Reading POD5 Files¶

escapepod.Reader gives efficient, memory-mapped access to a single POD5 file. escapepod.DatasetReader presents many files as one stream.

Opening a file¶

import escapepod

reader = escapepod.Reader("experiment.pod5")

Reader accepts a str or any os.PathLike (e.g. pathlib.Path). Use it as a context manager so the underlying file is released promptly:

from pathlib import Path

with escapepod.Reader(Path("experiment.pod5")) as reader:
    print(reader.read_count)

File information¶

Reader exposes file-level metadata as properties:

reader.read_count        # number of reads
reader.read_batch_count  # number of internal read batches
reader.signal_row_count  # number of signal rows
reader.file_identifier   # file UUID
reader.software          # writer software string
reader.pod5_version      # POD5 format version
reader.run_infos         # list[RunInfo]
reader.has_index         # whether a .p5s read index is available
len(reader)              # same as reader.read_count

Iterating over reads¶

Iterating the reader yields ReadData objects — one per read, metadata only:

for read in reader:
    print(read.read_id, read.channel, read.num_samples, read.end_reason)

reader.reads() returns the same reads as a list. Pass a selection of read IDs to restrict it:

reads = reader.reads()                              # every read
subset = reader.reads(selection=["<uuid-1>", "<uuid-2>"])
subset = reader.reads(selection=ids, missing_ok=True)  # skip IDs not present

By default a requested ID that isn't in the file raises KeyError; pass missing_ok=True to silently skip it.

reader.read_ids() returns just the IDs as a list[str].

Looking up specific reads¶

read = reader.get_read("<uuid>")                    # one read, ValueError if absent
reads = reader.get_reads(["<uuid-1>", "<uuid-2>"])  # many reads
reads = reader.get_reads(ids, missing_ok=True)      # skip absent IDs

Repeated lookups build and reuse an in-memory index. Call reader.build_index() up front to pay that cost once (it returns the number of reads indexed and persists the index in the .p5s sidecar); reader.has_index reports whether a sidecar exists.

Sidecar annotations¶

Per-read annotations recorded with escpod annotate (e.g. demux barcode assignments) live in the .p5s sidecar next to the POD5 and are exposed as plain dicts:

reader.annotation_names()          # e.g. ["barcode", "condition"]
barcodes = reader.annotation()     # dict[read_id, label]; name= if several
conditions = reader.annotation("condition")  # derived from the design
reader.design()                    # {"key_columns": …, "value_columns": …,
                                   #  "rows": [...]} or None

Numeric columns (e.g. a demux classifier's confidence/margin scores) live alongside the string annotations, under their own accessors — a separate Arrow type, so a separate pair of methods:

reader.score_names()                # e.g. ["ldx_confidence", "ldx_crf_margin"]
scores = reader.score("ldx_confidence")  # dict[read_id, float]

Unassigned/unscored reads are absent from the dict. A sidecar that does not match the POD5 (stale, or copied from another file) raises instead of returning wrong answers. The sidecar itself is a plain Arrow IPC table, so pyarrow.ipc.open_file("reads.pod5.p5s").read_all() works too.

Accessing signal data¶

Signal is stored separately from metadata and is fetched on demand from the reader — it is not an attribute of ReadData. Request it per read:

read = reader.reads()[0]

signal = reader.get_signal(read)      # numpy int16, raw ADC values
signal_pa = reader.get_signal_pa(read)  # numpy float32, picoamps (calibrated)

get_signal returns raw ADC counts as int16. get_signal_pa applies the read's calibration ((adc + offset) * scale) and returns float32 picoamps. Both take an optional max_samples, decoding only that many leading samples instead of the whole read (a read shorter than max_samples comes back whole) — cheaper than slicing after the fact when only a prefix is needed:

prefix = reader.get_signal(read, max_samples=1000)  # first 1000 ADC samples

For many reads at once, the bulk variants decode in parallel and return (read_id, signal) tuples; they take the same max_samples:

reads = reader.reads()
for read_id, signal in reader.get_signals(reads):       # int16 ADC
    ...
for read_id, signal_pa in reader.get_signals_pa(reads):  # float32 pA
    ...

reader.prefetch_signal() is an optional hint that warms the signal region of the file; reader.byte_count(read) reports the compressed on-disk size of a read's signal.

Reads as a DataFrame¶

Pull every read's metadata into a table in one call:

d = reader.to_dict()        # dict[str, list] — column name -> values
df = reader.to_pandas()     # pandas.DataFrame  (requires pandas)
df = reader.to_polars()     # polars.DataFrame  (requires polars)

All three accept the same selection / missing_ok arguments as reads(). Columns include read_id, channel, well, num_samples, calibration_offset, calibration_scale, end_reason, and the rest of the read metadata fields.

df = reader.to_pandas()
long_reads = df[df["num_samples"] > 100_000]
print(long_reads[["read_id", "channel", "num_samples"]])

The `ReadData` object¶

Each read is a ReadData with the POD5 read fields as read-only properties:

read.read_id             # str (UUID)
read.read_number
read.start_sample
read.channel
read.well
read.pore_type
read.calibration_offset
read.calibration_scale
read.median_before
read.end_reason          # e.g. "signal_positive", "mux_change", "unknown"
read.end_reason_forced
read.run_info_index      # index into reader.run_infos
read.num_samples
read.signal_rows
# plus scaling/mux fields: tracked_scaling_*, predicted_scaling_*,
# num_reads_since_mux_change, time_since_mux_change, open_pore_level, ...

If you already hold a raw ADC array, read.calibrate_signal_array(adc) converts it to picoamps using that read's calibration:

adc = reader.get_signal(read)
pa = read.calibrate_signal_array(adc)   # numpy float32

Reading many files (`DatasetReader`)¶

DatasetReader reads a single file, a directory (scanned for *.pod5), or a list mixing both, and presents every read across all files as one stream — the escapepod analogue of pod5.DatasetReader.

# A directory (recurses by default)
with escapepod.DatasetReader("run_dir/") as ds:
    print(ds.file_count, "files,", ds.read_count, "reads")
    for read in ds:
        signal = ds.get_signal(read)

# An explicit list of files and/or directories
ds = escapepod.DatasetReader(["a.pod5", "b.pod5", "more_reads/"])

Control the directory scan with keyword arguments:

ds = escapepod.DatasetReader("run_dir/", recursive=False, pattern="*.pod5")

DatasetReader offers the same reading surface as Reader — reads()/read_ids(), to_dict()/to_pandas()/to_polars(), get_signal()/get_signal_pa() and their bulk get_signals* forms, byte_count(), iteration, and len() — plus paths, file_count, read_count, and run_infos properties.