Skip to main content

A description of your project

Project description

PivotPy

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

Run in Azure Open In Colab

<style>a{text-decoration: none !important;color:lightkblue;font-weight:bold;} a:focus,a:active,a:hover{color:hotpink !important;}</style>

  Index● 
  XmlElementTree 
  StaticPlots 
  InteractivePlots 
  Utilities 
  StructureIO 
  Widgets 

Install

pip install pivotpy

How to use

New: Ipywidgets-based GUI

See GIF here: GIF

  • The code at end is used below to rebuild dataframe which can be use in many ways such as generating latex table. The matplotlib code is used to generate publication quality figure.
from IPython.display import Markdown
import pivotpy as pp
paths = ['e:/Research/graphene_example/ISPIN_1/bands/DOS/vasprun.xml',
         'e:/Research/graphene_example/ISPIN_1/bands/vasprun.xml',
         'e:/Research/graphene_example/ISPIN_1/dos/vasprun.xml',
         'e:/Research/graphene_example/ISPIN_2/bands/vasprun.xml',
         'e:/Research/graphene_example/ISPIN_2/dos/sigm0_01/vasprun.xml',
         'e:/Research/graphene_example/ISPIN_2/dos/vasprun.xml',
         'e:/Research/graphene_example/vasprun.xml']
df = pp.generate_summary(paths_list=paths)
print(df.caption)
Markdown(df.data.to_markdown())
Root Path: e:/Research/graphene_example/
sys V a b c VBM CBM so_max so_min rel_path E_gap
0 C2 105.493 2.46803 2.46803 19.9983 nan nan nan nan ISPIN_1/bands/DOS nan
1 C2 105.493 2.46803 2.46803 19.9983 -2.7733 -2.7733 nan nan ISPIN_1/bands nan
2 C2 105.493 2.46803 2.46803 19.9983 -12.7211 -5.2581 nan nan ISPIN_1/dos 7.463
3 C2 105.493 2.46803 2.46803 19.9983 -12.7211 -5.2581 nan nan ISPIN_2/bands 7.463
4 C2 105.493 2.46803 2.46803 19.9983 nan nan nan nan ISPIN_2/dos/sigm0_01 nan
5 nan nan nan nan nan nan nan nan nan ISPIN_2/dos nan
6 C2 105.493 2.46803 2.46803 19.9983 -3.7816 -1.7516 nan nan nan 2.03
print(df.data[:2].to_latex())
\begin{tabular}{llrrrrrrrrlr}
\toprule
{} & sys &          V &        a &        b &         c &     VBM &     CBM &  so\_max &  so\_min &           rel\_path &  E\_gap \\
\midrule
0 &  C2 &  105.49325 &  2.46803 &  2.46803 &  19.99829 &     NaN &     NaN &     NaN &     NaN &  ISPIN\_1/bands/DOS &    NaN \\
1 &  C2 &  105.49325 &  2.46803 &  2.46803 &  19.99829 & -2.7733 & -2.7733 &     NaN &     NaN &      ISPIN\_1/bands &    NaN \\
\bottomrule
\end{tabular}
ax = pp.init_figure(figsize=(3,1.5))
_ = df.data.sort_values('VBM').plot(ax=ax,x = 'VBM',y=['CBM','E_gap'])

svg

import os 
os.chdir('E:/Research/graphene_example/ISPIN_1/bands')
xml_data=pp.read_asxml()
vr=pp.export_vasprun(elim=[-5,5])
vr
Data(
    sys_info = Data(
        SYSTEM = C2
        NION = 2
        TypeION = 1
        ElemName = ['C']
        E_Fermi = -3.3501
        fields = ['s', 'py', 'pz', 'px', 'dxy', 'dyz', 'dz2', 'dxz', 'x2-y2']
        incar = Data(
            SYSTEM = C2
            PREC = high
            ALGO = N
            LSORBIT = T
            NELMIN = 7
            ISMEAR = 0
            SIGMA = 0.10000000
            LORBIT = 11
            GGA = PS
        )
        ElemIndex = [0, 2]
        ISPIN = 1
    )
    dim_info = Data(
        ⇅ = Each of SpinUp/SpinDown Arrays
        kpoints = (NKPTS,3)
        kpath = (NKPTS,1)
        bands = ⇅(NKPTS,NBANDS)
        dos = ⇅(grid_size,3)
        pro_dos = ⇅(NION,grid_size,en+pro_fields)
        pro_bands = ⇅(NION,NKPTS,NBANDS,pro_fields)
    )
    kpoints = <ndarray:shape=(90, 3)>
    kpath = <list:len=90>
    bands = Data(
        E_Fermi = -3.3501
        ISPIN = 1
        NBANDS = 21
        evals = <ndarray:shape=(90, 21)>
    )
    tdos = Data(
        E_Fermi = -3.3501
        ISPIN = 1
        tdos = <ndarray:shape=(301, 3)>
    )
    pro_bands = Data(
        labels = ['py', 'pz', 'px', 'dxy', 'dyz', 'dz2', 'dxz', 'x2-y2']
        pros = <ndarray:shape=(2, 90, 21, 9)>
    )
    pro_dos = Data(
        labels = ['s', 'py', 'pz', 'px', 'dxy', 'dyz', 'dz2', 'dxz', 'x2-y2']
        pros = <ndarray:shape=(2, 301, 10)>
    )
    poscar = Data(
        volume = 105.49324928
        basis = <ndarray:shape=(3, 3)>
        rec_basis = <ndarray:shape=(3, 3)>
        positions = <ndarray:shape=(2, 3)>
    )
)

Matplotlib's static plots

#collapse_input
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=[2,1,2.2],sharey=True,wspace=0.05,figsize=(8,2.6))
elements=[0,0,[0,1]]
orbs=[[0],[1],[2,3]]
labels=['s','$p_z$','$(p_x+p_y)$']
ti_cks=dict(xt_indices=[0,30,60,-1],xt_labels=['Γ','M','K','Γ'])
args_dict=dict(elements=elements,orbs=orbs,labels=labels,elim=[-20,15])
pp.quick_bplot(path_evr=vr1,ax=axs[0],**ti_cks,elim=[-20,15])
lg_k={'ncol': 3}
pp.quick_dos_lines(path_evr=vr2,ax=axs[1],vertical=True,spin='both',include_dos='pdos',**args_dict,legend_kwargs=lg_k)
pp.quick_rgb_lines(path_evr=vr1,ax=axs[2],**args_dict,**ti_cks,colorbar=True)
pp.show() 

svg

Interactive plots using plotly

args_dict['labels'] = ['s','p_z','p_x+p_y']
fig1 = pp.plotly_rgb_lines(vr1,**args_dict)
#pp.plotly_to_html(fig1) #Do inside Google Colab, fig1 inside Jupyter
from IPython.display import Markdown
Markdown("[See Interactive Plot](https://massgh.github.io/InteractiveHTMLs/iGraphene.html)")

See Interactive Plot

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!).
  • Same color points lie on a sphere, with radius decreasing as red to blue and gamma point in gold color. These color help distinguishing points but the points not always be equivalent, for example in FCC, there are two points on mid of edges connecting square-hexagon and hexagon-hexagon at equal distance from center but not the same points.
  • Any colored point's hover text is in gold background.
import pivotpy as pp 
fig2 = pp.plot_bz([[1,0,0],[0,1,0],[0,0,1]])
#pp.plotly_to_html(fig2) #Do inside Google Colab, fig1 inside Jupyter
from IPython.display import Markdown
Markdown("[See Interactive BZ Plot](https://massgh.github.io/InteractiveHTMLs/BZ.html)")
E:\Research\pivotpy\pivotpy\sio.py:408: RuntimeWarning:

divide by zero encountered in double_scalars

See Interactive BZ Plot

Plotting Two Calculations Side by Side

  • Here we will use shift_kpath to demonstrate plot of two calculations on same axes side by side
#nbdev_collapse_input
import matplotlib.pyplot as plt
import pivotpy as pp 
plt.style.use('bmh')
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)

svg

Interpolation

#collapse_input
import pivotpy as pp
plt.style.use('ggplot')
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,'m',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')

svg

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!

pp.ps_to_std(ps_command='(Get-Process)[0..4]')
NPM(K)    PM(M)      WS(M)     CPU(s)      Id  SI ProcessName
------    -----      -----     ------      --  -- -----------
50    38.14      61.34     185.53   17860   1 AltC
24    13.38      51.61       2.80   17324   1 ApplicationFrameHost
8     1.59       5.84       0.00    5884   0 armsvc
39    33.12      46.57      10.81   15428   1 BingWallpaperApp
22    42.45      88.01       5.38    2916   1 Code

Advancaed: Poweshell Cell/Line Magic %%ps/%ps

  • You can create a IPython cell magic to run powershell commands directly in IPython Shell/Notebook (Powershell core installation required).
  • Cell magic can be assigned to a variable foo by %%ps --out foo
  • Line magic can be assigned to a variable by foo = %ps powershell_command

Put below code in ipython profile's startup file (create one) "~/.ipython/profile_default/startup/powershell_magic.py"

from IPython.core.magic import register_line_cell_magic
from IPython import get_ipython
@register_line_cell_magic
def ps(line, cell=None):
    if cell:
        return get_ipython().run_cell_magic('powershell',line,cell)
    else:
        get_ipython().run_cell_magic('powershell','--out posh_output',line)
        return posh_output.splitlines()

Additionally you need to add following lines in "~/.ipython/profile_default/ipython_config.py" file to make above magic work.

from traitlets.config.application import get_config
c = get_config()
c.ScriptMagics.script_magics = ['powershell']
c.ScriptMagics.script_paths = {
    'powershell' : 'powershell.exe -noprofile -command -',
    'pwsh': 'pwsh.exe -noprofile -command -'
}
%%ps 
Get-ChildItem 'E:\Research\graphene_example\'
    Directory: E:\Research\graphene_example





Mode                 LastWriteTime         Length Name                                                                 

----                 -------------         ------ ----                                                                 

da----        10/31/2020   1:30 PM                ISPIN_1                                                              

da----          5/9/2020   1:05 PM                ISPIN_2                                                              

-a----          5/9/2020   1:01 PM          75331 OUTCAR                                                               

-a----        11/22/2020   7:02 PM            162 result.json                                                          

-a----        11/22/2020   6:08 PM            693 sys_info.pickle                                                      

-a----        11/22/2020   6:08 PM          91850 vasprun.pickle                                                       

-a----          5/9/2020   1:01 PM         240755 vasprun.xml                                                          
x = %ps (Get-ChildItem 'E:\Research\graphene_example\').FullName
x
['E:\\Research\\graphene_example\\ISPIN_1',
 'E:\\Research\\graphene_example\\ISPIN_2',
 'E:\\Research\\graphene_example\\OUTCAR',
 'E:\\Research\\graphene_example\\result.json',
 'E:\\Research\\graphene_example\\sys_info.pickle',
 'E:\\Research\\graphene_example\\vasprun.pickle',
 'E:\\Research\\graphene_example\\vasprun.xml']

Functions Reference

<style>a{text-decoration: none !important;color:lightkblue;font-weight:bold;} a:focus,a:active,a:hover{color:hotpink !important;}</style>

  Index● 
  XmlElementTree 
  StaticPlots 
  InteractivePlots 
  Utilities 
  StructureIO 
  Widgets 

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pivotpy-0.9.1.tar.gz (66.8 kB view hashes)

Uploaded Source

Built Distribution

pivotpy-0.9.1-py3-none-any.whl (64.8 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page