flipchem provides a python wrapper of the flipchem ionospheric photochemistry model developed by Phil Richards [Richards2011]. Specifically, this code wraps the version of flipchem that was used for [Richards2010]. The model requires NRLMSIS-00 neutral density and the f107, f107a, and AP geophysical parameters, so both of these have been packaged with flipchem. NRLMSIS-00 is provided by wrapping the C version of the code written by Dominik Brodowski, which is based on the original Fortran version of the model [Picone2002].

The package is intended to be used for fitting Incoherent Scatter Radar (ISR) data, which involves fitting multiple gates of data at fixed time intervals. Also, the geophysical indicies are available with approximately daily time resolution. This is why the indicies are read when the Flipchem class is initialized and the get_point* functions do not accept date as an argument. The intended usage is:

1. initialize the Flipchem class for one time interval
2. call one of the get_point* functions for each of the range gates in the one time interval
3. initialize a new Flipchem class for the next time interval
4. goto 2

This facilitates usage of the multiprocessing library when processing ISR data, if each time interval is treated as a pool of asynchronous jobs.

Since this package is a wrapper around both C and Fortran source code, one must have a C and Fortran compiler installed and one must also install numpy before attempting installation. Numpy is used during installation of flipchem to compile the Fortran via f2py [Oliphant2006].

Then, installation of flipchem can be accomplished using pip:

pip install git+https://github.com/amisr/[email protected]

And to make a profile of ion densities, one can try this:

from datetime import datetime
import numpy as np
import flipchem
date = datetime(2017,1,4,18)
fc = flipchem.Flipchem(date)

glat = 74.72955
glon = -94.90576
alts = np.linspace(90,350,num=100)
nes = 5.0e11*np.exp(1-(alts-250)/70-np.exp(-(alts-250)/70))
tes = 2500*(1-np.exp(-(alts-90)/70))+300
tis = 1750*(1-np.exp(-(alts-90)/70))+300

Op = np.zeros((alts.shape))
O2p = np.zeros((alts.shape))
NOp = np.zeros((alts.shape))
N2p = np.zeros((alts.shape))
Np = np.zeros((alts.shape))
iters = np.zeros((alts.shape))

for i,(alt,ne,te,ti) in enumerate(zip(alts,nes,tes,tis)):
    outputs = fc.get_point(glat,glon,alt,ne,te,ti)
    Op[i] = outputs[3]
    O2p[i] = outputs[4]
    NOp[i] = outputs[5]
    N2p[i] = outputs[6]
    Np[i] = outputs[7]
    iters[i] = outputs[-1]

fig = pyplot.figure(figsize=(15,10))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.plot(nes,alts,'--',label='$N_e$',lw=4)
ax1.plot(Op,alts,label='$O^+$')
ax1.plot(O2p,alts,label='$O_2^+$')
ax1.plot(NOp,alts,label='$NO^+$')
ax1.plot(N2p,alts,label='$N_2^+$')
ax1.plot(Np,alts,label='$N^+$')
ax1.set_xscale('log')
ax1.set_xlim([1e9,1e12])
ax1.set_ylabel('Altitude (km)')
ax1.set_xlabel('Number Density (m$^{-3}$)')
l = ax1.legend()

ax2.plot(tes,alts,label='$T_e$')
ax2.plot(tis,alts,label='$T_i$')
ax2.set_xlabel('Temperature (K)')
l = ax2.legend()

You can finde more detailed documentation, including installation guides for Windows 10, macOS, and Linux here: https://flipchem.readthedocs.io