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Merged
JoshLoecker merged 2 commits into from
Feb 12, 2026
Merged

Use raise instead of log_and_raise function #247

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abd45e1
revert: use `raise <error>` instead of `log_and_raise` function
JoshLoecker Feb 12, 2026
cf990b3
chore: remove non-existant import
JoshLoecker Feb 12, 2026
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80 changes: 23 additions & 57 deletions main/como/cluster_rnaseq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
import numpy as np

from como.data_types import LogLevel
from como.utils import log_and_raise_error, stringlist_to_list
from como.utils import stringlist_to_list


@dataclass
Expand All @@ -35,77 +35,43 @@ def __post_init__(self): # noqa: C901, ignore too complex
self.seed = np.random.randint(0, 100_000)

if (isdigit(self.min_active_count) and int(self.min_active_count) < 0) or self.min_active_count != "default":
log_and_raise_error(
"min_active_count must be either 'default' or an integer > 0",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("min_active_count must be either 'default' or an integer > 0")

if (isdigit(self.quantile) and 0 > int(self.quantile) > 100) or self.quantile != "default":
log_and_raise_error(
"quantile must be either 'default' or an integer between 0 and 100",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("quantile must be either 'default' or an integer between 0 and 100")

if (isdigit(self.replicate_ratio) and 0 > self.replicate_ratio > 1.0) or self.replicate_ratio != "default":
log_and_raise_error(
"--rep-ratio must be either 'default' or a float between 0 and 1",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("--rep-ratio must be either 'default' or a float between 0 and 1")

if (isdigit(self.batch_ratio) and 0 > self.batch_ratio > 1.0) or self.batch_ratio != "default":
log_and_raise_error(
"--batch-ratio must be either 'default' or a float between 0 and 1",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("--batch-ratio must be either 'default' or a float between 0 and 1")

if self.filtering_technique.lower() not in {"quantile", "tpm", "cpm", "zfpkm"}:
log_and_raise_error(
"--technique must be either 'quantile', 'tpm', 'cpm', 'zfpkm'",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("--technique must be either 'quantile', 'tpm', 'cpm', 'zfpkm'")

if self.filtering_technique.lower() == "tpm":
self.filtering_technique = "quantile"

if self.cluster_algorithm.lower() not in {"mca", "umap"}:
log_and_raise_error(
"--clust_algo must be either 'mca', 'umap'",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("--clust_algo must be either 'mca', 'umap'")

if 0 > self.min_distance > 1.0:
log_and_raise_error(
"--min_dist must be a float between 0 and 1",
error=ValueError,
level=LogLevel.ERROR,
)

if (isdigit(self.num_replicate_neighbors) and self.num_replicate_neighbors < 1) or self.num_replicate_neighbors != "default":
log_and_raise_error(
"--n-neighbors-rep must be either 'default' or an integer > 1",
error=ValueError,
level=LogLevel.ERROR,
)

if (isdigit(self.num_batch_neighbors) and self.num_batch_neighbors < 1) or self.num_batch_neighbors != "default":
log_and_raise_error(
"--n-neighbors-batch must be either 'default' or an integer > 1",
error=ValueError,
level=LogLevel.ERROR,
)

if (isdigit(self.num_context_neighbors) and self.num_context_neighbors < 1) or self.num_context_neighbors != "default":
log_and_raise_error(
"--n-neighbors-context must be either 'default' or an integer > 1",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("--min_dist must be a float between 0 and 1")

if (
isdigit(self.num_replicate_neighbors) and self.num_replicate_neighbors < 1
) or self.num_replicate_neighbors != "default":
raise ValueError("--n-neighbors-rep must be either 'default' or an integer > 1")

if (
isdigit(self.num_batch_neighbors) and self.num_batch_neighbors < 1
) or self.num_batch_neighbors != "default":
raise ValueError("--n-neighbors-batch must be either 'default' or an integer > 1")

if (
isdigit(self.num_context_neighbors) and self.num_context_neighbors < 1
) or self.num_context_neighbors != "default":
raise ValueError("--n-neighbors-context must be either 'default' or an integer > 1")


def _parse_args() -> _Arguments:
Expand Down
15 changes: 7 additions & 8 deletions main/como/combine_distributions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
)
from como.pipelines.identifier import convert
from como.utils import LogLevel, get_missing_gene_data, log_and_raise_error, num_columns
from como.utils import num_columns


def _combine_z_distribution_for_batch(
Expand Down Expand Up @@ -191,10 +192,9 @@ def _combine_z_distribution_for_context(
for res in zscore_results:
matrix = res.z_score_matrix.copy()
if len(matrix.columns) > 1:
log_and_raise_error(
f"Expected a single column for combined z-score dataframe for data '{res.type.value.lower()}'. Got '{len(matrix.columns)}' columns",
error=ValueError,
level=LogLevel.ERROR,
raise ValueError(
f"Expected a single column for combined z-score dataframe for data '{res.type.value.lower()}'. "
f"Got '{len(matrix.columns)}' columns"
)

matrix.columns = [res.type.value.lower()]
Expand Down Expand Up @@ -327,10 +327,9 @@ async def _begin_combining_distributions(
else ""
)
if not index_name:
log_and_raise_error(
f"Unable to find common gene identifier across batches for source '{source.value}' in context '{context_name}'",
error=ValueError,
level=LogLevel.ERROR,
raise ValueError(
f"Unable to find common gene identifier across batches for source "
f"'{source.value}' in context '{context_name}'"
)
merged_batch_results = pd.concat(batch_results, axis="columns")
merged_batch_results.index.name = index_name
Expand Down
140 changes: 36 additions & 104 deletions main/como/create_context_specific_model.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@
Solver,
_BoundaryReactions,
)
from como.utils import log_and_raise_error, set_up_logging, split_gene_expression_data
from como.utils import set_up_logging, split_gene_expression_data


def _reaction_indices_to_ids(
Expand Down Expand Up @@ -235,10 +235,8 @@ def _build_with_fastcore(
)
s_matrix = cast(npt.NDArray[np.floating], cobra.util.create_stoichiometric_matrix(model=model))
if lower_bounds.shape[0] != upper_bounds.shape[0] != s_matrix.shape[1]:
log_and_raise_error(
message="Lower bounds, upper bounds, and stoichiometric matrix must have the same number of reactions.",
error=ValueError,
level=LogLevel.ERROR,
raise ValueError(
"Lower bounds, upper bounds, and stoichiometric matrix must have the same number of reactions."
)
logger.debug("Creating feasible model")
_, cobra_model = _feasibility_test(cobra_model, "other")
Expand Down Expand Up @@ -301,7 +299,7 @@ def _build_with_tinit(
solver,
idx_force,
) -> Model:
log_and_raise_error("tINIT is not yet implemented.", error=NotImplementedError, level=LogLevel.CRITICAL)
raise NotImplementedError("tINIT is not yet implemented.")
model = reference_model
properties = tINITProperties(
reactions_scores=expr_vector,
Expand Down Expand Up @@ -331,7 +329,7 @@ def _build_with_corda(
:param neg_expression_threshold: Reactions expressed below this value will be placed in "negative" expression bin
:param high_expression_threshold: Reactions expressed above this value will be placed in the "high" expression bin
"""
log_and_raise_error("CORDA is not yet implemented", error=NotImplementedError, level=LogLevel.CRITICAL)
raise NotImplementedError("CORDA is not yet implemented")
model = reference_model
properties = CORDAProperties(
high_conf_rx=[],
Expand Down Expand Up @@ -450,12 +448,7 @@ def _read_reference_model(filepath: Path) -> cobra.Model:
case ".json":
reference_model = cobra.io.load_json_model(filepath)
case _:
log_and_raise_error(
f"Reference model format must be .xml, .mat, or .json; found '{filepath.suffix}'",
error=ValueError,
level=LogLevel.ERROR,
)
return reference_model
raise ValueError(f"Reference model format must be .xml, .mat, or .json; found '{filepath.suffix}'")


async def _build_model(
Expand Down Expand Up @@ -523,28 +516,16 @@ async def _build_model(
ref_ub[i] = float(rxn.upper_bound)
reaction_ids.append(rxn.id)
if ref_lb.shape[0] != ref_ub.shape[0] != len(reaction_ids):
log_and_raise_error(
message=(
"Lower bounds, upper bounds, and reaction IDs must have the same length.\n"
f"Number of reactions: {len(reaction_ids)}\n"
f"Number of upper bounds: {ref_ub.shape[0]}\n"
f"Number of lower bounds: {ref_lb.shape[0]}"
),
error=ValueError,
level=LogLevel.ERROR,
raise ValueError(
"Lower bounds, upper bounds, and reaction IDs must have the same length.\n"
f"Number of reactions: {len(reaction_ids)}\n"
f"Number of upper bounds: {ref_ub.shape[0]}\n"
f"Number of lower bounds: {ref_lb.shape[0]}"
)
if np.isnan(ref_lb).any():
log_and_raise_error(
message="Lower bounds contains unfilled values!",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("Lower bounds contains unfilled values!")
if np.isnan(ref_ub).any():
log_and_raise_error(
message="Upper bounds contains unfilled values!",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("Upper bounds contains unfilled values!")

# get expressed reactions
reaction_expression: collections.OrderedDict[str, int] = await _map_expression_to_reaction(
Expand Down Expand Up @@ -635,14 +616,10 @@ async def _build_model(
idx_force=force_reaction_indices,
)
else:
log_and_raise_error(
(
f"Reconstruction algorithm must be {Algorithm.GIMME.value}, "
f"{Algorithm.FASTCORE.value}, {Algorithm.IMAT.value}, or {Algorithm.TINIT.value}. "
f"Got: {recon_algorithm.value}"
),
error=ValueError,
level=LogLevel.ERROR,
raise ValueError(
f"Reconstruction algorithm must be {Algorithm.GIMME.value}, "
f"{Algorithm.FASTCORE.value}, {Algorithm.IMAT.value}, or {Algorithm.TINIT.value}. "
f"Got: {recon_algorithm.value}"
)

inconsistent_and_infeasible_reactions: pd.DataFrame = pd.concat(
Expand Down Expand Up @@ -690,13 +667,9 @@ async def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
"minimum reaction rate",
"maximum reaction rate",
]:
log_and_raise_error(
(
f"Boundary reactions file must have columns named 'Reaction', 'Abbreviation', 'Compartment', "
f"'Minimum Reaction Rate', and 'Maximum Reaction Rate'. Found: {column}"
),
error=ValueError,
level=LogLevel.ERROR,
raise ValueError(
f"Boundary reactions file must have columns named 'Reaction', 'Abbreviation', 'Compartment', "
f"'Minimum Reaction Rate', and 'Maximum Reaction Rate'. Found: {column}"
)

reactions: list[str] = [""] * len(df)
Expand All @@ -707,11 +680,7 @@ async def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
for i in range(len(boundary_type)):
boundary: str = boundary_type[i].lower()
if boundary not in boundary_map:
log_and_raise_error(
f"Boundary reaction type must be 'Exchange', 'Demand', or 'Sink'. Found: {boundary}",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError(f"Boundary reaction type must be 'Exchange', 'Demand', or 'Sink'. Found: {boundary}")

shorthand_compartment = CobraCompartments.get_shorthand(reaction_compartment[i])
reactions[i] = f"{boundary_map.get(boundary)}_{reaction_abbreviation[i]}[{shorthand_compartment}]"
Expand Down Expand Up @@ -741,10 +710,8 @@ async def _write_model_to_disk(
elif path.suffix in xml_suffix:
tasks.add(asyncio.to_thread(cobra.io.write_sbml_model, model=model, filename=path))
else:
log_and_raise_error(
f"Invalid output model filetype. Should be one of .xml, .sbml, .mat, or .json. Got '{path.suffix}'",
error=ValueError,
level=LogLevel.ERROR,
raise ValueError(
f"Invalid output model filetype. Should be one of .xml, .sbml, .mat, or .json. Got '{path.suffix}'"
)
logger.success(f"Will save metabolic model for context '{context_name}' to: '{path}'")
await asyncio.gather(*tasks)
Expand Down Expand Up @@ -809,43 +776,19 @@ async def create_context_specific_model( # noqa: C901
output_model_filepaths = [output_model_filepaths] if isinstance(output_model_filepaths, Path) else output_model_filepaths

if not reference_model.exists():
log_and_raise_error(
f"Reference model not found at {reference_model}",
error=FileNotFoundError,
level=LogLevel.ERROR,
)
raise FileNotFoundError(f"Reference model not found at {reference_model}")
if not active_genes_filepath.exists():
log_and_raise_error(
f"Active genes file not found at {active_genes_filepath}",
error=FileNotFoundError,
level=LogLevel.ERROR,
)
raise FileNotFoundError(f"Active genes file not found at {active_genes_filepath}")
if algorithm == Algorithm.FASTCORE and not output_fastcore_expression_index_filepath:
log_and_raise_error(
"The fastcore expression index output filepath must be provided",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("The fastcore expression index output filepath must be provided")
if boundary_rxns_filepath and not boundary_rxns_filepath.exists():
log_and_raise_error(
f"Boundary reactions file not found at {boundary_rxns_filepath}",
error=FileNotFoundError,
level=LogLevel.ERROR,
)
raise FileNotFoundError(f"Boundary reactions file not found at {boundary_rxns_filepath}")

if algorithm not in Algorithm:
log_and_raise_error(
f"Algorithm {algorithm} not supported. Use one of {', '.join(a.value for a in Algorithm)}",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError(f"Algorithm {algorithm} not supported. Use one of {', '.join(a.value for a in Algorithm)}")

if solver not in Solver:
log_and_raise_error(
f"Solver '{solver}' not supported. Use one of {', '.join(s.value for s in Solver)}",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError(f"Solver '{solver}' not supported. Use one of {', '.join(s.value for s in Solver)}")

mat_suffix, json_suffix, xml_suffix = {".mat"}, {".json"}, {".sbml", ".xml"}
if any(path.suffix not in {*mat_suffix, *json_suffix, *xml_suffix} for path in output_model_filepaths):
Expand All @@ -858,6 +801,7 @@ async def create_context_specific_model( # noqa: C901
f"Invalid output filetype. Should be 'xml', 'sbml', 'mat', or 'json'. Got:\n{invalid_suffix}'",
error=ValueError,
level=LogLevel.ERROR,
raise ValueError(f"Invalid output filetype. Should be 'xml', 'sbml', 'mat', or 'json'. Got:\n{invalid_suffix}'")
)

boundary_reactions = None
Expand All @@ -869,23 +813,15 @@ async def create_context_specific_model( # noqa: C901
exclude_rxns_filepath: Path = Path(exclude_rxns_filepath)
df = await _create_df(exclude_rxns_filepath)
if "abbreviation" not in df.columns:
log_and_raise_error(
"The exclude reactions file should have a single column with a header named Abbreviation",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("The exclude reactions file should have a single column with a header named Abbreviation")
exclude_rxns = df["abbreviation"].tolist()

force_rxns: list[str] = []
if force_rxns_filepath:
force_rxns_filepath: Path = Path(force_rxns_filepath)
df = await _create_df(force_rxns_filepath, lowercase_col_names=True)
if "abbreviation" not in df.columns:
log_and_raise_error(
"The force reactions file should have a single column with a header named Abbreviation",
error=ValueError,
level=LogLevel.ERROR,
)
raise ValueError("The force reactions file should have a single column with a header named Abbreviation")
force_rxns = df["abbreviation"].tolist()

# Test that gurobi is using a valid license file
Expand All @@ -894,14 +830,10 @@ async def create_context_specific_model( # noqa: C901

gurobi_present = find_spec("gurobipy")
if not gurobi_present:
log_and_raise_error(
message=(
"The gurobi solver requires the gurobipy package to be installed. "
"Please install gurobipy and try again. "
"This can be done by installing the 'gurobi' optional dependency."
),
error=ImportError,
level=LogLevel.ERROR,
raise ImportError(
"The gurobi solver requires the gurobipy package to be installed. "
"Please install gurobipy and try again. "
"This can be done by installing the 'gurobi' optional dependency."
)

if not Path(f"{os.environ['HOME']}/gurobi.lic").exists():
Expand Down
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