Data Ingestion & Setup 🏗️

The first step in any TEC analysis is loading the raw data and preparing the execution environment. PyTECGg uses a high-performance Rust backend to parse RINEX files, delivering data directly into a Polars DataFrame for maximum efficiency.

Parse RINEX files — fast ⚡

The parsing module provides two main functions: read_rinex_obs for observations and read_rinex_nav for navigation messages.

from pathlib import Path
from pytecgg.parsing import read_rinex_nav, read_rinex_obs

# Define paths to your local files
NAV_PATH = Path("./path/to/your/nav_file.rnx")
OBS_PATH = Path("./path/to/your/obs_file.rnx")

# Load navigation data
nav_dict = read_rinex_nav(NAV_PATH)

# Load observations and metadata
df_obs, rec_pos, rinex_version = read_rinex_obs(OBS_PATH)

# Derive a standard 4-character receiver name from the filename
rec_name = OBS_PATH.name[:4].lower()

Metadata Extraction

read_rinex_obs returns a tuple: along with the observation DataFrame, it automatically extracts the approximate ECEF position of the receiver and the RINEX version directly from the file header.

Setting the Context ⚙️

The GNSSContext acts as a centralised configuration and state manager for the entire processing pipeline. It ensures that coordinate validation, constellation normalisation, and metadata management are handled consistently across all modules.

from pytecgg import GNSSContext

ctx = GNSSContext(
    receiver_pos=rec_pos,
    receiver_name=rec_name,
    rinex_version=rinex_version,
    h_ipp=350_000,
    systems=['G', 'E'],
)

Context parameters are:

• receiver_pos, receiver_name, rinex_version: receiver position in ECEF coordinates (in meters), station identifier, and RINEX version string, respectively.
• h_ipp: the height (in meters) of the Ionospheric Pierce Point (IPP). This defines the altitude of the theoretical "thin shell" used for vertical mapping (typically 350 km).
• systems: a list of constellation identifiers to be processed. PyTECGg supports GPS (G), Galileo (E), GLONASS (R), and BeiDou (C). You can provide full names (e.g. Galileo) or symbols (e.g. E).

Initialising this object is mandatory: it serves as the required argument for all subsequent processing steps, including cycle-slip detection, IPP calculation, and bias estimation.

Internal State Management

The GNSSContext also maintains internal attributes like glonass_channels and freq_meta: these are automatically populated during the ephemeris and signal processing stages.

RINEX utilities 🔧

PyTECGg also provides utility functions to perform quick health checks on the parsed datasets and automate data retrieval.

Data Diagnostics

Once data is loaded, you can use summarise_rinex_data to verify signal availability and navigation coverage.

CodeExample Output

from pytecgg.utils import summarise_rinex_data

summarise_rinex_data(obs_data, nav_dict)

=======================================================
🛰️  GNSS RINEX DATA SUMMARY
=======================================================

📅 Temporal Coverage (OBS)

- Start:    2025-03-28 00:00:00+00:00
- End:      2025-03-28 23:59:30+00:00
- Duration: 23:59:30
- Sampling: 30.0s

📡 Constellations Breakdown (OBS)

Sys  | SVs  | Total Records   | Available Signals
-------------------------------------------------
C    | 39   | 561,006         | C1X, C2I, C5X, C6I, C7I, D2I, L1X, L2I, L5X, L6I, L7I, S1X, S2I, S5X, S6I, S7I
E    | 27   | 352,425         | C1X, C5X, C6X, C7X, C8X, D1X, L1X, L5X, L6X, L7X, L8X, S1X, S5X, S6X, S7X, S8X
G    | 31   | 220,278         | C1C, C2W, C5X, L1C, L2W, L5X, S1C, S2W, S5X
R    | 23   | 153,268         | C1C, C1P, C2C, D1C, L1C, L2C, S1C, S2C

📖 Ephemeris Coverage (NAV)

- BEIDOU  :  54 satellites have at least an ephemeris
- GALILEO :  31 satellites have at least an ephemeris
- GLONASS :  25 satellites have at least an ephemeris
- GPS     :  31 satellites have at least an ephemeris

Download RINEX files

INGV RING (ObS)BKG IGS (NAV)

The download_obs_ring function targets the INGV RING network. This network manages continuous GNSS stations across Italy and archives GNSS data from thousands of stations along the Eurasia-Africa plate boundary. The function handles station naming conventions: if a 4-character code like GRO2 is provided, it is converted to the long-name format (e.g., GRO200ITA).

from pytecgg.utils import download_obs_ring

download_obs_ring(
    station_code="GRO200ITA",
    year=2025,
    doys=[1, 55, 252],
    output_path=Path("./data")
)

The function creates a subdirectory named after the station_code under the provided output_path. The above example will produce the following structure:

data/
└── GRO2/
    ├── GRO200ITA_R_20250010000_01D_30S_MO.crx.gz
    ├── GRO200ITA_R_20250550000_01D_30S_MO.crx.gz
    └── GRO200ITA_R_20252520000_01D_30S_MO.crx.gz

Multi-constellation navigation messages, providing aggregated data from the global IGS network, can be downloaded from the BKG GNSS Data Center.

from pytecgg.utils import download_nav_bkg

download_nav_bkg(
    year=2025,
    doys=[1, 55, 252],
    output_path=Path("./data")
)

The RINEX files are saved under the provided output_path.