Station Database and Queries

Station Lookup

The station_info command lets you search for stations by name fragment or ID:

$ station_info francisco

See dms_datastore Command Reference for the full CLI reference.

Configuration System

The datastore uses YAML files and Python modules to manage station metadata, variable mappings, and screening configurations.

Main configuration files

dstore_config.yaml

The central configuration file. Defines paths to station databases, repository locations, source priorities, and screening configurations.

dstore_config.py

Python module that reads the YAML configuration and exposes helper functions.

Key data files

station_dbase.csv

Master database of all stations. Key columns:

• id — internal unique identifier

• agency_id — ID used by the collecting agency

• name — descriptive station name

• lat, lon — geographic coordinates

• x, y — projected coordinates (SCHISM mesh-corrected)

variable_mappings.csv

Maps agency-specific variable codes/names to standardized variable names used within the datastore.

variable_definitions.csv

Defines standard variables with their units and descriptive information.

station_subloc.csv

Defines sublocations (e.g., depths, sensor positions) for stations where the station ID alone is insufficient to identify a unique datastream.

Configuration API

The dstore_config module exposes these functions:

station_dbase()

Returns the station database as a pandas.DataFrame.

sublocation_df()

Returns the sublocations table.

configuration()

Returns the full configuration dictionary.

config_file(label)

Returns the path to a named configuration file. Checks the current working directory first, then the built-in config_data/ package directory.

All functions cache their results to avoid repeated filesystem reads.

Screen Configuration

The screening configuration YAML (referenced by screen_config in dstore_config.yaml) drives auto_screen and contains rule sets for:

• Bounds checking — acceptable min/max values per variable

• Spike detection — parameters for flagging data spikes

• Repetition checking — rules for flagging suspicious repeated values

• Custom screening functions — advanced algorithms for specific data types