Skip to content
Snippets Groups Projects
Commit 30ca7f20 authored by Mate Balajti's avatar Mate Balajti
Browse files

refactor: remove tqdm, update cli and main

parent f8aefb82
No related branches found
No related tags found
2 merge requests!48refactor: setup.py, CI,!47feat: add support for reassigned Transcript Support Level
Pipeline #17351 passed
Stage: build
Stage: test
Hide whitespace changes
Inline Side-by-side
requirements.txt
+ 0
− 1
View file @ 30ca7f20
matplotlib
pandas
tqdm
requirements_dev.txt
+ 0
− 1
View file @ 30ca7f20
matplotlib
pandas
pip
tqdm
mypy
flake8
flake8-docstrings
......
tsg/cli.py
+ 2
− 2
View file @ 30ca7f20
......@@ -30,8 +30,8 @@ def setup_logging(loglevel: str) -> None:
raise
logging.basicConfig(
format='[%(asctime)s: %(levelname)s] \
%(message)s (module "%(module)s")',
format=('[%(asctime)s: %(levelname)s] '
'%(message)s (module "%(module)s")'),
level=numeric_level,
)
......
tsg/main.py
+ 51
− 42
View file @ 30ca7f20
......@@ -4,8 +4,6 @@ import logging
import numpy as np
import pandas as pd # type: ignore
from tqdm import tqdm # type: ignore
LOG = logging.getLogger(__name__)
......@@ -31,15 +29,17 @@ def read_abundances(transcripts_file: str) -> pd.DataFrame:
def filter_df(gtf_df: pd.DataFrame, transcripts: list) -> pd.DataFrame:
"""Filter annotations to include only exons \
with the highest transcript support level, i.e. TSL1.
"""Filter dataframe.
Filter annotations to include only exons
with the highest transcript support level, i.e. TSL1.
`feature` column is filtered on value "exon" and
`free_text` column is filtered to include the string \
`free_text` column is filtered to include the string
denoting the highest transcript support level
('transcript_support_level "1"').
If a list of transcript IDs is given, `free_text` column \
If a list of transcript IDs is given, `free_text` column
is filtered to include one of the IDs.
Args:
......@@ -47,7 +47,7 @@ def filter_df(gtf_df: pd.DataFrame, transcripts: list) -> pd.DataFrame:
transcript: list of transcript IDs
Returns:
A pd.DataFrame containing only rows with exon annotations \
A pd.DataFrame containing only rows with exon annotations
of highest transcript support level and,
if provided, belonging to one of the given transcripts
"""
......@@ -68,7 +68,7 @@ def filter_df(gtf_df: pd.DataFrame, transcripts: list) -> pd.DataFrame:
def str_to_dict(gene_string: str) -> dict:
"""Split between key/value pairs.
Split string based on delimiter ';' into items, remove empty items and \
Split string based on delimiter ';' into items, remove empty items and
split items on delimiter ' ' into
key/value pairs. Remove quotes from value strings and create a dictionary.
......@@ -92,9 +92,9 @@ def dict_to_str(gene_dict: dict) -> str:
Takes e.g. dictionary {'gene_id': 'GENE1', 'transcript_id': 'TRANSCRIPT1'}
and returns string 'gene_id "GENE1"; transcript_id "TRANSCRIPT1";'.
Key/value pairs are joined by space to form an item and items are \
Key/value pairs are joined by space to form an item and items are
joinded by ';' to form a string.
If a value is Not a Number (nan), the key/value pair is omitted \
If a value is Not a Number (nan), the key/value pair is omitted
from the string.
Args:
......@@ -115,13 +115,15 @@ def dict_to_str(gene_dict: dict) -> str:
def reverse_parse_free_text(df_all: pd.DataFrame) -> pd.DataFrame:
"""Reverse parsing of gtf based pd.DataFrame to include only columns that \
are well defnined by gtf-file standards.
"""Reverse parse a gtf based pd.DataFrame.
The data frame will include only columns that
are well defnined by gtf-file standards.
The first 8 defined columns are constant as defined by gtf-file standards.
Further columns are assumed to be parsed free-text columns \
Further columns are assumed to be parsed free-text columns
(see Gtf.parse_free_text()).
The parsed free-text columns are aggregated as a dictionary and \
The parsed free-text columns are aggregated as a dictionary and
the dictionry is parsed as a string in gtf format.
Args:
......@@ -165,8 +167,9 @@ def write_gtf(gtf_df: pd.DataFrame, filename: str) -> None:
def write_header(annotations_file: str) -> None:
"""Write the header of an annotations file, consisting of the \
tab delimited column names.
"""Write the header of an annotations file.
It consists of the tab delimited column names.
Args:
annotations_file: Filename to write header to.
......@@ -182,7 +185,7 @@ class Gtf:
dtypes: A dictionary containing column names and respective data types.
parsed: A boolean indicating if the pd.DataFrame is parsed.
original_columns: A list of columns not touched by parsing.
free_text_columns: A list of columns created during parsing \
free_text_columns: A list of columns created during parsing
of column `free_text`.
"""
......@@ -240,7 +243,7 @@ class Gtf:
Part of initialization is:
Set dataframe attribute
Check which columns belong to the free-text part of the gtf-file.
Check if there are no columns called free-text and if so, sets \
Check if there are no columns called free-text and if so, sets
the value of parsed attribute to TRUE.
Args:
......@@ -254,11 +257,11 @@ class Gtf:
self.parsed = True
def parse_key_value(self):
"""Parse key/value pairs from `free_text` column into column `key` \
with row entry `value`.
"""Parse key/value pairs.
Creates a dataframe with columns for keys in the free-text column \
instead of `free_text` column.
From `free_text` column into column `key` with row entry `value`.
Creates a dataframe with columns for keys in the free-text column
instead of `free_text` column.
Saves it to Gtf.df attribute.
"""
assert self.parsed is False
......@@ -316,15 +319,15 @@ class TranscriptGenerator:
strands = transcript_df["strand"].unique()
if len(transcript_df) == 0:
LOG.warning(
"Transcript %s can't be sampled. "
"Transcript \"%s\" can't be sampled: "
"Annotation is missing or TSL is not 1.", transcript_id
)
instance = None
elif len(strands) > 1:
LOG.warning(
"Transcript %s can't be sampled. Transcript generator is "
"Transcript \"%s\" can't be sampled: Transcript generator is "
"not implemented for transcripts with exons annotated on "
"different strands", transcript_id,
"different strands.", transcript_id,
)
instance = None
else:
......@@ -350,8 +353,8 @@ class TranscriptGenerator:
def get_inclusions(self) -> np.ndarray:
"""Generate inclusions array.
Each column corresponds to one sample and the number of columns \
corresponds to the number of samples.
Each column corresponds to one sample and the number of columns
corresponds to the number of samples.
Returns:
A boolean np.array, where True means intron inclusion.
......@@ -367,16 +370,18 @@ class TranscriptGenerator:
return inclusion_arr
def get_unique_inclusions(self) -> tuple[list, np.ndarray, np.ndarray]:
"""Inclusion of unique intron inclusion via arrays and counts and \
name generation of each unique count.
"""Get unique inclusions.
Inclusion of unique intron inclusion via arrays and counts and
name generation of each unique count.
Args:
Returns:
- List of names for generated exons.
- A boolean np.array where columns correspond to generated \
- A boolean np.array where columns correspond to generated
transcripts and rows to intron inclusion.
- A np.array containing sample number per generated inclusions, \
- A np.array containing sample number per generated inclusions,
i.e. transcript.
"""
inclusion_arr = self.get_inclusions()
......@@ -397,8 +402,10 @@ class TranscriptGenerator:
def get_df(
self, inclusions: np.ndarray, transcript_id: str
) -> pd.DataFrame:
"""Take as input a dataframe filtered to one transcript and \
a boolean vector denoting intron inclusions.
"""Get dataframe.
Take as input a dataframe filtered to one transcript and
a boolean vector denoting intron inclusions.
Args:
inclusions: A boolean vector denoting intron inclusion.
......@@ -466,7 +473,7 @@ class TranscriptGenerator:
data_frame = reverse_parse_free_text(data_frame)
write_gtf(data_frame, filename)
LOG.debug("Transcript %s sampled", self.ts_id)
LOG.debug("Transcript \"%s\" sampled.", self.ts_id)
def sample_transcripts(
......@@ -476,19 +483,21 @@ def sample_transcripts(
output_transcripts_file: str,
output_annotations_file: str,
):
"""Read input files, iterate over transcript IDs, \
sample each transcript and save results.
"""Sample transcripts.
Read input files, iterate over transcript IDs,
sample each transcript and save results.
Args:
input_transcripts_file: Filename of transcript abundances, \
input_transcripts_file: Filename of transcript abundances,
needs to be csv or tsv.
input_annotations_file: Filename of annotations, \
input_annotations_file: Filename of annotations,
needs to be gtf.
prob_inclusion: Probability of intron inclusion, \
prob_inclusion: Probability of intron inclusion,
needs to be float in range [0,1].
output_transcripts_file: Filename of file to write \
output_transcripts_file: Filename of file to write
sampled transcripts to.
output_annotations_file: Filename of file to write \
output_annotations_file: Filename of file to write
generated annotations to.
"""
LOG.info("Probability of intron inclusion: %s", str(prob_inclusion))
......@@ -508,7 +517,7 @@ def sample_transcripts(
# Set up output file, write header once and append data in loop
write_header(output_annotations_file)
for _, row in tqdm(transcripts.iterrows()):
for _, row in transcripts.iterrows():
transcript_id = row["id"]
transcript_count = row["count"]
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment