GeoRinex

RINEX 3 and RINEX 2 reader and batch conversion to NetCDF4 / HDF5 in Python or Matlab. Batch converts NAV and OBS GPS RINEX (including Hatanaka compressed OBS) data into xarray.Dataset for easy use in analysis and plotting. This gives remarkable speed vs. legacy iterative methods, and allows for HPC / out-of-core operations on massive amounts of GNSS data. GeoRinex has over 125 unit tests driven by Pytest.

Pure compiled language RINEX processors such as within Fortran NAPEOS give perhaps 2x faster performance than this Python program--that's pretty good for a scripted language like Python! However, the initial goal of this Python program was to be for one-time offline conversion of ASCII (and compressed ASCII) RINEX to HDF5/NetCDF4, where ease of cross-platform install and correctness are primary goals.

RINEX plot

Input data types

RINEX 3.x or RINEX 2.x
- NAV
- OBS
Plain ASCII or seamlessly read compressed ASCII in:
- .gz GZIP
- .Z LZW
- .bz2 bzip2
- .zip
Hatanaka compressed RINEX (plain .crx or .crx.gz etc.)
Python io.StringIO text stream RINEX

Also SP3 ephemeris:

Output

File: NetCDF4 (subset of HDF5), with zlib compression. This yields orders of magnitude speedup in reading/converting RINEX data and allows filtering/processing of gigantic files too large to fit into RAM.
In-memory: Xarray.Dataset. This allows all the database-like indexing power of Pandas to be unleashed.

Install

Latest stable release:

pip install georinex

Current development version:

git clone https://github.com/geospace-code/georinex

python -m pip install -e ./georinex

Selftest

It can be useful to check the setup of your system with:

python -m pytest

158 passed, 1 skipped

Usage

The simplest command-line use is through the top-level python -m georinex.read script. Normally you'd use the -p option with single files to plot, if not converting.

Read single RINEX3 or RINEX 2 Obs or Nav file:

python -m georinex.read myrinex.XXx

Read times from a file (helpful for debugging a file that doesn't read properly):

python -m georinex.time myrinex.XXx

Read NetCDF converted RINEX data:

python -m georinex.read myrinex.nc

Batch convert RINEX to NetCDF4 / HDF5:

python -m georinex.rinex2hdf5 ~/data "*o" -o ~/data

in this example, the suffix .nc is appended to the original RINEX filename: my.15o => my.15o.nc

It's suggested to save the GNSS data to NetCDF4 (a subset of HDF5) with the -ooption, as NetCDF4 is also human-readable, yet say 1000x faster to load than RINEX.

You can also of course use the package as a python imported module as in the following examples. Each example assumes you have first done:

import georinex as gr

Time limits

Time bounds can be set for reading -- load only data between those time bounds:

--tlim start stop

option, where start and stop are formatted like 2017-02-23T12:00

dat = gr.load('my.rnx', tlim=['2017-02-23T12:59', '2017-02-23T13:13'])

read RINEX

This convenience function reads any possible format (including compressed, Hatanaka) RINEX 2/3 OBS/NAV or .nc file:

obs = gr.load('tests/demo.10o')

Analysis

A significant reason for using xarray as the base class of GeoRinex is that big data operations are fast, easy and efficient. It's suggested to load the original RINEX files with the -use or use= option to greatly speed loading and conserve memory.

A copy of the processed data can be saved to NetCDF4 for fast reloading and out-of-core processing by:

obs.to_netcdf('process.nc', group='OBS')

georinex.__init.py__ shows examples of using compression and other options if desired.

Join data from multiple files

Please see documentation for xarray.concat and xarray.merge for more details. Assuming you loaded OBS data from one file into obs1 and data from another file into obs2, and the data needs to be concatenated in time:

obs = xarray.concat((obs1, obs2), dim='time')

The xarray.concatoperation may fail if there are different SV observation types in the files. you can try the more general:

obs = xarray.merge((obs1, obs2))

Receiver location

While APPROX LOCATION XYZ gives ECEF location in RINEX OBS files, this is OPTIONAL for moving platforms. If available, the location is written to the NetCDF4 / HDF5 output file on conversion. To convert ECEF to Latitude, Longitude, Altitude or other coordinate systems, use PyMap3d.

Read location from NetCDF4 / HDF5 file can be accomplished in a few ways:

python -m georinex.loc to load and plot all RINEX and .nc files in a directory

using xarray

obs = xarray.open_dataset('my.nc)

ecef = obs.position
latlon = obs.position_geodetic  # only if pymap3d was used

Using h5py:

with h5py.File('my.nc') as f:
  ecef = h['OBS'].attrs['position']
  latlon = h['OBS'].attrs['position_geodetic']

Convert to Pandas DataFrames

Although Pandas DataFrames are 2-D, using say df = nav.to_dataframe() will result in a reshaped 2-D DataFrame. Satellites can be selected like df.loc['G12'].dropna(0, 'all') using the usual Pandas Multiindexing methods.

Benchmark

An Intel Haswell i7-3770 CPU with plain uncompressed RINEX 2 OBS processes in about:

6 MB file: 5 seconds
13 MB file: 10 seconds

This processing speed is about within a factor of 2 of compiled RINEX parsers, with the convenience of Python, Xarray, Pandas and HDF5 / NetCDF4.

OBS2 and NAV2 currently have the fast pure Python read that has C-like speed.

Obs3

OBS3 / NAV3 are not yet updated to new fast pure Python method.

On Haswell laptop:

time python -m georinex.read tests/CEDA00USA_R_20182100000_23H_15S_MO.rnx.gz -u E

real 48.6 s

time python -m georinex.read tests/CEDA00USA_R_20182100000_23H_15S_MO.rnx.gz -u E -m C1C

real 17.6 s

Profiling

using

conda install line_profiler

and ipython:

%load_ext line_profiler

%lprun -f gr.obs3._epoch gr.load('tests/CEDA00USA_R_20182100000_23H_15S_MO.rnx.gz', use='E', meas='C1C')

shows that np.genfromtxt() is consuming about 30% of processing time, and xarray.concat and xarray.Datasetnested insideconcat` takes over 60% of time.

Notes

RINEX 3.03 specification release notes
RINEX 3.04 specification release notes
RINEX 3.05 specification release notes
GPS satellite position is given for each time in the NAV file as Keplerian parameters, which can be converted to ECEF.
https://downloads.rene-schwarz.com/download/M001-Keplerian_Orbit_Elements_to_Cartesian_State_Vectors.pdf
http://www.gage.es/gFD

Number of SVs visible

With the GNSS constellations in 2018, per the Trimble Planner the min/max visible SV would be about:

Maximum: ~60 SV maximum near the equator in Asia / Oceania with 5 degree elev. cutoff
Minimum: ~6 SV minimum at poles with 20 degree elev. cutoff and GPS only

RINEX OBS reader algorithm

read overall OBS header (so we know what to expect in the rest of the OBS file)
fill the xarray.Dataset with the data by reading in blocks -- another key difference from other programs out there, instead of reading character by character, I ingest a whole time step of text at once, helping keep the processing closer to CPU cache making it much faster.

Data

For capable Android devices, you can log RINEX 3 using the built-in GPS receiver.

UNAVCO site map: identify the 4-letter callsign of a station, and look in the FTP sites below for data from a site.

UNAVCO RINEX 3 data:

OBS: ftp://data-out.unavco.org/pub/rinex3/obs/
NAV: ftp://data-out.unavco.org/pub/rinex3/nav/

UNAVCO RINEX 2 data:

OBS: ftp://data-out.unavco.org/pub/rinex/obs/
NAV: ftp://data-out.unavco.org/pub/rinex/nav/

Hatanaka compressed RINEX .crx

Compressed Hatanaka CRINEX files are supported seamlessly via hatanaka Python package. These are distinct from the supported .rnx, .gz, or .zip RINEX files.

Hatanaka, Y. (2008), A Compression Format and Tools for GNSS Observation Data, Bulletin of the Geospatioal Information Authority of Japan, 55, 21-30. (available at http://www.gsi.go.jp/ENGLISH/Bulletin55.html)

geospace-code / georinex

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