from ..utils import Mappings, path_converter, is_numeric
class VarID:
"""
Clinical variable identifier. Contains logic to convert to string for
jstree json.
"""
def __new__(cls, *args, **kwargs):
if len(args) == 1:
# highdim or tags
if (len(args[0]) == 32 and '_' not in args[0]) or 'tags_id_' in args[0]:
return args[0]
return super(VarID, cls).__new__(cls)
def __init__(self, *args):
if len(args) == 1 and '__' in args[0]:
l = args[0].rsplit('__', 1)
if '_' in l[1]:
l += l.pop(1).split('_')
args = l
elif len(args) == 1 and type(args[0]) == tuple:
args = args[0]
self.filename = args[0]
self.column = args[1]
self.category = args[2] if len(args) > 2 else None
def __eq__(self, other):
return hash(self) == hash(other)
def __hash__(self):
return hash(self.tuple)
def __repr__(self):
if self.category:
return 'VarID({!r}, {}, {})'.format(*self.tuple)
else:
return 'VarID({!r}, {})'.format(*self.tuple)
def __str__(self):
if self.category:
return '{}__{}_{}'.format(*self.tuple)
else:
return '{}__{}'.format(*self.tuple)
def __getitem__(self, key):
return self.tuple[key]
def __iter__(self):
for item in self.filename, self.column, self.category:
if item:
yield item
@property
def tuple(self):
return tuple(self)
@property
def parent(self):
return self.filename, self.column
def create_category(self, i):
"""
Create a category ID and return it.
:param i: Integer
:return: new VarID
"""
assert not self.category, 'VarID already has category.'
return VarID(self.filename, self.column, int(i))
[docs]class Variable:
"""
Base class for clinical variables
"""
def __init__(self, datafile, column: int = None, clinical_parent=None):
self.datafile = datafile
self.column = column
self._zero_column = column - 1
self.parent = clinical_parent
def __repr__(self):
return '{} {!r}: {}'.format(self.__class__.__name__,
self.var_id,
self.concept_path)
@property
def values(self):
"""
:return: All values as found in the datafile.
"""
return self.datafile.df.iloc[:, self._zero_column]
@property
def unique_values(self):
"""
:return: Unique set of values in the datafile.
"""
return self.values.unique()
@property
def var_id(self):
"""
:return: Variable identifier tuple (datafile.name, column).
"""
return VarID(self.datafile.name, self.column)
@property
def is_numeric_in_datafile(self):
"""
True if the datafile contains only numerical items.
:return: bool.
"""
try:
set(map(float, self.values))
return True
except ValueError:
return False
@property
def min(self):
if self.is_numeric_in_datafile:
return min(set(map(float, self.values)))
@property
def max(self):
if self.is_numeric_in_datafile:
return max(set(map(float, self.values)))
@property
def is_numeric(self):
"""
True if transmart-batch will load this concept as numerical. This includes
information from word mapping and column mapping.
:return: bool.
"""
if self.forced_categorical:
return False
if not self.is_in_wordmap and self.is_numeric_in_datafile:
return True
else:
return is_numeric(self.mapped_values)
@property
def is_empty(self):
"""
Check if variable is fully empty.
:return: bool.
"""
return not self.values.any(skipna=True)
@property
def concept_path(self):
"""
Concept path after conversions by transmart-batch.
:return: str.
"""
cp = self.parent.ColumnMapping.get_concept_path(self.var_id)
return path_converter(cp)
@concept_path.setter
def concept_path(self, value):
self.parent.ColumnMapping.set_concept_path(self.var_id, path=value)
@property
def column_map_data(self):
"""Column mapping row as dictionary where keys are short descriptors.
:return: dict.
"""
row = self.parent.ColumnMapping.select_row(self.var_id)
data_args = {}
for i, s in enumerate(Mappings.column_mapping_s):
data_args.update({s: row[i] if len(row) > i else None})
return data_args
@property
def data_label(self):
"""
Variable data label.
:return: str.
"""
return self.column_map_data.get(Mappings.data_label_s)
@data_label.setter
def data_label(self, value):
self.parent.ColumnMapping.set_concept_path(self.var_id, label=value)
@property
def word_map_dict(self):
"""
A dictionary with word mapped categoricals. Keys are items in the datafile,
values are what they will be mapped to through the word mapping file. Unmapped
items are also added as key, value pair.
:return: dict.
"""
values = self.values
d = dict(zip(values, values))
d.update(self.parent.WordMapping.get_word_map(self.var_id))
return d
@word_map_dict.setter
def word_map_dict(self, d):
self.parent.WordMapping.set_word_map(self.var_id, d)
@property
def mapped_values(self):
"""
Data items after word mapping.
:return: list.
"""
return [v for k, v in self.word_map_dict.items()]
@property
def forced_categorical(self):
"""Check if forced categorical by entering 'CATEGORICAL' in 7th column.
Can be changed by setting this to True or False.
:return: bool.
"""
return self.column_map_data.get(Mappings.concept_type_s) == 'CATEGORICAL'
@forced_categorical.setter
def forced_categorical(self, value: bool):
self.parent.ColumnMapping.force_categorical(self.var_id, bool(value))
@property
def is_in_wordmap(self):
"""
Check if variable is represented in word mapping file.
:return: bool.
"""
return tuple(self.var_id) in self.parent.WordMapping.df.index
[docs] def word_mapped_not_present(self):
"""
Gets the values that are in the word map but not in the data file.
:return: set.
"""
if not self.is_in_wordmap:
return set()
mapped_value_column = self.parent.WordMapping.df.columns[2]
t_index = tuple(self.var_id)
mapped_values = self.parent.WordMapping.df.loc[t_index, mapped_value_column]
if type(mapped_values) != str:
mapped_values = set(mapped_values)
else:
mapped_values = {mapped_values}
return mapped_values - set(self.values)
@property
def header(self):
return self.datafile.df.columns[self._zero_column]