Usage

Command Line

To run the AMISR synthetic data generator, simply call amisrsynthdata from the command line with a configuration file.

amisrsynthdata config.yaml

Use the -v or --verbose options to print status messages to the screen as the code runs. Depending on the options used, the code may take several mintues to run.

See Configuration File section of the documentation for details on the fields that should be in the configuration file. An example configuration file can be downloaded from the root of the GitHub repository.

Python

The modules of amisrsynthdata can be imported directly into python programs. This is most useful to get truth values at arbitrary points and compare with inversion and interpolation codes. Create YAML config object to initialize the amisrsynthdata modules by loading an existing config.yaml file.

import yaml

config_file = 'config.yaml'
with open(config_file, 'r') as cf:
    config = yaml.load(cf, Loader=yaml.FullLoader)

Alternatively, hard-code a dictionary with the appropriate fields. It is important to note that the module classes are initialized with an actual YAML object or python dictionary, NOT just the filename string as is used with the command line option.

The amisrsynthdata package contains three classes: Radar, Ionosphere, and SyntheticData. All three are initialized with the YAML config object discussed above, but all fields may not be necessary for the Radar and Ionosphere classes. In general, Ionosphere contains functions for calculating ionospheric parameters at specified locations, Radar contains information about the radar’s position and the locations of all beams and range gates, and SyntheticData actually calculates ionospheric parameters at each radar gate location and creates output synthetic data files.

Radar Class Example

from amisrsynthdata.radar import Radar

rad = Radar(config)

# Print BeamCode array
print(rad.beam_codes)

# Print geodetic latitude of each range gate
print(rad.lat)

Ionosphere Class Example

import datetime as dt
from amisrsynthdata.ionosphere import Ionosphere

iono = Ionosphere(config)

# Calculate ionospheric parameters at a particular location and time
glat = 65.0
glon = 100.0
alt = 300000.
utime = (dt.datetime(2016, 9, 13, 0, 5, 0)-dt.datetime.utcfromtimestamp(0)).total_seconds()
Ne = iono.density(utime, glat, glon, alt)
Vi = iono.velocity(utime, glat, glon, alt)
Te = iono.etemp(utime, glat, glon, alt)
Ti = iono.itemp(utime, glat, glon, alt)

SyntheticData Class Example

from amisrsynthdata.syntheticdata import SyntheticData

sd = SyntheticData(config)

# Get get output range gate positions and electron density
fov_glat = sd.Geomag['Latitude']
fov_glon = sd.Geomag['Longitude']
fov_galt = sd.Geomag['Altitude']
fov_ne = sd.FittedParams['Ne']

# Can also get range gate positions from Radar
glat = sd.radar.lat
glon = sd.radar.lon
galt = sd.radar.alt

# And access Ionosphere functions directly
Ne = sd.iono.density(utime, glat, glon, galt)

Note that all functionality of the Radar and Ionosphere classes are available through the SyntheticData class. SyntheticData contains an instance of the Radar class named radar and an instance of the Ionosphere class named iono.

Benchmarking

The amount of time it takes to run amisrsynthdata depends on the ionosphere model that is chosen as well as the radar mode. Modes with more beams or finner range or time resolution will generally take longer to compute synthetic data files. Producing summary plots also increases the time it takes to run amisrsynthdata. The table below shows rough benchmarking of how long it takes to produce a synthetic data file from the command line for a simple case and a complex case. The simple case uses the example configuration file provided with the package which uses altitude-varying or uniform ionospheric state functions and 6 beams with relatively corse range resolution. The complex case uses an ionosphere specified from the output of the GEMINI numerical model (the slowest ionosphere option currently available) and mimics the 52 beam imaging mode. This benchmarking was performed on a laptop workstation and should only be considered approximate.

	Simple	Complex
No Plots	0.77 s	87.48 s
Plots	9.72 s	103.24 s