Commit ed6e2cef authored by Martin Larralde's avatar Martin Larralde
Browse files

Allow `gecco embed` to take more than one annotation table file for each input

parent 7660b97c
Pipeline #14347 passed with stages
in 46 minutes and 11 seconds
import csv
import itertools
import logging
import math
import multiprocessing
......@@ -31,7 +32,7 @@ class Embed(Command):
gecco embed (-h | --help)
gecco embed --bgc <data> --no-bgc <data> [options]
gecco embed [--bgc <data>]... [--no-bgc <data>]... [options]
--bgc <data> the path to the annotation table
......@@ -63,7 +64,7 @@ class Embed(Command):
return 1
# Check the input exists
for input_ in (self.args["--bgc"], self.args["--no-bgc"]):
for input_ in itertools.chain(self.args["--bgc"], self.args["--no-bgc"]):
if not os.path.exists(input_):
self.logger.error("could not locate input file: {!r}", input_)
return 1
......@@ -74,18 +75,22 @@ class Embed(Command):"Reading BGC and non-BGC feature tables")
# Read the non-BGC table, assign the Y column to `0`, sort and reshape
self.logger.debug("Reading non-BGC table from {!r}", self.args["--no-bgc"])
no_bgc_df = pandas.read_table(self.args["--no-bgc"], dtype={"domain": str})
no_bgc_df = no_bgc_df.assign(BGC="0")
rows = []
for no_bgc_path in self.args["--no-bgc"]:
self.logger.debug("Reading non-BGC table from {!r}", no_bgc_path)
rows.append(pandas.read_table(no_bgc_path, dtype={"domain": str}))
no_bgc_df = pandas.concat(rows).assign(BGC="0")
self.logger.debug("Sorting non-BGC table")
no_bgc_df = no_bgc_df.sort_values(by=["sequence_id", "start", "domain_start"])
no_bgc_df = no_bgc_df.groupby("sequence_id", sort=False)
no_bgc_list = [s for _, s in no_bgc_df if s.shape[0] > self.args["--min-size"]]
# Read the BGC table, assign the Y column to `1`, and sort
self.logger.debug("Reading BGC table from {!r}", self.args["--bgc"])
bgc_df = pandas.read_table(self.args["--bgc"], dtype={"domain": str})
bgc_df = bgc_df.assign(
rows = []
for bgc_path in self.args["--bgc"]:
self.logger.debug("Reading BGC table from {!r}", bgc_path)
rows.append(pandas.read_table(bgc_path, dtype={"domain": str}))
bgc_df = pandas.concat(rows).assign(
BGC_id=[id_[0] for id_ in bgc_df['protein_id'].str.split("|")]
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