Metadata-Version: 2.1
Name: pivotpy
Version: 0.7.2
Summary: A description of your project
Home-page: https://github.com/massgh/pivotpy/tree/master/
Author: Abdul Saboor
Author-email: mass_qau@outlook.com
License: Apache Software License 2.0
Description: # PivotPy
        > A Python Processing Tool for Vasp Input/Output. A CLI is available in Powershell, see <a href='https://github.com/massgh/Vasp2Visual'>Vasp2Visual</a>.
        
        
        ## Install
        `pip install pivotpy`
        
        ## How to use
        - See [Full Documentation](https://massgh.github.io/pivotpy/).
        - For CLI, use [Vasp2Visual](https://github.com/massgh/Vasp2Visual).
        - Run in Azure [![Run in Azure](https://notebooks.azure.com/launch.png)](https://testazurenotebooks-massaz.notebooks.azure.com/j/notebooks/index.ipynb)
        
        ```
        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, load_export, make_dot_dict, dump_dict, get_file_size, interpolate_data, ps_to_py, ps_to_std, select_dirs, select_files, get_child_items, invert_color, printr, printg, printb, printy, printm, printc, EncodeFromNumpy, DecodeToNumpy, link_to_class, 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, plt_to_html, get_rgb_data, rgb_to_plotly, plotly_to_html, plotly_rgb_lines, plotly_dos_lines, iplotfromtxt, save_mp_API, load_mp_data, get_crystal, get_poscar, trace_kpath, get_kmesh, intersect_3p_p_3v, centroid, order, in_vol_sector, out_bz_plane, to_xy, rad_angle, arctan_full, get_bz, plot_bz, 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'])
        
        
        
        ```
        from pivotpy import vr_parser as vp
        xml_data=vp.read_asxml()
        vr=vp.export_vasprun(elim=[-5,5])
        vr.keys()
        ```
        
        
        
        
            dict_keys(['sys_info', 'dim_info', 'kpoints', 'kpath', 'bands', 'tdos', 'pro_bands', 'pro_dos', 'poscar', 'xml'])
        
        
        
        ```
        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() 
        ```
        
        
        ![svg](docs/images/output_5_0.svg)
        
        
        ## 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.
        
        ```
        from IPython.display import Image
        Image(url="docs/images/plot_bz.jpg")
        ```
        
        
        
        
        <img src="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 
        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](docs/images/output_9_0.svg)
        
        
        ## Interpolation 
        
        ```
        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](docs/images/output_11_0.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
            
        
        ## Using Plotly in pivotpy
        - See video!
        <div><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></div>
        - Interact with chart below, hover, zoom, pan and more!
        <div><iframe width="700" height="400" frameborder="0" scrolling="no" src="//plotly.com/~massgh/36.embed"></iframe></div>
        
Keywords: dft vasp visualization
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
