"""DataInfo is a companion to the dataset. It contains metadata of the dataset
"""
from __future__ import annotations
import json
from collections.abc import Mapping, Sequence
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, Union, cast, overload
from pharmpy.basic import TUnit, Unit
from pharmpy.internals.fs.path import path_absolute, path_relative_to
from pharmpy.internals.immutable import Immutable, frozenmapping
if TYPE_CHECKING:
import pandas as pd
else:
from pharmpy.deps import pandas as pd
[docs]
class ColumnInfo(Immutable):
"""Information about one data column
Parameters
----------
name : str
Colum name
type : str
Type (see the "type" attribute)
unit : str
Unit (see the "unit" attribute)
scale : str
Scale of measurement (see the "scale" attribute)
continuous : bool
True if continuous or False if discrete
categories : Optional[Union[Tuple, Dict]]
Tuple of all possible categories or dict from value to label for each category
drop : bool
Should column be dropped (i.e. barred from being used)
datatype : str
Pandas datatype or special Pharmpy datatype (see the "dtype" attribute)
descriptor : str
Descriptor (kind) of data
"""
_all_types = (
'id',
'dv',
'dvid',
'idv',
'unknown',
'dose',
'rate',
'additional',
'ii',
'ss',
'event',
'covariate',
'mdv',
'compartment',
'admid',
'lloq',
'blq',
)
_all_scales = ('nominal', 'ordinal', 'interval', 'ratio')
_all_dtypes = (
'int8',
'int16',
'int32',
'int64',
'uint8',
'uint16',
'uint32',
'uint64',
'float16',
'float32',
'float64',
'float128',
'nmtran-time',
'nmtran-date',
'str',
)
_all_descriptors = (
None,
'age',
'body weight',
'lean body mass',
'fat free mass',
'time after dose',
'plasma concentration',
'subject identifier',
'observation identifier',
'pk measurement',
'pd measurement',
)
[docs]
@staticmethod
def convert_pd_dtype_to_datatype(dtype) -> str:
"""Convert pandas dtype to Pharmpy datatype
Parameters
----------
dtype : str
String representing a pandas dtype
Returns
-------
str
String representing a Pharmpy datatype
Examples
--------
>>> from pharmpy.model import ColumnInfo
>>> ColumnInfo.convert_pd_dtype_to_datatype("float64")
'float64'
"""
return dtype if dtype in ColumnInfo._all_dtypes else 'str'
[docs]
@staticmethod
def convert_datatype_to_pd_dtype(datatype) -> str:
"""Convert Pharmpy datatype to pandas dtype
Parameters
----------
datatype : str
String representing a Pharmpy datatype
Returns
-------
str
String representing a pandas dtype
Examples
--------
>>> from pharmpy.model import ColumnInfo
>>> ColumnInfo.convert_datatype_to_pd_dtype("float64")
'float64'
>>> ColumnInfo.convert_datatype_to_pd_dtype("nmtran-date")
'str'
"""
if datatype.startswith('nmtran'):
return 'str'
else:
return datatype
def __init__(
self,
name: str,
type: str = 'unknown',
unit: Unit = Unit.unitless(),
scale: str = 'ratio',
continuous: Optional[bool] = None,
categories: Optional[Union[frozenmapping[str, str], tuple[str, ...]]] = None,
drop: bool = False,
datatype: str = "float64",
descriptor: Optional[str] = None,
):
self._name = name
self._type = type
self._unit = unit
self._scale = scale
self._continuous = continuous
self._categories = categories
self._drop = drop
self._datatype = datatype
self._descriptor = descriptor
@staticmethod
def _canonicalize_categories(
categories: Union[Mapping[str, str], Sequence[str], None]
) -> Union[frozenmapping[str, str], tuple[str, ...], None]:
if isinstance(categories, dict):
return frozenmapping(categories)
elif isinstance(categories, frozenmapping):
return categories
elif isinstance(categories, tuple):
return categories
elif isinstance(categories, Sequence):
return tuple(categories)
elif categories is None:
return categories
else:
raise TypeError("categories must be None, list-like or dict-like")
[docs]
@classmethod
def create(
cls,
name: str,
type: str = 'unknown',
unit: Optional[TUnit] = None,
scale: str = 'ratio',
continuous: Optional[bool] = None,
categories: Optional[Union[Mapping[str, str], Sequence[str]]] = None,
drop: bool = False,
datatype: str = "float64",
descriptor: Optional[str] = None,
):
if scale in ('nominal', 'ordinal'):
if continuous is True:
raise ValueError("A nominal or ordinal column cannot be continuous")
else:
continuous = False
if continuous is None:
continuous = True
if not isinstance(name, str):
raise TypeError("Column name must be a string")
if type not in ColumnInfo._all_types:
raise TypeError(f"Unknown column type {type}")
if scale not in ColumnInfo._all_scales:
raise TypeError(
f"Unknown scale of measurement {scale}. Only {ColumnInfo._all_scales} are possible."
)
if unit is None:
unit = Unit.unitless()
else:
unit = Unit(unit)
if datatype not in ColumnInfo._all_dtypes:
raise ValueError(
f"{datatype} is not a valid datatype. Valid datatypes are {ColumnInfo._all_dtypes}"
)
if descriptor not in ColumnInfo._all_descriptors:
raise TypeError(f"Unknown column descriptor {descriptor}")
categories = ColumnInfo._canonicalize_categories(categories)
return cls(
name=name,
type=type,
unit=unit,
scale=scale,
continuous=continuous,
categories=categories,
drop=drop,
datatype=datatype,
descriptor=descriptor,
)
[docs]
def replace(self, **kwargs) -> ColumnInfo:
"""Replace properties and create a new ColumnInfo"""
d = {key[1:]: value for key, value in self.__dict__.items()}
d.update(kwargs)
new = ColumnInfo.create(**d)
return new
def __eq__(self, other: Any):
return (
isinstance(other, ColumnInfo)
and self._name == other._name
and self._type == other._type
and self._unit == other._unit
and self._scale == other._scale
and self._continuous == other._continuous
and self._categories == other._categories
and self._drop == other._drop
and self._datatype == other._datatype
)
def __hash__(self):
return hash(
(
self._name,
self._type,
self._unit,
self._scale,
self._continuous,
# FIXME: What are categories really?
# self._categories,
self._drop,
self._datatype,
self._descriptor,
)
)
[docs]
def to_dict(self) -> dict[str, Any]:
return {
'name': self._name,
'type': self._type,
'unit': self._unit.serialize(),
'scale': self._scale,
'continuous': self._continuous,
'categories': self._categories,
'drop': self._drop,
'datatype': self._datatype,
'descriptor': self._descriptor,
}
[docs]
@classmethod
def from_dict(cls, d: dict[str, Any]) -> ColumnInfo:
return cls(
name=d['name'],
type=d['type'],
unit=Unit.deserialize(d['unit']),
scale=d['scale'],
continuous=d['continuous'],
categories=d['categories'],
drop=d['drop'],
datatype=d['datatype'],
descriptor=d['descriptor'],
)
@property
def name(self) -> str:
"""Column name"""
return self._name
@property
def type(self) -> str:
"""Type of column
============ =============
type Description
============ =============
id Individual identifier. Max one per DataFrame. All values have to be unique
idv Independent variable. Max one per DataFrame.
dv Observations of the dependent variable
dvid Dependent variable ID
covariate Covariate
dose Dose amount
rate Rate of infusion
additional Number of additional doses
ii Interdose interval
ss Steady state dosing
event 0 = observation
mdv 0 = DV is observation value, 1 = DV is missing
admid Administration ID
compartment Compartment information (not yet exactly specified)
lloq Lower limit of quantification
blq Below limit of quantification indicator
unknown Unkown type. This will be the default for columns that hasn't been
assigned a type
============ =============
"""
return self._type
@property
def descriptor(self) -> Optional[str]:
"""Kind of data
====================== ============================================
descriptor Description
====================== ============================================
age Age (since birth)
body weight Human body weight
lean body mass Lean body mass
fat free mass Fat free mass
time after dose Time after dose
plasma concentration Concentration of substance in blood plasma
subject identifier Unique integer identifier for a subject
observation identifier Unique integer identifier for an observation
pk measurement Any kind of PK measurement
pd measurement Any kind of PD measurement
====================== ============================================
"""
return self._descriptor
@property
def unit(self) -> Unit:
"""Unit of the column data
Custom units are allowed, but units that are available in sympy.physics.units can be
recognized. The default unit is 1, i.e. without unit.
"""
return self._unit
@property
def scale(self) -> str:
"""Scale of measurement
The statistical scale of measurement for the column data. Can be one of
'nominal', 'ordinal', 'interval' and 'rational'.
"""
return self._scale
@property
def continuous(self) -> Optional[bool]:
"""Is the column data continuous
True for continuous data and False for discrete. Note that nominal and ordinal data have to
be discrete.
"""
return self._continuous
@property
def categories(self) -> Optional[Union[frozenmapping[str, str], tuple[str, ...]]]:
"""List or dict of allowed categories"""
return self._categories
@property
def drop(self) -> bool:
"""Should this column be dropped"""
return self._drop
@property
def datatype(self) -> str:
"""Column datatype
============ ================ ======== ================================= ===========
datatype Description Size Range NA allowed?
============ ================ ======== ================================= ===========
int8 Signed integer 8 bits -128 to +127. No
int16 Signed integer 16 bits -32,768 to +32,767. No
int32 Signed integer 32 bits -2,147,483,648 to +2,147,483,647. No
int64 Signed integer 64 bits -9,223,372,036,854,775,808 to No
9,223,372,036,854,775,807.
uint8 Unsigned integer 8 bits 0 to 256. No
uint16 Unsigned integer 16 bit 0 to 65,535. No
uint32 Unsigned integer 32 bit 0 to 4,294,967,295. No
uint64 Unsigned integer 64 bit 0 to 18,446,744,073,709,551,615 No
float16 Binary float 16 bits ≈ ±6.55×10⁴ Yes
float32 Binary float 32 bits ≈ ±3.4×10³⁸ Yes
float64 Binary float 64 bits ≈ ±1.8×10³⁰⁸ Yes
float128 Binary float 128 bits ≈ ±1.2×10⁴⁹³² Yes
nmtran-time NM-TRAN time n No
nmtran-date NM-TRAN date n No
str General string n No
============ ================ ========================================== ===========
The default, and most common datatype, is float64.
"""
return self._datatype
[docs]
def is_categorical(self) -> bool:
"""Check if the column data is categorical
Returns
-------
bool
True if categorical (nominal or ordinal) and False otherwise.
See also
--------
is_numerical : Check if the column data is numerical
Examples
--------
>>> from pharmpy.model import ColumnInfo
>>> col1 = ColumnInfo.create("WGT", scale='ratio')
>>> col1.is_categorical()
False
>>> col2 = ColumnInfo.create("ID", scale='nominal')
>>> col2.is_categorical()
True
"""
return self.scale in ['nominal', 'ordinal']
[docs]
def is_numerical(self) -> bool:
"""Check if the column data is numerical
Returns
-------
bool
True if numerical (interval or ratio) and False otherwise.
See also
--------
is_categorical : Check if the column data is categorical
Examples
--------
>>> from pharmpy.model import ColumnInfo
>>> col1 = ColumnInfo.create("WGT", scale='ratio')
>>> col1.is_numerical()
True
>>> col2 = ColumnInfo.create("ID", scale='nominal')
>>> col2.is_numerical()
False
"""
return self.scale in ['interval', 'ratio']
[docs]
def is_integer(self) -> bool:
"""Check if the column datatype is integral
Returns
-------
bool
True if of integral datatype
See also
--------
is_categorical : Check if the column data is categorical
Examples
--------
>>> from pharmpy.model import ColumnInfo
>>> col1 = ColumnInfo.create("WGT", scale='ratio')
>>> col1.is_integer()
False
"""
return self.datatype in [
'int8',
'int16',
'int32',
'int64',
'uint8',
'uint16',
'uint32',
'uint64',
]
[docs]
def get_all_categories(self) -> list[str]:
"""Get a list of all categories"""
if isinstance(self._categories, tuple):
return list(self._categories)
elif self._categories is None:
return []
else:
return list(self._categories.keys())
def __repr__(self):
ser = pd.Series(
[
self._type,
self._scale,
self._continuous,
self._categories,
self._unit,
self._drop,
self._datatype,
self._descriptor,
],
index=[
'type',
'scale',
'continuous',
'categories',
'unit',
'drop',
'datatype',
'descriptor',
],
name=self._name,
)
return ser.to_string(name=True)
[docs]
class DataInfo(Sequence, Immutable):
"""Metadata for the dataset
Can be indexed to get ColumnInfo for the columns.
Parameters
----------
columns : list
List of column names
path : Path
Path to dataset file
separator : str
Character or regexp separator for dataset
"""
def __init__(
self,
columns: tuple[ColumnInfo, ...] = (),
path: Optional[Path] = None,
separator: str = ',',
force_absolute_path: bool = True,
):
self._columns = columns
self._path = path
self._separator = separator
self._force_absolute_path = force_absolute_path
[docs]
@classmethod
def create(
cls,
columns: Optional[Union[Sequence[ColumnInfo], Sequence[str]]] = None,
path: Optional[Union[str, Path]] = None,
separator: str = ',',
force_absolute_path: bool = True,
):
if columns and not all(
isinstance(col, str) or isinstance(col, ColumnInfo) for col in columns
):
raise ValueError(
'Argument `columns` need to consist of either type `str` or `ColumnInfo`'
)
if columns is None or len(columns) == 0:
cols: tuple[ColumnInfo, ...] = ()
elif len(columns) > 0 and any(isinstance(col, str) for col in columns):
cols = tuple(ColumnInfo.create(col) if isinstance(col, str) else col for col in columns)
else:
cols = cast(tuple[ColumnInfo, ...], tuple(columns))
assert isinstance(cols, tuple)
if path is not None:
path = Path(path)
assert not force_absolute_path or path is None or path.is_absolute()
return cls(columns=cols, path=path, separator=separator)
[docs]
def replace(self, **kwargs) -> DataInfo:
if 'columns' in kwargs:
columns = tuple(kwargs['columns'])
else:
columns = self._columns
if 'path' in kwargs:
if kwargs['path'] is not None:
path = Path(kwargs['path'])
else:
path = None
else:
path = self._path
separator = kwargs.get('separator', self._separator)
return DataInfo.create(columns=columns, path=path, separator=separator)
def __add__(self, other: DataInfo) -> DataInfo:
if isinstance(other, DataInfo):
return DataInfo.create(
columns=self._columns + other._columns, path=self.path, separator=self.separator
)
elif isinstance(other, ColumnInfo):
return DataInfo.create(
columns=self._columns + (other,), path=self.path, separator=self.separator
)
else:
return DataInfo.create(
columns=self._columns + tuple(other), path=self.path, separator=self.separator
)
def __radd__(self, other: DataInfo) -> DataInfo:
if isinstance(other, ColumnInfo):
return DataInfo.create(
columns=(other,) + self._columns, path=self.path, separator=self.separator
)
else:
return DataInfo.create(
columns=tuple(other) + self._columns, path=self.path, separator=self.separator
)
def __eq__(self, other: Any):
if not isinstance(other, DataInfo):
return False
if len(self) != len(other):
return False
for col1, col2 in zip(self, other):
if col1 != col2:
return False
return True
def __hash__(self):
return hash(self._columns)
def __len__(self):
return len(self._columns)
def _getindex(self, i: Union[int, str]) -> int:
if isinstance(i, str):
for n, col in enumerate(self._columns):
if col.name == i:
return n
raise IndexError(f"Cannot find column {i} in DataInfo")
elif isinstance(i, int):
return i
else:
raise TypeError(f"Cannot index DataInfo by {type(i)}")
@overload
def __getitem__(self, index: Union[list, slice]) -> DataInfo: ...
@overload
def __getitem__(self, index: Union[int, str]) -> ColumnInfo: ...
def __getitem__( # pyright: ignore [reportIncompatibleMethodOverride]
self, index: Union[list, slice, int, str]
) -> Union[DataInfo, ColumnInfo]:
if isinstance(index, list):
cols = []
for ind in index:
i = self._getindex(ind)
cols.append(self._columns[i])
return DataInfo.create(columns=cols)
if isinstance(index, slice):
return DataInfo.create(self._columns[index], path=self._path, separator=self._separator)
return self._columns[self._getindex(index)]
@property
def path(self) -> Optional[Path]:
r"""Path of dataset file
Examples
--------
>>> from pharmpy.modeling import load_example_model
>>> model = load_example_model("pheno")
>>> str(model.datainfo.path).replace('\\', '/') # doctest: +ELLIPSIS
'.../pharmpy/internals/example_models/pheno.dta'
"""
return self._path
@property
def separator(self) -> str:
"""Separator for dataset file
Can be a single character or a regular expression
string.
"""
return self._separator
@property
def typeix(self) -> TypeIndexer:
"""Type indexer
Example
-------
>>> from pharmpy.modeling import load_example_model
>>> model = load_example_model("pheno")
>>> model.datainfo.typeix['covariate'].names
['WGT', 'APGR']
"""
return TypeIndexer(self)
@property
def descriptorix(self) -> DescriptorIndexer:
"""Descriptor indexer
Example
-------
>>> from pharmpy.modeling import load_example_model
>>> model = load_example_model("pheno")
>>> model.datainfo.descriptorix['body weight'].names
['WGT']
"""
return DescriptorIndexer(self)
[docs]
def set_column(self, col: ColumnInfo) -> DataInfo:
"""Set ColumnInfo of an existing column of the same name
Parameters
----------
col : ColumnInfo
New ColumnInfo
Returns
-------
DataInfo
Updated DataInfo
"""
newcols = []
for cur in self:
if cur.name != col.name:
newcols.append(cur)
else:
newcols.append(col)
return self.replace(columns=newcols)
@property
def id_column(self) -> ColumnInfo:
"""The id column
Examples
--------
>>> from pharmpy.modeling import load_example_model
>>> model = load_example_model("pheno")
>>> model.datainfo.id_column.name
'ID'
"""
return self.typeix['id'][0]
def _set_column_type(self, name: str, type: str) -> DataInfo:
for i, col in enumerate(self):
if col.name != name and col.type == type:
raise ValueError(
f"Cannot set new {type} column: column {col.name} already has type {type}"
)
for i, col in enumerate(self):
if col.name == name:
mycol = col
ind = i
break
else:
raise IndexError(f"No column {name} in DataInfo")
newcol = mycol.replace(type=type)
cols = self._columns[0:ind] + (newcol,) + self._columns[ind + 1 :]
return DataInfo.create(cols, path=self._path, separator=self._separator)
[docs]
def set_id_column(self, name: str) -> DataInfo:
return self._set_column_type(name, 'id')
@property
def dv_column(self) -> ColumnInfo:
"""The dv column
Examples
--------
>>> from pharmpy.modeling import load_example_model
>>> model = load_example_model("pheno")
>>> model.datainfo.dv_column.name
'DV'
"""
return self.typeix['dv'][0]
[docs]
def set_dv_column(self, name: str) -> DataInfo:
return self._set_column_type(name, 'dv')
@property
def idv_column(self) -> ColumnInfo:
"""The idv column
Examples
--------
>>> from pharmpy.modeling import load_example_model
>>> model = load_example_model("pheno")
>>> model.datainfo.idv_column.name
'TIME'
"""
return self.typeix['idv'][0]
[docs]
def set_idv_column(self, name: str) -> DataInfo:
return self._set_column_type(name, 'idv')
@property
def names(self) -> list[str]:
"""All column names
Examples
--------
>>> from pharmpy.modeling import load_example_model
>>> model = load_example_model("pheno")
>>> model.datainfo.names
['ID', 'TIME', 'AMT', 'WGT', 'APGR', 'DV', 'FA1', 'FA2']
"""
return [col.name for col in self._columns]
@property
def types(self) -> list[str]:
"""All column types"""
return [col.type for col in self._columns]
[docs]
def set_types(self, value: Union[list[str], str]) -> DataInfo:
"""Set types for all columns
Parameters
----------
value : list or str
Types to set. If only one this will be broadcast
Return
------
DataInfo
Updated datainfo
"""
if isinstance(value, str):
value = [value]
if len(value) == 1:
value *= len(self)
if len(value) != len(self):
raise ValueError(
"Length mismatch. "
"Can only set the same number of names as columns or 1 for broadcasting"
)
newcols = []
for v, col in zip(value, self._columns):
newcol = col.replace(type=v)
newcols.append(newcol)
return DataInfo.create(columns=newcols, path=self._path, separator=self._separator)
[docs]
def get_dtype_dict(self) -> dict[str, str]:
"""Create a dictionary from column names to pandas dtypes
This can be used as input to some pandas functions to convert
column to the correct pandas dtype.
Returns
-------
dict
Column name to pandas dtype
Examples
--------
>>> from pharmpy.modeling import *
>>> model = load_example_model("pheno")
>>> model.datainfo.get_dtype_dict()
{'ID': 'int32',
'TIME': 'float64',
'AMT': 'float64',
'WGT': 'float64',
'APGR': 'float64',
'DV': 'float64',
'FA1': 'float64',
'FA2': 'float64'}
"""
return {
col.name: (
col.datatype if not col.drop and not col.datatype.startswith('nmtran') else 'str'
)
for col in self
}
[docs]
def to_dict(self) -> dict[str, Any]:
return self._to_dict(path=None)
[docs]
@classmethod
def from_dict(cls, d: dict[str, Any]) -> DataInfo:
columns = tuple(ColumnInfo.from_dict(col) for col in d['columns'])
return cls(columns=columns, path=d['path'], separator=d['separator'])
def _to_dict(self, path: Optional[str]) -> dict[str, Any]:
a = []
for col in self._columns:
d = {
"name": col.name,
"type": col.type,
"scale": col.scale,
"continuous": col.continuous,
"categories": col.categories,
"unit": str(col.unit),
"datatype": col.datatype,
"drop": col.drop,
"descriptor": col.descriptor,
}
a.append(d)
return {
"columns": a,
"path": path,
"separator": self._separator,
}
[docs]
def to_json(self, path: Optional[Union[Path, str]] = None):
if path is None:
return json.dumps(self._to_dict(str(self.path) if self.path is not None else None))
else:
with open(path, 'w') as fp:
json.dump(
self._to_dict(
str(path_relative_to(Path(path).parent, self.path))
if self.path is not None
else None
),
fp,
)
[docs]
@staticmethod
def from_json(s: str) -> DataInfo:
"""Create DataInfo from JSON string
Parameters
----------
s : str
JSON string
Return
------
DataInfo
Created DataInfo object
"""
d = json.loads(s)
columns = []
for col in d['columns']:
ci = ColumnInfo.create(
name=col['name'],
type=col.get('type', 'unknown'),
scale=col['scale'],
continuous=col.get('continuous', None),
unit=col.get('unit', Unit.unitless()),
categories=col.get('categories', None),
datatype=col.get('datatype', 'float64'),
descriptor=col.get('descriptor', None),
drop=col.get('drop', False),
)
columns.append(ci)
path = d.get('path', None)
if path:
path = Path(path)
separator = d.get('separator', ',')
di = DataInfo.create(columns, path=path, separator=separator, force_absolute_path=False)
return di
[docs]
@staticmethod
def read_json(path: Union[Path, str]) -> DataInfo:
"""Read DataInfo from JSON file
Parameters
----------
path : Path or str
Path to JSON datainfo file
Return
------
DataInfo
Created DataInfo object
"""
with open(path, 'r') as fp:
s = fp.read()
di = DataInfo.from_json(s)
return (
di
if di.path is None or di.path.is_absolute()
else di.replace(path=path_absolute(Path(path).parent / di.path))
)
def __repr__(self):
labels = [col.name for col in self._columns]
types = [col.type for col in self._columns]
scales = [col.scale for col in self._columns]
cont = [col.continuous for col in self._columns]
cats = [col.categories for col in self._columns]
units = [col.unit for col in self._columns]
drop = [col.drop for col in self._columns]
datatype = [col.datatype for col in self._columns]
descriptor = [col.descriptor for col in self._columns]
df = pd.DataFrame(
{
'name': labels,
'type': types,
'scale': scales,
'continuous': cont,
'categories': cats,
'unit': units,
'drop': drop,
'datatype': datatype,
'descriptor': descriptor,
}
)
return df.to_string(index=False)
class TypeIndexer:
def __init__(self, obj):
self._obj = obj
def __getitem__(self, i) -> DataInfo:
cols = [col for col in self._obj if col.type == i and not col.drop]
if not cols:
raise IndexError(f"No columns of type {i} available")
return DataInfo.create(cols)
class DescriptorIndexer:
def __init__(self, obj):
self._obj = obj
def __getitem__(self, i) -> DataInfo:
cols = [col for col in self._obj if col.descriptor == i and not col.drop]
if not cols:
raise IndexError(f"No columns with descriptor {i} available")
return DataInfo.create(cols)
