DataSeries

class reciprocalspaceship.DataSeries(data=None, index=None, dtype: Dtype | None = None, name=None, copy: bool | None = None, fastpath: bool | lib.NoDefault = <no_default>)

Bases: Series

One-dimensional ndarray with axis labels, representing a slice of a DataSet. DataSeries objects inherit methods from pd.Series, and as such have support for statistical methods that automatically exclude missing data (represented as NaN).

Operations between DataSeries align values on their associated index values so they do not need to have the same length.

For more information on the attributes and methods available with DataSeries objects, see the Pandas documentation.

Parameters:

• data (array-like, Iterable, dict, or scalar value) – data to be stored in DataSeries.

• index (array-like or Index) – Values must be hashable and have the same length as data. Non-unique index values are allowed. Will default to RangeIndex (0, 1, 2, …, n) if not provided. If a dict is provided as data and a index is given, index will override the keys found in the dict.

• dtype (str, numpy.dtype, or ExtensionDtype, optional) – Data type for the DataSeries.

• name (str, optional) – The name to give to the DataSeries.

• copy (bool, default False) – Copy input data.

Attributes

Methods

from_friedel_dtype()	Convert dtype of DataSeries from the Friedel equivalent.
infer_mtz_dtype()	Infer MTZ dtype from column name and underlying data.
to_friedel_dtype()	Convert dtype of DataSeries to the Friedel equivalent.

from_friedel_dtype()

Convert dtype of DataSeries from the Friedel equivalent. If DataSeries is not a Friedel-related dtype, it is returned unchanged.

Returns:

DataSeries

See also

DataSeries.to_friedel_dtype

Convert dtype of DataSeries to the Friedel equivalent

Examples

DataSeries has a Friedel equivalent:

>>> s = rs.DataSeries([1, 2, 3], dtype="FriedelIntensity")
>>> s
0   1.0
1   2.0
2   3.0
dtype: FriedelIntensity
>>> s.from_friedel_dtype()
0   1.0
1   2.0
2   3.0
dtype: Intensity

DataSeries does not have a Friedel equivalent:

>>> s = rs.DataSeries([1, 2, 3], dtype="HKL")
>>> s
0    1
1    2
2    3
dtype: HKL
>>> s.from_friedel_dtype()
0    1
1    2
2    3
dtype: HKL

infer_mtz_dtype()

Infer MTZ dtype from column name and underlying data.

If name does not match a common MTZ column, the method will return an MTZInt or MTZReal depending on whether the data is composed of integers or floats, respectively. If the data is non-numeric, the returned dtype will be unchanged. If input dataseries is already a MTZDtype, it will be returned unchanged.

Returns:

DataSeries

See also

DataSet.infer_mtz_dtypes

Infer MTZ dtypes for columns in DataSet

Examples

Common intensity column name:

>>> s = rs.DataSeries([1, 2, 3], name="I")
>>> s.infer_mtz_dtype()
0   1.0
1   2.0
2   3.0
Name: I, dtype: Intensity

Common intensity column name for anomalous data:

>>> s = rs.DataSeries([1, 2, 3], name="I(+)")
>>> s.infer_mtz_dtype()
0   1.0
1   2.0
2   3.0
Name: I(+), dtype: FriedelIntensity

Ambiguous name case:

>>> s = rs.DataSeries([1, 2, 3], name="Something")
>>> s.infer_mtz_dtype()
0    1
1    2
2    3
Name: Something, dtype: MTZInt

to_friedel_dtype()

Convert dtype of DataSeries to the Friedel equivalent. If there is not a Friedel equivalent dtype, the DataSeries is returned unchanged.

Returns:

DataSeries

See also

DataSeries.from_friedel_dtype

Convert dtype of DataSeries from the Friedel equivalent.

Examples

DataSeries has a Friedel equivalent:

>>> s = rs.DataSeries([1, 2, 3], dtype="Intensity")
>>> s
0   1.0
1   2.0
2   3.0
dtype: Intensity
>>> s.to_friedel_dtype()
0   1.0
1   2.0
2   3.0
dtype: FriedelIntensity

DataSeries does not have a Friedel equivalent:

>>> s = rs.DataSeries([1, 2, 3], dtype="HKL")
>>> s
0    1
1    2
2    3
dtype: HKL
>>> s.to_friedel_dtype()
0    1
1    2
2    3
dtype: HKL