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API Reference

This document details the functions available in the slicks package.

Core Functions

slicks.connect_influxdb3

Updates the global InfluxDB connection settings dynamically.

slicks.connect_influxdb3(url=None, token=None, org=None, db=None)
- url (str): The InfluxDB host URL (e.g., "http://localhost:8086"). - token (str): Authentication token. - org (str): Organization name (default: "Docs"). - db (str): Database/Bucket name (default: "WFR25").

slicks.fetch_telemetry

The primary function to retrieve data. It handles querying, pivoting, resampling, and movement filtering.

slicks.fetch_telemetry(start_time, end_time, signals=None, client=None, filter_movement=True)
  • start_time (datetime): Start of the query range.
  • end_time (datetime): End of the query range.
  • signals (str or list[str]): A single sensor name or a list of sensor names to fetch. Defaults to standard configuration if None.
  • client (InfluxDBClient3, optional): An existing client instance (advanced use).
  • filter_movement (bool): If True (default), strips out rows where the car is stationary. If False, returns all raw data.

Returns: pandas.DataFrame indexed by time, with 1-second resolution. Returns None if no data is found.

slicks.discover_sensors

Scans the database to find which sensors actually recorded data during a time period.

slicks.discover_sensors(start_time, end_time, chunk_size_days=1)
  • start_time (datetime): Start of scan.
  • end_time (datetime): End of scan.
  • chunk_size_days (int): How many days to query at once (prevents timeouts).

Returns: list[str] of unique sensor names sorted alphabetically.

Analysis Tools

slicks.get_movement_segments

Identifies distinct "laps" or driving sessions by detecting gaps in movement.

slicks.get_movement_segments(df, speed_column="INV_Motor_Speed", threshold=100.0, max_gap_seconds=60.0)
  • df (pd.DataFrame): DataFrame containing at least a speed column.
  • max_gap_seconds (float): Time in seconds to wait before declaring a new "segment" (default: 60s).

Returns: pandas.DataFrame with columns start_time, end_time, duration, state ("Moving"/"Idle"), and mean_speed.

slicks.detect_movement_ratio

Calculates the percentage of time the car was active.

slicks.detect_movement_ratio(df, speed_column="INV_Motor_Speed")

Returns: dict containing total_rows, moving_rows, idle_rows, and movement_ratio (0.0 - 1.0).