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Writing POD5 Files

escapepod.Writer creates new POD5 files. Signal is compressed with the VBZ codec automatically. Use it as a context manager so the footer is written and the file is finalized on exit.

A minimal round trip

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import numpy as np
import escapepod

# 1. Describe the acquisition/run
run_info = escapepod.create_run_info(
    acquisition_id="acq-001",
    sample_rate=4000,
    experiment_name="demo",
)

# 2. Write a file
signal = np.arange(1000, dtype=np.int16)   # raw ADC values

with escapepod.Writer("out.pod5") as writer:
    ri_idx = writer.add_run_info(run_info)
    writer.add_read(
        read_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
        read_number=1,
        start_sample=0,
        channel=42,
        well=1,
        pore_type="not_set",
        calibration_offset=0.0,
        calibration_scale=1.0,
        median_before=200.0,
        end_reason="signal_positive",
        end_reason_forced=False,
        run_info_index=ri_idx,
        num_minknow_events=100,
        signal=signal,
    )

# 3. Read it back
with escapepod.Reader("out.pod5") as reader:
    read = reader.reads()[0]
    assert reader.get_signal(read).tolist() == signal.tolist()

Run info

Every read references a run info by index. Create one with create_run_info (or construct a RunInfo directly) and register it with writer.add_run_info, which returns the index to pass as run_info_index:

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run_info = escapepod.create_run_info(
    acquisition_id="acq-001",     # required
    sample_rate=4000,
    experiment_name="demo",
    flow_cell_id="FA01234",
    sequencing_kit="SQK-RNA004",
    context_tags={"experiment_type": "rna"},
    tracking_id={"device_id": "MN00001"},
)

with escapepod.Writer("out.pod5") as writer:
    ri_idx = writer.add_run_info(run_info)
    # ... add reads referencing ri_idx

Only acquisition_id is required; every other field has a sensible default (see the RunInfo constructor for the full list).

Adding reads

add_read takes the read fields as keyword arguments plus a numpy int16 signal array. The required fields are shown in the round trip above; the optional scaling/mux fields (tracked_scaling_scale, predicted_scaling_shift, open_pore_level, …) default to zero/one and can be omitted.

Signal must be numpy.int16 (raw ADC). num_samples is inferred from the array length unless you pass it explicitly.

From an existing ReadData

If you already have a ReadData — for example when copying or transforming reads from another file — write it directly with add_read_data:

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with escapepod.Reader("in.pod5") as reader, escapepod.Writer("out.pod5") as writer:
    src_ri = reader.run_infos[0]
    ri_idx = writer.add_run_info(escapepod.create_run_info(
        acquisition_id=src_ri.acquisition_id,
        sample_rate=src_ri.sample_rate,
    ))
    for read in reader:
        signal = reader.get_signal(read)
        writer.add_read_data(read, signal)

For many reads at once, add_reads(reads, signals) takes a list of ReadData and a matching list of int16 signal arrays.

Writer options

Tuning options are keyword arguments on the constructor; all are optional:

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writer = escapepod.Writer(
    "out.pod5",
    compress_signal=True,        # VBZ-compress signal (default on)
    signal_batch_size=None,      # reads per signal record batch
    read_batch_size=None,        # reads per reads record batch
    max_signal_chunk_size=None,  # max samples per signal chunk
    software="my-tool 1.0",      # writer software string
)

Closing

Exiting the with block finalizes the file. If you don't use a context manager, call writer.close() explicitly — the file is not valid until it is closed.

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writer = escapepod.Writer("out.pod5")
# ... add_run_info / add_read ...
writer.close()

The RunInfo object

create_run_info(...) returns a RunInfo; you can also construct one directly with escapepod.RunInfo(acquisition_id, ...). Its fields are exposed as read-only properties: acquisition_id, acquisition_start_time, adc_min, adc_max, experiment_name, flow_cell_id, flow_cell_product_code, protocol_name, protocol_run_id, protocol_start_time, sample_id, sample_rate, sequencing_kit, sequencer_position, sequencer_position_type, software, system_name, system_type, context_tags, and tracking_id.