def __init__(self,
wrapper,
mapped_arr,
col_arr,
id_arr=None,
idx_arr=None,
value_map=None,
**kwargs):
Wrapping.__init__(self,
wrapper,
mapped_arr=mapped_arr,
col_arr=col_arr,
id_arr=id_arr,
idx_arr=idx_arr,
value_map=value_map,
**kwargs)
mapped_arr = np.asarray(mapped_arr)
col_arr = np.asarray(col_arr)
checks.assert_shape_equal(mapped_arr, col_arr, axis=0)
if id_arr is None:
id_arr = np.arange(len(mapped_arr))
if idx_arr is not None:
idx_arr = np.asarray(idx_arr)
checks.assert_shape_equal(mapped_arr, idx_arr, axis=0)
if value_map is not None:
if checks.is_namedtuple(value_map):
value_map = to_value_map(value_map)
self._mapped_arr = mapped_arr
self._id_arr = id_arr
self._col_arr = col_arr
self._idx_arr = idx_arr
self._value_map = value_map
self._col_mapper = ColumnMapper(wrapper, col_arr)
def __init__(self,
wrapper: ArrayWrapper,
data: tp.Data,
tz_localize: tp.Optional[tp.TimezoneLike],
tz_convert: tp.Optional[tp.TimezoneLike],
missing_index: str,
missing_columns: str,
download_kwargs: dict,
**kwargs) -> None:
Wrapping.__init__(
self,
wrapper,
data=data,
tz_localize=tz_localize,
tz_convert=tz_convert,
missing_index=missing_index,
missing_columns=missing_columns,
download_kwargs=download_kwargs,
**kwargs
)
StatsBuilderMixin.__init__(self)
PlotsBuilderMixin.__init__(self)
checks.assert_instance_of(data, dict)
for k, v in data.items():
checks.assert_meta_equal(v, data[list(data.keys())[0]])
self._data = data
self._tz_localize = tz_localize
self._tz_convert = tz_convert
self._missing_index = missing_index
self._missing_columns = missing_columns
self._download_kwargs = download_kwargs
def __init__(self,
wrapper,
data,
tz_localize=None,
tz_convert=None,
missing_index=None,
missing_columns=None,
download_kwargs=None,
**kwargs):
Wrapping.__init__(self,
wrapper,
data=data,
tz_localize=tz_localize,
tz_convert=tz_convert,
missing_index=missing_index,
missing_columns=missing_columns,
download_kwargs=download_kwargs,
**kwargs)
checks.assert_type(data, dict)
for k, v in data.items():
checks.assert_meta_equal(v, data[list(data.keys())[0]])
self._data = data
self._tz_localize = tz_localize
self._tz_convert = tz_convert
self._missing_index = missing_index
self._missing_columns = missing_columns
self._download_kwargs = download_kwargs
def __init__(self,
wrapper: ArrayWrapper,
records_arr: tp.RecordArray,
col_mapper: tp.Optional[ColumnMapper] = None,
**kwargs) -> None:
Wrapping.__init__(
self,
wrapper,
records_arr=records_arr,
col_mapper=col_mapper,
**kwargs
)
StatsBuilderMixin.__init__(self)
# Check fields
records_arr = np.asarray(records_arr)
checks.assert_not_none(records_arr.dtype.fields)
field_names = {
dct.get('name', field_name)
for field_name, dct in self.field_config.get('settings', {}).items()
}
dtype = self.field_config.get('dtype', None)
if dtype is not None:
for field in dtype.names:
if field not in records_arr.dtype.names:
if field not in field_names:
raise TypeError(f"Field '{field}' from {dtype} cannot be found in records or config")
self._records_arr = records_arr
if col_mapper is None:
col_mapper = ColumnMapper(wrapper, self.col_arr)
self._col_mapper = col_mapper
def __init__(self, wrapper: ArrayWrapper, col_arr: tp.Array1d, **kwargs) -> None:
Wrapping.__init__(
self,
wrapper,
col_arr=col_arr,
**kwargs
)
self._wrapper = wrapper
self._col_arr = col_arr
def __init__(self, obj: tp.SeriesFrame, wrapper: tp.Optional[ArrayWrapper] = None, **kwargs) -> None:
checks.assert_instance_of(obj, (pd.Series, pd.DataFrame))
self._obj = obj
wrapper_arg_names = get_func_arg_names(ArrayWrapper.__init__)
grouper_arg_names = get_func_arg_names(ColumnGrouper.__init__)
wrapping_kwargs = dict()
for k in list(kwargs.keys()):
if k in wrapper_arg_names or k in grouper_arg_names:
wrapping_kwargs[k] = kwargs.pop(k)
if wrapper is None:
wrapper = ArrayWrapper.from_obj(obj, **wrapping_kwargs)
else:
wrapper = wrapper.replace(**wrapping_kwargs)
Wrapping.__init__(self, wrapper, obj=obj, **kwargs)
def __init__(self,
wrapper: ArrayWrapper,
mapped_arr: tp.ArrayLike,
col_arr: tp.ArrayLike,
id_arr: tp.Optional[tp.ArrayLike] = None,
idx_arr: tp.Optional[tp.ArrayLike] = None,
mapping: tp.Optional[tp.MappingLike] = None,
col_mapper: tp.Optional[ColumnMapper] = None,
**kwargs) -> None:
Wrapping.__init__(self,
wrapper,
mapped_arr=mapped_arr,
col_arr=col_arr,
id_arr=id_arr,
idx_arr=idx_arr,
mapping=mapping,
col_mapper=col_mapper,
**kwargs)
StatsBuilderMixin.__init__(self)
mapped_arr = np.asarray(mapped_arr)
col_arr = np.asarray(col_arr)
checks.assert_shape_equal(mapped_arr, col_arr, axis=0)
if id_arr is None:
id_arr = np.arange(len(mapped_arr))
else:
id_arr = np.asarray(id_arr)
if idx_arr is not None:
idx_arr = np.asarray(idx_arr)
checks.assert_shape_equal(mapped_arr, idx_arr, axis=0)
if mapping is not None:
if isinstance(mapping, str):
if mapping.lower() == 'index':
mapping = self.wrapper.index
elif mapping.lower() == 'columns':
mapping = self.wrapper.columns
mapping = to_mapping(mapping)
self._mapped_arr = mapped_arr
self._id_arr = id_arr
self._col_arr = col_arr
self._idx_arr = idx_arr
self._mapping = mapping
if col_mapper is None:
col_mapper = ColumnMapper(wrapper, col_arr)
self._col_mapper = col_mapper
def __init__(self, wrapper, records_arr, idx_field='auto', **kwargs):
Wrapping.__init__(self,
wrapper,
records_arr=records_arr,
idx_field=idx_field,
**kwargs)
records_arr = np.asarray(records_arr)
checks.assert_not_none(records_arr.dtype.fields)
checks.assert_in('id', records_arr.dtype.names)
checks.assert_in('col', records_arr.dtype.names)
if idx_field == 'auto':
if 'idx' in records_arr.dtype.names:
idx_field = 'idx'
elif idx_field is not None:
checks.assert_in(idx_field, records_arr.dtype.names)
self._records_arr = records_arr
self._idx_field = idx_field
self._col_mapper = ColumnMapper(wrapper, records_arr['col'])
