pod5_format_pybind

class EmbeddedFileData(*args, **kwargs)

property length → int

property offset → int

property file_path → str

class FileWriter(*args, **kwargs)

add_end_reason(end_reason_enum: int) → int

add_pore(pore_type: str) → int

add_reads(count: int, read_ids: numpy.typing.NDArray[numpy.uint8], read_numbers: numpy.typing.NDArray[np.uint32], start_samples: numpy.typing.NDArray[np.uint64], channels: numpy.typing.NDArray[np.uint16], wells: numpy.typing.NDArray[np.uint8], pore_types: numpy.typing.NDArray[np.int16], calibration_offsets: numpy.typing.NDArray[np.float32], calibration_scales: numpy.typing.NDArray[np.float32], median_befores: numpy.typing.NDArray[np.float32], end_reasons: numpy.typing.NDArray[np.int16], end_reason_forceds: numpy.typing.NDArray[bool], run_infos: numpy.typing.NDArray[np.int16], num_minknow_events: numpy.typing.NDArray[np.uint64], tracked_scaling_scales: numpy.typing.NDArray[np.float32], tracked_scaling_shifts: numpy.typing.NDArray[np.float32], predicted_scaling_scales: numpy.typing.NDArray[np.float32], predicted_scaling_shifts: numpy.typing.NDArray[np.float32], num_reads_since_mux_changes: numpy.typing.NDArray[np.uint32], time_since_mux_changes: numpy.typing.NDArray[np.float32], signals: List[npt.NDArray[np.int16]) → None

add_reads_pre_compressed(count: int, read_ids: numpy.typing.NDArray[np.uint8], read_numbers: numpy.typing.NDArray[np.uint32], start_samples: numpy.typing.NDArray[np.uint64], channels: numpy.typing.NDArray[np.uint16], wells: numpy.typing.NDArray[np.uint8], pore_types: numpy.typing.NDArray[np.int16], calibration_offsets: numpy.typing.NDArray[np.float32], calibration_scales: numpy.typing.NDArray[np.float32], median_befores: numpy.typing.NDArray[np.float32], end_reasons: numpy.typing.NDArray[np.int16], end_reason_forceds: numpy.typing.NDArray[bool], run_infos: numpy.typing.NDArray[np.int16], num_minknow_events: numpy.typing.NDArray[np.uint64], tracked_scaling_scales: numpy.typing.NDArray[np.float32], tracked_scaling_shifts: numpy.typing.NDArray[np.float32], predicted_scaling_scales: numpy.typing.NDArray[np.float32], predicted_scaling_shifts: numpy.typing.NDArray[np.float32], num_reads_since_mux_changes: numpy.typing.NDArray[np.uint32], time_since_mux_changes: numpy.typing.NDArray[np.float32], signal_chunks: List[npt.NDArray[np.uint8]], signal_chunk_lengths: npt.NDArray[np.uint32], signal_chunk_counts: npt.NDArray[np.uint32]) → None

add_run_info(acquisition_id: str, acquisition_start_time: int, adc_max: int, adc_min: int, context_tags: List[Tuple[str, str]], experiment_name: str, flow_cell_id: str, flow_cell_product_code: str, protocol_name: str, protocol_run_id: str, protocol_start_time: int, sample_id: str, sample_rate: int, sequencing_kit: str, sequencer_position: str, sequencer_position_type: str, software: str, system_name: str, system_type: str, tracking_id: List[Tuple[str, str]]) → int

close() → None

class FileWriterOptions(*args, **kwargs)

max_signal_chunk_size :int

read_table_batch_size :int

signal_compression_type :Any

signal_table_batch_size :int

class Pod5AsyncSignalLoader(*args, **kwargs)

release_next_batch() → Pod5SignalCacheBatch

class Pod5FileReader(*args, **kwargs)

batch_get_signal(get_samples: bool, get_sample_count: bool) → Pod5AsyncSignalLoader

batch_get_signal_batches(get_samples: bool, get_samples_count: bool, batches: numpy.typing.NDArray[numpy.uint32]) → Pod5AsyncSignalLoader

batch_get_signal_selection(get_samples: bool, get_sample_count: bool, batch_counts: numpy.typing.NDArray[numpy.uint32], batch_rows: numpy.typing.NDArray[numpy.uint32]) → Pod5AsyncSignalLoader

close() → None

get_file_read_table_location() → EmbeddedFileData

get_file_run_info_table_location() → EmbeddedFileData

get_file_signal_table_location() → EmbeddedFileData

plan_traversal(read_id_data: numpy.typing.NDArray[numpy.uint8], batch_counts: numpy.typing.NDArray[numpy.uint32], batch_rows: numpy.typing.NDArray[numpy.uint32]) → int

class Pod5RepackerOutput(*args, **kwargs)

class Pod5SignalCacheBatch(*args, **kwargs)

property batch_index → int

property sample_count → numpy.typing.NDArray[numpy.uint64]

property samples → List[numpy.typing.NDArray[numpy.int16]]

class Repacker

add_all_reads_to_output(output: Pod5RepackerOutput, input: Pod5FileReader) → None

add_output(output: FileWriter) → Pod5RepackerOutput

add_selected_reads_to_output(output: Pod5RepackerOutput, input: Pod5FileReader, batch_counts: numpy.typing.NDArray[numpy.uint32], all_batch_rows: numpy.typing.NDArray[numpy.uint32]) → None

finish() → None

property batches_completed → int

property batches_requested → int

property is_complete → bool

property pending_batch_writes → int

property reads_completed → int

property reads_sample_bytes_completed → int

create_file(filename: str, writer_name: str, options: FileWriterOptions = ...) → FileWriter

open_file(filename: str) → Pod5FileReader

get_error_string() → str

format_read_id_to_str(read_id_data_out: numpy.typing.NDArray[numpy.uint8]) → List[numpy.typing.NDArray[numpy.uint8]]

load_read_id_iterable(read_ids_str: Iterable, read_id_data_out: numpy.typing.NDArray[numpy.uint8]) → None

compress_signal(signal: numpy.typing.NDArray[numpy.int16], compressed_signal_out: numpy.typing.NDArray[numpy.uint8]) → int

decompress_signal(compressed_signal: numpy.typing.NDArray[numpy.uint8], signal_out: numpy.typing.NDArray[numpy.int16]) → None

vbz_compressed_signal_max_size(sample_count: int) → int