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style: update of docstrings
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thomas-bailly committed Apr 24, 2024
1 parent 27475ca commit cab11ed
Showing 1 changed file with 21 additions and 21 deletions.
42 changes: 21 additions & 21 deletions asmc/utils.py
Original file line number Diff line number Diff line change
Expand Up @@ -35,13 +35,13 @@ def __init__(self, pos: int, limit: int) -> None:
super().__init__(self.message)

class AminoAcidTypeError(Exception):
"""Execption raised for Amino Acid does not correspond to a 1-letter code or
a valid amino acid type
"""Exception raised for Amino Acid which does not correspond to a 1-letter
code or a valid amino acid type
valid amino acid type : 'aromatic', 'acidic', 'basic', 'polar', 'hydrophobic'
Attribute:
aa (str): the amino acid string wich caused the error
aa (str): the amino acid string which caused the error
"""

def __init__(self, aa: str) -> None:
Expand Down Expand Up @@ -87,14 +87,14 @@ def get_seq_from_pdb(pdb: Path) -> str:
def read_models(models: Path) -> Dict[str, str]:
"""Reads the model file
For each model, add its id as key in a dictionnary and add as value the
For each model, add its id as key in a dictionary and add as value the
sequence return by get_seq_from_pdb().
Args:
models (pathlib.Path): The file containing the model paths
Returns:
all_seq (dict): A dictionnary with each pair of id - seq
all_seq (dict): A dictionary with each pair of id - seq
"""

all_seq = {}
Expand All @@ -110,13 +110,13 @@ def read_models(models: Path) -> Dict[str, str]:
def read_multi_fasta(fasta: Path) -> Dict[str, str]:
"""Reads a multi fasta file
Add in a dictionnary all pair id - sequence.
Add in a dictionary all pair id - sequence.
Args:
fasta (pathlib.Path): The multi fasta file
Returns:
all_seq (dict): A dictionnary with each pair of id - seq
all_seq (dict): A dictionary with each pair of id - seq
"""

all_seq = {}
Expand All @@ -141,11 +141,11 @@ def read_multi_fasta(fasta: Path) -> Dict[str, str]:
def get_identity(ref_seq: Dict[str, str], target: str) -> Tuple[str, float]:
"""Get the % identity between two sequences
For each pair reference - target, build a global alignment and calculates
For each pair reference - target, build a global alignment and calculate
the percentage of identity.
Args:
ref_seq (dict): Dictionnary with ids of reference as key and their
ref_seq (dict): Dictionary with ids of reference as key and their
sequences as value
target (str): The target sequence
Expand Down Expand Up @@ -184,13 +184,13 @@ def get_identity(ref_seq: Dict[str, str], target: str) -> Tuple[str, float]:
def build_comparison_data(id_dict: Optional[Dict[str, Union[str, int, None]]],
file: Path,
empty=True) -> Dict[str, Union[str, int, None]]:
"""Read the ASMC groups.tsv and load information in a dictionnary
"""Read the ASMC groups.tsv and load information in a dictionary
Args:
id_dict (dict): An empty dictionnary or contaning sub dict as value of
id_dict (dict): An empty dictionary or containing sub dict as value of
seqID (key)
file (pathlib.Path): The ASMC groups.tsv
empty (bool, optional): Defaults to True.
empty (bool, optional): Default to True.
Returns:
id_dict (dict): The updated id_dict
Expand Down Expand Up @@ -232,15 +232,15 @@ def build_comparison_data(id_dict: Optional[Dict[str, Union[str, int, None]]],

def add_ref_data_to_comparison_data(id_dict:Dict[str, Union[str, int, None]],
file:Path) -> Tuple[Dict[str, Union[str, int, None]], Set[str]]:
"""Reads the identity_target_ref.tsv file and add information to a
"""Reads the identity_targets_refs.tsv file and add information to a
comparison data
Args:
id_dict (dict): Dict with sub dict as value and seqID as key
file (pathlib.Path): The identity_target_ref.tsv
file (pathlib.Path): The identity_targets_refs.tsv
Returns:
id_dict (dict): The updated id_dict,
id_dict (dict): The updated id_dict
ref_set (set): Set containing the reference IDs
"""

Expand All @@ -261,7 +261,7 @@ def add_ref_data_to_comparison_data(id_dict:Dict[str, Union[str, int, None]],
return id_dict, ref_set

def LD_two_rows(s1: str, s2: str) -> int:
"""Calcultes Levenshtein distance between two strings
"""Calculates Levenshtein distance between two strings
Simple implementation of Levenshtein distance based on the two rows
algorithm.
Expand All @@ -274,7 +274,7 @@ def LD_two_rows(s1: str, s2: str) -> int:
int: The Levenshtein/edit distance
"""

# Switch s1 and s2 for reduce the columns number
# Switch s1 and s2 to reduce the columns number
if len(s1) > len(s2):
s1, s2 = s2, s1

Expand Down Expand Up @@ -363,7 +363,7 @@ def build_active_site_checking_file(id_dict: Dict[str, Union[str, int, None]],
g2 = id_dict[key]["g2"]
ref_pid = id_dict[key]["ref_pid"]

# Add dictionnary items
# Add dictionary items
text += f"{key}\t{g1}\t{seq1}\t{g2}\t{seq2}\t{d}\t{ref}\t{ref_pid}\t"
text += f"{seq_ref}\t{d1}\t{d2}\t"

Expand Down Expand Up @@ -456,7 +456,7 @@ def extract_aa(file: Path, pos: int, aa: str, group: Optional[int]):

def get_unique(group_file: Path) -> Tuple[Dict[str, Tuple[str, Set[str]]],
Dict[str, Tuple[int, int, float]]]:
"""Calculates statistics on the number of unique sequences per groups
"""Calculates statistics on the number of unique sequences per group
Args:
group_file (Path): ASMC tsv output
Expand All @@ -465,8 +465,8 @@ def get_unique(group_file: Path) -> Tuple[Dict[str, Tuple[str, Set[str]]],
FileFormatError: Raised if the tsv contains less than 3 columns
Returns:
unique_seq (dict): Dict with seq as key and as value tuple containing
the group id and a set of sequence ids
unique_seq (dict): Dict with seq as key and a tuple containing
the group id and a set of sequence ids as values
groups_stats (dict): Dict with group id as key and a tuple of int and
float as value
Expand Down

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