A description of your project

These details have not been verified by PyPI

Project links

Homepage

GitHub Statistics

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Development Status
- 5 - Production/Stable
Intended Audience
- Developers
License
- OSI Approved :: Apache Software License
Natural Language
- English
Programming Language

Project description

PivotPy

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

Install

pip install pivotpy

How to use

Read Function References
See Full Documentation.
For CLI, use Vasp2Visual.
Run in Azure

{% include note.html content='In case links of documentation pages do not work, navigate through here. You can aslo troubleshoot page’s view by removing .html at end of address in browser’s addressbar.' %}- Index

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.

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)
df.data

Root Path: e:/Research/graphene_example/

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	sys	V	a	b	c	VBM	CBM	so_max	so_min	E_gap	Δ_SO	rel_path
0	C2	105.49325	2.46803	2.46803	19.99829	-3.9468	-0.8125	-3.9491	4.7810	3.1343	-8.7301	ISPIN_1/bands/DOS
1	C2	105.49325	2.46803	2.46803	19.99829	-2.7733	-1.1682	NaN	NaN	1.6051	NaN	ISPIN_1/bands
2	C2	105.49325	2.46803	2.46803	19.99829	4.5161	4.5591	-9.8405	-12.2355	0.0430	2.3950	ISPIN_2/bands

print(df.data[:2].to_latex())

\begin{tabular}{llrrrrrrrrrrl}
\toprule
{} & sys &          V &        a &        b &         c &     VBM &     CBM &  so\_max &  so\_min &   E\_gap &    Δ\_SO &           rel\_path \\
\midrule
0 &  C2 &  105.49325 &  2.46803 &  2.46803 &  19.99829 & -3.9468 & -0.8125 & -3.9491 &   4.781 &  3.1343 & -8.7301 &  ISPIN\_1/bands/DOS \\
1 &  C2 &  105.49325 &  2.46803 &  2.46803 &  19.99829 & -2.7733 & -1.1682 &     NaN &     NaN &  1.6051 &     NaN &      ISPIN\_1/bands \\
\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']
        ElemIndex = [0, 2]
        E_Fermi = -3.35005822
        ISPIN = 1
        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
            KPOINT_BSE = -1     0     0     0
            GGA = PS
        )
    )
    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 = -3.35005822
        ISPIN = 1
        NBANDS = 10
        bands_range = range(4, 14)
        evals = <ndarray:shape=(90, 10)>
    )
    tdos = Data(
        E_Fermi = -3.35005822
        ISPIN = 1
        grid_range = range(124, 203)
        tdos = <ndarray:shape=(79, 3)>
    )
    pro_bands = Data(
        labels = ['s', 'py', 'pz', 'px', 'dxy', 'dyz', 'dz2', 'dxz', 'x2-y2']
        pros = <ndarray:shape=(2, 90, 10, 9)>
    )
    pro_dos = Data(
        labels = ['energy', 's', 'py', 'pz', 'px', 'dxy', 'dyz', 'dz2', 'dxz', 'x2-y2']
        pros = <ndarray:shape=(2, 79, 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']
fig = pp.plotly_rgb_lines(vr1,**args_dict)
pp.plotly_to_html(fig)

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 
from IPython.display import HTML,display
fig = pp.plot_bz([[1,0,0],[0,1,0],[0,0,1]])
pp.plotly_to_html(fig)

Plotting Two Calculations Side by Side

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

#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
------    -----      -----     ------      --  -- -----------
53    39.77      18.03     901.41   13988   1 AltC
38    40.05      33.10      45.00     792   1 ApplicationFrameHost
8     1.64       4.39       0.00    7532   0 AppVShNotify
8     1.88       4.60       0.09   18180   1 AppVShNotify
19     4.77       4.37       0.00    4992   0 armsvc

Project details

These details have not been verified by PyPI

Project links

Homepage

GitHub Statistics

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Development Status
- 5 - Production/Stable
Intended Audience
- Developers
License
- OSI Approved :: Apache Software License
Natural Language
- English
Programming Language

Release history Release notifications | RSS feed

1.3.11

Sep 4, 2022

1.3.9

Aug 30, 2022

1.3.8

Aug 18, 2022

1.3.7

Aug 16, 2022

1.3.6

Aug 16, 2022

1.3.5

Aug 16, 2022

1.3.4

Aug 9, 2022

1.3.3

Jul 31, 2022

1.3.2

Jul 27, 2022

1.3.1

Jul 17, 2022

1.3.0

Jul 14, 2022

1.2.12

Jul 14, 2022

1.2.11

Jul 13, 2022

1.2.10

Jul 12, 2022

1.2.9

Jul 12, 2022

1.2.8

Jul 11, 2022

1.2.7

Jul 4, 2022

1.2.6

Jun 20, 2022

1.2.5

Jun 18, 2022

1.2.4

Jun 12, 2022

1.2.3

Jun 11, 2022

1.2.2

Jun 6, 2022

1.2.1

Jun 2, 2022

1.2.0

Jun 2, 2022

1.1.14

May 31, 2022

1.1.13

May 31, 2022

1.1.12

May 31, 2022

1.1.11

May 30, 2022

1.1.10

May 30, 2022

1.1.9

May 1, 2022

1.1.8

Apr 26, 2022

1.1.7

Apr 25, 2022

1.1.5

Apr 25, 2022

1.1.4

Apr 24, 2022

1.1.3

Apr 24, 2022

1.1.2

Feb 13, 2022

1.1.1

Dec 3, 2021

1.1.0

Nov 30, 2021

1.0.13

Nov 23, 2021

1.0.11

Nov 22, 2021

1.0.10

Nov 21, 2021

1.0.9

Sep 5, 2021

1.0.8

Aug 24, 2021

1.0.7

Aug 22, 2021

1.0.6

Aug 1, 2021

1.0.4

Jul 10, 2021

1.0.3

May 2, 2021

1.0.2

Apr 30, 2021

1.0.1

Mar 14, 2021

1.0.0

Mar 8, 2021

0.9.20

Feb 28, 2021

0.9.16

Feb 11, 2021

0.9.14

Feb 7, 2021

0.9.12

Jan 29, 2021

0.9.10

Jan 2, 2021

0.9.9

Dec 27, 2020

0.9.8

Dec 24, 2020

0.9.5

Dec 23, 2020

0.9.3

Nov 24, 2020

0.9.2

Nov 24, 2020

0.9.1

Nov 24, 2020

0.9.0

Nov 23, 2020

This version

0.8.5

Nov 22, 2020

0.8.3

Nov 16, 2020

0.8.2

Nov 12, 2020

0.8.0

Nov 12, 2020

0.7.6

Nov 6, 2020

0.7.5

Nov 6, 2020

0.7.4

Nov 6, 2020

0.7.3

Nov 4, 2020

0.7.2

Oct 20, 2020

0.7

Oct 20, 2020

0.6

Oct 19, 2020

0.5.0

Sep 22, 2020

0.4.1

Sep 16, 2020

0.4.0

Sep 4, 2020

0.3.9

Sep 3, 2020

0.3.8

Aug 27, 2020

0.3.7

Aug 22, 2020

Download files

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

Source Distribution

pivotpy-0.8.5.tar.gz (188.8 kB view hashes)

Uploaded Nov 22, 2020 Source

Built Distribution

pivotpy-0.8.5-py3-none-any.whl (105.8 kB view hashes)

Uploaded Nov 22, 2020 Python 3

Hashes for pivotpy-0.8.5.tar.gz

Hashes for pivotpy-0.8.5.tar.gz
Algorithm	Hash digest
SHA256	`d30d47cfa51c49230d35e2992c5fc6f33e1b574bd91524cf0679b5664cef1c7d`
MD5	`b04a9414ab724583e8fbe62d4a36cbc2`
BLAKE2b-256	`40a1fba529e1dde289e933ad876c48b1f723e9d139210d2ace431ac1238f71ef`

Hashes for pivotpy-0.8.5-py3-none-any.whl

Hashes for pivotpy-0.8.5-py3-none-any.whl
Algorithm	Hash digest
SHA256	`73e9c91448b75199f4d7f4e858a52492aa7288cec415898f30b64f3d8d867129`
MD5	`c6dbc8aab3a0e504a02584252e529fe0`
BLAKE2b-256	`238a533b4950360dfeeaf6c691ef2e99f5a68eb06234cd2bebda4802e504d951`