def load(self):
super().load()
start = perf_counter()
parsed_nfasta_headers = jb.Parallel(n_jobs=-1)(
jb.delayed(mp_parse_nfasta_header)(i)
for i in self.raw_nucleotide_fasta_headers)
self.nucleotide_header_df = pd.DataFrame(parsed_nfasta_headers)
print(
f"Nucleotide FASTA headers parsed into table with shape {self.nucleotide_header_df.shape} with {count_elapsed_seconds(start)}"
)
start = perf_counter()
pfasta_file = find_file_by_tail(self.reference_dir,
"VFDB_setB_pro.fas")
print(f"Use the protein FASTA file: '{pfasta_file}'")
raw_pfasta_headers = load_headers_from_fasta(pfasta_file)
print(f"Loaded {len(raw_pfasta_headers)} protein FASTA headers")
parsed_pfasta_headers = jb.Parallel(n_jobs=-1)(
jb.delayed(mp_parse_pfasta_header)(i) for i in raw_pfasta_headers)
self.protein_header_df = pd.DataFrame(parsed_pfasta_headers)
print(
f"Protein FASTA headers parsed into table with shape {self.protein_header_df.shape} with {count_elapsed_seconds(start)}"
)
vfs_table_file = find_file_by_tail(self.reference_dir, "VFs.xls")
print(f"Use the VFs description file: '{vfs_table_file}'")
self.vfs_df = pd.read_excel(vfs_table_file, engine="xlrd",
header=1).fillna("")
print(f"Loaded VFs description table with shape {self.vfs_df.shape}")
self.vfs_df["vfdb_number"] = self.vfs_df["VFID"].str.extract(
"([0-9]+)").astype(int)
def load(self):
super().load()
start = perf_counter()
parsed_nfasta_headers = jb.Parallel(n_jobs=-1)(
jb.delayed(mp_parse_nfasta_header)(i)
for i in self.raw_nucleotide_fasta_headers)
self.nucleotide_header_df = pd.DataFrame(parsed_nfasta_headers)
print(
f"Nucleotide FASTA headers parsed into table with shape {self.nucleotide_header_df.shape} with {count_elapsed_seconds(start)}"
)
reference_file = find_file_by_tail(self.reference_dir,
"completeMvirDBTable.txt")
print(f"Use the reference description file: '{reference_file}'")
self.reference_df = pd.read_csv(
reference_file,
engine="python",
header=0,
on_bad_lines="warn",
sep="\t").rename(columns={
"#Virulence Factor ID": "vfid"
}).sort_values("vfid")
print(
f"Loaded reference description table with shape {self.reference_df.shape}"
)
def find_and_load_refdata(directory: str):
print(f"Looking for '*refdata.json' in '{directory}'")
refdata_file = find_file_by_tail(directory, "_refdata.json")
if len(refdata_file) == 0:
print(
f"Cannot find a ReferenceData compatible file within the directory '{directory}'"
)
raise ValueError
print(f"Reference data found at '{refdata_file}'")
return ReferenceData.load(refdata_file)
def retrieve(self):
if len(self.download_page_soup) == 0:
self.get_latest_version()
self.download_links = [
urljoin(self.DOMAIN_ROOT, j) for j in [
i["href"]
for i in self.download_page_soup.find_all("a", href=True)
] if not j.endswith("htm")
]
self.download()
downloaded_nfasta = find_file_by_tail(
self.REFERENCE_DOWNLOAD_DIRECTORY, "VFDB_setB_nt.fas")
self.create_nucleotide_fasta_symlink(downloaded_nfasta,
default_nfasta=True)
def create_sampledata_dict_from_dir(directory: str,
reads_extension: str = DEFAULT_REGEX):
reads_files = find_file_by_tail(directory,
".{}".format(reads_extension.strip(".")),
multiple=True)
tokenized_reads_files = [tokenize_reads_file_name(i) for i in reads_files]
out = dict()
for token_dict in tokenized_reads_files:
sample_name = token_dict["sample_name"]
if sample_name in out.keys():
out[sample_name]["reads"].append(token_dict["reads_file"])
out[sample_name]["reads"].sort()
else:
out[sample_name] = {
"name": sample_name,
"reads": [
token_dict["reads_file"],
],
"taxa": ""
}
return out
help="nBee stage directory")
parser.add_argument("-o",
"--output_file",
metavar="<dir>",
required=True,
help="Output file")
_namespace = parser.parse_args()
return (_namespace.rgi_dir, _namespace.card_version, _namespace.nbee_dir,
_namespace.output_file)
if __name__ == '__main__':
(rgi_dir, card_version, nbee_dir, out_file) = _parse_args()
# RGI
rgi_tables = find_file_by_tail(dir_name=rgi_dir,
multiple=True,
tail=".txt")
merged_rgi_df = pd.DataFrame()
for rgi_table in rgi_tables:
rgi_df = load_tsv(rgi_table)
if rgi_df.shape[0] == 0:
continue
rgi_df = remove_longest_columns(rgi_df, CELL_SIZE_LIMIT)
columns = rgi_df.columns.tolist()
rgi_df["sample_name"] = filename_only(rgi_table)
rgi_df = rgi_df.loc[:, ["sample_name"] + columns]
print(
f"Concatenate dataframes with shapes {rgi_df.shape}, {merged_rgi_df.shape}"
)
merged_rgi_df = pd.concat([merged_rgi_df, rgi_df],
axis=0,