ePSproc base and multijob class intro

16/10/20

As of Oct. 2020, v1.3.0-dev, basic data classes are now implemented, and are now the easiest/preferred method for using ePSproc (as opposed to calling core functions directly, as illustrated in the functions guide).

A brief intro and guide to use is given here.

Aims:

• Provide unified data architecture for ePSproc, ePSdata and PEMtk.

• Wrap plotting and computational functions for ease of use.

• Handle multiple datasets inc. comparitive plots.

Setup

# For module testing, include path to module here, otherwise use global installation
local = True

if local:
    import sys
    if sys.platform == "win32":
        modPath = r'D:\code\github\ePSproc'  # Win test machine
        winFlag = True
    else:
        modPath = r'/home/femtolab/github/ePSproc/'  # Linux test machine
        winFlag = False

    sys.path.append(modPath)

# Base
import epsproc as ep

# Class dev code
from epsproc.classes.multiJob import ePSmultiJob
from epsproc.classes.base import ePSbase

* pyevtk not found, VTK export not available.

ePSbase class

The ePSbase class wraps most of the core functionality, and will handle all ePolyScat output files in a single data directory. In general, we’ll assume:

• an ePS job constitutes a single ionization event/channel (ionizing orbital) for a given molecule, stored in one or more output files.

• the data dir contains one or more files, where each file will contain a set of symmetries and energies, with either

• one file per ionizing event. In this case, each file will equate to one job, and one entry in the class datastructure.

• a single ionizing event, where each file contains a different set of energies for the given event (energy chunked fileset). In this case, the files will be stacked, and the dir will equate to one job and one entry in the class datastructure.

The class datastructure is (currently) a set of dictionaries, with entries per job as above, and various data for each job. In general the data is stored in Xarrays.

The multiJob class extends the base class with reading from multple directories.

Load data

Firstly, set the data path, instantiate a class object and load the data.

# Set for ePSproc test data, available from https://github.com/phockett/ePSproc/tree/master/data
# Here this is assumed to be on the epsproc path
import os
dataPath = os.path.join(sys.path[-1], 'data', 'photoionization', 'n2_multiorb')

# Instantiate class object.
# Minimally this needs just the dataPath, if verbose = 1 is set then some useful output will also be printed.
data = ePSbase(dataPath, verbose = 1)

# ScanFiles() - this will look for data files on the path provided, and read from them.
data.scanFiles()

*** Job orb6 details
Key: orb6
Dir D:\code\github\ePSproc\data\photoionization\n2_multiorb, 1 files.
{   'batch': 'ePS n2, batch n2_1pu_0.1-50.1eV, orbital A2',
    'event': ' N2 A-state (1piu-1)',
    'orbE': -17.096913836366,
    'orbLabel': '1piu-1'}

*** Job orb5 details
Key: orb5
Dir D:\code\github\ePSproc\data\photoionization\n2_multiorb, 1 files.
{   'batch': 'ePS n2, batch n2_3sg_0.1-50.1eV, orbital A2',
    'event': ' N2 X-state (3sg-1)',
    'orbE': -17.341816310545997,
    'orbLabel': '3sg-1'}

In this case, two files are read, and each file is a different ePS job - here the \(3\sigma_g^{-1}\) and \(1\pi_u^{-1}\) channels in N2. The keys for the job are also used as the job names.

Basic info & plots

A few basic methods to summarise the data…

# Summarise jobs, this will also be output by scanFile() if verbose = 1 is set, as illustrated above.
data.jobsSummary()

*** Job orb6 details
Key: orb6
Dir D:\code\github\ePSproc\data\photoionization\n2_multiorb, 1 files.
{   'batch': 'ePS n2, batch n2_1pu_0.1-50.1eV, orbital A2',
    'event': ' N2 A-state (1piu-1)',
    'orbE': -17.096913836366,
    'orbLabel': '1piu-1'}

*** Job orb5 details
Key: orb5
Dir D:\code\github\ePSproc\data\photoionization\n2_multiorb, 1 files.
{   'batch': 'ePS n2, batch n2_3sg_0.1-50.1eV, orbital A2',
    'event': ' N2 X-state (3sg-1)',
    'orbE': -17.341816310545997,
    'orbLabel': '3sg-1'}

# Molecular info
# Note that this is currently assumed to be the same for all jobs in the data dir.
data.molSummary()

*** Molecular structure

*** Molecular orbital list (from ePS output file)
EH = Energy (Hartrees), E = Energy (eV), NOrbGrp, OrbGrp, GrpDegen = degeneracies and corresponding orbital numbering by group in ePS, NormInt = single centre expansion convergence (should be ~1.0).

props	Sym	EH	Occ	E	NOrbGrp	OrbGrp	GrpDegen	NormInt
orb
1	SG	-15.6719	2.0	-426.454121	1.0	1.0	1.0	0.999532
2	SU	-15.6676	2.0	-426.337112	1.0	2.0	1.0	0.999458
3	SG	-1.4948	2.0	-40.675580	1.0	3.0	1.0	0.999979
4	SU	-0.7687	2.0	-20.917392	1.0	4.0	1.0	0.999979
5	SG	-0.6373	2.0	-17.341816	1.0	5.0	1.0	1.000000
6	PU	-0.6283	2.0	-17.096914	1.0	6.0	2.0	1.000000
7	PU	-0.6283	2.0	-17.096914	2.0	6.0	2.0	1.000000

Plot cross-sections and betas

These are taken from the GetCro segments in the ePS output files, and correspond to results for an isotropic ensemble of molecules, i.e. observables in the lab frame (LF) for 1-photon ionization (see the ePS tutorial for more details).

# Minimal method call will plot cross-sections for all ePS jobs found in the data directory.
data.plotGetCro()

# Plot beta parameters with the 'BETA' flag
data.plotGetCro(pType = 'BETA')

TODO: fix labelling here.

Compute MFPADs

The class currently wraps just the basic numerical routine for MFPADs. This defaults to computing MFPADs for all energies and \((z,x,y)\) polarization geometries (where the z-axis is the molecular symmetry axis, and corresponds to the molecular structure plot shown above).

# Compute MFPADs...
data.mfpadNumeric()

To plot it’s advisable to set an enery slice, Erange = [start, stop, step], since MFPADs are currently shown as individual plots in the default case, and there may be a lot of them.

We’ll also just set for a single key here, otherwise all jobs will be plotted.

data.padPlot(keys = 'orb5', Erange = [5, 10, 4])

#TODO: fix plot layout!

Found dims ('Labels', 'Phi', 'Theta', 'Eke', 'Sym'), summing to reduce for plot. Pass selDims to avoid.
Sph plots: Pol geom: z
Plotting with mpl
Data dims: ('Phi', 'Theta', 'Eke'), subplots on Eke
Sph plots: Pol geom: x
Plotting with mpl
Data dims: ('Phi', 'Theta', 'Eke'), subplots on Eke
Sph plots: Pol geom: y
Plotting with mpl
Data dims: ('Phi', 'Theta', 'Eke'), subplots on Eke

To view multiple results in a more concise fashion, a Cartesian gridded output is also available.

data.padPlot(keys = 'orb5', Erange = [5, 10, 4], pStyle='grid')

Found dims ('Labels', 'Phi', 'Theta', 'Eke', 'Sym'), summing to reduce for plot. Pass selDims to avoid.
Grid plot: 3sg-1

# Various other args can be passed...

# Set a plotting backend, currently 'mpl' (Matplotlib - default) or 'pl' (Plotly - interactive, but may give issues in some environments)
backend = 'pl'

# Subselect on dimensions, this is set as a dictionary for Xarray selection (see http://xarray.pydata.org/en/stable/indexing.html#indexing-with-dimension-names)
selDims = {'Labels':'z'}  # Plot z-pol case only.

data.padPlot(Erange = [5, 10, 4], selDims=selDims, backend = backend)

Found dims ('Phi', 'Theta', 'Eke', 'Sym'), summing to reduce for plot. Pass selDims to avoid.
Sph plots: 1piu-1
Plotting with pl