deepbiop.fq¶

Classes¶

`EncoderOption`
`FastqDataset`
`FastqIterator`
`FastqRecord`
`ParquetEncoder`
`Predict`	A struct to store the prediction result.
`RecordData`

Functions¶

`convert_multiple_fqs_to_one_fq`(paths, result_path, ...)
`encode_fq_path_to_parquet`(fq_path, bases, qual_offset)
`encode_fq_path_to_parquet_chunk`(fq_path, chunk_size, ...)
`encode_fq_paths_to_parquet`(fq_path, bases, qual_offset)
`encode_qual`(qual, qual_offset)	Convert ASCII quality to Phred score for Phred+33 encoding.
`fastq_to_fasta`(fastq_path, fasta_path)
`get_label_region`(labels)
`load_predicts_from_batch_pt`(pt_path, ignore_label, ...)
`load_predicts_from_batch_pts`(pt_path, ignore_label, ...)
`select_record_from_fq`(selected_reads, fq, output)
`select_record_from_fq_by_random`(fq, number, output)
`test_predicts`(predicts)
`write_fq`(records_data[, file_path])
`write_fq_parallel`(records_data, file_path, threads)

Module Contents¶

class deepbiop.fq.EncoderOption¶

property qual_offset: int¶

Return type:: int

property bases: list[int]¶

Return type:: list[int]

property threads: int¶

Return type:: int

class deepbiop.fq.FastqDataset¶

__len__()¶

Return type:: int

__getitem__(idx)¶

Parameters:: idx (int)
Return type:: Any

__iter__()¶

Return type:: FastqIterator

static from_file(file_path, chunk_size)¶

Parameters:

file_path (str)
chunk_size (int)

Return type:

FastqDataset

get_stats()¶

Return type:: Any

get_records(start, end)¶

Parameters:

start (int)
end (int)

Return type:

list[FastqRecord]

get_records_indexed(start, end)¶

Parameters:

start (int)
end (int)

Return type:

list[FastqRecord]

get_records_sequential(start, end)¶

Parameters:

start (int)
end (int)

Return type:

list[FastqRecord]

class deepbiop.fq.FastqIterator¶

__iter__()¶

Return type:: FastqIterator

__next__()¶

Return type:: Any | None

class deepbiop.fq.FastqRecord¶

property header: str¶

Return type:: str

property sequence: str¶

Return type:: str

property quality: str¶

Return type:: str

__repr__()¶

Return type:: str

class deepbiop.fq.ParquetEncoder¶

class deepbiop.fq.Predict¶

A struct to store the prediction result.

property prediction: list[int]¶

Return type:: list[int]

property seq: str¶

Return type:: str

property id: str¶

Return type:: str

property is_truncated: bool¶

Return type:: bool

property qual: str | None¶

Return type:: str | None

__repr__()¶

Return type:: str

prediction_region()¶

Get the prediction region.

Return type:: list[tuple[int, int]]

smooth_prediction(window_size)¶

Get the smooth prediction region.

Parameters:: window_size (int)
Return type:: list[tuple[int, int]]

smooth_label(window_size)¶

Get the smooth label.

Parameters:: window_size (int)
Return type:: list[int]

smooth_and_select_intervals(smooth_window_size, min_interval_size, append_interval_number)¶

Smooth and select intervals.

Parameters:

smooth_window_size (int)
min_interval_size (int)
append_interval_number (int)

Return type:

list[tuple[int, int]]

seq_len()¶

Get the sequence length.

Return type:: int

qual_array()¶

Get the quality score array.

Return type:: list[int]

show_info(smooth_interval, text_width=None)¶

Show the information of the prediction.

Parameters:

smooth_interval (Sequence[tuple[int, int]])
text_width (int | None)

Return type:

str

__getstate__()¶

Return type:: Any

__setstate__(state)¶

Parameters:: state (Any)
Return type:: None

class deepbiop.fq.RecordData¶

property id: str¶

Return type:: str

property seq: str¶

Return type:: str

property qual: str¶

Return type:: str

deepbiop.fq.convert_multiple_fqs_to_one_fq(paths, result_path, parallel)¶

Parameters:

paths (Sequence[str | os.PathLike | pathlib.Path])
result_path (str | os.PathLike | pathlib.Path)
parallel (bool)

Return type:

None

deepbiop.fq.encode_fq_path_to_parquet(fq_path, bases, qual_offset, result_path=None)¶

Parameters:

fq_path (str | os.PathLike | pathlib.Path)
bases (str)
qual_offset (int)
result_path (str | os.PathLike | pathlib.Path | None)

Return type:

None

deepbiop.fq.encode_fq_path_to_parquet_chunk(fq_path, chunk_size, parallel, bases, qual_offset)¶

Parameters:

fq_path (str | os.PathLike | pathlib.Path)
chunk_size (int)
parallel (bool)
bases (str)
qual_offset (int)

Return type:

None

deepbiop.fq.encode_fq_paths_to_parquet(fq_path, bases, qual_offset)¶

Parameters:

fq_path (Sequence[str | os.PathLike | pathlib.Path])
bases (str)
qual_offset (int)

Return type:

None

deepbiop.fq.encode_qual(qual, qual_offset)¶

Convert ASCII quality to Phred score for Phred+33 encoding.

Parameters:

qual (str)
qual_offset (int)

Return type:

list[int]

deepbiop.fq.fastq_to_fasta(fastq_path, fasta_path)¶

Parameters:

fastq_path (str | os.PathLike | pathlib.Path)
fasta_path (str | os.PathLike | pathlib.Path)

Return type:

None

deepbiop.fq.get_label_region(labels)¶

Parameters:: labels (Sequence[int])
Return type:: list[tuple[int, int]]

deepbiop.fq.load_predicts_from_batch_pt(pt_path, ignore_label, id_table)¶

Parameters:

pt_path (str | os.PathLike | pathlib.Path)
ignore_label (int)
id_table (Mapping[int, str])

Return type:

dict[str, Predict]

deepbiop.fq.load_predicts_from_batch_pts(pt_path, ignore_label, id_table, max_predicts=None)¶

Parameters:

pt_path (str | os.PathLike | pathlib.Path)
ignore_label (int)
id_table (Mapping[int, str])
max_predicts (int | None)

Return type:

dict[str, Predict]

deepbiop.fq.select_record_from_fq(selected_reads, fq, output)¶

Parameters:

selected_reads (Sequence[str])
fq (str | os.PathLike | pathlib.Path)
output (str | os.PathLike | pathlib.Path)

Return type:

None

deepbiop.fq.select_record_from_fq_by_random(fq, number, output)¶

Parameters:

fq (str | os.PathLike | pathlib.Path)
number (int)
output (str | os.PathLike | pathlib.Path)

Return type:

None

deepbiop.fq.test_predicts(predicts)¶

Parameters:: predicts (Sequence[Predict])
Return type:: None

deepbiop.fq.write_fq(records_data, file_path=None)¶

Parameters:

records_data (Sequence[RecordData])
file_path (str | os.PathLike | pathlib.Path | None)

Return type:

None

deepbiop.fq.write_fq_parallel(records_data, file_path, threads)¶

Parameters:

records_data (Sequence[RecordData])
file_path (str | os.PathLike | pathlib.Path)
threads (int)

Return type:

None