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Python RINEX 2/3 NAV/OBS reader with speed and simplicity.

Project description


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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 works in Python ≥ 3.6 and 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
    • .zip
  • Hatanaka compressed RINEX (plain .crx or .crx.gz etc.)
  • Python io.StringIO text stream RINEX'
  • .sp3 SP3-c ephemeris
  • .sp3d SP3-d extended ephemeris


  • 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.


Latest stable release:

pip install georinex

Current development version:

git clone

cd georinex

python -m pip install -e .

The Hatanaka CRINEX converter automatically compiles if needed and a C compiler is available.


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

python -m pytest
158 passed, 1 skipped


The simplest command-line use is through the top-level ReadRinex 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:
    ReadRinex myrinex.XXx
  • Read NetCDF converted RINEX data:
  • Batch convert RINEX to NetCDF4 / HDF5 (this example for RINEX 2 OBS):
    rnx2hdf5 ~/data "*o" -o ~/data
    in this example, the suffix .nc is appended to the original RINEX filename: my.15o =>

By default all plots and status messages are off, unless using the -p option to save processing time.

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

Uses speculative time preallocation gr.load(..., fast=True) by default. Set fast=False or georinex_read -strict to fall back to double-read strict (slow) preallocation. Please open a GitHub issue if this is a problem.

Time limits

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

--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'])

Measurement selection

Further speed increase can arise from reading only wanted measurements:

--meas C1C L1C
dat = gr.load('my.rnx', meas=['C1C', 'L1C'])

Use Signal and Loss of Lock indicators

By default, the SSI and LLI (loss of lock indicators) are not loaded to speed up the program and save memory. If you need them, the -useindicators option loads SSI and LLI for OBS 2/3 files.

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')

read times in OBS, NAV file(s)

Print start, stop times and measurement interval in a RINEX OBS or NAV file:

TimeRinex ~/my.rnx

Print start, stop times and measurement interval for all files in a directory:

TimeRinex ~/data *.rnx

Get vector of datetime.datetime in RINEX file:

times = gr.gettimes('~/my.rnx')

read Obs

If you desire to specifically read a RINEX 2 or 3 OBS file:

obs = gr.load('tests/demo_MO.rnx')

This returns an xarray.Dataset of data within the .XXo observation file.

NaN is used as a filler value, so the commands typically end with .dropna(dim='time',how='all') to eliminate the non-observable data vs time. As per pg. 15-20 of RINEX 3.03 specification, only certain fields are valid for particular satellite systems. Not every receiver receives every type of GNSS system. Most Android devices in the Americas receive at least GPS and GLONASS.

read OBS header

To get a dict() of the RINEX file header:

hdr = gr.rinexheader('myfile.rnx')

Index OBS data

assume the OBS data from a file is loaded in variable obs.

Select satellite(s) (here, G13) by


Pick any parameter (say, L1) across all satellites and time (or index via .sel() by time and/or satellite too) by:


Indexing only a particular satellite system (here, Galileo) using Boolean indexing.

import georinex as gr
obs = gr.load('myfile.o', use='E')

would load only Galileo data by the parameter E. ReadRinex allow this to be specified as the -use command line parameter.

If however you want to do this after loading all the data anyway, you can make a Boolean indexer

Eind ='E')  # returns a simple Numpy Boolean 1-D array
Edata = obs.isel(sv=Eind)  # any combination of other indices at same time or before/after also possible

Plot OBS data

Plot for all satellites L1C:

from matplotlib.pyplot import figure, show
ax = figure().gca()
ax.plot(obs.time, obs['L1C'])

Suppose L1C pseudorange plot is desired for G13:


read Nav

If you desire to specifically read a RINEX 2 or 3 NAV file:

nav = gr.load('tests/demo_MN.rnx')

This returns an xarray.Dataset of the data within the RINEX 3 or RINEX 2 Navigation file. Indexed by time x quantity

Index NAV data

assume the NAV data from a file is loaded in variable nav. Select satellite(s) (here, G13) by


Pick any parameter (say, M0) across all satellites and time (or index by that first) by:



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('', 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:

  • using georinex_loc script, which loads and plots all RINEX and .nc files in a directory
  • using xarray
    obs = xarray.open_dataset('
    ecef = obs.position
    latlon = obs.position_geodetic  # only if pymap3d was used
  • Using h5py:
    with h5py.File('') as f:
        ecef = h['OBS'].attrs['position']
        latlon = h['OBS'].attrs['position_geodetic']

Converting 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.


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

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

Done on 5 year old Haswell laptop:

time georinex_read tests/CEDA00USA_R_20182100000_23H_15S_MO.rnx.gz -u E

real 48.6 s

time georinex_read tests/CEDA00USA_R_20182100000_23H_15S_MO.rnx.gz -u E -m C1C

real 17.6 s



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.


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

  1. read overall OBS header (so we know what to expect in the rest of the OBS file)
  2. 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.


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.



Hatanaka compressed RINEX .crx

The compressed Hatanaka .crx or .crx.gz files are supported seamlessly via crx2rnx as noted in the Install section. There 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

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