Model molecular excited state populations over time
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Python package to calculate the population of molecules in particular quantum states using a master equation approach. Designed for (and currently only usable) for excitation of the hydroxyl radical (OH) to one excited vibrational state and one excited electronic state, with two different lasers, and up to four vibronic states being tracked.

This represents a slow accumulation of calculations I've needed to do for a research project, and would need some work to become more generalized.


  • Extract absorption feature information (upper/lower states, energy gap, degeneracies, Einstein coefficients, ...) from a HITRAN-type file using loadhitran. (Vibrational lines of OH only, with limited parsing of H2O.)
  • Calculate shape of absorption feature from Doppler and pressure broadening (absprofile.AbsProfile object).
  • Automatically define fast modulation of narrow laser linewidth over broadened absorption feature (sweep.Sweep object).
  • Solve system of ODEs to calculate population in each state over time. Processes included in the ODEs are stimulated absorption/emission, spontaneous emission, and lambda doublet/rotational/vibrational/electronic relaxation (main.KineticsRun object).
  • Create matplotlib figures of populations or laser frequency over time; create figure of infrared absorption feature (main.KineticsRun.[...]figure() functions).
  • convenience unit conversion functions related to atmospheric science (atmcalcs)
  • constants and functions related to OH spectroscopy (ohcalcs)
  • Simulate absorption cross-section spectrum (popmodel.simspec module -- needs to be imported separately from popmodel package).

The core of popmodel is the KineticsRun object. Each KineticsRun instance requires dictionaries of parameters describing rates of spectroscopic transitions, lasers, detection cell, transition lines, and ODE integration. The expected dictionary format is designed for extraction from a YAML file and compatible with command line use.

Required input files

Hitran file

Infrared line parameters are extracted from the 140-character-format HITRAN 2012 file for OH (default filename 13_hit12.par), which can be accessed at Some low-level functions within loadhitran module can also read other molecules' HITRAN files, but trying to go through the full workflow called by loadhitran.processhitran() used in setting up a KineticsRun will not work due to the need to parse strings describing molecule-specific term descriptions. See the HITRAN website for more documentation related to the record format.

An 200-line excerpt from the OH HITRAN file is included at src/popmodel/data/hitran_sample.par for use by the test module. To extract the path to hitran_sample.par:

from pkg_resources import resource_filename
hpath = resource_filename('popmodel','data/hitran_sample.par')

YAML parameter file

Parameters for setting up a KineticsRun instance are organized in dictionaries corresponding to a YAML parameter file. A template for the format that the YAML file must follow can be found at src/popmodel/data/parameters_template.yaml.

To extract the path to parameters_template.yaml within popmodel:

from pkg_resources import resource_filename
yamlpath = resource_filename('popmodel','data/parameters_template.yaml')

Example usage

Command line

Installation using pip creates command-line command popmodel. Format of command line arguments: HITFILE PARAMETERS [-l] LOGFILE [-c] CSVOUTPUT [-i] IMAGE [-v]

For example:

$ popmodel 13_hit12.par parameters.yaml -l output.log -c output.csv -i output.png

Python session

Basic usage:

import popmodel as pm
pm.add_streamhandler() # optional, print logging.INFO to screen
pm.add_filehandler("path/to.log") # optional, write logging.INFO to file
par = pm.importyaml("path_to/yaml/parameters.yaml")
hpar = pm.loadhitran.processhitran("path_to/13_hit12.par")
k = pm.KineticsRun(hpar,**par)


Install from PyPI:

$ pip install popmodel

Install most recent commit on Github (less stable):

$ pip install git+


Tested for Python 2.7 and 3.5.

Requires numpy, scipy, pandas, pyyaml and matplotlib>=1.5 (automatically handled if using pip to install). I recommend using conda to install the Scipy stack on a Windows machine if pip is having issues.

Developed in a Windows environment. Travis-CI tests performed on Linux virtual machine.


Tests written using pytest with the pytest-mpl plugin to check matplotlib image output and the pytest-cov plugin to assess coverage. To create baseline images that will be compared against (from root project folder):

$ py.test --mpl-generate-path=tests/baseline

Then to run full test suite with matplotlib and coverage plugins:

$ py.test --mpl --cov=popmodel

Travis-CI tests do not run the --mpl image comparison.


  • fix error in oh.MASS (was 106 too large)
  • fix absolute scaling of cross-sections in simspec (missing factor of 3E10)
  • refactor simspec to make cross-section functions accessible without using hitran line

0.4.0 (2016-04-12)

  • add support for Python 3 (Python 3.5 tested)