pivotpy 0.3.7

A description of your project

PivotPy

A Python Processing Tool for Vasp Input/Output. A CLI is available in Powershell, see Vasp2Visual.

Install

pip install pivotpy

How to use

• See Full Documentation.
• For CLI, use Vasp2Visual.
• Run in Azure

import pivotpy as pp
print(', '.join(pp.__all__))

Dic2Dot, read_asxml, exclude_kpts, get_ispin, get_summary, get_kpts, get_tdos, get_evals, get_bands_pro_set, get_dos_pro_set, get_structure, export_vasprun, interpolate_data, ps_to_py, ps_to_std, select_dirs, select_files, invert_color, plot_bands, modify_axes, quick_bplot, add_text, add_legend, add_colorbar, create_rgb_lines, quick_rgb_lines, quick_color_lines, init_figure, select_pdos, collect_dos, quick_dos_lines, rgb_to_plotly, plotly_to_html, plotly_rgb_lines, plotly_dos_lines, show, savefig

import os 
os.chdir('E:/Research/graphene_example/ISPIN_1/bands')
xml_data=pp.read_asxml()
vr=pp.export_vasprun(elim=[-5,5])
vr.keys()

dict_keys(['sys_info', 'dim_info', 'kpoints', 'kpath', 'bands', 'tdos', 'pro_bands', 'pro_dos', 'poscar', 'xml'])

print(pp.exclude_kpts(xml_data=xml_data))
pp.get_summary(xml_data=xml_data)

14





{'SYSTEM': 'C2',
 'NION': 2,
 'TypeION': 1,
 'ElemName': ['C'],
 'ElemIndex': [0, 2],
 'ISPIN': 1}

Example Plot: Graphene Spin Polarized

import pivotpy as pp 
import matplotlib.pyplot as plt 
vr1=pp.export_vasprun('E:/Research/graphene_example/ISPIN_2/bands/vasprun.xml')
vr2=pp.export_vasprun('E:/Research/graphene_example/ISPIN_2/dos/vasprun.xml')
axs=pp.init_figure(ncols=3,widths=[1,1,1],sharey=True,wspace=0.05,figsize=(10,2.6))
elements=[0,0,[0,1]]
orbs=[[0],[1],[2,3]]
orblabels=['s','p_z','(p_x+p_y)']
colors=['r',(0,0.9,0),'b']
ti_cks=dict(xt_indices=[0,30,60,-1],xt_labels=['Γ','M','K','Γ'])
args_dict=dict(elements=elements,orbs=orbs,orblabels=orblabels,elim=[-20,15])
pp.quick_bplot(path_evr=vr1,ax=axs[0],**ti_cks,elim=[-20,15])
pp.quick_rgb_lines(path_evr=vr1,ax=axs[1],**args_dict,**ti_cks)
lg_k={'ncol': 3}
pp.quick_dos_lines(path_evr=vr2,ax=axs[2],vertical=True,include_dos='pdos',**args_dict,colors=colors,legend_kwargs=lg_k)
pp.add_colorbar(ax=plt.gcf().add_axes([0.399,1.02,0.23,0.05]),ticklabels=[r'$s^{⇅}$',r'$p_z^{⇅}$',r'$(p_x+p_y)^{⇅}$'])
pp.show() 

Brillouin Zone (BZ) Processing

Look in pivotpy.sio module for details on generating mesh and path of KPOINTS as well as using Materials Projects' API to get POSCAR right in the working folder with command get_poscar. Below is a screenshot of interactive BZ plot. You can double click on blue points and hit Ctrl + C to copy the high symmetry points relative to reciprocal lattice basis vectors. (You will be able to draw kpath in Pivotpy-Dash application and generate KPOINTS automatically from a web interface later on!).

from IPython.display import Image
Image('./docs/images/plot_bz.jpg')

Plotting Two Calculations Side by Side

• Here we will use shift_kpath to demonstrate plot of two calculations on same axes side by side

import matplotlib.pyplot as plt
import pivotpy as pp 
vr1=pp.export_vasprun('E:/Research/graphene_example/ISPIN_1/bands/vasprun.xml')
shift_kpath=vr1.kpath[-1] # Add last point from first export in second one.
vr2=pp.export_vasprun('E:/Research/graphene_example/ISPIN_2/bands/vasprun.xml',shift_kpath=shift_kpath)
last_k=vr2.kpath[-1]
axs=pp.init_figure(figsize=(5,2.6))
K_all=[*vr1.kpath,*vr2.kpath] # Merge kpath for ticks
kticks=[K_all[i] for i in [0,30,60,90,120,150,-1]]
ti_cks=dict(xticks=kticks,xt_labels=['Γ','M','K','Γ','M','K','Γ'])
pp.quick_bplot(path_evr=vr1,ax=axs)
pp.quick_bplot(path_evr=vr2,ax=axs,txt='Graphene(Left: ISPIN=1, Right: ISPIN=2)',ctxt='m')
pp.modify_axes(ax=axs,xlim=[0,last_k],ylim=[-10,10],**ti_cks)

Interpolation

import pivotpy as pp
k=vr1.kpath
ef=vr1.bands.E_Fermi
evals=vr1.bands.evals-ef
#Let's interpolate our graph to see effect. It is useful for colored graphs.
knew,enew=pp.interpolate_data(x=k,y=evals,n=10,k=3)
plot=plt.plot(k,evals,'b',lw=5,label='real data')
plot=plt.plot(k,evals,'w',lw=1,label='interpolated',ls='dashed')
pp.add_text(ax=plt.gca(),txts='Graphene')

Running powershell commands from python.

Some tasks are very tideious in python while just a click way in powershell. See below, and try to list processes in python yourself to see the difference!

gu.ps_to_std(ps_command='(Get-Process)[0..4]')

Handles  NPM(K)    PM(K)      WS(K)     CPU(s)     Id  SI ProcessName
-------  ------    -----      -----     ------     --  -- -----------
563      49    39564      75820      17.41  16184   1 AltC
271      17     6072      24916       1.67   6776   1 ApplicationFrameHost
147       9     1360       5832              5320   0 armsvc
413      22   300208      39312       7.42   3556   0 audiodg
331      17     4724       6628       2.38  14560   1 CastSrv

Using Plotly in pivotpy

• See video!

<iframe width="560" height="315" src="https://www.youtube.com/embed/uda0ubF-cnQ" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

- Interact with chart below, hover, zoom, pan and more!

<iframe width="700" height="400" frameborder="0" scrolling="no" src="//plotly.com/~massgh/36.embed"></iframe>