Fetching Data

Use arrakis.api.fetch to retrieve historical timeseries data for a GPS time range. This loads all requested data into memory as a single arrakis.block.SeriesBlock.

Basic Usage

import arrakis

block = arrakis.fetch(
    ["H1:CAL-DELTAL_EXTERNAL_DQ", "H1:LSC-POP_A_LF_OUT_DQ"],
    start=1187000000,
    end=1187001000,
)

The start and end parameters are GPS times in seconds. The returned SeriesBlock contains data for all requested channels spanning the full time range.

Working with the Result

A SeriesBlock behaves like a dictionary keyed by channel name:

# access a single channel
series = block["H1:CAL-DELTAL_EXTERNAL_DQ"]
print(series.data)         # numpy.ndarray
print(series.sample_rate)  # Hz
print(series.time)         # GPS start time in seconds
print(series.duration)     # seconds

Iterate over all channels:

for channel, series in block.items():
    print(f"{channel}: {len(series)} samples at {series.sample_rate} Hz")

See Working with Series Blocks for more on inspecting and manipulating the data.

Using a Client

The top-level arrakis.fetch() creates a new arrakis.client.Client for each call. For multiple requests, reuse a client:

client = arrakis.Client()

block_a = client.fetch(channels, start=1187000000, end=1187000500)
block_b = client.fetch(channels, start=1187000500, end=1187001000)

Fetch vs Stream

fetch() loads the entire time range into memory. Under the hood, it streams the data and concatenates the blocks:

# these are equivalent
block = client.fetch(channels, start, end)

# manual equivalent
from arrakis.block import concatenate_blocks
block = concatenate_blocks(*client.stream(channels, start, end))

For large time ranges where loading everything at once is impractical, use streaming to process data incrementally.