"""Functions operating on the PEtab condition table"""
from collections.abc import Iterable
from pathlib import Path
import numpy as np
import pandas as pd
from . import core, lint
from .C import *
__all__ = [
"get_condition_df",
"write_condition_df",
"create_condition_df",
"get_parametric_overrides",
]
[docs]
def get_condition_df(
condition_file: str | pd.DataFrame | Path | None,
) -> pd.DataFrame:
"""Read the provided condition file into a ``pandas.Dataframe``
Conditions are rows, parameters are columns, conditionId is index.
Arguments:
condition_file: File name of PEtab condition file or pandas.Dataframe
"""
if condition_file is None:
return condition_file
if isinstance(condition_file, str | Path):
condition_file = pd.read_csv(
condition_file, sep="\t", float_precision="round_trip"
)
lint.assert_no_leading_trailing_whitespace(
condition_file.columns.values, "condition"
)
if not isinstance(condition_file.index, pd.RangeIndex):
condition_file.reset_index(
drop=condition_file.index.name != CONDITION_ID,
inplace=True,
)
try:
condition_file.set_index([CONDITION_ID], inplace=True)
except KeyError:
raise KeyError(
f"Condition table missing mandatory field {CONDITION_ID}."
) from None
return condition_file
[docs]
def write_condition_df(df: pd.DataFrame, filename: str | Path) -> None:
"""Write PEtab condition table
Arguments:
df: PEtab condition table
filename: Destination file name. The parent directory will be created
if necessary.
"""
df = get_condition_df(df)
Path(filename).parent.mkdir(parents=True, exist_ok=True)
df.to_csv(filename, sep="\t", index=True)
[docs]
def create_condition_df(
parameter_ids: Iterable[str], condition_ids: Iterable[str] | None = None
) -> pd.DataFrame:
"""Create empty condition DataFrame
Arguments:
parameter_ids: the columns
condition_ids: the rows
Returns:
A :py:class:`pandas.DataFrame` with empty given rows and columns and
all nan values
"""
condition_ids = [] if condition_ids is None else list(condition_ids)
data = {CONDITION_ID: condition_ids}
df = pd.DataFrame(data)
for p in parameter_ids:
if not lint.is_valid_identifier(p):
raise ValueError("Invalid parameter ID: " + p)
df[p] = np.nan
df.set_index(CONDITION_ID, inplace=True)
return df
[docs]
def get_parametric_overrides(condition_df: pd.DataFrame) -> list[str]:
"""Get parametric overrides from condition table
Arguments:
condition_df: PEtab condition table
Returns:
List of parameter IDs that are mapped in a condition-specific way
"""
constant_parameters = set(condition_df.columns.values.tolist()) - {
CONDITION_ID,
CONDITION_NAME,
}
result = []
for column in constant_parameters:
if not pd.api.types.is_string_dtype(condition_df[column].dtype):
continue
floatified = condition_df.loc[:, column].apply(core.to_float_if_float)
result.extend(x for x in floatified if not isinstance(x, float))
return result