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import collections
import pandas as pd
import numpy as np
__all__ = [
"update_default_colnames",
"is_chrom_dtype",
]
_rc = {"colnames": {"chrom": "chrom", "start": "start", "end": "end"}}
def _get_default_colnames():
"""
Returns default column names.
These defaults be updated with :func:`update_default_colnames`.
Returns
-------
colnames : triplet (str, str, str)
"""
return _rc["colnames"]["chrom"], _rc["colnames"]["start"], _rc["colnames"]["end"]
class update_default_colnames:
def __init__(self, new_colnames):
self._old_colnames = dict(_rc["colnames"])
if isinstance(new_colnames, collections.abc.Iterable):
if len(new_colnames) != 3:
raise ValueError(
"Please, specify new columns using a list of "
"3 strings or a dict!"
)
(
_rc["colnames"]["chrom"],
_rc["colnames"]["start"],
_rc["colnames"]["end"],
) = new_colnames
elif isinstance(new_colnames, collections.abc.Mapping):
_rc["colnames"].update(
{
k: v
for k, v in new_colnames.items()
if k in ["chrom", "start", "end"]
}
)
else:
raise ValueError(
"Please, specify new columns using a list of " "3 strings or a dict!"
)
def __enter__(self):
return self
def __exit__(self, *args):
_rc["colnames"] = self._old_colnames
def _verify_columns(df, colnames, unique_cols=False, return_as_bool=False):
"""
Raises ValueError if columns with colnames are not present in dataframe df.
Parameters
----------
df: pandas.DataFrame
colnames: list of column names
return_as_bool : bool
If True, returns as a boolean instead of raising errors. Default False.
"""
if not type(df) is pd.core.frame.DataFrame:
if return_as_bool:
return False
raise ValueError("df is not a dataframe")
if unique_cols:
if len(set(colnames)) < len(colnames):
raise ValueError("column names must be unique")
if not set(colnames).issubset(df.columns):
if return_as_bool:
return False
raise ValueError(
", ".join(set(colnames).difference(set(df.columns)))
+ " not in keys of df.columns"
)
if return_as_bool:
return True
def _verify_column_dtypes(df, cols=None, return_as_bool=False):
"""
Checks that dataframe `df` has chrom, start, end columns with valid dtypes.
Raises TypeErrors if cols have invalid dtypes.
Parameters
----------
df : pandas.DataFrame
cols : (str, str, str) or None
The names of columns containing the chromosome, start and end of the
genomic intervals, provided separately for each set. The default
values are 'chrom', 'start', 'end'.
return_as_bool : bool
If true, returns as a boolean instead of raising errors. Default False.
"""
ck1, sk1, ek1 = _get_default_colnames() if cols is None else cols
if not _verify_columns(df, [ck1, sk1, ek1], return_as_bool=True):
if return_as_bool:
return False
raise ValueError("could not verify columns")
chrom_dtype, start_dtype, end_dtype = df.dtypes[[ck1, sk1, ek1]]
if not is_chrom_dtype(chrom_dtype):
if return_as_bool:
return False
raise TypeError(
"invalid df['chrom'] dtype, must be object, string, or categorical"
)
if not pd.api.types.is_integer_dtype(start_dtype):
if return_as_bool:
return False
raise TypeError("invalid df['start'] dtype, must be integer")
if not pd.api.types.is_integer_dtype(end_dtype):
if return_as_bool:
return False
raise TypeError("invalid df['end'] dtype, must be integer")
if return_as_bool:
return True
def is_chrom_dtype(chrom_dtype):
"""
Returns True if dtype is any of the allowed bioframe chrom dtypes, False otherwise.
"""
return np.any(
[
pd.api.types.is_string_dtype(chrom_dtype),
pd.api.types.is_object_dtype(chrom_dtype),
pd.api.types.is_categorical_dtype(chrom_dtype),
]
)
|
