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Model molecular excited state populations over time

Project description

# popmodel

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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.

## Capabilities

  • 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)

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) k.solveode() k.popsfigure() ~~~

## Installation

pip install popmodel install from PyPI

pip install git+ installs most recent commit on github

## Dependencies

Tested for Python 2.7 and 3.5.

Requires numpy, scipy, pandas, pyyaml and matplotlib>=1.5 (automatically handled if using pip to install).

Tests written using pytest using the [pytest-mpl plugin](

Developed in a Windows environment.

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