This is a fork of the original project available at https://github.com/stggh/PyDiatomic Here I have targeted calculations towards ultracold strontium-86. The most substantive change is the inclusion of JuPyter notebooks under the /examples directory.
PyDiatomic solves the time-independent coupled-channel Schroedinger equation using the Johnson renormalized Numerov method [1]. This is very compact and stable algorithm.
The code is directed to the computation of photodissociation cross sections for diatomic molecules. The coupling of electronic states results in transition profile broadening, line-shape asymmetry, and intensity sharing. A coupled-channel calculation is the only correct method compute the photodissociation cross-section.
PyDiatomic requires Python 3.5 (*), numpy and scipy. If you don't already have Python, we recommend an "all in one" Python package such as the Anaconda Python Distribution, which is available for free.
Download the latest version from github :
git clone https://github.com/stggh/PyDiatomic.gitcd to the PyDiatomic directory, and use :
python setup.py install --userOr, if you wish to edit the PyAbel source code without re-installing each time :
python setup.py develop --user(*) due to the use of infix matrix multiplication @. To run with python < 3.5, replace A @ B with np.dot(A, B) in cse.py and expectation.py.
PyDiatomic has a wrapper classes cse.Cse() and cse.Transition()
cse.Cse() set ups the CSE problem (interaction matrix of potential energy curves, and couplings) and solves the coupled channel Schroedinger equation for an initial guess energy.
Input parameters may be specified in the class instance, or they will be requested if required.
import cse
X = cse.Cse('16O16O') # class instance
# CSE: potential energy curves [X3S-1.dat]: # requested parameter
X.solve(800) # solves TISE for energy ~ 800 cm-1
# attributes
# AM limits rot
# Bv molecule set_coupling()
# calc mu set_mu()
# cm node_count() solve()
# diabatic2adiabatic() openchann vib
# energy pecfs VT
# levels() R wavefunction
X.cm
# 787.3978354211097
X.vib
# 0
X.calc
# {0: (787.3981436364634, 1.4376793143458806)} {vib: (eigenvalue, Bv}
X # class representation
# Molecule: O2 mass: 1.32801e-26 kg
# Electronic state: X3S-1.dat
# eigenvalues (that have been evaluated for this state):
# v energy(cm-1) Bv(cm-1)
# 0 787.398 1.43768import cse
X = cse.Cse('16O16O', VT=['X3S-1.dat'])
X.levels(vmax=5) # evaluates energy levels for v=0, .., vmax
# attribute .calc
X # class representation
# Molecule: O2 mass: 1.32801e-26 kg
# Electronic state: X3S-1.dat
# evaluated eigenvalues:
# v energy(cm-1) Bv(cm-1)
# 0 787.398 1.43768
# 1 2337.360 1.42051
# 2 3867.008 1.40407
# 3 5375.938 1.38823
# 4 6863.744 1.37288
# 5 8335.901 1.35919
# 7 11196.366 1.32867
# 11 16131.082 1.22378
# 15 21719.531 1.20443
# 17 24119.541 1.17186
# 24 31559.738 0.99627
# 25 32754.587 1.03787
# 35 40566.037 0.74300cse.Transition() evaluates two couple channel problems, for an intitial and final set of coupled channels, to calculate the photodissociation cross section.
import numpy as np
import cse
# initial state
O2X = cse.Cse('16O16O', VT=['potentials/X3S-1.dat'], en=800)
# final state
O2B = cse.Cse('16O16O', VT=['potentials/B3S-1.dat'])
# transition
BX = cse.Transition(O2B, O2X)
# methods
# BX.calculate_xs()
BX.calculate_xs(transition_energy=np.arange(110, 174, 0.1), eni=800)
# attributes
# the calculated cross section BX.xs and those of the initial BX.gs and
# final coupled states BS.usA simple 3Σu− ↔ 3Σu− Rydberg-valence coupling in O2
import numpy as np
import cse
import matplotlib.pyplot as plt
O2X = cse.Cse('16O16O', VT=['X3S-1.dat'], en=800)
O2B = cse.Cse('16O16O', VT=['B3S-1.dat', 'E3S-1.dat'], coup=[4000])
O2BX = cse.Transition(B, X, dipolemoment=[1, 0],
transition_energy=np.arange(110, 174, 0.1))
plt.plot(O2BX.wavenumber, O2BX.xs*1.0e16)
plt.xlabel("Wavenumber (cm$^{-1}$)")
plt.ylabel("Cross section ($10^{-16}$ cm$^{2}$)")
plt.axis(ymin=-0.2)
plt.title("O$_{2}$ $^{3}\Sigma_{u}^{-}$ Rydberg-valence interaction")
plt.savefig("RVxs.png", dpi=75)
plt.show()
`example_O2xs.py`:
`example_O2_continuity.py`:
`example_O2X_fine_structure.py`:
PyDiatomic O2 X-state fine-structure levels
energy diffences (cm-1): Rouille - PyDiatomic
N F1 F2 F3
1 -0.000 0.000 0.000
3 -0.005 0.000 0.009
5 -0.009 0.000 0.013
7 -0.013 0.000 0.017
9 -0.017 0.000 0.022
11 -0.021 0.000 0.026
13 -0.025 0.000 0.030
15 -0.029 -0.000 0.034
17 -0.033 -0.000 0.039
19 -0.037 -0.000 0.043
21 -0.041 -0.000 0.047`example_O2_SRB4.py`:
`example_HO.py`:
`example_rkr.py`:
import cse
X = cse.Cse('16O16O', VT=['X3S-1.dat']) # include path to potential curve
X.solve(900, rot=0)
X.cm
# 787.3978354211097
X.Bv
# 1.4376793638070153
X.solve(900, 20)
X.cm
# 1390.369249612629
# (1390.369-787.398)/(20*21) = 1.4356Each transition energy solution to the coupled-channel Schroedinger equation is a separate calculation. PyDiatomic uses multiprocessing to perform these calculations in parallel, resulting in a substantial reduction in execution time on multiprocessor systems. e.g. for example_O2xs.py:
| machine | GHz | CPU(s) | time (sec) |
|---|---|---|---|
| Xenon E5-2697 | 2.6 | 64 | 6 |
| i7-6700 | 3.4 | 8 | 17 |
| Macbook pro i5 | 2.4 | 4 | 63 |
| raspberry pi 3 | 1.35 | 4 | 127 |
PyDiatomic documentation is available at readthedocs.
PyDiatomic is a Python implementation of the Johnson renormalized Numerov method. It provides a simple introduction to the profound effects of channel-coupling in the calculation of diatomic photodissociation spectra.
More sophisticated C and Fortran implementations have been in use for a number of years, see references below. These were developed by Stephen Gibson (ANU), Brenton Lewis (ANU), and Alan Heays (ANU, Leiden, and ASU).
[4] A. N. Heays "Photoabsorption and photodissociation in molecular nitrogen, PhD Thesis (2011)
If you find PyDiatomic useful in your work please consider citing this project.
