkouts/readthedocs.org/user_builds/sisl/conda/v0.12.2/lib/python3.10/site-packages/plotly/io/_kaleido.py:133, in to_image(fig, format, width, height, scale, validate, engine)
131 # Raise informative error message if Kaleido is not installed
132 if scope is None:
--> 133 raise ValueError(
134 """
135 Image export using the "kaleido" engine requires the kaleido package,
136 which can be installed using pip:
137 $ pip install -U kaleido
138 """
139 )
141 # Validate figure
142 # ---------------
143 fig_dict = validate_coerce_fig_to_dict(fig, validate)
ValueError:
Image export using the "kaleido" engine requires the kaleido package,
which can be installed using pip:
$ pip install -U kaleido