def run_scrape(
name: str,
input_specification: Path,
jobs: int,
minscans: int,
scanwindow: int,
mztol: float,
minintensity: int,
minrt: float,
configfile: Optional[Path] = None,
):
"""Collects relevant scan data for all members of the ground truth feature list"""
click.echo(f"Running scrape for {name} using {input_specification}")
spec = InputSpec.from_csv(input_specification)
config = get_config(configfile, mztol=mztol)
source_dir = Path(name.replace(" ", "_"))
core.isotope_scraper(
input_spec=spec,
source_dir=source_dir,
exp_name=name,
n_jobs=jobs,
min_scans=minscans,
scanwindow=scanwindow,
min_intensity=minintensity,
min_rt=minrt,
config=config,
)
def test_get_filepath_info():
inp = InputSpec.from_csv(TESTFILE)
data = inp.get_filepath_info(
Path("tests/test_files/full_scan_labelled_rep3.mzml"))
assert data["organism"] == "ORG1"
assert data["type"] == "s"
assert data["isotope"] == 13
assert data["element"] == "C"
assert data["condition"] == "COND1"
def test_get_feature_filepaths_blanks():
inp = InputSpec.from_csv(TESTFILE)
expected = list(
map(
Path,
[
"tests/test_files/blanks_rep1.csv",
"tests/test_files/blanks_rep2.csv"
],
))
assert expected == inp.get_feature_filepaths("blank")
def test_get_scan_filepaths():
inp = InputSpec.from_csv(TESTFILE)
expected = list(
map(
Path,
[
"tests/test_files/full_scan_nat_rep1.mzml",
"tests/test_files/full_scan_nat_rep2.mzml",
"tests/test_files/full_scan_nat_rep3.mzml",
],
))
assert expected == inp.get_scan_filepaths("12cond1")
def test_get_feature_filepaths_condition():
inp = InputSpec.from_csv(TESTFILE)
expected = list(
map(
Path,
[
"tests/test_files/feature_list_rep1.csv",
"tests/test_files/feature_list_rep2.csv",
"tests/test_files/feature_list_rep3.csv",
],
))
assert expected == inp.get_feature_filepaths("cond1")
def run_validate(
name: str, input_specification: Path, configfile: Optional[Path] = None
):
"""
Performs some simple checks on your input specification file
"""
click.echo(f"Running validation for {name} on {input_specification}")
spec = InputSpec.from_csv(input_specification)
try:
core.validate_input(spec)
click.echo(click.style("Validation successful! ✅", fg="green"))
except AssertionError as e:
click.echo(click.style("Validation failed... ❌", fg="red"), err=True)
sys.exit(1)
def run_analyze(
name: str,
input_specification: Path,
jobs: int,
minscans: int,
minconditions: int,
configfile: Optional[Path] = None,
):
"""Performs Stable Isotope Labelling detecting and analysis"""
click.echo(f"Running analysis for {name} using {input_specification}")
spec = InputSpec.from_csv(input_specification)
source_dir = Path(name.replace(" ", "_"))
core.isotope_label_detector(
input_spec=spec,
source_dir=source_dir,
exp_name=name,
min_scans=minscans,
num_cond=minconditions,
n_jobs=jobs,
)
def run_prep(
name: str,
input_specification: Path,
jobs: int,
blank_remove: bool,
minreps: int,
mztol: int,
rttol: int,
configfile: Optional[Path] = None,
):
"""Prepares the ground truth feature list"""
click.echo(f"Running prep for {name} using {input_specification}")
spec = InputSpec.from_csv(input_specification)
config = get_config(configfile, minreps=minreps, mztol=mztol, rttol=rttol)
source_dir = Path(name.replace(" ", "_"))
core.generate_featurelist(
input_spec=spec,
source_dir=source_dir,
exp_name=name,
n_jobs=jobs,
config=config,
blank_remove=blank_remove,
)
def test_csv_factory():
inp = InputSpec.from_csv(TESTFILE)
assert isinstance(inp, InputSpec)
assert isinstance(inp.df, pd.DataFrame)
def test_validate_success():
inp = InputSpec.from_csv(TESTFILE)
print(inp.df.head())
assert inp.validate()
def test_get_filepath_info_missing():
inp = InputSpec.from_csv(TESTFILE)
data = inp.get_filepath_info("MISSING")
assert len(data) == 0
def test_get_conditions():
inp = InputSpec.from_csv(TESTFILE)
expected = ["COND1"]
assert expected == inp.get_conditions()