This page was auto-generated from a Jupyter notebook: aniline/aniline_wf_0.1-10.1eV_orb33_A2.

DOI Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)

ePSproc: Aniline Orb 33 (A2) ionization

Job details

  • ePS aniline, batch aniline_wf_0.1-10.1eV, orbital orb33_A2

  • Aniline Orb 33 (A2) ionization, S1/LUMO test using B2 initial symmetry, B1 ion state, wavefn run on AntonJr

  • E=0.1:0.2:10.1 (51 points)

  • Thu Mar 7 17:45:08 EST 2019

Set-up

Load modules

[1]:
import sys
import os
import numpy as np
import epsproc as ep

from datetime import datetime as dt
timeString = dt.now()
* pyevtk not found, VTK export not available.
* plotly not found, plotly plots not available.

Load data

[2]:
# File path only, from env var DATAFILE
# dataPath = os.getcwd()
dataFile = os.environ.get('DATAFILE', '')
[3]:
jobInfo = ep.headerFileParse(dataFile)
molInfo = ep.molInfoParse(dataFile)
*** Job info from file header.

 ePS aniline, batch aniline_wf_0.1-10.1eV, orbital orb33_A2
 Aniline Orb 33 (A2) ionization, S1/LUMO test using B2 initial symmetry, B1 ion state, wavefn run on AntonJr
 E=0.1:0.2:10.1 (51 points)
 Thu Mar  7 17:45:08 EST 2019

*** Found orbitals
1   1 Ene =     -15.5686 Spin =Alpha Occup =   2.000000
2   2 Ene =     -11.2822 Spin =Alpha Occup =   2.000000
3   3 Ene =     -11.2336 Spin =Alpha Occup =   2.000000
4   4 Ene =     -11.2335 Spin =Alpha Occup =   2.000000
5   5 Ene =     -11.2203 Spin =Alpha Occup =   2.000000
6   6 Ene =     -11.2203 Spin =Alpha Occup =   2.000000
7   7 Ene =     -11.2144 Spin =Alpha Occup =   2.000000
8   8 Ene =      -1.2347 Spin =Alpha Occup =   2.000000
9   9 Ene =      -1.1330 Spin =Alpha Occup =   2.000000
10  10 Ene =      -1.0156 Spin =Alpha Occup =   2.000000
11  11 Ene =      -0.9909 Spin =Alpha Occup =   2.000000
12  12 Ene =      -0.8355 Spin =Alpha Occup =   2.000000
13  13 Ene =      -0.8200 Spin =Alpha Occup =   2.000000
14  14 Ene =      -0.7152 Spin =Alpha Occup =   2.000000
15  15 Ene =      -0.7034 Spin =Alpha Occup =   2.000000
16  16 Ene =      -0.6603 Spin =Alpha Occup =   2.000000
17  17 Ene =      -0.6006 Spin =Alpha Occup =   2.000000
18  18 Ene =      -0.5992 Spin =Alpha Occup =   2.000000
19  19 Ene =      -0.5820 Spin =Alpha Occup =   2.000000
20  20 Ene =      -0.5361 Spin =Alpha Occup =   2.000000
21  21 Ene =      -0.5084 Spin =Alpha Occup =   2.000000
22  22 Ene =      -0.4866 Spin =Alpha Occup =   2.000000
23  23 Ene =      -0.4446 Spin =Alpha Occup =   2.000000
24  24 Ene =      -0.3358 Spin =Alpha Occup =   2.000000
25  25 Ene =      -0.2853 Spin =Alpha Occup =   2.000000
26  26 Ene =       0.0318 Spin =Alpha Occup =   0.000000
27  27 Ene =       0.0415 Spin =Alpha Occup =   0.000000
28  28 Ene =       0.0427 Spin =Alpha Occup =   0.000000
29  29 Ene =       0.0566 Spin =Alpha Occup =   0.000000
30  30 Ene =       0.0578 Spin =Alpha Occup =   0.000000
31  31 Ene =       0.0768 Spin =Alpha Occup =   0.000000
32  32 Ene =       0.0875 Spin =Alpha Occup =   0.000000
33  33 Ene =       0.1133 Spin =Alpha Occup =   0.000000

*** Found atoms
Z =  7 ZS =  7 r =   0.0000000000   0.0000000000   2.3057780000
Z =  6 ZS =  6 r =   0.0000000000   0.0000000000   0.9332450000
Z =  6 ZS =  6 r =   0.0000000000   1.1965340000   0.2222240000
Z =  6 ZS =  6 r =   0.0000000000  -1.1965340000   0.2222240000
Z =  6 ZS =  6 r =   0.0000000000   1.1882610000  -1.1569230000
Z =  6 ZS =  6 r =   0.0000000000  -1.1882610000  -1.1569230000
Z =  6 ZS =  6 r =   0.0000000000   0.0000000000  -1.8637550000
Z =  1 ZS =  1 r =   0.0000000000   2.1302880000   0.7533710000
Z =  1 ZS =  1 r =   0.0000000000  -2.1302880000   0.7533710000
Z =  1 ZS =  1 r =   0.0000000000   2.1240450000  -1.6836310000
Z =  1 ZS =  1 r =   0.0000000000  -2.1240450000  -1.6836310000
Z =  1 ZS =  1 r =   0.0000000000   0.0000000000  -2.9358510000
Z =  1 ZS =  1 r =   0.0000000000  -0.8462720000   2.8135300000
Z =  1 ZS =  1 r =   0.0000000000   0.8462720000   2.8135300000
[4]:
# Scan file(s) for various data types...

# For dir scan
# dataXS = ep.readMatEle(fileBase = dataPath, recordType = 'CrossSection')
# dataMatE = ep.readMatEle(fileBase = dataPath, recordType = 'DumpIdy')

# For single file
dataXS = ep.readMatEle(fileIn = dataFile, recordType = 'CrossSection')
dataMatE = ep.readMatEle(fileIn = dataFile, recordType = 'DumpIdy')

*** ePSproc readMatEle(): scanning files for CrossSection segments.

*** Scanning file(s)
['/home/paul/ePS_results/aniline/aniline_wf_0.1-10.1eV/aniline_wf_0.1-10.1eV_orb33_A2.inp.out']

*** Reading ePS output file:  /home/paul/ePS_results/aniline/aniline_wf_0.1-10.1eV/aniline_wf_0.1-10.1eV_orb33_A2.inp.out
Expecting 51 energy points.
Expecting 3 symmetries.
Scanning CrossSection segments.
Expecting 4 CrossSection segments.
Found 4 CrossSection segments (sets of results).
Processed 4 sets of CrossSection file segments, (0 blank)
*** ePSproc readMatEle(): scanning files for DumpIdy segments.

*** Scanning file(s)
['/home/paul/ePS_results/aniline/aniline_wf_0.1-10.1eV/aniline_wf_0.1-10.1eV_orb33_A2.inp.out']

*** Reading ePS output file:  /home/paul/ePS_results/aniline/aniline_wf_0.1-10.1eV/aniline_wf_0.1-10.1eV_orb33_A2.inp.out
Expecting 51 energy points.
Expecting 3 symmetries.
Scanning CrossSection segments.
Expecting 153 DumpIdy segments.
Found 153 dumpIdy segments (sets of matrix elements).

Processing segments to Xarrays...
Processed 153 sets of DumpIdy file segments, (0 blank)

Job & molecule info

[5]:
ep.jobSummary(jobInfo, molInfo);

*** Job summary data
 ePS aniline, batch aniline_wf_0.1-10.1eV, orbital orb33_A2
 Aniline Orb 33 (A2) ionization, S1/LUMO test using B2 initial symmetry, B1 ion state, wavefn run on AntonJr
 E=0.1:0.2:10.1 (51 points)
 Thu Mar  7 17:45:08 EST 2019

Electronic structure input: '/home/paul/ePS_stuff/aniline/electronic_structure/aniline_aug-cc-pVDZ_E_orbLT56.molden'
Initial state occ:               [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 1]
Final state occ:                 [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0]
IPot (input vertical IP, eV):            7.70

*** Additional orbital info (SymProd)
Ionizing orb:                    [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]
Ionizing orb sym:                ['A2']
Orb energy (eV):                 [3.08305004]
Orb energy (H):                  [0.1133]
Orb energy (cm^-1):              [24866.47573488]
Threshold wavelength (nm):       402.1478598985443

*** Molecular structure

../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_9_1.png

1-photon ePS Cross-Sections

Plot 1-photon cross-sections and \(beta_2\) parameters (for an unaligned ensemble) from ePS calculations. These are taken directly from the ePS output file, CrossSection segments. See the ePS manual, ``GetCro` command, for further details <https://www.chem.tamu.edu/rgroup/lucchese/ePolyScat.E3.manual/GetCro.html>`__.

Cross-sections by symmetry & type

Types correspond to:

  • ‘L’: length gauge results.

  • ‘V’: velocity gauge results.

  • ‘M’: mixed gauge results.

Symmetries correspond to allowed ionizing transitions for the molecular point group (IRs typically corresponding to (x,y,z) polarization geometries), see the ePS manual for a list of symmetries. Symmetry All corresponds to the sum over all allowed sets of symmetries.

Cross-section units are MBarn.

[6]:
# Plot cross sections using Xarray functionality
# Set here to plot per file - should add some logic to combine files.
for data in dataXS:
    daPlot = data.sel(XC='SIGMA')
    daPlot.plot.line(x='Eke', col='Type')
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_12_0.png

\(\beta_{2}\) by symmetry & type

Types & symmetries as per cross-sections. Normalized \(\beta_{2}\) paramters, dimensionless.

[7]:
# Repeat for betas
for data in dataXS:
    daPlot = data.sel(XC='BETA')
    daPlot.plot.line(x='Eke', col='Type')
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_14_0.png

Dipole matrix elements

For 1-photon ionization. These are taken directly from ePS DumpIdy segments. See the ePS manual, ``DumpIdy` command, for further details <https://www.chem.tamu.edu/rgroup/lucchese/ePolyScat.E3.manual/DumpIdy.html>`__.

[8]:
# Set threshold for significance, only matrix elements with abs values > thres % will be plotted
thres = 0.1
[9]:
# Plot for each fie
for data in dataMatE:
    # Plot with sensible defaults - all dims with lmPlot()

    # Plot only values > theshold
    daPlot, daPlotpd, legendList, gFig = ep.lmPlot(data, thres = thres, thresType = 'pc', figsize = (15,10))

    # Plot phases, with unwrap
    daPlot, daPlotpd, legendList, gFig = ep.lmPlot(data, thres = thres, thresType = 'pc', figsize = (15,10), pType='phaseUW')
/home/paul/anaconda3/envs/ePSproc-v1.2/lib/python3.7/site-packages/xarray/core/nputils.py:223: RuntimeWarning: All-NaN slice encountered
  result = getattr(npmodule, name)(values, axis=axis, **kwargs)
Plotting data aniline_wf_0.1-10.1eV_orb33_A2.inp.out, pType=a, thres=3.0759236685324947, with Seaborn
/home/paul/anaconda3/envs/ePSproc-v1.2/lib/python3.7/site-packages/xarray/core/nputils.py:223: RuntimeWarning: All-NaN slice encountered
  result = getattr(npmodule, name)(values, axis=axis, **kwargs)
/home/paul/anaconda3/envs/ePSproc-v1.2/lib/python3.7/site-packages/numpy/lib/function_base.py:1520: RuntimeWarning: invalid value encountered in greater
  _nx.copyto(ddmod, pi, where=(ddmod == -pi) & (dd > 0))
/home/paul/anaconda3/envs/ePSproc-v1.2/lib/python3.7/site-packages/numpy/lib/function_base.py:1522: RuntimeWarning: invalid value encountered in less
  _nx.copyto(ph_correct, 0, where=abs(dd) < discont)
Plotting data aniline_wf_0.1-10.1eV_orb33_A2.inp.out, pType=phaseUW, thres=3.0759236685324947, with Seaborn
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_17_4.png
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_17_5.png

MFPADs

Calculated MF \(\beta\) parameters, using ePS dipole matrix elements. These are calculated by ep.mfblm(), as a function of energy and polarization geometry. See the ePSproc docs on ``ep.mfblm()` <https://epsproc.readthedocs.io/en/latest/modules/epsproc.MFBLM.html>`__ for further details, and this demo notebook.

[10]:
# Set pol geoms - these correspond to (z,x,y) in molecular frame (relative to principle/symmetry axis)
eAngs = ep.setPolGeoms()
[11]:
# Calculate for each fie & pol geom
# TODO - file logic, and parallelize
BLM = []
for data in dataMatE:
    BLM.append(ep.mfblmEuler(data, selDims = {'Type':'L'}, eAngs = eAngs, thres = thres,
                             SFflag = True, verbose = 0))  # Run for all Eke, selected gauge only
[12]:
# Save BLM data - defaults to working dir and 'ep_timestamp' file
# TODO - testing for array/multiple file case
for data in BLM:
    fileName = dataFile + '_BLM-L_' + timeString.strftime('%Y-%m-%d_%H-%M-%S')
    ep.writeXarray(data, fileName = fileName)
['Written to h5netcdf format', '/home/paul/ePS_results/aniline/aniline_wf_0.1-10.1eV/aniline_wf_0.1-10.1eV_orb33_A2.inp.out_BLM-L_2020-01-14_08-50-18.nc']
/home/paul/anaconda3/envs/ePSproc-v1.2/lib/python3.7/site-packages/h5netcdf/core.py:481: H5pyDeprecationWarning: other_ds.dims.create_scale(ds, name) is deprecated. Use ds.make_scale(name) instead.
  h5ds.dims.create_scale(h5ds, scale_name)
[13]:
# Normalize and plot results
for BLMplot in BLM:
    # Plot unnormalized B00 only, real part
    # This is/should be in units of MBarn (TBC).
#     BLMplot.where(np.abs(BLMplot) > thres, drop = True).real.squeeze().sel({'l':0, 'm':0}).plot.line(x='Eke', col='Euler');
    BLMplot.XS.real.squeeze().plot.line(x='Eke', col='Euler');

    # Plot values normalised by B00 - now set in calculation function
    # Plot results with lmPlot(), ordering by Euler sets
    # Version with (semi-manual) Euler grouping
    daPlot, daPlotpd, legendList, gFig = ep.lmPlot(BLMplot.swap_dims({'Euler':'Labels'}), SFflag = False, eulerGroup = True,
                                                   thresType = 'pc', thres = thres,
                                                   plotDims = ('Labels','l','m'),
                                                   figsize = (15,10))
Plotting data aniline_wf_0.1-10.1eV_orb33_A2.inp.out, pType=a, thres=0.1, with Seaborn
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_22_1.png
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_22_2.png

Error & consistency checks

[14]:
# Check SF values
for data in dataMatE:
    # Plot values, single plot
    data.SF.pipe(np.abs).plot.line(x='Eke')
    data.SF.real.plot.line(x='Eke')
    data.SF.imag.plot.line(x='Eke')

    # Plot values, facet plot
#     data.SF.pipe(np.abs).plot.line(x='Eke', col='Sym')

../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_24_0.png
[15]:
# Compare calculated BLMs for L and V types (dafault above for L)

# Calculate for each fie & pol geom, and compare.
BLMv = []
BLMdiff = []
for n, data in enumerate(dataMatE):
    BLMv.append(ep.mfblmEuler(data, selDims = {'Type':'V'}, eAngs = eAngs, thres = thres,
                             SFflag = True, verbose = 0))  # Run for all Eke, selected gauge only

    BLMdiff.append(BLM[n] - BLMv[n])
    BLMdiff[n]['dXS'] = BLM[n].XS - BLMv[n].XS  # Set XS too, dropped in calc above

    BLMdiff[n].attrs['dataType'] = 'matE'
[16]:
# Save BLM data - defaults to working dir and 'ep_timestamp' file
# TODO - testing for array/multiple file case
for data in BLMv:
    fileName = dataFile + '_BLM-V_' + timeString.strftime('%Y-%m-%d_%H-%M-%S')
    ep.writeXarray(data, fileName = fileName)
['Written to h5netcdf format', '/home/paul/ePS_results/aniline/aniline_wf_0.1-10.1eV/aniline_wf_0.1-10.1eV_orb33_A2.inp.out_BLM-V_2020-01-14_08-50-18.nc']
/home/paul/anaconda3/envs/ePSproc-v1.2/lib/python3.7/site-packages/h5netcdf/core.py:481: H5pyDeprecationWarning: other_ds.dims.create_scale(ds, name) is deprecated. Use ds.make_scale(name) instead.
  h5ds.dims.create_scale(h5ds, scale_name)
[17]:
# Normalize and plot results
for BLMplot in BLMv:
    # Plot unnormalized B00 only, real part
    # This is/should be in units of MBarn (TBC).
#     BLMplot.where(np.abs(BLMplot) > thres, drop = True).real.squeeze().sel({'l':0, 'm':0}).plot.line(x='Eke', col='Euler');
    BLMplot.XS.real.squeeze().plot.line(x='Eke', col='Euler');

    # Plot values normalised by B00 - now set in calculation function
    # Plot results with lmPlot(), ordering by Euler sets
    # Version with (semi-manual) Euler grouping
    daPlot, daPlotpd, legendList, gFig = ep.lmPlot(BLMplot.swap_dims({'Euler':'Labels'}), SFflag = False, eulerGroup = True,
                                                   thresType = 'pc', thres = thres,
                                                   plotDims = ('Labels','l','m'),
                                                   figsize = (15,10))
Plotting data aniline_wf_0.1-10.1eV_orb33_A2.inp.out, pType=a, thres=0.1, with Seaborn
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_27_1.png
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_27_2.png
[18]:
# Difference between 'L' and 'V' results
# NOTE - this currently drops XS

print('Differences, L vs. V gauge BLMs')

for BLMplot in BLMdiff:
    maxDiff = BLMplot.max()
    print(f'Max difference in BLMs (L-V): {0}', maxDiff.data)

    if np.abs(maxDiff) > thres:
        # Plot B00 only, real part
#         BLMplot.where(np.abs(BLMplot) > thres, drop = True).real.squeeze().sel({'l':0, 'm':0}).plot.line(x='Eke', col='Euler');
        BLMplot.dXS.real.squeeze().plot.line(x='Eke', col='Euler');

        # Plot values normalised by B00 - now set in calculation function
        # Plot results with lmPlot(), ordering by Euler sets
        # Version with (semi-manual) Euler grouping
        daPlot, daPlotpd, legendList, gFig = ep.lmPlot(BLMplot.swap_dims({'Euler':'Labels'}), SFflag = False, eulerGroup = True,
                                                       thresType = 'pc', thres = thres,
                                                       plotDims = ('Labels','l','m'),
                                                       figsize = (15,10))

Differences, L vs. V gauge BLMs
Max difference in BLMs (L-V): 0 (0.5109455268460952+0j)
Plotting data (No filename), pType=a, thres=0.05109455268460952, with Seaborn
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_28_1.png
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_28_2.png
[19]:
# Check imaginary components - should be around machine tolerance.
print('Machine tolerance: ', np.finfo(float).eps)
for BLMplot in BLM:
    maxImag = BLMplot.imag.max()
    print(f'Max imaginary value: {0}', maxImag.data)

#     BLMplot.where(np.abs(BLMplot) > thres, drop = True).imag.squeeze().plot.line(x='Eke', col='Euler');

    BLMplot = ep.matEleSelector(BLMplot, thres=thres, dims = 'Eke')
    BLMplot.imag.squeeze().plot.line(x='Eke', col='Euler');
Machine tolerance:  2.220446049250313e-16
Max imaginary value: 0 5.429235533647399e-16
../_images/aniline_aniline_wf_0.1-10.1eV_orb33_A2_29_1.png

Version info

Original job details

[20]:
print(jobInfo['ePolyScat'][0])
print('Run: ' + jobInfo['Starting'][0].split('at')[1])
ePolyScat Version E3
Run:  2019-03-08  08:32:07.372 (GMT -0500)

ePSproc details

[21]:
templateVersion = '0.0.6'
templateDate = '12/01/20'
[22]:
%load_ext version_information
[23]:
%version_information epsproc, xarray
[23]:
SoftwareVersion
Python3.7.5 64bit [GCC 7.3.0]
IPython7.9.0
OSLinux 5.0.0 36 generic x86_64 with debian buster sid
epsproc1.2.3
xarray0.14.0
Tue Jan 14 14:50:10 2020 EST
[24]:
print('Run: {}'.format(timeString.strftime('%Y-%m-%d_%H-%M-%S')))
host = !hostname
print('Host: {}'.format(host[0]))
Run: 2020-01-14_08-50-18
Host: jake

Cite this dataset

Hockett, Paul (2019). ePSproc: Aniline Orb 33 (A2) ionization. Dataset on Zenodo. DOI: 10.5281/zenodo.3612509. URL: https://phockett.github.io/ePSdata/aniline/aniline_wf_0.1-10.1eV_orb33_A2.html

Bibtex:

@data{ Aniline Orb 33 (A2) ionization,
    title = {ePSproc:  Aniline Orb 33 (A2) ionization}
    author = {Hockett, Paul},
    doi = {10.5281/zenodo.3612509},
    publisher = {Zenodo},
    year = {2019},
    url = {https://phockett.github.io/ePSdata/aniline/aniline_wf_0.1-10.1eV_orb33_A2.html}
  }

See citation notes on ePSdata for further details.

DOI Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)